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Oceans (Conference)

作者： Taylor Bruce Colt Morris Lauren Adoram-Kershner Rachel Holser Daniel Costa Paul G. Flikkema Michael W. Shafer School of Informatics Computing and Cyber Systems Northern Arizona University Department of Mechanical Engineering Northern Arizona University Costa Lab University of California Santa Cruz

ISBN: (纸本)9781509064298;9780692946909

Solar power plays a significant and sometimes primary role in the energy budgets of many terrestrial sensor systems due to its reliability, power density, and simplicity. More importantly, photoelectric generation can be accurately predicted for a given terrestrial location. This knowledge of expected harvested power is critical for both provisioning in design and scheduling of activities in operations. The importance of reliable prediction algorithms is highlighted by the dearth of solar energy harvesting designs for submarine sensor systems. Platforms such as autonomous underwater vehicles and marine wildlife telemetry tags almost exclusively rely on battery power for their entire deployments. Reliance on fixed charge batteries significantly impacts the frequency of measurements, types of measurements, and the transmission of those data to operators. For these reasons, our group has worked to develop a method of predicting photovoltaic energy production for solar cells in marine environments. To validate and improve the model, we have developed a device, referred to as a datalogger, for the characterization of solar energy harvesting, with an initial focus on harvesting when attached to marine wildlife. This device measures and logs for analysis the current-voltage relationship of a silicon solar cell as its host animal moves through the water column. This paper discusses the development of this datalogger, presenting design requirements, design decisions, test results, and preliminary data resulting from a deployment on a Northern elephant seal (Mirounga angustirostris) in the spring of 2017. Results are discussed and future design changes for improving the system are presented.

关键词： Marine Telemetry Solar Power Modeling Submerged Irradiance Datalogger

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A computational approach to relative aesthetics

A computational approach to relative aesthetics

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International Conference on Pattern Recognition

作者： Vijetha Gattupalli Parag S. Chandakkar Baoxin Li School of Computing Informatics and Decision Systems Engineering Arizona State University

ISBN: (纸本)9781509048489

Computational visual aesthetics has recently become an active research area. Existing state-of-art methods formulate this as a binary classification task where a given image is predicted to be beautiful or not. In many applications such as image retrieval and enhancement, it is more important to rank images based on their aesthetic quality instead of binary-categorizing them. Furthermore, in such applications, it may be possible that all images belong to the same category. Hence determining the aesthetic ranking of the images is more appropriate. To this end, we formulate a novel problem of ranking images with respect to their aesthetic quality. We construct a new dataset of image pairs with relative labels by carefully selecting images from the popular AVA dataset. Unlike in aesthetics classification, there is no single threshold which would determine the ranking order of the images across our entire dataset. We propose a deep neural network based approach that is trained on image pairs by incorporating principles from relative learning. Results show that such relative training procedure allows our network to rank the images with a higher accuracy than a state-of-art network trained on the same set of images using binary labels.

关键词： Image color analysis Neural networks Data models Training Semantics Convolutional codes Visualization

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Differences in the learning principles dominating student-student vs. Student-instructor interactions while working on programming tasks 16

Differences in the learning principles dominating student-st...

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47th ACM Technical Symposium on computing Science Education, SIGCSE 2016

作者： Gaspar, Alessio Torsella, Joni Honken, Nora Sohoni, Sohum Arnold, Colin Department of Computer Science and Engineering University of South Florida United States Department of Engineering Education University of Cincinnati United States School of Computing Informatics and Decision Systems Engineering Arizona State University United States

ISBN: (纸本)9781450338561

Peer learning principles have been successfully applied to novice programmers. Pedagogies such as Pair Programming, Peer Testing, Peer review of code or tests, or, more generally Peer Instruction, have repeatedly demonstrated their effectiveness in improving both individual performance and retention rates. This paper proposes to supplement the existing literature by investigating how students interact with one another during collaborative programming tasks. More specifically, we are interested in comparing the learning principles used during student-student interactions with those used during studentinstructor or student-teaching assistant dialogs. Students in online and face to face courses, who worked collaboratively on programming assignments, were surveyed to gain an understanding of the frequency with which they engaged in specific activities. These that are representative of the learning principles that have been supported by research to promote learning. Results suggest that some learning principles, may be absent from student-student interactions. We discuss how the success of collaborative programming pedagogies put into question the role of these principles and whether they may contribute to further improve peer-based approaches.

关键词： Students

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Learning generalized reactive policies using deep neural networks

arXiv

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arXiv 2017年

作者： Groshev, Edward Goldstein, Maxwell Tamar, Aviv Srivastava, Siddharth Abbeel, Pieter Department of Computer Science University of California Berkeley BerkeleyCA94720 United States Department of Mathematics Princeton University PrincetonNJ08544 United States School of Computing Informatics Decision Systems Engineering Arizona State University TempeAZ85281 United States

