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Unbiased estimation using a Class of Diffusion Processes

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arXiv 2022年

作者： Ruzayqat, Hamza Beskos, Alexandros Crisan, Dan Jasra, Ajay Kantas, Nikolas Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 KSA Saudi Arabia Department of Statistical Science University College London LondonWC1E 6BT United Kingdom Department of Mathematics Imperial College London LondonSW7 2AZ United Kingdom

We study the problem of unbiased estimation of expectations with respect to (w.r.t.) π a given, general probability measure on (d, B(d)) that is absolutely continuous with respect to a standard Gaussian measure. We focus on simulation associated to a particular class of diffusion processes, sometimes termed the Schrödinger-Föllmer Sampler, which is a simulation technique that approximates the law of a particular diffusion bridge process {Xt}tϵ[0,1]on d, d ϵ 0. This latter process is constructed such that, starting at X0= 0, one has X1∼ π. Typically, the drift of the diffusion is intractable and, even if it were not, exact sampling of the associated diffusion is not possible. As a result, [10, 16] consider a stochastic euler-Maruyama scheme that allows the development of biased estimators for expectations w.r.t. π. We show that for this methodology to achieve a mean square error of O(ϵ2), for arbitrary ϵ > 0, the associated cost is O(ϵ-5). We then introduce an alternative approach that provides unbiased estimates of expectations w.r.t. π, that is, it does not suffer from the time discretization bias or the bias related with the approximation of the drift function. We prove that to achieve a mean square error of O(ϵ2), the associated cost (which is random) is, with high probability, O(ϵ-2| log(ϵ)|2+δ), for any δ > 0. We implement our method on several examples including Bayesian inverse *** Codes 60J60, 62D05, 65C40 © 2022, CC BY.

关键词： Diffusion

Best practices for machine learning in antibody discovery and development

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arXiv 2023年

作者： Wossnig, Leonard Furtmann, Norbert Buchanan, Andrew Kumar, Sandeep Greiff, Victor LabGenius Ltd. The Biscuit Factory 100 Drummond Road London LondonSE16 4DG United Kingdom Department of Computer Science University College London 66-72 Gower St LondonWC1E 6EA United Kingdom R&D Large Molecules Research Platform Sanofi Deutschland GmbH Industriepark Höchst Frankfurt am Main Germany Biologics Engineering R&D AstraZeneca CambridgeCB2 0AA United Kingdom Computational Protein Design and Modeling Group Computational Science Moderna Therapeutics 200 Technology Square CambridgeMA02139 United States Department of Immunology Oslo University Hospital University of Oslo Oslo Norway

Over the past 40 years, the discovery and development of therapeutic antibodies to treat disease has become common practice. However, as therapeutic antibody constructs are becoming more sophisticated (e.g., multi-specifics), conventional approaches to optimisation are increasingly inefficient. Machine learning (ML) promises to open up an in silico route to antibody discovery and help accelerate the development of drug products using a reduced number of experiments and hence cost. Over the past few years, we have observed rapid developments in the field of ML-guided antibody discovery and development (D&D). However, many of the results are difficult to compare or hard to assess for utility by other experts in the field due to the high diversity in the datasets and evaluation techniques and metrics that are across industry and academia. This limitation of the literature curtails the broad adoption of ML across the industry and slows down overall progress in the field, highlighting the need to develop standards and guidelines that may help improve the reproducibility of ML models across different research groups. To address these challenges, we set out in this perspective to critically review current practices, explain common pitfalls, and clearly define a set of method development and evaluation guidelines that can be applied to different types of ML-based techniques for therapeutic antibody D&D. Specifically, we address in an end-to-end analysis, challenges associated with all aspects of the ML process and recommend a set of best practices for each stage. © 2023, CC BY-NC-ND.

关键词： Antibodies

Improved Accuracy of Cardiac Tissue-Level Simulations by Considering Membrane Resistance as a Cellular-Level Optimization Objective

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Improved Accuracy of Cardiac Tissue-Level Simulations by Con...

