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arXiv 2019年

作者： Klein, Natalie Orellana, Josue Brincat, Scott Miller, Earl K. Kass, Robert E. Department of Statistics and Data Science Carnegie Mellon University Machine Learning Department Carnegie Mellon University Center for the Neural Basis of Cognition Carnegie Mellon University University of Pittsburgh Department of Brain and Cognitive Science Massachusetts Institute of Technology

Angular measurements are often modeled as circular random variables, where there are natural circular analogues of moments, including correlation. Because a product of circles is a torus, a d-dimensional vector of circular random variables lies on a d-dimensional torus. For such vectors we present here a class of graphical models, which we call torus graphs, based on the full exponential family with pairwise interactions. The topological distinction between a torus and Euclidean space has several important consequences. Our development was motivated by the problem of identifying phase coupling among oscillatory signals recorded from multiple electrodes in the brain: oscillatory phases across electrodes might tend to advance or recede together, indicating coordination across brain areas. The data analyzed here consisted of 24 phase angles measured repeatedly across 840 experimental trials (replications) during a memory task, where the electrodes were in 4 distinct brain regions, all known to be active while memories are being stored or retrieved. In realistic numerical simulations, we found that a standard pairwise assessment, known as phase locking value, is unable to describe multivariate phase interactions, but that torus graphs can accurately identify conditional associations. Torus graphs generalize several more restrictive approaches that have appeared in various scientific literatures, and produced intuitive results in the data we analyzed. Torus graphs thus unify multivariate analysis of circular data and present fertile territory for future research. Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

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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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Minimax rates of distribution estimation in wasserstein distance

arXiv

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arXiv 2018年

作者： Singh, Shashank Poczos, Barnabas Machine Learning Department and Department of Statistics and Data Science Carnegie Mellon University Machine Learning Department Carnegie Mellon University

The Wasserstein metric is an important measure of distance between probability distributions, with many applications in machine learning, statistics, probability theory, and data analysis. This paper provides new upper and lower bounds on statistical minimax rates for the problem of estimating a probability distribution under Wasserstein loss. Specifically, we provide matching rates in a very general setting, using only metric properties, such as covering and packing numbers of balls in the sample space, and moment bounds on the probability distribution. Copyright © 2018, The Authors. All rights reserved.

关键词： Probability distributions

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A multi-objective deep reinforcement learning algorithm for spatio-temporal latency optimization in mobile IoT-enabled edge computing networks

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Simulation Modelling Practice and Theory 2025年 143卷

作者： Parisa Khoshvaght Amir Haider Amir Masoud Rahmani Farhad Soleimanian Gharehchopogh Ferzat Anka Jan Lansky Mehdi Hosseinzadeh Institute of Research and Development Duy Tan University Da Nang Vietnam School of Engineering & Technology Duy Tan University Da Nang Vietnam Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of AI and Robotics Sejong University Seoul 05006 Republic of Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Ur. C. Islamic Azad University Urmia Iran Data Science Application and Research Center (VEBIM) Fatih Sultan Mehmet Vakif University Istanbul Türkiye Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam Republic of Korea

The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge Computing (MEC) systems. Spatio-temporal dynamics, which include the position of edge servers and the timing of task schedules, pose a complex optimization problem. These challenges are further exacerbated by the heterogeneity of IoT workloads and the constraints imposed by device mobility. The balance between computational overhead and communication challenges is also a problem. To solve these issues, advanced methods are needed for resource management and dynamic task scheduling in mobile IoT and edge computing environments. In this paper, we propose a Deep Reinforcement learning (DRL) multi-objective algorithm, called a Double Deep Q-learning (DDQN) framework enhanced with Spatio-temporal mobility prediction, latency-aware task offloading, and energy-constrained IoT device trajectory optimization for federated edge computing networks. DDQN was chosen for its optimize stability and reduced overestimation in Q-values. The framework employs a reward-driven optimization model that dynamically prioritizes latency-sensitive tasks, minimizes task migration overhead, and balances energy efficiency across devices and edge servers. It integrates dynamic resource allocation algorithms to address random task arrival patterns and real-time computational demands. Simulations demonstrate up to a 35 % reduction in end-to-end latency, a 28 % improvement in energy efficiency, and a 20 % decrease in the deadline-miss ratio compared to benchmark algorithms.

