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IEEE Conference on Nanotechnology

作者： Viraj Senevirathne Malin Premaratne Advanced Computing and Simulation Laboratory (AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton VIC Australia

Nanohybrid comprising of a nanoresonator and a quantum emitter have shown enhanced optical properties compared to its constituents. Such nanohybrids are highly sensitive to their surrounding medium permittivity, making them suitable for sensing applications which relies on the background permittivity. This feature is a result of the change in resonance conditions in the nanoresonator, which is part of the nanohybrid. Here we investigate how different nanoresonators affect the sensing capabilities of the nanohybrid and use that information to make recommendations on the best nanores-onator for the nanohybrid depending on the application. Our analysis is based on cavity quantum electrodynamics formalism and thus rigorous and accurate. Our results indicate that the graphene resonator based nanohybrids can be used to detect broader permittivity range than MNP based nanohybrids.

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Mitosis domain generalization in histopathology images - The MIDOG challenge

arXiv

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arXiv 2022年

作者： Aubreville, Marc Stathonikos, Nikolas Bertram, Christof A. Klopfleisch, Robert ter Hoeve, Natalie Ciompi, Francesco Wilm, Frauke Marzahl, Christian Donovan, Taryn A. Maier, Andreas Breen, Jack Ravikumar, Nishant Chung, Youjin Park, Jinah Nateghi, Ramin Pourakpour, Fattaneh Fick, Rutger H.J. Hadj, Saima Ben Jahanifar, Mostafa Rajpoot, Nasir Dexl, Jakob Wittenberg, Thomas Kondo, Satoshi Lafarge, Maxime W. Koelzer, Viktor H. Liang, Jingtang Wang, Yubo Long, Xi Liu, Jingxin Razavi, Salar Khademi, April Yang, Sen Wang, Xiyue Veta, Mitko Breininger, Katharina Technische Hochschule Ingolstadt Ingolstadt Germany Pathology Department UMC Utrecht Netherlands Institute of Pathology University of Veterinary Medicine Vienna Austria Institute of Veterinary Pathology Freie Universität Berlin Berlin Germany Computational Pathology Group Radboud UMC Nijmegen Netherlands Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Anatomic Pathology Schwarzman Animal Medical Center New York United States CISTIB Center for Computational Imaging and Simulation Technologies in Biomedicine School of Computing University of Leeds United Kingdom Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Electrical and Electronics Engineering Department Shiraz University of Technology Shiraz Iran Tehran Iran Tribun Health Paris France Tissue Image Analytics Centre Department of Computer Science University of Warwick United Kingdom Fraunhofer-Institute for Integrated Circuits IIS Erlangen Germany Muroran Institute of Technology Hokkaido Japan Department of Pathology and Molecular Pathology University Hospital University of Zurich Zurich Switzerland School of Life Science and Technology Xidian University Shannxi China Histo Pathology Diagnostic Center Shanghai China Xi'an Jiaotong-Liverpool University Suzhou China Electrical Computer and Biomedical Engineering Ryerson University TorontoON Canada Tencent AI Lab Shenzhen518057 China College of Computer Science Sichuan University Chengdu610065 China Medical Image Analysis Group TU Eindhoven Netherlands Department of Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitotic figures by pathologists is known to be subject to a strong inter-rater bias, which limits the prognostic value. State-of-the-art deep learning methods can support the expert in this assessment but are known to strongly deteriorate when applied in a different clinical environment than was used for training. One decisive component in the underlying domain shift has been identified as the variability caused by using different whole slide scanners. The goal of the MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As a test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were given. The best approaches performed on an expert level, with the winning algorithm yielding an F1 score of 0.748 (CI95: 0.704-0.781). In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Scanning

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Rapid Initial-State Preparation for the Quantum simulation of Strongly Correlated Molecules

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PRX Quantum 2025年第2期6卷 020327-020327页

