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作者： Robledo-Rella, Víctor Neri, Luis Noguez, Julieta González-Nucamendi, Andrés Physics and Math Department School of Engineering and Science Tecnológico de Monterrey Campus Ciudad de México Mexico Information Technologies and Computing Department School of Engineering and Science Tecnológico de Monterrey Campus Ciudad de México Mexico

The use of mobile devices for learning purposes has increased in recent years, but there has been little effort to measure its impact on student outcomes. The goals of this work are: to quantify the impact of using mobile learning resources on academic performance and to know the student perception about these resources. Our hypothesis is that mobile learning resources have a positive effect on the learning *** the effectiveness of two mobile learning resources for engineering students regarding free body diagram and conservation of linear momentum. We gave pre and post-tests to experimental and control groups during a 6-year time span and analyzed the differences in the learning gains for both groups. We also gave perception questionnaires to our students about the use of mobile learning resources. With a sample of N = 645 students, we found that the experimental group obtained learning gains 7-10 points higher (on a 0-100 scale) than the control group. We found robust evidence regarding the effectiveness of our mobile learning resources through linear regressions (p = 0.001-0.053) and t-Student tests (p = 0.002-0.045). However, the observed effect size was only ES = 0.28. The student perception questionnaires indicate that students found the implementation of mobile learning resources to be very useful. Our results are indicative of a positive impact of the use of mobile learning resources to enhance student concept comprehension and problem-solving skills in undergraduate Physics courses. © 2017 TEMPUS Publications.

关键词： Students

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Schur multipliers and mixed unitary maps

arXiv

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arXiv 2018年

作者： Harris, Samuel J. Levene, Rupert H. Paulsen, Vern I. Plosker, Sarah Rahaman, Mizanur Institute for Quantum Computing Dept. of Pure Math. University of Waterloo WaterlooONN2L 3G1 Canada School of Mathematics and Statistics University College Dublin Belfield Dublin 4 Ireland Department of Mathematics and Computer Science Brandon University BrandonMBR7A 6A9 Canada

We consider the tensor product of the completely depolar- ising channel on d×d matrices with the map of Schur multiplication by a k ×k correlation matrix and characterise, via matrix theory methods, when such a map is a mixed (random) unitary channel. When d = 1, this recovers a result of O'Meara and Pereira, and for larger d is equiva- lent to a result of Haagerup and Musat that was originally obtained via the theory of factorisation through von Neumann algebras. We obtain a bound on the distance between a given correlation matrix for which this tensor product is nearly mixed unitary and a correlation matrix for which such a map is exactly mixed unitary. This bound allows us to give an elementary proof of another result of Haagerup and Musat about the closure of such correlation matrices without appealing to the theory of von Neumann algebras. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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Modularity based community detection with deep learning 25

Modularity based community detection with deep learning

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Liang, Yang Cao, Xiaochun He, Dongxiao Chuan, Wang Xiao, Wang Weixiong, Zhang State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences China School of Information Engineering Tianjin University of Commerce China School of Computer Science and Technology Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University China Department of Computer Science and Technology Tsinghua University China College of Math and Computer Science Institute for Systems Biology Jianghan University China Department of Computer Science and Engineering Washington University St. Louis United States

Identification of module or community structures is important for characterizing and understanding complex systems. While designed with different objectives, i.e., stochastic models for regeneration and modularity maximization models for discrimination, both these two types of model look for low-rank embedding to best represent and reconstruct network topology. However, the mapping through such embedding is linear, whereas real networks have various nonlinear features, making these models less effective in practice. Inspired by the strong representation power of deep neural networks, we propose a novel nonlinear reconstruction method by adopting deep neural networks for representation. We then extend the method to a semi-supervised community detection algorithm by incorporating pairwise constraints among graph nodes. Extensive experimental results on synthetic and real networks show that the new methods are effective, outperforming most state-of-the-art methods for community detection.

