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International Conference on computer Vision (ICCV)

作者： Michael J. Wilber Iljung S. Kwak David Kriegman Serge Belongie Cornell Tech Department of Computer Science Cornell University Department of Computer Science and Engineering UC San Diego Dropbox

ISBN: (纸本)9781467383929

This paper presents our work on "SNaCK," a low-dimensional concept embedding algorithm that combines human expertise with automatic machine similarity kernels. Both parts are complimentary: human insight can capture relationships that are not apparent from the object's visual similarity and the machine can help relieve the human from having to exhaustively specify many constraints. We show that our SNaCK embeddings are useful in several tasks: distinguishing prime and nonprime numbers on MNIST, discovering labeling mistakes in the Caltech ucSD Birds (CUB) dataset with the help of deep-learned features, creating training datasets for bird classifiers, capturing subjective human taste on a new dataset of 10,000 foods, and qualitatively exploring an unstructured set of pictographic characters. Comparisons with the state-of-the-art in these tasks show that SNaCK produces better concept embeddings that require less human supervision than the leading methods.

关键词： Kernel Visualization Labeling Birds Crowdsourcing Machine learning computer vision

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The Apache Point Observatory Galactic Evolution Experiment (APOGEE) spectrographs

arXiv

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arXiv 2019年

作者： Wilson, J.C. Hearty, F.R. Skrutskie, M.F. Majewski, S.R. Holtzman, J.A. Eisenstein, D. Gunn, J. Blank, B. Henderson, C. Smee, S. Nelson, M. Nidever, D. Arns, J. Barkhouser, R. Barr, J. Beland, S. Bershady, M.A. Blanton, M.R. Brunner, S. Burton, A. Carey, L. Carr, M. Colque, J.P. Crane, J. Damke, G.J. Davidson, J.W. Dean, J. Di Mille, F. Don, K.W. Ebelke, G. Evans, M. Fitzgerald, G. Gillespie, B. Hall, M. Harding, A. Harding, P. Hammond, R. Hancock, D. Harrison, C. Hope, S. Horne, T. Karakla, J. Lam, C. Leger, F. MacDonald, N. Maseman, P. Matsunari, J. Melton, S. Mitcheltree, T. O'Brien, T. O'Connell, R.W. Patten, A. Richardson, W. Rieke, G. Rieke, M. Roman-Lopes, A. Schiavon, R.P. Sobeck, J.S. Stolberg, T. Stoll, R. Tembe, M. Trujillo, J.D. Uomoto, A. Vernieri, M. Walker, E. Weinberg, D.H. Young, E. Anthony-Brumfield, B. Bizyaev, D. Breslauer, B. de Lee, N. Downey, J. Halverson, S. Huehnerhoff, J. Klaene, M. Leon, E. Long, D. Mahadevan, S. Malanushenko, E. Nguyen, D.C. Owen, R. Sánchez-Gallego, J.R. Sayres, C. Shane, N. Shectman, S.A. Shetrone, M. Skinner, D. Stauffer, F. Zhao, B. Astronomy Department University of Virginia CharlottesvilleVA22901 United States Department of Astronomy and Astrophysics Pennsylvania State University University ParkPA16802 United States Dept of Astronomy New Mexico State Univ P. O. Box 30001 MSC 4500 Las CrucesNM88003 United States Harvard-Smithsonian Center for Astrophysics 60 Garden Street MS 20 CambridgeMA02138 United States Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States PulseRay 4583 State Route 414 Beaver DamsNY14812 United States Department of Physics and Astronomy Johns Hopkins University BaltimoreMD21218 United States National Optical Astronomy Observatory 950 North Cherry Avenue TucsonAZ85719 United States Kaiser Optical Systems Inc. 371 Parkland Plaza Ann ArborMI48103 United States Laboratory for Atmospheric and Space Physics University of Colorado 3665 Discovery Dr. BoulderCO80303 United States Department of Astronomy University of Wisconsin-Madison 475 N. Charter St. MadisonWI53726 United States South African Astronomical Observatory P.O. Box 9 Observatory Cape Town7935 South Africa Center for Cosmology and Particle Physics Department of Physics New York University 726 Broadway Rm. 1005 New YorkNY10003 United States Department of Astronomy University of Washington Box 351580 SeattleWA98195 United States Universidad de Antofagasta Avenida Angamos 601 Antofagasta Chile Observatories of the Carnegie Institution for Science 813 Santa Barbara Street PasadenaCA91101 United States AURA Observatory in Chile Cisternas 1500 La Serena Chile Centro Multidisciplinario de Ciencia y Tecnología Universidad de La Serena Cisternas 1200 La Serena Chile Las Campanas Observatory Colina El Pino Casilla La Serena601 Chile Steward Observatory University of Arizona 933 N. Cherry Ave TucsonAZ85721 United States New England Optical Systems Inc. 237 Cedar Hill St. MarlboroughMA01752 United States Apache Point Observatory P.

