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arXiv 2024年

作者： Zi, Wei Wang, Siyi Kim, Hyunji Sun, Xiaoming Chattopadhyay, Anupam Rebentrost, Patrick State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Hansung University Seoul Korea Republic of Centre for Quantum Technologies National University of Singapore Singapore School of Computing National University of Singapore Singapore

In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers. Among the components in this domain, activation functions hold a fundamental and indispensable role. Our research focuses on the development of activation functions quantum circuits for integration into fault-tolerant quantum computing architectures, with an emphasis on minimizing T-depth. Specifically, we present novel implementations of ReLU and leaky ReLU activation functions, achieving constant T-depths of 4 and 8, respectively. Leveraging quantum lookup tables, we extend our exploration to other activation functions such as the sigmoid. This approach enables us to customize precision and T-depth by adjusting the number of qubits, making our results more adaptable to various application scenarios. This study represents a significant advancement towards enhancing the practicality and application of quantum machine learning. Copyright © 2024, The Authors. All rights reserved.

关键词： Table lookup

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Probing the limits of variational quantum algorithms for nonlinear ground states on real quantum hardware: The effects of noise

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Physical Review A 2025年第1期111卷 012626-012626页

作者： Muhammad Umer Eleftherios Mastorakis Sofia Evangelou Dimitris G. Angelakis Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore 117543 School of Electrical and Computer Engineering Technical University of Crete Chania 73100 Greece AngelQ Quantum Computing 531A Upper Cross Street #04-95 Hong Lim Complex Singapore 051531

A recently proposed variational quantum algorithm has expanded the horizon of variational quantum computing to nonlinear physics and fluid dynamics. In this work, we probe the ability of such approach to capture the ground state of the nonlinear Schrödinger equation for a range of parameters on real superconducting quantum processors. Specifically, we study the expressivity of real-amplitude, hardware-efficient Ansatz to capture the ground state of this nonlinear system across various interaction regimes and implement different noise scenarios in both simulators and cloud processors. Our investigation reveals that although quantum hardware noise impairs the evaluation of the energy cost function, certain small instances of the problem consistently converge to the ground state. We test and demonstrate a variety of cases on IBM Q superconducting devices and analyze the discrepancies in the energy cost function evaluation due to quantum hardware noise. These discrepancies are absent in the state fidelity estimation because of the shallow state preparation circuit. Our comprehensive analysis offers valuable insights into the practical implementation and advancement of the variational algorithms for nonlinear problems.

关键词： Cost functions

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A Study in Zucker: Insights on Interactions Between Humans and Small Service Robots

arXiv

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arXiv 2023年

作者： Day, Alex Karamouzas, Ioannis The School of Computing Clemson University SC United States Department of Computer Science and Engineering University of California Riverside United States CYENS Centre of Excellence Nicosia Cyprus

Despite recent advancements in human-robot interaction (HRI), there is still limited knowledge about how humans interact and behave in the presence of small service indoor robots and, subsequently, about the human-centered behavior of such robots. This also raises concerns about the applicability of current trajectory prediction methods to indoor HRI settings as well as the accuracy of existing crowd simulation models in shared environments. To address these issues, we introduce a new HRI dataset focusing on interactions between humans and small differential drive robots running different types of controllers. Our analysis shows that anticipatory and non-anticipatory robot controllers impose similar constraints to humans’ safety and efficiency. Additionally, we found that current state-of-the-art models for human trajectory prediction can adequately extend to indoor HRI settings. Finally, we show that humans respond differently to small differential drives than to other humans when collisions are imminent, since interacting with small robots can only cause a finite level of social discomfort as compared to human-human interactions. Our dataset and related code and models are available at: https://***/ZuckerDataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Human robot interaction

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Forecasting Hierarchical Time Series Using Non-Linear Mappings

