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arXiv 2021年

作者： Andéol, Léo Kawakami, Yusei Wada, Yuichiro Kanamori, Takafumi Müller, Klaus-Robert Montavon, Grégoire Machine Learning group Technische Universität Berlin Berlin10587 Germany Tokyo Institute of Technology Tokyo Japan Berlin Institute for the Foundations of Learning and Data – BIFOLD Berlin10587 Germany Fujitsu Laboratories Ltd. Japan RIKEN AIP Japan Max Planck Institute for Informatics Stuhlsatzenhausweg 4 Saarbrücken66123 Germany Department of Artificial Intelligence Korea University Seoul136-713 Korea Republic of Google Deepmind Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin14195 Germany

Domain shifts in the training data are common in practical applications of machine learning;they occur for instance when the data is coming from different sources. Ideally, a ML model should work well independently of these shifts, for example, by learning a domain-invariant representation. However, common ML losses do not give strong guarantees on how consistently the ML model performs for different domains, in particular, whether the model performs well on a domain at the expense of its performance on another domain. In this paper, we build new theoretical foundations for this problem, by contributing a set of mathematical relations between classical losses for supervised ML and the Wasserstein distance in joint space (i.e. representation and output space). We show that classification or regression losses, when combined with a GAN-type discriminator between domains, form an upper-bound to the true Wasserstein distance between domains. This implies a more invariant representation and also more stable prediction performance across domains. Theoretical results are corroborated empirically on several image datasets. Our proposed approach systematically produces the highest minimum classification accuracy across domains, and the most invariant representation. Copyright © 2021, The Authors. All rights reserved.

关键词： machine learning

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Real-time gravitational-wave inference for binary neutron stars using machine learning

arXiv

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arXiv 2024年

作者： Dax, Maximilian Green, Stephen R. Gair, Jonathan Gupte, Nihar Pürrer, Michael Raymond, Vivien Wildberger, Jonas Macke, Jakob H. Buonanno, Alessandra Schölkopf, Bernhard Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Tübingen72076 Germany School of Mathematical Sciences University of Nottingham University Park NottinghamNG7 2RD United Kingdom Am Mühlenberg 1 Potsdam14476 Germany Department of Physics University of Maryland College ParkMD20742 United States Department of Physics University of Rhode Island East Hall KingstonRI02881 United States Center for Computational Research Carothers Library University of Rhode Island KingstonRI02881 United States Gravity Exploration Institute Cardiff University CardiffCF24 3AA United Kingdom ELLIS Institute Tübingen Maria-von-Linden-Straße 2 Tübingen72076 Germany Machine Learning in Science University of Tübingen & Tübingen AI Center Tübingen72076 Germany

Mergers of binary neutron stars (BNSs) emit signals in both the gravitational-wave (GW) and electromagnetic (EM) spectra. Famously, the 2017 multi-messenger observation of GW170817 [1, 2] led to scientific discoveries across cosmology [3], nuclear physics [4–6], and gravity [7]. Central to these results were the sky localization and distance obtained from GW data, which, in the case of GW170817, helped to identify the associated EM transient, AT 2017gfo [8], 11 hours after the GW signal. Fast analysis of GW data is critical for directing time-sensitive EM observations;however, due to challenges arising from the length and complexity of signals, it is often necessary to make approximations that sacrifice accuracy. Here, we present a machine learning framework that performs complete BNS inference in just one second without making any such approximations. Our approach enhances multi-messenger observations by providing (i) accurate localization even before the merger;(ii) improved localization precision by ∼ 30% compared to approximate low-latency methods;and (iii) detailed information on luminosity distance, inclination, and masses, which can be used to prioritize expensive telescope time. Additionally, the flexibility and reduced cost of our method open new opportunities for equation-of-state studies. Finally, we demonstrate that our method scales to extremely long signals, up to an hour in length, thus serving as a blueprint for data analysis for next-generation ground- and space-based detectors. Copyright © 2024, The Authors. All rights reserved.

关键词： Stars

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Author Correction: Efficient interatomic descriptors for accurate machine learning force fields of extended molecules

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Nature communications 2023年第1期14卷 4116页

作者： Adil Kabylda Valentin Vassilev-Galindo Stefan Chmiela Igor Poltavsky Alexandre Tkatchenko Department of Physics and Materials Science University of Luxembourg L-1511 Luxembourg City Luxembourg. Machine Learning Group Technische Universität Berlin 10587 Berlin Germany. BIFOLD - Berlin Institute for the Foundations of Learning and Data 10587 Berlin Germany. Department of Physics and Materials Science University of Luxembourg L-1511 Luxembourg City Luxembourg. alexandre.tkatchenko@uni.lu.

