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Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network 23rd

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Cerebrovascular Segmentation in MRA via Reverse Edge Attenti...

23rd International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zhang, Hao Xia, Likun Song, Ran Yang, Jianlong Hao, Huaying Liu, Jiang Zhao, Yitian College of Information Engineering Capital Normal University Beijing China Cixi Institute of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China School of Control Science and Engineering Shandong University Jinan China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China International Science and Technology Cooperation Base of Electronic System Reliability and Mathematical Interdisciplinary Capital Normal University Beijing China Laboratory of Neural Computing and Intelligent Perception Capital Normal University Beijing China Beijing Advanced Innovation Center for Imaging Theory and Technology Capital Normal University Beijing China

ISBN: (纸本)9783030597245

Automated extraction of cerebrovascular is of great importance in understanding the mechanism, diagnosis, and treatment of many cerebrovascular pathologies. However, segmentation of cerebrovascular networks from magnetic resonance angiography (MRA) imagery continues to be challenging because of relatively poor contrast and inhomogeneous backgrounds, and the anatomical variations, complex geometry and topology of the networks themselves. In this paper, we present a novel cerebrovascular segmentation framework that consists of image enhancement and segmentation phases. We aim to remove redundant features, while retaining edge information in shallow features when combining these with deep features. We first employ a Retinex model, which is able to model noise explicitly to aid removal of imaging noise, as well as reducing redundancy within an image and emphasizing the vessel regions, thereby simplifying the subsequent segmentation problem. Subsequently, a reverse edge attention module is employed to discover edge information by paying particular attention to the regions that are not salient in high-level semantic features. The experimental results show that the proposed framework enables the reverse edge attention network to deliver a reliable cerebrovascular segmentation. © 2020, Springer Nature Switzerland AG.

关键词： Image enhancement

Galois conjugates of String-net Model

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arXiv 2021年

作者： Chen, Chao-Yi Lao, Bing-Xin Yu, Xin-Yang Hung, Ling-Yan State Key Laboratory of Surface Physics Fudan University Shanghai200433 China Shanghai Qi Zhi Institute AI Tower No. 701 Yunjin Road Xuhui District Shanghai200232 China Department of Physics Center for Field Theory and Particle Physics Fudan University Shanghai200433 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Department of Physics University of Science and Technology of China China

We revisit a class of non-Hermitian topological models that are Galois conjugates of their Hermitian counter parts. Particularly, these are Galois conjugates of unitary string-net models. We demonstrate that these models necessarily have real spectra, and that topological numbers are recovered as matrix elements of operators evaluated in appropriate bi-orthogonal basis, that we conveniently reformulate as a concomitant Hilbert space here. We also compute in the bi-orthogonal basis the topological entanglement entropy, demonstrating that its real part is related to the quantum dimension of the topological order. While we focus mostly on the Yang-Lee model, the results in the paper apply generally to Galois conjugates. © 2021, CC BY.

关键词： Quantum entanglement

An emission-free, energy-independent mobility system for 169 European and Chinese cities

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Societal Impacts

Societal Impacts 2025年 5卷

作者： Bardia Mashhoodi Ying-En Ge Huijun Sun Qianwen Xu Liujiang Kang School of Computing and Mathematical Sciences Faculty of Engineering and Science University of Greenwich the United Kingdom School of Transportation Engineering Chang’an University Xi’an Shaanxi China School of Systems Science Beijing Jiaotong University Beijing 100044 China Department of Electric Power and Energy Systems KTH Royal Institute of Technology Sweden Key Laboratory of Transport Industry of Comprehensive Transportation Theory Ministry of Transport Beijing Jiaotong University China

The project, “Sustainable mobility and logistics for post-pandemic second-tier cities” (NEW NORMAL), one of the studies granted by ERA-Net Urban Accessibility and Connectivity Sino-European, consisted of four universities, two municipalities and two SMEs from the Netherlands, China and Sweden. It introduces a novel emission-free, energy-independent and space-efficient mobility system (MaaS-Lane), designed based on the potentials of edge technologies in drones-based logistics, block-chain technologies, on-surface solar panels, wireless EV charging, and hydrogen generation and storage. The project aims at developing and assessing the potentials of MaaS-Lanes in 169 s-tier, and new first-tier, EU and Chinese cities. The project seeks broader impact by publishing scientific results, developing knowledge hubs and interactive GIS database on the 169 cities, facilitating knowledge spillover between European and Chinese scientists and professionals, and detailed application of the approach for one European and one Chinese cities.

