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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Anomalously enhanced thermal stability of phosphorene via metal adatom doping: An experimental and first-principles study

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Nano Research 2016年第9期9卷 2687-2695页

作者： Xuewei Feng Vadym V.Kulish Ping Wu xinke Liu Kah-Wee Ang Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Singapore Entropic Interface Group(EIG) Engineering Product DevelopmentSingapore University of Technology and Design(SUTD)8 Somapah RoadSingapore 487372Singapore College of Materials Science and Engineering Shenzhen Engineering Laboratory for Advanced Technology of CeramicsNanshan District Key Lab for Biopolymer and Safety EvaluationShenzhen University3688 Nanhai AvenueShenzhen 518060China

Atomically thin black phosphorus, also known as phosphorene, is an emerging two-dimensional (2D) material, which has attracted increasing attention due to its unique electronic and optoelectronic properties. However, the reduced thermal stability of phosphorene limits its suitability for high-temperature fabrication processes, which could be detrimental for the performance of phosphorenebased devices. Here, we investigate the impact of doping by Al and Hf transition metal adatoms on the thermal stability of phosphorene. The formation of Al–P covalent bonds was found to significantly improve the thermal coefficients of the Ag1, B2g, and Ag2phonon modes to 0.00044, 0.00081, and 0.00012 cm–1·°C–1, respectively, which are two orders of magnitude lower than those observed for pristine P–P bonds (~0.01 cm–1·°C–1). First-principles calculations within the density functional theory framework reveal that the observed thermal stability enhancement in the Al-doped material reflects a significantly higher Al binding energy, due to the stronger Al–P bonds compared to the weak van der Waals interactions between adjacent P atoms in the undoped material. The present work thus paves the way towards phosphorene materials with improved structural stability, which could be promising candidates for potential nanoelectronic and optoelectronic applications. [Figure not available: see fulltext.] © 2016, Tsinghua University Press and Springer-Verlag Berlin Heidelberg.

关键词： phosphorene black phosphorus adatom doping transition metals thermal stability

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Multifrequency superscattering from subwavelength hyperbolic structures

arXiv

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arXiv 2018年

作者： Qian, Chao Lin, Xiao Yang, Yi Gao, Fei Shen, Yichen Lopez, Josue Kaminer, Ido Zhang, Baile Li, Erping Soljačić, Marin Chen, Hongsheng Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Electromagnetics Academy at Zhejiang University Zhejiang University Hangzhou310027 China ZJU-UIUC Institute College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Centre for Disruptive Photonic Technologies NTU Singapore637371 Singapore

Superscattering, i.e., a phenomenon of the scattering cross section from a subwavelength object exceeding the single-channel limit, has important prospects in enhanced sensing/spectroscopy, solar cells, and biomedical imaging. Superscattering can be typically constructed only at a single frequency regime, and depends critically on the inescapable material losses. Under such realistic conditions, superscattering has not been predicted nor observed to exist simultaneously at multiple frequency regimes. Here we introduce multifrequency superscattering in a subwavelength hyperbolic structure, which can be made from artificial metamaterials or from naturally-existing materials, such as hexagonal boron nitride (BN), and show the advantage of such hyperbolic materials for reducing structural complexity. The underlying mechanism is revealed to be the multimode resonances at multiple frequency regimes as appear in BN due to the peculiar dispersion of phonon-polaritons. Importantly, the multifrequency superscattering has a high tolerance to material losses and some structural variations, bringing the concept of multifrequency superscattering closer to useful and realistic conditions. Copyright © 2018, The Authors. All rights reserved.

关键词： III-V semiconductors

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A simple and novel method for the quantitative detection of 5-hydroxymethylcytosine using carbon nanotube field- effect transistors

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Nano Research 2016年第6期9卷 1701-1708页

作者： Fang Yuan Yanyan Deng Wenyu Zhou Min Zhang Zigang Li School of Chemical Biology & Biotechnology Peking University Shenzhen 518055 China School of Electronic and Computer Engineering Peking University Shenzhen 518055 China Shenzhen Thin Film Transistor and Advanced Display Lab Shenzhen 518055 China Shenzhen Second People's Hospital Shenzhen 518035 China

5-hydroxymethylcytosine （5-hmC） is an important epigenetic derivative of cytosine and quantitative detection of 5-hmC could be used as a reliable biomarker for a variety of human diseases. Current technologies used in 5-hmC detection are complicated and time/cost inefficient. In this work, we report the first application of antibody-functionalized carbon nanotube field-effect transistors （CNT-FETs） in quantitative detection of 5-hmC from mouse tissues. This method achieves facile and ultra-sensitive 5-hmC detection based on electrical performance device and avoids complicated processing for DNA samples. The 5-hmC content percentages of normal mouse cerebrum, cerebellum, spleen, lung, liver, and heart samples presented in the genomic DNA were measured as 0.653, 0.573, 0.002, 0.020, 0.076, and 0.009, respectively, which is consistent with previous reports. This technology could be developed into fadle routine 5-hmC monitoring devices for clinic human disease diagnoses.

关键词： 5-hydroxymethylcytosine carbon nanotubefield-effect transistors biosensor

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X-Ray Beam Induced Voltage: A Novel Technique for Electrical Nanocharacterization of Solar Cells

X-Ray Beam Induced Voltage: A Novel Technique for Electrical...

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2017 IEEE 44th Photovoltaic Specialist Conference (PVSC)

作者： Michael E. Stuckelberger Tara Nietzold Bradley M. West Barry Lai Jörg M. Maser Volker Rose Mariana I. Bertoni Defect Lab Computer and Energy Engineering Arizona State University Tempe AZ USA Advanced Photon Source Argonne National Laboratory Argonne IL USA Center for Nanoscale Materials Argonne National Laboratory Argonne IL USA

The efficiency of solar cells with a polycrystalline absorber is typically limited by grain boundaries. Many questions need to be answered such as: which roles do elemental and structural inhomogeneities play with respect to electrical performance? By which mechanisms does degradation occur and how can it be mitigated? The answers to these questions lie at the nanoscale. Ideally, we would measure performance, composition, and structure in-situ and operando all at once. Correlative, synchrotron-based X-ray microscopy offers a step in this direction. Here, we present a novel technique, X-ray beam induced voltage (XBIV) measurements, which complements the set of X-ray microscopy techniques. Combining the penetration depth of visible-light microscopy with the spatial resolution of electron-beam methods, XBIV measurements shine light on recombination and absorber-layer bandgap variations at nanoscale resolution. We give experimental details and discuss the use of lock-in amplification based on first applications of XBIV measurements to CuIn x Ga 1-x Se 2 solar cells.

关键词： Current measurement Voltage measurement Photovoltaic cells Photonics Measurement by laser beam Microscopy Atmospheric measurements

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An environment for sustainable research software in Germany and beyond: Current state, open challenges, and call for action

