Abstract: A method for executing a multi-task deep learning model for learning trends in multivariate time series is presented. The method includes collecting multi-variate time series data from a plurality of sensors, jointly learning both local and global contextual features for predicting a trend of the multivariate time series by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends, and employing a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data to model a short-term dependency between local time series data and subsequent trends.

Abstract: A method for unsupervised multivariate time series trend detection for group behavior analysis is presented. The method includes collecting multi-variate time series data from a plurality of sensors, learning piecewise linear trends jointly for all of the multi-variate time series data, dividing the multi-variate time series data into a plurality of time segments, counting a number of up/down trends in each of the plurality of time segments, for a training phase, employing a cumulative sum (CUSUM), and, for a testing phase, monitoring the CUSUM for trend changes.

Abstract: A method for acquiring skills through imitation learning by employing a meta imitation learning framework with structured skill discovery (MILD) is presented. The method includes learning behaviors or tasks, by an agent, from demonstrations: by learning to decompose the demonstrations into segments, via a segmentation component, the segments corresponding to skills that are transferrable across different tasks, learning relationships between the skills that are transferrable across the different tasks, employing, via a graph generator, a graph neural network for learning implicit structures of the skills from the demonstrations to define structured skills, and generating policies from the structured skills to allow the agent to acquire the structured skills for application to one or more target tasks.

Abstract: A method that includes: transmitting, by an electronic device, a first detecting signal when the electronic device is in a reverse wireless charging mode; receiving, by the electronic device at a gap moment between at least two adjacent moments at which the first detecting signal is transmitted, a second detecting signal transmitted by a wireless charging device; and if the second detecting signal received by the electronic device meets a preset condition, automatically switching, by the electronic device, from the reverse wireless charging mode to a forward wireless charging mode.

Abstract: Systems and methods for predicting system device failure are provided. The method includes performing graph-based predictive maintenance (GBPM) to determine a trained ensemble classification model for detecting maintenance ready components that includes extracted node features and graph features. The method includes constructing, based on testing data and the trained ensemble classification model, an attributed temporal graph and the extracted node features and graph features. The method further includes concatenating the extracted node features and graph features. The method also includes determining, based on the trained ensemble classification model, a list of prediction results of components that are to be scheduled for component maintenance.

Abstract: A method for learning prototypical options for interpretable imitation learning is presented. The method includes initializing options by bottleneck state discovery, each of the options presented by an instance of trajectories generated by experts, applying segmentation embedding learning to extract features to represent current states in segmentations by dividing the trajectories into a set of segmentations, learning prototypical options for each segment of the set of segmentations to mimic expert policies by minimizing loss of a policy and projecting prototypes to the current states, training option policy with imitation learning techniques to learn a conditional policy, generating interpretable policies by comparing the current states in the segmentations to one or more prototypical option embeddings, and taking an action based on the interpretable policies generated.

Abstract: A method is provided for peptide-based vaccine generation. The method receives a dataset of positive and negative binding peptide sequences. The method pre-trains a set of peptide binding property predictors on the dataset to generate training data. The method trains a Wasserstein Generative Adversarial Network (WGAN) only on the positive binding peptide sequences, in which a discriminator of the WGAN is updated to distinguish generated peptide sequences from sampled positive peptide sequences from the training data, and a generator of the WGAN is updated to fool the discriminator. The method trains the WGAN only on the positive binding peptide sequences while simultaneously updating the generator to minimize a kernel Maximum Mean Discrepancy (MMD) loss between the generated peptide sequences and the sampled peptide sequences and maximize prediction accuracies of a set of pre-trained peptide binding property predictors with parameters of the set of pre-trained peptide binding property predictors being fixed.

Abstract: A method for employing a graph enhanced attention network for explainable point-of-interest (POI) recommendation (GEAPR) is presented. The method includes interpreting POI prediction in an end-to-end fashion by adopting an adaptive neural network, learning user representations by aggregating a plurality of factors, the plurality of factors including structural context, neighbor impact, user attributes, and geolocation influence, and quantifying each of the plurality of factors by numeric values as feature salience indicators.

Abstract: A computer-implemented method is provided for hierarchical multi-agent imitation learning. The method includes learning sub-policies for sub-tasks of a hierarchical multi-agent imitation learning task by imitating expert trajectories of expert demonstrations of the subtasks with guidance from a high-level policy corresponding to the hierarchical multi-agent imitation learning task. The method further includes collecting feedback from the sub-policies relating to updating the high-level-policy with a new observation. The method also includes updating the high-level policy with the new observation responsive to the feedback from the sub-policies.

Abstract: Methods and systems for training a graph neural network (GNN) include training a denoising network in a GNN model, which generates a subgraph of an input graph by removing at least one edge of the input graph. At least one GNN layer in the GNN model, which performs a GNN task on the subgraph, is jointly trained with the denoising network.