关键词： Deep neural networks

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Sharp large deviation results for sums of independent random variables

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science China mathematics 2015年第9期58卷 1939-1958页

作者： FAN XieQuan GRAMA Ion LIU QuanSheng Regularity Team Institut National de Recherche en Informatique et en Automatique Laboratoire de Mathmatiques Appliques aux Systèmes Ecole Centrale Paris-Grande Voie des Vignes Laboratoire de Mathmatiques de Bretagne Atlantique UMR 6205 University Bretagne-Sud School of Mathematics and Computing Science Changsha University of Science and Technology

We show sharp bounds for probabilities of large deviations for sums of independent random variables satisfying Bernstein's condition. One such bound is very close to the tail of the standard Gaussian law in certain case; other bounds improve the inequalities of Bennett and Hoeffding by adding missing factors in the spirit of Talagrand(1995). We also complete Talagrand's inequality by giving a lower bound of the same form, leading to an equality. As a consequence, we obtain large deviation expansions similar to those of Cram′er(1938),Bahadur-Rao(1960) and Sakhanenko(1991). We also show that our bound can be used to improve a recent inequality of Pinelis(2014).

关键词： Bernstein’s inequality sharp large deviations Cramér large deviations expansion of BahadurRao sums of independent random variabl

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Information technology and computer science programs: How do we relate? 15

Information technology and computer science programs: How do...

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16th Annual ACM Conference on Information Technology Education, SIGITE 2015

作者： MacKellar, Bonnie Sabin, Mihaela Hislop, Gregory Settle, Amber Div. of Computer Science Math and Science St John's University QueensNY United States Computing Technology University of New Hampshire ManchesterNH03101 United States College of Computing and Informatics Drexel University PhiladelphiaPA United States School of Computing DePaul University ChicagoIL60604 United States

ISBN: (纸本)9781450338356

In this panel session, the relationship between computer science programs and information technology programs at universities that house both will be explored. People outside the computing disciplines often find the distinction between these programs confusing. The panelists, who have experience with both types of program, will discuss strategies for differentiating the programs in the eyes of administrators, for advising students into the correct program, and for maintaining focus and excellence in both computer science and information technology programs.

关键词： Engineering education

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Human mobility: Models and applications

arXiv

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arXiv 2017年

作者： Barbosa-Filho, Hugo Barthelemy, Marc Ghoshal, Gourab James, Charlotte R. Lenormand, Maxime Louail, Thomas Menezes, Ronaldo Ramasco, José J. Simini, Filippo Tomasini, Marcello Department of Physics & Astronomy Goergen Institute of Data Science University of Rochester RochesterNY United States Institut de Physique Théorique CEA CNRS-URA 2306 Gif-sur-YvetteF-91191 France Centre d'Etudes et de Mathématique Sociales EHESS Paris75006 France Department of Engineering Mathematics University of Bristol United Kingdom Irstea UMR TETIS 500 rue JF Breton Montpellier34093 France Campus UIB Palma de Mallorca07122 Spain CNRS UMR 8504 Géographie-cités 13 rue du four ParisF-75006 France BioComplex Laboratory School of Computing Florida Institute of Technology Melbourne United States

Recent years have witnessed an explosion of extensive geolocated datasets related to human movement, enabling scientists to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. The study of human mobility is especially important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling. In this survey, we review the approaches developed to reproduce various mobility patterns, with the main focus on recent developments. This review can be used both as an introduction to the fundamental modeling principles of human mobility, and as a collection of technical methods applicable to specific mobility-related problems. The review organizes the subject by differentiating between individual and population mobility and also between shortrange and long-range mobility. Throughout the text the description of the theory is intertwined with real-world applications. Copyright © 2017, The Authors. All rights reserved.

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Testing the magnetar scenario for superluminous supernovae with circular polarimetry

