A5100635306- Advanced Memory and Neural Computing
- HVDC Systems and Fault Protection
- Real-time simulation and control systems
- Advanced Neural Network Applications
- High-Voltage Power Transmission Systems
- Ferroelectric and Negative Capacitance Devices
- Neural Networks and Applications
- Adversarial Robustness in Machine Learning
- Silicon Carbide Semiconductor Technologies
- Microgrid Control and Optimization
- Neural Networks and Reservoir Computing
- Advanced Image and Video Retrieval Techniques
- Medical Image Segmentation Techniques
- Optical Network Technologies
- Parallel Computing and Optimization Techniques
- RNA Research and Splicing
- Domain Adaptation and Few-Shot Learning
- Privacy-Preserving Technologies in Data
- Photonic Crystal and Fiber Optics
- Smart Grid Security and Resilience
- Machine Learning and ELM
- Stochastic Gradient Optimization Techniques
- Cancer-related molecular mechanisms research
- Interconnection Networks and Systems
- Iterative Learning Control Systems
Chinese Academy of Sciences
2019-2025
University of Hong Kong
1999-2025
Institute of Microelectronics
2025
Hong Kong Science and Technology Parks Corporation
2023-2025
Liaoning Shihua University
2025
University of Electronic Science and Technology of China
2023-2024
Neusoft (China)
2023-2024
Chengdu Neusoft University
2023-2024
Development Research Center
2022-2024
Chinese University of Hong Kong
2024
Intrinsic plasticity of neurons, such as spontaneous threshold lowering (STL) to modulate neuronal excitability, is key spatial attention biological neural systems. In-memory computing with emerging memristors expected solve the memory bottleneck von Neumann architecture commonly used in conventional digital computers and deemed a promising solution this bioinspired paradigm. Nonetheless, are incapable implementing STL neurons due their first-order dynamics. Here, second-order memristor...
Abstract Alkaline zinc‐based flow batteries (AZFBs) have emerged as a promising electrochemical energy storage technology owing to Zn abundance, high safety, and low cost. However, zinc dendrite growth the formation of dead greatly impede development AZFBs. Herein, dual‐function electrolyte additive strategy is proposed regulate nucleation mitigate hydroxide corrosion depositions for stable This strategy, exemplified by urea, introduces an coordinate with 2+ /Zn proper strength, slowing...
Despite the significant progress made in deep learning on digital computers, their energy consumption and computational speed still fall short of meeting standards for brain-like computing. To address these limitations, reservoir computing (RC) has been gaining increasing attention across communities electronic devices, systems, machine learning, notably with its in-memory or in-sensor implementation hardware–software co-design. Hardware regarded, computers leverage emerging optoelectronic...
Neural networks are increasingly used to solve optimization problems in various fields, including operations research, design automation, and gene sequencing. However, these face challenges due the nondeterministic polynomial time (NP)-hard issue, which results exponentially increasing computational complexity as problem size grows. Conventional digital hardware struggles with von Neumann bottleneck, slowdown of Moore's law, arising from heterogeneous system design. Two-dimensional (2D)...
In this era of artificial intelligence and Internet Things, emerging new computing paradigms such as in-sensor in-memory call for both structurally simple multifunctional memory devices. Although two-dimensional (2D) devices provide promising solutions, the most reported either suffer from single functionalities or structural complexity. Here, work reports a reconfigurable device (RMD) based on MoS
Despite the high accuracy achieved by deep neural network (DNN) technique, there is still a lack of satisfying methodologies to protect intellectual property (IP) DNNs, which involves extensive valuable training data, abundant hardware resources, and fine-tuning skills experienced experts. Existing solutions based on watermarking cannot prevent malicious/unauthorized users from using well-trained DNNs. This paper proposes chaotic weights (ChaoWs), novel framework Chaotic Map theory, IP DNN...
