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Galaxy clusters at z ∼ 1 imaged by ALMA with the Sunyaev–Zel’dovich effect

Publications of the Astronomical Society of Japan 2023年第2期75卷 311-337页

作者： Kitayama, Tetsu Ueda, Shutaro Okabe, Nobuhiro Akahori, Takuya Hilton, Matt Hughes, John P. Ichinohe, Yuto Kohno, Kotaro Komatsu, Eiichiro Lin, Yen-Ting Miyatake, Hironao Oguri, Masamune Sifón, Cristóbal Takakuwa, Shigehisa Takizawa, Motokazu Tsutsumi, Takahiro van Marrewijk, Joshiwa Wollack, Edward J. Department of Physics Toho University 2-2-1 Miyama Chiba Funabashi274-8510 Japan No. 1 Section 4 Roosevelt Road Taipei10617 Taiwan Institute of Astronomy National Tsing Hua University Hsinchu30013 Taiwan Department of Physics Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Astrophysical Science Center Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Core Research for Energetic Universe Department of Physics Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Physics Program Graduate School of Advanced Science and Engineering Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Mizusawa VLBI observatory National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Operation Division SKA Observatory Jodrell Bank Lower Withington MacclesfieldSK11 9DL United Kingdom Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban4041 South Africa School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Westville Campus Durban4041 South Africa Wits Centre for Astrophysics School of Physics University of the Witwatersrand Private Bag 3 Johannesburg2050 South Africa Department of Physics and Astronomy Rutgers The State University of New Jersey PiscatawayNJ08854-8019 United States Department of Physics Rikkyo University 3-34-1 Nishi-Ikebukuro Toshima-ku Tokyo171-8501 Japan Institute of Astronomy School of Science The University of Tokyo 2-21-1 Osawa Mitaka Tokyo181-0015 Japan Research Center for the Early Universe School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan Max-Planck-Institut für Astrophysik Karl-Schwarzschild Str. 1 GarchingD-85741 Germany The University of Tokyo Institutes for Advanced Study The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8583 Japan Nagoya University Furo-cho Chikusa-

We present high angular resolution measurements of the thermal Sunyaev–Zel’dovich effect (SZE) toward two galaxy clusters, RCS J2319+0038 at z = 0.9 and HSC J0947−0119 at z = 1.1, by the Atacama Large Millimeter/submillimeter Array (ALMA) in Band 3. They are supplemented with available Chandra X-ray data, optical data taken by Hyper Suprime-Cam on Subaru, and millimeter-wave SZE data from the Atacama Cosmology Telescope. Taking into account departures from spherical symmetry, we have reconstructed non-parametrically the inner pressure profile of two clusters as well as electron temperature and density profiles for RCS J2319+0038. This is one of the first such measurements for an individual cluster at z 0.9. We find that the inner pressure profile of both clusters is much shallower than that of local cool-core clusters. Our results consistently suggest that RCS J2319+0038 hosts a weak cool core, where radiative cooling is less significant than in local cool cores. On the other hand, HSC J0947−0119 exhibits an even shallower pressure profile than RCS J2319+0038 and is more likely to be a non-cool-core cluster. The SZE centroid position is offset by more than 140 h−170 kpc from the peaks of galaxy distribution in HSC J0947−0119, suggesting a stronger influence of mergers in this cluster. We conclude that these distant clusters are at a very early stage of developing the cool cores typically found in clusters at lower redshifts. © The Author(s) 2023. Published by Oxford University Press on behalf of the Astronomical Society of Japan.

关键词： Millimeter waves

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Augment Reality-Based Teaching Practice

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Biomedical engineering education 2021年第1期1卷 237-241页

作者： You Wu Mingzhen Zhang Xiaosi Li Yu Gan Chao Zhao Department of Chemical and Biological Engineering The University of Alabama P. O. Box870203 Tuscaloosa AL 35401 USA. Computational Structural Biology Section Basic Science Program Frederick National Laboratory for Cancer Research Frederick MD 21702 USA. Department of Electrical and Computer Engineering The University of Alabama P. O. Box870286 Tuscaloosa AL 35401 USA.

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Simulating urban freight flows in e-grocery scenarios accounting for consumer heterogeneous preferences

Simulating urban freight flows in e-grocery scenarios accoun...

