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arXiv 2020年

作者： Raykov, Yordan P. Evers, Luc J.W. Badawy, Reham Bloem, Bastiaan Heskes, Tom M. Meinders, Marjan Claes, Kasper Little, Max A. Aston University School of Engineering and Applied Sciences Department of Mathematics Birmingham United Kingdom Radboud university medical center Donders Institute for Brain Cognition and Behaviour Department of Neurology Center of Expertise for Parkinson & Movement Disorders Nijmegen Netherlands Radboud University Institute for Computing and Information Sciences Nijmegen Netherlands University of Birmingham School of Computer Science Birmingham United Kingdom Nijmegen Netherlands UCB Biopharma Brussels Belgium Media Lab Massachusetts Institute of Technology CambridgeMA United States

Passive monitoring in daily life may provide invaluable insights about a person's health throughout the day. Wearable sensor devices are likely to play a key role in enabling such monitoring in a non-obtrusive fashion. However, sensor data collected in daily life reflects multiple health and behavior-related factors together. This creates the need for structured principled analysis to produce reliable and interpretable predictions that can be used to support clinical diagnosis and treatment. In this work we develop a principled modelling approach for free-living gait (walking) analysis. Gait is a promising target for non-obtrusive monitoring because it is common and indicative of various movement disorders such as Parkinson's disease (PD), yet its analysis has largely been limited to experimentally controlled lab settings. To locate and characterize stationary gait segments in free living using accelerometers, we present an unsupervised statistical framework designed to segment signals into differing gait and non-gait patterns. Our flexible probabilistic framework combines empirical assumptions about gait into a principled graphical model with all of its merits. We demonstrate the approach on a new video-referenced dataset including unscripted daily living activities of 25 PD patients and 25 controls, in and around their own houses. We evaluate our ability to detect gait and predict medication induced fluctuations in PD patients based on modelled gait. Our evaluation includes a comparison between sensors attached at multiple body locations including wrist, ankle, trouser pocket and lower back. Copyright © 2020, The Authors. All rights reserved.

关键词： Wearable sensors

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Some bounds on zeroth-order general randic index

arXiv

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arXiv 2019年

作者： Jamil, Muhammad Kamran Tomescu, Ioan Imran, Muhammad Department of Mathematical Sciences United Arab University Al Ain United Arab Emirates Faculty of Mathematics and Computer Science University of Bucharest Bucharest Romania Department of Mathematics School of Natural Sciences National University of Science and Technology Islamabad Pakistan Department of Mathematics Riphah Institute of Computing and Applied Sciences Riphah International University Lahore

For a graph G without isolated vertices, the inverse degree of a graph G is defined as ID(G) = £u∈V(G)d(u)-1where d(u) is the number of vertices adjacent to the vertex u in G By replacing -1 by any non-zero real number we obtain zeroth-order general Randic index, i.e. 0Rγ(G) = T,u∈V(G)d(u)γwhere γ is any non-zero real number. In [12], Xu et. al. determined some upper and lower bounds on the inverse degree for a connected graph G in terms of chromatic number, clique number, connectivity, number of cut edges. In this paper, we extend their results and investigate if the same results hold for γ Copyright © 2019, The Authors. All rights reserved.

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Randomized compiling for scalable quantum computing on a noisy superconducting quantum processor

arXiv

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arXiv 2020年

作者： Hashim, Akel Naik, Ravi K. Morvan, Alexis Ville, Jean-Loup Mitchell, Bradley Kreikebaum, John Mark Davis, Marc Smith, Ethan Iancu, Costin O'Brien, Kevin P. Hincks, Ian Wallman, Joel J. Emerson, Joseph Siddiqi, Irfan Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley BerkeleyCA94720 United States Graduate Group in Applied Science and Technology University of California at Berkeley BerkeleyCA94720 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Quantum Benchmark Inc. KitchenerONN2H 5G5 Canada Institute for Quantum Computing and Department of Applied Mathematics University of Waterloo WaterlooONN2L 3G1 Canada

