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arXiv 2024年

作者： Bastidas, Victor M. Fitzpatrick, Nathan Joven, K.J. Rossi, Zane M. Islam, Shariful Van Voorhis, Troy Chuang, Isaac L. Liu, Yuan Department of Chemistry Massachusetts Institute of Technology CambridgeMA02139 United States Physics and Informatics Laboratory NTT Research Inc. 940 Stewart Dr. SunnyvaleCA94085 United States Quantinuum Terrington House 13-15 Hills Road CambridgeCB2 1NL United Kingdom Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics North Carolina State University RaleighNC27606 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC27606 United States Department of Computer Science North Carolina State University RaleighNC27606 United States

Symmetry is fundamental in the description and simulation of quantum systems. Leveraging symmetries in classical simulations of many-body quantum systems often results in an exponential overhead due to the exponentially growing size of some symmetry groups as the number of particles increases. Quantum computers hold the promise of achieving exponential speedup in simulating quantum many-body systems;however, a general method for utilizing symmetries in quantum simulations has not yet been established. In this work, we present a unified framework for incorporating symmetry groups into the simulation of many-body systems on quantum computers. The core of our approach lies in the development of efficient quantum circuits for symmetry-adapted projection onto irreducible representations of a group or pairs of commuting groups. We provide resource estimations for common groups, including the cyclic and permutation groups. Our algorithms demonstrate the capability to prepare coherent superpositions of symmetry-adapted states and to perform quantum evolution across a wide range of models in condensed matter physics and ab initio electronic structure in quantum chemistry. Specifically, we execute a symmetry-adapted quantum subroutine for small molecules in first quantization on noisy hardware, and demonstrate the emulation of symmetry-adapted quantum phase estimation for preparing coherent superpositions of quantum states in various irreps of a symmetry group. In addition, we present a discussion of major open problems regarding the use of symmetries in digital quantum simulations of many-body systems, paving the way for future systematic investigations into leveraging symmetries for practical quantum advantage. The broad applicability and rigorous resource estimation for symmetry transformations make our framework appealing for achieving provable quantum advantage on fault-tolerant quantum computers. The efficiency of the proposed symmetry-adapted subroutine holds the promi

关键词： Quantum computers

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A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart rate Estimation from Wearables

arXiv

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arXiv 2023年

作者： Jain, Pranay Ding, Cheng Rudin, Cynthia Hu, Xiao Department of Computer Science Duke University United States Department of Biomedical Engineering Georgia Institute of Technology Emory University United States Center for Data Science Nell Hodgson Woodruff School of Nursing Associated Faculty of Biomedical Informatics School of Medicine Associated Faculty of Computer Science College of Arts and Sciences Emory University United States

Smart watches and other wearable devices are equipped with photoplethysmography (PPG) sensors for monitoring heart rate and other aspects of cardiovascular health. However, PPG signals collected from such devices are susceptible to corruption from noise and motion artifacts, which cause errors in heart rate estimation. Typical denoising approaches filter or reconstruct the signal in ways that eliminate much of the morphological information, even from the clean parts of the signal that would be useful to preserve. In this work, we develop an algorithm for denoising PPG signals that reconstructs the corrupted parts of the signal, while preserving the clean parts of the PPG signal. Our novel framework relies on self-supervised training, where we leverage a large database of clean PPG signals to train a denoising autoencoder. As we show, our reconstructed signals provide better estimates of heart rate from PPG signals than the leading heart rate estimation methods. Further experiments show significant improvement in Heart rate Variability (HRV) estimation from PPG signals using our algorithm. We conclude that our algorithm denoises PPG signals in a way that can improve downstream analysis of many different health metrics from wearable devices. Copyright © 2023, The Authors. All rights reserved.

