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601 Sentences With "input data"

How to use input data in a sentence? Find typical usage patterns (collocations)/phrases/context for "input data" and check conjugation/comparative form for "input data". Mastering all the usages of "input data" from sentence examples published by news publications.

Algorithms can have an unfair bias depending on their input data.
The program runs on specially enabled iPads where parents input data.
Some teachers had asked for a more streamlined way to input data.
For most of AI history, slow computational speeds have severely limited the scope of applied AI. An algorithm's prediction depends on the input data, and the input data represents a snapshot in time at the moment it was recorded.
Deep learning allows systems to process and evaluate raw input data from their environments.
But knowing exactly what input data a predictive policing system is using is critical.
"If you have biased input data, that really can be a problem," said Gabriel.
This randomization is effectively the same as compressing the system's representation of the input data.
Deep learning consists of networks of interconnected nodes that autonomously run computations on input data.
You have to input data on when you're having sex and when your period is.
In fact, what we see is the brain's interpretation of the input data provided by our eyes.
First, machine learning requires well-defined problems where input data can reliably be mapped to output predictions.
Back then, it most likely would have been women helping input data into a machine-readable format.
MIT engineers have developed biological computational circuits capable of both remembering and responding to sequential input data.
They then tracked what happened as the networks engaged in deep learning with 3,000 sample input data sets.
Both companies now appear to be battling to control the main way you input data into an iPhone.
It may require us to give it some parameters (or input data) of a certain type, for example.
The algorithms here only work on talking head style videos, for example, and require 40 minutes of input data.
The expected utility function does not allow modelers to indicate their subjective confidence in various sources of input data.
I later sorted out all the input data and realized she had hoodwinked me—a conversational sleight of hand.
The US Navy's solution, Naval Tactical Data System, could also input data automatically that DATAR required manual input for.
The NYPD has still not provided the Brennan Center with documents related to the input data these algorithms use.
In a typical AI-human workflow, the human feeds input data into the algorithm, the algorithm runs calculations on that input data and returns an output that predicts a certain outcome or recommends a course of action; the human interprets that information to decide on a course of action, then takes action.
This allows us to learn features from the input data which are most relevant for control, making computation very efficient.
Industrial managers would input data which would then be centrally analysed; instructions for any necessary changes would be sent back.
For instance, the words "Curry" and "Dingle" weren't in the input data, but did show up in the AI-generated carols.
The second step is to make the neural network learn the dynamics of the evolving flame front from the input data.
The input data triggers the neurons to fire, triggering connected neurons in turn and sending a cascade of signals throughout the network.
Instead of starting with some input data, executing a series of operations and displaying the output, it works by finding internal consistency.
It's the type of ratcheting up that's necessary if the visual cortex is going to create full images from sparse input data.
For an AI program to, say, play Quake III, it first needs to be "trained" on a large amount of input data.
One of the major building blocks of such AI-powered recognition systems is image annotation delivered with a human inputdata training.
Many AI-driven platform act as a sort of black box — you input data and get a result without really knowing why.
It's easy to forget that even with the fanciest of machine learning models, we still need humans in the trenches cleaning input data.
Dr Datta feeds the system under test a range of input data and examines its output for dodgy, potentially harmful or discriminatory results.
Deep learning systems, or neural networks, are "layers" of nodes that run semi-random computations on input data, like millions of cat photos.
The important part of this step is realizing that the quality of a model is highly dependent on the quality of its input data.
The error, Harry writes, has to do with the technical assumption that the input data signal be "cyclical," repeating itself without any breaks or discontinuities.
You can also use inking to add comments in Office documents, and ink within Excel cells to input data, writing over them to update numbers.
Eurostat's release contained only figures for January, reflecting more detailed input data and a change in the way it deals with German package holiday prices.
"When people complain that algorithms aren't transparent, the real problem is usually that someone is keeping the algorithm or its input data secret," Felten wrote.
In the middle we have the meat of the pipeline, the model, which is the machine learning algorithm that learns to predict given input data.
That's exactly what Charles, programmer Harrison Kinsley's self-driving neural network (a computer program that teaches itself to estimate things using input data), does best.
The more powerful ones have something akin to layers of neurons, each processing the input data and sending the results up to the next layer.
GANs are algorithms that "learn" from a large amount of input data, and use that knowledge to produce new results after a long training period.
"With a vanilla neural network you take a set of input data, pass it through the network, and get a set of outputs," said Thoutt.
Still, for doctors and nurses in this hospital network outside Boston, worrying about security when they input data into the system's computers requires a balancing act.
The generator is "trained" on input data (in this case, more than 1,000 Doom maps), and it creates new levels based on the model it's learned.
That includes clipboards, stop watches, and Excel spreadsheets, which are far from real time and simply collect troves of manually input data without pulling insights automatically.
The network starts to shed information about the input data, keeping track of only the strongest features—those correlations that are most relevant to the output label.
Turning AI against itselfAnother growing trend of AI-based threats are adversarial attacks, where malicious actors manipulate input data to force neural networks to act in erratic manners.
Among them: the state party's chief financial officer, Melissa Watson, "did not know how to operate a Google spreadsheet application used to input data," the Times report says.
Allowing bots to share conversational context with one another also greatly increases the speed of interaction because users no longer need to re-input data for each communication.
If caseworkers input data on their cases into a shared system, Congress would suddenly have a powerful tool for tracking trends and protecting the interests of American consumers.
Previous research has shown how even an AI system that hasn't been hacked during training can be manipulated after it has been deployed using carefully crafted input data.
Mr. Paslow is grateful to have work, but he chafes at all the software he has to use, and misses the secretaries who used to help him input data.
According to Vondrick it will also help the field of unsupervised machine learning, since this type of machine vision algorithm received all of its input data from unlabeled videos.
The idea is that a network rids noisy input data of extraneous details as if by squeezing the information through a bottleneck, retaining only the features most relevant to general concepts.
Human error by doctors and medical examiners whose job it is to input data into the state's electronic death registration system, researchers from the Texas Department of Health Services Center found.
Often, the answer to those questions is small armies of human employees, listening to recorded conversations and reading transcripts as they input data for the underlying machine learning algorithms to digest.
Samasource works through a discovery phase with customers — to determine the problems they're trying to solve and their sources of input data — and customizes an approach to providing what they need.
" He continued, "What people don't realize is that what the scientists have done is input data into a computer and produced modeling as far as the trajectory of what's going to occur.
Though this service is advertised for public locations and businesses, it supports private residences as well, and SPD offers steps to input data and add a "swatting concerns" tab to your profile.
"To make it work, the key players in the chain must agree a set of input data to define its features," said Chow, whose team has $425 billion of assets under advice.
We'll get into this in more detail later, but in broad strokes, deep learning systems are "layers" of digital neurons that each run their own computations on input data and rearrange themselves.
Field workers will be able to input data on their apps, according a level of priority so that it can be quickly followed up by child protection teams based in their offices.
We input data to Waze, share our location and speed with GPS, drive through speed monitor zones and even provide license plate information and traffic patterns with intersection cameras and toll booths.
To get started, developers can use what's basically a variant of standard SQL to say what kind of model they are trying to build and what the input data is supposed to be.
This both provides enough audio input data for the speaker-separating AI to work with, and also means multiple meeting participants can participate in grabbing the record of the meeting if they wish.
In their experiments, Tishby and Shwartz-Ziv tracked how much information each layer of a deep neural network retained about the input data and how much information each one retained about the output label.
Melissa Watson, the state party's chief financial officer, who was in charge of the boiler room, did not know how to operate a Google spreadsheet application used to input data, Democratic officials later acknowledged.
This procedure, called "convolution," lets a layer of the neural network perform a mathematical operation on small patches of the input data and then pass the results to the next layer in the network.
In one case, the researchers used small networks that could be trained to label input data with a 1 or 0 (think "dog" or "no dog") and gave their 282 neural connections random initial strengths.
The software is collaborative: Data such as humidity, temperature and even noise and light intensity is collected and recorded, and government agencies can also input data to build a comprehensive 3D model of the city.
He and his colleagues have also written an algorithm that generates images specifically designed to maximally activate individual neurons in an effort to determine what they are "looking" for in a stream of input data.
Neural networks are computer programs made up of "layers" of connected nodes that run semi-random computations on input data like images, and rearrange themselves until they "learn" how to recognize the objects in them.
To create a master fingerprint the researchers fed an artificial neural network—a type of computing architecture loosely modeled on the human brain that "learns" based on input data—the real fingerprints from over 6,000 individuals.
Afterward, the Oregon Department of Environmental Quality (DEQ) worked with the Portland Bureau of Planning and Sustainability (BPS) to develop a CBEI more narrowly targeted at Multnomah County, where Portland is located, using local input data.
The app enables horse owners to carefully document each animal they own by allowing them to upload up to 10 photos per horse and input data on their ancestry, race history, age, sex, and health conditions.
Child protection case workers - who come from various charities as well as the government - will now be able to directly input data on separated children into the app so that other field workers can easily access it.
The first thing to know is that deep learning, a highly advanced form of machine learning, is made up of a "neural network"—an interconnected network of "nodes" that all run semi-random computations on input data.
Over the past decade, scientists have resurrected an old concept that doesn't rely on a massive encyclopedic memory bank, but instead on a simple and systematic way of analyzing input data that's loosely modeled after human thinking.
The process, though still arduous, was much faster than before: All told, the students managed to input data for some 21975,21973 images, including at least two of each face, at a rate of about 19703 an hour.
Robin integrates with the major electronic health records companies, Epic and Cerner, through third-party integrations that are designed to make it easier to input data automatically as doctors are assessing a patient's condition and delivering treatments.
The end product is the result of a long process of carefully selecting input data, tweaking mathematical parameters, and then sifting through the results to find the very best examples of whatever it is you're looking for.
It's based on a recurrent neural network—computing architecture that "learns" patterns in a large amount of input data (in this case, death metal) in order to predict what musical elements and sequences are most common and recreates them.
In its short run, the startup has focused on the "sparse data" problem of how to build artificial intelligence that can quickly recognize objects or situations with a much smaller amount of "training" input data than is required by today's techniques.
The "deep networks" referred to by Liu are generative neural networks, a type of computing architecture loosely based on the human brain that "learns" to produce new things after being tuned to recognize patterns in a large amount of input data.
Anybody can look up the transactions and see the drawings using either a typical blockchain explorer (just navigate to the transaction input data and click the ASCII option or, on Etherscan, UTF-8) or a service specifically for viewing transaction data.
The term "Jacquard" comes from the name of the man who invented the process of using perforated cards to input pattern designs into the first automated looms, just like the punch cards used to input data for the first IBM computers.
If the environment described by the data changes faster than the algorithm can compute the input data, by the time the algorithm completes its computations and returns a prediction, the prediction will only describe a moment in the past and will not be actionable.
"Now you have all you need for liquidity planning and revenue/expense reports close to real-time in the tool w/o the need to input data yourself or wait for your external account to do it for you at month's end," says Erxleben.
Finally, the physics engine's predicted values are mapped on to static images of the objects picked out by the tracking algorithm and fed into a neural network—"layers" of simulated neurons that run calculations on input data and self-correct until a desired output is achieved.
As a deep neural network tweaks its connections by stochastic gradient descent, at first the number of bits it stores about the input data stays roughly constant or increases slightly, as connections adjust to encode patterns in the input and the network gets good at fitting labels to it.
As each of our detailed actions is captured in software, analyzed along with hundreds of thousands of others, and provided back to users in the form of recommended next steps, software will no longer be a place to input data, but a place to go for specifically-tailored advice.
Artificial intelligence, obviously they're going to input data that is going to create a whole new set of data where crimes might happen, what kind of people are likely to commit crimes, but the whole worry around this is first the designers of these systems are largely white men, essentially.
Traditional résumé review leads to women and minorities being at a 50 percent to 67 percent disadvantage, according to start-up pymetrics, which attempts to go well beyond the résumé in assessing job applicants using neuroscience games and AI. Companies using AI can reduce those figures dramatically, pymetrics said, as long as the input data is accurate and remains unbiased.
"Although our company was a late entrant and small in scale, we recognized the potential for the electronic pen and sensor board input system based on electromagnetic induction phenomena from an early date, and decided to concentrate our development efforts in hand-drawn CG production on the assumption that it would help people input data more freely," he said in the interview.
This has happened with something as inert and easy to observe as seat belts, so we should expect a rougher ride with distributed IT like AI. We might guard against this with enough attention from those familiar with dynamics of complex social systems and by expanding the range of input data available to these systems, but then again, these might be the very things that ensure its occurrence.
Input data is classified into two categories: relative input data and absolute input data.
It is used to solve the inverse problem with incomplete input data, similar to local tomography. However this concept of local inverse also can be applied to complete input data.
Once the input data is believed correct, the design itself must still be verified for correctness. Some tool flows verify designs by first producing a design, and then scanning the design to produce compatible input data for the tool flow. If the scanned data matches the input data, then the tool flow has probably not introduced errors. The functional verification data are usually called "test vectors".
It should be distinguished from the kinetic convex hull, which studies similar problems for continuously moving points. Dynamic convex hull problems may be distinguished by the types of the input data and the allowed types of modification of the input data.
Furthermore, It improves productivity by reducing the need for internal staff to input data.
When the input data to an algorithm is too large to be processed and it is suspected to be redundant (e.g. the same measurement in both feet and meters) then the input data will be transformed into a reduced representation set of features (also named features vector). Transforming the input data into the set of features is called feature extraction. If the features extracted are carefully chosen it is expected that the features set will extract the relevant information from the input data in order to perform the desired task using this reduced representation instead of the full size input.
It requires highly developed approaches to store, retrieve and analyze them in reasonable time. During the preprocessing stage, input data requires to be normalized. The normalization of input data includes noise reduction, and filtering. Processing may contain a few sub-steps depending on applications.
The dynamic convex hull problem is a class of dynamic problems in computational geometry. The problem consists in the maintenance, i.e., keeping track, of the convex hull for input data undergoing a sequence of discrete changes, i.e., when input data elements may be inserted, deleted, or modified.
Typically an input-output technique would be more accurate, but the input data is not always available.
The few core steps for BPBEMD algorithm are: ;Step 1 Assuming the size of original input data and resultant data to be N\times N and (N+2M)\times(N+2M), respectively, we can also define that original input data matrix to be in the middle of resultant data matrix. ;Step 2 Divide both original input data matrix and resultant data matrix into blocks of M\times M size. ;Step 3 Find the block which is the most similar to its neighbor block in the original input data matrix, and put it into the corresponding resultant data matrix. ;Step 4 Form a distance matrix which the matrix elements are weighted by different distances between each block from those boundaries.
Climate files like EnergyPlus weather files (EPW) or ASHRAE climate files, can be downloaded and installed. The table- based input structure allows full interoperability with MS Excel and comparable software. Modern features like copy and paste and Drag&drop; combined with visual input data check make input data management easier.
Dictionary learning develops a set (dictionary) of representative elements from the input data such that each data point can be represented as a weighted sum of the representative elements. The dictionary elements and the weights may be found by minimizing the average representation error (over the input data), together with L1 regularization on the weights to enable sparsity (i.e., the representation of each data point has only a few nonzero weights). Supervised dictionary learning exploits both the structure underlying the input data and the labels for optimizing the dictionary elements.
Many of these problems are related to erroneous assumptions of what input data is possible, or the effects of special data.
The example encoder is systematic because the input data is also used in the output symbols (Output 2). Codes with output symbols that do not include the input data are called non- systematic. Recursive codes are typically systematic and, conversely, non- recursive codes are typically non-systematic. It isn't a strict requirement, but a common practice.
Principal component analysis (PCA) is often used for dimension reduction. Given an unlabeled set of n input data vectors, PCA generates p (which is much smaller than the dimension of the input data) right singular vectors corresponding to the p largest singular values of the data matrix, where the kth row of the data matrix is the kth input data vector shifted by the sample mean of the input (i.e., subtracting the sample mean from the data vector). Equivalently, these singular vectors are the eigenvectors corresponding to the p largest eigenvalues of the sample covariance matrix of the input vectors.
Non-blocking, synchronous: device = IO.open() ready = False while not ready: print("There is no data to read!") ready = IO.poll(device, IO.INPUT, 5) # returns control if 5 seconds have elapsed or there is data to read (INPUT) data = device.read() print(data) 3\. Non-blocking, asynchronous: ios = IO.IOService() device = IO.open(ios) def inputHandler(data, err): "Input data handler" if not err: print(data) device.
ERC can take input data like text, audio, video or a combination form to detect several emotions such as fear, lust, pain, and pleasure.
There's no block transmission of entire screens (input forms) of input data. By contrast mainframes and minicomputers in closed architectures commonly use Block-oriented terminals.
Relative input data are the textual descriptions of a location which, alone, cannot output a spatial representation of that location. Such data outputs a relative geocode, which is dependent and geographically relative of other reference locations. An example of a relative geocode is address-interpolation using areal units or line vectors. "Across the street from the Empire State Building" is an example of a relative input data.
A hierarchical classifier is a classifier that maps input data into defined subsumptive output categories. The classification occurs first on a low-level with highly specific pieces of input data. The classifications of the individual pieces of data are then combined systematically and classified on a higher level iteratively until one output is produced. This final output is the overall classification of the data.
Absolute input data are the textual descriptions of a location which, alone, can output a spatial representation of that location. This data type outputs an absolute known location independently of other locations. For example, USPS ZIP codes; USPS ZIP+4 codes; complete and partial postal addresses; USPS PO boxes; rural routes; cities; counties; intersections; and named places can all be referenced in a data source absolutely. When there is a lot of variability in the way addresses can be represented – such as too much input data or too little input data – geocoders use address normalization and address standardization in order to resolve this problem.
Collapse operators reduce the dimensionality of an input data array by one or more dimensions. For example, summing over elements collapses the input array by 1 dimension.
Matrices are the input data for performing network analysis, factorial analysis or multidimensional scaling analysis; # Text mining of manuscripts (title, abstract, authors' keywords, etc.); # Co-word analysis.
The input data are the elements fij of the performance (decision) matrix, where fij is the value of the i-th criterion function for the alternative Aj.
Unsupervised dictionary learning does not utilize data labels and exploits the structure underlying the data for optimizing dictionary elements. An example of unsupervised dictionary learning is sparse coding, which aims to learn basis functions (dictionary elements) for data representation from unlabeled input data. Sparse coding can be applied to learn overcomplete dictionaries, where the number of dictionary elements is larger than the dimension of the input data. Aharon et al.
The algorithm has to recognize correlations between the images and the features, so that it is possible to extrapolate from the data base material to the input data.
Since the system automatically takes care of details like partitioning the input data, scheduling and executing tasks across a processing cluster, and managing the communications between nodes, programmers with no experience in parallel programming can easily use a large distributed processing environment. The programming model for MapReduce architecture is a simple abstraction where the computation takes a set of input key-value pairs associated with the input data and produces a set of output key-value pairs. In the Map phase, the input data is partitioned into input splits and assigned to Map tasks associated with processing nodes in the cluster. The Map task typically executes on the same node containing its assigned partition of data in the cluster.
Trellis quantization effectively finds the optimal quantization for each block to maximize the PSNR relative to bitrate. It has varying effectiveness depending on the input data and compression method.
Input data are the descriptive, textual information (address or building name) which the user wants to turn into numerical, spatial data (latitude and longitude) – through the process of geocoding.
Each leg on the input modules reads the process data and passes that information to its respective Main Processor. The three Main Processors communicate with each other using a proprietary high- speed bus system called the TriBus. Once per scan, the three Main Processors synchronize and communicate with their two neighbors over the TriBus. The Tricon votes digital input data, compares output data, and sends copies of analog input data to each Main Processor.
As multitasking greatly improved the throughput of computers, programmers started to implement applications as sets of cooperating processes (e. g., one process gathering input data, one process processing input data, one process writing out results on disk). This, however, required some tools to allow processes to efficiently exchange data. Threads were born from the idea that the most efficient way for cooperating processes to exchange data would be to share their entire memory space.
In contrast, a thin client generally does as little processing as possible on the client, relying on access to the server each time input data needs to be processed or validated.
Despite the many developments that CAD has achieved since the dawn of computers, there are still certain challenges that CAD systems face today. Some challenges are related to various algorithmic limitations in the procedures of a CAD system including input data collection, preprocessing, processing and system assessments. Algorithms are generally designed to select a single likely diagnosis, thus providing suboptimal results for patients with multiple, concurrent disorders. Today input data for CAD mostly come from electronic health records (EHR).
In practice, if the engineer can manually remove irrelevant features from the input data, this is likely to improve the accuracy of the learned function. In addition, there are many algorithms for feature selection that seek to identify the relevant features and discard the irrelevant ones. This is an instance of the more general strategy of dimensionality reduction, which seeks to map the input data into a lower-dimensional space prior to running the supervised learning algorithm.
In data structures, the range mode query problem asks to build a data structure on some input data to efficiently answer queries asking for the mode of any consecutive subset of the input.
A Model Test Data Generator A Test Data Generator follows the following steps # Program Control Flow Graph Construction # Path Selection # Generating Test Data The basis of the Generator is simple. The path selector identifies the paths. Once a set of test paths is determined the test generator derives input data for every path that results in the execution of the selected path. Essentially, our aim is to find an input data set that will traverse the path chosen by the path selector.
A governing equation may also be a state equation, an equation describing the state of the system, and thus actually be a constitutive equation that has "stepped up the ranks" because the model in question was not meant to include a time-dependent term in the equation. This is the case for a model of an oil production plant which on the average operates in a steady state mode. Results from one thermodynamic equilibrium calculation are input data to the next equilibrium calculation together with some new state parameters, and so on. In this case the algorithm and sequence of input data form a chain of actions, or calculations, that describes change of states from the first state (based solely on input data) to the last state that finally comes out of the calculation sequence.
Input data to the proofing combined process usually required both interpreting (with the exception of JDF ByteMap) and rendering. In these cases they will be included in the combined process describing the proofing step.
For instance, in Transport Layer Security (TLS), the input data is split in halves that are each processed with a different hashing primitive (SHA-1 and SHA-2) then XORed together to output the MAC.
A filter to sort lines in the input data stream and send them to the output data stream. Similar to the Unix command `sort`. Handles files up to 64k. This sort is always case insensitive.
A command to compile a FORTRAN program would look like "@FOR[,options] sourcefile, objectfile". Input data for an application could be read from a file (generally card images), or immediately follow the @ command in the run stream. All lines until the sentinel command "@END" were assumed to be input data, so forgetting to insert it led to the compiler interpreting subsequent commands as program data. For this reason, it was preferable to process data in files rather than inputting it in the run stream.
The output is mouse sensitive. The Lisp listener can display forms to input data for the various built-in commands. The user interface provides extensive online help and context sensitive help, completion of choices in various contexts.
There is one particular problem that we will face in this method. Sometimes, there will be only one local maxima or minima element in the input data, so it will cause the distance array to be empty.
The Watson computer system will be used to generate the Deep Thunder weather forecasts. Input data will be collected from over 200,000 Weather Underground personal weather stations, weather satellite data, smartphone barometer and data from other sources.
Worst case is the function which performs the maximum number of steps on input data of size n. Average case is the function which performs an average number of steps on input data of n elements. In real-time computing, the worst-case execution time is often of particular concern since it is important to know how much time might be needed in the worst case to guarantee that the algorithm will always finish on time. Average performance and worst- case performance are the most used in algorithm analysis.
The maximum-likelihood approach uses probability theory to complete all three steps simultaneously. It estimates critical parameters, including the divergence between sequences and the transition/transversion ratio, by deducing the most likely values to produce the input data.
Under sparsity assumptions and when input data is pre-processed with the whitening transformation, k-means produces the solution to the linear independent component analysis (ICA) task. This aids in explaining the successful application of k-means to feature learning.
Kahan suggests several rules of thumb that can substantially decrease by orders of magnitude the risk of numerical anomalies, in addition to, or in lieu of, a more careful numerical analysis. These include: as noted above, computing all expressions and intermediate results in the highest precision supported in hardware (a common rule of thumb is to carry twice the precision of the desired result i.e. compute in double precision for a final single precision result, or in double extended or quad precision for up to double precision results); and rounding input data and results to only the precision required and supported by the input data (carrying excess precision in the final result beyond that required and supported by the input data can be misleading, increases storage cost and decreases speed, and the excess bits can affect convergence of numerical procedures: notably, the first form of the iterative example given below converges correctly when using this rule of thumb). Brief descriptions of several additional issues and techniques follow.
When keying input data, the operator would be viewing the character display, which was also common to the then current IBM 3740 family of data entry to floppy disk media. A computer specialist was not required for the operation of System/32.
System identification techniques can utilize both input and output data (e.g. eigensystem realization algorithm) or can include only the output data (e.g. frequency domain decomposition). Typically an input-output technique would be more accurate, but the input data is not always available.
Commercial data processing "involves a large volume of input data, relatively few computational operations, and a large volume of output." Accounting programs are the prototypical examples of data processing applications. Information Systems (IS) is the field that studies such organizational computer systems.
However, both produce support information for each branch. The assumptions of these methods are overt and are verifiable. The complexity of the model can be increased if required. The model parameters are estimated directly from the input data so assumptions about evolutionary rate are avoided.
The user can also introduce farmers' responses by manually changing the relevant input data. Perhaps it will be useful first to study the automatic farmers' responses and their effect first and thereafter decide what the farmers' responses will be in the view of the user.
Determining a subset of the initial features is called feature selection. The selected features are expected to contain the relevant information from the input data, so that the desired task can be performed by using this reduced representation instead of the complete initial data.
In computer science, a preprocessor is a program that processes its input data to produce output that is used as input to another program. The output is said to be a preprocessed form of the input data, which is often used by some subsequent programs like compilers. The amount and kind of processing done depends on the nature of the preprocessor; some preprocessors are only capable of performing relatively simple textual substitutions and macro expansions, while others have the power of full-fledged programming languages. A common example from computer programming is the processing performed on source code before the next step of compilation.
