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International Joint Conference on Neural Networks

作者： Tong Zhao Julian McAuley Mengya Li Irwin King Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong Department of Computer Science and Engineering UC San Diego La Jolla CA USA State Information Center China

ISBN: (纸本)9781509061839

Recommender systems are ubiquitous in applications ranging from e-commerce to social media, helping users to navigate a huge selection of items and to meet a variety of special needs and user tastes. Incorporating contextual knowledge into such systems - such as relational information - has proven to be an effective way to improve recommendation accuracy. A popular line of research aims to model relationships between users, through their connections in a social network. In contrast, we aim to model complex relationships between products, using data based on co-purchase and co-browsing behavior. Modeling such networks presents a variety of challenges, in particular because the features that make two items complementary (or likely to be co-purchased) are far more complex than mere similarity. To model these complex relationships we develop a method based on pairwise ranking and embedding learning to build representations of items based on their co-purchasing and co-browsing statistics. We conduct experiments on Amazon product data to demonstrate that modeling such relationships significantly improves accuracy compared to competitive baselines.

关键词： Estimation Recommender systems Matrix decomposition Predictive models Silicon Distance measurement

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Mineral Detection of Neutrinos and Dark Matter. A Whitepaper

arXiv

引用

arXiv 2023年

作者： Baum, Sebastian Stengel, Patrick Abe, Natsue Acevedo, Javier F. Araujo, Gabriela R. Asahara, Yoshihiro Avignone, Frank Balogh, Levente Baudis, Laura Boukhtouchen, Yilda Bramante, Joseph Breur, Pieter Alexander Caccianiga, Lorenzo Capozzi, Francesco Collar, Juan I. Ebadi, Reza Edwards, Thomas Eitel, Klaus Elykov, Alexey Ewing, Rodney C. Freese, Katherine Fung, Audrey Galelli, Claudio Glasmacher, Ulrich A. Gleason, Arianna Hasebe, Noriko Hirose, Shigenobu Horiuchi, Shunsaku Hoshino, Yasushi Huber, Patrick Ido, Yuki Igami, Yohei Ishikawa, Norito Itow, Yoshitaka Kamiyama, Takashi Kato, Takenori Kavanagh, Bradley J. Kawamura, Yoji Kazama, Shingo Kenney, Christopher J. Kilminster, Ben Kouketsu, Yui Kozaka, Yukiko Kurinsky, Noah A. Leybourne, Matthew Lucas, Thalles McDonough, William F. Marshall, Mason C. Mateos, Jose Maria Mathur, Anubhav Michibayashi, Katsuyoshi Mkhonto, Sharlotte Murase, Kohta Naka, Tatsuhiro Oguni, Kenji Rajendran, Surjeet Sakane, Hitoshi Sala, Paola Scholberg, Kate Semenec, Ingrida Shiraishi, Takuya Spitz, Joshua Sun, Kai Suzuki, Katsuhiko Tanin, Erwin H. Vincent, Aaron Vladimirov, Nikita Walsworth, Ronald L. Watanabe, Hiroko Stanford Institute for Theoretical Physics Department of Physics Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Ferrara via Giuseppe Saragat 1 FerraraI-44122 Italy Kanagawa Yokohama236-0001 Japan SLAC National Accelerator Laboratory Stanford University 2575 Sand Hill Road Menlo ParkCA94025 United States Department of Physics University of Zurich Winterthurerstrasse 190 ZurichCH-8057 Switzerland Department of Earth and Environmental Sciences Graduate School of Environmental Studies Nagoya University Furo-cho Chikusa-ku Nagoya464-8601 Japan Department of Physics and Astronomy University of South Carolina ColumbiaSC29208 United States Mechanical and Materials Engineering Queen’s University 130 Stuart Street KingstonONK7L 2V9 Canada Department of Physics Engineering Physics and Astronomy Queen’s University 64 Bader Lane KingstonONK7L 2S8 Canada Perimeter Institute for Theoretical Physics WaterlooONN2J 2W9 Canada Istituto Nazionale di Fisica Nucleare Sezione di Milano Via Celoria 16 Milan Italy Dipartimento di Scienze Fisiche e Chimiche Università degli Studi dell’Aquila L’Aquila67100 Italy Department of Physics University of Chicago ChicagoIL60637 United States Department of Physics University of Maryland College ParkMD20742 United States Quantum Technology Center University of Maryland College ParkMD20742 United States The William H. Miller III Department of Physics and Astronomy The Johns Hopkins University BaltimoreMD21218 United States Institute for Astroparticle Physics Karlsruhe Institute of Technology Karlsruhe76021 Germany Earth and Planetary Sciences Stanford University StanfordCA94305-2115 United States Texas Center for Cosmology and Astroparticle Physics Weinberg Institute for Theoretical Physics Department of Physics The University of Texas at Austin AustinTX78712 United States The Oskar Klein Centre Department of Physics Stockholm University AlbaNova

Minerals are solid state nuclear track detectors – nuclear recoils in a mineral leave latent damage to the crystal structure. Depending on the mineral and its temperature, the damage features are retained in the material from minutes (in low-melting point materials such as salts at a few hundred ◦C) to timescales much larger than the 4.5 Gyr-age of the Solar System (in refractory materials at room temperature). The damage features from the O(50) MeV fission fragments left by spontaneous fission of 238U and other heavy unstable isotopes have long been used for fission track dating of geological samples. Laboratory studies have demonstrated the readout of defects caused by nuclear recoils with energies as small as O(1) keV. This whitepaper discusses a wide range of possible applications of minerals as detectors for ER O(1) keV nuclear recoils: Using natural minerals, one could use the damage features accumulated over O(10) Myr–O(1) Gyr to measure astrophysical neutrino fluxes (from the Sun, supernovae, or cosmic rays interacting with the atmosphere) as well as search for Dark Matter. Using signals accumulated over months to few-years timescales in laboratory-manufactured minerals, one could measure reactor neutrinos or use them as Dark Matter detectors, potentially with directional sensitivity. Research groups in Europe, Asia, and America have started developing microscopy techniques to read out the O(1)–O(100) nm damage features in crystals left by O(0.1)–O(100) keV nuclear recoils. We report on the status and plans of these programs. The research program towards the realization of such detectors is highly interdisciplinary, combining geoscience, material science, applied and fundamental physics with techniques from quantum information and Artificial Intelligence. Copyright © 2023, The Authors. All rights reserved.

关键词： Minerals

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Control improvisation

arXiv

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arXiv 2017年

作者： Fremont, Daniel J. Donzé, Alexandre Seshia, Sanjit A. University of California Berkeley United States Group in Logic and the Methodology of Science UC Berkeley BerkeleyCA94720 Department of Electrical Engineering and Computer Sciences UC Berkeley BerkeleyCA94720 United States

We formalize and analyze a new problem in formal language theory termed control improvisation. Given a specification language, the problem is to produce an improviser, a probabilistic algorithm that randomly generates words in the language, subject to two additional constraints: the satisfaction of a quantitative soft constraint, and the exhibition of a specified amount of randomness. Control improvisation has many applications, including for example systematically generating random test vectors satisfying format constraints or preconditions while being similar to a library of seed inputs. Other applications include robotic surveillance, machine improvisation of music, and randomized variants of the supervisory control problem. We describe a general framework for solving the control improvisation problem, and use it to give efficient algorithms for several practical classes of instances with finite automaton and context-free grammar specifications. We also provide a detailed complexity analysis, establishing #P-hardness of the problem in many other cases. For these intractable cases, we show how symbolic techniques based on Boolean satisfiability (SAT) solvers can be used to find approximate solutions. Finally, we discuss an extension of control improvisation to multiple soft constraints that is useful in some applications. Copyright © 2017, The Authors. All rights reserved.

关键词： Specification languages

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A low-power hybrid magnetic cache architecture exploiting narrow-width values 5

A low-power hybrid magnetic cache architecture exploiting na...

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5th Non-Volatile Memory Systems and Applications Symposium, NVMSA 2016

作者： Imani, Mohsen Rahimi, Abbas Kim, Yeseong Rosing, Tajana Computer Science and Engineering UC San Diego San DiegoCA92093 United States Electrical Engineering and Computer Sciences UC Berkeley BerkeleyCA94720 United States

ISBN: (纸本)9781509041367

Modern microprocessors have increased the word width to 64-bits to support larger main memory sizes. It has been observed that data can often be represented by relatively few bits, so-called narrow-width values. To leverage narrow-width data, we propose a hybrid cache architecture composed of magnetic RAM (MRAM) and SRAM to save the upper and lower 32-bits of each word in MRAM and SRAM respectively. To address write performance issue of MRAM, we propose an optimal dynamic write buffer (DWB) allocation mechanism. To enhance efficacy of our hybrid cache in the absence of narrow-width values, we propose a double row write (DRW) technique that adaptively partitions non-narrow data to two 32-bit pieces for consecutive row writes in the SRAM part. DWB and DRW jointly guarantee the performance of the proposed hybrid cache and balance a tradeoff between the buffer size and the number of double row writes. Our evaluation on SPEC CPU2000, SPEC CPU2006 and Mibench benchmarks shows that our hybrid cache can achieve up to 46% power and 24% area savings at the same performance as the conventional SRAM cache. © 2016 IEEE.

关键词： Static random access storage

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

引用

NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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Unified acceleration method for packing and covering problems via diameter reduction 43

Unified acceleration method for packing and covering problem...

