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Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference

Physical Review Letters 2023年第17期130卷 171403-171403页

作者： Maximilian Dax Stephen R. Green Jonathan Gair Michael Pürrer Jonas Wildberger Jakob H. Macke Alessandra Buonanno Bernhard Schölkopf Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 72076 Tübingen Germany Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Am Mühlenberg 1 14476 Potsdam Germany School of Mathematical Sciences University of Nottingham University Park Nottingham NG7 2RD United Kingdom Department of Physics East Hall University of Rhode Island Kingston Rhode Island 02881 USA URI Research Computing Tyler Hall University of Rhode Island Kingston Rhode Island 02881 USA Machine Learning in Science University of Tübingen 72076 Tübingen Germany Department of Physics University of Maryland College Park Maryland 20742 USA

We combine amortized neural posterior estimation with importance sampling for fast and accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian posterior using neural networks, and then attach importance weights based on the underlying likelihood and prior. This provides (1) a corrected posterior free from network inaccuracies, (2) a performance diagnostic (the sample efficiency) for assessing the proposal and identifying failure cases, and (3) an unbiased estimate of the Bayesian evidence. By establishing this independent verification and correction mechanism we address some of the most frequent criticisms against deep learning for scientific inference. We carry out a large study analyzing 42 binary black hole mergers observed by LIGO and Virgo with the SEOBNRv4PHM and IMRPhenomXPHM waveform models. This shows a median sample efficiency of ≈10% (2 orders of magnitude better than standard samplers) as well as a tenfold reduction in the statistical uncertainty in the log evidence. Given these advantages, we expect a significant impact on gravitational-wave inference, and for this approach to serve as a paradigm for harnessing deep learning methods in scientific applications.

关键词： Classical black holes General relativity Gravitational wave sources Gravitational waves Astronomical black holes data analysis Gravitational wave detectors machine learning

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Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distribution

arXiv

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arXiv 2024年

作者： Xiong, Jiaqi Yin, Nan Liang, Shiyang Li, Haoyang Wang, Yingxu Ai, Duo Pan, Fang Wang, Jingjie Department of Gastroenterology Tangdu Hospital Fourth Military Medical University Shaanxi 710038 China Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University Guangzhou528225 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Internal Medicine The No. 944 Hospital of Joint Logistic Support Force of PLA Xiongguan Road Jiu Quan735000 China Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Dermatology Xijing Hospital Fourth Military Medical University No 127 of West Changle Road Shaanxi Xi’an710032 China

Inferencing Gene Regulatory Networks (GRNs) from gene expression data is a pivotal challenge in systems biology, and several innovative computational methods have been introduced. However, most of these studies have not considered the skewed degree distribution of genes. Specifically, some genes may regulate multiple target genes while some genes may be regulated by multiple regulator genes. Such a skewed degree distribution issue significantly complicates the application of directed graph embedding methods. To tackle this issue, we propose the Cross-Attention Complex Dual Graph Embedding Model (XATGRN). Our XATGRN employs a cross-attention mechanism to effectively capture intricate gene interactions from gene expression profiles. Additionally, it uses a Dual Complex Graph Embedding approach to manage the skewed degree distribution, thereby ensuring precise prediction of regulatory relationships and their directionality. Our model consistently outperforms existing state-of-the-art methods across various datasets, underscoring its efficacy in elucidating complex gene regulatory mechanisms. Our codes used in this paper are publicly available at: https://***/kikixiong/XATGRN. © 2024, CC BY.

关键词： Graph embeddings

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TEEM: Two-Factor Energy Evaluation Metric Toward Green Big data System

TEEM: Two-Factor Energy Evaluation Metric Toward Green Big D...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Weidong Fang Chunsheng Zhu Mohsen Guizani Zhiqi Li Wuxiong Zhang Joel J.P.C. Rodrigues Science and Technology on Micro-system Laboratory Shanghai Institute of Micro-system and Information Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China Shanghai Research and Development Center for Micro-Nano Electronics Shanghai China College of Big Data and Internet Shenzhen Technology University Shenzhen China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE COPELABS Lusófona University Lisbon Portugal

Toward green Big data System (BDS), one of the key requirements is to save energy consumption so that the system lifetime can be prolonged. Hence, the energy evaluation metric for the measurement of energy efficiency in green BDS, is very critical. Unfortunately, most current energy evaluation metrics are based on a single factor, which might be difficult to meet the diverse application requirements. In this paper, we propose a novel two-factor energy evaluation metric (TEEM) for green BDS. Specifically, the transmission distance and the modulation level are taken into account simultaneously, generating a metric named the bit-per energy consumption (BEC). Extensive simulation results demonstrate that the system performance in energy consumption can be more effectively evaluated by BEC.

