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arXiv 2023年

作者： Abdallah, Waleed De Freitas, Tiago Carlos Adorno Afanaciev, Konstantin Ahmad, Shakeel Ahmed, Ijaz Ai, Xiaocong Aleem, Abid Altmannshofer, Wolfgang Alves, Fabio An, Weiming An, Rui Anderle, Daniele Paolo Antusch, Stefan Arai, Yasuo Arbuzov, Andrej Arhrib, Abdesslam Ashry, Mustafa Bai, Sha Bai, Yu Bai, Yang Bairathi, Vipul Balazs, Csaba Bambade, Philip Ban, Yong Bandyopadhyay, Triparno Bao, Shou-Shan Barber, Desmond P. Bat, Ayse Batozskaya, Varvara Behera, Subash Chandra Belyaev, Alexander Bertucci, Michele Bi, Xiao-Jun Bi, Yuanjie Bian, Tianjian Bianchi, Fabrizio Biekötter, Thomas Biglietti, Michela Bilanishvili, Shalva Binglin, Deng Bodrov, Denis Bogomyagkov, Anton Bondarenko, Serge Boogert, Stewart Boonekamp, Maarten Borri, Marcello Bosotti, Angelo Boudry, Vincent Boukidi, Mohammed Boyko, Igor Bozovic, Ivanka Bozzi, Giuseppe Brient, Jean-Claude Budzinskaya, Anastasiia Bukhari, Masroor Bytev, Vladimir Cacciapaglia, Giacomo Cai, Hua Cai, Wenyong Cai, Wujun Cai, Yijian Cai, Yizhou Cai, Yuchen Cai, Haiying Cai, Huacheng Calibbi, Lorenzo Cang, Junsong Cao, Guofu Cao, Jianshe Chance, Antoine Chang, Xuejun Chang, Yue Chang, Zhe Chang, Xinyuan Chao, Wei Chatrabhuti, Auttakit Che, Yimin Che, Yuzhi Chen, Bin Chen, Danping Chen, Fuqing Chen, Fusan Chen, Gang Chen, Guoming Chen, Hua-Xing Chen, Huirun Chen, Jinhui Chen, Ji-Yuan Chen, Kai Chen, Mali Chen, Mingjun Chen, Mingshui Chen, Ning Chen, Shanhong Chen, Shanzhen Chen, Shao-Long Chen, Shaomin Chen, Shiqiang Chen, Tianlu Chen, Wei Chen, Xiang Chen, Xiaoyu Chen, Xin Chen, Xun Chen, Xurong Chen, Ye Chen, Ying Chen, Yukai Chen, Zelin Chen, Zilin Chen, Boping Chen, Chunhui Cheng, Hok Chuen Cheng, Huajie Cheng, Shan Cheng, Tongguang Chi, Yunlong Chimenti, Pietro Chiu, Wen Han Cho, Guk Chu, Ming-Chung Chu, Xiaotong Chu, Ziliang Coloretti, Guglielmo Crivellin, Andreas Cui, Hanhua Cui, Xiaohao Cui, Zhaoyuan D'Anzi, Brunella Dai, Ling-Yun Dai, Xinchen Dai, Xuwen De Maria, Antonio De Filippis, Nicola De La Taille, Christophe De Mori, Francesca De Sio, Chiara Del Core, Elisa Deng, S Agh University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Anhui University Anhui Hefei China Applied Physics Department Federal Urdu University of Arts Science and Technology Islamabad Pakistan Banaras Hindu University Institute of Science Varanasi India Bandirma Onyedi Eylul University Bandirma Turkey Beihang University Beijing China Beijing Computational Science Research Center Beijing China Beijing Conveyi Ltd Beijing China Beijing Glass Research Institute Co. Ltd Beijing China Beijing Institute of Technology Beijing China Beijing Normal University Beijing China Brown University ProvidenceRI United States Budker Institute of Nuclear Physics Novosibirsk Russia Cadi Ayyad University Laboratory of Fundamental and Applied Physics Polydisciplinary Faculty Marrakech Morocco Cairo University Giza Egypt Center for Theory and Computation National Tsing Hua University Hsinchu Taiwan Central China Normal University Hubei Wuhan China Central South University Hunan Changsha China Centro de Fisica Teorica de Particulas Universidade Tecnica de Lisboa Lisboa Portugal Chengdu KaitengSifang Digital Radio & Tv Equipment Co. Ltd Sichuan Chengdu China China Academy of Engineering Physics Sichuan Mianyang China China Building Materials Academy Beijing China China Center of Advanced Science and Technology Beijing China China Institute of Atomic Energy Beijing China China Jiliang University Zhejiang Hangzhou China China Nuclear Instrument CO. Ltd Beijing China China University of Geosciences Beijing China China University of Geosciences Hubei Wuhan China China University of Mining and Technology Jiangsu Xuzhou China China West Normal University Sichuan Nanchong China Chongqing University Department of Physics Chongqing China Chulalongkorn University Bangkok Thailand City University of Hong Kong Department of Physics Hong Kong Cockcroft Institute Daresbury United Kingdom Cornell University IthacaNY

