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arXiv 2022年

作者： Chen, Kevin C. Dhara, Prajit Heuck, Mikkel Lee, Yuan Dai, Wenhan Guha, Saikat Englund, Dirk Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Wyant College of Optical Sciences The University of Arizona TucsonAZ85721 United States NSF-ERC Center for Quantum Networks The University of Arizona TucsonAZ85721 United States Department of Electrical and Photonics Engineering Technical University of Denmark Kgs. Lyngby2800 Denmark College of Information and Computer Science University of Massachusetts AmherstMA01003 United States Brookhaven National Laboratory UptonNY11973 United States

We propose a scheme for optical entanglement distribution in quantum networks based on a quasi-deterministic entangled photon pair source. By combining heralded photonic Bell pair generation with spectral mode conversion to interface with quantum memories, the scheme eliminates switching losses due to multiplexing in the source. We analyze this 'zero-added-loss multiplexing' (ZALM) Bell pair source for the particularly challenging problem of long-baseline entanglement distribution via satellites and ground-based memories, where it unlocks additional advantages: (i) the substantially higher channel efficiency η of downlinks vs. uplinks with realistic adaptive optics, and (ii) photon loss occurring before interaction with the quantum memory - i.e., Alice and Bob receiving rather than transmitting - improve entanglement generation rate scaling by O(√η). Based on numerical analyses, we estimate our protocol to achieve > 10 ebit/s at memory multiplexing of 102 spin qubits for ground distance > 102 km, with the spin-spin Bell state fidelity exceeding 99%. Our architecture presents a blueprint for realizing global-scale quantum networks in the near-term. © 2022, CC BY.

关键词： Quantum entanglement

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Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node data: Whole City Traffic and ETA from Stationary Vehicle Detectors

arXiv

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arXiv 2023年

作者： Neun, Moritz Eichenberger, Christian Martin, Henry Spanring, Markus Siripurapu, Rahul Springer, Daniel Deng, Leyan Wu, Chenwang Lian, Defu Zhou, Min Lumiste, Martin Ilie, Andrei Wu, Xinhua Lyu, Cheng Lu, Qing-Long Mahajan, Vishal Lu, Yichao Li, Jiezhang Li, Junjun Gong, Yue-Jiao Grötschla, Florian Mathys, Joël Wei, Ye Haitao, He Fang, Hui Malm, Kevin Tang, Fei Kopp, Michael Kreil, David Hochreiter, Sepp Vienna Austria Institute of Cartography and Geoinformation ETH Zurich Switzerland School of Data Science University of Science and Technology of China China Huawei Noah’s Ark Lab Bolt Technology Tallinn Estonia University of Bucharest Bucharest Romania Department of Civil and Environmental Engineering Northeastern University BostonMA United States Transportation Systems Engineering Technical University of Munich Germany Layer 6 AI Toronto Canada School of Coumpute Science and Engineering South China University of Technology Guangzhou China ETH Zurich Switzerland Department of Computer Science Loughborough University Loughborough United Kingdom School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom HERE Technologies ChicagoIL United States Kaiko Zurich Switzerland Machine Learning Institute Johannes Kepler University Linz Austria

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future – super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data. © 2023, CC BY-NC-SA.

关键词： Travel time

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A Survey on Reconfigurable Intelligent Surface-Assisted Orthogonal Time Frequency Space Systems

IEEE Open Journal of Vehicular Technology

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IEEE Open Journal of Vehicular Technology 2025年

作者： Tao, Qin Li, Zhongjie Zhi, Kangda Li, Shuangyang Yuan, Weijie Zaniboni, Lorenzo Stanczak, Slawomir Viterbo, Emanuele Wang, Xianbin Hangzhou Normal University School of Information Science and Technology Hangzhou311121 China School of Automation and Intelligent Manufacturing Southern University of Science and Techonology Shenzhen518055 China Technical University of Berlin School of Electrical Engineering and Computer Science Berlin10623 Germany Technical University of Munich Institute for Communications Engineering Munich80333 Germany Fraunhofer Heinrich Hertz Institute Wireless Communications and Networks Department Berlin10587 Germany Monash University Department of Electrical and Computer Systems Engineering ClaytonVIC3800 Australia Western University Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada

The vision for 6th-generation (6G) wireless communication systems emphasizes the need for robust and reliable communication in extremely high-mobility scenarios, while also addressing critical demands for energy and spectral efficiency. Under such scenarios, doubly time-frequency selective fading channels often significantly degrade the performance of orthogonal frequency-division multiplexing (OFDM) based systems due to the impact of large delay and Doppler shifts. Recently, orthogonal time frequency space (OTFS) modulation has emerged as a promising alternative. By processing signals in the delay-Doppler (DD) domain, OTFS offers several advantages, including quasi-static channel characteristics, full-time-frequency diversity, and low peak-to-average power ratio (PAPR), making it a promising candidate for high-mobility communications. Reconfigurable intelligent surfaces (RIS) are being further integrated to enhance the performance of OTFS systems cost-effectively. With their ability to dynamically reconfigure the wireless environment, the integration of RIS can offer significant performance improvements for OTFS systems. This survey offers a comprehensive review of RIS-assisted OTFS systems, including the fundamental principles, recent advances, and future research directions. Specifically, we first introduce the background of RIS-assisted OTFS systems, outlining the opportunities and challenges of their integration. To ensure the survey is self-contained, we provide a brief overview of the fundamental principles of OTFS and RIS technologies. Building on these foundations, we present a general input-output relationship and capacity characterization for RIS-assited MIMO-OTFS systems. Then, this survey further explores cutting-edge research in areas such as input-output analysis, RIS phase shift design, channel estimation, detection techniques, RIS-assisted integrated sensing and communication (ISAC), and other novel technologies. Finally, we outline some future rese

关键词： Orthogonal frequency division multiplexing

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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OPINION MANIPULATION ON FARSI TWITTER

arXiv

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arXiv 2022年

作者： Farzam, Amirhossein Moradi, Parham Mohammadi, Saeedeh Padar, Zahra Siegel, Alexandra A. Department of Political Science Duke University DurhamNC27708 United States Department of Engineering Sciences and Applied Mathematics Northwestern University EvanstonIL60208 United States Max Planck Institute for Mathematics in the Sciences Leipzig04103 Germany Center for Complex Networks and Social Data Science Tehran Iran Department of Physics Shahid Beheshti University Tehran Iran School of Mathematics and Statistics University College Dublin BelfieldDublin 4 Ireland Department of Computer Science University of Freiburg Freiburg Germany Department of Political Science University of Colorado BoulderCO80309 United States

For Iranians and the Iranian diaspora, the Farsi Twittersphere provides an important alternative to state media and an outlet for political discourse. But this understudied online space has become an opinion manipulation battleground, with diverse actors using inauthentic accounts to advance their goals and shape online narratives. Examining trending discussions crossing social cleavages in Iran, we explore how the dynamics of opinion manipulation differ across diverse issue areas. Our analysis suggests that opinion manipulation by inauthentic accounts is more prevalent in divisive political discussions than non-divisive or apolitical discussions. We show how Twitter's network structures help to reinforce the content propagated by clusters of inauthentic accounts in divisive political discussions. Analyzing both the content and structure of online discussions in the Iranian Twittersphere, this work contributes to a growing body of literature exploring the dynamics of online opinion manipulation, while improving our understanding of how information is controlled in the digital age. © 2022, CC BY.

关键词： Social networking (online)

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Robust Self-Expression Learning with Adaptive Noise Perception

SSRN

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SSRN 2023年

作者： Wang, Yangbo Zhou, Jie Lu, Jianglin Wan, Jun Gao, Can Lin, Qingshui College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China SMILE Lab Department of ECE College of Engineering Northeastern University Boston02115 United States School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan430073 China Basic Teaching Department Liaoning Technical University Huludao125105 China

Self-expression learning methods often obtain a coefficient matrix to measure the similarity between pairs of samples. However, directly using the raw data to represent each sample under the self-expression framework may not be ideal, as noise points are inevitably involved in the process of representing clean samples. To address this issue, this work proposes a novel self-expression model called robust Self-Expression learning with adaptive Noise Perception (SENP). SENP decomposes each sample into a clean part and a noisy part, and samples with large self-expression losses can be recognized as the noise points. A reliable coefficient matrix can then be learned by using only the clean points to reconstruct the clean part of each sample. By simultaneously detecting the noisy part of each sample and noise points, and adaptively mitigating their negative impacts, the representative ability of the generated coefficient matrix is improved. Moreover, inspired by the solution of non-negative matrix factorization (NMF), an effective algorithm is formed to optimize SENP. Extensive experiments on well-known benchmark datasets demonstrate the superiority of SENP compared to several state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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Proof methods for robust low-rank matrix recovery

arXiv

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arXiv 2021年

作者： Fuchs, Tim Gross, David Jung, Peter Krahmer, Felix Kueng, Richard Stöger, Dominik Department of Mathematics Technical University of Munich Germany Institute for Theoretical Physics University of Cologne Germany Communications and Information Theory Group Technische Universität Berlin Germany Data Science in Earth Observation Technical University of Munich Germany Institute for Integrated Circuits Johannes Kepler University Linz Austria Ming Hsieh Department of Electrical and Computer Engineering University of Southern California United States

