Search · four archives
Search · four archives
27 papers · ranked by Valyu relevance
Hao Li
Control science is a core representative of the third industrial revolution and is so important to modern civilization. Control systems are the main subject of control science and may involve many aspects of consideration, such as hardware consideration, software consideration, operation consideration, maintenance…
Ridma Ganganath, Simone Servadio, David Daeyoung Lee
This paper presents an adaptive multi-model framework for jointly estimating spacecraft attitude and star-tracker misalignments in GPS-denied deep-space CubeSat missions. A Multiplicative Extended Kalman Filter (MEKF) estimates attitude, angular velocity, and gyro bias, while a Bayesian Multiple-Model Adaptive…
Xiaoliang Feng, Jiawei Zhang, Mohammad Reza Rahimi Tabar
This paper investigates state estimation for strongly nonlinear systems with unknown inputs under non-Gaussian heavy-tailed impulsive noise. Conventional recursive three-step filters (RTSF) based on the minimum-variance criterion are sensitive to outliers, while a single local linearization is often inadequate for…
Wenxin Zhang, Mark van der Laan
Adaptive designs are increasingly used in clinical trials and digital experiments to improve estimation efficiency by updating treatment randomization probabilities as data accumulate. While most existing work focuses on settings with a single-stage treatment, adaptive designs for longitudinal studies with multi-stage…
Ibrahim Salim, Nermeen Okasha, Mohamed Barbary, Wagdy Anis
Multi-object tracking (MOT) in cluttered and dynamic environments remains challenging, especially for maneuvering objects. While trajectory-based random finite set (RFS) filters provide principled solutions for trajectory estimation, they typically rely on a single motion model, limiting their adaptability. To address…
Yong See Foo, Torkel E. Loman, Alexander P. Browning, Ivo Siekmann + 2 more
Mathematical models are invaluable for understanding and predicting how biological systems behave, although their construction requires specifying mechanisms and relationships that are often not perfectly known. In the presence of multiple competing models, model uncertainty should be accounted for when performing…
Maria Selezneva, Konstantin Neusypin, Anastasia Surkova
This study investigates the concept of the adaptability degree of state variables in mathematical models used in estimation algorithms. Two numerical criteria are proposed for calculating the adaptability degree of state variables in linear models. Qualitative characteristics of the adaptability of inertial navigation…
Andrea Polo-Rodríguez, David R. Penas, Julio R. Banga
Parameter estimation is a central challenge in systems biology, particularly for large dynamic models described by nonlinear ordinary differential equations (ODEs). These global optimization problems exhibit landscapes which are topologically heterogeneous, often exhibiting a pathological mixture of stiff, smooth…
Authors not listed
This conceptual paper introduces the Adaptive Multi-Resolution Modeling Framework (AMRMF), a novel technique designed to revolutionize chemical engineering by integrating multi-scale simulations, quantum-inspired algorithms, advanced uncertainty quantification, and Bayesian inference. The framework bridges theoretical…
Aron Fink, Christoph König, Andreas Frey
This paper introduces a fully Bayesian approach to multidimensional adaptive testing (MBAT). By incorporating uncertainty in both item and person parameter estimates, MBAT addresses limitations in conventional multidimensional adaptive testing (MAT), which relies on point item and person parameter estimates. A Monte…
Cyrus Mehta, Ajoy Mukhopadhyay, Martin Posch
The graph-based approach to multiple testing is an intuitive method that enables a study team to represent clearly, through a directed graph, its priorities for hierarchical testing of multiple hypotheses, and for propagating the available type-1 error from rejected or dropped hypotheses to hypotheses yet to be tested.…
Daniel Ajuzie, Seyed A. Arshad, Komal S. Rasaputra, Bert Debusschere + 1 more
Developing effective antimicrobial strategies requires a predictive understanding of bacterial responses to multiple stress conditions which often result in multiple phenotypes. A microbe’s survival and proliferation depend on its ability to manage concurrent, dynamically varying stressors within its microenvironment.…
Sungmin Kim, Youndo Do, Fan Zhang, Yuan Yao