SSRN

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SSRN 2024年

作者： Wickramasuriya, Shanika L. Bandara, Kasun Hewamalage, Hansika Perera, Maneesha Department of Econometrics and Business Statistics Monash University Melbourne Australia School of Computing and Information Systems Melbourne Centre for Data Science The University of Melbourne Melbourne Australia School of Computer Science & Engineering University of New South Wales Sydney Australia Department of Mechanical Engineering School of Electrical Mechanical and Infrastructure Engineering The University of Melbourne Melbourne Australia

Forecast reconciliation is a procedure for forecasting multivariate time series with linear aggregation constraints to ensure that forecasts satisfy the same set of constraints. Most state-of-the-art reconciliation methods reconcile the forecasts using linear mappings to project the base forecasts onto a coherent subspace. They also use assumptions such as the forecasts are unbiased and residuals are jointly covariance stationary. In this study, we propose a non-linear forecast reconciliation approach by using machine learning methods, relaxing the assumptions of traditional linear reconciliation approaches. We introduce a novel loss function incorporating non-linear mappings to obtain a set of coherent forecasts from the individual base forecasts. To obtain the weights of the non-linear mapping between the base forecasts, we train a feed-forward neural network with the proposed loss function using transformed fitted values of the base models as inputs to the network. We evaluate the proposed methodology against state-of-the-art linear reconciliation approaches on multiple benchmark datasets. The results indicate that the proposed method works well when there is a reasonable number of observations in comparison to the number of bottom-level and non-bottom level series. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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FairTP: A Prolonged Fairness Framework for Traffic Prediction

arXiv

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arXiv 2024年

作者： Xia, Jiangnan Yang, Yu Shen, Jiaxing Wang, Senzhang Cao, Jiannong School of Computer Science and Engineering Central South University China Centre for Learning Teaching and Technology The Education University of Hong Kong Hong Kong School of Data Science Lingnan University Hong Kong The Department of Computing The Hong Kong Polytechnic University Hong Kong

Traffic prediction is pivotal in intelligent transportation systems. Existing works mainly focus on improving the overall accuracy, overlooking a crucial problem of whether prediction results will lead to biased decisions by transportation authorities. In practice, the uneven deployment of traffic sensors in different urban areas produces imbalanced data, making the traffic prediction model fail in some areas and leading to unfair regional decision-making that eventually severely affects equity and quality of residents' life. Additionally, existing fairness machine learning models fail to preserve fair traffic prediction for a prolonged time. Although they can achieve fairness at certain time points, such static fairness will be broken as the traffic conditions change. To fill this research gap, we investigate prolonged fair traffic prediction, introduce two novel fairness definitions tailored to dynamic traffic scenarios, and propose a prolonged fairness traffic prediction framework, namely FairTP. We argue that fairness in traffic scenarios changes dynamically over time and across areas. Each traffic sensor or city area has state that alternates between "sacrifice" and "benefit" based on its prediction accuracy (high accuracy indicates "benefit" state). Prolonged fairness is achieved when the overall states of sensors similar within a given ***, we first define region-based static fairness and sensor-based dynamic fairness. Next, we designed a state identification module in FairTP to discriminate between states of "sacrifice" or "benefit" to enable prolonged fairness-aware traffic predictions. Lastly, a state-guided balanced sampling strategy is designed to select training examples to promote prediction fairness further, mitigating the performance disparities among regions with imbalanced traffic sensors. Extensive experiments in two real-world datasets show that FairTP significantly improves prediction fairness without causing much accuracy degrada

关键词： Intelligent systems

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Quantifying White Matter Hyperintensity and Brain Volumes in Heterogeneous Clinical and Low-Field Portable MRI

Quantifying White Matter Hyperintensity and Brain Volumes in...