作者： Dominic W. Berry Yu Tong Tanuj Khattar Alec White Tae In Kim Guang Hao Low Sergio Boixo Zhiyan Ding Lin Lin Seunghoon Lee Garnet Kin-Lic Chan Ryan Babbush Nicholas C. Rubin School of Mathematical and Physical Sciences Macquarie University Sydney New South Wales 2109 Australia Institute of Quantum Information and Matter California Institute of Technology Pasadena California 91125 USA Department of Mathematics Duke University Durham North Carolina 27708 USA Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA Google Quantum AI Venice California 90291 USA Quantum Simulation Technologies Inc. Boston Massachusetts 02135 USA Department of Chemistry Seoul National University Seoul 08826 Republic of Korea Department of Mathematics University of California Berkeley California 94720 USA Division of Chemistry and Chemical Engineering California Institute of Technology Pasadena California 91125 USA

Studies on quantum algorithms for ground-state energy estimation often assume perfect ground-state preparation; however, in reality the initial state will have imperfect overlap with the true ground state. Here, we address that problem in two ways: by faster preparation of matrix-product-state (MPS) approximations and by more efficient filtering of the prepared state to find the ground-state energy. We show how to achieve unitary synthesis with a Toffoli complexity about 7× lower than that in prior work and use that to derive a more efficient MPS-preparation method. For filtering, we present two different approaches: sampling and binary search. For both, we use the theory of window functions to avoid large phase errors and minimize the complexity. We find that the binary-search approach provides better scaling with the overlap at the cost of a larger constant factor, such that it will be preferred for overlaps less than about 0.003. Finally, we estimate the total resources to perform ground-state energy estimation of Fe-S cluster systems, including the FeMo cofactor by estimating the overlap of different MPS initial states with potential ground states of the FeMo cofactor using an extrapolation procedure. With a modest MPS bond dimension of 4000, our procedure produces an estimate of approximately 0.9 overlap squared with a candidate ground state of the FeMo cofactor, producing a total resource estimate of 7.3×1010 Toffoli gates; neglecting the search over candidates and assuming the accuracy of the extrapolation, this validates prior estimates that have used perfect ground-state overlap. This presents an example of a practical path to prepare states of high overlap in a challenging-to-compute chemical system.

关键词： Digital arithmetic

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Benchmarking Advantage and D-Wave 2000Q quantum annealers with exact cover problems

arXiv

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arXiv 2021年

作者： Willsch, Dennis Willsch, Madita Gonzalez Calaza, Carlos D. Jin, Fengping De Raedt, Hans Svensson, Marika Michielsen, Kristel Institute for Advanced Simulation Jülich Supercomputing Centre Forschungszentrum Jülich Jülich52425 Germany AIDAS Jülich52425 Germany Zernike Institute for Advanced Materials University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands Jeppesen Gothenburg411 03 Sweden Department of Computer Science and Engineering Chalmers University of Technology GothenburgSE-412 96 Sweden RWTH Aachen University Aachen52056 Germany

We benchmark the quantum processing units of the largest quantum annealers to date, the 5000+ qubit quantum annealer Advantage and its 2000+ qubit predecessor D-Wave 2000Q, using tail assignment and exact cover problems from aircraft scheduling scenarios. The benchmark set contains small, intermediate, and large problems with both sparsely connected and almost fully connected instances. We find that Advantage outperforms D-Wave 2000Q for almost all problems, with a notable increase in success rate and problem size. In particular, Advantage is also able to solve the largest problems with 120 logical qubits that D-Wave 2000Q cannot solve anymore. Furthermore, problems that can still be solved by D-Wave 2000Q are solved faster by Advantage. We find, however, that D-Wave 2000Q can achieve better success rates for sparsely connected problems that do not require the many new couplers present on Advantage, so improving the connectivity of a quantum annealer does not per se improve its performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Benchmarking

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Generative Adversarial Network for Improving Deep Learning Based Malware Classification

Generative Adversarial Network for Improving Deep Learning B...

引用

simulation Winter Conference

作者： Yan Lu Jiang Li Department of Modeling Simulation and Visualization Engineering Old Dominion University Norfolk VA USA Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

ISBN: (数字)9781728132839

ISBN: (纸本)9781728120522

The generative adversarial network (GAN) had been successfully applied in many domains in the past, the GAN network provides a new approach for solving computer vision, object detection and classification problems by learning, mimicking and generating any distribution of data. One of the difficulties in deep learning-based malware detection and classification tasks is lacking of training malware samples. With insufficient training data the classification performance of the deep model could be compromised significantly. To solve this issue, in this paper, we propose a method which uses the Deep Convolutional Generative Adversarial Network (DCGAN) to generate synthetic malware samples. Our experiment results show that by using the DCGAN generated adversarial synthetic malware samples, the classification accuracy of the classifier - a 18-layer deep residual network is significantly improved by approximately 6%.