The Circular Electron Positron Collider (CEPC) is a large scientific projectinitiated and hosted by China, fostered through extensive collaboration withinternational partners. The complex comprises four accelerators: a 30 GeVLinac, a 1.1 GeV Damping Ring, a Booster capable of achieving energies up to180 GeV, and a Collider operating at varying energy modes (Z, W, H, and ttbar).The Linac and Damping Ring are situated on the surface, while the Booster andCollider are housed in a 100 km circumference underground tunnel, strategicallyaccommodating future expansion with provisions for a Super Proton ProtonCollider (SPPC). The CEPC primarily serves as a Higgs factory. In its baselinedesign with synchrotron radiation (SR) power of 30 MW per beam, it can achievea luminosity of 5e34 /cm^2/s^1, resulting in an integrated luminosity of 13 /abfor two interaction points over a decade, producing 2.6 million Higgs *** the SR power to 50 MW per beam expands the CEPC\'s capability togenerate 4.3 million Higgs bosons, facilitating precise measurements of Higgscoupling at sub-percent levels, exceeding the precision expected from theHL-LHC by an order of magnitude. This Technical Design Report (TDR) follows thePreliminary Conceptual Design Report (Pre-CDR, 2015) and the Conceptual DesignReport (CDR, 2018), comprehensively detailing the machine\'s layout andperformance, physical design and analysis, technical systems design, R&D andprototyping efforts, and associated civil engineering aspects. Additionally, itincludes a cost estimate and a preliminary construction timeline, establishinga framework for forthcoming engineering design phase and site selectionprocedures. Construction is anticipated to begin around 2027-2028, pendinggovernment approval, with an estimated duration of 8 years. The commencement ofexperiments could potentially initiate in the mid-2030s. © 2023, CC BY-NC-SA.

关键词： Synchrotron radiation

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Multi-Classification of Cancer Samples Based on Co-Expression Analyses

Multi-Classification of Cancer Samples Based on Co-Expressio...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hongyang Jiang Qiang Huang Liang Chen Zhi Li Ying Xu Huiyan Sun Yi Chang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China College of Engineering Key Laboratory of Intelligent ManufacturingTechnology of Ministry of Education Shantou University Shantou China Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province The First Hospital of China Medical University Shenyang China The China-Japan Union Hospital Cancer Systems Biology Center Jilin University Changchun China The China-Japan Union Hospital School of Artificial Intelligence Cancer Systems Biology Center Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China

Cancer staging, grading and subtyping all represent important problems for precision diagnosis, treatment and mechanistic studies of cancer. The majority of the existing computational methods solve this problem via multi-classification of differential gene-expressions of cancer samples of specific classes (Stages, Grades and subtypes) vs. controls. However, the performance of such classification techniques is generally not satisfactory since the discerning power of differential expression patterns in such classifications is limited. We present here a multi-classification technique, based on co-expression patterns specific to individual subclasses in provided training data as co-expression patterns tend to be more conserved than differential expressions within each subclass. A challenge in implementing this strategy lies in how to effectively derive co-expression patterns in individual samples, which is solved through comparing co-expression patterns within a subclass and those in the subclass plus a new sample. Compared with the state-of-the-art gene expression-based classification methods, our method outperforms them in cancer staging, grading and subtyping of cancer samples from TCGA in almost all the measures used. In addition, the co-expressed genes computationally selected for classifications are biologically meaningful, which will prove important for diagnostic biomarker design, treatment plan selection and possibly mechanistic studies of cancer.

