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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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FPGA acceleration of short read alignment

arXiv

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arXiv 2018年

作者： McVicar, Nathaniel Hoshino, Akina La Torre, Anna Reh, Thomas A. Ruzzo, Walter L. Hauck, Scott Department of Electrical Engineer-ing University of Washington 185 Stevens Way SeattleWA98195 United States Department of Biological Structure University of Washington 1959 NE Pacific Street SeattleWA98195 United States Department of Cell Biology and Human Anatomy UC Davis School of Medicine 4303 Tupper Hall DavisCA95616 United States Allen School of Computer Science and Engineering University of Washington 185 E Stevens Way NE SeattleWA98195 United States Department of Genome Science University of Washington Fred Hutchinson Cancer Research Center SeattleWA98109 United States

Aligning millions of short DNA or RNA reads, of 75 to 250 base pairs each, to a reference genome is a significant computation problem in bioinformatics. We present a flexible and fast FPGA-based short read alignment tool. Our aligner makes use of the processing power of FPGAs in conjunction with the greater host memory bandwidth and flexibility of software to improve performance and achieve a high level of configurability. This flexible design supports a variety of reference genome sizes without the performance degradation suffered by other software and FPGA-based aligners. It is also better able to support the features of new alignment algorithms, which frequently crop up in the rapidly evolving field of bioinformatics. We demonstrate these advantages in a case study where we align RNA-Seq data from a hypothesized mouse/human xenograft. In this case study, our aligner provides a speedup of 5.6x over BWA-SW with energy savings of 21%, while also reducing incorrect short read classification by 29%. To demonstrate the flexibility of our system we show that the speedup can be substantially improved while retaining most of the accuracy gains over BWA-SW. The speedup can be increased to 71.3x, while still enjoying a 28% incorrect classification improvement and 52% improvement in unaligned reads. Copyright © 2018, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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Neuromatch academy: Teaching computational neuroscience with global accessibility

arXiv

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arXiv 2020年

作者： van Viegen, Tara Akrami, Athena Bonnen, Kate DeWitt, Eric Hyafil, Alexandre Ledmyr, Helena Lindsay, Grace W. Mineault, Patrick Murray, John D. Pitkow, Xaq Puce, Aina Sedigh-Sarvestani, Madineh Stringer, Carsen Achakulvisut, Titipat Alikarami, Elnaz Atay, Melvin Selim Batty, Eleanor Erlich, Jeffrey C. Galbraith, Byron V. Guo, Yueqi Juavinett, Ashley L. Krause, Matthew R. Li, Songting Pachitariu, Marius Straley, Elizabeth Valeriani, Davide Vaughan, Emma Vaziri-Pashkam, Maryam Waskom, Michael L. Blohm, Gunnar Kording, Konrad Schrater, Paul Wyble, Brad Escola, Sean Peters, Megan A.K. Princeton Neuroscience Institute Princeton University PrincetonNJ United States Sainsbury Wellcome Centre University College London London United Kingdom Center for Neural Science New York University New York CityNY United States Champalimaud Research Champalimaud Foundation Lisbon Portugal Centre de Recerca Matemàtica Bellaterra Universitat Autònoma de Barcelona Barcelona Spain International Neuroinformatics Coordinating Facility Sweden Gatsby Computational Neuroscience Unit Sainsbury Wellcome Centre University College London London United Kingdom Department of Psychiatry Yale University New HavenCT United States Baylor College of Medicine Dept. of Neuroscience HoustonTX United States Baylor College of Medicine Center for Neuroscience and Artificial Intelligence HoustonTX United States Rice University Dept of Electrical and Computer Engineering HoustonTX United States Psychological & Brain Sciences Indiana University BloomingtonIN United States Max Planck Florida Institute for Neuroscience JupiterFL United States HHMI Janelia Research Campus AshburnVA United States University of Pennsylvania Dept of Neuroscience and Bioengineering PhiladelphiaPA United States Faculty of dentistry McGill University MontrealQB Canada Middle East Technical University Ankara Turkey Department of Neurobiology Harvard University CambridgeMA United States New York University Shanghai Shanghai China Talla Inc BostonMA United States Johns Hopkins University Department of Biomedical Engineering BaltimoreMD United States Division of Biological Sciences UC San Diego San diegoCA United States Montreal Neurological Institute McGill University MontrealQB Canada School of Mathematical Sciences MOE-LSC Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China Department of Otolaryngology - Head & Neck Surgery Massachusetts Eye and Ear BostonMA United States Department of Otolaryngology - Head & Neck Surgery Harvard Medical School BostonM

