Abstract: A detection structure, a display panel, a detection device and a detection system, the detection structure is located in display panel, and the detection structure includes test units and groups of first connecting lines; each of the test units includes chip bonding part, test pin parts located at chip bonding part, and groups of second connecting lines; in each test units, first end of each group in groups of second connecting lines is connected with test pin parts, and second end of each group of second connecting lines is connected with chip bonding part; in two adjacent test units in test units, two adjacent test pin parts are respectively connected with first end and second end of one group of first connecting lines; and when detection structure is used in lightening test, at least two test units arranged at intervals are configured for being electrically connected with detection device respectively.

Abstract: Systems and methods for personalized federated learning. The method may include receiving at a central server local models from a plurality of clients, and aggregating a heterogeneous data distribution extracted from the local models. The method can further include processing the data distribution as a linear mixture of joint distributions to provide a global learning model, and transmitting the global learning model to the clients. The global learning model is used to update the local model.

Abstract: Disclosed are a prefabricated energy pile, a construction method and a heat pump heat exchange system. The energy pile includes a pile body and a heat exchange pipeline. The pile body includes a stainless steel pipe, a metal lining pipe extending within the stainless steel pipe and a concrete pipe between the metal lining pipe and the stainless steel pipe. A heat conductor between the stainless steel pipe and the metal lining pipe is in contact connection with the stainless steel pipe and the metal lining pipe. The heat exchange pipeline includes a first pipe section and a second pipe section in communication with each other, through which a heat exchange medium flows in turn. Tops of the first pipe section and the second pipe section protrude upward out of the energy pile. The second pipe section is in contact connection with the metal lining pipe.

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: Disclosed are a prefabricated energy pile, a construction method and a heat pump heat exchange system. The energy pile includes a pile body and a heat exchange pipeline. The pile body includes a stainless steel pipe, a metal lining pipe extending within the stainless steel pipe and a concrete pipe between the metal lining pipe and the stainless steel pipe. A heat conductor between the stainless steel pipe and the metal lining pipe is in contact connection with the stainless steel pipe and the metal lining pipe. The heat exchange pipeline includes a first pipe section and a second pipe section in communication with each other, through which a heat exchange medium flows in turn. Tops of the first pipe section and the second pipe section protrude upward out of the energy pile. The second pipe section is in contact connection with the metal lining pipe.

Abstract: Methods and systems for training a model include performing skill discovery, using a set of demonstrations that includes known-good demonstrations and noisy demonstrations, to generate a set of skills. A unidirectional skill embedding model is trained in a first training while parameters of a skill matching model and low-level policies that relate skills to actions are held constant. The unidirectional skill embedding model, the skill matching model, and the low-level policies are trained together in an end-to-end fashion in a second training.

Abstract: A computer-implemented method for employing a time-series-to-text generation model to generate accurate description texts is provided. The method includes passing time series data through a time series encoder and a multilayer perceptron (MLP) classifier to obtain predicted concept labels, converting the predicted concept labels, by a serializer, to a text token sequence by concatenating an aspect term and an option term of every aspect, inputting the text token sequence into a pretrained language model including a bidirectional encoder and an autoregressive decoder, and using adapter layers to fine-tune the pretrained language model to generate description texts.

Abstract: A method for learning a self-explainable imitator by discovering causal relationships between states and actions is presented. The method includes obtaining, via an acquisition component, demonstrations of a target task from experts for training a model to generate a learned policy, training the model, via a learning component, the learning component computing actions to be taken with respect to states, generating, via a dynamic causal discovery component, dynamic causal graphs for each environment state, encoding, via a causal encoding component, discovered causal relationships by updating state variable embeddings, and outputting, via an output component, the learned policy including trajectories similar to the demonstrations from the experts.

Abstract: A method for learning a self-explainable imitator by discovering causal relationships between states and actions is presented. The method includes obtaining, via an acquisition component, demonstrations of a target task from experts for training a model to generate a learned policy, training the model, via a learning component, the learning component computing actions to be taken with respect to states, generating, via a dynamic causal discovery component, dynamic causal graphs for each environment state, encoding, via a causal encoding component, discovered causal relationships by updating state variable embeddings, and outputting, via an output component, the learned policy including trajectories similar to the demonstrations from the experts.

Abstract: A method for learning a self-explainable imitator by discovering causal relationships between states and actions is presented. The method includes obtaining, via an acquisition component, demonstrations of a target task from experts for training a model to generate a learned policy, training the model, via a learning component, the learning component computing actions to be taken with respect to states, generating, via a dynamic causal discovery component, dynamic causal graphs for each environment state, encoding, via a causal encoding component, discovered causal relationships by updating state variable embeddings, and outputting, via an output component, the learned policy including trajectories similar to the demonstrations from the experts.

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 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 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: 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 event detection include training a joint neural network model with respective neural networks for audio data and video data relating to a same scene. The joint neural network model is configured to output a belief value, a disbelief value, and an uncertainty value. It is determined that an event has occurred based on the belief value, the disbelief value, and the uncertainty value.

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 computer-implemented method for personalizing heterogeneous clients is provided. The method includes initializing a federated modular network including a plurality of clients communicating with a server, maintaining, within the server, a heterogenous module pool having sub-blocks and a routing hypernetwork, partitioning the plurality of clients by modeling a joint distribution of each client into clusters, enabling each client to make a decision in each update to assemble a personalized model by selecting a combination of sub-blocks from the heterogenous module pool, and generating, by the routing hypernetwork, the decision for each client.

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 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: 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.