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Demonstration of long-range correlations via susceptibility measurements in a one-dimensional superconducting Josephson spin chain

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arXiv 2021年

作者： Tennant, Daniel M. Dai, Xi Martinez, Antonio J. Trappen, Robbyn Melanson, Denis Yurtalan, M.A. Tang, Yongchao Bedkihal, Salil Yang, Rui Novikov, Sergei Grover, Jeffery A. Disseler, Steven M. Basham, James I. Das, Rabindra Kim, David K. Melville, Alexander J. Niedzielski, Bethany M. Weber, Steven J. Yoder, Jonilyn L. Kerman, Andrew J. Mozgunov, Evgeny Lidar, Daniel A. Lupascu, Adrian Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Northrop Grumman Corporation LinthicumMD21090 United States MIT Lincoln Laboratory LexingtonMA02421 United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Departments of Electrical & Computer Engineering Chemistry and Physics Waterloo Institute for Nanotechnology University of Waterloo WaterlooONN2L 3G1 Canada

Spin chains have long been considered an effective medium for long-range interactions, entanglement generation, and quantum state transfer. In this work, we explore the properties of a spin chain implemented with superconducting flux circuits, designed to act as a connectivity medium between two superconducting qubits. The susceptibility of the chain is probed and shown to support long-range, cross chain correlations. In addition, interactions between the two end qubits, mediated by the coupler chain, are demonstrated. This work has direct applicability in near term quantum annealing processors as a means of generating long-range, coherent coupling between qubits. © 2021, CC BY.

关键词： Spin dynamics

Kitchen-based light tomography - a DIY toolkit for advancing tomography - by and for the tomography community

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Tomography of Materials and Structures

Tomography of Materials and Structures 2023年 1卷

作者： Emanuel Larsson Doğa Gürsoy Stephen A. Hall Division of Solid Mechanics Lund University Ole Römers väg 1 22100 Lund Sweden LUNARC Centre for Scientific and Technical Computing Lund University John Ericssons väg 1 22100 Lund Sweden Lund Institute of Advanced Neutron and X-ray Science (LINXS) Scheelevägen 19 22370 Lund Sweden Institute for Globally Distributed Open Research and Education (IGDORE) Advanced Photon Source Argonne National Laboratory 9700 S Cass Ave Lemont 60439 IL USA Department of Electrical Engineering and Computer Science Northwestern University 2145 Sheridan Rd Evanston 60208 IL USA

We present a recipe for building a portable DIY toolkit, entitled Kitchen-Based Light Tomography (KBLT) for performing tomography using visible light with low-cost and easily accessible components. We also present different use cases to mimic different challenges in tomography, such as imaging time evolving samples. All the software for motor controls, image acquisition, image reconstruction and analysis is open-sourced and available online. The fast acquisition of KBLT datasets permits 4D scanning (3D plus time), also in combination with so-called sample environments, which can support the advancement of improved image reconstruction algorithms. We believe this ‘ Do it yourself ’ (DIY) toolkit will be useful to tomography users, beamline scientists and computational researchers, and the tomography community in general.

关键词： Kitchen-based light tomography KBLT Educational training tool Synchrotron X-ray microtomography SR μ CT XCT Neutron tomography NCT

Artificial intelligence reveals brain aging patterns in 27,402 individuals without diagnosed cognitive impairment that are linked to genetics, biomedical measures, and cognitive decline

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Ioanna Skampardoni Ilya M. Nasrallah Junhao Wen Yuhan Cui Ahmed Abdulkadir Zhijian Yang Guray Erus Elizabeth Mamourian Ashish Singh Haochang Shou Li Shen Konstantina Nikita Christos Davatzikos iSTAGING consortium School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Correspondece Department of Radiology University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Laboratory for Research in Neuroimaging Department of Clinical Neurosciences Lausanne University Hospital (CHUV) and University of Lausanne Lausanne Switzerland 1Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA

Background Understanding heterogeneity of structural brain changes in aging may provide insights into susceptibility to neurodegenerative diseases. We characterize the genetics underlying brain structural heterogeneity within cognitively unimpaired (CU) individuals using data-driven machine learning applied to a diverse dataset of 27,402 individuals from 11 neuroimaging studies from the iSTAGING consortium. Method Structural brain morphologic patterns of CU individuals were independently examined in four decade-long intervals spanning ages 45 to 85. Within each interval, Smile-GAN (Yang et al., 2021) was trained on baseline anatomic and white matter hyperintensity (WMH) volumes. Smile-GAN probability scores were used as phenotypes in genome-wide association studies (GWAS). Specifically, we performed multiple linear regressions controlling for confounders (e.g., age) via Plink (Purcell et al., 2007). We observed longitudinal clustering stability across decades, so individuals from adjacent age groups were combined into broader age groups ([45,65), [65,85)) due to the large sample requirement of GWAS. Genomic loci, represented by the top leading single nucleotide polymorphisms (SNPs), were defined considering linkage disequilibrium. We investigated associations of SNPs with clinical traits and mapped them to genes using the GWAS Catalog (Buniello et al., 2019). Result Three structural brain aging patterns, relative to resilient agers (A0), consistent across decades, emerged: A1, or ‘typical’ aging with low atrophy and WMHs, and two ‘advanced’ aging patterns, one showing elevated WMHs and modest atrophy (A2) and the other displaying severe, widespread atrophy and moderate WMH load (A3) (Figure 1). GWAS discovered eight and six genomic loci in [45,65) and [65,85) age groups, respectively (Table 1, Figure 2). The lead SNPs for A1 and A2 were previously associated with several cardiometabolic risk factors, WMHs, and regional brain volumes. Interestingly, rs4843552, previo

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Josephson-junction infrared single-photon detector

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arXiv 2020年

作者： Walsh, Evan D. Jung, Woochan Lee, Gil-Ho Efetov, Dmitri K. Wu, Bae-Ian Huang, K.-F. Ohki, Thomas A. Taniguchi, Takashi Watanabe, Kenji Kim, Philip Englund, Dirk Fong, Kin Chung Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Physics Pohang University of Science and Technology Pohang790-784 Korea Republic of Department of Physics Harvard University CambridgeMA02138 United States 08860 Spain Air Force Research Laboratory Wright-Patterson AFB OH45433 United States Raytheon BBN Technologies Quantum Engineering and Computing Group CambridgeMA02138 United States International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba Japan Research Center for Functional Materials National Institute for Materials Science Tsukuba Japan

Josephson junctions (JJs) are ubiquitous superconducting devices, enabling high sensitivity magnetometers and voltage amplifiers, as well as forming the basis of high performance cryogenic computer and superconducting quantum computers. While JJ performance can be degraded by quasiparticles (QPs) formed from broken Cooper pairs, this phenomenon also opens opportunities to sensitively detect electromagnetic radiation. Here we demonstrate single near-infrared photon detection by coupling photons to the localized surface plasmons of a graphene-based JJ. Using the photon-induced switching statistics of the current-biased JJ, we reveal the critical role of QPs generated by the absorbed photon in the detection mechanism. The photon-sensitive JJ will enable a high-speed, low-power optical interconnect for future JJ-based computing architectures. Copyright © 2020, The Authors. All rights reserved.

关键词： Photons

Transfer learning-based online multiperson tracking with Gaussian process regression

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Transfer learning-based online multiperson tracking with Gau...

