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13th Innovations in Theoretical computer Science Conference, ITCS 2022

作者： Chia, Nai-Hui Chou, Chi-Ning Zhang, Jiayu Zhang, Ruizhe Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States School of Engineering and Applied Sciences Harvard University BostonMA United States Department of Computer Science Boston University MA United States California Institute and Technology PasadenaCA United States Department of Computer Science The University of Texas AustinTX United States

ISBN: (纸本)9783959772174

In this work, we initiate the study of the Minimum Circuit Size Problem (MCSP) in the quantum setting. MCSP is a problem to compute the circuit complexity of Boolean functions. It is a fascinating problem in complexity theory - its hardness is mysterious, and a better understanding of its hardness can have surprising implications to many fields in computer science. We first define and investigate the basic complexity-theoretic properties of minimum quantum circuit size problems for three natural objects: Boolean functions, unitaries, and quantum states. We show that these problems are not trivially in NP but in QCMA (or have QCMA protocols). Next, we explore the relations between the three quantum MCSPs and their variants. We discover that some reductions that are not known for classical MCSP exist for quantum MCSPs for unitaries and states, e.g., search-to-decision reductions and self-reductions. Finally, we systematically generalize results known for classical MCSP to the quantum setting (including quantum cryptography, quantum learning theory, quantum circuit lower bounds, and quantum fine-grained complexity) and also find new connections to tomography and quantum gravity. Due to the fundamental differences between classical and quantum circuits, most of our results require extra care and reveal properties and phenomena unique to the quantum setting. Our findings could be of interest for future studies, and we post several open problems for further exploration along this direction. © Nai-Hui Chia, Chi-Ning Chou, Jiayu Zhang, and Ruizhe Zhang;licensed under Creative Commons License CC-BY 4.0

关键词： Boolean functions

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Wide-range Angle-sensitive Plasmonic Color Printing with a Lossy Resonator

Wide-range Angle-sensitive Plasmonic Color Printing with a L...

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CLEO: Applications and technology, CLEO:A and T 2023 - Part of Conference on Lasers and Electro-Optics 2023

作者： Chowdhury, Sarah N. Simon, Jeffrey Nowak, Michal P. Prokopeva, Ludmila J. Pagadala, Karthik Fruhling, Colton Nyga, Piotr Bravo, Esteban Garcia Kildishev, Alexander V. Shalaev, Vladimir M. Boltasseva, Alexandra School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University 1205 W State St West LafayetteIN47906 United States Institute of Optoelectronics Military University of Technology 2 Kaliskiego St Warsaw00-908 Poland Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Toruń87-100 Poland Department of Computer Graphics Technology Purdue University 401 N. Grant St Knoy Hall West LafayetteIN47907 United States

ISBN: (纸本)9781957171258

We demonstrate a wide gamut of color generation by large-scale, lithography-free, and environment-friendly plasmonic structures with a resolution of 100 µm for macroscopic color printing by utilizing femtosecond laser photomodification of multi-functional optical elements. CLEO 2023 © Optica Publishing Group 2023, © 2023 The Author(s)

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Application of machine learning models for risk estimation and risk prediction of classical swine fever in Assam, India

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VirusDisease 2023年第4期34卷 514-525页

作者： Suresh, Kuralayanapalya Puttahonnappa Barman, Nagendra Nath Bari, Tarushree Jagadish, Dikshitha Sushma, Bylaiah Darshan, H.V. Patil, Sharanagouda S. Bora, Mousumi Deka, Abhijit Spatial Epidemiology Laboratory Indian Council of Agricultural Research-National Institute of Veterinary Epidemiology and Disease Informatics Karnataka Yelahanka Bengaluru India Department of Veterinary Microbiology College of Veterinary Science Assam Agricultural University Guwahati India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Matthikere Karnataka Bengaluru India Virology Laboratory Indian Council of Agricultural Research-National Institute of Veterinary Epidemiology and Disease Informatics Karnataka Yelahanka Bengaluru India Department of Veterinary Pathology College of Veterinary Science Assam Agricultural University Guwahati India