关键词： Double deep Q-learning Latency and energy efficiency Mobile edge computing Spatio-temporal optimization

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machine learning Force Fields

arXiv

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arXiv 2020年

作者： Unke, Oliver T. Chmiela, Stefan Sauceda, Huziel E. Gastegger, Michael Poltavsky, Igor Schütt, Kristof T. Tkatchenko, Alexandre Müller, Klaus-Robert Machine Learning Group Technische Universität Berlin Berlin10587 Germany Technische Universität Berlin Berlin10623 Germany BASLEARN BASF-TU joint Lab Technische Universität Berlin Berlin10587 Germany Department of Physics and Materials Science University of Luxembourg Luxembourg CityL-1511 Luxembourg BIFOLD Berlin Institute for the Foundations of Learning and Data Berlin Germany Department of Artificial Intelligence Korea University Anam-dong Seongbuk-gu Seoul02841 Korea Republic of Max Planck Institute for Informatics Stuhlsatzenhausweg Saarbrücken66123 Germany

In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs. Copyright © 2020, The Authors. All rights reserved.

关键词： machine learning

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Epitopedia: identifying molecular mimicry between pathogens and known immune epitopes

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ImmunoInformatics 2023年 9卷

作者： Christian A Balbin Janelle Nunez-Castilla Vitalii Stebliankin Prabin Baral Masrur Sobhan Trevor Cickovski Ananda Mohan Mondal Giri Narasimhan Prem Chapagain Kalai Mathee Jessica Siltberg-Liberles Department of Biological Sciences College of Arts Science and Education Florida International University Miami United States Bioinformatics Research Group (BioRG) Knight Foundation School of Computing and Information Sciences Florida International University Miami United States Department of Physics College of Arts Science and Education Florida International University Miami United States Machine Learning and Data Analytics Group (MLDAG) Knight Foundation School of Computing and Information Sciences Florida International University Miami United States Biomolecular Sciences Institute Florida International University Miami United States Department of Human and Molecular Genetics Herbert Wertheim College of Medicine Florida International University Miami United States

Upon infection, foreign antigenic proteins stimulate the host's immune system to produce antibodies targeting the pathogen. These antibodies bind to regions on the antigen called epitopes. Structural similarity (molecular mimicry) of epitopes between an infecting pathogen and host proteins or other pathogenic proteins the host has previously encountered can impact the host immune response to the pathogen and may lead to cross-reactive antibodies. The ability to identify potential regions of molecular mimicry in a pathogen can illuminate immune effects which are especially important to pathogen treatment and vaccine design. Here we present Epitopedia, a software pipeline that facilitates the identification of regions that may exhibit potential three-dimensional molecular mimicry between an antigenic pathogen protein and known immune epitopes as catalogued by the Immune Epitope database (IEDB). Epitopedia is open-source software released under the MIT license and is freely available on GitHub, including a Docker container with all other software dependencies preinstalled. We performed an analysis describing how various secondary structure states, identity between pentapeptide pairs, and identity between the parent sequences of pentapeptide pairs affects RMSD. We found that pentapeptides pairs in a helical conformation had considerably lower RMSD values than those in extended or coil conformations. We also found that RMSD is significantly increased when pentapeptide pairs are from non-homologous sequences.

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The limits of distribution-free conditional predictive inference

arXiv

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arXiv 2019年

作者： Barber, Rina Foygel Candès, Emmanuel J. Ramdas, Aaditya Tibshirani, Ryan J. Department of Statistics University of Chicago United States Departments of Statistics and Mathematics Stanford University United States Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States

We consider the problem of distribution-free predictive inference, with the goal of producing predictive coverage guarantees that hold conditionally rather than marginally. Existing methods such as conformal prediction offer marginal coverage guarantees, where predictive coverage holds on average over all possible test points, but this is not sufficient for many practical applications where we would like to know that our predictions are valid for a given individual, not merely on average over a population. On the other hand, exact conditional inference guarantees are known to be impossible without imposing assumptions on the underlying distribution. In this work we aim to explore the space in between these two, and examine what types of relaxations of the conditional coverage property would alleviate some of the practical concerns with marginal coverage guarantees while still being possible to achieve in a distribution-free setting. Copyright © 2019, The Authors. All rights reserved.