In many industrial facilities, online monitoring systems have improved the reliability of key equipment, reducing the cost of operation and maintenance over recent decades. However, it often requires additional on-site inspection of target facilities due to limited information from installed sensors. To systematically…
Anders Granholm, Aksel Karl Georg Jensen, Theis Lange, Anders Perner + 2 more
Advanced adaptive randomised clinical trials are increasingly used. Compared to their conventional counterparts, their flexibility may make them more efficient, increase the probability of obtaining conclusive results without larger samples than necessary, and increase the probability that individual participants are…
Zhengxi Chen, Holly Hartman
Sequential multiple assignment randomized trials (SMARTs) provide a systematic framework for constructing and evaluating dynamic treatment regimens (DTRs). In clinical studies, longitudinal biomarkers are routinely collected to monitor disease progression and define treatment response. However, the integration of…
Nadav Ben Nun, Saharon Rosset, David Gresham, Yoav Ram
High-throughput experimental platforms now routinely generate data from dozens or hundreds of independent observations. Simulation-based inference (SBI) offers a powerful framework for estimating model parameters from such complex datasets, but standard methods struggle to scale to the noisy multiple-replicates regime…
Authors not listed
Continuous manufacturing processes offer significant advantages over batch processes, including easier scalability, reduced costs, lower raw material and solvent consumption, and improved energy efficiency. A robust techno-economic assessment is therefore essential to evaluate and facilitate the adoption of such…
Yue Yao, Caleb N. Ellington, Jingyun Jia, Baiheng Chen + 8 more
Modern predictive systems are expected to adapt their behavior to the specific situation they are facing. A clinical model should not treat every patient the same; a retrieval-augmented model should change its answer when given different evidence; a mixture-of-experts model should route different inputs to different…
Rabea Turon, Lars C. Reining, Philipp A. Hummel, Lynn Schmittwilken + 5 more
Behavioral experiments are often infeasible when stimulus spaces have many dimensions or when testing time is limited. One way to address this challenge is adaptive stimulus selection, where informative stimuli are chosen dynamically based on participants’ responses. However, in high-dimensional spaces, identifying…
Siao Liu, Yongjiu Li, Chunxiao Sun, Yi Wang + 2 more
This paper addresses issues such as nonlinearity, model uncertainty, and multiple policy constraints within the dynamic evolution of new quality productive forces systems. It proposes a research framework integrating data-driven modelling with adaptive tracking control. By merging control theory with economic dynamics…
Paola Montoya, Anne-Claire Fabre, Anjali Goswami, Helene Morlon + 1 more
Multivariate phylogenetic comparative methods for modelling high-dimensional traits such as 3D shapes or gene expression proBiles have been recently developed. However, these approaches are impractical and almost impossible to use when the number of traits exceeds a few thousands, as they become computationally…
Authors not listed
Developing a transferable classical force field (FF) has historically been a lengthy, expert-informed process. In this work, we integrate optimization, machine learning, and data science techniques to accelerate the systematic design and parameterization of transferable FF models. As a demonstration, we create…
Authors not listed
Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Authors not listed
Graph Neural Networks (GNNs) are powerful tools for molecular property prediction, but they are not magic. When applied to molecules unlike their training data, they produce unreliable predictions that are difficult to detect. The Applicability Domain (AD) concept addresses this by defining regions of chemical space…
Zinan Lu, Jonathan Anns, Yishan Mai, Rou Zhang + 10 more
Data analysis in experimental science mainly relies on null-hypothesis significance testing, despite its well-known limitations. A powerful alternative is estimation statistics, which focuses on effect-size quantification. However, current estimation tools struggle with the complex, multi-group comparisons common in…
Rolf Ergon
Hunt’s ancestor-descendant parameterization for fitting of evolutionary models to empirical paleontological sequences assumes independent log-likelihoods for the transitions between populations (2). This is not quite correct, as he also pointed out in his paper. The reason is that adjacent trait differences share a…