检索结果-内蒙古大学图书馆

Coulomb-corrected quantum-trajectories method for recollision-impact double ionization in an intense laser field

学校读者我要写书评

暂无评论

Physical Review A 2023年第1期108卷 013115-013115页

作者： Yuxing Bai Xiaolei Hao Weidong Li Weifeng Yang Jing Chen Institute of Theoretical Physics and Department of Physics State Key Laboratory of Quantum Optics and Quantum Optics Devices Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan 030006 China Department of Computer Science and Technology Taiyuan University Taiyuan 237016 China Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology Center for Advanced Material Diagnostic Technology and College of Engineering Physics Shenzhen Technology University Shenzhen 518118 China Department of Physics School of Science Hainan University Haikou 570228 China Institute of Applied Physics and Computational Mathematics P. O. Box 8009 Beijing 100088 China

By incorporating the quantum effects and the Coulomb interaction between the first ionized electron and the residual ion in a uniform theory, the Coulomb-corrected quantum-trajectories (CCQT) method is developed to describe the recollision-impact ionization (RII) process in nonsequential double ionization of atoms in intense laser field. The results simulated by CCQT are found to be in better agreement with the experimental results, compared with those calculated by the classical-trajectory Monte Carlo (CTMC) and strong-field approximation (SFA) methods. Considering its advantage of small computational cost and back-trajectory analysis, the CCQT method will be an efficient theoretical tool to quantitatively investigate the underlying physics of the complex and coherent multielectron dynamics in intense laser field.

关键词： Atomic & molecular processes in external fields Electronic excitation & ionization Strong field excitation Atomic systems Electronic structure High-harmonic generation Optical techniques Quantum Monte Carlo Semiclassical methods

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

Learning shared neural manifolds from multi-subject FMRI data

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Huang, Jessie Busch, Erica L. Wallenstein, Tom Gerasimiuk, Michal Benz, Andrew Lajoie, Guillaume Wolf, Guy Turk-Browne, Nicholas B. Krishnaswamy, Smita Department Of Computer Science Department Of Psychology Department Of Mathematics Department Of Genetics Program In Applied Mathematics Wu Tsai Institute Yale University United States Department Of Mathematics And Statistics Université De Montréal Canada Mila Quebec AI Institute Canada

Functional magnetic resonance imaging (fMRI) is a notoriously noisy measurement of brain activity because of the large variations between individuals, signals marred by environmental differences during collection, and spatiotemporal averaging required by the measurement resolution. In addition, the data is extremely high dimensional, with the space of the activity typically having much lower intrinsic dimension. In order to understand the connection between stimuli of interest and brain activity, and analyze differences and commonalities between subjects, it becomes important to learn a meaningful embedding of the data that denoises, and reveals its intrinsic structure. Specifically, we assume that while noise varies significantly between individuals, true responses to stimuli will share common, low-dimensional features between subjects which are jointly discoverable. Similar approaches have been exploited previously but they have mainly used linear methods such as PCA and shared response modeling (SRM). In contrast, we propose a neural network called MRMD-AE (manifold-regularized multiple decoder, autoencoder), that learns a common embedding from multiple subjects in an experiment while retaining the ability to decode to individual raw fMRI signals. We show that our learned common space represents an extensible manifold (where new points not seen during training can be mapped), improves the classification accuracy of stimulus features of unseen timepoints, as well as improves cross-subject translation of fMRI signals. We believe this framework can be used for many downstream applications such as guided brain-computer interface (BCI) training in the future. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Developing a medical artificial intelligence course for high school students

学校读者我要写书评

暂无评论

Developing a medical artificial intelligence course for high...

International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Huang, Chao-Jung Wu, Tinghui Lu, Jui-Ting Lin, Beatrice Chang, Dawei Wang, Pochuan Wang, Mei-Chi Lee, Peijung Wang, Weichung National Taiwan University-Stanford Joint Program Office of AI in Biotechnology Ministry of Science and Technology Joint Research Center for Artificial Intelligence Technology and All Vista Healthcare Taipei Taiwan Institute of Applied Mathematical Sciences National Taiwan University Taipei Taiwan Department of Mathematics Ecole Normale Supérieure de Lyon Lyon France Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Mathematics National Taiwan University Taipei Taiwan

