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A multimodal framework for extraction and fusion of satellite images and public health data

Scientific Data 2024年第1期11卷 1-20页

作者： Moukheiber, Dana Restrepo, David Cajas, Sebastián Andrés Montoya, María Patricia Arbeláez Celi, Leo Anthony Kuo, Kuan-Ting López, Diego M. Moukheiber, Lama Moukheiber, Mira Moukheiber, Sulaiman Osorio-Valencia, Juan Sebastian Purkayastha, Saptarshi Paddo, Atika Rahman Wu, Chenwei Kuo, Po-Chih Laboratory for Computational Physiology Massachusetts Institute of Technology Cambridge MA United States Departamento de Telemática Universidad del Cauca Cauca Popayán Colombia John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA United States School of Computer Science University College Dublin Dublin Ireland Grupo de Epidemiología Facultad Nacional de Salud Pública Universidad de Antioquia Medellín Colombia Department of Biostatistics Harvard TH Chan School of Public Health Boston MA United States Department of Medicine Beth Israel Deaconess Medical Center Boston MA United States Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science Worcester Polytechnic Institute Worcester MA United States ScienteLab Cundinamarca Bogota Colombia Department of BioHealth Informatics Indiana University Luddy School of Informatics Computing and Engineering Indianapolis IN United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States

In low- and middle-income countries, the substantial costs associated with traditional data collection pose an obstacle to facilitating decision-making in the field of public health. Satellite imagery offers a potential solution, but the image extraction and analysis can be costly and requires specialized expertise. We introduce SatelliteBench, a scalable framework for satellite image extraction and vector embeddings generation. We also propose a novel multimodal fusion pipeline that utilizes a series of satellite imagery and metadata. The framework was evaluated generating a dataset with a collection of 12,636 images and embeddings accompanied by comprehensive metadata, from 81 municipalities in Colombia between 2016 and 2018. The dataset was then evaluated in 3 tasks: including dengue case prediction, poverty assessment, and access to education. The performance showcases the versatility and practicality of SatelliteBench, offering a reproducible, accessible and open tool to enhance decision-making in public health. © The Author(s) 2024.

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Neural-network quantum states for ultra-cold Fermi gases

arXiv

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arXiv 2023年

作者： Kim, Jane Fore, Bryce Carleo, Giuseppe Nys, Jannes Pescia, Gabriel Gandolfi, Stefano Hjorth-Jensen, Morten Lovato, Alessandro Department of Physics and Astronomy Facility for Rare Isotope Beams Michigan State University East LansingMI48824 United States Physics Division Argonne National Laboratory ArgonneIL60439 United States LausanneCH-1015 Switzerland Center for Quantum Science and Engineering École Polytechnique Fédérale de Lausanne [EPFL LausanneCH-1015 Switzerland Theory Division T-2 Los Alamos National Laboratory Los AlamosNM87545 United States Department of Physics Center for Computing in Science Education University of Oslo OsloN-0316 Norway Computational Science Division Argonne National Laboratory ArgonneIL60439 United States INFN-TIFPA Trento Institute of Fundamental Physics and Applications Trento38123 Italy

Ultra-cold Fermi gases display diverse quantum mechanical properties, including the transition from a fermionic superfluid BCS state to a bosonic superfluid BEC state, which can be probed experimentally with high precision. However, the theoretical description of these properties is challenging due to the onset of strong pairing correlations and the non-perturbative nature of the interaction among the constituent particles. This work introduces a novel Pfaffian-Jastrow neural-network quantum state that includes backflow transformation based on message-passing architecture to efficiently encode pairing, and other quantum mechanical correlations. Our approach offers substantial improvements over comparable ansätze constructed within the Slater-Jastrow framework and outperforms state-of-the-art diffusion Monte Carlo methods, as indicated by our lower ground-state energies. We observe the emergence of strong pairing correlations through the opposite-spin pair distribution functions. Moreover, we demonstrate that transfer learning stabilizes and accelerates the training of the neural-network wave function, enabling the exploration of the BCS-BEC crossover region near unitarity. Our findings suggest that neural-network quantum states provide a promising strategy for studying ultra-cold Fermi gases. Copyright © 2023, The Authors. All rights reserved.

关键词： Wave functions

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Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation

arXiv

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arXiv 2023年

作者： Acosta, Fernando Torales Mikuni, Vinicius Nachman, Benjamin Arratia, Miguel Karki, Bishnu Milton, Ryan Karande, Piyush Angerami, Aaron Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States Department of Physics and Astronomy University of California RiversideCA92521 United States Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Nuclear and Chemical Science Division Lawrence Livermore National Laboratory LivermoreCA94550 United States

Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared. © 2023, CC BY.

