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arXiv 2018年

作者： Panousis, Konstantinos P. Chatzis, Sotirios Theodoridis, Sergios Dept. of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Dept. of Electrical Eng. Computer Eng. and Informatics Cyprus University of Technology Limassol Cyprus Chinese University of Hong Kong Shenzen China

The aim of this work is to enable inference of deep networks that retain high accuracy for the least possible model complexity, with the latter deduced from the data during inference. To this end, we revisit deep networks that comprise competing linear units, as opposed to nonlinear units that do not entail any form of (local) competition. In this context, our main technical innovation consists in an inferential setup that leverages solid arguments from Bayesian nonparametrics. We infer both the needed set of connections or locally competing sets of units, as well as the required floatingpoint precision for storing the network parameters. Specifically, we introduce auxiliary discrete latent variables representing which initial network components are actually needed for modeling the data at hand, and perform Bayesian inference over them by imposing appropriate stick-breaking priors. As we experimentally show using benchmark datasets, our approach yields networks with less computational footprint than the state-of-the-art, and with no compromises in predictive accuracy. Copyright © 2018, The Authors. All rights reserved.

关键词： Bayesian networks

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Solving dependency quantified boolean formulas using quantifier localization

arXiv

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arXiv 2019年

作者： Ge-Ernst, Aile Scholl, Christoph Síč, Juraj Wimmer, Ralf Department of Computer Science Albert-Ludwigs-Universität Freiburg Freiburg im Breisgau Germany Faculty of Informatics Masaryk University Brno Czech Republic Faculty of Information Technology Brno University of Technology Brno Czech Republic Concept Engineering GmbH Freiburg im Breisgau Germany

Dependency quantified Boolean formulas (DQBFs) are a powerful formalism, which subsumes quantified Boolean formulas (QBFs) and allows an explicit specification of dependencies of existential variables on universal variables. Driven by the needs of various applications which can be encoded by DQBFs in a natural, compact, and elegant way, research on DQBF solving has emerged in the past few years. However, research focused on closed DQBFs in prenex form (where all quantifiers are placed in front of a propositional formula), while non-prenex DQBFs have almost not been studied in the literature. In this paper, we provide a formal definition for syntax and semantics of non-closed non-prenex DQBFs and prove useful properties enabling quantifier localization. Moreover, we make use of our theory by integrating quantifier localization into a state-of-the-art DQBF solver. Experiments with prenex DQBF benchmarks, including all instances from the QBFEVAL’18–’20 competitions, clearly show that quantifier localization pays off in this context. Copyright © 2019, The Authors. All rights reserved.

关键词： Boolean functions

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Robustness of DNA Strand Displacement Systems⋆⁎

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IFAC-PapersOnLine 2018年第33期51卷 32-37页

作者： Nakakuki, Takashi Imura, Jun-ichi Kawamata, Ibuki Murata, Satoshi Department of Systems Design and Informatics School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu IizukaFukuoka820-8502 Japan Department of Mechanical and Environmental Informatics Tokyo Institute of Technology 2-12-1-W8-1 O-okayama Meguro-ku Tokyo152-8552 Japan Department of Robotics Graduate School of Engineering Tohoku University 6-6-1 Aoba Aramaki-Aza Aoba-Ward SendaiMiyagi980-8579 Japan

How to construct a reliable deoxyribonucleic acid (DNA) circuit is one of the most important issues in the field of molecular programming and computing. Such a circuit frequently suffers from various kinds of unintended binding reactions on account of a lower specificity of the molecular interaction emerging from base complementarity between DNA strands, which results in low reliability of the molecular circuit. In this study, we propose a method to quantitatively evaluate the robustness of a DNA circuit created by DNA strand displacement reactions. The highlight of our method is representing the circuit system contaminated by unintended reactions by a standard form of a disturbed nonlinear system and evaluating the robustness with the L2 gain by solving the Hamilton–Jacobi inequality. © 2018

关键词： Nucleic acids Differential equations DNA Electric network analysis Large scale systems Nonlinear equations Substitution reactions Binding reactions Circuit systems Dna strand displacements DNA strands Molecular circuits Molecular programming Nonlinear circuit Uncertainty

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Preface

Smart Innovation, Systems and Technologies

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Smart Innovation, Systems and Technologies 2020年 179卷 ix-x页

作者： Kountchev, Roumen Patnaik, Srikanta Shi, Junsheng Favorskaya, Margarita N. Technical University of Sofia Sofia Bulgaria Department of Electro-Optical Engineering Yunnan Normal University Yunnan China Department of Computer Science and Engineering SOA University BhubaneswarOdisha India Informatics and Computer Techniques Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia

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Predicting Community Engagement on Twitter on Environmental Health Hazards

Predicting Community Engagement on Twitter on Environmental ...

