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13th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Q2SWinet 2017

作者： Nahar, Badrun Alam, Mohammad Shah Shawkat, Shamim Ara Hoque, Mohammad A. Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Bangladesh Dept. of Electrical Engineering and Computer Science University of Tennessee Knoxville United States Department of Computing East Tennessee State University United States

ISBN: (纸本)9781450351652

In recent years, Smart Grid (SG) is envisioned to be the next generation electric power system by replacing traditional power grid due to its advantage of using two way communications. To implement reliable SG wireless communication networks, IEEE introduced a new wireless standard (IEEE802.15.4g) for Smart Utility Networks (SUNs). However, SUN operates on 2.4 GHz unlicensed band which is overlapped with Wireless Local Area Networks (WLANs) that leads to coexistence in Smart Utility Networks. In this paper, the coexistence problem of SUN is addressed in terms of homogeneous and heterogeneous interferences. To mitigate the homogeneous interference, Contention Access Period (CAP) and Contention Free Period (CFP) of a super frame of IEEE 802.15.4g is used to access the channel using slotted CSMA/CA algorithm by modifying the Backoff Period (BP) and Clear Channel Assessment (CCA) period for different priority data. An analytical model is developed using Markov chain, through which we demonstrate the accuracy of the proposed model in terms of throughput, channel access delay, probability of successful transmission and collision for nodes with different priority data. Performance evaluation is further investigated by comparing the proposed scheme with the existing PA-MAC. A channel switching mechanism is explored to mitigate the heterogeneous interference by the prediction of Naive Bayes Classifier. Predicted result shows that proposed mechanism effectively mitigates the heterogeneous interference by choosing the non-overlapping and non-coexisting channel. © 2017 Association for computing Machinery.

关键词： Markov processes

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Memory attention networks for skeleton-based action recognition

arXiv

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arXiv 2018年

作者： Xie, Chunyu Li, Ce Zhang, Baochang Chen, Chen Han, Jungong Zou, Changqing Liu, Jianzhuang School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Computer Science and Technology China University of Mining & Technology Beijing China Department of Electrical and Computer Engineering University of North Carolina at Charlotte School of Computing & Communications Lancaster University LA1 4YW United Kingdom University of Maryland Institute for Advanced Computer Studies College ParkMD20742 United States Noah's Ark Lab Huawei Technologies Co. Ltd. China

Skeleton-based action recognition task is entangled with complex spatio-temporal variations of skeleton joints, and remains challenging for Recurrent Neural Networks (RNNs). In this work, we propose a temporal-then-spatial recalibration scheme to alleviate such complex variations, resulting in an end-to-end Memory Attention Networks (MANs) which consist of a Temporal Attention Recalibration Module (TARM) and a Spatio-Temporal Convolution Module (STCM). Specifically, the TARM is deployed in a residual learning module that employs a novel attention learning network to recalibrate the temporal attention of frames in a skeleton sequence. The STCM treats the attention calibrated skeleton joint sequences as images and leverages the Convolution Neural Networks (CNNs) to further model the spatial and temporal information of skeleton data. These two modules (TARM and STCM) seamlessly form a single network architecture that can be trained in an end-to-end fashion. MANs significantly boost the performance of skeleton-based action recognition and achieve the best results on four challenging benchmark datasets: NTU RGB+D, HDM05, SYSU-3D and UT-Kinect. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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Optical control of single-photon negative-feedback avalanche diode detector

arXiv

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arXiv 2019年

作者： Gras, Gaëtan Sultana, Nigar Huang, Anqi Jennewein, Thomas Bussières, Félix Makarov, Vadim Zbinden, Hugo ID Quantique SA CarougeCH-1227 Switzerland Group of Applied Physics University of Geneva GenevaCH-1211 Switzerland Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Russian Quantum Center Skolkovo Moscow121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai201315 China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia

We experimentally demonstrate optical control of negative-feedback avalanche diode (NFAD) detectors using bright light. We deterministically generate fake single-photon detections with a better timing precision than normal operation. This could potentially open a security loophole in quantum cryptography systems. We then show how monitoring the photocurrent through the avalanche photodiode can be used to reveal the detector is being blinded. Copyright © 2019, The Authors. All rights reserved.