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

Artificial intelligence (AI) training courses often require prerequisites such as calculus or statistics. It is hence challenging to design and develop an introductory AI course for students of secondary education. This research intends to develop a medical AI course, provide high school students with an overview of deep learning applications in medical image analysis, and inspire them to pursue careers in the field of medical AI. We designed a 20-hour course, including lectures and two hands-on projects based on medical image classification. The proposed courses provided medical AI disciplines and built up their knowledge from basic to advanced levels. During the ten-day online courses, all the students were fully engaged and gave us positive feedback. The students endeavored to complete the experimental study in training, testing, and hypothesis of medical images application in the course. Their performance exceeded all expectations, for they did further analysis by tuning different hyperparameters. We designed a course evaluation form, which suggested that the students found it essential and expected to interact with the instructors. The results indicate that combining lectures with hands-on sessions would lead to evidently better achievement in terms of high school students' medical AI knowledge and positive attitudes while addressing real-world problems in the projects. Through this innovative education model, high school students regained their enthusiasm and were encouraged to cultivate their medical AI skills through self-learning while finishing the project. We conclude that this course could be successfully applied to interdisciplinary education in high school. © 2021 SPIE.

关键词： Students

Lower Bounds on Quantum Annealing Times

学校读者我要写书评

暂无评论

Physical Review Letters 2023年第14期130卷 140601-140601页

作者： Luis Pedro García-Pintos Lucas T. Brady Jacob Bringewatt Yi-Kai Liu Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland 20742 USA Joint Quantum Institute University of Maryland College Park Maryland 20742 USA Theoretical Division (T4) Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Quantum Artificial Intelligence Laboratory NASA Ames Research Center Moffett Field California 94035 USA KBR 601 Jefferson Street Houston Texas 77002 USA Applied and Computational Mathematics Division National Institute of Standards and Technology Gaithersburg Maryland 20899 USA

The adiabatic theorem provides sufficient conditions for the time needed to prepare a target ground state. While it is possible to prepare a target state much faster with more general quantum annealing protocols, rigorous results beyond the adiabatic regime are rare. Here, we provide such a result, deriving lower bounds on the time needed to successfully perform quantum annealing. The bounds are asymptotically saturated by three toy models where fast annealing schedules are known: the Roland and Cerf unstructured search model, the Hamming spike problem, and the ferromagnetic p-spin model. Our bounds demonstrate that these schedules have optimal scaling. Our results also show that rapid annealing requires coherent superpositions of energy eigenstates, singling out quantum coherence as a computational resource.

关键词： Adiabatic quantum optimization Quantum algorithms Quantum computation

TensorKMC: Kinetic Monte Carlo Simulation of 50 Trillion Atoms Driven by Deep Learning on a New Generation of Sunway Supercomputer

学校读者我要写书评

暂无评论

TensorKMC: Kinetic Monte Carlo Simulation of 50 Trillion Ato...

Supercomputing Conference

作者： Honghui Shang Xin Chen Xingyu Gao Rongfen Lin Lifang Wang Fang Li Qian Xiao Lei Xu Qiang Sun Leilei Zhu Fei Wang Yunquan Zhang Haifeng Song SKL of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Applied Physics and Computational Mathematics Beijing China Department of Computer Science and Technology Tsinghua University Wuxin China National Supercomputer Center in Wuxi Wuxi China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

The atomic kinetic Monte Carlo method plays an important role in multi-scale physical simulations because it bridges the micro and macro worlds. However, its accuracy is limited by empirical potentials. We therefore propose herein a triple-encoding algorithm and vacancy-cache mechanism to efficiently integrate ab initio neural network potentials (NNPs) with AKMC and implement them in our TensorKMC codes. We port our program to SW26010-pro and innovate a fast feature operator and a big fusion operator for the NNPs for fully utilizing the powerful heterogeneous computing units of the new-generation Sunway supercomputer. We further optimize memory usage. With these improvements, TensorKMC can simulate up to 54 trillions of atoms and achieve excellent strong and weak scaling performance up to 27,456,000 cores.