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DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm

arXiv

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arXiv 2023年

作者： Ding, Lisang Jin, Kexin Ying, Bicheng Yuan, Kun Yin, Wotao Department of Mathematics University of California Los AngelesCA United States Department of Mathematics Princeton University PrincetonNJ United States Google Inc. Los AngelesCA United States Center for Machine Learning Research Peking University Beijing China AI for Science Institute Beijing China National Engineering Labratory for Big Data Analytics and Applications Beijing China Decision Intelligence Lab. Alibaba US BellevueWA United States

Decentralized Stochastic Gradient Descent (SGD) is an emerging neural network training approach that enables multiple agents to train a model collaboratively and simultaneously. Rather than using a central parameter server to collect gradients from all the agents, each agent keeps a copy of the model parameters and communicates with a small number of other agents to exchange model updates. Their communication, governed by the communication topology and gossip weight matrices, facilitates the exchange of model updates. The state-of-the-art approach uses the dynamic one-peer exponential-2 topology, achieving faster training times and improved scalability than the ring, grid, torus, and hypercube topologies. However, this approach requires a power-of-2 number of agents, which is impractical at scale. In this paper, we remove this restriction and propose Decentralized SGD with Communication-optimal Exact Consensus Algorithm (DSGD-CECA), which works for any number of agents while still achieving state-of-the-art properties. In particular, DSGD-CECA incurs a unit per-iteration communication overhead and an Õ(n3) transient iteration complexity. Our proof is based on newly discovered properties of gossip weight matrices and a novel approach to combine them with DSGD’s convergence analysis. Numerical experiments show the efficiency of DSGD-CECA. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Improved decision making with similarity based machine learning: Applications in chemistry

arXiv

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arXiv 2022年

作者： Lemm, Dominik von Rudorff, Guido Falk von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 14-16 ViennaAT-1090 Austria University of Vienna Vienna Doctoral School in Physics Boltzmanngasse 5 ViennaAT-1090 Austria Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

Despite the fundamental progress in autonomous molecular and materials discovery, data scarcity throughout chemical compound space still severely hampers the use of modern ready-made machine learning models as they rely heavily on the paradigm,’the bigger the data the better’. Presenting similarity based machine learning (SML), we show an approach to select data and train a model on-the-fly for specific queries, enabling decision making in data scarce scenarios in chemistry. By solely relying on query and training data proximity to choose training points, only a fraction of data is necessary to converge to competitive performance. After introducing SML for the harmonic oscillator and the Rosenbrock function, we describe applications to scarce data scenarios in chemistry which include quantum mechanics based molecular design and organic synthesis planning. Finally, we derive a relationship between the intrinsic dimensionality and volume of feature space, governing the overall model accuracy. © 2022, CC BY.

关键词： Decision making

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Set-valued prediction in multi-class classification 31

Set-valued prediction in multi-class classification

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31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on machine learning, BNAIC/BENELEARN 2019

作者： Mortier, Thomas Wydmuch, Marek Dembczýnski, Krzysztof Hüllermeier, Eyke Waegeman, Willem Department of Data Analysis and Mathematical Modelling Ghent University Belgium Institute of Computing Science Poznán University of Technology Poland Intelligent Systems and Machine Learning Universität Paderborn Germany

In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. More precisely, the classifier should strive for an optimal balance between the correctness (the true class is among the candidates) and the precision (the candidates are not too many) of its prediction. We formalize this problem within a general decision-theoretic framework that unifies most of the existing work in this area. In this framework, uncertainty is quantified in terms of conditional class probabilities, and the quality of a predicted set is measured in terms of a utility function. We then address the problem of finding the Bayes-optimal prediction, i.e., the subset of class labels with highest expected utility. © 2019 for this paper by its authors.

关键词： Forecasting

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Dynamics, Statistics, and Predictability of Rossby Waves, Heat Waves, and Spatially Compounding Extreme Events

Dynamics, Statistics, and Predictability of Rossby Waves, He...

