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Strongly Enhanced Tunneling at Total Charge Neutrality in Double-Bilayer Graphene-WSe2 Heterostructures

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Physical Review Letters 2018年第17期120卷 177702-177702页

作者： G. William Burg Nitin Prasad Kyounghwan Kim Takashi Taniguchi Kenji Watanabe Allan H. MacDonald Leonard F. Register Emanuel Tutuc Microelectronics Research Center Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78758 USA National Institute for Materials Science 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan Department of Physics The University of Texas at Austin Austin Texas 78712 USA

We report the experimental observation of strongly enhanced tunneling between graphene bilayers through a WSe2 barrier when the graphene bilayers are populated with carriers of opposite polarity and equal density. The enhanced tunneling increases sharply in strength with decreasing temperature, and the tunneling current exhibits a vertical onset as a function of interlayer voltage at a temperature of 1.5 K. The strongly enhanced tunneling at overall neutrality departs markedly from single-particle model calculations that otherwise match the measured tunneling current-voltage characteristics well, and suggests the emergence of a many-body state with condensed interbilayer excitons when electrons and holes of equal densities populate the two layers.

关键词： Excitons Pairing mechanisms 2-dimensional systems Graphene Tunnel junctions

The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

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arXiv 2023年

作者： Moawad, Ahmed W. Janas, Anastasia Baid, Ujjwal Ramakrishnan, Divya Saluja, Rachit Ashraf, Nader Jekel, Leon Amiruddin, Raisa Adewole, Maruf Albrecht, Jake Anazodo, Udunna Aneja, Sanjay Anwar, Syed Muhammad Bergquist, Timothy Calabrese, Evan Chiang, Veronica Chung, Verena Conte, Gian Marco Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kofler, Florian Krantchev, Kiril LaBella, Dominic Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Link, Katherine E. Liu, Xinyang Maleki, Nazanin Meier, Zeke Menze, Bjoern H. Moy, Harrison Osenberg, Klara Piraud, Marie Reitman, Zachary Shinohara, Russel Takeshi Tahon, Nourel hoda Nada, Ayman Velichko, Yuri S. Wang, Chunhao Wiestler, Benedikt Wiggins, Walter Shafique, Umber Willms, Klara Avesta, Arman Bousabarah, Khaled Chakrabarty, Satrajit Gennaro, Nicolo Holler, Wolfgang Kaur, Manpreet LaMontagne, Pamela Lin, MingDe Lost, Jan Marcus, Daniel S. Maresca, Ryan Merkaj, Sarah Nada, Ayaman Pedersen, Gabriel Cassinelli von Reppert, Marc Sotiras, Aristeidis Teytelboym, Oleg Tillmans, Niklas Westerhoff, Malte Youssef, Ayda Godfrey, Devon Floyd, Scott Rauschecker, Andreas Villanueva-Meyer, Javier Pflüger, Irada Cho, Jaeyoung Bendszus, Martin Brugnara, Gianluca Cramer, Justin Perez-Carillo, Gloria Guzman J. Johnson, Derek R. Kam, Anthony Kwan, Benjamin Yin Ming Lai, Lillian Lall, Neil U. Memon, Fatima Patro, Satya Narayana Petrovic, Bojan So, Tiffany Y. Thompson, Gerard Wu, Lei Schrickel, E. Brooke Bansal, Anu Barkhof, Frederik Besada, Cristina Chu, Sammy Druzgal, Jason Dusoi, Alexandru Farage, Luciano Feltrin, Fabricio Fong, Amy Fung, Steve H. Gray, R. Ian Ikuta, Ichiro Iv, Michael Postma, Alida A. Mahajan, Amit Joyner, David Krumpelman, Chase Letourneau-Guillon, Laurent Lincoln, Christie M. Maros, Mate E. Miller, Elka Morón, Fanny Nimchinsky, Esther A. Ozsarlak, Ozkan Patel, Uresh Rohatgi, Saurabh Saha, Atin Sayah, Anousheh Schwartz, Eric D. Shih, Robert Shiroishi, Mark Trinity health Mid Atlantic Hosiptals DarbyPA United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States ImagineQuant Children’s Hospital of Philadelphia PhiladelphiaPA United States College of Medicine Alfaisal University Riyadh Saudi Arabia DKFZ Division of Translational Neurooncology The WTZ German Cancer Consortium DKTK Partner Site University Hospital Essen Essen Germany Medical Artificial Intelligence Lab Crestview Radiology Lagos Nigeria Sage Bionetworks SeattleWA United States Montreal Neurological Institute McGill University Montreal Canada lab Crestview Radiology Lagos Nigeria Department of Therapeutic Radiology Yale School of Medicine New HavenCT United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Radiology Mayo Clinic RochesterMN United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Neurosurgery Yale School of Medicine New HavenCT United States Center for Global Health Perelman School of Medicine University of Pennsylvania PA United States Department of Informatics Technical University Munich Germany Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Children’s National Hospital WashingtonDC United States PrecisionFDA U.S. Food and Drug Administration S