The present study is aimed to develop an early warning system of Classical swine fever (CSF) disease by applying machine learning models and to study the climate-disease relationship with respect to the spatial occurrence and outbreaks of the disease in the north-eastern state of Assam, India. The disease incidence data from the year 2005 to 2021 was used. The linear discriminant analysis (LDA) revealed that significant environmental and remote sensing risk factors like air temperature, enhanced vegetation index, land surface temperature, potential evaporation rate and wind speed were significantly contributing to CSF incidences in Assam. Furthermore, the climate-based disease modelling was applied to relevant ecological and environmental risk factors determined using LDA and risk maps were generated. The western and eastern regions of the state were predicted to be at high risk of CSF with presence of significant hotspots. For the districts that are significantly clustered, the Basic reproduction number (R₀) was calculated after the predicted results were superimposed onto the risk maps. The R₀ value ranged from 1.04 to 2.07, implying that the eastern and western regions of Assam are more susceptible to CSF. Machine learning models were implemented using R statistical software version 3.1.3. The random forest, classification tree analysis and gradient boosting machine were found to be the best-fitted models for the study group. The models’ performance was measured using the Receiving Operating Characteristic (ROC) curve, Cohen’s Kappa, True Skill Statistics, Area Under ROC Curve, ACCUraCY, ERROR raTE, F1 SCORE, and Logistic Loss. As a part of the suggested study, these models will help us to understand the disease transmission dynamics, risk factors and spatio-temporal pattern of spread and evaluate the efficacy of control measures to battle the economic losses caused by CSF outbreaks. © 2023, The Author(s), under exclusive licence to Indian V

关键词： Assam Classical swine fever Incidence Machine learning Risk modelling Swine

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Annealing-Based Optimization for Selecting Training Space in Ensemble Learning

Annealing-Based Optimization for Selecting Training Space in...

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International Conference of Advanced informatics: Concept, Theory and Application (ICAICTA)

作者： Lenny Putri Yulianti Agung Trisetyarso Judhi Santoso Kridanto Surendro School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta Indonesia

Ensemble learning integrates many learners to achieve better performance, however, it can increase the complexity dramatically. Redundant learners should be pruned to reduce ensemble complexity while allowing for increased accuracy. There are two common ways to achieve this, which include selecting the optimal training space and the optimal trained learners. This study contributes to reducing redundant learners by selecting the optimal training space using annealing-based optimization. The optimal training space was defined by 1) measuring the similarity between training spaces (clusters) and 2) searching for the best set of training spaces by minimizing the similarity between the selected training spaces. The proposed method was tested on 10 datasets from the UCI repository and compared to five state-of-the-art random subspace methods. The results showed that the proposed method could reduce the size of the ensemble while significantly increasing its accuracy.

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Assessment of Post-Disaster Concerns using Social Network Analysis and Machine Learning: A Study of 2018 Palu Disaster

Assessment of Post-Disaster Concerns using Social Network An...

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International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

作者： Tyanita Puti Marindah Wardhani Subhajyoti Samaddar Hirokazu Tatano Zulkifli Tahir Sudip Roy Department of Informatics Faculty of Engineering Universitas Hasanuddin Makassar Indonesia Disaster Prevention Research Institute Kyoto University Kyoto Japan Centre of Excellence in Disaster Mitigation and Management (CoEDMM) Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

ISBN: (数字)9798350367928

ISBN: (纸本)9798350367935

Post-disaster needs assessment plays a critical role in developing emergency response programs and disaster preparedness. An assessment of needs identifies possible interventions or assistance and the necessary resources for emergency responses. This emergency need assessment assists stakeholders in defining possible response options, evaluating the priorities of needs of affected communities, and determining what support will be required immediately. The prevalent use of social media in Indonesia highlights its significance as a crucial means of acquiring prompt, effortless, individualized, and geo-specific information regarding the demands and requirements of communities impacted by a calamity. This study aims to analyze the tweets posted by local communities affected by the 2018 Central Sulawesi earthquakes, tsunami, and liquefaction using the Social Network Analysis (SNA) method based on machine learning and natural language processing. Through this analysis, we intend to observe the spatial interaction and relationship among the affected areas of communities for the needs and concerns after the disaster to enhance the relief operation. We found that social media users, government, and humanitarian organizations effectively shared information during the disaster. This study also underlined that the behavioral responses of individuals to disasters were not limited to the disaster-stricken areas but extended far beyond them. This action suggests that they were willing to exhibit a more empathetic, benevolent, and philanthropic demeanor in the face of such traumatic events.

