Question: Secure input and output handling is a type of what, which protects computers from theft or damage?

Context articles: Fiber optic coupler
Secure input and output handling
Conditional factor demands
Schmitt trigger
Bidirectional recurrent neural networks
Olfactory bulb mitral cell
Computer security
Microcomputer
Standard streams
Function (mathematics)

A fiber optic coupler is a device used in optical fiber systems with one or more input fibers and one or several output fibers.
 Light entering an input fiber can appear at one or more outputs and its power distribution potentially depending on the wavelength and polarization.
 Such couplers can be fabricated in different ways, for example by thermally fusing fibers so that their cores get into intimate contact.
 If all involved fibers are single-mode (supporting only a single mode per polarization direction for a given wavelength), there are certain physical restrictions on the performance of the coupler.
 In particular, it is not possible to combine two or more inputs of the same optical frequency into one single-polarization output without significant excess losses.
 However, such a restriction does not occur for different input wavelengths: there are couplers that can combine two inputs at different wavelengths into one output without exhibiting significant losses.
 Wavelength-sensitive couplers are used as multiplexers in wavelength-division multiplexing (WDM) telecom systems to combine several input channels with different wavelengths, or to separate channels.
Secure input and output handling are secure programming techniques designed to prevent security bugs and the exploitation thereof.
In economics, a conditional factor demand is the cost-minimizing level of an input (factor of production) such as labor or capital, required to produce a given level of output, for given unit input costs (wage rate and rental rate) of the input factors.
 A conditional factor demand function expresses the conditional factor demand as a function of the output level and the input costs.
 The conditional portion of this phrase refers to the fact that this function is conditional on a given level of output, so output is one argument of the function.
 Typically this concept arises in a long run context in which both labor and capital usage are choosable by the firm, so a single optimization gives rise to conditional factor demands for each of labor and capital.
In electronics, a Schmitt trigger is a comparator circuit with hysteresis implemented by applying positive feedback to the noninverting input of a comparator or differential amplifier.
 It is an active circuit which converts an analog input signal to a digital output signal.
 The circuit is named a "trigger" because the output retains its value until the input changes sufficiently to trigger a change.
 In the non-inverting configuration, when the input is higher than a chosen threshold, the output is high.
 When the input is below a different (lower) chosen threshold the output is low, and when the input is between the two levels the output retains its value.
 This dual threshold action is called "hysteresis" and implies that the Schmitt trigger possesses memory and can act as a bistable multivibrator (latch or flip-flop).
 There is a close relation between the two kinds of circuits: a Schmitt trigger can be converted into a latch and a latch can be converted into a Schmitt trigger.
Bidirectional Recurrent Neural Networks (BRNN) were invented in 1997 by Schuster and Paliwal.
 BRNNs were introduced to increase the amount of input information available to the network.
 For example, multilayer perceptron (MLPs) and time delay neural network (TDNNs) have limitations on the input data flexibility, as they require their input data to be fixed.
 Standard recurrent neural network (RNNs) also have restrictions as the future input information cannot be reached from the current state.
 On the contrary, BRNNs do not require their input data to be fixed.
 Moreover, their future input information is reachable from the current state.
 The basic idea of BRNNs is to connect two hidden layers of opposite directions to the same output.
 By this structure, the output layer can get information from past and future states.
Mitral cells are neurons that are part of the olfactory system.
 They are located in the olfactory bulb in the mammalian central nervous system.
 They receive information from the axons of olfactory receptor neurons, forming synapses in neuropils called glomeruli.
 Axons of the mitral cells transfer information to a number of areas in the brain, including the piriform cortex, entorhinal cortex, and amygdala.
 Mitral cells receive excitatory input from olfactory sensory neurons and external tufted cells on their primary dendrites, whereas inhibitory input arises either from granule cells onto their lateral dendrites and soma or from periglomerular cells onto their dendritic tuft.
 Mitral cells together with tufted cells form an obligatory relay for all olfactory information entering from the olfactory nerve.
 Mitral cell output is not a passive reflection of their input from the olfactory nerve.
 In mice, each mitral cell sends a single primary dendrite into a glomerulus receiving input from a population of olfactory sensory neurons expressing identical olfactory receptor proteins, yet the odor responsiveness of the 20-40 mitral cells connected to a single glomerulus (called sister mitral cells) is not identical to the tuning curve of the input cells, and also differs between sister mitral cells.
 The exact type of processing that mitral cells perform with their inputs still a matter of controversy.
 One prominent hypothesis is the notion that mitral cells transform the strength of olfactory input into a timing code, where odor concentration is encoded in the phase of mitral cell firing relative to the sniff cycle.
 A second (not necessarily exclusive) hypothesis is the idea of decorrelation in the olfactory bulb network, where the olfactory bulb network acts as a dynamical system whose action over time increases some (abstract) measure of distance between representations of highly similar odorants.
 Support for the second hypothesis comes primarily from research in zebrafish (where mitral and tufted cells cannot be distinguished).
Computer security, also known as cyber security or IT security, is the protection of computer systems from the theft or damage to their hardware, software or information, as well as from disruption or misdirection of the services they provide.
A microcomputer is a small, relatively inexpensive computer with a microprocessor as its central processing unit (CPU).
 It includes a microprocessor, memory, and minimal input/output (I/O) circuitry mounted on a single printed circuit board.
 Microcomputers became popular in the 1970s and 1980s with the advent of increasingly powerful microprocessors.
 The predecessors to these computers, mainframes and minicomputers, were comparatively much larger and more expensive (though indeed present-day mainframes such as the IBM System z machines use one or more custom microprocessors as their CPUs).
 Many microcomputers (when equipped with a keyboard and screen for input and output) are also personal computers (in the generic sense).
In computer programming, standard streams are preconnected input and output communication channels between a computer program and its environment when it begins execution.
 The three input/output (I/O) connections are called standard input (stdin), standard output (stdout) and standard error (stderr).
 Originally I/O happened via a physically connected system console (input via keyboard, output via monitor), but standard streams abstract this.
 When a command is executed via an interactive shell, the streams are typically connected to the text terminal on which the shell is running, but can be changed with redirection, e.g. via a pipeline.
 More generally, a child process will inherit the standard streams of its parent process.
In mathematics, a function is a relation between a set of inputs and a set of permissible outputs with the property that each input is related to exactly one output.
 An example is the function that relates each real number "x" to its square "x".
 The output of a function "f" corresponding to an input "x" is denoted by "f"("x") (read ""f" of "x"").
 In this example, if the input is −3, then the output is 9, and we may write "f"(−3) = 9 .
 Likewise, if the input is 3, then the output is also 9, and we may write "f"(3) = 9 .
 (The same output may be produced by more than one input, but each input gives only one output.)
 The input variable(s) are sometimes referred to as the argument(s) of the function.