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Superiorization of preconditioned conjugate gradient algorithms for tomographic image reconstruction

arXiv

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arXiv 2018年

作者： Helou, Elias S. Herman, Gabor T. Lin, Chuan Zibetti, Marcelo V.W. Institute of Mathematical Science and Computation University of São Paulo SP Brazil Department of Computer Science Graduate Center City University of New York NY United States School of Electrical Engineering Southwest Jiaotong University Chengdu China Program in Electrical and Computer Engineering Federal University of Technology PR Brazil

Properties of Superiorized Preconditioned Conjugate Gradient (SupPCG) algorithms in image reconstruction from projections are examined. Least squares (LS) is usually chosen for measuring data-inconsistency in these inverse problems. Preconditioned Conjugate Gradient algorithms are fast methods for finding an LS solution. However, for ill-posed problems, such as image reconstruction, an LS solution may not provide good image quality. This can be taken care of by superiorization. A superiorized algorithm leads to images with the value of a secondary criterion (a merit function such as the total variation) improved as compared to images with similar data-inconsistency obtained by the algorithm without superiorization. Numerical experimentation shows that SupPCG can lead to high-quality reconstructions within a remarkably short time. A theoretical analysis is also provided.15A29, 65F22, 65F10, 94A08 Copyright © 2018, The Authors. All rights reserved.

关键词： Image reconstruction

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Coupled-projection residual network for MRI super-resolution

arXiv

引用

arXiv 2019年

作者： Feng, Chun-Mei Wang, Kai Lu, Shijian Xu, Yong Kong, Heng Shao, Ling Bio-Computing Research Center Harbin Institute of Technology Shenzhen518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Key Laboratory of Network Oriented Intelligent Computation Shenzhen518055 China Shenzhen Univerisity General Hospital Shenzhen University Shenzhen518055 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

Magnetic Resonance Imaging (MRI) has been widely used in clinical application and pathology research by helping doctors make more accurate diagnoses. On the other hand, accurate diagnosis by MRI remains a great challenge as images obtained via present MRI techniques usually have low resolutions. Improving MRI image quality and resolution thus becomes a critically important task. This paper presents an innovative Coupled-Projection Residual Network (CPRN) for MRI super-resolution. The CPRN consists of two complementary sub-networks: a shallow network and a deep network that keep the content consistency while learning high frequency differences between low-resolution and high-resolution images. The shallow sub-network employs coupled-projection for better retaining the MRI image details, where a novel feedback mechanism is introduced to guide the reconstruction of high-resolution images. The deep sub-network learns from the residuals of the high-frequency image information, where multiple residual blocks are cascaded to magnify the MRI images at the last network layer. Finally, the features from the shallow and deep sub-networks are fused for the reconstruction of high-resolution MRI images. For effective fusion of features from the deep and shallow sub-networks, a step-wise connection (CPRN S) is designed as inspired by the human cognitive processes (from simple to complex). Experiments over three public MRI datasets show that our proposed CPRN achieves superior MRI super-resolution performance as compared with the state-of-the-art. Our source code will be publicly available at http://***/***. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Gaussian lower bound for the information bottleneck limit

arXiv

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arXiv 2017年

作者： Painsky, Amichai Tishby, Naftali School of Computer Science and Engineering Interdisciplinary Center for Neural Computation Hebrew University of Jerusalem Givat Ram Jerusalem91904 Israel

The Information Bottleneck (IB) is a conceptual method for extracting the most compact, yet informative, representation of a set of variables, with respect to the target. It generalizes the notion of minimal sufficient statistics from classical parametric statistics to a broader information-theoretic sense. The IB curve defines the optimal trade-off between representation complexity and its predictive power. Specifically, it is achieved by minimizing the level of mutual information (MI) between the representation and the original variables, subject to a minimal level of MI between the representation and the target. This problem is shown to be in general NP hard. One important exception is the multivariate Gaussian case, for which the Gaussian IB (GIB) is known to obtain an analytical closed form solution, similar to Canonical Correlation Analysis (CCA). In this work we introduce a Gaussian lower bound to the IB curve;we find an embedding of the data which maximizes its "Gaussian part", on which we apply the GIB. This embedding provides an efficient (and practical) representation of any arbitrary data-set (in the IB sense), which in addition holds the favorable properties of a Gaussian distribution. Importantly, we show that the optimal Gaussian embedding is bounded from above by non-linear CCA. This allows a fundamental limit for our ability to Gaussianize arbitrary data-sets and solve complex problems by linear methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Gaussian distribution

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Nonretarded edge plasmon-polaritons in anisotropic two-dimensional materials

arXiv

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arXiv 2019年

作者： Margetis, Dionisios Maier, Matthias Stauber, Tobias Low, Tony Luskin, Mitchell Institute for Physical Science and Technology Department of Mathematics Center for Scientific Computation and Mathematical Modeling University of Maryland College ParkMD20742 United States Department of Mathematics Texas A&M University College StationTX77843 United States Materials Science Factory Instituto de Ciencia de Materiales de Madrid CSIC MadridE-28049 Spain Department of Electrical & Computer Engineering University of Minnesota MinneapolisMN55455 United States School of Mathematics University of Minnesota MinneapolisMN55455 United States