关键词： Feedback

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A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters (vol 14, pg 352, 2018)

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NATURE CHEMICAL BIOLOGY 2018年第9期14卷 901-901页

作者： Piatkevich, Kiryl D. Jung, Erica E. Straub, Christoph Linghu, Changyang Park, Demian Suk, Ho-Jun Hochbaum, Daniel R. Goodwin, Daniel Pnevmatikakis, Eftychios Pak, Nikita Kawashima, Takashi Yang, Chao-Tsung Rhoades, Jeffrey L. Shemesh, Or Asano, Shoh Yoon, Young-Gyu Freifeld, Limor Saulnier, Jessica L. Riegler, Clemens Engert, Florian Hughes, Thom Drobizhev, Mikhail Szabo, Balint Ahrens, Misha B. Flavell, Steven W. Sabatini, Bernardo L. Boyden, Edward S. Media Lab Massachusetts Institute of Technology (MIT) Cambridge MA USA. Howard Hughes Medical Institute Department of Neurobiology Harvard Medical School Boston MA USA. Department of Electrical Engineering and Computer Science MIT Cambridge MA USA. Harvard-MIT Division of Health Sciences and Technology MIT Cambridge MA USA. Simons Center Data Analysis Simons Foundation New York NY USA. Department of Mechanical Engineering MIT Cambridge MA USA. Janelia Research Campus Howard Hughes Medical Institute Ashburn Virginia USA. Picower Institute for Learning & Memory and Department of Brain & Cognitive Sciences MIT Cambridge MA USA. Department of Molecular and Cellular Biology and Center for Brain Science Harvard University Cambridge MA USA. Department of Neurobiology Faculty of Life Sciences University of Vienna Wien Austria. Department of Cell Biology and Neuroscience Montana State University Bozeman Montana USA. Department of Biological Physics Eotvos University Budapest Hungary. Media Lab Massachusetts Institute of Technology (MIT) Cambridge MA USA. esb@media.mit.edu. Department of Biological Engineering MIT Cambridge MA USA. esb@media.mit.edu. MIT Center for Neurobiological Engineering MIT Cambridge MA USA. esb@media.mit.edu. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. esb@media.mit.edu. MIT McGovern Institute for Brain Research MIT Cambridge MA USA. esb@media.mit.edu.

We developed a new way to engineer complex proteins toward multidimensional specifications using a simple, yet scalable, directed evolution strategy. By robotically picking mammalian cells that were identified, under a microscope, as expressing proteins that simultaneously exhibit several specific properties, we can screen hundreds of thousands of proteins in a library in just a few hours, evaluating each along multiple performance axes. To demonstrate the power of this approach, we created a genetically encoded fluorescent voltage indicator, simultaneously optimizing its brightness and membrane localization using our microscopy-guided cell-picking strategy. We produced the high-performance opsin-based fluorescent voltage reporter Archon1 and demonstrated its utility by imaging spiking and millivolt-scale subthreshold and synaptic activity in acute mouse brain slices and in larval zebrafish in vivo. We also measured postsynaptic responses downstream of optogenetically controlled neurons in C. elegans.

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Controlling single-photon detector ID210 with bright light

arXiv

引用

arXiv 2019年

作者： Chistiakov, Vladimir Huang, Anqi Egorov, Vladimir Makarov, Vadim Faculty of Photonics and Optical Information ITMO University St. Petersburg Russia Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Russian Quantum Center Skolkovo Moscow143025 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai201315 China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada

We experimentally demonstrate that a single-photon detector ID210 commercially available from ID Quantique is vulnerable to blinding and can be fully controlled by bright illumination. In quantum key distribution, this vulnerability can be exploited by an eavesdropper to perform a faked-state attack giving her full knowledge of the key without being noticed. We consider the attack on standard BB84 protocol and a subcarrier-wave scheme, and outline a possible countermeasure. Copyright © 2019, The Authors. All rights reserved.

关键词： Avalanche photodiodes

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Alert classification for the alerce broker system: The real-time stamp classifier