The successful implementation of algorithms on quantum processors relies on the accurate control of quantum bits (qubits) to perform logic gate operations. In this era of noisy intermediate-scale quantum (NISQ) computing, systematic miscalibrations, drift, and crosstalk in the control of qubits can lead to a coherent form of error which has no classical analog. Coherent errors severely limit the performance of quantum algorithms in an unpredictable manner, and mitigating their impact is necessary for realizing reliable quantum computations. Moreover, the average error rates measured by randomized benchmarking and related protocols are not sensitive to the full impact of coherent errors, and therefore do not reliably predict the global performance of quantum algorithms, leaving us unprepared to validate the accuracy of future large-scale quantum computations. Randomized compiling is a protocol designed to overcome these performance limitations by converting coherent errors into stochastic noise, dramatically reducing unpredictable errors in quantum algorithms and enabling accurate predictions of algorithmic performance from error rates measured via cycle benchmarking. In this work, we demonstrate significant performance gains under randomized compiling for the four-qubit quantum Fourier transform algorithm and for random circuits of variable depth on a superconducting quantum processor. Additionally, we accurately predict algorithm performance using experimentally-measured error rates. Our results demonstrate that randomized compiling can be utilized to leverage and predict the capabilities of modern-day noisy quantum processors, paving the way forward for scalable quantum computing. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

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Josephson-junction infrared single-photon detector

arXiv

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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

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Core imaging library - Part II: Multichannel reconstruction for dynamic and spectral tomography

arXiv

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arXiv 2021年

作者： Papoutsellis, Evangelos Ametova, Evelina Delplancke, Claire Fardell, Gemma Jørgensen, Jakob S. Pasca, Edoardo Turner, Martin Warr, Ryan Lionheart, William R.B. Withers, Philip J. Henry Royce Institute Department of Materials The University of Manchester Oxford Road ManchesterM13 9PL United Kingdom Scientific Computing Department STFC UKRI Rutherford Appleton Laboratory Harwell Campus DidcotOX11 0QX United Kingdom Department of Mathematics The University of Manchester Oxford Road ManchesterM13 9PL United Kingdom Department of Mathematical Sciences University of Bath Claverton Down BathBA2 7AY United Kingdom Department of Applied Mathematics and Computer Science Technical University of Denmark Richard Petersens Plads Building 324 Kgs. Lyngby2800 Denmark Laboratory for Applications of Synchrotron Radiation Karlsruhe Institute of Technology Kaiserstr. 12 Karlsruhe76131 Germany Research IT Services The University of Manchester Oxford Road ManchesterM13 9PL United Kingdom

The newly developed Core Imaging Library (CIL) is a flexible plug and play library for tomographic imaging with a specific focus on iterative reconstruction. CIL provides building blocks for tailored regularised reconstruction algorithms and explicitly supports multichannel tomographic data. In the first part of this two-part publication, we introduced the fundamentals of CIL. This paper focuses on applications of CIL for multichannel data, e.g., dynamic and spectral. We formalise different optimisation problems for colour processing, dynamic and hyperspectral tomography and demonstrate CIL's capabilities for designing state of the art reconstruction methods through case studies and code *** Codes 65K10, 65R32, 65F10 © 2021, CC BY.

关键词： Tomography

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Particle Swarm Optimization for Feature Selection: A Review of Filter-based Classification to Identify Challenges and Opportunities

Particle Swarm Optimization for Feature Selection: A Review ...

引用

International Conference and Workshop on computing and Communication (IEMCON)

作者： Marianne Cherrington David Airehrour Joan Lu Fadi Thabtah Qiang Xu Samaneh Madanian School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom School of Applied Business Unitec Institute of Technology Auckland New Zealand Department of Computer Science University of Huddersfield Huddersfield United Kingdom Department of Digital Technologies Manukau Institute of Technology Auckland New Zealand Department of Engineering Technology University of Huddersfield Huddersfield United Kingdom Department of Computer Science Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9781728125312

Feature selection (FS) is a fundamental big data task, improving classification performance by selecting a relevant feature subset to mitigate the `curse of dimensionality'. As the number of attributes increase, search algorithms can limit FS methods. Particle swarm optimization (PSO) is a global search metaheuristic, with the ability to search a space of large dimension quickly, with few assumptions. This review explores filter FS classification methods that exploit contemporary particle swarm optimization research, categorizing state-of-the-art techniques. The major contribution of this review is in highlighting the uses and limitations of these currently underrepresented techniques, to identify current challenges and opportunities, so further productive research may be exploited.