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IEEE International Symposium on Biomedical Imaging

作者： Pablo Laso Stefano Cerri Annabel Sorby-Adams Jennifer Guo Farrah Mateen Philipp Goebl Jiaming Wu Peirong Liu Hongwei Bran Li Sean I. Young Benjamin Billot Oula Puonti Gordon Sze Sam Payabavash Adam DeHavenon Kevin N. Sheth Matthew S. Rosen John Kirsch Nicola Strisciuglio Jelmer M. Wolterink Arman Eshaghi Frederik Barkhof W. Taylor Kimberly Juan Eugenio Iglesias Massachusetts General Hospital Harvard Medical School USA Electrical Engineering Mathematics and Computer Science University of Twente The Netherlands Pioneer Centre for AI University of Copenhagen Denmark Centre for Medical Image Computing University College London UK Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology USA Danish Research Centre for Magnetic Resonance Denmark Yale School of Medicine USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated segmentation and quantification is desirable but existing methods require high-resolution MRI with good signal-to-noise ratio (SNR). This precludes application to clinical and low-field portable MRI (pMRI) scans, thus hampering large-scale tracking of atrophy and WMH progression, especially in underserved areas where pMRI has huge potential. Here we present a method that segments white matter hyperintensity and 36 brain regions from scans of any resolution and contrast (including pMRI) without retraining. We show results on eight public datasets and on a private dataset with paired high- and low-field scans (3T and 64mT), where we attain strong correlation between the WMH (ρ=.85) and hippocampal volumes (ρ=.89) estimated at both fields. Our method is publicly available as part of FreeSurfer, at: http://***/fswiki/WMH-SynthSeg.

关键词： Atrophy Neuroimaging Image segmentation Multiple sclerosis Image resolution Magnetic resonance imaging White matter

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Metaverse for Education: Developments, Challenges, and Future Direction

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computer Applications in engineering Education 2025年第3期33卷

作者： Chamola, Vinay Peelam, Mritunjay Shall Mittal, Uday Hassija, Vikas Singh, Anushka Pareek, Ritawari Mangal, Poorvi Sangwan, Devika de Albuquerque, Victor Hugo C. Mahmud, Mufti Brown, David J. Department of Electrical and Electronics Engineering Internet of Things Research Group APPCAIR BITS-Pilani Rajasthan Pilani India Department of Electrical and Electronics Engineering BITS-Pilani Rajasthan Pilani India School of Computer Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar India Department of Computer Science and Engineering Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Humanities and Social Sciences BITS-Pilani Rajasthan Pilani India Fortaleza Brazil Department of Computer Science Computing and Informatics Research Centre Medical Technologies Innovation Facility at Nottingham Trent University Clifton Lane Nottingham United Kingdom Department of Computer Science Computing and Informatics Research Centre at Nottingham Trent University Clifton Lane Nottingham United Kingdom

The rapid advancements in digital technologies such as artificial intelligence (AI), virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), and the internet of things (IoT) have revolutionized various sectors, including education. Metaverse, a convergence of these transformative technologies, offers immersive, personalized, and interactive experiences, making it a powerful tool in modern education. This paper explores the Metaverse's role in enhancing education by examining its architecture, types, and components while addressing practical implementation challenges, and follows a structured review protocol to ensure a comprehensive analysis, including systematic research, paper selection, and a critical examination of relevant studies from reputable databases such as Google Scholar, IEEE Xplore, ACM, and Springer. The research objectives focus on evaluating the Metaverse's applications in education, ethical challenges, technological limitations, and potential strategies for sustainable integration. Key research questions address the need for Metaverse adoption in education, its benefits, challenges, and future directions. The Metaverse cultivates essential skills such as empathy, ethical reasoning, and effective communication by providing students with customized, immersive learning environments. However, ethical concerns, technical barriers, and infrastructural costs pose significant obstacles to its widespread adoption. It discusses strategies to solve these barriers, explores applications in distance learning, and proposes future research directions to create scalable and sustainable educational models in the Metaverse. Through this structured inquiry, the paper establishes the Metaverse as a transformative force in education, blending technological innovation with instructional advancement. © 2025 Wiley Periodicals LLC.

关键词： Contrastive Learning

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare 7

Artificial Intelligence-based Service Chains Scheduling for ...

