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arXiv 2018年

作者： Wang, Zi Wang, Dali Li, Chengcheng Xu, Yichi Li, Husheng Bao, Zhirong Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37934 United States Developmental Biology Program Sloan-Kettering Institute New YorkNY10065 United States Environmental Science Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

Cell movement in the early phase of C. elegans development is regulated by a highly complex process in which a set of rules and connections are formulated at distinct scales. Previous efforts have demonstrated that agent-based, multiscale modeling systems can integrate physical and biological rules and provide new avenues to study developmental systems. However, the application of these systems to model cell movement is still challenging and requires a comprehensive understanding of regulation networks at the right scales. Recent developments in deep learning and reinforcement learning provide an unprecedented opportunity to explore cell movement using 3D time-lapse microscopy images. We present a deep reinforcement learning approach within an agent-based modeling system to characterize cell movement in the embryonic development of C. elegans. Our modeling system captures the complexity of cell movement patterns in the embryo and overcomes the local optimization problem encountered by traditional rule-based, agent-based modeling that uses greedy algorithms. We tested our model with two real developmental processes: the anterior movement of the Cpaaa cell via intercalation and the rearrangement of the superficial left-right asymmetry. In the first case, the model results suggested that Cpaaa’s intercalation is an active directional cell movement caused by the continuous effects from a longer distance, as opposed to a passive movement caused by neighbor cell movements. This is because the learning-based simulation found that a passive movement model could not lead Cpaaa to the predefined destination. In the second case, a leader-follower mechanism well explained the collective cell movement pattern in the asymmetry rearrangement. These results showed that our approach to introduce deep reinforcement learning into agent-based modeling can test regulatory mechanisms by exploring cell migration paths in a reverse engineering perspective. This model opens new doors to exp

关键词： Deep learning

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Nonlocal Metasurfaces for Optical Signal Processing

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Physical Review Letters 2018年第17期121卷 173004-173004页

作者： Hoyeong Kwon Dimitrios Sounas Andrea Cordaro Albert Polman Andrea Alù Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Institute of Physics University of Amsterdam Science Park 904 1098 XH Amsterdam Netherlands Center for Nanophotonics AMOLF Science Park 104 1098 XG Amsterdam Netherlands Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10016 USA Department of Electrical Engineering City College of The City University of New York New York New York 10031 USA

Optical analog signal processing has been gaining significant attention as a way to overcome the speed and energy limitations of digital techniques. Metasurfaces offer a promising avenue towards this goal due to their efficient manipulation of optical signals over deeply subwavelength volumes. To date, metasurfaces have been proposed to transform signals in the spatial domain, e.g., for beam steering, focusing, or holography, for which angular-dependent responses, or nonlocality, are unwanted features that must be avoided or mitigated. Here, we show that the metasurface nonlocality can be engineered to enable signal manipulation in the momentum domain over an ultrathin platform. We explore nonlocal metasurfaces performing basic mathematical operations, paving the way towards fast and power-efficient ultrathin devices for edge detection and optical image processing.

关键词： Geometrical & wave optics Imaging & optical processing Metamaterials Nanophotonics Photonics Transformation optics

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Planar Hall effect in antiferromagnetic MnTe thin films

arXiv

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arXiv 2018年

作者： Yin, Gen Yu, Jie-Xiang Liu, Yizhou Lake, Roger K. Zang, Jiadong Wang, Kang L. Department of Electrical and Computer Engineering University of California – Los Angeles Los AngelesCA90095 United States Department of Physics and Materials Science Program University of New Hampshire DurhamNH03824 United States Institute of Physics Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering University of California – Riverside RiversideCA92521 United States Department of Physics and Astronomy University of California – Los Angeles Los AngelesCA90095 United States

We show that the spin-orbit coupling (SOC) in α-MnTe impacts the transport behavior by generating an anisotropic valence-band splitting, resulting in four spin-polarized pockets near Γ. A minimal k·p model is constructed to capture this splitting by group theory analysis, a tight-binding model and ab initio calculations. The model is shown to describe the rotation symmetry of the zero-field planer Hall effect (PHE). The PHE originates from the band anisotropy given by SOC, and is quantitatively estimated to be 25% ∼ 31% for an ideal thin film with a single antiferromagnetic domain. Copyright © 2018, The Authors. All rights reserved.

