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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Bin Zhang Shenyao Jin Yili Xia Yongming Huang Zixiang Xiong School of Information Science and Engineering Southeast University Nanjing P. R. China Jiangsu Industrial Technology Research Institute Nanjing P. R. China Department of Electrical and Computer Systems Engineering Texas A&M University TX USA

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Deep learning based image denoising methods have been extensively investigated. In this paper, attention mechanism enhanced kernel prediction networks (AME-KPNs) are proposed for burst image denoising, in which, nearly cost-free attention modules are adopted to first refine the feature maps and to further make a full use of the inter-frame and intra-frame redundancies within the whole image burst. The proposed AME-KPNs output per-pixel spatially-adaptive kernels, residual maps and corresponding weight maps, in which, the predicted kernels roughly restore clean pixels at their corresponding locations via an adaptive convolution operation, and subsequently, residuals are weighted and summed to compensate the limited receptive field of predicted kernels. Simulations and real-world experiments are conducted to illustrate the robustness of the proposed AME-KPNs in burst image denoising.

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A Novel granular-based bi-clustering method of deep mining the co-expressed genes

arXiv

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arXiv 2020年

作者： Xu, Kaijie Pedrycz, Witold Li, Zhiwu Quan, Yinghui Nie, Weike Xidian University Xian China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Institute of Systems Engineering Macau University of Science and Technology Taipa China School of Information Science and Technology Northwest University Xi'an China Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia

Traditional clustering methods are limited when dealing with huge and heterogeneous groups of gene expression data, which motivates the development of bi-clustering methods. Bi-clustering methods are used to mine bi-clusters whose subsets of samples (genes) are co-regulated under their test conditions. Studies show that mining bi-clusters of consistent trends and trends with similar degrees of fluctuations from the gene expression data is essential in bioinformatics research. Unfortunately, traditional bi-clustering methods are not fully effective in discovering such bi-clusters. Therefore, we propose a novel bi-clustering method by involving here the theory of Granular Computing. In the proposed scheme, the gene data matrix, considered as a group of time series, is transformed into a series of ordered information granules. With the information granules we build a characteristic matrix of the gene data to capture the fluctuation trend of the expression value between consecutive conditions to mine the ideal bi-clusters. The experimental results are in agreement with the theoretical analysis, and show the excellent performance of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Cluster analysis

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A 400 MHz, 8-Bit, 1.75-ps Resolution Pipelined-Two-Step Time-to-Digital Converter with Dynamic Time Amplification

A 400 MHz, 8-Bit, 1.75-ps Resolution Pipelined-Two-Step Time...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Yuting Tu Rongjin Xu Dawei Ye Liangjian Lyu C.-J. Richard Shi State Key Laboratory of ASIC and System Fudan University Shanghai 201203 China Institute of Brain-inspired Circuits and Systems (iBiCAS) Fudan University Shanghai 201203 China Department of Electrical and Computer Engineering University of Washington Seattle 98195 USA

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

This work proposes a high-speed pipelined-two-step time-to-digital converter (TDC) with a dynamic time amplification (DTA) to improve the resolution at low power. The key element of this TDC is the DTA. It samples the residual time errors as voltages held in the MOM capacitors and discharges them to generate the amplified time difference. Thanks to the dynamic time-voltage-time conversion, the DTA realizes high linearity and power efficiency, and can be employed to build a pipeline TDC architecture with high sampling frequency because of its sample and hold operation. Moreover, the DTA maintains constant gain, so only a one-time forground calibration for gain mismatch is required in this TDC. Simulations show that the TDC designed in 65 nm CMOS achieves 8-bit, 1.75 ps of time resolution, and 1 LSB INL and 1.6 LSB DNL with one-time foreground calibration at 400 MHz sampling frequency while just consuming 726 μW power, which corresponds to 18.45 fJ/Conv. FoM.

关键词： Delays Calibration Inverters Capacitors Linearity Tin

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Why botnets work: Distributed brute-force attacks need no synchronization

Why botnets work: Distributed brute-force attacks need no sy...