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d

关键词： Image segmentation

Modeling and thermal metrology of thermally isolated MEMS electrothermal actuators for strain engineering of 2D materials

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arXiv 2018年

作者： Vutukuru, Mounika Christopher, Jason W. Pollock, Corey Bishop, David J. Swan, Anna K. Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Physics Boston University BostonMA02215 United States Division of Materials Science and Engineering Boston University BostonMA02215 United States Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Physics Boston University BostonMA02215 United States

We present electrothermal microelectromechanical (MEMS) actuators as a practical platform for straining 2D materials. The advantages of the electrothermal actuator is its high output force and displacement for low input voltage, but its drawback is that it is actuated by generating high amounts of heat. It is crucial to mitigate the high temperatures generated during actuation for reliable 2D material strain device implementation. Here, we implement a chevron actuator design that incorporates a thermal isolation stage in order to avoid heating the 2D material from the high temperatures generated during the actuation. By comparing experiment and simulation, we ensure our design does not compromise output force and displacement, while keeping the 2D material strain device stage cool. We also provide a simple analytical model useful for quickly evaluating different thermal isolation stage designs . Copyright © 2018, The Authors. All rights reserved.

关键词： Finite element method

Measurements of the Temperature and E-mode Polarization of the Cosmic Microwave Background from the Full 500-square-degree SPTpol Dataset

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arXiv 2025年

作者： Chou, T.-L. Ade, P.A.R. Anderson, A.J. Austermann, J.E. Balkenhol, L. Beall, J.A. Bender, A.N. Benson, B.A. Bianchini, F. Bleem, L.E. Carlstrom, J.E. Chang, C.L. Chaubal, P. Chiang, H.C. Citron, R. Moran, C. Corbett Crawford, T.M. Crites, A.T. de Haan, T. Dobbs, M.A. Dutcher, D. Everett, W. Gallicchio, J. George, E.M. Gupta, N. Halverson, N.W. Holder, G.P. Holzapfel, W.L. Hrubes, J.D. Huang, N. Hubmayr, J. Irwin, K.D. Knox, L. Lee, A.T. Li, D. Lowitz, A. McMahon, J.J. Montgomery, J. Natoli, T. Nibarger, J.P. Noble, G.I. Novosad, V. Omori, Y. Padin, S. Patil, S. Pryke, C. Quan, W. Reichardt, C.L. Ruhl, J.E. Saliwanchik, B.R. Schaffer, K.K. Sievers, C. Smecher, G. Stark, A.A. Tucker, C. Veach, T. Vieira, J.D. Wang, G. Whitehorn, N. Wu, W.L.K. Yefremenko, V. Zebrowski, J.A. Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Cardiff University CardiffCF10 3XQ United Kingdom Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Department of Physics University of Colorado BoulderCO80309 United States Sorbonne Universit’e CNRS UMR 7095 Institut d’Astrophysique de Paris 98 bis bd Arago Paris75014 France High Energy Physics Division Argonne National Laboratory 9700 S. Cass Avenue ArgonneIL60439 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Department of Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Enrico Fermi Institute University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States School of Physics University of Melbourne ParkvilleVIC3010 Australia Department of Physics McGill University 3600 Rue University MontrealQCH3A 2T8 Canada School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Durban South Africa University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Jet Propulsion Laboratory PasadenaCA91109 United States Department of Physics Cornell University IthacaNY United States Department of Astronomy Cornell University IthacaNY United States Department of Physics University of California BerkeleyCA94720 United States Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Ibaraki Tsukuba305-0801 Japan Canadian Institute for Advanced Research CIFAR Program in Gravity and the Extreme Universe TorontoONM5G 1Z8 Canada Joseph Henry Laboratories of Physics Jadwin Hall Princeton University PrincetonNJ08544

Using the full four-year SPTpol 500 deg2 dataset in both the 95 GHz and 150 GHz frequency bands, we present measurements of the temperature and E-mode polarization of the cosmic microwave background (CMB), as well as the E-mode polarization auto-power spectrum (EE) and temperature-E-mode cross-power spectrum (TE) in the angular multipole range 50 0 = 70.48 ± 2.16 km s−1 Mpc−1 and Ωm = 0.271 ± 0.026, when using only the SPTpol data and a Planck-based prior on the optical depth to reionization. The ΛCDM parameter constraints are consistent across the 95 GHz-only, 150 GHz-only, TE-only, and EE-only data splits. Between the 1000 data splits, the ΛCDM parameter constraints are borderline consistent at the ∼ 2σ level. This consistency improves when including a parameter AL, the degree of lensing of the CMB inferred from the smearing of acoustic peaks. When marginalized over AL, the ΛCDM parameter constraints from SPTpol are consistent with those from Planck. The power spectra presented here are the most sensitive measurements of the lensed CMB damping tail to date for roughly > 1700 in TE and > 2000 in EE. Copyright © 2025, The Authors. All rights reserved.