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2020 Forum on Integrated and Sustainable Transportation Systems, FISTS 2020

作者： Pira, M. Le Marcucci, E. Gatta, V. Pluchino, A. Fazio, M. Inturri, G. Ignaccolo, M. University of Catania Department of Civil Engineering and Architecture Catania95125 Italy Roma Tre University Department of Political Science Rome00145 Italy Faculty of Logistics Molde University College Molde6410 Norway University of Catania Department of Physics and Astronomy Catania95125 Italy INFN Section of Catania Catania95125 Italy University of Catania Department of Electric Electronic and Computer Engineering Catania95125 Italy

ISBN: (纸本)9781728195032

E-grocery is a growing phenomenon that has the potential to rapidly change the way shopping is performed and goods distributed in cities. The impacts of this innovation can vary according to the delivery service performed, thus affecting positively or negatively the overall sustainability of urban freight transport. Understanding the dynamics of demand (i.e. consumers) will be useful to know how supply (i.e. supermarkets) should adapt, and how policy-makers should deal with this phenomenon. This paper presents an agent-based model to simulate different e-grocery scenarios at a neighbourhood scale. Agents are characterized with individual utility functions from a latent class model based on a stated preference survey performed in Rome (Italy). First results of a one-month simulation allow deriving some useful conclusion on the impact different grocery channels can have on travelled distance and consumer shopping time and formulating some policy implications. © 2020 IEEE.

关键词： Technology transfer

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NetMoST: A network-based machine learning approach for subtyping schizophrenia using polygenic SNP allele biomarkers

arXiv

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arXiv 2023年

作者： Wei, Xinru Dong, Shuai Su, Zhao Tang, Lili Zhao, Pengfei Pan, Chunyu Wang, Fei Tang, Yanqing Zhang, Weixiong Zhang, Xizhe Early Intervention Unit Department of Psychiatry The Affiliated Brain Hospital of Nanjing Medical University Jiangsu Nanjing210029 China School of Biomedical Engineering and Informatics Nanjing Medical University Jiangsu Nanjing210001 China School of Computer Science and Engineering Northeastern University Shenyang China Department of Health Technology and Informatics Department of Computing The Hong Kong Polytechnic University Hong Kong Functional Brain Imaging Institute of Nanjing Medical University Nanjing China Department of Psychiatry The First Affiliated Hospital of China Medical University Shenyang China Brain Function Research Section The First Affiliated Hospital of China Medical University Shenyang China Department of Gerontology The First Affiliated Hospital of China Medical University Shenyang China

Subtyping neuropsychiatric disorders like schizophrenia is essential for improving the diagnosis and treatment of complex diseases. Subtyping schizophrenia is challenging because it is polygenic and genetically heterogeneous, rendering the standard symptom-based diagnosis often unreliable and unrepeatable. We developed a novel network-based machine-learning approach, netMoST, to subtyping psychiatric disorders. NetMoST identifies polygenic risk SNP-allele modules from genome-wide genotyping data as polygenic haplotype biomarkers (PHBs) for disease subtyping. We applied netMoST to subtype a cohort of schizophrenia subjects into three distinct biotypes with differentiable genetic, neuroimaging and functional characteristics. The PHBs of the first biotype (36.9% of all patients) were related to neurodevelopment and cognition, the PHBs of the second biotype (28.4%) were enriched for neuroimmune functions, and the PHBs of the third biotype (34.7%) were associated with the transport of calcium ions and neurotransmitters. Neuroimaging patterns provided additional support to the new biotypes, with unique regional homogeneity (ReHo) patterns observed in the brains of each biotype compared with healthy controls. Our findings demonstrated netMoST’s capability for uncovering novel biotypes of complex diseases such as schizophrenia. The results also showed the power of exploring polygenic allelic patterns that transcend the conventional GWAS approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

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Are There Any Overlooked Catalysts for Electrochemical NH 3 Synthesis? – New Insights from the Analysis of Thermochemical Data

SSRN

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SSRN 2020年

作者： Drazevic, Emil Skúlason, Egill Aarhus University Department of Engineering Biotechnology and Chemical Engineering Section Aabogade 40 Aarhus N8200 Denmark Egill Skúlason University of Iceland Science Institute and Faculty of Industrial Engineering Mechanical Engineering and Computer Science Hjarðarhagi 2 VR-III Reykjavík107 Iceland

Standard enthalpies and entropies of formation of many metal nitrides (MN) were measured in 60s, 70s and 80s of the last century. We have used this available thermochemical data and made a unique analysis in the NH3 electrocatalysis field. Briefly, we have calculated M→MN and MN→NH3 standard cell potentials for 36 metal nitrides. We found that potentials needed to start the onset for nitrogen reduction reaction (NRR), earlier calculated using density functional theory, scale linearly with the cell potentials we calculated. We have used the linear relations as "calibration curves" and from known cell potentials calculated NRR onset potentials for all elements analysed. This allowed us to build a large Volcano plot for NRR. We found that: i) transition metal Mn is the most reactive catalyst;ii) there is a gap of unidentified materials close to the top of the Volcano plot;and iii) posttransition metals, Ga and In, are close to the top. We have calculated the potentials needed to start the onset for HER and NRR at different pH and we have identified new, overlooked metal catalysts which might selectively catalyse NRR at pH>4. © 2020, The Authors. All rights reserved.