We present a new approach to learning for planning, where knowledge acquired while solving a given set of planning problems is used to plan faster in related, but new problem instances. We show that a deep neural network can be used to learn and represent a generalized reactive policy (GRP) that maps a problem instance and a state to an action, and that the learned GRPs efficiently solve large classes of challenging problem instances. In contrast to prior efforts in this direction, our approach significantly reduces the dependence of learning on handcrafted domain knowledge or feature selection. Instead, the GRP is trained from scratch using a set of successful execution traces. We show that our approach can also be used to automatically learn a heuristic function that can be used in directed search algorithms. We evaluate our approach using an extensive suite of experiments on two challenging planning problem domains and show that our approach facilitates learning complex decision making policies and powerful heuristic functions with minimal human input. Videos of our results are available at ***/Hpy4e3. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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Visualizing Malaria Spread Under Climate Variability 4

Visualizing Malaria Spread Under Climate Variability

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4th Workshop on Visualisation in Environmental Sciences, EnvirVis 2016 at EuroVis 2016

作者： Liang, X. Aggarwal, R. Cherif, A. Gumel, A. Mascaro, G. Maciejewski, R. School of Computing Informatics and Decision Systems Engineering Arizona State University United States School of Mathematical and Statistical Sciences Arizona State University United States School of Sustainable Engineering and the Built Environment Arizona State University United States

ISBN: (纸本)9783038680185

In order to better control and prevent the infectious diseases, measures of vulnerability and risk to increased infectious disease outbreaks have been explored. Research investigating possible links between variations in climate and transmission of infectious diseases has led to a variety of predictive models for estimating the future impact of infectious disease under projected climate change. Underlying all of these approaches is the connection of multiple data sources and the need for computational models that can capture the spatio-temporal dynamics of emerging infectious diseases and climate variability, especially as the impact of climate variability on the land surface is becoming increasingly critical in predicting the geo-temporal evolution of infectious disease outbreaks. This paper presents an initial visualization prototype that combines data from population and climate simulations as inputs to a patch-based mosquito spread model for analyzing potential disease spread vectors and their relationship to climate variability. © 2016 The Author(s) Eurographics Proceedings © 2016 The Eurographics Association.

关键词： Climate change

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Performance comparison between broadcast authentication methods for vehicular networks 16

Performance comparison between broadcast authentication meth...

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4th International Conference on Information and Network Security, ICINS 2016

作者： Grover, Kanika Lim, Alvin Arizona State University School of Computing Informatics and Decision Systems Engineering TempeAZ85287 United States Auburn University Department of Computer Science and Software Engineering AuburnAL36849 United States

ISBN: (纸本)9781450347969

For authenticating time critical broadcast messages, IEEE 1609.2 security standard for Vehicular Ad hoc Networks (VANETs) suggests the use of secure Elliptic Curve Digital Signature Algorithm (ECDSA). Since ECDSA has an expensive verification in terms of time, most commonly suggested alternate algorithms are TESLA and signature amortization. Unfortunately, these algorithms lack immediate authentication and non-repudiation. Therefore, we introduce a probabilistic verification scheme for an ECDSA-based authentication protocol. Using ns2 simulation tools, we compare the performance of all above-mentioned broadcast authentication algorithms. The results show with our proposed scheme, there is an increase in packet processed ratio over that of all the other algorithms. © 2016 ACM.

关键词： Authentication

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Robot as a Service in Information Science & Electronic engineering Education

Robot as a Service in Information Science & Electronic Engin...

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International Symposium on Autonomous Decentralized systems (ISADAS)

作者： Yuehai Wang Yinong Chen Xiaofan Tong Yubo Lee Jianyi Yang School of Information Science & Electronic- Engineering Zhejiang University Hangzhou China Zhejiang Electric Power Design Institute China Informatics and Decision Systems Engineering Arizona State University Tempe AZ U.S.A

ISBN: (纸本)9781509040438

This paper reports the newly designed programming course developed and taught at Zhejiang University for a freshman class majoring in information science and electronic engineering, and disseminated to many other universities, to address the enrollment crisis as reported by ACM Curriculum Committee Review Task Force. The course teaches the basic information science and electronic engineering concepts and gives the students practical programming experience through robotics programming. The initial curriculum and the ongoing improvement of the course are presented. The course started with its experiment environment using ASU Visual IoT/Robotics Programming Environment (VIPLE). The environment supports a variety of platforms, including open architecture-based robots, such as Intel robots, PCDuino robots, vendor-specific robots, such as Lego EV3, as well as simulated robots, such as Web simulated robots in HTML5 and Unity-based 3D robots.

关键词： Robot sensing systems Robot programming Programming profession Programming environments Software

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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

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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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Neural style transfer: A review

arXiv

引用

arXiv 2017年

作者： Jing, Yongcheng Yang, Yezhou Feng, Zunlei Ye, Jingwen Yu, Yizhou Song, Mingli Microsoft Visual Perception Laboratory College of Computer Science and Technology Zhejiang University Hangzhou310027 School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States Department of Computer Science University of Hong Kong Pokfulam Road Hong Kong

The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. In this paper, we aim to provide a comprehensive overview of the current progress towards NST. We first propose a taxonomy of current algorithms in the field of NST. Then, we present several evaluation methods and compare different NST algorithms both qualitatively and quantitatively. The review concludes with a discussion of various applications of NST and open problems for future research. A list of papers discussed in this review, corresponding codes, pre-trained models and more comparison results are publicly available at: https://***/ycjing/Neural-Style-Transfer-Papers. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

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