引用

43rd International Colloquium on Automata, Languages, and Programming, ICALP 2016

作者： Wang, Di Rao, Satish Mahoney, Michael W. Department of Electrical Engineering and Computer Sciences UC Berkeley Berkeley United States International Computer Science Institute Department of Statistics UC Berkeley Berkeley United States

ISBN: (纸本)9783959770132

In a series of recent breakthroughs, Allen-Zhu and Orecchia [2, 1] leveraged insights from the linear coupling method [15], which is a first-order optimization scheme, to provide improved algorithms for packing and covering linear programs. The result in [1] is particularly interesting, as the algorithm for packing LP achieves both width-independence and Nesterov-like acceleration, which was not known to be possible before. Somewhat surprisingly, however, while the dependence of the convergence rate on the error parameter ϵ for packing problems was improved to O(1/ϵ), which corresponds to what accelerated gradient methods are designed to achieve, the dependence for covering problems was only improved to O(1/ϵ1.5), and even that required a different more complicated algorithm, rather than from Nesterov-like acceleration. Given the primal-dual connection between packing and covering problems and since previous algorithms for these very related problems have led to the same - dependence, this discrepancy is surprising, and it leaves open the question of the exact role that the linear coupling is playing in coordinating the complementary gradient and mirror descent step of the algorithm. In this paper, we clarify these issues, illustrating that the linear coupling method can lead to improved O(1/ϵ) dependence for both packing and covering problems in a unified manner, i.e., with the same algorithm and almost identical analysis. Our main technical result is a novel dimension lifting method that reduces the coordinate-wise diameters of the feasible region for covering LPs, which is the key structural property to enable the same Nesterov-like acceleration as in the case of packing LPs. The technique is of independent interest and that may be useful in applying the accelerated linear coupling method to other combinatorial problems.

关键词： Convex optimization

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The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

引用

NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-Bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

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Approximating the solution to mixed packing and covering LPs in parallel Õ(ϵ-3) time 43

Approximating the solution to mixed packing and covering LPs...

引用

43rd International Colloquium on Automata, Languages, and Programming, ICALP 2016

作者： Mahoney, Michael W. Rao, Satish Wang, Di Zhang, Peng International Computer Science Institute Department of Statistics UC Berkeley Berkeley United States Department of Electrical Engineering and Computer Sciences UC Berkeley Berkeley United States Department of Computer Science Georgia Tech Atlanta United States

ISBN: (纸本)9783959770132

We study the problem of approximately solving positive linear programs (LPs). This class of LPs models a wide range of fundamental problems in combinatorial optimization and operations research, such as many resource allocation problems, solving non-negative linear systems, computing tomography, single/multi commodity flows on graphs, etc. For the special cases of pure packing or pure covering LPs, recent result by Allen-Zhu and Orecchia [2] gives Õ(1/ϵ3)-time parallel algorithm, which breaks the longstanding Õ(1/ϵ4) running time bound by the seminal work of Luby and Nisan [10]. We present new parallel algorithm with running time Õ(1/ϵ3) for the more general mixed packing and covering LPs, which improves upon the Õ(1/ϵ4)-time algorithm of Young [18, 19]. Our work leverages the ideas from both the optimization oriented approach [2, 17], as well as the more combinatorial approach with phases [18, 19]. In addition, our algorithm, when directly applied to pure packing or pure covering LPs, gives a improved running time of Õ(1/ϵ2).

关键词： Parallel algorithms

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A graph degeneracy-based approach to keyword extraction

A graph degeneracy-based approach to keyword extraction

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2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016

作者： Tixier, Antoine J.-P. Malliaros, Fragkiskos D. Vazirgiannis, Michalis Computer Science Laboratory École Polytechnique Palaiseau France Department of Computer Science and Engineering UC San Diego San DiegoCA United States

ISBN: (纸本)9781945626258

We operate a change of paradigm and hypothesize that keywords are more likely to be found among influential nodes of a graph-of-words rather than among its nodes high on eigenvector-related centrality measures. To test this hypothesis, we introduce unsupervised techniques that capitalize on graph degeneracy. Our methods strongly and significantly outperform all baselines on two datasets (short and medium size documents), and reach best performance on the third one (long documents). © 2016 Association for Computational Linguistics

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On Identification of Distribution Grids

arXiv

引用

arXiv 2017年

作者： Ardakanian, Omid Wong, Vincent W.S. Dobbe, Roel Low, Steven H. Meier, Alexandra Von Tomlin, Claire Yuan, Ye Department of Computing Science University of Alberta Canada Department of Electrical and Computer Engineering University of British Columbia Canada Department of Computing and Mathematical Science California Institute of Technology United States Department of Electrical Engineering and Computer Sciences Uc Berkeley United States School of Automation Huazhong University of Science and Technology Canada

Large-scale integration of distributed energy resources into residential distribution feeders necessitates careful control of their operation through power flow analysis. While the knowledge of the distribution system model is crucial for this type of analysis, it is often unavailable or outdated. The recent introduction of synchrophasor technology in low-voltage distribution grids has created an unprecedented opportunity to learn this model from high-precision, time-synchronized measurements of voltage and current phasors at various locations. This paper focuses on joint estimation of model parameters (admittance values) and operational structure of a poly-phase distribution network from the available telemetry data via the lasso, a method for regression shrinkage and selection. We propose tractable convex programs capable of tackling the low rank structure of the distribution system and develop an online algorithm for early detection and localization of critical events that induce a change in the admittance matrix. The efficacy of these techniques is corroborated through power flow studies on four three-phase radial distribution systems serving real household demands. Copyright © 2017, The Authors. All rights reserved.

关键词： Phasor measurement units

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