arXiv

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arXiv 2018年

作者： Cikota, Aleksandar Leloudas, Giorgos Bulla, Mattia Inserra, Cosimo Chen, Ting-Wan Spyromilio, Jason Patat, Ferdinando Cano, Zach Cikota, Stefan Coughlin, Michael W. Kankare, Erkki Lowe, Thomas B. Maund, Justyn R. Rest, Armin Smartt, Stephen J. Smith, Ken W. Wainscoat, Richard J. Young, David R. European Southern Observatory Karl-Schwarzschild-Str. 2 Garching b. München85748 Germany Dark Cosmology centre Niels Bohr Institute University of Copenhagen Juliane Maries vej 30 Copenhagen2100 Denmark Oskar Klein Centre Department of Physics Stockholm University StockholmSE 106 91 Sweden Department of Physics & Astronomy University of Southampton Southampton HampshireSO17 1BJ United Kingdom Max-Planck-Institut für Extraterrestrische Physik Giessenbachstraße 1 85748 Garching Germany Glorieta de la Astronomía s/n GranadaE-18008 Spain University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Physics Unska 3 Zagreb10000 Croatia Ruđer Bošković Institute Bijenička cesta 54 Zagreb10000 Croatia Division of Physics Math and Astronomy California Institute of Technology PasadenaCA91125 United States Astrophysics Research Centre School of Mathematics and Physics Queens University Belfast BelfastBT7 1NN United Kingdom Institute for Astronomy University of Hawaii 2680 Woodlawn Drive HonoluluHI96822 United States Department of Physics and Astronomy University of Sheffield Hicks Building Hounsfield Road SheffieldS3 7RH United Kingdom Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Department of Physics and Astronomy Johns Hopkins University 3400 North Charles Street BaltimoreMD21218 United States

Superluminous supernovae (SLSNe) are at least ∼5 times more luminous than common supernovae (SNe). Especially hydrogen-poor SLSN-I are difficult to explain with conventional powering mechanisms. One possible scenario that might explain such luminosities is that SLSNe-I are powered by an internal engine, such as a magnetar or an accreting black hole. Strong magnetic fields or collimated jets can circularly polarize light. In this work, we measured circular polarization of two SLSNe-I with the FOcal Reducer and low dispersion Spectrograph (FORS2) mounted at the ESO’s Very Large Telescope (VLT). PS17bek, a fast evolving SLSN-I, was observed around peak, while OGLE16dmu, a slowly evolving SLSN-I, was observed 100 days after maximum. Neither SLSN shows evidence of circularly polarized light, however, these non-detections do not rule out the magnetar scenario as the powering engine for SLSNe-I. We calculate the strength of the magnetic field and the expected circular polarization as a function of distance from the magnetar, which decreases very fast. Additionally, we observed no significant linear polarization for PS17bek at four epochs, suggesting that the photosphere near peak is close to spherical symmetry. Copyright © 2018, The Authors. All rights reserved.

关键词： Circular polarization

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Automatic Classification of Disaster-Related Tweets

Automatic Classification of Disaster-Related Tweets

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International Conference on Innovative Engineering Technologies (ICIET’2014, Thailand)

作者： Beverly Estephany Parilla-Ferrer Proceso L.Fernandez Jaime T.Ballena IV Department of Information Technology and Computer Science School of Information and Computing Science Saint Louis University Department of Information Systems and Computer Science School of Science and EngineeringAteneo de Manila University Math Department School of Information and Computing Science Saint Louis University

The social networking site Twitter has become one of the quickest sources of news and other information. Twitter information feeds known as tweets,are voluntarily sent by registered users and reach even non-registered users,sometimes ahead of traditional sources of mass news. In this study,we develop some machine learning models that can automatically detect informative disaster-related tweets. A dataset of tweets,collected during the Habagat flooding of Metro Manila in 2012,was used in building the classifier models. A random subset of this dataset was manually labeled as either informative or uninformative to produce the ground truth. Two machine learning algorithms,Naive Bayes and Support Vector Machine(SVM),were used to build models for the automatic classification of the tweets,and these models were evaluated across the metrics of accuracy,precision,recall,area under curve and Fmeasure. Experimental results show that the model generated from SVM has significantly better results compared to that of the Naive Bayes. This study also revealed that uninformative tweets outnumbered informative tweets,suggesting that the subscribers used Twitter to broadcast more of tweets that express their subjective messages and emotions regarding the Habagat event. However,the informative tweets were more likely to be retweeted than uninformative tweets,indicating that subscribers retweet messages they deem informative and useful for public awareness. These insights,together with the built classifier models,can help in the development of a system that can sift through the voluminous Twitter data and in real-time detect informative disaster-related tweets so that appropriate action may be done promptly.

关键词： Disaster Machine learning Text mining Tweets

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Separating decision tree complexity from subcube partition complexity

Separating decision tree complexity from subcube partition c...