关键词： Social networking (online) Disasters Government Information sharing Machine learning Tsunami Natural language processing Stakeholders Open data Videos

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Cyber-Physical Steganography in Robotic Motion Control

arXiv

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arXiv 2025年

作者： Chang, Ching-Chun Lin, Yijie Echizen, Isao The Information and Society Research Division National Institute of Informatics Japan The Department of Information Engineering and Computer Science Feng Chia University Taiwan The Graduate School of Information Science and Technology University of Tokyo Japan

Steganography, the art of information hiding, has continually evolved across visual, auditory and linguistic domains, adapting to the ceaseless interplay between steganographic concealment and steganalytic revelation. This study seeks to extend the horizons of what constitutes a viable steganographic medium by introducing a steganographic paradigm in robotic motion control. Based on the observation of the robot's inherent sensitivity to changes in its environment, we propose a methodology to encode messages as environmental stimuli influencing the motions of the robotic agent and to decode messages from the resulting motion trajectory. The constraints of maximal robot integrity and minimal motion deviation are established as fundamental principles underlying secrecy. As a proof of concept, we conduct experiments in simulated environments across various manipulation tasks, incorporating robotic embodiments equipped with generalist multimodal policies. © 2025, CC BY.

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ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization

arXiv

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arXiv 2023年

作者： Shivashankara, Kshama Kodthalu Deepanshi Shervedani, Afagh Mehri Clifford, Gari D. Reyna, Matthew A. Sameni, Reza The School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States The Electrical and Computer Engineering Department University of Illinois Chicago ChicagoIL United States The Department of Biomedical Informatics Emory University AtlantaGA United States The Department of Biomedical Engineering Georgia Institute of Technology GA United States

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, and the electrocardiogram (ECG) plays a crucial role in diagnosing CVDs. For decades, ECGs have been recorded in printed formats, and their digitization offers significant potential for training machine learning (ML) models in algorithmic ECG diagnosis. However, the valuable information in physical ECG archives risks deterioration, leading to a considerable loss in CVD research, particularly in low and low-middle-income countries where paper-based ECGs are common. Furthermore, merely scanning printed ECGs is inadequate, as most ML models require ECG time-series data. Therefore, digitizing and converting paper ECG archives into time-series data is essential. In this context, deep learning models for image processing are promising. Nonetheless, the scarcity of clinical ECG archives with reference time-series data presents a challenge. To overcome this, data augmentation techniques using digital twins offer a potential solution. We introduce ECG-Image-Kit, an open-source toolbox for generating synthetic ECG images with realistic artifacts from time-series data, and showcase its application in developing algorithms for ECG image digitization. Synthetic data is generated by producing distortionless ECG images on a standard ECG paper background. Subsequently, various distortions, including handwritten text artifacts, wrinkles, creases, and perspective transformations, are applied to these ECG images. The artifacts and text are synthetically generated, excluding personally identifiable information. The toolbox is used for data augmentation in the 2024 PhysioNet Challenge on Digitization and Classification of ECG Images. As a case study, we employed ECG-Image-Kit to create an ECG image dataset of 21,801 records from the PhysioNet QT database [1, 2]. A denoising convolutional neural network (DnCNN)-based model was developed and trained on this synthetic dataset and used to convert the synthetically ge

关键词： Electrocardiograms

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Real Time Prediction Model for Air Pollution and Air Quality Index based on Machine Learning

Real Time Prediction Model for Air Pollution and Air Quality...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： rakesh Kumar Bhisham Sharma Shashi Shekhar Imed Ben Dhaou Saurabh Singhal Department of Computer Engineering & Applications GLA University Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

