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EcoVis:visual analysis of industrial-level spatio-temporal correlations in electricity consumption

Frontiers of computer science 2022年第2期16卷 98-108页

作者： Yong XIAO Kaihong ZHENG Supaporn LONAPALAWONG Wenjie LU Zexian CHEN Bin QIAN Tianye ZHANG Xin WANG Wei CHEN Electric Power Research Institute China Southern Power GridGuangzhou 510663China Digital Grid Research Institute China Southern Power GridGuangzhou 510663China The State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China School of Computer Science Zhejiang UniversityHangzhou 310058China

Closely related to the economy,the analysis and management of electricity consumption has been widely *** approaches mainly focus on the prediction and anomaly detection of electricity consumption,which fails to reveal the in-depth relationships between electricity consumption and various factors such as industry,weather etc..In the meantime,the lack of analysis tools has increased the difficulty in analytical tasks such as correlation analysis and comparative *** this paper,we introduce EcoVis,a visual analysis system that supports the industrial-level spatio-temporal correlation analysis in the electricity consumption *** not only propose a novel approach to model spatio-temporal data into a graph structure for easier correlation analysis,but also introduce a novel visual representation to display the distributions of multiple instances in a single *** implement the system with the cooperation with domain *** are conducted to demonstrate the effectiveness of our method.

关键词： spatio-temporal data electricity consumption correlation analysis visual analysis visualization

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Toward Optimal Filler Cell Insertion with Complex Implant Layer Constraints 23

Toward Optimal Filler Cell Insertion with Complex Implant La...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Peng Zou Guohao Chen Zhifeng Lin Jun Yu Jianli Chen State Key Lab of ASIC & System Fudan University Shanghai China Center for Discrete Mathematics and Theoretical Computer Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350323481

Modern circuits often contain standard cells of different threshold voltages (multi-VTs) to achieve a better trade-off between timing and power consumption. Due to the heterogeneous cell structures, the multi-VTs cells impose various implant layer constraints, further complicating the already time-consuming filler cell insertion process. In this paper, we present a fast and near-optimal algorithm to solve the filler insertion problem with complex implant layer rules and minimum filler width constraints. We first propose an inference-driven detecting algorithm to identify each design rule violation accurately. Then, a dynamic-programming-based insertion method is developed to reduce the implant layer violations. Finally, we design a contour-driven violation refinement strategy to further improve manufacturability. Experimental results show that our algorithm can reduce the number of violations significantly compared with state-of-the-art works. Besides, with our identifier in the legalization stage, we can avoid conflicts in advance and solve almost all violations after filler insertion in industrial cases.

关键词： filler insertion

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MACRO: Multi-agent Reinforcement Learning-based Cross-layer Optimization of Operational Amplifier

MACRO: Multi-agent Reinforcement Learning-based Cross-layer ...

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Asia and South Pacific Design Automation Conference

作者： Zihao Chen Songlei Meng Fan Yang Li Shang Xuan Zeng State Key Lab of Integrated Chips and Systems School of Microelectronics Fudan University Shanghai China China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

The optimization of operational amplifiers, including topology design and parameter tuning, is significantly challenging, given the high-dimensional and heterogeneous characteristics of the design space. This paper presents MACRO, a novel approach to operational amplifier design that employs multi-agent reinforcement learning for cross-layer optimization. We model the sequentially executed topology design and parameter tuning tasks as a Markov decision process, where the high-dimensional design space is effectively transformed into a series of manageable action spaces at each step. Two agents are meticulously tailored to specialize in these two distinct tasks respectively. The co-evolution of the agents is ensured by sharing design information and customizing the policy-gradient training method. Experimental results show that, compared with state-of-the-art methods, MACRO can produce superior-performing circuits while maintaining competitive design efficiency.

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The long-term health outcomes, pathophysiological mechanisms and multidisciplinary management of long COVID

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Signal Transduction and Targeted Therapy 2023年第12期8卷 5584-5602页

作者： Jingwei Li Yun Zhou Jiechao Ma Qin Zhang Jun Shao Shufan Liang Yizhou Yu Weimin Li Chengdi Wang Department of Pulmonary and Critical Care Medicine State Key Laboratory of Respiratory Health and MultimorbidityMed-X Center for ManufacturingFrontiers Science Center for Disease-related Molecular NetworkWest China HospitalSichuan UniversityChengduChina AI Lab Deepwise HealthcareBeijingChina Department of Postgraduate Student West China HospitalWest China School of MedicineSichuan UniversityChengduChina Department of Computer Science The University of Hong KongHong KongChina

