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arXiv 2023年

作者： Innan, Nouhaila Siddiqui, Owais Ishtiaq Arora, Shivang Ghosh, Tamojit Koçak, Yasemin Poyraz Paragas, Dominic Galib, Abdullah Al Omar Khan, Muhammad Al-Zafar Bennai, Mohamed Quantum Physics and Magnetism Team LPMC Faculty of Sciences Ben M’sick Hassan II University of Casablanca Morocco Quantum Formalism Fellow Zaiku Group Ltd Liverpool United Kingdom Department of Physics COMSATS University Islamabad45550 Pakistan Technical University of Munich Munich Germany Department of Physics Indian Institute of Technology Madras Tamil Nadu Chennai600036 India Istanbul University-Cerrahpaşa Computer Technology Department Istanbul Turkey University of California BerkeleyCA United States School of Computer Science and Applied Mathematics University of the Witwatersrand 1 Jan Smuts Ave Braamfontein Johannesburg Gauteng2000 South Africa

Quantum State Tomography (QST) is a fundamental technique in Quantum Information Processing (QIP) for reconstructing unknown quantum states. However, the conventional QST methods are limited by the number of measurements required, which makes them impractical for large-scale quantum systems. To overcome this challenge, we propose the integration of Quantum Machine Learning (QML) techniques to enhance the efficiency of QST. In this paper, we conduct a comprehensive investigation into various approaches for QST, encompassing both classical and quantum methodologies;We also implement different QML approaches for QST and demonstrate their effectiveness on various simulated and experimental quantum systems, including multi-qubit networks. Our results show that our QML-based QST approach can achieve high fidelity (98%) with significantly fewer measurements than conventional methods, making it a promising tool for practical QIP applications. © 2023, CC BY.

关键词： Machine learning

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Monolithically integrated, broadband, high-efficiency silicon nitride-on-silicon waveguide photodetectors in a visible-light integrated photonics platform

arXiv

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arXiv 2022年

作者： Lin, Yiding Yong, Zheng Luo, Xianshu Azadeh, Saeed Sharif Mikkelsen, Jared Sharma, Ankita Chen, Hong Mak, Jason C.C. Lo, Patrick Guo Qiang Sacher, Wesley D. Poon, Joyce K.S. Max Planck Institute of Microstructure Physics Weinberg 2 Halle06120 Germany Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S 3G4 Canada Advanced Micro Foundry Pte Ltd 11 Science Park Road Singapore Science Park II 117685 Singapore 138634 Singapore

Visible and near-infrared spectrum photonic integrated circuits are quickly becoming a key technology to address the scaling challenges in quantum information and biosensing. Thus far, integrated photonic platforms in this spectral range have lacked integrated photodetectors. Here, we report the first silicon nitride-on-silicon waveguide photodetectors that are monolithically integrated in a visible light photonic platform on silicon. Owing to a leaky-wave silicon nitride-on-silicon design, the devices achieved a high external quantum efficiency of > 60% across a record wavelength span from λ ∼ 400 nm to ∼ 640 nm, an opto-electronic bandwidth up to 9 GHz, and an avalanche gain-bandwidth product up to 173 ± 30 GHz. As an example, a photodetector was integrated with a wavelength-tunable microring in a single chip for on-chip power monitoring. © 2022, CC BY-NC-ND.

关键词： Photons

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Object Detection for Smart Factory Processes by Machine Learning

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Procedia computer science 2021年 184卷 581-588页

作者： Lukas Malburg Manfred-Peter Rieder Ronny Seiger Patrick Klein Ralph Bergmann Business Information Systems II University of Trier 54296 Trier Germany Institute of Computer Science University of St.Gallen 9000 St.Gallen Switzerland German Research Center for Artificial Intelligence (DFKI) Branch University of Trier Behringstraße 21 54296 Trier Germany

The production industry is in a transformation towards more autonomous and intelligent manufacturing. In addition to more flexible production processes to dynamically respond to changes in the environment, it is also essential that production processes are continuously monitored and completed in time. Video-based methods such as object detection systems are still in their infancy and rarely used as basis for process monitoring. In this paper, we present a framework for video-based monitoring of manufacturing processes with the help of a physical smart factory simulation model. We evaluate three state-of-the-art object detection systems regarding their suitability to detect workpieces and to recognize failure situations that require adaptations. In our experiments, we are able to show that detection accuracies above 90% can be achieved with current object detection methods.

