A simulation method includes a process of calculating a transient charge density q.sub.T of trapped charges after applying a voltage between a gate electrode and a semiconductor layer of a transistor, the charge density q.sub.T being calculated with a time variance of the charge density q.sub.T being expressed by a function obtained by superimposing multiple exponential functions having mutually different time constants.

A computer-implemented method for inferring a device feature of a device produced on a wafer. The method includes: providing a wafer feature model associating a wafer position indicating a position of a produced device on the wafer to a device feature, wherein the wafer feature model is configured to be trained by one or more wafer feature maps and particularly configured as a Gaussian process model, providing a sample device feature of at least one device at a sample wafer position, and inferring the device feature of at least one other device of the wafer depending on the provided wafer feature model.

An apparatus includes a memory and a hardware processor. The memory stores a threshold. The processor receives first, second, and third messages. The processor determines a number of occurrences of words in the messages. The processor also calculates probabilities that a word in the messages is a particular word and co-occurrence probabilities. The processor further calculates probability distributions of words in the messages. The processor also calculates probabilities based on the probability distributions. The processor compares these probabilities to a threshold to determine whether the first message is related to the second message and/or whether the first message is related to the third message.

A system and method are provided to enable non-quantum experts to schematically represent, simulate and quantify the performance of physically realistic photonic quantum circuits. The framework offers the flexibility for usersnot necessarily familiar with the fundamentals of quantum mechanicsto create circuits and work with simple inputs and outputs, while the complexities of manipulating high dimensionality quantum Hilbert spaces supporting photonic and physical quantum object states are handled with the use of purpose-built tools. The tools include a user-friendly method for defining classical photonic circuits which may be coupled to physical objects such as qubits, quantum input states, as well as classical and quantum measurement devices. The tools feature classical-to-quantum S-matrix conversion, quantum S-matrix extraction, as well as capabilities for defining and extracting quantum error parameters. The framework also supports extraction of post-measurement quantum states for use in subsequent circuits or simulators.

According to an embodiment of the disclosure, an electronic device includes a processor and a memory operationally connected to the processor and configured to store instructions that, when executed by the processor, cause the processor to configure a time period comprising multiple unit durations, check for utilization of the processor for each of the multiple unit durations of the time period, collect at least one variation of the utilization of the processor based on the utilization of the processor for each of the multiple unit durations, acquire a temporal probability density function based on the at least one collected variation, determine a probability density function corresponding to the temporal probability density function based on a previously stored probability density function table, and determine an operating frequency for a next unit duration based on at least part of the identified probability density function. Various other embodiments are possible.

An apparatus includes a memory and a hardware processor. The memory stores a threshold. The processor receives first, second, and third messages. The processor determines a number of occurrences of words in the messages. The processor also calculates probabilities that a word in the messages is a particular word and co-occurrence probabilities. The processor further calculates probability distributions of words in the messages. The processor also calculates probabilities based on the probability distributions. The processor compares these probabilities to a threshold to determine whether the first message is related to the second message and/or whether the first message is related to the third message.

An anomaly detection device according to the embodiment includes a prediction unit and an anomaly score calculation unit. The prediction unit performs a process to obtain, at each time step of the time series data of m dimensions, distribution parameters required to express a continuous probability distribution representing a distribution state of predicted values that can be obtained at a time step t of the time series data of m dimensions. The anomaly score calculation unit performs a process to calculate, using distribution parameters obtained by the prediction unit, an anomaly score corresponding to an evaluation value representing evaluation of a magnitude of anomaly in an actual measurement value at the time step t of time series data of m dimensions.

An apparatus includes a memory and a hardware processor. The memory stores a threshold. The processor receives first, second, and third messages. The processor determines a number of occurrences of words in the messages. The processor also calculates probabilities that a word in the messages is a particular word and co-occurrence probabilities. The processor further calculates probability distributions of words in the messages. The processor also calculates probabilities based on the probability distributions. The processor compares these probabilities to a threshold to determine whether the first message is related to the second message and/or whether the first message is related to the third message.

System and method that allow to control density distributions of multiple particles (micro-or-nano-sized objects) to desired positions are described. A kernel density estimation (KDE) is used as a proxy for the initial particle density distribution and an optimal control problem is defined and solved using this approximation. A sequence of electrode electric potentials is computed so that the initial particle distribution is shaped into a target distribution after applying this sequence over time. The optimal control cost function is defined in terms of an L2 metric, with the L2 function that is used to compute the error between the particle density at the end of a time horizon and a target density. The KDE depends on the predicted trajectories of a set of particles, where the trajectory of a single particle is determined by a lumped, 2D, capacitive-based, nonlinear model describing the particle's motion.

An apparatus includes a memory and a hardware processor. The memory stores a threshold. The processor receives first, second, and third messages. The processor determines a number of occurrences of words in the messages. The processor also calculates probabilities that a word in the messages is a particular word and co-occurrence probabilities. The processor further calculates probability distributions of words in the messages. The processor also calculates probabilities based on the probability distributions. The processor compares these probabilities to a threshold to determine whether the first message is related to the second message and/or whether the first message is related to the third message.