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Enhanced Quantum Convolutional Neural Network for Signature Authentication in Consumer Products

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IEEE Transactions on Consumer Electronics 2024年

作者： Raghupathy, Bala Krishnan Vairavasundaram, Subramaniyaswamy Ganesan, Manikandan Namachivayam, Rajesh Kumar Kotecha, Ketan Herencsar, Norbert SASTRA Deemed University Department of CSE India Vellore Institute of Technology School of Computer Science and Engineering Vellore632 014 India SASTRA Deemed University School of Computing India Symbiosis Centre for Applied Artificial Intelligence Pune India Brno University of Technology Faculty of Electrical Engineering and Communication Department of Telecommunications Technicka 3082/12 Brno61600 Czech Republic

Product tracking applications utilize the Internet of Things and cyber-physical systems to identify permitted or unauthorized user intrusions into the system. Classical machine learning algorithms cannot detect every risk in an environment that evolves constantly and where new abnormalities are visible. This article investigates the potential of quantum machine learning (QML) for real-time product purchase monitoring and intrusion detection using an enhanced quantum convolutional neural network (EQCNN) with signature-based detection over a massive volume of search space data (qubits). We suggest a three-stage technique to effectively handle the sensitive content: Pre-processing, EQCNN-based feature extraction, and syntactic pattern recognition. Signature-based identification is a feature of the EQCNN architecture that helps detect particular patterns linked to goods purchases or invasions. The model can minimize product tracking mistakes by utilizing the QML-based EQCNN with signature-based detection, resulting in a more efficient supply chain. © 2024 IEEE.

关键词： Convolutional neural networks

Experimental demonstration of a high-fidelity virtual two-qubit gate

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Physical Review Research 2024年第1期6卷 013235-013235页

作者： Akhil Pratap Singh Kosuke Mitarai Yasunari Suzuki Kentaro Heya Yutaka Tabuchi Keisuke Fujii Yasunobu Nakamura Department of Applied Physics Graduate School of Engineering The University of Tokyo Bunkyo-ku Tokyo 113-8656 Japan Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino 180-8585 Japan RIKEN Center for Quantum Computing (RQC) Wako Saitama 351-0198 Japan

We experimentally demonstrate a virtual two-qubit gate and characterize it using quantum process tomography (QPT). The virtual two-qubit gate decomposes an actual two-qubit gate into single-qubit unitary gates and projection gates in quantum circuits for expectation-value estimation. We implement projection gates via midcircuit measurements. The deterministic sampling scheme reduces the number of experimental circuit evaluations required for decomposing a virtual two-qubit gate. We also apply quantum error mitigation to suppress the effect of measurement errors and improve the average gate fidelity of a virtual controlled-Z (CZ) gate to fav=0.9938±0.0002. Our results highlight a practical approach to implement virtual two-qubit gates with high fidelities, which are useful for simulating quantum circuits using fewer qubits and implementing two-qubit gates on a distant pair of qubits.

关键词： Quantum algorithms Quantum algorithms & computation Quantum benchmarking Quantum circuits Quantum computation Quantum gates Quantum nondemolition measurement Quantum simulation

Multi-graph Graph Matching for Coronary Artery Semantic Labeling in Invasive Coronary Angiograms

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arXiv 2024年

作者： Zhao, Chen Xu, Zhihui Baral, Pukar Esposito, Michel Zhou, Weihua Department of Computer Science Kennesaw State University MariettaGA United States Department of Cardiology The First Affiliated Hospital of Nanjing Medical University Nanjing China Department of Applied Computing Michigan Technological University HoughtonMI United States Department of Cardiology Medical University of South Carolina CharlestonSC United States Center for Biocomputing and Digital Health Institute of Computing and Cyber-systems Health Research Institute Michigan Technological University HoughtonMI United States