By an integral equation approach to the time-harmonic classical Maxwell equations, we describe the dispersion in the nonretarded frequency regime of the edge plasmon-polariton (EPP) on a semi-infinite flat sheet. The sheet has an arbitrary, physically admissible, tensor valued and spatially homogeneous conductivity, and serves as a model for a family of two-dimensional conducting materials. We formulate a system of integral equations for the electric field tangential to the sheet in a homogeneous and isotropic ambient medium. We show how this system is simplified via a length scale separation. This view entails the quasi-electrostatic approximation, by which the tangential electric field is replaced by the gradient of a scalar potential, . By the Wiener-Hopf method, we solve an integral equation for in some generality. The EPP dispersion relation comes from the elimination of a divergent limiting Fourier integral for at the edge. We connect the existence, or lack thereof, of the EPP dispersion relation to the index for Wiener-Hopf integral equations, an integer of topological character. We indicate that the values of this index may express an asymmetry due to the material anisotropy in the number of wave modes propagating on the sheet away from the edge with respect to the EPP direction of propagation. We discuss extensions such as the setting of two semi-infinite, coplanar sheets. Our theory forms a generalization of the treatment by Volkov and Mikhailov (1988 Sov. Phys. JETP 67 1639). Copyright © 2019, The Authors. All rights reserved.

关键词： Integral equations

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Gaslight: A comprehensive fuzzing architecture for memory forensics frameworks 17

Gaslight: A comprehensive fuzzing architecture for memory fo...

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17th Annual DFRWS USA, DFRWS 2017 USA

作者： Case, Andrew Das, Arghya Kusum Park, Seung-Jong Ramanujam, J. Richard, Golden G. Volatility Foundation United States Center for Computation and Technology Louisiana State University United States School of Electrical Engineering and Computer Science Louisiana State University United States

Memory forensics is now a standard component of digital forensic investigations and incident response handling, since memory forensic techniques are quite effective in uncovering artifacts that might be missed by traditional storage forensics or live analysis techniques. Because of the crucial role that memory forensics plays in investigations and because of the increasing use of automation of memory forensics techniques, it is imperative that these tools be resilient to memory smear and deliberate tampering. Without robust algorithms, malware may go undetected, frameworks may crash when attempting to process memory samples, and automation of memory forensics techniques is difficult. In this paper we present Gaslight, a powerful and flexible fuzz-testing architecture for stress-testing both open and closed-source memory forensics frameworks. Gaslight automatically targets critical code paths that process memory samples and mutates samples in an efficient way to reveal implementation errors. In experiments we conducted against several popular memory forensics frameworks, Gaslight revealed a number of critical previously undiscovered bugs. © 2017 The Author(s). Published by Elsevier Ltd. on behalf of DFRWS.

关键词： Malware

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Author Correction: Spatial communication systems across languages reflect universal action constraints

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Nature human behaviour 2024年第1期8卷 181页

