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arXiv 2024年

作者： Sasseville, Gabriel Hlavacek-Larrondo, Julie Berek, Samantha C. Eadie, Gwendolyn M. Rhea, Carter Lee Springford, Aaron Mezcua, Mar Haggard, Daryl Department of Physics University of Montreal MontrealQCH2V 0B3 Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning MontrealQCH2V 0B3 Canada Centre de recherche en astrophysique du Québec [CRAQ Canada Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St George Street TorontoONM5S 3H4 Canada Data Sciences Institute University of Toronto 17th Floor Ontario Power Building 700 University Ave TorontoONM5G 1Z5 Canada Department of Statistical Sciences University of Toronto 9th Floor Ontario Power Building 700 University Avenue TorontoONM5G 1Z5 Canada Toronto ON Canada Institute of Space Sciences [ICE CSIC Campus UAB Carrer de Magrans Barcelona08193 Spain Institut d’Estudis Espacials de Catalunya [IEEC Edifici RDIT Campus UPC Castelldefels Barcelona08860 Spain Department of Physics McGill University 3600 rue University MontréalQCH3A2T8 Canada Trottier Space Institute at McGill 3550 rue University MontréalQCH3A2A7 Canada

The strongest and most universal scaling relation between a supermassive black hole and its host galaxy is known as the M• − σ relation, where M• is the mass of the central black hole and σ is the stellar velocity dispersion of the host galaxy. This relation has been studied for decades and is crucial for estimating black hole masses of distant galaxies. However, recent studies suggest the potential absence of central black holes in some galaxies, and a significant portion of current data only provides upper limits for the mass. Here, we introduce a novel approach using a Bayesian hurdle model to analyze the M• − σ relation across 244 galaxies. This model integrates upper mass limits and the likelihood of hosting a central black hole, combining logistic regression for black hole hosting probability with a linear regression of mass on σ. From the logistic regression, we find that galaxies with a velocity dispersion of 11, 34 and 126 km/s have a 50%, 90% and 99% probability of hosting a central black hole, respectively. Furthermore, from the linear regression portion of the model, we find that M• ∝ σ5.8, which is significantly steeper than the slope reported in earlier studies. Our model also predicts a population of under-massive black holes (M• = 10 − 105M☉) in galaxies with σ 127 km/s and over-massive black holes (M• ≥ 1.8 × 107) above this threshold. This reveals an unexpected abundance of galaxies with intermediate-mass and ultramassive black holes, accessible to next-generation telescopes like the Extremely Large Telescope. Copyright © 2024, The Authors. All rights reserved.

关键词： Black holes

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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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Efficient Algorithms for Set-Valued Prediction in Multi-Class Classification

arXiv

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arXiv 2019年

作者： Mortier, Thomas Wydmuch, Marek Dembczyński, Krzysztof Hüllermeier, Eyke Waegeman, Willem Department of Data Analysis and Mathematical Modelling Ghent University Belgium Institute of Computing Science Poznań 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. For this problem, which is computationally challenging as there are exponentially (in the number of classes) many predictions to choose from, we propose efficient algorithms that can be applied to a broad family of utility scores. Two of these algorithms make use of structural information in the form of a class hierarchy, which is often available in prediction problems with many classes. Our theoretical results are complemented by experimental studies, in which we analyze the proposed algorithms in terms of predictive accuracy and runtime efficiency. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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222P Artificial intelligence (AI) based prognostication from baseline computed tomography (CT) scans in a phase III advanced non-small cell lung cancer (aNSCLC) trial

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Annals of Oncology 2024年 35卷 S304-S304页

作者： O.F. Khan J. Montalt-Tordera J. Riskas S.A. Haider V. Sivan O. Samorodova J. Hennessy A. Mitchell S.S. Mohammadi E. Di Tomaso T. Banerji F. Baldauf-Lenschen C. Glaus Oncology Department Cumming School of Medicine Tom Baker Cancer Centre Calgary AB Canada Computer Vision & Sound Analysis Bayer Hispania S.L. Sant Joan Despí Spain Machine Learning Altis Labs Inc. Toronto ON Canada Radiology Altis Labs Inc. Toronto ON Canada Data Altis Labs Inc. Toronto ON Canada Biostatistics Altis Labs Inc. Toronto ON Canada Computer Vision & Sound Analysis Bayer AG Leverkusen Germany Translational Sciences Oncology Bayer U.S. LLC Cambridge MA USA Digital Lead Global Brands Bayer U.S. LLC Whippany NJ USA Executive Altis Labs Inc. Toronto ON Canada