Phylogenetic trees generated by computational phylogenetics can be either rooted or unrooted depending on the input data and the algorithm used. A rooted tree is a directed graph that explicitly identifies a most recent common ancestor (MRCA), usually an imputed sequence that is not represented in the input. Genetic distance measures can be used to plot a tree with the input sequences as leaf nodes and their distances from the root proportional to their genetic distance from the hypothesized MRCA. Identification of a root usually requires the inclusion in the input data of at least one "outgroup" known to be only distantly related to the sequences of interest.
On the other hand, in numerical algorithms for differential equations the concern is the growth of round-off errors and/or small fluctuations in initial data which might cause a large deviation of final answer from the exact solution . Some numerical algorithms may damp out the small fluctuations (errors) in the input data; others might magnify such errors. Calculations that can be proven not to magnify approximation errors are called numerically stable. One of the common tasks of numerical analysis is to try to select algorithms which are robust – that is to say, do not produce a wildly different result for very small change in the input data.
Systolic arrays (< wavefront processors), first described by H. T. Kung and Charles E. Leiserson are an example of MISD architecture. In a typical systolic array, parallel input data flows through a network of hard-wired processor nodes, resembling the human brain which combine, process, merge or sort the input data into a derived result. Systolic arrays are often hard-wired for a specific operation, such as "multiply and accumulate", to perform massively parallel integration, convolution, correlation, matrix multiplication or data sorting tasks. A Systolic array typically consists of a large monolithic network of primitive computing nodes which can be hardwired or software configured for a specific application.
A normal distribution is shown at left; this is the input data, in radiocarbon years. The central darker part of the normal curve is the range within one standard deviation of the mean; the lighter grey area shows the range within two standard deviations of the mean.
The command File=>Define Ini File can be used to define the location of the ini file. The ini file will save the conversion project input data files and directories. The SWMMM3 and SWMM 3.5 files are fixed format. The SWMM 4 files are free format.
Ledger is a command-line based double-entry bookkeeping application. Accounting data is stored in a plain text file, using a simple format, which the users prepare themselves using other tools. Ledger does not write or modify data, it only parses the input data and produces reports.
There are several steps in conducting MDS research: # Formulating the problem – What variables do you want to compare? How many variables do you want to compare? What purpose is the study to be used for? # Obtaining input data – For example, :- Respondents are asked a series of questions.
This can improve performance, and reduce heat and cost. Unfortunately, the compiler lacks accurate knowledge of runtime scheduling issues. Merely changing the CPU core frequency multiplier will have an effect on scheduling. Operation of the program, as determined by input data, will have major effects on scheduling.
Because most of image inputs are non-stationary which don’t exist boundary problems, the BPBEMD method is still lack of enough evidence that it is adaptive to all kinds of input data. Also, this method is narrowly restricted to be use on texture analysis and image processing.
Commercial data processing involves a large volume of input data, relatively few computational operations, and a large volume of output. For example, an insurance company needs to keep records on tens or hundreds of thousands of policies, print and mail bills, and receive and post payments.
This way of storing is robust and not deterministic. A memory cell is not addressed directly. If input data (logical addresses) are partially damaged at all, we can still get correct output data. The theory of the memory is mathematically complete and has been verified by computer simulation.
The input to the second phase, the query phase, is a query datum. The problem is to determine if the query datum was included in the original input data set. Operations are free except to access memory cells. This model is useful in the analysis of data structures.
The FIND command is a filter to find lines in the input data stream that contain or don't contain a specified string and send these to the output data stream. It may also be used as a pipe. The command is available in MS-DOS versions 2 and later.
A user of the 'Stand Manger' is required to create log grade sets (log specifications), regimes (sequence of events over the life of the rotation detailing events, including timing and costs/returns), as input data. This Tool can also use the output from 'Site Productivity' and 'Inventory' tools.
The algorithm described above requires full pairwise correspondence information between input data sets; a supervised learning paradigm. However, this information is usually difficult or impossible to obtain in real world applications. Recent work has extended the core manifold alignment algorithm to semi-supervised , unsupervised , and multiple- instance settings.
The method uses seasonal water balance components as input data. These are related to the surface hydrology (like rainfall, evaporation, irrigation, use of drain and well water for irrigation, runoff), and the aquifer hydrology (like upward seepage, natural drainage, pumping from wells). The other water balance components (like downward percolation, upward capillary rise, subsurface drainage) are given as output. The quantity of drainage water, as an output, is determined by two drainage intensity factors for drainage above and below drain level respectively (to be given with the input data), a drainage reduction factor (to simulate a limited operation of the drainage system), and the height of the water table, resulting from the computed water balance.
Most lossless compression programs do two things in sequence: the first step generates a statistical model for the input data, and the second step uses this model to map input data to bit sequences in such a way that "probable" (e.g. frequently encountered) data will produce shorter output than "improbable" data. The primary encoding algorithms used to produce bit sequences are Huffman coding (also used by DEFLATE) and arithmetic coding. Arithmetic coding achieves compression rates close to the best possible for a particular statistical model, which is given by the information entropy, whereas Huffman compression is simpler and faster but produces poor results for models that deal with symbol probabilities close to 1.
If the ordered pairs representing the original input function are equally spaced in their input variable (for example, equal time steps), then the Fourier transform is known as a discrete Fourier transform (DFT), which can be computed either by explicit numerical integration, by explicit evaluation of the DFT definition, or by fast Fourier transform (FFT) methods. In contrast to explicit integration of input data, use of the DFT and FFT methods produces Fourier transforms described by ordered pairs of step size equal to the reciprocal of the original sampling interval. For example, if the input data is sampled every 10 seconds, the output of DFT and FFT methods will have a 0.1 Hz frequency spacing.
Siamese neural network is composed of two twin networks whose output is jointly trained. There is a function above to learn the relationship between input data sample pairs. The two networks are the same, sharing the same weight and network parameters. Gregory Koch GKOCH, Richard Zemel ZEMEL, Ruslan Salakhutdinov(2015).
The contest consists of 2 rounds: # An elimination round, where entrants have to complete a five-hour challenge via the Deadline24 website. # The finals, which lasts 24 consecutive clock hours. The qualifying round lasts 5 hours and is conducted via the Internet. The teams receive tasks and necessary input data.
All of the codes can be described by stating 3 octal values. This is done with a naming convention of "Dxx.x" or "Kxx.x". Example: :Input Data Bits: ABCDEFGH :Data is split: ABC DEFGH :Data is shuffled: DEFGH ABC Now these bits are converted to decimal in the way they are paired.
Therefore, it is possible for computer programs to operate on other computer programs, by manipulating their programmatic data. The line between program and data can become blurry. An interpreter, for example, is a program. The input data to an interpreter is itself a program, just not one expressed in native machine language.
If one wishes to distinguish an upper and lower part of the transition zone in the absence of a subsurface drainage system, one may specify in the input data a drainage system with zero intensity. The aquifer has mainly horizontal flow. Pumped wells, if present, receive their water from the aquifer only.
They are variations of multilayer perceptrons that use minimal preprocessing. This architecture allows CNNs to take advantage of the 2D structure of input data. Its unit connectivity pattern is inspired by the organization of the visual cortex. Units respond to stimuli in a restricted region of space known as the receptive field.
RAMP allows the user to set the time unit of interest, according to scale and fidelity considerations. The only requirement is that time units should be used consistently across a model to avoid misleading results. Time units are expressed in the following input data: # Element failure probability distributions. # Element repair probability distributions.
This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task. Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning.
Speedup is by a factor of two. This is only possible because lifting is restricted to perfect-reconstruction filter banks. That is, lifting somehow squeezes out redundancies caused by perfect reconstruction. The transformation can be performed immediately in the memory of the input data (in place, in situ) with only constant memory overhead.
Ultimately an equilibrium situation will be brought about. The user can also introduce farmers' responses by manually changing the relevant input data. Perhaps it will be useful first to study the automatic farmers' responses and their effect and thereafter decide what the farmers' responses will be in the view of the user. The responses influence the water and salt balances, which, in their turn, slow down the process of water logging and salinization. Ultimately an equilibrium situation will be brought about. The user can also introduce farmers' responses by manually changing the relevant input data. Perhaps it will be useful first to study the automatic farmers' responses and their effect and thereafter decide what the farmers' responses will be in the view of the user.
In functional programming, an iteratee is a composable abstraction for incrementally processing sequentially presented chunks of input data in a purely functional fashion. With iteratees, it is possible to lazily transform how a resource will emit data, for example, by converting each chunk of the input to uppercase as they are retrieved or by limiting the data to only the five first chunks without loading the whole input data into memory. Iteratees are also responsible for opening and closing resources, providing predictable resource management. On each step, an iteratee is presented with one of three possible types of values: the next chunk of data, a value to indicate no data is available, or a value to indicate the iteration process has finished.
Before input validation is performed, the input is usually normalized by eliminating encoding (e.g., HTML encoding) and reducing the input data to a single common character set. Other forms of data, typically associated with signal processing (including audio and imaging) or machine learning, can be normalized in order to provide a limited range of values.
The dissolution of solid soil minerals or the chemical precipitation of poorly soluble salts is not included in the computation method, but to some extent it can be accounted for through the input data, e.g. by increasing or decreasing the salt concentration of the irrigation water or of the incoming water in the aquifer.
A mathematical approach requires ground rules for handling input data for each pin in a uniform way. 1\. A pin is designated as either bendable or not bendable. 2\. All pins are equally likely to fail in the same way. 3\. A pin, if inadvertently bent, is equally likely to bend in any direction. 4\.
The frequency domain decomposition (FDD) is an output-only system identification technique popular in civil engineering, in particular in structural health monitoring. As an output-only algorithm, it is useful when the input data is unknown. FDD is a modal analysis technique which generates a system realization using the frequency response given (multi-)output data.
She was awarded a National Institutes of Health Trailblazer Award in 2018. The award uses machine learning to improve the quality of ultrasound images. She will explore convolutional neural networks that input data and output readable images that are free from artefacts. She took part in the 2017 Deep Learning in Healthcare Summit in Boston.
Vehicle conflict points and yield points are automatically calculated inside of intersections. Road networks can be imported from Synchro, CORSIM, and TransCAD line layers. TransModeler includes various tools for managing a simulation project and the associated scenarios. Project databases, traffic signal timing plans, and other input data can be shared between multiple projects and scenarios.
The handling of roundoff errors increases the code complexity and execution time of AA operations. In applications where those errors are known to be unimportant (because they are dominated by uncertainties in the input data and/or by the linearization errors), one may use a simplified AA library that does not implement roundoff error control.
The examples shown here come from WIPL-D. Please keep in mind, these software packages must be used by someone who understands the process and can decide whether the calculated is real or if an error in the model and input data generated false output data (the old adage of garbage in equals garbage out).
In computer science, garbage in, garbage out (GIGO) is the concept that flawed, or nonsense input data produces nonsense output or "garbage". In the UK the term sometimes used is rubbish in, rubbish out (RIRO). The principle also applies more generally to all analysis and logic, in that arguments are unsound if their premises are flawed.
For example, an output of a passenger ship is the movement of people from departure to destination. ;System model :A system comprises multiple views. Man-made systems may have such views as concept, analysis, design, implementation, deployment, structure, behavior, input data, and output data views. A system model is required to describe and represent all these views.
Nearly all of the datasets are free to all registered users; a few are restricted to certain users (e.g. university researchers). In addition, the catalog shows data sets available in these categories: atmospheric, oceanographic, geophysical, hydrology, gridded analysis and MM5 model input data. The archive also maintains climate model output products for use in assessment and impact studies.
This is done in two steps: #Find the path predicate for the path #Solve the path predicate The solution will ideally be a system of equations which will describe the nature of input data so as to traverse the path. In some cases the generator provides the selector with feedback concerning paths which are infeasible etc.
Similar to the external solver interfaces, FEATool features built-in support for the Gmsh and Triangle mesh generators. If requested instead of the built-in mesh generation algorithm, FEATool will convert and export appropriate Gridgen2D, Gmsh, or Triangle input data files, call the mesh generators through external system calls, and re-import the resulting grids into FEATool.
In particular, the visible variables correspond to input data, and the hidden variables correspond to feature detectors. The weights can be trained by maximizing the probability of visible variables using Hinton's contrastive divergence (CD) algorithm. In general training RBM by solving the maximization problem tends to result in non-sparse representations. Sparse RBM was proposed to enable sparse representations.
WindStation is a wind energy software which uses computational fluid dynamics (CFD) to conduct wind resource assessments in complex terrain. The physical background and its numerical implementation are described in. and the official manual of the software. WindStation takes the terrain description in raster format as well as wind observations and atmospheric stability as input data.
The purpose of this standard is to specify an input data file, a measurement procedure and an output data format to characterize any four-color printing process. The output data (characterization) file should be transferred with any four-color (cyan, magenta, yellow and black) halftone image files to enable a color transformation to be undertaken when required. 29 pp.
MIDI patch bays also clean up any skewing of MIDI data bits that occurs at the input stage. MIDI data processors are used for utility tasks and special effects. These include MIDI filters, which remove unwanted MIDI data from the stream, and MIDI delays, effects that send a repeated copy of the input data at a set time.
Different kinds of machine learning regression and classification models can be used for having machines produce continuous or discrete labels. Sometimes models are also built that allow combinations across the categories, e.g. a happy-surprised face or a fearful- surprised face. The following sections consider many of the kinds of input data used for the task of emotion recognition.
VC Dimension uses the principles of measure theory and finds the maximum capacity under the best possible circumstances. This is, given input data in a specific form. As noted in, the VC Dimension for arbitrary inputs is half the information capacity of a Perceptron. The VC Dimension for arbitrary points is sometimes referred to as Memory Capacity.
In the early days of computing, computer use was typically limited to batch processing, i.e., non-interactive tasks, each producing output data from given input data. Computability theory, which studies computability of functions from inputs to outputs, and for which Turing machines were invented, reflects this practice. Since the 1970s, interactive use of computers became much more common.
On the contrary, BRNNs do not require their input data to be fixed. Moreover, their future input information is reachable from the current state. BRNN are especially useful when the context of the input is needed. For example, in handwriting recognition, the performance can be enhanced by knowledge of the letters located before and after the current letter.
In intuitionistic logic, the function f is called a realization of this formula. A precondition can be a proposition stating that input data exists, e.g. Xi may have the meaning “variable xi has received a value”, but it may denote also some other condition, e.g. that resources needed for using the function f are available, etc.
Programs like Cmix and SuperCollider are script-based rather than driven by a graphical interface. (Input data is frequently in the form of a program rather than a notelist.) This facilitated the creation of complex textures in works such as Idle Chatter, which contain thousands of short notes, frequently selected using random methods. This is sometimes called algorithmic composition.
The algorithm therefore produces a 2:1 compression ratio. The compression ratio is sometimes stated as being "up to 4:1" as it is common to use 16-bit precision for input data rather than 8-bit. This produces compressed output that is literally 1/4 the size of the input but it is not of comparable precision.
Salt concentrations of outgoing water (either from one reservoir into the other or by subsurface drainage) are computed on the basis of salt balances, using different leaching or salt mixing efficiencies to be given with the input data. The effects of different leaching efficiencies can be simulated by varying their input value. If drain or well water is used for irrigation, the method computes the salt concentration of the mixed irrigation water in the course of the time and the subsequent effect on the soil and ground water salinities, which again influences the salt concentration of the drain and well water. By varying the fraction of used drain or well water (to be given in the input data), the long-term effect of different fractions can be simulated.
Once events and event chains are defined, quantitative analysis using Monte Carlo simulation can be performed to quantify the cumulative effect of the events. Probabilities and impacts of risks assigned to activities are used as input data for Monte Carlo simulation of the project schedule.Williams, T. "Why Monte Carlo simulations of project networks can mislead". Project Management Journal, Vol 35.
The strict consensus tree is the least resolved and contains those splits that are in every tree. Bootstrapping (a statistical resampling strategy) is used to provide branch support values. The technique randomly picks characters from the input data matrix and then the same analysis is used. The support value is the fraction of the runs with that bipartition in the observed tree.
The older type is the RS-232 barcode scanner. This type requires special programming for transferring the input data to the application program. Keyboard interface scanners connect to a computer using a PS/2 or AT keyboard–compatible adaptor cable (a "keyboard wedge"). The barcode's data is sent to the computer as if it had been typed on the keyboard.
Other classifiers work by comparing observations to previous observations by means of a similarity or distance function. An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. The term "classifier" sometimes also refers to the mathematical function, implemented by a classification algorithm, that maps input data to a category. Terminology across fields is quite varied.
By means of its "Report Generator", FAMOS enables creation of documentations / lab reports consisting of a variety of dialog elements and plots as well as graphics with controls which can be automatically hidden when printing. The reports generated can be subject to post-processing using various input data, and there are templates for partially or fully automated composition of reports.
The X.Org Server communicates with its clients, e.g. Amarok, over the X11 protocol X Window System logo One example of a display server is the X.Org Server, which runs on top of the kernel (usually a Unix-based kernel, such as Linux or BSD). It receives user input data (e.g. from evdev on Linux) and passes it to one of its clients.
Pre-charge half buffer (PCHB) uses domino logic to implement a more complex computational pipeline stage. This removes the long pull-up network problem, but also introduces an isochronic fork on the input data which must be resolved later in the cycle. This causes the pipeline cycle to be 14 transitions long (or 10 using the half-cycle timing assumption).
Functional testing typically involves six steps # The identification of functions that the software is expected to perform # The creation of input data based on the function's specifications # The determination of output based on the function's specifications # The execution of the test case # The comparison of actual and expected outputs # To check whether the application works as per the customer need.
The programming language used in VisualAp to describe a system is a dataflow programming language. Execution is determined by the structure of the graphical block diagram on which the programmer connects different components by drawing connectors. These connectors propagate variables and any component can execute as soon as all its input data become available. Internally the VisualAp programming language is based on XML.
Xpress includes its modelling language Xpress Mosel and the integrated development environment Xpress Workbench. Mosel includes distributed computing features to solve multiple scenarios of an optimization problem in parallel. Uncertainty in the input data can be handled via robust optimization methods. Xpress has a modeling module called BCL (Builder Component Library) that interfaces to the C, C++, Java programming languages, and to the .
The injected code will then automatically get executed. This type of attack exploits the fact that most computers (which use a Von Neumann architecture) do not make a general distinction between code and data, so that malicious code can be camouflaged as harmless input data. Many newer CPUs have mechanisms to make this harder, such as a no-execute bit.
On May 4, 2020, the NCAA announced that it would replace the RPI with the NET (NCAA Evaluation Tool), a metric that has been used in the selection process for the D-I men's tournament since 2019. The women's version of the NET uses input data specific to the women's game, but is otherwise functionally identical to the men's version.
SahysMod components The method uses seasonal water balance components as input data. These are related to the surface hydrology (like rainfall, potential evaporation, irrigation, use of drain and well water for irrigation, runoff), and the aquifer hydrology (e.g., pumping from wells). The other water balance components (like actual evaporation, downward percolation, upward capillary rise, subsurface drainage, groundwater flow) are given as output.
In standard NMF, matrix factor , i.e., can be anything in that space. Convex NMFC Ding, T Li, MI Jordan, Convex and semi-nonnegative matrix factorizations, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 45-55, 2010 restricts the columns of to convex combinations of the input data vectors (v_1, \cdots, v_n) . This greatly improves the quality of data representation of .
The NSS software crypto module has been validated five times (1997, 1999, 2002, 2007, and 2010) for conformance to FIPS 140 at Security Levels 1 and 2. NSS was the first open source cryptographic library to receive FIPS 140 validation. The NSS libraries passed the NISCC TLS/SSL and S/MIME test suites (1.6 million test cases of invalid input data).
In computer science, best, worst, and average cases of a given algorithm express what the resource usage is at least, at most and on average, respectively. Usually the resource being considered is running time, i.e. time complexity, but could also be memory or other resource. Best case is the function which performs the minimum number of steps on input data of n elements.
A graphical example of insertion sort. The partial sorted list (black) initially contains only the first element in the list. With each iteration one element (red) is removed from the "not yet checked for order" input data and inserted in-place into the sorted list. Insertion sort iterates, consuming one input element each repetition, and growing a sorted output list.
The primary calculation in GRAPE hardware is a summation of the forces between a particular star and every other star in the simulation. Several versions (GRAPE-1, GRAPE-3 and GRAPE-5) use the Logarithmic Number System (LNS) in the pipeline to calculate the approximate force between two stars, and take the antilogarithms of the x, y and z components before adding them to their corresponding total. The GRAPE-2, GRAPE-4 and GRAPE-6 use floating point arithmetic for more accurate calculation of such forces. The advantage of the logarithmic-arithmetic versions is they allow more and faster parallel pipes for a given hardware cost because all but the sum portion of the GRAPE algorithm (1.5 power of the sum of the squares of the input data divided by the input data) is easy to perform with LNS.
This agreement set a research target of 150 tonnes per country, intended to input data to scientific research on the fish population structure. Australian fishermen were allowed to catch three times as much fish as the New Zealanders. The quota was set at 2100 and then 2400 tonnes for 1999-2000. However it was exceeded so the fishing ground was closed till the end of February 2000.
CrimeStat can input data both attribute and GIS files but requires that all datasets have geographical coordinates assigned for the objects. The basic file format is dBase (dbf) but shape (shp), and Ascii text files can also be read. The program requires a Primary File but many routines also use a Secondary File. CrimeStat uses three coordinate systems: spherical (longitude, latitude), projected and directional (angles).
The shock pulse meters measure the shock signal on a decibel scale, at two levels. A micro processor evaluates the signal. It needs input data defining the bearing type (ISO number) and the rolling velocity (RPM and bearing diameter). Surface damage in bearings causes a large increase in shock pulse strength, combined with a notable change in the characteristics between stronger and weaker pulses.
The IWFM source code is released under the GNU General Public License. Groundwater flow is simulated using the finite element method. Surface water flow can be simulated as a simple one-dimensional flow-through network or with the kinematic wave method. IWFM input data sets incorporate a time stamp, allowing users to run a model for a specified time period without editing the input files.
Rule learning algorithm are taking training data as input and creating rules by partitioning the table with cluster analysis. A possible alternative over the ID3 algorithm is genetic programming which evolves a program until it fits to the data. Creating different algorithm and testing them with input data can be realized in the WEKA software. Additional tools are machine learning libraries for Python like scikit-learn.
No lossless compression algorithm can efficiently compress all possible data (see the section Limitations below for details). For this reason, many different algorithms exist that are designed either with a specific type of input data in mind or with specific assumptions about what kinds of redundancy the uncompressed data are likely to contain. Some of the most common lossless compression algorithms are listed below.
Model users can set input data on the dashboard screen, run the model, and analyze the output. AnyLogic Cloud allows users to run models using web browsers, on desktop computers and mobile devices, with the model being executed on the server side. Multiple run experiments are performed using several nodes. The results of all executed experiments are stored in the database and can be immediately accessed.
Machine learning can make it possible to recognize the shared characteristics of promotional events and identify their effect on normal sales. Learning machines use simpler versions of nonlinear functions to model complex nonlinear phenomena. Learning machines process sets of input and output data and develop a model of their relationship. Based on this model, learning machines forecast outputs associated with new sets of input data.
Input data for the model included habitat data, daily minimum, maximum, and mean temperatures, and wind speed and direction. For the Aphid agents, age, position, and morphology (alate or apterous) were considered. Age ranged from 0.00 to 2.00, with 1.00 being the point at which the agent becomes an adult. Reproduction by the Aphid agents is dependent on age, morphology, and daily minimum, maximum, and mean temperatures.
In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization and various forms of clustering. Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros.
A cyclic redundancy check (CRC) is a non- secure hash function designed to detect accidental changes to digital data in computer networks. It is not suitable for detecting maliciously introduced errors. It is characterized by specification of a generator polynomial, which is used as the divisor in a polynomial long division over a finite field, taking the input data as the dividend. The remainder becomes the result.
OpenPGP, described in RFC 4880, describes Radix-64 encoding, also known as "ASCII armor". Radix-64 is identical to the "Base64" encoding described from MIME, with the addition of an optional 24-bit CRC. The checksum is calculated on the input data before encoding; the checksum is then encoded with the same Base64 algorithm and, prefixed by "`=`" symbol as separator, appended to the encoded output data.
A signal processing algorithm that cannot keep up with the flow of input data with output falling farther and farther behind the input, is not real-time. But if the delay of the output (relative to the input) is bounded regarding a process that operates over an unlimited time, then that signal processing algorithm is real-time, even if the throughput delay may be very long.
Audification is an auditory display technique for representing a sequence of data values as sound. By definition, it is described as a "direct translation of a data waveform to the audible domain." Audification interprets a data sequence and usually a time series, as an audio waveform where input data are mapped to sound pressure levels. Various signal processing techniques are used to assess data features.
Edifi was divided into five departments: Sales, Accounting, Reservations, which set up appointments for the seminars, Customer Service, which dealt with the clients and input data from client phone interviews and tax documents to the company's proprietary software (a customized "front end" for Microsoft Access), and Forms, which was in charge of completing financial aid forms, and whose new hires were tested for their printing ability.
A DataSet is a basic unit in NetMiner and used as an input data for all the analysis and visualization Modules. A DataSet is composed of four types of data items: Main Nodeset, Sub Nodeset, 1-mode Network data and 2-mode Network data. A DataSet can have only one Main Nodeset. But multiple 1-mode Network data can be contained in a DataSet.
By contrast, when we wish to check whether a Boolean MSO formula is satisfied by an input finite tree, this problem can be solved in linear time in the tree, by translating the Boolean MSO formula to a tree automaton and evaluating the automaton on the tree. In terms of the query, however, the complexity of this process is generally nonelementary. Thanks to Courcelle's theorem, we can also evaluate a Boolean MSO formula in linear time on an input graph if the treewidth of the graph is bounded by a constant. For MSO formulas that have free variables, when the input data is a tree or has bounded treewidth, there are efficient enumeration algorithms to produce the set of all solutions, ensuring that the input data is preprocessed in linear time and that each solution is then produced in a delay linear in the size of each solution, i.e.
