A5100438233- Water Quality Monitoring Technologies
- IoT and Edge/Fog Computing
- Hydrological Forecasting Using AI
- Anomaly Detection Techniques and Applications
- Cloud Computing and Resource Management
- Evolutionary Algorithms and Applications
- Advanced Multi-Objective Optimization Algorithms
- Metaheuristic Optimization Algorithms Research
- Water Quality Monitoring and Analysis
- Explainable Artificial Intelligence (XAI)
- Blockchain Technology Applications and Security
- Energy Efficient Wireless Sensor Networks
- Superconducting Materials and Applications
- Age of Information Optimization
- Laser-Plasma Interactions and Diagnostics
- Magnetic confinement fusion research
- Topic Modeling
- Digital Mental Health Interventions
- IoT Networks and Protocols
- Psychological Well-being and Life Satisfaction
- Cloud Data Security Solutions
- Vehicular Ad Hoc Networks (VANETs)
- Poverty, Education, and Child Welfare
- Advanced Data Processing Techniques
- Silicon Carbide Semiconductor Technologies
Beijing University of Technology
2022-2025
China Medical University
2024
Qingdao University
2024
Zhejiang Ocean University
2024
Zhengzhou University
2024
Xiamen University
2023
Central South University
2022
The University of Queensland
2022
Carnegie Mellon University
2017
Smart mobile devices (SMDs) are integral for running advanced applications that demand significant computing resources and quick response time, e.g., immersive gaming image editing. However, SMDs often face constraints in computational capacity battery duration, restricting their ability to process these tasks instantaneously. Cloud can circumvent limitations by computation offloading, but cloud data centers (CDCs) deployed at long distances from users, which results longer latency. To...
Here, we used RNA capture-seq to identify a large population of lncRNAs that are expressed in the infralimbic prefrontal cortex adult male mice response fear-related learning. Combining these data with cell-type-specific ATAC-seq on neurons had been selectively activated by fear extinction learning, find inducible 434 derived from enhancer regions vicinity protein-coding genes. In particular, discover an experience-induced lncRNA call ADRAM (activity-dependent associated memory) acts as both...
ABSTRACT Background In recent years, Large Models (LMs) have been rapidly developed, including large language models, visual foundation and multimodal LMs. They are updated iterated at a very fast pace. These LMs can accomplish many tasks, e.g ., daily work assistant, intelligent customer service, factory scheduling. Their development has contributed to various industries in human society. Aims The architectural flaws of lead several problems, illusions difficulty locating errors, limiting...
Radio Frequency (RF) sensing has emerged as a pivotal technology for non-intrusive human perception in various applications. However, the challenge of collecting extensive labeled RF data hampers scalability and effectiveness machine learning models this domain. Our prior work introduced innovative generative AI frameworks - RF-Artificial Intelligence Generated Content using conditional adversarial networks RF-Activity Class Conditional Latent Diffusion Model employing latent diffusion to...
Anxiety, depression, and sleep problems are prevalent comorbid mental disorders among university students. The World Health Organization (WHO) emphasized a health promotion objective, recommending the consideration of protective health-promoting factors in strategies aimed at preventing disorders. Integrating theoretically significant constructs (such as factors) enhances our comprehension intricate mechanisms that underpin This study employed network analysis to first identify core bridge...
High-dimensional expensive problems are often encountered in the design and optimization of complex robotic automated systems distributed computing systems, they suffer from a time-consuming fitness evaluation process. It is extremely challenging difficult to produce promising solutions high-dimensional search space. This work proposes an evolutionary framework with embedded autoencoders that effectively solve Autoencoders provide strong dimension reduction feature extraction abilities...
While deep neural networks have achieved impressive performance on a range of NLP tasks, these data-hungry models heavily rely labeled data, which restricts their applications in scenarios where data annotation is expensive. Natural language (NL) explanations been demonstrated very useful additional supervision, can provide sufficient domain knowledge for generating more over new instances, while the time only doubles. However, directly applying them augmenting model learning encounters two...
Evolutionary algorithms are commonly used to solve many complex optimization problems in such fields as robotics, industrial automation, and system design. Yet, their performance is limited when dealing with high-dimensional because they often require enormous computational resources yield desired solutions, may easily trap into local optima. To this problem, work proposes a Self-adaptive Teaching-learning-based Optimizer an improved Radial basis function model sparse Autoencoder (STORA). In...
Nowadays, Internet of Things Devices (IOTDs) support numerous applications that require extensive computational resources and are sensitive to delays. Nevertheless, IOTDs constrained by limited power battery life, preventing them from processing all tasks in real-time. Computation offloading provides a solution these problems where can offload part their edge servers for execution. Small Base Stations (SBSs) located closer IOTDs, which act as servers. However, SBSs have computing compared...
Large language Models (LLMs) have achieved promising performance on arithmetic reasoning tasks by incorporating step-by-step chain-of-thought (CoT) prompting. However, LLMs face challenges in maintaining factual consistency during reasoning, exhibiting tendencies to condition overlooking, question misinterpretation, and hallucination over given problems. Existing methods use coarse-grained feedback (e.g., whether the answer is correct) improve consistency. In this work, we propose RCoT...
Abstract In this study, high-frequency instabilities driven by runaway electrons in the EXL-50 spherical torus have been reported using a magnetic pickup coil. The frequency of these is found to be power function dependent on plasma density, similar dispersion relation whistler wave. observed instability seems exhibit fluctuating pattern, resembling chirping behavior, which appears align with expected outcomes predicted Berk-Breizman model. Theoretically, excitation threshold related ratio...
Abstract In order to improve transmission performance, opportunistic social networks are widely used in 5G mobile network communication for congestion. However, transferring large amounts of data an instant will lead redundancy problems, which affect the quality. The authors propose a method called user‐optimized scheduling based on edge community service (ECSUO). This constructs model by introducing computing into network. Then several factors set coordinate dynamic priority assessment....
Nowadays, the rapid advancement of Connected and Automated Vehicles (CAVs) has led to their integration with various capabilities, encompassing environmental sensing, decision-making, multi-level assisted driving. However, computationally intensive applications like navigation autonomous driving challenges CAVs due limited computational resources, necessitating timely completion computations. Vehicular Edge Computing (VEC) offers a solution by enabling partially offload computation-intensive...
Nowadays, smart mobile devices (SMDs) support various computation-intensive and delay-sensitive applications, e.g., online games, figure compression. However, SMDs have limited computing resources battery energy cannot execute all tasks of the above applications in a real-time manner. Cloud provides enormous that can easily offloaded from SMDs. could data centers (CDCs) are often located remote sites, which leads to long transmission time. Small base stations (SBSs) offer high-bandwidth...
In recent years, Large Models (LMs) have been rapidly developed, including large language models, visual foundation and multimodal LMs.They are updated iterated at a very fast pace.These LMs can accomplish many tasks, e.g., daily work assistant, intelligent customer service, factory scheduling.Their development has contributed to various industries in human society.However, the architectural flaws of lead several problems, illusions difficulty locating errors, limiting their...