Computational studies on Escherichia coli metabolism : from constitutive bricks to large-scale behaviour

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Whereas the ability of deep networks to produce useful predictions on many kinds of data has been amply demonstrated, estimating the reliability of these predictions remains challenging. Sampling approaches such as MC-Dropout and Deep Ensembles have emerge ...

The outstanding information processing capacity of the brain relies on numerous molecular mechanisms. It is an extremely energy-expensive function, which involves specialization and collaboration of different cell types. A common framework to study the com ...

Aluminium is one of the most widely used metals in the world, but its production is highly energy intensive and largely dependent on the use of fossil fuels. A typical secondary aluminium production facility consumes 700-1,000 kWh of natural gas and 200-40 ...

We propose a framework for the construction of 4D scientific models for cities of the past. It leverages both the use of computational methods for bootstrapping, and a collaborative interface for visualisation and edition. This permits to continuously enri ...

For the dimensioning and assessment of structures, it is common practice to compare action effects with sectional resistances. Extensive studies have been performed to quantify the model uncertainty on the resistance side. However, for statically indetermi ...

We propose an image-based elastography method to measure the heterogeneous stiffness inside a cell and its nucleus. It uses a widely accessible setup consisting of plate compression imaged with fluorescence microscopy. Our framework recovers a spatial map ...

Background and Objective: Computational models of the cardiovascular system allow for a detailed and quantitative investigation of both physiological and pathological conditions, thanks to their ability to combine clinical-possibly patient-specific-data wi ...

Cryopreservation and lyophilization processes are widely used for conservation purposes in the pharmaceutical, biotechnological, and food industries or in medical transplantation. Such processes deal with extremely low temperatures (e.g., -196 degrees C) a ...

ML-based edge devices may face memory and computational errors that affect applications' reliability and performance. These errors can be the result of particular working conditions (e.g., radiation areas in physical experiments or avionics) or could be th ...

Deep learning models (DLM) are efficient replacements for computationally intensive optimization techniques. Musculoskeletal models (MSM) typically involve resource-intensive optimization processes for determining joint and muscle forces. Consequently, DLM ...

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterog ...

In the field of choice modeling, the availability of ever-larger datasets has the potential to significantly expand our understanding of human behavior, but this prospect is limited by the poor scalability of discrete choice models (DCMs): as sample sizes ...

Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ra ...

Computational studies on Escherichia coli metabolism : from constitutive bricks to large-scale behaviour

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ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference

Molecular reconstruction and simulation of the Neuron-Glia-Vasculature system

Modelling The Effect of Future Uncertainty in Energy Prices on Decarbonization Pathways for Secondary Aluminium Production

From Automated Bootstrapping to Collaborative Editing: A Framework for 4D City Reconstruction

Model uncertainties in action effects and load bearing capacity calculation in statically indeterminate reinforced concrete structures

PROBING INTRACELLULAR ELASTICITY WITH MINIMAL-HESSIAN REGISTRATION

The role of computational methods in cardiovascular medicine: a narrative review

Industrial Biotechnology Conservation Processes: Similarities with Natural Long-Term Preservation of Biological Organisms

Hardware-Software co-design Methodologies for Edge AI Optimization

Glenohumeral joint force prediction with deep learning

Surface segregation in high-entropy alloys from alchemical machine learning

Stochastic adaptive resampling for the estimation of discrete choice models

On double-descent in uncertainty quantification in overparametrized models