Low-rank matrix recovery problems arise naturally as mathematical formulations of various inverse problems, such as matrix completion, blind deconvolution, and phase retrieval. Over the last two decades, a number of works have rigorously analyzed the reconstruction performance for such scenarios, giving rise to a rather general understanding of the potential and the limitations of low-rank matrix models in sensing problems. In this article, we compare the two main proof techniques that have been paving the way to a rigorous analysis, discuss their potential and limitations, and survey their successful applications. On the one hand, we review approaches based on descent cone analysis, showing that they often lead to strong guarantees even in the presence of adversarial noise, but face limitations when it comes to structured observations. On the other hand, we discuss techniques using approximate dual certificates and the golfing scheme, which are often better suited to deal with practical measurement structures, but sometimes lead to weaker guarantees. Lastly, we review recent progress towards analyzing descent cones also for structured scenarios—exploiting the idea of splitting the cones into multiple parts that are analyzed via different techniques. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Weisfeiler and Leman go Machine Learning: The Story so far

arXiv

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arXiv 2021年

作者： Morris, Christopher Lipman, Yaron Maron, Haggai Rieck, Bastian Kriege, Nils M. Grohe, Martin Fey, Matthias Borgwardt, Karsten Department of Computer Science RWTH Aachen University Aachen Germany Meta AI Research Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel NVIDIA Research Tel Aviv Israel AIDOS Lab Institute of AI for Health Helmholtz Zentrum München and Technical University of Munich Munich Germany Faculty of Computer Science University of Vienna Vienna Austria Research Network Data Science University of Vienna Vienna Austria Kumo.AI Mountain ViewCA United States Machine Learning & Computational Biology Lab Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland

In recent years, algorithms and neural architectures based on the Weisfeiler–Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machine learning with graphs and relational data. Here, we give a comprehensive overview of the algorithm’s use in a machine-learning setting, focusing on the supervised regime. We discuss the theoretical background, show how to use it for supervised graph and node representation learning, discuss recent extensions, and outline the algorithm’s connection to (permutation-)equivariant neural architectures. Moreover, we give an overview of current applications and future directions to stimulate further research. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Air quality data prediction using data analytics

引用

AIP Conference Proceedings 2024年第1期2742卷

作者： Senthil Kumar Sidharthan Kalaiarasan Nancy Anvar Shathik Kavietha Dept. of Computer Science and Egg Chennai Institute of Technology Chennai Tamilnadu India Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore Tamilnadu India Saveetha School of Engineering Saveetha Institute of Medical and technical sciences(Deemed to be university) Chennai India Department of Cloud Technology & Data Science iNurture Education Solutions Srinivas University College of Engineering & Technology Bangalore India. Department of Computer Science and Engineering Sri Sairam Institute of Technology Chennai India.

Air pollution is usually described as the release into the atmosphere of contaminants which are harmful to human health and the environment. It may be described as one of the most serious risks human beings have ever faced because to the growing more catastrophic problem of air pollution in the globe. A description is provided of how our air quality sensor system is designed, implemented and optimised, which provides a live and fine grain air quality map of the area under surveillance. The average joint failure in the generated real-time air quality map should be maintained to a minimal when unmeasured data values as part of the objective are deducted. In terms of studying the quality issues, Big data Technology has been recommended to be utilised in order to organise the air quality sensing equipment sensing tasks appropriately. In addition, the prediction of air quality based on climatic conditions is explored using various machine learning techniques for the data given.

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Narrow-linewidth optical frequency comb reference to a fiber delay line

Narrow-linewidth optical frequency comb reference to a fiber...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Haochen Tian Fei Meng Baike Lin Shiying Cao Zhanjun Fang Youjian Song Minglie Hu Ultrafast Laser Laboratory Key Laboratory of Opto-Electronic Information Technical Science of Ministry of Education School of Precision Instrument and Opto-Electronics Engineering Tianjin University Tianjin China National Institute of Metrology Beijing China Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Engineering and Computer Science Peking University Beijing China

A fully-stabilized Er:fiber optical frequency comb referenced to a km-long fiber delay line is presented. The comb mode’s fractional frequency stability is at 10 −12 level in 12.8 ms. The absolute linewidth is 587 Hz.

关键词： Phase measurement Laser noise Laser mode locking Measurement by laser beam Delay lines Lasers and electrooptics Optical harmonic generation

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