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arXiv 2018年

作者： Nazari, Mina Xi, Min Aronson, Mark Hong, Mi K. Gummuluru, Suryaram Sgro, Allyson E. Ziegler, Lawrence D. Gillespie, Christopher Souza, Kathleen Nguyen, Nhung Smith, Robert M. Silva, Edward Miura, Ayako Erramilli, Shyamsunder Reinhard, Björn M. Photonics Center Biological Design Center Departments of Electrical and Computer Engineering Chemistry Biomedical Engineering Physics Boston University BostonMA02446 United States Department of Microbiology Boston University School of Medicine BostonMA02118 United States Immunogen 830 Winter St WalthamMA02451 United States Process Solutions MilliporeSigma BedfordMA01730 United States Takeda Pharmaceuticals 35 Landsdowne St CambridgeMA02139 United States

Pan-microbial inactivation technologies that do not require high temperatures, reactive chemical compounds, or UV radiation could address gaps in current infection control strategies and provide efficient sterilization of biologics in the biotechnological industry. Here, we demonstrate that femtosecond (fs) laser irradiation of resonant gold nanoparticles (NPs) under conditions that allow for E-field mediated cavitation and shockwave generation achieve an efficient plasmon-enhanced photonic microbial pathogen inactivation. We demonstrate that this NP-enhanced, physical inactivation approach is effective against a diverse group of pathogens, including both enveloped and non-enveloped viruses, and a variety of bacteria and mycoplasma. Photonic inactivation is wavelength-dependent and in the absence of plasmonic enhancement from NPs, negligible levels of microbial inactivation are observed in the near-infrared (NIR) at 800 nm. This changes upon addition of resonant plasmonic NPs, which provide a strong enhancement of inactivation of viral and bacterial contaminants. Importantly, the plasmon-enhanced 800 nm femtosecond (fs)-pulse induced inactivation was selective to pathogens. No measurable damage was observed for antibodies included as representative biologics under identical conditions. Copyright © 2018, The Authors. All rights reserved.

关键词： Metal nanoparticles

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Real-space-transfer mechanism of negative differential conductivity in gated graphene-phosphorene hybrid structures: Phenomenological heating model

arXiv

引用

arXiv 2018年

作者： Ryzhii, V. Ryzhii, M. Svintsov, D. Leiman, V. Maltsev, P.P. Ponomarev, D.S. Mitin, V. Shur, M.S. Otsuji, T. Research Institute of Electrical Communication Tohoku University Sendai980-8577 Japan Institute of Ultra High Frequency Semiconductor Electronics of RAS Moscow117105 Russia Center for Photonics and Infrared Engineering Bauman Moscow State Technical University Moscow111005 Russia Department of Computer Science and Engineering University of Aizu Aizu-Wakamatsu965-8580 Japan Laboratory of 2D Material's Optoelectronics Moscow Institute of Physics and Technology Dolgoprudny141700 Russia Department of Electrical Engineering University at Buffalo SUNY BuffaloNY1460-1920 United States Departments of Electrical Electronics and Systems Engineering and Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States Electronics of the Future Inc. ViennaVA22181 United States

We analyze the nonlinear carrier transport in the gated graphene-phosphorene (G-P) hybrid structures - the G-P field-effect transistors (G-P-FETs) using a phenomenological model. This model assumes that due to high carrier densities in the G-P-channel, the carrier system, including the electrons and holes in both the G- and P-layers, is characterized by a single effective temperature. We demonstrate that a strong electric-field dependence of the G-P-channel conductivity and substantially non-linear current-voltage characteristics, exhibiting a negative differential conductivity, are associated with the carrier heating and the real-space carrier transfer between the G- and P-layers. The predicted features of the G-P-systems can be used in the detectors and sources of electromagnetic radiation and in the logical circuits. Copyright © 2018, The Authors. All rights reserved.

关键词： Graphene

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Tuning windowed chi-squared detectors for sensor attacks

arXiv

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arXiv 2017年

作者： Tunga, R. Murguia, Carlos Ruths, Justin Departments of Electrical and Mechanical and Systems Engineering University of Texas at Dallas RichardsonTX United States ITrust Centre Singapore University of Technology and Design Singapore

A model-based windowed chi-squared procedure is proposed for identifying falsified sensor measurements. We employ the widely-used static chi-squared and the dynamic cumulative sum (CUSUM) fault/attack detection procedures as benchmarks to compare the performance of the windowed chisquared detector. In particular, we characterize the state degradation that a class of attacks can induce to the system while enforcing that the detectors do not raise alarms (zero-alarm attacks). We quantify the advantage of using dynamic detectors (windowed chi-squared and CUSUM detectors), which leverages the history of the state, over a static detector (chi-squared) which uses a single measurement at a time. Simulations using a chemical reactor are presented to illustrate the performance of our tools. Copyright © 2017, The Authors. All rights reserved.