arXiv

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arXiv 2020年

作者： Anzt, Hartwig Bach, Felix Druskat, Stephan Löffler, Frank Loewe, Axel Renard, Bernhard Y. Seemann, Gunnar Struck, Alexander Achhammer, Elke Aggarwal, Piush Appel, Franziska Bader, Michael Brusch, Lutz Busse, Christian Chourdakis, Gerasimos Dabrowski, Piotr W. Ebert, Peter Flemisch, Bernd Friedl, Sven Fritzsch, Bernadette Funk, Maximilian D. Gast, Volker Goth, Florian Grad, Jean-Noël Hermann, Sibylle Hohmann, Florian Janosch, Stephan Kutra, Dominik Linxweiler, Jan Muth, Thilo Peters-Kottig, Wolfgang Rack, Fabian Raters, Fabian H.C. Rave, Stephan Reina, Guido Reißig, Malte Ropinski, Timo Schaarschmidt, Joerg Seibold, Heidi Thiele, Jan P. Uekerman, Benjamin Unger, Stefan Weeber, Rudolf Germany Innovative Computing Lab University of Tennessee KnoxvilleTN United States Department of English Studies Friedrich Schiller University Jena Germany Germany Department of Computer Science Humboldt-Universität zu Berlin Germany Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany Michael Stifel Center Jena Germany Center for Computation and Technology Louisiana State University Baton Rouge United States Germany Robert Koch Institute Berlin Germany Hasso Plattner Institute Faculty of Digital Engineering University of Potsdam Germany Institute for Experimental Cardiovascular Medicine University Heart Centre Freiburg Bad Krozingen Germany Faculty of Medicine University of Freiburg Freiburg Germany Matters of Activity. Image Space Material. Cluster of Excellence at Humboldt-Universität zu Berlin Germany Technische Universität München Germany Language Technology Lab Universität Duisburg-Essen Germany Germany Department of Informatics Technical University of Munich Germany Technische Universität Dresden Germany Deutsches Krebsforschungszentrum Heidelberg Germany Scientific Computing in Computer Science Technical University Munich Germany School of Computing Communication and Business Hochschule für Technik und Wirtschaft Berlin Germany Center for Bioinformatics Saar Saarland Informatics Campus Germany Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Germany Berlin Institute of Health Germany Scientific Computing Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Bremerhaven Germany Max-Planck-Gesellschaft e.V. Institut für Theoretische Physik und Astrophysik Universität Würzburg Germany Institut für Computerphysik University of Stuttgart Germany University Library University of Stuttgart Germany Universität Bremen Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany European Molecular Biology Laboratory Heidelb

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin. Copyright © 2020, The Authors. All rights reserved.

关键词： Software design

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A community-powered search of machine learning strategy space to find NMR property prediction models

arXiv

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arXiv 2020年

作者： Bratholm, Lars A. Gerrard, Will Anderson, Brandon Bai, Shaojie Choi, Sunghwan Dang, Lam Hanchar, Pavel Howard, Addison Huard, Guillaume Kim, Sanghoon Kolter, Zico Kondor, Risi Kornbluth, Mordechai Lee, Youhan Lee, Youngsoo Mailoa, Jonathan P. Nguyen, Thanh Tu Popovic, Milos Rakocevic, Goran Reade, Walter Song, Wonho Stojanovic, Luka Thiede, Erik H. Tijanic, Nebojsa Torrubia, Andres Willmott, Devin Butts, Craig P. Glowacki, David R. Participants, Kaggle School of Chemistry University of Bristol Cantock’s Close BristolBS8 1TS United Kingdom School of Mathematics University of Bristol Fry Building Woodland Road BristolBS1 1UG United Kingdom Dept. of Computer Science University of Bristol Merchant Venturer’s Building BristolBS8 1UB United Kingdom Intangible Realities Laboratory University of Bristol Cantock’s Close BristolBS8 1TS United Kingdom Bosch Center for Artificial Intelligence PittsburghPA15222 United States Carnegie Mellon University PittsburghPA15213 United States National Institute of Supercomputing and Network Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of BNP Paribas Cardif Fyusion Inc. Kaggle Google Inc. Mountain View United States Ebay Korea 34F Gangnam Finance Center 152 Teheran Ro Gangnam Gu Seoul Korea Republic of Department of Computer Science University of Chicago 5730 S Ellis Ave ChicagoIL60637 United States Department of Statistics University of Chicago 5747 S Ellis Ave ChicagoIL60637 United States Center for Computational Mathematics Flatiron Institute 162 5th Ave. New YorkNY10010 United States Bosch Research and Technology Center CambridgeMA02139 United States Department of Chemical and Biomolecular Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of 111 Daedeok-daero 989beon-gil Yuseong-gu Daejeon Korea Republic of MINDS AND COMPANY 2621 Nambusunhwan-ro Gangnam-gu Seoul06267 Korea Republic of Totient Inc Sindjeliceva 9 Belgrade11000 Serbia KAIST Web Security & Privacy Lab 291 Daehak-ro Yuseong-guDaejeon34141 Korea Republic of *** Alicante Spain

