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Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions with Applications

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arXiv 2024年

作者： Iguchi, Yuga Jasra, Ajay Maama, Mohamed Beskos, Alexandros Department of Statistical Science University College London LondonWC1E 6BT United Kingdom School of Data Science The Chinese University of Hong Kong Shenzhen China 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

In this paper we present a new antithetic multilevel Monte Carlo (MLMC) method for the estimation of expectations with respect to laws of diffusion processes that can be elliptic or hypo-elliptic. In particular, we consider the case where one has to resort to time discretization of the diffusion and numerical simulation of such schemes. Motivated by recent developments, we introduce a new MLMC estimator of expectations, which does not require simulation of intractable Lévy areas but has a weak error of order 2 and achieves the optimal computational complexity. We then show how this approach can be used in the context of the filtering problem associated to partially observed diffusions with discrete time observations. We illustrate with numerical simulations that our new approaches provide efficiency gains for several problems relative to some existing methods. © 2024, CC BY.

关键词： Diffusion

A robust incomplete large-scale group decision-making model for metaverse metro operations and maintenance

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Applied Soft Computing 2024年 156卷

作者： Bai, Wenhui Zhang, Chao Zhai, Yanhui Sangaiah, Arun Kumar School of Computer and Information Technology Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

The metaverse, constructed through digital technology, serves as a virtual realm intertwining with reality. Within this context, the challenge of evaluating data from diverse sources arises, and the application of large-scale group decision-making (LSGDM) methods emerges as a viable solution. Handling incomplete information and reducing dimensionality for large-scale decision-makers (DMs) is crucial in addressing complex decision-making problems. Moreover, addressing missing data is a fundamental and pivotal concern in tackling real-world decision challenges, given the ubiquitous presence of information gaps that cannot be straightforwardly integrated into decision models. Besides, the intricacies of LSGDM amplify this challenge by introducing a wealth of DMs, thereby augmenting the complexity and diversity of decision-related information. This paper proposes an approach to supplement missing data by double-dimensions. This paper explores various facets of similarity relationships within the data to enhance data completeness. Additionally, this paper categorizes DMs into clusters based on their relevance and establishes a two-stage consensus-reaching process (CRP) that takes into account both group sizes and individual consensus contributions. These CRPs play a crucial role in enhancing the overall consistency and consensus within the decision group. Subsequently, this paper applies a robust decision-making method rooted in MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the complete MULTIplicative form) to rank decision objects. Finally, this paper employs this proposed methodology in a practical case study that involves evaluating the operational status of a metaverse's urban construction metro system. Following these considerations, a comprehensive stability analysis of relevant parameters is conducted to guarantee the robustness and reliability of the decision-making process. © 2024 Elsevier B.V.

关键词： Multiobjective optimization

Biosynthesis of microrubble-shaped nanoporous Co₃O₄/rGO NCs using leaves extract of Neolamarckia cadamba and its antibacterial/antioxidant performance

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Nano-Structures and Nano-Objects 2024年 39卷

作者： Bhilkar, Pavan R. Madankar, Rohit S. Chaudhary, Ankita R. Chaudhary, Rupali R. Somkuwar, Subhash R. Potbhare, Ajay K. Dadure, Kanhaiya Abdala, Ahmed A. Chaudhary, Ratiram G. Post Graduate Department of Chemistry Seth Kesarimal Porwal College Department of Botany L.A.D College Department of Botany Sant Gadge Maharaj Mahavidyalaya Department of Botany Dr. Ambedkar College Post Graduate Department of Chemistry Bajaj College of Science Chemical Engineering Program Texas A&M University at Qatar

Nowadays, the whole globe is paying great attention for the sustainable development, eco-friendly and economical production of nanostructures materials. With this motivation, the present work portrayed one-step ‘in-situ’ bioinspired green fabrication of Co₃O₄–reduced graphene oxide nanocomposites (Co₃O₄–rGO NCs) for the first time utilizing an aqueous leaf extract of Neolamarckia cadamba. As-fabricated NCs were characterized by several analytical and electron microscopic techniques viz. X-ray diffraction (XRD), Fourier Transform-Infrared (FT-IR), Energy dispersive spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), BET (Brunauer-Emmett-Teller), Scanning electron microscopy (SEM), and Transmission electron microscopy (TEM) to evaluate their phases, structure, shape, composition, chemical state, porosity, optical and morphological aspects. The XRD study revealed a cubic spinel symmetry of NCs, while crystallite size estimated around 25 nm. TEM spectroscopy exposed an average particle size 8–10 nm of Co₃O₄ grown on the surface of reduced graphene oxide (rGO NCs), whereas SEM analysis showed sharped microrubble-shaped morphology. The formation of NCs by doping metal oxide with rGO through in-situ method were confirmed by XRD, EDS, Raman, and XPS spectroscopy. The BET study estimated the specific surface area and pore size of NCs as 98 m²/g and 8.9 nm respectively. The Co₃O₄–rGO NCs demonstrated an effective bactericidal activity against S. aureus, E. coli, K. pneumoniae, and B. subtillis. Prominently it was found that, a significant antibacterial activity seen against E. coli and S. aureus at a concentration 0.1 mg/100 µL may be due to scaffold morphology, nanoscale-structures and nanoporosity of the NCs. Due to receiving significant antibacterial activity, material's molecular docking was assessed. Furthermore, an antioxidant activity of leaf extract and NCs was e

关键词： Antibacterial Activity Antioxidant evaluation Bioinspired Co3O4-rGO NCs Microrubble-shaped NCs Nature-inspired synthesis Neolamarckia cadamba

Empowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic Regression

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arXiv 2024年

作者： Lou, Siyu Liu, Chengchun Chen, Yuntian Mo, Fanyang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315200 China School of Materials Science and Engineering Peking University Beijing100871 China -Preferred Program Peking University Shenzhen Graduate School Shenzhen518500 China

Thin-layer chromatography (TLC) is a crucial technique in molecular polarity analysis. Despite its importance, the interpretability of predictive models for TLC, especially those driven by artificial intelligence, remains a challenge. Current approaches, utilizing either high-dimensional molecular fingerprints or domain-knowledge-driven feature engineering, often face a dilemma between expressiveness and interpretability. To bridge this gap, we introduce Unsupervised Hierarchical Symbolic Regression (UHiSR), combining hierarchical neural networks and symbolic regression. UHiSR automatically distills chemical-intuitive polarity indices, and discovers interpretable equations that link molecular structure to chromatographic behavior. Copyright © 2024, The Authors. All rights reserved.

关键词： Thin layer chromatography

Temporal Mode Conversion in a Waveguide by Fast Motion of its Plates

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Temporal Mode Conversion in a Waveguide by Fast Motion of it...

CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Solís, Diego M. Yin, Shixiong Galiffi, Emanuele Alù, Andrea Engheta, Nader Departamento de Tecnología de los Computadores y de las Comunicaciones University of Extremadura Cáceres10003 Spain City University of New York Photonics Initiative Advanced Science Research Center New York10031 United States City University of New York Physics Program Graduate Center New York10031 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States

ISBN: (纸本)9781957171395

We show, semi-analytically and numerically, how the relativistic accelerated motion of a waveguide's parallel plates induces fast mode conversion including reflection, similar to the temporal interface by sudden variation of interplate distance through conductivity switching. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Waveguides

Bacterial Mobility and Motility in Porous Media Mimicked By Microspheres

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SSRN

SSRN 2022年

作者： Shrestha, Diksha Ou, Jun Rogers, Ariel Jereb, Amani Wang, Yong Department of Physics Cell and Molecular Graduate Program Materials Science and Engineering Program University of Arkansas FayettevilleAR72701 United States Department of Environmental Resources Engineering Mechanical Engineering Program California State Polytechnic University Humboldt ArcataCA95521 United States

Bacterial motion in porous media are essential for their survival, proper functioning, and variousapplications. Here we investigated the motion of Escherichia coli bacteria in microsphere-mimicked porousmedia. We observed reduced bacterial velocity and enhanced directional changes of bacteria as the densityof microspheres increased, while such changes happened mostly around the microspheres and due to thecollisions with the microspheres. More importantly, we established and quantified the correlation betweenthe bacterial trapping in porous media and the geometric confinement imposed by the microspheres. In addition,numerical simulations showed that the active Brownian motion model in the presence of microspheresresulted in bacterial motion that are consistent with the experimental observations. Our study suggested thatit is important to distinguish the ability of bacteria to move easily – bacterial mobility – from the abilityof bacteria to move independently – bacteria motility. Our results showed that bacterial motility remainssimilar in porous media, but bacterial mobility was significantly affected by the pore-scale confinement. © 2022, The Authors. All rights reserved.

