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arXiv 2020年

作者： Nishimura, Naoki Sukegawa, Noriyoshi Takano, Yuichi Iwanaga, Jiro Graduate School of Systems and Information Engineering University of Tsukuba Ibaraki305-8577 Japan Recruit Lifestyle Co. Ltd. Tokyo100-6640 Japan Department of Information and Computer Technology Tokyo University of Science Tokyo125-8585 Faculty of Engineering Information and Systems University of Tsukuba Ibaraki305-8577 Doctoral Program in Policy and Planning Sciences University of Tsukuba Ibaraki305-8577 Erdos Inc. Kanagawa222-0033

This paper examines the relationship between user pageview (PV) histories and their item-choice behavior on an e-commerce website. We focus on PV sequences, which represent time series of the number of PVs for each user-item pair. We propose a shape-restricted optimization model that accurately estimates item-choice probabilities for all possible PV sequences. This model imposes monotonicity constraints on item-choice probabilities by exploiting partial orders for PV sequences, according to the recency and frequency of a user's previous PVs. To improve the computational efficiency of our optimization model, we devise efficient algorithms for eliminating all redundant constraints according to the transitivity of the partial orders. Experimental results using real-world clickstream data demonstrate that our method achieves higher prediction performance than that of a state-of-the-art optimization model and common machine learning methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph theory

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AN EXPANDED GENETICALLY ENCODED VOLTAGE INDICATOR TOOLBOX FOR DEEP, RAPID, AND PROLONGED VOLTAGE RECORDINGS IN VIVO

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IBRO Neuroscience Reports 2023年 15卷 S264-S265页

作者： Shuyuan Yang Xiaoyu Lu Michelle Land Zhuohe Liu Alex Mcdonald Yueyang Gou Yunmiao Wang Elsayed Zaabout Xiaoyu Dong Shujuan Lai Dieter Jaeger Jacob Reimer François St-Pierre Rice University Department Of Chemical And Biomolecular Engineering Houston United States of America Rice University Systems Synthetic And Physical Biology Program Houston United States of America Baylor College of Medicine Department Of Neuroscience Houston United States of America Emory University Department Of Biology Atlanta United States of America Emory University Neuroscience Graduate Program Atlanta United States of America Baylor College of Medicine Center For Neuroscience And Artificial Intelligence Houston United States of America Rice University Department Of Electrical And Computer Engineering Houston United States of America Baylor College of Medicine Department Of Biochemistry And Molecular Biology Houston United States of America

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Erratum to "Automated parameter selection for accelerated MRI reconstruction via low-rank modeling of local k-space neighborhoods" [Z Med. Phys. 33 (2023) 203-219]

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Zeitschrift fur medizinische Physik 2024年第1期35卷 119-119页

作者： Efe Ilicak Emine Ulku Saritas Tolga Çukur Computer Assisted Clinical Medicine Mannheim Institute for Intelligent Systems in Medicine Medical Faculty Mannheim Heidelberg University Mannheim Germany National Magnetic Resonance Research Center (UMRAM) Bilkent University Ankara Turkey. Electronic address: efe.ilicak@medma.uni-heidelberg.de. National Magnetic Resonance Research Center (UMRAM) Bilkent University Ankara Turkey Department of Electrical and Electronics Engineering Bilkent University Ankara Turkey Neuroscience Program Bilkent University Ankara Turkey.

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Leveraging multivariate information for community detection in functional brain networks

Communications biology

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Communications biology 2025年第1期8卷 840页

作者： Maria Grazia Puxeddu Maria Pope Thomas F Varley Joshua Faskowitz Olaf Sporns Department of Psychological and Brain Sciences Indiana University Bloomington IN USA. mpuxeddu@iu.edu. Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Rome Italy. mpuxeddu@iu.edu. Program in Neuroscience Indiana University Bloomington IN USA. Luddy School of Informatics Indiana University Bloomington IN USA. Department of Psychological and Brain Sciences Indiana University Bloomington IN USA. Vermont Complex Systems Institute University of Vermont Burlington VT USA.

Brain functioning relies on specialized systems whose integration enables cognition and behavior. Network science provides tools to model the brain as a set of interconnected brain regions wherein those segregated systems (modules) can be identified by optimizing the weights of pairwise connections within them. However, knowledge alone of these pairwise connections might not suffice: brain dynamics are also engendered by higher-order interactions that simultaneously involve multiple brain areas. Here, we propose a community detection algorithm that accounts for multivariate interactions and finds modules of brain regions whose activity is maximally redundant. We compared redundancy-dominated modules to those identified with conventional methods, uncovering a new organization of the transmodal cortex. Moreover, by identifying a spatial resolution where within-module redundancy and between-module synergy are maximally balanced, we captured a higher-order manifestation of the interplay between segregation and integration of information. Finally, we distinguish brain regions with high and low topological specialization based on their contribution to within- or between-module redundancy, and we observed how redundant modules reconfigure across the lifespan. Altogether, the results show a modular organization of the brain that accounts for higher-order interactions and pave the way for future investigations that might link it to cognition, behavior, or disease.