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Field-level simulation-based inference of galaxy clustering with convolutional neural networks

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Physical Review D 2024年第8期109卷 083536-083536页

作者： Pablo Lemos Liam Parker ChangHoon Hahn Shirley Ho Michael Eickenberg Jiamin Hou Elena Massara Chirag Modi Azadeh Moradinezhad Dizgah Bruno Régaldo-Saint Blancard David Spergel Department of Physics Université de Montréal Montréal 1375 Avenue Thérèse-Lavoie-Roux Montréal QC H2V 0B3 Canada Mila—Quebec Artificial Intelligence Institute Montréal 6666 Rue Saint-Urbain Montréal QC H2S 3H1 Canada Ciela—Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New York New York 10010 USA Department of Physics Princeton University Princeton New Jersey 08544 USA Center for Cosmology and Particle Physics Department of Physics New York University New York New York 10003 USA Department of Physics Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA Center for Computational Mathematics Flatiron Institute 162 5th Avenue New York New York 10010 USA Department of Astronomy University of Florida 211 Bryant Space Science Center Gainesville Florida 32611 USA Max-Planck-Institut für Extraterrestrische Physik Postfach 1312 Giessenbachstrasse 1 85748 Garching bei München Germany Waterloo Centre for Astrophysics University of Waterloo 200 University Avenue W. Waterloo Ontario N2L 3G1 Canada Department of Physics and Astronomy University of Waterloo 200 University Avenue W. Waterloo Ontario N2L 3G1 Canada Département de Physique Théorique Université de Genève 24 quai Ernest Ansermet 1211 Genève 4 Switzerland

We present the first simulation-based inference (SBI) of cosmological parameters from field-level analysis of galaxy clustering. Standard galaxy clustering analyses rely on analyzing summary statistics, such as the power spectrum Pℓ, with analytic models based on perturbation theory. Consequently, they do not fully exploit the nonlinear and non-Gaussian features of the galaxy distribution. To address these limitations, we use the SimBIG forward modeling framework to perform SBI using normalizing flows. We apply SimBIG to a subset of the Baryon Oscillation Spectroscopic Survey CMASS galaxy sample using a convolutional neural network with stochastic weight averaging to perform massive data compression of the galaxy field. We infer constraints on Ωm=0.267−0.029+0.033 and σ8=0.762−0.035+0.036. While our constraints on Ωm are in line with standard Pℓ analyses, ours on σ8 are 2.65× tighter. Our analysis also provides constraints on the Hubble constant H0=64.5±3.8 km/s/Mpc from galaxy clustering alone. This higher constraining power comes from additional non-Gaussian cosmological information, inaccessible with Pℓ. We demonstrate the robustness of our analysis by showcasing our ability to infer unbiased cosmological constraints from a series of test simulations that are constructed using different forward models than the one used in our training dataset. This work not only presents competitive cosmological constraints but also introduces novel methods for leveraging additional cosmological information in upcoming galaxy surveys like the Dark Energy Spectroscopic Instrument, Prime Focus Spectrograph, and Euclid.

关键词： Cosmology Dark energy Dark matter Large scale structure of the Universe Astrophysical & cosmological simulations Bayesian methods Deep learning machine learning Numerical simulations in gravitation & astrophysics

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Optimal Neural Summarisation for Full-Field Weak Lensing Cosmological Implicit Inference

arXiv

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arXiv 2024年

作者： Lanzieri, Denise Zeghal, Justine Makinen, T. Lucas Boucaud, Alexandre Starck, Jean-Luc Lanusse, François Université Paris Cité Université Paris-Saclay CEA CNRS AIM Gif-sur-YvetteF-91191 France Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France Astrophysics Group Imperial College London Blackett Laboratory Prince Consort Road LondonSW7 2AZ United Kingdom Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-Yvette91191 France Sony Computer Science Laboratories - Rome Joint Initiative CREF-SONY Centro Ricerche Enrico Fermi Via Panisperna 89/A Rome00184 Italy Greece Center for Computational Astrophysics Flatiron Institute 162 5th Ave New YorkNY10010 United States Department of Physics Université de Montréal MontréalH2V 0B3 Canada Mila – Quebec Artificial Intelligence Institute MontréalH2S 3H1 Canada Ciela – Montreal Institute for Astrophysical Data Analysis and Machine Learning MontréalH2V 0B3 Canada

