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Sharpening the dark matter signature in gravitational waveforms. II. Numerical simulations

Physical Review D 2025年第6期111卷 063071-063071页

作者： Bradley J. Kavanagh Theophanes K. Karydas Gianfranco Bertone Pierfrancesco Di Cintio Mario Pasquato Instituto de Física de Cantabria (IFCA UC-CSIC) Avenue de Los Castros s 39005 Santander Spain Gravitation Astroparticle Physics Amsterdam (GRAPPA) Institute for Theoretical Physics Amsterdam and Delta Institute for Theoretical Physics University of Amsterdam Science Park 904 1098 XH Amsterdam The Netherlands Consiglio Nazionale delle Ricerche Istituto dei Sistemi Complessi (CNR-ISC) via Madonna del Piano 17 50022 Sesto Fiorentino (FI) Italy INAF-Osservatorio Astronomico di Arcetri Largo Enrico Fermi 5 50125 Firenze Italy INFN-Sezione di Firenze Via Giovanni Sansone 1 50022 Sesto Fiorentino Italy Département de Physique Université de Montréal 1375 Avenue Thérèse-Lavoie-Roux Montréal Canada Mila—Quebec Artificial Intelligence Institute 6666 Rue Saint-Urbain Montréal Canada Ciela—Montréal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Dipartimento di Fisica e Astronomia Università di Padova Vicolo dell’Osservatorio 5 Padova Italy Istituto Nazionale di Fisica Nucleare Padova Via Marzolo 8 Padova Italy

Future gravitational wave observatories can probe dark matter by detecting the dephasing in the waveform of binary black hole mergers induced by dark matter overdensities. Such a detection hinges on the accurate modeling of the dynamical friction, induced by dark matter on the secondary compact object in intermediate and extreme mass ratio inspirals. In this paper, we introduce nbodyimri, a new publicly available code designed for simulating binary systems within cold dark matter spikes. Leveraging higher particle counts and finer time steps, we validate the applicability of the standard dynamical friction formalism and provide an accurate determination of the maximum impact parameter of particles which can effectively scatter with a compact object, across various mass ratios. We also show that in addition to feedback due to dynamical friction, the dark matter also evolves through a stirring effect driven by the time-dependent potential of the binary. We introduce a simple semianalytical scheme to account for this effect and demonstrate that including stirring tends to slow the rate of dark matter depletion and therefore enhances the impact of dark matter on the dynamics of the binary.

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On Convergence of Federated Averaging Langevin Dynamics

arXiv

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arXiv 2021年

作者： Deng, Wei Zhang, Qian Ma, Yi-An Song, Zhao Lin, Guang Machine Learning Research Morgan Stanley NY United States Department of Statistics Purdue University West LafayetteIN United States Haliciog˜lu Data Science Institute University of California La Jolla San DiegoCA United States Adobe Research San JoseCA United States Department of Mathematics School of Mechanical Engineering Purdue University United States

We propose a federated averaging Langevin algorithm (FA-LD) for uncertainty quantification and mean predictions with distributed clients. In particular, we generalize beyond normal posterior distributions and consider a general class of models. We develop theoretical guarantees for FA-LD for strongly log-concave distributions with non-i.i.d data and study how the injected noise and the stochastic-gradient noise, the heterogeneity of data, and the varying learning rates affect the convergence. Such an analysis sheds light on the optimal choice of local updates to minimize the communication cost. Important to our approach is that the communication efficiency does not deteriorate with the injected noise in the Langevin algorithms. In addition, we examine in our FA-LD algorithm both independent and correlated noise used over different clients. We observe there is a trade-off between the pairs among communication, accuracy, and data privacy. As local devices may become inactive in federated networks, we also show convergence results based on different averaging schemes where only partial device updates are available. In such a case, we discover an additional bias that does not decay to zero. © 2021, CC BY.

