Synthesis and Analysis of 3D shapes with Geometric Deep Learning in Computer-Aided Engineering

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Given a convex set Q ⊆ R m and an integer matrix W ∈ Z m×n , we consider statements of the form ∀b ∈ Q ∩ Z m ∃x ∈ Z n s.t. W x ≤ b. Such statements can be verified in polynomial time with the algorithm of Kannan and its improvements if n is fixed and Q is ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

The Songdo Vision dataset provides high-resolution (4K, 3840×2160 pixels) RGB images annotated with categorized axis-aligned bounding boxes (BBs) for vehicle detection from a high-altitude bird’s-eye view (BeV) perspective. Captured over Songdo Internation ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Structured interviews often include past-behavior questions inviting applicants to recount a past work experience. While optimal responses to these questions should take the form of a story, applicants struggle to produce them extemporaneously. Asynchronou ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

This study aims to enhance the Environmental Performance (EP) of structural elements focusing on isostatic beams to mitigate the construction sector’s impact. The objective of the study is to determine the optimum typology and geometry to maximize EP for a ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Coral reefs are crucial for biodiversity and provide vital resources for humankind. But despite such a central role, they are confronted to increasing threats linked to climate change, pollution, and local stressors. To ensure effective conservation, effic ...

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

In computational physics, chemistry, and biology, the implementation of new techniques in shared and open-source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several cha ...

The goal of this work is to use anisotropic adaptive finite elements for the numerical simulation of aluminium electrolysis. The anisotropic adaptive criteria are based on a posteriori error estimates derived for simplified problems. First, we consider an ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

Zoning reform is a crucial tool for cities to adapt to contemporary challenges. However, its implementation remains challenging. Property owners, with a vested interest in the value of their neighborhoods, are sensitive to local developments and the potent ...

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

The rise of robotic body augmentation brings forth new developments that will transform robotics, human-machine interaction, and wearable electronics. Extra robotic limbs, although building upon restorative technologies, bring their own set of challenges i ...

Synthesis and Analysis of 3D shapes with Geometric Deep Learning in Computer-Aided Engineering

Chattez avec Graph Search

Forall-exist statements in pseudopolynomial time

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Songdo Vision: Vehicle Annotations from High-Altitude BeV Drone Imagery in a Smart City

Topics in statistical physics of high-dimensional machine learning

Identifying storytelling in job interviews using deep learning

Understanding generalization and robustness in modern deep learning

Safe Deep Neural Networks

Maximisation of the environmental performance of beams through the optimisation and comparison of different typologies

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Semantic Segmentation of Benthic Classes in Reef Environments Using a Large Vision Transformer

Scalable constrained optimization

PLUMED Tutorials: A collaborative, community-driven learning ecosystem

Adaptive Finite Elements with Large Aspect Ratio. Application to Aluminium Electrolysis

Random matrix methods for high-dimensional machine learning models

Incorporating Projective Geometry into Deep Learning

Spatial Modeling for Building Design Evaluation: from Visual Landscape Quality Assessment to Devaluation Risk Estimation

Optimizing Dynamic Aperture Studies with Active Learning

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Investigating neural resource allocation in the sensorimotor control of extra limbs