引用

作者： Lembo, Valerio Bordoni, Simona Bevacqua, Emanuele Domeisen, Daniela I.V. Franzke, Christian L.E. Galfi, Vera M. Garfinkel, Chaim I. Grams, Christian M. Hochman, Assaf Jha, Roshan Kornhuber, Kai Kwasniok, Frank Lucarini, Valerio Messori, Gabriele Pappert, Duncan Perez-Fernandez, Iago Riboldi, Jacopo Russo, Emmanuele Shaw, Tiffany A. Strigunova, Iana Strnad, Felix Yiou, Pascal Zagar, Nedjeljka Bologna Italy University of Trento Trento Italy Department of Compound Environmental Risks Helmholtz Centre for Environmental Research – UFZ Leipzig Germany University of Lausanne Lausanne Switzerland Institute for Atmospheric and Climate Science ETH Zurich Zurich Switzerland Center for Climate Physics Institute for Basic Science Busan Korea Republic of Pusan National University Busan Korea Republic of Institute for Environmental Studies Vrije Universiteit Amsterdam Amsterdam Netherlands Fredy and Nadine Herrmann Institute of Earth Sciences The Hebrew University of Jerusalem Jerusalem Israel Federal Office of Meteorology and Climatology MeteoSwiss Zurich-Airport Switzerland IDP in Climate Studies Indian Institute of Technology Bombay Mumbai India International Institute of Applied Systems Analysis Laxenburg Austria Potsdam Institute for Climate impact Research Potsdam Germany Lamont-Doherty Earth Observatory Columbia University New YorkNY United States Department of Mathematics and Statistics University of Exeter Exeter United Kingdom School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Earth Sciences Uppsala University Uppsala Sweden Uppsala University Uppsala Sweden Department of Meteorology Bolin Centre for Climate Research Stockholm University Stockholm Sweden Institute of Geography University of Bern Bern Switzerland University of Bern Bern Switzerland Departamento de Ciencias de la atmósfera y Física de los Océanos Facultad de Ciencias Universidad de la República Montevideo Uruguay Department of the Geophysical Sciences The University of Chicago ChicagoIL United States Machine Learning in Climate Science University of Tübingen Tübingen Germany Laboratoire des Sciences du Climat et de’Environnement UMR8212 CEA-CNRS-UVSQ IPSL & Université Paris-Saclay Gif-sur-Yvette France Meteorological Institute CEN Universität Hamburg Hamburg Germany

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The unrealized potential of agroforestry for an emissions-intensive agricultural commodity

arXiv

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arXiv 2024年

作者： Becker, Alexander Wegner, Jan D. Dawoe, Evans Schindler, Konrad Thompson, William J. Bunn, Christian Garrett, Rachael D. Castro, Fabio Hart, Simon P. Blaser-Hart, Wilma J. ETH Zurich Switzerland EcoVision Lab Dept. of Mathematical Modeling and Machine Learning University of Zurich Switzerland Dept. of Agroforestry Faculty of Renewable Natural Resources Kwame Nkrumah University of Science and Technology Kumasi Ghana Nature-based Solutions Initiative Department of Biology University of Oxford OxfordOX12JD United Kingdom Alliance of Bioversity International and CIAT Rome00153 Italy Department of Geography Conservation Research Institute University of Cambridge Cambridge United Kingdom School of the Environment The University of Queensland St LuciaQLD4072 Australia

Reconciling agricultural production with climate-change mitigation and adaptation is one of the most formidable problems in sustainability. One proposed strategy for addressing this problem is the judicious retention of trees in agricultural systems. However, the magnitude of the current and future-potential benefit that trees contribute remains uncertain, particularly in the agricultural sector where trees can also limit production. Here we help to resolve these issues across a West African region responsible for producing ≈60% of the world’s cocoa, a crop that contributes one of the highest per unit carbon footprints of all foods. We use machine learning to generate spatially-explicit estimates of shade-tree cover and carbon stocks across the region. We find that existing shade-tree cover is low, and not spatially aligned with climate threat. But we also find enormous unrealized potential for the sector to counterbalance a large proportion of their high carbon footprint annually, without threatening production. Our methods can be applied to other globally significant commodities that can be grown in agroforests, and align with accounting requirements of carbon markets, and emerging legislative requirements for sustainability reporting. © 2024, CC BY.