The National Chornobyl Museum in Kyiv, Ukraine supports the "Remembrance Book" (, Kneega Pahmyati) an open to the public online database of liquidators featuring personal pages with photo and brief structured information on their input. Data fields include "Radiation damage suffered", "Field of liquidation activity" and "Subsequent fate". The project started in 1997, containing over 5,000 entries as of February, 2013.Memory The database is currently available in Ukrainian language only.
The 2009 contest required participants to write a program that sifts through routing directives but redirects a piece of luggage based on some innocuous- looking comment in the space-delimited input data file. The contest began December 29, 2009, and was due to end on March 1, 2010.The Underhanded C Contest », xcott.com (archived from the original on 2011-07-18) However, no activity occurred for three years.
An exerciser bar is supported for rotation and acts against an hydraulic cylinder with the angle of the bar and the pressure in the cylinder measured and fed to a micro computer which, using this input data, controls the cylinder pressure in accordance with a selected exercise program, the micro computer also providing outputs to displays so that the person exercising can monitor his progress. Patent US 4354676 A, 1982.
The Main Processors execute the userwritten application and send outputs generated by the application to the output modules. In addition to voting the input data, the TriBus votes the output data. This is done on the output modules as close to the field as possible, in order to detect and compensate for any errors that could occur between the Tricon voting and the final output driven to the field.
For Search and Rescue planning the latest SARIS software is used allowing the officers to input data relating to an incident which then uses multiple calculations involving wind, tide and drift formulas to predict where a person or object may have drifted to. Jersey Coastguard also has an emergency response vehicle, which is equipped with TETRA and VHF radios. 123 lifebelts are located around the harbours and island.
It has been noted that results of Fuzzy ART and ART 1 (i.e., the learnt categories) depend critically upon the order in which the training data are processed. The effect can be reduced to some extent by using a slower learning rate, but is present regardless of the size of the input data set. Hence Fuzzy ART and ART 1 estimates do not possess the statistical property of consistency.
4-connectivity 8-connectivity A graph, containing vertices and connecting edges, is constructed from relevant input data. The vertices contain information required by the comparison heuristic, while the edges indicate connected 'neighbors'. An algorithm traverses the graph, labeling the vertices based on the connectivity and relative values of their neighbors. Connectivity is determined by the medium; image graphs, for example, can be 4-connected neighborhood or 8-connected neighborhood.
In computer science, dynamization is the process of transforming a static data structure into a dynamic one. Although static data structures may provide very good functionality and fast queries, their utility is limited because of their inability to grow/shrink quickly, thus making them inapplicable for the solution of dynamic problems, where the amount of the input data changes. Dynamization techniques provide uniform ways of creating dynamic data structures.
Such reusable asynchronous procedure is named Actor. Programming using Actors is described in Actor model and Dataflow programming. The difference is that Actor in the Actor model has exactly two ports: one port to receive input data, and another (hidden) port to provide serial handling of input messages, while Actor in Dataflow programming can have many, and goes to execution service when all inputs contain data or permissions.
Water balance factors in the top soil The water balances are calculated for each reservoir separately as shown in the article Hydrology (agriculture). The excess water leaving one reservoir is converted into incoming water for the next reservoir. The three soil reservoirs can be assigned a different thickness and storage coefficients, to be given as input data. In a particular situation, the transition zone or the aquifer need not be present.
The output of Saltmod is given for each season of any year during any number of years, as specified with the input data. The output data comprise hydrological and salinity aspects. The data are filed in the form of tables that can be inspected directly or further analyzed with spreadsheet programs. As the soil salinity is very variable from place to place (figure left) SaltMod includes frequency distributions in the output.
Maximum Variance Unfolding (MVU), also known as Semidefinite Embedding (SDE), is an algorithm in computer science that uses semidefinite programming to perform non-linear dimensionality reduction of high-dimensional vectorial input data. It is motivated by the observation that kernel Principal Component Analysis (kPCA) does not reduce the data dimensionality, as it leverages the Kernel trick to non-linearly map the original data into an inner-product space.
Wavelets are extracted individually for each well. A final "multi-well" wavelet is then extracted for each volume using the best individual well ties and used as input to the inversion. Histograms and variograms are generated for each stratigraphic layer and lithology, and preliminary simulations are run on small areas. The AVA geostatistical inversion is then run to generate the desired number of realizations, which match all the input data.
The spreadsheet allowed blocks of cells to be protected from editing or other user input. The BTOS version allowed scripts to be written that included opening the spreadsheet for user input, then automatically printing graphs based on the input data. The system shell was extensible, making it possible to define new commands. To get the parameters, the system would display the form which was to be filled out by the user.
Basic three sub-steps on medical imaging are segmentation, feature extraction / selection and classification. These sub-steps require advanced techniques to analyze input data with less computational time. Although much effort has been devoted on creating innovative techniques for these procedures of CAD systems, there is still not the single best algorithm for each step. Ongoing studies in building innovative algorithms for all the aspects of CAD systems is essential.
The Todd–Coxeter algorithm can be applied to infinite groups and is known to terminate in a finite number of steps, provided that the index of H in G is finite. On the other hand, for a general pair consisting of a group presentation and a subgroup, its running time is not bounded by any computable function of the index of the subgroup and the size of the input data.
This mapping is usually done at the time when parallel input data is converted into a serial output stream for transmission over a fibre channel link. The odd/even selection is done in such a way that a long-term zero disparity between ones and zeroes is maintained. This is often called "DC balancing". The 8-bit to 10-bit conversion scheme uses only 512 of the possible 1024 output values.
A master node ensures that only one copy of the redundant input data is processed. # Shuffle: worker nodes redistribute data based on the output keys (produced by the `map` function), such that all data belonging to one key is located on the same worker node. # Reduce: worker nodes now process each group of output data, per key, in parallel. MapReduce allows for the distributed processing of the map and reduction operations.
PACT programs are constructed as data flow graphs that consist of data sources, PACTs, and data sinks. One or more data sources read files that contain the input data and generate records from those files. Those records are processed by one or more PACTs, each consisting of an Input Contract, user code, and optional code annotations. Finally, the results are written back to output files by one or more data sinks.
Galaxy objects are anything that can be saved, persisted, and shared in Galaxy: ; Histories: : Histories are computational analyses (recipes) run with specified input datasets, computational steps and parameters. Histories include all intermediate and output datasets as well. ; Workflows: : Workflows are computational analyses that specify all the steps (and parameters) in the analysis, but none of the data. Workflows are used to run the same analysis against multiple sets of input data.
Computerized clinical psychological test interpretations: Unvalidated plus all mean and no sigma. American Psychologist, 41, 14-24. and the validity of individual CBTI systems has been found to vary. However, many validity studies are flawed due to small samples, criterion contamination, the Barnum effect, inadequate input data to generate powerful statistical prediction rules, unreliability of measures and the practice of generalizing across testing situations and populations without considering potential moderators.
4B5B codes are designed to produce at least two transitions per 5 bits of output code regardless of input data. When NRZI-encoded, the transitions provide necessary clock transitions for the receiver. For example, a run of 4 bits such as 0000 contains no transitions and that causes clocking problems for the receiver. 4B5B solves this problem by assigning the 4-bit block a 5-bit code, in this case, 11110.
The operating system was multi-programming with a variable number of tasks. In the field, the system did not perform well on account of input data being stored on disc as 80-byte records, and output as 160-byte records. In about 1971, the then supplier, ICL, rewrote I/O modules to remove trailing blanks on input and output, and to block to 384 bytes, which improved performance considerably.
In data structures, a range query consists of preprocessing some input data into a data structure to efficiently answer any number of queries on any subset of the input. Particularly, there is a group of problems that have been extensively studied where the input is an array of unsorted numbers and a query consists of computing some function, such as the minimum, on a specific range of the array.
Similarly to , PTA accounts for high-throughput data for every gene. In addition, specific topological information is used about role, position, and interaction directions of the pathway genes. This requires additional input data from a pathway database in a pre-specified format, such as KEGG Markup Language (KGML). Using this information, PTA estimates a pathway significance by considering how much each individual gene alteration might have affected the whole pathway.
A mail merge programming language allows the user to input data from a text file or Prodata database. This is achieved by 'Stored commands' within the body of the text, an idea borrowed from the 'dot commands' of WordStar. Stored commands are similarly used to control formatting and layout of the text for printing. A preview mode showed the formatted layout but Protext did not display the fonts on screen.
The `find` command is a filter to find lines in the input data stream that contain or don't contain a specified string and send these to the output data stream. It does not support wildcard characters. The command is available in DOS, Digital Research FlexOS, IBM/Toshiba 4690 OS, IBM OS/2, Microsoft Windows, and ReactOS. On MS-DOS, the command is available in versions 2 and later.
Prof. Pleszczyńska is known for her criticism of the classic statistical approach. Classic parametric methods, like Pearson correlation coefficient, or least squares method produce comparable results only for comparable distribution types (in practice multivariate normal distribution is being assumed). Parametric statistical tests are derived from distribution assumptions. Classic methods fail if the input data contain strong outliers, and interpretation of their results should be different for different distribution types.
Bootstrapping is a technique used to iteratively improve a classifier's performance. Typically, multiple classifiers will be trained on different sets of the input data, and on prediction tasks the output of the different classifiers will be combined together. Seed AI is a hypothesized type of artificial intelligence capable of recursive self- improvement. Having improved itself, it would become better at improving itself, potentially leading to an exponential increase in intelligence.
In order to obtain input data for PyClone, cell lysis is a required step to prepare bulk sample sequencing. This results in the loss of information on the complete set of mutations defining a clonal population. PyClone can distinguish and identify the frequency of different clonal populations but can not identify exact mutations defining these populations. Instead of clustering cells by mutational composition, PyClone clusters mutations that have similar cellular frequencies.
It is often desirable that the output of a hash function have fixed size (but see below). If, for example, the output is constrained to 32-bit integer values, the hash values can be used to index into an array. Such hashing is commonly used to accelerate data searches. Producing fixed-length output from variable length input can be accomplished by breaking the input data into chunks of specific size.
In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function by making data-driven predictions or decisions, through building a mathematical model from input data. The data used to build the final model usually comes from multiple datasets. In particular, three datasets are commonly used in different stages of the creation of the model.
In computer science, incremental learning is a method of machine learning in which input data is continuously used to extend the existing model's knowledge i.e. to further train the model. It represents a dynamic technique of supervised learning and unsupervised learning that can be applied when training data becomes available gradually over time or its size is out of system memory limits. Algorithms that can facilitate incremental learning are known as incremental machine learning algorithms.
If the new system is accepted, the existing system will stop running and will be replaced by the new one. If both old and new systems are computerized, the input data can be held on a disk or tape and run concurrently on both systems. If changing from a manual system to computerized system, the main problem is inputting the data. Data needs to be input manually and this may take a long time.
Unlike the other products, POWER required a dedicated partition. It allowed a single printer (1403/2311), punch (2520, 2540) or reader (2540, 2501) to be shared by two or more processing partitions. Input data was asynchronously loaded and directed to the proper partition by Job class. Output was directed to disk and stored there - then directed to a printer or punch by the writer type, (PRT, PUN), Job Class, Priority and form code.
Figure 1 illustrates the functional components of most LDPC encoders. Fig. 1. LDPC Encoder During the encoding of a frame, the input data bits (D) are repeated and distributed to a set of constituent encoders. The constituent encoders are typically accumulators and each accumulator is used to generate a parity symbol. A single copy of the original data (S0,K-1) is transmitted with the parity bits (P) to make up the code symbols.
Each language is represented by a path, the paths showing the different states as it evolves. There is only one path between every pair of vertices. Unrooted trees plot the relationship between the input data without assumptions regarding their descent. A rooted tree explicitly identifies a common ancestor, often by specifying a direction of evolution or by including an "outgroup" that is known to be only distantly related to the set of languages being classified.
In order to apply the VR DIF algorithm the input data is to be formulated and rearranged as follows.S. C. Chan and K. L. Ho, “Direct methods for computing discrete sinusoidal transforms,” in Proc. Inst. Elect. Eng. Radar Signal Process., vol. 137, Dec. 1990, pp. 433–442.O. Alshibami and S. Boussakta, “Three-dimensional algorithm for the 3-D DCT-III,” in Proc. Sixth Int. Symp. Commun., Theory Applications, July 2001, pp. 104–107.
OpenCPN (Open Chart Plotter Navigator) is a free software project to create a concise chart plotter and navigation software, for use underway or as a planning tool. OpenCPN is developed by a team of active sailors using real world conditions for program testing and refinement. OpenCPN uses satellite navigation input data to determine the ship's own position and data from an AIS receiver to plot the positions of ships in the neighborhood.
Because analogue modelling involves the simplification of geodynamic processes, it also has several disadvantages and limitations: # The study of natural rock properties still needs more research. The more accurate the input data, the more accurate the analogue modelling. # There are many more factors in nature that affect the geodynamic processes (such as isostatic compensation and erosion), and these are most likely heterogeneous systems. Thus they are challenging for simulations (some factors are not even known).
Bollinger bands have been applied to manufacturing data to detect defects (anomalies) in patterned fabrics. In this application, the upper and lower bands of Bollinger Bands are sensitive to subtle changes in the input data obtained from samples. The International Civil Aviation Organization is using Bollinger bands to measure the accident rate as a safety indicator to measure efficacy of global safety initiatives. %b and bandwidth are also used in this analysis.
It uses 16 rounds of a balanced Feistel network to process the input data blocks (see diagram right). The complex round function f incorporates two substitution- permutation layers in each round. The key schedule is also a Feistel structure – an unbalanced one unlike the main network — but using the same F-function. Overall LOKI97 cipher structure The LOKI97 round function (shown right) uses two columns each with multiple copies of two basic S-boxes.
SUHA is most commonly used as a foundation for mathematical proofs describing the properties and behavior of hash tables in theoretical computer science. Minimizing hashing collisions can be achieved with a uniform hashing function. These functions often rely on the specific input data set and can be quite difficult to implement. Assuming uniform hashing allows hash table analysis to be made without exact knowledge of the input or the hash function used.
PCA has several limitations. First, it assumes that the directions with large variance are of most interest, which may not be the case. PCA only relies on orthogonal transformations of the original data, and it exploits only the first- and second-order moments of the data, which may not well characterize the data distribution. Furthermore, PCA can effectively reduce dimension only when the input data vectors are correlated (which results in a few dominant eigenvalues).
Other problem solving tools are linear and nonlinear programming, queuing systems, and simulation. Much of computer science involves designing completely automatic systems that will later solve some specific problem—systems to accept input data and, in a reasonable amount of time, calculate the correct response or a correct-enough approximation. In addition, people in computer science spend a surprisingly large amount of human time finding and fixing problems in their programs: Debugging.
In regression problems this can be found in one matrix operation. In classification problems the fixed non-linearity introduced by the sigmoid output function is most efficiently dealt with using iteratively re-weighted least squares. RBF networks have the disadvantage of requiring good coverage of the input space by radial basis functions. RBF centres are determined with reference to the distribution of the input data, but without reference to the prediction task.
Flaga-Maryanczyk et al. conducted a study in Sweden which examined a passive ventilation system which integrated a run-around system using a ground source heat pump as the heat source to warm incoming air. Experimental measurements and weather data were taken from the passive house used in the study. A CFD model of the passive house was created with the measurements taken from the sensors and weather station used as input data.
Competitive learning is a form of unsupervised learning in artificial neural networks, in which nodes compete for the right to respond to a subset of the input data. A variant of Hebbian learning, competitive learning works by increasing the specialization of each node in the network. It is well suited to finding clusters within data. Models and algorithms based on the principle of competitive learning include vector quantization and self-organizing maps (Kohonen maps).
Data-driven personas (sometimes also called quantitative personas) have been suggested by McGinn and Kotamraju. These personas are claimed to address the shortcomings of qualitative persona generation (see Criticism). Academic scholars have proposed several methods for data-driven persona development, such as clustering, factor analysis, principal component analysis, latent semantic analysis, and non-negative matrix factorization. These methods generally take numerical input data, reduce its dimensionality, and output higher level abstractions (e.g.
This is known as a linear search or brute-force search, each element being checked for equality in turn and the associated value, if any, used as a result of the search. This is often the slowest search method unless frequently occurring values occur early in the list. For a one-dimensional array or linked list, the lookup is usually to determine whether or not there is a match with an 'input' data value.
Generative modelling gains efficiency through the possibility of creating high-level shape operators from low-level shape operators. Any sequence of processing steps can be grouped together to create a new combined operator. It may use elementary operators as well as other combined operators. Concrete values can easily be replaced by parameters, which makes it possible to separate data from operations: The same processing sequence can be applied to different input data sets.
The output of a cryptographic hash function, also known as a message digest, can provide strong assurances about data integrity, whether changes of the data are accidental (e.g., due to transmission errors) or maliciously introduced. Any modification to the data will likely be detected through a mismatching hash value. Furthermore, given some hash value, it is typically infeasible to find some input data (other than the one given) that will yield the same hash value.
Cumulative frequency distribution of soil salinity Example of the polygonal mapping facilities using the depth of the watertable The output is given for each season of any year during any number of years, as specified with the input data. The output data comprise hydrological and salinity aspects. As the soil salinity is very variable from place to place (figure left) SahysMod includes frequency distributions in the output. The figure was made with the CumFreq program .
Linear subspace learning algorithms are traditional dimensionality reduction techniques that represent input data as vectors and solve for an optimal linear mapping to a lower-dimensional space. Unfortunately, they often become inadequate when dealing with massive multidimensional data. They result in very-high-dimensional vectors, lead to the estimation of a large number of parameters.H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, "MPCA: Multilinear principal component analysis of tensor objects," IEEE Trans.
Reliability design begins with the development of a (system) model. Reliability and availability models use block diagrams and Fault Tree Analysis to provide a graphical means of evaluating the relationships between different parts of the system. These models may incorporate predictions based on failure rates taken from historical data. While the (input data) predictions are often not accurate in an absolute sense, they are valuable to assess relative differences in design alternatives.
The museum supports the "Remembrance Book" (, Knyha Pam'yati) - a unique online database of Liquidators (Chernobyl disaster management personnel some of whom sacrificed their lives) featuring personal pages with photo and brief structured information on their input. Data fields include "Radiation damage suffered", "Field of liquidation activity" and "Subsequent fate". The project started in 1997, containing over 5000 entries as of February, 2013.Memory The database is currently available in Ukrainian language only.
Knowledge discovery describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data. It is often described as deriving knowledge from the input data. Knowledge discovery developed out of the data mining domain, and is closely related to it both in terms of methodology and terminology. The most well-known branch of data mining is knowledge discovery, also known as knowledge discovery in databases (KDD).
Data can then be retrieved from Akonadi by a model designed to collect specific data (mail, calendar, contacts, etc.). The application itself is made of viewers and editors to display data to the user and let them input data. Akonadi also supports metadata created by applications. Development of PIM applications is made much easier because Akonadi takes care of data storage and retrieval, which are traditionally the difficult parts of creating a PIM application.
Consumers should not rely on CARFAX alone when checking out a used vehicle. Although CARFAX continuously expands its database and resources, some information is not allowed to be provided. Under the 1994 U.S. Drivers Privacy Protection Act, personal information such as names, telephone numbers and addresses of current or previous owners are neither collected nor reported. CARFAX does not have access to every facility and mistakes are sometimes made by those who input data.
This is a stratigraphic representation of the seismic data using the seismic interpretation to define the layers. The stratigraphic grid model is then mapped to the corner point grid by adjusting the zones. Using the porosity and permeability models and a saturation height function, initial saturation models are built. If volumetric calculations identify problems in the model, changes are made in the petrophysical model without causing the model to stray from the original input data.
Likewise, computerized system can affect managers in terms of their management role and decision making process. Systems which are user friendly often meet less refusal as users feel comfortable with the system, have sense of control and be able to evaluate their stored input data. The system itself should also be sufficiently adaptable to suit different backgrounds and proficiency levels of users. Overcoming refusal to change and the adoption of the new system is a management issue.
The salt balances are calculated for each reservoir separately. They are based on their water balances, using the salt concentrations of the incoming and outgoing water. Some concentrations must be given as input data, like the initial salt concentrations of the water in the different soil reservoirs, of the irrigation water and of the incoming ground water in the aquifer. Graphic presentation of soil salinity trends The concentrations are expressed in terms of electric conductivity (EC in dS/m).
Analyze the algorithm, typically using time complexity analysis to get an estimate of the running time as a function of the size of the input data. The result is normally expressed using Big O notation. This is useful for comparing algorithms, especially when a large amount of data is to be processed. More detailed estimates are needed to compare algorithm performance when the amount of data is small, although this is likely to be of less importance.
A trust-based decision in a specific domain is a multi-stage process. The first step of this process consists in identifying and selecting the proper input data, that is, the trust evidence. In general, these are domain-specific and are derived from an analysis conducted over the application involved. In the next step, a trust computation is performed on the evidence to produce trust values, that means the estimation of the trustworthiness of entities in that particular domain.
As most systems involve stochastic processes, simulations frequently make use of random number generators to create input data which approximates the random nature of real-world events. Computer generated [random numbers] are usually not random in the strictest sense, as they are calculated using a set of equations. Such numbers are known as pseudo-random numbers. When making use of pseudo-random numbers the analyst must make certain that the true randomness of the numbers is checked.
While boosting is not algorithmically constrained, most boosting algorithms consist of iteratively learning weak classifiers with respect to a distribution and adding them to a final strong classifier. When they are added, they are weighted in a way that is related to the weak learners' accuracy. After a weak learner is added, the data weights are readjusted, known as "re-weighting". Misclassified input data gain a higher weight and examples that are classified correctly lose weight.
When the model is run, the system automatically reads input data from the spreadsheet and provides it to the model, and then writes the model results back to the spreadsheet. SolverStudio works with a range of commercial and open source modelling systems. By default, it uses PuLP, an open-source Python COIN-OR modelling language. A second open-source Python option is Pyomo which supports non-linear and stochastic programming and provides access to a larger range of solvers.
Unsupervised feature learning is learning features from unlabeled data. The goal of unsupervised feature learning is often to discover low-dimensional features that captures some structure underlying the high- dimensional input data. When the feature learning is performed in an unsupervised way, it enables a form of semisupervised learning where features learned from an unlabeled dataset are then employed to improve performance in a supervised setting with labeled data. Several approaches are introduced in the following.
A land use regression model (LUR model) is an algorithm often used for analyzing pollution, particularly in densely populated areas. The model is based on predictable pollution patterns to estimate concentrations in a particular area. This requires some linkage to the environmental characteristics of the area, especially characteristics that influence pollutant emission intensity and dispersion efficiency. LUR modeling is a useful approach for screening studies and can substitute for dispersion models given insufficient input data or dispersion models.
Equil 2 is a computer program used to estimate the risk of nephrolithiasis (renal stones). The input data includes excretion, concentration, and the saturation of trace elements or other substances which are involved in the creation of kidney stones and the output will be provided in terms of PSF score (probability of stone formation) or other equivalent formats. In some studies SUPERSAT, another program, provided more accurate measurements in some of the parameters such as relative supersaturation (RSS).
Journal of the ACM, 52, 4 (Jul. 2005). p. 553 It can be used to efficiently find the number of occurrences of a pattern within the compressed text, as well as locate the position of each occurrence. The query time, as well as the required storage space, has a sublinear complexity with respect to the size of the input data. The original authors have devised improvements to their original approach and dubbed it "FM-Index version 2".
The implementation proposed in the paper uses two arrays of size n (the original array containing the input data and a temporary one) for an efficient implementation. Hence, this version of the implementation is not an in-place algorithm. In each recursion step, the data gets copied to the other array in a partitioned fashion. If the data is in the temporary array in the last recursion step, then the data is copied back to the original array.
Here is a simple competitive learning algorithm to find three clusters within some input data. 1\. (Set-up.) Let a set of sensors all feed into three different nodes, so that every node is connected to every sensor. Let the weights that each node gives to its sensors be set randomly between 0.0 and 1.0. Let the output of each node be the sum of all its sensors, each sensor's signal strength being multiplied by its weight. 2\.
The human must have a clear method to input data and be able to easily access the information in output. The inability of rapid and accurate corrections can sometimes lead to drastic consequences, as summed up by many stories in Set Phasers on Stun.Casey, S.M. (1998). Set Phasers on Stun: And Other True Tales of Design, Technology, and Human Error The engineering psychologists wants to make the process of inputs and outputs as intuitive as possible for the user.
Here the basic input data to be fixed at the beginning are, firstly, the kinds of quantum fields carrying the theory's degrees of freedom and, secondly, the underlying symmetries. For any theory considered, these data determine the stage the renormalization group dynamics takes place on, the so-called theory space. It consists of all possible action functionals depending on the fields selected and respecting the prescribed symmetry principles. Each point in this theory space thus represents one possible action.
Encapsulation is the very essence of an FBP component, which may be thought of as a black box, performing some conversion of its input data into its output data. In FBP, part of the specification of a component is the data formats and stream structures that it can accept, and those it will generate. This constitutes a form of design by contract. In addition, the data in an IP can only be accessed directly by the currently owning process.
Then, a prefix sum computation is used to determine the range of positions in the sorted output at which the values with each key should be placed. Finally, in a second pass over the input, each item is moved to its key's position in the output array., 8.2 Counting Sort, pp. 168–169. Both algorithms involve only simple loops over the input data (taking time ) and over the set of possible keys (taking time ), giving their overall time bound.
Seeks to avoid the impact of outliers, that not fit with the model, so only considers inline which match the model in question. If an outlier is chosen to calculate the current setting, then the resulting line will have little support from the rest of the points. The algorithm that is performed is a loop that performs the following steps: # Of the entire input data set, takes a subset randomly to estimate the model. # Compute model subset.
If one wishes to distinguish an upper and lower part of the transition zone in the absence of a subsurface drainage system, one may specify in the input data a drainage system with zero intensity. The aquifer has mainly horizontal flow. Pumped wells, if present, receive their water from the aquifer only. The flow in the aquifer is determined in dependence of spatially varying depths of the aquifer, levels of the water table, and hydraulic conductivity.
