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Stability, metallicity, and magnetism in niobium silicide nanofilms

Physical Review Materials 2022年第6期6卷 064402-064402页

作者： Xuezeng Lu Dominic P. Goronzy Carlos G. Torres-Castanedo Paul Masih Das Maryam Kazemzadeh-Atoufi Anthony McFadden Corey Rae H. McRae Peter W. Voorhees Vinayak P. Dravid Michael J. Bedzyk Mark C. Hersam James M. Rondinelli Department of Materials Science and Engineering Northwestern University Evanston Illinois 60208 USA International Institute for Nanotechnology Northwestern University Evanston Illinois 60208 USA National Institute of Standards and Technology Boulder Colorado 80305 USA Department of Electrical Computer & Energy Engineering University of Colorado Boulder Boulder Colorado 80309 USA Department of Physics University of Colorado Boulder Colorado 80309 USA Northwestern University Atomic and Nanoscale Characterization Experimental Center (NUANCE) Northwestern University Evanston Illinois 60208 USA Department of Physics and Astronomy Northwestern University Evanston Illinois 60208 USA Applied Physics Program Northwestern University Evanston Illinois 60208 USA Department of Chemistry Northwestern University Evanston Illinois 60208 USA Department of Electrical and Computer Engineering Northwestern University Evanston Illinois 60208 USA

Modern superconducting qubits based on two-dimensional (2D) transmons typically involve the growth of Nb thin films on high-resistivity Si substrates. Since imperfections at the Nb-Si heterointerface have been implicated as a source of two-level systems that limit quantum coherence times, detailed characterization and understanding of niobium silicide interfacial layers are critical to improving superconducting qubit technology. While bulk binary intermetallic niobium silicide phases are well understood, the thermodynamic phase stability and properties of ultrathin niobium silicides, such as those found at the Nb-Si heterointerface in 2D transmons, have not yet been explored. Here, we report finite-sized effects for ultrathin niobium silicide films using density functional theory calculations and predict nanoscale stabilization of Nb6Si5 over the bulk α−Nb5Si3 phase. This result is consistent with our experimental observations of a niobium silicide interfacial layer between a sputtered Nb thin film and the underlying Si substrate. Furthermore, our calculations show that Nb6Si5 nanofilms are nonmagnetic, making them superior to nanofilms of α−Nb5Si3 that exhibit antiferromagnetic correlations detrimental to long coherence times in superconducting qubits. By providing atomic-scale insight into niobium silicide nanofilms, this paper can help guide ongoing efforts to optimize Nb-Si heterointerfaces for long coherence times in superconducting qubits.

关键词： Interface & surface thermodynamics Interfaces Superconducting qubits Thin films Density functional theory

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MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing 24

MRP 2020: The Second Shared Task on Cross-Framework and Cros...

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Shared Task: Cross-Framework Meaning Representation Parsing at 24th Conference on Computational Natural Language Learning, CoNLL 2020

作者： Oepen, Stephan Abend, Omri Abzianidze, Lasha Bos, Johan Hajič, Jan Hershcovich, Daniel Li, Bin O’Gorman, Tim Xue, Nianwen Zeman, Daniel University of Oslo Department of Informatics Norway The Hebrew University of Jerusalem School of Computer Science and Engineering Israel Utrecht University UiL OTS Netherlands University of Groningen Center for Language and Cognition Netherlands Charles University Prague Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Czech Republic University of Copenhagen Department of Computer Science Denmark Nanjing Normal University School of Chinese Language and Literature China University of Massachusetts at Amherst College of Information and Computer Sciences United States Brandeis University Department of Computer Science United States

ISBN: (纸本)9781952148644

The 2020 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and languages. Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization;for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework. The task received submissions from eight teams, of which two do not participate in the official ranking because they arrived after the closing deadline or made use of additional training data. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://***. © 2020 Association for Computational Linguistics.

