Background and Research Motivation Alcohol abuse is a global health issue, affecting over 8% of the male population. Prolonged alcohol exposure can alter the homeostasis of neurons and synapses in the brain, leading to various mental disorders and cognitive deficits, especially in motor learning. Previous studies have shown that alcohol abuse cause...
Background Introduction The brain is composed of different subtypes of neurons, which form complex neural networks through local and long-range synaptic connections. Understanding the functions of these neural networks requires knowledge of their connection patterns (projectome) and neuronal dynamics. Although advancements in mesoscale connectomics...
Thalamic Reuniens Nucleus Glutamatergic Neurons Mediated Colorectal Visceral Pain in Mice through 5-HT2B Receptors Background Irritable Bowel Syndrome (IBS) is a common functional bowel disorder characterized by abdominal pain and visceral hypersensitivity. Reducing visceral hypersensitivity is crucial for effectively alleviating abdominal pain in ...
Academic Report: Advanced Optimal Tracking Integrating Neural Critic Technique for Asymmetric Constrained Zero-Sum Games Background and Research Problem In the field of modern control, game theory is the mathematical model that studies the competition and cooperation between intelligent decision-makers, involving an interaction decision problem wit...
Invisible and Robust Protection Method for Content Generated by Deep Neural Networks Academic Background In recent years, with the revolutionary development and widespread application of deep learning models in engineering applications, phenomenon-level applications such as ChatGPT and DALL⋅E 2 have emerged, profoundly impacting people’s daily live...
Academic Report Background A Knowledge Graph (KG) is structured data that records information about entities and relationships, widely used in question-answering systems, information retrieval, machine reading, and other fields. Knowledge Graph Embedding (KGE) technology maps entities and relationships in the graph into a low-dimensional dense vect...
In recent years, applications such as privacy protection and large-scale data transmission have posed significant challenges to the inaccessibility of data. Researchers have proposed Data-Free Knowledge Distillation (DFKD) methods to address these issues. Knowledge Distillation (KD) is a method for training a lightweight model (student model) to le...
Background and Research Objectives Reinforcement Learning (RL) has recently become a dynamic and transformative field within artificial intelligence, aiming to maximize cumulative rewards through the interaction between agents and the environment. However, the application of RL faces challenges in optimizing the Bellman Error. This error is particu...
Structurally Enhanced Prototype Alignment for Unsupervised Cross-Domain Node Classification Introduction With the advancement of modern information technology, Graph Neural Networks (GNNs) have demonstrated significant success in handling complex network node classification tasks. However, one key challenge is the need for a large amount of high-qu...
Semantic segmentation aims to predict category labels for each pixel in an image (Liu et al., 2021; Wang et al., 2021) and is widely used in scene understanding, medical image analysis, autonomous driving, geographic information systems, and augmented reality (Strudel et al., 2021; Sun et al., 2023). Although the development of deep neural networks...