Annual International Conference of the Ieee engineering in Medicine and Biology Society (eMBC)

作者： e. Pouranbarani L. A. Berg R. S. Oliveira R. W. dos Santos A. Nygren Department of Electrical and Computer Engineering University of Calgary Alberta Canada Department of Computer Science and the Graduate Program of Computational Modeling Federal University of Juiz de Fora Juiz de Fora Brazil Department of Computer Science University of São João del-Rei São João del-Rei Brazil

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Cardiac cellular models are utilized as the building blocks for tissue simulation. One of the imprecisions of conventional cellular modeling, especially when the models are used in tissue-level modeling, stems from the mere consideration of cellular properties (e.g., action potential shape) in parameter tuning of the model. In our previous work, we put forward an accurate framework in which membrane resistance (R m ) reflecting inter-cellular characteristics, i.e., electrotonic effects, was considered alongside cellular features in cellular model fitting. This paper, for the first time, examines the hypothesis that considering R m as an additional optimization objective improves the accuracy of tissue-level modeling. To study this hypothesis, after cellular-level optimization of a well-known model, source-sink mismatch configurations in a 2-dimensional model are investigated. The results demonstrate that including R m in the optimization protocol yields a substantial improvement in the relative error of the critical transition border which is defined as the minimum window size between source and sink that wave propagates. Model developers can utilize the proposed concept during parameter tuning to increase the accuracy of models.

关键词： Mathematical model Optimization computational modeling Resistance Tuning Fitting Protocols

Revisiting the Transit Timing and Atmosphere Characterization of the Neptune-mass Planet HAT-P-26 b

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arXiv 2023年

作者： A-Thano, Napaporn Awiphan, Supachai Jiang, Ing-Guey Kerins, eamonn Priyadarshi, Akshay McDonald, Iain Joshi, Yogesh C. Chulikorn, Thansuda Hayes, Joshua J.C. Charles, Stephen Huang, Chung-Kai Rattanamala, Ronnakrit Yeh, Li-Chin Dhillon, Vik S. Department of Physics Institute of Astronomy National Tsing-Hua University Hsinchu30013 Taiwan National Astronomical Research Institute of Thailand 260 Moo 4 Donkaew Mae Rim Chiang Mai50180 Thailand Jodrell Bank Centre for Astrophysics University of Manchester Oxford Road ManchesterM13 9PL United Kingdom Department of Physical Sciences The Open University Walton Hall Milton KeynesMK7 6AA United Kingdom Manora Peak Nainital263001 India Department of Physics Faculty of Science Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok10330 Thailand Institute of Astronomy and Astrophysics Academia Sinica No.1 Sec. 4 Roosevelt Rd Taipei10617 Taiwan PhD Program in Astronomy Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai50200 Thailand Department of Physics and General Science Faculty of Science and Technology Nakhon Ratchasima Rajabhat University Nakhon Ratchasima30000 Thailand Institute of Computational and Modeling Science National Tsing-Hua University Hsinchu30013 Taiwan Department of Physics and Astronomy University of Sheffield SheffieldS3 7RH United Kingdom Instituto de Astrofísica de Canarias Tenerife La LagunaE-38205 Spain

We present the transit timing variation (TTV) and planetary atmosphere analysis of the Neptune-mass planet HAT-P-26 b. We present a new set of 13 transit light curves from optical ground-based observations and combine them with light curves from the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope (HST), Transiting exoplanet Survey Satellite (TeSS), and previously published ground-based data. We refine the planetary parameters of HAT-P-26 b and undertake a TTV analysis using 33 transits obtained over seven years. The TTV analysis shows an amplitude signal of 1.98 ± 0.05 minutes, which could result from the presence of an additional ∼ 0.02 MJup planet at the 1:2 mean-motion resonance orbit. Using a combination of transit depths spanning optical to near-infrared wavelengths, we find that the atmosphere of HAT-P-26 b contains (formula presented) of H2O with a derived temperature of (formula presented) K. Copyright © 2023, The Authors. All rights reserved.

