Concept

Data modeling

Related publications (422)

Reality-assisted evolution of soft robots through large-scale physical experimentation: a review

Josephine Anna Eleanor Hughes, Simon Lukas Hauser

We introduce the framework of reality-assisted evolution to summarize a growing trend towards combining model-based and model-free approaches to improve the design of physically embodied soft robots. In silico, data-driven models build, adapt, and improve ...

MIT Press Journals2021

Data management and modelling of composite materials fatigue behavior

In this paper, we will present an update for the previously developed CCFatigue computational tool. This tool has been built around several analysis methods for fatigue life predictions of composite materials and includes modules that allow analysis over c ...

2021

Representation Learning for Multi-relational Data

Eda Bayram

Recent years have witnessed a rise in real-world data captured with rich structural information that can be better depicted by multi-relational or heterogeneous graphs.However, research on relational representation learning has so far mostly focused on the ...

EPFL2021

The Genesis of Molecular Volcano Plots

Matthew Wodrich, Michael Markus Busch, Boodsarin Sawatlon

For the past two decades, linear free energy scaling relationships and volcano plots have seen frequent use as computational tools that aid in understanding and predicting the catalytic behavior of heterogeneous and electrocatalysts. Based on Sabatier's pr ...

2021

Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features

Michaël Unser, Cathrin Brisken, Daniel Sage, Olivier Burri, Martin Weigert, Fabio De Martino, Quentin Juppet

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis gener ...

2021

SiML: Sieved Maximum Likelihood for Array Signal Processing

Paul Hurley, Matthieu Martin Jean-André Simeoni

Stochastic Maximum Likelihood (SML) is a popular direction of arrival (DOA) estimation technique in array signal processing. It is a parametric method that jointly estimates signal and instrument noise by maximum likelihood, achieving excellent statistical ...

2021

Representing graphs through data with learning and optimal transport

Hermina Petric Maretic

Graphs offer a simple yet meaningful representation of relationships between data. Thisrepresentation is often used in machine learning algorithms in order to incorporate structuralor geometric information about data. However, it can also be used in an inv ...

EPFL2021

Propagation on Multi-relational Graphs for Node Regression

Eda Bayram

Recent years have witnessed a rise in real-world data captured with rich structural information that can be conveniently depicted by multi-relational graphs. While inference of continuous node features across a simple graph is rather under-studied by the c ...

2021

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

Damien Bouffard, Camille Roland Marie Minaudo, Theo Baracchini

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters als ...

MDPI2021

OMiLAB: A Smart Innovation Environment for Digital Engineers

This position paper introduces a Smart Innovation Environment for experimentation related to digital transformation projects, for the consolidation of a proposed "Digital Engineer" skill profile (with a business-oriented facet labelled as "Digital Innovato ...

SPRINGER INTERNATIONAL PUBLISHING AG2021

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