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...

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...

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...

DeepDTI: High-Fidelity Six-Direction Diffusion Tensor Imaging Using Deep Learning Research Background and Motivation Diffusion Tensor Imaging (DTI) boasts unparalleled advantages in mapping the microstructure and structural connectivity of live human brain tissue. However, traditional DTI techniques require extensive angular sampling, leading to pr...

Geometric Enhanced Pretraining for Interatomic Potentials Introduction Molecular dynamics (MD) simulations play an important role in fields such as physics, chemistry, biology, and materials science, providing insights into atomic-level processes. The accuracy and efficiency of MD simulations depend on the choice of interatomic potential functions ...

Major Breakthrough in Neuroscience Research: Deep Learning Technique Achieves Decoding of Natural Speech from Brain Signals A cross-disciplinary research team at New York University recently achieved a major breakthrough in the fields of neuroscience and artificial intelligence. They developed a novel deep learning-based framework that can directly...

This research proposes an online learning method for efficiently constructing surrogate models that can accurately emulate complex systems. The method consists of three key components: Sampling strategy for generating new training and testing data; Learning strategy for generating candidate surrogate models based on the training data; Validation me...

Current Situation Nowadays, large language models (LLMs) have demonstrated expert-level performance in multiple fields, which has sparked great interest in understanding their internal reasoning processes. Comprehending how LLMs generate these remarkable results is crucial for the future development of artificial intelligence agents and ensuring th...

Introduction: Currently, pre-trained language models (PLMs) are widely applied in the field of natural language processing, but they have the problem of inheriting and amplifying social biases present in the training corpora. Social biases may lead to unpredictable risks in real-world applications of PLMs, such as automatic job screening systems te...