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arXiv 2024年

作者： Ruzayqat, Hamza Knio, Omar Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

This paper presents a new data assimilation (DA) scheme based on a sequential Markov Chain Monte Carlo (SMCMC) DA technique [36] which is provably convergent and has been recently used for filtering, particularly for high-dimensional non-linear, and potentially, non-Gaussian state-space models. Unlike particle filters, which can be considered exact methods and can be used for filtering non-linear, non-Gaussian models, SMCMC does not assign weights to the samples/particles, and therefore, the method does not suffer from the issue of weight-degeneracy when a relatively small number of samples is used. We design a localization approach within the SMCMC framework that focuses on regions where observations are located and restricts the transition densities included in the filtering distribution of the state to these regions. This results in immensely reducing the effective degrees of freedom and thus improving the efficiency. We test the new technique on high-dimensional (d ∼ 104 − 105) linear Gaussian model and non-linear shallow water models with Gaussian noise with real and synthetic observations. For two of the numerical examples, the observations mimic the data generated by the Surface Water and Ocean Topography (SWOT) mission led by NASA, which is a swath of ocean height observations that changes location at every assimilation time step. We also use a set of ocean drifters' real observations in which the drifters are moving according the ocean kinematics and assumed to have uncertain locations at the time of assimilation. We show that when higher accuracy is required, the proposed algorithm is superior in terms of efficiency and accuracy over competing ensemble methods and the original SMCMC *** Codes 62M20, 60G35, 60J20, 94A12, 93E11, 65C40 © 2024, CC BY.

关键词： Markov chains

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NOMAD: nonlinear manifold decoders for operator learning 22

NOMAD: nonlinear manifold decoders for operator learning

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Jacob H. Seidman Georgios Kissas Paris Perdikaris George J. Pappas Graduate Program in Applied Mathematics and Computational Science University of Pennsylvania Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania Department of Electrical and Systems Engineering University of Pennsylvania

ISBN: (纸本)9781713871088

Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid mechanics, and climate modeling. By directly learning maps (operators) between infinite dimensional function spaces, these models are able to learn discretization invariant representations of target functions. A common approach is to represent such target functions as linear combinations of basis elements learned from data. However, there are simple scenarios where, even though the target functions form a low dimensional submanifold, a very large number of basis elements is needed for an accurate linear representation. Here we present NOMAD, a novel operator learning framework with a nonlinear decoder map capable of learning finite dimensional representations of nonlinear submanifolds in function spaces. We show this method is able to accurately learn low dimensional representations of solution manifolds to partial differential equations while outperforming linear models of larger size. Additionally, we compare to state-of-the-art operator learning methods on a complex fluid dynamics benchmark and achieve competitive performance with a significantly smaller model size and training cost.

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Hybrid Optimization Schemes for Global Optimization: Wing Modeling of Micro-Aerial Vehicles

Hybrid Optimization Schemes for Global Optimization: Wing Mo...

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DoD High Performance Computing Modernization program Users Group

作者： Leticia Vel´zquez Miguel Arg´ez Reinaldo Sanchez Carlos Ramirez Miguel Hernandez IV Matt Culbreth Antony Jameson Program in Computational Science University of Texas at El Paso El Paso TX Department of Aerospace Engineering Stanford University Palo Alto CA

In this paper, we present a parallel hybrid algorithm for solving global optimization problems that is based on the coupling of a stochastic global (Simultaneous-Perturbation Stochastic Approximation, Simulated Annealing, Genetic Algorithms) and a local method (Newton-Krylov Interior-Point) via a surrogate model. There exist several algorithms for finding approximate global solutions, but our technique will further guarantee that such solutions satisfy physical bounds of the problem. First, the Simultaneous-Perturbation Stochastic Approximation (SPSA) algorithm conjectures regions where a global solution may exist. Next, some data points from the regions are selected to generate a continuously differentiable surrogate model that approximates the original function. Finally, the Newton-Krylov Interior-Point (NKIP) algorithm is applied to the surrogate model subject to bound constraints for obtaining a feasible approximate global solution. The hybrid optimization code is being applied to Stanford's UFLO computational Fluid Dynamics (CFD) code. This code is used by the US Army High Performance Computing Research Center (AHPCRC) to develop flapping- and twisting-wing models for Micro Aerial Vehicles (MAV), hummingbird-sized airborne vehicles that can be used for sensing and surveillance. We present some preliminary numerical results of the large-scale high performance computing (HPC) hybrid optimization C code that is being run on the Department of Defense MANA machine from Maui, Hawaii.