Previous research on the viability and challenges of commercial shipping along Northern Sea Route (NSR) has thus far neglected to fully explain connections between operational models for viable year-round NSR port infrastructure services. In particular, little attention been paid function ports as transshipment hubs emerging polar routes. The purpose this paper is synthetize extant knowledge topic Arctic their Following a systematic literature review methodology using configurative...
Intercellular adhesion molecule-5 (ICAM-5) is a dendrite-specific molecule, which functions in both the immune and nervous systems. ICAM-5 only negative regulator that has been identified for maturation of dendritic spines so far. Shedding ectodomain promotes spine enhances synaptic activity. However, mechanism by regulates development remains poorly understood. In this study, we found ablation ICAM5 expression resulted significant increase formation contacts frequency miniature excitatory...
Abstract RNA‐dependent liquid‐liquid phase separation (LLPS) proteins play critical roles in cellular processes such as stress granule formation, DNA repair, RNA metabolism, germ cell development, and protein translation regulation. The abnormal behavior of these is associated with various diseases, particularly neurodegenerative disorders like amyotrophic lateral sclerosis frontotemporal dementia, making their identification crucial. However, conventional biochemistry‐based methods for...
Objective To analyze the value of abdominal CT images combined with serological indicators in predicting ureteral involvement idiopathic retroperitoneal fibrosis(IRF). Methods The 79 IRF patients were analyzed retrospectively,including involved sites and enhancement characteristics lesions.According to inclusion exclusion criteria,43 complete data selected assigned into a group(n=29)and non-ureteral group(n=14) according whether ureters IRF.Logistic regression analysis was performed select...
Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while bits non-zero values, as another major source of often ignored. The reason lies difficulty extracting essential during operating multiply-and-accumulate (MAC) processing element. Based fact that occupy high 68.9% fraction overall weights modern convolutional neural network models, this paper...
The bigger pictureThe fast-paced advancements in artificial intelligence (AI) and the internet of things (IoT) necessitate highly efficient edge computing. A crucial component this scenario is memristor, a specialized sensor adept at processing information within memory. This technology essential for swift decision-making, optimized data utilization, reduced energy consumption. However, there need innovative materials memristors. In context, metal halide perovskites possess unique properties...
Accurate prediction of subcellular localization viral proteins is crucial for understanding their functions and developing effective antiviral drugs. However, this task poses a significant challenge, especially when relying on expensive time-consuming classical biological experiments. In study, we introduced computational model called E-MuLA, based deep learning network that combines multiple local attention modules to enhance feature extraction from protein sequences. The superior...
In this paper we present a novel and automated technique based on the generalized robust point matching (G-RPM) framework using an extended free-form deformation (EFFD) model for analysis of left ventricular (LV) motion. Unlike deformations (FFDs) that employ parallelepipedical lattice shapes, EFFD models use arbitrarily-shaped lattices. Our extends set possible deformations, thus more accurate estimate LV can be obtained. The computation is efficient since number control points largely...
Convolutional Neural Networks (CNN) are being actively explored for safety-critical applications such as autonomous vehicles and aerospace, where it is essential to ensure the reliability of inference results in presence possible memory faults. Traditional methods error correction codes (ECC) Triple Modular Redundancy (TMR) CNN-oblivious incur substantial overhead energy cost. This paper introduces in-place zero-space ECC assisted with a new training scheme weight distribution-oriented...
Over the years, extensive research has highlighted functional roles of small nucleolar RNAs in various biological processes associated with development complex human diseases. Therefore, understanding existing relationships between different snoRNAs and diseases is crucial for advancing disease diagnosis treatment. However, classical experiments identifying snoRNA-disease associations are expensive time-consuming. there an urgent need cost-effective computational techniques that can enhance...
Most existing systems recommend songs to the user based on popularity of and singers. However, system proposed in this paper is driven by an emerging somewhat different need music industry-promoting new talents. The recommends novelty singers (or artists) their similarity user's favorite artists. Novel artists whose rise have a higher priority be recommended. Specifically, given artists, first determines candidate with then selects those who score than Then, outputs playlist composed most...