关键词： machine learning

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Analyzing the Structure of Attention in a Transformer Language Model

arXiv

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arXiv 2019年

作者： Vig, Jesse Belinkov, Yonatan Palo Alto Research Center Machine Learning and Data Science Group Interaction and Analytics Lab Palo AltoCA United States Harvard John A. Paulson School of Engineering and Applied Sciences MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA United States

The Transformer is a fully attention-based alternative to recurrent networks that has achieved state-of-the-art results across a range of NLP tasks. In this paper, we analyze the structure of attention in a Transformer language model, the GPT-2 small pretrained model. We visualize attention for individual instances and analyze the interaction between attention and syntax over a large corpus. We find that attention targets different parts of speech at different layer depths within the model, and that attention aligns with dependency relations most strongly in the middle layers. We also find that the deepest layers of the model capture the most distant relationships. Finally, we extract exemplar sentences that reveal highly specific patterns targeted by particular attention heads. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Incremental intervention effects in studies with dropout and many timepoints

arXiv

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arXiv 2019年

作者： Kim, Kwangho Kennedy, Edward H. Naimi, Ashley I. Department of Statistics & Data Science Machine Learning Department Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Department of Statistics & Data Science Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Department of Epidemiology Rollins School of Public Health Emory University AtlantaGA United States

Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by dropout and positivity violations. We tackle these problems by generalizing effects of recent incremental interventions (which shift propensity scores rather than set treatment values deterministically) to accommodate multiple outcomes and subject dropout. We give an identifying expression for incremental intervention effects when dropout is conditionally ignorable (without requiring treatment positivity), and derive the nonparametric efficiency bound for estimating such effects. Then we present efficient nonparametric estimators, showing that they converge at fast parametric rates and yield uniform inferential guarantees, even when nuisance functions are estimated flexibly at slower rates. We also study the variance ratio of incremental intervention effects relative to more conventional deterministic effects in a novel infinite time horizon setting, where the number of timepoints can grow with sample size, and show that incremental intervention effects yield near-exponential gains in statistical precision in this setup. Finally we conclude with simulations and apply our methods in a study of the effect of low-dose aspirin on pregnancy *** Codes 62G05 Copyright © 2019, The Authors. All rights reserved.

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L1 Trend Filtering: A Modern Statistical Tool for Time-Domain Astronomy and Astronomical Spectroscopy

arXiv

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arXiv 2019年

作者： Politsch, Collin A. Cisewski-Kehe, Jessi Croft, Rupert A.C. Wasserman, Larry Department of Statistics & Data Science Carnegie Mellon University PittsburghPA15213 Machine Learning Department Carnegie Mellon University PittsburghPA15213 Department of Statistics and Data Science Yale University New HavenCT06520 Department of Physics Carnegie Mellon University PittsburghPA15213 McWilliams Center for Cosmology Carnegie Mellon University PittsburghPA15213

The problem of estimating a one-dimensional signal possessing mixed degrees of smoothness is ubiquitous in time-domain astronomy and astronomical spectroscopy. For example, in the time domain, an astronomical object may exhibit a smoothly varying intensity that is occasionally interrupted by abrupt dips or spikes. Likewise, in the spectroscopic setting, a noiseless spectrum typically contains intervals of relative smoothness mixed with localized higher frequency components such as emission peaks and absorption lines. In this work, we present L1 trend filtering (Steidl et al.;Kim et al.), a modern nonparametric statistical tool that yields significant improvements in this broad problem space of estimating spatially heterogeneous signals. When the underlying signal is spatially heterogeneous, the L1 trend filter has been shown to be strictly superior to any estimator that is a linear combination of the observed data, including smoothing splines, kernels, and Gaussian processes. Moreover, the L1 trend filter does not require the restrictive setup of wavelets — the definitive classical approach for modeling spatially heterogeneous signals. In the spirit of illustrating the wide applicability of L1 trend filtering, we briefly demonstrate its utility on several relevant astrophysical data sets: two Kepler light curves (an exoplanet transit and an eclipsing binary system), a Palomar Transient Factory supernova light curve, and an SDSS galaxy spectrum. Furthermore, we present a more rigorous analysis of the Lyman-alpha forest of SDSS quasar spectra — a standard cosmological tool for probing the large-scale structure of the high redshift intergalactic medium. Copyright © 2019, The Authors. All rights reserved.

关键词： Cosmology

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