Measuring the structural parameters (size, total brightness, light concentration, etc.) of galaxies is a significant first step towards a quantitative description of different galaxy populations. In this work, we demonstrate that a Bayesian Neural Network (BNN) can be used for the inference, with uncertainty quantification, of such morphological parameters from simulated low-surface-brightness galaxy images. Compared to traditional profile-fitting methods, we show that the uncertainties obtained using BNNs are comparable in magnitude, well-calibrated, and the point estimates of the parameters are closer to the true values. Our method is also significantly faster, which is very important with the advent of the era of large galaxy surveys and big data in astrophysics.
Georgia Institute of Technology（佐治亚理工学院）；Duke University（杜克大学）
Multivariate Hawkes processes are commonly used to model streaming networked event data in a wide variety of applications. However, it remains a challenge to extract reliable inference from complex datasets with uncertainty quantification. Aiming towards this, we develop a statistical inference framework to learn causal relationships between nodes from networked data, where the underlying directed graph implies Granger causality. We provide uncertainty quantification for the maximum likelihood estimate of the network multivariate Hawkes process by providing a non-asymptotic confidence set. The…
California Institute of Technology（加州理工学院）；Perimeter Institute for Theoretical Physics（圆周理论物理研究所）；University of Waterloo（滑铁卢大学）；Harvard University（哈佛大学）；Center for Astrophysics — Harvard & Smithsonian（哈佛-史密森尼天体物理中心）
Inference is crucial in modern astronomical research, where hidden astrophysical features and patterns are often estimated from indirect and noisy measurements. Inferring the posterior of hidden features, conditioned on the observed measurements, is essential for understanding the uncertainty of results and downstream scientific interpretations. Traditional approaches for posterior estimation include sampling-based methods and variational inference. However, sampling-based methods are typically slow for high-dimensional inverse problems, while variational inference often lacks estimation accur…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；National University of Singapore（新加坡国立大学）；Singapore Management University（新加坡管理大学）
The availability of commercial wearable trackers equipped with features to monitor sleep duration and quality has enabled more useful sleep health monitoring applications and analyses. However, much research has reported the challenge of long-term user retention in sleep monitoring through these modalities. Since modern Internet users own multiple mobile devices, our work explores the possibility of employing ubiquitous mobile devices and passive WiFi sensing techniques to predict sleep duration as the fundamental measure for complementing long-term sleep monitoring initiatives. In this paper,…
Uncertainty Quantification in Cardiac Model Personalisation from Ultrafast Ultrasound
Université Côte d'Azur（蔚蓝海岸大学）；Inria（法国国家信息与自动化研究所）；Bordeaux University Hospital (CHU de Bordeaux)（波尔多大学医院）；IHU Liryc, Electrophysiology and Heart Modeling Institute（Liryc大学医院研究所（电生理与心脏建模研究所））
Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health
Department of Data Science, Norwegian University of Life Sciences（数据科学系，挪威生命科学大学）；Department for Mathematics and Cybernetics, SINTEF Digital（数学与 cybernetics 系，SINTEF 数字）
Université de Montréal（蒙特利尔大学）；Mila（米拉（魁北克人工智能研究所））；Ciela（Ciela研究所）；Flatiron Institute（熨斗研究所）；CIFAR（加拿大高等研究院）
Inferring accurate posteriors for high-dimensional representations of the brightness of gravitationally-lensed sources is a major challenge, in part due to the difficulties of accurately quantifying the priors. Here, we report the use of a score-based model to encode the prior for the inference of undistorted images of background galaxies. This model is trained on a set of high-resolution images of undistorted galaxies. By adding the likelihood score to the prior score and using a reverse-time stochastic differential equation solver, we obtain samples from the posterior. Our method produces in…
SIFARI: Self-Supervised Interferometric Fitting for Astronomical Radio Imaging
Rice University（莱斯大学）；Rice Space Institute（莱斯空间研究所）；National Radio Astronomy Observatory（美国国家射电天文台）；NSF-Simons AI Institute for Cosmic Origins（NSF-西蒙斯宇宙起源人工智能研究所）；University of Pennsylvania（宾夕法尼亚大学）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
Metropolis-Scale Resilient and Trustworthy Traffic Flow Inference Using Multi-Source Data
School of Transportation, Jilin University（吉林大学交通运输学院）；Department of Computer Science, City University of Hong Kong (Dongguan)（香港城市大学（东莞）计算机科学系）；Institute for Transport Planning and Systems, ETH Zurich（苏黎世联邦理工学院交通规划与系统研究所）；Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University（浙江大学智能交通系统研究所）；ZJU-UIUC Institute, Zhejiang University（浙江大学-UIUC研究院）
University of Science and Technology of China（中国科学技术大学）；Huawei Technologies（华为技术有限公司）
Spatiotemporal forecasting is an imperative topic in data science due to its diverse and critical applications in smart cities. Existing works mostly perform consecutive predictions of following steps with observations completely and continuously obtained, where nearest observations can be exploited as key knowledge for instantaneous status estimation. However, the practical issues of early activity planning and sensor failures elicit a brand-new task, i.e., non-consecutive forecasting. In this paper, we define spatiotemporal learning systems with missing observation as Grey Spatiotemporal Sys…