We describe the design and performance of the near-infrared (1.51-1.70µm), fiber-fed, multi-object (300 fibers), high resolution (R = λ/∆λ ∼ 22,500) spectrograph built for the Apache Point Observatory Galactic Evolution Experiment (APOGEE). APOGEE is a survey of ∼ 105 red giant stars that systematically sampled all Milky Way populations (bulge, disk, and halo) to study the Galaxy's chemical and kinematical history. It was part of the Sloan Digital Sky Survey III (SDSS-III) from 2011 - 2014 using the 2.5 m Sloan Foundation Telescope at Apache Point Observatory, New Mexico. The APOGEE-2 survey is now using the spectrograph as part of SDSS-IV, as well as a second spectrograph, a close copy of the first, operating at the 2.5 m du Pont Telescope at Las Campanas Observatory in Chile. Although several fiber-fed, multi-object, high resolution spectrographs have been built for visual wavelength spectroscopy, the APOGEE spectrograph is one of the first such instruments built for observations in the near-infrared. The instrument's successful development was enabled by several key innovations, including a "gang connector" to allow simultaneous connections of 300 fibers;hermetically sealed feedthroughs to allow fibers to pass through the cryostat wall continuously;the first cryogenically deployed mosaic volume phase holographic grating;and a large refractive camera that includes mono-crystalline silicon and fused silica elements with diameters as large as ∼ 400 mm. This paper contains a comprehensive description of all aspects of the instrument including the fiber system, optics and opto-mechanics, detector arrays, mechanics and cryogenics, instrument control, calibration system, optical performance and stability, lessons learned, and design changes for the second instrument. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectrographs

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Improving latent factor models via personalized feature projection for one class recommendation 15

Improving latent factor models via personalized feature proj...

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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Zhao, Tong McAuley, Julian King, Irwin Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong Department of Computer Science and Engineering UC San Diego San DiegoCA United States

ISBN: (纸本)9781450337946

Latent Factor models, which transform both users and items into the same latent feature space, are one of the most successful and ubiquitous models in recommender systems. Most existing models in this paradigm define both users' and items' latent factors to be of the same size and use an inner product to represent a user's 'compatibility' with an item. Intuitively, users' factors encode 'preferences' while item factors encode 'properties', so that the inner product encodes how well an item matches a user's preferences. However, a user's opinion of an item may be more complex, for example each dimension of each user's opinion may depend on a combination of multiple item factors simultaneously. Thus it may be better to view each dimension of a user's preference as a personalized projection of an item's properties so that the preference model can capture complex relationships between items' properties and users' preferences. Therefore, in this paper we propose a novel personalized feature projection method to model users' preferences over items. Specifically, for each user, we define a personalized projection matrix, which takes the place of user-specific factors from existing models. This matrix describes a mapping between items' factors and users' preferences in order to build personalized preference models for each user and item. The proposed personalized feature projection method is quite general and existing latent factor models, for example, can be cast as a special case. We present three objective functions to optimize predictions in the form of ranked lists of users' preferences over items, and demonstrate how each can be used to improve one-class recommendation performance. Experiments are conducted on four real-world datasets and our results show that our personalized feature projection method outperforms several state-of-the-art methods on various evaluation metrics. © 2015 ACM.

关键词： Collaborative filtering

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Ultrasound-based automated carotid lumen diameter/stenosis measurement and its validation system

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Journal for Vascular Ultrasound 2016年第3期40卷 120-134页

作者： Asheed M. Kumar P. Krishna Kumar Ajay Gupta Luca Saba Jeny Rajan Francesco Lavra Aditya M. Sharma Shoaib Shafique Andrew Nicolaides John R. Laird Jasjit S. Suri Division of Cardiovascular Medicine Toho University Ohashi Medical Center Tokyo Japan Department of Computer Science and Engineering National Institute of Technology Karnataka India Point-of-Care Devices Global Biomedical Technologies Inc Roseville CA United States Radiology Department Brain and Mind Research Institute Weill Cornell Medical College New York NY United States Department of Radiology University of Cagliari Cagliari Italy Division of Cardiovascular Medicine Department of Medicine University of Virginia Charlottesville VA United States CorVasc Vascular Laboratory Indianapolis IN United States Vascular Diagnostic Center University of Cyprus Nicosia Cyprus UC Davis Vascular Center University of California Davis CA United States Monitoring and Diagnostic Division AtheroPoint™ Roseville CA United States Department of Electrical Engineering University of Idaho Moscow ID United States

Objective.—Degree of carotid stenosis is an important predictor to assess risk of stroke. Systolic velocity-based methods for lumen diameter and stenosis measurement are subjective. Image-based methods face a challenge because of low gradients in media and intima walls. Methods.—This article presents AtheroEdge™ 2.0, a two-stage process for automated carotid lumen diameter measurement that combats the above challenges. Stage one uses spectral analysis based on the hypothesis that far-wall adventitia is brightest. Stage two uses lumen pixel region identification based on the assumption that blood flow has constant density. Using global and local processing, lumen boundaries are detected. This clinical system outputs lumen diameter along with stenosis severity index (SSI). Results.—Our database consists of institutional review board–approved 202 patients (males/ females: 155/47) left and right common carotid artery images (404 images, Toshiba scanner). Two trained neuro radiologists performed manual lumen border tracings using ImgTracer™ software. The coefficient of correlation between automated and two manual readings was 0.91 and 0.92. Dice similarity and Jaccard index were 95.82%, 95.72% and 92.10%, 91.92%, respectively. The mean diameter error between automated and two manual readings was 0.27 ± 0.26 and 0.26 ± 0.28 mm, respectively. Precision of merit was 98.05% and 99.03% with respect to two readings. SSI showed 97% accuracy. Conclusions.—The image-based automated carotid lumen diameter and stenosis measurement system is fast, accurate, and reliable. © 2018, SAGE Publications Ltd. All rights reserved.

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A histogram density modeling approach to music emotion recognition

A histogram density modeling approach to music emotion recog...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ju-Chiang Wang Hsin-Min Wang Gert Lanckriet Department of Electrical and Computer Engineering UC San Diego Institute of Information Science Academia Sinica

Music emotion recognition is concerned with developing predictive models that comprehend the affective content of musical signals. Recently, a growing number of attempts has been made to model the music emotion as a probability distribution in the valence-arousal (VA) space to better account for the subjectivity. In this paper, we present a novel histogram density modeling approach that models the emotion distribution by a 2-D histogram over the quantized VA space and learns a set of latent histograms to predict the emotion probability density of a song from audio. The proposed model is free from parametric distribution assumptions over the VA space, easy to implement, and extremely fast to train. We also extend our model to deal with the temporal dynamics of time-varying emotion labels. Comprehensive performance study on two larger-scale datasets demonstrates that our approach achieves comparable performance to the state-of-the-art ones, but with much better training and testing efficiency.

