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arXiv 2025年

作者： Zhou, Weiqing Zheng, Daye Liu, Qianrui Lu, Denghui Liu, Yu Lin, Peize Huang, Yike Peng, Xingliang Bao, Jie J. Cai, Chun Jin, Zuxin Wu, Jing Zhang, Haochong Jin, Gan Ji, Yuyang Shen, Zhenxiong Liu, Xiaohui Sun, Liang Cao, Yu Sun, Menglin Liu, Jianchuan Chen, Tao Liu, Renxi Li, Yuanbo Han, Haozhi Liang, Xinyuan Bao, Taoni Chen, Nuo Ren, Hongxu Zhang, Xiaoyang Liu, Zhaoqing Fu, Yiwei Liu, Maochang Li, Zhuoyuan Wen, Tongqi Tang, Zechen Xu, Yong Duan, Wenhui Wang, Xiaoyang Gu, Qiangqiang Dai, Fu-Zhi Zheng, Qijing Zhao, Jin Zhang, Yuzhi Ou, Qi Jiang, Hong Liu, Shi Xu, Ben Xu, Shenzhen Ren, Xinguo He, Lixin Zhang, Linfeng Chen, Mohan AI for Science Institute Beijing100080 China HEDPS CAPT School of Physics College of Engineering Peking University Beijing100871 China College of Engineering Peking University Beijing100871 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China Supercomputing Center University of Science and Technology of China Anhui Hefei230026 China Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Materials Science and Engineering Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China College of Chemistry and Molecular Engineering Peking University Beijing100871 China International Research Center for Renewable Energy State Key Laboratory of Multiphase Flow Xi’an Jiaotong University Shaanxi Xi’an710049 China Suzhou Academy of Xi’an Jiaotong University Jiangsu Suzhou215123 China Center for Structural Materials Department of Mechanical Engineering The University of Hong Kong Hong Kong The University of Hong Kong Shenzhen China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Frontier Science Center for Quantum Information Beijing China Saitama Wako351-0198 Japan Institute for Advanced Study Tsinghua University Beijing100084 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China School of Materials Science and Engineering University of Science and Technology Beijing Beijing100083 China Department of Physics University of Science and

ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It mainly features density functional theory (DFT) and is compatible with both plane-wave basis sets and numerical atomic orbital basis sets. ABACUS serves as a platform that facilitates the integration of various electronic structure methods, such as Kohn-Sham DFT, stochastic DFT, orbital-free DFT, and real-time time-dependent DFT, etc. In addition, with the aid of high-performance computing, ABACUS is designed to perform efficiently and provide massive amounts of first-principles data for generating general-purpose machine learning potentials, such as DPA models. Furthermore, ABACUS serves as an electronic structure platform that interfaces with several AI-assisted algorithms and packages, such as DeePKS-kit, DeePMD, DP-GEN, DeepH, DeePTB, etc. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Efficient numerical computation of the basic reproduction number for structured populations

arXiv

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arXiv 2020年

作者： Breda, Dimitri Florian, Francesco Ripoll, Jordi Vermiglio, Rossana CDLab - Computational Dynamics Laboratory Department of Mathematics Computer Science and Physics University of Udine via delle scienze 206 Udine33100 Italy Institute of Mathematics-University of Zürich Winterthurerstrasse 190 ZürichCH-8057 Switzerland CDLab - Computational Dynamics Laboratory Department of Computer Science Applied Mathematics and Statistics University of Girona Campus Montilivi Girona17003

As widely known, the basic reproduction number plays a key role in weighing birth/infection and death/recovery processes in several models of population dynamics. In this general setting, its characterization as the spectral radius of next generation operators is rather elegant, but simultaneously poses serious obstacles to its practical determination. In this work we address the problem numerically by reducing the relevant operators to matrices through a pseudospectral collo-cation, eventually computing the sought quantity by solving finite-dimensional eigenvalue problems. The approach is illustrated for two classes of models, re-spectively from ecology and epidemiology. Several numerical tests demonstrate experimentally important features of the method, like fast convergence and influ-ence of the smoothness of the models' coefficients. Examples of robust analysis of instances of specific models are also presented to show potentialities and ease of *** Codes 65Pxx, 65L03, 65L15, 65M70, 65J10, 92D25, 92D30, 92D40, 47D06, 47A75 Copyright © 2020, The Authors. All rights reserved.