关键词： Stochastic systems

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Objective frequentist uncertainty quantification for atmospheric CO2 retrievals

arXiv

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arXiv 2020年

作者： Patil, Pratik Kuusela, Mikael Hobbs, Jonathan Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States Department of Statistics and Data Science Carnegie Mellon University PittsburghPA15213 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States

The steadily increasing amount of atmospheric carbon dioxide (CO2) is affecting the global climate system and threatening the long-term sustainability of Earth's ecosystem. In order to better understand the sources and sinks of CO2, NASA operates the Orbiting Carbon Observatory-2 & 3 satellites to monitor CO2 from space. These satellites make passive radiance measurements of the sunlight reflected off the Earth's surface in different spectral bands, which are then inverted in an ill-posed inverse problem to obtain estimates of the atmospheric CO2 concentration. In this work, we propose a new CO2 retrieval method that uses known physical constraints on the state variables and direct inversion of the target functional of interest to construct well-calibrated frequentist confidence intervals based on convex programming. We compare the method with the current operational retrieval procedure, which uses prior knowledge in the form of probability distributions to regularize the problem. We demonstrate that the proposed intervals consistently achieve the desired frequentist coverage, while the operational uncertainties are poorly calibrated in a frequentist sense both at individual locations and over a spatial region in a realistic simulation experiment. We also study the influence of specific nuisance state variables on the length of the proposed intervals and identify certain key variables that can greatly reduce the final uncertainty given additional deterministic or probabilistic constraints, and develop a principled framework to incorporate such information into our *** Codes 62P12, 15A29, 62F30, 90C90 Copyright © 2020, The Authors. All rights reserved.

关键词： Carbon dioxide

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End-to-end topographic networks as models of cortical map formation and human visual behaviour

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Nature human behaviour 2025年 2025 Jun 6页

作者： Zejin Lu Adrien Doerig Victoria Bosch Bas Krahmer Daniel Kaiser Radoslaw M Cichy Tim C Kietzmann Machine Learning Group Institute for Cognitive Science Osnabrück University Osnabrück Germany. zekinglu@***. Neural Dynamics of Visual Cognition Group Department of Education and Psychology Freie Universität Berlin Berlin Germany. zekinglu@***. Machine Learning Group Institute for Cognitive Science Osnabrück University Osnabrück Germany. Cognitive Computational Neuroscience Lab Department of Education and Psychology Freie Universität Berlin Berlin Germany. Donders Institute for Brain Cognition and Behaviour Department for Artificial Intelligence Radboud University Nijmegen the Netherlands. Neural Computation Group Mathematical Institute Justus-Liebig-Universität Gießen Gießen Germany. Center for Mind Brain and Behavior Philipps-Universität Marburg and Justus-Liebig-Universität Gießen Marburg Germany. Neural Dynamics of Visual Cognition Group Department of Education and Psychology Freie Universität Berlin Berlin Germany. Machine Learning Group Institute for Cognitive Science Osnabrück University Osnabrück Germany. tim.kietzmann@uni-osnabrueck.de.

A prominent feature of the primate visual system is its topographic organization. For understanding its origins, its computational role and its behavioural implications, computational models are of central importance. Yet, vision is commonly modelled using convolutional neural networks, which are hard-wired to learn identical features across space and thus lack topography. Here we overcome this limitation by introducing all-topographic neural networks (All-TNNs). All-TNNs develop several features reminiscent of primate topography, including smooth orientation and category selectivity maps, and enhanced processing of regions with task-relevant information. In addition, All-TNNs operate on a low energy budget, suggesting a metabolic benefit of smooth topographic organization. To test our model against behaviour, we collected a dataset of human spatial biases in object recognition and found that All-TNNs significantly outperform control models. All-TNNs thereby offer a promising candidate for modelling primate visual topography and its role in downstream behaviour.

