Explanipedia

On Efficiency, Fairness and Security in AI Accelerator Resource Sharing: A Survey Open

Jiahua Huang, Weiwei Lin, Wentai Wu, Yang Wang, Haocheng Zhong , et al. · 2025

The effective and efficient utilization of AI accelerators represents a critical issue for the practitioners engaged in the field of deep learning. Practical evidence from companies such as Alibaba, SenseTime, and Microsoft reveals that th…

Targeted Vaccine: Safety Alignment for Large Language Models against Harmful Fine-Tuning via Layer-wise Perturbation Open

G. H. Liu, Weiwei Lin, Tiansheng Huang, Ruichao Mo, Qi Mu , et al. · 2024

Harmful fine-tuning attack poses a serious threat to the online fine-tuning service. Vaccine, a recent alignment-stage defense, applies uniform perturbation to all layers of embedding to make the model robust to the simulated embedding dri…

CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns Open

Shengsheng Lin, Weiwei Lin, Xinyi Hu, Wentai Wu, Ruichao Mo , et al. · 2024

The stable periodic patterns present in time series data serve as the foundation for conducting long-horizon forecasts. In this paper, we pioneer the exploration of explicitly modeling this periodicity to enhance the performance of models …

Research on reliable data communication optimization strategy for energy storage power stations considering link-service importance Open

Weiwei Lin, Zhiqiang Xu, Qin Li, Huifang Zhang, Yong Wang , et al. · 2024

Aiming at the reliability of large-capacity data transmission of energy storage power stations, an optimization strategy for reliable data communication of energy storage power stations considering the importance of link service is propose…

Study on green power supply modes for heavy load in remote areas Open

Li Yu, Yixi Cuomu, Yiming Gao, Guoqin Lv, Weiwei Lin , et al. · 2024

In this study, the present situation and characteristics of power supply in remote areas are summarized. By studying the cases of power supply projects in remote areas, the experience is analyzed and described, and the applicability of rel…

Physiologically-based pharmacokinetic modeling to predict the exposure and provide dosage regimens of Ustekinumab in pediatric patients with inflammatory bowel disease Open

Xiaoxi Cai, Wanhong Wu, Guimu Guo, Jiarui Chen, Jianwen Xu , et al. · 2024

Ustekinumab (UST), a fully human immunoglobulin G1 κ monoclonal antibody, exhibiting high affinity for the p40 subunit shared by IL-12 and IL-23, which play key roles in the pathogenesis of inflammatory bowel disease (IBD). By scaling the …

SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters Open

Shengsheng Lin, Weiwei Lin, Wentai Wu, Haojun Chen, Junjie Yang · 2024

This paper introduces SparseTSF, a novel, extremely lightweight model for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal dependencies over extended horizons with minimal computatio…

Three-Dimensional Turbulent Simulation of Bivariate Normal Distribution Protection Device Open

Jing Liu, Zongyu Li, Hanming Huang, Weiwei Lin, Zhilin Sun , et al. · 2024

In response to the deficiencies in existing bridge pier scour protection technologies, this paper introduces a novel protective device, namely a normal distribution-shaped surface (BND) protection devices formed by rotating a concave norma…

A robust system model for the photovoltaic in industrial parks considering photovoltaic uncertainties and low-carbon demand response Open

Juhua Hong, Linyao Zhang, Zhenda Hu, Weiwei Lin, Yichao Zou · 2024

Against the backdrop of carbon peaking and carbon neutrality initiatives, industrial parks have the potential to mitigate external electricity procurement and reduce carbon emissions by incorporating photovoltaic and energy storage systems…

A Priority-and-Parallelism-Aware Scheduling Algorithm for Customized Manufacturing Task in Industry 5.0 Open

Haotong Zhang, Weiwei Lin, Guan-Yu Chen, Keyi Wu, Keqin Li · 2024

Hpcsight: Fusing Intelligence with Usability in High-Performance Computing System Monitoring Open

xiaoxuan luo, Weiwei Lin, fan chen, haocheng zhong · 2024

Interference Modeling and Scheduling for Cpu-Intensive Batch Applications Open

Chennian Xiong, Weiwei Lin, Hong‐Li Huang, Jianpeng Lin, Keqin Li · 2024

Double U-Net: Semi-Supervised Ultrasound Image Segmentation Combining Cnn and Transformer's U-Shape Network Open

Huabiao Zhou, Yanmin Luo, Jingjing Guo, Zhikui Chen, Wanyuan Gong , et al. · 2024

A Server Placement Algorithm for Reducing Risk and Improving Power Utilization in Data Centers Open

Rui Chen, Huikang Huang, Xiaoxuan Luo, Weiwei Lin · 2023

As the power demand in data centers is increasing, the power capacity of the power supply system has become an essential resource to be optimized. Although many data centers use power oversubscription to make full use of the power capacity…

An optimal container update method for edge‐cloud collaboration Open

Haotong Zhang, Weiwei Lin, Rong Xie, Shenghai Li, Zhiyan Dai , et al. · 2023

Emerging computing paradigms provide field‐level service responses for users, for example, edge computing, fog computing, and MEC. Edge virtualization technologies represented by Docker can provide a platform‐independent, low‐resource‐cons…