By varying the fraction of used drain or well water (through the input), the long-term effect of different fractions can be simulated. The dissolution of solid soil minerals or the chemical precipitation of poorly soluble salts is not included in the computation method. However, but to some extent, it can be accounted for through the input data, e.g. increasing or decreasing the salt concentration of the irrigation water or of the incoming water in the aquifer.
Job descriptions were read in from cards or paper tape, peripherals and magnetic tape files were dynamically allocated to the job which was then run, producing output on the line printer. George 2 added the concept of spooling. Jobs and input data were read in from cards or paper tape to an input well on disk or tape. The jobs were then run, writing output to disk or tape spool files, which were then written to the output peripherals.
A sensitivity analysis can also be useful, as it determines what will happen if some of the original data upon which the forecast was developed turned out to be wrong. Determining forecast accuracy, like forecasting itself, can never be performed with certainty and so it is advisable to ensure that input data is measured and obtained as accurately as possible, the most appropriate forecasting methods are selected, and the forecasting process is conducted as rigorously as possible.
The programming paradigm used in LabVIEW, sometimes called G, is based on data availability. If there is enough data available to a subVI or function, that subVI or function will execute. Execution flow is determined by the structure of a graphical block diagram (the LabVIEW-source code) on which the programmer connects different function-nodes by drawing wires. These wires propagate variables and any node can execute as soon as all its input data become available.
At each iteration, insertion sort removes one element from the input data, finds the location it belongs within the sorted list, and inserts it there. It repeats until no input elements remain. Sorting is typically done in-place, by iterating up the array, growing the sorted list behind it. At each array-position, it checks the value there against the largest value in the sorted list (which happens to be next to it, in the previous array-position checked).
While the T52a/b and T52c were cryptologically weak, the last two were more advanced devices; the movement of the wheels was intermittent, the decision on whether or not to advance them being controlled by logic circuits which took as input data from the wheels themselves. In addition, a number of conceptual flaws (including very subtle ones) had been eliminated. One such flaw was the ability to reset the keystream to a fixed point, which led to key reuse by undisciplined machine operators.
The MAA-1 Piranha is a supersonic, short-range air-to-air missile relying on infrared passive guidance which seeks the target's heat emissions coming primarily from an aircraft's engines. It is highly maneuverable and can turn at accelerations of up to 50g. The Piranha performs as a 'fire and forget' missile, that means once launched the missile does not require input data coming the aircraft's sensors to hit its target. A laser fuze is responsible for detonating the high-explosive warhead.
Collectively they had managed to make about $10,000. As a science experiment, the group's objective was accomplished: to prove that there was a way of statistically predicting where a ball would fall in a roulette wheel given some input data. This outcome precursed data science and embodied the infancy of predictive analytics . A previous wearable roulette computer had been built and used in a casino by Edward O. Thorp and Claude Shannon in 1960–1961, though it had only been used briefly.
Space frame used in a building structure Tubular frame used in a competition car Structural mechanics or Mechanics of structures is the computation of deformations, deflections, and internal forces or stresses (stress equivalents) within structures, either for design or for performance evaluation of existing structures. It is one subset of structural analysis. Structural mechanics analysis needs input data such as structural loads, the structure's geometric representation and support conditions, and the materials' properties. Output quantities may include support reactions, stresses and displacements.
A bank of receivers can be created by performing a sequence of FFTs on overlapping segments of the input data stream. A weighting function (aka window function) is applied to each segment to control the shape of the frequency responses of the filters. The wider the shape, the more often the FFTs have to be done to satisfy the Nyquist sampling criteria. For a fixed segment length, the amount of overlap determines how often the FFTs are done (and vice versa).
For example, a supervised dictionary learning technique applied dictionary learning on classification problems by jointly optimizing the dictionary elements, weights for representing data points, and parameters of the classifier based on the input data. In particular, a minimization problem is formulated, where the objective function consists of the classification error, the representation error, an L1 regularization on the representing weights for each data point (to enable sparse representation of data), and an L2 regularization on the parameters of the classifier.
In applied mathematics, K-SVD is a dictionary learning algorithm for creating a dictionary for sparse representations, via a singular value decomposition approach. K-SVD is a generalization of the k-means clustering method, and it works by iteratively alternating between sparse coding the input data based on the current dictionary, and updating the atoms in the dictionary to better fit the data. K-SVD can be found widely in use in applications such as image processing, audio processing, biology, and document analysis.
In this scenario, the compiler cannot restrict code from bypassing the mutator method and changing the variable directly. The responsibility falls to the developers to ensure the variable is only modified through the mutator method and not modified directly. In programming languages that support them, properties offer a convenient alternative without giving up the utility of encapsulation. In the examples below, a fully implemented mutator method can also validate the input data or take further action such as triggering an event.
The instrument transformers with protective relays are used to sense the power-system voltage and current. They are physically connected to power-system apparatus and convert the actual power-system signals. The transducers convert the analog output of an instrument transformer from one magnitude to another or from one value type to another, such as from an ac current to dc voltage. Also the input data is taken from the auxiliary contacts of switch gears and power-system control equipment.
The input data used in a maximum parsimony analysis is in the form of "characters" for a range of taxa. There is no generally agreed-upon definition of a phylogenetic character, but operationally a character can be thought of as an attribute, an axis along which taxa are observed to vary. These attributes can be physical (morphological), molecular, genetic, physiological, or behavioral. The only widespread agreement on characters seems to be that variation used for character analysis should reflect heritable variation.
The validation process establishes the credibility of the model by demonstrating its ability to replicate reality. The importance of model validation underscores the need for careful planning, thoroughness and accuracy of the input data collection program that has this purpose. Efforts should be made to ensure collected data is consistent with expected values. For example, in traffic analysis it is typical for a traffic engineer to perform a site visit to verify traffic counts and become familiar with traffic patterns in the area.
Land change is visible in this image from Japan. Models cannot be as certain as satellite imagery. A notable property of all land change models is that they have some irreducible level of uncertainty in the model structure, parameter values, and/or input data. For instance, one uncertainty within land change models is a result from temporal non-stationarity that exists in land change processes, so the further into the future the model is applied, the more uncertain it is.
The original tool to produce AFP output and to drive the IBM printers was Print Service Facility (PSF), which is still in use on IBM mainframes today. It formats the input data to be printed based on definitions on how to place the data on the page, called PAGEDEF and FORMDEF. This service also allowed the definition of electronic forms, named OVERLAYS. PSF is not only able to format the documents, but also to drive AFP or, more precisely, IPDS printers.
The origins of CTI can be found in simple screen population (or "screen pop") technology. This allows data collected from the telephone systems to be used as input data to query databases with customer information and populate that data instantaneously in the customer service representative screen. The net effect is the agent already has the required screen on his/her terminal before speaking with the customer. This technology started gaining widespread adoption in markets like North America and West European countries.
Mikrotron Digital Microcomputer and Analog Technology GmbH was established by Bernhard Mindermann and Andreas Stockhausen, two Kontron AG employees, in 1976 in Eching, near Munich, Germany, and entered into the commercial registry on January 19, 1977, to develop microcomputer programs, devices and systems. The Mikrotron name is derived from Kontron. In the 1980s, the company supplied data logging systems that can input data into other systems. The company continued to grow and evolve, as they developed customized electronic data logging systems.
Arithmetic coding applies especially well to adaptive data compression tasks where the statistics vary and are context-dependent, as it can be easily coupled with an adaptive model of the probability distribution of the input data. An early example of the use of arithmetic coding was in an optional (but not widely used) feature of the JPEG image coding standard. It has since been applied in various other designs including H.263, H.264/MPEG-4 AVC and HEVC for video coding.
The HP 30b (NW238AA, variously codenamed "Big Euro", "Mid Euro" and "Fox") is a programmable financial calculator from HP which was released on 7 January 2010. The HP 30b is an advanced version of the HP's prior model HP 20b. Featuring a two line alpha numeric display, ability to input data via Reverse Polish Notation, Algebraic and normal Chain algebraic methods, and twelve digit display. This ARM powered calculator also has some limited scientific functions which is relatively rare in financial calculators.
In a 2019 study, a Convolutional Neural Network (CNN) was constructed with the ability to identify individual chess pieces the same way other CNNs can identify facial features. It was then fed eye-tracking input data from thirty chess players of various skill levels. With this data, the CNN used gaze estimation to determine parts of the chess board to which a player was paying close attention. It then generated a saliency map to illustrate those parts of the board.
Graphic example of soil salinity trends in the transition zone The salt balances are calculated for each soil reservoir separately. They are based on their water balances, using the salt concentrations of the incoming and outgoing water. Some concentrations must be given as input data, like the initial salt concentrations of the water in the different soil reservoirs, of the irrigation water and of the incoming groundwater in the aquifer. The concentrations are expressed in terms of electric conductivity (EC in dS/m).
A lot of systems use enhanced Wi-Fi infrastructure to provide location information. None of these systems serves for proper operation with any infrastructure as is. Unfortunately, Wi-Fi signal strength measurements are extremely noisy, so there is ongoing research focused on making more accurate systems by using statistics to filter out the inaccurate input data. Wi-Fi Positioning Systems are sometimes used outdoors as a supplement to GPS on mobile devices, where only few erratic reflections disturb the results.
Later supervised learning usually works much better when the raw input data is first translated into such a factorial code. For example, suppose the final goal is to classify images with highly redundant pixels. A naive Bayes classifier will assume the pixels are statistically independent random variables and therefore fail to produce good results. If the data are first encoded in a factorial way, however, then the naive Bayes classifier will achieve its optimal performance (compare Schmidhuber et al. 1996).
The τ-p transform is a special case of the Radon transform, and is simpler to apply than the Fourier transform. It allows one to study different wave modes as a function of their slowness values, p . Application of this transform involves summing (stacking) all traces in a record along a slope (slant), which results in a single trace (called the p value, slowness or the ray parameter). It transforms the input data from the space-time domain to intercept time-slowness domain.
Input data and output parameters of MAgPIE The model is based on static yield functions in order to model potential crop productivity and its related water use. For the biophysical supply simulation, spatially explicit 0.5° data is aggregated to a consistent number of clusters. Ten world regions represent the demand side of the model. Required calories for the demand categories (food and non-food energy intake) are determined by a cross- sectional country regression based on population and income projections.
Despite its limited range of characters, uuencoded data is sometimes corrupted on passage through certain computers using non-ASCII character sets such as EBCDIC. One attempt to fix the problem was the xxencode format, which used only alphanumeric characters and the plus and minus symbols. More common today is the Base64 format which is based on the same concept of alphanumeric- only as opposed to ASCII 32–95. All three formats use 6 bits (64 different characters) to represent their input data.
Furthermore, a deterministic hash function does not allow for rehashing: sometimes the input data turns out to be bad for the hash function (e.g. there are too many collisions), so one would like to change the hash function. The solution to these problems is to pick a function randomly from a large family of hash functions. The randomness in choosing the hash function can be used to guarantee some desired random behavior of the hash codes of any keys of interest.
In a typical machine learning application, practitioners have a set of input data points to train on. The raw data may not be in a form that all algorithms can be applied to it. To make the data amenable for machine learning, an expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods. After these steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their model.
Hash functions can be vulnerable to attack if a user can choose input data in such as way to intentionally cause hash collisions. Jean-Philippe Aumasson and Daniel J. Bernstein were able to show that even implementations of MurmurHash using a randomized seed are vulnerable to so-called HashDoS attacks. With the use of differential cryptanalysis they were able to generate inputs that would lead to a hash collision. The authors of the attack recommend to use their own SipHash instead.
Initially the rendering was on early Cathode ray tube screens or through plotters drawing on paper. Molecular structures have always been an attractive choice for developing new computer graphics tools, since the input data are easy to create and the results are usually highly appealing. The first example of MG was a display of a protein molecule (Project MAC, 1966) by Cyrus Levinthal and Robert Langridge. Among the milestones in high-performance MG was the work of Nelson Max in "realistic" rendering of macromolecules using reflecting spheres.
Represent a data model to store geographic information on top of EER model, GEIS define the input data model and provide the following for data model Geometry. In the GISER model, geometry is an entity that is related to a spatial object by the relationship determines shape of. Additional entities represent the primitives such as points, lines, and polygons as proposed in related models. Topology. Topology is a property belonging to a spatial object and that property remains unaltered even when the object deforms.
Flux balance analysis (FBA) is a mathematical method for simulating metabolism in genome-scale reconstructions of metabolic networks. In comparison to traditional methods of modeling, FBA is less intensive in terms of the input data required for constructing the model. Simulations performed using FBA are computationally inexpensive and can calculate steady-state metabolic fluxes for large models (over 2000 reactions) in a few seconds on modern personal computers. The results of FBA on a prepared metabolic network of the top six reactions of glycolysis.
Relatively simple to implement and understand, the two-pass algorithm, (also known as the Hoshen–Kopelman algorithm) iterates through 2-dimensional binary data. The algorithm makes two passes over the image. The first pass to assign temporary labels and record equivalences and the second pass to replace each temporary label by the smallest label of its equivalence class. The input data can be modified in situ (which carries the risk of data corruption), or labeling information can be maintained in an additional data structure.
For solving most problems, it is required to read all input data, which, normally, needs a time proportional to the size of the data. Thus, such problems have a complexity that is at least linear, that is, using big omega notation, a complexity \Omega(n). The solution of some problems, typically in computer algebra and computational algebraic geometry, may be very large. In such a case, the complexity is lower bounded by the maximal size of the output, since the output must be written.
Illustration of the implicit even/odd extensions of DCT input data, for N=11 data points (red dots), for the four most common types of DCT (types I-IV). However, because DCTs operate on finite, discrete sequences, two issues arise that do not apply for the continuous cosine transform. First, one has to specify whether the function is even or odd at both the left and right boundaries of the domain (i.e. the min-n and max-n boundaries in the definitions below, respectively).
When talking about computer hardware for an EIS environment, we should focus on the hardware that meets the executive's need. The executive must be put first and the executive's needs must be defined before the hardware can be selected. The basic hardware needed for a typical EIS includes four components: # Input data-entry devices. These devices allow the executive to enter, verify, and update data immediately # The central processing unit (CPU), which is the most important because it controls the other computer system components # Data storage files.
Typical paper tapes showing holes punched to input data to early computers. The newly graduated Berdichevsky studied computing from the visiting English software engineer Cicely Popplewell (famous for having worked with Alan Turing in Manchester) and with the Spanish mathematician Ernesto García Camarero. Popplewell herself motivated Berdichevsky to write and run the first program for the new computer, which required multiple arithmetic calculations. A photoelectric device read a punched paper ribbon that was used to submit the data and Clementina produced the desired result in only seconds.
Segmentation is accurate to the extent that it matches distinctions among letters in the actual inscriptions presented to the system for recognition (the input data). This is sometimes referred to as “explicit segmentation”.Alessandro Vinciarelli, “A Survey on [sic] Offline Cursive Word Recognition,” op. cit. “Implicit segmentation,” by contrast, is division of the cursive line into more parts than the number of actual letters in the cursive line itself. Processing these “implicit parts” to achieve eventual word identification requires specific statistical procedures involving Hidden Markov Models (HMM).
Many papers report large gaps between simulation results and measurements, while other studies show that they can match very well. The reliability of results from BPS depends on many different things, e.g. on the quality of input data, the competence of the simulation engineers and on the applied methods in the simulation engine. An overview about possible causes for the widely discussed performance gap from design stage to operation is given by de Wilde (2014) and a progress report by the Zero Carbon Hub (2013).
The concept of weird machine is a theoretical framework to understand the existence of exploits for security vulnerabilities. Exploits exist empirically, but were not studied from a theoretical perspective prior to the emergence of the framework of weird machines. In computer security, the weird machine is a computational artifact where additional code execution can happen outside the original specification of the program. It is closely related to the concept of weird instructions, which are the building blocks of an exploit based on crafted input data.
With a payload of feature vectors one-way encrypted, there is no need to decrypt and no need for key management. A promising method of homomorphic encryption on biometric data is the use of machine learning models to generate feature vectors. For black-box models, such as neural networks, these vectors can not by themselves be used to recreate the initial input data and are therefore a form of one-way encryption. However, the vectors are euclidean measurable, so similarity between vectors can be calculated.
Algorithms that perform optimization tasks (such as building cladograms) can be sensitive to the order in which the input data (the list of species and their characteristics) is presented. Inputting the data in various orders can cause the same algorithm to produce different "best" cladograms. In these situations, the user should input the data in various orders and compare the results. Using different algorithms on a single data set can sometimes yield different "best" cladograms, because each algorithm may have a unique definition of what is "best".
Digital image processing is the use of a digital computer to process digital images through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems.
A perceptual paradox illustrates the failure of a theoretical prediction. Theories of perception are supposed to help a researcher predict what will be perceived when senses are stimulated. A theory usually comprises a mathematical model (formula), rules for collecting physical measurements for input into the model, and rules for collecting physical measurements to which model outputs should map. When arbitrarily choosing valid input data, the model should reliably generate output data that is indistinguishable from that which is measured in the system being modeled.
Martin Lewis, founder of MoneySavingExpert, stated in a number of interviews that he believed the energy market was broken. The service monitors the users energy tariff to ensure they are on the cheapest gas and electricity deal. The scheme requires the user to input data regarding your current energy tariffs and state the amount of saving for which you would be willing to switch providers. Tariffs that are available are then reviewed every month and you are notified when switching would trigger your target saving.
These errors can originate either from the electron optical control hardware or the input data that was taped out. As might be expected, larger data files are more susceptible to data-related defects. Physical defects are more varied, and can include sample charging (either negative or positive), backscattering calculation errors, dose errors, fogging (long-range reflection of backscattered electrons), outgassing, contamination, beam drift and particles. Since the write time for electron beam lithography can easily exceed a day, "randomly occurring" defects are more likely to occur.
The principles established for the validation of chemistry sets are that: # There is experimental bench-marking from open sources (where available) and also directly provided by industrial partners (collaborating on the Powerbase project) and database contributors. # Calculations are performed for a range of models thereby reflecting the underlying quality of input data (example models used for validation include HPEM, Global_Kin, ChemKin). # The models used to produce the data are validated on a case-by-case basis. # Numerical uncertainties are quantified with thresholds set for validation where possible.
The Transformer is a deep learning model introduced in 2017, used primarily in the field of natural language processing (NLP). Like recurrent neural networks (RNNs), Transformers are designed to handle sequential data, such as natural language, for tasks such as translation and text summarization. However, unlike RNNs, Transformers do not require that the sequential data be processed in order. For example, if the input data is a natural language sentence, the Transformer does not need to process the beginning of it before the end.
ZX81 Apple II The standard solved one of the aforementioned problems, the incompatible data formats. However, programs still had to be adapted to each computer's BASIC dialect and hardware capabilities. Limiting the programs to only use instructions common across all dialects meant big limitations in terms of functionality, for example completely refraining from using graphics and sound and only uncomfortable methods to input data using the keyboard and to control character output on the screen. For this reasons, in 1984 the enhanced standard BASICODE 2 was created.
Just as many other forms of knowledge discovery it creates abstractions of the input data. The knowledge obtained through the process may become additional data that can be used for further usage and discovery. Often the outcomes from knowledge discovery are not actionable, actionable knowledge discovery, also known as domain driven data mining, aims to discover and deliver actionable knowledge and insights. Another promising application of knowledge discovery is in the area of software modernization, weakness discovery and compliance which involves understanding existing software artifacts.
Similarly, for "Customer", natural aggregations may arrange customers according to geographic location or industry. The number of aggregate values implied by a set of input data can become surprisingly large. If the Customer and Product dimensions are each in fact six "generations" deep, then 36 (6 × 6) aggregate values are affected by a single data point. It follows that if all these aggregate values are to be stored, the amount of space required is proportional to the product of the depth of all aggregating dimensions.
The system and survey parameters are stored with the input data allowing the user freedom from continually specifying these parameters for every model. Synthetic measurements at the receiver due to the model are what are calculated during a simulation. Early versions of EMIGMA could simulate the responses of 3d blocks, thin plates and the response of a many layered earth model. Simulation algorithms now include one for a sphere model, and alternate algorithms for thin plates and various algorithms for 3D prisms and polyhedra.
Generally, these types of attacks arise when an adversary manipulates the call stack by taking advantage of a bug in the program, often a buffer overrun. In a buffer overrun, a function that does not perform proper bounds checking before storing user-provided data into memory will accept more input data than it can store properly. If the data is being written onto the stack, the excess data may overflow the space allocated to the function's variables (e.g., "locals" in the stack diagram to the right) and overwrite the return address.
A command center is a central place for carrying out orders and for supervising tasks, also known as a headquarters, or HQ. Common to every command center are three general activities: inputs, processes, and outputs. The inbound aspect is communications (usually intelligence and other field reports). Inbound elements are "sitreps" (situation reports of what is happening) and "progreps" (progress reports relative to a goal that has been set) from the field back to the command element. The process aspect involves a command element that makes decisions about what should be done about the input data.
In statistics, one-way analysis of variance (abbreviated one-way ANOVA) is a technique that can be used to compare means of two or more samples (using the F distribution). This technique can be used only for numerical response data, the "Y", usually one variable, and numerical or (usually) categorical input data, the "X", always one variable, hence "one-way". The ANOVA tests the null hypothesis, which states that samples in all groups are drawn from populations with the same mean values. To do this, two estimates are made of the population variance.
In many practical situations, only incomplete and inaccurate statistical knowledge on uncertain input parameters are available. Fortunately, to construct a finite-order expansion, only some partial information on the probability measure is required that can be simply represented by a finite number of statistical moments. Any order of expansion is only justified if accompanied by reliable statistical information on input data. Thus, incomplete statistical information limits the utility of high-order polynomial chaos expansionsOladyshkin S. and Nowak W. Incomplete statistical information limits the utility of high-order polynomial chaos expansions.
Parts of the reporting website are designed for consumers, while other parts are designed for the more sophisticated data user. While the MONAHRQ software is available to anyone, its primary host users – those interested in generating and hosting a MONARHQ reporting website – may be state data organizations, chartered value exchanges, and hospital organizations. To generate the website, host users must provide the appropriate input data: hospital administrative data and/or any of the other publicly available measure results that MONAHRQ is able to load and report. Minimal technical knowledge is required to use MONAHRQ.
Once the SOM is trained using the input data, the final map is not expected to have any twists. If the map is twist-free, the distance between the codebook vectors of neighboring neurons gives an approximation of the distance between different parts of the underlying data. When such distances are depicted in a grayscale image, light colors depict closely spaced node codebook vectors and darker colors indicate more widely separated node codebook vectors. Thus, groups of light colors can be considered as clusters, and the dark parts as the boundaries between the clusters.
Second- generation FHE scheme implementations typically operate in the leveled FHE mode (though bootstrapping is still available in some libraries) and support efficient SIMD-like packing of data; they are typically used to compute on encrypted integers or real/complex numbers. Third-generation FHE scheme implementations often bootstrap after each Boolean gate operation but have limited support for packing and efficient arithmetic computations; they are typically used to compute Boolean circuits over encrypted bits. The choice of using a second-generation vs. third-generation scheme depends on the input data types and the desired computation.
Frasca distinguishes between simulational and representational media, with videogames being part of the former and 'traditional' media being the latter. The key difference, he argues, is that simulations react to certain stimuli, such as configurative input data (button presses etc.), according to a set of conditions. Generally, representational media (he provides the example of a photograph) produce a fixed description of traits and sequences of events (narrative), and cannot be manipulated. He places emphasis on the importance of serious games, most notably the use of games for political purposes.
Recent research has increasingly focused on unsupervised and semi-supervised learning algorithms. Such algorithms are able to learn from data that has not been hand-annotated with the desired answers, or using a combination of annotated and non- annotated data. Generally, this task is much more difficult than supervised learning, and typically produces less accurate results for a given amount of input data. However, there is an enormous amount of non-annotated data available (including, among other things, the entire content of the World Wide Web), which can often make up for the inferior results.
Artillery games have been described as a type of "shooting game", though they are more often classified as a type of strategy video game. Early precursors to the modern artillery-type games were text-only games that simulated artillery entirely with input data values. One of the earliest known games in the genre is War 3 for two or three players, written in FOCAL Mod V by Mike Forman (date unknown). The game was then ported to TSS-8 BASIC IV by M. E. Lyon Jr. in 1972.
EPANET provides an integrated environment for editing network input data, running hydraulic and water quality simulations, and viewing the results in a variety of formats. EPANET provides a fully equipped and extended period of hydraulic analysis that can handle systems of any size. The package also supports the simulation of spatially and temporally varying water demand, constant or variable speed pumps, and the minor head losses for bends and fittings. The modeling provides information such as flows in pipes, pressures at junctions, propagation of a contaminant, chlorine concentration, water age, and even alternative scenario analysis.
Colossus K2 switch panel showing switches for specifying the algorithm (on the left) and the counters to be selected (on the right). Colossus 'set total' switch panel Howard Campaigne, a mathematician and cryptanalyst from the US Navy's OP-20-G, wrote the following in a foreword to Flowers' 1983 paper "The Design of Colossus". Colossus was not a stored-program computer. The input data for the five parallel processors was read from the looped message paper tape and the electronic pattern generators for the chi, psi and motor wheels.
The first phase of patience sort, the card game simulation, can be implemented to take comparisons in the worst case for an -element input array: there will be at most piles, and by construction, the top cards of the piles form an increasing sequence from left to right, so the desired pile can be found by binary search. The second phase, the merging of piles, can be done in time as well using a priority queue. When the input data contain natural "runs", i.e., non-decreasing subarrays, then performance can be strictly better.
Like the keyboard interface scanner, USB scanners do not need custom code for transferring input data to the application program. On PCs running Windows the human interface device emulates the data merging action of a hardware "keyboard wedge", and the scanner automatically behaves like an additional keyboard. Most modern smartphones are able to decode barcode using their built-in camera. Google's mobile Android operating system can use their own Google Lens application to scan QR codes, or third party apps like Barcode Scanner to read both one-dimensional barcodes and QR codes.
