Diffusion-Based Feature Augmentation: A Novel Approach for Multiple Instance Learning in Whole Slide Images Academic Background and Research Motivation In computational pathology, effectively analyzing Whole Slide Images (WSIs) is a burgeoning area of research. WSIs are ultra-high-resolution images with a broad field of view and are widely employed...
The paper, titled “Higher-Order Directed Community Detection by a Multiobjective Evolutionary Framework”, authored by Jing Xiao, Jing Cao, and Xiao-Ke Xu, was published in the IEEE Transactions on Artificial Intelligence in December 2024. The authors introduce a novel approach for detecting higher-order communities in directed networks, addressing ...
Research on Cost-Efficient Feature Selection in Horizontal Federated Learning Background and Motivation As Federated Learning (FL) is increasingly recognized as a distributed machine learning paradigm that safeguards data privacy, its application to multi-client scenarios has garnered significant attention. In Horizontal Federated Learning (HFL), a...
Self-Model-Free Learning vs. Learning with External Rewards in Information-Constrained Environments: A New Reinforcement Learning Framework In recent years, with the development of networks and artificial intelligence systems, networked learning mechanisms face significant security challenges. In the domain of reinforcement learning (RL), the loss ...
Title: Generating and Detecting Spatio-Temporal Graph-Based Adversarial False Data Injection Evasion Attacks in Smart Grids Background With the continuous development of modern smart grids, the grid, as a typical Cyber-Physical System (CPS), faces numerous security threats due to the extensive exchange of data between its components. Among these, F...
Research Report on the Simplified Kernel-Based Cost-Sensitive Broad Learning System (SKCSBLS) for Imbalanced Fault Diagnosis Research Background and Significance With the advent of Industry 4.0, smart manufacturing increasingly relies on industrial big data analytics. By extracting critical insights from machine operation data, the effectiveness of...
Optimal Control of Stochastic Markovian Jump Systems with Wiener and Poisson Noises: Two Reinforcement Learning Approaches Academic Context In modern control theory, optimal control is a crucial research field, aiming to design an optimal control strategy under various constraints for dynamic systems to minimize a given cost function. For stochasti...
Discussion of “Sound Bubbles” and the Future of Hearable Devices: Innovations Based on Real-Time Neural Networks In daily life, noise and complex acoustic scenes often make speech difficult to distinguish, particularly in crowded environments such as restaurants, conference rooms, or airplanes. While traditional noise-canceling headphones can suppr...
The Impact of AI Explanation Types on Physician Diagnostic Performance and Trust Academic Background In recent years, the development of artificial intelligence (AI) diagnostic systems in healthcare and radiology has progressed rapidly, particularly in assisting overburdened healthcare providers, showcasing the potential to improve patient care. As...
Precision Autofocus in Optical Microscopy with Liquid Lenses Controlled by Deep Reinforcement Learning Academic Background Microscopic imaging plays a crucial role in scientific research, biomedical studies, and engineering applications. However, traditional microscopes and autofocus techniques face hardware limitations and slow software speeds in ...