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The medical algorithmic audit (vol 4, pg e384, 2022)

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LANCET DIGITAL HEALTH 2022年第6期4卷 E405-E405页

作者： Liu, X. Glocker, B. McCradden, M. M. Ghassemi, M. Denniston, A. K. Oakden-Rayner, L. Academic Unit of Ophthalmology Institute of Inflammation and Ageing College of Medical and Dental Sciences University of Birmingham UK Department of Ophthalmology University Hospitals Birmingham NHS Foundation Trust Birmingham UK Moorfields Eye Hospital NHS Foundation Trust London UK Health Data Research UK London UK Birmingham Health Partners Centre for Regulatory Science and Innovation University of Birmingham Birmingham UK Biomedical Image Analysis Group Department of Computing Imperial College London London UK The Hospital for Sick Children Toronto ON Canada Dalla Lana School of Public Health Toronto ON Canada Institute for Medical Engineering and Science and Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA National Institute of Health Research Biomedical Research Centre for Ophthalmology Moorfields Hospital London NHS Foundation Trust London UK University College London Institute of Ophthalmology London UK Australian Institute for Machine Learning University of Adelaide Adelaide SA Australia. lauren.oakden-rayner@adelaide.edu.au

Artificial intelligence systems for health care, like any other medical device, have the potential to fail. However, specific qualities of artificial intelligence systems, such as the tendency to learn spurious correlates in training data, poor generalisability to new deployment settings, and a paucity of reliable explainability mechanisms, mean they can yield unpredictable errors that might be entirely missed without proactive investigation. We propose a medical algorithmic audit framework that guides the auditor through a process of considering potential algorithmic errors in the context of a clinical task, mapping the components that might contribute to the occurrence of errors, and anticipating their potential consequences. We suggest several approaches for testing algorithmic errors, including exploratory error analysis, subgroup testing, and adversarial testing, and provide examples from our own work and previous studies. The medical algorithmic audit is a tool that can be used to better understand the weaknesses of an artificial intelligence system and put in place mechanisms to mitigate their impact. We propose that safety monitoring and medical algorithmic auditing should be a joint responsibility between users and developers, and encourage the use of feedback mechanisms between these groups to promote learning and maintain safe deployment of artificial intelligence *** © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

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DeePN2: A deep learning-based non-Newtonian hydrodynamic model

arXiv

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arXiv 2021年

作者： Fang, Lidong Ge, Pei Zhang, Lei Weinan, E. Lei, Huan Department of Computational Mathematics Science and Engineering Michigan State University MI48824 United States School of Mathematical Sciences Institute of Natural Sciences and MOE-LSC Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Center for Machine Learning Research School of Mathematical Sciences Peking University Beijing100871 China AI for Science Institute Beijing100080 China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University NJ08544 United States Department of Statistics and Probability Michigan State University MI48824 United States

A long standing problem in the modeling of non-Newtonian hydrodynamics of polymeric flows is the availability of reliable and interpretable hydrodynamic models that faithfully encode the underlying micro-scale polymer dynamics. The main complication arises from the long polymer relaxation time, the complex molecular structure and heterogeneous interaction. DeePN2, a deep learning-based non-Newtonian hydrodynamic model, has been proposed and has shown some success in systematically passing the micro-scale structural mechanics information to the macro-scale hydrodynamics for suspensions with simple polymer conformation and bond potential. The model retains a multi-scaled nature by mapping the polymer configurations into a set of symmetry-preserving macro-scale features. The extended constitutive laws for these macro-scale features can be directly learned from the kinetics of their micro-scale counterparts. In this paper, we develop DeePN2 using more complex micro-structural models. We show that DeePN2 can faithfully capture the broadly overlooked viscoelastic differences arising from the specific molecular structural mechanics without human intervention. © 2021, CC BY.

关键词： Non Newtonian flow

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Conformational and state-specific effects in reactions of 2,3-dibromobutadiene with Coulomb-crystallized calcium ions

arXiv

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arXiv 2023年

作者： Kilaj, Ardita Käser, Silvan Wang, Jia Straňák, Patrik Schwilk, Max Xu, Lei von Lilienfeld, O. Anatole Küpper, Jochen Meuwly, Markus Willitsch, Stefan Department of Chemistry University of Basel Klingelbergstrasse 80 Basel4056 Switzerland Center for Free-Electron Laser Science CFEL Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoONM5S 3H6 Canada Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data BIFOLD Germany Department of Physics Universität Hamburg Luruper Chaussee 149 Hamburg22761 Germany Department of Chemistry Universität Hamburg Martin-Luther-King-Platz 6 Hamburg20146 Germany Center for Ultrafast Imaging Universität Hamburg Luruper Chaussee 149 Hamburg22761 Germany Department of Chemistry Brown University ProvidenceRI02912 United States