关键词： Ammonia

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Demonstration of machine-learning-enhanced Bayesian quantum state estimation

arXiv

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arXiv 2022年

作者： Lohani, Sanjaya Lukens, Joseph M. Davis, Atiyya A. Khannejad, Amirali Regmi, Sangita Jones, Daniel E. Glasser, Ryan T. Searles, Thomas A. Kirby, Brian T. Dept. of Electrical & Computer Engineering University of Illinois Chicago ChicagoIL60607 United States Research Technology Office Arizona State University TempeAZ85287 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States DEVCOM US Army Research Laboratory AdelphiMD20783 United States Tulane University New OrleansLA70118 United States

Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations. Here, we experimentally realize an approach for defining custom prior distributions that are automatically tuned using ML for use with Bayesian quantum state estimation methods. Previously, researchers have looked to Bayesian quantum state tomography due to its unique advantages like natural uncertainty quantification, the return of reliable estimates under any measurement condition, and minimal mean-squared error. However, practical challenges related to long computation times and conceptual issues concerning how to incorporate prior knowledge most suitably can overshadow these benefits. Using both simulated and experimental measurement results, we demonstrate that ML-defined prior distributions reduce net convergence times and provide a natural way to incorporate both implicit and explicit information directly into the prior distribution. These results constitute a promising path toward practical implementations of Bayesian quantum state tomography. © 2022, CC BY.

关键词： Machine learning

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Reduced basis methods for numerical room acoustic simulations with parametrized boundaries

arXiv

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arXiv 2021年

作者： Llopis, Hermes Sampedro Engsig-Karup, Allan P. Jeong, Cheol-Ho Pind, Finnur Hesthaven, Jan S. Rambøll Denmark Copenhagen Denmark Acoustic Technology Group Department of Electrical Engineering Technical University of Denmark Kongens Lyngby Denmark Scientific Computing Section Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark Treble Technologies Reykjavík Iceland Computational Mathematics and Simulation Science Ecole Polytechnique Federale de Lausanne Lausanne Switzerland

The use of model-based numerical simulation of wave propagation in rooms for engineering applications requires that acoustic conditions for multiple parameters are evaluated iteratively and this is computationally expensive. We present a reduced basis methods (RBM) to achieve a computational cost reduction relative to a traditional full order model (FOM), for wave-based room acoustic simulations with parametrized boundary conditions. In this study, the FOM solver is based on the spectral element method, however other numerical methods could be applied. The RBM reduces the computational burden by solving the problem in a low-dimensional subspace for parametrized frequency-independent and frequency-dependent boundary conditions. The problem is formulated and solved in the Laplace domain, which ensures the stability of the reduced order model based on the RBM approach. We study the potential of the proposed RBM framework in terms of computational efficiency, accuracy and storage requirements and we show that the RBM leads to 100-fold speed-ups for a 2D case with an upper frequency of 2kHz and around 1000-fold speed-ups for an analogous 3D case with an upper frequency of 1kHz. While the FOM simulations needed to construct the ROM are expensive, we demonstrate that despite this cost, the ROM has a potential of three orders of magnitude faster than the FOM when four different boundary conditions are simulated per room surface. Moreover, results show that the storage model for the ROM is relatively high but affordable for the presented 2D and 3D cases. © 2021, CC BY-NC-ND.

关键词： Boundary conditions

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Control design and Lyapunov Functions via Bernstein Approximations: Exact Results ⁎

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IFAC-PapersOnLine 2020年第2期53卷 6459-6464页

作者： Tareq Hamadneh Nikolaos Athanasopoulos Rafael Wisniewski Al Zaytoonah University of Jordan Department of Mathematics Amman Jordan School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Northern Ireland Section of Automation and Control Aalborg University 9220 Aalborg East Denmark

We study the control problem for polynomial continuous-time dynamical systems. We consider polynomial Lyapunov functions and controllers, both parameterised in Bernstein form. Specifically, we present necessary and sufficient conditions for existence of polynomial controllers and Lyapunov functions of some maximum degree, providing at the same time explicit upper bounds on the degree of the involved Bernstein polynomials. The formulated conditions are a set of algebraic inequalities, in the space of Bernstein coefficients.

关键词： Bernstein polynomials control design certificates of positivity Lyapunov stability Control Lyapunov Functions polynomial systems

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Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems

arXiv

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arXiv 2020年

作者： Nguyen, Minh N.H. Pandey, Shashi Raj Dang, Tri Nguyen Huh, Eui-Nam Tran, Nguyen H. Saad, Walid Hong, Choong Seon The University of Danang Vietnam-Korea University of Information and Communication Technology Viet Nam The Department of Computer Science and Engineering Kyung Hee University Korea Republic of The Department of Computer Science and Engineering Kyung Hee University Yongin-si17104 Korea Republic of The Connectivity Section Department of Electronic Systems Aalborg University Denmark School of Computer Science The University of Sydney SydneyNSW2006 Australia The Wireless VT Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States The Department of Computer Science and Engineering Kyung Hee University Yongin446-701 Korea Republic of

Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform complex learning tasks. In this regard, democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. The outlined principles are meant to study a generalization in distributed learning systems that goes beyond existing mechanisms such as federated learning. Moreover, such learning systems rely on hierarchical self-organization of well-connected distributed learning agents who have limited and highly personalized data and can evolve and regulate themselves based on the underlying duality of specialized and generalized processes. Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this paper. The approach consists of a self-organizing hierarchical structuring mechanism based on agglomerative clustering, hierarchical generalization, and corresponding learning mechanism. Subsequently, hierarchical generalized learning problems in recursive forms are formulated and shown to be approximately solved using the solutions of distributed personalized learning problems and hierarchical update mechanisms. To that end, a distributed learning algorithm, namely DemLearn is proposed. Extensive experiments on benchmark MNIST, Fashion-MNIST, FE-MNIST, and CIFAR-10 datasets show that the proposed algorithm demonstrates better results in the generalization performance of learning models in agents compared to the conventional FL algorithms. The detailed analysis provides useful observations to further handle both the generalization and specialization performance of the learning models in Dem-AI systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Learning systems

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Renal Cell Type and State Estimation in Brightfield Histology Images: A Pilot Study on Diabetic Nephropathy

Renal Cell Type and State Estimation in Brightfield Histolog...

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Medical Imaging 2025: Digital and Computational Pathology

作者： Fermin, Jamie L. Border, Samuel Naglah, Ahmed Shickel, Benjamin La Rosa, Patricio S. Tomaszewski, John E. Jain, Sanjay El-Achkar, Tarek M. Eadon, Michael T. Sarder, Pinaki Dept. of Electrical and Computer Engineering Univ. of Florida GainesvilleFL United States Dept. of Biomedical Engineering Univ. of Florida GainesvilleFL United States Univ. of Florida Intelligent Critical Care Center GainesvilleFL United States Seed Production Innovation Crop Science Division Bayer Company St. LouisMO United States Dept. of Pathology & Anatomical Sciences Univ. at Buffalo BuffaloNY United States Div. of Nephrology Dept. of Medicine Washington Univ. School of Medicine St. LouisMO United States Div. of Nephrology Dept. of Medicine. Indiana Univ School of Medicine IndianapolisIN United States Div. of Nephrology Hypertension and Renal Transplantation Quantitative Health Section Dept. of Medicine Univ. of Florida GainesvilleFL United States

ISBN: (纸本)9781510686045

Multi-omics data, such as 10X Genomics Visium (spatial transcriptomics), measure gene expressions, molecular pathway activities, and can predict cell types/states but are often expensive and inaccessible in clinical settings. Thus, despite the emergence of multi-omics technologies, histopathological assessments under brightfield microscopy remain the diagnostic gold standard. In this work, we examine machine learning-based pipelines for predicting cell types/states from brightfield h i stology i m ages u s ing s t ate-of-the-art (SOTA) d e ep l e arning (DL) models, aiming to enhance diagnostics and prognostics in clinical medicine. Our proposed pipeline consists of two stages: (1) an Image-To-Text retrieval Network (ITTN) that leverages the CONtrastive learning from Captions for Histopathology (CONCH) model to assign histopathological text prompt from brightfield h i stology i m age, a n d (2) a V i sion L a nguage M odel (V LM), w h ich i s b u ilt o n t h e same CONCH model used in ITTN but incorporates a regression head to predict cell type/state proportions based on the paired image and text inputs. During training, we classify the image into one of four structural types (glomerulus, tubules, vessels, and interstitium) using the ITTN. These classification l a bels a r e t h en u s ed to construct a new text prompt with a suitable histopathological description for each image in the test set. The new text prompt and raw image are used as paired inputs to the VLM to predict cell types/states. We also utilize SOTA models, such as CONCH (using only the vision encoder), ViT, and ResNet, which employ image-only inputs in separate regression pipelines. We experimented and tested our proposed pipelines on a set of 10X Visium formalin-fixed paraffin-embedded whole slides images of diabetic nephropathy samples collected at Indiana University. Our experiments yielded a mean squared error of 0.0027 for the proposed pipeline, showing improvements of 20.59%, 27.03%, and 32.50

关键词： Genes

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