Abstract: Provided is a display panel, which includes a substrate, a first signal trace, a first insulating layer, a second insulating layer, and an isolation protection portion. In the first region, the second insulating layer directly contacts the first signal trace, and in the second region, the first insulating layer is located between the second insulating layer and the first signal trace. In the second region, the isolation protection portion overlaps with the first signal trace and is located on a side of the first insulating layer away from the substrate. A first voltage signal received by the first signal trace and a second voltage signal received by the isolation protection portion have opposite voltage types.

Abstract: An epitaxial structure, a semiconductor device and a manufacturing method thereof are provided. The epitaxial structure includes a substrate and an epitaxial stack disposed on the substrate. The epitaxial stack includes: a first insulating isolation layer, a channel layer, a barrier layer, a second insulating isolation layer, an N-type semiconductor layer, an active region, and a P-type semiconductor layer, sequentially stacked from bottom to top on the substrate.

Abstract: Disclosed are an anti-peeping film and a display device. In the present disclosure, an angle between long axes of liquid crystal molecules in a liquid crystal light-adjusting layer and planes in which polarizers are located is set to be an acute angle in a first mode, so that there is an angle between effective long axes of the liquid crystal molecules in the liquid crystal light-adjusting layer and a direction of polarized light in a side-view direction.

Abstract: A computer-implemented method for model building is provided. The method includes receiving a training set of medical records and model hyperparameters. The method further includes initializing an encoder as a Dual-Channel Combiner Network (DCNN) and initialize distribution related parameters. The method also includes performing, by a hardware processor, a forward computation to (1) the DCNN to obtain the embeddings of the medical records, and (2) the distribution related parameters to obtain class probabilities. The method additionally includes checking by a convergence evaluator if the iterative optimization has converged. The method further includes performing model personalization responsive to model convergence by encoding the support data of a new patient and using the embeddings and event subtype labels to train a personalized classifier.

Abstract: A charging and discharging device and a charging and discharging method are provided. The charging and discharging device includes a rack, a pressing assembly, a first driving assembly, a probe mechanism and a supporting plate. The supporting plate is provided between the pressing assembly and the probe mechanism. After the first driving assembly drives the pressing assembly to move to abut against a battery in a tray, the pressing assembly and the supporting plate move in the first direction so that the battery in the tray is engaged with the probe mechanism. The first driving assembly drives the pressing assembly to move toward the tray, the pressing assembly abuts against the battery, and the first driving assembly further drives the pressing assembly to move, so that the pressing assembly and the tray move toward the probe mechanism, and the battery abuts against the probe mechanism.

Abstract: A computer implemented method is provided. The method includes jointly encoding, by a dual-channel feature extractor, a current time series segment with corresponding static statuses into a compact feature. The method further includes converting, by a binary code extractor, the compact feature into a binary code. The method also includes computing distances between the binary code and all binary codes stored in a binary code database. The method additionally includes retrieving the top relevant multivariate time series segments based on the distances.

Abstract: Methods and systems include annotating a set of training data to indicate tokens that are sensitive. Instructions are generated based on the training data, including original token sequences and respective substituted token sequences. A language model is fine-tuned using the instructions with a penalty-based loss function to generate a privacy-protected language model.

Abstract: Systems and methods for predicting an occurrence of a medical event for a patient using a trained neural network. Historical patient data is preprocessed to generate normalized training samples, and the normalized training samples are sent to a personalized deep convolutional neural network for model pretraining and updating of model parameters. The pretrained model is stored in a remote server for utilization by a local machine for personalization during a preparation time period for a medical treatment. A normalized finetuning set is generated as output, and the model parameters are iteratively finetuned. A personal prediction score for future medical events is generated, and an operation of a medical treatment device is controlled responsive to the prediction score.

Abstract: In a shift register of a driver circuit of a display panel, a drive control circuit is electrically connected to a signal input terminal, a first node, and a second node and controls a signal of the first node and a signal of the second node in response to an input signal of the signal input terminal; an output circuit is electrically connected to the first node, the second node, a first level terminal, a first clock terminal, and a signal output terminal and controls an output signal of the signal output terminal according to the signal of the first node, the signal of the second node, a first level signal of the first level terminal, and a first clock signal of the first clock terminal; the signal output terminal of an x-th stage shift register is electrically connected to a signal input terminal of a y-th stage shift register.

