0 is a constant, it takes no input parameter(s) but returns a Nat.
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Many rock mass classification systems use core recovery parameters as input parameter, such as Rock Mass Rating and Q-system.
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The most commonly-used progressive input parameter is frequency varied over the standard audio bandwidth of 20 Hz to 20 kHz.
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Similar properties of the 1-D FT transform apply, but instead of the input parameter being just a single entry, it's a Multi-dimensional (MD) array or vector. Hence, it's x(n1,…,nM) instead of x(n).
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A program could be considered as a function, P:S → R, where is the set of all possible inputs and the set of all possible outputs. An input variable of function P is mapped to an input parameter of P. P(x) denotes execution of program for certain input x.
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The notions in an expression of r-value, l-value, and r-value/l-value are analogous to the parameter modes of input parameter (has a value), output parameter (can be assigned), and input/output parameter (has a value and can be assigned), though the technical details differ between contexts and languages.
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K Nearest Neighbor (k-NN) query takes the cardinality of the input set as an input parameter. For a given query object Q ∈ D and an integer k ≥ 1, the k-NN query NN(Q, k) selects the k indexed objects which have the shortest distance from Q, according to the distance function d.
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In this example region proposal (an input parameter) has size 7x5. "Region of Interest" pooling (also known as RoI pooling) is a variant of max pooling, in which output size is fixed and input rectangle is a parameter. Pooling is an important component of convolutional neural networks for object detection based on Fast R-CNN architecture.
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In applied statistics, the Morris method for global sensitivity analysis is a so-called one-step-at-a-time method (OAT), meaning that in each run only one input parameter is given a new value. It facilitates a global sensitivity analysis by making a number r of local changes at different points x(1 → r) of the possible range of input values.
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Example 6 shows bolus control on this screen. The insulin to carbohydrate ratio is stored in the pump as a function of the meal eaten (time of day). This screen selects grams of carbohydrate as the input parameter from the user, such that the pump can calculate the insulin bolus desired from the grams entered and the insulin to carb ratio that is stored.
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In a stepped sweep, one variable input parameter (frequency or amplitude) is incremented or decremented in discrete steps. After each change, the analyzer waits until a stable reading is detected before switching to the next step. The scaling of the steps is linear or logarithmic. Since the settling time of different test objects cannot be predicted, the duration of a stepped sweep cannot be determined exactly in advance.
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In complexity-based modern cryptography, a security scheme is provably secure if the probability of security failure (e.g., inverting a one-way function, distinguishing cryptographically strong pseudorandom bits from truly random bits) is negligible in terms of the input x = cryptographic key length n. Hence comes the definition at the top of the page because key length n must be a natural number. Nevertheless, the general notion of negligibility doesn't require that the input parameter x is the key length n.
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Biological applications of bifurcation theory provide a framework for understanding the behavior of biological networks modeled as dynamical systems. In the context of a biological system, bifurcation theory describes how small changes in an input parameter can cause a bifurcation or qualitative change in the behavior of the system. The ability to make dramatic change in system output is often essential to organism function, and bifurcations are therefore ubiquitous in biological networks such as the switches of the cell cycle.
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An algorithm is called scalable for an input parameter when its performance remains relatively independent of the size of that parameter. When the algorithm is capable of adapting to a varying number of computing units, but the number of computing units must be fixed before execution, it is called moldable. If, on the other hand, the algorithm is capable of dealing with a fluctuating amount of processors during its execution, the algorithm is said to be malleable. Most load balancing algorithms are at least moldable .
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An algorithm is said to take superpolynomial time if T(n) is not bounded above by any polynomial. Using little omega notation, it is ω(nc) time for all constants c, where n is the input parameter, typically the number of bits in the input. For example, an algorithm that runs for 2n steps on an input of size n requires superpolynomial time (more specifically, exponential time). An algorithm that uses exponential resources is clearly superpolynomial, but some algorithms are only very weakly superpolynomial.
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A sweep generator is a piece of electronic test equipment similar to, and sometimes included on, a function generator which creates an electrical waveform with a linearly varying frequency and a constant amplitude. Sweep generators are commonly used to test the frequency response of electronic filter circuits. These circuits are mostly transistor circuits with inductors and capacitors to create linear characteristics. Sweeps are a popular method in the field of audio measurementLet's Clear Up Some Things About Sweeps to describe the change in a measured output value over a progressing input parameter.
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Curves with such knot vectors start and end in a control point. The values of the knots control the mapping between the input parameter and the corresponding NURBS value. For example, if a NURBS describes a path through space over time, the knots control the time that the function proceeds past the control points. For the purposes of representing shapes, however, only the ratios of the difference between the knot values matter; in that case, the knot vectors (0, 0, 1, 2, 3, 3) and (0, 0, 2, 4, 6, 6) produce the same curve.