关键词： Optimization computational modeling Approximation methods Aircraft Stochastic processes Force Vehicles

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Fluorescent imaging technologies for in situ measurement of drug target engagement and cell signaling pathways 4

Fluorescent imaging technologies for in situ measurement of ...

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Visualizing and Quantifying Drug Distribution in Tissue IV 2020

作者： McMahon, Nathan P. Solanki, Allison Jones, Jocelyn Kwon, Sunjong Chang, Young-Hwan Chin, Koei Nederlof, Michel A. Gray, Joe W. Gibbs, Summer L. Department of Biomedical Engineering Oregon Health and Science University PortlandOR97201 United States Knight Cancer Institute Oregon Health and Science University PortlandOR97201 United States Center for Spatial Systems Biomedicine Oregon Health and Science University PortlandOR97201 United States Computational Biology Program Oregon Health and Science University PortlandOR97201 United States Quantitative Imaging Systems PittsburghPA15238 United States

ISBN: (纸本)9781510632011

Successful cancer treatment continues to elude modern medicine and its arsenal of therapeutic strategies. Therapy resistance is driven by significant tumor heterogeneity, complex interactions between malignant, microenvironmental and immune cells and cross talk between signaling pathways. Advances in molecular characterization technologies such as next generation sequencing have helped unravel this network of interactions and identify druggable therapeutic targets. Tyrosine kinase inhibitors (TKI) are a class of drugs seeking to inhibit signaling pathways critical to sustaining proliferative signaling, resisting cell death, and the other hallmarks of cancer. While tumors may initially respond to TKI therapy, disease progression is near universal due to mechanisms of acquired resistance largely involving cellular signaling pathway reprogramming. With the ultimate goal of improved TKI therapeutic efficacy our group has developed intracellular paired agent imaging (iPAI) to quantify drug target interactions and oligonucleotide conjugated antibody (Ab-oligo) cyclic immunofluorescence (cycIF) imaging to characterize perturbed signaling pathways in response to therapy. iPAI uses spectrally distinct, fluorescently labeled targeted and untargeted drug derivatives, correcting for non-specific drug distribution and facilitating quantitative assessment of the drug binding before and after therapy. Ab-oligo cycIF exploits in situ hybridization of complementary oligonucleotides for biomarker labeling while oligonucleotide modifications facilitate signal removal for sequential rounds of fluorescent tagging and imaging. Aboligo CycIF is capable of generating extreme multi-parametric images for quantifying total and phosphorylated protein expression to quantify protein activation, expression, and spatial distribution. Together iPAI and Ab-oligo cycIF can be applied to interrogate drug uptake and target binding as well as changes to heterogenous cell populations within tumors that d

关键词： Fluorescence imaging

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Numerical Simulation of Nanopulse Penetration of Biological Matter Using the z -Transform

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Journal of Mathematical Modelling and Algorithms 2005年第1期4卷 99-110页

作者： S. Su W. Dai D. T. Haynie R. Nassar N. Simicevic Ph.D. Program in Computational Analysis and Modeling College of Engineering & Science Louisiana Tech University Ruston USA Mathematics & Statistics College of Engineering & Science Louisiana Tech University Ruston USA Biomedical Engineering College of Engineering & Science Louisiana Tech University Ruston USA Physics College of Engineering & Science Louisiana Tech University Ruston USA Institute for Micromanufacturing College of Engineering & Science Louisiana Tech University Ruston USA

Short duration, fast rise time ultra-wideband (UWB) electromagnetic pulses (“nanopulses”) are generated by numerous electronic devices in use today. Moreover, many new technologies involving nanopulses are under development and expected to become widely available soon. Study of nanopulse bioeffects is needed to probe their useful range in possible biomedical and biotechnological applications, and to ensure human safety. In this work we develop a computational approach to investigate electromagnetic fields in biological cells exposed to nanopulses. The simulation is based on a z-transformation of the electric displacement and a second-order Taylor approximation of a Cole–Cole expression for the frequency dependence of the dielectric properties of tissues, useful for converting from the frequency domain to the time domain. Maxwell’s equations are then calculated using the finite difference time domain method (FDTD), coupled with a perfectly matched layer to eliminate reflections from the boundary. Numerical results for a biological cell model are presented and discussed.