关键词： Machine learning

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Spread, then target, and advertise in waves: Optimal capital allocation across advertising channels

Spread, then target, and advertise in waves: Optimal capital...

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Information Theory and Applications Workshop, ITA

作者： Soheil Eshghi Victor M. Preciado Saswati Sarkar Santosh S. Venkatesh Qing Zhao Raissa D'Souza Ananthram Swami School of Electrical Engineering Yale University New Haven CT School of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA School of Electrical and Computer Engineering Cornell University Ithaca NY Departments of Computer Science and Mechanical and Aerospace Engineering University of California Davis Davis CA Army Research Laboratory Adelphi MD

ISBN: (纸本)9781509052943

We obtain optimal strategies for the allocation of influence budget across multiple channels and across time for an external influencer, e.g., a political campaign, seeking to maximize its effect on an election given a network of agents with linear consensus-seeking opinion dynamics. We show that for a general set of objective functions, the optimal influence strategy at every time uses all channels at either their maximum rate or not at all. Furthermore, we prove that the number of switches between these extremes is bounded above both by a term that is typically much smaller than the number of agents. This means that the optimal influence strategy is to exert maximum effort in waves for every channel, and then cease effort and let the effects propagate. We also show that at the beginning, the total cost-adjusted reach of a channel determines its relative value, while targeting matters more closer to election time. We demonstrate that the optimal influence structures are easily computable in several practical cases. We explicitly characterize the optimal controls for the case of linear objective functions via a closed form. Finally, we see that in the canonical election example, identifying late-deciders approximately determines the optimal campaign resource allocation strategy.

关键词： Legged locomotion Eigenvalues and eigenfunctions Laplace equations

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Stability of a giant connected component in a complex network

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Physical Review E 2018年第1期97卷 012309-012309页

作者： Maksim Kitsak Alexander A. Ganin Daniel A. Eisenberg Pavel L. Krapivsky Dmitri Krioukov David L. Alderson Igor Linkov Department of Physics Northeastern University 110 Forsyth Street 111 Dana Research Center Boston Massachusetts 02115 USA. University of Virginia Department of Systems and Information Engineering Charlottesville Virginia 22904 USA U.S. Army Engineer Research and Development Center Contractor Concord Massachusetts 01742 USA School of Sustainable Engineering and the Built Environment Arizona State University Tempe Arizona 85281 USA Department of Physics Boston University Boston Massachusetts 02215 USA Departments of Physics Mathematics and Electrical and Computer Engineering Northeastern University Boston Massachusetts 02115 USA Operations Research Department Naval Postgraduate School Monterey California 93943 USA U.S. Army Engineer Research and Development Center Environmental Laboratory Concord Massachusetts 01742 USA

We analyze the stability of the network's giant connected component under impact of adverse events, which we model through the link percolation. Specifically, we quantify the extent to which the largest connected component of a network consists of the same nodes, regardless of the specific set of deactivated links. Our results are intuitive in the case of single-layered systems: the presence of large degree nodes in a single-layered network ensures both its robustness and stability. In contrast, we find that interdependent networks that are robust to adverse events have unstable connected components. Our results bring novel insights to the design of resilient network topologies and the reinforcement of existing networked systems.

关键词： Cascades Random attacks Robustness Topological tools

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Keep Your Friends Close and Your Colleagues Nearby: The Hidden Ties That Improve STEM Education

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Change 2019年第1期51卷 42页

作者： Mestre, Jose P. Herman, Geoffrey L. Tomkin, Jonathan H. West, Matthew Jose P. Mestre (mestre@illinois.edu). A theoretical nuclear physicist by training Mestre’s research focuses on the learning of physics contributing in areas such as the acquisition and use of knowledge by experts and novices transfer of learning problem solving and instructional design. He has offered Congressional testimony on The Science of Learning has published numerous articles and books and is a Fellow of the American Physical Society.http://***/0000-0003-2110-1954 Geoffrey L. Herman (glherman@illinois.edu). Herman has a Ph.D. in Electrical and Computer Engineering and is a Teaching Assistant Professor in Computer Science and a Research Assistant Professor in the Department of Curriculum & Instruction at the University of Illinois. His research focuses on how students learn engineering and computing concepts and studying processes for creating systemic instructional change in college settings. Previously he was a postdoctoral researcher in Engineering Education at Purdue University.http://***/0000-0002-9501-2295 Jonathan H. Tomkin (tomkin@illinois.edu). Tomkin is a mathematically based geoscientist with two research focus areas: first he uses models and large data sets to determine how climates glaciers tectonics and landscapes interact. Second he explores how new technological and organizational approaches can improve STEM education. He produced the first geoscience MOOC is the editor of an open-source textbook and has published widely in both geoscience and STEM education. Matthew West (mwest@illinois.edu). Matthew West is an Associate Professor of Mechanical Science and Engineering at the University of Illinois with research interests in scientific computing that include time integration of deterministic and stochastic systems distributed computation and particle methods for simulation and estimation. In education he researches computer-based assessment and the development of innovative teaching cultures. Professor West is a University of Illinois Distinguished Teacher-Scholar and Colleg

A science department on the campus of the University of Illinois has received numerous national awards for excellence in undergraduate teaching. The secret to their success? For nearly two decades, they have collaboratively used evidence-based instructional practices (EBIP) in their introductory level courses. Despite these successes and efforts to spread their EBIP, no other departments followed this department's lead. Then, suddenly six years ago, many instructors from other departments began adopting EBIP in their classrooms. This article asks what was the catalyst for this change? Why had previous attempts at institutional change failed? The authors answer these questions by analyzing their efforts to transform the teaching practices in large-enrollment science and engineering introductory courses on their campus.

关键词： STEM Education Evidence Based Practice Teaching Methods Undergraduate Study College Faculty Educational Change Communities of Practice Mentors School CultureIllinois

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Gradient methods for submodular maximization

arXiv

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arXiv 2017年

作者： Hassani, Hamed Soltanolkotabi, Mahdi Karbasi, Amin Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Ming Hsieh Department of Electrical Engineering University of Southern California Los AngelesCA United States Departments of Electrical Engineering and Computer Science Yale University New HavenCT United States

In this paper, we study the problem of maximizing continuous submodular functions that naturally arise in many learning applications such as those involving utility functions in active learning and sensing, matrix approximations and network inference. Despite the apparent lack of convexity in such functions, we prove that stochastic projected gradient methods can provide strong approximation guarantees for maximizing continuous submodular functions with convex constraints. More specifically, we prove that for monotone continuous DR-submodular functions, all fixed points of projected gradient ascent provide a factor 1/2 approximation to the global maxima. We also study stochastic gradient and mirror methods and show that after O(1/ϵ2) iterations these methods reach solutions which achieve in expectation objective values exceeding (OPT2 − ϵ). An immediate application of our results is to maximize submodular functions that are defined stochastically, i.e. the submodular function is defined as an expectation over a family of submodular functions with an unknown distribution. We will show how stochastic gradient methods are naturally well-suited for this setting, leading to a factor 1/2 approximation when the function is monotone. In particular, it allows us to approximately maximize discrete, monotone submodular optimization problems via projected gradient descent on a continuous relaxation, directly connecting the discrete and continuous domains. Finally, experiments on real data demonstrate that our projected gradient methods consistently achieve the best utility compared to other continuous baselines while remaining competitive in terms of computational effort. Copyright © 2017, The Authors. All rights reserved.

关键词： Gradient methods

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Delay-driven layer assignment for advanced technology nodes

Delay-driven layer assignment for advanced technology nodes

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Asia and South Pacific design Automation Conference

作者： Szu-Yuan Han Wen-Hao Liu Rickard Ewetz Cheng-Kok Koh Kai-Yuan Chao Ting-Chi Wang Department of Computer Science National Tsing Hua University Hsinchu Taiwan Block Implementation Cadence Design Systems Austin TX USA Department of Electrical and Computer Engineering University of Central Florida Orlando FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Intel Corporation Hillsboro OR USA

ISBN: (纸本)9781509015597

This paper addresses a delay-driven layer assignment problem with consideration of via delay and coupling effect in the global routing stage. A negotiation-based framework is proposed to balance delay, congestion, and via count. Coupling capacitance is considered using a probabilistic look-up table. Finally, the proposed algorithm uses both parallel wires and wide wires to reduce wire delay. The effectiveness of our layer assignment algorithm is supported by extensive experimental results.

关键词： Wires Routing Delays Couplings Capacitance Three-dimensional displays Two dimensional displays

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Experimental observation of the topological structure of exceptional points in an ultrathin hybridized metamaterial

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Physical Review A 2017年第6期96卷 063823-063823页

作者： Ming Kang Weiren Zhu Ivan D. Rukhlenko College of Physics and Materials Science Tianjin Normal University Tianjin 300387 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai 200240 China Modeling and Design of Nanostructures Laboratory ITMO University Saint Petersburg 197101 Russia Department of Electrical and Computer Systems Engineering Monash University Clayton Victoria 3800 Australia

The exceptional point (EP), which is one of the most important branch-type singularities exclusive to non-Hermitian systems, has been observed recently in various synthetic materials, giving rise to counterintuitive phenomena due to the nontrivial topology of the EP. Here, we present a direct experimental observation of the topological structure of the EPs via the angle-resolved transmission measurement of a hybridized metamaterial. Both eigenvalues and eigenvectors show branch-point singularities in the investigated biparametric space of frequency and incident angle. Importantly, the angle-resolved transmission coefficients provide all the information about the eigenvalues as well as the corresponding eigenvectors in the biparametric space, revealing the nontrivial topological structure of the EP, such as mode switching and the topological phase for a parameter loop encircling the EP. It is shown that the appearance of the EP in the scattering matrix is related directly to the perfect unidirectional transmission and the chirality of the EP corresponds to the maximum or minimum value of the asymmetric factor. Our investigation uncovers the capabilities of metamaterials for exploring the physics of EPs and their potential for having extreme optical properties, which provide potential applications in the spectral band ranging from microwaves to visible frequencies.

关键词： Metamaterials

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On the role of theory and modeling in neuroscience

arXiv

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arXiv 2020年

作者： Levenstein, Daniel Alvarez, Veronica A. Amarasingham, Asohan Azab, Habiba Chen, Zhe S. Gerkin, Richard C. Hasenstaub, Andrea Iyer, Ramakrishnan Jolivet, Renaud B. Marzen, Sarah Monaco, Joseph D. Prinz, Astrid A. Quraishi, Salma Santamaria, Fidel Shivkumar, Sabyasachi Singh, Matthew F. Traub, Roger Rotstein, Horacio G. Nadim, Farzan Redish, A. David Montreal Neurological Institute McGill University MontrealQC Canada Laboratory on Neurobiology of Compulsive Behaviors National Institute on Alcohol Abuse and Alcoholism NIH BethesdaMD United States Department of Neuroscience Center for Magnetic Resonance Research University of Minnesota MinneapolisMN United States School of Life Sciences Arizona State University TempeAZ United States Department of Otolaryngology - Head and Neck Surgery University of California San Francisco San FranciscoCA United States Allen Institute for Brain Science SeattleWA United States Maastricht University Maastricht Netherlands Department of Biomedical Engineering Johns Hopkins University School of Medicine BaltimoreMD United States Department of Biology University of Texas at San Antonio San AntonioTX United States Department of Psychological & Brain Sciences Department of Electrical & Systems Engineering Washington University in St. Louis St. LouisMO United States IBM T.J. Watson Research Center AI Foundations Yorktown HeightsNY United States W. M. Keck Science Department Pitzer Scripps and Claremont McKenna Colleges ClaremontCA United States Department of Biology Emory University AtlantaGA United States Department of Mathematics City College of New York United States Departments of Biology Computer Science and Psychology The Graduate Center City University of New York United States Brain and Cognitive Sciences University of Rochester RochesterNY United States Federated Department of Biological Sciences New Jersey Institute of Technology Rutgers University Institute for Brain and Neuroscience Research New Jersey Institute of Technology NewarkNJ United States Department of Neuroscience University of Minnesota MinneapolisMN United States Department of Psychiatry Neuroscience & Physiology New York University School of Medicine United States Mila - Quebec AI Institute MontrealQC Canada

In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena that cross a wide range of scales and often require consideration at varying degrees of abstraction, from precise biophysical interactions to the computations they implement. We argue that a pragmatic perspective of science, in which descriptive, mechanistic, and normative models and theories each play a distinct role in defining and bridging levels of abstraction will facilitate neuroscientific practice. This analysis leads to methodological suggestions, including selecting a level of abstraction that is appropriate for a given problem, identifying transfer functions to connect models and data, and the use of models themselves as a form of experiment. © 2020, CC BY.

关键词： Computation theory

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