Controlling air pollution is a difficult issue for governments in densely populated and developing nations. The burning of fossil fuels, industrial parameters and traffic assume critical parts in contamination of air. There is distinctive particulate matter which decide the nature of the air however among all the particulate matter, consideration towards particulate matter (PM 2.5) is become a necessity. In this paper we detect the PM value using image processing technology. Image processing uses edge detection and depth estimation techniques to get the contaminated regions of the picture. Accordingly, image processing is used to detect air pollution. It detects and quantifies contamination in the air with the image features like time, day/night, outdoor conditions for determining the correlation. The proposal uses the learning model based on these parameters to predict PM level on collected photos. High-level of PM can cause major issues on individuals’ wellbeing. As a result, regulating it by just being vigilant on its overall visibility is critical. This paper proposes a method for identifying and evaluating PM contamination by distinguishing six image features: transmission, sky perfection and shading, complete and neighborhood picture difference, and picture entropy. To assess the association between PM level and numerous elements, we also analyze the time, terrain, and climate state of each image. We created a relapse model based on these data to forecast PM2.5 levels in a specific city.

关键词： Technological innovation Smart cities Atmospheric modeling Predictive models Air pollution Real-time systems Indexes

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Integrating V2X solutions in intelligent green cities: an AI-driven point exchange system approach

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Neural Computing and Applications 2025年

作者： Talaat, Fatma M. Shaban, Warda M. ZainEldin, Hanaa Badawy, Mahmoud Elhosseini, Mostafa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & Engineering New Mansoura University Gamasa35712 Egypt Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence and informatics Horus University New Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah42353 Saudi Arabia College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

With rapid urbanization, smart cities have become essential for enhancing urban management and sustainability by integrating technological, social, and institutional innovations. Among these innovations, vehicle-to-everything (V2X) communications and electric vehicles (EVs) play a critical role in reducing carbon emissions and optimizing urban mobility. To address the existing gaps in holistic V2X integration, this paper presents a novel eco-assistive fog-based traffic management system (EAFTMS) that leverages a four-tier architecture (IoT, fog, cloud, and application) for scalable, real-time traffic optimization. A key innovation of this system is the AI-driven point exchange system (PES), designed to incentivize sustainable behaviors such as reducing unnecessary vehicle usage and promoting green lifestyle choices. Unlike conventional models, the proposed framework incorporates real-time behavioral monitoring, rewards-based sustainability programs, and V2X-enabled dynamic traffic control. Empirical validation demonstrates that EAFTMS outperforms existing models, including support vector regression (SVR), achieving a 30% reduction in latency, a 40% improvement in response time, a 25% increase in traffic flow efficiency, and a 35% reduction in CO2 emissions. These results highlight the framework’s potential to set new standards in intelligent green cities by offering scalable, practical, and environmentally impactful solutions to urban transportation challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Urban transportation

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Reducing the Number of Qubits from n2 to nlog2(n) to Solve the Traveling Salesman Problem with Quantum computers: A Proposal for Demonstrating Quantum Supremacy in the NISQ Era

arXiv

引用

arXiv 2024年

作者： ramezani, Mehdi Salami, Sadegh Shokhmkar, Mehdi Moradi, Morteza Bahrampour, Alireza Department of Physics Sharif University of Technology Tehran14588 Iran Centre for Quantum Engineering and Photonics Technology Sharif University of Technology Tehran14588 Iran Department of Computer Engineering Sharif University of Technology Tehran14588 Iran Institute of Informatics Faculty of Mathematics Informatics and Mechanics University of Warsaw Banacha 2 Warsaw02-097 Poland

In our pursuit of quantum supremacy during the NISQ era, this research introduces a novel approach rooted in the Quantum Approximate Optimization Algorithm (QAOA) framework to address the Traveling Salesman Problem (TSP). By strategically reducing the requisite qubit count from n2 to n log2 (n), our QAOA-based algorithm not only contributes to the ongoing discourse on qubit efficiency but also demonstrates improved performance based on established metrics, underscoring its potential for achieving NISQ-era supremacy in solving real-world optimization challenges. © 2024, CC BY-NC-ND.

关键词： Traveling salesman problem

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