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Mid-Range science Objectives for the Event Horizon Telescope

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arXiv 2024年

作者： Akiyama, Kazunori Albentosa-Ruíz, Ezequiel Alberdi, Antxon Alef, Walter Algaba, Juan Carlos Anantua, Richard Asada, Keiichi Azulay, Rebecca Bach, Uwe Baczko, Anne-Kathrin Ball, David Balokovi, Mislav Bandyopadhyay, Bidisha Barrett, John Baubck, Michi Benson, Bradford A. Bintley, Dan Blackburn, Lindy Blundell, Raymond Bouman, Katherine L. Bower, Geoffrey C. Bremer, Michael Brissenden, Roger Britzen, Silke Broderick, Avery E. Broguiere, Dominique Bronzwaer, Thomas Bustamante, Sandra Carlstrom, John E. Chael, Andrew Chan, Chi-Kwan Chang, Dominic O. Chatterjee, Koushik Chatterjee, Shami Chen, Ming-Tang Chen, Yongjun Cheng, Xiaopeng Cho, Ilje Christian, Pierre Conroy, Nicholas S. Conway, John E. Crawford, Thomas M. Crew, Geoffrey B. Cruz-Osorio, Alejandro Cui, Yuzhu Curd, Brandon Dahale, Rohan Davelaar, Jordy De Laurentis, Mariafelicia Deane, Roger Dempsey, Jessica Desvignes, Gregory Dexter, Jason Dhruv, Vedant Dihingia, Indu K. Doeleman, Sheperd S. Dzib, Sergio A. Eatough, Ralph P. Emami, Razieh Falcke, Heino Farah, Joseph Fish, Vincent L. Fomalont, Edward Ford, H. Alyson Foschi, Marianna Fraga-Encinas, Raquel Freeman, William T. Friberg, Per Fromm, Christian M. Fuentes, Antonio Galison, Peter Gammie, Charles F. García, Roberto Gentaz, Olivier Georgiev, Boris Goddi, Ciriaco Gold, Roman Gómez-Ruiz, Arturo I. Gómez, JoséL Gu, Minfeng Gurwell, Mark Hada, Kazuhiro Haggard, Daryl Hesper, Ronald Heumann, Dirk Ho, Luis C. Ho, Paul Honma, Mareki Huang, Chih-Wei L. Huang, Lei Hughes, David H. Ikeda, Shiro Impellizzeri, C.M. Violette Inoue, Makoto Issaoun, Sara James, David J. Jannuzi, Buell T. Janssen, Michael Jeter, Britton Jiang, Wu Jiménez-Rosales, Alejandra Johnson, Michael D. Jorstad, Svetlana Jones, Adam C. Joshi, Abhishek V. Jung, Taehyun Karuppusamy, Ramesh Kawashima, Tomohisa Keating, Garrett K. Kettenis, Mark Kim, Dong-Jin Kim, Jae-Young Kim, Jongsoo Kim, Junhan Kino, Motoki Koay, Jun Yi Kocherlakota, Prashant Kofuji, Yutaro Koch, Patrick M. Koyama, Shoko Kramer, Carsten Kramer, Joana A. Kramer, Michael Krichbaum, T Massachusetts Institute of Technology Haystack Observatory 99 Millstone Road WestfordMA01886 United States National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Black Hole Initiative Harvard University 20 Garden Street CambridgeMA02138 United States Departament d’Astronomia i Astrofísica Universitat de València C. Dr. Moliner 50 Burjassot ValènciaE-46100 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n GranadaE-18008 Spain Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Department of Physics & Astronomy The University of Texas at San Antonio One UTSA Circle San AntonioTX78249 United States Institute of Astronomy and Astrophysics Academia Sinica 11F of Astronomy-Mathematics Building AS/NTU No. 1 Sec. 4 Roosevelt Rd. Taipei106216 Taiwan Observatori Astronòmic Universitat de València C. Catedrático JoséBeltrán 2 Paterna ValènciaE-46980 Spain Department of Space Earth and Environment Chalmers University of Technology Onsala Space Observatory OnsalaSE-43992 Sweden Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States Yale Center for Astronomy & Astrophysics Yale University 52 Hillhouse Avenue New HavenCT06511 United States Astronomy Department Universidad de Concepción Casilla 160-C Concepción Chile Department of Physics University of Illinois 1110 West Green Street UrbanaIL61801 United States Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States East Asian Observatory 660 N. A’ohoku Place HiloHI96720 United States 660 N. A’ohoku Place HiloHI

