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vailá: VERSATILE ANARCHO INTEGRATED LIBERATION ÁNALYSIS IN MULTIMODAL TOOLBOX

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arXiv 2024年

作者： Santiago, Paulo Roberto Pereira Chinaglia, Abel Gonçalves Flanagan, Kira Bedo, Bruno L.S. Mochida, Ligia Yumi Aceros, Juan Bononi, Aline Cesar, Guilherme Manna Biomechanics and Motor Control Laboratory School of Physical Education and Sport USP Brazil Graduate Program in Rehabilitation and Functional Performance Medical School USP Brazil Technology and Sports Performance Analysis Laboratory School of Physical Education USP Brazil Laboratory of Applied Biomechanics and Engineering Brooks College of Health UNF United States Department of Physical Therapy Brooks College of Health UNF United States College of Computing Engineering and Construction UNF United States Municipal Pharmacy Ribeirão Preto Brazil

Human movement analysis is crucial in health and sports biomechanics for understanding physical performance, guiding rehabilitation, and preventing injuries. However, existing tools are often proprietary, expensive, and function as "black boxes", limiting user control and customization. This paper introduces vailá—Versatile Anarcho Integrated Liberation Ánalysis in Multimodal Toolbox—an open-source, Python-based platform designed to enhance human movement analysis by integrating data from multiple biomechanical systems. vailá supports data from diverse sources, including retroreflective motion capture systems, inertial measurement units (IMUs), markerless video capture technology, electromyography (EMG), force plates, and GPS/GNSS systems, enabling comprehensive analysis of movement patterns. Developed entirely in Python 3.11.9, which offers improved efficiency and long-term support, and featuring a straightforward installation process, vailá is accessible to users without extensive programming experience. In this paper, we also present several workflow examples that demonstrate how vailá allows the rapid processing of large batches of data, independent of the type of collection method. This flexibility is especially valuable in research scenarios where unexpected data collection challenges arise, ensuring no valuable data point is lost. We demonstrate the application of vailá in analyzing sit-to-stand movements in pediatric disability, showcasing its capability to provide deeper insights even with unexpected movement patterns. By fostering a collaborative and open environment, vailá encourages users to innovate, customize, and freely explore their analysis needs, potentially contributing to the advancement of rehabilitation strategies and performance optimization. For more details in GitHub repository: https://***/vaila-multimodaltoolbox/vaila and the online documentation: https://*** Codes 92C10, 68U10, 65D18, 65K10 © 2024, CC BY-SA.

关键词： Electromyography

A Tutorial on Trusted and Untrusted Non-3GPP Accesses in 5G Systems - First Steps Toward a Unified Communications Infrastructure

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arXiv 2021年

作者： Lemes, Mario Teixeira Alberti, Antonio Marcos Both, Cristiano Bonato de Oliveira, Antonio Carlos Cardoso, Kleber Vieira Instituto Federal de Educação Ciência e Tecnologia de Goiás Formosa73813-816 Brazil Instituto de Informática Universidade Federal de Goiás Goiânia74690-900 Brazil Instituto Nacional de Telecomunicações Santa Rita do Sapucaí37540-000 Brazil Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos São Leopoldo93022-750 Brazil Fraunhofer AICOS Porto4200-135 Portugal

fifth-generation (5G) systems are designed to enable convergent access-agnostic service availability. This means that 5G services will be available over 5G New Radio air interface and also through other non-Third Generation Partnership Project (3GPP) access networks, e.g., IEEE 802.11 (Wi-Fi). 3GPP has recently published the Release 16 that includes trusted non-3GPP access network concept and wireless wireline convergence. The main goal of this tutorial is to present an overview of access to 5G core via non-3GPP access networks specified by 3GPP until Release 16 (i.e., untrusted, trusted, and wireline access). The tutorial describes convergence aspects of a 5G system and these non-3GPP access networks, such as the authentication and authorization procedures and the data session establishment from the point of view of the protocol stack and exchanged messages among the network functions. In order to illustrate several concepts and part of 3GPP specification, we present a basic but fully operational implementation of untrusted non-3GPP access using WLAN. We perform experiments illustrating how a Wi-Fi user is authorized in a 5G core, establishing user plane connectivity to a data network. Moreover, we evaluate the performance of this access in terms of time consumed, the number of messages, and protocol overhead to established data sessions. © 2021, CC BY.