关键词： Matrix algebra

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

arXiv

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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

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Optimized large hyperuniform binary colloidal suspensions in two dimensions

arXiv

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arXiv 2020年

作者： Ma, Zheng Lomba, Enrique Torquato, Salvatore Department of Physics Princeton University PrincetonNJ08544 United States Instituto de Química Física Rocasolano CSIC Calle Serrano 119 MadridE-28006 Spain Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

The creation of disordered hyperuniform materials with potentially extraordinary optical properties requires a capacity to synthesize large samples that are effectively hyperuniform down to the nanoscale. Motivated by this challenge, we propose a fabrication protocol using binary paramagnetic colloidal particles confined in a 2D plane. The strong and long-ranged dipolar interaction induced by a tunable magnetic field is free from screening effects that attenuates long-ranged electrostatic interactions in charged colloidal systems. Specifically, we find a family of optimal size ratios that makes the two-phase system effectively hyperuniform. We show that hyperuniformity is a general consequence of low isothermal compressibilities, which makes our protocol suitable to systems with other long-ranged soft interactions, dimensionalities and/or polydispersity. Our methodology paves the way to synthesize large photonic hyperuniform materials that function in the visible to infrared range and hence may accelerate the discovery of novel photonic materials. Copyright © 2020, The Authors. All rights reserved.

关键词： Suspensions (fluids)

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Innovation and utilization of coffee skin waste (Coffea robusta L) as a superior compost for the growth of pepper cuttings (Piper nigrum L.)

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AIP Conference Proceedings 2025年第1期3186卷

作者： Awalul Fatiqin Dewi Lestari R. A. Hoetary Tirta Amalia Riri Novita Sunarti Ike Apriani Anita Restu Puji Raharjeng Rr Mini Sari Wulan Thathit Suprayogi Yahya Febrianto Sudarman Rahman Shesanthi Citrariana Rokiy Alfanaar Mu’afa Purwa Arsana Yithro Serang Wansadaj Jaroenram Biology Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Biology Study Program Faculty of Science and Technology Universitas Islam Negeri Raden Fatah Palembang Palembang South Sumatra Indonesia Education Biology Study Program Faculty of Teacher Training and Education Universitas Islam Negeri Raden Fatah Palembang Palembang South Sumatra Indonesia Physics Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Pharmacy Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Chemistry Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Mathematics Study Program Faculty of Mathematics and Natural Sciences Palangka Raya University Palangka Raya Central Kalimantan Indonesia Department of applied chemistry Chaoyang University of Technology Taichung City Taiwan National center for genetic engineering and biotechnology (BIOTEC) Pathum Thani Thailand

This study aimed to determine the potential of coffee skin waste (Coffea robusta L) as a superior compost innovation for the growth of pepper-cutting seedlings (Piper nigrum L). This study used an experimental method with a completely randomized design using five treatments and five repeats, variations in the dose of coffee skin waste used P0 (control), P1 (90 g), P2 (135 g), P3 (180 g), and P4 (225 g). The shoot height, number of leaves, number of roots, and length of roots were measured. The results showed that coffee skin waste compost significantly affected shoot height, especially at P1 (90 g), resulting in an average growth height of 7.24 cm. In addition, the highest number of leaves was found at a concentration of P1 (90 g), with an average of two leaves. Although there was no significant effect on the number of roots, the concentration of P1 (90 g) yielded the best results, with an average of 33 root blades, and the best root length was obtained at the concentration of P1 (90 g), with an average of 44.6 cm. Therefore, coffee skin waste significantly influenced shoot height and the number of leaves on pepper-cut seedlings (Piper nigrum L.) in the P1 treatment (90 g).

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Dispersive estimates for time and space fractional Schrödinger equations

arXiv

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arXiv 2019年

作者： Su, Xiaoyan Zhao, Shiliang Li, Miao Institute of Applied Physics and Computational Mathematics 100094 China Department of Mathematics Sichuan University 610064 China

In this paper, we consider the Cauchy problem for the fractional Schrödinger equation iDtα u + (−∆) β 2 u = 0 with 0 0. We establish the dispersive estimates for the solutions. In particular, we prove that the ... 详细信息

关键词： Decay (organic)

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Training of Physical Neural Networks

arXiv

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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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An optimal algorithm for strict circular seriation

arXiv

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arXiv 2021年

作者： Armstrong, Santiago Guzmán, Cristóbal Long, Carlos A. Sing Institute for Mathematical and Computational Engineering Pontificia Universidad Católica de Chile Santiago Chile Anid - Millennium Science Initiative Program Millennium Nucleus Center for the Discovery of Structures in Complex Data Santiago Chile Department of Applied Mathematics University of Twente Netherlands Institute for Biological and Medical Engineering Pontificia Universidad Católica de Chile Santiago Chile