The figure was made with the CumFreq program . The program offers the possibility to develop a multitude of relations between varied input data, resulting outputs and time. However, as it is not possible to foresee all different uses that may be made, the program offers only a limited number of standard graphics. The program is designed to make use of spreadsheet programs for the detailed output analysis, in which the relations between various input and output variables can be established according to the scenario developed by the user.
Speech Application Language Tags enables multimodal and telephony-enabled access to information, applications, and Web services from PCs, telephones, tablet PCs, and wireless personal digital assistants (PDAs). The Speech Application Language Tags extend existing mark- up languages such as HTML, XHTML, and XML. Multimodal access will enable users to interact with an application in a variety of ways: they will be able to input data using speech, a keyboard, keypad, mouse and/or stylus, and produce data as synthesized speech, audio, plain text, motion video, and/or graphics.
The generator is typically a deconvolutional neural network, and the discriminator is a convolutional neural network. GANs often suffer from a "mode collapse" where they fail to generalize properly, missing entire modes from the input data. For example, a GAN trained on the MNIST dataset containing many samples of each digit, might nevertheless timidly omit a subset of the digits from its output. Some researchers perceive the root problem to be a weak discriminative network that fails to notice the pattern of omission, while others assign blame to a bad choice of objective function.
Captchas are used by free-mail services to prevent automatic creation of a huge number of email accounts and to protect automatic form submissions on blogs, forums and article directories. As of November 2012, Xrumer has once again cracked Recaptcha, and is able to successfully post to Forums/Blogs that use it. Averaging is a common method in physics to reduce noise in input data. The averaging attack can be used on image-based captchas if the following conditions are met: The predominant distortion in the captcha is of noise-like nature.
The construction of a supertree scales exponentially with the number of taxa included; therefore for a tree of any reasonable size, it is not possible to examine every possible supertree and weigh its success at combining the input information. Heuristic methods are thus essential, although these methods may be unreliable; the result extracted is often biased or affected by irrelevant characteristics of the input data. The most well known method for supertree construction is Matrix Representation with Parsimony (MRP), in which the input source trees are represented by matrices with 0s, 1s, and ?s (i.e.
Hard coding requires the program's source code to be changed any time the input data or desired format changes, when it might be more convenient to the end user to change the detail by some means outside the program. Hard coding is often required, but can also be considered an anti-pattern. Programmers may not have a dynamic user interface solution for the end user worked out but must still deliver the feature or release the program. This is usually temporary but does resolve, in a short term sense, the pressure to deliver the code.
Neural networks are a family of learning algorithms that use a "network" consisting of multiple layers of inter-connected nodes. It is inspired by the animal nervous system, where the nodes are viewed as neurons and edges are viewed as synapses. Each edge has an associated weight, and the network defines computational rules for passing input data from the network's input layer to the output layer. A network function associated with a neural network characterizes the relationship between input and output layers, which is parameterized by the weights.
In the second step, lower-dimensional points are optimized with fixed weights, which can be solved via sparse eigenvalue decomposition. The reconstruction weights obtained in the first step capture the "intrinsic geometric properties" of a neighborhood in the input data. It is assumed that original data lie on a smooth lower-dimensional manifold, and the "intrinsic geometric properties" captured by the weights of the original data are also expected to be on the manifold. This is why the same weights are used in the second step of LLE.
The hierarchical architecture of the biological neural system inspires deep learning architectures for feature learning by stacking multiple layers of learning nodes. These architectures are often designed based on the assumption of distributed representation: observed data is generated by the interactions of many different factors on multiple levels. In a deep learning architecture, the output of each intermediate layer can be viewed as a representation of the original input data. Each level uses the representation produced by previous level as input, and produces new representations as output, which is then fed to higher levels.
Exponential smoothing and moving average have similar defects of introducing a lag relative to the input data. While this can be corrected by shifting the result by half the window length for a symmetrical kernel, such as a moving average or gaussian, it is unclear how appropriate this would be for exponential smoothing. They also both have roughly the same distribution of forecast error when α = 2/(k+1). They differ in that exponential smoothing takes into account all past data, whereas moving average only takes into account k past data points.
For the sake of simplicity, the description below assumes that the points are in general position, i.e., no three points are collinear. The algorithm may be easily modified to deal with collinearity, including the choice whether it should report only extreme points (vertices of the convex hull) or all points that lie on the convex hull. Also, the complete implementation must deal with degenerate cases when the convex hull has only 1 or 2 vertices, as well as with the issues of limited arithmetic precision, both of computer computations and input data.
Sphere is a parallel data processing engine integrated in Sector and it can be used to process data stored in Sector in parallel. It can broadly compared to MapReduce, but it uses generic user defined functions (UDFs) instead of the map and reduce functions. A UDF can be either a map function or a reduce function, or even others. Sphere can manipulate the locality of both input data and output data, thus it can effectively support multiple input datasets, combinative and iterative operations and even legacy application executable.
Numerical certification is the process of verifying the correctness of a candidate solution to a system of equations. In (numerical) computational mathematics, such as numerical algebraic geometry, candidate solutions are computed algorithmically, but there is the possibility that errors have corrupted the candidates. For instance, in addition to the inexactness of input data and candidate solutions, numerical errors or errors in the discretization of the problem may result in corrupted candidate solutions. The goal of numerical certification is to provide a certificate which proves which of these candidates are, indeed, approximate solutions.
The addition of structure, with DSC comments exposing that structure, helps provide a way for, e.g., an intelligent print spooler to have the ability to rearrange the pages for printing, or for a page layout program to find the bounding box of a PostScript file used as a graphic image. Collectively, any such program that takes PostScript files as input data is called a document manager. In order for a PostScript print file to properly distill to PDF using Adobe tools, it should conform to basic DSC standards.
Red (A), Green (B), Blue (C) 16-bit lookup table file sample. (Lines 14 to 65524 not shown) In data analysis applications, such as image processing, a lookup table (LUT) is used to transform the input data into a more desirable output format. For example, a grayscale picture of the planet Saturn will be transformed into a color image to emphasize the differences in its rings. A classic example of reducing run-time computations using lookup tables is to obtain the result of a trigonometry calculation, such as the sine of a value.
In ten- print searching, using a "search threshold" parameter to increase accuracy, there should seldom be more than a single candidate unless there are multiple records from the same candidate in the database. Many systems use a broader search in order to reduce the number of missed identifications, and these searches can return from one to ten possible matches. Latent to tenprint searching will frequently return many (often fifty or more) candidates because of limited and poor quality input data. The confirmation of system-suggested candidates is usually performed by a technician in forensic systems.
Input Contracts split the input data of a PACT into independently processable subsets that are handed to the user function of the PACT. Input Contracts vary in the number of data inputs and the way how independent subsets are generated. More formally, Input Contracts are second-order functions with a first-order function (the user code), one or more input sets, and none or more key fields per input as parameters. The first-order function is called (one or) multiple times with subsets of the input set(s).
Repeaters have HDMI inputs and outputs. Examples include home theater audio-visual receivers that separate and amplify the audio signal, while re-transmitting the video for display on a TV. A repeater could also simply send the input data stream to multiple outputs for simultaneous display on several screens. Each device may contain one or more HDCP transmitters and/or receivers. (A single transmitter or receiver chip may combine HDCP and HDMI functionality.) In the United States, the Federal Communications Commission (FCC) approved HDCP as a "Digital Output Protection Technology" on August 4, 2004.
The term block code may also refer to any error-correcting code that acts on a block of k bits of input data to produce n bits of output data (n,k). Consequently, the block coder is a memoryless device. Under this definition codes such as turbo codes, terminated convolutional codes and other iteratively decodable codes (turbo-like codes) would also be considered block codes. A non-terminated convolutional encoder would be an example of a non- block (unframed) code, which has memory and is instead classified as a tree code.
Many different classes of machine-learning algorithms have been applied to natural-language-processing tasks. These algorithms take as input a large set of "features" that are generated from the input data. Increasingly, however, research has focused on statistical models, which make soft, probabilistic decisions based on attaching real-valued weights to each input feature. Such models have the advantage that they can express the relative certainty of many different possible answers rather than only one, producing more reliable results when such a model is included as a component of a larger system.
Simulation of soil leaching losses and certain measures of soil nutrient availability require input data that define cation and anion exchange capacity data for organic matter and mineral soil, and sorption- desorption processes. The second aspect of calibration requires running the model in “set-up” mode to establish initial site conditions. The detailed representation of many different litter types and soil organic matter conditions makes it impractical to measure initial litter and soil pools and conditions directly in the field; consequently, the model is used to generate starting conditions.
A trace is a sequence of instructions, including branches but not including loops, that is executed for some input data. Trace scheduling uses a basic block scheduling method to schedule the instructions in each entire trace, beginning with the trace with the highest frequency. It then adds compensation code at the entry and exit of each trace to compensate for any effects that out of order execution may have had. This can result in large increases in code sizes and poor or erratic performance if program's behavior varies significantly with the input.
Inflow and outflow factors of water into and out from the soil reservoirs needed to find the waterbalances The agricultural water balances are calculated for each soil reservoir separately as shown in the article Hydrology (agriculture). The excess water leaving one reservoir is converted into incoming water for the next reservoir. The three soil reservoirs can be assigned different thickness and storage coefficients, to be given as input data. When, in a particular situation the transition zone or the aquifer is not present, they must be given a minimum thickness of 0.1 m.
Performance data can be directly obtained from computer simulators, within which each instruction of the target program is actually dynamically executed given a particular input data set. Simulators can predict program's performance very accurately, but takes considerable time to handle large programs. Examples include the PACE and Wisconsin Wind Tunnel simulators as well as the more recent WARPP simulation toolkit which attempts to significantly reduce the time required for parallel system simulation. Another approach, based on trace-based simulation does not run every instruction, but runs a trace file which store important program events only.
For the practical realisation of handovers in a cellular network each cell is assigned a list of potential target cells, which can be used for handing over calls from this source cell to them. These potential target cells are called neighbors and the list is called neighbor list. Creating such a list for a given cell is not trivial and specialized computer tools are used. They implement different algorithms and may use for input data from field measurements or computer predictions of radio wave propagation in the areas covered by the cells.
In cluster analysis, the k-means algorithm can be used to partition the input data set into k partitions (clusters). However, the pure k-means algorithm is not very flexible, and as such is of limited use (except for when vector quantization as above is actually the desired use case). In particular, the parameter k is known to be hard to choose (as discussed above) when not given by external constraints. Another limitation is that it cannot be used with arbitrary distance functions or on non-numerical data.
Once airborne the crew checked the take-off performance data: the N level was 81.5%, far below the required level of 92.7%. Thrust was only increased when the aircraft reached 800 feet, about 4 km after becoming airborne. Neither the installed flight management computer software nor the Electronic flight bags (EFBs) in use helped in detecting the data input error. A recent software release had not yet been installed, and the software omitted the cross-check of the pilot input data against the outside air temperature actually measured.
In computer science, a family of hash functions is said to be k-independent or k-universal if selecting a function at random from the family guarantees that the hash codes of any designated k keys are independent random variables (see precise mathematical definitions below). Such families allow good average case performance in randomized algorithms or data structures, even if the input data is chosen by an adversary. The trade-offs between the degree of independence and the efficiency of evaluating the hash function are well studied, and many k-independent families have been proposed.
To search for a logical inference path the following actions are implemented: # Known variables are denoted by z and required variables are denoted by w in the row (m+1). For example, z denotes positions: 1,2,3 in the row (m+1)), the variable w denotes the position (n-2). # The search of such rules that can be fired, that is, all the input variables of which are known, is implemented successively, for example, top-down. Absent such rules, no logical inference path exists and input data refinement (addition) is requested.
The purpose of phylogenetic software is to generate cladograms, a special kind of tree in which the links only bifurcate; that is, at any node in the same direction only two branches are offered. The input data is a set of characters that can be assigned states in different languages, such as present (1) or absent (0). A language therefore can be described by a unique coordinate set consisting of the state values for all of the characters considered. These coordinates can be like each other or less so.
Once barcodes and inventory management programs started spreading through grocery stores, inventory management by hand became less practical. Writing inventory data by hand on paper was replaced by scanning products and inputting information into a computer by hand. Starting in the early 2000s, inventory management software progressed to the point where businesspeople no longer needed to input data by hand but could instantly update their database with barcode readers. Also, the existence of cloud based business software and their increasing adoption by businesses mark a new era for inventory management software.
A more fundamental problem lies in the chaotic nature of the partial differential equations that govern the atmosphere. It is impossible to solve these equations exactly, and small errors grow with time (doubling about every five days). Present understanding is that this chaotic behavior limits accurate forecasts to about 14 days even with accurate input data and a flawless model. In addition, the partial differential equations used in the model need to be supplemented with parameterizations for solar radiation, moist processes (clouds and precipitation), heat exchange, soil, vegetation, surface water, and the effects of terrain.
In some applications, the input data may contain features that are irrelevant for comparison purposes. For example, when looking up a personal name, it may be desirable to ignore the distinction between upper and lower case letters. For such data, one must use a hash function that is compatible with the data equivalence criterion being used: that is, any two inputs that are considered equivalent must yield the same hash value. This can be accomplished by normalizing the input before hashing it, as by upper-casing all letters.
The ability to perform economical maximum likelihood soft decision decoding is one of the major benefits of convolutional codes. This is in contrast to classic block codes, which are generally represented by a time-variant trellis and therefore are typically hard-decision decoded. Convolutional codes are often characterized by the base code rate and the depth (or memory) of the encoder [n,k,K]. The base code rate is typically given as n/k, where n is the raw input data rate and k is the data rate of output channel encoded stream.
SELDM was developed as a Microsoft Access® database software application to facilitate storage, handling, and use of the hydrologic dataset with a simple graphical user interface (GUI). The program's menu-driven GUI uses standard Microsoft Visual Basic for Applications® (VBA) interface controls to facilitate entry, processing, and output of data. Appendix 4 of the SELDM manual has detailed instructions for using the GUI. The SELDM user interface has one or more GUI forms that are used to enter four categories of input data, which include documentation, site and region information, hydrologic statistics, and water-quality data.
The FEP is a processing device (usually a computer) which is closer to the input source than is the main processor. It performs some task such as telemetry control, data collection, reduction of raw sensor data, analysis of keyboard input, etc. Front-end processes relates to the software interface between the user (client) and the application processes (server) in the client/server architecture. The user enters input (data) into the front-end process where it is collected and processed in such a way that it conforms to what the receiving application (back end) on the server can accept and process.
The multiple redundant flight control computers continuously monitor each other's output. If one computer begins to give aberrant results for any reason, potentially including software or hardware failures or flawed input data, then the combined system is designed to exclude the results from that computer in deciding the appropriate actions for the flight controls. Depending on specific system details there may be the potential to reboot an aberrant flight control computer, or to reincorporate its inputs if they return to agreement. Complex logic exists to deal with multiple failures, which may prompt the system to revert to simpler back-up modes.
The BioCompute Object is in json format and, at a minimum, contains all the software versions and parameters necessary to evaluate or verify a computational pipeline. It may also contain input data as files or links, reference genomes, or executable Docker components. A BioCompute Object can be integrated with HL7 FHIR as a Provenance Resource. The effort is seen by many to be redundant and unnecessary as the bioinformatics community has already embraced the Common Workflow Language which contains all of these, and superior capabilities, despite the BCO objective to treat the CWL as a Research Object.
Receiver Operating Characteristic Curve Explorer and Tester (ROCCET) is an open-access web server for performing biomarker analysis using ROC (Receiver Operating Characteristic) curve analyses on metabolomic data sets. ROCCET is designed specifically for performing and assessing a standard binary classification test (disease vs. control). ROCCET accepts metabolite data tables, with or without clinical/observational variables, as input and performs extensive biomarker analysis and biomarker identification using these input data. It operates through a menu-based navigation system that allows users to identify or assess those clinical variables and/or metabolites that contain the maximal diagnostic or class-predictive information.
In the database example, all of the sorts could take place at the same time if the computer were capable of supplying the data. Dataflow languages tend to be inherently concurrent, meaning they are capable of running on multiprocessor systems "naturally", one of the reasons that it garnered so much interest in the 1980s. Loops and branches are constructed by modifying operations with annotations. For instance, a loop that calls the `doit` method on a list of input data is constructed by first dragging in the doit operator, then attaching the loop modifier and providing the list as the input to the loop.
Asynchronous procedure call is a unit of work in a computer. Usually a program works by executing a series of synchronous procedure calls on some thread. But if some data are not ready (for example, a program waits user to reply), then keeping thread in wait state is impractical, as a thread allocates considerable amount of memory for procedure stack, and this memory is not used. So such a procedure call is formed as an object with small amount of memory for input data, and this object is passed to the service which receive user inputs.
So life cycle of an asynchronous procedure call consists of 2 stages: passive stage, when it passively waits for input data, and active state, when that data is calculated in the same way as at usual procedure call. The object of the asynchronous procedure call can be reused for subsequent procedure calls with new data, received later. This allows to accumulate computed output data in that object, as it is usually done in objects, programmed with Object-oriented programming paradigm. Special care should be paid to avoid simultaneous execution of the same procedure call in order to keep computed data in consistent state.
During 2003 and 2004 permits to establish the Lysekil Research site were obtained and the first wave measuring buoy was deployed in 2004. The first experimental setup was deployed in March 2005 and the purpose was to measure the maximum line force from a buoy with a diameter of and a height of . This set up simulated a generator that had been disconnected from the grid and thereby operating without any damping in the system. The results from these experiment were used as input data to the first wave generator and to verify the calculations of the dynamics of non-damped systems.
Lossless compression is a class of data compression algorithms that allows the original data to be perfectly reconstructed from the compressed data. By contrast, lossy compression permits reconstruction only of an approximation of the original data, though usually with greatly improved compression rates (and therefore reduced media sizes). By operation of the pigeonhole principle, no lossless compression algorithm can efficiently compress all possible data. For this reason, many different algorithms exist that are designed either with a specific type of input data in mind or with specific assumptions about what kinds of redundancy the uncompressed data are likely to contain.
The independent Kohonen networks provide output independently and in parallel with the other independent networks in the system. Once presented to the independent Kohonen networks, the groups are then combined for a final time and sent to a final Kohonen network. After being sent to the final Kohnen network, the system will suggest the preliminary classifications that will be sent on to the next and final phase. By the end of the neural network phase, all of the input data will have been analyzed, grouped, and classified into patterns that will become the basis for which the final results depend on.
Refilling and vectorial concentration model in Traditional Balsamic Vinegar production Each cask of a barrel set contains a blend of vinegars with different compositions and ages due to the refilling procedure. As a consequence, the mean age of vinegar can be calculated as the weighted residence time of the different aliquots of vinegars introduced through the years. A theoretical model has been recently developed to estimate the mean age of TBV requiring refilling, withdrawn, and casks volumes as input data. The refilling procedure imposes an upper limit for the residence time of the vinegar inside the barrel set.
EIA provides access to NEMS to outside users through its model archival program. An archive includes the model source code and input data used to derive a given projection case, such as the "reference case." The stated archive purpose "is to demonstrate that the published results from the AEO reference case can be replicated with the model and to disclose the source code and inputs used." The model is not widely used outside of EIA, as deploying the model requires additional commercial software, such as compilers and optimization modeling packages, in addition to the model source code and data.
Proceedings of the 2016 Winter Simulation Conference This feature can be used for animating processes inside objects like factories, warehouses, hospitals, etc. This functionality is mostly used in Discrete Event (process-based) models in manufacturing, healthcare, civil engineering, and construction. AnyLogic software also supports 3D animation and includes a collection of ready-to-use 3D objects for animation related to different industries, including buildings, road, rail, maritime, transport, energy, warehouse, hospital, equipment, airport-related items, supermarket-related items, cranes, and other objects. Models can include custom UI for users to configure experiments and change input data.
The Droid X features a 1.0 GHz TI OMAP3630-1000 SoC, a FWVGA (854 × 480) TFT LCD display, 8 GB of internal flash memory and a 16 GB microSDHC card, and is compatible with microSDHC cards up to 32 GB. When the Droid X was first released it came standard with a microSDHC card of 16 GB, but Motorola reduced the size to 2 GB. Users input data to the phone via a multi-touch capacitive touchscreen. The Droid X includes an 8-megapixel camera with autofocus and LED flash and can record video at 720p resolution up to 24 fps also.
In one typical usage scenario, the system will load the SPEs with small programs (similar to threads), chaining the SPEs together to handle each step in a complex operation. For instance, a set-top box might load programs for reading a DVD, video and audio decoding, and display, and the data would be passed off from SPE to SPE until finally ending up on the TV. Another possibility is to partition the input data set and have several SPEs performing the same kind of operation in parallel. At 3.2 GHz, each SPE gives a theoretical 25.6 GFLOPS of single precision performance.
Predictive modeling uses statistics to predict outcomes. Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. For example, predictive models are often used to detect crimes and identify suspects, after the crime has taken place. In many cases the model is chosen on the basis of detection theory to try to guess the probability of an outcome given a set amount of input data, for example given an email determining how likely that it is spam.
In some implementations, the learning coefficient α and the neighborhood function Θ decrease steadily with increasing s, in others (in particular those where t scans the training data set) they decrease in step-wise fashion, once every T steps. Training process of SOM on two-dimensional data set This process is repeated for each input vector for a (usually large) number of cycles λ. The network winds up associating output nodes with groups or patterns in the input data set. If these patterns can be named, the names can be attached to the associated nodes in the trained net.
During mapping, there will be one single winning neuron: the neuron whose weight vector lies closest to the input vector. This can be simply determined by calculating the Euclidean distance between input vector and weight vector. While representing input data as vectors has been emphasized in this article, any kind of object which can be represented digitally, which has an appropriate distance measure associated with it, and in which the necessary operations for training are possible can be used to construct a self-organizing map. This includes matrices, continuous functions or even other self-organizing maps.
"Binning" describes the process of converting continuously variable data (in the present case, spatial locations in degrees of latitude and longitude) into a set of discrete "bins" in order to apply the indexing and subsequent search/retrieval processes, other processing and reporting, etc. The optimal size of the spatial data "bins" can depend on the user's requirements (e.g. geographic coverage), desire for handling large vs. small datasets (which can affect the time required for processing or production of on-demand maps) and density of available data (with sparse input data, many "bins" may end up being empty).
Agenda is a DOS-based personal information manager, designed by Mitch Kapor, Ed Belove and Jerry Kaplan, and marketed by Lotus Software. Lotus Agenda is a "free-form" information manager: the information need not be structured at all before it is entered into the database. A phrase such as "See Wendy on Tuesday 3pm" can be entered as is without any pre-processing. Its distinguishing feature was the ability to allow users to input data before the creation of database tables, giving the program flexibility to accommodate the myriad pieces of information a person may need to keep track of.
Reconstruction of phylogenies using Bayesian inference generates a posterior distribution of highly probable trees given the data and evolutionary model, rather than a single "best" tree. The trees in the posterior distribution generally have many different topologies. When the input data is variant allelic frequency data (VAF), the tool EXACT can compute the probabilities of trees exactly, for small, biologically relevant tree sizes, by exhaustively searching the entire tree space. Most Bayesian inference methods utilize a Markov-chain Monte Carlo iteration, and the initial steps of this chain are not considered reliable reconstructions of the phylogeny.
The goal of radiomics is to be able to use this database for new patients. This means that we need algorithms that run new input data through the database which return a result with information about what the course of the patients’ disease might look like. For example, how fast the tumor will grow or how good the chances are that the patient survives for a certain time, whether distant metastases are possible and where. This determines how the further treatment (like surgery, chemotherapy, radiotherapy or targeted drugs etc.) and the best solution which maximizes survival or improvement is selected.
These p singular vectors are the feature vectors learned from the input data, and they represent directions along which the data has the largest variations. PCA is a linear feature learning approach since the p singular vectors are linear functions of the data matrix. The singular vectors can be generated via a simple algorithm with p iterations. In the ith iteration, the projection of the data matrix on the (i-1)th eigenvector is subtracted, and the ith singular vector is found as the right singular vector corresponding to the largest singular of the residual data matrix.
Early political campaigns on which Penn and Schoen worked include Hugh Carey's New York gubernatorial campaign in 1974 and Ed Koch's New York mayoral campaign in 1977, for which the company supervised the direct-mail campaign and polling. Initially, Penn and Schoen manually input data from surveys onto punch cards, which was labor-intensive and subject to errors. For Koch's campaign, Penn built and programmed a computer to process the survey results, allowing them to analyze polling data much more quickly. The company also pioneered the use of overnight tracking polls, the results of which helped Koch to win the election.
According to Susan Metros of the University of Southern California,Susan E. Metros biography, Associate Vice Provost/Deputy CIO/Professor, University of Southern California students find themselves able to view pictures, read a map and input data, but are unable to create an image, map data and understand why one chart is better than another. To better prepare students, school districts are taking it upon themselves to add a technology component to their curriculum. For example, instead of submitting papers, students can create short films or interactive essays. This promotes a hands-on approach to multimedia for students to learn new tools.
Data channels are required to use some other form of pulse-stuffing, such as always setting bit 8 to '1', in order to maintain a sufficient density of ones. Of course, this lowers the effective data throughput to 56 kbit/s per channel.Telecom Dictionary, retrieved 25 January 2007 If the characteristics of the input data do not follow the pattern that every eighth bit is '1', the coder using alternate mark inversion adds a '1' after seven consecutive zeros to maintain synchronisation. On the decoder side, this extra '1' added by the coder is removed, recreating the correct data.
Different ways of tracking and analyzing gestures exist, and some basic layout is given is in the diagram above. For example, volumetric models convey the necessary information required for an elaborate analysis, however they prove to be very intensive in terms of computational power and require further technological developments in order to be implemented for real-time analysis. On the other hand, appearance- based models are easier to process but usually lack the generality required for Human-Computer Interaction. Depending on the type of the input data, the approach for interpreting a gesture could be done in different ways.
LZ78 algorithms achieve compression by replacing repeated occurrences of data with references to a dictionary that is built based on the input data stream. Each dictionary entry is of the form `dictionary[...] = {index, character}`, where index is the index to a previous dictionary entry, and character is appended to the string represented by . For example, "abc" would be stored (in reverse order) as follows: `dictionary[k] = {j, 'c'}, dictionary[j] = {i, 'b'}, dictionary[i] = {0, 'a'}`, where an index of 0 specifies the first character of a string. The algorithm initializes last matching index = 0 and next available index = 1.
Early computer resident monitors and operating systems were relatively primitive and were not capable of sophisticated resource allocation. Typically such allocation decisions were made by the computer operator or the user who submitted a job. Batch processing was common, and interactive computer systems rare and expensive. Job control languages (JCLs) developed as primitive instructions, typically punched on cards at the head of a deck containing input data, requesting resources such as memory allocation, serial numbers or names of magnetic tape spools to be made available during execution, or assignment of filenames or devices to device numbers referenced by the job.
The net signal is calculated from the average signal and background, as in signal to noise ratio (imaging)#Calculations. The SiTF curve is then given by the signal output data, (net signal data), plotted against the signal input data (see graph of SiTF to the right). All the data points in the linear region of the SiTF curve can be used in the method of least squares to find a linear approximation. Given n\, data points (x_i\,,y_i\,) a best fit line parameterized as y = mx + b\, is given by:Aboufadel, E. F., Goldberg, J. L., Potter, M. C. (2005).
The Microwave Humidity Sounder (MHS) is a five-channel passive microwave radiometer, with channels from 89 to 190 GHz. It is very similar in design to the AMSU-B instrument, but some channel frequencies have been altered. It is used to study profiles of atmospheric water vapor and provide improved input data to the cloud-clearing algorithms in IR/MW sounder suites. Instruments were launched on NOAA's POES satellite series starting with NOAA-18 launched in May 2005 and the European Space Agency's MetOp series starting with MetOp-A launched in October 2006, continuing with MetOp-B launched in September 2012.
Ceperley's methods have turned the path-integral formulation of the quantum mechanics of strongly interacting many-particle systems into a precise tool to elucidate quantitatively the properties of electrons in solids, superfluids, and other complex quantum systems. His calculation, with Berni Alder, of the equation of state of the 3 dimensional electron gas using a stochastic method has provided basic and definitive input data for numerical applications of density functional theory to electron systems. Their joint publication is one of the most cited articles in Physical Review Letters. The Tanatar-Ceperley exchange- correlation functional is used for the 2 dimensional electron gas.
Xbox 360 Play & Charge Kit - xbox.com When connected, the controller does not act as a wired controller, but continues to communicate with the console or receiver wirelessly; data is sent via USB to the host only to allow automatic syncing and to initiate charging and does not send controller input data. As a result, the cable need not be plugged into the console or computer the controller is being used with — any convenient powered USB port may be used. The Play and Charge Kit will also automatically sync the controller to a Wireless Gaming Receiver when both are plugged into a Windows computer.
A bad game choice may contribute to a lack of entertainment. In this context, a "bad game" may represent a goal choice that does not demonstrate the merits of tool-assistance, so choosing a different goal may alleviate this issue. In other cases, such as the Excitebike TAS by Thomas Seufert, a previously unpopular game had achieved notable entertainment boost due to massive improvements brought into play by increased tool-assisted precision. When someone submits a finished movie file of their input data for publication on the TASvideos website, the audience will vote on if they find the movie entertaining or not.
Most traffic models have typical default values but they may need to be adjusted to better match the driver behavior at the specific location being studied. Model verification is achieved by obtaining output data from the model and comparing them to what is expected from the input data. For example, in traffic simulation, traffic volume can be verified to ensure that actual volume throughput in the model is reasonably close to traffic volumes input into the model. Ten percent is a typical threshold used in traffic simulation to determine if output volumes are reasonably close to input volumes.
An additional way to improve land change modeling is through improvement of model evaluation approaches. Improvement in sensitivity analysis are needed to gain a better understand of the variation in model output in response to model elements like input data, model parameters, initial conditions, boundary conditions, and model structure. Improvement in pattern validation can help land change modelers make comparisons between model outputs parameterized for some historic case, like maps, and observations for that case. Improvement in uncertainty sources is needed to improve forecasting of future states that are non-stationary in processes, input variables, and boundary conditions.
A T52d on display at the Imperial War Museum, London. Siemens produced several and mostly incompatible versions of the T52: the T52a and T52b, which differed only in their electrical noise suppression, and the T52c, T52d and T52e. While the T52a/b and T52c were cryptologically weak, the last two were more advanced devices; the movement of the wheels was intermittent, the decision on whether or not to advance them being controlled by logic circuits which took as input data from the wheels themselves. In addition, a number of conceptual flaws, including very subtle ones, had been eliminated.
In unsupervised learning, input data is given along with the cost function, some function of the data \textstyle x and the network's output. The cost function is dependent on the task (the model domain) and any a priori assumptions (the implicit properties of the model, its parameters and the observed variables). As a trivial example, consider the model \textstyle f(x) = a where \textstyle a is a constant and the cost \textstyle C=E[(x - f(x))^2]. Minimizing this cost produces a value of \textstyle a that is equal to the mean of the data.
Results from simulating lake levels using the historical climate record with the area afforested and abstractions levels fixed at 2014 values indicate that no sustainable additional yield is possible because of the sustained decline in both the simulated lake levels and conceptual groundwater store, which would be environmentally, socially and ecologically unacceptable. Preliminary simulated results indicate that the removal of approximately 5 km2 of forestry is required to release 1 MCM/yr for domestic abstractions. However, these preliminary results require improved verification of input data and a review of the modelling for increased confidence in the results.
Reentrancy is not the same thing as idempotence, in which the function may be called more than once yet generate exactly the same output as if it had only been called once. Generally speaking, a function produces output data based on some input data (though both are optional, in general). Shared data could be accessed by any function at any time. If data can be changed by any function (and none keep track of those changes), there is no guarantee to those that share a datum that that datum is the same as at any time before.
The quantity of drainage water, as output, is determined by two drainage intensity factors for drainage above and below drain level respectively (to be given with the input data) and the height of the water table above the given drain level. This height results from the computed water balance Further, a drainage reduction factor can be applied to simulate a limited operation of the drainage system. Variation of the drainage intensity factors and the drainage reduction factor gives the opportunity to simulate the effect of different drainage options. To obtain accuracy in the computations of the ground water flow (sect.
Each layer of a neural network has inputs with a corresponding distribution, which is affected during the training process by the randomness in the parameter initialization and the randomness in the input data. The effect of these sources of randomness on the distribution of the inputs to internal layers during training is described as internal covariate shift. Although a clear-cut precise definition seems to be missing, the phenomenon observed in experiments is the change on means and variances of the inputs to internal layers during training. Batch normalization was initially proposed to mitigate internal covariate shift.
In numerical partial differential equations, the Ladyzhenskaya–Babuška–Brezzi condition is a sufficient condition for a saddle point problem to have a unique solution that depends continuously on the input data. Saddle point problems arise in the discretization of Stokes flow and in the mixed finite element discretization of Poisson's equation. For positive-definite problems, like the unmixed formulation of the Poisson equation, most discretization schemes will converge to the true solution in the limit as the mesh is refined. For saddle point problems, however, many discretizations are unstable, giving rise to artifacts such as spurious oscillations.
In SOP, runtime properties stored on the service interface metadata serve as a contract with the service virtual machine (SVM). One example for the use of runtime properties is that in declarative service synchronization. A service interface can be declared as a fully synchronized interface, meaning that only a single instance of that service can run at any given time. Or, it can be synchronized based on the actual value of key inputs at runtime, meaning that no two service instances of that service with the same value for their key input data can run at the same time.
The algorithm described above performs a "one-step" alignment, finding embeddings for both data sets at the same time. A similar effect can also be achieved with "two- step" alignments , following a slightly modified procedure: # Project each input data set to a lower-dimensional space independently, using any of a variety of dimension reduction algorithms. # Perform linear manifold alignment on the embedded data, holding the first data set fixed, mapping each additional data set onto the first's manifold. This approach has the benefit of decomposing the required computation, which lowers memory overhead and allows parallel implementations.
The MapReduce architecture and programming model pioneered by Google is an example of a modern systems architecture designed for data-intensive computing. MapReduce: Simplified Data Processing on Large Clusters by J. Dean, and S. Ghemawat. Proceedings of the Sixth Symposium on Operating System Design and Implementation (OSDI), 2004. The MapReduce architecture allows programmers to use a functional programming style to create a map function that processes a key-value pair associated with the input data to generate a set of intermediate key-value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key.
It also included any data contributed based on input data that was not compatible with the new terms. Estimates suggested that over 97% of data would be retained globally, however certain regions would be affected more than others, such as in Australia where 24 to 84% of objects would be retained, depending on the type of object. Ultimately, more than 99% of the data was retained, with Australia and Poland being the countries most severely affected by the change. All data added to the project needs to have a licence compatible with the Open Database Licence.
Traditional computers, as mainly used in the computational creativity application, do not support creativity, as they fundamentally transform a set of discrete, limited domain of input parameters into a set of discrete, limited domain of output parameters using a limited set of computational functions . As such, a computer cannot be creative, as everything in the output must have been already present in the input data or the algorithms . For some related discussions and references to related work are captured in some recent work on philosophical foundations of simulation. Mathematically, the same set of arguments against creativity has been made by Chaitin.
In the following example: select { case left :> v: out <: v; break; case right :> v: out <: v; break; } the select statement merges data from and channels on to an channel. A select case can be guarded, so that the case is only selected if the guard expression is true at the same time the event is enabled. For example, with a guard: case enable => left :> v: out <: v; break; the left- hand channel of the above example can only input data when the variable is true. The selection of events is arbitrary, but event priority can be enforced with the attribute for selects.
In computer engineering, out-of-order execution (or more formally dynamic execution) is a paradigm used in most high-performance central processing units to make use of instruction cycles that would otherwise be wasted. In this paradigm, a processor executes instructions in an order governed by the availability of input data and execution units, rather than by their original order in a program. In doing so, the processor can avoid being idle while waiting for the preceding instruction to complete and can, in the meantime, process the next instructions that are able to run immediately and independently.
Through this interface, the engineer can modify specific input parameters for the particular object under investigation. Once the appropriate values have been set by the engineer, the job is submitted to VE-CE, which schedules the appropriate models for execution and sends the input data to the respective models. Once the models have been executed, the data generated by the models is accessible in VE- Xplorer within the graphical decision-making environment. Everything that has occurred up to this point has occurred without user intervention; the software tools contained within VE-Suite have handled the information integration and model execution.
All field data is incorporated into the geostatistical inversion process through the use of probability distribution functions (PDFs). Each PDF describes a particular input data in geostatistical terms using histograms and variograms, which identify the odds of a given value at a specific place and the overall expected scale and texture based on geologic insight. Once constructed, the PDFs are combined using Bayesian inference, resulting in a posterior PDF that conforms to everything that is known about the field."Incorporating Geophysics into Geologic Models: New Approach Makes Geophysical Models Available to Engineers in a Form They Can Use", Fugro-Jason White Paper, 2008.
The term "hash" offers a natural analogy with its non-technical meaning (to "chop" or "make a mess" out of something), given how hash functions scramble their input data to derive their output. In his research for the precise origin of the term, Donald Knuth notes that, while Hans Peter Luhn of IBM appears to have been the first to use the concept of a hash function in a memo dated January 1953, the term itself would only appear in published literature in the late 1960s, on Herbert Hellerman's Digital Computer System Principles, even though it was already widespread jargon by then.
DVB Additive scramblers (they are also referred to as synchronous) transform the input data stream by applying a pseudo-random binary sequence (PRBS) (by modulo-two addition). Sometimes a pre-calculated PRBS stored in the read-only memory is used, but more often it is generated by a linear-feedback shift register (LFSR). In order to assure a synchronous operation of the transmitting and receiving LFSR (that is, scrambler and descrambler), a sync-word must be used. A sync-word is a pattern that is placed in the data stream through equal intervals (that is, in each frame).
The 7502 system enclosure had two levels to include space for the dual, 8-inch floppy disc unit. The interior of the cabinet was covered with acoustic-absorbent foam material to cut the noise from the cooling fans. The maximum connectivity was 8 x 7561 VDU stations and four serial printers, but in the early systems it was necessary to reduce the VDU attachments if floppy disc storage was attached. The rear of the 7502 system carried the connectors for VDUs, modem and serial printers and a set of 8 "engineer's switches" which could be used to input data and set options for "teleloading" software.
In contrast to other Hough transform-based approaches for analytical shapes, Fernandes' technique does not depend on the shape one wants to detect nor on the input data type. The detection can be driven to a type of analytical shape by changing the assumed model of geometry where data have been encoded (e.g., euclidean space, projective space, conformal geometry, and so on), while the proposed formulation remains unchanged. Also, it guarantees that the intended shapes are represented with the smallest possible number of parameters, and it allows the concurrent detection of different kinds of shapes that best fit an input set of entries with different dimensionalities and different geometric definitions (e.g.
Some of the earliest-used machine learning algorithms, such as decision trees, produced systems of hard if-then rules similar to existing hand-written rules. Increasingly, however, research has focused on statistical models, which make soft, probabilistic decisions based on attaching real- valued weights to the features making up the input data. The cache language models upon which many speech recognition systems now rely are examples of such statistical models. Such models are generally more robust when given unfamiliar input, especially input that contains errors (as is very common for real-world data), and produce more reliable results when integrated into a larger system comprising multiple subtasks.
Let s(t) be an unknown signal which must be estimated from a measurement signal x(t). The Wiener filter problem has solutions for three possible cases: one where a noncausal filter is acceptable (requiring an infinite amount of both past and future data), the case where a causal filter is desired (using an infinite amount of past data), and the finite impulse response (FIR) case where only input data is used (i.e. the result or output is not fed back into the filter as in the IIR case). The first case is simple to solve but is not suited for real-time applications.
Evaluating the complexity of an algorithm is an important part of algorithm design, as this gives useful information on the performance that may be expected. It is a common misconception that the evaluation of the complexity of algorithms will become less important as a result of Moore's law, which posits the exponential growth of the power of modern computers. This is wrong because this power increase allows working with large input data (big data). For example, when one wants to sort alphabetically a list of a few hundreds of entries, such as the bibliography of a book, any algorithm should work well in less than a second.
There is a need for a computer program that is easier to operate and that requires a simpler data structure than most currently available models. Therefore, the SaltModod program was designed keeping in mind a relative simplicity of operation to facilitate the use by field technicians, engineers and project planners instead of specialized geo-hydrologists. It aims at using input data that are generally available, or that can be estimated with reasonable accuracy, or that can be measured with relative ease. Although the calculations are done numerically and have to be repeated many times, the final results can be checked by hand using the formulas in the manual.
In 2009, the signal detection hardware and software was called Prelude, which was composed of rack-mounted PCs augmented by two custom accelerator cards based on digital signal processing (DSP) and field-programmable gate array (FPGA) chips. Each Programmable Detection Module (one of 28 PCs) can analyze 2 MHz of dual- polarization input data to generate spectra with spectral resolution of 0.7 Hz and time samples of 1.4 seconds. In 2009, the array had a 40 Mbit/s internet connection, adequate for remote access and transferring of data products for ATA-256. An upgrade to 40 Gbit/s was planned, which would enable direct distribution of raw data for offsite computing.
Often the more general terms (large scale) data analysis and analytics—or, when referring to actual methods, artificial intelligence and machine learning—are more appropriate. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics.
Geostatistical inversion integrates data from many sources and creates models that have greater resolution than the original seismic, match known geological patterns, and can be used for risk assessment and reduction. Seismic, well logs and other input data are each represented as a probability density function (PDF), which provides a geostatistical description based on histograms and variograms. Together these define the chances of a particular value at a particular location, and the expected geological scale and composition throughout the modeled area. Unlike conventional inversion and geomodeling algorithms, geostatistical inversion takes a one-step approach, solving for impedance and discrete property types or lithofacies at the same time.
Amplitude versus offset (AVO) (AVA) geostatistical inversion incorporates simultaneous AVO (AVA) inversion into the geostatistical inversion algorithm so high resolution, geostatistics, and AVO may be attained in a single method. The output model (realizations) are consistent with well log information, AVO seismic data, and honor rock property relationships found in the wells. The algorithm also simultaneously produces elastic properties (P-impedance, S-impedance and density) and lithology volumes, instead of sequentially solving for lithology first and then populating the cell with impedance and density values. Because all output models match all input data, uncertainty can be quantitatively assessed to determine the range of reservoir possibilities within the constraining data.
Each node is associated with a "weight" vector, which is a position in the input space; that is, it has the same dimension as each input vector. While nodes in the map space stay fixed, training consists in moving weight vectors toward the input data (reducing a distance metric) without spoiling the topology induced from the map space. Thus, the self-organizing map describes a mapping from a higher-dimensional input space to a lower-dimensional map space. Once trained, the map can classify a vector from the input space by finding the node with the closest (smallest distance metric) weight vector to the input space vector.
Shows the principle of a SerDes The basic SerDes function is made up of two functional blocks: the Parallel In Serial Out (PISO) block (aka Parallel-to-Serial converter) and the Serial In Parallel Out (SIPO) block (aka Serial-to-Parallel converter). There are 4 different SerDes architectures: (1) Parallel clock SerDes, (2) Embedded clock SerDes, (3) 8b/10b SerDes, (4) Bit interleaved SerDes. The PISO (Parallel Input, Serial Output) block typically has a parallel clock input, a set of data input lines, and input data latches. It may use an internal or external phase-locked loop (PLL) to multiply the incoming parallel clock up to the serial frequency.
The lower button arrangement and platform is the same for both model 50 and Ironman triathlon, but Ironman sports an additional start/split button on its face, indicating its additional chronograph functions. All three models are water resistant to 100 m. The model 50 (Timex models 70502/70518) was worn by astronaut James H. Newman on STS-88. Although there are other watches capable of storing all kinds of data, most had either a small keyboardManual for Casio watch module 2888, typical databank watch with keyboard input or buttons,Manual for Casio watch module 2747, typical databank watch with button input which could be used to input data.
Two central categories of mitigation to the problems caused by weird machine functionality include input validation within the software and protecting against problems arising from the platform on which the program runs, such as memory errors. Input validation aims to limit the scope and forms of unexpected inputs e.g. through whitelists of allowed inputs, so that the software program itself would not end up in an unexpected state by interpreting the data internally. Equally importantly, secure programming practices such as protecting against buffer overflows make it less likely that input data becomes interpreted in unintended ways by lower layers, such as the hardware on which the program is executed.
The typical implementation of the LDA technique requires that all the samples are available in advance. However, there are situations where the entire data set is not available and the input data are observed as a stream. In this case, it is desirable for the LDA feature extraction to have the ability to update the computed LDA features by observing the new samples without running the algorithm on the whole data set. For example, in many real-time applications such as mobile robotics or on-line face recognition, it is important to update the extracted LDA features as soon as new observations are available.
As a result, representational resources may be wasted on areas of the input space that are irrelevant to the task. A common solution is to associate each data point with its own centre, although this can expand the linear system to be solved in the final layer and requires shrinkage techniques to avoid overfitting. Associating each input datum with an RBF leads naturally to kernel methods such as support vector machines (SVM) and Gaussian processes (the RBF is the kernel function). All three approaches use a non-linear kernel function to project the input data into a space where the learning problem can be solved using a linear model.
The computational conversion of the ion sequence data, as obtained from a position sensitive detector, to a three-dimensional visualisation of atomic types, is termed "reconstruction". Reconstruction algorithms are typically geometrically based, and have several literature formulations. Most models for reconstruction assume that the tip is a spherical object, and use empirical corrections to stereographic projection to convert detector positions back to a 2D surface embedded in 3D space, R3. By sweeping this surface through R3 as a function of the ion sequence input data, such as via ion-ordering, a volume is generated onto which positions the 2D detector positions can be computed and placed three-dimensional space.
Josef Stoer and Roland Bulirsch, "Introduction to Numerical Analysis (3rd ed.)", Springer-Verlag, 2002, p. 610 Sheets often grow very complex with input data, intermediate values from formulas and output areas, separated by blank areas. In order to manage this complexity, Excel allows one to hide data that is not of interest,David Ringstrom, "Tricks for hiding and unhiding Excel rows and columns", accounting web, April 17, 2009 often intermediate values. Quattro Pro commonly introduced the idea of multiple sheets in a single book, allowing further subdivision of the data; Excel implements this as a set of tabs along the bottom of the workbook.
Illustration of the implicit even/odd extensions of DST input data, for N=9 data points (red dots), for the four most common types of DST (types I–IV). Like any Fourier-related transform, discrete sine transforms (DSTs) express a function or a signal in terms of a sum of sinusoids with different frequencies and amplitudes. Like the discrete Fourier transform (DFT), a DST operates on a function at a finite number of discrete data points. The obvious distinction between a DST and a DFT is that the former uses only sine functions, while the latter uses both cosines and sines (in the form of complex exponentials).
In computing, a shebang is the character sequence consisting of the characters number sign and exclamation mark () at the beginning of a script. It is also called sha-bang, hashbang, pound-bang, or hash-pling. When a text file with a shebang is used as if it is an executable in a Unix-like operating system, the program loader mechanism parses the rest of the file's initial line as an interpreter directive. The loader executes the specified interpreter program, passing to it as an argument the path that was initially used when attempting to run the script, so that the program may use the file as input data.
It was the 1401 that transferred input data from slow peripherals (such as the IBM 1402 Card Read-Punch) to tape, and transferred output data from tape to the card punch, the IBM 1403 Printer, or other peripherals. This allowed the mainframe's throughput to not be limited by the speed of a card reader or printer. (For more information, see Spooling.) Elements within IBM, notably John Haanstra, an executive in charge of 1401 deployment, supported its continuation in larger models for evolving needs (e.g., the IBM 1410) but the 1964 decision at the top to focus resources on the System/360 ended these efforts rather suddenly.
There is a need for a computer program that is easier to operate and that requires a simpler data structure then most currently available models. Therefore, the SahysMod program was designed keeping in mind a relative simplicity of operation to facilitate the use by field technicians, engineers and project planners instead of specialized geo- hydrologists. It aims at using input data that are generally available, or that can be estimated with reasonable accuracy, or that can be measured with relative ease. Although the calculations are done numerically and have to be repeated many times, the final results can be checked by hand using the formulas in this manual.
In addition to the ability to call any service, Service Request Events and Shared Memory are two of the SOP built-in mechanisms provided for inter-service communication. The consumption of a service is treated as an Event in SOP. SOP provides a correlation-based event mechanism that results in the pre-emption of a running composite that has declared, through a "wait" construct, the need to wait for one or more other service consumption events to happen with specified input data values. The execution of the composite service continues when services are consumed with specific correlation key inputs associated with the wait construct.
Most financial models are produced using Microsoft Excel. The model will routinely contain sheets for input data, formulas (the 'workings') which drive the model, and outputs, which are usually in the form of financial statements (balance sheet, income statement, cash flow statement, etc.). Model auditors may undertake a detailed 'bottom- up' review (cell-by-cell checks) of each unique formula, and/or combine a 'top-down' analysis such as the reperformance of calculations based upon the project's documentation There is some debate as to whether a "cell-by-cell" or reperformance approach is most appropriate. A model auditor may emphasise one or the other or use a combination of both approaches.
Research Analysts at the Sentinel Project use ThreatWiki to input data from reliable sources to track SOCs with the help of a visual time-line that enables them to track the SOCs more closely. ThreatWiki shows exactly where events such as arrests, arson, or raids have taken place and the data point is not just a vague point on the map; "we are talking about cities, towns, latitude, and longitude of the area where the incident occurred." ThreatWiki also shows correlations on how incidents are related to one another according to how they are tagged. Improvements to ThreatWiki will soon make visualizations more interactive and informative.
Grey-box testing (American spelling: gray-box testing) involves having knowledge of internal data structures and algorithms for purposes of designing tests, while executing those tests at the user, or black-box level. The tester is not required to have full access to the software's source code. Manipulating input data and formatting output do not qualify as grey-box, because the input and output are clearly outside of the "black box" that we are calling the system under test. This distinction is particularly important when conducting integration testing between two modules of code written by two different developers, where only the interfaces are exposed for test.
Neighbourhood components analysis aims at "learning" a distance metric by finding a linear transformation of input data such that the average leave-one-out (LOO) classification performance is maximized in the transformed space. The key insight to the algorithm is that a matrix A corresponding to the transformation can be found by defining a differentiable objective function for A, followed by use of an iterative solver such as conjugate gradient descent. One of the benefits of this algorithm is that the number of classes k can be determined as a function of A, up to a scalar constant. This use of the algorithm therefore addresses the issue of model selection.
Rate distortion theory has been applied to choosing k called the "jump" method, which determines the number of clusters that maximizes efficiency while minimizing error by information-theoretic standards. The strategy of the algorithm is to generate a distortion curve for the input data by running a standard clustering algorithm such as k-means for all values of k between 1 and n, and computing the distortion (described below) of the resulting clustering. The distortion curve is then transformed by a negative power chosen based on the dimensionality of the data. Jumps in the resulting values then signify reasonable choices for k, with the largest jump representing the best choice.
In the run-up to the election, both the CHP and the HDP developed computer systems that allowed the party to shadow the official election results by running their own counts alongside the Anadolu Agency and the Cihan News Agency. The systems would allow both parties to input data from hard copy statements of results for each ballot box to ensure that there was no discrepancy between the actual counted votes and the results entered into the YSK's SEÇSİS system. On 5 September, the HDP requested that the YSK place cameras to film the vote counting procedures in 126 'high security risk' areas. Their proposal was rejected on 14 September.
In machine learning, pattern recognition and in image processing, feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations. Feature extraction is related to dimensionality reduction. When the input data to an algorithm is too large to be processed and it is suspected to be redundant (e.g. the same measurement in both feet and meters, or the repetitiveness of images presented as pixels), then it can be transformed into a reduced set of features (also named a feature vector).