关键词： Directed graphs

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Ruth Obe Brandt Kaufmann Kaelen Baird Sam Kadel Yasmin Soltani Mostafa Cham Matthias K. Gobbert Carlos A. Barajas Zhuoran Jiang Vijay R. Sharma Lei Ren Stephen W. Peterson Jerimy C. PoIf Dept. of Computer Science and of Software Engineering U. of Houston-Clear Lake USA Dept. of Mathematics and Statistics U. of San Francisco USA Dept. of Computer Science and of Mathematics Skidmore College USA Dept. of Computer Science and of Psychology Mount Holyoke College USA Dept. of Biomedical Engineering U. of Houston USA Dept. of Information Systems U. of Maryland Baltimore County Dept. of Mathematics and Statistics U. of Maryland Baltimore County Medical Physics Graduate Program Duke University USA Dept. of Radiation Oncology U. of Maryland School of Medicine USA Department of Radiation Oncology U. of Maryland School of Medicine USA Dept. of Physics U. of Cape Town South Africa H3D Inc. USA

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy.

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Entanglement and Purification Transitions in Non-Hermitian Quantum Mechanics

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Physical Review Letters 2021年第17期126卷 170503-170503页

作者： Sarang Gopalakrishnan Michael J. Gullans Department of Physics The Pennsylvania State University University Park Pennsylvania 16802 USA Department of Physics and Astronomy CUNY College of Staten Island Staten Island New York 10314 and Physics Program and Initiative for the Theoretical Sciences The Graduate Center CUNY New York New York 10016 USA Joint Center for Quantum Information and Computer Science NIST/University of Maryland College Park Maryland 20742 USA

A quantum system subject to continuous measurement and postselection evolves according to a non-Hermitian Hamiltonian. We show that, as one increases the strength of postselection, this non-Hermitian Hamiltonian can undergo a spectral phase transition. On one side of this phase transition (for weak postselection), an initially mixed density matrix remains mixed at all times, and an initially unentangled state develops volume-law entanglement; on the other side, an arbitrary initial state approaches a unique pure state with low entanglement. We identify this transition with an exceptional point in the spectrum of the non-Hermitian Hamiltonian, at which PT symmetry is spontaneously broken. We characterize the transition as well as the nontrivial steady state that emerges at late times in the mixed phase using exact diagonalization and an approximate, analytically tractable mean-field theory; these methods yield consistent conclusions.

关键词： Dynamical phase transitions Entanglement production Open quantum systems PT-symmetric quantum mechanics Quantum measurements Quantum phase transitions

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A resetting particle embedded in a viscoelastic bath

arXiv

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arXiv 2024年

作者： Biswas, Arup Dubbeldam, Johan L.A. Sandev, Trifce Pal, Arnab The Institute of Mathematical Sciences CIT Campus Taramani Chennai600113 India Homi Bhabha National Institute Training School Complex Anushakti Nagar Mumbai400094 India Delft Institute of Applied Mathematics Delft University of Technology Delft2628 CD Netherlands Research Center for Computer Science and Information Technologies Macedonian Academy of Sciences and Arts Bul. Krste Misirkov 2 Skopje1000 Macedonia Institute of Physics Faculty of Natural Sciences and Mathematics Ss. Cyril and Methodius University Arhimedova 3 Skopje1000 Macedonia Department of Physics Korea University Seoul02841 Korea Republic of

We examine the behavior of a colloidal particle immersed in a viscoelastic bath undergoing stochastic resetting at a rate r. Microscopic probes suspended in viscoelastic environment do not follow the classical theory of Brownian motion. This is primarily because the memory from successive collisions between the medium particles and the probes does not necessarily decay instantly as opposed to the classical Langevin equation. To treat such a system one needs to incorporate the memory effects to the Langevin equation. The resulting equation formulated by Kubo, known as the Generalized Langevin equation (GLE), has been instrumental to describe the transport of particles in inhomogeneous or viscoelastic environments. The purpose of this work, henceforth, is to study the behavior of such a colloidal particle governed by the GLE under resetting dynamics. To this end, we extend the renewal formalism to compute the general expression for the position variance and the correlation function of the resetting particle driven by the environmental memory. These generic results are then illustrated for the prototypical example of the Jeffreys viscoelastic fluid model. In particular, we identify various timescales and intermittent plateaus in the transient phase before the system relaxes to the steady state;and further discuss the effect of resetting pertaining to these behaviors. Our results are supported by numerical simulations showing an excellent agreement. © 2024, CC BY.

关键词： Stochastic systems

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Real-time human detection with integration of visual and thermal data from high altitude suas

Real-time human detection with integration of visual and the...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2021

作者： Hayner, Chris Zhou, Timothy Gupta, Neil Liu, Echo Mayhew, Parker Vagners, Juris Autonomous Flight Systems Laboratory University of Washington SeattleWA98195 United States Department of Physics University of Washington SeattleWA98195 United States Department of Computer Science University of Illinois at Urbana–Champaign ChampaignIL61820 United States College of Information & Computer Sciences University of Massachusetts Amherst AmherstMA01003 United States Department of Applied Mathematics University of Washington SeattleWA98195 United States Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Aeronautics and Astronautics University of Washington SeattleWA98195 United States

ISBN: (纸本)9781624106095

A major challenge with Search and Rescue operations is covering a large amount of area quickly, efficiently, and accurately. With the rising prevalence of both computer vision algorithms for object detection and small Unmanned Aerial Systems (sUAS), an effective solution emerges. This work proposes a methodology for the integration of a sUAS, sensors, and software into a search and rescue workflow. Compared to ground search and rescue operations, sUAS allow for larger and faster area coverage at a fraction of the cost of manned aircraft missions. When combined with a versatile suite of object detection algorithms incorporating aerial data from both the visual and radiometric spectrum, the proposed system is able to analyze and process real-time data faster than existing systems at greater or comparable accuracy. © 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All Rights Reserved.

关键词： Object detection

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BUILDING MACHINE LEARNING LIMITED AREA MODELS: KILOMETER-SCALE WEATHER FORECASTING IN REALISTIC SETTINGS

arXiv

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arXiv 2025年

作者： Adamov, Simon Oskarsson, Joel Denby, Leif Landelius, Tomas Hintz, Kasper Christiansen, Simon Schicker, Irene Osuna, Carlos Lindsten, Fredrik Fuhrer, Oliver Schemm, Sebastian Federal Office for Meteorology and Climatology MeteoSwiss Institute of Atmospheric and Climate Science Department of Environmental Science ETH Zurich Switzerland Division of Statistics and Machine Learning Department of Computer and Information Science Linköping University Sweden Danish Meteorological Institute Swedish Meteorological and Hydrological Institute Sweden GeoSphere Austria Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom

Machine learning is revolutionizing global weather forecasting, with models that efficiently produce highly accurate forecasts. Apart from global forecasting there is also a large value in high-resolution regional weather forecasts, focusing on accurate simulations of the atmosphere for a limited area. Initial attempts have been made to use machine learning for such limited area scenarios, but these experiments do not consider realistic forecasting settings and do not investigate the many design choices involved. We present a framework for building kilometer-scale machine learning limited area models with boundary conditions imposed through a flexible boundary forcing method. This enables boundary conditions defined either from reanalysis or operational forecast data. Our approach employs specialized graph constructions with rectangular and triangular meshes, along with multi-step rollout training strategies to improve temporal consistency. We perform systematic evaluation of different design choices, including the boundary width, graph construction and boundary forcing integration. Models are evaluated across both a Danish and a Swiss domain, two regions that exhibit different orographical characteristics. Verification is performed against both gridded analysis data and in-situ observations, including a case study for the storm Ciara in February 2020. Both models achieve skillful predictions across a wide range of variables, with our Swiss model outperforming the numerical weather prediction baseline for key surface variables. With their substantially lower computational cost, our findings demonstrate great potential for machine learning limited area models in the future of regional weather forecasting. Copyright © 2025, The Authors. All rights reserved.

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Blue-White Veil Classification in Dermoscopy Images of the Skin Lesions Using Convolutional Neural Networks 19th

Blue-White Veil Classification in Dermoscopy Images of the S...

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19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020

作者： Milczarski, Piotr Wąs, Lukasz Faculty of Physics and Applied Informatics Department of Computer Science University of Lodz Pomorska Str. 149/153 Lodz90-226 Poland Institute of Mechatronics and Information Systems Lodz University of Technology Stefanowskiego Str. 18/22 Lodz Poland

ISBN: (纸本)9783030614003

In the dermatology, Three-Point Checklist of Dermatology is defined and it is proved to be a sufficient screening method in the skin lesions assessments during the checking by dermatology expert. In the method there is a criterion of blue-whitish veil appearance within the lesion defined and it can be classified using a binary classifier. In the paper, we show the results of CNN application to the problem of the assessment of whether the blue-white veil is present or absent within the lesion using the pre-trained VGG19 CNN network, trained and tested on the prepared images taken from the PH2 dataset. © 2020, Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Dual-GSE: Resource-efficient Generalized Quantum Subspace Expansion

arXiv

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arXiv 2023年

作者： Yang, Bo Yoshioka, Nobuyuki Harada, Hiroyuki Hakkaku, Shigeo Tokunaga, Yuuki Hakoshima, Hideaki Yamamoto, Kaoru Endo, Suguru LIP6 Sorbonne Université 4 Place Jussieu Paris75005 France Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Department of Applied Physics School of Engineering University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Wako-shi Saitama351-0198 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku Yokohama223-8522 Japan NTT Computer and Data Science Laboratories NTT Corporation 3-9-11 Midori-cho Musashino-shi Tokyo180-8585 Japan Center for Quantum Information and Quantum Biology Osaka University 1-3 Machikaneyama Toyonaka Osaka560-8531 Japan

There are considerable obstacles against realizing practical quantum computing: a significant amount of computation errors and the restricted qubit count. As a unified method of noise-agnostic quantum error mitigation (QEM) methods, i.e., the quantum subspace expansion and virtual purification, a generalized quantum subspace expansion (GSE) has recently been proposed that is significantly robust against stochastic and coherent errors. However, GSE requires entangled measurements between copies of the quantum states, which is a significant drawback under the current situation of the restricted number of qubits and their connectivity. In this work, we propose a resource-efficient implementation of GSE, which we name "Dual-GSE", circumventing significant overheads of state copies by constructing an ansatz of error-mitigated quantum states via dual-state purification. Remarkably, Dual-GSE can further simulate larger quantum systems beyond the size of available quantum hardware with a suitable ansatz construction inspired by divide-and-conquer methods that forge entanglement classically. This also significantly reduces the measurement overhead because we only need to measure subsystems’ Pauli operators. The proposed method is demonstrated by a numerical simulation of the eight-qubit transverse-field Ising model, showing that our method estimates the ground state energy with high precision under gate noise with low mitigation overhead and practical sampling cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Ground state

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Graphene Terahertz Devices for Sensing and Communication

arXiv

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arXiv 2024年

作者： Samaha, Anna-Christina Doumani, Jacques Kritzell, T. Elijah Xu, Hongjing Baydin, Andrey Ajayan, Pulickel M. Tahchi, Mario El Kono, Junichiro Laboratory of Biomaterials and Intelligent Materials Department of Physics Faculty of Sciences 2 Lebanese University P.O. Box 90656 Jdeidet Lebanon Department of Electrical and Computer Engineering Rice University 6100 Main Street HoustonTX77005 United States Applied Physics Graduate Program Smalley–Curl Institute Rice University 6100 Main Street HoustonTX77005 United States Department of Physics and Astronomy Rice University 6100 Main Street HoustonTX77005 United States Smalley–Curl Institute Rice University 6100 Main Street HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University 6100 Main Street HoustonTX77005 United States Carbon Hub Rice University 6100 Main Street HoustonTX77005 United States

Graphene-based terahertz (THz) devices have emerged as promising platforms for a variety of applications, leveraging graphene’s unique optoelectronic properties. This review explores recent advancements in utilizing graphene in THz technology, focusing on two main aspects: THz molecular sensing and THz wave modulation. In molecular sensing, the environment-sensitive THz transmission and emission properties of graphene are utilized for enabling molecular adsorption detection and biomolecular sensing. This capability holds significant potential, from the detection of pesticides to DNA at high sensitivity and selectivity. In THz wave modulation, crucial for next-generation wireless communication systems, graphene demonstrates remarkable potential in absorption modulation when gated. Novel device structures, spectroscopic systems, and metasurface architectures have enabled enhanced absorption and wave modulation. Furthermore, techniques such as spatial phase modulation and polarization manipulation have been explored. From sensing to communication, graphene-based THz devices present a wide array of opportunities for future research and development. Finally, advancements in sensing techniques not only enhance biomolecular analysis but also contribute to optimizing graphene’s properties for communication by enabling efficient modulation of electromagnetic waves. Conversely, developments in communication strategies inform and enhance sensing capabilities, establishing a mutually beneficial relationship. © 2024, CC BY.

关键词： Graphene

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