Study of Heterogeneous Hopfield Neural Networks: Dynamic Behavior Analysis Combining Adaptive Activation Functions and Memristors This study investigates the impact of nonlinear factors on the dynamic behavior of neural networks. Specifically, activation functions and memristors are commonly used as nonlinear factors to construct chaotic systems an...
Salient Object Detection in Low-Light RGB-T Scenarios by Mining Spatial-Frequency Cues Salient Object Detection (SOD) holds a significant position in the field of computer vision. Its main task is to identify the most visually attractive regions or objects in an image. Although SOD models have made certain progress in normal lighting environments o...
A Grid Fault Diagnosis Framework Based on Adaptive Integrated Decomposition and Cross-Modal Attention Fusion Research Background With the continuous expansion and increasing complexity of modern power systems, the stable operation of the grid faces growing challenges. Grid faults can occur due to natural disasters, equipment failures, and local gri...
Fast Synchronization Control and Application for Encryption-Decryption of Coupled Neural Networks With Intermittent Random Disturbance I. Background and Research Motivation In recent years, neural networks have been widely applied in various fields such as data classification, image recognition, and combinatorial optimization problems. Regarding th...
InA: Inhibition Adaptation Method on Pre-trained Language Models Pre-trained Language Models (LMs) have achieved significant results in Natural Language Processing (NLP) tasks. However, traditional fine-tuning methods suffer from the problem of redundant parameters, which affects efficiency and effectiveness. To address this challenge, this paper p...
Heterogeneous Coexisting Attractors, Large-Scale Amplitude Control and Finite-Time Synchronization of Central Cyclic Memristive Neural Networks Academic Background Due to their memory and nonlinearity characteristics similar to brain synapses, memristors hold significant theoretical and practical importance in the study of chaotic dynamics in brain...
Adaptive Identification and Optimization of Ill-Posed Regions for Accurate Stereo Matching Research Background and Motivation With the rapid development of computer vision technology, stereo matching technology has played a crucial role in various fields such as robotics, aerospace, autonomous driving, and industrial manufacturing due to its high a...
Federated Learning Using Model Projection for Multi-Center Disease Diagnosis Background Introduction With the rapid development of medical imaging technology, research on automated diagnostic methods has shown good performance on single-center datasets. However, these methods often find it difficult to generalize to data from other healthcare facil...
Adaptive Sampling Artificial-Actual Control for Non-Zero-Sum Games of Constrained Systems Background In modern industrial and scientific research fields, the rapid development of intelligent technology and control systems makes traditional control methods difficult to meet the strict requirements of ensuring system stability and minimizing energy c...
Multi-Scale Vision-Centric Multi-Modal Neural Machine Translation: Text-Aware Cross-Modality Contrastive Decoupling Academic Background Multi-Modal Neural Machine Translation (MNMT) aims to incorporate language-independent visual information into text to enhance machine translation performance. However, due to the significant modal differences betw...