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Biclique- free graphs have been used in parameterized complexity to develop algorithms that are efficient for sparse graphs with suitably small input parameter values. In particular, finding a dominating set of size , on -biclique-free graphs, is fixed-parameter tractable when parameterized by , even though there is strong evidence that this is not possible using alone as a parameter. Similar results are true for many variations of the dominating set problem. It is also possible to test whether one dominating set of size at most can be converted to another one by a chain of vertex insertions and deletions, preserving the dominating property, with the same parameterization..
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In the following example, both the service provider and the identity provider use an HTTP POST binding. Initially, the service provider responds to a request from the user agent with a document containing an XHTML form: ... other input parameter.... The value of the `SAMLRequest` parameter is the base64-encoding of a `` element, which is transmitted to the identity provider via the browser. The SSO service at the identity provider validates the request and responds with a document containing another XHTML form: ... The value of the `SAMLResponse` parameter is the base64 encoding of a `` element, which likewise is transmitted to the service provider via the browser. To automate the submission of the form, the following line of JavaScript may appear anywhere on the XHTML page: window.
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If there are more particles on the left than on the right, then t, and if there are more particles on the right than on the left, then t>t_0. Each step of this decision algorithm compares the input parameter t to the time that one of the particles crosses the origin. Using this decision algorithm as both the test algorithm and the decision algorithm of a parametric search leads to an algorithm for finding the optimal time t_0 in quadratic total time. To simulate the decision algorithm for parameter t_0, the simulation must determine, for each particle, whether its crossing time is before or after t_0, and therefore whether it is to the left or right of the origin at time t_0.
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The primary use of output parameters is to return multiple values from a function, while the use of input/output parameters is to modify state using parameter passing (rather than by shared environment, as in global variables). An important use of returning multiple values is to solve the semipredicate problem of returning both a value and an error status – see Semipredicate problem: Multivalued return. For example, to return two variables from a function in C, one may write: int width int height; F(x, &width;, &height;); where `x` is an input parameter and `width` and `height` are output parameters. A common use case in C and related languages is for exception handling, where a function places the return value in an output variable, and returns a boolean corresponding to whether the function succeeded or not.
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By using dynamic programming on a tree decomposition or branch-decomposition of a planar graph, many NP-hard optimization problems may be solved in time exponential in √n or √n log n. For instance, bounds of this form are known for finding maximum independent sets, Steiner trees, and Hamiltonian cycles, and for solving the travelling salesman problem on planar graphs.; ; ; . Similar methods involving separator theorems for geometric graphs may be used to solve Euclidean travelling salesman problem and Steiner tree construction problems in time bounds of the same form.. For parameterized problems that admit a kernelization that preserves planarity and reduces the input graph to a kernel of size linear in the input parameter, this approach can be used to design fixed-parameter tractable algorithms the running time of which depends polynomially on the size of the input graph and exponentially on √k, where k is the parameter of the algorithm.
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Conceptually, this is achieved by averaging the result of different FORM simulations. In practice, this is made possible by identifying the importance direction \boldsymbol \alpha in the input parameter space, which points towards the region which most strongly contributes to the overall failure probability. The importance direction can be closely related to the center of mass of the failure region, or to the failure point with the highest probability density, which often falls at the closest point to the origin of the limit state function, when the random variables of the problem have been transformed into the standard normal space. Once the importance direction has been set to point towards the failure region, samples are randomly generated from the standard normal space and lines are drawn parallel to the importance direction in order to compute the distance to the limit state function, which enables the probability of failure to be estimated for each sample.
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AgriHouse Smart Leaf Sensor (SG-1000) A Phase I research grant from the National Science Foundation in 2007 showed that the leaf sensor technology has the potential to save between 30% and 50% of irrigation water by reducing irrigation from once every 24 hours to about every 2 to 2.5 days by sensing impending water deficit stress. Leaf sensor technology developed by AgriHouse indicates water deficit stress by measuring the turgor pressure of a leaf, which decreases dramatically at the onset of leaf dehydration. Early detection of impending water deficit stress in plants can be used as an input parameter for precision irrigation control by allowing plants to communicate water requirements directly to humans and/or electronic interfaces. For example, a base system utilizing the wirelessly transmitted information of several sensors appropriately distributed over various sectors of a round field irrigated by a center-pivot irrigation system could tell the irrigation lever exactly when and what field sector needs to be irrigated.
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NET Framework,ParameterDirection Enumeration Swift,Functions — The Swift Programming Language (Swift 4.2) and the scripting language TScript (see TScript function declarations). More precisely, one may distinguish three types of parameters or parameter modes: s, output parameters, and s; these are often denoted `in`, `out`, and `in out` or `inout`. An input argument (the argument to an input parameter) must be a value, such as an initialized variable or literal, and must not be redefined or assigned to; an output argument must be an assignable variable, but it need not be initialized, any existing value is not accessible, and must be assigned a value; and an input/output argument must be an initialized, assignable variable, and can optionally be assigned a value. The exact requirements and enforcement vary between languages – for example, in Ada 83 output parameters can only be assigned to, not read, even after assignment (this was removed in Ada 95 to remove the need for an auxiliary accumulator variable).
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