Multithermal-Multibaric Molecular Simulations from a Variational Principle

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Given the patchy nature of gas plumes and the slow response of conventional gas sensors, the use of mobile robots for Gas Source Localization (GSL) tasks presents significant challenges. These aspects increase the difficulties in obtaining gas measurements ...

Metal-organic frameworks (MOFs) are a rapidly growing class of materials that offer great promise in various applications. However, the synthesis remains challenging: for example, a range of crystal structures can often be accessed from the same building b ...

Deep-learning-based digital twins (DDT) are a promising tool for data-driven system health management because they can be trained directly on operational data. A major challenge for efficient training however is that industrial datasets remain unlabeled. T ...

Exploiting the recent advancements in artificial intelligence, showcased by ChatGPT and DALL-E, in real-world applications necessitates vast, domain-specific, and publicly accessible datasets. Unfortunately, the scarcity of such datasets poses a significan ...

We show that including pairing and repulsion into the description of one-dimensional spinless fermions, as in the domain wall theory of commensurate melting or the interacting Kitaev chain, leads, for strong enough repulsion, to a line of critical points i ...

Domain generalization (DG) aims to learn a model from multiple training (i.e., source) domains that can generalize well to the unseen test (i.e., target) data coming from a different distribution. Single domain generalization (SingleDG) has recently emerge ...

Bioaerosols are emitted from various sources into the indoor environment and can positively and negatively impact human health. Humans are the major source of bioaerosol emissions indoors, specifically for bacteria. However, efficient sampling to guarantee ...

We investigate the nature of the phase transitions in the quantum Ashkin-Teller chain in the presence of chiral perturbations. We locate the Lifshitz line separating a region of direct chiral transitions from the region where the transition is through an i ...

We study elastic ribbons subject to large, tensile pre-stress confined to a central region within the cross-section. These ribbons can buckle spontaneously to form helical shapes, featuring regions of alternating chirality (phases) that are separated by so ...

Deep neural networks have completely revolutionized the field of machine learning by achieving state-of-the-art results on various tasks ranging from computer vision to protein folding. However, their application is hindered by their large computational an ...

Despite space heating and cooling, the energy use for hot water production has not changed significantly over time and accounts for a big share in modern, well-insulated buildings. The main challenge of hot water generation lies in the highly stochastic na ...

Neural functions operate in tightly controlled conditions that are mediated by multiple electrical and chemical phenomena. Brain disorders such as Parkinson's Disease and Alzheimer's Disease perturb these conditions and cause a loss of neurons, which impai ...

In event-based sensing, many sensors independently and asynchronously emit events when there is a change in their input. Event-based sensing can present significant improvements in power efficiency when compared to traditional sampling, because (1) the out ...

This work introduces a diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations. Our E(3) Equivariant Diffusion Model (EDM) learns to denoise a diffusion process with an equivariant network that jointly operates on bot ...

Production quality and process efficiency are the two main drivers that lead any industrial strategy. To ensure product quality, a duality historically existed between two approaches, namely batch sampling and systematic sampling. In batch sampling, the ba ...

Intelligence involves processing sensory experiences into representations useful for prediction. Understanding sensory experiences and building these contextual representations without prior knowledge of sensor models and environment is a challenging unsup ...

Using the corner-transfer matrix renormalization group to contract the tensor network that describes its partition function, we investigate the nature of the phase transitions of the hard-square model, one of the exactly solved models of statistical physic ...

This thesis investigates the water vapour sorption behaviour of samples with maximized calcium-silicate-hydrate (C-S-H) content. Primarily, the role of heterogeneous and homogeneous cavitation has been studied during the drying of cementitious materials. C ...

The largest source of systematic errors in the time-delay cosmography method likely arises from the lens model mass distribution, where an inaccurate choice of model could in principle bias the value of H-0. A Bayesian hierarchical framework has been propo ...

Sampling has always been at the heart of signal processing providing a bridge between the analogue world and discrete representations of it, as our ability to process data in continuous space is quite limited. Furthermore, sampling plays a key part in unde ...

Multithermal-Multibaric Molecular Simulations from a Variational Principle

Chattez avec Graph Search

Sense in Motion with Belief Clustering: Efficient Gas Source Localization with Mobile Robots

How Reproducible is the Synthesis of Zr-Porphyrin Metal-Organic Frameworks? An Interlaboratory Study

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Dated: Guidelines for Creating Synthetic Datasets for Engineering Design Applications

Eight-vertex criticality in the interacting Kitaev chain

Center-aware Adversarial Augmentation for Single Domain Generalization

Comparison of two methods for bioaerosol sampling and characterization in a low-biomass chamber environment

Critical properties of the quantum Ashkin-Teller chain with chiral perturbations

Twisting instabilities in elastic ribbons with inhomogeneous pre-stress: A macroscopic analog of thermodynamic phase transition

Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

Customized Neural Network training to predict the highly imbalanced data of domestic hot water usage

A neural probe to sample brain fluid in nanoliter volumes for quantitative molecular studies

Asynchrony Increases Efficiency: Time Encoding of Videos and Low-Rank Signals

Equivariant Diffusion for Molecule Generation in 3D

Virtual metrology applied to milling process

Mechanical intelligence for learning embodied sensor-object relationships

Tensor network investigation of the hard-square model

Characterization of water sorption cycle in the cement microstructure of controlled oxide composition

TOCOSMO VIII. A key test of systematics in the hierarchical method of time-delay cosmography

Uncertain Sampling with Certain Priors