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14th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys 2016

作者： Wang, Gang Wang, Bolun Wang, Tianyi Nika, Ana Zheng, Haitao Zhao, Ben Y. Department of Computer Science UC Santa Barbara United States Department of Electronic Engineering Tsinghua University China

ISBN: (纸本)9781450342698

Real-time crowdsourced maps such as Waze provide timely updates on traffic, congestion, accidents and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based Sybil devices that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc onWaze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. We propose a new approach to defend against Sybil devices based on co-location edges, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, colocation edges combine to form large proximity graphs that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and discuss how they can be used to dramatically reduce the impact of attacks against crowdsourced mapping services. © 2016 ACM.

关键词： Location

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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CYCLADES: Conflict-free asynchronous machine learning 30

CYCLADES: Conflict-free asynchronous machine learning

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30th Annual Conference on Neural Information Processing Systems, NIPS 2016

作者： Pan, Xinghao Lam, Maximilian Tu, Stephen Papailiopoulos, Dimitris Zhang, Ce Jordan, Michael I. Ramchandran, Kannan Re, Chris Recht, Benjamin Department of Electrical Engineering and Computer Science UC Berkeley BerkeleyCA United States Department of Computer Science Stanford University Palo AltoCA United States Department of Statistics UC Berkeley BerkeleyCA United States

We present CYCLADES, a general framework for parallelizing stochastic optimization algorithms in a shared memory setting. CYCLADES is asynchronous during model updates, and requires no memory locking mechanisms, similar to HOG-WILD!-type algorithms. Unlike HOGWILD!, CYCLADES introduces no conflicts during parallel execution, and offers a black-box analysis for provable speedups across a large family of algorithms. Due to its inherent cache locality and conflict-free nature, our multi-core implementation of CYCLADES consistently outperforms HOGWILD!-type algorithms on sufficiently sparse datasets, leading to up to 40% speedup gains compared to HOGWILD!, and up to 5× gains over asynchronous implementations of variance reduction algorithms. © 2016 NIPS Foundation - All Rights Reserved.

关键词： Optimization

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Learning privately from multiparty data 33

Learning privately from multiparty data

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33rd International Conference on Machine Learning, ICML 2016

作者： Hamm, Jihun Cao, Paul Belkin, Mikhail Dept. Computer Science and Engineering Ohio State University ColumbusOH43210 United States Dept. Computer Science and Engineering UC-San Diego San DiegoCA92093 United States

ISBN: (纸本)9781510829008

Learning a classifier from private data collected by multiple parties is an important problem that has many potential applications. How can we build an accurate and differentially private global classifier by combining locally-trained classifiers from different parties, without access to any party's private data? We propose to transfer the 'knowledge' of the local classifier ensemble by first creating labeled data from auxiliary unlabeled data, and then train a global e-differentially private classifier. We show that majority voting is too sensitive and therefore propose a new risk weighted by class probabilities estimated from the ensemble. Relative to a non-private solution, our private solution has a generalization error bounded by 0(∈-2M-2) where M is the number of parties. This allows strong privacy without performance loss when M is large, such as in crowdsensing applications. We demonstrate the performance of our method with realistic tasks of activity recognition, network intrusion detection, and malicious URL detection.

关键词： Intrusion detection

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Data-dependent initializations of convolutional neural networks 4

Data-dependent initializations of convolutional neural netwo...

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4th International Conference on Learning Representations, ICLR 2016

作者： Krähenbühl, Philipp Doersch, Carl Donahue, Jeff Darrell, Trevor Department of Electrical Engineering and Computer Science UC Berkeley United States Machine Learning Department Carnegie Mellon United States

Convolutional Neural Networks spread through computer vision like a wildfire, impacting almost all visual tasks imaginable. Despite this, few researchers dare to train their models from scratch. Most work builds on one of a handful of ImageNet pre-trained models, and fine-tunes or adapts these for specific tasks. This is in large part due to the difficulty of properly initializing these networks from scratch. A small miscalibration of the initial weights leads to vanishing or exploding gradients, as well as poor convergence properties. In this work we present a fast and simple data-dependent initialization procedure, that sets the weights of a network such that all units in the network train at roughly the same rate, avoiding vanishing or exploding gradients. Our initialization matches the current state-of-the-art unsupervised or self-supervised pre-training methods on standard computer vision tasks, such as image classification and object detection, while reducing the pre-training time by three orders of magnitude. When combined with pre-training methods, our initialization significantly outperforms prior work, narrowing the gap between supervised and unsupervised pre-training. © ICLR 2016: San Juan, Puerto Rico. All Rights Reserved.