关键词： Feature extraction Filtering algorithms Filtering theory Information filters Particle swarm optimization Classification algorithms

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Leveraging randomized compiling for the QITE algorithm

arXiv

引用

arXiv 2021年

作者： Ville, Jean-Loup Morvan, Alexis Hashim, Akel Naik, Ravi K. Lu, Marie Mitchell, Bradley Kreikebaum, John-Mark O’Brien, Kevin P. Wallman, Joel J. Hincks, Ian Emerson, Joseph Smith, Ethan Younis, Ed Iancu, Costin Santiago, David I. Siddiqi, Irfan Quantum Nanoelectronics Laboratory Dept. of Physics University of California at Berkeley BerkeleyCA94720 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Inst. for Quantum Computing Dept. of Applied Mathematics University of Waterloo WaterlooONN2L 3G1 Canada Quantum Benchmark Inc. KitchenerONN2H 5G5 Canada

The success of the current generation of Noisy Intermediate-Scale Quantum (NISQ) hardware shows that quantum hardware may be able to tackle complex problems even without error correction. One outstanding issue is that of coherent errors arising from the increased complexity of these devices. These errors can accumulate through a circuit, making their impact on algorithms hard to predict and mitigate. Iterative algorithms like Quantum Imaginary Time Evolution are susceptible to these errors. This article presents the combination of both noise tailoring using Randomized Compiling and error mitigation with a purification. We also show that Cycle Benchmarking gives an estimate of the reliability of the purification. We apply this method to the Quantum Imaginary Time Evolution of a Transverse Field Ising Model and report an energy estimation and a ground state infidelity both below 1%. Our methodology is general and can be used for other algorithms and platforms. We show how combining noise tailoring and error mitigation will push forward the performance of NISQ devices. © 2021, CC BY.

关键词： Benchmarking

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A non-perturbative approach to computing seismic normal modes in rotating planets

arXiv

引用

arXiv 2019年

作者： Shi, Jia Li, Ruipeng Xi, Yuanzhe Saad, Yousef de Hoop, Maarten V. Department of Earth Environmental and Planetary Sciences Rice University TX United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory CA United States Department of Mathematics Emory University AtlantaGA United States Department of Computer Science and Engineering University of Minnesota MN United States Department of Computational and Applied Mathematics Rice University TX United States

MSC Codes 86-08, 86-04, 85-04, 85-08, 85-10, 15A18, 65N25, 65N30A Continuous Galerkin method based approach is presented to compute the seismic normal modes of rotating planets. Special care is taken to separate out the essential spectrum in the presence of a fluid outer core using a polynomial filtering eigensolver. The relevant elastic-gravitational system of equations, including the Coriolis force, is subjected to a mixed finite-element method, while self-gravitation is accounted for with the fast multipole method. Our discretization utilizes fully unstructured tetrahedral meshes for both solid and fluid regions. The relevant eigenvalue problem is solved by a combination of several highly parallel and computationally efficient methods. We validate our three-dimensional results in the non-rotating case using analytical results for constant elastic balls, as well as numerical results for an isotropic Earth model from standard "radial" algorithms. We also validate the computations in the rotating case, but only in the slowly-rotating regime where perturbation theory applies, because no other independent algorithms are available in the general case. The algorithm and code are used to compute the point spectra of eigenfrequencies in several Earth and Mars models studying the effects of heterogeneity on a large range of scales. Copyright © 2019, The Authors. All rights reserved.

关键词： Seismology

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The computation of seismic normal modes with rotation as a quadratic eigenvalue problem

arXiv

引用

arXiv 2019年

作者： Shi, Jia Li, Ruipeng Xi, Yuanzhe Saad, Yousef de Hoop, Maarten V. Department of Earth Environmental and Planetary Sciences Rice University United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States Department of Mathematics Emory University AtlantaGA United States Department of Computer Science and Engineering University of Minnesota MN United States Simons Chair in Computational and Applied Mathematics and Earth Science Rice University HoustonTX United States

A new approach is presented to compute the seismic normal modes of a fully heterogeneous, rotating planet. Special care is taken to separate out the essential spectrum in the presence of a fluid outer core. The relevant elastic-gravitational system of equations, including the Coriolis force, is subjected to a mixed finite-element method, while self-gravitation is accounted for with the fast multipole method (FMM). To solve the resulting quadratic eigenvalue problem (QEP), the approach utilizes extended Lanczos vectors forming a subspace computed from a non-rotating planet – with the shape of boundaries of a rotating planet and accounting for the centrifugal potential – to reduce the dimension of the original problem significantly. The subspace is guaranteed to be contained in the space of functions to which the seismic normal modes belong. The reduced system can further be solved with a standard eigensolver. The computational accuracy is illustrated using all the modes with relative small meshes and also tested against standard perturbation calculations relative to a standard Earth model. The algorithm and code are used to compute the point spectra of eigenfrequencies in several Mars models studying the effects of heterogeneity on a large range of scales. Copyright © 2019, The Authors. All rights reserved.

关键词： Seismology

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Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

arXiv

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arXiv 2024年

作者： LaBella, Dominic Baid, Ujjwal Khanna, Omaditya McBurney-Lin, Shan McLean, Ryan Nedelec, Pierre Rashid, Arif Tahon, Nourel Hoda Altes, Talissa Bhalerao, Radhika Dhemesh, Yaseen Godfrey, Devon Hilal, Fathi Floyd, Scott Janas, Anastasia Kazerooni, Anahita Fathi Kirkpatrick, John Kent, Collin Kofler, Florian Leu, Kevin Maleki, Nazanin Menze, Bjoern Pajot, Maxence Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Velichko, Yury Wang, Chunhao Warman, Pranav Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anwar, Syed Muhammad Bergquist, Timothy Chen, Sully Francis Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Khalili, Nastaran Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Liu, Xinyang Mahtabfar, Aria Meier, Zeke Moawad, Ahmed W. Mongan, John Piraud, Marie Shinohara, Russell Takeshi Wiggins, Walter F. Abayazeed, Aly H. Akinola, Rachel Jakab, András Bilello, Michel de Verdier, Maria Correia Crivellaro, Priscila Davatzikos, Christos Farahani, Keyvan Freymann, John Hess, Christopher Huang, Raymond Lohmann, Philipp Moassefi, Mana Pease, Matthew W. Vollmuth, Phillipp Sollmann, Nico Diffley, David Nandolia, Khanak K. Warren, Daniel I. Hussain, Ali Fehringer, Pascal Bronstein, Yulia Deptula, Lisa Stein, Evan G. Taherzadeh, Mahsa de Oliveira, Eduardo Portela Haughey, Aoife Kontzialis, Marinos Saba, Luca Turner, Benjamin Brüßeler, Melanie M.T. Ansari, Shehbaz Gkampenis, Athanasios Weiss, David Maximilian Mansour, Aya Shawali, Islam H. Yordanov, Nikolay Stein, Joel M. Hourani, Roula Moshebah, Mohammed Yahya Abouelatta, Ahmed Magdy Rizvi, Tanvir Willms, Klara Martin, Dann C. Okar, Abdullah D'Anna, Gennaro Taha, Ahmed Sharifi, Yasaman Faghani, Shahriar Kite, Dominic Pinho, Marco Haider, Muhammad Ammar Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Alonso-Basanta, Michelle Villanueva-Meyer, Javier Rauschecker, Andreas M. Nada, Ayman Aboian, Mariam Flanders, Adam E. Wiestler, Benedikt Bakas, Spyridon Ca Duke University Medical Center Department of Radiation Oncology DurhamNC United States Division of Computational Pathology Department of Pathology Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning in Medicine Indiana University IndianapolisIN United States Department of Neurosurgery Thomas Jefferson University PhiladelphiaPA United States University of California San FranciscoCA United States Yale University New HavenCT United States Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University of Missouri ColumbiaMO United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Helmholtz AI Helmholtz Munich Germany Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany San FranciscoCA United States Biomedical Image Analysis and Machine Learning Department of Quantitative Biomedicine University of Zurich Switzerland Department of Neuroradiology Technical University of Munich Munich Germany University of San Diego CA United States Cornell University IthacaNY United States Department of Radiology Northwestern University EvanstonIL United States Duke University Medical Center School of Medicine DurhamNC United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United State

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps. © 2024, CC BY.

关键词： Tumors

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