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TechRxiv 2020年

作者： Ye, Cong Slavakis, Konstantinos Nakuci, Johan Muldoon, Sarah F. Medaglia, John NY14260 United States State University of New York at Buffalo China The Neuroscience Program UB SUNY United States The Department of Mathematics and the Computational and Data-Enabled Science and Engineering Program UB SUNY United States The Department of Psychology Drexel University PA19104 United States The Perelman School of Medicine University of Pennsylvania PA19104 United States

This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nodal time-series. The discussion follows a bottom-up path, starting from feature extraction from time-series and reaching up to Riemannian manifolds (feature spaces) to address clustering tasks such as state clustering, community detection (a.k.a. network-topology identification), and subnetwork-sequence tracking. Kernel autoregressive-moving-average modeling and kernel (partial) correlations serve as case studies of generating features in the Riemannian manifolds of Grassmann and positive-(semi)definite matrices, respectively. Feature point-clouds form clusters which are viewed as submanifolds according to Riemannian multi-manifold modeling. A novel sequential-clustering scheme of Riemannian features is also established: feature points are first sampled in a non-random way to reveal the underlying geometric information, and, then, a fast sequential-clustering scheme is brought forth that takes advantage of Riemannian distances and the angular information of tangent spaces. By virtue of the landmark points and the sequential processing of the Riemannian features, the computational complexity of the framework is rendered free from the length of the available time-series data. The effectiveness and computational efficiency of the proposed framework are validated by extensive numerical tests against several state-of-the-art manifold-learning and brain-network-clustering schemes on synthetic as well as real functional-magnetic-resonance-imaging (fMRI) and electroencephalogram (EEG) data. © 2020, CC BY.

关键词： Time series

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Mahakala: a Python-based Modular Ray-tracing and Radiative Transfer Algorithm for Curved Space-times

arXiv

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arXiv 2023年

作者： Sharma, Aniket Medeiros, Lia Chan, Chi-Kwan Halevi, Goni Mullen, Patrick D. Stone, James M. Wong, George N. Indian Institute of Science Education and Research Mohali Sector 81 SAS Nagar PO Manauli Punjab Mohali140306 India School of Natural Sciences Institute for Advanced Study 1 Einstein Drive PrincetonNJ08540 United States Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States CCS-2 Los Alamos National Laboratory Los AlamosNM United States Center for Theoretical Astrophysics Los Alamos National Laboratory Los AlamosNM United States Princeton Gravity Initiative Princeton University PrincetonNJ08544 United States

We introduce Mahakala, a Python-based, modular, radiative ray-tracing code for curved space-times. We employ Google’s JAX framework for accelerated automatic differentiation, which can efficiently compute Christoffel symbols directly from the metric, allowing the user to easily and quickly simulate photon trajectories through non-Kerr metrics. JAX also enables Mahakala to run in parallel on both CPUs and GPUs and achieve speeds comparable to C-based codes. Mahakala natively uses the Cartesian Kerr-Schild coordinate system, which avoids numerical issues caused by the "pole" of spherical coordinates. We demonstrate Mahakala’s capabilities by simulating the 1.3 mm wavelength images (the wavelength of Event Horizon Telescope observations) of general relativistic magnetohydrodynamic simulations of low-accretion rate supermassive black holes. The modular nature of Mahakala allows us to easily quantify the relative contribution of different regions of the flow to image features. We show that most of the emission seen in 1.3 mm images originates close to the black hole. We also quantify the relative contribution of the disk, forward jet, and counter jet to 1.3 mm images. Copyright © 2023, The Authors. All rights reserved.

关键词： Python

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Generative models of simultaneously heavy-tailed distributions of interevent times on nodes and edges

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Physical Review E 2020年第5期102卷 052303-052303页

作者： Elohim Fonseca dos Reis Aming Li Naoki Masuda Department of Mathematics State University of New York at Buffalo Buffalo New York 14260 USA Department of Zoology University of Oxford Oxford OX1 3PS United Kingdom Department of Biochemistry University of Oxford Oxford OX1 3QU United Kingdom Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260 USA Faculty of Science and Engineering Waseda University 169-8555 Tokyo Japan

Intervals between discrete events representing human activities, as well as other types of events, often obey heavy-tailed distributions, and their impacts on collective dynamics on networks such as contagion processes have been intensively studied. The literature supports that such heavy-tailed distributions are present for interevent times associated with both individual nodes and individual edges in networks. However, the simultaneous presence of heavy-tailed distributions of interevent times for nodes and edges is a nontrivial phenomenon, and its origin has been elusive. In the present study, we propose a generative model and its variants to explain this phenomenon. We assume that each node independently transits between a high-activity and low-activity state according to a continuous-time two-state Markov process and that, for the main model, events on an edge occur at a high rate if and only if both end nodes of the edge are in the high-activity state. In other words, two nodes interact frequently only when both nodes prefer to interact with others. The model produces distributions of interevent times for both individual nodes and edges that resemble heavy-tailed distributions across some scales. It also produces positive correlation in consecutive interevent times, which is another stylized observation for empirical data of human activity. We expect that our modeling framework provides a useful benchmark for investigating dynamics on temporal networks driven by non-Poissonian event sequences.

关键词： Network structure Social networks Stochastic networks Network Models

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Generative models of simultaneously heavy-tailed distributions of inter-event times on nodes and edges

arXiv

引用

arXiv 2020年

作者： dos Reis, Elohim Fonseca Li, Aming Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY United States Department of Zoology University of Oxford Oxford United Kingdom Department of Biochemistry University ofOxford Oxford United Kingdom Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY United States

Intervals between discrete events representing human activities, as well as other types of events, often obey heavy-tailed distributions, and their impacts on collective dynamics on networks such as contagion processes have been intensively studied. The literature supports that such heavy-tailed distributions are present for inter-event times associated with both individual nodes and individual edges in networks. However, the simultaneous presence of heavy-tailed distributions of inter-event times for nodes and edges is a non-trivial phenomenon, and its origin has been elusive. In the present study, we propose a generative model and its variants to explain this phenomenon. We assume that each node independently transits between a high-activity and low-activity state according to a continuous-time two-state Markov process and that, for the main model, events on an edge occur at a high rate if and only if both end nodes of the edge are in the high-activity state. In other words, two nodes interact frequently only when both nodes prefer to interact with others. The model produces distributions of inter-event times for both individual nodes and edges that resemble heavy-tailed distributions across some scales. It also produces positive correlation in consecutive inter-event times, which is another stylized observation for empirical data of human activity. We expect that our modeling framework provides a useful benchmark for investigating dynamics on temporal networks driven by non-Poissonian event sequences. Copyright © 2020, The Authors. All rights reserved.

关键词： Continuous time systems

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Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

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The Lancet 2023年第10397期402卷 203-234页

作者： Ong, Kanyin Liane Stafford, Lauryn K. McLaughlin, Susan A. Boyko, Edward J. Vollset, Stein Emil Smith, Amanda E. Dalton, Bronte E. Duprey, Joe Cruz, Jessica A. Hagins, Hailey Lindstedt, Paulina A. Aali, Amirali Abate, Yohannes Habtegiorgis Abate, Melsew Dagne Abbasian, Mohammadreza Abbasi-Kangevari, Zeinab Abbasi-Kangevari, Mohsen ElHafeez, Samar Abd Abd-Rabu, Rami Abdulah, Deldar Morad Abdullah, Abu Yousuf Md Abedi, Vida Abidi, Hassan Aboagye, Richard Gyan Abolhassani, Hassan Abu-Gharbieh, Eman Abu-Zaid, Ahmed Adane, Tigist Demssew Adane, Denberu Eshetie Addo, Isaac Yeboah Adegboye, Oyelola A. Adekanmbi, Victor Adepoju, Abiola Victor Adnani, Qorinah Estiningtyas Sakilah Afolabi, Rotimi Felix Agarwal, Gina Aghdam, Zahra Babaei Agudelo-Botero, Marcela Arriagada, Constanza Elizabeth Aguilera Agyemang-Duah, Williams Ahinkorah, Bright Opoku Ahmad, Danish Ahmad, Rizwan Ahmad, Sajjad Ahmad, Aqeel Ahmadi, Ali Ahmadi, Keivan Ahmed, Ali Ahmed, Luai A. Ahmed, Syed Anees Ajami, Marjan Akinyemi, Rufus Olusola Al Hamad, Hanadi Al Hasan, Syed Mahfuz AL-Ahdal, Tareq Mohammed Ali Alalwan, Tariq A. Al-Aly, Ziyad AlBataineh, Mohammad T. Alcalde-Rabanal, Jacqueline Elizabeth Alemi, Sharifullah Ali, Hassam Alinia, Tahereh Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Amare, Firehiwot Ameyaw, Edward Kwabena Amiri, Sohrab Amusa, Ganiyu Adeniyi Andrei, Catalina Liliana Anjana, Ranjit Mohan Ansar, Adnan Ansari, Golnoosh Ansari-Moghaddam, Alireza Anyasodor, Anayochukwu Edward Arabloo, Jalal Aravkin, Aleksandr Y. Areda, Demelash Arifin, Hidayat Arkew, Mesay Armocida, Benedetta Arnlov, Johan Artamonov, Anton A. Arulappan, Judie Aruleba, Raphael Taiwo Arumugam, Ashokan Aryan, Zahra Asemu, Mulu Tiruneh Asghari-Jafarabadi, Mohammad Askari, Elaheh Asmelash, Daniel Astell-Burt, Thomas Athar, Mohammad Athari, Seyyed Shamsadin Atout, Maha Moh'd Wahbi Avila-Burgos, Leticia Awaisu, Ahmed Azadnajafabad, Sina Darshan, B.B. Babamohamadi, Hassan Badar, Muhammad Badawi, Alaa Badiye, Ashish D. Baghcheghi, Nayereh Baghe Institute for Health Metrics and Evaluation School of Medicine General Medicine Service Department of Veterans Affairs Seattle WA United States Department of Health Metrics Sciences School of Medicine Faculty of Medicine France Department of Clinical Governance and Quality Improvement Aleta Wondo Hospital Aleta Wondo Ethiopia Department of Nursing Woldia University Woldia Ethiopia Department of Nursing Department of Orthopedic Surgery Orthopaedic Surgery Social Determinants of Health Research Center Non-communicable Diseases Research Center Epidemiology Department Evidence-Based Practice Center Community and Maternity Nursing Unit University of Duhok Duhok Iraq School of Planning Faculty of Environment Department of Public Health Sciences Penn State College of Medicine Pennsylvania State University Hershey PA United States Biocomplexity Institute Virginia Tech Blacksburg VA United States College of Medicine Department of Public Health Laboratory Technology Sciences Department Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Research Center for Immunodeficiencies Department of Biosciences and Nutrition Karolinska University Hospital Huddinge Sweden Clinical Sciences Department Department of Surgery Department of Surgery Department of Obstetrics & Gynecology College of Graduate Health Sciences University of Tennessee Memphis TN United States Department of Obstetrics & Gynecology Department of Clinical and Psychosocial Epidemiology Department of Anesthesia and Critical Care Centre for Social Research in Health Quality and Systems Performance Unit Cancer Institute NSW Sydney NSW Australia Department of Public Health and Tropical Medicine HIV and Infectious Diseases Department Jhpiego Abuja Nigeria Adolescent Research and Care Department Adolescent Friendly Research Initiative and Care Ado Ekiti Nigeria Department of Epidemiology Department of Epidemiology and Medical Statistics Department of Family Medicine McMaster University

Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined;these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs;defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims;type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative r

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How the world’s collective attention is being paid to a pandemic: COVID-19 related 1-gram time series for 24 languages on Twitter

arXiv

引用

arXiv 2020年

作者： Alshaabi, Thayer Arnold, Michael V. Minot, Joshua R. Adams, Jane Lydia Dewhurst, David Rushing Reagan, Andrew J. Muhamad, Roby Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT05401 MassMutual Data Science AmherstMA01002 International Class Program Faculty of Social and Political Sciences University of Indonesia Jakarta Indonesia Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States

In confronting the global spread of the coronavirus disease COVID-19 pandemic we must have coordinated medical, operational, and political responses. In all efforts, data is crucial. Fundamentally, and in the possible absence of a vaccine for 12 to 18 months, we need universal, well-documented testing for both the presence of the disease as well as confirmed recovery through serological tests for antibodies, and we need to track major socioeconomic indices. But we also need auxiliary data of all kinds, including data related to how populations are talking about the unfolding pandemic through news and stories. To in part help on the social media side, we curate a set of 1000 day-scale time series of 1-grams across 24 languages on Twitter that are most ‘important’ for March 2020 with respect to March 2019. We determine importance through our allotaxonometric instrument, rank-turbulence divergence. We make some basic observations about some of the time series, including a comparison to numbers of confirmed deaths due to COVID-19 over time. We broadly observe across all languages a peak for the language-specific word for ‘virus’ in January followed by a decline through February and a recent surge through March. The world’s collective attention dropped away while the virus spread out from China. We host the time series on Gitlab, updating them on a daily basis while relevant. Our main intent is for other researchers to use these time series to enhance whatever analyses that may be of use during the pandemic as well as for retrospective investigations. Copyright © 2020, The Authors. All rights reserved.

关键词： Time series

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ZokorDB: tissue specific regulatory network annotation for non-coding elements of plateau zokor

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Quantitative Biology 2020年第1期8卷 43-50页

作者： Jingxue Xin Junjun Hao Lang Chen Tao Zhang Lei Li Luonan Chen Wenmin Zhao Xuemei Lu Peng Shi Yong Wang CEMS NCMISMDISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China State Key Laboratory of Genetic Resources and Evolution Laboratory of Evolutionary and Functional GenomicsKunming Institute of ZoologyChinese Academy of SciencesKunming 650223China Key Laboratory of Systems Biology Innovation Center for Cell Signaling NetworkInstitute of Biochemistry and Cell BiologyShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghai 200031China Beijing Institute of Genomics Chinese Academy of SciencesBeijing 100101China Center for Excellence in Animal Evolution and Genetics Chinese Academy of SciencesKunming 650223China Department of Statistics Department of Biomedical Data ScienceBio-X ProgramStanford UniversityStanfordCA 94305USA University of Chinese Academy of Sciences Beijing 100049China

Background:Plateau zokor inhabits in sealed burrows from 2,000 to 4,200 meters at Qinghai-Tibet *** extreme living env ironment makes it a great model to study animal adaptation to hypoxia,low temperature,and high carbon dioxide ***:We provide an integrated resource,ZokorDB,for tissue specific regulatory network annotation for *** is based on a high-quality draft genome of a plateau zokor at 3,300 m and its transcriptional profiles in brain,heart,liver,kidney,and *** conserved non-coding elements of zokor are annotated by their nearest genes and upstream transcriptional factor motif binding ***:ZokorDB provides a general draft gene regulatory network(GRN),Le?potential transcription factor(TF)binds to non-coding regulatory elements and regulates the expression of target genes(TG).Furthermore,we refined the GRN by incorporating matched RNA-seq and DNase-seq data from mouse ENCODE project and reconstructed five tissue-specific regulatory ***:A web-based,open-access database is developed for easily searching,visualizing,and downloading the annotation and *** pipeline of non-coding region annotation for zokor will be useful for other non-model *** is free available at the website(***/zokordb/).

关键词： tissue specific regulatory network non-coding element plateau zokor non-model species

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Tracking X-Ray Variability in Next-generation EHT Low-luminosity Active Galactic Nucleus Targets

arXiv

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arXiv 2025年

作者： Ford, Nicole M. Nowak, Michael Ramakrishnan, Venkatessh Haggard, Daryl Dage, Kristen Nair, Dhanya G. Chan, Chi-Kwan Department of Physics McGill University 3600 Rue University MontrealQCH3A 2T8 Canada Trottier Space Institute 3550 Rue University MontréalQCH3A 2A7 Canada Department of Physics Washington University in Saint Louis MSC 1105-109-02 One Brookings Drive St. LouisMO63130-4899 United States Finnish Centre for Astronomy with ESO University of Turku TurkuFI-20014 Finland Aalto University Metsähovi Radio Observatory Metsähovintie 114 KylmäläFI-02540 Finland International Centre for Radio Astronomy Research Curtin University GPO Box U1987 PerthWA6845 Australia Astronomy Department Universidad de Concepción Casilla Concepción160-C Chile Max Planck Institute for Radio Astronomy Auf dem Hügel 69 Bonn53121 Germany Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Avenue TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Avenue TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States

We present a 5 month NICER X-ray monitoring campaign for two low-luminosity active galactic nuclei (LLAGNs)—NGC 4594 and IC 1459—with complementary Swift and NuSTAR observations. Utilizing an absorbed power-law and thermal source model combined with NICER’s SCORPEON background model, we demonstrate the effectiveness of joint source–background modeling for constraining emission from faint, background-dominated targets. Both sources are dominated by nuclear power-law emission with photon indices Γ ∼ 1.5–2, with NGC 4594 being slightly harder than IC 1459. The thermal contribution in both sources is fainter, but constant, with kT ∼ 0.5 keV (∼5 × 106 K). The power-law flux and Γ are strongly anticorrelated in both sources, as has been seen for other LLAGNs with radiatively inefficient accretion flows. NGC 4594 is the brighter source and exhibits significant aperiodic variability. Its variability timescale with an upper limit of 5–7 days indicates emission originating from ≾100 rg, at the scale of the inner accretion flow. A spectral break found at ∼6 keV, while tentative, could arise from synchrotron/inverse Compton emission. This high-cadence LLAGN X-ray monitoring campaign underlines the importance of multiwavelength variability studies for a sample of LLAGNs to truly understand their accretion and outflow physics. © 2025, CC BY.

关键词： Active Galactic Nuclei

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Learning over inherently distributed data

arXiv

引用

arXiv 2019年

作者： Yan, Donghui Xu, Ying Department of Mathematics and Program in Data Science University of Massachusetts DartmouthMA United States Indigo Agroculture Inc BostonMA United States

The recent decades have seen a surge of interests in distributed computing. Existing work focus primarily on either distributed computing platforms, data query tools, or, algorithms to divide big data and conquer at individual machines etc. It is, however, increasingly often that the data of interest are inherently distributed, i.e., data are stored at multiple distributed sites due to diverse collection channels, business operations etc. We propose to enable learning and inference in such a setting via a general framework based on the distortion minimizing local transformations. This framework only requires a small amount of local signatures to be shared among distributed sites, eliminating the need of having to transmitting big data. Computation can be done very efficiently via parallel local computation. The error incurred due to distributed computing vanishes when increasing the size of local signatures. As the shared data need not be in their original form, data privacy may also be preserved. Experiments on linear (logistic) regression and Random Forests have shown promise of this approach. This framework is expected to apply to a general class of tools in learning and inference with the "continuity" property. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

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Novel Polarimetric Analysis of Near Horizon Flaring Episodes in M87* in Millimeter Wavelength

arXiv

引用

arXiv 2025年

作者： Emami, Razieh Liska, Matthew Chatterjee, Koushik Bower, Geoffrey C. Benbow, Wystan Finkbeiner, Douglas Wielgus, Maciek Hernquist, Lars Smith, Randall Tremblay, Grant Ricarte, Angelo Steiner, James F. Broderick, Avery E. Saurabh Davelaar, Jordy Grindlay, Josh Vogelsberger, Mark Chan, Chi-Kwan Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Center for Relativistic Astrophysics Georgia Institute of Technology Howey Physics Bldg 837 State Street NW AtlantaGA30332 United States Department of Physics University of Maryland College ParkMD United States Academia Sinica Institute of Astronomy and Astrophysics 645 N. A’ohoku Place HiloHI96720 United States Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n GranadaE-18008 Spain Black Hole Initiative Harvard University 20 Garden Street CambridgeMA02138 United States Perimeter Institute for Theoretical Physics 31 Caroline Street North WaterlooONN2L 2Y5 Canada Department of Physics and Astronomy University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Department of Astrophysical Sciences Peyton Hall Princeton University PrincetonNJ08544 United States NASA Hubble Fellowship Program Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Department of Astronomy and Steward Observatory University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States

Recent multi-wavelength observations of M87* (Algaba et al. 2024) revealed a high-energy γ-ray flare without a corresponding millimeter counterpart. We present a theoretical polarimetric study to evaluate the presence and nature of a potential millimeter flare in M87*, using a suite of general relativistic magnetohydrodynamical simulations with varying black hole (BH) spins and magnetic field configurations. We find that the emergence of a millimeter flare is strongly influenced by both spin and magnetic structure, with limited sensitivity to the electron distribution (thermal vs. non-thermal). We model the intensity light curve with a damped random walk (DRW) and compare the characteristic timescale (τ) with recent SMA observations, finding that the simulated τ exceeds observed values by over an order of magnitude. In a flaring case with BH spin a=+0.5, we identify a distinct millimeter flare followed by an order-of-magnitude flux drop. All Stokes parameters show variability near the flare, including a sign reversal in the electric vector position angle. While most βm modes remain stable, the EB-correlation phase is highly sensitive to both the flare peak and decay. We examine polarimetric signatures in photon sub-rings, focusing on modes ns=0 and ns=1. The ns=0 signal closely matches the full image, while ns=1 reveals distinct behaviors, highlighting the potential of space VLBI to isolate sub-ring features. Finally, we analyze the magnetic and velocity field evolution during the flare, finding that magnetic reconnection weakens during the flux decay, and the clockwise velocity flow transitions into an outflow-dominated regime. These results suggest that transient radio variability near flares encodes key information about BH spin and magnetic field structure, offering a novel probe into the physics of active galactic nuclei. © 2025, CC BY.

关键词： Magnetohydrodynamics

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