Recent advances in experimental methodology enabled studies of the quantum-state- and conformational dependence of chemical reactions under precisely controlled conditions in the gas phase. Here, we generated samples of selected gauche and s-trans 2,3-dibromobutadiene (DBB) by electrostatic deflection in a molecular beam and studied their reaction with Coulomb crystals of laser-cooled Ca+ ions in an ion trap. The rate coefficients for the total reaction were found to strongly depend on both the conformation of DBB and the electronic state of Ca+. In the (4p) 2P1/2 and (3d) 2D3/2 excited states of Ca+, the reaction is capture-limited and faster for the gauche conformer due to long-range ion-dipole interactions. In the (4s) 2S1/2 ground state of Ca+, the reaction rate for s-trans DBB still conforms with the capture limit, while that for gauche DBB is strongly suppressed. The experimental observations were analysed with the help of adiabatic capture theory, ab-initio calculations and reactive molecular dynamics simulations on a machine-learned full-dimensional potential energy surface of the system. The theory yields near-quantitative agreement for s-trans-DBB, but overestimates the reactivity of the gauche- conformer compared to the experiment. The present study points to the important role of molecular geometry even in strongly reactive exothermic systems and illustrates striking differences in the reactivity of individual conformers in gas-phase ion-molecule reactions. © 2023, CC BY-SA.

关键词： Electronic states

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Multi-label Classification with High-rank and High-order Label Correlations

arXiv

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arXiv 2022年

作者： Si, Chongjie Jia, Yuheng Wang, Ran Zhang, Min-Ling Feng, Yanghe Qu, Chongxiao The Chien-Shiung Wu College Southeast University Nanjing210096 China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Mathematical Science Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Systems Engineering National University of Defense Technology China The 52nd Research Institute of China Electronics Technology Group China

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://***/Chongjie-Si/HOMI. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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Sharpening the dark matter signature in gravitational waveforms II: Numerical simulations with the NbodyIMRI code

arXiv

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arXiv 2024年

作者： Kavanagh, Bradley J. Karydas, Theophanes K. Bertone, Gianfranco Di Cintio, Pierfrancesco Pasquato, Mario Av. de Los Castros s/n Santander39005 Spain Institute for Theoretical Physics Amsterdam and Delta Institute for Theoretical Physics University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands 50022 Italy INAF-Osservatorio Astronomico di Arcetri Largo Enrico Fermi 5 Firenze50125 Italy INFN-Sezione di Firenze via G. Sansone 1 Sesto Fiorentino50022 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 modelling 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 timesteps, 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 semi-analytical 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. Copyright © 2024, The Authors. All rights reserved.

关键词： Gravity waves

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Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions

Watch Your Up-Convolution: CNN Based Generative Deep Neural ...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ricard Durall Margret Keuper Janis Keuper Competence Center High Performance Computing Fraunhofer ITWM Kaiserslautern Germany IWR University of Heidelberg Germany Data and Web Science Group University of Mannheim Germany Institute for Machine Learning and Analytics Offenburg University Germany

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e. known as up-convolution or transposed convolution, are causing the inability of such models to reproduce spectral distributions of natural training data correctly. This effect is independent of the underlying architecture and we show that it can be used to easily detect generated data like deepfakes with up to 100% accuracy on public benchmarks. To overcome this drawback of current generative models, we propose to add a novel spectral regularization term to the training optimization objective. We show that this approach not only allows to train spectral consistent GANs that are avoiding high frequency errors. Also, we show that a correct approximation of the frequency spectrum has positive effects on the training stability and output quality of generative networks.

关键词： Gallium nitride Convolution Distortion Neural networks Training Generative adversarial networks

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Modeling the dielectric constant of silicon-based nanocomposites using machine learning

Modeling the dielectric constant of silicon-based nanocompos...

引用

2020 International Conference on Actual Problems of Electron Devices Engineering, APEDE 2020

作者： Korchagin, Sergey Alekseevich Klinaev, Yuri Vasilievich Terin, Denis Vladimirovich Romanchuk, Sergey Petrovich Financial University Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Yuri Gagarin Saratov State Technical University Department of Natural and Mathematical Sciences Engels Russia Technol. and Qual. Mgmt. Saratov National Research State University Named after N. G. Chernyshevsky Department of Materials Science Saratov Russia Yuri Gagarin Saratov State Technical University Department of Information Security of Automated Systems Saratov Russia

ISBN: (数字)9781728143095

ISBN: (纸本)9781728143088

In this work, we solve the problem of predicting the dielectric constant of silicon-based nanocomposites using machine learning methods. mathematical models and programs have been developed to predict the electrophysical properties of new materials, as well as to select the optimal composition. The results obtained will be useful for faster and cheaper creation of new functional composites. © 2020 IEEE.

关键词： Nanocomposites

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