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作者： Salamatian, Salman Huleihel, Wasim Beirami, Ahmad Cohen, Asaf Medard, Muriel Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering-Systems Tel-Aviv University Tel-Aviv6997801 Israel EA Digital Platform-Data and AI Electronic Arts Redwood CityCA94065 United States Department of Electrical Engineering Ben-Gurion University of the Negev Beersheba8410501 Israel

In September 2017, the McAfee Labs quarterly report estimated that brute-force attacks represent 20% of total network attacks, making them the most prevalent type of attack ex-aequo with browser-based vulnerabilities. These attacks have sometimes catastrophic consequences, and understanding their fundamental limits may play an important role in the risk assessment of password-secured systems and in the design of better security protocols. While some solutions exist to prevent online brute-force attacks that arise from one single IP address, attacks performed by botnets are more challenging. In this paper, we analyze these distributed attacks by using a simplified model. Our aim is to understand the impact of distribution and asynchronization on the overall computational effort necessary to breach a system. Our result is based on guesswork, a measure of the number of queries (guesses) before a correct sequence, such as a password, is found in an optimal attack. Guesswork is a direct surrogate for time and computational effort of guessing a sequence from a set of sequences with the associated likelihoods. We model the lack of synchronization by a worst-case optimization in which the queries made by multiple adversarial agents are received in the worst possible order for the adversary, resulting in a min-max formulation. We show that, even without synchronization, and for sequences of growing length, the asymptotic optimal performance is achievable by using randomized guesses drawn from an appropriate distribution. Therefore, randomization is key for the distributed asynchronous attacks. In other words, asynchronous guessers can asymptotically perform brute-force attacks as efficiently as synchronized guessers. © 2019 IEEE.

关键词： Botnet

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Optical Interconnects using Single-Mode and Multi-Mode VCSEL and Multi-Mode Fiber

Optical Interconnects using Single-Mode and Multi-Mode VCSEL...

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Optical Fiber Communication (OFC) Conference

作者： N. N. Ledentsov V. A. Shchukin V. P. Kalosha N. Ledentsov L. Chorchos J. P. Turkiewicz U. Hecht P. Kurth F. Gerfers J. Lavrencik S. Varughese S. E. Ralph VI Systems GmbH Berlin Germany Warsaw University of Technology Warsaw Poland Mixed Signal Circuit Design Technische Universität Berlin Berlin Germany Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Single mode (SM) VCSELs, produced in industrial 4” technology, are suitable for 100Gb/s PAM2 and >160Gb/s PAM4 data transmission. >107Gb/s transmission over 1km of multimode (MM) fiber at 850nm and 910nm is rea... 详细信息

ISBN: (数字)9781943580712

ISBN: (纸本)9781728167626

关键词： Bragg reflectors Multimode fibers Optical amplifiers Optical feedback Optical interconnects Vertical cavity surface emitting lasers

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Visualizing group dynamics based on multiparty meeting understanding

Visualizing group dynamics based on multiparty meeting under...

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2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2018

作者： Zhang, Ni Zhang, Tongtao Bhattacharya, Indrani Ji, Heng Radke, Richard J. Department of Computer Science United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States

ISBN: (纸本)9781948087858

Group discussions are usually aimed at sharing opinions, reaching consensus and making good decisions based on group knowledge. During a discussion, participants might adjust their own opinions as well as tune their attitudes towards others’ opinions, based on the unfolding interactions. In this paper, we demonstrate a framework to visualize such dynamics;at each instant of a conversation, the participants’ opinions and potential influence on their counterparts is easily visualized. We use multi-party meeting opinion mining based on bipartite graphs to extract opinions and calculate mutual influential factors, using the Lunar Survival Task as a study case. © 2018 Association for Computational Linguistics.

关键词： Sentiment analysis

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Entropy Measurement of a Strongly Coupled Quantum Dot

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Physical Review Letters 2022年第22期129卷 227702-227702页

作者： Timothy Child Owen Sheekey Silvia Lüscher Saeed Fallahi Geoffrey C. Gardner Michael Manfra Andrew Mitchell Eran Sela Yaakov Kleeorin Yigal Meir Joshua Folk Stewart Blusson Quantum Matter Institute University of British Columbia Vancouver British Columbia V6T1Z4 Canada Department of Physics and Astronomy University of British Columbia Vancouver British Columbia V6T1Z1 Canada Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA Birck Nanotechnology Center Purdue University West Lafayette Indiana 47907 USA School of Materials Engineering Purdue University West Lafayette Indiana 47907 USA Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA School of Physics University College Dublin Belfield Dublin 4 Ireland Centre for Quantum Engineering Science and Technology University College Dublin Dublin 4 Ireland Raymond and Beverly Sackler School of Physics and Astronomy Tel-Aviv University IL-69978 Tel Aviv Israel Center for the Physics of Evolving Systems University of Chicago Chicago Illinois 60637 USA Department of Physics Ben-Gurion University of the Negev Beer Sheva 84105 Israel The Ilse Katz Institute for Nanoscale Science and Technology Ben-Gurion University of the Negev Beer Sheva 84105 Israel

The spin 1/2 entropy of electrons trapped in a quantum dot has previously been measured with great accuracy, but the protocol used for that measurement is valid only within a restrictive set of conditions. Here, we demonstrate a novel entropy measurement protocol that is universal for arbitrary mesoscopic circuits and apply this new approach to measure the entropy of a quantum dot hybridized with a reservoir. The experimental results match closely to numerical renormalization group (NRG) calculations for small and intermediate coupling. For the largest couplings investigated in this Letter, NRG calculations predict a suppression of spin entropy at the charge transition due to the formation of a Kondo singlet, but that suppression is not observed in the experiment.