关键词： Timing circuits

*** - A plug-and-play solution for online systems medicine and network-based drug repurposing

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arXiv 2023年

作者： Maier, Andreas Hartung, Michael Abovsky, Mark Adamowicz, Klaudia Bader, Gary D. Baier, Sylvie Blumenthal, David B. Chen, Jing elkjaer, Maria L. Garcia-Hernandez, Carlos Helmy, Mohamed Hoffmann, Markus Jurisica, Igor Kotlyar, Max Lazareva, Olga Levi, Hagai List, Markus Lobentanzer, Sebastian Loscalzo, Joseph Malod-Dognin, Noel Manz, Quirin Matschinske, Julian Mee, Miles Oubounyt, Mhaned Pico, Alexander R. Pillich, Rudolf T. Poschenrieder, Julian M. Pratt, Dexter Pržulj, Nataša Sadegh, Sepideh Saez-Rodriguez, Julio Sarkar, Suryadipto Shaked, Gideon Shamir, Ron Trummer, Nico Turhan, Ugur Wang, Ruisheng Zolotareva, Olga Baumbach, Jan Institute for Computational Systems Biology University of Hamburg Hamburg Germany Division of Orthopaedic Surgery Schroeder Arthritis Institute Data Science Discovery Centre Osteoarthritis Research Program Krembil Research Institute UHN Toronto Canada Data Science Discovery Centre for Chronic Diseases Krembil Research Institute University Health Network 60 Leonard Avenue 5KD-407 TorontoONM5T 0S8 Canada Department of Molecular Genetics University of Toronto TorontoON Canada The Donnelly Centre University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada The Lunenfeld-Tanenbaum Research Institute Mount Sinai Hospital TorontoON Canada TUM School of Life Sciences Technical University of Munich Munich Germany Erlangen91052 Germany Department of Medicine University of California San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Neurology Odense University Hospital Odense Denmark Institute of Clinical Research University of Southern Denmark Odense Denmark Institute of Molecular Medicine University of Southern Denmark Odense Denmark Barcelona08034 Spain Institute for Advanced Study Lichtenbergstrasse 2a GarchingD-85748 Germany Technical University of Munich Germany National Institute of Diabetes Digestive and Kidney Diseases BethesdaMD20892 United States Departments of Medical Biophysics and Computer Science University of Toronto Toronto Canada Institute of Neuroimmunology Slovak Academy of Sciences Bratislava Slovakia Heidelberg69120 Germany Heidelberg Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg69117 Germany Blavatnik School of Computer Science Tel-Aviv University Tel-Aviv Israel Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany Department of Medicine Bri

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce ***, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, *** turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, *** has been successfully integrated with 21 computational systems medicine tools. Available at https://***, *** has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research. © 2023, CC BY.

关键词： Data visualization

The impact of AI on engineering design procedures for dynamical systems

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arXiv 2024年

作者： de Payrebrune, Kristin M. Flaßkamp, Kathrin Ströhla, Tom Sattel, Thomas Bestle, Dieter Röder, Benedict eberhard, Peter Peitz, Sebastian Stoffel, Marcus Rutwik, Gulakala Aditya, Borse Wohlleben, Meike Sextro, Walter Raff, Maximilian Remy, C. David Yadav, Manish Stender, Merten van Delden, Jan Lüddecke, Timo Langer, Sabine C. Schultz, Julius Blech, Christopher RPTU Kaiserslautern-Landau Institute for Computational Physics in Engneering Gottlieb-Daimler-Straße 74 Kaiserslautern67663 Germany Saarland University Systems Engineering Systems Modeling and Simulation Campus Saarbrücken66123 Germany TU Ilmenau Mechatronics group Max-Planck-Ring 12 Ilmenau98693 Germany Brandenburg University of Technology Cottbus-Senftenberg Chair of Engineering Mechanics and Vehicle Dynamics Siemens-Halske-Ring 14 Cottbus03046 Germany University of Stuttgart Institute of Engineering and Computational Mechanics Pfaffenwaldring 9 Stuttgart70569 Germany TU Dortmund Department of Computer Science & Lamarr Institute Joseph-von-Fraunhofer-Str. 25 Dortmund44227 Germany RWTH Aachen University Institute of General Mechanics Eilfschornsteinstraße 18 Aachen52062 Germany Paderborn University Faculty of Mechanical Engineering Dynamics and Mechatronics Warburger Str. 100 Paderborn33098 Germany University of Stuttgart Institute for Nonlinear Mechanics Pfaffenwaldring 9 Stuttgart70569 Germany Technische Universität Berlin Chair of Cyber-Physical Systems in Mechanical Engineering Straße des 17. Juni 135 Berlin10623 Germany University of Göttingen Institute of Informatics Goldschmidtstr. 7 Göttingen37077 Germany Technische Universität Braunschweig Institute for Acoustics and Dynamics Langer Kamp 19 Braunschweig38106 Germany