关键词： Generative adversarial networks Machine learning Data models Computational modeling Trojan horses Gallium nitride

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Modelling of III-Nitride Epitaxial Layers Grown on Silicon Substrates with Low Dislocation-Densities

Modelling of III-Nitride Epitaxial Layers Grown on Silicon S...

引用

MRS Advances

作者： Khafagy, Khaled H. Hatem, Tarek M. Bedair, Salah M. Centre for Simulation Innovation and Advanced Manufacturing British University in Egypt El-Sherouk City Cairo11837 Egypt Department of Electrical and Computer Engineering North Carolina State University RaleighNC27695 United States Faculty of Energy and Environmental Engineering British University in Egypt El-Sherouk City Cairo11837 Egypt

Large lattice and thermal expansion coefficients mismatches between III-Nitride (III N) epitaxial layers and their substrates inevitably generate defects on the interfaces. Such defects as dislocations affect the reliability, life time, and performance of photovoltaic (PV) devices. High dislocation densities in epitaxial layer generate higher v-shaped pits densities on the layer top surface that also directly affect the device performance. Therefore, using an approach such as the embedded void approach (EVA) for defects reduction in the epitaxial layers is essential. EVA relies on the generation of high densities of embedded microvoids (∼108/cm2), with ellipsoidal shapes. These tremendous number of microvoids are etched near the interface between the III N thin-film and its substrate where the dislocation densities present with higher values. This article used a 3-D constitutive model that accounts the crystal plasticity formulas and specialized finite element (FE) formulas to model the EVA in multi-junction PV and therefore to study the effect of the embedded void approach on the defects reduction. Mesh convergence and 2-D analytical solution validation is conducted with accounting thermal stresses. Several aspect and volume ratios of the embedded microvoids are used to optimize the microvoid dimensions. © 2019 Materials Research Society.

关键词： Substrates

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RadSense: Enabling one hand and no hands interaction for sterile manipulation of medical images using Doppler radar

引用

Smart Health 2020年 15卷

作者： Miller, Elishiah Li, Zheng Mentis, Helena Park, Adrian Zhu, Ting Banerjee, Nilanjan Department of Computer Science and Electrical Engineering University of Maryland Baltimore County United States Department of Information Systems University of Maryland Baltimore County United States Simulation to Advance Innovation and Learning Center Anne Arundel Medical Center Annapolis MD United States

In this paper, we show how surgeons can interact with medical images using finger and hand gestures in two situations: one hand-free and no hands-free interaction. We explain how interaction with only one hand or a couple of fingers is beneficial and can help surgeons have continuous interaction, without the need to release their tools and leave the operating table, saving valuable patient time. To this end, we present RadSense, an end-to-end and unobtrusive system that uses Doppler radar-sensing to recognize hand and finger gestures when either one or both hands are busy. Our system permits the following important capabilities: (1) touch-less input for sterile interaction with connected health applications, (2) hand and finger gesture recognition when either one or both hands are busy holding tools, extending multitasking capabilities for health professionals, and (3) mobile and networked, allowing for custom wearable and non-wearable configurations. We evaluated our system in a simulated operating room to manipulate preoperative images using four gestures: circle, double tap, swipe, and finger click. We collected data from five subjects and trained a K-Nearest-Neighbor multi-class classifier using 15-fold cross validation, achieving a 94.5% precision for gesture classification. We conclude that our system performs with high accuracy and is useful in cases where only one hand or a few fingers are free to interact when the hands are busy. © 2019 Elsevier Inc.

