Research Based on Genotype Representation Graph (GRG): A New Framework to Enhance Biobank-Scale Data Analysis Efficiency Academic Background and Research Motivation With the rapid advancement of sequencing technologies, the collection of large-scale genomic data has become increasingly common, especially in the field of human disease association st...
Research on Deep Learning Framework for Spin-Symmetry-Enforced Solutions to the Many-Body Schrödinger Equation: A Groundbreaking Achievement In the fields of quantum physics and quantum chemistry, the description of many-body electron systems has always been an important yet highly challenging topic. Accurately characterizing strong electron-electr...
Predicting the Emergence of Crystals from Amorphous Precursors: Deep Learning Empowers Breakthroughs in Materials Science Background Introduction The process of crystallization from amorphous materials holds significant importance in both natural and laboratory settings. This phenomenon is widespread in various processes ranging from geological to ...
Exploring Biomarkers and Biosensors for Motion Sickness: Innovative Approaches to Diagnostic Challenges Motion sickness (MS) is a common syndrome experienced by humans, triggered by unnatural motions such as those encountered during transportation or virtual reality (VR). It manifests through symptoms like headaches, nausea, vomiting, cold sweats, ...
Can Large Language Models Tackle Misinformation? — In-Depth Research on LLMs In today’s digital era of rapid information dissemination, the spread of misinformation and fake news has become a significant societal challenge. The widespread use of the internet and social media has dramatically lowered the barriers to information sharing, enabling any...
In-Depth Exploration of Sector-Based Pairs Trading Strategies and Innovative Pair Selection Techniques Background and Research Objectives Pairs Trading Strategy (PTS) is a widely used financial arbitrage strategy that leverages the relative performance of two highly correlated stocks to profit from temporary price deviations. The core concept of tr...
A Research Report on a New Framework for Solving Migrant Resettlement Using Multiobjective Evolutionary Optimization Against the backdrop of accelerated globalization and evolving socio-economic conditions, migration has become an undeniable global trend. From the perspective of humanitarian relief or the sustainable development of a globalized eco...
Multi-agent Cooperative Navigation in Hybrid Environments: A New Reinforcement Learning Approach Based on Relational Graph Learning Mobile robotics is witnessing a surge in applications, fueled by advancements in artificial intelligence, with navigation capabilities being one of the core focus areas of research. Traditional navigation methods often...
Intrusion Detection Approach for Industrial Internet of Things Traffic Using Deep Recurrent Reinforcement Learning and Federated Learning Academic Background The rapid development of the Industrial Internet of Things (IIoT) has profoundly transformed intelligent industrial systems, enabling data exchange, remote control, and smart decision-making b...
Nonlinear Fixed-Time Reinforcement Learning Optimized Control for Intelligent Ship Autopilots In recent years, intelligent autopilot technology has gradually become a research hotspot in the field of automation control. For complex nonlinear systems, the design of optimized control strategies, especially the achievement of system stability and perf...