Abstract: A computer-implemented method is provided for cross-lingual knowledge graph alignment. The method includes formulating a credible aligned entity pair selection problem for cross-lingual knowledge graph alignment as a Markov decision problem having a state space, an action space, a state transition probability and a reward function. The method further includes calculating a reward for a language entity selection policy responsive to the reward function. The method also includes performing credible aligned entity selection by optimizing task-specific rewards from an alignment-oriented entity representation learning phrase. The method additionally includes providing selected entity pairs as augmented alignments to the representation learning phase.

Abstract: Some embodiments of the present invention provide a charger. According to the charger, a detection signal generation circuit actively sends a detection signal; a feedback circuit detects level signals on a sampling component, and generates a feedback signal according to the level signals; a switch component controls, according to the feedback signal, the charger to output a charging voltage; and when the level signals meet a preset condition (the preset condition is a preset condition that the level signals meet when an abnormal short circuit occurs on a charging interface), the switch component controls the charger to stop outputting the charging voltage.

Abstract: Methods and systems for performing a knowledge graph task include aligning multiple knowledge graphs and performing a knowledge graph task using the aligned multiple knowledge graphs. Aligning the multiple knowledge graphs includes updating entity representations based on representations of neighboring entities within each knowledge graph, updating entity representations based on representations of entities from different knowledge graphs, and learning machine learning model parameters to align the multiple knowledge graphs, based on the updated entity representations.

Abstract: A method for unsupervised multivariate time series trend detection for group behavior analysis is presented. The method includes collecting multi-variate time series data from a plurality of sensors, learning piecewise linear trends jointly for all of the multi-variate time series data, dividing the multi-variate time series data into a plurality of time segments, counting a number of up/down trends in each of the plurality of time segments, for a training phase, employing a cumulative sum (CUSUM), and, for a testing phase, monitoring the CUSUM for trend changes.

Abstract: A method for executing a multi-task deep learning model for learning trends in multivariate time series is presented. The method includes collecting multi-variate time series data from a plurality of sensors, jointly learning both local and global contextual features for predicting a trend of the multivariate time series by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends, and employing a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data to model a short-term dependency between local time series data and subsequent trends.

Abstract: Methods and systems for responding to changing conditions include training a model, using a processor, using trajectories that resulted in a positive outcome and trajectories that resulted in a negative outcome. Training is performed using an adversarial discriminator to train the model to generate trajectories that are similar to historical trajectories that resulted in a positive outcome, and using a cooperative discriminator to train the model to generate trajectories that are dissimilar to historical trajectories that resulted in a negative outcome. A dynamic response regime is generated using the trained model and environment information. A response to changing environment conditions is performed in accordance with the dynamic response regime.

Abstract: Methods and systems for detecting anomalous behavior in a network include identifying topological state information in a dynamic network using a first neural network. Attribute state information in the dynamic network is identified, based on a partial labeling of nodes in the dynamic network, using a second neural network. The topological state information and the attribute state information are concatenated. Labels for unlabeled nodes in the dynamic network are predicted using a multi-factor attention, based on the concatenated state information. A security action is performed responsive to a determination that at least one node in the dynamic network is anomalous.

Abstract: The present application discloses a multi-spectral fundus imaging system and method using dynamic visual stimulation, where the imaging system includes: a multi-spectral light source capable of emitting multiple different wavelengths; a mid-pass mirror being a reflecting mirror with a central hole penetrating the reflecting mirror; an imaging focusing lens group; an image acquisition device; and a controller configured to control the multi-spectral light source and the image acquisition device to work synchronously; a pattern sent by the optical stimulation device is transmitted to the fundus through the image focusing lens group and the central hole of the mid-pass mirror in sequence; an imaging light reflected from the fundus passes through the central hole of the mid-pass mirror and the imaging focusing lens group in sequence; the image acquisition device acquires the image to complete a multi-spectral fundus image acquisition.

Abstract: Methods and systems for determining a network embedding include training a network embedding model using training data that includes topology information for networks and attribute information relating to vertices of the networks. An embedded representation is generated using the trained network embedding model to represent an input network, with associated attribute information, in a network topology space. A machine learning task is performed using the embedded representation as input to a machine learning model.

Abstract: Systems and methods for predicting system device failure are provided. The method includes performing graph-based predictive maintenance (GBPM) to determine a trained ensemble classification model for detecting maintenance ready components that includes extracted node features and graph features. The method includes constructing, based on testing data and the trained ensemble classification model, an attributed temporal graph and the extracted node features and graph features. The method further includes concatenating the extracted node features and graph features. The method also includes determining, based on the trained ensemble classification model, a list of prediction results of components that are to be scheduled for component maintenance.