关键词： Calorimeters

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Continuous black-box optimization with an Ising machine and random subspace coding

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Physical Review Research 2022年第2期4卷 023062-023062页

作者： Syun Izawa Koki Kitai Shu Tanaka Ryo Tamura Koji Tsuda Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Kashiwa 277-8561 Japan Department of Mechanical Engineering The University of Tokyo Tokyo 113-8654 Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama 223-8522 Japan Green Computing System Research Organization Waseda University Tokyo 162-0042 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0047 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0047 Japan RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan

A black-box optimization algorithm such as Bayesian optimization finds the extremum of an unknown function by alternating the inference of the underlying function and optimization of an acquisition function. In a high-dimensional space, such algorithms perform poorly due to the difficulty of acquisition function optimization. Herein, we apply Ising machines to overcome the difficulty in the continuous black-box optimization. As an Ising machine specializes in optimization of binary problems, a continuous vector has to be encoded to binary, and the solution by Ising machines has to be translated back. Our method has the following three parts: (1) random subspace coding based on axis-parallel hyperrectangles from continuous vector to binary vector, (2) a quadratic unconstrained binary optimization (QUBO) defined by the acquisition function based on the nonnegative-weighted linear regression model, which is solved by Ising machines, and (3) a penalization scheme to ensure that the solution can be translated back. It is shown with benchmark tests that its performance using the D-Wave Advantage quantum annealer and simulated annealing is competitive with a state-of-the-art method based on the Gaussian process in high-dimensional problems. Our method may open up the possibility of Ising machines and other QUBO solvers, including a quantum approximate optimization algorithm using gated-quantum computers, and may expand its range of application to continuous-valued problems.

关键词： Adiabatic quantum optimization Machine learning Quantum algorithms Quantum computation

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

arXiv

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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

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HETEROGENEOUS MULTISCALE METHODS FOR FOURTH-ORDER SINGULAR PERTURBATIONS

arXiv

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arXiv 2025年

作者： Liao, Yulei Ming, Pingbing Department of Mathematics Faculty of Science National University of Singapore 10 Lower Kent Ridge Road Singapore119076 Singapore LSEC Institute of Computational Mathematics and Scientific/Engineering Computing AMSS Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

We develop a numerical homogenization method for fourth-order singular perturbation problems within the framework of heterogeneous multiscale method. These problems arise from heterogeneous strain gradient elasticity and elasticity models for architectured materials. We establish an error estimate for the homogenized solution applicable to general media and derive an explicit convergence for the locally periodic media with the fine-scale Ε. For cell problems of size δ = NΕ, the classical resonance error O(Ε/δ) can be eliminated due to the dominance of the higher-order operator. Despite the occurrence of boundary layer effects, discretization errors do not necessarily deteriorate for general boundary conditions. Numerical simulations corroborate these theoretical findings. Copyright © 2025, The Authors. All rights reserved.

关键词： Convergence of numerical methods

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Quantum technologies for climate change: Preliminary assessment

arXiv

引用

arXiv 2021年

作者： Berger, Casey Paolo, Agustin Di Forrest, Tracey Hadfield, Stuart Sawaya, Nicolas Stęchly, Michal Thibault, Karl Department of Physics Smith College NorthamptonMA01063 United States Boston University Rafik B Hariri Institute for Computing and Computational Science & Engineering BostonMA02215 United States Département de physique Institut quantique Université de Sherbrooke SherbrookeQCJ1K 2R1 Canada Transformative Quantum Technologies University of Waterloo WaterlooON Canada USRA Research Institute for Advanced Computer Science Mountain ViewCA94043 United States Intel Labs Santa ClaraCA95054 United States Zapata Computing Canada Inc. 325 Front St W TorontoONM5V 2Y1 Canada

Climate change presents an existential threat to human societies and the Earth’s ecosystems more generally. Mitigation strategies naturally require solving a wide range of challenging problems in science, engineering, and economics. In this context, rapidly developing quantum technologies in computing, sensing, and communication could become useful tools to diagnose and help mitigate the effects of climate change. However, the intersection between climate and quantum sciences remains largely unexplored. This preliminary report aims to identify potential high-impact use-cases of quantum technologies for climate change with a focus on four main areas: simulating physical systems, combinatorial optimization, sensing, and energy efficiency. We hope this report provides a useful resource towards connecting the climate and quantum science communities, and to this end we identify relevant research questions and next steps. Copyright © 2021, The Authors. All rights reserved.

