GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning.

Deep neural networks often exhibit sub-optimal performance under covariate and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma, yet most SFDA approaches are restricted to closed-set scenarios. In this paper, we explore Source-Free Universal Domain Adaptation (SF-UniDA) aiming to accurately classify "known" data belonging to common categories and segregate them from target-private "unknown" data.

Antibacterial activity of baicalein against : and evaluation.

Introduction: (AH) is a pathogenic bacterium commonly found in aquatic organisms, particularly in fish products. Baicalein, a bioactive flavonoid derived from traditional Chinese herbal medicine, possesses a wide range of pharmacological properties, including anticancer, antibacterial, antiviral, antioxidant, hepatoprotective, and anti-inflammatory effects.

Methods: , the Oxford cup method was employed to assess the antibacterial activity of baicalein, while the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were determined using the microtiter broth dilution technique.

Future scientific paradigms in the integration of materials, aerospace and information.

This perspective dissects the fourth paradigm in aerospace systems and explores how future paradigms drive collaborative advancement by integrating materials, aerospace, and information to address deep space exploration and interdisciplinary research challenges.

Federated Aggregation With Interlayer Personalized Contribution: Preference-Based Optimization Between Performance and Privacy.

Currently, due to the different distribution of data for each user, many personalized federated learning (PFL) methods have emerged to meet the personalized needs of different users. However, existing methods have two problems: 1) in the aggregation process, the contribution between the internal layers of the client model is not considered and 2) it is difficult to match the quantitative weight information of both user privacy protection and performance with their qualitative preferences during the training process. Therefore, we first propose a framework for federated aggregation with interlayer personalized contribution named FedIPC, which completes model aggregation based on the contribution of internal layers and improves client model performance.

Preferential Selective-Aware Graph Neural Network for Preventing Attacks in Interbank Credit Rating.

Accurately assessing and forecasting bank credit ratings at an early stage is vitally important for a healthy financial environment and sustainable economic development. However, the evaluation process faces challenges due to individual attacks on the rating model. Some participants may provide manipulated information in an attempt to undermine the rating model and secure higher scores, further complicating the evaluation process.

Association between physical activity levels and stroke risk among Chinese adults aged 45 and over based on CHARLS.

Stroke is one of the leading causes of death in developing countries, and China bears the largest global burden of stroke. This study aims to investigate the relationship between different dimensions of physical activity levels and stroke risk using a nationally representative database. We performed a cross-sectional analysis using data from the China Health and Retirement Longitudinal Study (CHARLS) 2020.

HFTCRNet: Hierarchical Fusion Transformer for Interbank Credit Rating and Risk Assessment.

As a prominent application of deep neural networks in financial literature, bank credit ratings play a pivotal role in safeguarding global economic stability and preventing crises. In the contemporary financial system, interconnectivity among banks has reached unprecedented levels. However, many existing credit risk models continue to assess each bank independently, resulting in inevitable suboptimal performance.

Will the fibers of the future be considered fibers?

This work explores the pivotal breakthroughs and historical developments in fibers over the past century, while also identifying future research directions and emerging trends that promise to shape the future of this field.

Graph Multi-Convolution and Attention Pooling for Graph Classification.

Many studies have achieved excellent performance in analyzing graph-structured data. However, learning graph-level representations for graph classification is still a challenging task. Existing graph classification methods usually pay less attention to the fusion of node features and ignore the effects of different-hop neighborhoods on nodes in the graph convolution process.

Graph Convolutional Networks With Adaptive Neighborhood Awareness.

Graph convolutional networks (GCNs) can quickly and accurately learn graph representations and have shown powerful performance in many graph learning domains. Despite their effectiveness, neighborhood awareness remains essential and challenging for GCNs. Existing methods usually perform neighborhood-aware steps only from the node or hop level, which leads to a lack of capability to learn the neighborhood information of nodes from both global and local perspectives.

Observation of the out-of-plane orbital antidamping-like torque.

The out-of-plane antidamping-like orbital torque fosters great hope for high-efficiency spintronic devices. Here we report experimentally the observation of out-of-plane antidamping-like torque that could be generated by -polarized orbital current in ferromagnetic-metal/oxidized Cu (CuO) bilayers, which is presented unambiguously by the magnetic field angle dependence of the spin-torque ferromagnetic resonance signal. The CuO thickness dependence of the orbital torque ratios highlights that the interfacial effect would be responsible for the generation of orbital current.

A recent survey on controllable text generation: A causal perspective.

As an important subject of natural language generation, Controllable Text Generation (CTG) focuses on integrating additional constraints and controls while generating texts and has attracted a lot of attention. Existing controllable text generation approaches mainly capture the statistical association implied within training texts, but generated texts lack causality consideration. This paper intends to review recent CTG approaches from a causal perspective.

Hybrid Residual Multiexpert Reinforcement Learning for Spatial Scheduling of High-Density Parking Lots.

Industries, such as manufacturing, are accelerating their embrace of the metaverse to achieve higher productivity, especially in complex industrial scheduling. In view of the growing parking challenges in large cities, high-density vehicle spatial scheduling is one of the potential solutions. Stack-based parking lots utilize parking robots to densely park vehicles in the vertical stacks like container stacking, which greatly reduces the aisle area in the parking lot, but requires complex scheduling algorithms to park and take out the vehicles.