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A Full Eulerian Fluid-Membrane Coupling Method with a Smoothed Volume-of-Fluid Approach

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Communications in computational Physics 2012年第7期12卷 544-576页

作者： Satoshi Ii Xiaobo Gong Kazuyasu Sugiyama Jinbiao Wu Huaxiong Huang Shu Takagi Department of Mechanical Engineering The University of Tokyo7-3-1 Hongo Bunkyo-kuTokyo113-8656Japan Department of Engineering Mechanics NAOCEShanghai Jiaotong UniversityShanghai 200240China LMAM&School of Mathematical Sciences Peking UniversityBeijing 100871China Department of Mathematics and Statistics York University4700 Keele StreetTorontoOntarioCanada Computational Science Research Program RIKEN2-1 Hirosawa WakoSaitama351-0198Japan.

A novel full Eulerian fluid-elastic membrane coupling method on the fixed Cartesian coordinate mesh is proposed within the framework of the volume-of-fluid *** present method is based on a full Eulerian fluid-(bulk)structure coupling solver(Sugiyama et al.,***.,230(2011)596–627),with the bulk structure replaced by elastic *** this study,a closed membrane is considered,and it is described by a volume-of-fluid or volume-fraction information generally called VOF function.A smoothed indicator(or characteristic)function is introduced as a phase indicator which results in a smoothed VOF *** smoothed VOF function uses a smoothed delta function,and it enables a membrane singular force to be incorporated into a mixture momentum *** order to deal with a membrane deformation on the Eulerian mesh,a deformation tensor is introduced and updated within a compactly supported region near the *** the neo-Hookean and the Skalak models are employed in the numerical simulations.A smoothed(and less dissipative)interface capturing method is employed for the advection of the VOF function and the quantities defined on the *** stability restriction due to membrane stiffness is relaxed by using a quasi-implicit *** present method is validated by using the spherical membrane deformation problems,and is applied to a pressure-driven flow with the biconcave membrane capsules(red blood cells).

关键词： Fluid-membrane interaction Eulerian formulation Cartesian mesh volume-of-fluid quasi-implicit approach red blood cell

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Exploring Approaches to Answer Biomedical Questions: From Pre-processing to GPT-4 Notebook for the BioASQ Lab at CLEF 2023 24

Exploring Approaches to Answer Biomedical Questions: From Pr...

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24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023

作者： Kim, Hyunjae Hwang, Hyeon Lee, Chaeeun Seo, Minju Yoon, Wonjin Kang, Jaewoo Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of Computational Health Informatics Program Boston Children’s Hospital BostonMA02115 United States Harvard Medical School BostonMA02115 United States AIGEN Sciences Seoul04778 Korea Republic of

Biomedical question answering (QA) plays a crucial role in assisting researchers, healthcare professionals, and even patients in accessing and retrieving accurate and up-to-date information from the vast amount of biomedical knowledge available in literature. To enhance the efficiency of knowledge discovery and information retrieval, we investigate the efficacy of various pre-processing, model training, data augmentation, and ensemble methods and evaluate a range of advanced pre-trained models such as BioLinkBERT and GPT-4. Additionally, we explore data augmentation and ensemble methods to further improve system performance. In our participation in BioASQ Task 11b-Phase B, our systems achieved a top ranking in all four batches for the yes/no type of questions, in one out of four batches for factoid questions, and in two out of four batches for list-type questions. © 2023 Copyright for this paper by its authors.

关键词： BioASQ 11b BioLinkBERT Data Augmentation Ensemble GPT-4

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ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

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A Method for Automatic Generation of 3D Background Models Using Fractal Models and Genetic Algorithms 11

A Method for Automatic Generation of 3D Background Models Us...