Abstract: Provided are a display panel and a preparation method therefor and a display device. The display panel includes a base substrate, a first planarization layer, a first metal layer, a photosensitive element, an etching stop layer, and a light-emitting element. The photosensitive element includes a first electrode and a semiconductor layer that are electrically connected to each other, the first electrode is located in the first metal layer, and the semiconductor layer is located on one side of the etching stop layer facing away from the first metal layer. In the display region, the etching stop layer includes at least one first opening. Along a direction perpendicular to the plane where the base substrate is located, a first opening does not overlap with the first metal layer.

Abstract: An RRAM structure includes a dielectric layer. A bottom electrode, a resistive switching layer and a top electrode are disposed from bottom to top on the dielectric layer. A spacer is disposed at sidewalls of the bottom electrode, the resistive switching layer and the top electrode. The spacer includes an L-shaped spacer and a sail-shaped spacer. The L-shaped spacer contacts the sidewall of the bottom electrode, the sidewall of the resistive switching layer and the sidewall of the top electrode. The sail-shaped spacer is disposed on the L-shaped spacer. A metal line is disposed on the top electrode and contacts the top electrode and the spacer.

Abstract: Systems and methods for detecting machine-generated texts by detecting motifs. The system and method including tokenizing documents into token sequences, generating at least one graph where tokens and documents are nodes, wherein the tokens are one-hot features and the documents are zero features, and wherein the graph has at least two types of connections, at least one type connects documents to tokens that the documents contain, and at least one other type links tokens that frequently appear together across all documents, classifying each node in the at least one graph using a graph neural network (GNN), and generating an optimal explanation subgraph from the classified graph, wherein the optimal explanation subgraph represents a motif. The optimal explanation subgraph can later be used to support artificial intelligence (AI) agent decision-making.

Abstract: Methods and systems include inferring a first prediction and an explanation based on a time series input and a text input using a multi-modal prototype-based encoder. A second prediction is inferred based on the text input and the explanation using a large language model. The first prediction and the second prediction are fused to generate a fused prediction. A reflection is generated based on the fused prediction, the second prediction, and the text input. The text input is refined based on the reflection.

Abstract: According to an aspect of the present invention, a system method is provided for solution decomposition, the system and method comprising in response to a query, decomposing a solution using a recursive subprocess including generating complete solutions using a policy until there are enough complete solutions to determine a reward model, selecting at least one target step of the complete solutions based on the reward model, partitioning the at least one target step into at least two segments based on a predefined partition fraction, wherein for each segment, a priority metric is estimated, wherein the at least one target step is a first step or a segment of a previously partitioned step, appending the partitioned segment with a lower priority metric, determining whether an end condition has been met, and ending the recursive subprocess if the end condition is met; and outputting a decomposition.

Abstract: Systems and methods for Evidence-based Sound Event Early Detection is provided. The system/method includes parsing collected labeled audio corpus data and real time audio streaming data utilizing mel-spectrogram, encoding features of the parsed mel-spectrograms using a trained neural network, and generating a final predicted result for a sound event based on the belief, disbelief and uncertainty outputs from the encoded mel-spectrograms.

Abstract: Disclosed are an anti-peeping film and a display device. In the present disclosure, an angle between long axes of liquid crystal molecules in a liquid crystal light-adjusting layer and planes in which polarizers are located is set to be an acute angle in a first mode, so that there is an angle between effective long axes of the liquid crystal molecules in the liquid crystal light-adjusting layer and a direction of polarized light in a side-view direction. Therefore, light in the side-view direction of the anti-peeping film is darker. Thus, an anti-peeping effect can be realized, while sharing can be realized in a non-first mode.

Abstract: A computer-implemented method for meta-learning is provided. The method includes receiving a training time series and labels corresponding to some of the training time series. The method further includes optimizing time series augmentations of the training time series using a time series augmentation selection process performed by a meta learner to obtain a selected augmentation from a plurality of candidate augmentations. The method also includes training a time series encoder with contrastive loss using the selected augmentation to obtain a learned time series encoder. The method additionally includes learning, by the learned time series encoder, a vector representation of another time series. The method further includes performing, by the learned time series encoder, a downstream task of label classification for at least a portion of the other time series.

Abstract: Methods and systems for training and deploying a neural network mode include training a modular encoder model using training data collected from heterogeneous system types. The modular encoder model includes layers of neural network blocks and a selectively enabled connections between neural network blocks of adjacent layers. Each neural network block includes neural network layers. The modular encoder model is deployed to a system corresponding to one of the heterogeneous system types.