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arXiv 2024年

作者： Shen, Siyuan Huang, Langwen Chrapek, Marcin Schneider, Timo Dayal, Jai Gajbe, Manisha Wisniewski, Robert Hoefler, Torsten Department of Computer Science ETH Zürich Zürich Switzerland SAIT Systems Architecture Lab. Samsung Semiconductor San Jose United States

The shift towards high-bandwidth networks driven by AI workloads in data centers and HPC clusters has unintentionally aggravated network latency, adversely affecting the performance of communication-intensive HPC applications. As large-scale MPI applications often exhibit significant differences in their network latency tolerance, it is crucial to accurately determine the extent of network latency an application can withstand without significant performance degradation. Current approaches to assessing this metric often rely on specialized hardware or network simulators, which can be inflexible and time-consuming. In response, we introduce LLAMP, a novel toolchain that offers an efficient, analytical approach to evaluating HPC applications’ network latency tolerance using the LogGPS model and linear programming. LLAMP equips software developers and network architects with essential insights for optimizing HPC infrastructures and strategically deploying applications to minimize latency impacts. Through our validation on a variety of MPI applications like MILC, LULESH, and LAMMPS, we demonstrate our tool’s high accuracy, with relative prediction errors generally below 2%. Additionally, we include a case study of the ICON weather and climate model to illustrate LLAMP’s broad applicability in evaluating collective algorithms and network topologies. © 2024, CC BY-SA.

关键词： Linear programming

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Electrification of Transportation: Policy Framework, Technical Aspects and Challenges in Pakistan - A Case Study

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 10卷

作者： Masood, Arsalan Hassan, Syed Zulqadar Kamal, Tariq Khan, Salman Rizvi, Syed Asad Abbas Salman, Salman Department of Electrical Engineering Faculty of Engineering and Architecture University of Sialkot Pakistan Department of Computer Science Faculty of Information Technology and Computer Science University of Central Pubjab Lahore Pakistan School of Technology and Innovations Electrical Engineering University of Vaasa Vaasa Finland Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Estonia State Key Lab. for Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University) Beijing China Department of Electrical Engineering Balochistan University of Engineering and Technology Balochistan Khuzdar Pakistan

Pakistan faces severe air pollution and high dependency on imported fossil fuels for transportation, urgently needing solutions. Electrification of transportation could address these issues, but Pakistan lacks a clear roadmap for adopting electric vehicles. This paper examines the electrification of transportation in Pakistan, focusing on policies, technical aspects, and standards. It looks at best practices from developed countries and discusses incentives offered by the Pakistani government to boost electric vehicle adoption. The study also explores challenges and opportunities in adopting electric vehicles within existing power systems. A SWOT analysis is included, highlighting strengths, weaknesses, opportunities, and threats related to electric vehicle policies and market integration in Pakistan. By reviewing these factors, the paper provides insights into the obstacles and potential benefits of electric vehicle adoption. It also suggests ways for developing nations like Pakistan to encourage widespread use of electric vehicles, aiming to reduce fossil fuel dependency and meet environmental goals. © 2024

关键词： Electric transportation Electric vehicles Key challenges Policy SWOT analysis

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Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors 36

Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph...

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36th Annual Conference on Neural Information Processing Systems - Competition Track, NeurIPS 2022

作者： Neun, Moritz Eichenberger, Christian Martin, Henry Spanring, Markus Siripurapu, Rahul Springer, Daniel Deng, Leyan Wu, Chenwang Lian, Defu Zhou, Min Lumiste, Martin Ilie, Andrei Wu, Xinhua Lyu, Cheng Lu, Qing-Long Mahajan, Vishal Lu, Yichao Li, Jiezhang Li, Junjun Gong, Yue-Jiao Grötschla, Florian Mathys, Joël Wei, Ye Haitao, He Fang, Hui Malm, Kevin Tang, Fei Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland School of Data Science University of Science and Technology of China China Huawei Noah’s Ark Lab. China Bolt Technology Tallinn Estonia University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering Northeastern University BostonMA United States Technical University of Munich Germany Layer 6 AI Toronto Canada School of Coumpute Science and Engineering South China University of Technology Guangzhou China ETH Zurich Switzerland Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom HERE Technologies ChicagoIL United States Kaiko Zurich Switzerland Machine Learning Institute Johannes Kepler University Linz Austria

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only availab.e in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future – super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly availab.e sparse vehicle data and without the need for large amounts of real-time floating vehicle data. © 2023 M. Neun et al.

关键词： Travel time

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Inductor Design Optimization Using FEA Supervised Machine Learning

Inductor Design Optimization Using FEA Supervised Machine Le...

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European Conference on Power Electronics and Applications

作者： D. Cajander I. Viarouge P. Viarouge D. Aguglia ENERGY Inst. Haute Ecole d'Ingénieurie et d’Architecture Fribourg HES-SO University of Applied Sciences and Arts Western Switzerland Boulevard de Pérolles 80 Fribourg Switzerland Electrical and Computer Eng. Dept. LEEPCI Lab. Laval University GIK 7P4 Quebec (QC) Canada ELECTROTECHNOLOGIES Selem Inc 2610 Rue Gérard-Lajoie GIP 3G1 Quebec (QC) Canada Accelerator Systems Dept. Electric Power Converter Group CERN - European Organization for Nuclear Research Geneva 23 Switzerland

ISBN: (数字)9789075815399

ISBN: (纸本)9781665487009

An optimal inductor design methodology using dimensioning models derived from Finite Element Analysis (FEA) supervised Artificial Neural Networks (ANN) is presented. The efficiency of such trained ANN dimensioning models in terms of compromise between precision and computing time is demonstrated for the cylindrical inductor topology with air and magnetic material core including saturation.

关键词： Training Analytical models Systematics Network topology Computational modeling Atmospheric modeling Artificial neural networks

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Effective fusion of deep multitasking representations for robust visual tracking

arXiv

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arXiv 2020年

作者： Marvasti-Zadeh, Seyed Mojtaba Ghanei-Yakhdan, Hossein Kasaei, Shohreh Nasrollahi, Kamal Moeslund, Thomas B. Department of Electrical Engineering Yazd University Yazd Iran Sharif University of Technology Tehran Iran Vision and Learning Lab. University of Alberta Edmonton Canada Department of Electrical Engineering Yazd University Yazd Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Department of Architecture Design and Media Technology Aalborg University Aalborg Denmark

Visual object tracking remains an active research field in computer vision due to persisting challenges with various problem-specific factors in real-world scenes. Many existing tracking methods based on discriminative correlation filters (DCFs) employ feature extraction networks (FENs) to model the target appearance during the learning process. However, using deep feature maps extracted from FENs based on different residual neural networks (ResNets) has not previously been investigated. This paper aims to evaluate the performance of twelve state-of-the-art ResNet-based FENs in a DCF-based framework to determine the best for visual tracking purposes. First, it ranks their best feature maps and explores the generalized adoption of the best ResNet-based FEN into another DCF-based method. Then, the proposed method extracts deep semantic information from a fully convolutional FEN and fuses it with the best ResNet-based feature maps to strengthen the target representation in the learning process of continuous convolution filters. Finally, it introduces a new and efficient semantic weighting method (using semantic segmentation feature maps on each video frame) to reduce the drift problem. Extensive experimental results on the well-known OTB-2013, OTB-2015, TC-128, and VOT-2018 visual tracking datasets demonstrate that the proposed method effectively outperforms state-of-the-art methods in terms of precision and robustness of visual tracking. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Sensations on the body: Varying duration, overlap, ratios and placement on dynamic vibrotactile patterns 18

Sensations on the body: Varying duration, overlap, ratios an...

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19th International Conference on Human-computer Interaction, Interaccion 2018

作者： Morrison, Ann Manresa-Yee, Cristina Jensen, Walther Digital Life Lab. University of Southern Queensland Toowoomba Australia Computer Science and Mathematics Science Department University of Balearic Islands Palma Spain Department of Architecture Design and Media Technology Aalborg University Denmark

ISBN: (纸本)9781450364911

Prior research revealed that people have strong responses to various vibrotactile patterns acting on the trunk of the body. Moreover, they experienced patterns on the back and around the waist more positively compared to those on the front or mid-front of the body. In this study, we use a vibrotactile vest designed according to Traditional Chinese principles to identify the effect of individual factors in the patterns. We examined variations of duration, overlap, placement and repetition. Our research questions are: 'What influenced peoples' perception of the sensations?' 'How can we design for the wearer with specific sensations in mind?' As an example, we found using actuators with the same placement but varying the ratio between duration and overlap gave wearers' opposing perceived effects. One ratio was perceived as calming/feel good (0.5), another as activating (>0.5), another again as annoying ( © 2018 Association for Computing Machinery.

关键词： Wearable computers

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POSTER: STAR (Space-Time Adaptive and Reductive) Algorithms for Real-World Space-Time Optimality

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ACM SIGPLAN Notices 2017年第8期52卷 455-456页

作者： Tang, Yuan You, Ronghui School of Computer Science School of Software Fudan University Shanghai Key Lab. of Intelligent Information Processing State Key Lab. of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

It's important to hit a space-time balance for a real-world algorithm to achieve high performance on modern shared-memory multi-core or many-core systems. However, a large class of dynamic programs with more than $O(1)$ dependency achieve optimality either in space or time, but not both. In the literature, the problem is known as the fundamental space-time tradeoff. By exploiting properly on the runtime system, we show that our STAR (Space-Time Adaptive and Reductive) technique can help these dynamic programs to achieve sublinear parallel time bounds while still maintaining work-, space-, and cache-optimality in a processor- and cache-oblivious fashion. © 2017 ACM.

关键词： Real time systems

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Using semantic differential scales to assess the subjective perception of auditory warning signals 21

Using semantic differential scales to assess the subjective ...

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21st International Conference on Digital Audio Effects, DAFx 2018

作者： Vieira, Joana Santos, Jorge Almeida Noriega, Paulo ALGORITMI University of Minho Braga CCG - Centre for Computer Graphics Guimarães Portugal School of Psychology University of Minho Braga Portugal Ergonomics Lab. Faculty of Human Kinetics CIAUD Lisbon School of Architecture Universidade de Lisboa Portugal

The relationship between physical acoustic parameters and the subjective responses they evoke is important to assess in audio alarm design. While the perception of urgency has been thoroughly investigated, the perception of other variables such as pleasantness, negativeness and irritability has not. To characterize the psychological correlates of variables such as frequency, speed, rhythm and onset, twenty-six participants evaluated fifty-four audio warning signals according to six different semantic differential scales. Regression analysis showed that speed predicted mostly the perception of urgency, preoccupation and negativity;frequency predicted the perception of pleasantness and irritability;and rhythm affected the perception of urgency. No correlation was found with onset and offset times. These findings are important to human-centred design recommendations for auditory warning signals. Copyright © 2018 *** rights reserved.

关键词： Regression analysis

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Publisher Correction: Twisted moiré conductive thermal metasurface

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Nature communications 2024年第1期15卷 2876页

作者： Huagen Li Dong Wang Guoqiang Xu Kaipeng Liu Tan Zhang Jiaxin Li Guangming Tao Shuihua Yang Yanghua Lu Run Hu Shisheng Lin Ying Li Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Republic of Singapore. State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China. International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University Haining 314400 China. Wuhan National Laboratory for Optoelectronics and State Key Laboratory of Material Processing and Die and Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China. Smart Materials for Architecture Research Lab Innovation Center of Yangtze River Delta Zhejiang University Jiaxing 314100 China. State Key Laboratory of Coal Combustion School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan 430074 China. Chongqing 2D Materials Institute Chongqing 400015 China. State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China. eleying@***. International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University Haining 314400 China. eleying@***. Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Republic of Singapore. chengwei.qiu@nus.edu.sg.

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Foreward

Disciplines and Disruption - Proceedings Catalog of the 37th...

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Disciplines and Disruption - Proceedings Catalog of the 37th Annual Conference of the Association for computer Aided Design in architecture, ACADIA 2017 2017年 8-9页

作者： Johnson, Jason Kelly Association for Computer Aided Design in Architecture United States California College of the Arts United States Future Cites Lab. United States

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