A machine learning system that incorporates arbitrary constraints into deep learning model is provided. The machine learning system provides a set of penalty data points en a set of arbitrary constraints in addition to a set of original training data points. The machine learning system assigns a penalty to each penalty data point in the set of penalty data points. The machine learning system optimizes a machine learning model by solving an objective function based on an original loss function and a penalty loss function. The original loss function is evaluated over a set of original training data points and the penalty loss function is evaluated over the set of penalty data points. The machine learning system provides the optimized machine learning model based on a solution of the objective function.

An adaptive characteristic spectral line screening method and system based on atomic emission spectrum are provided, the method includes: using a set characteristic screening optimization method to perform a plurality of optimization rounds of characteristic screening, obtaining an initialized spectral dataset of each round of the characteristic screening and initialized characteristic population genes; obtaining an optimal characteristic population gene of each round by a set analysis method, a fitness function, and an iteration of a genetic algorithm; obtaining an optimized characteristic spectral information set when the plurality of optimization rounds reach set optimization rounds; performing combination statistics and discriminant analyses on the optimized characteristic spectral information set to complete an adaptive characteristic spectral line screening. The disclosure can efficiently and automatically screen out the characteristic spectral lines that meet the analysis requirements in the complex atomic emission spectrum, thus ensuring the effectiveness and accuracy of screening the characteristic spectral lines.

A system for rideshare matching using locality sensitive hashing is disclosed, including at least one rider device and at least one driver device in operable connection with a network. A rideshare application is in operable communication with the network and configured for matching a driver to a rider within a match pool via an artificial intelligence engine operating a locality sensitive hashing module.

An information processing system includes: an acquisition processing unit that acquires a plurality of pieces of data to be classified; a classification processing unit that classifies the plurality of pieces of data acquired by the acquisition processing circuit into a plurality of groups, extracts a feature element representing a feature of a group for each of the plurality of classified groups, and generates a classification map in which the feature element is displayed in association with the group; a reception processing unit that receives an operation of selecting the predetermined feature element in the classification map from a user; and an update processing unit that executes processing of changing the group based on the feature element selected by the user and updates the classification map.

A computer-implemented method, computer program product, and computer system for automated model predictive control. The computer system trains multiple step look-ahead regression models, using historical states and historical actions for a to-be-optimized system, for each timestep of a past time horizon. Regression models may be either linear or nonlinear in order to capture process dynamics and nonlinearity. The computer system generates optimization constraints for each timestep of a future time horizon. The computer system generates optimization variables, based on the multiple step look-ahead regression models, for each timestep of the future time horizon. The computer system constructs a mixed integer linear programming based optimization model that includes an objective function, the optimization constraints, and the optimization variables. Nonlinear regression models are converted into piecewise linear approximation functions. The computer system solves the optimization model to produce actions for the to-be-optimized system, over the future time horizon, and recommend commitment-look-ahead actions.

A method of hop count matrix recovery based on a decision tree classifier, includes: S1: performing a flooding process to acquire a hop count matrix {tilde over (H)} with missing entries; S2: constructing a training sample set according to relationships between a part of observed hop counts in the hop count matrix {tilde over (H)}, and modeling the observed hop counts in the hop count matrix as labels of the training sample set, wherein a maximum hop count represents a number of classes; S3: training a decision tree classifier according to the training sample set obtained in step S2; and S4: constructing a feature for an unobserved hop count, to obtain an unknown sample; and inputting the unknown sample to the trained decision tree classifier, to obtain a class of the unknown sample which represents a missing hop count at a corresponding position in the matrix, to recover a complete hop count matrix H.

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for analyzing samples. The method includes acquiring a set of feature representations associated with a set of samples. The set of samples illustratively have classification information for indicating classifications of the set of samples. The method further includes adjusting the set of feature representations so that distances between feature representations of samples corresponding to the same classification are less than a first distance threshold. The method further includes training a classification model based on the adjusted set of feature representations and the classification information. The classification model is illustratively configured to receive an input sample and determine a classification of the input sample. In this manner, a relatively accurate classification model can be trained using a small number of samples, thereby reducing computation time and required computation capacity.

The present invention relates to a parameter estimation system, a parameter estimation method, and a program, and more particularly to a parameter estimation system, a parameter estimation method, and a program that efficiently estimate parameters of machine learning and simulation, etc. An objective of the present invention is to provide a parameter estimation system and a parameter estimation method that may rapidly determine the optimum input parameter.

Methods and systems for measurements of semiconductor structures based on a trained parameter conditioned measurement model are described herein. The shape of a measured structure is characterized by a geometric model parameterized by one or more conditioning parameters and one or more non-conditioning parameters. A trained parameter conditioned measurement model predicts a set of values of each non-conditioning parameter based on measurement data and a corresponding set of predetermined values for each conditioning parameter. In this manner, the trained parameter conditioned measurement model predicts the shape of a measured structure. Although a parameter conditioned measurement model is trained at discrete geometric points of a structure, the trained model predicts values of non-conditioning parameters for any corresponding conditioning parameter value. In some examples, training data is augmented by interpolation of conditioning parameters and corresponding non-conditioning parameters that lie between discrete DOE points. This improves prediction accuracy of the trained model.

According to one embodiment, a data analyzing apparatus acquires data containing the number N of analysis target samples (where N is an integer larger than or equal to 2). The apparatus performs a matrix factorization upon the data to factorize the data into the number K of basis samples and the number K of weights corresponding to the number K of basis samples (where K is an integer larger than or equal to 2), and fixes part of the K basis samples to specific basis samples in the matrix factorization.