关键词： Wi-Fi

Inter-comparison of multiple Global Climate Model (GCM) data based on spatial pattern of rainfall over Indonesia

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IOP Conference Series: Earth and Environmental Science 2019年第1期284卷

作者： Elania Aflahah Arnida Lailatul Latifah Rahmat Hidayat Rini Hidayati Andi Ihwan Applied Climatology Study Program Graduate School Bogor Agricultural University Bogor 16680 Laboratorium of High-Performance Computing Research Center for Informatics Indonesian Institute of Sciences Bogor West Java 16916 Department of Geophysics and Meteorology Bogor Agricultural University Bogor 16680

The Coupled Model Inter-comparison Project Phase 5 (CMIP5) is the output of many coupled atmosphere-ocean of global climate models (GCMs) and widely used for climate research, especially for driving regional climate model. There are more than 40 CMIP5 GCMs data available, but no single model can be considered as the best for every region. The use of CMIP5 GCMs data for rainfall projection in Indonesia is important to improve the accuracy of the monthly and seasonal rainfall forecast. Then, this study evaluates the capability of the CMIP5 GCMs data for Indonesia region by quantitatively comparing the spatial pattern of the precipitation mean and standard deviation of the CMIP5 data against GPCP, GPCC, and CRU data in the period 1980-2005. Furthermore, the composite analysis is conducted to observe the model performance in reproducing the precipitation characteristic over some areas in Indonesia. In conclusion, the models NorESM1-M, NorESM1-ME, GFDL-ESM2M, CSIRO-MK3-6-0 perform the rainfall mean better than others, while the standard deviation of the rainfall show that the models NorESM1-M, BNU-ESM, CMCC-CMS are superior in which NorESM1-M gives the best performance. The annual precipitation pattern of the model NorESM1-M over various areas in Indonesia is also highly correlated with the observations. Thus, the most suitable model for Indonesia region is NorESM1-M.

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Deep Machine Learning Techniques for the Detection and Classification of Sperm Whale Bioacoustics (vol 9, 12588, 2019)

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SCIENTIFIC REPORTS 2019年第1期9卷 12588-12588页

作者： Bermant, Peter C. Bronstein, Michael M. Wood, Robert J. Gero, Shane Gruber, David F. Radcliffe Institute for Advanced Study Harvard University Cambridge MA USA Department of Computing Imperial College London UK Twitter 20 Air Street London W1B 5DL United Kingdom Wyss Institute for Biologically Inspired Engineering Harvard University Cambridge MA USA Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA USA Department of Zoophysiology Institute for Bioscience Aarhus University C.F. Møllers Allé 3 8000 Aarhus Denmark Department of Natural Sciences Baruch College and The Graduate Center PhD Program in Biology City University of New York New York NY USA

We implemented Machine Learning (ML) techniques to advance the study of sperm whale (Physeter macrocephalus) bioacoustics. This entailed employing Convolutional Neural Networks (CNNs) to construct an echolocation click detector designed to classify spectrograms generated from sperm whale acoustic data according to the presence or absence of a click. The click detector achieved 99.5% accuracy in classifying 650 spectrograms. The successful application of CNNs to clicks reveals the potential of future studies to train CNN-based architectures to extract finer-scale details from cetacean spectrograms. Long short-term memory and gated recurrent unit recurrent neural networks were trained to perform classification tasks, including (1) 'coda type classification' where we obtained 97.5% accuracy in categorizing 23 coda types from a Dominica dataset containing 8,719 codas and 93.6% accuracy in categorizing 43 coda types from an Eastern Tropical Pacific (ETP) dataset with 16,995 codas; (2) 'vocal clan classification' where we obtained 95.3% accuracy for two clan classes from Dominica and 93.1% for four ETP clan types; and (3) 'individual whale identification' where we obtained 99.4% accuracy using two Dominica sperm whales. These results demonstrate the feasibility of applying ML to sperm whale bioacoustics and establish the validity of constructing neural networks to learn meaningful representations of whale vocalizations. [ABSTRACT FROM AUTHOR]

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Enhanced Superconducting Qubit Performance Through Ammonium Fluoride Etch

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arXiv 2024年

作者： Kopas, Cameron J. Goronzy, Dominic P. Pham, Thang Torres Castanedo, Carlos G. Cheng, Matthew Cochrane, Rory Nast, Patrick Lachman, Ella Zhelev, Nikolay Z. Vallières, André Murthy, Akshay A. Oh, Jin-Su Zhou, Lin Kramer, Matthew J. Cansizoglu, Hilal Bedzyk, Michael J. Dravid, Vinayak P. Romanenko, Alexander Grassellino, Anna Mutus, Josh Y. Hersam, Mark C. Yadavalli, Kameshwar Rigetti Computing BerkeleyCA United States Department of Materials Science and Engineering Northwestern University EvanstonIL United States Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL United States Department of Physics and Astronomy Northwestern University EvanstonIL United States Department of Physics University of Oregon EugeneOR United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Fermi National Accelerator Laboratory BataviaIL United States Ames Laboratory U.S. Department of Energy AmesIA United States Northwestern University EvanstonIL United States Department of Chemistry Northwestern University EvanstonIL United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL United States

The performance of superconducting qubits is often limited by dissipation and two-level systems (TLS) losses. The dominant sources of these losses are believed to originate from amorphous materials and defects at interfaces and surfaces, likely as a result of fabrication processes or ambient exposure. Here, we explore a novel wet chemical surface treatment at the Josephson junction-substrate and the substrate-air interfaces by replacing a buffered oxide etch (BOE) cleaning process with one that uses hydrofluoric acid followed by aqueous ammonium fluoride. We show that the ammonium fluoride etch process results in a statistically significant improvement in median T1 by ∼ 22% (p = 0.002), and a reduction in the number of strongly-coupled TLS in the tunable frequency range. Microwave resonator measurements on samples treated with the ammonium fluoride etch prior to niobium deposition also show ∼ 33% lower TLS-induced loss tangent compared to the BOE treated samples. As the chemical treatment primarily modifies the Josephson junction-substrate interface and substrate-air interface, we perform targeted chemical and structural characterizations to examine materials’ differences at these interfaces and identify multiple microscopic changes that could contribute to decreased TLS. © 2024, CC BY-NC-ND.

关键词： Fluorine compounds

Loss tangent fluctuations due to two-level systems in superconducting microwave resonators

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arXiv 2024年

作者： Vallières, A. Russell, M.E. You, X. Garcia-Wetten, D.A. Goronzy, D.P. Walker, M.J. Bedzyk, M.J. Hersam, M.C. Romanenko, A. Lu, Y. Grassellino, A. Koch, J. McRae, C.R.H. Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL60208 United States Electrical Computing and Energy Engineering Department University of Colorado BoulderCO80309 United States Department of Physics University of Colorado BoulderCO80309 United States National Institute of Standards and Technology BoulderCO80305 United States

Superconducting microwave resonators are critical to quantum computing and sensing technologies. Additionally, they are common proxies for superconducting qubits when determining the effects of performance-limiting loss mechanisms such as from two-level systems (TLS). The extraction of these loss mechanisms is often performed by measuring the internal quality factor Qi as a function of power or temperature. In this work, we investigate large temporal fluctuations of Qi at low powers over periods of 12 to 16 hours (relative standard deviation σQi/Qi = 13%). These fluctuations are ubiquitous across multiple resonators, chips and cooldowns. We are able to attribute these fluctuations to variations in the TLS loss tangent due to two main indicators. First, measured fluctuations decrease as power and temperature increase. Second, for interleaved measurements, we observe correlations between low- and medium-power Qi fluctuations and an absence of correlations with high-power fluctuations. Agreement with the TLS loss tangent mean is obtained by performing measurements over a time span of a few hours. We hypothesize that, in addition to decoherence due to coupling to individual near-resonant TLS, superconducting qubits are affected by these observed TLS loss tangent fluctuations. © 2024, CC BY.

关键词： Qubits

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

Cetacean translation initiative: A roadmap to deciphering the communication of sperm whales

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arXiv 2021年

作者： Andreas, Jacob Beguš, Gašper Bronstein, Michael M. Diamant, Roee Delaney, Denley Gero, Shane Goldwasser, Shafi Gruber, David F. de Haas, Sarah Malkin, Peter Payne, Roger Petri, Giovanni Rus, Daniela Sharma, Pratyusha Tchernov, Dan Tønnesen, Pernille Torralba, Antonio Vogt, Daniel Wood, Robert J. MIT CSAIL CambridgeMA United States Department of Linguistics University of California BerkeleyCA United States Department of Computing Imperial College London United Kingdom IDSIA University of Lugano Switzerland Twitter United Kingdom School of Marine Sciences Haifa University Israel Exploration Technology Lab National Geographic Society Dominica Sperm Whale Project Dominica Department of Biology Carleton University Canada Simons Institute for the Theory of Computing University of California BerkeleyCA United States Department of Natural Sciences Baruch College and The Graduate Center PhD Program in Biology City University of New York New YorkNY United States Google Research Ocean Alliance GloucesterMA United States ISI Foundation Turin Italy Marine Bioacoustics Lab Zoophysiology Department of Biology Aarhus University Denmark School of Engineering and Applied Sciences Harvard University CambridgeMA United States

The past decade has witnessed a groundbreaking rise of machine learning for human language analysis, with current methods capable of automatically accurately recovering various aspects of syntax and semantics — including sentence structure and grounded word meaning — from large data collections. Recent research showed the promise of such tools for analyzing acoustic communication in nonhuman species. We posit that machine learning will be the cornerstone of future collection, processing, and analysis of multimodal streams of data in animal communication studies, including bioacoustic, behavioral, biological, and environmental data. Cetaceans are unique non-human model species as they possess sophisticated acoustic communications, but utilize a very different encoding system that evolved in an aquatic rather than terrestrial medium. Sperm whales, in particular, with their highly-developed neuroanatomical features, cognitive abilities, social structures, and discrete click-based encoding make for an excellent starting point for advanced machine learning tools that can be applied to other animals in the future. This paper details a roadmap toward this goal based on currently existing technology and multidisciplinary scientific community effort. We outline the key elements required for the collection and processing of massive bioacoustic data of sperm whales, detecting their basic communication units and language-like higher-level structures, and validating these models through interactive playback experiments. The technological capabilities developed by such an undertaking are likely to yield cross-applications and advancements in broader communities investigating non-human communication and animal behavioral research. © 2021, CC BY-NC-ND.

关键词： Signal encoding

Doe's effort to reduce truck aerodynamic drag - joint experiments and computations lead to smart design

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Doe's effort to reduce truck aerodynamic drag - joint experi...

34th AIAA Fluid Dynamics Conference and Exhibit 2004

作者： McCallen, Rose C. Salari, Kambiz Ortega, Jason M. DeChant, Larry J. Hassan, Basil Roy, Christopher J. Pointer, W. David Browand, Fred Hammache, Mustapha Hsu, Tsun-Ya Leonard, Anthony Rubel, Mike Chatalain, Philippe Englar, Robert Ross, James Satran, D. Heineck, James T. Walker, Stephen Yaste, D. Storms, B. Lawrence Livermore National Laboratory Livermore CA 94551 United States Sandia National Laboratories Albuquerque NM 87185-0825 United States Auburn University Auburn AL 36849 United States Argonne National Laboratory Argonne IL 60439 United States University of Southern California LosAngeles CA 90089-1191 United States Georgia Tech Research Institute Atlanta GA 30332 United States NASA Ames Research Center Moffet Field CA 94035 United States Center for Applied Scientific Computing P.O. Box 808 L-98 United States New Technologies Engineering Division P.O. Box 808 L-644 United States Aerosciences and Compressible Fluid Mechanics Dept. PO Box 5800 MS 0825 United States Aerospace Engineering Dept. 211 Aerospace Engineering Bldg United States Nuclear Engineering Division NE-208 United States Aerospace and Mechanical Engineering MS 1191 United States Engineering and Applied Science 1200 East California Blvd. MC 301-46 United States Graduate Aeronautical Laboratories 1200 East California Blvd. MC 205-45 United States Aerospace Transportation and Advanced Systems Code 0844 United States Systems Analysis Branch MS 260-1 United States Aeronautical Projects and Program Office MS 260-1 United States

ISBN: (纸本)9781624100314

At 70 miles per hour, overcoming aerodynamic drag represents about 65% of the total energy expenditure for a typical heavy truck vehicle. The goal of this US Department of Energy supported consortium is to establish a clear understanding of the drag producing flow phenomena. This is being accomplished through joint experiments and computations, leading to the 'smart' design of drag reducing devices. This paper will describe our objective and approach, provide an overview of our efforts and accomplishments, and discuss our future direction. © 2004 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Aerodynamic drag