arXiv

引用

arXiv 2020年

作者： Carrasco-Davis, Rodrigo Reyes, E. Valenzuela, C. Förster, F. Estévez, P.A. Pignata, G. Bauer, F.E. Reyes, I. Sánchez-Sáez, P. Cabrera-Vives, G. Eyheramendy, S. Catelan, M. Arredondo, J. Castillo-Navarrete, E. Rodríguez-Mancini, D. Ruz-Mieres, D. Moya, A. Sabatini-Gacitúa, L. Sepúlveda-Cobo, C. Mahabal, A.A. Silva-Farfán, J. Camacho-Iñiguez, E. Galbany, L. Nuncio Monseñor Sótero Sanz 100 Providencia Santiago Chile Department of Electrical Engineering Universidad de Chile Av. Tupper 2007 Santiago8320000 Chile Center for Mathematical Modeling Universidad de Chile Beauchef 851 North building 7th floor Santiago8320000 Chile Faculty of Engineering and Sciences Universidad Adolfo Ibañez Diagonal Las Torres 2700 Peñalolén Santiago Chile Data Observatory Santiago Chile Departamento de Astronomía Universidad de Chile Casilla 36D Santiago Chile Departamento de Ciencias Físicas Universidad Andres Bello Av. Republica 230 Santiago8370146 Chile Instituto de Astrofísica and Centro de Astroingeniería Facultad de Física Pontificia Universidad Católica de Chile Casilla 306 Santiago 22 Chile Space Science Institute 4750 Walnut Street Suite 205 BoulderCO80301 United States Inria Chile Research Center Av. Apoquindo 2827 Las Condes Chile Department of Computer Science Universidad de Concepción Edmundo Larenas 219 Concepción Chile Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Cahill Center for Astrophysics California Institute of Technology 1200 E. California Boulevard PasadenaCA91125 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Departamento de Física Teórica y del Cosmos Universidad de Granada GranadaE-18071 Spain

We present a real-time stamp classifier of astronomical events for the ALeRCE (Automatic Learning for the Rapid Classification of Events) broker. The classifier is based on a convolutional neural network, trained on alerts ingested from the Zwicky Transient Facility (ZTF). Using only the science, reference and difference images of the first detection as inputs, along with the metadata of the alert as features, the classifier is able to correctly classify alerts from active galactic nuclei, supernovae (SNe), variable stars, asteroids and bogus classes, with high accuracy (∼94%) in a balanced test set. In order to find and analyze SN candidates selected by our classifier from the ZTF alert stream, we designed and deployed a visualization tool called SN Hunter, where relevant information about each possible SN is displayed for the experts to choose among candidates to report to the Transient Name Server database. From June 26th 2019 to February 28th 2021, we have reported 6846 SN candidates to date (11.8 candidates per day on average), of which 971 have been confirmed spectroscopically. Our ability to report objects using only a single detection means that 70% of the reported SNe occurred within one day after the first detection. ALeRCE has only reported candidates not otherwise detected or selected by other groups, therefore adding new early transients to the bulk of objects available for early follow-up. Our work represents an important milestone toward rapid alert classifications with the next generation of large etendue telescopes, such as the Vera C. Rubin Observatory. Copyright © 2020, The Authors. All rights reserved.

关键词： Supernovae

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Not just privacy: Improving performance of private deep learning in mobile cloud

arXiv

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arXiv 2018年

作者： Wang, Ji Zhang, Jianguo Bao, Weidong Zhu, Xiaomin Cao, Bokai Yu, Philip S. College of Systems Engineering National University of Defense Technology Changsha China Department of Computer Science University of Illinois at Chicago Chicago United States College of Systems Engineering State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Facebook Inc. Menlo Park United States Department of Computer Science University of Illinois at Chicago Chicago United States Institute for Data Science Tsinghua University Beijing China

The increasing demand for on-device deep learning services calls for a highly efficient manner to deploy deep neural networks (DNNs) on mobile devices with limited capacity. The cloud-based solution is a promising approach to enabling deep learning applications on mobile devices where the large portions of a DNN are offloaded to the cloud. However, revealing data to the cloud leads to potential privacy risk. To benefit from the cloud data center without the privacy risk, we design, evaluate, and implement a cloud-based framework Arden which partitions the DNN across mobile devices and cloud data centers. A simple data transformation is performed on the mobile device, while the resource-hungry training and the complex inference rely on the cloud data center. To protect the sensitive information, a lightweight privacy-preserving mechanism consisting of arbitrary data nullification and random noise addition is introduced, which provides strong privacy guarantee. A rigorous privacy budget analysis is given. Nonetheless, the private perturbation to the original data inevitably has a negative impact on the performance of further inference on the cloud side. To mitigate this influence, we propose a noisy training method to enhance the cloud-side network robustness to perturbed data. Through the sophisticated design, Arden can not only preserve privacy but also improve the inference performance. To validate the proposed Arden, a series of experiments based on three image datasets and a real mobile application are conducted. The experimental results demonstrate the effectiveness of Arden. Finally, we implement Arden on a demo system to verify its practicality. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Probing physical origin of anisotropic thermal transport in black phosphorus nanoribbons