关键词： Entropy Kondo effect Mesoscopics Nanostructures Quantum dots

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MammoFL: Mammographic Breast Density Estimation using Federated Learning

arXiv

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arXiv 2022年

作者： Muthukrishnan, Ramya Heyler, Angelina Katti, Keshava Pati, Sarthak Mankowski, Walter Alahari, Aprupa Sanborn, Michael Conant, Emily F. Scott, Christopher Winham, Stacey Vachon, Celine Chaudhari, Pratik Kontos, Despina Bakas, Spyridon Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Massachusetts Institute of Technology CambridgeMA United States Research and Development GSK CambridgeMA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine Indiana IN United States Research Center for Federated Learning in Medicine Indiana University School of Medicine Indiana IN United States University of Pennsylvania PhiladelphiaPA United States Department of Pathology & Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Health Sciences Research Mayo Clinic RochesterMN United States

In this study, we automate quantitative mammographic breast density estimation with neural networks and show that this tool is a strong use case for federated learning on multi-institutional datasets. Our dataset included bilateral CC-view and MLO-view mammographic images from two separate institutions. Two U-Nets were separately trained on algorithm-generated labels to perform segmentation of the breast and dense tissue from these images and subsequently calculate breast percent density (PD). The networks were trained with federated learning and compared to three non-federated baselines, one trained on each single-institution dataset and one trained on the aggregated multi-institution dataset. We demonstrate that training on multi-institution datasets is critical to algorithm generalizability. We further show that federated learning on multi-institutional datasets improves model generalization to unseen data at nearly the same level as centralized training on multi-institutional datasets, indicating that federated learning can be applied to our method to improve algorithm generalizability while maintaining patient privacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Image segmentation

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FAIR for AI: An interdisciplinary and international community building perspective

arXiv

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arXiv 2022年

作者： Huerta, E.A. Blaiszik, Ben Brinson, L. Catherine Bouchard, Kristofer E. Diaz, Daniel Doglioni, Caterina Duarte, Javier M. Emani, Murali Foster, Ian Fox, Geoffrey Harris, Philip Heinrich, Lukas Jha, Shantenu Katz, Daniel S. Kindratenko, Volodymyr Kirkpatrick, Christine R. Lassila-Perini, Kati Madduri, Ravi K. Neubauer, Mark S. Psomopoulos, Fotis E. Roy, Avik Rübel, Oliver Zhao, Zhizhen Zhu, Ruike Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Globus University of Chicago ChicagoIL60637 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Biological Systems & Engineering Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Helen Wills Neuroscience Institute University of California Berkeley BerkeleyCA94720 United States Department of Physics University of California La Jolla San DiegoCA92093 United States Lund University Department of Physics Box 118 Lund221 00 Sweden School of Physics & Astronomy The University of Manchester ManchesterM13 9PL United Kingdom Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States Biocomplexity Institute Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Technical University Munich Arcisstraße 21 München80333 Germany Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States Electrical and Computer Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States San Diego Supercomputer Center University of California La Jolla San DiegoCA

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been reinterpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Information management

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Multi-model Short-term Prediction Schema for mHealth Empowering Asthma Self-management

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Electronic Notes in Theoretical computer Science 2019年 343卷 3-17页

作者： Otilia Kocsis Aris Lalos Gerasimos Arvanitis Konstantinos Moustakas Department of Electrical and Computer Engineering University of Patras Patras Greece Industrial Systems Institute ATHENA Research and Innovation Center Platani-Patras 26504 Greece

Ambient intelligence and machine learning techniques are widely proposed by various eHealth and mHealth applications for home-care and self-management of various chronic health conditions. Their adoption for self-management of asthma, a multifactorial chronic disease, requires evaluation and validation in a real-life setups along with optimization at patient level to personalize predictions with respect to asthma control status and exacerbation risk. The current work proposes a novel short-term prediction approach for asthma control status, considering training of multiple classification models for each monitored parameters along with necessary pre-processing methods to enhance robustness and efficiency. The machine learning algorithms considered in this study are the Support Vector Machines, the Random Forests, AdaBoost and Bayesian Network. The Random Forests and Support Vector Machines classifiers demonstrated overall superior performance for the case studies (models) considered.

关键词： asthma control personalized self-management short-term prediction machine learning algorithms decision support system mHealth

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