关键词： Climate change

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Max-Min Fairness in IRS-Aided Multi-Cell MISO Systems with Joint Transmit and Reflective Beamforming

arXiv

引用

arXiv 2020年

作者： Xie, Hailiang Xu, Jie Liu, Ya-Feng School of Information Engineering Guangdong University of Technology Guangzhou510006 China Shenzhen518172 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

This paper investigates an intelligent reflecting surface (IRS)-aided multi-cell multiple-input single-output (MISO) system with a set of multi-antenna base stations (BSs) each communicating with a single-antenna user, in which an IRS is dedicatedly deployed for assisting the wireless transmission and suppressing the inter-cell interference. Under this setup, we jointly optimize the coordinated transmit beamforming vectors at the BSs and the reflective beamforming vector at the IRS, for the purpose of maximizing the minimum weighted signal-to-interference-plus-noise ratio (SINR) at the users, subject to the individual maximum transmit power constraints at the BSs and the reflection constraints at the IRS. To solve the non-convex min-weighted-SINR maximization problem, we first present an exact-alternating-optimization approach to optimize the transmit and reflective beamforming vectors in an alternating manner, in which the transmit and reflective beamforming optimization subproblems are solved exactly in each iteration by using the techniques of second-order-cone program (SOCP) and semi-definite relaxation (SDR), respectively. However, the exact-alternating-optimization approach has high computational complexity, and may lead to compromised performance due to the uncertainty of randomization in SDR. To avoid these drawbacks, we further propose an inexact-alternating-optimization approach, in which the transmit and reflective beamforming optimization subproblems are solved inexactly in each iteration based on the principle of successive convex approximation (SCA). In addition, to further reduce the computational complexity, we propose a low-complexity inexact-alternating-optimization design, in which the reflective beamforming optimization subproblem is solved more inexactly. Via numerical results, it is shown that the proposed three designs achieve significantly increased min-weighted-SINR values, as compared with benchmark schemes without the IRS or with random re

关键词： Beamforming

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Optically Heralded Entanglement of Superconducting Systems in Quantum Networks

引用

Physical Review Letters 2021年第4期127卷 040503-040503页

作者： Stefan Krastanov Hamza Raniwala Jeffrey Holzgrafe Kurt Jacobs Marko Lončar Matthew J. Reagor Dirk R. Englund Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA U.S. Army Research Laboratory Computational and Information Sciences Directorate Adelphi Maryland 20783 USA Department of Physics University of Massachusetts at Boston Boston Massachusetts 02125 USA Rigetti Computing 775 Heinz Avenue Berkeley California 94710 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

Networking superconducting quantum computers is a longstanding challenge in quantum science. The typical approach has been to cascade transducers: converting to optical frequencies at the transmitter and to microwave frequencies at the receiver. However, the small microwave-optical coupling and added noise have proven formidable obstacles. Instead, we propose optical networking via heralding end-to-end entanglement with one detected photon and teleportation. This new protocol can be implemented on standard transduction hardware while providing significant performance improvements over transduction. In contrast to cascaded direct transduction, our scheme absorbs the low optical-microwave coupling efficiency into the heralding step, thus breaking the rate-fidelity trade-off. Moreover, this technique unifies and simplifies entanglement generation between superconducting devices and other physical modalities in quantum networks.

关键词： Entanglement production Optical & microwave phenomena Quantum entanglement Quantum networks Superconducting qubits

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Nonuniform 3D finite difference elastic wave simulation on staggered grids

arXiv

引用

arXiv 2020年

作者： Gao, Longfei Ghattas, Omar Keyes, David Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX78712 United States Oden Institute for Computational Engineering and Sciences Jackson School of Geosciences Department of Mechanical Engineering The University of Texas at Austin AustinTX78712 United States Applied Mathematics and Computational Science Program Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

We present an approach to simulate the 3D isotropic elastic wave propagation using nonuniform finite difference discretization on staggered grids. Specifically, we consider simulation domains composed of layers of uniform grids with different grid spacings, separated by nonconforming interfaces. We demonstrate that this layer-wise finite difference discretization has the potential to significantly reduce the simulation cost, compared to its fully uniform counterpart. Stability of such a discretization is achieved by using specially designed difference operators, which are variants of the standard difference operators with adaptations near boundaries or interfaces, and penalty terms, which are appended to the discretized wave system to weakly impose boundary or interface conditions. Combined with specially designed interpolation operators, the discretized wave system is shown to preserve the energy conserving property of the continuous elastic wave equation, and a fortiori ensure the stability of the simulation. Numerical examples are presented to demonstrate the efficacy of the proposed simulation approach. © 2020, CC BY.

关键词： Elastic waves

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