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7th International Conference on Contemporary computing and Informatics, IC3I 2024

作者： Soni, Mukesh Rusho, Maher Ali Byeon, Haewon Alghamdi, Azzah Hannan, Shaikh Abdul Dave, Parth Rameshchandra Dr. D. Y. Patil School of Science & Technology Tathawade Dr. D. Y. Patil Vidyapeeth Marashtra Pune India University of Colorado Department of Lockheed Martin Engineering Management BoulderCO80309 United States Inje University Department of Ai and Software & Medical Big Data Research Centre Gimhae50834 Korea Republic of Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia AlBaha University Faculty of Computing and Information AlBaha Saudi Arabia L. D. College of Engineering Department of Computer Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350350067

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare. © 2024 IEEE.

关键词： Electronic health record

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Novel Applications of Cloud computing and Deep Learning Techniques for Data Analysis

Novel Applications of Cloud Computing and Deep Learning Tech...

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International Conference on computing and Networking Technology (ICCNT)

作者： V.Aravinda Rajan Devesh Tiwari H. Summia Parveen Aditya Verma Rishi Prakash Shukla Vikas V Pawar Department of Computer Science and Engineering (School of Computing) Kalasalingam Academy of Research and Education Krishnankoil Srivilliputhur Tamil Nadu Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Computer Science Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Jaipuriya Institute of Management Jaipur India Dr. D Y Patil Vidyapeeth Centre for Online Learning Pune

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In recent years, cloud computing and deep getting to know strategies have emerged as effective gear for information analysis. This combination permits for the efficient processing of large amounts of statistics, permitting researchers to gain precious insights and make informed selections. This technical abstract will talk the unconventional packages of cloud computing and deep learning strategies for statistics evaluation and how they’ve revolutionized the sphere. Cloud computing has made it feasible to save and get admission to big amounts of statistics from any region, eliminating the need for costly and cumbersome hardware. This has opened up new possibilities for data evaluation as researchers can now access and examine information from a couple of resources: social media, medical studies, and monetary statistics. Deep studying techniques, then again, have proven terrific fulfillment in processing complicated and unstructured statistics. Via mimicking the shape and functions of the human brain, deep mastering fashions can identify styles and traits in information that traditional methods may additionally leave out.

关键词： Deep learning Cloud computing Accuracy Social networking (online) Shape Scalability Organizations Prediction algorithms Hardware Information analysis

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Image Registration and Deep NeuroFuzzy Networks for Mitigating Atmospheric Turbulence Effects in Consumer-Based Optical Imaging

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IEEE Transactions on Consumer Electronics 2025年

作者： Khowaja, Sunder Ali Ali, Usman Dev, Kapal Lee, Ik Hyun Dublin City University and ADAPT Centre School of Computing Dublin Ireland Munster Technological University Department of Computer Science Cork Ireland Tech University of Korea Department of Mechatronics Engineering Siheung15073 Korea Republic of IKLAB Inc Seoul08513 Korea Republic of

Consumer-based optical imaging systems are characterized as big data processing systems, which are drastically affected by atmospheric turbulences that add geometric distortions and blur effect to the images when used in outdoor condition. Physics-grounded simulators have been proposed recently to generate synthetic data but the generalization to the real-world turbulent images is not so good. In this paper, we combine the characteristics of image registration, deep neurofuzzy methods, and channel-attention based discriminative learning strategy to propose image registration, neurofuzzy based denoising, and deblurring network (RND2Net). The RND2Net is designed on a principle that it does not require turbulent image pairs (ground truth images) to train the network, which closely resembles the real-world situation used as consumer devices. The registration module focuses on the region-based fusion techniques while the denoising and deblurring module incorporates deep neurofuzzy network along with dense residual blocks and channel attention mechanism to train the network. The RND2Net is also designed to reduce the noise and blur effect from images, while generalizing on the down-stream tasks, such as text recognition. Experimental results show that the RND2Net yields better performance quantitatively as qualitatively on synthetic and real-world datasets in comparison to existing state-of-the-art methods. © 2025 IEEE.

关键词： Image registration

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