Scientization or parametrization? Which system is more advantageous to environmental conservation in the reform of China's state-owned forest farms?

Government leadership and grassroots participation are the most typical institutional arrangements in natural resource management, a topic which has been the subject of vigorous debate for a long time. Individually, these systems are referred to as scientization and parametrization. This paper takes the reform of China's state-owned forest farms (SSFs) as a pointcut, comparing the effects of the 2011 policy (representing scientization) and the 2015 policy (representing parametrization) on environmental conservation.

HRegNet: A Hierarchical Network for Efficient and Accurate Outdoor LiDAR Point Cloud Registration.

Point cloud registration is a fundamental problem in 3D computer vision. Outdoor LiDAR point clouds are typically large-scale and complexly distributed, which makes the registration challenging. In this paper, we propose an efficient hierarchical network named HRegNet for large-scale outdoor LiDAR point cloud registration.

A Multichannel Convolutional Decoding Network for Graph Classification.

Graph convolutional networks (GCNs) have shown superior performance on graph classification tasks, and their structure can be considered as an encoder-decoder pair. However, most existing methods lack the comprehensive consideration of global and local in decoding, resulting in the loss of global information or ignoring some local information of large graphs. And the commonly used cross-entropy loss is essentially an encoder-decoder global loss, which cannot supervise the training states of the two local components (encoder and decoder).

[Effect of aerobic exercise on AKT/GSK3β mediated hepatocyte apoptosis in nonalcoholic fatty liver disease].

Objective: To investigate the effect of aerobic exercise on AKT/GSK3β pathway-mediated hepatocyte apoptosis in non-alcoholic fatty liver diseases(NAFLD).

Methods: A total of 30 6-week-old male C57BL/6J mice, and mice were fed adaptively for one week. The control group was fed with ordinary diet, and the model group and model exercise group were fed with high-fat diet until 18 weeks.

Sparse-to-Dense Matching Network for Large-Scale LiDAR Point Cloud Registration.

Point cloud registration is a fundamental problem in 3D computer vision. Previous learning-based methods for LiDAR point cloud registration can be categorized into two schemes: dense-to-dense matching methods and sparse-to-sparse matching methods. However, for large-scale outdoor LiDAR point clouds, solving dense point correspondences is time-consuming, whereas sparse keypoint matching easily suffers from keypoint detection error.

PSDC: A Prototype-Based Shared-Dummy Classifier Model for Open-Set Domain Adaptation.

Open-set domain adaptation (OSDA) aims to achieve knowledge transfer in the presence of both domain shift and label shift, which assumes that there exist additional unknown target classes not presented in the source domain. To solve the OSDA problem, most existing methods introduce an additional unknown class to the source classifier and represent the unknown target instances as a whole. However, it is unreasonable to treat all unknown target instances as a group since these unknown instances typically consist of distinct categories and distributions.

Electrical Manipulation of Orbital Current Via Oxygen Migration in Ni Fe /CuO /TaN Heterostructure.

The orbital Hall effect and the interfacial Rashba effect provide new approaches to generate orbital current and spin-orbit torque (SOT) efficiently without the use of heavy metals. However, achieving efficient dynamic control of orbital current and SOT in light metal oxides has proven challenging. In this study, it is demonstrated that a sizable magnetoresistance effect related to orbital current and SOT can be observed in Ni Fe /CuO /TaN heterostructures with various CuO oxidization concentrations.

Enhanced Spin Current in Ni Fe /Cu-CuO Bilayer with Top and Sideways Oxidization.

Generation and manipulation of spin current are the cores of spintronic devices, which are intensely pursued. Heavy metals with strong spin-orbit coupling are commonly used for the generation of spin current, but are incompatible with the mass production of devices, and the polarization of spin current is limited to be in-plane. Here, it is shown that the spin current with strong out-of-plane polarization component can be generated and transmitted in Ni Fe /Cu-CuO bilayer with sideways and top oxidizations.

[Research progress on mechanism of exercise improving non-alcoholic fatty liver disease through regulating endoplasmic reticulum stress].

Non-alcoholic fatty liver disease (NAFLD) is one of the main diseases of metabolic syndrome. With the increasing popularity of NAFLD in the world, the prevention and therapy of NAFLD are facing great challenges. In recent years, scholars at home and abroad have carried out a large number of studies on NAFLD, but its pathogenesis is still unclear.

Learning Transactional Behavioral Representations for Credit Card Fraud Detection.

Credit card fraud detection is a challenging task since fraudulent actions are hidden in massive legitimate behaviors. This work aims to learn a new representation for each transaction record based on the historical transactions of users in order to capture fraudulent patterns accurately and, thus, automatically detect a fraudulent transaction. We propose a novel model by improving long short-term memory with a time-aware gate that can capture the behavioral changes caused by consecutive transactions of users.

Transcriptome Profiling to the Effects of Drought Stress on Different Propagation Modes of Tea Plant ().

Tea plant () is an important economic beverage crop. Drought stress seriously affects the growth and development of tea plant and the accumulation of metabolites, as well as the production, processing, yield and quality of tea. Therefore, it is necessary to understand the reaction mechanism of tea plant under drought conditions and find efficient control methods.