Abstract: A collaborative learning framework is presented. The collaborative framework is implemented by multiple network nodes interconnected by a network. The network nodes belong to multiple client systems of the framework. A network node belonging to a first client system constructs a predictive model for the first client system by using a pattern dictionary that is a built based on a consensus among the multiple client systems. The network node calculates a set of local statistics for the first client system based on raw data of the first client system. The network node computes a consensus set of local statistics by aggregating sets of local statistics from the multiple client systems. The network node updates the pattern dictionary based on current values of the pattern dictionary and the consensus set of local statistics.

Abstract: Provided are a plant-based fibrous base material in which any unpleasant odor is suppressed, and a product containing a replica meat obtained by processing said base material. The plant-based textured base material contains a plant-protein-containing fibrous structure, linoleic acid and linolenic acid in an amount of no more than 25 mg/dry weight base material (g) in total, phospholipids in an amount of at least 0.5 mg/dry weight base material (g), and protein in an amount of no more than 540 mg/base material dry weight (g). The base material preferably has stress in an amount of at least 0.7×105 Pa.

Abstract: An embodiment identifies, by a probabilistic black-box anomaly attribution engine, an anomalous sample in test data associated with a black-box model, the black-box model comprising a plurality of variables. The embodiment generates, by the probabilistic black-box anomaly attribution engine, a variable distribution based on the test data using a plurality of outputs generated using a plurality of perturbations. The embodiment generates, by the probabilistic black-box anomaly attribution engine based on the variable distribution, an attribution score representing a responsibility of a variable for the anomalous sample.

Abstract: A computer-implemented method for discovering causality includes accessing time-series data describing past events. The time-series data is input into a machine learning model, the machine learning model implementing an intensity function including a kernel function, where the kernel function determines a causal strength value computed from a transformer model, and where the kernel function contributes to an additive function that sums intensity of repeated individual events of the past events. In response to the inputting, an output is received from the machine learning model that indicates a causality that links two or more events of the past events.

Abstract: In a method of machine learning inferencing, access, via a computer, raw data including data elements; and produce, via the computer, a respective positional encoding vector for each of the data elements. The producing includes computing coefficients using a discrete functional transform on a sequence of the data elements in the raw data. Produce, via the computer, one or more representational encoding vectors based upon the positional encoding vectors and that represent the raw data. Input via the computer, the one or more representational encoding vectors into a neural network. In response to the inputting, receive, via the computer, output from the neural network. The output includes an inference related to the raw data.

Abstract: Provided are a plant-based fibrous base material in which any unpleasant odor is suppressed, and a product containing a replica meat obtained by processing said base material. The plant-based textured base material contains a plant-protein-containing fibrous structure, linoleic acid and linolenic acid in an amount of no more than 25 mg/dry weight base material (g) in total, phospholipids in an amount of at least 0.5 mg/dry weight base material (g), and protein in an amount of no more than 540 mg/base material dry weight (g). The base material preferably has stress in an amount of at least 0.7×105 Pa.

Abstract: A directed graph autoencoder device includes one or more memories and a processor coupled to the one or more memories and configured to implement a graph convolutional layer. The graph convolutional layer comprises a plurality of nodes and is configured to generate transformed dual vector representations by applying a source weight matrix and a target weight matrix to input dual vector representations of the plurality of nodes. The input dual vector representations comprise, for each node of the plurality of nodes, a source vector representation that corresponds to the node in its role as a source and a target vector representation that corresponds to the node in its role as a target. The graph convolutional layer is further configured to scale the transformed dual vector representations to generate scaled transformed dual vector representations. The graph convolutional layer is further configured to perform message passing using the scaled transformed dual vector representations.

