Application of Sliding Mode Control in Uncertain Fractional-Order Reaction-Diffusion Memristor Neural Networks In recent years, as neural networks have been widely applied in various fields, the research on their control and stability has gained increasing attention. Fractional-order (FO) memristor neural networks (MNNs), due to their ability to si...

Background and Motivation Understanding fluid motion is crucial for comprehension of our environment and our interactions with it in the field of physics. However, traditional fluid simulation methods face limitations in practical applications due to high computational demands. In recent years, physics-driven neural networks have emerged as a promi...

Distillation of Multi-Class Cervical Lesion Cell Detection via Synthesis-Aided Pre-Training and Patch-Level Feature Alignment Background and Research Significance Cervical cancer is a disease that seriously threatens the life and health of women. According to data from the International Agency for Research on Cancer (IARC), there were approximately...

With the continuous advancement of data acquisition technology, obtaining large amounts of high-dimensional data containing multiple features has become very easy, such as images and vision data. However, traditional machine learning methods, especially those based on vectors and matrices, face challenges such as the curse of dimensionality, increa...

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...