作者： Zhang, Baobing Li, Siguang Huang, Zhengwen Rahi, Babak H. Wang, Qicong Li, Maozhen Department of Electronic and Computer Engineering Brunel University London London United Kingdom The Key Laboratory of Embedded Systems and Service Computing Tongji University Shanghai China College of Electrical Engineering Binzhou University Binzhou China Department of Computer Science Xiamen University Xiamen China

Most existing tracking-by-detection approaches are affected by abrupt pedestrian pose changes, lighting conditions, scale changes, and real-time processing, which leads to issues such as detection errors and drifts. To deal with these issues, we present a novel multi-person tracking framework by introducing a new Gaussian Process Regression based observation model, which learns in a semi-supervised manner. The background information is taken into consideration to build the discriminative tracker, training samples are re-weighted appropriately to ease the impact of the potential sample misalignment and noisy during model updating. Unlabeled samples from the current frame provide rich information, which is used for enhancing the tracking inference. Experimental results show that the proposed approach outperforms a number of state-of-the-art methods on some benchmark datasets. © 2018 John Wiley & Sons, Ltd.

关键词： Gaussian noise (electronic)

Demand-driven single- and multitarget mixture preparation using digital microfluidic biochips

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Demand-driven single- and multitarget mixture preparation us...

作者： Shalu Kumar, Srijan Singla, Ananya Roy, Sudip Chakrabarty, Krishnendu Chakrabarti, Partha P. Bhattacharya, Bhargab B. CoDA Laboratory Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee247667 India Department of Computer Science Stanford University StanfordCA94305 United States Department of Electrical and Computer Engineering Duke University Durham27708 United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur721302 India Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata Kolkata700108 India

Recent studies in algorithmic microfluidics have led to the development of several techniques for automated solution preparation using droplet-based digital microfluidic (DMF) biochips. A major challenge in this direction is to produce a mixture of several reactants with a desired ratio while optimizing reactant cost and preparation time. The sequence of mix-split operations that are to be performed on the droplets is usually represented as a mixing tree (or graph). In this article, we present an efficient mixing algorithm, namely, Mixing Tree with Common Subtrees (MTCS), for preparing single-target mixtures. MTCS attempts to best utilize intermediate droplets, which were otherwise wasted, and uses morphing based on permutation of leaf nodes to further reduce the graph size. The technique can be generalized to produce multitarget ratios, and we present another algorithm, namely, Multiple Target Ratios (MTR). Additionally, in order to enhance the output load, we also propose an algorithm for droplet streaming called Multitarget Multidemand (MTMD). Simulation results on a large set of target ratios show that MTCS can reduce the mean values of the total number of mix-split steps (Tms) and waste droplets (W) by 16% and 29% over Min-Mix (Thies et al. 2008) and by 22% and 34% over RMA (Roy et al. 2015), respectively. Experimental results also suggest that MTR can reduce the average values of Tms andW by 23% and 44% over the repeated version of Min-Mix, by 30% and 49% over the repeated version of RMA, and by 9% and 22% over the repeated-version of MTCS, respectively. It is observed that MTMD can reduce the mean values ofTms andW by 64% and 85%, respectively, over MTR. Thus, the proposed multitarget techniques MTR and MTMD provide efficient solutions to multidemand, multitarget mixture preparation on a DMF platform. © 2018 ACM.

关键词： Digital microfluidics

ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

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arXiv 2019年

作者： Liao, Haofu Lin, Wei-An Kevin, S. Zhou Luo, Jiebo Department of Computer Science University of Rochester RochesterNY14627 United States Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen China

—Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training. However, as synthesized data may not accurately simulate the underlying physical mechanisms of CT imaging, the supervised methods often generalize poorly to clinical applications. To address this problem, we propose, to the best of our knowledge, the first unsupervised learning approach to MAR. Specifically, we introduce a novel artifact disentanglement network that disentangles the metal artifacts from CT images in the latent space. It supports different forms of generations (artifact reduction, artifact transfer, and self-reconstruction, etc.) with specialized loss functions to obviate the need for supervision with synthesized data. Extensive experiments show that when applied to a synthesized dataset, our method addresses metal artifacts significantly better than the existing unsupervised models designed for natural image-to-image translation problems, and achieves comparable performance to existing supervised models for MAR. When applied to clinical datasets, our method demonstrates better generalization ability over the supervised models. The source code of this paper is publicly available at https://***/liaohaofu/adn. Copyright © 2019, The Authors. All rights reserved.