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Weisfeiler and Leman go machine learning: The Story so far

arXiv

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arXiv 2021年

作者： Morris, Christopher Lipman, Yaron Maron, Haggai Rieck, Bastian Kriege, Nils M. Grohe, Martin Fey, Matthias Borgwardt, Karsten Department of Computer Science RWTH Aachen University Aachen Germany Meta AI Research Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel NVIDIA Research Tel Aviv Israel AIDOS Lab Institute of AI for Health Helmholtz Zentrum München and Technical University of Munich Munich Germany Faculty of Computer Science University of Vienna Vienna Austria Research Network Data Science University of Vienna Vienna Austria Kumo.AI Mountain ViewCA United States Machine Learning & Computational Biology Lab Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland

In recent years, algorithms and neural architectures based on the Weisfeiler–Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machine learning with graphs and relational data. Here, we give a comprehensive overview of the algorithm’s use in a machine-learning setting, focusing on the supervised regime. We discuss the theoretical background, show how to use it for supervised graph and node representation learning, discuss recent extensions, and outline the algorithm’s connection to (permutation-)equivariant neural architectures. Moreover, we give an overview of current applications and future directions to stimulate further research. Copyright © 2021, The Authors. All rights reserved.

关键词： machine learning

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machine learning Approaches in Polymer science:Progress and Fundamental for a New

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SmartMat 2025年第1期6卷 102-142页

作者： 482.T.-S.Lin C.W.ColeyH.Mochigaseet al.“Bigsmiles:A Structurally-Based Line Notation for Describing Macromolecules"ACs Central Science 5no.9(2019):1523-1531.483.J.Wu and M.Gu“Perfecting Liquid-State Theories With Machine Intelligence"Journal of Physical Chemistry Letters 14no.47(2023):10545-10552.484.M.Rubinstein and R.H.ColbyPolymer Physics(Oxford University Press2003).485.M.E.DeagenB.Dalle-CortN.J.RebelloT.S.LinD.J.Walshand B.D.Olsen“Machine Translation Between BigSMILES Line Notation and Chemical Structure Diagrams"Macromolecules 57no.1(2023):42-53.486.S.M.McDonaldE.K.AugustineQ.LannersC.RudinL.Catherine Brinsonand M.L.Becker“Applied Machine Learning as a Driver for Polymeric Biomaterials Design"Nature Communications 14no.1(2023):4838.487.B.HuA.Linand L.C.Brinson“Tackling Structured Knowledge Extraction From Polymer Nanocomposite Literature as an NER/RE Task With seq2seq"Integrating Materials and Manufacturing Innovation 13no.3(2024):656-668.488.P.V.CoveneyE.R.Doughertyand R.R.Highfield“Big Data Need Big Theory Too"Philosophical Transactions of the Royal Society A:MathematicalPhysical and Engineering Sciences 374no.2080(2016):201601 489.M.Tang R.ZhangS.Liet al.“Towards a Supertough Thermo-plastic Polyisoprene Elastomer Based on a Biomimic Strategy"Angewandte Chemie International Edition557no.48(2018):15836-15840.490.M.Z.Naser“An Engineer's Guide to Explainable Artificial Intel-ligence and Interpretable Machine Learning:Navigating CausalityForced Goodnessand the False Perception of Inference"Automation in Construction 129(2021):103821 .491.T.K.Patra V.MeenakshisundaramJ.H.Hungand D.S.Simmons“Neural-Network-Biased Genetic Algorithms for Materials Design:Evolutionary Algorithms That LearnACS Combinatorial Science 19no.2(2017):96-107.

machine learning(ML),material genome,and big data approaches are highly overlapped in their strategies,algorithms,and *** can target various definitions,distributions,and correlations of concerned physical parameters in given polymer systems,and have expanding applications as a new paradigm indispensable to conventional *** inherent advantages in building quantitative multivariate correlations have largely enhanced the capability of scientific understanding and discoveries,thus facilitating mechanism exploration,target prediction,high-throughput screening,optimization,and rational and inverse *** article summarizes representative progress in the recent two decades focusing on the design,preparation,application,and sustainable development of polymer materials based on the exploration of key physical parameters in the composition-process-structure-property-performance *** integration of both data-driven and physical insights through ML approaches to deepen fundamental understanding and discover novel polymer materials is categorically *** the construction and application of robust ML models,strategies and algorithms to deal with variant tasks in polymer science are still in rapid *** challenges and prospects are then *** believe that the innovation in polymer materials will thrive along the development of ML approaches,from efficient design to sustainable applications.