We study the problem of circular seriation, where we are given a matrix of pairwise dissimilarities between n objects, and the goal is to find a circular order of the objects in a manner that is consistent with their dissimilarity. This problem is a generalization of the classical linear seriation problem where the goal is to find a linear order, and for which optimal O(n2) algorithms are known. Our contributions can be summarized as follows. First, we introduce circular Robinson matrices as the natural class of dissimilarity matrices for the circular seriation problem. Second, for the case of strict circular Robinson dissimilarity matrices we provide an optimal O(n2) algorithm for the circular seriation problem. Finally, we propose a statistical model to analyze the well-posedness of the circular seriation problem for large n. In particular, we establish O(log(n)/n) rates on the distance between any circular ordering found by solving the circular seriation problem to the underlying order of the model, in the Kendall-tau *** Codes 68R01, 05C85, 05C50, 05C25, 65C20 Copyright © 2021, The Authors. All rights reserved.

关键词： Trees (mathematics)

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Local well-posedness of semilinear space-time fractional Schrödinger equation

arXiv

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arXiv 2019年

作者： Su, Xiaoyan Zhao, Shiliang Li, Miao Institute of Applied Physics and Computational Mathematics 100094 China Department of Mathematics Sichuan University 610064 China

The semilinear space-time fractional Schrödinger equation is considered. First, we give the explicit form for the fundamental solutions by using the Fox H-functions in order to to establish some Ls decay estimates. After that, we give some space-time estimates for the mild solutions from which the local well-posedness is derived on some proper Banach space. Copyright © 2019, The Authors. All rights reserved.

关键词： Banach spaces

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A Robust Light-Curve Diagnostic for Electron-Capture Supernovae and Low-Mass Fe-Core-Collapse Supernovae

arXiv

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arXiv 2024年

作者： Sato, Masato Tominaga, Nozomu Blinnikov, Sergei I. Potashov, Marat Sh. Moriya, Takashi J. Hiramatsu, Daichi Astronomical Science Program Graduate Institute for Advanced Studies SOKENDAI 2-21-1 Osawa Tokyo Mitaka181-8588 Japan National Astronomical Observatory of Japan National Institutes of Natural Sciences 2-21-1 Osawa Tokyo Mitaka181-8588 Japan Department of Physics Faculty of Science and Engineering Konan University 8-9-1 Okamoto Hyogo Kobe658-8501 Japan NRC Kurchatov Institute Moscow123182 Russia Moscow127055 Russia Keldysh Institute of Applied Mathematics RAS 4 Miusskaya Square Moscow125047 Russia School of Physics and Astronomy Monash University ClaytonVIC3800 Australia Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138-1516 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States

Core-collapse supernovae (CCSNe) are the terminal explosions of massive stars. While most massive stars explode as iron-core-collapse supernovae (FeCCSNe), slightly less massive stars explode as electron-capture supernovae (ECSNe), shaping the low-mass end of CCSNe. ECSNe was proposed ∼ 40 years ago and first-principles simulations also predict their successful explosions. Observational identification and investigation of ECSNe are important for the completion of stellar evolution theory. To date, only one promising candidate has been proposed, SN 2018zd, other than the historical progenitor of the Crab Nebula, SN 1054. We present representative synthetic light curves of low-mass FeCCSNe and ECSNe exploding with energies in circumstellar media (CSM) estimated with theoretically or observationally plausible methods. The plateaus of the ECSNe are shorter, brighter, and bluer than those of the FeCCSNe. To investigate the robustness of their intrinsic differences, we adopted various explosion energies and CSM. Although they may have similar bolometric light-curve plateaus, ECSNe are bluer than FeCCSNe in the absence of strong CSM interaction, illustrating that multicolor observations are essential to identify ECSNe. This provides a robust indicator of ECSNe because the bluer plateaus stem from the low-density envelopes of their super-asymptotic-giant-branch progenitors. Furthermore, we propose a distance-independent method to identify ECSNe: (g − r)tPT/2 PT − 0.4, i.e., blue g − r at the middle of the plateau (g − r)tPT/2, where tPT is the transition epoch from plateau to tail. Using this method, we identified SN 2018zd as an ECSN, which we believe to be the first ECSN identified with modern observing techniques. © 2024, CC BY.

关键词： Supernovae

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