关键词： Histograms Unified modeling language

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Cover Image, Volume 84, Issue 10

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Proteins: Structure, Function, and Bioinformatics 2016年第10期84卷

作者： Carter T. Butts Jan C. Bierma Rachel W. Martin Department of Electrical Engineering and Computer Science UC Irvine Irvine California 92697 Department of Sociology UC Irvine Irvine California 92697 Department of Statistics UC Irvine Irvine California 92697 Department of Molecular Biology and Biochemistry UC Irvine Irvine California 92697 Department of Chemistry UC Irvine Irvine California 92697

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Modeling and control battery aging in energy harvesting systems

Modeling and control battery aging in energy harvesting syst...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Roberto Valentini Nga Dang Marco Levorato Eli Bozorgzadeh The Donald Bren School of Information and Computer Science UC Irvine CA US Dept. of Information Engineering Computer Science and Mathematics University of L'Aquila IT

Energy storage is a fundamental component for the development of sustainable and environment-aware technologies. One of the critical challenges that needs to be overcome is preserving the State of Health (SoH) in energy harvesting systems, where bursty arrival of energy and load may severely degrade the battery. Tools from Markov process and Dynamic Programming theory are becoming an increasingly popular choice to control dynamics of these systems due to their ability to seamlessly incorporate heterogeneous components and support a wide range of applications. Mapping aging rate measures to fit within the boundaries of these tools is non-trivial. In this paper, a framework for modeling and controlling the aging rate of batteries based on Markov process theory is presented. Numerical results illustrate the tradeoff between battery degradation and task completion delay enabled by the proposed framework.

关键词： Batteries Aging Degradation Smart grids Measurement Energy harvesting

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Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop

Developing a framework for digital objects in the Big Data t...

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作者： Jagodnik, Kathleen M. Koplev, Simon Jenkins, Sherry L. Ohno-Machado, Lucila Paten, Benedict Schurer, Stephan C. Dumontier, Michel Verborgh, Ruben Bui, Alex Ping, Peipei McKenna, Neil J. Madduri, Ravi Pillai, Ajay Ma'ayan, Avi Department of Pharmacological Sciences BD2K-LINCS Data Coordination and Integration Center Mount Sinai Center for Bioinformatics Icahn School of Medicine at Mount Sinai One Gustave L. Levy Place Box 1215 New YorkNY10029 United States Health System Department of Biomedical Informatics University of California San Diego 9500 Gilman Dr. La Jolla CA92083 United States Health Services Research San Diego Veterans Administration Health System San DiegoCA92083 United States UC Santa Cruz Genomics Institute University of California Santa Cruz 1156 High St. Santa CruzCA95060 United States MiamiFL33136 United States Institute for Data Science Universiteit Maastricht Minderbroedersberg 4-6 LK Maastricht6211 Netherlands Ghent University – iMinds Research Foundation Flanders St. Pietersnieuwstraat 33 Gent9000 Belgium Department of Radiological Sciences UCLA School of Medicine Los AngelesCA90095 United States Department of Bioengineering UCLA Henri Samueli School of Engineering Los AngelesCA90095 United States Departments of Physiology Medicine and Bioinformatics UCLA School of Medicine Los AngelesCA90095 United States Department of Molecular and Cellular Biology Baylor College of Medicine 1 Baylor Plaza HoustonTX77030 United States Department of Mathematics and Computer Science Argonne National Laboratory 9700 S. Cass Avenue ArgonneIL60439 United States Division of Genome Sciences National Human Genome Research Institute National Institutes of Health 31 Center Drive MSC 2152 9000 Rockville Pike Bethesda MD20892 United States

