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Calibrating mathematical models of biological processes is essential for achieving predictive accuracy and gaining mechanistic insight. However, this task remains challenging due to limited and noisy data, significant biological…

Quantitative Methods · Quantitative Biology 2025-12-04 Piotr Gwiazda , Alexey Kazarnikov , Anna Marciniak-Czochra , Zuzanna Szymańska

Systems biology seeks to create math models of biological systems to reduce inherent biological complexity and provide predictions for applications such as therapeutic development. However, it remains a challenge to determine which math…

Quantitative Methods · Quantitative Biology 2022-08-05 Vincent D. Zaballa , Elliot E. Hui

The scattered field formalism is combined to the particle-in-cell method to model relativistic laser-plasma dynamics in complex field configurations. Despite the strong nonlinearity of the interactions, we demonstrate the validity of this…

Optics · Physics 2023-10-18 M. Lytova , F. Fillion-Gourdeau , S. Vallières , F. Légaré , S. MacLean

Scanning transmission electron microscopy (STEM) is an extremely versatile method for studying materials on the atomic scale. Many STEM experiments are supported or validated with electron scattering simulations. However, using the…

Reinforcement learning suffers from limitations in real practices primarily due to the number of required interactions with virtual environments. It results in a challenging problem because we are implausible to obtain a local optimal…

Machine Learning · Computer Science 2024-10-28 Qizhen Wu , Kexin Liu , Lei Chen

Machine learning (ML) has emerged as a powerful tool for tackling complex regression and classification tasks, yet its success often hinges on the quality of training data. This study introduces an ML paradigm inspired by domain knowledge…

Machine Learning · Computer Science 2025-01-10 Mohsen Rashki

The framework of reinforcement learning or optimal control provides a mathematical formalization of intelligent decision making that is powerful and broadly applicable. While the general form of the reinforcement learning problem enables…

Machine Learning · Computer Science 2018-05-22 Sergey Levine

Reinforcement learning has shown a wide usage in robotics tasks, such as insertion and grasping. However, without a practical sim2real strategy, the policy trained in simulation could fail on the real task. There are also wide researches in…

Robotics · Computer Science 2022-06-07 Yiwen Chen , Xue Li , Sheng Guo , Xian Yao Ng , Marcelo Ang

Rapid progress in imitation learning, foundation models, and large-scale datasets has led to robot manipulation policies that generalize to a wide-range of tasks and environments. However, rigorous evaluation of these policies remains a…

Modern distributed systems include a class of applications in which non-functional requirements are important. In particular, these applications include multimedia facilities where real time constraints are crucial to their correct…

Multimedia · Computer Science 2007-05-23 Jeremy Bryans , Howard Bowman , John Derrick

The industrial insertion of flexible flat cables (FFCs) into receptacles presents a significant challenge owing to the need for submillimeter precision when handling the deformable cables. In manufacturing processes, FFC insertion with…

Robotics · Computer Science 2025-09-18 Jeongwoo Park , Seabin Lee , Changmin Park , Wonjong Lee , Changjoo Nam

A new type of microfluidic system for biological cell manipulation, a CMOS/microfluidic hybrid, is demonstrated. The hybrid system starts with a custom-designed CMOS (complementary metal-oxide semiconductor) chip fabricated in a…

Quantitative Methods · Quantitative Biology 2007-05-23 Hakho Lee , Yong Liu , Donhee Ham , Robert M. Westervelt

Recently, there has been a growing interest in rescue robots due to their vital role in addressing emergency scenarios and providing crucial support in challenging or hazardous situations where human intervention is difficult. However, very…

Robotics · Computer Science 2025-05-19 Qianwen Zhao , Rajarshi Roy , Chad Spurlock , Kevin Lister , Long Wang

Validation is often defined as the process of determining the degree to which a model is an accurate representation of the real world from the perspective of its intended uses. Validation is crucial as industries and governments depend…

Data Analysis, Statistics and Probability · Physics 2015-06-26 D. Sornette , A. B. Davis , K. Ide , K. R. Vixie , V. Pisarenko , J. R. Kamm

Hydrogen fuel cells are a key technology in the transition toward carbon-neutral energy systems, offering clean power with water as the only byproduct. Microfluidic fuel cells, which operate at the microliter scale, are an emerging variant…

Computational Engineering, Finance, and Science · Computer Science 2026-03-31 Michel Takken , Robert Wille

The Finite Element Method (FEM) is a powerful modeling tool for predicting soft robots' behavior, but its computation time can limit practical applications. In this paper, a learning-based approach based on condensation of the FEM model is…

Microscopy imaging plays a vital role in understanding many biological processes in development and disease. The recent advances in automation of microscopes and development of methods and markers for live cell imaging has led to rapid…

Computer Vision and Pattern Recognition · Computer Science 2017-05-10 Saad Ullah Akram , Juho Kannala , Lauri Eklund , Janne Heikkilä

Experiments of cell migration and chemotaxis assays have been classically performed in the so-called Boyden Chambers. A recent technology, xCELLigence Real Time Cell Analysis, is now allowing to monitor the cell migration in real time. This…

Probabilistic model checking is a widely used formal verification technique to automatically verify qualitative and quantitative properties for probabilistic models. However, capturing such systems, writing corresponding properties, and…

Logic in Computer Science · Computer Science 2024-03-04 Kangfeng Ye , Fang Yan , Simos Gerasimou

The objective of this work is to quantify the uncertainty in probability of failure estimates resulting from incomplete knowledge of the probability distributions for the input random variables. We propose a framework that couples the…

Methodology · Statistics 2021-10-26 Dimitris G. Giovanis , Michael Shields