关键词： Photoplethysmography

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Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis

arXiv

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arXiv 2023年

作者： Bera, Asish Nasipuri, Mita Krejcar, Ondrej Bhattacharjee, Debotosh The Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Campus Rajasthan Pilani333031 India The Department of Computer Science and Engineering Jadavpur University Kolkata32 India The Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic

Human body-pose estimation is a complex problem in computer vision. Recent research interests have been widened specifically on the Sports, Yoga, and Dance (SYD) postures for maintaining health conditions. The SYD pose categories are regarded as a fine-grained image classification task due to the complex movement of body parts. Deep Convolutional Neural Networks (CNNs) have attained significantly improved performance in solving various human body-pose estimation problems. Though decent progress has been achieved in yoga postures recognition using deep learning techniques, fine-grained sports, and dance recognition necessitates ample research attention. However, no benchmark public image dataset with sufficient inter-class and intra-class variations is available yet to address sports and dance postures classification. To solve this limitation, we have proposed two image datasets, one for 102 sport categories and another for 12 dance styles. Two public datasets, Yoga-82 which contains 82 classes and Yoga-107 represents 107 classes are collected for yoga postures. These four SYD datasets are experimented with the proposed deep model, SYD-Net, which integrates a patch-based attention (PbA) mechanism on top of standard backbone CNNs. The PbA module leverages the self-attention mechanism that learns contextual information from a set of uniform and multi-scale patches and emphasizes discriminative features to understand the semantic correlation among patches. Moreover, random erasing data augmentation is applied to improve performance. The proposed SYD-Net has achieved state-of-the-art accuracy on Yoga-82 using five base CNNs. SYD-Net’s accuracy on other datasets is remarkable, implying its efficiency. Our Sports-102 and Dance-12 datasets are publicly available at https://***/view/syd-net/home. © 2023, CC BY.

关键词： Sports

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Development and implementation of unmanned vehicles through artificial intelligence involving communication system with sensors and control parameters

引用

Measurement: Sensors 2024年 33卷

作者： Srinivas, T. Aditya Sai Yadav, ranjeet Gowri, V. Chandraprabha, K. Ponnusamy, Sivakumar Mavaluru, Dinesh Department of AIML Jayaprakash Narayan College of Engineering Telangana Mahabubnagar India Government Polytechnic Daman UT Administration of Dadra and Nagar Haveli and Daman and Diu Varkund Nani Daman 396210 India Electronics and Communication Engineering J.J.College of Engineering and Technology Ammapettai Trichy620009 India Department of Information Technology Bannari Amman Institute of Technology Tamilnadu Sathyamangalam India Department of Computer Science and Engineering KSR College of Engineering Tiruchengode TamilNadu Namakkal India Department of Information Technology College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

Physical situations that are duplicated in virtual settings are what the term "digital twin" refers to. Utilizing the Internet of Things and sensors to improve microgrid operation, the Microgrid Digital Twin (MGDT) exhibits bidirectional data sharing and sophisticated communication characteristics. Smart grid functionality is enhanced, limits are set in real-time, and data is securely stored by MGDT. It directs the planning and execution phases of microgrid operations. Concurrently, industry and transportation innovation is propelled by the Industrial Revolution. AI-driven ground-dependent controls, communication systems, and sensors are essential for breakthroughs in unmanned vehicle safety. This abstract clarifies how important digital twins more specifically, MGDTs are to system operations optimization. In addition to highlighting technological advancement and the use of AI to ensure the safety of unmanned vehicles, it provides insights into the potentially revolutionary applications of digital twins across a range of industries. © 2024 The Authors

关键词： Accident prevention

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Enhancing Acceptance of Automated Vehicles Through Gamification: Insights from a Driving Simulator Study

SSRN

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SSRN 2025年

作者： Li, Chenchang Yin, rao Yang, Bo Guan, Muhua Xie, Kun Nakano, Kimihiko Institute of Industrial Science University of Tokyo Tokyo153-8505 Japan School of Design & Innovation China Academy of Art Hangzhou311100 China School of Computer Science & System Engineering Kyushu Institute of Technology Fukuoka820-8502 Japan Department of Civil and Environmental Engineering Transportation Informatics Lab Old Dominion University NorfolkVA23529 United States

