Publications associées à Efficient characterization of high-dimensional parameter spaces for systems biology

Efficient and insightful descriptors for representing molecular and material space

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

EPFL2024

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

Alfio Quarteroni, Francesco Regazzoni, Stefano Pagani

Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equ ...

Nature Portfolio2024

Spectral Estimators for High-Dimensional Matrix Inference

Farzad Pourkamali

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

EPFL2024

Resistances to change in urban travel behaviors

Marc-Edouard Baptiste Grégoire Schultheiss

The social discourse surrounding the climate emergency progressively infuses the society, transforming into both micro- and macro-social injunctions to change. Yet, society - grounded in a territorial, social, and cultural contingency - appears to resist t ...

EPFL2023

Explicit representations for Banach subspaces of Lizorkin distributions

Michaël Unser, Sebastian Jonas Neumayer

The Lizorkin space is well suited to the study of operators like fractional Laplacians and the Radon transform. In this paper, we show that the space is unfortunately not complemented in the Schwartz space. In return, we show that it is dense in C0(Double- ...

2023

An adaptive space-time algorithm for the incompressible Navier-Stokes equations

Marco Picasso, Samuel Dubuis

A space-time adaptive algorithm is presented to solve the incompressible Navier-Stokes equations. Time discretization is performed with the BDF2 method while continuous, piecewise linear anisotropic finite elements are used for the space discretization. Th ...

Academic Press Inc Elsevier Science2023

Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models

Vassily Hatzimanikatis, Georgios Fengos, Pierre Guy Rémy Salvy, Daniel Robert Weilandt, Zhaleh Hosseini, Maria Masid Barcon

Motivation: Large-scale kinetic models are an invaluable tool to understand the dynamic and adaptive responses of biological systems. The development and application of these models have been limited by the availability of computational tools to build and ...

OXFORD UNIV PRESS2022

Propagation of singularities for the stochastic wave equation

Cheuk Yin Lee

We study the existence and propagation of singularities of the solution to a one-dimensional linear stochastic wave equation driven by an additive Gaussian noise that is white in time and colored in space. Our approach is based on a simultaneous law of the ...

ELSEVIER2022

Search for relativistic fractionally charged particles in space

Songmei Wu, Johan Juul Chang, Zhirui Xu, Jennifer Maria Frieden, Siyuan Wang, Junqiu Liu, Jian Wang, Yi Zhang, Yong Yang, Hao Liu, Ming Gao, Cheng Zhao, Xuan Li, Arshia Ruina, Chiara Perrina, Andrii Kotenko, Wenyu Zhang, Xiao Wang, Yi Wang, Wei Li, Jie Chen, Shuai Liu, Yao Zhang

More than a century after the performance of the oil drop experiment, the possible existence of fractionally charged particles (FCPs) still remains unsettled. The search for FCPs is crucial for some extensions of the Standard Model in particle physics. Mos ...

American Physical Society2022

A Functional Perspective on Information Measures

Amedeo Roberto Esposito

Since the birth of Information Theory, researchers have defined and exploited various information measures, as well as endowed them with operational meanings. Some were born as a "solution to a problem", like Shannon's Entropy and Mutual Information. Other ...

EPFL2022

A machine learning approach to portfolio pricing and risk management for high-dimensional problems

Damir Filipovic

We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. Our method learns the features necessary for an effective low-dimensi ...

WILEY2022

Intensifying space activity calls for increased scrutiny of risks

Romain Buchs

As the pace of space activities accelerates and societies become more reliant on space-based systems, the associated risks deserve more attention. Satellites form a critical infrastructure on which military, civil and commercial entities rely. The services ...

EPFL International Risk Governance Center2021

New and improved methods for integrated models of metabolism and gene expression

Pierre Guy Rémy Salvy

The beginning of the 21st century was marked by the advent of disruptive technologies, which ushered an era of groundbreaking advances in fundamental sciences, carried by the great pace at which computational capabilities spread and evolved. But the new ce ...

EPFL2021

Home, Spatial Familiarity And Routines In Mobility Behaviours

Marc-Edouard Baptiste Grégoire Schultheiss

This research explores the unreasoned influences of locational behaviour – i.e. locational habits – in Activity- Travel-Behaviour (ATB). In particular, the interrelations between Activity Space characteristics and mobility patterns. In a wider research eff ...

2021

Modelling the Intracellular Environment under Uncertainty, from Post-translational Modification to Metabolic Networks

Robin Alexander Denhardt-Eriksson

Modelling and analysis of biological systems is crucial in order to quantitatively explain and predict their behaviour. The importance of modelling in systems biology becomes even more evident when tackling complex, large systems. Approaches that rely on ...

EPFL2021

A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience

Daniel Keller, Andrii Stepaniuk

Neuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a ...

HUMANA PRESS INC2022

Network Classifiers Based On Social Learning

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon

This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to ...

IEEE2021

The effects of model complexity and size on metabolic flux distribution and control: case study in Escherichia coli

Vassily Hatzimanikatis, Georgios Fengos, Tuure Eelis Hameri

Background Significant efforts have been made in building large-scale kinetic models of cellular metabolism in the past two decades. However, most kinetic models published to date, remain focused around central carbon pathways or are built around ad hoc re ...

BMC2021

Lieu hologrammatique, un concept et une méthodologie complexe de mesure de l'organisation spatiale des réalités sociales

Thibault Pierre Louis Romany

The urban is a complex phenomenon whose exploration remains a important scientific challenge. Choosing complex thought in order to renew our capacities for understanding and action on and with the urban is not trivial. The complex thought developed by Edga ...

EPFL2020

Oblivious Sketching of High-Degree Polynomial Kernels

Mikhail Kapralov, Amir Zandieh

Kernel methods are fundamental tools in machine learning that allow detection of non-linear dependencies between data without explicitly constructing feature vectors in high dimensional spaces. A major disadvantage of kernel methods is their poor scalabili ...

ASSOC COMPUTING MACHINERY2020