Context. Traditionally, weak lensing cosmological surveys have been analyzed using summary statistics that were either motivated by their analytically tractable likelihoods (e.g. power spectrum), or by their ability to access some higher-order information (e.g. peak counts) but in that case at the cost of requiring a Simulation-Based Inference approach. In both cases, even if these statistics can be very informative, they are not designed nor guaranteed to be statistically sufficient (i.e. to capture all of the cosmological information content of the data). With the rise of deep learning, however, it becomes possible to create summary statistics that are specifically optimized to extract the full cosmological information content of the data. However, a fairly wide range of loss functions have been used in practice in the weak lensing literature to train such neural networks, leading to the natural question of whether a given loss should be preferred and whether sufficient statistics can be achieved in theory and in practice under these different choices. Aims. We compare different neural summarization strategies that have been proposed in the literature, to answer the question of what loss function leads to theoretically optimal summary statistics for performing full-field cosmological inference. In doing so, we aim to provide guidelines and insights to the community to help guide future neural network-based cosmological inference analyses. Methods. We design an experimental setup which allows us to isolate the specific impact of the loss function used to train neural summary statistics on weak lensing data, at fixed neural architecture and Simulation-Based Inference pipeline. To achieve this, we have developed the sbi_lens JAX package, which implements an automatically differentiable lognormal weak lensing simulator and the tools needed to perform explicit full-field inference with a Hamiltonian-Monte Carlo (HMC) sampler over this model. Using sbi_lens, we simulate

关键词： Cosmology

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LtU-ILI: AN ALL-IN-ONE FRAMEWORK FOR IMPLICIT INFERENCE IN ASTROPHYSICS AND COSMOLOGY

arXiv

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arXiv 2024年

作者： Ho, Matthew Bartlett, Deaglan J. Chartier, Nicolas Cuesta-Lazaro, Carolina Ding, Simon Lapel, Axel Lemos, Pablo Lovell, Christopher C. Makinen, T. Lucas Modi, Chirag Pandya, Viraj Pandey, Shivam Perez, Lucia A. Wandelt, Benjamin Bryan, Greg L. UMR 7095 98 bis bd Arago ParisF-75014 France Center for the Gravitational-Wave Universe Astronomy Program Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Center for Astrophysics Harvard & Smithsonian 60 Garden St CambridgeMA02138 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Université de Montréal Montréal Canada Mila - Quebec Artificial Intelligence Institute Montréal Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Institute of Cosmology and Gravitation University of Portsmouth Burnaby Road PortsmouthPO1 3FX United Kingdom & Astrophysics Group Imperial College London Blackett Laboratory Prince Consort Road LondonSW7 2AZ United Kingdom Center for Computational Mathematics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States 4 place Jussieu ParisF-75252 Cedex 5 France

This paper presents the learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. The pipeline includes software for implementing various neural architectures, training schemata, priors, and density estimators in a manner easily adaptable to any research workflow. It includes comprehensive validation metrics to assess posterior estimate coverage, enhancing the reliability of inferred results. Additionally, the pipeline is easily parallelizable and is designed for efficient exploration of modeling hyperparameters. To demonstrate its capabilities, we present real applications across a range of astrophysics and cosmology problems, such as: estimating galaxy cluster masses from X-ray photometry;inferring cosmology from matter power spectra and halo point clouds;characterizing progenitors in gravitational wave signals;capturing physical dust parameters from galaxy colors and luminosities;and establishing properties of semi-analytic models of galaxy formation. We also include exhaustive benchmarking and comparisons of all implemented methods as well as discussions about the challenges and pitfalls of ML inference in astronomical sciences. All code and examples are made publicly available at https://***/maho3/ltu-ili. © 2024, CC BY.

