Abstract: A device may receive log data from application logs associated with applications, service logs associated with services, and server logs associated with server devices. The device may store the log data. The device may perform natural language processing on the log data to convert the log data into event data identifying events associated with categories. The device may process the event data, with a first machine learning model, to identify patterns in the event data and to generate an alert based on the patterns. The device may process the event data, with a second machine learning model, to generate a correlation matrix for the event data and to predict an event based on the correlation matrix. The device may process the event data, with a third machine learning model, to classify the event data based on the categories and to generate a recommendation based on classifying the event data.

Abstract: A system and method includes generating approximate distributions for distinct classes of data samples; computing a first partial Jensen-Shannon (JS) divergence and a second partial JS divergence based on the approximate distribution of the disparity affected class of data samples with reference to the approximate distribution of the control class of data samples; computing a disparity divergence based on the first partial JS divergence and the second partial JS divergence; generating a distribution-matching term based on the disparity divergence, wherein the distribution-matching term mitigates an inferential disparity between the control class of data samples and the disparity affected class of data samples during a training of an unconstrained artificial neural network; constructing a disparity-constrained loss function based on augmenting a target loss function with the distribution-matching term; and transforming the unconstrained ANN to a disparity-constrained ANN based on a training of the unconstraine

Abstract: A reasoning engine is disclosed. Contemplated reasoning engines acquire data relating to one or more aspects of various environments. Inference engines within the reasoning engines review the acquire data, historical or current, to generate one or more hypotheses about how the aspects of the environments might be correlated, if at all. The reasoning engine can attempt to validate the hypotheses through controlling acquisition of the environment data.

Abstract: A neuron circuit can switch between two functions: as an input neuron circuit, and as a hidden neuron circuit. An error circuit can switch between two functions: as a hidden error circuit, and as an output neuron circuit. A switching circuit is configured to be capable of changing the connections between the neuron circuit, a synapse circuit, and the error circuit. The synapse circuit includes an analog memory that stores data that corresponds to the connection strength between the input neuron circuit and the hidden neuron circuit or between the hidden neuron circuit and the output neuron circuit, a writing circuit that changes the data in the analog memory, and a weighting circuit that weights an input signal in reaction to the data of the analog memory and outputs the weighted output signal. The analog memory includes a transistor comprising an oxide semiconductor with extremely low off-state current.

Abstract: A method for determining a final architecture for a neural network to perform a particular machine learning task is described.

Abstract: Methods and systems for disentangled data generation include accessing a dataset including pairs, each formed from a given input text structure and a given style label for the input text structures. An encoder is trained to disentangle a sequential text input into disentangled representations, including a content embedding and a style embedding, based on a subset of the dataset, using an objective function that includes a regularization term that minimizes mutual information between the content embedding and the style embedding. A generator is trained to generate a text output that includes content from the style embedding, expressed in a style other than that represented by the style embedding of the text input.

Abstract: The present invention provides a method and a system for train periodic message scheduling based on a multi-objective evolutionary algorithm, relating to the field of information and communications technology, mainly including: acquiring an MVB periodic message table; binary encoding the MVB periodic message table and initializing it randomly, to generate an iterative population; performing crossover and mutation operations on the individuals of the iterative population using a genetic algorithm, to update the iterative population; constructing an MVB periodic scheduling table that meets scheduling needs and minimizes the macro cycle according to a multi-objective algorithm and the updated iterative population; scheduling train periodic messages according to the MVB periodic scheduling table, thereby meeting the real-time requirement of periodic data transmission in actual scheduling scenarios.

Abstract: The present invention relates to the field of smart grids, and provides a non-intrusive load monitoring method and device based on temporal attention mechanism. The method comprises the following steps: obtaining a total load data, an equipment load data, and corresponding sampling time of a building during a certain period of time; integrating the total load data and the equipment load data with the corresponding sampling time to obtain an enhanced total load data and an enhanced equipment load data; using a sliding window method to segment the enhanced total load data and the enhanced equipment load data, and constructing a deep learning training dataset; constructing a neural network model based on a deep learning training framework and training the model using the training dataset. The present invention can effectively extract the working time mode of the load and its inherent dependencies, thereby improving the accuracy of load monitoring.

Abstract: Disclosed is a multi-device based inference method and apparatus, where the multi-device based inference method includes receiving information related to operation devices performing an operation included in a neural network and a graph corresponding to the neural network, obtaining a size of an output of the operation in a forward direction of the graph based on the information and the graph, dividing an input of the operation in a backward direction of the graph based on the information, the graph, and the size of the output, and performing an inference based on the divided input.

Abstract: A method and apparatus for data analysis according to various aspects of the present invention is configured to test a set of components and generate test data for the components. A diagnostic system automatically analyzes the test data to identify a characteristic of a component fabrication process by recognizing a pattern in the test data and classifying the pattern using a neural network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reinforcement learning. The embodiments described herein apply meta-learning (and in particular, meta-gradient reinforcement learning) to learn an optimum return function G so that the training of the system is improved. This provides a more effective and efficient means of training a reinforcement learning system as the system is able to converge on an optimum set of one or more policy parameters ? more quickly by training the return function G as it goes. In particular, the return function G is made dependent on the one or more policy parameters ? and a meta-objective function J? is used that is differentiated with respect to the one or more return parameters ? to improve the training of the return function G.

Abstract: A fully or semi-automated, integrated learning, labeling and classification system and method have closed, self-sustaining pattern recognition, labeling and classification operation, wherein unclassified data sets are selected and converted to an assembly of graphic and text data forming compound data sets that are to be classified. By means of feature vectors, which can be automatically generated, a machine learning classifier is trained for improving the classification operation of the automated system during training as a measure of the classification performance if the automated labeling and classification system is applied to unlabeled and unclassified data sets, and wherein unclassified data sets are classified automatically by applying the machine learning classifier of the system to the compound data set of the unclassified data sets.

Abstract: Reinforcement learning is applied in a multi-agent environment to enable effective determination of user intent classification from documents (e.g., chat, emails or another mode of communication by a user). Although different agents may implement different learning algorithms, they communicate with each other to learn and adjust their behavior by observing peer agents. Using a reinforcement learning (RL) framework, a method integrates each agent's prediction of user intent, as a sequence of tokens in the document are being analyzed. The method continues to refine its observation until it reaches the end of the document. This approach is more effective in uncovering refined linguistic features of words in the document, when read sequentially from start to end.

Abstract: The present invention provides a processing method and device for data of a well site test based on a knowledge graph. The processing method for the data of the well site test based on the knowledge graph comprises: carrying out format identification on received historical data of the well site test to generate format identification results; establishing a mind map according to the format identification results; generating the knowledge graph of the data of the well site test according to the mind map; and processing the historical data of the well site test and new data of the well site test according to the knowledge graph.

Abstract: A surrogate-assisted evolutionary optimization method, ESP, discovers decision strategies in real-world applications. Based on historical data, a surrogate is learned and used to evaluate candidate policies with minimal exploration cost. Extended into sequential decision making, ESP is highly sample efficient, has low variance, and low regret, making the policies reliable and safe. As an unexpected result, the surrogate also regularizes decision making, making it sometimes possible to discover good policies even when direct evolution fails.

Abstract: Methods, systems, and computer-readable storage media for providing an insight provider including a logic component and a configuration component, the logic component including a domain-specific model, the configuration component including one or more parameter values for processing data using the domain-specific model, receiving a set of assets including data indicative of one or more assets, retrieving asset data associated with at least one asset of the first set of assets, the asset data including OT data and IT data, the OT data being provided from one or more networked devices, the IT data being provided from one or more enterprise systems, and processing the OT data and the IT data using the domain-specific model of the logic component to provide a result set, the result set including one or more of a second set of assets and enriched data.

Abstract: An explainable surrogate-assisted evolutionary optimization method, E-ESP, discovers rule-based decision strategies for which actions to take to achieve certain outcomes when historical training data is limited or unavailable. The resulting rules are human readable and thus facilitate explainability and trustworthiness unlike the black box solutions resulting from neural network solutions.

Abstract: The invention employs sense element engagement theory to provide a sense element engagement process for producing self-organized patterns of interaction strengths among electronic neurons in an electronic neural network which coordinate with models of patterns of phosphenes experiences and the visual space in which they are located; documented by individuals whose primary visual cortex is stimulated by electric current. The process may be employed for other experiential patterns by substituting desired model patterns for patterns comprising the experience of phosphenes in a visual space. If such experiential patterns are produced, then devices that implement the process can be sentient. Also provided is an understanding of how neural networks produce a specific quality of experience. The phenomena that comprise cortical prosthetic vision (CPV) include two properties: subjective qualities of CPV models can be constructed; and such models can be related to objective aspects of CPV models.

Abstract: Methods and systems enable an omniphysical mind or descriptive self supportable by a computing device, to evaluate its current platform and seek or build a new or replacement platform. The descriptive system includes infrastructure for translating sensor readings into descriptive terms, comparing the descriptive terms with template requirements, and initiating an action as the result of the comparison. The descriptive system also includes infrastructure for communicating with other platforms to receive information representing functionality and/or sensor readings, to translate the information into descriptive terms, and compare the descriptive terms with template requirements. In evaluating a new or replacement platform, if template requirements are met the descriptive system reports a database that includes symbols, definitions of symbols, and processing rules, which are provided to the new/replacement system, the database comprising an infrastructure of an omniphysical mind.

Abstract: A generalizable and interpretable deep learning model for predicting microsatellite instability from histopathology slide images is provided. Microsatellite instability (MSI) is an important genomic phenotype that can direct clinical treatment decisions, especially in the context of cancer immunotherapies. A deep learning framework is provided to predict MSI from histopathology images, to improve the generalizability of the predictive model using adversarial training to new domains, such as on new data sources or tumor types, and to provide techniques to visually interpret the topological and morphological features that influence the MSI predictions.