关键词： computer vision

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Mixing rates for the Alternating Gibbs sampler over restricted Boltzmann Machines and friends 33

Mixing rates for the Alternating Gibbs sampler over restrict...

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33rd International Conference on Machine Learning, ICML 2016

作者： Tosh, Christopher Department of Computer Science and Engineering UC San Diego 9500 Gilman Dr. San DiegoCA92093 United States

Alternating Gibbs sampling is a modification of classical Gibbs sampling where several variables are simultaneously sampled from their joint conditional distribution. In this work, we investigate the mixing rate of al... 详细信息

ISBN: (纸本)9781510829008

关键词： Mixing

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Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation

arXiv

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arXiv 2017年

作者： Koanantakool, Penporn Ali, Alnur Azad, Ariful Buluç, Aydın Morozov, Dmitriy Oliker, Leonid Yelick, Katherine Oh, Sang-Yun Department of Electrical Engineering and Computer Sciences UC Berkeley United States Machine Learning Department Carnegie Mellon University United States Computational Research Division Lawrence Berkeley National Laboratory United States Berkeley Institute for Data Science UC Berkeley United States Department of Statistics and Applied Probability UC Santa Barbara United States Google Brain United States

Across a variety of scientific disciplines, sparse inverse covariance estimation is a popular tool for capturing the underlying dependency relationships in multivariate data. Unfortunately, most estimators are not scalable enough to handle the sizes of modern high-dimensional data sets (often on the order of terabytes), and assume Gaussian samples. To address these deficiencies, we introduce HP-CONCORD, a highly scalable optimization method for estimating a sparse inverse covariance matrix based on a regularized pseudolikelihood framework, without assuming Gaussianity. Our parallel proximal gradient method uses a novel communication-avoiding linear algebra algorithm and runs across a multi-node cluster with up to 1k nodes (24k cores), achieving parallel scalability on problems with up to ≈819 billion parameters (1.28 million dimensions);even on a single node, HP-CONCORD demonstrates scalability, outperforming a state-of-the-art method. We also use HP-CONCORD to estimate the underlying dependency structure of the brain from fMRI data, and use the result to identify functional regions automatically. The results show good agreement with a clustering from the neuroscience literature. Copyright © 2017, The Authors. All rights reserved.

关键词： Scalability

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Stochastic primal-dual proximal extragradient descent for compositely regularized optimization

arXiv

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arXiv 2017年

作者： Lin, Tianyi Qiao, Linbo Zhang, Teng Feng, Jiashi Zhang, Bofeng Department of Industrial Engineering and Operations Research UC BerkeleyCA United States College of Computer National University of Defense Technology Changsha China Department of Management Science and Engineering Stanford University United States National University of Singapore Singapore Singapore

We consider a wide range of regularized stochastic minimization problems with two regularization terms, one of which is composed with a linear function. This optimization model abstracts a number of important applications in artificial intelligence and machine learning, such as fused Lasso, fused logistic regression, and a class of graph-guided regularized minimization. The computational challenges of this model are in two folds. On one hand, the closed-form solution of the proximal mapping associated with the composed regularization term or the expected objective function is not available. On the other hand, the calculation of the full gradient of the expectation in the objective is very expensive when the number of input data samples is considerably large. To address these issues, we propose a stochastic variant of extra-gradient type methods, namely Stochastic Primal-Dual Proximal ExtraGradient descent (SPDPEG), and analyze its convergence property for both convex and strongly convex objectives. For general convex objectives, the uniformly average iterates generated by SPDPEG converge in expectation with O(1/√t) rate. While for strongly convex objectives, the uniformly and non-uniformly average iterates generated by SPDPEG converge with O(log(t)/t) and O(1/t) rates, respectively. The order of the rate of the proposed algorithm is known to match the best convergence rate for first-order stochastic algorithms. Experiments on fused logistic regression and graph-guided regularized logistic regression problems show that the proposed algorithm performs very efficiently and consistently outperforms other competing algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Stochastic systems

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Enabling Synergy in IoT: Platform to Service and Beyond

Enabling Synergy in IoT: Platform to Service and Beyond

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IEEE International Conference on Internet-of-Things Design and Implementation (IoTDI)

作者： Michael P Andersen Gabe Fierro David E. Culler Electrical Engineering and Computer Science UC Berkeley

To enable a prosperous Internet of Things, devices and services must be extensible and adapt to changes in the environment or user interaction patterns. These requirements manifest as a set of design principles for each of the layers in an IoT ecosystem, from hardware to cloud services. This paper gives concrete guidelines learned from building a full-stack Synergistic IoT platform.

关键词： Hardware IEEE 802.15 Standard Context Protocols Metadata Software Complexity theory

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