The data reduction procedure for radio interferometers can be viewed as a combined calibration and imaging problem. We present an algorithm that unifies cross-calibration, self-calibration, and imaging. Being a Bayesian method, that algorithm does not only calculate an estimate of the sky brightness distribution, but also provides an estimate of the joint uncertainty which entails both the uncertainty of the calibration and the one of the actual observation. The algorithm is formulated in the language of information field theory and uses Metric Gaussian Variational Inference (MGVI) as the unde…
We present ${\tt nimbus}$ : a hierarchical Bayesian framework to infer the intrinsic luminosity parameters of kilonovae (KNe) associated with gravitational-wave (GW) events, based purely on non-detections. This framework makes use of GW 3-D distance information and electromagnetic upper limits from multiple surveys for multiple events, and self-consistently accounts for finite sky-coverage and probability of astrophysical origin. The framework is agnostic to the brightness evolution assumed and can account for multiple electromagnetic passbands simultaneously. Our analyses highlight the import…
A recursive Bayesian neural network for constitutive modeling of sands under monotonic and cyclic loading
Department of Civil Engineering（土木工程系）；Indian Institute of Technology Delhi（德里印度理工学院）；Department of Transportation Systems Engineering（交通运输系统工程系）；Technical University of Munich（慕尼黑技术大学）；Department of Applied Mechanics（应用力学系）
Towards real-time surrogate-free Bayesian inversion for neutron reflectometry
Dynamics Research Group University of Sheffield（动态研究组谢菲尔德大学）；School of Mathematical and Physical Sciences University of Sheffield（数学与物理科学学院谢菲尔德大学）；Institut Laue-Langevin（劳厄-朗之万研究所）；ISIS Pulsed Neutron and Muon Source Rutherford Appleton Laboratory（ISIS脉冲中子与muon源劳瑟福-苹果顿实验室）；School of Mechanical, Aerospace and Civil Engineering University of Sheffield（机械、航空航天与土木工程学院谢菲尔德大学）；School of Chemical, Material and Biological Engineering University of Sheffield（化学、材料与生物工程学院谢菲尔德大学）
Reduced Order Modeling with Shallow Recurrent Decoder Networks
Department of Mechanical Engineering, Politecnico di Milano, Milano, Italy（米兰理工学院机械工程系）；Department of Mechanical Engineering, University of Washington, Seattle, WA（华盛顿大学机械工程系）；MOX - Department of Mathematics, Politecnico di Milano, Milano, Italy（米兰理工学院数学系）；Department of Applied Mathematics and Electrical and Computer Engineering, University of Washington, Seattle, WA（华盛顿大学应用数学与电气与计算机工程系）
Artificial lights raise night sky luminance, creating the most visible effect of light pollution-artificial skyglow. Despite the increasing interest among scientists in fields such as ecology, astronomy, health care, and land-use planning, light pollution lacks a current quantification of its magnitude on a global scale. To overcome this, we present the world atlas of artificial sky luminance, computed with our light pollution propagation software using new high-resolution satellite data and new precision sky brightness measurements. This atlas shows that more than 80% of the world and more th…
Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach
Applied Modelling and Computation Group, Department of Earth Science and Engineering（应用建模与计算组，地球科学与工程系）；Institute of GeoEnergy Engineering（地球能源工程研究所）；Department of Civil and Environmental Engineering（土木与环境工程系）；Centre for AI-Physics Modelling, Imperial-X, White City Campus（人工智能-物理建模中心，Imperial-X，白城校区）；Data Assimilation Lab., Data Science Institute（数据同化实验室，数据科学研究院）
Creating a database of 21cm brightness temperature signals from the Epoch of Reionisation (EoR) for an array of reionisation histories is a complex and computationally expensive task, given the range of astrophysical processes involved and the possibly high-dimensional parameter space that is to be probed. We utilise a specific type of neural network, a Progressively Growing Generative Adversarial Network (PGGAN), to produce realistic tomography images of the 21cm brightness temperature during the EoR, covering a continuous three-dimensional parameter space that models varying X-ray emissivity…
With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of Simulation-Based inference (SBI) and amortized Neural Posterior Estimation (NPE) has been successfully used to analyse simulated and real galaxy photometry both precisely and efficiently. In this work, we utilise this combination and build on existing literature to analyse simulated noisy galaxy spectra. Here, we demonstrate a proof-of-concept study of spectra that is a) an efficient anal…
A fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are > $10^9$ photometrically cataloged sources, yet modern spectroscopic surveys are limited to ~few x $10^6$ targets. As we approach the Large Synoptic Survey Telescope (LSST) era, new algorithmic solutions are required to cope with the data deluge. Here we report the development of a machine-learning framework capable of inferring fundamental stellar parameters (Teff, log g, and [Fe/H]) using photometric-brightness variations and color alone. A training set is constructed from a sy…
