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arXiv 2017年

作者： Banerjee, Joydeep Pal, Anamitra Basu, Kaustav Padhee, Malhar Sen, Arunabha School of Computing Informatics and Decision System Engineering Arizona State University TempeAZ United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ United States

Smart grid systems are composed of power and communication network components. The components in either network exhibit complex dependencies on components in its own as well as the other network to drive their functionality. Existing, models fail to capture these complex dependencies. In this paper, we restrict to the dependencies in the power network and propose the Multi-scale Implicative Interdependency Relation (MIIR) model that address the existing limitations. A formal description of the model along with its working dynamics and a brief validation with respect to the 2011 Southwest blackout are provided. Utilizing the MIIR model, the K Contingency List problem is proposed. For a given time instant, the problem solves for a set of K entities in a power network which when failed at that time instant would cause the maximum number of entities to fail eventually. Owing to the problem being NP-complete we devised a Mixed Integer Program (MIP) to obtain the optimal solution and a polynomial time sub-optimal heuristic. The efficacy of the heuristic with respect to the MIP is compared by using different bus system data. In general, the heuristic is shown to provide near optimal solution at a much faster time than the MIP. Copyright © 2017, The Authors. All rights reserved.

关键词： Electric power transmission networks

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VC-bots: A vehicular cloud computing testbed with mobile robots 1

VC-bots: A vehicular cloud computing testbed with mobile rob...

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1st MobiHoc International Workshop on Internet of Vehicles and Vehicles of Internet, IoV-VoI 2016

作者： Lu, Duo Li, Zhichao Huang, Dijiang Lu, Xianglong Deng, Yuli Chowdhary, Ankur Li, Bing School of Computing Informatics and Decision Systems Arizona State University United States School of Electrical Computer and Energy Engineering Arizona State University United States

ISBN: (纸本)9781450343459

Smart vehicles with computing, sensing, and communication capabilities are gaining popularity. With various vehicular applications equipped, these smart vehicles not only improve driving safety, but also facilitate data collection and information sharing for traffic optimization, insurance estimation, and infotainment. However, developing and testing such cloud based vehicular application is challenging due to the high cost of running the application on actual cars in various traffic scenarios. For the same reason it is also difficult to understand and model the network protocol behavior among multiple vehicles. In this paper we proposed VC-bots, a vehicular cloud testbed using mobile robot vehicles, which can emulate different types of vehicles for testing vehicular network protocols and vehicular cloud applications in various scenarios, which can be easily reconfigured without any infrastructure assistance. To facilitate software integration, we also developed a message based service framework for applications running on the robot vehicle and in the cloud. © 2016 ACM.

关键词： Mobile robots

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Seeking programming-related information from large scaled discussion forums, help or harm? 9

Seeking programming-related information from large scaled di...

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9th International Conference on Educational Data Mining, EDM 2016

作者： Lu, Yihan Hsiao, I-Han School of Computing Informatics and Decision Systems Engineering Arizona State University 699 S. Mill Ave. TempeAZ United States

Online programming discussion forums have grown increasingly and have formed sizable repositories of problem solving-solutions. In this paper, we investigate programming learners’ information seeking behaviors from online discussion forums. We design engines to collect students’ information seeking processes, including query formulation, refinement, results examination, and reading processes. We model these behaviors and conduct sequence pattern mining. The results show that programming learners indeed seek for programming related information from discussion forums by actively searching on the site and reading posts progressively according to course schedule topics. Advanced students consistently perform query refinements, examine search results and commit to read, however, novices do not. In addition, advanced students commit to read posts, but novices only skim. © 2016 International Educational Data Mining Society. All rights reserved.

关键词： Hidden Markov models

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TripletGAN: Training generative model with triplet loss

arXiv

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arXiv 2017年

作者： Cao, Gongze Yang, Yezhou Lei, Jie Jin, Cheng Liu, Yang Song, Mingli Zhejiang Provincial Key Laboratory of Service Robot Zhejiang University School of Computing Informatics and Decision Systems Engineering Arizona State University School of Computer Science Fudan University Shanghai China School of Mathematics Zhejiang University Shanghai Alibaba Group Hang Zhou

As an effective way of metric learning, triplet loss has been widely used in many deep learning tasks, including face recognition and person-ReID, leading to many states of the arts. The main innovation of triplet loss is using feature map to replace softmax in the classification task. Inspired by this concept, we propose here a new adversarial modeling method by substituting the classification loss of discriminator to triplet loss. Theoretical proof based on IPM (Integral probability metric) demonstrates that such setting will help generator converge to the given distribution theoretically under some conditions. Moreover, since triplet loss requires the generator to maximize distance within a class, we justify tripletGAN is also helpful to prevent mode collapse through both theory and experiment. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep learning

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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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Design of experiments: A key to successful innovation 12th

Design of experiments: A key to successful innovation

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12th International Workshop on Intelligent Statistical Quality Control, IWISQC 2016

作者： Montgomery, Douglas C. Silvestrini, Rachel T. School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Department of Industrial and Systems Engineering Rochester Institute of Technology RochesterNY United States

Does the use of statistical methodology such as design of experiments stifle innovation? This is an important theme in this paper. Design of experiments is viewed as part of a process for enabling both breakthrough innovation and incremental innovation, without which western society will fail to be competitive. Quality engineering technology in general is part of a broader approach to innovation and business improvement called statistical engineering. The most powerful statistical technique in statistical engineering is design of experiments. Several important developments in this field are reviewed, the role of designed experiments in innovation examined, and new developments and applications of the methods discussed.

关键词： Design of experiments

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Traffic light status detection using movement patterns of vehicles 19

Traffic light status detection using movement patterns of ve...

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19th IEEE International Conference on Intelligent Transportation systems, ITSC 2016

作者： Campbell, Joseph Amor, Heni Ben Ang, Marcelo H. Fainekos, Georgios School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States National University of Singapore Kent Ridge Singapore

ISBN: (纸本)9781509018895

Vision-based methods for detecting the status of traffic lights used in autonomous vehicles may be unreliable due to occluded views, poor lighting conditions, or a dependence on unavailable high-precision meta-data, which is troublesome in such a safety-critical application. This paper proposes a complementary detection approach based on an entirely new source of information: The movement patterns of other nearby vehicles. This approach is robust to traditional sources of error, and may serve as a viable supplemental detection method. Several different classification models are presented for inferring traffic light status based on these patterns. Their performance is evaluated over real and simulated data sets, resulting in up to 97% accuracy in each set. © 2016 IEEE.

关键词： Vehicles

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Multi-task dictionary learning based convolutional neural network for computer aided diagnosis with longitudinal images

arXiv

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arXiv 2017年

作者： Zhang, Jie Li, Qingyang Caselli, Richard J. Ye, Jieping Wang, Yalin School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Department of Neurology Mayo Clinic ScottsdaleAZ United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States

Algorithmic image-based diagnosis and prognosis of neurodegenerative diseases on longitudinal data has drawn great interest from computer vision researchers. The current state-of-the-art models for many image classification tasks are based on the Convolutional Neural Networks (CNN). However, a key challenge in applying CNN to biological problems is that the available labeled training samples are very limited. Another issue for CNN to be applied in computer aided diagnosis applications is that to achieve better diagnosis and prognosis accuracy, one usually has to deal with the longitudinal dataset, i.e., the dataset of images scanned at different time points. Here we argue that an enhanced CNN model with transfer learning for the joint analysis of tasks from multiple time points or regions of interests may have a potential to improve the accuracy of computer aided diagnosis. To reach this goal, we innovate a CNN based deep learning multi-task dictionary learning framework to address the above challenges. Firstly, we pretrain CNN on the ImageNet dataset and transfer the knowledge from the pre-trained model to the medical imaging progression representation, generating the features for different tasks. Then, we propose a novel unsupervised learning method, termed Multi-task Stochastic Coordinate Coding (MSCC), for learning different tasks by using shared and individual dictionaries and generating the sparse features required to predict the future cognitive clinical scores. We apply our new model in a publicly available neuroimaging cohort to predict clinical measures with two different feature sets and compare them with seven other state-of-the-art methods. The experimental results show our proposed method achieved superior results. Copyright © 2017, The Authors. All rights reserved.

关键词： Computer aided diagnosis

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From the lab to the desert: Fast prototyping and learning of robot locomotion

arXiv

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arXiv 2017年

作者： Luck, Kevin Sebastian Campbell, Joseph Jansen, Michael Andrew Aukes, Daniel M. Amor, Heni Ben School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States School of Life Sciences Arizona State University TempeAZ85281 United States Polytechnic School Arizona State University TempeAZ85281 United States

We present a methodology for fast prototyping of morphologies and controllers for robot locomotion. Going beyond simulation-based approaches, we argue that the form and function of a robot, as well as their interplay with real-world environmental conditions are critical. Hence, fast design and learning cycles are necessary to adapt robot shape and behavior to their environment. To this end, we present a combination of laminate robot manufacturing and sample-efficient reinforcement learning. We leverage this methodology to conduct an extensive robot learning experiment. Inspired by locomotion in sea turtles, we design a low-cost crawling robot with variable, interchangeable fins. Learning is performed using both bio-inspired and original fin designs in an artificial indoor environment as well as a natural environment in the Arizona desert. The findings of this study show that static policies developed in the laboratory do not translate to effective locomotion strategies in natural environments. In contrast to that, sample-efficient reinforcement learning can help to rapidly accommodate changes in the environment or the robot. Copyright © 2017, The Authors. All rights reserved.

关键词： Fins (heat exchange)

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Toward a more scientific science

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Science 2018年第6408期361卷 1194-1197页

作者： Wible, Brad Azoulay, Pierre Graff-Zivin, Joshua Uzzi, Brian Wang, Dashun Williams, Heidi Evans, James A. Zhe Jin, Ginger Feng Lu, Susan Jones, Benjamin F. Börner, Katy Lakhani, Karim R. Boudreau, Kevin J. Guinan, Eva C. Massachusetts Institute of Technology (MIT) Cambridge MA United States National Bureau of Economic Research (NBER) Cambridge MA United States University of California San Diego San Diego CA United States Kellogg School of Management Northwestern University Evanston IL United States McCormick School of Engineering Northwestern University Evanston IL United States Northwestern Institute on Complex Systems (NICO) Northwestern University Evanston IL United States University of Chicago Chicago IL United States University of Maryland College Park MD United States Purdue University West Lafayette IN United States School of Informatics Computing and Engineering Indiana University Bloomington IN United States Alexander von Humboldt Fellow Technische Universität Dresden Dresden 01062 Germany Harvard Business School Boston MA United States Laboratory for Innovation Science Harvard University Cambridge MA United States Northeastern University Boston MA United States Dana Farber Cancer Institute Boston MA United States Harvard Medical School Boston MA United States

Climb atop shoulders and wait for funerals. That, suggested Newton and then Planck, is how science advances (more or less). We’ve come far since then, but many notions about how people and practices, policies, and resources influence the course of science are still more rooted in traditions and intuitions than in evidence. We can and must do better, lest we resign ourselves to “intuition-based policy” when making decisions and investments aimed at driving scientific progress. Science invited experts to highlight key aspects of the scientific enterprise that are steadily yielding to empirical investigation—and to explain how Newton and Planck got it right (and Einstein got it wrong). 2017 © The Authors, some rights reserved.

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