检索结果-内蒙古大学图书馆

Innovative computing Curricula and the CC2020 Project

学校读者我要写书评

暂无评论

Innovative Computing Curricula and the CC2020 Project

Frontiers in Education (FIE) Conference

作者： John Impagliazzo Allen Parrish Alison Clear School of Engineering and Applied Science Hofstra University Hempstead New York USA Department of Computer Science and Engineering Mississippi State University MS USA School of Computing Eastern Institute of Technology Auckland New Zealand

This summary is an extension of a paper presented at the IEEE EduNine 2018 conference [1]. That paper contains much more detail than the content presented here.

ISBN: (纸本)9781538611753;9781538611746

This summary is an extension of a paper presented at the IEEE EduNine 2018 conference [1]. That paper contains much more detail than the content presented here.

关键词： Information technology Guidelines Education Task analysis Force computer science Information systems

Embeddability in the 3-Sphere is decidable

学校读者我要写书评

暂无评论

Embeddability in the 3-Sphere is decidable

作者： Matoušek, Jiří Sedgwick, Eric Tancer, Martin Wagner, Uli Department of Applied Mathematics Charles University Malostranské nám. 25 118 00 Praha 1 Czech Republic Department of Computer Science ETH Zürich Zürich8092 Switzerland School of Computing DePaul University 243 S. Wabash Ave ChicagoIL60604 United States Department of Applied Mathematics Charles University Malostranské nám. 25 Praha 1118 00 Czech Republic IST Austria Am Campus 1 Klosterneuburg3400 Austria

We show that the following algorithmic problem is decidable: given a 2-dimensional simplicial complex, can it be embedded (topologically, or equivalently, piecewise linearly) in R3? By a known reduction, it suffices to decide the embeddability of a given triangulated 3-manifold X into the 3-sphere S3. The main step, which allows us to simplify X and recurse, is in proving that if X can be embedded in S3, then there is also an embedding in which X has a short meridian, that is, an essential curve in the boundary of X bounding a disk in S3 \ X with length bounded by a computable function of the number of tetrahedra of X. © 2018 ACM.

关键词： Topology

Generalized bent boolean functions and strongly regular cayley graphs

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Riera, Constanza Stanica, Pantelimon Gangopadhyay, Sugata Department of Computing Mathematics and Physics Western Norway University of Applied Sciences Bergen5020 Norway Department of Applied Mathematics Naval Postgraduate School MontereyCA93943 United States Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee247667 India

In this paper we define the (edge-weighted) Cayley graph associated to a generalized Boolean function, introduce a notion of strong regularity and give several of its properties. We show some connections between this concept and generalized bent functions (gbent), that is, functions with at Walsh-Hadamard spectrum. In particular, we find a complete characterization of quartic gbent functions in terms of the strong regularity of their associated Cayley graph.94C10, 94C15, 05E30, 06E30 Copyright © 2018, The Authors. All rights reserved.

关键词： Boolean functions

Evolution of interlayer and intralayer magnetism in three atomically thin chromium trihalides

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Kim, Hyun Ho Yang, Bowen Li, Siwen Jiang, Shengwei Jin, Chenhao Tao, Zui Nichols, George Sfigakis, Francois Zhong, Shazhou Li, Chenghe Tian, Shangjie Cory, David G. Miao, Guo-Xing Shan, Jie Mak, Kin Fai Lei, Hechang Sun, Kai Zhao, Liuyan Tsen, Adam W. Institute for Quantum Computing Department of Chemistry Department of Physics and Astronomy Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics University of Michigan 450 Church Street Ann ArborMI48109 United States School of Applied and Engineering Physics Department of Physics Kavli Institute at Cornell for Nanoscale Science IthacaNY14853 United States Department of Physics Beijing Key Laboratory of Opto-electronic Functional Materials & Micro-Nano Devices Renmin University of China Beijing100872 China

We conduct a comprehensive study of three different magnetic semiconductors, CrI3, CrBr3, and CrCl3, by incorporating both few- and bi-layer samples in van der Waals tunnel junctions. We find that the interlayer magnetic ordering, exchange gap, magnetic anisotropy, as well as magnon excitations evolve systematically with changing halogen atom. By fitting to a spin wave theory that accounts for nearest neighbor exchange interactions, we are able to further determine a simple spin Hamiltonian describing all three systems. These results extend the 2D magnetism platform to Ising, Heisenberg, and XY spin classes in a single material family. Using magneto-optical measurements, we additionally demonstrate that ferromagnetism can be stabilized down to monolayer in more isotropic CrBr3, with transition temperature still close to that of the bulk. Copyright © 2019, The Authors. All rights reserved.

关键词： Van der Waals forces

Erratum to: Search for single top-quark production via flavour-changing neutral currents at 8 TeV with the ATLAS detector

学校读者我要写书评

暂无评论

The European Physical Journal C 2022年第1期82卷 1-15页

作者： Aad, G. Abbott, B. Abdallah, J. Abdinov, O. Aben, R. Abolins, M. AbouZeid, O. S. Abramowicz, H. Abreu, H. Abreu, R. Abulaiti, Y. Acharya, B. S. Adamczyk, L. Adams, D. L. Adelman, J. Adomeit, S. Adye, T. Affolder, A. A. Agatonovic-Jovin, T. Agricola, J. Aguilar-Saavedra, J. A. Ahlen, S. P. Ahmadov, F. Aielli, G. Akerstedt, H. Åkesson, T. P. A. Akimov, A. V. Alberghi, G. L. Albert, J. Albrand, S. Alconada Verzini, M. J. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexander, G. Alexopoulos, T. Alhroob, M. Alimonti, G. Alio, L. Alison, J. Alkire, S. P. Allbrooke, B. M. M. Allport, P. P. Aloisio, A. Alonso, A. Alonso, F. Alpigiani, C. Altheimer, A. Alvarez Gonzalez, B. Álvarez Piqueras, D. Alviggi, M. G. Amadio, B. T. Amako, K. Amaral Coutinho, Y. Amelung, C. Amidei, D. Amor Dos Santos, S. P. Amorim, A. Amoroso, S. Amram, N. Amundsen, G. Anastopoulos, C. Ancu, L. S. Andari, N. Andeen, T. Anders, C. F. Anders, G. Anders, J. K. Anderson, K. J. Andreazza, A. Andrei, V. Angelidakis, S. Angelozzi, I. Anger, P. Angerami, A. Anghinolfi, F. Anisenkov, A. V. Anjos, N. Annovi, A. Antonelli, M. Antonov, A. Antos, J. Anulli, F. Aoki, M. Aperio Bella, L. Arabidze, G. Arai, Y. Araque, J. P. Arce, A. T. H. Arduh, F. A. Arguin, J-F. Argyropoulos, S. Arik, M. Armbruster, A. J. Arnaez, O. Arnal, V. Arnold, H. Arratia, M. Arslan, O. Artamonov, A. Artoni, G. Asai, S. Asbah, N. Ashkenazi, A. Åsman, B. Asquith, L. Assamagan, K. Astalos, R. Atkinson, M. Atlay, N. B. Augsten, K. Aurousseau, M. Avolio, G. Axen, B. Ayoub, M. K. Azuelos, G. Baak, M. A. Baas, A. E. Baca, M. J. Bacci, C. Bachacou, H. Bachas, K. Backes, M. Backhaus, M. Bagiacchi, P. Bagnaia, P. Bai, Y. Bain, T. Baines, J. T. Baker, O. K. Baldin, E. M. Balek, P. Balestri, T. Balli, F. Banas, E. Banerjee, Sw. Bannoura, A. A. E. Bansil, H. S. Barak, L. Barberio, E. L. Barberis, D. Barbero, M. Barillari, T. Barisonzi, M. Barklow, T. Barlow, N. Barnes, S. L. Barnett, B. M. Barnett, R. M. Barnovska, Z. Baroncelli, A. Barone, G. Barr, A. J. Barreiro, F. Barreiro Guimarães da Costa, J. Bartol CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman USA Institute of Physics Academia Sinica Taipei Taiwan Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan Nikhef National Institute for Subatomic Physics and University of Amsterdam Amsterdam The Netherlands Department of Physics and Astronomy Michigan State University East Lansing USA Department of Physics University of Toronto Toronto Canada Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion: Israel Institute of Technology Haifa Israel Center for High Energy Physics University of Oregon Eugene USA Department of Physics Stockholm University Stockholm Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London UK AGH University of Science and Technology Faculty of Physics and Applied Computer Science Kraków Poland Physics Department Brookhaven National Laboratory Upton USA Department of Physics Northern Illinois University De Kalb USA Fakultät für Physik Ludwig-Maximilians-Universität München Munich Germany Particle Physics Department Rutherford Appleton Laboratory Didcot UK Oliver Lodge Laboratory University of Liverpool Liverpool UK Institute of Physics University of Belgrade Belgrade Serbia II Physikalisches Institut Georg-August-Universität Göttingen Germany Laboratório de Instrumentação e Física Experimental de Partículas-LIP Lisbon Portugal Departamento de Fisica Teorica y del Cosmos and CAFPE Universidad de Granada Granada Spain Department of Physics Boston University Boston USA Joint Institute for Nuclear Research JINR Dubna Dubna Russia INFN Sezione di Roma Tor Vergata Rome Italy Dipartimento di Fisica Università di Roma Tor Vergata Rome Italy Fysiska institutionen Lunds uni

Structure optimization for parameterized quantum circuits

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Ostaszewski, Mateusz Grant, Edward Benedetti, Marcello Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland Department of Computer Science University College London LondonWC1E 6BT United Kingdom Rahko Limited LondonN4 3JP United Kingdom Cambridge Quantum Computing Limited CambridgeCB2 1UB United Kingdom

We propose an efficient method for simultaneously optimizing both the structure and parameter values of quantum circuits with only a small computational overhead. Shallow circuits that use structure optimization perform significantly better than circuits that use parameter updates alone, making this method particularly suitable for noisy intermediate-scale quantum computers. We demonstrate the method for optimizing a variational quantum eigensolver for finding the ground states of Lithium Hydride and the Heisenberg model in simulation, and for finding the ground state of Hydrogen gas on the IBM Melbourne quantum computer. © 2019, CC BY.

关键词： Structural optimization

Information geometry of orthogonal initializations and training

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Sokól, Piotr Aleksander Park, Il Memming Department of Neurobiology and Behavior Departments of Applied Mathematics and Statistics Electrical and Computer Engineering Institutes for Advanced Computing Science and AI-driven Discovery and Innovation Stony Brook University Stony BrookNY11733

Recently mean field theory has been successfully used to analyze properties of wide, random neural networks. It gave rise to a prescriptive theory for initializing feed-forward neural networks with orthogonal weights, which ensures that both the forward propagated activations and the backpropagated gradients are near `2 isometries and as a consequence training is orders of magnitude faster. Despite strong empirical performance, the mechanisms by which critical initializations confer an advantage in the optimization of deep neural networks are poorly understood. Here we show a novel connection between the maximum curvature of the optimization landscape (gradient smoothness) as measured by the Fisher information matrix (FIM) and the spectral radius of the input-output Jacobian, which partially explains why more isometric networks can train much faster. Furthermore, given that orthogonal weights are necessary to ensure that gradient norms are approximately preserved at initialization, we experimentally investigate the benefits of maintaining orthogonality throughout training, from which we conclude that manifold optimization of weights performs well regardless of the smoothness of the gradients. Moreover, motivated by experimental results we show that a low condition number of the FIM is not predictive of faster learning. Copyright © 2018, The Authors. All rights reserved.

关键词： Fisher information matrix

Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Pfeiffer, Micha Funke, Isabel Robu, Maria R. Bodenstedt, Sebastian Strenger, Leon Engelhardt, Sandy Roß, Tobias Clarkson, Matthew J. Gurusamy, Kurinchi Davidson, Brian R. Maier-Hein, Lena Riediger, Carina Welsch, Thilo Weitz, Jürgen Speidel, Stefanie Translational Surgical Oncology National Center for Tumor Diseases Dresden Germany Wellcome/EPSRC Centre for Interventional & Surgical Sciences University College London United Kingdom Centre for Medical Image Computing University College London United Kingdom Faculty of Computer Science Mannheim University of Applied Sciences Germany German Cancer Research Center Heidelberg Germany Division of Surgery and Interventional Science University College London United Kingdom Department for Visceral Thoracic and Vascular Surgery University Hospital Dresden Germany

In the medical domain, the lack of large training data sets and benchmarks is often a limiting factor for training deep neural networks. In contrast to expensive manual labeling, computer simulations can generate large and fully labeled data sets with a minimum of manual effort. However, models that are trained on simulated data usually do not translate well to real scenarios. To bridge the domain gap between simulated and real laparoscopic images, we exploit recent advances in unpaired image-to-image translation. We extent an image-to-image translation method to generate a diverse multitude of realistically looking synthetic images based on images from a simple laparoscopy simulation. By incorporating means to ensure that the image content is preserved during the translation process, we ensure that the labels given for the simulated images remain valid for their realistically looking translations. This way, we are able to generate a large, fully labeled synthetic data set of laparoscopic images with realistic appearance. We show that this data set can be used to train models for the task of liver segmentation of laparoscopic images. We achieve average dice scores of up to 0.89 in some patients without manually labeling a single laparoscopic image and show that using our synthetic data to pre-train models can greatly improve their performance. The synthetic data set will be made publicly available, fully labeled with segmentation maps, depth maps, normal maps, and positions of tools and camera (http://***/image2image). Copyright © 2019, The Authors. All rights reserved.

关键词： Laparoscopy

Learning from learning machines: A new generation of AI technology to meet the needs of science

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems