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arXiv 2022年

作者： Kungurtsev, Vyacheslav Korpas, Georgios Marecek, Jakub Zhu, Elton Yechao Department of Computer Science Czech Technical University in Prague Karlovo nam. 13 Prague 2 Czech Republic Archimedes Research Unit on AI Data Science and Algorithms Athena Research and Innovation Center Marousi15125 Greece Fidelity Center for Applied Technology FMR LLC BostonMA02210 United States

There has been much recent interest in near-term applications of quantum computers, i.e., using quantum circuits that have short decoherence times due to hardware limitations. Variational quantum algorithms (VQA), wherein an optimization algorithm implemented on a classical computer evaluates a parametrized quantum circuit as an objective function, are a leading framework in this space. An enormous breadth of algorithms in this framework have been proposed for solving a range of problems in machine learning, forecasting, applied physics, and combinatorial optimization, among others. In this paper, we analyze the iteration complexity of VQA, that is, the number of steps that VQA requires until its iterates satisfy a surrogate measure of optimality. We argue that although VQA procedures incorporate algorithms that can, in the idealized case, be modeled as classic procedures in the optimization literature, the particular nature of noise in near-term devices invalidates the claim of applicability of off-the-shelf analyses of these algorithms. Specifically, noise makes the evaluations of the objective function via quantum circuits biased. Commonly used optimization procedures, such as SPSA and the parameter shift rule, can thus be seen as derivative-free optimization algorithms with biased function evaluations, for which there are currently no iteration complexity guarantees in the literature. We derive the missing guarantees and find that the rate of convergence is unaffected. However, the level of bias contributes unfavorably to both the constant therein, and the asymptotic distance to stationarity, i.e., the more bias, the farther one is guaranteed, at best, to reach a stationary point of the VQA objective. © 2022, CC BY.

关键词： Quantum computers

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Suboptimal subspace construction for log-determinant approximation

arXiv

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arXiv 2023年

作者： Han, Zongyuan Li, Wenhao Huang, Yixuan Zhu, Shengxin School of Mathematical Sciences Beijing Normal University Laboratory of Mathematics and Complex Systems Ministry of Education Beijing100875 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science Department of Applied Mathematics BNU-HKBU United International College Zhuhai519087 China Research Center for Mathematics Beijing Normal University Zhuhai519087 China

Variance reduction is a crucial idea for Monte Carlo simulation and the stochastic Lanczos quadrature method is a dedicated method to approximate the trace of a matrix function. Inspired by their advantages, we combine these two techniques to approximate the log-determinant of large-scale symmetric positive definite matrices. Key questions to be answered for such a method are how to construct or choose an appropriate projection subspace and derive guaranteed theoretical analysis. This paper applies some probabilistic approaches including the projection-cost-preserving sketch and matrix concentration inequalities to construct a suboptimal subspace. Furthermore, we provide some insights on choosing design parameters in the underlying algorithm by deriving corresponding approximation error and probabilistic error estimations. Numerical experiments demonstrate our method’s effectiveness and illustrate the quality of the derived error *** Codes 65C05, 65D32, 65F15, 65F60, 65G99, 65Y20, 68Q10, 68Q87 Copyright © 2023, The Authors. All rights reserved.

关键词： Error analysis

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Indirect influence in social networks as an induced percolation phenomenon (vol 119, e2100151119, 2022)

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PROCEEDINGS OF THE NATIONAL ACADEMY OF scienceS OF THE unitED STATES OF AMERICA 2022年第24期119卷 e2100151119-e2100151119页

作者： Xie, Jiarong Wang, Xiangrong Feng, Ling Zhao, Jin-Hua Liu, Wenyuan Moreno, Yamir Hu, Yanqing School of Computer Science and Engineering Sun Yat-sen University 510006 Guangzhou China Institute of Future Networks Southern University of Science and Technology 518055 Shenzhen China Peng Cheng Laboratory 518066 Shenzhen China Institute of High Performance Computing A*STAR 138632 Singapore Department of Physics National University of Singapore 117551 Singapore Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University 510006 Guangzhou China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University 510006 Guangzhou China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Institute for Biocomputation and Physics of Complex Systems University of Zaragoza 50018 Zaragoza Spain Department of Theoretical Physics University of Zaragoza 50018 Zaragoza Spain ISI Foundation 10126 Torino Italy Department of Statistics and Data Science College of Science Southern University of Science and Technology 518055 Shenzhen China

Percolation theory has been widely used to study phase transitions in network systems. It has also successfully explained various macroscopic spreading phenomena across different fields. Yet, the theoretical frameworks have been focusing on direct interactions among nodes, while recent empirical observations have shown that indirect interactions are common in many network systems like social and ecological networks, among others. By investigating the detailed mechanism of both direct and indirect influence on scientific collaboration networks, here we show that indirect influence can play the dominant role in behavioral influence. To address the lack of theoretical understanding of such indirect influence on the macroscopic behavior of the system, we propose a percolation mechanism of indirect interactions called induced percolation. Surprisingly, our model exhibits a unique anisotropy property. Specifically, directed networks show first-order abrupt transitions as opposed to the second-order continuous transition in the same network structure but with undirected links. A mix of directed and undirected links leads to rich hybrid phase transitions. Furthermore, a unique feature of the nonmonotonic pattern is observed in network connectivities near the critical point. We also present an analytical framework to characterize the proposed induced percolation, paving the way to further understanding network dynamics with indirect interactions.

关键词： percolation indirect interactions social network phase transition behavioral contagion

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Automated Soccer Event Detection and Highlight Generation for Short and Long Views

SSRN

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SSRN 2024年

作者： Afzal, Maira Shah, Jamal Hussain Rehman, Saeed ur Dahan, Fadl Yasmin, Mussarat Kadry, Seifedine Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Department of Management Information Systems College of Business Administration - Hawtat Bani Tamim Prince Sattam bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon MEU Research Unit Middle East University Amman11831 Jordan

The computer vision field has wide applications in various areas, including sports. Almost all sports events have been exploiting the best features. Sports videos are structure-based, and due to this characteristic, these videos can be categorized into interesting and non-interesting events. Identifying the view of the video and separating important events from non-interesting events is challenging. However, correct view detection can lead to correct event detection. Various researchers have proposed many strategies for detecting events in sports videos. Significant research shows some gaps while generating highlights due to limited work available in the long or short view;there is still a need for some automated methods. The main purpose of this research work is to detect key events by normalizing the data, extracting features, fusing those features, and classifying them. In this research, events are detected by classification. A new dataset is created for research purposes. The benchmark dataset is divided into two subsets, which can be used separately or as part of a larger dataset. A proposed novel approach for event highlight generation in long and short view is presented using a fusion of AlexNet and VGGNet architectures to explore the model's efficiency in the context of accurate highlight generation deep learning models fused with handcrafted. AlexNet and VGG16 pre-trained deep CNN models are applied to extract deep prior features, which are then combined with HOG to improve the results. The proposed methodology undergoes evaluation on a dataset that we created, resulting in an accuracy of 99.6%. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Role of Future SNIa data from Rubin LSST in Reinvestigating Cosmological Models

arXiv

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arXiv 2023年

作者： Shah, Rahul Mitra, Ayan Mukherjee, Purba Pal, Barun Pal, Supratik Physics and Applied Mathematics Unit Indian Statistical Institute 203 B.T. Road Kolkata700 108 India Center for AstroPhysical Surveys National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States Kazakh-British Technical University 59 Tole bi street Almaty050000 Kazakhstan Netaji Nagar College for Women 170/13/1 N.S.C. Bose Road Regent Estate Kolkata700092 India Technology Innovation Hub on Data Science Big Data Analytics and Data Curation Indian Statistical Institute 203 B.T. Road Kolkata700 108 India

We study how future Type-Ia supernovae (SNIa) standard candles detected by the Vera C. Rubin Observatory (LSST) can constrain some cosmological models. We use a realistic three-year SNIa simulated dataset generated by the LSST Dark Energy science Collaboration (DESC) Time Domain pipeline, which includes a mix of spectroscopic and photometrically identified candidates. We combine this data with Cosmic Microwave Background (CMB) and Baryon Acoustic Oscillation (BAO) measurements to estimate the dark energy model parameters for two models – the baseline ΛCDM and Chevallier-Polarski-Linder (CPL) dark energy parametrization. We compare them with the current constraints obtained from joint analysis of the latest real data from the Pantheon SNIa compilation, CMB from Planck 2018 and BAO. Our analysis finds tighter constraints on the model parameters along with a significant reduction of correlation between H0 and σ8,0. We find that LSST is expected to significantly improve upon the existing SNIa data in the critical analysis of cosmological models. Copyright © 2023, The Authors. All rights reserved.

关键词： Cosmology

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How to design the biofeedback training for the rehabilitation of PTSD patients?

How to design the biofeedback training for the rehabilitatio...

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2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive science, Genomics and Biomedicine, CSGB 2023

作者： Bazanova, Olga Zakharov, Alexander Larkova, Irina Maryanovskaya, Tatiana Melnikov, Andrey Ermolaeva, Sargylana Nikolenko, Ekaterina Shirolapov, Igor The Research Institute of Molecular Biology and Biophysics Laboratory of Computer Systems of Biofeedback Dolgoprudny Russia The Moscow Institute of Physics and Technology Educational and Scientific Center for Humanities and Social Science Novosibirsk Russia Research Institute of Neurosciences Samara State Medical University Samara Russia Lugansk Republican Center for Emergency Medical Care and Disaster Medicine Laboratory of Psychophysiological Support Lugansk Russia Novosibirsk National Research State University Mechanic-Mathematic Department Novosibirsk Russia The Russian University of Sport «gtsolifk» Department of Physiology Moscow Russia The Moscow Institute of Physics and Technology Master's Degree in Applied Data Analysis in the Medical Field Dolgoprudny Russia Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Department of Clinical Neuroscience Behavior and Neurotechnology Novosibirsk Russia

ISBN: (纸本)9798350307979

Rationale for this review is the pressing medical and social problem of the posttraumatic stress disorder (PTSD) treatment. It is known that biofeedback technology is one of the most effective methods of treatment and rehabilitation for emotional profile disorders. In order to develop biofeedback aimed to PTSD rehabilitation, we conducted a literature search on this problem. However, it appears that at the moment there is not enough unambiguous data on this type of biofeedback design. The pathogenesis of PTSD is closely related to impaired efficiency of sensorimotor integration (SMI). The effectiveness of therapy for psychosomatic disorders in patients with PTSD can be increased by restoring normal sensorimotor integration. The review examines various autonomic, electrophysiological and postural markers of destroyed sensorimotor integration (SMI) in individuals with PTSD. We have found that the most informative indicators of SMI in norm include: an increase in EEG power in the individually determined high-frequency alpha subrange, a decrease in the speed of body sway and energy demands to maintain a vertical posture, and a decrease in EMG activity of muscles not involved in cognitive or psychomotor performance. We intend to use these indicators in diagnostic purposes and to develop neurofeedback technology for SMI recovery in patients with PTSD. © 2023 IEEE.

关键词： Electroencephalography

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Deep learning for model parameter calibration in power systems

Deep learning for model parameter calibration in power syste...

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2020 IEEE International Conference on Power systems Technology, POWERCON 2020

作者： Wshah, Safwan Shadid, Reem Wu, Yuhao Matar, Mustafa Xu, Beilei Wu, Wencheng Lin, Lei Elmoudi, Ramadan Vermont Complex Systems Center University of Vermont United States Applied Science Private University Amman Jordan Rochester Data Science Consortium University of Rochester United States New York Power Authority NYPA NY United States

ISBN: (数字)9781728163505

ISBN: (纸本)9781728163505

In power systems, having accurate device models is crucial for grid reliability, availability, and resiliency. Existing model calibration methods based on mathematical approaches often lead to multiple solutions due to the ill-posed nature of the problem, which would require further interventions from the field engineers in order to select the optimal solution. In this paper, we present a novel deep-learning-based approach for model parameter calibration in power systems. Our study focused on the generator model as an example. We studied several deep-learning-based approaches including 1-D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent units (GRU), which were trained to estimate model parameters using simulated Phasor Measurement unit (PMU) data. Quantitative evaluations showed that our proposed methods can achieve high accuracy in estimating the model parameters, i.e., achieved a 0.0079 MSE on the testing dataset. We consider these promising results to be the basis for further exploration and development of advanced tools for model validation and calibration. © 2020 IEEE.

关键词： Parameter estimation

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The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies

arXiv

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arXiv 2024年

作者： Joachimiak, Marcin P. Miller, Mark A. Caufield, J. Harry Ly, Ryan Harris, Nomi L. Tritt, Andrew Mungall, Christopher J. Bouchard, Kristofer E. Biosystems Data Science Department Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Scientific Data Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Biological Systems & Engineering Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Helen Wills Neuroscience Institute UC Berkeley BerkeleyCA94720 United States Redwood Center for Theoretical Neuroscience UC Berkeley BerkeleyCA94720 United States

The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. The ontology is structured around six top-level branches: Networks, Layers, Functions, LLMs, Preprocessing, and Bias, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO's development utilized the Ontology Development Kit (ODK) for its creation and maintenance, with its content being dynamically updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting it potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub (https://***/berkeleybop/artificial-intelligence-ontology) and BioPortal (https://***/ontologies/AIO). © 2024, CC BY.

关键词： Deep neural networks

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Interlayer antisynchronization in degree-biased duplex networks

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Physical Review E 2023年第3期107卷 034313-034313页

作者： Sayantan Nag Chowdhury Sarbendu Rakshit Chittaranjan Hens Dibakar Ghosh Department of Environmental Science and Policy University of California Davis California 95616 USA Technology Innovation Hub (TIH) IDEAS (Institute of Data Engineering Analytics and Science Foundation) Indian Statistical Institute 203 B. T. Road Kolkata 700108 India Department of Mechanical Engineering University of California Riverside California 92521 USA Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Gachibowli Hyderabad 500032 India Physics and Applied Mathematics Unit Indian Statistical Institute 203 B. T. Road Kolkata 700108 India

With synchronization being one of nature's most ubiquitous collective behaviors, the field of network synchronization has experienced tremendous growth, leading to significant theoretical developments. However, most previous studies consider uniform connection weights and undirected networks with positive coupling. In the present article, we incorporate the asymmetry in a two-layer multiplex network by assigning the ratio of the adjacent nodes' degrees as the weights to the intralayer edges. Despite the presence of degree-biased weighting mechanism and attractive-repulsive coupling strengths, we are able to find the necessary conditions for intralayer synchronization and interlayer antisynchronization and test whether these two macroscopic states can withstand demultiplexing in a network. During the occurrence of these two states, we analytically calculate the oscillator's amplitude. In addition to deriving the local stability conditions for interlayer antisynchronization via the master stability function approach, we also construct a suitable Lyapunov function to determine a sufficient condition for global stability. We provide numerical evidence to show the necessity of negative interlayer coupling strength for the occurrence of antisynchronization, and such repulsive interlayer coupling coefficients cannot destroy intralayer synchronization.

关键词： Coupled oscillators Dynamics of networks Synchronization

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Optical Imaging of Epigenetic Modifications in Cancer:A Systematic Review

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Phenomics 2022年第2期2卷 88-101页

作者： Yang Du Pei Zhang Wei Liu Jie Tian CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing 100190China The University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Department of Supportive Care Center and Day Oncology UnitPeking University Cancer Hospital and InstituteBeijing 100142China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang UniversityBeijing 100191China School of Life Science and Technology Xidian UniversityXi’an 710071ShaanxiChina

Increasing evidence has demonstrated that abnormal epigenetic modifications are strongly related to cancer ***,sensitive and specific detection of epigenetic modifications could markedly improve biological investigations and cancer precision medicine.A rapid development of molecular imaging approaches for the diagnosis and prognosis of cancer has been observed during the past few *** biomarkers unique to epigenetic modifications and targeted imaging probes have been characterized and used to discriminate cancer from healthy tissues,as well as evaluate therapeutic *** this study,we summarize the latest studies associated with optical molecular imaging of epigenetic modification targets,such as those involving DNA methylation,histone modification,noncoding RNA regulation,and chromosome remodeling,and further review their clinical application on cancer diagnosis and ***,we further propose the future direc-tions for precision imaging of epigenetic modification in *** by promising clinical and preclinical studies associated with optical molecular imaging technology and epigenetic drugs,the central role of epigenetics in cancer should be increasingly recognized and accepted.

关键词： Epigenetic modification Optical imaging Cancer Imaging probe

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