关键词： Electricity grid Drone logistics Hydrogen storage Sustainable mobility Mobility as a service

CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening

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Matter 2025年

作者： Zhao, Guobin Brabson, Logan M. Chheda, Saumil Huang, Ju Kim, Haewon Liu, Kunhuan Mochida, Kenji Pham, Thang D. Prerna Terrones, Gianmarco G. Yoon, Sunghyun Zoubritzky, Lionel Coudert, François-Xavier Haranczyk, Maciej Kulik, Heather J. Moosavi, Seyed Mohamad Sholl, David S. Siepmann, J. Ilja Snurr, Randall.Q. Chung, Yongchul G. School of Chemical and Biomolecular Engineering Pusan National University Busan46241 Korea Republic of School of Chemical and Biomolecular Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Chemistry and Chemical Theory Center University of Minnesota 207 Pleasant Street SE MinneapolisMN55455 United States Department of Chemical Engineering and Materials Science University of Minnesota 421 Washington Avenue SE MinneapolisMN55455 United States Department of Chemistry Kavli Energy Nanoscience Institute Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley BerkeleyCA94720 United States Chemical Engineering and Applied Chemistry University of Toronto TorontoONM5S 3E5 Canada Department of Chemical and Biological Engineering Northwestern University EvanstonIL60208 United States Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Chimie ParisTech PSL University CNRS Institut de Recherche de Chimie Paris Paris75005 France Air Liquide Campus Innovation Paris Les Loges-en-Josas78350 France IMDEA Materials Institute Getafe Madrid28906 Spain Department of Chemistry Massachusetts Institute of Technology CambridgeMA02139 United States Oak Ridge National Laboratory Oak RidgeTN37830 United States of Data Science Pusan National University Busan46241 Korea Republic of

We present an updated version of the Computation-Ready, Experimental (CoRE) Metal-Organic Framework (MOF) database, which includes a curated set of computation-ready MOF crystal structures designed for high-throughput computational materials discovery. Data collection and curation procedures were improved from the previous version to enable more frequent updates in the future. Machine-learning-predicted properties, such as stability metrics and heat capacities, are included in the dataset to streamline screening activities. An updated version of MOFid was developed to provide detailed information on metal nodes, organic linkers, and topologies of an MOF structure. DDEC6 partial atomic charges of MOFs were assigned based on a machine-learning model. Gibbs ensemble Monte Carlo simulations were used to classify the hydrophobicity of MOFs. The finalized dataset was subsequently used to perform integrated material-process screening for various carbon-capture conditions using high-fidelity temperature-swing adsorption (TSA) simulations. Our workflow identified multiple MOF candidates that are predicted to outperform CALF-20 for these applications. © 2025 Elsevier Inc.

关键词： Crystal structure

Generating Approximate Ground States of Molecules Using Quantum Machine Learning

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arXiv 2022年

作者： Ceroni, Jack Stetina, Torin F. Kieferová, Mária Marrero, Carlos Ortiz Arrazola, Juan Miguel Wiebe, Nathan Xanadu TorontoONM5G 2C8 Canada Department of Mathematics University of Toronto TorontoONM5S 3E1 Canada Simons Institute for the Theory of Computing BerkeleyCA94704 United States Berkeley Quantum Information and Computation Center University of California BerkeleyCA94720 United States Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology SydneyNSW2007 Australia AI & Data Analytics Division Pacific Northwest National Laboratory RichlandWA99354 United States Department of Electrical & Computer Engineering North Carolina State University RaleighNC27607 United States Department of Computer Science University of Toronto ONM5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory RichlandWA99354 United States