The volume and diversity of data in biomedical research have been rapidly increasing in recent years. While such data hold significant promise for accelerating discovery, their use entails many challenges including: the need for adequate computational infrastructure, secure processes for data sharing and access, tools that allow researchers to find and integrate diverse datasets, and standardized methods of analysis. These are just some elements of a complex ecosystem that needs to be built to support the rapid accumulation of these data. The NIH Big Data to Knowledge (BD2K) initiative aims to facilitate digitally enabled biomedical research. Within the BD2K framework, the Commons initiative is intended to establish a virtual environment that will facilitate the use, interoperability, and discoverability of shared digital objects used for research. The BD2K Commons Framework Pilots Working Group (CFPWG) was established to clarify goals and work on pilot projects that address existing gaps toward realizing the vision of the BD2K Commons. This report reviews highlights from a two-day meeting involving the BD2K CFPWG to provide insights on trends and considerations in advancing Big Data science for biomedical research in the United States. © 2017 Elsevier Inc.

关键词： Reusability

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Beating the random assignment on constraint satisfaction problems of bounded degree

Beating the random assignment on constraint satisfaction pro...

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18th International Workshop on ApproximationAlgorithms for Combinatorial Optimization Problems, APPROX 2015, and 19th International Workshop on Randomization and Computation, RANDOM 2015

作者： Barak, Boaz Moitra, Ankur O'Donnell, Ryan Raghavendra, Prasad Regev, Oded Steurer, David Trevisan, Luca Vijayaraghavan, Aravindan Witmer, David Wright, John Microsoft Research New England United States MIT Mathematics Department United States Department of Computer Science Carnegie Mellon University United States UC Berkeley Department of Electrical Engineering and Computer Sciences United States Courant Institute of Mathematical Sciences New York University United States Cornell University United States

ISBN: (纸本)9783939897897

We show that for any odd k and any instance = of the Max-kXOR constraint satisfaction problem, there is an efficient algorithm that finds an assignment satisfying at least a 1/2 +Ω (1/√ D) fraction of ß's constraints, where D is a bound on the number of constraints that each variable occurs in. This improves both qualitatively and quantitatively on the recent work of Farhi, Goldstone, and Gutmann (2014), which gave a quantum algorithm to find an assignment satisfying a 1/2 +Ω (D-3/4) fraction of the equations. For arbitrary constraint satisfaction problems, we give a similar result for "triangle-free" instances;i.e., an efficient algorithm that finds an assignment satisfying at least a μ + Ω (1/√ D) fraction of constraints, where μ is the fraction that would be satisfied by a uniformly random assignment. © Boaz Barak, Ankur Moitra, Ryan O'Donnell, Prasad Raghavendra, Oded Regev, David Steurer, Luca Trevisan, Aravindan Vijayaraghavan, David Witmer, and John Wright.

关键词： Constraint satisfaction problems

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Cloud-assisted distributed private data sharing 15

Cloud-assisted distributed private data sharing

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6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015

作者： Chen, Feng Mohammed, Noman Wang, Shuang He, Wenbo Cheng, Samuel Jiang, Xiaoqian School of Electrical and Computer Engineering University of Oklahoma TulsaOK74135 United States Department of Computer Science University of Manitoba WinnipegMBR3T 2N2 Canada Division of Biomedical Informatics UC San Diego San DiegoCA92093 United States School of Computer Science McGill University MontrealQCH3A 0G4 Canada

ISBN: (纸本)9781450338530

Data privacy is an important issue to address when multiple data owners are required to integrate and share sensitive information for data analysis. In this article, we study the privacy threats caused by distributed data sharing and present the first cloud-based data sharing framework to integrate horizontally partitioned data from multiple data owners. The cloud performs the anonymization in a top-down fashion. It proceeds from the most generalized values of attributes (serve as the root of the tree) and specializes them (i.e., generate less generalized values as siblings of the parent node) in every iteration. A candidate value is selected for specialization in each iteration based on its score. The score of each candidate is calculated securely using multiple cryptographic protocols to ensure security. Finally, the cloud adds noise to the integrated data and releases them in a differentially private manner. Experimental results on real-life data set demonstrate that the proposed algorithm retains data utility for supporting classification analysis and provide similar classification accuracy compared to that of the centralized data dissemination mechanism. Copyright 2015 ACM.

关键词： Iterative methods

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