关键词： Sustainable agriculture

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Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting

arXiv

引用

arXiv 2022年

作者： He, Hui Zhang, Qi Yi, Kun Shi, Kaize Niu, Zhendong Cao, Longbing The School of Medical Technology Beijing Institute of Technology Beijing100081 China The School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China The Department of Computer Science Tongji University Shanghai201804 China The Data Science and Machine Intelligence Lab University of Technology SydneyNSW2007 Australia The Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education China The DataX Research Centre School of Computing Macquarie University SydneyNSW2109 Australia

Due to the non-stationary nature, the distribution of real-world multivariate time series (MTS) changes over time, which is known as distribution drift. Most existing MTS forecasting models greatly suffer from distribution drift and degrade the forecasting performance over time. Existing methods address distribution drift via adapting to the latest arrived data or self-correcting per the meta knowledge derived from future data. Despite their great success in MTS forecasting, these methods hardly capture the intrinsic distribution changes, especially from a distributional perspective. Accordingly, we propose a novel framework temporal conditional variational autoencoder (TCVAE) to model the dynamic distributional dependencies over time between historical observations and future data in MTSs and infer the dependencies as a temporal conditional distribution to leverage latent variables. Specifically, a novel temporal Hawkes attention mechanism represents temporal factors subsequently fed into feed-forward networks to estimate the prior Gaussian distribution of latent variables. The representation of temporal factors further dynamically adjusts the structures of Transformer-based encoder and decoder to distribution changes by leveraging a gated attention mechanism. Moreover, we introduce conditional continuous normalization flow to transform the prior Gaussian to a complex and form-free distribution to facilitate flexible inference of the temporal conditional distribution. Extensive experiments conducted on six real-world MTS datasets demonstrate the TCVAE’s superior robustness and effectiveness over the state-of-the-art MTS forecasting baselines. We further illustrate the TCVAE applicability through multifaceted case studies and visualization in real-world *** Codes 68Txx Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Traffic4cast at NeurIPS 2021 – Temporal and Spatial Few-Shot Transfer learning in Gridded Geo-Spatial Processes

arXiv

引用

arXiv 2022年

作者： Eichenberger, Christian Neun, Moritz Martin, Henry Herruzo, Pedro Spanring, Markus Lu, Yichao Choi, Sungbin Konyakhin, Vsevolod Lukashina, Nina Shpilman, Aleksei Wiedemann, Nina Raubal, Martin Wang, Bo Vu, Hai L. Mohajerpoor, Reza Cai, Chen Kim, Inhi Hermes, Luca Melnik, Andrew Velioglu, Riza Vieth, Markus Schilling, Malte Bojesomo, Alabi Al Marzouqi, Hasan Liatsis, Panos Santokhi, Jay Hillier, Dylan Yang, Yiming Sarwar, Joned Jordan, Anna Hewage, Emil Jonietz, David Tang, Fei Gruca, Aleksandra Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland Layer 6 AI Toronto Canada ITMO University Saint Petersburg Russia JetBrains Research Saint Petersburg Russia HSE University Saint Petersburg Russia Institute of Transport Studies Monash University ClaytonVIC Australia CSIRO’s Data61 Eveleigh Australia Institute Civil and Environmental Engineering Department Kongju National University Korea Republic of Machine Learning & Neuroinformatics Group Bielefeld University Germany Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Alchera Data Technologies Ltd Cambridge United Kingdom HERE Technologies Zurich Switzerland Silesian University of Technology Gliwice Poland Machine Learning Institute Johannes Kepler University Linz Austria

The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins. We thus reinterpreted the challenge of forecasting traffic conditions as a movie completion task. U-Nets proved to be the winning architecture, demonstrating an ability to extract relevant features in this complex real-world geo-spatial process. Building on the previous competitions, Traffic4cast 2021 now focuses on the question of model robustness and generalizability across time and space. Moving from one city to an entirely different city, or moving from pre-COVID times to times after COVID hit the world thus introduces a clear domain shift. We thus, for the first time, release data featuring such domain shifts. The competition now covers ten cities over 2 years, providing data compiled from over 1012 GPS probe data. Winning solutions captured traffic dynamics sufficiently well to even cope with these complex domain shifts. Surprisingly, this seemed to require only the previous 1h traffic dynamic history and static road graph as input. © 2022, CC BY-NC-SA.

关键词： Probes

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