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National Science Review 2025年第5期 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

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Marangoni convection-driven laser fountains and waves on free surfaces of liquids

arXiv

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arXiv 2021年

作者： Lin, Feng Quraishy, Aamir Nasir Tong, Tian Li, Runjia Yang, Guang Mohebinia, Mohammadjavad Qiu, Yi Vishal, Talari Zhao, Junyi Zhang, Wei Zhong, Hong Zhang, Hang Zhou, Chaofu Tong, Xin Yu, Peng Hu, Jonathan Dong, Suchuan Liu, Dong Wang, Zhiming Schaibley, John R. Bao, Jiming Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China ChengduSichuan610054 China Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States Department of Physics University of Arizona TucsonAZ85721 United States James C. Wyant College of Optical Sciences University of Arizona TucsonAZ85721 United States Department of Mechanical Engineering University of Houston HoustonTX77204 United States Materials Science and Engineering Program University of Houston HoustonTX77204 United States School of Science Southwest Petroleum University ChengduSichuan610500 China Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO63130 United States Department of Electrical & Computer Engineering Baylor University WacoTX76798 United States Department of Mathematics Purdue University West LafayetteIN47907 United States Department of Physics Texas Center for Superconductivity University of Houston HoustonTX77204 United States

It is well accepted that an outward Marangoni convection from a low surface tension region will make the surface depressed. Here, we report that this established perception is only valid for thin liquid films. Using surface laser heating, we show that in deep liquids a laser beam actually pulls up the fluid above the free surface generating fountains with different shapes. Whereas with decreasing liquid depth a transition from fountain to indentation with fountain-in-indentation is observed. Further, high-speed imaging reveals a transient surface process before steady elevation is formed, and this dynamic deformation is subsequently utilized to resonantly excite giant surface waves by a modulated laser beam. Computational fluid dynamics models reveal the underlying flow patterns and quantify the depth-dependent and time-resolved surface deformations. Our discoveries and techniques have upended the century-old perception and opened up a new regime of interdisciplinary research and applications of Marangoni-induced interface phenomena and optocapillary fluidic surfaces - the control of fluids with light. © 2021, CC BY.

关键词： Deformation

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Levels and trends in the sex ratio at birth in seven provinces of Nepal between 1980 and 2016 with probabilistic projections to 2050: A Bayesian modeling approach

arXiv

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arXiv 2020年

作者： Chao, Fengqing Samir, K.C. Ombao, Hernando Statistics Program Computer Electrical And Mathematical Sciences And Engineering Division King Abdullah University Of Science And Technology 4700 KAUST Thuwal23955-6900 Saudi Arabia Asian Demographic Research Institute Shanghai University Shangda Road Shanghai200444 China International Institute For Applied Systems Analysis Schlossplatz 1 Laxenburg2361 Austria

Background: The sex ratio at birth (SRB;ratio of male to female births) in Nepal has been reported without imbalance on the national level. However, the national SRB could mask the disparity within the country. Given the demographic and cultural heterogeneities in Nepal, it is crucial to model Nepal SRB on the subnational level. Prior studies on subnational SRB in Nepal are mostly based on reporting observed values from surveys and census, and no study has provided probabilistic projections. We aim to estimate and project SRB for the seven provinces of Nepal from 1980 to 2050 using a Bayesian modeling approach. Methods: We compiled an extensive database on provincial SRB of Nepal, consisting 2001, 2006, 2011, and 2016 Nepal Demographic and Health Surveys and 2011 Census. We adopted a Bayesian hierarchical time series model to estimate and project the provincial SRB, with a focus on modelling the potential SRB imbalance. Results: In 2016, the highest SRB is estimated in Province 5 at 1.102 with a 95% credible interval (1.044, 1.127) and the lowest SRB is in Province 2 at 1.053 (1.035, 1.109). During 1980-2016, the provincial SRB was around the same level as the national SRB baseline of 1.049. The SRB imbalance probabilities in all provinces are generally low and vary from 16% in Province 2 to 81% in Province 5. SRB imbalances are estimated to have begun at the earliest in 2001 in Province 5 with a 95% credible interval (1992, 2022) and the latest in 2017 (1998, 2040) in Province 2. We project SRB in all provinces to begin converging back to the national baseline in the mid-2030s. By 2050, the SRBs in all provinces are projected to be around the SRB baseline level. Conclusion: Our findings imply that the majority of provinces in Nepal have low risks of SRB imbalance for the period 1980-2016. However, we identify a few provinces with higher probabilities of having SRB inflation. The projected SRB is an important illustration of potential future prenatal sex discriminat

关键词： Bayesian networks

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Success Factors of the Blockchain Adoption for Smart Manufacture

Success Factors of the Blockchain Adoption for Smart Manufac...