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Nuclear Neural Networks: Emulating Late Burning Stages in Core Collapse Supernova Progenitors

arXiv

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arXiv 2025年

作者： Grichener, Aldana Renzo, Mathieu Kerzendorf, Wolfgang E. Farmer, Rob de Mink, Selma E. Bellinger, Earl Patrick Chan, Chi-Kwan Chen, Nutan Farag, Ebraheem Justham, Stephen Steward Steward Observatory Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Max Planck Institute for Astrophysics Karl-Schwarzschild-Str. 1 Garching85748 Germany Department of Physics Technion Haifa3200003 Israel Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Ludwig-Maximilians-Universitat Munchen Geschwister-Scholl-Platz 1 Munchen80539 Germany Department of Astronomy Yale University New HavenCT06511 United States Steward Observatory Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Avenue TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 North Santa Rita TucsonAZ85721 United States Machine Learning Research Lab Volkswagen AG Munich38440 Germany

One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of fully-coupled stiff ordinary differential equations (ODEs), making the simulations computationally intensive and prone to numerical instability. To overcome this barrier, we design a nuclear neural network (NNN) framework with multiple hidden layers to emulate nucleosynthesis calculations and conduct a proof-of-concept to evaluate its performance. The NNN takes the temperature, density and composition of a burning region as input and predicts the resulting isotopic abundances along with the energy generation and loss rates. We generate training sets for initial conditions corresponding to oxygen core depletion and beyond using large nuclear reaction networks, and compare the predictions of the NNNs to results from a commonly used small net. We find that the NNNs improve the accuracy of the electron fraction by 280 − 660 % and the nuclear energy generation by 250 − 750 %, consistently outperforming the small network across all timesteps. They also achieve significantly better predictions of neutrino losses on relatively short timescales, with improvements ranging from 100 − 106 %. While further work is needed to enhance their accuracy and applicability to different stellar conditions, integrating NNN trained models into stellar evolution codes is promising for facilitating large-scale generation of core-collapse supernova (CCSN) progenitors with higher physical fidelity. © 2025, CC BY.

关键词： Nucleosynthesis

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Cross-population coupling of neural activity based on Gaussian process current source densities

arXiv

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arXiv 2021年

作者： Klein, Natalie Siegle, Joshua H. Teichert, Tobias Kass, Robert E. Department of Statistics and Data Science Carnegie Mellon University PittsburghPA United States Machine Learning Department Carnegie Mellon University PittsburghPA United States MindScope Program Allen Institute SeattleWA United States Department of Psychiatry University of Pittsburgh PittsburghPA United States Department of Bioengineering University of Pittsburgh PittsburghPA United States Neuroscience Institute Carnegie Mellon University PittsburghPA United States

Because local field potentials (LFPs) arise from multiple sources in different spatial locations, they do not easily reveal coordinated activity across neural populations on a trial-to-trial basis. As we show here, however, once disparate source signals are decoupled, their trial-to-trial fluctuations become more accessible, and cross-population correlations become more apparent. To decouple sources we introduce a general framework for estimation of current source densities (CSDs). In this framework, the set of LFPs result from noise being added to the transform of the CSD by a biophysical forward model, while the CSD is considered to be the sum of a zero-mean, stationary, spatiotemporal Gaussian process, having fast and slow components, and a mean function, which is the sum of multiple time-varying functions distributed across space, each varying across trials. We derived biophysical forward models relevant to the data we analyzed. In simulation studies this approach improved identification of source signals compared to existing CSD estimation methods. Using data recorded from primate auditory cortex, we analyzed trial-to-trial fluctuations in both steady-state and task-evoked signals. We found cortical layer-specific phase coupling between two probes and showed that the same analysis applied directly to LFPs did not recover these patterns. We also found task-evoked CSDs to be correlated across probes, at specific cortical depths. Using data from Neuropixels probes in mouse visual areas, we again found evidence for depth-specific phase coupling of primary visual cortex and lateromedial area based on the CSDs. © 2021, CC BY.

