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Metaheuristic conditional neural network for harvesting skyrmionic metastable states

Physical Review research 2023年第4期5卷 043199-043199页

作者： Qichen Xu I. P. Miranda Manuel Pereiro Filipp N. Rybakov Danny Thonig Erik Sjöqvist Pavel F. Bessarab Anders Bergman Olle Eriksson Pawel Herman Anna Delin Department of Applied Physics School of Engineering Sciences KTH Royal Institute of Technology AlbaNova University Center SE-10691 Stockholm Sweden Swedish e-Science Research Center (SeRC) KTH Royal Institute of Technology SE-10044 Stockholm Sweden Department of Physics and Astronomy Uppsala University Box 516 SE-75120 Uppsala Sweden School of Science and Technology Örebro University SE-701 82 Örebro Sweden Department of Physics and Electrical Engineering Linnaeus University Hus Magna SE-39231 Kalmar Sweden Science Institute University of Iceland 107 Reykjavík Iceland Division of Computational Science and Technology School of Electrical Engineering and Computer Science KTH Royal Institute of Technology AlbaNova University Center SE-10691 Stockholm Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) KTH Royal Institute of Technology SE-10044 Stockholm Sweden

We present a metaheuristic conditional neural-network-based method aimed at identifying physically interesting metastable states in a potential energy surface of high rugosity. To demonstrate how this method works, we identify and analyze spin textures with topological charge Q ranging from 1 to −13 (where antiskyrmions have Q<0) in the Pd/Fe/Ir(111) system, which we model using a classical atomistic spin Hamiltonian based on parameters computed from density functional theory. To facilitate the harvest of relevant spin textures, we make use of the newly developed segment anything model. Spin textures with Q ranging from −3 to −6 are further analyzed using finite-temperature spin-dynamics simulations. We observe that for temperatures up to around 20 K, lifetimes longer than 200 ps are predicted, and that when these textures decay, new topological spin textures are formed. We also find that the relative stability of the spin textures depend linearly on the topological charge, but only when comparing the most stable antiskyrmions for each topological charge. In general, the number of holes (i.e., non-self-intersecting curves that define closed domain walls in the structure) in the spin texture is an important predictor of stability—the more holes, the less stable the texture. Methods for systematic identification and characterization of complex metastable skyrmionic textures—such as the one demonstrated here—are highly relevant for advancements in the field of topological spintronics.

关键词： Image processing Magnetic texture Optimization problems Density functional theory Neural network simulations Skyrmions

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Optical Quantum Sensing for Agnostic Environments via Deep Learning

arXiv

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arXiv 2023年

作者： Zhou, Zeqiao Du, Yuxuan Yin, Xu-Fei Zhao, Shanshan Tian, Xinmei Tao, Dacheng Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China JD Explore Academy Beijing101111 China Hefei National Research Center for Physical Sciences at the Microscale School of Physical Sciences University of Science and Technology of China Hefei230026 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China School of Computer Science The University of Sydney NSW2008 Australia

Optical quantum sensing promises measurement precision beyond classical sensors termed the Heisenberg limit (HL). However, conventional methodologies often rely on prior knowledge of the target system to achieve HL, presenting challenges in practical applications. Addressing this limitation, we introduce an innovative Deep Learning-based Quantum Sensing scheme (DQS), enabling optical quantum sensors to attain HL in agnostic environments. DQS incorporates two essential components: a Graph Neural Network (GNN) predictor and a trigonometric interpolation algorithm. Operating within a data-driven paradigm, DQS utilizes the GNN predictor, trained on offline data, to unveil the intrinsic relationships between the optical setups employed in preparing the probe state and the resulting quantum Fisher information (QFI) after interaction with the agnostic environment. This distilled knowledge facilitates the identification of optimal optical setups associated with maximal QFI. Subsequently, DQS employs a trigonometric interpolation algorithm to recover the unknown parameter estimates for the identified optical setups. Extensive experiments are conducted to investigate the performance of DQS under different settings up to eight photons. Our findings not only offer a new lens through which to accelerate optical quantum sensing tasks but also catalyze future research integrating deep learning and quantum mechanics. © 2023, CC BY.

关键词： Quantum theory

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Architectures for Heterogeneous Quantum Error Correction Codes

arXiv

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arXiv 2024年

作者： Stein, Samuel Xu, Shifan Cross, Andrew W. Yoder, Theodore J. Javadi-Abhari, Ali Liu, Chenxu Liu, Kun Zhou, Zeyuan Guinn, Charles Ding, Yufei Ding, Yongshan Li, Ang Pacific Northwest National Laboratory RichlandWA United States Departments of Applied Physics Yale University New HavenCT United States Yale Quantum Institute Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States IBM Quantum IBM T. J. Watson Research Center Yorktown HeightsNY United States Department of Physics Princeton University PrincetonNJ United States Department of Computer Science & Engineering University of Califronia San Diego San DiegoCA United States Department of Electrical & Computer Engineering University of Washington WashingtonWA United States

Quantum Error Correction (QEC) is essential for future quantum computers due to its ability to exponentially suppress physical errors. The surface code is a leading error-correcting code candidate because of its local topological structure, experimentally achievable thresholds, and support for universal gate operations with magic states. However, its physical overhead scales quadratically with number of correctable errors. Conversely, quantum low-density parity-check (qLDPC) codes offer superior scaling but lack, on their own, a clear path to universal logical computation. Therefore, it is becoming increasingly evident that there are significant advantages to designing architectures using multiple codes. Heterogeneous architectures provide a clear path to universal logical computation as well as the ability to access different resource trade offs. To address this, we propose integrating the surface code and gross code using an ancilla bus for inter-code data movement. This approach involves managing trade-offs, including qubit overhead, a constrained instruction set, and gross code (memory) routing and management. While our focus is on the gross-surface code architecture, our method is adaptable to any code combination and the constraints generated by that specific architecture. Motivated by the potential reduction of physical qubit overhead, an ever important feature in the realization of fault tolerant computation, we perform the first full system study of heterogeneous error-correcting codes, discovering architectural trade-offs and optimizing around them. We demonstrate physical qubit reductions of up to 6.42× when executing an algorithm to a specific logical error rate, at the cost of up to a 3.43× increase in execution time. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

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Value Stream Mapping for Smart Pharmaceutical Management in a Thai Hospital

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Procedia computer science 2025年 253卷 495-504页

作者： Ganda Boonsothonsatit Rardchawadee Silapunt Supachai Vongbunyong Kamol Kaemarungsi Wirachchaya Chanpuypetch Nilubon Chonsawat Graduate School of Management and Innovation King Mongkut’s University of technology Thonburi Thailand Electronic and Telecommunication Engineering King Mongkut’s University of Technology Thonburi Thailand Hospital Automation Research Center Institute of Field Robotics King Mongkut’s University of Technology Thonburi Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand Faculty of Agro-Industry Chiang Mai University Thailand Department of Agricultural Economy and Developement Faculty of Agriculture Chiang Mai University Thailand

The evolution of healthcare systems worldwide necessitates continual improvement in hospital management practices, particularly pharmaceutical management. This paper explores transforming traditional pharmaceutical management systems in large and medium-sized hospitals in Thailand into smart systems using Value Stream Mapping (VSM) as a guiding tool. Traditional systems often face challenges such as inefficiencies and medication errors. VSM, rooted in lean management principles, offers a structured approach to visualize and optimize processes. Additionally, smart technologies like automated inventory systems and electronic health records enhance pharmaceutical management effectiveness. Through a comprehensive research methodology encompassing case studies, data collection, and VSM analysis, this study identifies inefficiencies and proposes evidence-based strategies for improvement. Results from a case study highlight the benefits of VSM implementation in enhancing pharmacy operations, emphasizing improved patient care and operational efficiency. The implementation of a smart medication dispensing system shows promising results in reducing workload and accommodating increased patient arrivals. Continued evaluation and refinement are crucial for maximizing benefits and addressing emerging challenges effectively.

关键词： lean management smart medication dispensing smart pharmaceutical smart technologies Value Stream Mapping

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Microwave signal processing using an analog quantum reservoir computer

arXiv

引用

arXiv 2023年

作者： Senanian, Alen Prabhu, Sridhar Kremenetski, Vladimir Roy, Saswata Cao, Yingkang Kline, Jeremy Onodera, Tatsuhiro Wright, Logan G. Wu, Xiaodi Fatemi, Valla McMahon, Peter L. Department of Physics Cornell University NY United States School of Applied and Engineering Physics Cornell University NY United States Department of Computer Science University of Maryland MD United States Joint Center for Quantum Information and Computer Science University of Maryland MD United States NTT Physics and Informatics Laboratories NTT Research Inc. CA United States Kavli Institute Cornell for Nanoscale Science Cornell University NY United States

Quantum reservoir computing (QRC) has been proposed as a paradigm for performing machine learning with quantum processors where the training is efficient in the number of required runs of the quantum processor and takes place in the classical domain, avoiding the issue of barren plateaus in parameterized-circuit quantum neural networks. It is natural to consider using a quantum processor based on superconducting circuits to classify microwave signals that are analog—continuous in time. However, while theoretical proposals of analog QRC exist, to date QRC has been implemented using circuit-model quantum systems—imposing a discretization of the incoming signal in time, with each time point input by executing a gate operation. In this paper we show how a quantum superconducting circuit comprising an oscillator coupled to a qubit can be used as an analog quantum reservoir for a variety of classification tasks, achieving high accuracy on all of them. Our quantum system was operated without artificially discretizing the input data, directly taking in microwave signals. Our work does not attempt to address the question of whether QRCs could provide a quantum computational advantage in classifying pre-recorded classical signals. However, beyond illustrating that sophisticated tasks can be performed with a modest-size quantum system and inexpensive training, our work opens up the possibility of achieving a different kind of advantage than a purely computational advantage: superconducting circuits can act as extremely sensitive detectors of microwave photons;our work demonstrates processing of ultra-low-power microwave signals in our superconducting circuit, and by combining sensitive detection with QRC processing within the same system, one could achieve a quantum sensing-computational advantage, i.e., an advantage in the overall analysis of microwave signals comprising just a few photons. © 2023, CC BY.

关键词： Photons

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Strong nonreciprocal acoustic extinction and asymmetric audibility from spinning fluid scatterers

引用

Physical Review B 2023年第16期108卷 165129-165129页

作者： Yao-Ting Wang Heedong Goh Sander A. Mann Andrea Alù Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Department of Photonics National Sun Yat-Sen University Kaohsiung 804201 Taiwan Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Physics Program Graduate Center City University of New York New York New York 10026 USA

Lorentz reciprocity places important constraints on the response of systems to wave excitations, and surpassing these constraints is of both fundamental and applied interest. In the context of scattering, reciprocity requires that the total extinction of an object is identical when excited from opposite directions, regardless of the asymmetry of the scatterer. Here, we demonstrate that, by combining multiple scatterers that break time-reversal symmetry, this extinction symmetry can be largely violated. We consider spinning cylinders, demonstrating that nonreciprocity must be paired with broken parity symmetry to enable large extinction contrast. As a dramatic example, we show a system that strongly scatters when excited from one side, but it is cloaked for the opposite excitation. Our results pave the way for approaches to asymmetric audibility and scattering manipulation, and they may readily translate to other wave domains.

关键词： Acoustic metamaterials Acoustics Non-reciprocal propagation

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Persistent Mayer homology and persistent Mayer Laplacian

arXiv

引用

arXiv 2023年

作者： Shen, Li Liu, Jian Wei, Guo-Wei Department of Mathematics Michigan State University MI48824 United States Mathematical Science Research Center Chongqing University of Technology Chongqing400054 China Department of Electrical and Computer Engineering Michigan State University MI48824 United States Department of Biochemistry and Molecular Biology Michigan State University MI48824 United States

In algebraic topology, the differential (i.e., boundary operator) typically satisfies d2 = 0. However, the generalized differential dN = 0 for an integer N ≥ 2 has been studied in terms of Mayer homology on N-chain complexes for more than eighty years. We introduce Mayer Laplacians on N-chain complexes. We show that both Mayer homology and Mayer Laplacians offer considerable application potential, providing topological and geometric insights to spaces. We also introduce persistent Mayer homology and persistent Mayer Laplacians at various N. The Wasserstein distance and stability of persistence diagrams associated with Mayer homology are investigated. Our computational experiments indicate that the topological features offered by persistent Mayer homology and spectrum given by persistent Mayer Laplacians hold substantial promise for large, complex, and diverse data. We envision that the present work serves as an inaugural step towards integrating Mayer homology and Mayer Laplacians into the realm of topological data analysis. Copyright © 2023, The Authors. All rights reserved.

关键词： Topology

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Deep Automatic Alignment of Mpox Dermatological Hand Photography

Deep Automatic Alignment of Mpox Dermatological Hand Photogr...

引用

IEEE International Symposium on Biomedical Imaging

作者： Bohan Jiang Andrew McNeil Yihao Liu Gaurav Rudravaram Inga Saknite Placide Mbala-Kingebeni Olivier Tshiani Mbaya Tyra Silaphet Rachel Weiss Lori E. Dodd Veronique Nussenblatt Daniel Moyer Bennett A. Landman Benoit M. Dawant Eric R. Tkaczyk Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA Department of Dermatology Vanderbilt University Medical Center Nashville TN USA Department of Veterans Affairs Dermatology Service and Research Service Nashville TN USA Biophotonics Laboratory University of Latvia Riga Latvia Institut National de Recherche Biomedicale Kinshasa Democratic Republic of the Congo Clinical Monitoring Research Program Directorate Frederick National Laboratory for Cancer Research Frederick MD USA Clinical Trials Research Section Division of Clinical Research National Institute of Allergy and Infectious Disease Bethesda MD USA Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases Bethesda MD USA Department of Computer Science Vanderbilt University Nashville TN USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Mpox is a viral illness with heavy cutaneous involvement. Automatic tracking of mpox lesion progression is critical in determining the resolution of evolving lesions. This work introduces a novel application of deep learning for lesion monitoring through alignment of dermatological hand photographs. By adapting the VoxelMorph framework for 2D photographic data, we explore key point alignment across serial images. We trained our neural network model on a unique dataset of 1,658 hand images and evaluated its performance on a test set of 254 images. Additionally, we validated the method's generalizability with a supplementary set of 500 images, which included extensive Mpox infection. Our findings indicate modest yet significant improvements in key points and lesion center registration across different regularization strengths. Although promising, the complexity of hand structure presents challenges, requiring cautious application and further refinement, especially in regions with intense spatial discontinuities, such as interdigital areas.

关键词： Hands Photography Deep learning Training Three-dimensional displays Image resolution Neural networks Parametric statistics Lesions Monitoring

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Enrichment, Borrowing, And Mining: A Data-Driven Approach To Colonoscopic Lesion Classification

Enrichment, Borrowing, And Mining: A Data-Driven Approach To...

引用

IEEE International Symposium on Biomedical Imaging

作者： Shuangyi Tan Yuhao Du ZhenHua Wu Dejun Fan Xutao Lin Zhen Li Xiang Wan Guanbin Li The Chinese University of Hong Kong(Shenzhen) Shenzhen Research Institute of Big Data School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Department of Gastrointestinal Endoscopy Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases Biomedical Innovation Center The Sixth Affiliated Hospital Sun Yat-Sen University

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The automatic detection and diagnosis of colorectal lesions during colonoscopy are vital for the prevention and treatment of colorectal cancer. To date, numerous ROI-based neural network models have been proposed to classify colonoscopic lesions. However, they commonly encounter two main issues: 1) Their performance is limited by the dataset size and the diversity of lesion types included; 2) They tend to directly extract deep features from the original Region of Interests (ROIs) of the lesion, neglecting features of the lesion’s edge background as well as detailed texture and color information. To address these issues, we first propose a publicly available colonoscopic lesion classification dataset that includes all major categories of intestinal lesions. Secondly, we innovatively utilize additional colonoscopy video detection data with consistency between frames to enhance the lesion classification model by leveraging an online-target network. Simultaneously, to better mine the characteristics of lesions in data, we propose an ROI broaden strategy and a deep-fusion module, enabling the model to learn lesion-related information such as lesion edges, morphology, texture, and color. Experiments show that our method outperforms existing supervised and semi-supervised state-of-the-art methods, demonstrating the effectiveness of our approach. The GitHub link for our work is https://***/Zoe-TAN/EBM-polyp-cls.

关键词： Image color analysis Image edge detection Prevention and mitigation Colonoscopy Morphology Feature extraction Data models

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Comparative Study of Plasma, Laser, and Flame Induced Polarity on Hdpe Liner Surfaces of Type 4 Hydrogen Vessels

SSRN

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SSRN 2024年

作者： Varga, Erika Palásti, Ferenc Bata, Attila Kovács, Péter I. Hydrogen Technology Research Center John Von Neumann University KecskemétH-6000 Hungary Department of Innovative Vehicles and Materials GAMF Faculty of Engineering and Computer Science John von Neumann University KecskemétH-6000 Hungary Széchenyi István University GyőrH-9000 Hungary

Despite the numerous advantages of HDPE, its surface adjustment to achieve better compatibility with coatings and adhesives to open new pathways of development is a challenge. In our study we compare the surface modifications of HDPE induced by air plasma, CO2 laser and flame treatments. Changes of contact angles and components of surface free energy were determined by sessile drop method, and chemical characterization of the surface species was carried out with FT-IR and EDS, while surface structures were studied through SEM images. Surface free energy components were analyzed after treatments and short-term aging, too. It was concluded that surface activation took place in a tunable way in all the cases, and the effectiveness could be ranged as air plasma>flame>CO2 laser methods. It was proven that different oxygen compounds were formed in the outermost atomic layers after treatments, part of which was resistant to hydrophobic recovery in air within one week. Depth of the presence of these compounds can be ranged as air plasma>CO2 laser>flame. Among the tested methods, plasma treatment may provide the best base of Type 4 vessel developments, while the effects of CO2 irradiation were proven to be nonlinear, and flame activation showed a saturation character. © 2024, The Authors. All rights reserved.

关键词： Contact angle

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