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arXiv 2018年

作者： Nguyen, Duong Malliaros, Fragkiskos D. Department of Computer Science and Engineering UC San Diego La JollaCA United States Center for Visual Computing CentraleSupélec and Inria Saclay Gif-Sur-Yvette France

Network embedding algorithms are able to learn latent feature representations of nodes, transforming networks into lower dimensional vector representations. Typical key applications, which have effectively been addressed using network embeddings, include link prediction, multilabel classification and community detection. In this paper, we propose BiasedWalk, a scalable, unsupervised feature learning algorithm that is based on biased random walks to sample context information about each node in the network. Our random-walk based sampling can behave as Breath-First-Search (BFS) and Depth-First-Search (DFS) samplings with the goal to capture homophily and role equivalence between the nodes in the network. We have performed a detailed experimental evaluation comparing the performance of the proposed algorithm against various baseline methods, on several datasets and learning tasks. The experiment results show that the proposed method outperforms the baseline ones in most of the tasks and datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Network embeddings

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A 4-mm2 180-nm-CMOS 15-Giga-cell-updates-per-second DNA sequence alignment engine based on asynchronous race conditions

A 4-mm2 180-nm-CMOS 15-Giga-cell-updates-per-second DNA sequ...

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Custom Integrated Circuits Conference (CICC)

作者： Advait Madhavan Timothy Sherwood Dmitri Strukov Electrical and Computer Engineering UC Banta Barbara Computer Science UC Banta Barbara

We have fabricated and successfully tested, for the first time, a prototype chip of a Race Logic computing paradigm, which makes positive use of race conditions for accelerating a broad class of optimization problems, such as ones solved by dynamic programming algorithms. In Race Logic, information is encoded in signal propagation delay, rather than conventional logic levels, and the result of the computation is observed from relative timing differences between injected signals, i.e. the outcome of races. The 2×2 mm 2 chip, fabricated in standard 180nm CMOS technology, is designed to perform real-world DNA sequence alignment. Measurement results on typical benchmark data show 15 GCUPS sustained throughput at 70 mW power consumption, with only ~ 15 mW spent for actual computation. These numbers compare very favorably with the state-of-the-art implementations.

关键词： Delays DNA Logic gates Logic arrays Switches Clocks

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Learning from learning machines: A new generation of AI technology to meet the needs of science

arXiv

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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

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A first look at user switching behaviors over multiple video content providers 11

A first look at user switching behaviors over multiple video...

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11th International Conference on Web and Social Media, ICWSM 2017

作者： Yan, Huan Lin, Tzu-Heng Wang, Gang Li, Yong Zheng, Haitao Jin, Depeng Zhao, Ben Y. Department of Electronic Engineering Tsinghua University China Department of Computer Science Virginia Tech BlacksburgVA United States Department of Computer Science UC Santa Barbara Santa BarbaraCA United States

ISBN: (纸本)9781577357889

Watching videos from multiple content providers (CP) has become prevalent. For individual CPs, understanding user video consumption patterns among CPs is critical for improving on-site user experience and CP's opportunity of success. In this paper, based on a two-month dataset recording 9 million users' 269 million video viewing requests over 6 most popular video CPs in China, we provide a first look at users' video consumption and switching behaviors across different CPs. We find that rarely do active video watchers stick to one single CP. When users switch from one CP to another, they are more likely to switch to larger CPs with more videos. © Copyright 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Behavioral research

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Self-synchronization of multiple vehicles using ambient impulsive noise

Self-synchronization of multiple vehicles using ambient impu...

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Underwater Communications and Networking Conference

作者： Perry Naughton Tahiya Salam Peter Tueller Philippe Roux Curt Schurgers Ryan Kastner Jules Jaffe Paul Roberts Department of Electrical and Computer Engineering UC San Diego Department of Computer Science and Engineering UC San Diego ISTerre Universite Grenoble Alpes CNRS Scripps Institution of Oceanography UC San Diego

ISBN: (纸本)9781538664438

Clock synchronization is important when considering a swarm of underwater vehicles. Unfortunately, accurate clocks are expensive, and acoustic communication is power intensive, preventing large numbers of vehicles from being deployed for long periods of time. We propose a technique where vehicles achieve relative synchronization by leveraging sounds already present in the underwater environment. This provides a synchronization method that does not rely on expensive clocks, communication, or additional infrastructure. We demonstrate that our technique accurately tracks clock offsets between vehicles during an at sea deployment.

关键词： vehicle Timing devices automatic synchronization clock recovery Wet underwater environment Impulse noise ACOUSTIC COMMUNICATIONS dike swarms time periods clock skew

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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Improved oracle complexity for stochastic compositional variance reduced gradient

arXiv

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arXiv 2018年

作者： Lin, Tianyi Fan, Chenyou Wang, Mengdi Jordan, Michael I. Department of Industrial Engineering and Operations Research UC Berkeley BerkeleyCA94720 United States School of Informatics and Computing Indiana University BloomingtonIN47405 United States Department of Operations Research and Financial Engineering Princeton University PrincetonNJ08544 United States Department of Electrical Engineering and Computer Science Department of Statistics UC Berkeley BerkeleyCA94720 United States

We propose an accelerated stochastic compositional variance reduced gradient method for optimizing the sum of a composition function and a convex nonsmooth function. We provide an incremental first-order oracle (IFO) complexity analysis for the proposed algorithm and show that it is provably faster than all the existing methods. Indeed, we show that our method achieves an asymptotic IFO complexity of O (Equation Presented) where m and n are the number of inner/outer component functions, improving the best-known results of O (Equation Presented) and achieving for the best known linear run time for convex composition problem. Experiment results on sparse mean-variance optimization with 21 real-world financial datasets confirm that our method outperforms other competing methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Accuracy-resource tradeoff for edge devices in Internet of Things

Accuracy-resource tradeoff for edge devices in Internet of T...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Nima Mousavi Baris Aksanli Alper Sinan Akyurek Tajana Šimunic̀ Rosing Electrical and Computer Engineering UC San Diego Electrical and Computer Engineering San Diego State University Computer Science and Engineering UC San Diego

Modern power grid has evolved from a passive network into an application of Internet of Things with numerous interconnected elements and users. In this environment, household users greatly benefit from a prediction algorithm that estimates their future power demand to help them control off-grid generation, battery storage, and power consumption. In particular, household power consumption prediction plays a pivotal role in optimal utilization of batteries used alongside photovoltaic generation, creating saving opportunities for users. Since edge devices in Internet of Things offer limited capabilities, the computational complexity and memory and energy consumption of the prediction algorithms are capped. In this paper we forecast 24-hour demand from power consumption, weather, and time data, using Support Vector Regression models, and compare it to state-of-the-art prediction methods such as Linear Regression and persistence. We use power consumption traces from real datasets and a Raspberry Pi 3 embedded computer as testbed to evaluate the resource-accuracy trade-off. Our study reveals that Support Vector Regression is able to achieve 21% less prediction error on average compared to Linear Regression, which translates into 16% more cost savings for users when using residential batteries with photovoltaic generation.

关键词： Power demand Prediction algorithms Batteries Predictive models Biological system modeling Support vector machines Mathematical model

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Animal Models for Microbiome Research and Drug Discovery

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Drug Discovery Today: Disease Models 2018年 28卷 1-2页

作者： Philip J. Richter Pieter C. Dorrestein Rob Knight Department of Pathology and Animal Care Program and Center for Veterinary Sciences and Comparative Medicine UC San Diego La Jolla CA 92093 USA Skaggs School of Pharmacy and Pharmaceutical Sciences Center for Microbiome Innovation and Departments of Pharmacology and Pediatrics UC San Diego La Jolla CA 92093 USA Center for Microbiome Innovation and Departments of Pediatrics Bioengineering and Computer Science & Engineering UC San Diego La Jolla CA 92093 USA

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Exploring Mixed Reality in specialized surgical environments

Exploring Mixed Reality in specialized surgical environments

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2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI EA 2017

作者： Rodrigues, Danilo Gasques Jain, Ankur Rick, Steven R. Liu, Shangley Suresh, Preetham Weibel, Nadir Dept. of Computer Science and Engineering UC San Diego San DiegoCA United States Dept. of Surgery UC San Diego San DiegoCA United States Dept. of Anesthesiology UC San Diego San DiegoCA United States

ISBN: (纸本)9781450346566

Recent technology advances in both Virtual Reality and Augmented Reality are creating an opportunity for a paradigm shift in the design of human-computer interaction systems. Delving into the Reality-Virtuality Continuum, we find Mixed Reality - systems designed to augment the physical world with virtual entities that embody characteristics of real world objects. In the medical field, Mixed Reality systems can overlay real-time and spatially accurate results onto a patient's body without the need for external screens. The complexity of these systems previously required specialized prototypes, but newly available commercial products like the Microsoft HoloLens make the technology more available. Through a combination of literature review, expert analysis, and prototyping we explore the use of Mixed Reality in healthcare. From the experience of prototyping Patiently and HoloSim, two applications for augmenting medical training, we outline considerations for the future design and development of virtual interfaces grounded in reality. Copyright © 2017 by the Association for Computing Machinery, Inc. (ACM).

关键词： Mixed reality

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