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International Conference on Semantic Computing

作者： Adel Alshehri Wainella Isaacs Aseel Addawood Maya Trotz Sriram Chellappan King Abdulaziz City for Science and Technology Riyadh Saudi Arabia Dept. of Civil and Environmental Engineering University of South Florida Tampa Florida Illinois Informatics Institute University of Illinois at Urbana Champaign Champaign Illinois Dept. of Computer Science and Engineering University of South Florida Tampa Florida

In this empirical study, a framework was developed for binary and multi-class classification of Twitter data. We first introduce a manually built gold standard dataset of 4000 tweets related to the environmental health hazards in Barbados for the period 2014 - 2018. Then, the binary classification was used to categorize each tweet as relevant or irrelevant. Next, the multiclass classification was then used to further classify relevant tweets into four types of community engagement: reporting information, expressing negative engagement, expressing positive engagement, and asking for information. Results indicate that (combination of TF-IDF, psychometric, linguistic, sentiment and Twitter-specific features) using a Random Forest algorithm is the best feature for detecting and predicting binary classification with (87% F1 score). For multi-class classification, TF-IDF using Decision Tree algorithm was the best with (74% F1 score).

关键词： Twitter Feature extraction Diseases Hazards Support vector machines Data mining

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ClsGAN: Selective attribute editing based on classification adversarial network

arXiv

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arXiv 2019年

作者： Liu, Ying Fan, Heng Ni, Fuchuan Xiang, Jinhai College of Informatics Huazhong Agricultural University Wuhan430070 China Hubei Engineering Technology Research Center of Agricultural Big Data Huazhong Agricultural University Wuhan430070 China Department of Computer & Information Sciences Temple University Philadelphia19122 United States

Attribution editing has shown remarking progress by the incorporating of encoder-decoder structure and generative adversarial network. However, there are still some challenges in the quality and attribute transformation of the generated images. Encoder-decoder structure leads to blurring of images and the skip-connection of encoder-decoder structure weakens the attribute transfer ability. To address these limitations, we propose a classification adversarial model(Cls-GAN) that can balance between attribute transfer and generated photo-realistic images. Considering that the transfer images are affected by the original attribute using skip-connection, we introduce upper convolution residual network(Tr-resnet) to selectively extract information from the source image and target label. Specially, we apply to the attribute classification adversarial network to learn about the defects of attribute transfer images so as to guide the generator. Finally, to meet the requirement of multimodal and improve reconstruction effect, we build two encoders including the content and style network, and select a attribute label approximation between source label and the output of style network. Experiments that operates at the dataset of CelebA show that images are superiority against the existing state-of-the-art models in image quality and transfer accuracy. Experiments on wikiart and seasonal datasets demonstrate that ClsGAN can effectively implement styel transfer. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Electromagnetic aspects of practical approaches to realization of intelligent metasurfaces

arXiv

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arXiv 2019年

作者： Liu, F. Tsilipakos, O. Wang, X. Pitilakis, A. Tasolamprou, A.C. Mirmoosa, M.S. Kwon, D.-H. Kossifos, K. Georgiou, J. Kafesaki, M. Soukoulis, C.M. Tretyakov, S.A. Aalto University Department of Electronics and Nanoengineering Maarintie 8 Espoo02150 Finland Foundation for Research and Technology Hellas Heraklion71110 Greece Aristotle University of Thessaloniki Dept. of Electrical and Computer Engineering Thessaloniki Greece University of Massachusetts Amherst Dept. of Electrical and Computer Engineering Massachusetts United States University of Cyprus Department of Electrical and Computer Engineering 1 Panepistimiou Avenue Aglanzia Nicosia2109 Singapore University of Crete Department of Materials Science and Technology Heraklion Crete71003 Greece Ames Laboratory Dept. of Physics and Astronomy Iowa State University AmesIW50011 United States

We thoroughly investigate the electromagnetic response of intelligent functional metasurfaces. We study two distinct designs operating at different frequency regimes, namely, a switch-fabric-based design for GHz frequencies and a graphene-based approach for THz band, and discuss the respective practical design considerations. The performance for tunable perfect absorption applications is assessed in both cases. Copyright © 2019, The Authors. All rights reserved.

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A cyber-enabled mission-critical system for post-flood response: Exploiting TV white space as network backhaul links

A cyber-enabled mission-critical system for post-flood respo...

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作者： Rahman, M.D. Arafatur Asyhari, A. Taufiq Azad, Saiful Hasan, M.D. Munirul Munaiseche, Cindy P.C. Krisnanda, Made Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan26300 Malaysia IBM Center of Excellence and Earth Resources and Sustainability Center Universiti Malaysia Pahang Kuantan26300 Malaysia School of Computing Electronics and Mathematics Coventry University CoventryCV1 5FB United Kingdom Telkom University Jalan Telekomunikasi Terusan Buah Batu Bandung40257 Indonesia Faculty of Engineering Department of Informatics Universitas Negeri Manado Tondano95618 Indonesia Faculty of Engineering Department of Information and Communication Technology Education Universitas Negeri Manado Tondano95618 Indonesia

