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International conference on New Interfaces for Musical Expression, NIME 2017

作者： Kıratlı, Şölen Cadambi, Akshay Visell, Yon Media Arts and Technology University of California Santa Barbara Santa BarbaraCA United States Media Arts and Technology Electrical and Computer Engineering University of California Santa Barbara Santa BarbaraCA United States

In this paper we present HIVE (2016), a parametrically designed interactive sound sculpture with embedded multichannel digital audio which explores the intersection of sculptural form and musical instrument design. We examine sculpture as an integral part of music composition and performance, expanding the definition of musical instrument to include the gestalt of loudspeakers, architectural spaces, and material form. After examining some related works, we frame HIVE as an interactive sculpture for musical expression. We then describe our design and production process, which hinges on the relationship between sound, space, and sculptural form. Finally, we discuss the installation and its implications. © 2017 Steering Committee of the International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： Musical instruments

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Pre-training of deep bidirectional protein sequence representations with structural information

arXiv

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arXiv 2019年

作者： Min, Seonwoo Park, Seunghyun Kim, Siwon Choi, Hyun-Soo Lee, Byunghan Yoon, Sungroh Department of Electrical and Computer engineering Seoul National University Seoul08826 Korea Republic of LG AI Research Seoul07796 Korea Republic of Clova AI Research NAVER Corp. Seongnam13561 Korea Republic of Department of Computer Science and Engineering Kangwon National University Chuncheon24341 Korea Republic of Department of Electronic and IT Media Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Interdisciplinary Program in Artificial Intelligence ASRI INMC Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of

Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling. In these studies, models were pre-trained with a substantial amount of unlabeled data, and the representations were transferred to various downstream tasks. Most pre-training methods solely rely on language modeling and often exhibit limited performance. In this paper, we introduce a novel pre-training scheme called PLUS, which stands for Protein sequence representations Learned Using Structural information. PLUS consists of masked language modeling and a complementary protein-specific pre-training task, namely same-family prediction. PLUS can be used to pre-train various model architectures. In this work, we use PLUS to pre-train a bidirectional recurrent neural network and refer to the resulting model as PLUS-RNN. Our experiment results demonstrate that PLUS-RNN outperforms other models of similar size solely pre-trained with the language modeling in six out of seven widely used protein biology tasks. Furthermore, we present the results from our qualitative interpretation analyses to illustrate the strengths of PLUS-RNN. PLUS provides a novel way to exploit evolutionary relationships among unlabeled proteins and is broadly applicable across a variety of protein biology tasks. We expect that the gap between the numbers of unlabeled and labeled proteins will continue to grow exponentially, and the proposed pre-training method will play a larger role. All the data and codes used in this study are available at https://***/mswzeus/PLUS. © 2019, CC BY-NC-ND.

关键词： Proteins

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Machine learning used to study risk factors for chronic diseases: A scoping review

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Canadian journal of public health = Revue canadienne de sante publique 2025年 2025 Jun 11页

作者： Mahek Shergill Steve Durant Sharon Birdi Roxana Rabet Carolyn Ziegler Shehzad Ali David Buckeridge Marzyeh Ghassemi Jennifer Gibson Ava John-Baptiste Jillian Macklin Melissa McCradden Kwame McKenzie Parisa Naraei Akwasi Owusu-Bempah Laura C Rosella James Shaw Ross Upshur Sharmistha Mishra Andrew D Pinto Upstream Lab MAP Centre for Urban Health Solutions Li Ka Shing Knowledge Institute St. Michael's Hospital Toronto ON Canada. Michael G. DeGroote School of Medicine McMaster University Hamilton ON Canada. Health Science Library Unity Health Toronto Toronto ON Canada. Department of Epidemiology and Biostatistics Western Centre for Public Health & Family Medicine Western University London ON Canada. Department of Health Sciences University of York York UK. WHO Collaborating Centre for Knowledge Translation and Health Technology Assessment in Health Equity Ottawa ON Canada. Department of Epidemiology Biostatistics and Occupational Health School of Population and Global Health McGill University Montreal QC Canada. Department of Electrical Engineering and Computer Science (EECS) Institute for Medical Engineering & Science (IMES) MIT Cambridge MA USA. Joint Centre for Bioethics University of Toronto Toronto ON Canada. Departments of Epidemiology & Biostatistics Anesthesia & Perioperative Medicine Schulich Interfaculty Program in Public Health Western University London ON Canada. Undergraduate Medical Education Faculty of Medicine University of Toronto Toronto ON Canada. Department of Bioethics The Hospital for Sick Children Toronto ON Canada. Genetics & Genome Biology SickKids Research Institute Toronto ON Canada. Division of Clinical Public Health Dalla Lana School of Public Health University of Toronto Toronto ON Canada. Wellesley Institute Toronto ON Canada. CAMH Toronto ON Canada. Department of Computer Science Toronto Metropolitan University Toronto ON Canada. Department of Sociology Faculty of Arts & Sciences University of Toronto Toronto ON Canada. Institute for Better Health Trillium Health Partners Toronto ON Canada. Department of Physical Therapy Faculty of Medicine University of Toronto Toronto ON Canada. Department of Family and Community Medicine Faculty of Medicine University of Toronto Toronto ON Canada. Division