The first images of the black holes in Sgr A∗ and M87∗ have created a wide range of new scientific opportunities in gravitational physics, compact objects, and relativistic astrophysics. We discuss here the scientific opportunities that arise from the rich data sets that have already been obtained and the new data sets that will be obtained, exploiting a wide range of technical advances, including observational agility, receiver upgrades, and the addition of new stations. This document provides a 5-year framework for Event Horizon Telescope (EHT) science structured around four fundamental questions that are used to prioritize the analysis of existing data, guide technical upgrades, and determine the optimal use of future observational opportunities with EHT, ALMA, and multi-wavelength facilities. Through enhancements over this period, the EHT will create the first movie of M87∗ connecting black hole and jet physics, provide detailed studies of the structure and dynamics of Sgr A∗, characterize the magnetospheres of both systems through polarimetric imaging, and explore the spacetime properties of black holes with greater precision and range. © 2024, CC BY.

关键词： Astrophysics

Enhancing stock market prediction with extended coupled Hidden Markov Model over multi-sourced data

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arXiv 2018年

作者： Zhang, Xi Li, Yixuan Wang, Senzhang Fang, Binxing Yu, Philip S. Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education Beijing China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Institute of Electronic and Information Engineering UESTC Guangdong China Department of Computer Science University of Illinois ChicagoIL United States Institute for Data Science Tsinghua University Beijing China

Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market uctuations can be impacted by various other information sources such as stock related events. Although some recent works propose event-driven prediction approaches by considering the event data, how to leverage the joint impacts of multiple data sources still remains an open research problem. In this work, we study how to explore multiple data sources to improve the performance of the stock prediction. We introduce an Extended Coupled Hidden Markov Model incorporating the news events with the historical trading data. To address the data sparsity issue of news events for each single stock, we further study the uctuation correlations between the stocks and incorporate the correlations into the model to facilitate the prediction task. Evaluations on China A-share market data in 2016 show the superior performance of our model against previous methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Hidden Markov models

Minimizing cost and backorder risk through customer priority on spares inventory

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Journal of Physics: Conference Series 2019年第1期1218卷

作者： V Lukitosari A P Subriadi I Siyamah Mathematics Department Faculty of Mathematics Computing and Data Science Sepuluh Nopember Institute of Technology (ITS) Surabaya 60111 Indonesia Department of Information System Sepuluh Nopember Institute of Technology (ITS) Surabaya 60111 Indonesia

Spare parts demands usually occur due to the need to replace spare parts on damaged equipment. Uncertain demands and many types of spare parts require complex management. Therefore, the management of spare parts is very challenging. In this study, we developed a spare part inventory method by considering priorities based on customer class. Customers will be grouped according to the number of demands. The number of requests becomes a fill rate priority. Any request that cannot be fulfilled will have backorder risk. This customer prioritization aims to minimize costs and backorder risks. The difference in customer class aims to minimize total inventory costs. Spare parts will be issued based on the queue discipline which is first in first out. The inventory model is solved by the Lagrange multiplier method. Model verification is carried out in real practice in illustrative examples. The results of the research show that customer priorities that are carried out in the provision of the fulfilment of spare parts can reduce costs and backorder risks.

