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A deep learning approach for lower back-pain risk prediction during manual lifting

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arXiv 2020年

作者： Snyder, Kristian Thomas, Brennan Lu, Ming-Lun Jha, Rashmi Barim, Menekse S. Hayden, Marie Werren, Dwight Department of Electrical Engineering and Computing Systems University of Cincinnati CincinnatiOH United States National Institute for Occupational Safety and Health CincinnatiOH United States Department of Engineering and Computer Science University of Cincinnati CincinnatiOH United States

Occupationally-induced back pain is a leading cause of reduced productivity in industry. Detecting when a worker is lifting incorrectly and at increased risk of back injury presents significant possible benefits. These include increased quality of life for the worker due to lower rates of back injury and fewer workers' compensation claims and missed time for the employer. However, recognizing lifting risk provides a challenge due to typically small datasets and subtle underlying features in accelerometer and gyroscope data. A novel method to classify a lifting dataset using a 2D convolutional neural network (CNN) and no manual feature extraction is proposed in this paper;the dataset consisted of 10 subjects lifting at various relative distances from the body with 720 total trials. The proposed deep CNN displayed greater accuracy (90.6%) compared to an alternative CNN and multilayer perceptron (MLP). A deep CNN could be adapted to classify many other activities that traditionally pose greater challenges in industrial environments due to their size and complexity. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

On the Philosophical, Cognitive and Mathematical Foundations of Symbiotic Autonomous systems (SAS)

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arXiv 2021年

作者： Wang, Yingxu Karray, Fakhri Kwong, Sam Plataniotis, Konstantinos N. Leung, Henry Hou, Ming Tunstel, Edward Rudas, Imre J. Trajkovic, Ljiljana Kaynak, Okyay Kacprzyk, Janusz Zhou, Mengchu Smith, Michael H. Chen, Philip Patel, Shushma University of Calgary Canada FIEEE Dept. of Electrical & Computer Engineering University of Waterloo ON Canada FIEEE Department of Computer Science City University of Hong Kong Hong Kong FIEEE Dept. of Electrical & Computer Engineering University of Toronto ON Canada SMIEEE Toronto Research Centre DRDC Canada FIEEE Autonomous & Intelligent Systems Dept. Raytheon Technologies Research Center United States Óbuda University Budapest Hungary FIEEE School of Engineering Science Simon Fraser University BurnabyBC Canada FIEEE Bogazici University Bebek Istanbul Turkey FIEEE Systems Research Institute Polish Academy of Sciences Warsaw Poland FIEEE Dept. of Computer Science New Jersey Institute of Technology NJ United States SMIEEE Furaxa Inc. OrindaCA United States FIEEE School of Computer Science and Engineering South China University of Technology Guangzhou China FBCS Faculty of Computing Engineering and Media De Montfort University LeicesterLE1 9BH United Kingdom

Symbiotic Autonomous systems (SAS) are advanced intelligent and cognitive systems exhibiting autonomous collective intelligence enabled by coherent symbiosis of human-machine interactions in hybrid societies. Basic research in the emerging field of SAS has triggered advanced general AI technologies functioning without human intervention or hybrid symbiotic systems synergizing humans and intelligent machines into coherent cognitive systems. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences. SAS are characterized by the composition of autonomous and symbiotic systems that adopt bio-brain-social-inspired and heterogeneously synergized structures and autonomous behaviors. This paper explores their cognitive and mathematical foundations. The challenge to seamless human-machine interactions in a hybrid environment is addressed. SAS-based collective intelligence is explored in order to augment human capability by autonomous machine intelligence towards the next generation of general AI, autonomous computers, and trustworthy mission-critical intelligent systems. Emerging paradigms and engineering applications of SAS are elaborated via an autonomous knowledge learning system that symbiotically works between humans and cognitive robots. © 2021, CC BY-NC-ND.

关键词： Cognitive systems

Accelerating surgical robotics research: A review of 10 years with the da Vinci research kit

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arXiv 2021年

作者： D’Ettorre, Claudia Mariani, Andrea Stilli, Agostino Rodriguez y Baena, Ferdinando Valdastri, Pietro Deguet, Anton Kazanzides, Peter Taylor, Russell H. Fischer, Gregory S. DiMaio, Simon P. Menciassi, Arianna Stoyanov, Danail University College London LondonW1W 7EJ United Kingdom Pisa Italy Mechanical Engineering Department Imperial College London United Kingdom STORM Lab Institute of Robotics Autonomous Systems and Sensing School of Electronic and Electrical Engineering University of Leeds Leeds United Kingdom Johns Hopkins University Baltimore United States Automation and Interventional Medicine Laboratory Worcester Polytechnic Institute WorcesterMA United States Intuitive Surgical Inc SunnyvaleCA United States

Robotic-assisted surgery is now well-established in clinical practice and has become the gold standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade with a range of new robotic devices emerging to address unmet clinical needs across different specialties. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an academic and industry collaborative effort to re-purpose decommissioned da Vinci surgical systems (Intuitive Surgical Inc, CA, USA) as a research platform for surgical robotics research, has been a key initiative for addressing a barrier to entry for new research groups in surgical robotics. In this paper, we present an extensive review of the publications that have been facilitated by the dVRK over the past decade. We classify research efforts into different categories and outline some of the major challenges and needs for the robotics community to maintain this initiative and build upon it. © 2021, CC BY.