OBJECTIVES:Machine learning (ML) has received significant attention for its potential to process and learn from vast amounts of data. Our aim was to perform a scoping review to identify studies that used ML to study risk factors for chronic diseases at a population level, notably those that incorporated methods to mitigate algorithmic bias. We focused on ML applications for the most common risk factors for chronic disease: tobacco use, alcohol use, unhealthy eating, physical activity, and psychological stress. METHODS:We searched the peer-reviewed, indexed literature using Medline (Ovid), Embase (Ovid), Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews (Ovid), Scopus, ACM Digital Library, INSPEC, and Web of Science's Science Citation Index, Social Sciences Citation Index, and Emerging Sources Citation Index. Among the included studies, we examined whether bias was considered and identified strategies employed to mitigate bias. SYNTHESIS:The search identified 10,329 studies, and 20 met our inclusion criteria. The studies we identified used ML for a wide range of goals, from prediction of chronic disease development to automating the classification of data to identifying new associations between risk factors and disease. Nine studies (45%) included some discussion of algorithmic bias. Studies that incorporated a broad array of sociodemographic variables did so primarily to improve the performance of a ML model rather than to mitigate potential harms to populations made vulnerable by social and economic policies. CONCLUSION:This work contributes to our understanding of how ML can be used to advance population and public health.

关键词： Artificial intelligence Machine learning Non-communicable diseases Population health Public health Risk factors

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Noninvasive assessment of jugular venous pressure via force-coupled single crystal ultrasound

Noninvasive assessment of jugular venous pressure via force-...

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作者： Singh, Robin Jaffe, Alex Frydman, Galit H. Najia, Mohamad Ali Toufic Wei, Zijun Yang, Jason Majmudar, Maulik D. Department of Mechanical Engineering Medical Electronic Device Realization Centre Institute of Medical Engineering and Science Massachusetts Institute of Technology United States Research Laboratory of Electronics Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Division of Comparative Medicine Department of Biological Engineering Massachusetts Institute of Technology United States Center for Surgery Innovation and Bioengineering Department of Surgery Massachusetts General Hospital United States Harvard-MIT Health Sciences and Technology United States Broad Institute of MIT and Harvard United States Department of Hematology/Oncology Boston Children's Hospital United States Department of Media Arts and Sciences Massachusetts Institute of Technology United States Healthcare Transformation Lab Division of Cardiology Department of Medicine Massachusetts General Hospital BostonMA02114-2696 United States

Objective: we have developed a handheld device for noninvasive quantitative assessment of jugular venous pressure (JVP). Methods: we used a single crystal ultrasound coupled to a force-sensing load cell to measure JVP based on the force necessary to collapse the internal jugular vein (IJV) walls. We used a gelatin-based model system of the IJV to test the ability of single crystal ultrasound to identify the IJV and verified the cross-sectional position and diameter of the vessels with conventional imaging ultrasound. We also tested our prototype device on healthy human volunteers. Results: experiments on model system demonstrated that vessel diameters determined with single crystal ultrasound were in close agreement with the diameters derived from conventional 2-D ultrasound. Proof-of-concept human experiments demonstrate that single crystal ultrasound can detect the IJV in basal and collapsed states, as compared to gold-standard sonography (insert stats). Assessment of JVP in human volunteers was physiologically consistent with and sensitive to postural changes (supine JVP 6.6 ± 2.4 mmHg;standing JVP 4.2 ± 1.9 mmHg (p © 1964-2012 IEEE.

关键词： Single crystals

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Probing the universality of topological defect formation in a quantum annealer: Kibble-Zurek mechanism and beyond

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Physical Review Research 2020年第3期2卷 033369-033369页

作者： Yuki Bando Yuki Susa Hiroki Oshiyama Naokazu Shibata Masayuki Ohzeki Fernando Javier Gómez-Ruiz Daniel A. Lidar Sei Suzuki Adolfo del Campo Hidetoshi Nishimori Institute of Innovative Research Tokyo Institute of Technology Nagatsuta-cho Midori-ku Yokohama 226-8503 Japan Institute of Innovative Research Tokyo Institute of Technology Oh-okayama Meguro-ku Tokyo 152-8550 Japan Department of Physics Tohoku University Sendai 980-8578 Japan Graduate School of Information Sciences Tohoku University Sendai 980-8579 Japan Sigma-i Co. Ltd. Konan Minato-ku Tokyo 108-0075 Japan Donostia International Physics Center E-20018 San Sebastián Spain Departments of Electrical and Computer Engineering Chemistry and Physics University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science & Technology University of Southern California Los Angeles California 90089 USA Department of Liberal Arts Saitama Medical University Moroyama Saitama 350-0495 Japan IKERBASQUE Basque Foundation for Science E-48013 Bilbao Spain Department of Physics University of Massachusetts Boston 100 Morrissey Boulevard Boston Massachusetts 02125 USA RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) Wako Saitama 351-0198 Japan

The number of topological defects created in a system driven through a quantum phase transition exhibits a power-law scaling with the driving time. This universal scaling law is the key prediction of the Kibble-Zurek mechanism (KZM), and testing it using a hardware-based quantum simulator is a coveted goal of quantum information science. Here we provide such a test using quantum annealing. Specifically, we report on extensive experimental tests of topological defect formation via the one-dimensional transverse-field Ising model on two different D-Wave quantum annealing devices. We find that the quantum simulator results can indeed be explained by the KZM for open-system quantum dynamics with phase-flip errors, with certain quantitative deviations from the theory likely caused by factors such as random control errors and transient effects. In addition, we probe physics beyond the KZM by identifying signatures of universality in the distribution and cumulants of the number of kinks and their decay, and again find agreement with the quantum simulator results. This implies that the theoretical predictions of the generalized KZM theory, which assumes isolation from the environment, applies beyond its original scope to an open system. We support this result by extensive numerical computations. To check whether an alternative, classical interpretation of these results is possible, we used the spin-vector Monte Carlo model, a candidate classical description of the D-Wave device. We find that the degree of agreement with the experimental data from the D-Wave annealing devices is better for the KZM, a quantum theory, than for the classical spin-vector Monte Carlo model, thus favoring a quantum description of the device. Our work provides an experimental test of quantum critical dynamics in an open quantum system, and paves the way to new directions in quantum simulation experiments.

关键词： Critical exponents Nonequilibrium statistical mechanics Open quantum systems Quantum computation Quantum master equation Quantum simulation Quantum statistical mechanics Quantum spin models

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Wavelet-based Technique for Mobile Device with Low Resolution Depth Image-based Rendering

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Journal of computers (Taiwan) 2018年第6期29卷 168-180页

作者： Lu, Shengrong Hsia, Chih-Hsien Cheng, Wei-Han Li, Hsin-Ting Chiang, Jen-Shiun Zhang, Ling Tseng, Hsien-Wei Gong, Cihun-Siyong Alex School of Information Engineering Longyan University Longyan city Fujian364012 China Department of Computer Science and Information Engineering National Ilan University Yilan Taiwan Department of Electrical and Computer Engineering Tamkang University New Taiepi Taiwan Department of Electrical Engineering Chang Gung University Taoyuan Taiwan Portable Energy System Group of Green Technology Research Center Chang Gung University Taoyuan Taiwan Department of Ophthalmology Chang Gung Memorial Hospital Taoyuan Taiwan Master Program of Arts and Technology Chinese Culture University Taiwan

Depth Image-Based Rendering (DIBR) is an approach to generate a 3-D image by the original 2-D color image with the corresponding 2-D depth map. Although DIBR is a quite convenient to convert 2D images to 3D ones, there is a disadvantage in DIBR system that it cannot reach real-time processing due to the computing time. Against this, this paper proposes a method which is based on discrete wavelet transform and adaptive edge-oriented smoothing process, to speed up the computing of the system. The proposed method still preserves the original texture and thus the proposed method not only preserves the vertical texture but also reduces at least 60% of the computing time of the DIBR system. © 2018 computer Society of the Republic of China. All rights reserved.

关键词： Discrete wavelet transforms

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Room-temperature lasing action in GaN quantum wells in the infrared 1.5 µm region

arXiv

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arXiv 2018年

作者： Ho, V.X. Al tahtamouni, T.M. Jiang, H.X. Lin, J.Y. Zavada, J.M. Vinh, N.Q. Department of Physics Center for Soft Matter and Biological Physics Virginia Tech BlacksburgVA24061 United States Materials Science & Technology Program College of Arts & Sciences Qatar University Doha2713 Qatar Department of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States Department of Electrical and Computer Engineering New York University BrooklynNY11201 United States

Large-scale optoelectronics integration is strongly limited by the lack of efficient light sources, which could be integrated with the silicon complementary metal-oxide-semiconductor (CMOS) technology. Persistent efforts continue to achieve efficient light emission from silicon in the extending the silicon technology into fully integrated optoelectronic circuits. Here, we report the realization of room-temperature stimulated emission in the technologically crucial 1.5 µm wavelength range from Er-doped GaN multiple-quantum wells on silicon and sapphire. Employing the well-acknowledged variable stripe technique, we have demonstrated an optical gain up to 170 cm-1 in the multiple-quantum well structures. The observation of the stimulated emission is accompanied by the characteristic threshold behavior of emission intensity as a function of pump fluence, spectral linewidth narrowing and excitation length. The demonstration of room-temperature lasing at the minimum loss window of optical fibers and in the eye-safe wavelength region of 1.5 µm are highly sought-after for use in many applications including defense, industrial processing, communication, medicine, spectroscopy and imaging. As the synthesis of Er-doped GaN epitaxial layers on silicon and sapphire has been successfully demonstrated, the results laid the foundation for achieving hybrid GaN-Si lasers providing a new pathway towards full photonic integration for silicon optoelectronics. Driven by the strong need for cheap and integrable Si-based optoelectronic devices for a wide range of applications, continuing endeavors have been made to develop structures for light emission, modulation, and detection in this material system. Recent breakthroughs including the demonstration of a high-speed optical modulator in Si,1-2 photodetectors3, and waveguides4 have brought the concept of transition from electrical to optical interconnects closer to realization. However, the base for silicon photonics, namely, a group IV la

关键词： Semiconductor quantum wells

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Predictable and precise template-free CRISPR editing of pathogenic variants (vol 563, pg 646, 2018)

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NATURE 2019年第7746期567卷 E1-E2页

作者： Shen, Max W. Arbab, Mandana Hsu, Jonathan Y. Worstell, Daniel Culbertson, Sannie J. Krabbe, Olga Cassa, Christopher A. Liu, David R. Gifford, David K. Sherwood, Richard I. Computational and Systems Biology Program Massachusetts Institute of Technology Cambridge USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge USA Merkin Institute of Transformative Technologies in Healthcare Broad Institute of Harvard and MIT Cambridge USA Department of Chemistry and Chemical Biology Harvard University Cambridge USA Howard Hughes Medical Institute Harvard University Cambridge USA Department of Biological Engineering Massachusetts Institute of Technology Cambridge USA Molecular Pathology Unit Center for Cancer Research and Center for Computational and Integrative Biology Massachusetts General Hospital Charlestown USA Division of Genetics Department of Medicine Brigham and Women’s Hospital and Harvard Medical School Boston USA Hubrecht Institute for Developmental Biology and Stem Cell Research Royal Netherlands Academy of Arts and Sciences (KNAW) Utrecht The Netherlands Broad Institute of MIT and Harvard Cambridge USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge USA

In this Article, a data processing error affected Fig. 3e and Extended Data Table 2; these errors have been corrected online.

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The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future (vol 398, pg 1619, 2021)

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LANCET 2021年第10317期398卷 2148-2148页

作者： Romanello, M. McGushin, A. Di Napoli, C. [a]Institute for Global Health University College London London UK [b]Institute for Sustainable Resources University College London London UK [c]UCL Energy Institute University College London London UK [d]Institute for Environmental Design and Engineering University College London London UK [e]Department of Geography University College London London UK [f]The Bartlett School of Sustainable Construction University College London London UK [g]Centre for Human Health and Performance University College London London UK [h]School of Agriculture Policy and Development University of Reading Reading UK [i]Department of Meteorology University of Reading Reading UK [j]The Walker Institute University of Reading Reading UK [k]Department of Health Sciences University of York York UK [l]Institute for Environment and Human Security United Nations University Bonn Germany [m]Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK [n]Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London UK [o]Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London UK [p]Department of Earth System Science Tsinghua University Beijing China [q]Department of Environment Climate Change and Health World Health Organization Geneva Switzerland [r]Institute for Environmental Sciences World Health Organization Geneva Switzerland [s]Centre for Climate Change and Social Transformations School of Psychology Cardiff University Cardiff UK [t]Yale Center on Climate Change and Health Yale University New Haven CT USA [u]School of Government University of Birmingham Birmingham UK [v]Economic analysis of Climate Impacts and Policy Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy [w]Natural Resources Institute University of Greenwich London UK [x]Department of Global Health University of Washington Seattle WA USA [y]Centre for He

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Soft Electromagnetic Actuators: Miniature Soft Electromagnetic Actuators for Robotic Applications (Adv. Funct. Mater. 18/2018)

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Advanced Functional Materials 2018年第18期28卷

作者： Thanh Nho Do Hung Phan Thuc‐Quyen Nguyen Yon Visell Department of Electrical Computer Engineering Media Arts and Technology Program California NanoSystems Institute University of California Santa Barbara CA 93106 USA Department of Chemistry and Biochemistry University of California Santa Barbara CA 93106 USA

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