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published ‘in-house’ efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties. Copyright © 2020, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance

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Correction to: Asymmetric RF MEMS resonant switch for high speed switching applications

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Microsystem Technologies 2018年第12期24卷 4737-4737页

作者： Sara Rizehbandi Midia Reshadi Masoud Baghelani Ahmad Khademzadeh Department of Electrical Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Microsystems and Advanced Instrumentation Lab. (MAILab.) Engineering School Ilam University Ilam Iran Research Institute for Information and Communication Technology Tehran Iran

Unfortunately, the affiliation details of the authors have been published incorrectly. The correct details are given below.

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Toward multidiversified ensemble clustering of high-dimensional data: From subspaces to metrics and beyond

arXiv

引用

arXiv 2017年

作者： Huang, Dong Wang, Chang-Dong Lai, Jian-Huang Kwoh, Chee-Keong The College of Mathematics and Informatics South China Agricultural University Guangzhou China Pazhou Lab Guangzhou China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China The School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

The rapid emergence of high-dimensional data in various areas has brought new challenges to current ensemble clustering research. To deal with the curse of dimensionality, recently considerable efforts in ensemble clustering have been made by means of different subspace-based techniques. However, besides the emphasis on subspaces, rather limited attention has been paid to the potential diversity in similarity/dissimilarity metrics. It remains a surprisingly open problem in ensemble clustering how to create and aggregate a large population of diversified metrics, and furthermore, how to jointly investigate the multi-level diversity in the large populations of metrics, subspaces, and clusters in a unified framework. To tackle this problem, this paper proposes a novel multidiversified ensemble clustering approach. In particular, we create a large number of diversified metrics by randomizing a scaled exponential similarity kernel, which are then coupled with random subspaces to form a large set of metric-subspace pairs. Based on the similarity matrices derived from these metric-subspace pairs, an ensemble of diversified base clusterings can thereby be constructed. Further, an entropy-based criterion is utilized to explore the cluster-wise diversity in ensembles, based on which three specific ensemble clustering algorithms are presented by incorporating three types of consensus functions. Extensive experiments are conducted on 30 high-dimensional datasets, including 18 cancer gene expression datasets and 12 image/speech datasets, which demonstrate the superiority of our algorithms over the state-of-the-art. The source code is available at https://***/huangdonghere/MDEC. Copyright © 2017, The Authors. All rights reserved.

关键词： Cluster analysis

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Correlation-based detection of TCM signals for cognitive radios

Correlation-based detection of TCM signals for cognitive rad...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Reza Soosahabi Mort Naraghi-Pour Nasim Nasirian Magdy Bayoumi Distributed Computing & Communications Lab Infolink-USA Inc. Lafayette LA70508 United States of America Division of Electrical & Computer Engineering Louisiana State University Baton Rouge LA70803 United States of America The Center for Advanced Computer Studies The University of Louisiana Lafayette LA70503 United States of America

ISBN: (纸本)9781509041183

In this work, the inter-dependency of TCM signals is studied. Using this inter-dependency, correlation-based detectors are proposed for spectrum sensing of TCM signals in white Gaussian noise. In particular, a constant false alarm rate (CFAR) detector is presented and its performance is evaluated using simulations. We also describe an application of our detector for the classification of uncoded modulation systems vs. their TCM counterparts and present numerical results on their performance.

关键词： Spectrum Sensing Cognitive Radio Trellis Coded Modulation Correlation Signal Detection trellis coded modulation Cognitive radio signal acquisition detector Gaussian white noise constant false alarm rate signals

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