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2018 International Seminar on Research of Information Technology and Intelligent systems, ISRITI 2018

作者： Surjandy Fernando, Erick Meyliana Kosala, Raymond Warnars, Harco Leslie Hendric Spits Abdurachman, Edi Supangkat, Suhono Harso Computer Science Department BINUS Graduate Program - Doctor of Computer Science Information Systems Audit Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9781538674222

Currently, the discussion about smart city becomes a trend topic in which the development of the Internet of Thing (IoT) is used in various sectors including in the manufacturing sector where the Indonesian government is intensively increasing the capacity and production quality. The use of IoT in both smart manufacture and smart city become an interesting thing topic for research and the combination of the new technology Blockchain, may proof the reliability of data. Trust, transparent, secure, real time online process as a new phenomenon to merge IoT with Blockchain technology into smart manufacturing technology. However, for maintaining quality and sustainability, required technology governance so the achievement of implementation can be done well. This research uses the method called Systematic Literature Review (SLR) find the Blockchain adoption factors for Smart Manufacturing Technology Governance and the research conducted on several reputable publications. The result found, there are 35 adoption success factors found from the adoption of blockchain technology into smart manufacture industry. The influence of the technology governance process in smart manufacture industry increase sustainability, realtime and accurate decision making for the industry. © 2018 IEEE.

关键词： Manufacture

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Emergent Dimer-Model Topological Order and Quasiparticle Excitations in Liquid Crystals: Combinatorial Vortex Lattices

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Physical Review X 2025年第2期15卷 021084-021084页

作者： Cuiling Meng Jin-Sheng Wu Žiga Kos Jörn Dunkel Cristiano Nisoli Ivan I. Smalyukh Department of Physics and Chemical Physics Program University of Colorado Boulder Colorado 80309 USA International Institute for Sustainability with Knotted Chiral Meta Matter (WPI-SKCM) Hiroshima University Higashihiroshima Hiroshima Japan Department of Mathematics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Faculty of Mathematics and Physics University of Ljubljana Ljubljana Slovenia Department of Condensed Matter Physics Jožef Stefan Institute Ljubljana Slovenia Theoretical Division Physics of Condensed Matter and Complex Systems and Center For Nonlinear Studies Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Electrical Computer and Energy Engineering Materials Science and Engineering Program University of Colorado Boulder Colorado 80309 USA Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado Boulder Colorado 80309 USA

Liquid crystals have proven to provide a versatile experimental and theoretical platform for studying topological objects such as vortices, skyrmions, and hopfions. In parallel, in hard condensed matter physics, the concept of topological phases and topological order has been introduced in the context of spin liquids to investigate emergent phenomena like quantum Hall effects and high-temperature superconductivity. Here, we bridge these two seemingly disparate perspectives on topology in physics. Combining experiments and simulations, we show how topological defects in liquid crystals can be used as versatile building blocks to create complex, highly degenerate topological phases, which we refer to as “combinatorial vortex lattices” (CVLs). CVLs exhibit extensive residual entropy and support locally stable quasiparticle excitations in the form of charge-conserving topological monopoles, which can act as mobile information carriers and be linked via Dirac strings. CVLs can be rewritten and reconfigured on demand, endowed with various symmetries, and modified through laser-induced topological surgery—an essential capability for information storage and retrieval. We demonstrate experimentally the realization, stability, and precise optical manipulation of CVLs, thus opening new avenues for understanding and technologically exploiting higher-hierarchy topology in liquid crystals and other ordered media.

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Time-Varying ℓ₀ Optimization for Spike Inference from Multi-Trial Calcium Recordings

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Data Science in Science 2024年第1期3卷

作者： Shen, Tong Du, Mingyu Johnston, Kevin Grieco, Steven F. Crary, Rachel Guzowski, John F. Lur, Gyorgy Xu, Xiangmin Ombao, Hernando Guindani, Michele Yu, Zhaoxia Department of Statistics University of California Irvine 92697 CA United States Center for Complex Biological Systems University of California Irvine 92697 CA United States Department of Mathematics and The NSF-Simons Center for Multiscale Cell Fate Research University of California Irvine 92697 CA United States Department of Anatomy and Neurobiology School of Medicine University of California Irvine 92697 CA United States Department of Neurobiology and Behavior University of California Irvine 92697 CA United States The Center for the Neurobiology of Learning and Memory University of California Irvine 92697 CA United States Department of Biomedical Engineering University of California Irvine 92697 CA United States Department of Computer Science University of California Irvine 92697 CA United States The Center for Neural Circuit Mapping University of California Irvine 92697 CA United States Statistics Program KAUST Thuwal Saudi Arabia Department of Biostatistics University of California Los Angeles CA United States

Optical imaging of genetically encoded calcium indicators is a powerful tool to record the activity of a large number of neurons simultaneously over a long period of time from freely behaving animals. However, determining the exact time at which a neuron spikes and estimating the underlying firing rate from calcium fluorescence data remains challenging, especially for calcium imaging data obtained from a longitudinal study. We propose a multi-trial time-varying ℓ₀ penalized method to jointly detect spikes and estimate firing rates by robustly integrating evolving neural dynamics across trials. Our simulation study shows that the proposed method performs well in both spike detection and firing rate estimation. We demonstrate the usefulness of our method on calcium fluorescence trace data from two studies, with the first study showing differential firing rate functions between two behaviors and the second study showing evolving firing rate functions across trials due to learning. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： calcium imaging firing rate spike detection Time-varying penalty ℓ0 regularization

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