A crucial problem in post-flood recovery actions is the ability to rapidly establish communication and collaboration among rescuers to conduct timely and effective search and rescue (SAR) mission given disrupted telecommunication infrastructure to support the service. Aimed at providing such proximity service (ProSe) for mission-critical data exchange in the post-flood environment, the majority of existing solutions rely heavily upon ad-hoc networking approaches, which suffer from restricted communication range and the limited scope of interaction. As an effort to broaden the ProSe coverage and expand integrated global-local information exchange in the post-flood SAR activities, this paper proposes a novel network architecture in the form of a cyber-enabled mission-critical system (CEMCS) for acquiring and communicating post-flood emergency data by exploiting TV white space spectrum as network backhaul links. The primary method of developing the proposed system builds upon a layered architecture of wireless local, regional and wide-area communications, and incorporates collaborative network components among these layers. The desirable functionalities of CEMCS are showcased through formulation and the development of an efficient global search strategy exploiting a wide range of collaboration among network agents. The simulation results demonstrate the capability of CEMCS to provide ProSe in the post-flood scenarios as reflected by reliable network performance (e.g., packet delivery ratio nearing 80%–90%) and the optimality of efficient search algorithm. © 2017 Oxford university Press. All rights reserved.

关键词： Cyber Physical System

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A faster v-order string comparison algorithm 22

A faster v-order string comparison algorithm

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22nd Prague Stringology Conference, PSC 2018

作者： Alatabbi, Ali Daykin, Jacqueline W. Mhaskar, Neerja Rahman, M. Sohel Smyth, William F. Department of Informatics King's College London London United Kingdom Department of Computer Science Aberystwyth University Department of Information Science Stellenbosch University South Africa Algorithms Research Group Department of Computing and Software McMaster University Canada Department of Computer Science and Engineering Bangladesh University of Engineering and Science Bangladesh School of Engineering and Information Technology Murdoch University WA Australia

ISBN: (纸本)9788001064849

V -order is a total order on strings that determines an instance of Unique Maximal Factorization Families (UMFFs) [7-10], a generalization of Lyndon words [12]. V -order has also recently been proposed as an alternative to lexicographic order (lexorder) in the computation of suffix arrays and in the suffix-sorting induced by the Burrows-Wheeler Transform (BWT) [11]. The central problem of efficient V -ordering of strings was considered in [2-4, 9, 10], culminating in a remarkably simple, linear time, constant space comparison algorithm [1]. In this paper we improve on this result to achieve significant speed-up in almost all cases of interest. © Proceedings of the Prague Stringology Conference, PSC 2018. All rights reserved.

关键词： Combinatorial mathematics

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Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients

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Nature communications 2023年第1期14卷 2102页

作者： Pei-Chen Tsai Tsung-Hua Lee Kun-Chi Kuo Fang-Yi Su Tsung-Lu Michael Lee Eliana Marostica Tomotaka Ugai Melissa Zhao Mai Chan Lau Juha P Väyrynen Marios Giannakis Yasutoshi Takashima Seyed Mousavi Kahaki Kana Wu Mingyang Song Jeffrey A Meyerhardt Andrew T Chan Jung-Hsien Chiang Jonathan Nowak Shuji Ogino Kun-Hsing Yu Department of Biomedical Informatics Harvard Medical School Boston MA USA. Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan ROC. Department of Computer Science and Information Engineering Southern Taiwan University of Science and Technology Tainan Taiwan ROC. Division of Health Sciences and Technology Harvard-Massachusetts Institute of Technology Boston MA USA. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA USA. Department of Pathology Brigham and Women's Hospital Boston MA USA. Cancer and Translational Medicine Research Unit Medical Research Center Oulu Oulu University Hospital and University of Oulu Oulu Finland. Department of Medicine Dana Farber Cancer Institute Boston MA USA. Department of Nutrition Harvard T.H. Chan School of Public Health Boston MA USA. Department of Medicine Massachusetts General Hospital Boston MA USA. Channing Division of Network Medicine Department of Medicine Brigham and Women's Hospital Boston MA USA. Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan ROC. jchiang@mail.ncku.edu.tw. Broad Institute of MIT and Harvard Cambridge MA USA. Department of Biomedical Informatics Harvard Medical School Boston MA USA. Kun-Hsing_Yu@hms.harvard.edu. Department of Pathology Brigham and Women's Hospital Boston MA USA. Kun-Hsing_Yu@hms.harvard.edu.

Histopathologic assessment is indispensable for diagnosing colorectal cancer (CRC). However, manual evaluation of the diseased tissues under the microscope cannot reliably inform patient prognosis or genomic variations crucial for treatment selections. To address these challenges, we develop the Multi-omics Multi-cohort Assessment (MOMA) platform, an explainable machine learning approach, to systematically identify and interpret the relationship between patients' histologic patterns, multi-omics, and clinical profiles in three large patient cohorts (n = 1888). MOMA successfully predicts the overall survival, disease-free survival (log-rank test P-value<0.05), and copy number alterations of CRC patients. In addition, our approaches identify interpretable pathology patterns predictive of gene expression profiles, microsatellite instability status, and clinically actionable genetic alterations. We show that MOMA models are generalizable to multiple patient populations with different demographic compositions and pathology images collected from distinctive digitization methods. Our machine learning approaches provide clinically actionable predictions that could inform treatments for colorectal cancer patients.

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