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11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Watanabe, Kaito Nakayama, Hirotaka Shiraki, Atsushi Ito, Tomoyoshi Hirata, Katsumi Hoshikawa, Naoto National Institute of Technology Oyama College Advanced Course General Engineering Program Oyama Japan National Astronomical Observatory of Japan Center for Computational Astrophysics Mitaka Japan Chiba University Graduate School of Engineering Chiba Japan National Institute of Technology Oyama College Department of Innovative Electrical and Electronic Engineering Oyama Japan University of Tsukuba Graduate School of Science and Tecnology Tsukuba Japan

ISBN: (纸本)9798350306941

In recent years, 3DCG-related industries have grown rapidly and the demand for 3DCG content has increased. However, 3DCG production requires special skills and a lot of time. In particular, the creation of background data requires a lot of human resources at the production site because the data is huge and complex. Therefore, we propose an automatic creation system that generates 3D background data for 3DCG production. The proposed system consists of three functions: 3D model generation, generation parameter optimization, and fitness evaluation. The user inputs a landscape photograph as idea information into the system, and the system optimizes the generation parameters so that the 3D object shape approximates the input image. To evaluate the accuracy of the system's performance, we observe the optimization process and the 3D object output when a previously generated fractal image or landscape photos are input. Experimental results show that the system accurately optimizes the generation parameters and generates 3D objects that are almost identical to the shape of the fractal image. In addition, the parameters were also optimized with a certain degree of accuracy for landscape photographs for which no specific solution had been determined. © 2023 IEEE.

关键词： Genetic algorithms

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New insight into pyrrolic-N site effect towards the first NIR window absorption of pyrrolic-N-rich carbon dots

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Nano Research 2023年第4期16卷 6001-6009页

作者： Fitri Aulia Permatasari Reza Umami Citra Deliana Dewi Sundari Tirta Rona Mayangsari Atthar Luqman Ivansyah Fahdzi Muttaqien Takashi Ogi Ferry Iskandar Department of Physics Faculty Mathematics and Natural SciencesInstitut Teknologi BandungJalan Ganesha 10BandungWest Java 40132Indonesia Collaboration Research Center for Advanced Energy Materials National Research and Innovation Agency-Institut Teknologi BandungJalan Ganesha 10BandungWest Java 40132Indonesia Department of Chemistry Faculty of Mathematics and Natural SciencesInstitut Teknologi BandungJl.Ganesha 10BandungWest Java 40132Indonesia Department of Chemistry Education UIN Sunan Gunung Djati BandungJl.CimincrangBandungWest Java 40292Indonesia Department of Chemistry Universitas PertaminaJakarta 12220Indonesia Master Program in Computational Science Faculty of Mathematics and Natural SciencesInstitut Teknologi BandungJl.Ganesha 10BandungWest Java 40132Indonesia Instrumentation and Computational Physics Research Group Faculty of Mathematics and Natural SciencesInstitut Teknologi BandungJl.Ganesha 10Bandung 40132Indonesia Research Center for Nanoscience and Nanotechnology Institut Teknologi BandungJalan Ganesha 10BandungWest Java 40132Indonesia Chemical Engineering Program Department of Advanced Science and EngineeringGraduate School of Advanced Science and EngineeringHiroshima UniversityHigashi-Hiroshima 739-8527Japan

Controlled C-N configurations,i.e.,pyrrolic-N,pyridinic-N,and graphitic-N,are promising strategies to tailor the carbon dots’(CDs)optical properties into the first near infrared(NIR)window(650-900 nm),a responsive range for biomedical ***,a deep understanding of the role of the C-N configuration in the CDs’properties is still challenging and thoughtprovoking owing to their complex ***,an underlying pyrrolic-N concentration and position effect on the pyrrolic-N-rich CDs’absorption was comprehensively elucidated based on the integrated experimental and computational *** assynthesized pyrrolic-N-rich CDs exhibit a first NIR window absorption centered at 650 nm with high photothermal ***-N concentrations from 1.4%to 11.3%and positions(edge and mid-site)were systematically investigated.A mid-site pyrrolic-N was subsequently generated after the pyrrolic-N concentration more than 10%.Edge-site pyrrolic-N induces a frontier orbital hybridization,reducing bandgap energy,while mid-site pyrrolic-N plays a critical role in inducing a first NIR window absorption owing to their high charge ***,pyrrolic-N-rich CDs inherit a bowl-like topological feature,elevating the CDs’layer thickness as much as 0.71 *** study shed light on the design and optimization of pyrrolic-N on CDs for the first NIR window responsive materials in any biomedical application.

关键词： absorption carbon dots charge transfer first near infrared(NIR)window frontier orbital hybridization mid-site pyrrolic-N

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