Artificial intelligence (AI) is driving transformative changes across numerous fields, revolutionizing conventional processes and creating new opportunities for innovation. The development of mechatronic systems is undergoing a similar transformation. Over the past decade, modeling, simulation, and optimization techniques have become integral to the design process, paving the way for the adoption of AI-based methods. In this paper, we examine the potential for integrating AI into the engineering design process, using the V-model from the VDI guideline 2206, considered the state-of-the-art in product design, as a foundation. We identify and classify AI methods based on their suitability for specific stages within the engineering product design workflow. Furthermore, we present a series of application examples where AI-assisted design has been successfully implemented by the authors. These examples, drawn from research projects within the DFG Priority program SPP 2353: Daring More Intelligence - Design Assistants in Mechanics and Dynamics, showcase a diverse range of applications across mechanics and mechatronics, including areas such as acoustics and robotics. © 2024, CC BY.

关键词： Mechatronics

Revisiting the Transit Timing and Atmosphere Characterization of the Neptune-mass Planet HAT-P-26 b

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Astronomical Journal 2023年第6期166卷 223页

作者： A-Thano, Napaporn Awiphan, Supachai Jiang, Ing-Guey Kerins, eamonn Priyadarshi, Akshay McDonald, Iain Joshi, Yogesh C. Chulikorn, Thansuda Hayes, Joshua J. C. Charles, Stephen Huang, Chung-Kai Rattanamala, Ronnakrit Yeh, Li-Chin Dhillon, Vik S Department of Physics Institute of Astronomy National Tsing-Hua University Hsinchu 30013 Taiwan National Astronomical Research Institute of Thail 260 Moo 4 Donkaew Mae Rim Chiang Mai 50180 Thailand Jodrell Bank Centre for Astrophysics University of Manchester Oxford Road Manchester M13 9PL United Kingdom Department of Physical Sciences The Open University Walton Hall Milton Keynes MK7 6AA United Kingdom Aryabhatta Research Institute of Observational Sciences (ARIES) Manora Peak Nainital 263001 India Department of Physics Faculty of Science Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok 10330 Thailand Institute of Astronomy and Astrophysics Academia Sinica No.1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan PhD Program in Astronomy Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand Department of Physics and General Science Faculty of Science and Technology Nakhon Ratchasima Rajabhat University Nakhon Ratchasima 30000 Thailand Institute of Computational and Modeling Science National Tsing-Hua University Hsinchu 30013 Taiwan Department of Physics and Astronomy University of Sheffield Sheffield S3 7RH United Kingdom Instituto de Astrofísica de Canarias La Laguna Tenerife E-38205 Spain

We present a transit-timing variation (TTV) and planetary atmosphere analysis of the Neptune-mass planet HAT-P-26 b. We present a new set of 13 transit light curves from optical ground-based observations and combine them with light curves from the Wide Field Camera 3 on the Hubble Space Telescope, the Transiting exoplanet Survey Satellite, and previously published ground-based data. We refine the planetary parameters of HAT-P-26 b and undertake a TTV analysis using 33 transits obtained over seven years. The TTV analysis shows an amplitude signal of 1.98 ± 0.05 minutes, which could result from the presence of an additional ∼0.02 M _Jup planet at a 1:2 mean-motion resonance orbit. Using a combination of transit depths spanning optical to near-infrared wavelengths, we find that the atmosphere of HAT-P-26 b contains 2.4 − 1.6 + 2.9 % H₂O with a derived temperature of 590 − 50 + 60 K. © 2023. The Author(s). Published by the American Astronomical Society.