关键词： Anisotropy

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A Supervised stdp-based training algorithm for living neural networks

arXiv

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arXiv 2017年

作者： Zeng, Yuan Devincentis, Kevin Xiao, Yao Ferdous, Zubayer Ibne Guo, Xiaochen Yan, Zhiyuan Berdichevsky, Yevgeny Electrical and Computer Engineering Department Lehigh University Bioengineering Department Lehigh University Electrical and Computer Engineering Department Carnegie Mellon University School of Gifted Young University of Science and Technology of China

Neural networks have shown great potential in many applications like speech recognition, drug discovery, image classification, and object detection. Neural network models are inspired by biological neural networks, but they are optimized to perform machine learning tasks on digital computers. The proposed work explores the possibility of using living neural networks in vitro as the basic computational elements for machine learning applications. A new supervised STDP-based learning algorithm is proposed in this work, which considers neuron engineering constraints. A 74.7% accuracy is achieved on the MNIST benchmark for handwritten digit recognition. Copyright © 2017, The Authors. All rights reserved.

关键词： Neural networks

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Resonant photonic structures in porous silicon for biosensing 9

Resonant photonic structures in porous silicon for biosensin...

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Frontiers in Biological Detection: From Nanosensors to Systems IX

作者： Zhao, Yiliang Rodriguez, Gilberto A. Graham, Yasmin M. Cao, Tengfei Gaur, Girija Weiss, Sharon M. Interdisciplinary Graduate Program in Materials Science Vanderbilt University NashvilleTN37235 United States Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN37235 United States Department of Mechanical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States

ISBN: (数字)9781510606043

ISBN: (纸本)9781510606036

The formation of resonant photonic structures in porous silicon leverages the benefit of high surface area for improved molecular capture that is characteristic of porous materials with the advantage of high detection sensitivity that is a feature of resonant optical devices. This review provides an overview of the biosensing capabilities of a variety of resonant porous silicon photonic structures including microcavities, Bloch surface waves, ring resonators, and annular Bragg resonators. Detection sensitivities > 1000 nm/RIU are achieved for small molecule detection. The challenge of detecting molecules that approach and exceed the pore diameter is also addressed. © 2017 SPIE.

关键词： Porous silicon

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Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain

arXiv

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arXiv 2018年

作者： Thakur, Chetan Singh Molin, Jamal Cauwenberghs, Gert Indiveri, Giacomo Kumar, Kundan Qiao, Ning Schemmel, Johannes Wang, Runchun Chicca, Elisabetta Hasler, Jennifer Olson Seoa, Jae sun Yu, Shimeng Cao, Yu van Schaik, André Etienne-Cummings, Ralph Department of Electronic Systems Engineering Indian Institute of Science Bangalore India Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States Department of Bioengineering and Institute for Neural Computation University of California San Diego La JollaCA United States Institute of Neuroinformatics University of Zurich ETH Zurich Zurich Switzerland Kirchhoff Institute for Physics University of Heidelberg Heidelberg Germany MARCS Institute Western Sydney University KingswoodNSW2751 Australia Cognitive Interaction Technology Center of Excellence Bielefeld University Bielefeld Germany School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States School of Electrical Computer and Engineering Arizona State University TempeAZ United States

Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems. The brain has evolved over billions of years to solve difficult engineering problems by using efficient, parallel, low-power computation. The goal of NE is to design systems capable of brain-like computation. Numerous large-scale neuromorphic projects have emerged recently. This interdisciplinary field was listed among the top 10 technology breakthroughs of 2014 by the MIT Technology Review and among the top 10 emerging technologies of 2015 by the World Economic Forum. NE has two-way goals: one, a scientific goal to understand the computational properties of biological neural systems by using models implemented in integrated circuits (ICs);second, an engineering goal to exploit the known properties of biological systems to design and implement efficient devices for engineering applications. Building hardware neural emulators can be extremely useful for simulating large-scale neural models to explain how intelligent behavior arises in the brain. The principle advantages of neuromorphic emulators are that they are highly energy efficient, parallel and distributed, and require a small silicon area. Thus, compared to conventional CPUs, these neuromorphic emulators are beneficial in many engineering applications such as for the porting of deep learning algorithms for various recognitions tasks. In this review article, we describe some of the most significant neuromorphic spiking emulators, compare the different architectures and approaches used by them, illustrate their advantages and drawbacks, and highlight the capabilities that each can deliver to neural modelers. This article focuses on the discussion of large-scale emulators and is a continuation of a previous review of various neural and synapse circuits [1]. We also explore application

关键词： Large scale systems

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Stretchable Electronics: Biaxially Stretchable Ultrathin Si Enabled by Serpentine Structures on Prestrained Elastomers (Adv. Mater. Technol. 1/2019)

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Advanced Materials Technologies 2019年第1期4卷

作者： Kyoseung Sim Yuhang Li Jizhou Song Cunjiang Yu Materials Science and Engineering Program University of Houston Houston TX 77204 USA Institute of Solid Mechanics Beihang University Beijing 100191 China Department of Engineering Mechanics Soft Matter Research Center and Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang University Hangzhou Zhejiang 310027 China Department of Mechanical Engineering Department of Electrical and Computer Engineering Department of Biomedical Engineering Materials Science and Engineering Program Texas Center for Superconductivity at the University of Houston University of Houston Houston TX 77204 USA

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High-Throughput Screening: One-Step Generation of a Drug-Releasing Hydrogel Microarray-On-A-Chip for Large-Scale Sequential Drug Combination Screening (Adv. Sci. 3/2019)

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Advanced science 2019年第3期6卷 1970014-1970014页

作者： Seo Woo Song Su Deok Kim Dong Yoon Oh Yongju Lee Amos Chungwon Lee Yunjin Jeong Hyung Jong Bae Daewon Lee Sumin Lee Jiyun Kim Sunghoon Kwon Department of Electrical and Computer Engineering Seoul National University Seoul 08826 South Korea Institutes of Entrepreneurial BioConvergence Seoul National University Seoul 08826 South Korea Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign Urbana IL 61801 USA Nano Systems Institute Seoul National University Seoul 08826 South Korea Interdisciplinary Program in Bioengineering Seoul National University Seoul 08826 South Korea School of Materials Science and Engineering Ulsan National Institute of Science and Technology Ulsan 44919 South Korea

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Laser micro-ablated multi-point side-firing optical fiber for deep-tissue light delivery 30

Laser micro-ablated multi-point side-firing optical fiber fo...

引用

30th Annual Conference of the IEEE Photonics Society, IPC 2017

作者： Nguyen, Hoang ParvezArnob, Masud Shih, Wei-Chuan Program of Materials Science and Engineering University of Houston TX77204 United States Department of Electrical and Computer Engineering University of Houston TX77204 United States Department of Biomedical Engineering University of Houston TX77204 United States Department of Chemistry University of Houston TX77204 United States

ISBN: (纸本)9781509065783

A compact light delivery device capable of delivering light to multiple desired locations is essential for many biomedical applications. Here, we demonstrate the use of laser micro-ablation to create controllable conicalshaped cavities on optical fiber to enable a multi-point side-firing configuration using a single fiber. © 2017 IEEE.

关键词： Tissue

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A phase variable approach for improved rhythmic and non-rhythmic control of a powered knee-ankle prosthesis

arXiv

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arXiv 2018年

作者： Rezazadeh, Siavash Quintero, David Divekar, Nikhil Reznick, Emma Gray, Leslie Gregg, Robert D. Department of Bioengineering University of Texas at Dallas RichardsonTX75080 United States School of Engineering San Francisco State University San FranciscoCA94132 United States Department of Health Care Sciences University of Texas Southwestern Medical Center DallasTX75390 United States Department of Electrical Engineering and Computer Science Robotics Institute University of Michigan Ann ArborMI48109 United States

Although there has been recent progress in control of multi-joint prosthetic legs for rhythmic tasks such as walking, control of these systems for non-rhythmic motions and general real-world maneuvers is still an open problem. In this article, we develop a new controller that is capable of both rhythmic (constant-speed) walking, transitions between speeds and/or tasks, and some common volitional leg motions. We introduce a new piecewise holonomic phase variable, which, through a finite state machine, forms the basis of our controller. The phase variable is constructed by measuring the thigh angle, and the transitions in the finite state machine are formulated through sensing foot contact along with attributes of a nominal reference gait trajectory. The controller was implemented on a powered knee-ankle prosthesis and tested with a transfemoral amputee subject, who successfully performed a wide range of rhythmic and non-rhythmic tasks, including slow and fast walking, quick start and stop, backward walking, walking over obstacles, and kicking a soccer ball. Use of the powered leg resulted in clinically significant reductions in amputee compensations for rhythmic tasks (including vaulting and hip circumduction) when compared to use of the take-home passive leg. In addition, considerable improvements were also observed in the performance for nonrhythmic tasks. The proposed approach is expected to provide a better understanding of rhythmic and non-rhythmic motions in a unified framework, which in turn can lead to more reliable control of multi-joint prostheses for a wider range of real-world tasks. Copyright © 2018, The Authors. All rights reserved.

关键词： Artificial limbs

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