Automated vehicles hold great potential to revolutionize transportation, but subjective user acceptance remains a major obstacle to their widespread adoption and the realization of anticipated safety benefits. Recognizing this challenge, this study explores the role of gamification in enhancing user acceptance of automated vehicles and promoting safer driver engagement. By designing a gamified interaction framework and implementing it in a driving simulator experiment, the research addresses three core questions: Does gamification work? Why does it work? How can it be effectively implemented? In the experiment, participants experienced automated driving scenarios with integrated gamified interactions during overtaking tasks. Data on users' attitudes, behaviors, and eye movements were collected and analyzed through qualitative and quantitative methods. Furthermore, the study incorporated design research techniques, including interviews and user journey mapping, into the scientific framework to derive meaningful and actionable insights. The results reveal that gamification significantly improves user acceptance by enhancing Perceived Ease of Use and strengthening Behavioral Intentions. At a deeper mechanistic level, gamification enhances users' situational awareness and trust by increasing their perception efficiency during interactions and raising their risk tolerance, thereby shifting the balance between safety and reward. Based on these mechanisms and further findings, the study proposes three design strategies to maximize the benefits of gamification: designing system transparency, tailoring experiences to individual user characteristics, and leveraging incentive mechanisms to engage and motivate users effectively. This study's findings provide theoretical foundations and practical design directions for leveraging gamification to enhance human-vehicle interactions, utimately contributing to the development of safer and more efficient automated driving systems. © 2

关键词： Eye movements

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VILMA: A ZERO-SHOT BENCHMARK FOR LINGUISTIC AND TEMPOraL GROUNDING IN VIDEO-LANGUAGE MODELS

arXiv

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arXiv 2023年

作者： Kesen, Ilker Pedrotti, Andrea Dogan, Mustafa Cafagna, Michele Acikgoz, Emre Can Parcalabescu, Letitia Calixto, Iacer Frank, Anette Gatt, Albert Erdem, Aykut Erdem, Erkut Koç University KUIS AI Center 2 Koç University Department of Computer Engineering Turkey University of Pisa Department of Computer Science Italy Institute of Information Science and Technologies "Alessandro Faedo" Italy Hacettepe University Department of Computer Engineering 6 Aselsan Research Turkey University of Malta Institute of Linguistics and Language Technology Malta Heidelberg University Department of Computational Linguistics Germany Amsterdam UMC University of Amsterdam Department of Medical Informatics Netherlands Amsterdam Public Health Methodology & Mental Health Amsterdam Netherlands Utrecht University Department of Information and Computing Sciences Netherlands

With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their visio-linguistic capabilities. To address this challenge, we present VILMA1 (Video Language Model Assessment), a task-agnostic benchmark that places the assessment of fine-grained capabilities of these models on a firm footing. Task-based evaluations, while valuable, fail to capture the complexities and specific temporal aspects of moving images that VidLMs need to process. Through carefully curated counterfactuals, VILMA offers a controlled evaluation suite that sheds light on the true potential of these models, as well as their performance gaps compared to human-level understanding. VILMA also includes proficiency tests, which assess basic capabilities deemed essential to solving the main counterfactual tests. We show that current VidLMs’ grounding abilities are no better than those of vision-language models which use static images. This is especially striking once the performance on proficiency tests is factored in. Our benchmark serves as a catalyst for future research on VidLMs, helping to highlight areas that still need to be explored. © 2023, CC BY.

关键词： Computational linguistics

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Evaluating RNN Models for Multi-Step Traffic Matrix Prediction

Evaluating RNN Models for Multi-Step Traffic Matrix Predicti...

引用

Information technology and Data Science (CITDS), IEEE Conference on

作者： Mohammed Nsaif Gergely Kovásznai Hasanein D. Rjeib Ali Malik Ruairí de Fréin Faculty of informatics University of Debrecen Hungary Faculty of Computer Science and Mathematics University of Kufa Iraq Department of Business Management Al-imam University College Balad Iraq Faculty of informatics Eszterházy Károly Catholic University Hungary Faculty of Engineering University of Kufa Iraq Institute of Information Technology University of Miskolc Hungary School of Electrical and Electronic Engineering Technological University Dublin Ireland