Many implementations of mass customization are operational today, such as software-based product configurators that make it possible to add and/or change functionalities of a core product or to build fully custom enclosures from scratch. This degree of mass customization, however, has only seen limited adoption. If an enterprise's marketing department offers individual products (atomic market fragmentation), it doesn't often mean that a product is produced individually, but rather that similar variants of the same mass-produced item are available. Additionally, in a fashion context, existing technologies to predict clothing size from user input data have been shown to be not yet of high enough suitability for mass customisation purposes.
Alternatively, if the variables are accorded different names and perhaps employ different numeric measurement scales but are highly correlated with each other, then they suffer from redundancy. One of the features of multicollinearity is that the standard errors of the affected coefficients tend to be large. In that case, the test of the hypothesis that the coefficient is equal to zero may lead to a failure to reject a false null hypothesis of no effect of the explanator, a type II error. Another issue with multicollinearity is that small changes to the input data can lead to large changes in the model, even resulting in changes of sign of parameter estimates.
A universal hashing scheme is a randomized algorithm that selects a hashing function h among a family of such functions, in such a way that the probability of a collision of any two distinct keys is 1/m, where m is the number of distinct hash values desired—independently of the two keys. Universal hashing ensures (in a probabilistic sense) that the hash function application will behave as well as if it were using a random function, for any distribution of the input data. It will, however, have more collisions than perfect hashing and may require more operations than a special-purpose hash function.
In many applications, the range of hash values may be different for each run of the program, or may change along the same run (for instance, when a hash table needs to be expanded). In those situations, one needs a hash function which takes two parameters—the input data z, and the number n of allowed hash values. A common solution is to compute a fixed hash function with a very large range (say, 0 to 232 − 1), divide the result by n, and use the division's remainder. If n is itself a power of 2, this can be done by bit masking and bit shifting.
The computing load of the inverse problem of an ordinary Kalman recursion is roughly proportional to the cube of the number of the measurements processed simultaneously. This number can always be set to 1 by processing each scalar measurement independently and (if necessary) performing a simple pre-filtering algorithm to de-correlate these measurements. However, for any large and complex system this pre-filtering may need the HWB computing. Any continued use of a too narrow window of input data weakens observability of the calibration parameters and, in the long run, this may lead to serious controllability problems totally unacceptable in safety-critical applications.
Multiple errors scattered throughout a table can be a sign of deeper problems, and other statistical tests can be used to analyze the suspect data. The GRIM test works best with data sets in which: the sample size is relatively small, the number of subcomponents in composite measures is also small, and the mean is reported to multiple decimal places. In some cases, a valid mean may appear to fail the test if the input data is not discretized as expected – for example, if people are asked how many slices of pizza they ate at a buffet, some people may respond with a fraction such as "three and a half" instead of a whole number as expected.
Casio fx-115ES—A modern scientific calculator from Casio with a dot matrix "Natural Textbook" LCD solar-powered scientific calculator from the 1980s using a single-line LCD A scientific calculator is a type of electronic calculator, usually but not always handheld, designed to calculate problems in science, engineering, and mathematics. They have completely replaced slide rules in traditional applications, and are widely used in both education and professional settings. In certain contexts such as higher education, scientific calculators have been superseded by graphing calculators, which offer a superset of scientific calculator functionality along with the ability to graph input data and write and store programs for the device. There is also some overlap with the financial calculator market.
The 803 has a little-known interrupt facility. Whilst it is not mentioned in the programming guide and is not used by any of the standard peripherals, the operation of the interrupt logic is described in the 803 hardware handbooks and the logic is shown in the 803 maintenance diagrams (Diagram 1:LB7 Gb). Interrupts are probably used mostly in conjunction with custom interfaces provided as part of ARCH real time process control systems. Since all input and output instructions causes the 803 to become "busy" if input data is not available or if an output device has not completed a previous operation, interrupts are not needed and are not used for driving the standard peripherals.
Signal chain, or signal-processing chain is a term used in signal processingSmith, Steven W., The Scientist and Engineer's Guide to Digital Signal Processing, 1999, California Technical Publishing, San Diego, California, and mixed-signal system designKester, W. (Editor-in-Chief), Mixed-Signal and DSP Design Techniques, 2000, Analog Devices, Norwood, MA, to describe a series of signal-conditioning electronic components that receive input (data acquired from sampling either real-time phenomena or from stored data) in tandem, with the output of one portion of the chain supplying input to the next. Signal chains are often used in signal processing applications to gather and process data or to apply system controls based on analysis of real- time phenomena.
The key, which is given as one input to the cipher, defines the mapping between plaintext and ciphertext. If data of arbitrary length is to be encrypted, a simple strategy is to split the data into blocks each matching the cipher's block size, and encrypt each block separately using the same key. This method is not secure as equal plaintext blocks get transformed into equal ciphertexts, and a third party observing the encrypted data may easily determine its content even when not knowing the encryption key. To hide patterns in encrypted data while avoiding the re-issuing of a new key after each block cipher invocation, a method is needed to randomize the input data.
In the late 19th century, Herman Hollerith took the idea of using punched cards to store information a step further when he created a punched card tabulating machine which he used to input data for the 1890 U.S. Census. A large data processing industry using punched-card technology was developed in the first half of the twentieth centurydominated initially by the International Business Machine corporation (IBM), with its line of unit record equipment. The cards were used for data, however, with programming done by plugboards. Some early computers, such as the 1944 IBM Automatic Sequence Controlled Calculator (Harvard Mark I) received program instructions from a paper tape punched with holes, similar to Jacquard's string of cards.
The finite element method (FEM) is used to find approximate solution of partial differential equations (PDE) and integral equations. The solution approach is based either on eliminating the time derivatives completely (steady state problems), or rendering the PDE into an equivalent ordinary differential equation, which is then solved using standard techniques such as finite differences, etc. In solving partial differential equations, the primary challenge is to create an equation which approximates the equation to be studied, but which is numerically stable, meaning that errors in the input data and intermediate calculations do not accumulate and destroy the meaning of the resulting output. There are many ways of doing this, with various advantages and disadvantages.
Early precursors to the modern artillery-type games were text-only games that simulated artillery entirely with input data values. A BASIC game known simply as Artillery was written by Mike Forman and was published in Creative Computing magazine in 1976. This seminal home computer version of the game was revised in 1977 by M. E. Lyon and Brian West and was known as War 3; War 3 was revised further in 1979 and published as Artillery-3. These early versions of turn-based tank combat games interpreted human-entered data such as the distance between the tanks, the velocity or "power" of the shot fired and the angle of the tanks' turrets.
There are often other cost metrics in addition to execution time that are relevant to compare query plans . In a cloud computing scenario for instance, one should compare query plans not only in terms of how much time they take to execute but also in terms of how much money their execution costs. Or in the context of approximate query optimization, it is possible to execute query plans on randomly selected samples of the input data in order to obtain approximate results with reduced execution overhead. In such cases, alternative query plans must be compared in terms of their execution time but also in terms of the precision or reliability of the data they generate.
The SWMM 3 and SWMM 4 converter can convert up to two files from the earlier SWMM 3 and 4 versions at one time to SWMM 5. Typically one would convert a Runoff and Transport file to SWMM 5 or a Runoff and Extran File to SWMM 5. If there is a combination of a SWMM 4 Runoff, Transport and Extran network then it will have to be converted in pieces and the two data sets will have to be copied and pasted together to make one SWMM 5 data set. The x,y coordinate file is only necessary if there are not existing x, y coordinates on the D1 line of the SWMM 4 Extran input data[ set.
For planning and monitoring decompression using decompression tables, the input data usually consists of the maximum depth reached during the dive, the bottom time as defined by the dive table in use and the composition of the breathing gas. For repetitive dives it also includes the "surface interval" – the time spent at surface pressure between the previous dive and the start of the next dive. This information is used to estimate the levels of inert gas dissolved in the diver's tissues during and after completing a dive or series of dives. Residual gas may be expressed as a "repetitive group", which is an important input value for planning the decompression for the next dive when using tables.
Firing semantics of process P modeled with a Petri net displayed in the image above Assuming process P in the KPN above is constructed so that it first reads data from channel A, then channel B, computes something and then writes data to channel C, the execution model of the process can be modeled with the Petri net shown on the right. The single token in the PE resource place forbids that the process is executed simultaneously for different input data. When data arrives at channel A or B, tokens are placed into places FIFO A and FIFO B respectively. The transitions of the Petri net are associated with the respective I/O operations and computation.
The first step is for "neighbor-preserving", where each input data point Xi is reconstructed as a weighted sum of K nearest neighbor data points, and the optimal weights are found by minimizing the average squared reconstruction error (i.e., difference between an input point and its reconstruction) under the constraint that the weights associated with each point sum up to one. The second step is for "dimension reduction," by looking for vectors in a lower-dimensional space that minimizes the representation error using the optimized weights in the first step. Note that in the first step, the weights are optimized with fixed data, which can be solved as a least squares problem.
This results in a small error in distance, but makes calculations simpler and, given the inherent imprecision in the used input data, it is not the biggest error source. The relatively new FT8 narrowband digital mode transmits Maidenhead locator square as part of standard messages, with the 4 character locator square being efficiently represented within 15 bits of the transmitted string. Until the adoption of WGS 84 as the official geodetic datum of the Maidenhead locator system in 1999, operators had usually specified their location based on their local national datum. Consequently, stations very near the edges of squares (at denoted precision) may have changed their locators when changing over to the use of WGS 84.
In information theory, a soft-decision decoder is a kind of decoding methods – a class of algorithm used to decode data that has been encoded with an error correcting code. Whereas a hard-decision decoder operates on data that take on a fixed set of possible values (typically 0 or 1 in a binary code), the inputs to a soft-decision decoder may take on a whole range of values in-between. This extra information indicates the reliability of each input data point, and is used to form better estimates of the original data. Therefore, a soft- decision decoder will typically perform better in the presence of corrupted data than its hard-decision counterpart.
Supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, and occurs after a program sees a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. Both classifiers and regression learners can be viewed as "function approximators" trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, "spam" or "not spam".
AWK was initially developed in 1977 by Alfred Aho (author of egrep), Peter J. Weinberger (who worked on tiny relational databases), and Brian Kernighan; it takes its name from their respective initials. According to Kernighan, one of the goals of AWK was to have a tool that would easily manipulate both numbers and strings. AWK was also inspired by Marc Rochkind's programming language that was used to search for patterns in input data, and was implemented using yacc. As one of the early tools to appear in Version 7 Unix, AWK added computational features to a Unix pipeline besides the Bourne shell, the only scripting language available in a standard Unix environment.
Lexical choice modules must be informed by linguistic knowledge of how the system's input data maps onto words. This is a question of semantics, but it is also influenced by syntactic factors (such as collocation effects) and pragmatic factors (such as context). Hence NLG systems need linguistic models of how meaning is mapped to words in the target domain (genre) of the NLG system. Genre tends to be very important; for example the verb veer has a very specific meaning in weather forecasts (wind direction is changing in a clockwise direction) which it does not have in general English, and a weather-forecast generator must be aware of this genre-specific meaning.
A common example of feature vectors appears when each image point is to be classified as belonging to a specific class. Assuming that each image point has a corresponding feature vector based on a suitable set of features, meaning that each class is well separated in the corresponding feature space, the classification of each image point can be done using standard classification method. Another and related example occurs when neural network-based processing is applied to images. The input data fed to the neural network is often given in terms of a feature vector from each image point, where the vector is constructed from several different features extracted from the image data.
A simple example of a job stream is a system to print payroll checks which might consist of the following steps, performed on a batch of inputs: # Read a file of data containing employee id numbers and hours worked for the current pay period (batch of input data). Validate the data to check that the employee numbers are valid and that the hours worked are reasonable. # Compute salary and deductions for the current pay period based on hours input and pay rate and deductions from the employee's master record. Update the employee master "year-to-date" figures and create a file of records containing information to be used in the following steps.
In a further refinement of the direct use of probabilistic modelling, statistical estimates can be coupled to an algorithm called arithmetic coding. Arithmetic coding is a more modern coding technique that uses the mathematical calculations of a finite-state machine to produce a string of encoded bits from a series of input data symbols. It can achieve superior compression compared to other techniques such as the better-known Huffman algorithm. It uses an internal memory state to avoid the need to perform a one-to-one mapping of individual input symbols to distinct representations that use an integer number of bits, and it clears out the internal memory only after encoding the entire string of data symbols.
In some fields, the terminology is different: For example, in community ecology, the term "classification" is used to refer to what is commonly known as "clustering". The piece of input data for which an output value is generated is formally termed an instance. The instance is formally described by a vector of features, which together constitute a description of all known characteristics of the instance. (These feature vectors can be seen as defining points in an appropriate multidimensional space, and methods for manipulating vectors in vector spaces can be correspondingly applied to them, such as computing the dot product or the angle between two vectors.) Typically, features are either categorical (also known as nominal, i.e.
The threshold value to determine when a data point fits a model , and the number of close data points required to assert that a model fits well to data are determined based on specific requirements of the application and the dataset, and possibly based on experimental evaluation. The number of iterations , however, can be determined as a function of the desired probability of success using a theoretical result. Let be the desired probability that the RANSAC algorithm provides a useful result after running. RANSAC returns a successful result if in some iteration it selects only inliers from the input data set when it chooses the points from which the model parameters are estimated.
The principles used are correspond to those described in the article soil salinity control. Salt concentrations of outgoing water (either from one reservoir into the other or by subsurface drainage) are computed on the basis of salt balances, using different leaching or salt mixing efficiencies to be given with the input data. The effects of different leaching efficiencies can be simulated varying their input value. If drain or well water is used for irrigation, the method computes the salt concentration of the mixed irrigation water in the course of the time and the subsequent effect on the soil and ground water salinity, which again influences the salt concentration of the drain and well water.
A baseline is created by adjusting device settings under a given set of conditions and running test samples, measuring the samples, readjusting the settings, until the output process is brought to an optimal state. Once optimized, a final set of measurements are made of output samples and this data becomes part of the baseline information. The baseline is then characterized by outputting ECIEuropean Color Initiative or IT8.7/4ANSI IT8.7/4-2005 Graphic technology - Input data for characterization of 4-color process printing - Expanded data set test charts (samples of color patches), then the charts are scanned with a spectrophotometer to finally produce a color profile of the baseline. Over time, specific device performance (and other conditions) may vary.
Most of the necessary input data are obtained by defining the location of the site of interest and five simple basin properties. These basin properties are the drainage area, the basin length, the basin slope, the impervious fraction, and the basin development factorGranato, G.E., 2012, Estimating basin lagtime and hydrograph-timing indexes used to characterize stormflows for runoff-quality analysis: U.S. Geological Survey Scientific Investigations Report 2012–5110, 47 p.Stricker, V.A., and Sauer, V.B., 1982, Techniques for estimating flood hydrographs for ungaged urban watersheds: U.S. Geological Survey Open-File Report 82–365, 24 p. SELDM models the potential effect of mitigation measures by using Monte Carlo methods with statistics that approximate the net effects of structural and nonstructural best management practices (BMPs).
In a typical installation that incorporates cost recovery systems and electronic billing, there is a dedicated server to support the billing system (Host); a Local Area Network (LAN) to support user applications such as word processing, graphics, document management, spreadsheets; and cost recovery devices used to input data such as employee ID, client names, account numbers, etc. The accounting server and the cost recovery systems are usually connected to the LAN, and data must be transferred on a regular basis between each of the accounting server and the cost recovery systems. An ECRS can provide the ability to schedule tasks on both the accounting system server and the LAN. Individual tasks may be run at timed intervals separately, or grouped into task lists and run together.
This is more realistic than relying on one sigmoidal equation. A number of sigmoidal equations have been proposed that give rock mass modulus as a function of intact modulus and a rock mass rating. These equations may give a good estimate of modulus given the correct input data, however it is difficult to obtain reliable intact strength or intact modulus values from laboratory tests on samples from highly disturbed rock masses. Because of this limitation, something that is commonly done in practice is to base intact modulus values on test results done on good samples of intact rock from locations with competent rock, using either laboratory measurements of intact modulus or on an assumed ratio between intact strength and modulus for a particular rock type.
The goal of complexity analysis in this model is to find time bounds that depend only on and not on the actual size of the input values or the machine words... In modeling integer computation, it is necessary to assume that machine words are limited in size, because models with unlimited precision are unreasonably powerful (able to solve PSPACE-complete problems in polynomial time).. The transdichotomous model makes a minimal assumption of this type: that there is some limit, and that the limit is large enough to allow random access indexing into the input data. As well as its application to integer sorting, the transdichotomous model has also been applied to the design of priority queues. and to problems in computational geometry and graph algorithms..
Dwork is known for her research placing privacy-preserving data analysis on a mathematically rigorous foundation, including the co-invention of differential privacy, a strong privacy guarantee frequently permitting highly accurate data analysis (with McSherry, Nissim, and Smith, 2006). The differential privacy definition provides guidelines for preserving the privacy of people who may have contributed data to a dataset, by adding small amounts of noise either to the input data or to outputs of computations performed on the data. She uses a systems-based approach to studying fairness in algorithms including those used for placing ads. Dwork has also made contributions in cryptography and distributed computing, and is a recipient of the Edsger W. Dijkstra Prize for her early work on the foundations of fault-tolerant systems.
Equinox - "The Box", Channel 4, 2001. At the request of the NTSB, data from the Penny & Giles quick access recorder - "QAR" - of a British Airways (BA) Boeing 747-400 London- Bangkok flight in which the aircraft had suffered an uncommanded elevator movement and momentary elevator reversal on take-off, the aircraft then continuing its flight and landing safely, was supplied to the NTSB by BA. Unlike a standard FDR, the QAR sampled control input data at much shorter time intervals, as well as sampling and recording many more other aircraft parameters. The Quick Access Recorder had been pioneered by BA's predecessor airline, BEA, on its Hawker Siddeley Trident aircraft back in the 1960s. Subsequently, QARs were fitted to all BEA, and then BA, aircraft.
The scaling that results in the use of a smaller range of digital values than what might appear to be desirable for representation of the nominal range of the input data allows for some "overshoot" and "undershoot" during processing without necessitating undesirable clipping. This "head-room" and "toe-room" can also be used for extension of the nominal color gamut, as specified by xvYCC. The value 235 accommodates a maximum black-to-white overshoot of 255 - 235 = 20, or 20 / ( 235 - 16 ) = 9.1%, which is slightly larger than the theoretical maximum overshoot (Gibbs' Phenomenon) of about 8.9% of the maximum step. The toe-room is smaller, allowing only 16 / 219 = 7.3% overshoot, which is less than the theoretical maximum overshoot of 8.9%.
Further refinements include reserving a code to indicate that the code table should be cleared and restored to its initial state (a "clear code", typically the first value immediately after the values for the individual alphabet characters), and a code to indicate the end of data (a "stop code", typically one greater than the clear code). The clear code lets the table be reinitialized after it fills up, which lets the encoding adapt to changing patterns in the input data. Smart encoders can monitor the compression efficiency and clear the table whenever the existing table no longer matches the input well. Since codes are added in a manner determined by the data, the decoder mimics building the table as it sees the resulting codes.
QSAR modeling produces predictive models derived from application of statistical tools correlating biological activity (including desirable therapeutic effect and undesirable side effects) or physico-chemical properties in QSPR models of chemicals (drugs/toxicants/environmental pollutants) with descriptors representative of molecular structure or properties. QSARs are being applied in many disciplines, for example: risk assessment, toxicity prediction, and regulatory decisions in addition to drug discovery and lead optimization. Obtaining a good quality QSAR model depends on many factors, such as the quality of input data, the choice of descriptors and statistical methods for modeling and for validation. Any QSAR modeling should ultimately lead to statistically robust and predictive models capable of making accurate and reliable predictions of the modeled response of new compounds.
Another way to look at MapReduce is as a 5-step parallel and distributed computation: # Prepare the Map() input – the "MapReduce system" designates Map processors, assigns the input key K1 that each processor would work on, and provides that processor with all the input data associated with that key. # Run the user-provided Map() code – Map() is run exactly once for each K1 key, generating output organized by key K2. # "Shuffle" the Map output to the Reduce processors – the MapReduce system designates Reduce processors, assigns the K2 key each processor should work on, and provides that processor with all the Map-generated data associated with that key. # Run the user- provided Reduce() code – Reduce() is run exactly once for each K2 key produced by the Map step.
Within the context of polarizing topics such as political bias, the top search results can play a significant role in shaping opinions. Through the use of a bias quantification framework, bias can be measured within the political bias by rank, within the search system but further address the sources of the bias through the input data and ranking system. Within the context of information queries, the search results are determined through a ranking system which in the case of topics such as Politics, can return politically biased search results. This bias present in the search results can be a direct result of either biased data that collaborates the ranking system or because of the structure of the ranking system itself.
This questionable nature of search results raises questions of impact on users and to what degree the ranking system can impact political opinions and beliefs, which can directly translate into voter behavior. This can also affirm or encourage biased data within the google search results. Whilst research has shown that users do not place exclusive trust on the information provided by search engines, studies have shown that individuals that are undecided politically are susceptible to be manipulated by bias relative to political candidates and the light in which their policies and actions are presented and conveyed. In the quantification of political bias, both the input data for search results and further the ranking system in which they are presented to the user encapsulate bias to varying degrees.
The primary aim of knowledge engineering is to attain a productive interaction between the available knowledge base and problem solving techniques. This is possible through development of a procedure in which large amounts of task-specific information is encoded into heuristic programs. Thus, the first essential component of knowledge engineering is a large “knowledge base.” Dendral has specific knowledge about the mass spectrometry technique, a large amount of information that forms the basis of chemistry and graph theory, and information that might be helpful in finding the solution of a particular chemical structure elucidation problem. This “knowledge base” is used both to search for possible chemical structures that match the input data, and to learn new “general rules” that help prune searches.
Note that in cases of unsupervised learning, there may be no training data at all to speak of; in other words, the data to be labeled is the training data. Note that sometimes different terms are used to describe the corresponding supervised and unsupervised learning procedures for the same type of output. For example, the unsupervised equivalent of classification is normally known as clustering, based on the common perception of the task as involving no training data to speak of, and of grouping the input data into clusters based on some inherent similarity measure (e.g. the distance between instances, considered as vectors in a multi-dimensional vector space), rather than assigning each input instance into one of a set of pre-defined classes.
Some of the earliest-used machine learning algorithms, such as decision trees, produced systems of hard if-then rules similar to existing hand-written rules. However, part-of-speech tagging introduced the use of hidden Markov models to natural language processing, and increasingly, research has focused on statistical models, which make soft, probabilistic decisions based on attaching real- valued weights to the features making up the input data. The cache language models upon which many speech recognition systems now rely are examples of such statistical models. Such models are generally more robust when given unfamiliar input, especially input that contains errors (as is very common for real-world data), and produce more reliable results when integrated into a larger system comprising multiple subtasks.
An atomic service is an in-memory extension of the SOP runtime environment through a service native interface (SNI) it is essentially a plug-in mechanism. For example, if SOP is automated through an SVM, a service plug-in is dynamically loaded into the SVM when any associated service is consumed. An example of a service plug- in would be a SOAP communicator plug-in that can on-the-fly translate any in- memory service input data to a Web Service SOAP request, post it to a service producer, and then translate the corresponding SOAP response to in-memory output data on the service. Another example of a service plug-in is a standard database SQL plug-in that supports data access, modification and query operations.
In some cases where results must be refereed (such as legal cases), model validation may be needed with field test data in the local setting; this step is not usually warranted, because the best models have been extensively validated over a wide spectrum of input data variables. The product of the calculations is usually a set of isopleths or mapped contour lines either in plan view or cross sectional view. Typically these might be stated as concentrations of carbon monoxide, total reactive hydrocarbons, oxides of nitrogen, particulate or benzene. The air quality scientist can run the model successively to study techniques of reducing adverse air pollutant concentrations (for example, by redesigning roadway geometry, altering speed controls or limiting certain types of trucks).
Bluetooth 5 has introduced two new modes with lower data rate. The symbol rate of the new "Coded PHY" is the same as the Base Rate 1M PHY but in mode S=2 there are two symbols transmitted per data bit. In mode S=2 only a simple Pattern Mapping P=1 is used which simply produces the same stuffing bit for each input data bit. In mode S=8 there are eight symbols per data bit with a Pattern Mapping P=4 producing contrasting symbol sequences - a 0 bit is encoded as binary 0011 and a 1 bit is encoded as binary 1100. In mode S=2 using P=1 the range doubles approximately, while in mode S=8 using P=4 it does quadruple.
Examples of fixtures include loading a database with a specific known set of data, erasing a hard disk and installing a known clean operating system installation, copying a specific known set of files, or the preparation of input data as well as set-up and creation of mock objects. Software which is used to run reproducible tests systematically on a piece of software under test is known as a test harness; part of its job is to set up suitable test fixtures. In generic xUnit, a test fixture is all the things that must be in place in order to run a test and expect a particular outcome. Frequently fixtures are created by handling setUp() and tearDown() events of the unit testing framework.
This has to be done by numerical methods rather than by a formula because the calibration curve is not describable as a formula. Programs to perform these calculations include OxCal and CALIB. These can be accessed online; they allow the user to enter a date range at one standard deviation confidence for the radiocarbon ages, select a calibration curve, and produce probabilistic output both as tabular data and in graphical form. In the example CALIB output shown at left, the input data is 1270 BP, with a standard deviation of 10 radiocarbon years. The curve selected is the northern hemisphere INTCAL13 curve, part of which is shown in the output; the vertical width of the curve corresponds to the width of the standard error in the calibration curve at that point.
Randomness tests (or tests for randomness), in data evaluation, are used to analyze the distribution of a set of data to see if it can be described as random (patternless). In stochastic modeling, as in some computer simulations, the hoped-for randomness of potential input data can be verified, by a formal test for randomness, to show that the data are valid for use in simulation runs. In some cases, data reveals an obvious non-random pattern, as with so- called "runs in the data" (such as expecting random 0–9 but finding "4 3 2 1 0 4 3 2 1..." and rarely going above 4). If a selected set of data fails the tests, then parameters can be changed or other randomized data can be used which does pass the tests for randomness.
A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map, and is therefore a method to do dimensionality reduction. Self-organizing maps differ from other artificial neural networks as they apply competitive learning as opposed to error-correction learning (such as backpropagation with gradient descent), and in the sense that they use a neighborhood function to preserve the topological properties of the input space. U.S. Congress voting patterns. The input data was a table with a row for each member of Congress, and columns for certain votes containing each member's yes/no/abstain vote.
Another application cited by concerns sorting permutations using stacks. An influential result of showed that a system that processes a data stream by pushing incoming elements onto a stack and then, at appropriately chosen times, popping them from the stack onto an output stream can sort the data if and only if its initial order is described by a permutation that avoids the permutation pattern 231.. Since then, there has been much work on similar problems of sorting data streams by more general systems of stacks and queues. In the system considered by , each element from an input data stream must be pushed onto one of several stacks. Then, once all of the data has been pushed in this way, the items are popped from these stacks (in an appropriate order) onto an output stream.
To create a BOE companies, throughout the past few decades, have used spreadsheet programs and skilled cost analysts to enter thousands of lines of data and create complex algorithms to calculate the costs. These positions require a high level of skill to ensure accuracy and knowledge of using these basic level programs. In recent times, software companies have begun releasing specific software designed to create a BOE with much less effort and time and expense of labor, which ultimately is the goal of a BOE in the first place. These software programs allow members of the project team to form the calculations and input data and create a final number with much less effort as the process is streamlined and much of the work is done by the internal software programming.
In 1936 Royal Aircraft Establishment scientist Leslie Bennet Craigie Cunningham suggested using a gyroscope's resistance to rotation to modify the aiming point in a gun sight to compensate for deflection caused by a turning aircraft.Spencer C. Tucker, World War II: The Definitive Encyclopedia and Document Collection [5 volumes]: The Definitive Encyclopedia and Document Collection, ABC-CLIO – 2016, page 752Lon O. Nordeen, Air warfare in the missile age, page 265 This arrangement meant the information presented to the pilot was of his own aircraft, that is the deflection/lead calculated was based on his own bank-level, rate of turn, airspeed etc. The assumption was that the flight path was following the flight path of the target aircraft, as in a dogfight, therefore the input data was close enough.
This classification leads to four classes: [minimum, m1], (m1, m2], (m2, m3], (m3, maximum]. In general, it can be represented as a recursive function as follows: Recursive function Head/tail Breaks: Rank the input data values from the biggest to the smallest; Compute the mean value of the data Break the data (around the mean) into the head and the tail; // the head for data values greater the mean // the tail for data values less the mean while (length(head)/length(data) <=40%): Head/tail Breaks(head); End Function The resulting number of classes is referred to as ht-index, an alternative index to fractal dimension for characterizing complexity of fractals or geographic features: the higher the ht-index, the more complex the fractals.Jiang, Bin and Yin Junjun (2014).
Those who are non-union production assistants are usually asked to complete a variety of tasks by a department head on a film set. The tasks of a non-union PA can match those of a union PA. These tasks asked to be completed by either a union or non-union PA are requirements that are related to the movie's production, such as setting up props on set and negotiating with the director on how a scene should be shot. However, non- union PAs commonly may be asked to complete a wider array of tasks since these tasks may not necessarily be associated with the film. For example, non-union PAs may be asked to input data in a computer, wash dishes, sort letters in the mail room, and buy coffee for department heads.
These Map tasks perform user-specified computations on each input key-value pair from the partition of input data assigned to the task, and generates a set of intermediate results for each key. The shuffle and sort phase then takes the intermediate data generated by each Map task, sorts this data with intermediate data from other nodes, divides this data into regions to be processed by the reduce tasks, and distributes this data as needed to nodes where the Reduce tasks will execute. The Reduce tasks perform additional user- specified operations on the intermediate data possibly merging values associated with a key to a smaller set of values to produce the output data. For more complex data processing procedures, multiple MapReduce calls may be linked together in sequence.
AquaMaps predictions have been validated successfully for a number of species using independent data sets and the model was shown to perform equally well or better than other standard species distribution models, when faced with the currently existing suboptimal input data sets. In addition to displaying individual maps per species, AquaMaps provides tools to generate species richness maps by higher taxon, plus a spatial search for all species overlapping a specified grid square. There is also the facility to create custom maps for any species via the web by modifying the input parameters and re-running the map generating algorithm in real time, and a variety of other tools including the investigation of effects of climate change on species distributions (see relevant section of the AquaMaps search page).
In coding theory, a zigzag code is a type of linear error-correcting code introduced by .. They are defined by partitioning the input data into segments of fixed size, and adding sequence of check bits to the data, where each check bit is the exclusive or of the bits in a single segment and of the previous check bit in the sequence. The code rate is high: where is the number of bits per segment. Its worst-case ability to correct transmission errors is very limited: in the worst case it can only detect a single bit error and cannot correct any errors. However, it works better in the soft-decision model of decoding: its regular structure allows the task of finding a maximum- likelihood decoding or a posteriori probability decoding to be performed in constant time per input bit.
Most modern deep learning models are based on artificial neural networks, specifically, Convolutional Neural Networks (CNN)s, although they can also include propositional formulas or latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep Boltzmann machines. In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation. In an image recognition application, the raw input may be a matrix of pixels; the first representational layer may abstract the pixels and encode edges; the second layer may compose and encode arrangements of edges; the third layer may encode a nose and eyes; and the fourth layer may recognize that the image contains a face. Importantly, a deep learning process can learn which features to optimally place in which level on its own.
Irrigated land The input data on irrigation, evaporation, and surface runoff are to be specified per season for three kinds of agricultural practices, which can be chosen at the discretion of the user: #A: irrigated land with crops of group A #B: irrigated land with crops of group B #U: non-irrigated land with rainfed crops or fallow land The groups, expressed in fractions of the total area, may consist of combinations of crops or just of a single kind of crop. For example, as the A type crops one may specify the lightly irrigated cultures, and as the B type the more heavily irrigated ones, such as sugarcane and rice. But one can also take A as rice and B as sugarcane, or perhaps trees and orchards. The A, B and/or U crops can be taken differently in different seasons, e.g.
Early neurophysiologists suggest that retinal and inertial signals were selected for about 450 million years ago by primitive brainstem- cerebellar circuitry because of their relationship with the environment. Microscopically, it is evident that Purkinje cell precursors arose from granule cells, first forming in irregular patterns, then progressively becoming organized in a layered fashion. Evolutionarily, the Purkinje cells then developed extensive dendritic trees that increasingly became confined to a single plane, through which the axons of granule cells threaded, eventually forming a neuronal grid of right angles. The origin of the cerebellum is in close association with that of the nuclei of the vestibular cranial nerve and lateral line nerves, perhaps suggesting that this part of the cerebellum originated as a means of carrying out transformations of the coordinate system from input data of the vestibular organ and the lateral line organs.
Likewise, binary-only distribution does not prevent the malicious modification of executable binary-code, either through a man-in-the-middle attack while being downloaded via the internet, or by the redistribution of binaries by a third-party that have been previously modified either in their binary state (i.e. patched), or by decompilingprobably using tools such as ERESI and recompiling them after modification. These modifications are possible unless the binary files – and the transport channel – are signed and the recipient person/system is able to verify the digital signature, in which case unwarranted modifications should be detectable, but not always. Either way, since in the case of Folding@home the input data and output result processed by the client-software are both digitally signed, the integrity of work can be verified independently from the integrity of the client software itself.
The -medoids or partitioning around medoids (PAM) algorithm is a clustering algorithm reminiscent of the -means algorithm. Both the -means and -medoids algorithms are partitional (breaking the dataset up into groups) and both attempt to minimize the distance between points labeled to be in a cluster and a point designated as the center of that cluster. In contrast to the -means algorithm, -medoids chooses data points as centers (medoids or exemplars) and can be used with arbitrary distances, while in -means the centre of a cluster is not necessarily one of the input data points (it is the average between the points in the cluster). The PAM method was proposed in 1987Kaufman, L. and Rousseeuw, P.J. (1987), Clustering by means of Medoids, in Statistical Data Analysis Based on the L_1–Norm and Related Methods, edited by Y. Dodge, North- Holland, 405–416.
Idiolect analysis is different for an individual depending on whether the data being analyzed is from a corpus made up entirely from texts or audio files, since written work is more thought out in planning and precise in wording than in (spontaneous) speech, where informal language and conversation fillers (i.e. umm..., you know, etc.) fill corpus samples. Corpora with large amounts of input data allow for the generation of word frequency and synonym lists to be generated, normally through the use of the top ten bigrams created from it (context of word usage is taken into account here, when determining whether a bigram is legitimate in certain circumstances). Determining whether a word or phrase is part of an idiolect, is determined by where the word is in comparison to the window's head word, the edge of the window.
The chapter subtitle A Critique of Artificial- intelligence Methodology indicates that this is a polemical article, in which David Chalmers, Robert French, and Hofstadter criticize most of the research going on at that time (the early '80s) as exaggerating results and missing the central features of human intelligence. Some of these AI projects, like the structure mapping engine (SME), claimed to model high faculties of the human mind and to be able to understand literary analogies and to rediscover important scientific breakthroughs. In the introduction, Hofstadter warns about the Eliza effect that leads people to attribute understanding to a computer program that only uses a few stock phrases. The authors claim that the input data for such impressive results are already heavily structured in the direction of the intended discovery and only a simple matching task is left to the computer.
The programs are for this purpose written in such a way that almost any application that can be run in a direct mode can equally well be run in an inverse mode, and thus for model calibration and parameter estimation. The HYDRUS packages use a Microsoft Windows based graphical user interface (GUI) to manage the input data required to run the program, as well as for nodal discretization and editing, parameter allocation, problem execution, and visualization of results. All spatially distributed parameters, such as those for various soil horizons, the root water uptake distribution, and the initial conditions for water, heat and solute movement, are specified in a graphical environment. The program offers graphs of the distributions of the pressure head, water content, water and solute fluxes, root water uptake, temperature and solute concentrations in the subsurface at pre-selected times.
The value for the semimajor axis (a) of the WGS 72 Ellipsoid is 6 378 135 meters. The adoption of an a-value 10 meters smaller than that for the WGS 66 Ellipsoid was based on several calculations and indicators including a combination of satellite and surface gravity data for position and gravitational field determinations. Sets of satellite derived station coordinates and gravimetric deflection of the vertical and geoid height data were used to determine local-to-geocentric datum shifts, datum rotation parameters, a datum scale parameter and a value for the semimajor axis of the WGS Ellipsoid. Eight solutions were made with the various sets of input data, both from an investigative point of view and also because of the limited number of unknowns which could be solved for in any individual solution due to computer limitations.
Finally, because the data swarm is transformed as it passes through the array from node to node, the multiple nodes are not operating on the same data, which makes the MISD classification a misnomer. The other reason why a systolic array should not qualify as a MISD is the same as the one which disqualifies it from the SISD category: The input data is typically a vector not a single data value, although one could argue that any given input vector is a single data set. In spite of all of the above, systolic arrays are often offered as a classic example of MISD architecture in textbooks on parallel computing and in the engineering class. If the array is viewed from the outside as atomic it should perhaps be classified as SFMuDMeR = Single Function, Multiple Data, Merged Result(s).
Map of Global Offshore Wind Speeds (Global Wind Atlas 3.0) Offshore wind resources are by their nature both huge in scale and highly dispersed, considering the ratio of the planet’s surface area that is covered by oceans and seas compared to land mass. Wind speeds offshore are known to be considerably higher than for the equivalent location onshore due to the absence of land mass obstacles and the lower surface roughness of water compared to land features such as forests and savannah, a fact that is illustrated by global wind speed maps that cover both onshore and offshore areas using the same input data and methodology. For the North Sea, wind turbine energy is around 30 kWh/m2 of sea area, per year, delivered to grid. The energy per sea area is roughly independent of turbine size.
Finally, because the data swarm is transformed as it passes through the array from node to node, the multiple nodes are not operating on the same data, which makes the MISD classification a misnomer. The other reason why a systolic array should not qualify as a MISD is the same as the one which disqualifies it from the SISD category: The input data is typically a vector not a single data value, although one could argue that any given input vector is a single dataset. All of the above not withstanding, systolic arrays are often offered as a classic example of MISD architecture in textbooks on parallel computing and in the engineering class. If the array is viewed from the outside as atomic it should perhaps be classified as SFMuDMeR = Single Function, Multiple Data, Merged Result(s).
Nutshell description of a RASP: :The RASP is a universal Turing machine (UTM) built on a random-access machine RAM chassis. The reader will remember that the UTM is a Turing machine with a "universal" finite-state table of instructions that can interpret any well- formed "program" written on the tape as a string of Turing 5-tuples, hence its universality. While the classical UTM model expects to find Turing 5-tuples on its tape, any program-set imaginable can be put there given that the Turing machine expects to find them—given that its finite-state table can interpret them and convert them to the desired action. Along with the program, printed on the tape will be the input data/parameters/numbers (usually to the program's right), and eventually the output data/numbers (usually to the right of both, or intermingled with the input, or replacing it).
The selection of an appropriate model is critical for the production of good phylogenetic analyses, both because underparameterized or overly restrictive models may produce aberrant behavior when their underlying assumptions are violated, and because overly complex or overparameterized models are computationally expensive and the parameters may be overfit. The most common method of model selection is the likelihood ratio test (LRT), which produces a likelihood estimate that can be interpreted as a measure of "goodness of fit" between the model and the input data. However, care must be taken in using these results, since a more complex model with more parameters will always have a higher likelihood than a simplified version of the same model, which can lead to the naive selection of models that are overly complex. For this reason model selection computer programs will choose the simplest model that is not significantly worse than more complex substitution models.
Time bounds for integer sorting algorithms typically depend on three parameters: the number of data values to be sorted, the magnitude of the largest possible key to be sorted, and the number of bits that can be represented in a single machine word of the computer on which the algorithm is to be performed. Typically, it is assumed that ; that is, that machine words are large enough to represent an index into the sequence of input data, and also large enough to represent a single key.. Integer sorting algorithms are usually designed to work in either the pointer machine or random access machine models of computing. The main difference between these two models is in how memory may be addressed. The random access machine allows any value that is stored in a register to be used as the address of memory read and write operations, with unit cost per operation.
As Ted Cooke-Yarborough wrote of his design in 1953 "a slow computer can only justify its existence if it is capable of running for long periods unattended and the time spent performing useful computations is a large proportion of the total time available". The design was noted for its reliability because in the period from May 1952 until February 1953 it averaged 80 hours per week running time. Dr Jack Howlett, Director of the Computer Laboratory at AERE 1948–61, said it "could be left unattended for long periods; I think the record was over one Christmas-New Year holiday when it was all by itself, with miles of input data on punched tape to keep it happy, for at least ten days and was still ticking away when we came back." It was the machine's untiring durability, rather than its speed, that was its main feature.
The game's "flick it" control system began development long before any graphics had been implemented: the initial prototype simply read analogue stick motions and displayed a basic text message saying what trick had been performed, along with speed and accuracy ratings. The developers found that in order to receive accurate information from the very fast analogue stick motions used when playing the game, input data from each control pad had to be read at a rate of 120 Hz. The game relies extensively on physics to model the skateboarders' movement. Havok, Endorphin and others were considered, but ultimately a RenderWare package called "Drives" was used to model the joints of the human body. Initially the development team planned to include the ability for the player to get off of the skateboard and walk around, but animating this proved to be too big a challenge for the team to handle.
They include (but are not limited to): (1) photosynthetic efficiency per unit foliage biomass and its nitrogen content based on relationships between foliage nitrogen, simulated self-shading, and net primary productivity after accounting for litterfall and mortality; (2) nutrient uptake requirements based on rates of biomass accumulation and literature- or field-based measures of nutrient concentrations in different biomass components on sites of different nutritional quality (i.e. fertility); (3) light-related measures of tree and branch mortality derived from stand density and live canopy height input data in combination with simulated vertical light profiles. Light levels at which mortality of branches and individual trees occur are estimated for each species. Many of FORECAST’s calculations are made at the stand level, but the model includes a sub-model which disaggregates stand-level productivity into the growth of individual stems with user-supplied information on stem size distributions at different stand ages.
The input data on irrigation, evaporation, and surface runoff are to be specified per season for three kinds of agricultural practices, which can be chosen at the discretion of the user: :A: irrigated land with crops of group A :B: irrigated land with crops of group B :U: non-irrigated land with rain-fed crops or fallow land Irrigated land The groups, expressed in fractions of the total area, may consist of combinations of crops or just of a single kind of crop. For example, as the A-type crops one may specify the lightly irrigated cultures, and as the B type the more heavily irrigated ones, such as sugarcane and rice. But one can also take A as rice and B as sugar cane, or perhaps trees and orchards. A, B and/or U crops can be taken differently in different seasons, e.g.
The larger the number of seasons becomes, the larger is the number of input data required. The duration of each season (Ts) is given in number of months (0 < Ts < 12). Day to day water balances are not considered for several reasons: #daily inputs would require much information, which may not be readily available; #the method is especially developed to predict long term, not day-to-day, trends and predictions for the future are more reliably made on a seasonal (long term) than on a daily (short term) basis, due to the high variability of short-term data; #even though the precision of the predictions for the future may still not be very high, a lot is gained when the trend is sufficiently clear; for example, it need not be a major constraint to design appropriate soil salinity control measures when a certain salinity level, predicted by Saltmod to occur after 20 years, will in reality occur after 15 or 25 years.
Before the global 2007–08 financial crisis, numerous market participants trusted the copula model uncritically and naively. However, the 2007–08 crisis was less a matter of a particular correlation model, but rather an issue of "irrational complacency". In the extremely benign time period from 2003 to 2006, proper hedging, proper risk management and stress test results were largely ignored. The prime example is AIG's London subsidiary, which had sold credit default swaps and collateralized debt obligations in an amount of close to $500 billion without conducting any major hedging. For an insightful paper on inadequate risk management leading up to the crisis, see “A personal view of the crisis – Confessions of a Risk Manager” (The Economist 2008). In particular, if any credit correlation model is fed with benign input data as low default intensities and low default correlation, the risk output figures will be benign, ‘garbage in garbage out’ in modeling terminology.
The solver is a set of computation algorithms that solve equations of motion. Types of components that can be studied through multibody simulation range from electronic control systems to noise, vibration and harshness. Complex models such as engines are composed of individually designed components, e.g. pistons/crankshafts. The MBS process often can be divided in 5 main activities. The first activity of the MBS process chain is the” 3D CAD master model”, in which product developers, designers and engineers are using the CAD system to generate a CAD model and its assembly structure related to given specifications. This 3D CAD master model is converted during the activity “Data transfer” to the MBS input data formats i.e. STEP. The “MBS Modeling” is the most complex activity in the process chain. Following rules and experiences, the 3D model in MBS format, multiple boundaries, kinematics, forces, moments or degrees of freedom are used as input to generate the MBS model.
Davis makes a persuasive argument that Turing's conception of what is now known as "the stored-program computer", of placing the "action table"—the instructions for the machine—in the same "memory" as the input data, strongly influenced John von Neumann's conception of the first American discrete-symbol (as opposed to analog) computer—the EDVAC. Davis quotes Time magazine to this effect, that "everyone who taps at a keyboard... is working on an incarnation of a Turing machine," and that "John von Neumann [built] on the work of Alan Turing" (Davis 2000:193 quoting Time magazine of 29 March 1999). Davis makes a case that Turing's Automatic Computing Engine (ACE) computer "anticipated" the notions of microprogramming (microcode) and RISC processors (Davis 2000:188). Knuth cites Turing's work on the ACE computer as designing "hardware to facilitate subroutine linkage" (Knuth 1973:225); Davis also references this work as Turing's use of a hardware "stack" (Davis 2000:237 footnote 18).
For validation of QSAR models, usually various strategies are adopted: # internal validation or cross-validation (actually, while extracting data, cross validation is a measure of model robustness, the more a model is robust (higher q2) the less data extraction perturb the original model); # external validation by splitting the available data set into training set for model development and prediction set for model predictivity check; # blind external validation by application of model on new external data and # data randomization or Y-scrambling for verifying the absence of chance correlation between the response and the modeling descriptors. The success of any QSAR model depends on accuracy of the input data, selection of appropriate descriptors and statistical tools, and most importantly validation of the developed model. Validation is the process by which the reliability and relevance of a procedure are established for a specific purpose; for QSAR models validation must be mainly for robustness, prediction performances and applicability domain (AD) of the models. Some validation methodologies can be problematic.
The basic function of the Profinet is the cyclic data exchange between the IO-Controller as producer and several IO-Devices as consumers of the output data and the IO-Devices as producers and the IO-Controller as consumer of the input data. Each communication relationship IO data CR between the IO-Controller and an IO-Device defines the number of data and the cycle times. All Profinet IO-Devices must support device diagnostics and the safe transmission of alarms via the communication relation for alarms Alarm CR. In addition, device parameters can be read and written with each Profinet device via the acyclic communication relation Record Data CR. The data set for the unique identification of an IO-Device, the Identification and Maintenance Data Set 0 (I&M; 0), must be installed by all Profinet IO-Devices. Optionally, further information can be stored in a standardized format as I&M; 1-4.
For certain applications, t may be 64 or 32, but the use of these two tag lengths constrains the length of the input data and the lifetime of the key. Appendix C in NIST SP 800-38D provides guidance for these constraints (for example, if and the maximal packet size is 210 bytes, the authentication decryption function should be invoked no more than 211 times; if and the maximal packet size is 215 bytes, the authentication decryption function should be invoked no more than 232 times). Like with any message authentication code, if the adversary chooses a t-bit tag at random, it is expected to be correct for given data with probability measure 2−t. With GCM, however, an adversary can increase their likelihood of success by choosing tags with n words – the total length of the ciphertext plus any additional authenticated data (AAD) – with probability measure 2−t by a factor of n.
Thus quoting an average value containing three significant digits in the output with just one significant digit in the input data could be recognized as an example of false precision. With the implied accuracy of the data points of ±0.5, the zeroth order approximation could at best yield the result for y of ~3.7±2.0 in the interval of x from -0.5 to 2.5, considering the standard deviation. If the data points are reported as :x=[0.00,1.00,2.00]\, :y=[3.00,3.00,5.00]\, the zeroth-order approximation results in :y\sim f(x)=3.67\, The accuracy of the result justifies an attempt to derive a multiplicative function for that average, for example, :y \sim\ x+2.67 One should be careful though because the multiplicative function will be defined for the whole interval. If only three data points are available, one has no knowledge about the rest of the interval, which may be a large part of it.
Maps can be performed in parallel, provided that each mapping operation is independent of the others; in practice, this is limited by the number of independent data sources and/or the number of CPUs near each source. Similarly, a set of 'reducers' can perform the reduction phase, provided that all outputs of the map operation that share the same key are presented to the same reducer at the same time, or that the reduction function is associative. While this process often appears inefficient compared to algorithms that are more sequential (because multiple instances of the reduction process must be run), MapReduce can be applied to significantly larger datasets than a single "commodity" server can handle - a large server farm can use MapReduce to sort a petabyte of data in only a few hours. The parallelism also offers some possibility of recovering from partial failure of servers or storage during the operation: if one mapper or reducer fails, the work can be rescheduled - assuming the input data are still available.
Privacy advocates and other critics have expressed concern regarding Windows10's privacy policies and its collection and use of customer data. Under the default "Express" settings, Windows10 is configured to send various information to Microsoft and other parties, including the collection of user contacts, calendar data, and "associated input data" to personalize "speech, typing, and inking input", typing and inking data to improve recognition, allowing apps to use a unique "advertisingID" for analytics and advertising personalization (functionality introduced by Windows 8.1) and allow apps to request the user's location data and send this data to Microsoft and "trusted partners" to improve location detection (Windows8 had similar settings, except that location data collection did not include "trusted partners"). Users can opt out from most of this data collection, but telemetry data for error reporting and usage is also sent to Microsoft, and this cannot be disabled on non-Enterprise editions of Windows10. Microsoft's privacy policy states, however, that "Basic"-level telemetry data is anonymized and cannot be used to identify an individual user or device.
A unique feature of HYDRUS-2D was that it used a Microsoft Windows based Graphics User Interface (GUI) to manage the input data required to run the program, as well as for nodal discretization and editing, parameter allocation, problem execution, and visualization of results. It could handle flow regions delineated by irregular boundaries, as well as three-dimensional regions exhibiting radial symmetry about the vertical axis. The code includes the MeshGen2D mesh generator, which was specifically designed for variably-saturated subsurface flow and transport problems. The mesh generator may be used for defining very general domain geometries, and for discretizing the transport domain into an unstructured finite element mesh. HYDRUS-2D has been recently fully replaced with HYDRUS (2D/3D) as described below. The HYDRUS (2D/3D) (version 1) software package (Šimůnek et al., 2006;Šimůnek, J., M. Th. van Genuchten, and M. Šejna. 2006. The HYDRUS Software Package for Simulating Two- and Three-Dimensional Movement of Water, Heat, and Multiple Solutes in Variably-Saturated Media, Technical Manual, Version 1.0, PC Progress, Prague, Czech Republic, pp. 241.
If the input function is a series of ordered pairs (for example, a time series from measuring an output variable repeatedly over a time interval) then the output function must also be a series of ordered pairs (for example, a complex number vs. frequency over a specified domain of frequencies), unless certain assumptions and approximations are made allowing the output function to be approximated by a closed-form expression. In the general case where the available input series of ordered pairs are assumed be samples representing a continuous function over an interval (amplitude vs. time, for example), the series of ordered pairs representing the desired output function can be obtained by numerical integration of the input data over the available interval at each value of the Fourier conjugate variable (frequency, for example) for which the value of the Fourier transform is desired.. Explicit numerical integration over the ordered pairs can yield the Fourier transform output value for any desired value of the conjugate Fourier transform variable (frequency, for example), so that a spectrum can be produced at any desired step size and over any desired variable range for accurate determination of amplitudes, frequencies, and phases corresponding to isolated peaks.

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