关键词： Probes

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Automating airborne pollen classification: Identifying and interpreting hard samples for classifiers

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Heliyon 2025年第2期11卷 e41656页

作者： Milling, Manuel Rampp, Simon D.N. Triantafyllopoulos, Andreas Plaza, Maria P. Brunner, Jens O. Traidl-Hoffmann, Claudia Schuller, Björn W. Damialis, Athanasios CHI – Chair of Health Informatics MRI Technical University of Munich Munich Germany MCML–Munich Center for Machine Learning Germany EIHW – Chair of Embedded Intelligence for Health Care & Wellbeing University of Augsburg Augsburg Germany Institute of Environmental Medicine and Integrative Health Faculty of Medicine University Clinic of Augsburg & University of Augsburg Augsburg Germany Institute of Environmental Medicine Helmholtz Center Munich German Research Center for Environmental Health Germany Faculty of Business and Economics and Faculty of Medicine University of Augsburg Augsburg Germany Department of Technology Management and Economics Technical University of Denmark Denmark Next Generation Technology Region Zealand Denmark Christine Kühne Center for Allergy Research and Education Davos Switzerland MDSI–Munich Data Science Institute Germany GLAM–the Group on Language Audio & Music Imperial College London London United Kingdom Terrestrial Ecology and Climate Change Department of Ecology School of Biology Faculty of Sciences Aristotle University of Thessaloniki Thessaloniki Greece

Deep-learning-based classification of pollen grains has been a major driver towards automatic monitoring of airborne pollen. Yet, despite an abundance of available datasets, little effort has been spent to investigate which aspects pose the biggest challenges to the (often black-box- resembling) pollen classification approaches. To shed some light on this issue, we conducted a sample-level difficulty analysis based on the likelihood for one of the largest automatically-generated datasets of pollen grains on microscopy images and investigated the reason for which certain airborne samples and specific pollen taxa pose particular problems to deep learning algorithms. It is here concluded that the main challenges lie in A) the (partly) co-occurring of multiple pollen grains in a single image, B) the occlusion of specific markers through the 2D capturing of microscopy images, and C) for some taxa, a general lack of salient, unique features. Our code is publicly available under https://***/millinma/SDPollen © 2025

关键词： Deep learning Pollen recognition Sample difficulty analysis

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An initiative on digital nephrology: the Kidney Imageomics Project

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National science Review 2025年第3期 92-96页

作者： Fangxu Zhou Zehua Li Haifeng Li Yao Lu Linjia Cheng Ying Zhang Zichen Wang Jing Nie Heping Cheng Bin Dong Lei Ma Li Yang Renal Division Peking University Institute of Nephrology Peking University First Hospital Key Laboratory of Renal Disease—Ministry of Health of China Key Laboratory of CKD Prevention and Treatment (Peking University)—Ministry of Education of China Peking University First Hospital Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases—Chinese Academy of Medical Sciences Peking University First Hospital National Biomedical Imaging Center College of Future Technology Peking University Beijing International Center for Mathematical Research and the New Cornerstone Science Laboratory Peking University Academy for Advanced Interdisciplinary Studies Peking University State Key Laboratory of Membrane Biology Peking University Institute of Molecular Medicine College of Future Technology Peking University Peking-Tsinghua Center for Life Sciences Peking University Center for Machine Learning Research Peking University KIP Consortium

The kidney's complex structure and functions are essential for *** human kidney contains one million nephrons surrounded by a vast network of peritubular capillaries that support oxygen and nutrients, and an inter...

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