关键词： Robotic surgery

MoDL-QSM: Model-based deep learning for quantitative susceptibility mapping

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arXiv 2021年

作者： Feng, Ruimin Zhao, Jiayi Wang, He Yang, Baofeng Feng, Jie Shi, Yuting Zhang, Ming Liu, Chunlei Zhang, Yuyao Zhuang, Jie Wei, Hongjiang School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China School of Psychology Shanghai University of Sport Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Helen Wills Neuroscience Institute University of California BerkeleyCA United States School of Information and Science and Technology ShanghaiTech University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

Quantitative susceptibility mapping (QSM) has demonstrated great potential in quantifying tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution affects the accuracy for quantifying tissue susceptibility. Recently, deep learning has shown promising results to improve accuracy by reducing the streaking artifacts. However, there exists a mismatch between the observed phase and the theoretical forward phase estimated by the susceptibility label. In this study, we proposed a model-based deep learning architecture that followed the STI (susceptibility tensor imaging) physical model, referred to as MoDL-QSM. Specifically, MoDL-QSM accounts for the relationship between STI-derived phase contrast induced by the susceptibility tensor terms (χ13, χ23 and χ33) and the acquired single-orientation phase. The convolution neural networks are embedded into the physical model to learn a regularization term containing prior information. χ33 and phase induced by χ13 and χ23 terms were used as the labels for network training. Quantitative evaluation metrics (RSME, SSIM, and HFEN) were compared with recently developed deep learning QSM methods. The results showed that MoDL-QSM achieved superior performance, demonstrating its potential for future applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Multi-Agent Coverage in Urban Environments

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arXiv 2020年

作者： Patel, Shivang Hariharan, Senthil Dhulipala, Pranav Lin, Ming C. Manocha, Dinesh Xu, Huan Otte, Michael Maryland Robotics Center University of Maryland College Park United States Department of Computer Science University of Maryland College Park United States Department of Electrical and Computer Engineering University of Maryland College Park United States Department of Aerospace Engineering University of Maryland College Park United States Instiitute for Systems Research University of Maryland College Park United States

We study multi-agent coverage algorithms for autonomous monitoring and patrol in urban environments. We consider scenarios in which a team of flying agents uses downward facing cameras (or similar sensors) to observe the environment outside of buildings at street-level. Buildings are considered obstacles that impede movement, and cameras are assumed to be ineffective above a maximum altitude. We study multi-agent urban coverage problems related to this scenario, including: (1) static multi-agent urban coverage, in which agents are expected to observe the environment from static locations, and (2) dynamic multi-agent urban coverage where agents move continuously through the environment. We experimentally evaluate six different multi-agent coverage methods, including: three types of ergodic coverage (that avoid buildings in different ways), lawn-mower sweep, voronoi region based control, and a naive grid method. We evaluate all algorithms with respect to four performance metrics (percent coverage, revist count, revist time, and the integral of area viewed over time), across four types of urban environments [low density, high density] × [short buildings, tall buildings], and for team sizes ranging from 2 to 25 agents. We believe this is the first extensive comparison of these methods in an urban setting. Our results highlight how the relative performance of static and dynamic methods changes based on the ratio of team size to search area, as well the relative effects that different characteristics of urban environments (tall, short, dense, sparse, mixed) have on each algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

An Early Stage Researcher's Primer on systems Medicine Terminology

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Network and Systems Medicine

Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

Inverse-designed multi-dimensional silicon photonic transmitters

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arXiv 2021年

作者： Yang, Ki Youl White, Alexander D. Ashtiani, Farshid Shirpurkar, Chinmay Pericherla, Srinivas V. Chang, Lin Song, Hao Zou, Kaiheng Zhou, Huibin Pang, Kai Yang, Joshua Guidry, Melissa A. Lukin, Daniil M. Hao, Han Trask, Lawrence Ahn, Geun Ho Netherton, Aandy Briles, Travis C. Stone, Jordan R. Rechtman, Lior Stone, Jeffery S. van Gasse, Kasper Skarda, Jinhie L. Su, Logan Vercruysse, Dries MacLean, Jean-Philippe W. Aghaeimeibodi, Shahriar Li, Ming-Jun Miller, David A.B. Marom, Dan Papp, Scott B. Willner, Alan E. Bowers, John E. Delfyett, Peter J. Aflatouni, Firooz Vučković, Jelena E.L.Ginzton Laboratory Stanford University StanfordCA United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States The College of Optics and Photonics University of Central Florida OrlandoFL32816 United States Department of Electrical and Computer Engineering University of California Santa BarbaraCA United States Department of Electrical Engineering University of Southern California Los AngelesCA United States Time and Frequency Division National Institute of Standards and Technology BoulderCO United States Applied Physics Department The Hebrew University of Jerusalem Jerusalem Israel Corning Incorporated CorningNY United States Department of Physics University of Colorado BoulderCO United States

Modern microelectronic processors have migrated towards parallel computing architectures with many-core processors. However, such expansion comes with diminishing returns exacted by the high cost of data movement between individual processors. The use of optical interconnects1,2 has burgeoned as a promising technology that can address the limits of this data transfer. While recent pushes to enhance optical communication have focused on developing wavelength-division multiplexing technology, this approach will eventually saturate the usable bandwidth, and new dimensions of data transfer will be paramount to fulfill the ever-growing need for speed3–6. Here we demonstrate an integrated intra- and inter-chip multi-dimensional communication scheme enabled by photonic inverse design. Using inverse-designed mode-division multiplexers, we combine wavelength- and mode- multiplexing and send massively parallel data through nano-photonic waveguides and optical fibres. Crucially, as we take advantage of an orthogonal optical basis, our approach is inherently scalable to a multiplicative enhancement over the current state of the art. © 2021, CC BY.

关键词： Parallel architectures

GlassLoc: Plenoptic Grasp Pose Detection in Transparent Clutter

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GlassLoc: Plenoptic Grasp Pose Detection in Transparent Clut...

2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zheming Zhou Tianyang Pan Shiyu Wu Haonan Chang Odest Chadwicke Jenkins Department of Electrical Engineering and Computer Science Robotics Institute University of Michigan Ann Arbor MI USA

Transparent objects are prevalent across many environments of interest for dexterous robotic manipulation. Such transparent material leads to considerable uncertainty for robot perception and manipulation, and remains an open challenge for robotics. This problem is exacerbated when multiple transparent objects cluster into piles of clutter. In household environments, for example, it is common to encounter piles of glassware in kitchens, dining rooms, and reception areas, which are essentially invisible to modern robots. We present the GlassLoc algorithm for grasp pose detection of transparent objects in transparent clutter using plenoptic sensing. GlassLoc classifies graspable locations in space informed by a Depth Likelihood Volume (DLV) descriptor. We extend the DLV to infer the occupancy of transparent objects over a given space from multiple plenoptic viewpoints. We demonstrate and evaluate the GlassLoc algorithm on a Michigan Progress Fetch mounted with a first generation Lytro. The effectiveness of our algorithm is evaluated through experiments for grasp detection and execution with a variety of transparent glassware in minor clutter.

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The Gender Gap in Scholarly Self-Promotion on Social Media

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arXiv 2022年

作者： Peng, Hao Teplitskiy, Misha Romero, Daniel M. Horvát, Emoke-Ágnes School of Information University of Michigan Ann ArborMI48109 United States Kellogg School of Management Northwestern University EvanstonIL60208 United States Northwestern University Institute on Complex Systems EvanstonIL60208 United States Laboratory for Innovation Science at Harvard Harvard University BostonMA United States Center for the Study of Complex Systems University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan MI United States School of Communication Northwestern University EvanstonIL60208 United States McCormick School of Engineering Northwestern University EvanstonIL60208 United States

Self-promotion in science is ubiquitous but may not be exercised equally by men and women. Research on self-promotion in other domains suggests that, due to bias in self-assessment and adverse reactions to non-gender-conforming behaviors ("pushback"), women tend to self-promote less often than men. We test whether this pattern extends to scholars by examining self-promotion over six years using 23M Tweets about 2.8M research papers by 3.5M authors. Overall, women are about 28% less likely than men to self-promote their papers even after accounting for important confounds, and this gap has grown over time. Moreover, differential adoption of Twitter does not explain the gender gap, which is large even in relatively gender-balanced broad research areas, where bias in self-assessment and pushback are expected to be smaller. Further, the gap increases with higher performance and status, being most pronounced for productive women from top-ranked institutions who publish in high-impact journals. Critically, we find differential returns with respect to gender: while self-promotion is associated with increased tweets of papers, the increase is smaller for women than for men. Our findings suggest that self-promotion varies meaningfully by gender and help explain gender differences in the visibility of scientific ideas. © 2022, CC BY-NC-SA.

关键词： Social networking (online)