作者： Kenny R Coventry Harmen B Gudde Holger Diessel Jacqueline Collier Pedro Guijarro-Fuentes Mila Vulchanova Valentin Vulchanov Emanuela Todisco Maria Reile Merlijn Breunesse Helen Plado Juergen Bohnemeyer Raed Bsili Michela Caldano Rositsa Dekova Katharine Donelson Diana Forker Yesol Park Lekhnath Sharma Pathak David Peeters Gabriella Pizzuto Baris Serhan Linda Apse Florian Hesse Linh Hoang Phuong Hoang Yoko Igari Keerthana Kapiley Tamar Haupt-Khutsishvili Sara Kolding Katri Priiki Ieva Mačiukaitytė Vaisnavi Mohite Tiina Nahkola Sum Yi Tsoi Stefan Williams Shunei Yasuda Angelo Cangelosi Jon Andoni Duñabeitia Ramesh Kumar Mishra Roberta Rocca Jurģis Šķilters Mikkel Wallentin Eglė Žilinskaitė-Šinkūnienė Ozlem Durmaz Incel School of Psychology University of East Anglia Norwich UK. k.coventry@uea.ac.uk. School of Psychology University of East Anglia Norwich UK. Helmholtz Institute Department of Experimental Psychology Utrecht University Utrecht the Netherlands. Hanse-Wissenschaftskolleg Delmenhorst Germany. Department of English Friedrich-Schiller-Universität Jena Jena Germany. Department of Spanish Modern and Classic Philology University of the Balearic Islands Palma Spain. Language Acquisition and Language Processing Lab Department of Language and Literature Norwegian University of Science and Technology Trondheim Norway. Department of Spanish Language Linguistics and Literature Theory University of Seville Seville Spain. Institute of Estonian and General Linguistics University of Tartu Tartu Estonia. Centre for the Arts in Society Leiden University Leiden the Netherlands. Võro Institute Võru Estonia. Department of Linguistics University at Buffalo Buffalo NY USA. Danieli Telerobot Srl Genoa Italy. Istituto Italiano di Tecnologia (IIT) Genoa Italy. Paisii Hilendarski University of Plovdiv Plovdiv Bulgaria. Department of English University of Nevada Reno NV USA. Department of Slavonic Languages and Caucasus Studies Friedrich-Schiller-Universität Jena Jena Germany. Cognitive Science Department of Humanities Social and Political Sciences ETH Zürich Zürich Switzerland. Cognitive Science and Psycholinguistics Lab Central Department of Linguistics Tribhuvan University Kathmandu Nepal. Centre for Neural and Cognitive Sciences School of Medical Sciences University of Hyderabad Hyderabad India. Department of Communication and Cognition TiCC Tilburg University Tilburg the Netherlands. Max Planck Institute for Psycholinguistics Nijmegen the Netherlands. Department of Chemistry University of Liverpool Liverpool UK. Department of Computer Science University of Manchester Manchester UK. Laboratory for Perceptual and Cognitive Systems Faculty of Computing University o

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Optimized ultrabroadband absorbing multilayer thin film structure

Optimized ultrabroadband absorbing multilayer thin film stru...

引用

Frontiers in Optics, FIO 2018

作者： Matyas, Corey You, Chenglong Huang, Yin Dowling, Jonathan P. Veronis, Georgios Hearne Institute for Theoretical Physics Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States Department of Optoelectrics Information Science and Engineering School of Physics and Electronics Central South University Changsha Hunan410012 China NYU-ECNU Institute of Physics NYU Shanghai 3663 Zhongshan Road North Shanghai200062 China CAS-Alibaba Quantum Computing Laboratory CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803 United States Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States

We design an optimized aperiodic multilayer thin film structure with ultra-broadband absorption over a broad angular range. Using a hybrid optimization algorithm, we achieve an average 97.9% absorption from 400 nm to ... 详细信息

ISBN: (纸本)9781943580460

关键词： Film preparation

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Hole spin echo envelope modulations

引用

Physical Review B 2019年第12期100卷 125402-125402页

作者： Pericles Philippopoulos Stefano Chesi Joe Salfi Sven Rogge W. A. Coish Department of Physics McGill University 3600 Rue University Montreal Quebec Canada H3A 2T8 Beijing Computational Science Research Center Beijing 100193 China Department of Physics Beijing Normal University Beijing 100875 China School of Physics The University of New South Wales Sydney New South Wales 2052 Australia Centre for Quantum Computation and Communication Technology The University of New South Wales Sydney New South Wales 2052 Australia Department of Electrical and Computer Engineering University of British Columbia Vancouver British Columbia Canada V6T 1Z4

Hole spins in semiconductor quantum dots or bound to acceptor impurities show promise as potential qubits, partly because of their weak and anisotropic hyperfine couplings to proximal nuclear spins. Since the hyperfine coupling is weak, it can be difficult to measure. However, an anisotropic hyperfine coupling can give rise to a substantial spin-echo envelope modulation that can be Fourier analyzed to accurately reveal the hyperfine tensor. Here, we give a general theoretical analysis for hole spin-echo envelope modulation, and apply this analysis to the specific case of a boron acceptor hole spin in silicon. For boron acceptor spins in unstrained silicon, both the hyperfine and Zeeman Hamiltonians are approximately isotropic, leading to negligible envelope modulations. In contrast, in strained silicon, where light-hole spin qubits can be energetically isolated, we find that the hyperfine Hamiltonian and g tensor are sufficiently anisotropic to give spin-echo envelope modulations. We show that there is an optimal magnetic-field orientation that maximizes the visibility of envelope modulations in this case. Based on microscopic estimates of the hyperfine coupling, we find that the maximum modulation depth can be substantial, reaching ≈10%, at a moderate laboratory magnetic field, B≲200mT.

关键词： Quantum information with solid state qubits Spin dynamics Quantum dots Semiconductors Electron paramagnetic resonance Electron spin resonance Luttinger–Kohn model k dot p method

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