关键词： Cosmology

Indoor localization at 5GHz using dynamic machine learning approach (DMLA)

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Indoor localization at 5GHz using dynamic machine learning a...

2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Belay, Abebe Yen, Lei Renu, Sakthidasan Lin, Hsin-Piao Jeng, Shiann-Shiun Department of International Graduate Program in Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Department of Electrical Engineering National Dong Hwa University Taipei Taiwan

ISBN: (纸本)9781509048977

In recent years, Wi-Fi based indoor localization using received signal strength (RSS) gets considerable attention. However, RSS based Wi-Fi localization at 2.4GHz is highly susceptible and unstable. We proposed dynamic machine learning approach (DMLA) at 5GHz to effectively localize Wi-Fi users by means of feed-forward neural network algorithm. The simulation result shows that 90% of tested result has less than 0.4m estimation error, and no result shows greater than 0.47m location estimation errors. © 2017 IEEE.

关键词： Feedforward neural networks

Self-Awareness for Autonomous Systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

Tunable Γ−K Valley Populations in Hole-Doped Trilayer WSe2

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Physical Review Letters 2018年第10期120卷 107703-107703页

作者： Hema C. P. Movva Timothy Lovorn Babak Fallahazad Stefano Larentis Kyounghwan Kim Takashi Taniguchi Kenji Watanabe Sanjay K. Banerjee Allan H. MacDonald Emanuel Tutuc Microelectronics Research Center Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78758 USA Department of Physics The University of Texas at Austin Austin Texas 78712 USA National Institute of Materials Science 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan

We present a combined experimental and theoretical study of valley populations in the valence bands of trilayer WSe2. Shubnikov–de Haas oscillations show that trilayer holes populate two distinct subbands associated with the K and Γ valleys, with effective masses 0.5me and 1.2me, respectively; me is the bare electron mass. At a fixed total hole density, an applied transverse electric field transfers holes from Γ orbitals to K orbitals. We are able to explain this behavior in terms of the larger layer polarizability of the K orbital subband.

关键词： Electronic structure Quantum Hall effect Valleytronics 2-dimensional systems Transition metal dichalcogenides Band structure methods Density functional theory Shubnikov-de Haas effect k dot p method

Immunity to scaling in MoS2transistors using edge contacts

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arXiv 2018年

作者： Cheng, Zhihui Price, Katherine Singh, Shreya Noyce, Steven Lin, Yuh-Chen Yu, Yifei Cao, Linyou Franklin, Aaron D. Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Materials Science and Engineering North Carolina State University RaleighNC27695 United States Department of Chemistry Duke University DurhamNC27708 United States

Atomically thin two-dimensional (2D) materials are promising candidates for sub-10 nm transistor channels due to their ultrathin body thickness, which results in strong electrostatic gate control. Properly scaling a transistor technology requires reducing both the channel length (distance from source to drain) and the contact length (distance that source and drain interface with semiconducting channel). Contact length scaling remains an unresolved epidemic for transistor scaling, affecting devices from all semiconductors - silicon to 2D materials. Here, we show that clean edge contacts to 2D MoS2provide immunity to the contact-scaling problem, with performance that is independent of contact length down to the 20 nm regime. Using a directional ion beam, in situ edge contacts of various metal-MoS2interfaces are studied. Characterization of the intricate edge interface using cross-sectional electron microscopy reveals distinct morphological effects on the MoS2depending on its thickness - from monolayer to few-layer films. Chromium is found to outperform other metals in the edge contact scheme, which is attributed to the shorter Cr-MoS2bond length. Compared to scaled top contacts with 20 nm contact length, in situ edge contacts yield better performance with an effective contact length of ∼ 1 nm and 18 times higher carrier injection efficiency. The in situ edge contacts also exhibit ∼8 times higher performance compared to the best-reported edge contacts. Our work provides experimental evidence for a solution to contact scaling in transistors, using 2D materials with clean edge contact interfaces, opening a new way of designing devices with 2D materials. Copyright © 2018, The Authors. All rights reserved.

关键词： Monolayers

Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit