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Quantum noise protects quantum classifiers against adversaries

Physical Review Research 2021年第2期3卷 023153-023153页

作者： Yuxuan Du Min-Hsiu Hsieh Tongliang Liu Dacheng Tao Nana Liu UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering University of Sydney Australia Hon Hai Quantum Computing Research Center No. 32 Jihu Road Neihu District Taipei 114 Taiwan Institute of Natural Sciences Shanghai Jiao Tong University Shanghai 200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai 200240 China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai 200240 China

Noise in quantum information processing is often viewed as a disruptive and difficult-to-avoid feature, especially in near-term quantum technologies. However, noise has often played beneficial roles, from enhancing weak signals in stochastic resonance to protecting the privacy of data in differential privacy. It is then natural to ask: Can we harness the power of quantum noise that is beneficial to quantum computing? An important current direction for quantum computing is its application to machine learning, such as classification problems. One outstanding problem in machine learning for classification is its sensitivity to adversarial examples. These are small, undetectable perturbations from the original data where the perturbed data is completely misclassified in otherwise extremely accurate classifiers. They can also be considered as worst-case perturbations by unknown noise sources. We show that by taking advantage of depolarization noise in quantum circuits for classification, a robustness bound against adversaries can be derived where the robustness improves with increasing noise. This robustness property is intimately connected with an important security concept called differential privacy, which can be extended to quantum differential privacy. For the protection of quantum data, this quantum protocol can be used against the most general adversaries. Furthermore, we show how the robustness in the classical case can be sensitive to the details of the classification model, but in the quantum case the details of the classification model are absent, thus also providing a potential quantum advantage for classical data. This opens the opportunity to explore other ways in which quantum noise can be used in our favor, as well as identifying other ways quantum algorithms can be helpful in a way which is distinct from quantum speedups.

关键词： Machine learning Quantum algorithms Quantum information processing Quantum protocols

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Faithful Transfer of 3D Propagation Characteristics of Deterministic and Random Optical Fields to Coded Aperture Imaging Systems Using Lucy-Richardson-Rosen Algorithm

Faithful Transfer of 3D Propagation Characteristics of Deter...

引用

Next Generation Electronics (NEleX), International Conference on

作者： Agnes Pristy Ignatius Xavier Francis Gracy Arockiaraj Shivasubramanian Gopinath Aravind Simon John Francis Rajeswary Andra Naresh Kumar Reddy Rashid A. Ganeev M. Scott Arockia Singh S.D. Milling Tania Vijayakumar Anand Institute of Physics University of Tartu Estonia School of Electrical and Computer Engineering Ben Gurion University of the Negev Israel Laboratory of Nonlinear Optics University of Latvia Riga Latvia Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Department of Physics and Astronomy Chirchik State Pedagogical University Chirchik Uzbekistan Dr. Jeyasekharan Hospital Nagercoil Tamil Nadu India Dr. Scott's Laser and Piles Fistula Center Nagercoil Tamil Nadu India Department of Orthodontics Rajas Dental College and Hospital Tirunelveli Tamil Nadu India Darshan Dental and Orthodontic Clinic Kanyakumari Tamil Nadu India Optical Sciences Center and ARC Training Centre in Surface Engineering for Advanced Materials (SEAM) School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne VIC Australia

engineering the complex amplitude and polarization of light is essential for various applications. In this direction, many deterministic and random optical beams such as Airy Bessel, and self-rotating beams were developed. While the above beams satisfied the requirements for the targeted applications, they are not suitable for imaging applications in spite of the valuable axial characteristics they possess, as they are not effective object-image mapping elements. Consequently, when exotic beams were implemented for direct imaging, only a distorted image was obtained. However, the scenario is different in coded aperture imaging (CAI) methods, where the imaging mode is indirect, consisting of optical recording and computational image recovery. Therefore, the point spread function (PSF) in CAI is not the recorded intensity distribution but the reconstructed intensity distribution. By employing a suitable computational reconstruction method, it is possible to convert the recorded intensity distribution into a Delta-like function. In this study, Lucy-Richardson-Rosen algorithm has been implemented as a generalized image recovery method for a wide range of optical beams, and the performance is validated in both simulation and optical experiments.

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DROID: Minimizing the Reality Gap using Single-Shot Human Demonstration

arXiv

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arXiv 2021年

作者： Tsai, Ya-Yen Xu, Hui Ding, Zihan Zhang, Chong Johns, Edward Huang, Bidan Hamlyn Centre for Robotic Surgery LondonSW7 2AZ United Kingdom Robot Learning Lab Department of Computing Imperial College London LondonSW7 2AZ United Kingdom School of Computer Science and Engineering University of Electronic Science and Technology of China China Tencent Robotics X China

Reinforcement learning (RL) has demonstrated great success in the past several years. However, most of the scenarios focus on simulated environments. One of the main challenges of transferring the policy learned in a simulated environment to real world, is the discrepancy between the dynamics of the two environments. In prior works, Domain Randomization (DR) has been used to address the reality gap for both robotic locomotion and manipulation tasks. In this paper, we propose Domain Randomization Optimization IDentification (DROID), a novel framework to exploit single-shot human demonstration for identifying the simulator’s distribution of dynamics parameters, and apply it to training a policy on a door opening task. Our results show that the proposed framework can identify the difference in dynamics between the simulated and the real worlds, and thus improve policy transfer by optimizing the simulator’s randomization ranges. We further illustrate that based on these same identified parameters, our method can generalize the learned policy to different but related tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Random processes

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Fock State-enhanced Expressivity of Quantum Machine Learning Models

arXiv

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arXiv 2021年

作者： Gan, Beng Yee Leykam, Daniel Angelakis, Dimitris G. Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore117543 Singapore School of Electrical and Computer Engineering Technical University of Crete Chania73100 Greece AngelQ Quantum Computing 531A Upper Cross Street #04-95 Hong Lim Complex Singapore051531 Singapore

The data-embedding process is one of the bottlenecks of quantum machine learning, potentially negating any quantum speedups. In light of this, more effective data-encoding strategies are necessary. We propose a photonic-based bosonic data-encoding scheme that embeds classical data points using fewer encoding layers and circumventing the need for nonlinear optical components by mapping the data points into the high-dimensional Fock space. The expressive power of the circuit can be controlled via the number of input photons. Our work shed some light on the unique advantages offered by quantum photonics on the expressive power of quantum machine learning models. By leveraging the photon-number dependent expressive power, we propose three different noisy intermediate-scale quantum-compatible binary classification methods with different scaling of required resources suitable for different supervised classification tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Photons

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Geometric Optimisation of Unmanned Aerial Vehicle Trajectories in Uncertain Environments

引用

Vehicular Communications 2025年 54卷

作者： Akshya, J. Sundarrajan, M. Amutha, S. Dhanaraj, Rajesh Kumar Khadidos, Adil O. Khadidos, Alaa O. Selvarajan, Shitharth Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Networking and Communications SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) Pune India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia School of Built Environment Engineering and Computing Leeds Beckett University LS6 3HF Leeds United Kingdom Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

The problem of efficient trajectory optimisation for Unmanned Aerial Vehicles (UAVS) in dynamic and constrained environments is one where energy efficiency, spatial coverage, and path smoothness need to be balanced. The existing methods, namely RRT*, A*, and Dijkstra, are popular but generally heuristic and do not provide globally optimal solutions. They face significant limitations while dealing with complex geometries, dynamic obstacles, and multi-objective requirements. These challenges call for a mathematically sound framework that seamlessly integrates convex analysis and computational geometry to provide an optimal trajectory planning framework. This research work introduces a convex optimisation framework for UAV trajectory planning which unifies multiple objectives, like minimising energy consumption, maximising spatial coverage, and ensuring the smoothness of the path, into a single convex objective function. More importantly, it indicates that obstacle dynamics and uncertain environmental conditions are handled better by it, so it is relatively easier for safe and efficient navigation. Proven to converge faster and with higher precision than RRT*, A*, and Dijkstra, the approach proposed here enjoys intrinsic convex properties, which ensure global optimality. Qualitative measurements show the efficiency of the proposed framework. The result is energy efficiency of 90%, with 92% coverage, 98% constraint satisfaction, and 95% path smoothness, which is 15-25% better on all metrics than traditional approaches can offer. By bridging between theory in convex optimisation and practice for solving multi-objective problems in a dynamic setting, this study provides a more robust solution for UAV trajectory planning. © 2025 The Author(s)

关键词： Computational Geometry Convex Optimisation Delaunay Triangulations Spatial Coverage UAV Trajectory Voronoi Diagrams

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Robust and Generalisable Segmentation of Subtle Epilepsy-causing Lesions: a Graph Convolutional Approach

arXiv

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arXiv 2023年

作者： Spitzer, Hannah Ripart, Mathilde Fawaz, Abdulah Williams, Logan Z.J. Project, Meld Robinson, Emma Iglesias, Juan Eugenio Adler, Sophie Wagstyl, Konrad Institute of Computational Biology Helmholtz Center Munich Munich85764 Germany University Hospital LMU Munich Munich Germany Department of Developmental Neuroscience UCL Great Ormond Street Institute for Child Health LondonWC1N 1EH United Kingdom Department of Biomedical Engineering School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Martinos Center for Biomedical Imaging MGH & Harvard Medical School United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Boston02139 United States Centre for Medical Image Computing University College London LondonWC1V 6LJ United Kingdom Wellcome Centre for Human Neuroimaging University College London LondonWC1N 3AR United Kingdom

Focal cortical dysplasia (FCD) is a leading cause of drug-resistant focal epilepsy, which can be cured by surgery. These lesions are extremely subtle and often missed even by expert neuroradiologists. "Ground truth" manual lesion masks are therefore expensive, limited and have large inter-rater variability. Existing FCD detection methods are limited by high numbers of false positive predictions, primarily due to vertex- or patch-based approaches that lack whole-brain context. Here, we propose to approach the problem as semantic segmentation using graph convolutional networks (GCN), which allows our model to learn spatial relationships between brain regions. To address the specific challenges of FCD identification, our proposed model includes an auxiliary loss to predict distance from the lesion to reduce false positives and a weak supervision classification loss to facilitate learning from uncertain lesion masks. On a multi-centre dataset of 1015 participants with surface-based features and manual lesion masks from structural MRI data, the proposed GCN achieved an AUC of 0.74, a significant improvement against a previously used vertex-wise multi-layer perceptron (MLP) classifier (AUC 0.64). With sensitivity thresholded at 67%, the GCN had a specificity of 71% in comparison to 49% when using the MLP. This improvement in specificity is vital for clinical integration of lesion-detection tools into the radiological workflow, through increasing clinical confidence in the use of AI radiological adjuncts and reducing the number of areas requiring expert review. © 2023, CC BY.

关键词： Brain

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The Quantum Internet (Technical Version)

arXiv

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arXiv 2025年

作者： Rohde, Peter P. Huang, Zixin Ouyang, Yingkai Huang, He-Liang Su, Zu-En Devitt, Simon Ramakrishnan, Rohit Mantri, Atul Tan, Si-Hui Liu, Nana Harrison, Scott Radhakrishnan, Chandrashekar Brennen, Gavin K. Baragiola, Ben Q. Dowling, Jonathan P. Byrnes, Tim Munro, William J. Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia Hearne Institute for Theoretical Physics Louisiana State University Baton Rouge United States School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia School of Mathematical and Physical Sciences University of Sheffield SheffieldS3 7RH United Kingdom Henan Key Laboratory of Quantum Information and Cryptography Henan Zhengzhou450000 China Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science & Technology of China Hefei China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information & Quantum Physics University of Science & Technology of China Hefei China CAS-Alibaba Quantum Computing Laboratory Shanghai China InstituteQ Aalto University Espoo02150 Finland Indian Institute of Science Bangalore India Singapore University of Technology & Design Singapore Centre for Quantum Technologies National University of Singapore Singapore Horizon Quantum Ireland 24 Fitzwilliam Place Dublin 2 D02 T296 Ireland Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China Shanghai Artificial Intelligence Laboratory Shanghai China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China Leibniz Institute for Research & Information in Education Frankfurt am Main Germany Department of Computer Science and Engineering NYU Shanghai 567 West Yangsi Road Shanghai200124 China Centre of Excellence in Engineered Quantum Systems Macquarie University NSW Australia Centre for Quantum Computation and Communication Technology School of Science RMIT University VIC

The desire to share and unite remote digital assets motivated the development of the classical internet, the enabler of the entire 21st century economy and our modern way of life. As we enter the quantum era, it is to be expected there will be a similar demand for networking quantum assets, motivating a global quantum internet for bringing together the world's quantum resources, leveraging off their exponential trajectory in capability. We present models for quantum networking, how they might be applied in the future, and the implications they will have. Socially, economically, politically and geostrategically, the upcoming era of quantum supremacy will be as significant for the 21st century as the transistor was for the 20th. The inherently different scaling in the computational power of quantum computers fundamentally changes the dynamics of how they will operate in the future. Given their high expected initial cost, a client/server model for outsourcing computation will be essential for enabling the accessibility and proliferation of this technology, and ensuring its economic viability. We therefore anticipate the emergence of cloud quantum computing, a model for outsourcing quantum computations to the network. We argue that economic efficiency will mandate that all future quantum computers be united into a single global virtual quantum computer, offering exponentially more power to all network participants than if they were to keep their resources to themselves. This model for the allocation of computational resources is uniquely quantum, with no classical analogue, completely altering the economic landscape for the future of computation. Given the sensitivity of much of the data to which future quantum computers are going to foreseeably be applied, protocols for encrypted quantum computation will be essential - the outsourcing of computations that neither an eavesdropper nor even the server performing the computation can spy upon. This will enable new models fo

关键词： Outsourcing

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Improved Fitness Dependent Optimizer for Solving Economic Load Dispatch Problem

arXiv

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arXiv 2022年

作者： Tahir, Barzan Hussein Rashid, Tarik A. Rauf, Hafiz Tayyab Bacanin, Nebojsa Chhabra, Amit Vimal, S. Yaseen, Zaher Mundher Department of Computer Science and Engineering University of Kurdistan Helwer Erbil Iraq Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Singidunum University Danijelova 32 Belgrade11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia USQ's Advanced Data Analytics Research Group School of Mathematics Physics and Computing University of Southern Queensland QLD4350 Australia New era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq

Economic Load Dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to Economic Load Dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness Dependent Optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness Dependent Optimizer (FDO) examines the search spaces based on the searching approach of Particle Swarm Optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have carried out Fitness Dependent Optimizer to solve the Economic Load Dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of Fitness Dependent Optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for Fitness Dependent Optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multi-dimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. Empirical results obtained using the enhanced variant of the Fitness Dependent Optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional Fitness Dependent Optimizer. The experimental study obtained 7.94E-12, the lowest transmission loss using the enhanced Fitness Dependent Optimizer. Correspondingly, various standard estimations are used to prove the stability

关键词： Decision making

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Polarization Characteristics of the Hyperactive FRB 20240114A

arXiv

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arXiv 2024年

作者： Xie, Jin-Tao Feng, Yi Li, Di Zhang, Yong-Kun Zhou, Deng-Ke Qu, Yuanhong Cui, Xianghan Fang, JianHua Xu, JiaYing Miao, ChenChen Yuan, Mao Tsai, Chao-Wei Wang, Pei Niu, Chen-Hui Chen, Xianglei Xue, Mengyao Zhang, Junshuo School of Computer Science and Engineering Sichuan University of Science and Engineering Yibin644000 China Research Center for Astronomical Computing Zhejiang Laboratory Hangzhou311100 China Institute for Astronomy School of Physics Zhejiang University Hangzhou310027 China New Cornerstone Science Laboratory Department of Astronomy Tsinghua University Beijing100084 China National Astronomical Observatories Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China Zhejiang Lab Zhejiang Hangzhou311121 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Nevada Center for Astrophysics University of Nevada Las VegasNV89154 United States Department of Physics and Astronomy University of Nevada Las VegasNV89154 United States International Centre for Radio Astronomy Research Curtin Institute of Radio Astronomy Perth6102 Australia National Space Science Center Chinese Academy of Sciences Beijing100101 China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Institute of Astrophysics Central China Normal University Hubei Wuhan430079 China

Fast radio bursts (FRBs) are transient radio bursts of extragalactic origin characterized by millisecond durations and high luminosities. We report on observations of FRB 20240114A conducted with the Robert C. Byrd Green Bank Telescope (GBT) at frequencies ranging from 720 to 920 MHz. A total of 437 bursts were detected, with a single observation recording 365 bursts over 1.38 hours, corresponding to a burst rate of 264 bursts per hour. The average rotation measures (RMs) were 347.0 ±1.0 rad m−2 on February 23, 2024, and 353.7 ± 0.6 rad m−2 on March 1, 2024. Of the 301 bursts with detected RMs, 81% have a linear polarization fraction greater than 90%, and 14% exhibit circular polarization with a signal-to-noise ratio > 5. Our sample also displayed polarization angle swings. We compared the linear polarization fraction of FRB 20240114A with those of the repeating sources FRB 20201124A and FRB 20220912A. Our analysis reveals that all three exhibit similar distributions in both linear and circular polarization fractions. These results indicate that the three sources share the same radiation mechanism. We analyze the fluence and waiting-time distributions of FRB 20240114A, revealing a right-skewed fluence distribution and a bimodal waiting-time structure, suggesting intrinsic emission timescales and potential multiple burst populations. Additionally, We present a novel method to determine the frequency range of bursts based on their spectral characteristics. This algorithm is independent of spectral models and remains unaffected by the removal of interference-affected channels in the data, ensuring robust performance. © 2024, CC BY.

关键词： Fading (radio)

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