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

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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Social stress drives the multi-wave dynamics of COVID-19 outbreaks

arXiv

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arXiv 2021年

作者： Kastalskiy, Innokentiy A. Pankratova, Evgeniya V. Mirkes, Evgeny M. Kazantsev, Victor B. Gorban, Alexander N. Department of Neurotechnology Lobachevsky University 23 Gagarin Ave. Nizhny Novgorod603022 Russia 46 Ulyanov St. Nizhny Novgorod603950 Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University 14 Nevsky St. Kaliningrad236016 Russia Laboratory of Perspective Methods for Analysis of Multidimensional Data Institute of Information Technology Mathematics and Mechanics Lobachevsky University 23 Gagarin Ave. Nizhny Novgorod603022 Russia Department of Applied Mathematics Institute of Information Technology Mathematics and Mechanics Lobachevsky University 23 Gagarin Ave. Nizhny Novgorod603022 Russia Department of Mathematics University of Leicester University Rd LeicesterLE1 7RH United Kingdom Neuroscience and Cognitive Technology Laboratory Innopolis University 1 Universitetskaya St. Innopolis420500 Russia Laboratory of Neuromodeling Samara State Medical University 18 Gagarin St. Samara443079 Russia

The dynamics of epidemics depend on how people's behavior changes during an outbreak. At the beginning of the epidemic, people do not know about the virus, then, after the outbreak of epidemics and alarm, they begin to comply with the restrictions and the spreading of epidemics may decline. Over time, some people get tired/frustrated by the restrictions and stop following them (exhaustion), especially if the number of new cases drops down. After resting for a while, they can follow the restrictions again. But during this pause the second wave can come and become even stronger then the first one. Studies based on SIR models do not predict the observed quick exit from the first wave of epidemics. Social dynamics should be considered. The appearance of the second wave also depends on social factors. Many generalizations of the SIR model have been developed that take into account the weakening of immunity over time, the evolution of the virus, vaccination and other medical and biological details. However, these more sophisticated models do not explain the apparent differences in outbreak profiles between countries with different intrinsic sociocultural features. In our work, a system of models of the COVID-19 pandemic is proposed, combining the dynamics of social stress with classical epidemic models. Social stress is described by the tools of sociophysics. The combination of a dynamic SIR-type model with the classical triad of stages of the general adaptation syndrome, alarm-resistance-exhaustion, makes it possible to describe with high accuracy the available statistical data for 13 countries. The sets of kinetic constants corresponding to optimal fit of model to data were found. These constants characterize the ability of society to mobilize efforts against epidemics and maintain this concentration over time, and can further help in the development of management strategies specific to a particular society. © 2021, CC BY.

关键词： Epidemiology

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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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Posterior sampling of the initial conditions of the universe from non-linear large scale structures using score-based generative models

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Monthly Notices of the Royal Astronomical Society: Letters 2024年第1期527卷 L173-L178页

作者： Legin, Ronan Ho, Matthew Lemos, Pablo Perreault-Levasseur, Laurence Ho, Shirley Hezaveh, Yashar Wandelt, Benjamin Department of Physics Universite de Montreal Montreal H2V 0B3 Canada Mila - Quebec Artificial Intelligence Institute Montreal H2S 3H1 Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montreal H2V 0B3 Canada Sorbonne Universite CNRS UMR 7095 Institut d'Astrophysique de Paris 98 bis bd Arago Paris 75014 France Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New York 10010 NY United States Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 ON Canada

Reconstructing the initial conditions of the universe is a key problem in cosmology. Methods based on simulating the forward evolution of the universe have provided a way to infer initial conditions consistent with present-day observations. However, due to the high complexity of the inference problem, these methods either fail to sample a distribution of possible initial density fields or require significant approximations in the simulation model to be tractable, potentially leading to biased results. In this work, we propose the use of score-based generative models to sample realizations of the early universe given present-day observations. We infer the initial density field of full high-resolution dark matter N-body simulations from the present-day density field and verify the quality of produced samples compared to the ground truth based on summary statistics. The proposed method is capable of providing plausible realizations of the early universe density field from the initial conditions posterior distribution marginalized over cosmological parameters and can sample orders of magnitude faster than current state-of-the-art methods. © 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society.

关键词： early Universe large-scale structure of Universe methods: statistical

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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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PSXI-17 Evaluation of Influence of the Total Accumulation of Toxic Elements in Blood Serum on the Indicators of Milk Productivity of the Holstein Cows

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Journal of Animal Science 2022年第SUPPLEMENT_3期100卷 221–221页

作者： Zubac, Isidora Sycheva, Irina Komarchev, Alexey Nikitin, Petr Vasilev, Vladimir Vasilyeva, Irina Stepantsevich, Marina 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 Moscow Regional Research and Clinical Institute "MONIKI" 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 main objective was to study the milk productivity of lactating cows depending on the level of total accumulation of toxic metals in the blood serum. The experimental part of work was carried out on clinically healthy Holstein cows (n=45; lactation stage - 20-40 days after calving) bred in a single farm. The elemental composition of blood serum was studied for 4 chemical elements (Al, Pb, Sn, Sr) by ICP AES and ICP MS. The indicators of milk productivity of cows were evaluated by the yield of milk fat, protein, lactose, dry matter, SOMO and the average daily milk yield. To assess the magnitude of the toxic load on the body of cows, the coefficient of the total toxic load Ktox (µg/ml) was calculated. To calculate the coefficient, the sum of the concentrations of some toxic elements in the blood serum was used: Ktox=Al+Pb+Sn+Sr. Based on the data on the value of the total toxic load coefficient Ktox in blood serum, three groups were formed: group I included cows with a minimum value of the toxic load coefficient (Ktox=0.128 (0.088-0.142) μg/ml relative to the study sample; group II with an average (Ktox=0.229 (0.206-0.276) µg/ml; Group III included cows with the maximum coefficient (Ktox=0.395 (0.306-0.453) µg/ml. Comparative analysis showed that cows with the minimum Ktox value (group I) outperformed individuals from I and group II in terms of milk fat yield by 15.4 (P£0.001) and 19.7% (P£0.001), protein by 5.6 and 12.5% (P£0.05), lactose -10.9 (P£0.01) and 18.4% (P£0.001), dry matter by -7.6 and 12.8% (P£0.05) and average daily milk yield by 9.4 and 17.5% (P£0.01), respectively. Thus, in the course of the study, it was registered that with an increase in the total accumulation of heavy metals in the body of animals, the milk productivity of cows decreases.

关键词： blood serum cattle milk productivity toxic elements

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A novel approach to understanding the link between supermassive black holes and host galaxies

arXiv

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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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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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