关键词： Microspheres

Direct Teaching/Playback Method for the Contact Task of a Six-DOF Manipulator Using a Parallel-Wire-Type Teaching Device with a Remote Center Compliance Device

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Direct Teaching/Playback Method for the Contact Task of a Si...

International Conference on Control, Automation and Systems ( ICCAS)

作者： Tatsuya Okochi Masao Sakai Yoshifumi Morita Department of Engineering Electrical and Mechanical Engineering Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Aichi Japan Industrial Research Center Aichi Center for Industry and Science Technology Kariya-shi Aichi Japan Department of Electrical and Mechanical Engineering Nagoya Institute of Technology Nagoya Aichi Japan

This study proposes a direct teaching method for the task of pressing an end effector attached to a robot manipulator against a workpiece with a constant force. When performing contact tasks, such as deburring or polishing using a manipulator, a passive compliance is required for the end effector to suppress excessive contact force and impact. Therefore, we developed an end effector that combines a “parallel wire-type teaching device,” which enables direct teaching, and a compliance device. In the case of pressing a workpiece with a constant force, after teaching the robot's trajectory to trace the edge of the workpiece, the amount of pressing can be calculated from the stiffness of the end effector and the desired pressing force; the robot's trajectory can be edited to teach contact tasks. An experiment was conducted to press a straight trajectory with a constant force as the target value and evaluate the accuracy of force teaching.

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Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation

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arXiv 2024年

作者： Yang, Soojung Nam, Juno Dietschreit, Johannes C.B. Gómez-Bombarelli, Rafael Computational and Systems Biology Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Institute of Theoretical Chemistry Faculty of Chemistry University of Vienna Vienna1090 Austria

In molecular dynamics simulations, rare events, such as protein folding, are typically studied using enhanced sampling techniques, most of which are based on the definition of a collective variable (CV) along which acceleration occurs. Obtaining an expressive CV is crucial, but often hindered by the lack of information about the particular event, e.g., the transition from unfolded to folded conformation. We propose a simulation-free data augmentation strategy using physics-inspired metrics to generate geodesic interpolations resembling protein folding transitions, thereby improving sampling efficiency without true transition state samples. This new data can be used to improve the accuracy of classifier-based methods. Alternatively, a regression-based learning scheme for CV models can be adopted by leveraging the interpolation progress parameter. © 2024, CC BY.

关键词： Geodesy

Heterogeneous quantization regularizes spiking neural network activity

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arXiv 2024年

作者： Moyal, Roy Mama, Kyrus R. Einhorn, Matthew Borthakur, Ayon Cleland, Thomas A. Computational Physiology Lab Department of Psychology Cornell University IthacaNY14853 United States AI for Science Institute Cornell University IthacaNY14853 United States Neurosciences Graduate Program Stanford University StanfordCA94305 United States Mehta Family School of Data Science and Artificial Intelligence IIT Guwahati Assam Guwahati781039 India

The learning and recognition of object features from unregulated input has been a longstanding challenge for artificial intelligence systems. Brains are adept at learning stable representations given small samples of noisy observations;across sensory modalities, this capacity is aided by a cascade of signal conditioning steps informed by domain knowledge. The olfactory system, in particular, solves a source separation and denoising problem compounded by concentration variability, environmental interference, and unpredictably correlated sensor affinities. To function optimally, its plastic network requires statistically well-behaved input. We present a data-blind neuromorphic signal conditioning strategy whereby analog data are normalized and quantized into spike phase representations. Input is delivered to a column of duplicated spiking principal neurons via heterogeneous synaptic weights;this regularizes layer utilization, yoking total activity to the network’s operating range and rendering internal representations robust to uncontrolled open-set stimulus variance. We extend this mechanism by adding a data-aware calibration step whereby the range and density of the quantization weights adapt to accumulated input statistics, optimizing resource utilization by balancing activity regularization and information retention. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantization (signal)