Coronary artery disease (CAD) stands as the leading cause of death worldwide, and invasive coronary angiography (ICA) remains the gold standard for assessing vascular anatomical information. However, deep learning-based methods encounter challenges in generating semantic labels for arterial segments, primarily due to the morphological similarity between arterial branches and varying anatomy of arterial system between different projection view angles and patients. To address this challenge, we model the vascular tree as a graph and propose a multi-graph graph matching (MGM) algorithm for coronary artery semantic labeling. The MGM algorithm assesses the similarity between arterials in multiple vascular tree graphs, considering the cycle consistency between each pair of graphs. As a result, the unannotated arterial segments are appropriately labeled by matching them with annotated segments. Through the incorporation of anatomical graph structure, radiomics features, and semantic mapping, the proposed MGM model achieves an impressive accuracy of 0.9471 for coronary artery semantic labeling using our multisite dataset with 718 ICAs. With the semantic labeled arteries, an overall accuracy of 0.9155 was achieved for stenosis detection. The proposed MGM presents a novel tool for coronary artery analysis using multiple ICA-derived graphs, offering valuable insights into vascular health and pathology. © 2024, CC BY.

关键词： Trees (mathematics)

Intelligent Solar Energy Harvesting and Management in IoT Nodes Using Deep Self-Organizing Maps

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Intelligent Solar Energy Harvesting and Management in IoT No...

Emerging Research in Computational science (ICERCS), International Conference on

作者： Anita Rajkumar Shinkar Drumil Joshi R V S Praveen Yelisela Rajesh Boopalan K Dharmendra Singh Department of Computer Engineering Dr. D.Y. Patil College of Engineering Varale Telangana Maharashtra India Monitoring & Diagnostics Department Southern Power Birmingham Alabama Digital Engineering and Assurance Hyderabad Telangana India Department of Computer Science & Engineering KLEF Andhra Pradesh India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu India Department of Applied Sciences & Humanities School of Management Sciences Lucknow India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

Smart city energy generation must be efficient and dependable. As a result of studies conducted in this field, reliable control schemes for microgrid management have been developed, which seamlessly integrate with smart building management systems. In order for a building microgrid's solar energy system to recover from problems, this article suggests reliable controllers and the hardware to install them. Training models and Internet of Things (IoT) sensors provide the backbone of this proposed approach. When it comes to organizational and industrial contexts, sensors have evolved significantly with the introduction of the IoT. Pressure, optical, temperature, chemical sensors, and proximity are just a few examples of the many types of data that IoT devices may collect and send through sensor networks, allowing for greater efficiency. The model achieves a 91.37% accuracy rate in solar energy harvesting prediction after being trained using the Extended DSOM.

关键词： Temperature sensors Training Chemical sensors Temperature distribution Solar energy Sensors Internet of Things Optical sensors Reliability Intelligent sensors

Tensor neural networks for high-dimensional Fokker-Planck equations

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arXiv 2024年

作者： Wang, Taorui Hu, Zheyuan Kawaguchi, Kenji Zhang, Zhongqiang Karniadakis, George Em Department of Mathematical Sciences Worcester Polytechnic Institute WorcesterMA United States Department of Computer Science National University of Singapore 119077 Singapore Division of Applied Mathematics Brown University ProvidenceRI02912 United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

We solve high-dimensional steady-state Fokker-Planck equations on the whole space by applying tensor neural networks. The tensor networks are a linear combination of tensor products of one-dimensional feedforward networks or a linear combination of several selected radial basis functions. The use of tensor feedforward networks allows us to efficiently exploit auto-differentiation (in physical variables) in major Python packages while using radial basis functions can fully avoid auto-differentiation, which is rather expensive in high dimensions. We then use the physics-informed neural networks and stochastic gradient descent methods to learn the tensor networks. One essential step is to determine a proper bounded domain or numerical support for the Fokker-Planck equation. To better train the tensor radial basis function networks, we impose some constraints on parameters, which lead to relatively high accuracy. We demonstrate numerically that the tensor neural networks in physics-informed machine learning are efficient for steady-state Fokker-Planck equations from two to ten dimensions. Copyright © 2024, The Authors. All rights reserved.