Neuromatch Academy designed and ran a fully online 3-week Computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity and universal accessibility. © 2020, CC BY.

关键词： Python

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Quantum bionanophotonics in life science and medicine

Quantum bionanophotonics in life science and medicine

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IEEE/LEOS International Conference on Optical MEMS

作者： Luke P. Lee Departments of Bioengineering Electrical Engineering & Computer Science UC Berkeley

Quantum bionanophotonics is making headways into the life sciences, enabling in-vivo interrogation at the molecular level and in-vitro applications for diagnostics and analysis. These and other emerging biomedical app... 详细信息

关键词： Plasmons Biomedical optical imaging Optical sensors Optical imaging Optical scattering Nanobioscience Ultrafast optics

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Estimation of the Investability of Real Estate Properties through Text Analysis

Estimation of the Investability of Real Estate Properties th...

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International Conference on Semantic Computing

作者： Moloud Shahbazi Joseph R. Barr Vagelis Hristidis Nani Narayanan Srinivasan Computer Science and Engineering UC Riverside HomeUnion Irvine California

ISBN: (纸本)9781509006632

The Multiple Listing Service, commonly known as the MLS, is the singularly most important database where real estate agents and brokers list real estate properties for sale. It is common that agents include textual comments pertinent to the property. Although the information content of comments varies, it is usually expressed in good faith and in many cases is helpful in shedding light on the overall condition and the value of the property. Therefore, it seems reasonable that semantic text analysis would be useful to evaluate properties, or aspects thereof. As far as we're aware of, no methodology to effectively extract insight from the MLS textual portion exists. In this paper we demonstrate how textual descriptions may be exploited for property ranking. The proposed methodology, which combines supervised and unsupervised methods, identifies domain-specific concepts and combines their contributions to assign a score to a listing. We evaluate the proposed methods using both human evaluators and data-driven evaluation metrics on real datasets (complied from actual listings), and compare them to baseline approaches.

关键词： Feature extraction Vocabulary Context Cost accounting Training Semantics Databases

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Model predictive control for signal temporal logic specifications

arXiv

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arXiv 2017年

作者： Raman, Vasumathi Donzé, Alexandre Maasoumy, Mehdi Murray, Richard M. Sangiovanni-Vincentelli, Alberto Seshia, Sanjit A. California Institute of Technology PasadenaCA91125 United States Department of Electrical Engineering and Computer Science Uc Berkeley BerkeleyCA94720 United States C3 Energy Inc. Redwood CityCA94063 United States

We present a mathematical programming-based method for model predictive control of cyber-physical systems subject to signal temporal logic (STL) specifications. We describe the use of STL to specify a wide range of properties of these systems, including safety, response and bounded liveness. For synthesis, we encode STL specifications as mixed integer-linear constraints on the system variables in the optimization problem at each step of a receding horizon control framework. We prove correctness of our algorithms, and present experimental results for controller synthesis for building energy and climate control. Copyright © 2017, The Authors. All rights reserved.