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Understanding adversarial attacks on deep learning based medical image analysis systems

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arXiv 2019年

作者： Ma, Xingjun Niu, Yuhao Gu, Lin Wang, Yisen Zhao, Yitian Bailey, James Lu, Feng State Key Laboratory of VR Technology and Systems School of CSE Beihang University Beijing China School of Computing and Information Systems The University of Melbourne ParkvilleVIC3010 Australia Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China National Institute of Informatics Tokyo101-8430 Japan Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Cixi Instuitue of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China

Deep neural networks (DNNs) have become popular for medical image analysis tasks like cancer diagnosis and lesion detection. However, a recent study demonstrates that medical deep learning systems can be compromised by carefully-engineered adversarial examples/attacks with small imperceptible perturbations. This raises safety concerns about the deployment of these systems in clinical settings. In this paper, we provide a deeper understanding of adversarial examples in the context of medical images. We find that medical DNN models can be more vulnerable to adversarial attacks compared to models for natural images, according to two different viewpoints. Surprisingly, we also find that medical adversarial attacks can be easily detected, i.e., simple detectors can achieve over 98% detection AUC against state-of-the-art attacks, due to fundamental feature differences compared to normal examples. We believe these findings may be a useful basis to approach the design of more explainable and secure medical deep learning systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

Angular triplet-center loss for multi-view 3D shape retrieval

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arXiv 2018年

作者： Li, Zhaoqun Xu, Cheng Leng, Biao School of Computer Science and Engineering Beihang University Beijing100191 Research Institute of Beihang University in Shenzhen Shenzhen518057 Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191

How to obtain the desirable representation of a 3D shape, which is discriminative across categories and polymerized within classes, is a significant challenge in 3D shape retrieval. Most existing 3D shape retrieval methods focus on capturing strong discriminative shape representation with softmax loss for the classification task, while the shape feature learning with metric loss is neglected for 3D shape retrieval. In this paper, we address this problem based on the intuition that the cosine distance of shape embeddings should be close enough within the same class and far away across categories. Since most of 3D shape retrieval tasks use cosine distance of shape features for measuring shape similarity, we propose a novel metric loss named angular triplet-center loss, which directly optimizes the cosine distances between the features. It inherits the triplet-center loss property to achieve larger inter-class distance and smaller intra-class distance simultaneously. Unlike previous metric loss utilized in 3D shape retrieval methods, where Euclidean distance is adopted and the margin design is difficult, the proposed method is more convenient to train feature embeddings and more suitable for 3D shape retrieval. Moreover, the angle margin is adopted to replace the cosine margin in order to provide more explicit discriminative constraints on an embedding space. Extensive experimental results on two popular 3D object retrieval benchmarks, ModelNet40 and ShapeNetCore 55, demonstrate the effectiveness of our proposed loss, and our method has achieved state-of-the-art results on various 3D shape datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

Blood pressure trajectories from early life and their association with cognitive function changes in midlife

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Alzheimer's & Dementia 2021年第S10期17卷

作者： Ileana De Anda-Duran Camilo Fernandez-Alonso Owen T Carmichael Rhoda Au Lydia Bazzano Vijaya B Kolachalama Tulane School of Public Health and Tropical Medicine New Orleans LA USA Tulane University New Orleans LA USA Pennington Biomedical Research Center Baton Rouge LA USA Boston University Alzheimer’s Disease Center Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston University School of Public Health Boston MA USA Boston University Schools of Medicine & Public Health Boston MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA College of Arts & Sciences Boston University Boston MA USA Boston University School of Medicine Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA Whitaker Cardiovascular Institute Boston University School of Medicine Boston MA USA