关键词： Cosmology

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Stability evaluation method for special purpose ACS based on sensitivity theory

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AIP Conference Proceedings 2022年第1期2383卷

作者： Filatov Vladimir Ivanovich Fedorova Veronika Anatolievna Chipchagov Mikhail Sergeevich Zaitsev Mikhail Alekseevich 1Department “Computer security” of faculty “Informatics and control systems” of Bauman Moscow State Technical University 5/1 2ya Baumanskaya st. Moscow 105005 Russia 2Faculty “Big Data Analysis and Machine Learning in Economics and Finance” of Financial University under the Government of the Russian Federation Moscow Russia 3Faculty “Informational system” of Moscow Witte University Moscow Russia

The article deals with the use of complex special purpose automated control systems (ACS), which are characterized by a significant complication of the control process by nodes or elements included in the control loop...

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PSXV-6 Influence of GDF5 Gene Polymorphism on the Concentration of Essential and Toxic Elements in Blood Serum and Milk Productivity of the Holstein Cows

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Journal of Animal Science 2022年第SUPPLEMENT_3期100卷 212–212页

作者： Zubac, Isidora Sycheva, Irina Komarchev, Alexey Nikitin, Petr Orishev, Aleksandr Vasilyeva, Irina Krasavina, Vera University of Belgrade Russian State Agrarian University–Moscow Timiryazev Agricultural Academy Federal Scientific Center «All-Russian Research and Technological Institute of Poultry» of Russian Academy of Sciences Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Federal State Autonomous Educational Institution of Higher Education I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)

The aim of this study was to evaluate the effect of GDF5 (growth differentiation factor 5) gene polymorphism on metabolism of chemical elements and milk production of cows. The studies were performed on Holstein cows (n=100). To determine the single nucleotide polymorphism of GDF5 (T586C in exon 1), blood samples were taken. DNA samples were isolated from whole blood using the IsoGeneLab reagent kit. The thermal cycler "MyCycler" was used for DNA amplification. The elemental composition of blood serum was estimated according to 13 chemical elements (Al, As, Pb, Sn, Sr, Co, Cr, Cu, Fe, I, Mn, Se, Zn) by ICP AES and ICP MS. The frequency of occurrence of polymorphic groups in the GDF5 gene was established, which was for homozygous alleles: TT (n=41), CC (n=10), heterozygous (TS)–(n=49). The analysis of the data obtained showed that blood serum of cows with the CC genotype, relative to animals with the TT and TC genotypes had a greater concentration of I - by 19.3 (P£0.001) and 10.4% (P£0.01), Se - by 18.3 (Р£0.001) and 11.7% (Р£0.01), Zn – by 15.8 (Р£0.01) and 13.2% (Р£0.01), while relatively low concentrations of Al were registered - by 10.8 (Р£0.001) and 21.2% (Р£0.01), Pb - by 16.4 (Р£0.01) and 23.7% (Р£0.001) respectively. In terms of milk fat yield (kg/day), animals with CC genotype advanced over animals with the TT and TC genotypes by 4.8 (P£0.05) and 5.2% (P£0.01), protein by 1.2 and 1.4% (Р£0.05), milk yield - by 4.8 (Р£0.05) and 5.3% (Р£0.01). Thus, we can conclude that the metabolism of chemical elements and the milk productivity of cows are significantly determined by the polymorphism of the GDF5 gene. In order to select cows of the Holstein breed according to the GDF5 gene polymorphism, it is advisable to select homozygous animals for the CC genotype.

关键词： blood serum cattle essential and toxic elements Holstein breed genotype GDF5

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Robust learning with implicit residual networks

arXiv

引用

arXiv 2019年

作者： Reshniak, Viktor Webster, Clayton G. Data Analysis and Machine Learning Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Mathematics University of Tennessee at Knoxville KnoxvilleTN37996 United States Lirio LLC KnoxvilleTN37923 United States

In this effort, we propose a new deep architecture utilizing residual blocks inspired by implicit discretization schemes. As opposed to the standard feed-forward networks, the outputs of the proposed implicit residual blocks are defined as the fixed points of the appropriately chosen nonlinear transformations. We show that this choice leads to the improved stability of both forward and backward propagations, has a favorable impact on the generalization power and allows to control the robustness of the network with only a few hyperparameters. In addition, the proposed reformulation of ResNet does not introduce new parameters and can potentially lead to a reduction in the number of required layers due to improved forward stability. Finally, we derive the memory-efficient training algorithm, propose a stochastic regularization technique and provide numerical results in support of our findings. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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