The potential energy surface (PES) of molecules with respect to their nuclear positions is a primary tool in understanding chemical reactions from first principles. However, obtaining this information is complicated by the fact that sampling a large number of ground states over a high-dimensional PES can require a vast number of state preparations. In this work, we propose using a generative quantum machine learning model to prepare quantum states at arbitrary points on the PES. The model is trained using quantum data consisting of ground-state wavefunctions associated with different classical nuclear coordinates. Our approach uses a classical neural network to convert the nuclear coordinates of a molecule into quantum parameters of a variational quantum circuit. The model is trained using a fidelity loss function to optimize the neural network parameters. We show that gradient evaluation is efficient and numerically demonstrate our method’s ability to prepare wavefunctions on the PES of hydrogen chains, water, and beryllium hydride. In all cases, we find that a small number of training points are needed to achieve very high overlap with the groundstates in practice. From a theoretical perspective, we further prove limitations on these protocols by showing that if we were able to learn across an avoided crossing using a small number of samples, then we would be able to violate Grover’s lower bound. Additionally, we prove lower bounds on the amount of quantum data needed to learn a locally optimal neural network function using arguments from quantum Fisher information. This work further identifies that quantum chemistry can be an important use case for quantum machine learning. © 2022, CC BY.

关键词： Molecules

Quantum computing hardware for HEP algorithms and sensing

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arXiv 2022年

作者： Alam, M. Sohaib Belomestnykh, Sergey Bornman, Nicholas Cancelo, Gustavo Chao, Yu-Chiu Checchin, Mattia Dinh, Vinh San Grassellino, Anna Gustafson, Erik J. Harnik, Roni McRae, Corey Rae Harrington Huang, Ziwen Kapoor, Keshav Kim, Taeyoon Kowalkowski, James B. Kramer, Matthew J. Krasnikova, Yulia Kumar, Prem Kurkcuoglu, Doga Murat Lamm, Henry Lyon, Adam L. Milathianaki, Despina Murthy, Akshay Mutus, Josh Nekrashevich, Ivan Oh, JinSu Özgüler, A. Barış Perdue, Gabriel Nathan Reagor, Matthew Romanenko, Alexander Sauls, James A. Stefanazzi, Leandro Tubman, Norm M. Venturelli, Davide Wang, Changqing You, Xinyuan van Zanten, David M.T. Zhou, Lin Zhu, Shaojiang Zorzetti, Silvia NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Scientific Computing Division Fermi National Accelerator Laboratory BataviaIL60510 United States Northwestern-Fermilab Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL60208 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Theory Division Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO80305 United States National Institute of Standards and Technology Boulder CO80305 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Ames Laboratory U.S. Department of Energy AmesIA50011 United States Center for Photonic Communication and Computing ECE Department Northwestern University EvanstonIL60208 United States Rigetti Computing Inc. BerkeleyCA94710 United States NASA Ames Research Center Moffett FieldCA94035 United States

Quantum information science harnesses the principles of quantum mechanics to realize computational algorithms with complexities vastly intractable by current computer platforms. Typical applications range from quantum chemistry to optimization problems and also include simulations for high energy physics. The recent maturing of quantum hardware has triggered preliminary explorations by several institutions (including Fermilab) of quantum hardware capable of demonstrating quantum advantage in multiple domains, from quantum computing to communications, to sensing. The Superconducting Quantum Materials and Systems (SQMS) Center, led by Fermilab, is dedicated to providing breakthroughs in quantum computing and sensing, mediating quantum engineering and HEP based material science. The main goal of the Center is to deploy quantum systems with superior performance tailored to the algorithms used in high energy physics. In this Snowmass paper, we discuss the two most promising superconducting quantum architectures for HEP algorithms, i.e. three-level systems (qutrits) supported by transmon devices coupled to planar devices and multi-level systems (qudits with arbitrary N energy levels) supported by superconducting 3D cavities. For each architecture, we demonstrate exemplary HEP algorithms and identify the current challenges, ongoing work and future opportunities. Furthermore, we discuss the prospects and complexities of interconnecting the different architectures and individual computational nodes. Finally, we review several different strategies of error protection and correction and discuss their potential to improve the performance of the two architectures. This whitepaper seeks to reach out to the HEP community and drive progress in both HEP research and QIS hardware. Copyright © 2022, The Authors. All rights reserved.