Background Hypertension (HTN) is a well-established risk factor for cognitive impairment and dementia. HTN-induced vascular endothelium and smooth muscle damage have been described as pivotal components of the pathophysiological cascade over the life-course. While the impact on cognitive function (CF) changes has been studied from midlife- to old age, the impact from childhood to middle age exposure remains poorly understood. We aimed to explore this relationship in a community-based cohort of middle-age adults. Methods The analysis included a total of 342 adult participants (67.3% Woman, 38.9% African American) from the Bogalusa Heart Study, with CF assessments at two time points, baseline (age 48.8 ± 4.9 years) and follow-up (age 51.7 ± 5.2 years), and at least 3 observations of BP since childhood. CF change was assessed by computing the difference of the two timepoints standardized global cognitive score (GCS) obtained from a battery of tests covering major neuropsychological domains. Systolic BP (SBP) trajectories were created using latent class trajectory modeling. Mixed linear models with unstructured covariance were used to determine the association between SBP trajectories and CF changes in midlife. Results Mean CF assessment follow-up was 2.9 ± 0.6 years, and mean follow-up for BP and other traditional cardiovascular (CV) risk factors was 41.8 ± 3.4 years. Three distinct trajectory classes of SBP were identified: increasing to plateau (n=226 [66.1%]), moderate- (n=54 [15.8%]) and high- (n=62 [18.1%]) increasing (see Figure 1). With the increasing to plateau SBP trajectory as reference, and after adjustment for modifiable dementia risk factors and traditional CV risk factors, the high-increasing SBP trajectory was associated with greater decrease in GCS (β= -1.22 [95%CI -2.39, -0.07]; p =0.038). The moderate-increasing SBP trajectory showed no statistically significant association with GCS change (β= -1.00 [95%CI -2.23, - 0.22]; p =0.109). (see Table 1) Conc

关键词：

Locally smoothed neural networks 9

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Locally smoothed neural networks

9th Asian Conference on Machine Learning, ACML 2017

作者： Pang, Liang Lan, Yanyan Xu, Jun Guo, Jiafeng Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Convolutional Neural Networks (CNN) and the locally connected layer are limited in capturing the importance and relations of different local receptive fields, which are often crucial for tasks such as face verification, visual question answering, and word sequence prediction. To tackle the issue, we propose a novel locally smoothed neural network (LSNN) in this paper. The main idea is to represent the weight matrix of the locally connected layer as the product of the kernel and the smoother, where the kernel is shared over different local receptive fields, and the smoother is for determining the importance and relations of different local receptive fields. Specifically, a multi-variate Gaussian function is utilized to generate the smoother, for modeling the location relations among different local receptive fields. Furthermore, the content information can also be leveraged by setting the mean and precision of the Gaussian function according to the content. Experiments on some variant of MNIST clearly show our advantages over CNN and locally connected layer. © 2017 L. Pang†∗, Y. Lan†, J. Xu†, J. Guo† & X. Cheng†.

关键词： Convolution

Assessing the Global Impact of Brain Small Vessel Disease on Cognition: The Multi-Ethnic Study of Atherosclerosis

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Alzheimer's and Dementia 2025年第6期21卷 e70326页

作者： Charisis, Sokratis Rashid, Tanweer Dintica, Christina Gonzales, Mitzi Liu, Hangfan Ware, Jeffrey B. Austin, Thomas R. Jensen, Paul N. Fohner, Alison E. Tanley, Jordan E. Ding, Jingzhong Luchsinger, José A. Sachs, Bonnie Nasrallah, Ilya M. Bryan, R. Nick Hayden, Kathleen M. Wolk, David Rascovsky, Katya Davatzikos, Christos Longstreth, William T. Yaffe, Kristine Seshadri, Sudha Heckbert, Susan R. Hughes, Timothy Habes, Mohamad Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases University of Texas Health Science Center San Antonio San Antonio TX United States Department of Neurology University of Texas Health Science Center at San Antonio San Antonio TX United States Department of Psychiatry and Behavioral Sciences University of California San Francisco San Francisco CA United States Department of Neurology Cedars Sinai Medical Center Los Angeles CA United States Center for Advanced Imaging Research (CAIR) University of Maryland School of Medicine Baltimore MD United States Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Epidemiology University of Washington Seattle WA United States Cardiovascular Health Research Unit University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Department of Internal Medicine Wake Forest School of Medicine Winston-Salem NC United States Departments of Medicine and Epidemiology Columbia University Medical Center New York NY United States Department of Neurology Wake Forest University School of Medicine Winston-Salem NC United States AI2D Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA United States Department of Social Sciences and Health Policy Division of Public Health Sciences Wake Forest University School of Medicine Winston-Salem NC United States Alzheimer's Disease Research Center University of Pennsylvania Philadelphia PA United States Department of Neurology University of Pennsylvania Philadelphia PA United States Department of Neurology University of Washington Seattle WA United States