关键词： Embedded systems

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Personalized radiotherapy design for glioblastoma: Integrating mathematical tumor models, multimodal scans and Bayesian inference

arXiv

引用

arXiv 2018年

作者： Lipková, Jana Angelikopoulos, Panagiotis Wu, Stephen Alberts, Esther Wiestler, Benedikt Diehl, Christian Preibisch, Christine Pyka, Thomas Combs, Stephanie Hadjidoukas, Panagiotis van Leemput, Koen Koumoutsakos, Petros Lowengrub, John Menze, Bjoern Germany Dept. of Neuroradiolog Klinikum Rechts der Isar TUM Germany D.E. Shaw Research L.L.C United States Institute of Statistical Mathematics Tokyo Japan Dept. of Diagnostic and Interventional Neuroradiology & Neuroimaging Center & Clinic for Neurology Klinikum Rechts der Isar TUM Germany Dept. of Nuclear Medicine Klinikum Rechts der Isar TUM Germany Dept. of Radiation Oncology Klinikum Rechts der Isar TUM Institute of Innovative Radiotherapy Helmholtz Zentrum Munich & Deutsches Konsortium für Translationale Krebsforschung Germany IBM Research - Zurich Switzerland Harvard Medical School Boston United States Dept. of Applied Mathematics and Computer Science TU Denmark Denmark Computational Science and Engineering Lab ETH Zürich Switzerland Dept. of Mathematics Biomedical Engineering Chemical Engineering and Materials Science Center for Complex Biological Systems Chao Family Comprehensive Cancer Center UC Irvine United States Institute for Advanced Study TUM Germany

Glioblastoma is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in glioblastoma patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with credible intervals. The proposed methodology relies only on data acquired at a single time point and thus is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized dose-escalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design. Copyright © 2018, The Authors. All rights reserved.

关键词： Radiotherapy

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Cyclades: Conflict-free Asynchronous Machine Learning 16

Cyclades: Conflict-free Asynchronous Machine Learning

引用

Annual Conference on Neural Information Processing Systems

作者： Xinghao Pan Maximilian Lam Stephen Ti Dimitris Papailiopoulos Ce Zhang Michael I. Jordan Kannan Ramchandran Chris Re Benjamin Recht Department of Electrical Engineering and Computer Science UC Berkeley Berkeley CA Department of Computer Science Stanford University Palo Alto CA Department of Electrical Engineering and Computer Science UC Berkeley Berkeley CADepartment of Statistics UC Berkeley Berkeley CA

ISBN: (纸本)9781510838819

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.

关键词： Cyclades Asynchronous parallel execution shared memory large family asynchronous machines Optimization algorithms Model updating Conflict (Psychology) algorithms

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Optimizing the Diamond Lane: A More Tractable Carpool Problem and Algorithms

Optimizing the Diamond Lane: A More Tractable Carpool Proble...

引用

IEEE International Conference on Intelligent Transportation Systems

作者： Cathy Wu K. Shankari Ece Kamar Randy Katz David Culler Christos Papadimitriou Eric Horvitz Alexandre Bayen Department of Electrical Engineering and Computer Science at UC Berkeley Microsoft Research

ISBN: (纸本)9781509018901

Carpooling has been long deemed a promising approach to better utilizing existing transportation infrastructure. However, there are several reasons carpooling is still not the preferred mode of commute in the United States: first, complex human factors, including time constraints and not having right incentive structures, discourage the sharing of rides;second, algorithmic and technical barriers inhibit the development of online services for matching riders. In this work, we study algorithms for 3+ high-occupancy vehicle (HOV) lanes, which permit vehicles that hold three or more people. We focus on the technical barriers but also address the aforementioned human factors. We formulate the HOV3 Carpool problem, and show that it is NP-Complete. We thus pose the relaxed problem HOV3- Carpool problem, allowing groups of up to size three, and propose several methods for solving the problem of finding globally optimal carpool groups that may utilize these 3- HOV lanes. Our methods include local search, integer programming, and dynamic programming. Our local search methods include sampling-based (hill-climbing and simulated annealing), classical neighborhood search, and a hybrid random neighborhood search. We assess the methods numerically in terms of objective value and scalability. Our findings show that our sampling-based local search methods scale up to 100K agents, thereby improving upon related previous work (which studies up to 1000 agents). The hill climbing local search method converges significantly closer and faster towards a naive lower bound on cumulative carpooling cost.

关键词： CARPOOLING Search methods Information Services Human factors algorithms High occupancy vehicle lanes Time constraints

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