关键词： Radial basis function networks

Narrative Analysis of True Crime Podcasts With Knowledge Graph-Augmented Large Language Models

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arXiv 2024年

作者： Leng, Xinyi Liang, Jason Mauro, Jack Wang, Xu Chapman, James Bertozzi, Andrea L. Lin, Junyuan Chen, Bohan Ye, Chenchen Daniel, Temple Brantingham, P. Jeffrey Carleton College Department of Mathematics and Statistics United States Claremont McKenna College Department of Mathematical Sciences United States University of California Los Angeles Department of Mathematics United States Loyola Marymount University Department of Mathematics Statistics and Data Science United States California Institute of Technology Computing + Mathematical Sciences Division of Engineering and Applied Science United States University of California Los Angeles Department of Computer Science United States University of California Los Angeles Department of Anthropology United States

Narrative data spans all disciplines and provides a coherent model of the world to the reader or viewer. Recent advancement in machine learning and Large Language Models (LLMs) have enable great strides in analyzing natural language. However, Large language models (LLMs) still struggle with complex narrative arcs as well as narratives containing conflicting information. Recent work indicates LLMs augmented with external knowledge bases can improve the accuracy and interpretability of the resulting models. In this work, we analyze the effectiveness of applying knowledge graphs (KGs) in understanding true-crime podcast data from both classical Natural Language Processing (NLP) and LLM approaches. We directly compare KG-augmented LLMs (KGLLMs) with classical methods for KG construction, topic modeling, and sentiment analysis. Additionally, the KGLLM allows us to query the knowledge base in natural language and test its ability to factually answer questions. We examine the robustness of the model to adversarial prompting in order to test the model's ability to deal with conflicting information. Finally, we apply classical methods to understand more subtle aspects of the text such as the use of hearsay and sentiment in narrative construction and propose future directions. Our results indicate that KGLLMs outperform LLMs on a variety of metrics, are more robust to adversarial prompts, and are more capable of summarizing the text into topics. © 2024, CC BY.

关键词： Knowledge graph

A graph-based convolutional neural network stock price prediction with leading indicators

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A graph-based convolutional neural network stock price predi...

作者： Wu, Jimmy Ming-Tai Li, Zhongcui Srivastava, Gautam Tasi, Meng-Hsiun Lin, Jerry Chun-Wei College of Computer Science and Engineering Shandong University of Science and Technology Shandong China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

The stock market is a capitalistic haven where the issued shares are transferred, traded, and circulated. It bases stock prices on the issue market, however, the structure and trading activities of the stock market are much more complicated than the issue market itself. Therefore, making an accurate prediction becomes an intricate as well as highly difficult task. On the other hand, because of the potential benefits of stock prediction, it attracts generation after generation of scholars as well as investors to continuously develop various prediction methods from different perspectives, a myriad of theories, a multitude of investment strategies, and different practical experiences. In this article, aiming at the task of time series (financial) feature extraction and prediction of price movements, a new convolutional novel neural network that can be called a framework to improve the prediction accuracy of stock trading is proposed. The method that is proposed is called SSACNN, a short form of stock sequence array convolutional neural network. SSACNN collects data including historical data of prices and its leading indicators (options/futures) for a stock to take an array as the input graph of the convolutional neural network framework. In our experimental results, five Taiwanese and American stocks were used as a benchmark to compare with the previous algorithms and proposed algorithm, the motion prediction performance of SSACNN has been improved significantly and proved that it has the potential to be applied in the real financial market. © 2020 John Wiley & Sons Ltd

关键词： Forecasting

An Ensemble Models for the Prediction of Sickle Cell Disease from Erythrocytes Smears

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EAI Endorsed Transactions on Pervasive Health and Technology 2023年 9卷