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Homogenisation for the monodomain model in the presence of microscopic fibrotic structures

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arXiv 2020年

作者： Lawson, Brodie A.J. dos Santos, Rodrigo Weber Turner, Ian W. Bueno-Orovio, Alfonso Burrage, Pamela Burrage, Kevin Centre for Data Science Queensland University of Technology QLD Australia ARC Centre of Excellence for Mathematical and Statistical Frontiers Queensland University of Technology QLD Australia Graduate Program on Computational Modeling Universidade de Juiz de Fora Minas Gerais Brazil Department of Computer Science University of Oxford Oxfordshire United Kingdom

MSC Codes 35B27computational models in cardiac electrophysiology are notorious for long runtimes, restricting the numbers of nodes and mesh elements in the numerical discretisations used for their solution. This makes it particularly challenging to incorporate structural heterogeneities on small spatial scales, preventing a full understanding of the critical arrhythmogenic effects of conditions such as cardiac fibrosis. In this work, we explore the technique of homogenisation by volume averaging for the inclusion of non-conductive micro-structures into larger-scale cardiac meshes with minor computational overhead. Importantly, our approach is not restricted to periodic patterns, enabling homogenised models to represent, for example, the intricate patterns of collagen deposition present in different types of fibrosis. We first highlight the importance of appropriate boundary condition choice for the closure problems that define the parameters of homogenised models. Then, we demonstrate the technique’s ability to correctly upscale the effects of fibrotic patterns with a spatial resolution of 10 µm into much larger numerical mesh sizes of 100-250 µm. The homogenised models using these coarser meshes correctly predict critical pro-arrhythmic effects of fibrosis, including slowed conduction, source/sink mismatch, and stabilisation of re-entrant activation patterns. As such, this approach to homogenisation represents a significant step towards whole organ simulations that unravel the effects of microscopic cardiac tissue heterogeneities. © 2020, CC BY.

关键词： Heart

Analyzing dominance move (MIP-DoM) indicator for multi- And many-objective optimization

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arXiv 2020年

作者： do Val Lopes, Claudio Lucio Martins, Flávio Vinícius Cruzeiro Wanner, elizabeth Fialho Deb, Kalyanmoy Postgraduate Program in Mathematical and Computational Modeling CEFET-MG Brazil Computer Department Centro Federal de Educação Tecnológica de Minas Gerais 30510-000 Brazil Computer Department Centro Federal de Educação Tecnológica de Minas Gerais 30510-000 Brazil Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States

—Dominance move (DoM) is a binary quality indicator that can be used in multi-objective and many-objective optimization to compare two solution sets obtained from different algorithms. The DoM indicator can differentiate the sets for certain important features, such as convergence, spread, uniformity, and cardinality. DoM does not use any reference, and it has an intuitive and physical meaning, similar to the -indicator, and calculates the minimum total move of members of one set so that all elements in another set are to be dominated or identical to at least one member of the first set. Despite the aforementioned properties, DoM is hard to calculate, particularly in higher dimensions. There is an efficient and exact method to calculate it in a bi-objective case only. This work proposes a novel approach to calculate DoM using a mixed integer programming (MIP) approach, which can handle sets with three or more objectives and is shown to overcome the -indicator’s information loss. experiments, in the bi-objective space, are done to verify the model’s correctness. Furthermore, other experiments, using 3, 5, 10, 15, 20, 25 and 30-objective problems are performed to show how the model behaves in higher-dimensional cases. Algorithms, such as IBeA, MOeA/D, NSGA-III, NSGA-II, and SPeA2 are used to generate the solution sets (however any other algorithms can also be used with the proposed MIP-DoM indicator). Further extensions are discussed to handle certain idiosyncrasies with some solution sets and also to improve the quality indicator and its use for other situations. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization