检索结果-内蒙古大学图书馆

arXiv 2018年

作者： Yao, Nan Huang, Zi-Gang Ren, Hai-Peng Grebogi, Celso Lai, Ying-Cheng Department of Applied Physics Xi’an University of Technology Xi’an710048 China School of Life Science and Technology Xi’an Jiao Tong University Xi’an710049 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Institute for Complex Systems and Mathematical Biology King’s College University of Aberdeen AberdeenAB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Physics Arizona State University TempeAZ85287 United States

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model. Not only is the model able to reproduce the numerical results, it can also predict the trajectory of drifting. Our finding is striking as it reveals that, inherently, chimera states possess a kind of "intelligence" in achieving robustness through self adaptation. The behavior can be exploited for controlled generation of chimera states with their coherent component placed in any desired spatial region of the system. Copyright © 2018, The Authors. All rights reserved.

关键词： Dynamical systems

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Zero-bias photocurrents in highly-disordered networks of Ge and Si nanowires

arXiv

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arXiv 2017年

作者： Rabbani, M. Golam Patil, Sunil R. Verma, Amit Villarreal, Julian E. Korgel, Brian A. Nekovei, Reza Khader, Mahmoud M. Darling, R.B. Anantram, M.P. Department of Electrical Engineering University of Washington SeattleWA98195 United States Department of Physics College of Engineering PuneMS411005 India Department of Electrical Engineering and Computer Science Texas A&M University – Kingsville KingsvilleTX78363 United States McKetta Department of Chemical Engineering Texas Materials Institute Center for Nano- and Molecular Science and Technology University of Texas at Austin AustinTX78712 United States Gas Processing Center College of Engineering Qatar University DohaP.O2713 Qatar

Semiconducting nanowire (NW) devices have garnered attention in self-powered electronic and optoelectronic applications. This work explores and exhibits, for the first time for visible light, a clear evidence of the zero-biased optoelectronic switching in randomly dispersed Ge and Si NW networks. The test bench, on which the NWs were dispersed for optoelectronic characterization, was fabricated using standard CMOS fabrication process, and utilized metal contacts with dissimilar work functions - Al and Ni. The randomly dispersed NWs respond to light by exhibiting substantial photocurrents and, most remarkably, demonstrate zero-bias photo-switching. The magnitude of the photocurrent is dependent on the NW material, as well as the channel length. The photocurrent in randomly dispersed GeNWs was found to be higher by orders of magnitude compared to SiNWs. In both of these material systems, when the length of the NWs was comparable to the channel length, the currents in sparse NW networks were found to be higher than that in dense NW networks, which can be explained by considering various possible arrangements of NWs in these devices. Copyright © 2017, The Authors. All rights reserved.

关键词： Photocurrents

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Consistency guarantees for greedy permutation-based causal inference algorithms

arXiv

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arXiv 2017年

作者： Solus, Liam Wang, Yuhao Uhler, Caroline Institutionen för Matematik KTH StockholmSE-100 44 Sweden Institute for Interdisciplinary Information Sciences Tsinghua University Beijing China Shanghai Qi Zhi Institute Shanghai China Department of Electrical Engineering and Computer Science Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States

Directed acyclic graphical models, or DAG models, are widely used to represent complex causal systems. Since the basic task of learning such a model from data is NP-hard, a standard approach is greedy search over the space of directed acyclic graphs or Markov equivalence classes of directed acyclic graphs. As the space of directed acyclic graphs on p nodes and the associated space of Markov equivalence classes are both much larger than the space of permutations, it is desirable to consider permutation-based greedy searches. Here, we provide the first consistency guarantees, both uniform and high-dimensional, of a greedy permutation-based search. This search corresponds to a simplex-like algorithm operating over the edge-graph of a sub-polytope of the permutohedron, called a DAG associahedron. Every vertex in this polytope is associated with a directed acyclic graph, and hence with a collection of permutations that are consistent with the directed acyclic graph ordering. A walk is performed on the edges of the polytope maximizing the sparsity of the associated directed acyclic graphs. We show via simulated and real data that this permutation search is competitive with current approaches. Copyright © 2017, The Authors. All rights reserved.

关键词： Bayesian networks

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Fast thermal relaxation in cavity-coupled graphene bolometers with a Johnson noise read-out

arXiv

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arXiv 2017年

作者： Efetov, Dmitri K. Shiue, Ren-Jye Gao, Yuanda Skinner, Brian Walsh, Evan D. Choi, Hyeongrak Zheng, Jiabao Tan, Cheng Grosso, Gabriele Peng, Cheng Hone, James Fong, Kin Chung Englund, Dirk ICFO - Institut de Ciencies Fotoniques Barcelona Institute of Science and Technology Castelldefels Barcelona08860 Spain Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Columbia University New YorkNY10027 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Raytheon BBN Technologies Quantum Information Processing Group CambridgeMA02138 United States

High sensitivity, fast response time and strong light absorption are the most important metrics for infrared sensing and imaging. The trade-off between these characteristics remains the primary challenge in bolometry. Graphene with its unique combination of a record small electronic heat capacity and a weak electron-phonon coupling has emerged as a sensitive bolometric medium that allows for high intrinsic bandwidths. Moreover, the materials light absorption can be enhanced to near unity by integration into photonic structures. Here, we introduce an integrated hot-electron bolometer based on Johnson noise readout of electrons in ultra-clean hexagonal boron nitride encapsulated graphene, which is critically coupled to incident radiation through a photonic nanocavity with Q=900. The device operates at telecom wavelengths and shows an enhanced bolometric response at charge neutrality. We report a noise equivalent power 1/2 , a record fast thermal relaxation time Copyright © 2017, The Authors. All rights reserved.

关键词： Light absorption

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Tunable magnetization relaxation of Fe2Cr1−xCoxSi half-metallic Heusler alloys by band structure engineering

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Physical Review Materials 2017年第6期1卷 064401-064401页

作者： Shikun He Yifan Liu Yuhong Zheng Qing Qin Zhenchao Wen Qingyun Wu Yi Yang Yupu Wang YuanPing Feng Kie Leong Teo Christos Panagopoulos Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Data Storage Institute Agency for Science Technology and Research (A*STAR) 2 Fusionopolis Way 08-01 Innovis Singapore 138634 Department of Electrical and Computer Engineering National University of Singapore Singapore 117582 Department of Materials Science and Engineering National University of Singapore Singapore 117574 Center for Spintronics Research Network (CSRN) Tohoku University Sendai 980-8577 Japan Department of Physics National University of Singapore Singapore 117542

We report a systematic investigation on the magnetization relaxation properties of iron-based half-metallic Heusler alloy Fe2Cr1−xCoxSi (FCCS) thin films using broadband angular-resolved ferromagnetic resonance. Band structure engineering through Co doping (x) demonstrated by first-principles calculations is shown to tune the intrinsic magnetic damping over an order of magnitude, namely 1×10−2–8×10−4. Notably, the intrinsic damping constants for samples with high Co concentration are among the lowest reported for Heusler alloys and even comparable to magnetic insulator yttrium iron garnet. Furthermore, a significant reduction of both isotropic and anisotropic contributions of extrinsic damping of the FCCS alloys was found in the FCCS films with x=0.5–0.75, which is of particular importance for applications. These results demonstrate a practical recipe to tailor functional magnetization for Heusler alloy-based spintronics at room temperature.

关键词： Ferromagnetism Spin relaxation Spin transfer torque

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Coherent oscillations of driven rf SQUID metamaterials

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Physical Review E 2017年第5期95卷 050201(R)-050201(R)页

作者： Melissa Trepanier Daimeng Zhang Oleg Mukhanov V. P. Koshelets Philipp Jung Susanne Butz Edward Ott Thomas M. Antonsen Alexey V. Ustinov Steven M. Anlage Department of Physics CNAM University of Maryland College Park Maryland 20742 USA Department of Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Hypres Inc. 175 Clearbrook Road Elmsford New York 10523 USA Laboratory of Superconducting Devices for Signal Detection and Processing Kotelnikov Institute of Radio Engineering and Electronics Moscow 125009 Russia Physikalisches Institut Karlsruhe Institute of Technology D-76049 Karlsruhe Germany Russian Quantum Center National University of Science and Technology MISIS Moscow 119049 Russia

Through experiments and numerical simulations we explore the behavior of rf SQUID (radio frequency superconducting quantum interference device) metamaterials, which show extreme tunability and nonlinearity. The emergent electromagnetic properties of this metamaterial are sensitive to the degree of coherent response of the driven interacting SQUIDs. Coherence suffers in the presence of disorder, which is experimentally found to be mainly due to a dc flux gradient. We demonstrate methods to recover the coherence, specifically by varying the coupling between the SQUID meta-atoms and increasing the temperature or the amplitude of the applied rf flux.

关键词： Collective dynamics Coupled oscillators Josephson effect Magnetic susceptibility Optical & microwave phenomena Synchronization Metamaterials

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Graphene-based Josephson junction single photon detector

arXiv

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arXiv 2017年

作者： Walsh, Evan D. Efetov, Dmitri K. Lee, Gil-Ho Heuck, Mikkel Crossno, Jesse Ohki, Thomas A. Kim, Philip Englund, Dirk Fong, Kin Chung Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States 08860 Spain Department of Physics Harvard University CambridgeMA02138 United States Raytheon BBN Technologies Quantum Information Processing Group CambridgeMA02138 United States

We propose to use graphene-based Josephson junctions (gJjs) to detect single photons in a wide electromagnetic spectrum from visible to radio frequencies. Our approach takes advantage of the exceptionally low electronic heat capacity of monolayer graphene and its constricted thermal conductance to its phonon degrees of freedom. Such a system could provide high sensitivity photon detection required for research areas including quantum information processing and radio-astronomy. As an example, we present our device concepts for gJj single photon detectors in both the microwave and infrared regimes. The dark count rate and intrinsic quantum efficiency are computed based on parameters from a measured gJj, demonstrating feasibility within existing technologies. Copyright © 2017, The Authors. All rights reserved.

关键词： Graphene

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Visual Reasoning Indexing and Retrieval Using In-Memory Computing

Visual Reasoning Indexing and Retrieval Using In-Memory Comp...

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作者： Cao, Hongfei Li, Yu Allen, Carla M. Phinney, Michael A. Shyu, Chi-Ren Computer Science Interdisciplinary Data Analytics and Search Lab University of Missouri-Columbia ColumbiaMO65211 United States Clinical and Diagnostic Science Department University of Missouri-Columbia ColumbiaMO65211 United States Computer Science Electrical and Computer Engineering Informatics Institute Interdisciplinary Data Analytics and Search Lab University of Missouri-Columbia ColumbiaMO65211 United States

Research has shown that visual information of multimedia is critical in highly-skilled applications, such as biomedicine and life sciences, and a certain visual reasoning process is essential for meaningful search in a timely manner. Relevant image characteristics are learned and verified with accumulated experiences during the reasoning processes. However, such processes are highly dynamic and elusive to computationally quantify and therefore challenging to analyze, let alone to make the knowledge sharable across users. In this paper we study real-time human visual reasoning processes with the aid of gaze tracking devices. Temporal and spatial representations are proposed for gaze modeling, and a visual reasoning retrieval system utilizing in-memory computing under Big data ecosystems is designed for real-time search of similar reasoning models. Simulated data derived from human subject experiments show that the system has a reasonably high accuracy and provides predictive estimations for hardware requirements versus data sizes for exhaustive searches. Comparison between various visual action classifiers show challenges in modeling visual actions. The proposed system provides a theoretical framework and computing platform for advancement in visual semantic computing, as well as potential applications in medicine, social science, and arts. © 2016 World Scientific Publishing Company.

关键词： Eye tracking

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Network slicing for service-oriented networks under resource constraints

arXiv

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arXiv 2017年

作者： Zhang, Nan Liu, Ya-Feng Farmanbar, Hamid Chang, Tsung-Hui Hong, Mingyi Luo, Zhi-Quan School of Mathematical Sciences Peking University State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Huawei Canada Research Center Ottawa Canada Department of Electrical and Computer Engineering University of Minnesota United States Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China

To support multiple on-demand services over fixed communication networks, network operators must allow flexible customization and fast provision of their network resources. One effective approach to this end is network virtualization, whereby each service is mapped to a virtual subnetwork providing dedicated on-demand support to network users. In practice, each service consists of a prespecified sequence of functions, called a service function chain (SFC), while each service function in a SFC can only be provided by some given network nodes. Thus, to support a given service, we must select network function nodes according to the SFC and determine the routing strategy through the function nodes in a specified order. A crucial network slicing problem that needs to be addressed is how to optimally localize the service functions in a physical network as specified by the SFCs, subject to link and node capacity constraints. In this paper, we formulate the network slicing problem as a mixed binary linear program and establish its strong NP-hardness. Furthermore, we propose efficient penalty successive upper bound minimization (PSUM) and PSUM-R(ounding) algorithms, and two heuristic algorithms to solve the problem. Simulation results are shown to demonstrate the effectiveness of the proposed algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Network function virtualization

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