作者： Ayoade, Oluwafisayo Babatope Oladele, Tinuke Omolewa Imoize, Agbotiname Lucky Adeloye, Jerome Adetoye Awotunde, Joseph Bamidele Olorunyomi, Segun Omotayo Faboya, Olusola Theophilus Idowu, Ayorinde Oladele Department of Computing and Information Science School of Pure and Applied Sciences College of Science Bamidele Olumilua University of Education Science & Technology Ekiti State Ikere-Ekiti Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Kwara State Nigeria Department of Electrical and Electronics Faculty of Engineering University of Lagos Lagos State Akoka 100213 Nigeria Ekiti State Data Center Office Old Governor’s Office Ekiti State Ado-Ekiti Nigeria

INTRODUCTION: The human blood as a collection of tissues containing Red Blood Cells (RBCs), circular in shape and acting as an oxygen carrier, are frequently deformed by multiple blood diseases inherited from parents. These hereditary diseases of blood involve abnormal haemoglobin (Hb) or anemia which are major public health issues. Sickle Cell Disease (SCD) is one of the common non-communicable disease and genetic disorder due to changes in hematological conditions of the RBCs which often causes the inheritance of mutant Hb genes by the patient.. OBJECTIVES: The process of manual valuation, predictions and diagnosis of SCD necessitate for a passionate time spending and if not done properly can lead to wrong predictions and diagnosis. Machine Learning (ML), a branch of AI which emphases on building systems that improve performance based on the data they consume is appropriate. Despite previous research efforts in predicting with single ML algorithm, the existing systems still suffer from high false and wrong predictions. METHODS: Thus, this paper aimed at performing comparative analysis of individual ML algorithms and their ensemble models for effective predictions of SCD (elongated shapes) in erythrocytes blood cells. Three ML algorithms were selected, and ensemble models were developed to perform the predictions and metrics were used to evaluate the performance of the model using accuracy, sensitivity, Receiver Operating Characteristics-Area under Curve (ROC-AUC) and F1 score metrics. The results were compared with existing literature for model(s) with the best prediction metrics performance.. RESULTS: The analysis was carried out using Python programming language. Individual ML algorithms reveals that their accuracies show MLR=87%, XGBoost=90%, and RF=93%, while hybridized RF-MLR=92% and RF-XGBoost=99%. The accuracy of RF-XGBoost of 99% outperformed other individual ML algorithms and Hybrid models. CONCLUSION: Thus, the study concluded that involving hybridized M

关键词： Forecasting

Pandemics in silico: Scaling Agent-based Simulations on Realistic Social Contact Networks

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arXiv 2024年

作者： Kitson, Joy Costello, Ian Chen, Jiangzhuo Jiménez, Diego Hoops, Stefan Mortveit, Henning Meneses, Esteban Yeom, Jae-Seung Marathe, Madhav V. Bhatele, Abhinav Department of Computer Science University of Maryland College Park United States Google Inc. Mountain View United States Biocomplexity Institute and Initiative University of Virginia Charlottesville United States Max Planck Computing and Data Facility Garching Germany National Advanced Computing Collaboratory National High Technology Center San José Costa Rica Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore United States

Preventing the spread of infectious diseases requires implementing interventions at various levels of government and evaluating the potential impact and efficacy of those preemptive measures. Agent-based modeling can be used for detailed studies of the spread of such diseases in the presence of possible interventions. The computational cost of modeling epidemic diffusion through large social contact networks necessitates the use of parallel algorithms and resources in order to achieve quick turnaround times. In this work, we present Loimos, a scalable parallel framework for simulating epidemic diffusion. Loimos uses a hybrid of time-stepping and discrete event simulation to model disease spread, and is implemented on top of Charm++, an asynchronous, many-task runtime that enables over-decomposition and adaptive overlap of computation and communication. We demonstrate that Loimos is able to achieve significant speedups while scaling to large core counts. In particular, Loimos is able to simulate 200 days of a COVID-19 outbreak on a digital twin of California in about 42 seconds, for an average of 4.6 billion traversed edges per second (TEPS), using 4096 cores on Perlmutter at NERSC. © 2024, CC BY.

关键词： Discrete event simulation