There have been hundreds of millions of cases of coronavirus disease 2019(COVID-19),which is caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).With the growing population of recovered patients,it is crucial to understand the long-term consequences of the disease and management *** COVID-19 was initially considered an acute respiratory illness,recent evidence suggests that manifestations including but not limited to those of the cardiovascular,respiratory,neuropsychiatric,gastrointestinal,reproductive,and musculoskeletal systems may persist long after the acute *** persistent manifestations,also referred to as long COVID,could impact all patients with COVID-19 across the full spectrum of illness ***,we comprehensively review the current literature on long COVID,highlighting its epidemiological understanding,the impact of vaccinations,organ-specific sequelae,pathophysiological mechanisms,and multidisciplinary management *** addition,the impact of psychological and psychosomatic factors is also *** these crucial findings on long COVID,the current diagnostic and therapeutic strategies based on previous experience and pilot studies remain inadequate,and well-designed clinical trials should be prioritized to validate existing ***,we propose the primary challenges concerning biological knowledge gaps and efficient remedies as well as discuss the corresponding recommendations.

关键词： mechanisms consequences hundreds

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CODESWIFT: Accelerating LLM Inference for Efficient Code Generation

arXiv

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arXiv 2025年

作者： Zhao, Qianhui Zhang, Li Liu, Fang Lian, Xiaoli Meng, Qiaoyuanhe Jiao, Ziqian Zhou, Zetong Zhang, Borui Guo, Runlin Li, Jia School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Beihang University China Key Lab of High Confidence Software Technology Peking University China

Code generation is a latency-sensitive task that demands high timeliness, but the autoregressive decoding mechanism of Large Language Models (LLMs) leads to poor inference efficiency. Existing LLM inference acceleration methods mainly focus on standalone functions using only built-in components. Moreover, they treat code like natural language sequences, ignoring its unique syntax and semantic characteristics. As a result, the effectiveness of these approaches in code generation tasks remains limited and fails to align with real-world programming scenarios. To alleviate this issue, we propose CODESWIFT, a simple yet highly efficient inference acceleration approach specifically designed for code generation, without comprising the quality of the output. CODESWIFT constructs a multi-source datastore, providing access to both general and project-specific knowledge, facilitating the retrieval of high-quality draft sequences. Moreover, CODESWIFT reduces retrieval cost by controlling retrieval timing, and enhances efficiency through parallel retrieval and a context- and LLM preference-aware cache. Experimental results show that CODESWIFT can reach up to 2.53× and 2.54× speedup compared to autoregressive decoding in repository-level and standalone code generation tasks, respectively, outperforming state-of-the-art inference acceleration approaches by up to 88%. Our code and data are available at https://***/r/CodeSwift. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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Container lifecycle-aware scheduling for serverless computing

Container lifecycle-aware scheduling for serverless computin...

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作者： Wu, Song Tao, Zhiheng Fan, Hao Huang, Zhuo Zhang, Xinmin Jin, Hai Yu, Chen Cao, Chun National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Elastic scaling in response to changes on demand is a main benefit of serverless computing. When bursty workloads arrive, a serverless platform launches many new containers and initializes function environments (known as cold starts), which incurs significant startup latency. To reduce cold starts, platforms usually pause a container after it serves a request, and reuse this container for subsequent requests. However, this reuse strategy cannot efficiently reduce cold starts because the schedulers are agnostic of container lifecycle. For example, it may ignore soon available containers or evict soon needed containers. We propose a container lifecycle-aware scheduling strategy for serverless computing, CAS. The key idea is to control distribution of requests and determine creation or eviction of containers according to different lifecycle phases of containers. We implement a prototype of CAS on OpenWhisk. Our evaluation shows that CAS reduces 81% cold starts and therefore brings a 63% reduction at 95th percentile latency compared with native scheduling strategy in OpenWhisk when there is worker contention between workloads, and does not add significant performance overhead. © 2021 John Wiley & Sons Ltd.

关键词： Containers

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A Video Surveillance System Based on Single-Mode Fiber for Energy and Signal Co-Transmission

A Video Surveillance System Based on Single-Mode Fiber for E...

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2024 China Automation Congress, CAC 2024

作者： Su, Yuanle Gu, Xueliang Zhang, Zhiguo Wang, Xu Wu, Haoyu Wei, Jialin Song, Xiangning State Key Lab of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350368604

This paper introduces an innovative video surveillance system that utilizes single-mode fiber for the simultaneous transmission of energy and information. Traditional surveillance systems relying on electrical cables pose safety risks and operational challenges, particularly in hazardous and remote environments. The proposed system leverages power-over-fiber (PoF) technology, using a single-mode fiber to co-transmit high-power laser energy and video signals. This approach overcomes the limitations of multimode fibers, such as signal loss and modal dispersion, and enables long-distance transmission up to 10 kilometers. At the remote end, the energy is converted into electrical power by a photovoltaic converter, ensuring the operation of surveillance equipment. The system also incorporates low-power microcontroller (MCU) and a lossless compression algorithm to enhance video transmission efficiency, achieving a frame rate of 16-17 frames per second at a resolution of 320x240. Experimental results demonstrate the system's effectiveness in delivering stable energy supply and high-quality video transmission, making it suitable for use in special environments with stringent safety and operational requirements. © 2024 IEEE.

关键词： Single mode fibers

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EGODE: An Event-attended Graph ODE Framework for Modeling Rigid Dynamics 38

EGODE: An Event-attended Graph ODE Framework for Modeling Ri...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yuan, Jingyang Sun, Gongbo Xiao, Zhiping Zhou, Hang Luo, Xiao Luo, Junyu Zhao, Yusheng Ju, Wei Zhang, Ming School of Computer Science State Key Laboratory for Multimedia Information Processing PKU-Anker LLM Lab Peking University China University of Wisconsin-Madison United States University of Washington United States University of California Davis United States University of California Los Angeles United States

This paper studies the problem of rigid dynamics modeling, which has a wide range of applications in robotics, graphics, and mechanical design. The problem is partly solved by graph neural network (GNN) simulators. However, these approaches cannot effectively handle the relationship between intrinsic continuity and instantaneous changes in rigid dynamics. Moreover, they usually neglect hierarchical structures across mesh nodes and objects in systems. In this paper, we propose a novel approach named Event-attend Graph ODE (EGODE) for effective rigid dynamics modeling. In particular, we describe the rigid system using both mesh node representations and object representations. To model continuous dynamics across hierarchical structures, we use a coupled graph ODE framework for the evolution of both types of representations over a long period. In addition, to capture instantaneous changes during the collision, we introduce an event module, which can effectively estimate the occurrence of the collision and update the states of both mesh node and object representations during evolution. Extensive experiments on a range of benchmark datasets validate the superiority of the proposed EGODE compared to various state-of-the-art baselines. The source code can be found at https://***/yuanjypku/EGODE. © 2024 Neural information processing systems foundation. All rights reserved.

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Long- and Short- Term Sequential Recommendation with Temporal Interval 19

Long- and Short- Term Sequential Recommendation with Tempora...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： He, Kun Liu, Qiyan Li, Qianmu Meng, Shunmei Nanjing University of Science and Technology Nanjing China University of Toronto Computer Engineering Canada Nanjing University State Key Lab. for Novel Software Technology Nanjing China

ISBN: (纸本)9798350346558

Sequential recommendation aims to learn the changes of users' interests according to their historical behaviors and predict the most likely next item. Since user's historical behavior are sequential actions, user's interest in different time periods has different emphases. For predicting a user's next item, not only the recent behavior is important, but also long-term preference with all historical behaviors could not be ignored. Recently, self-attention based user preference modeling has drawn much attention for its advantages of fewer parameters and parallelism. However, most of the existing self-attention based model do not make a good distinction between the long-term and short-term preferences of users. Based on the observations, we design a sequential recommendation model based on a combination of long-term and short-term preference. On the one hand, considering the changes of users' interests in different periods, we divide the sequence of users' behaviors into different temporal windows. Then, we use GRU to capture users' interests in different temporal window. On the other hand, to better combine the global and local information of user's, we adopt a locally constrained multi-head attention mechanism based on Transformer encoder. On three real-world public datasets, we finally validate the efficacy of our method. © 2022 IEEE.

关键词： User profile

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Camouflaged Object Segmentation with Transformer 6th

Camouflaged Object Segmentation with Transformer

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6th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2021

作者： Wang, Haiwen Wang, Xinzhou Sun, Fuchun Song, Yixu State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China School of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9789811692468

The Vision Transformer (ViT) [6] directly applies a Transformer architecture to image classification and achieves an impressive result compared with convolutional networks. This paper presents a new ViT-base camouflaged object segmentation method, called COS Transformer, which aims to identify and segment objects concealed in a complex environment. The high intrinsic similarities between object and surrounding makes the task challenging than salient object detection. Most recent camouflaged object segmentation methods(e.g., EGNet [29], PraNet [10] and SINet [9]) adopt convolutional network with an encoder-decoder architecture and focused on increasing the receptive field, which is limited by the depth of the network. In camouflaged object segmentation (COS) task, the camouflage is mainly relied on contrast of the whole surrounding instead of the local information. We introduce transformer with global context awareness in this paper, for self-attention allowing COS Transformer to aggregate features globally even in the lowest layers. Specifically, the architecture is composed of a transformer-based encoder and a multi-layers feature aggregation refinement module. After training on the COD10K [9] dataset, COS Transformer attains excellent results compared to state-of-the-art convolutional networks, e.g. 11.7% improvement of E [8] on the COD10K contrasted to SINet. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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