INTRODUCTION: We aimed to examine the global impact of brain small vessel disease (SVD) on cognitive performance. METHODS: In 892 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we derived perivascular spaces (PVS), white matter hyperintensities (WMH), microbleeds (MB), and white matter fractional anisotropy (FA) and trace (TR). Cognitive function was assessed with a comprehensive neuropsychological battery. RESULTS: A composite SVD measure was constructed as a linear combination of basal ganglia PVS, thalamus PVS, periventricular WMH, subcortical WMH, and white matter FA and TR, and exhibited associations with worse global and domain-specific cognitive performance. Additionally, SVD mediated the effect of age and cardiovascular disease risk on global cognitive function, both directly and through smaller gray matter (GM) volume. DISCUSSION: Integrating multiple individual SVD endophenotypes may more accurately reflect the neurobiology of SVD and capture its global impact on cognition. SVD mediates the effects of age and cardiovascular disease risk on cognition through both atrophy-related and non–atrophy-related pathways. Highlights: Associations between individual magnetic resonance imaging (MRI) markers of brain small vessel disease and cognitive outcomes might not fully capture the global impact of small vessel disease on cognition. We modeled small vessel disease as a latent construct, integrating multiple MRI endophenotypes in strategic brain regions. The small vessel disease construct was associated with worse global and domain-specific cognitive performance. The small vessel disease construct exhibited mediating effects in the relationships of aging and cardiovascular disease risk with cognition through pathways that both involve and are independent of brain atrophy. Integrating information from multiple relevant imaging endophenotypes could open new avenues in small vessel disease research, broadening our understanding of its risk factors

关键词： aging atrophy attention cardiovascular disease risk cerebral small vessel disease cognition cognitive performance deep learning delayed memory diffusion tensor imaging enlarged perivascular spaces executive function factor analysis fractional anisotropy Framingham risk score global cognition gray matter volume immediate memory language latent variable magnetic resonance imaging mediation microbleeds phonemic fluency processing speed semantic fluency structural equation modeling trace visuospatial functioning white matter hyperintensities

Max-min fairness user scheduling and power allocation in full-duplex OFDMA systems

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arXiv 2018年

作者： Zhang, Xiaozhou Chang, Tsung-Hui Liu, Ya-Feng Shen, Chao Zhu, Gang State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Science and Engineering Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen 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 Beijing China

In a full-duplex (FD) multi-user network, the system performance is not only limited by the self-interference but also by the co-channel interference due to the simultaneous uplink and downlink transmissions. Joint design of the uplink/downlink transmission direction of users and the power allocation is crucial for achieving high system performance in the FD multi-user network. In this paper, we investigate the joint uplink/downlink transmission direction assignment (TDA), user paring (UP) and power allocation problem for maximizing the system max-min fairness (MMF) rate in a FD multi-user orthogonal frequency division multiple access (OFDMA) system. The problem is formulated with a two-time-scale structure where the TDA and the UP variables are for optimizing a long-term MMF rate while the power allocation is for optimizing an instantaneous MMF rate during each channel coherence interval. We show that the studied joint MMF rate maximization problem is NP-hard in general. To obtain high-quality suboptimal solutions, we propose efficient methods based on simple relaxation and greedy rounding techniques. Simulation results are presented to show that the proposed algorithms are effective and achieve higher MMF rates than the existing heuristic methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Optimization