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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： Kothari, Robin Racicot-Desloges, David Santha, Miklos Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA United States David R. Cheriton School of Computer Science Institute for Quantum Computing University of Waterloo WaterlooON Canada Département de Mathématiques Faculté des Sciences Université de Sherbrooke SherbrookeQC Canada Centre for Quantum Technologies National University of Singapore Singapore Singapore LIAFA CNRS Université Paris Diderot Paris France

ISBN: (纸本)9783939897897

The subcube partition model of computation is at least as powerful as decision trees but no separation between these models was known. We show that there exists a function whose deterministic subcube partition complexity is asymptotically smaller than its randomized decision tree complexity, resolving an open problem of Friedgut, Kahn, and Wigderson ([7]). Our lower bound is based on the information-theoretic techniques first introduced to lower bound the randomized decision tree complexity of the recursive majority function. We also show that the public-coin partition bound, the best known lower bound method for randomized decision tree complexity subsuming other general techniques such as block sensitivity, approximate degree, randomized certificate complexity, and the classical adversary bound, also lower bounds randomized subcube partition complexity. This shows that all these lower bound techniques cannot prove optimal lower bounds for randomized decision tree complexity, which answers an open question of Jain and Klauck ([9]) and Jain, Lee, and Vishnoi ([10]). © Robin Kothari, David Racicot-Desloges, and Miklos Santha.

关键词： Decision trees

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Foreword

Proceedings of the 2016 International Conference on Image Pr...

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Proceedings of the 2016 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2016 2016年

作者： Abd-Wahab, Mohd Helmy Al-Bakry, Abbas Al-Holou, Nizar Arabnia, Hamid R. Bhattacharya, Mahua Martinez Castillo, Juan Jose Daimi, Kevin Deligiannidis, Leonidas Djoudi, Lamia Atma Duong, Trung Eshaghian-Wilner, Mary Mehrnoosh Gravvanis, George A. Huang, Ruizhu Jandieri, George Kim, Byung-Gyu Kim, Tai-hoon Korovin, Iakov Lai, Guoming Lee, Hyo Jong Bin Mansor, Muhammad Naufal Marsh, Andrew Mostafaeipour, Ali Park, James J. Patil, Shashikant Ponalagusamy, R. Schaefer, Gerald Singh, Akash Solo, Ashu M.G. Swee, Sim Kok Thomas, Jaya Tinetti, Fernando G. Vladimir, Hahanov Wang, Shiuh-Jeng Yang, Mary Yoe, Hyun You, Jane Zhao, Wenbing Department of Computer Engineering University Tun Hussein Onn Malaysia Malaysia University of IT and Communications Baghdad Iraq Electrical and Computer Engineering Department IEEE/SEM-Computer Chapter University of Detroit Mercy DetroitMI United States University of Georgia United States ABV Indian Institute of Information Technology and Management MHRD Government of India India Acantelys Alan Turing Nikola Tesla Research Group and GIPEB Universidad Nacional Abierta Venezuela Computer Science and Software Engineering Programs Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States Department of Computer Information Systems Wentworth Institute of Technology BostonMA United States Synchrone Technologies France Rutgers University State University of New Jersey New Jersey United States University of Southern California California United States Electrical Engineering University of California Los Angeles Los Angeles [UCLA CA United States Advanced Scientific Computing Applied Math and Applications Research Group Applied Mathematics and Numerical Computing and Department of ECE School of Engineering Democritus University of Thrace Xanthi Greece Texas Advanced Computing Center University of Texas AustinTX United States Georgian Technical University Tbilisi Georgia Institute of Cybernetics Georgian Academy of Science Georgia Multimedia Processing CommunicationsLab.[MPCL Department of Computer Science and Engineering College of Engineering SunMoon University Korea Republic of School of Information and Computing Science University of Tasmania Australia Southern Federal University Russia Computer Science and Technology Sun Yat-Sen University Guangzhou China Center for Advanced Image and Information Technology Division of Computer Science and Engineering Chonbuk National University Korea Republic of Faculty of Engineering Technology Kampus Uniciti Alam Universiti Malaysia Perlis UniMAP Malaysi

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