arXiv

引用

arXiv 2018年

作者： Zhao, Yunshan Zhang, Gang Nai, Mui Hoon Ding, Guangqian Li, Dengfeng Liu, Yi Hippalgaonkar, Kedar Lim, Chwee Teck Chi, Dongzhi Li, Baowen Wu, Jing Thong, John T.L. Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Institute of High Performance Computing Singapore138632 Singapore Department of Biomedical Engineering National University of Singapore Singapore117576 Singapore School of Science Chongqing University of Posts and Telecommunications Chongqing400065 China Institute of Materials Research and Engineering Agency for Science Technology and Research Singapore138634 Singapore Mechanobiology Institute National University of Singapore Singapore117411 Singapore Department of Mechanical Engineering University of Colorado Boulder80309 United States

Black phosphorus (BP) has emerged as a promising candidate for next generation electronics and optoelectronics among the 2D family materials due to its extraordinary electrical/optical/optoelectronic properties. Interestingly, BP shows strong anisotropic transport behaviour because of its puckered honeycomb structure. Previous studies have demonstrated the thermal transport anisotropy of BP and theoretically attribute this to the anisotropy in both phonon dispersion relation and phonon relaxation time. However, the exact origin of such strong anisotropy lacks clarity and has yet to be proven experimentally. In this work, we probe the thermal transport anisotropy of BP nanoribbons (NRs) by an electron beam technique. We provide direct evidence that the origin of this anisotropy is dominated by the anisotropic phonon group velocity for the first time, verified by Young’s modulus measurements along different directions. It turns out that the ratio of thermal conductivity between zigzag (ZZ) and armchair (AC) ribbons is almost same as that of the corresponding Young modulus values. The results from first-principles calculation are consistent with this experimental observation, where anisotropic phonon group velocity between ZZ and AC is shown. Our results provide fundamental insight into the anisotropic thermal transport in low symmetric crystals. Copyright © 2018, The Authors. All rights reserved.

关键词： Anisotropy

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Improving stock market prediction via heterogeneous information fusion

arXiv

引用

arXiv 2018年

作者： Zhang, Xi Zhang, Yunjia Wang, Senzhang Yao, Yuntao Fang, Binxing Yu, Philip S. Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Computer Science University of Illinois at ChicagoIL60607 United States Institute for Data Science Tsinghua University Beijing100084 China Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan Guangdong523808 China

Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. As the Web information grows, recently some works try to explore financial news to improve the prediction. Effective indicators, e.g., the events related to the stocks and the people’s sentiments towards the market and stocks, have been proved to play important roles in the stocks’ volatility, and are extracted to feed into the prediction models for improving the prediction accuracy. However, a major limitation of previous methods is that the indicators are obtained from only a single source whose reliability might be low, or from several data sources but their interactions and correlations among the multi-sourced data are largely ignored. In this work, we extract the events from Web news and the users’ sentiments from social media, and investigate their joint impacts on the stock price movements via a coupled matrix and tensor factorization framework. Specifically, a tensor is firstly constructed to fuse heterogeneous data and capture the intrinsicrelations among the events and the investors’ sentiments. Due to the sparsity of the tensor, two auxiliary matrices, the stock quantitative feature matrix and the stock correlation matrix, are constructed and incorporated to assist the tensor decomposition. The intuition behind is that stocks that are highly correlated with each other tend to be affected by the same event. Thus, instead of conducting each stock prediction task separately and independently, we predict multiple correlated stocks simultaneously through their commonalities, which are enabled via sharing the collaboratively factorized low rank matrices between matrices and the tensor. Evaluations on the China A-share stock data and the HK stock data in the year 2015 demonstrate the effectiveness of the proposed model. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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Pioneering the Use of Neural Network Architectures and Feature engineering for Real-Time Augmented Microscopy and Analysis

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Microscopy and Microanalysis 2018年第S1期24卷 514-515页

作者： Matthew L. Gong Su Jong Yoon Raymond R. Unocic Hope Ishii John P. Bradley Brandon D. Miller Daniel Masiel Bryan Reed Tolga Tasdizen Jeffery A. Aguiar University of Utah Scientific Computing Imaging Institute Department of Electrical and Computer Engineering Salt Lake City UT Idaho National Laboratory Nuclear Science and Technology Division Idaho Falls ID Oak Ridge National Laboratory Center for Nanophase Materials Science Oak Ridge TN University of Hawai’i at Manoa School of Ocean and Earth Science and Technology Honolulu HI Integrated Dynamic Electron Solutions Pleasanton CA University of Utah Department of Materials Science and Engineering Salt Lake City UT

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