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Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders

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arXiv 2021年

作者： Gajamannage, Kelum Park, Yonggi Paffenroth, Randy Jayasumana, Anura P. Department of Mathematics and Statistics Texas A&M University–Corpus Christi Corpus ChristiTX78412 United States Department of Mathematical Sciences Department of Computer Science Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Department of Electrical and Computer Engineering Colorado State University Fort CollinsCO80525 United States

Learning dynamics of collectively moving agents such as fish or humans is an active field in research. Due to natural phenomena such as occlusion and change of illumination, the multi-object methods tracking such dynamics might lose track of the agents where that might result fragmentation in the constructed trajectories. Here, we present an extended deep autoencoder (DA) that we train only on fully observed segments of the trajectories by defining its loss function as the Hadamard product of a binary indicator matrix with the absolute difference between the outputs and the labels. The trajectories of the agents practicing collective motion is low-rank due to mutual interactions and dependencies between the agents that we utilize as the underlying pattern that our Hadamard deep autoencoder (HDA) codes during its training. The performance of our HDA is compared with that of a low-rank matrix completion scheme in the context of fragmented trajectory reconstruction. © 2021, CC BY.

关键词： Learning 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

Decentralized Interactive Planning and Sensing in an Unknown Spatiotemporal Threat Field

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Decentralized Interactive Planning and Sensing in an Unknown...

Control Conference (ICC), 2019 Sixth Indian

作者： Benjamin Cooper Raghvendra V. Cowlagi Aerospace Engineering Program Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9781728138602

ISBN: (纸本)9781728138619

We address path-planning for a mobile vehicle, called the actor, to minimize exposure to an unknown spatiotemporally varying scalar threat field. Multiple sensor agents commanded by the actor take pointwise measurements and estimate the threat field using a consensus filter. The actor and sensors communicate over a network with fixed distance-based topology. We introduce a novel interactive planning and sensing algorithm, where sensor agents are iteratively reconfigured to minimize the actor's cost. Two sensor reconfiguration policies are proposed: one that enforces all sensors to remain within the actor's communication network, and the other that allows sensors to be deployed farther away. Through numerical simulation results we demonstrate that the proposed algorithm achieves near-optimal performance for the actor.

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The evolution of lithium-ion battery recycling

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Nature Reviews Clean Technology

Nature Reviews Clean Technology 2025年第1期1卷

作者： Xiaotu Ma Zifei Meng Yan Wang Marilena Velonia Bellonia Jeffrey Spangenberger Gavin Harper Eric Gratz Elsa Olivetti Renata Arsenault Mechanical and Materials Engineering Department Worcester Polytechnic Institute Worcester MA USA Technology and Policy Program Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge MA USA ReCell Center Argonne National Laboratory Lemont IL USA School of Metallurgy and Materials University of Birmingham Birmingham UK The Faraday Institution Harwell Science and Innovation Campus Didcot UK Ascend Elements Inc. Westborough MA USA Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge MA USA Ford Ion Park Battery Center of Excellence Romulus MI USA

Demand for lithium-ion batteries (LIBs) is increasing owing to the expanding use of electrical vehicles and stationary energy storage. Efficient and closed-loop battery recycling strategies are therefore needed, which will require recovering materials from spent LIBs and reintegrating them into new batteries. In this Review, we outline the current state of LIB recycling, evaluating industrial and developing technologies. Among industrial technologies, pyrometallurgy can be broadly applied to diverse electrode materials but requires operating temperatures of over 1,000 °C and therefore has high energy consumption. Hydrometallurgy can be performed at temperatures below 200 °C and has material recovery rates of up to 93% for lithium, nickel and cobalt, but it produces large amounts of wastewater. Developing technologies such as direct recycling and upcycling aim to increase the efficiency of LIB recycling and rely on improved pretreatment processes with automated disassembly and cleaner mechanical separation. Additionally, the range of materials recovered from spent LIBs is expanding from the cathode materials recycled with established methods to include anode materials, electrolytes, binders, separators and current collectors. Achieving an efficient recycling ecosystem will require collaboration between recyclers, battery manufacturers and electric vehicle manufacturers to aid the design and automation of battery disassembly lines.

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Transplantation of human kidney organoids elicited a robust allogeneic response in a humanized mouse model

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Kidney International 2025年第6期107卷 1011-1016页

作者： Mon-Wei Yu, Samuel Choi, John Yongjoon Kady, Jamie De Cos, Marina Wong, Jenny S. King, Emily Younis, Nour Al Rahy, Nadim Al Chaar, Soltan Djebli, Houda Eskandari, Siawosh Samuel, Stephen Merhej, Tamara Sukumar, Nivedha Lin, Jia-Ren Chen, Jia-Yun Xu, Yilin Santagata, Sandro Weins, Astrid Yeung, Melissa Cravedi, Paolo Campbell, Kirk N. Lemos, Dario Bonventre, Joseph V. Brehm, Michael A. Azzi, Jamil Department of Medicine Nephrology Division Mount Sinai Hospital New York NY United States Department of Medicine Renal Division Brigham and Women's Hospital Harvard Medical School Boston MA United States Program in Molecular Medicine Diabetes Center of Excellence University of Massachusetts Chan Medical School Worcester MA United States Doctoral School of Science and Technology Lebanese University Beirut Lebanon Institute of Biological Science Faculty of Medical Sciences Mohammed VI Polytechnic University Benguerir Morocco Department of Biotechnology Northeastern University Boston MA United States Department of Systems Biology Harvard Medical School Boston MA United States Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA United States Harvard Stem Cell Institute Cambridge MA United States Department of Medicine Division of Engineering in Medicine Brigham and Women's Hospital Harvard Medical School Boston MA United States

Human kidney organoids derived from embryonic stem cells (ESCs) or induced pluripotent stem cells (iPSCs) have become novel tools for studying various kidney pathologies. Here, we transplanted ESC-derived kidney organoids into humanized mice with a mature human adaptive immune system developed through thymic education. As judged by histology and immunophenotyping, the transplanted HLA-mismatched kidney organoids trigged a robust alloimmune response, characterized by a dense immune cell infiltrate and enhanced memory T cell phenotype in the allograft 30 days post-transplantation. Multiplexed immunofluorescence revealed expression of functional markers of various immune cell infiltrates in response to organoid allografts, mimicking the T cell-mediated rejection process in humans. This validated our model as a novel platform to study various therapeutic strategies to control alloimmunity. Splenocytes isolated from organoid-transplanted hosts showed an alloantigen-specific memory response against 2D kidney organoids ex vivo. Overall, our study indicates that transplanting kidney organoids in humanized mice may be a valuable tool for studying human allogeneic immunity. © 2025 International Society of Nephrology

关键词： human kidney organoids humanized mice model kidney transplantation

robotics as an undergraduate major: 10 Years' experience 125

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Robotics as an undergraduate major: 10 Years' experience

125th ASEE Annual Conference and Exposition

作者： Gennert, Michael A. Putnam, Craig B. Worcester Polytechnic Institute Department of Robotics Engineering CS and ECE United States

In 2007 worcester polytechnic institute (WPI) launched an undergraduate degree program in robotics. At that time, there were only a handful of universities worldwide offering undergraduate robotics programs, none in the United States, although many universities included robotics within a discipline such as Computer Science, Electrical engineering, or Mechanical engineering. WPI took a decidedly different approach. We introduced robotics as a multi-disciplinary engineering discipline to meet the needs of 21st century engineering. The curriculum, designed top-down, incorporates a number of best practices, including spiral curriculum, a unified set of core courses, multiple pathways, inclusion of social issues and entrepreneurship, an emphasis on project-based learning, and capstone design projects. This paper provides a brief synopsis, comparison with other approaches, and multi-year retrospective on the program. The curriculum has steadily evolved from the original to its current state, including changes in requirements, courses, hardware, software, labs, and projects. The guiding philosophy remains unchanged, however, providing continuity of purpose to the program. The program has been highly successful in meeting its desired outcomes, including: quantity and quality of enrolled students, ABET EAC accreditation, graduate placement in jobs and graduate school, and overall student learning. The program is assessed using several quantitative measures: enrollment, cohort survival within the program, course and project evaluations, and student placement success. Other, qualitative outcomes are also discussed: results from competitions, interaction with industry, accreditation, and external recognition. The paper concludes with a summary of lessons learned and recommendations for future actions to further robotics education. © American Society for engineering Education, 2018.

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Mixed-granularity human-swarm interaction

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arXiv 2019年

作者： Patel, Jayam Xu, Yicong Pinciroli, Carlo Robotics Engineering Worcester Polytechnic Institute MA United States PTC Inc MA United States

We present an augmented reality human-swarm interface that combines two modalities of interaction: environment-oriented and robot-oriented. The environmentoriented modality allows the user to modify the environment (either virtual or physical) to indicate a goal to attain for the robot swarm. The robot-oriented modality makes it possible to select individual robots to reassign them to other tasks to increase performance or remedy failures. Previous research has concluded that environment-oriented interaction might prove more difficult to grasp for untrained users. In this paper, we report a user study which indicates that, at least in collective transport, environment-oriented interaction is more effective than purely robot-oriented interaction, and that the two combined achieve remarkable efficacy. Copyright © 2019, The Authors. All rights reserved.

关键词： Augmented reality

Robot Co-design: Beyond the Monotone Case

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arXiv 2019年

作者： Carlone, Luca Pinciroli, Carlo Massachusetts Institute of Technology United States Robotics Engineering Department of Computer Science Worcester Polytechnic Institute United States

Recent advances in 3D printing and manufacturing of miniaturized robotic hardware and computing are paving the way to build inexpensive and disposable robots. This will have a large impact on several applications including scientific discovery (e.g., hurricane monitoring), search-and-rescue (e.g., operation in confined spaces), and entertainment (e.g., nano drones). The need for inexpensive and task-specific robots clashes with the current practice, where human experts are in charge of designing hardware and software aspects of the robotic platform. This makes the robot design process expensive and time consuming, and ultimately unsuitable for small-volumes low-cost applications. This paper considers the computational robot co-design problem, which aims to create an automatic algorithm that selects the best robotic modules (sensing, actuation, computing) in order to maximize the performance on a task, while satisfying given specifications (e.g., maximum cost of the resulting design). We propose a binary optimization formulation of the co-design problem and show that such formulation generalizes previous work based on strong modeling assumptions. We show that the proposed formulation can solve relatively large co-design problems in seconds and with minimal human intervention. We demonstrate the proposed approach in two applications: the co-design of an autonomous drone racing platform and the co-design of a multi-robot system.68T40, 70B15, 70E60, 46N10 Copyright © 2019, The Authors. All rights reserved.

关键词： 3D printers

IoT environmental analyzer using sensors and machine learning for migraine occurrence prevention 18

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IoT environmental analyzer using sensors and machine learnin...

18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

作者： Day, Rosemarie J. Salehi, Hassan Javadi, Mahsa Data Science Program Worcester Polytechnic Institute WorcesterMA United States Department of Electrical and Computer Engineering California State University Chico ChicoCA United States Division of Genetic Research University of Connecticut Health Center FarmingtonCT United States

ISBN: (纸本)9781728145495

Everyday migraines are affecting more than one billion people worldwide. This headache disorder is classified as the sixth most disabling disease in the world. Migraines are just one chronic illness affected by environmental triggers due to changes that occur inside the home. Migraines share this characteristic with sinus headaches and thus are often misdiagnosed. In this research work, an iOS-based environmental analyzer was designed, implemented and evaluated for migraine sufferers with the use of sensors. After the data collection and cleaning, five machine learning model were used to estimate prediction accuracy of migraines in terms of the environment. The data was evaluated against the models using K-Fold cross validation. The algorithm accuracy comparison showed that Linear Discriminant Analysis (LDA) produced highest accuracy for the testing data at a mean of 0.938. Preliminary results demonstrate the feasibility of using machine learning algorithms to perform the automated recognition of migraine trigger areas in the environment. © 2019 IEEE.

关键词： Internet of things