关键词： Process Monitoring Object Detection computer Vision Machine Learning Industry 4.0 Cyber-Physical Production Systems

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An Interval Difference Method of Second Order for Solving an Elliptical BVP 13th

An Interval Difference Method of Second Order for Solving an...

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13th International Conference on Parallel Processing and Applied Mathematics, PPAM 2019

作者： Marciniak, Andrzej Jankowska, Malgorzata A. Hoffmann, Tomasz Institute of Computing Science Poznan University of Technology Piotrowo 2 Poznan60-965 Poland Department of Computer Science State University of Applied Sciences in Kalisz Poznanska 201-205 Kalisz62-800 Poland Institute of Applied Mechanics Poznan University of Technology Jana Pawla II 24 Poznan60-965 Poland Poznan Supercomputing and Networking Center Jana Pawla II 10 Poznan61-139 Poland

ISBN: (纸本)9783030432218

In the article we present an interval difference scheme for solving a general elliptic boundary value problem with Dirichlet’ boundary conditions. The obtained interval enclosure of the solution contains all possible numerical errors. A numerical example we present confirms that the exact solution belongs to the resulting interval enclosure. © 2020, Springer Nature Switzerland AG.

关键词： Enclosures

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Identifying Traffic Congestion Pattern using K-means Clustering Technique 4

Identifying Traffic Congestion Pattern using K-means Cluster...

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4th International Conference on Internet of Things: Smart Innovation and Usages, IoT-SIU 2019

作者： Mondal, Md. Ashifuddin Rehena, Zeenat Dept. of Computer Science and Engineering Narula Institute of Technology Agarpara Kolkata700109 India Dept. of Computer Science and Engineering Aliah University II A/27 Newtown Kolkata700160 India

ISBN: (纸本)9781728112534

With increase in urbanization and socio-economical growth, the number of vehicles in major metropolitan cities is increasing day by day. Therefore, traffic congestion is becoming a major concern of metropolitan cities all over the world. This results in tremendous air pollution, loss of valuable time and money of citizens. Hence, traffic congestion monitoring of different road segments is very essential for analyzing the problem associated with smooth mobility. Identifying the problematic road segments within the city is one of the important job for the transport authority to assess the road condition. That will assist the government agencies or policy makers to optimize traffic rules and regulations. This work identifies traffic congestion pattern which can classify the different road segments based on traffic density and average speed of vehicles. The traffic parameters are captured by in-road stationary sensors deployed in road segments. The proposed system uses k-means clustering algorithm to categorize the different road segments. © 2019 IEEE.

关键词： Traffic congestion

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A novel binary modified beluga whale optimization algorithm using ring crossover and probabilistic state mutation for enhanced bladder cancer diagnosis

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Informatics in Medicine Unlocked 2024年 50卷

作者： Gharaibeh, Hasan Alawad, Noor Aldeen Nasayreh, Ahmad Al Mamlook, Rabia Emhamed Makhadmeh, Sharif Naser Bashkami, Ayah Abualigah, Laith Ezugwu, Absalom E. Computer Science Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid 21163 Jordan Department of Business Administration Trine University IN United States Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libya Department of Information Technology King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan Department Medical Laboratory Sciences – Faculty of Science Al Balqa Applied University Salt 19117 Jordan Department of Cyber Security and Cloud Computing Faculty of Information Technology Applied Science Private University Amman 11937 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq 25113 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Artificial Intelligence Research Center (AIRC) College of Engineering and Information Technology Ajman University Ajman P.O. Box 346 United Arab Emirates

Bladder cancer (BC) remains a significant global health challenge, requiring the development of accurate predictive models for diagnosis. In this study, a new Binary Modified White Whale Optimization (B-MBWO) algorithm is proposed to address the BC problem. The proposed method utilizes circular transitivity optimization and the Probabilistic State Mutation Algorithm (PSMA) to enhance its optimization performance. The new method is called the BBWORCPS algorithm. High-dimensional and complex medical datasets pose challenges to the original optimization algorithms in addressing the BC problem, motivating the proposed modifications to the original Beluga Whale Optimization algorithm. These enhancements, including quantum-inspired mutation and circular crossing, aim to improve solution space exploration and enhance the algorithm's effectiveness in handling intricate feature spaces. Through comprehensive experiments on BC datasets, the superiority of the BBWORCPS algorithm in terms of feature selection accuracy and computational efficiency is demonstrated compared to existing optimization methods. The obtained findings suggest that BBWORCPS offers a promising approach for developing more precise and reliable predictive models for bladder cancer analysis. © 2024 The Author(s)

关键词： Binary modified beluga whale optimization Bladder cancer Feature selection Machine learning Predictive modeling

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Financial Fraud Detection using Quantum Graph Neural Networks

arXiv

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arXiv 2023年

作者： Innan, Nouhaila Sawaika, Abhishek Dhor, Ashim Dutta, Siddhant Thota, Sairupa Gokal, Husayn Patel, Nandan Khan, Muhammad Al-Zafar Theodonis, Ioannis Bennai, Mohamed Quantum Physics and Magnetism Team LPMC Faculty of Sciences Ben M’sick Hassan II University of Casablanca Morocco Quantum Formalism Fellow Zaiku Group Ltd Liverpool United Kingdom Indian Institute of Technology Bombay India Department of Data Science and Engineering Indian Institute of Science Education and Research Bhopal India Dwarkadas J. Sanghvi College of Engineering India Guru Nanak Institutions Technical Campus Hyderabad India ASTI Academy Dubai United Arab Emirates Vellore Institute of Technology Chennai India School of Computer Science and Applied Mathematics University of the Witwatersrand 1 Jan Smuts Ave Braamfontein Johannesburg Gauteng2000 South Africa Department of Physics School of Applied Mathematical and Physical Sciences National Technical University of Athens Greece

Financial fraud detection is essential for preventing significant financial losses and maintaining the reputation of financial institutions. However, conventional methods of detecting financial fraud have limited effectiveness, necessitating the need for new approaches to improve detection rates. In this paper, we propose a novel approach for detecting financial fraud using Quantum Graph Neural Networks (QGNNs). QGNNs are a type of neural network that can process graph-structured data and leverage the power of Quantum Computing (QC) to perform computations more efficiently than classical neural networks. Our approach uses Variational Quantum Circuits (VQC) to enhance the performance of the QGNN. In order to evaluate the efficiency of our proposed method, we compared the performance of QGNNs to Classical Graph Neural Networks using a real-world financial fraud detection dataset. The results of our experiments showed that QGNNs achieved an AUC of 0.85, which outperformed classical GNNs. Our research highlights the potential of QGNNs and suggests that QGNNs are a promising new approach for improving financial fraud detection. © 2023, CC BY.

关键词： Timing circuits

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Revealing the small proteome of Haloferax volcanii by combining ribosome profling and small-protein optimized mass spectrometry

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MicroLife 2023年第4期4卷 uqad001页

作者： Hadjeras, Lydia Bartel, Jürgen Maier, Lisa-Katharina Maaß, Sandra Vogel, Verena Svensson, Sarah L. Eggenhofer, Florian Gelhausen, Rick Müller, Teresa Alkhnbashi, Omer S. Backofen, Rolf Becher, Dörte Sharma, Cynthia M. Marchfelder, Anita Department of Molecular Infection Biology II Institute of Molecular Infection Biology (IMIB) University of Würzburg Josef-Schneider-Straße 2 / D15 Würzburg 97080 Germany Department of Microbial Proteomics Institute of Microbiology University of Greifswald Felix-Hausdorff-Str. 8 Greifswald 17489 Germany Ulm University Albert-Einstein-Allee 11 Ulm 89081 Germany Bioinformatics Group Department of Computer Science University of Freiburg Georges-Koehler-Allee 106 Freiburg 79110 Germany Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Signalling Research Centres BIOSS and CIBSS University of Freiburg Schaenzlestr. 18 Freiburg 79104 Germany

In contrast to extensively studied prokaryotic 'small' transcriptomes (encompassing all small noncoding RNAs), small proteomes (here defned as including proteins ≤70 aa) are only now entering the limelight. The absence of a complete small protein catalogue in most prokaryotes precludes our understanding of how these molecules affect physiology. So far, archaeal genomes have not yet been analyzed broadly with a dedicated focus on small proteins. Here, we present a combinatorial approach, integrating experimental data from small protein-optimized mass spectrometry (MS) and ribosome profling (Ribo-seq), to generate a high confdence inventory of small proteins in the model archaeon Haloferax volcanii. We demonstrate by MS and Ribo-seq that 67% of the 317 annotated small open reading frames (sORFs) are translated under standard growth conditions. Furthermore, annotation-independent analysis of Ribo-seq data showed ribosomal engagement for 47 novel sORFs in intergenic regions. A total of seven of these were also detected by proteomics, in addition to an eighth novel small protein solely identifed by MS. We also provide independent experimental evidence in vivo for the translation of 12 sORFs (annotated and novel) using epitope tagging and western blotting, underlining the validity of our identifcation scheme. Several novel sORFs are conserved in Haloferax species and might have important functions. Based on our fndings, we conclude that the small proteome of H. volcanii is larger than previously appreciated, and that combining MS with Ribo-seq is a powerful approach for the discovery of novel small protein coding genes in archaea. © The Author(s) 2023.

关键词： archaea Haloferax volcanii mass spectrometry proteomics Ribo-seq ribosome profling small protein small proteome sORF sprotein

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

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Physical Review X 2024年第1期14卷 011053-011053页

作者： H. Miao J. Bouaziz G. Fabbris W. R. Meier F. Z. Yang H. X. Li C. Nelson E. Vescovo S. Zhang A. D. Christianson H. N. Lee Y. Zhang C. D. Batista S. Blügel Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Peter Grünberg Institut and Institute for Advanced Simulation Forschungszentrum Jülich and JARA D-52425 Jülich Germany Advanced Photon Source Argonne National Laboratory Argonne Illinois 60439 USA Department of Materials Science and Engineering University of Tennessee Knoxville Tennessee USA Advanced Materials Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China National Synchrotron Light Source II Brookhaven National Laboratory Upton New York 11973 USA Max-Planck-Institut fur Physik komplexer Systeme Nothnitzer Straße 38 01187 Dresden Germany Department of Physics and Astronomy The University of Tennessee Knoxville Tennessee 37996 USA Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee 37996 USA Quantum Condensed Matter Division and Shull-Wollan Center Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA

The asymmetric distribution of chiral objects with opposite chirality is of great fundamental interest ranging from molecular biology to particle physics. 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 fields, a spontaneous chirality flipping has yet to be discovered. We present experimental evidence of chirality flipping via changing temperature in a topological magnet EuAl4, which features orthogonal spin density waves (SDW) and charge density waves (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 lay the groundwork for a new functional manipulation of chirality through momentum-dependent spin-charge-lattice interactions.

关键词： Charge density waves Chirality Magnetic order Magnetic texture 3-dimensional systems Electron-correlation calculations X-ray diffraction X-ray magnetic circular dichroism

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Exploring the large-Nc limit with one quark flavour 39

Exploring the large-Nc limit with one quark flavour

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39th International Symposium on Lattice Field Theory, LATTICE 2022

作者： Della Morte, Michele Jäger, Benjamin Martins, Sofie Sannino, Francesco Tsang, J. Tobias Ziegler, Felix P.G. CP3-Origins Dept. of Mathematics and Computer Science University of Southern Denmark Campusvej 55 Odense M5230 Denmark Danish Institute for Advanced Study University of Southern Denmark Campusvej 55 Odense M5230 Denmark CP3-Origins Dept. of Physics Chemistry and Pharmacy University of Southern Denmark Campusvej 55 Odense M5230 Denmark Dipartimento di Fisica "E. Pancini" Università di Napoli Federico II INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Edificio 6 via Cintia Napoli80126 Italy Department of Theoretical Physics CERN Geneva 231211 Switzerland School of Physics and Astronomy The University of Edinburgh EdinburghEH9 3FD United Kingdom

We use one-flavour QCD (Nc = 3) as a proxy to understand N = 1 SYM. For our simulations, we use tree-level improved Wilson fermions and a Symanzik improved gauge action. The hadron spectrum is obtained by using LapH smearing for different masses and simulation volumes. We also report on our efforts to increase the number of colours in our simulations, where we find that the simulations show increasing topological freezing for larger Nc © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Lattice theory

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