ISBN: (数字)9798350387889

ISBN: (纸本)9798350387896

Network traffic matrix prediction is used to estimate the patterns of future network flows before they are initiated. Traffic matrix prediction is needed by a wide range of network management functions such as network monitoring and it relies on historical data. In this paper, we address the task of multi-time step traffic matrix prediction using Recurrent Neural Networks (RNN). Our objective is to conduct a comparative study of different types of RNNs and to evaluate their ability to predict multi-time step Origin-Destination traffic matrices. Experiments show that RNNs are capable of predicting multiple steps of traffic matrices, however, the RMSE of the predictions increases as we move further away from the last true value. Our primary finding is that the RNN-GRU show has the best prediction ability in the very beginning steps with an RMSE of 0.048, while RNN-LSTM demonstrated higher capability with the last steps, having an RMSE value of 0.058.

关键词： Recurrent neural networks Power demand Telecommunication traffic Predictive models Data science Data models computer networks Numerical models Information technology Monitoring

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Synergizing Airborne Non-Terrestrial Networks and Reconfigurable Intelligent Surfaces-Aided 6G IoT

arXiv

引用

arXiv 2023年

作者： Jamshed, Muhammad Ali Kaushik, Aryan Toka, Mesut Shin, Wonjae Shakir, Muhammad Zeeshan Dash, Soumya P. Dardari, Davide The College of Science and Engineering University of Glasgow United Kingdom The School of Engineering & Informatics University of Sussex United Kingdom The Department of Electrical and Computer Engineering Ajou University Korea Republic of The School of Electrical Engineering Korea University Korea Republic of The Department of Electrical and Computer Engineering Princeton University United States The School of Computing Engineering and Physical Sciences University of the West of Scotland United Kingdom The School of Electrical Sciences Indian Institute of Technology Bhubaneswar India The Department of Electrical Electronic and Information Engineering University of Bologna Italy

On the one hand, Reconfigurable Intelligent Surfaces (RISs) emerge as a promising solution to meet the demand for higher data rates, improved coverage, and efficient spectrum utilization. On the other hand, Non-Terrestrial Networks (NTNs) offer unprecedented possibilities for global connectivity. Moreover, the NTN can also support the upsurge in the number of Internet of Things (IoT) devices by providing reliable and ubiquitous connectivity. Although NTNs have shown promising results, there are several challenges associated with their usage, such as signal propagation delays, interference, security, etc. In this article, we have discussed the possibilities of integrating RIS with an NTN platform to overcome the issues associated with NTN. Furthermore, through experimental validation, we have demonstrated that the RIS-assisted NTN can play a pivotal role in improving the performance of the entire communication system. © 2023, CC BY.

关键词： Internet of things

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

引用

Procedia computer Science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

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Quantum advantage for noisy channel discrimination

引用

Physical Review A 2022年第3期105卷 032401-032401页

作者： Zane M. Rossi Jeffery Yu Isaac L. Chuang Sho Sugiura Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Department of Electrical Engineering and Computer Science and Co-Design Center for Quantum Advantage Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Physics and Informatics Laboratory NTT Research Inc. 940 Stewart Dr. Sunnyvale California 94085 USA

Many quantum mechanical experiments can be viewed as multiround interactive protocols between known quantum circuits and an unknown quantum process. Fully quantum “coherent” access to the unknown process is known to provide an advantage in many discrimination tasks compared to when only incoherent access is permitted, but it is unclear if this advantage persists when the process is noisy. Here, we show that a quantum advantage can be maintained when distinguishing between two noisy single-qubit rotation channels. Numerical and analytical calculations reveal a distinct transition between the performance of fully coherent and fully incoherent protocols as a function of noise strength. Moreover, the size of the region of coherent quantum advantage shrinks inverse polynomially in the number of channel uses, and in an intermediate regime an improved strategy is a hybrid of fully coherent and fully incoherent subroutines. The fully coherent protocol is based on quantum signal processing, suggesting a generalizable algorithmic framework for the study of quantum advantage in the presence of realistic noise.

关键词： Quantum algorithms Quantum channels Quantum computation Quantum information processing Quantum sensing Quantum simulation

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