关键词： Busy hand interaction Gesture recognition Healthcare Human centered computing Wearable devices

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Preface

Lecture Notes in Networks and Systems

引用

Lecture Notes in Networks and Systems 2023年 729 LNNS卷 v-vi页

作者： Vasant, Pandian Weber, Gerhard-Wilhelm Arefin, Mohammad Shamsul Rodriguez-Aguilar, Roman Panchenko, Vladimir Munapo, Elias Thomas, J. Joshua Faculty of Electrical and Electronics Engineering Modeling Evolutionary Algorithms Simulation and Artificial Intelligence Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science Chittagong University of Engineering and Technology Chittagong Bangladesh Laboratory of Non-traditional Energy Systems Department of Theoretical and Applied Mechanics Federal Scientific Agroengineering Center VIM Russian University of Transport Moscow Russia Department of Computer Science UOW Malaysia KDU Penang University College George Town Malaysia Faculty of Engineering Management Poznań University of Technology Poznan Poland School of Economics and Decision Sciences North West University Mmabatho South Africa Facultad de Ciencias Económicas y Empresariales School of Economic and Business Sciences Universidad Panamericana Mexico City Mexico

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The concept of augmented reality application for putting alignment in golf 5

The concept of augmented reality application for putting ali...

引用

5th International Conference of the Virtual and Augmented Reality in Education, VARE 2019

作者： Ginters, Egils Burkhardt, Dirk Nazemi, Kawa Merkuryev, Yuri Sociotechnical Systems Engineering Group Department of Modelling and Simulation Faculty of Computer Science and Information Technology Riga Technical University 2 Daugavgrivas Street RigaLV-1048 Latvia Sociotechnical Systems OU Sakala 7-2 Street Tallinn10141 Estonia Human-Computer Interaction and Visual Analytics Research Group Darmstadt University of Applied Sciences Darmstadt Germany

ISBN: (纸本)9788885741423

Virtual and augmented reality (VR / AR) applications have successfully overcome the critical part of the Gartner curve. Investments are made and new products entering the economy. However, a very small percentage of society have also heard about AR glasses, mainly linking these with potential identity threats and personal data breaches. The authors dealt with the design of application of AR to improve golf skills by improving the putting technique. The above solution is complicated by requiring complex object recognition, tracking and advanced AR software designing. © 5th International Conference of the Virtual and Augmented Reality in Education, VARE 2019. All rights reserved.

关键词： Augmented reality

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Spontaneous Chirality Flipping in an Orthogonal Spin-Charge Ordered Topological Magnet

arXiv

引用

arXiv 2023年

作者： Miao, H. Bouaziz, J. Fabbris, G. Meier, W.R. Yang, F.Z. Li, H.X. Nelson, C. Vescovo, E. Zhang, S. Christianson, A. Lee, H.N. Zhang, Y. Batista, C.D. Blügel, S. Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Peter Grünberg Institut Institute for Advanced Simulation Forschungszentrum Jülich &JARA JülichD-52425 Germany Advanced Photon Source Argonne National Laboratory ArgonneIL60439 United States Department of Physics and Astronomy The University of Tennessee KnoxvilleTN37996 United States Guangzhou China National Synchrotron Light Source II Brookhaven National Laboratory UptonNY11973 United States Max-Planck-Institut fur Physik komplexer Systeme Nothnitzer Straße 38 Dresden01187 Germany Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Quantum Condensed Matter Division Shull-Wollan Center Oak Ridge National Laboratory Oak RidgeTN37831 United States

The asymmetric distribution of chiral objects with opposite chirality is of great fundamental interests ranging from molecular biology to particle physics1. In quantum materials, chiral states can build on inversion-symmetry-breaking lattice structures or emerge from spontaneous magnetic ordering induced by competing interactions. Although the handedness of a chiral state can be changed through external fields2,3, a spontaneous chirality flipping has yet to be discovered. In this letter, we present experimental evidence of chirality flipping via changing temperature in a topological magnet EuAl4, which features orthogonal spin and charge density waves (SDW/CDW). Using circular dichroism of Bragg peaks in the resonant magnetic x-ray scattering, we find that the chirality of the helical SDW flips through a first order phase transition with modified SDW wavelength. Intriguingly, we observe that the CDW couples strongly with the SDW and displays a rare commensurate-to-incommensurate transition at the chirality flipping temperature. Combining with first principles calculations and angle resolved photoemission spectroscopy, our results support a Fermi surface origin of the helical SDW with intertwined spin, charge, and lattice degrees of freedom in EuAl4. Our results reveal an unprecedented spontaneous chirality flipping and lays the groundwork for a new functional manipulation of chirality through momentum dependent spin-charge-lattice interactions. © 2023, CC BY.

关键词： Topology

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