关键词： Technological innovation Monte Carlo methods Codes Quantum chemistry High performance computing Optimization methods Artificial neural networks

Autonomous Electron Tomography Reconstruction with Machine Learning

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Millsaps, William Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Hovden, Robert Department of Nuclear Engineering & Radiological Sciences University of Michigan Ann ArborMI United States Department of Materials Science and Engineering University of Michigan Ann ArborMI United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Advanced Photon Source Facility Argonne National Laboratory LemontIL United States Applied Physics Program University of Michigan Ann ArborMI United States

This manuscript is published in Microscopy and Microanalysis (DOI: 10.1093/micmic/ozad083). Modern electron tomography has progressed to higher resolution at lower doses by leveraging compressed sensing methods that minimize total variation (TV). However, these sparsity-emphasized reconstruction algorithms introduce tunable parameters that greatly influence the reconstruction quality. Here, Pareto front analysis shows that high-quality tomograms are reproducibly achieved when TV minimization is heavily weighted. However, in excess, compressed sensing tomography creates overly smoothed 3D reconstructions. Adding momentum to the gradient descent during reconstruction reduces the risk of over-smoothing and better ensures that compressed sensing is well behaved. For simulated data, the tedious process of tomography parameter selection is efficiently solved using Bayesian optimization with Gaussian processes. In combination, Bayesian optimization with momentum-based compressed sensing greatly reduces the required compute time-an 80% reduction was observed for the 3D reconstruction of SrTiO3 nanocubes. Automated parameter selection is necessary for large scale tomographic simulations that enable the 3D characterization of a wider range of inorganic and biological materials. © 2023, CC BY.

关键词： Compressed sensing

Tuning cooperative behavior in games with nonlinear opinion dynamics

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Park, Shinkyu Bizyaeva, Anastasia Kawakatsu, Mari Franci, Alessio Leonard, Naomi Ehrich Computer Electrical and Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia Department of Mechanical and Aerospace Engineering Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Mathematics Department National Autonomous University of Mexico Mexico City04510 Mexico

We examine the tuning of cooperative behavior in repeated multi-agent games using an analytically tractable, continuous-time, nonlinear model of opinion dynamics. Each modeled agent updates its real-valued opinion about each available strategy in response to payoffs and other agent opinions, as observed over a network. We show how the model provides a principled and systematic means to investigate behavior of agents that select strategies using rationality and reciprocity, key features of human decision-making in social dilemmas. For two-strategy games, we use bifurcation analysis to prove conditions for the bistability of two equilibria and conditions for the first (second) equilibrium to reflect all agents favoring the first (second) strategy. We prove how model parameters, e.g., level of attention to opinions of others (reciprocity), network structure, and payoffs, influence dynamics and, notably, the size of the region of attraction to each stable equilibrium. We provide insights by examining the tuning of the bistability of mutual cooperation and mutual defection and their regions of attraction for the repeated prisoner's dilemma and the repeated multi-agent public goods game. Our results generalize to games with more strategies, heterogeneity, and additional feedback dynamics, such as those designed to elicit cooperation. Copyright © 2021, The Authors. All rights reserved.

关键词： Bifurcation (mathematics)

Generalized forbidding matrix grammars and their membrane computing perspective 21

学校读者我要写书评

暂无评论

Generalized forbidding matrix grammars and their membrane co...

21st International Conference on Membrane Computing, CMC 2020

作者： Fernau, Henning Kuppusamy, Lakshmanan Raman, Indhumathi Fachbereich 4 – Abteilung Informatikwissenschaften Universität Trier Trier54286 Germany School of Computer Science and Engineering VIT University Vellore632 014 India Department of Applied Mathematics and Computational Sciences PSG College of Technology Coimbatore641 004 India

ISBN: (纸本)9783030771010

Matrix grammars are one of the first approaches ever proposed in regulated rewriting, prescribing that rules have to be applied in a certain order. In traditional regulated rewriting, the most interesting case shows up when all rules are context-free. Typical descriptional complexity measures incorporate the number of nonterminals or the length, i.e., the number of rules per matrix. When viewing matrices as program fragments, it becomes natural to consider additional applicability conditions for such matrices. Here, we focus on forbidding sets, i.e., a matrix is applicable to a sentential form w only if none of the words in its forbidding set occurs as a subword in w. This gives rise to further natural descriptional complexity measures: How long could words in forbidding sets be? How many words could be in any forbidding set? How many matrices contain non-empty forbidding contexts? As context-free grammars with forbidding sets are known as generalized forbidding grammars, we call this variant of matrix grammars also generalized forbidding. In this paper, we attempt to answer the above four questions while studying the computational completeness of generalized forbidding matrix grammars. We also link our research to processing strings with membrane computing and discuss appropriate variations of P systems. © Springer Nature Switzerland AG 2021.

关键词： Matrix algebra