关键词： High energy physics

Hard jet substructure in a multistage approach

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arXiv 2023年

作者： Tachibana, Y. Kumar, A. Majumder, A. Angerami, A. Arora, R. Bass, S.A. Cao, S. Chen, Y. Dai, T. Du, L. Ehlers, R. Elfner, H. Fan, W. Fries, R.J. Gale, C. He, Y. Heffernan, M. Heinz, U. Jacak, B.V. Jacobs, P.M. Jeon, S. Ji, Y. Kauder, K. Kasper, L. Ke, W. Kelsey, M. Kordell, M. Latessa, J. Lee, Y.-J. Liyanage, D. Lopez, A. Luzum, M. Mak, S. Mankolli, A. Martin, C. Mehryar, H. Mengel, T. Mulligan, J. Nattrass, C. Oliinychenko, D. Paquet, J.-F. Putschke, J.H. Roland, G. Schenke, B. Schwiebert, L. Sengupta, A. Shen, C. Silva, A. Sirimanna, C. Soeder, D. Soltz, R.A. Soudi, I. Staudenmaier, J. Strickland, M. Velkovska, J. Vujanovic, G. Wang, X.-N. Wolpert, R.L. Zhao, W. Akita International University Yuwa Akita010-1292 Japan Department of Physics McGill University MontréalQCH3A2T8 Canada Department of Physics and Astronomy Wayne State University DetroitMI48201 United States Department of Physics University of Regina ReginaSKS4S 0A2 Canada Lawrence Livermore National Laboratory LivermoreCA94550 United States Research Computing Group University Technology Solutions The University of Texas at San Antonio San AntonioTX78249 United States Department of Physics Duke University DurhamNC27708 United States Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Laboratory for Nuclear Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics and Astronomy Vanderbilt University NashvilleTN37235 United States Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Physics Division Oak Ridge National Laboratory Oak RidgeTN37830 United States Department of Physics University of California BerkeleyCA94270 United States Nuclear Science Division Lawrence Berkeley National Laboratory BerkeleyCA94270 United States GSI Helmholtzzentrum für Schwerionenforschung Darmstadt64291 Germany Institute for Theoretical Physics Goethe University Frankfurt am Main60438 Germany Frankfurt Institute for Advanced Studies Frankfurt am Main60438 Germany Cyclotron Institute Texas A&M University College StationTX77843 United States Department of Physics and Astronomy Texas A&M University College StationTX77843 United States Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou510006 China Depar

We present predictions and postdictions for a wide variety of hard jet-substructure observables using a multistage model within the jetscape framework. The details of the multistage model and the various parameter choices are described in [Phys. Rev. C 107, 034911 (2023)]. A novel feature of this model is the presence of two stages of jet modification: a high virtuality phase [modeled using the matter modular all twist transverse-scattering elastic-drag and radiation model (matter)], where modified coherence effects diminish medium-induced radiation, and a lower virtuality phase [modeled using the linear Boltzmann transport model (lbt)], where parton splits are fully resolved by the medium as they endure multiple scattering induced energy loss. Energy-loss calculations are carried out on event-by-event viscous fluid dynamic backgrounds constrained by experimental data. The uniform and consistent descriptions of multiple experimental observables demonstrate the essential role of modified coherence effects and the multistage modeling of jet evolution. Using the best choice of parameters from [Phys. Rev. C 107, 034911 (2023)], and with no further tuning, we present calculations for the medium modified jet fragmentation function, the groomed jet momentum fraction zg and angular separation rg distributions, as well as the nuclear modification factor of groomed jets. These calculations provide accurate descriptions of published data from experiments at the Large Hadron Collider. Furthermore, we provide predictions from the multistage model for future measurements at the BNL Relativistic Heavy Ion Collider. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiple scattering

CFTD from TQFTD+1 via Holographic Tensor Network, and Precision Discretisation of CFT2

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arXiv 2022年

作者： Chen, Lin Zhang, Haochen Ji, Kaixin Shen, Ce Wang, Ruoshui Zeng, Xiangdong Hung, Ling-Yan State Key Laboratory of Surface Physics Fudan University Shanghai200433 China Department of Physics Center for Field Theory and Particle Physics Fudan University Shanghai200433 China School of Physics and Optoelectronics South China University of Technology Guangzhou510641 China Department of Physics Shandong University Jinan250100 China International Centre for Theoretical Physics Asia-Pacific University of Chinese Academy of Sciences Beijing100190 China Cornell University IthacaNY14853 United States Institute for Advanced Study Tsinghua University Beijing100084 China Institute for Nanoelectronic devices and Quantum computing Fudan University Shanghai200433 China Yau Mathematical Sciences Center Tsinghua University Beijing100084 China

We show that the path-integral of conformal field theories in D dimensions (CFTD) can be constructed by solving for eigenstates of an RG operator following from the Turaev-Viro formulation of a topological field theory in D + 1 dimensions (TQFTD+1), explicitly realising the holographic sandwich relation between a symmetric theory and a TQFT. Generically, exact eigenstates corresponding to symmetric-TQFTD follow from Frobenius algebra in the TQFTD+1. For D = 2, we constructed eigenstates that produce 2D rational CFT path-integral exactly, which, curiously connects a continuous field theoretic path-integral with the Turaev-Viro state sum. We also devise and illustrate numerical methods for D = 2, 3 to search for CFTD as phase transition points between symmetric TQFTD. Finally since the RG operator is in fact an exact analytic holographic tensor network, we compute "bulk-boundary" correlator and compare with the AdS/CFT dictionary at D = 2. Promisingly, they are numerically compatible given our accuracy, although further works will be needed to explore the precise connection to the AdS/CFT correspondence. Copyright © 2022, The Authors. All rights reserved.

关键词： Tensors

Experimental Realization of Genuine Three-Copy Collective Measurements for Optimal Information Extraction

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Physical Review Letters 2025年第21期134卷 210201-210201页

作者： Kai Zhou Changhao Yi Wen-Zhe Yan Zhibo Hou Huangjun Zhu Guo-Yong Xiang Chuan-Feng Li Guang-Can Guo Laboratory of Quantum Information University of Science and Technology of China Hefei 230026 People’s Republic of China CAS Center For Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei 230026 People’s Republic of China State Key Laboratory of Surface Physics Department of Physics and Center for Field Theory and Particle Physics Fudan University Shanghai 200433 People’s Republic of China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 People’s Republic of China Shanghai Research Center for Quantum Sciences Shanghai 201315 People’s Republic of China Hefei National Laboratory Hefei 230088 People’s Republic of China

Nonclassical phenomena tied to entangled states are the focus of foundational studies and powerful resources in many applications. By contrast, the counterparts on quantum measurements are still poorly understood. Notably, genuine multipartite nonclassicality is barely discussed, let alone its experimental realization. Here we experimentally demonstrate the power of genuine tripartite nonclassicality in quantum measurements based on a simple estimation problem. To this end we realize an optimal genuine three-copy collective measurement via a nine-step two-dimensional photonic quantum walk with 30 elaborately designed coin operators. Then we realize an optimal estimation protocol and achieve an unprecedented high estimation fidelity, which can beat all strategies based on restricted collective measurements by more than 11 standard deviations. These results clearly demonstrate that genuine collective measurements can extract more information than local measurements and restricted collective measurements. Our Letter opens the door for exploring genuine multipartite nonclassical measurements and their power in quantum information processing.

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