Abstract: Anomaly detection in industrial dynamic process can include receiving a set of multivariate time series data representative of sensor data obtained over time. The set of multivariate time series data can be transformed into a set of signature vectors in an embedding space. A neural network can be trained to estimate a probability distribution of the set of signature vectors in the embedding space. Streaming data can be received. The streaming data can be appended with a previously stored time series data. The appended streaming data can be transformed into an embedding. The embedding can be input into the trained neural network, the trained neural network outputting a first probability distribution score. A second probability distribution score associated with the embedding can be determined based on a given proposed probability distribution. Anomaly score can be determined based on the first probability distribution score and the second probability distribution score.

Abstract: A computer-implemented method, a computer program product, and a computer system for stochastic event triage. A computer receives an event log including timestamps and event types. The computer determines a sparse impact matrix representing causal relationships between the event types, via a cardinality regularization. The computer determines triggering probabilities representing causal association probabilities between individual event instances, by leveraging a variational bound of a likelihood function. The computer provides a user with the triggering probabilities for event triage. The computer learns model parameters by iterating type-level causal analysis and instance-level causal analysis.

Abstract: A computer-implemented method, a computer program product, and a computer system for diagnosing anomalies detected by a black-box machine learning model. A computer determines a local variance of a test sample in a test dataset, where the local variance represents uncertainty of a prediction by the black-box machine learning model. The computer initializes optimal compensations for the test sample, where the optimal compensations are optimal perturbations to test sample values of respective components of a multivariate input variable. The computer determines local gradients for the test sample. Based on the local variance and the local gradients, the computer updates the optimal compensations until convergences of the optimal compensations are reached. Using the optimal compensations, the computer diagnoses the anomalies detected by the black-box machine learning model.

Abstract: A computer-implemented method, a computer program product, and a computer system for influence maximization on a social network. A computing device or server receives a graph of a social network and a user contextual tensor. With a tensor regression model, the computing device or server predicts activation probabilities of respective first users influencing respective second users, using a tensor inner product of the user contextual tensor and a susceptibility tensor and using an upper confidence bound. The computing device or server determines a set of seed users that maximizes influence in the social network, based on the activation probabilities. The computing device or server updates the susceptibility tensor by machine learning, based on user responses online and the user contextual tensor. The computing device or server updates the activation probabilities and the set of the seed users, based on an updated susceptibility tensor.

Abstract: A first dependency graph is constructed based on a first data set by solving an objective function constrained with a maximum number of non-zeros and formulated with a regularization term comprising a quadratic penalty to control sparsity. The quadratic penalty in constructing the second dependency graph is determined as a function of the first data set. A second dependency graph is constructed based on a second data set by solving the objective function constrained with the maximum number of non-zeros and formulated with the regularization term comprising a quadratic penalty. The quadratic penalty in constructing the second dependency graph is determined as a function of the first data set and the second data set. An anomaly score is determined for each of a plurality of sensors based on comparing the first dependency graph and the second dependency graph, nodes of which represent sensors.

Abstract: A computer-implemented method, a computer program product, and a computer system for diagnosing anomalies detected by a black-box machine learning model. A computer determines a local variance of a test sample in a test dataset, where the local variance represents uncertainty of a prediction by the black-box machine learning model. The computer initializes optimal compensations for the test sample, where the optimal compensations are optimal perturbations to test sample values of respective components of a multivariate input variable. The computer determines local gradients for the test sample. Based on the local variance and the local gradients, the computer updates the optimal compensations until convergences of the optimal compensations are reached. Using the optimal compensations, the computer diagnoses the anomalies detected by the black-box machine learning model.

Abstract: A collaborative learning framework is presented. The collaborative framework is implemented by multiple network nodes interconnected by a network. The network nodes belong to multiple client systems of the framework. A network node belonging to a first client system constructs a predictive model for the first client system by using a pattern dictionary that is a built based on a consensus among the multiple client systems. The network node calculates a set of local statistics for the first client system based on raw data of the first client system. The network node computes a consensus set of local statistics by aggregating sets of local statistics from the multiple client systems. The network node updates the pattern dictionary based on current values of the pattern dictionary and the consensus set of local statistics.

Abstract: A probability distribution of a manufacturing system's performance conditioned on a training dataset comprising a historical tensor and associated performance metric of a reference period is learned. An input tensor associated with a time window and the input tensor's associated performance metric may be received. The input tensor includes at least multiple sensor variables associated with the manufacturing system and multiple steps of the manufacturing system's manufacturing process. Based on the probability distribution, an overall change is determined between the training dataset's relationship of the historical tensor and associated performance metric, and the relationship of the input tensor and the input tensor's associated performance metric. Based on the probability distribution, contribution of at least one of the multiple variables and the multiple steps to the overall change is determined. An action is automatically triggered in the manufacturing system which reduces the overall change.

Abstract: A system and method for maintaining health of a fleet of assets implementing an asset maintenance framework for collective anomaly detection that provides for a more accurate maintenance planning solution for the fleet or assets that may be prioritized. Based on a Bayesian multi-task multi-modal sparse mixture of sparse Gaussian graphical models (MTL-MM GGM), the methods combine the variational Bayes framework with (1) Laplace prior-based sparse structure learning and (2) an 0-based sparse mixture weight selection approach. Dual sparsity is guaranteed over both variable-variable dependency and mixture components to efficiently learn multi-modal distributions that are observed in various applications. A generated model represents the fleet-level CbM model as a combination between two model components: 1) S sets of sparse mixture weights representing individuality of the assets in the fleet; and 2) One set of sparse GGMs that are shared with the S assets to represent commonality across the S assets.

Abstract: The present invention provides a novel method of manufacturing germinated plant seeds suitable for producing a large amount of phytoalexin, a method of manufacturing raw material seeds for germination induction for use in manufacture of the above germinated plant seeds, an extract composition of the germinated plant seeds and a screening method for a plant seed candidate for use in producing a target substance. The method of manufacturing raw material seeds for germination induction comprises a pre-treatment step of maintaining plant seeds under atmosphere conditions of a carbon dioxide concentration of 400 ppm or more and/or an oxygen concentration of 19 vol % or less continuously for 5 hours or more.

Abstract: A probability distribution of a manufacturing system's performance conditioned on a training dataset comprising a historical tensor and associated performance metric of a reference period is learned. An input tensor associated with a time window and the input tensor's associated performance metric may be received. The input tensor includes at least multiple sensor variables associated with the manufacturing system and multiple steps of the manufacturing system's manufacturing process. Based on the probability distribution, an overall change is determined between the training dataset's relationship of the historical tensor and associated performance metric, and the relationship of the input tensor and the input tensor's associated performance metric. Based on the probability distribution, contribution of at least one of the multiple variables and the multiple steps to the overall change is determined. An action is automatically triggered in the manufacturing system which reduces the overall change.

Abstract: A method for detecting early indications of equipment failure in an industrial system includes receiving sensor training data collected from industrial equipment under normal conditions and identifying periods of time in the sensor training data when the equipment was functioning normally; finding a pattern for each identified period of time to initialize a plurality of mixture models; learning weighting factors, mean and variance of each of the plurality of mixture models, and removing unimportant models from the plurality of mixture models; determining a Gaussian Markov random field model from surviving mixture models by calculating gating functions for each of the variables and individual mixture models; determining a threshold value of an anomaly score for each variable from the sensor training data; and deploying the model to monitor sensor data from industrial equipment using the threshold values to detect anomalous sensor data values indicative of an impending system failure.

Abstract: A computer-implemented method for controlling a manufacturing process. A non-limiting example of the computer-implemented method includes using a processor to perform discretization modeling of a continuous probability distribution to yield a prediction of a future probability distribution. Next, the method uses the processor to impose a smoothness condition on the predicted probability distribution. The method using the processor to perform a multi-step forecast of the probability distribution to create a predicted probability density function. The method uses the predicted probability density function as an input to a process control system and uses the processor to control a process using the predicted probability density function.