A resource scheduling method for cloud data centers based on thermal management Open

Li Mao, Rui Chen, Huiwen Cheng, Weiwei Lin, Bo Liu , et al. · 2023

With the rapid growth of cloud computing services, the high energy consumption of cloud data centers has become a critical concern of the cloud computing society. While virtual machine (VM) consolidation is often used to reduce energy cons…

A Resource Scheduling Method for Cloud Data Centers Based on Thermal Management Open

Li Mao, Rui Chen, Huiwen Cheng, Weiwei Lin, Bo Liu · 2023

With the continuous growth of cloud computing services, the high energy consumption of cloud data centers has become an urgent problem to be solved. Virtual machine consolidation (VMC) is an important way to optimize energy consumption, ho…

Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration Open

Huabin Wang, Ruichao Mo, Yuping Chen, Weiwei Lin, Minxian Xu , et al. · 2023

Nowadays, the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network, such as pedestrian and vehicle detection, to provide efficient intelligent services to mob…

Variation-Incentive Loss Re-weighting for Regression Analysis on Biased Data Open

Wentai Wu, Ligang He, Weiwei Lin · 2021

Both classification and regression tasks are susceptible to the biased distribution of training data. However, existing approaches are focused on the class-imbalanced learning and cannot be applied to the problems of numerical regression w…

PDMA: Probabilistic Service Migration Approach for Delay-aware and Mobility-aware Mobile Edge Computing Open

Minxian Xu, Qiheng Zhou, Huaming Wu, Weiwei Lin, Kejiang Ye , et al. · 2021

As a key technology in the 5G era, Mobile Edge Computing (MEC) has developed rapidly in recent years. MEC aims to reduce the service delay of mobile users, while alleviating the processing pressure on the core network. MEC can be regarded …

Adaptive Processor Frequency Adjustment for Mobile Edge Computing with Intermittent Energy Supply Open

Tiansheng Huang, Weiwei Lin, Ying Li, Xiumin Wang, Qingbo Wu , et al. · 2021

With astonishing speed, bandwidth, and scale, Mobile Edge Computing (MEC) has played an increasingly important role in the next generation of connectivity and service delivery. Yet, along with the massive deployment of MEC servers, the ens…

FedProf: Efficient Federated Learning with Data Representation Profiling Open

Wentai Wu, Ligang He, Weiwei Lin, Rui Mao · 2021

Federated Learning (FL) has shown great potential as a privacy-preserving solution to learning from decentralized data which are only accessible locally on end devices (i.e., clients). In many scenarios, however, a large proportion of the …

FedProf: Selective Federated Learning with Representation Profiling Open

Wentai Wu, Ligang He, Weiwei Lin, Rui Mao · 2021

Federated Learning (FL) has shown great potential as a privacy-preserving solution to learning from decentralized data that are only accessible to end devices (i.e., clients). In many scenarios, however, a large proportion of the clients a…

Developing an Unsupervised Real-Time Anomaly Detection Scheme for Time Series With Multi-Seasonality Open

Wentai Wu, Ligang He, Weiwei Lin, Yi Su, Yuhua Cui , et al. · 2020

On-line detection of anomalies in time series is a key technique used in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources…

Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems Open

Wentai Wu, Ligang He, Weiwei Lin, Rui Mao · 2020

Mobile Edge Computing (MEC), which incorporates the Cloud, edge nodes and end devices, has shown great potential in bringing data processing closer to the data sources. Meanwhile, Federated learning (FL) has emerged as a promising privacy-…

A Virtual Machine Consolidation Algorithm Based on Ant Colony System and Extreme Learning Machine for Cloud Data Center Open

Fagui Liu, Zhenjiang Ma, Bin Wang, Weiwei Lin · 2019

The energy consumption issue of large-scale data centers is attracting more and more attention. Virtual machine consolidation can significantly reduce energy consumption by migrating virtual machines from one physical machine to another. H…

A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation Open

Yan Zhong, Simon Fong, Shimin Hu, Raymond K. Wong, Weiwei Lin · 2019

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at freque…

Local Trend Inconsistency: A Prediction-driven Approach to Unsupervised Anomaly Detection in Multi-seasonal Time Series. Open

Wentai Wu, Ligang He, Weiwei Lin · 2019

On-line detection of anomalies in time series is a key technique in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources and …

A Dual-Attention-Based Stock Price Trend Prediction Model With Dual Features Open

Yingxuan Chen, Weiwei Lin, James Z. Wang · 2019

Modeling and predicting stock prices is an important and challenging task in the field of financial market. Due to the high volatility of stock prices, traditional data mining methods cannot identify the most relevant and critical market d…

Predicting Long-Term Scientific Impact Based on Multi-Field Feature Extraction Open

Ziming Wu, Weiwei Lin, Pan Liu, Jingbang Chen, Li Mao · 2019

Nowadays, there have been many studies on evaluating the scientific impact of scholars. However, we still lack effective methods to predict long-term impact, especially 10 years in the future. Therefore, we propose a long-term scientific i…

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