关键词： Image enhancement

Master stability functions for complete, intralayer, and interlayer synchronization in multiplex networks of coupled Rössler oscillators

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Physical Review E 2019年第1期99卷 012304-012304页

作者： Longkun Tang Xiaoqun Wu Jinhu Lü Jun-an Lu Raissa M. D'Souza Fujian Province University Key Laboratory of Computation Science School of Mathematical Science Huaqiao University Quanzhou 362021 China School of Mathematics and Statistics Wuhan University Wuhan 430072 China Department of Computer Science University of California Davis California 95616 USA School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

Synchronization phenomena are of broad interest across disciplines and increasingly of interest in a multiplex network setting. For the multiplex network of coupled Rössler oscillators, here we show how the master stability function, a celebrated framework for analyzing synchronization on a single network, can be extended to certain classes of multiplex networks with different intralayer and interlayer coupling functions. We derive three master stability equations that determine, respectively, the necessary regions of complete synchronization, intralayer synchronization, and interlayer synchronization. We calculate these three regions explicitly for the case of a two-layer network of Rössler oscillators and show that the overlap of the regions determines the type of synchronization achieved. In particular, if the interlayer or intralayer coupling function is such that the interlayer or intralayer synchronization region is empty, complete synchronization cannot be achieved regardless of the coupling strength. Furthermore, for any network structure, the occurrence of intralayer and interlayer synchronization depends mainly on the coupling functions of nodes within a layer and across layers, respectively. Our mathematical analysis requires that the intralayer and interlayer supra-Laplacians commute. But, we show this is only a sufficient, and not necessary, condition and that the results can be applied more generally.

关键词： Collective dynamics Synchronization Synchronization transition

The medical segmentation decathlon

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arXiv 2021年

作者： Antonelli, Michela Reinke, Annika Bakas, Spyridon Farahani, Keyvan Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. van Ginneken, Bram Bilello, Michel Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc J. Heckers, Stephan H. Huisman, Henkjan Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Golia Pernicka, Jennifer S. Rhode, Kawal Tobon-Gomez, Catalina Vorontsov, Eugene Hulsmon, Hankan Meakin, James A. Ourselin, Sebastien Wiesenfarth, Manuel Arbeláez, Pablo Bae, Byeonguk Chen, Sihong Daza, Laura Feng, Jianjiang He, Baochun Isensee, Fabian Ji, Yuanfeng Jia, Fucang Kim, Namkug Kim, Ildoo Merhof, Dorit Pai, Akshay Park, Beomhee Perslev, Mathias Rezaiifar, Ramin Rippel, Oliver Sarasua, Ignacio Shen, Wei Son, Jaemin Wachinger, Christian Wang, Liansheng Wang, Yan Xia, Yingda Xu, Daguang Xu, Zhanwei Zheng, Yefeng Simpson, Amber L. Maier-Hein, Lena Cardoso, M. Jorge School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany University of Pennsylvania PhiladelphiaPA United States BethesdaMD United States Heidelberg Germany Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen Netherlands Quantitative Biomedicine University of Zurich Zurich Switzerland DeepMind London United Kingdom School of Computing Department of Biomedical and Molecular Sciences Queen’s University KingstonON Canada Department of Surgery Memorial Sloan Kettering Cancer Center New YorkNY United States BethesdaMD United States Department of Informatics Technische Universität München Germany Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science and Software Engineering École Polytechnique de Montréal Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Medical Faculty University of Heidelberg Heidelberg Germany VUNO Inc. Seoul Korea Republic of Universidad de los Andes Colombia Department of Automation Tsinghua University Beijing China Heidelberg Germany Department of Computer Science Xiamen University Xiamen361005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Kakao Brain Korea Republic of Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Cere

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks;(2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability;(3) the training of accurate AI segmentation models is now commoditized to non AI *** Codes 68T07. © 2021, CC BY-NC-SA.

关键词： Image segmentation