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Molecular simulations and biophysical experiments can be used to provide independent and complementary insights into the molecular origin of biological processes. A particularly useful strategy is to use molecular simulations as a modelling…

Chemical Physics · Physics 2019-12-10 Simone Orioli , Andreas Haahr Larsen , Sandro Bottaro , Kresten Lindorff-Larsen

Organisms have evolved a variety of mechanisms to cope with the unpredictability of environmental conditions, and yet mainstream models of metabolic regulation are typically based on strict optimality principles that do not account for…

Molecular Networks · Quantitative Biology 2020-07-08 David S. Tourigny

The rapid advances in 3D scanning and acquisition techniques have given rise to the explosive increase of volumetric digital models in recent years. This dissertation systematically trailblazes a novel volumetric modeling framework to…

Graphics · Computer Science 2013-08-06 Bo Li

Diffusion models have emerged as powerful generative frameworks by progressively adding noise to data through a forward process and then reversing this process to generate realistic samples. While these models have achieved strong…

Machine Learning · Computer Science 2025-03-04 Xingzhuo Guo , Yu Zhang , Baixu Chen , Haoran Xu , Jianmin Wang , Mingsheng Long

With the development of web technology, multi-modal or multi-view data has surged as a major stream for big data, where each modal/view encodes individual property of data objects. Often, different modalities are complementary to each…

Computer Vision and Pattern Recognition · Computer Science 2020-06-16 Yang Wang

Commercial activity trackers are set to become an essential tool in health research, due to increasing availability in the general population. The corresponding vast amounts of mostly unlabeled data pose a challenge to statistical modeling…

Machine Learning · Statistics 2018-03-01 Martin Treppner , Stefan Lenz , Harald Binder , Daniela Zöller

Use of machine learning to perform database operations, such as indexing, cardinality estimation, and sorting, is shown to provide substantial performance benefits. However, when datasets change and data distribution shifts, empirical…

Machine Learning · Computer Science 2024-11-12 Sepanta Zeighami , Cyrus Shahahbi

Dynamic data physicalisation is an emerging field of research, investigating the representation and exploration of data via multiple modalities, beyond traditional visual methods. Despite the development of various data physicalisation…

Human-Computer Interaction · Computer Science 2025-03-11 Xuyao Zhang , Milan Ilić , Beat Signer

Leveraging hypergraph structures to model advanced processes has gained much attention over the last few years in many areas, ranging from protein-interaction in computational biology to image retrieval using machine learning. Hypergraph…

Human-Computer Interaction · Computer Science 2021-12-07 Maximilian T. Fischer , Alexander Frings , Daniel A. Keim , Daniel Seebacher

Model selection methods are used in different scientific contexts to represent a characteristic data set in terms of a reduced number of parameters. Apparently, these methods have not found their way into the literature on multibody systems…

Robotics · Computer Science 2017-05-30 Javier Ros , Xabier Iriarte , Aitor Plaza , Vicente Mata

Scientists often use observational time series data to study complex natural processes, but regression analyses often assume simplistic dynamics. Recent advances in deep learning have yielded startling improvements to the performance of…

Machine Learning · Computer Science 2023-04-21 Cory Shain , William Schuler

A deep latent variable model is a powerful method for capturing complex distributions. These models assume that underlying structures, but unobserved, are present within the data. In this dissertation, we explore high-dimensional problems…

Machine Learning · Computer Science 2024-06-13 Khuong Vo

As information becomes increasingly sizable for organizations to maintain the challenge of organizing data still remains. More importantly, the on-going process of analysing incoming data occurs on a continual basis and organizations should…

Databases · Computer Science 2021-05-19 Bryar A. Hassan , Shko M. Qader

Precise kinematic modeling is critical in calibration and controller design for soft robots, yet remains a challenging issue due to their highly nonlinear and complex behaviors. To tackle the issue, numerous data-driven machine learning…

Robotics · Computer Science 2025-07-11 Zhanhong Jiang , Dylan Shah , Hsin-Jung Yang , Soumik Sarkar

Metamodeling is used as a general technique for integrating and defining models from different domains. This technique can be used in diverse application domains, especially for purposes of standardization. Also, this process mainly has a…

Cryptography and Security · Computer Science 2021-08-13 Omair Ameerbakhsh , Fahad M Ghabban , Ibrahim Alfadli , Amer Nizar AbuAli , Arafat Al-Dhaqm , Mahmoud Ahmad Al-Khasawneh

Understanding and simulating how a quantum system interacts and exchanges information or energy with its surroundings is a ubiquitous problem, one which must be carefully addressed in order to establish a coherent framework to describe the…

Quantum Physics · Physics 2021-05-25 Steve Campbell , Bassano Vacchini

Dynamic factor models have been developed out of the need of analyzing and forecasting time series in increasingly high dimensions. While mathematical statisticians faced with inference problems in high-dimensional observation spaces were…

Econometrics · Economics 2024-01-03 Matteo Barigozzi , Marc Hallin

The development of accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics. Challenges arise both in considering the appropriate model assumptions…

Machine Learning · Computer Science 2023-02-22 Jan N. Fuhg , Craig M. Hamel , Kyle Johnson , Reese Jones , Nikolaos Bouklas

The digital transformation of the energy infrastructure enables new, data driven, applications often supported by machine learning models. However, domain specific data transformations, pre-processing and management in modern data driven…

Artificial Intelligence · Computer Science 2022-09-12 Gregor Cerar , Blaž Bertalanič , Anže Pirnat , Andrej Čampa , Carolina Fortuna

Dynamical systems theory describes how interacting quantities change over time and space, from molecular oscillators to large-scale biological patterns. Such systems often involve nonlinear feedbacks, delays, and interactions across scales.…

Quantitative Methods · Quantitative Biology 2025-09-09 Bartosz Prokop , Lendert Gelens