In the past few years, Differentiable Neural Architecture Search (DNAS) rapidly imposed itself as the trending approach to automate the discovery of deep neural network architectures. This rise is mainly due to the popularity of DARTS, one of the first major DNAS methods. In contrast with previous works based on Reinforcement Learning or Evolutiona...
Physics-based Patient-Specific Biomechanical models (PSBMs), particularly those using finite element methods (FEM), simulate organ behaviors accurately but are computationally intensive, especially for hyper-elastic tissues. To address this, U-Mesh \cite{Mendizabal2020} introduced a data-driven approach using U-Net architecture, achieving real-time...
Morphological operations such as erosion, dilation, and skeletonization offer valuable tools for processing and analyzing segmentation masks. Several studies have investigated the integration of differentiable morphological operations within deep segmentation neural networks, particularly for the computation of loss functions. However, those method...
This work introduces a new approach for accelerating the numerical analysis of time-domain partial differential equations (PDEs) governing complex physical systems. The methodology is based on a combination of a classical reduced-order modeling (ROM) framework and recently-introduced Graph Neural Networks (GNNs), where the latter is trained on high...
Attack paths represent the sequences of network nodes compromised by attackers while exploiting their respective vulnerabilities. Current methods for predicting such attack paths largely depend on existing human expertise or established heuristics. These traditional methods are time-consuming and require highly skilled threat-hunting analysts to id...
Recent work has shown that deep learning algorithms are efficient for various tasks, whether in Natural Language Processing (NLP) or in Computer Vision (CV). One of the particularities of these algorithms is that they are so efficient as the amount of data used is large. However, sequential execution of these algorithms on large amounts of data can...
Mental disorders are a major public health concern, affecting millions of people worldwide. The quest for reliable biomarkers for diagnosis and treatment remains a complex challenge. Autism Spectrum Disorder (ASD), a common psychiatric condition, is characterized by atypical patterns in cognitive, emotional, and social domains. Early and accurate d...
The limitations and complexity of traditional noncontact sensors in terms of sensitivity and threshold settings pose great challenges to extend the traditional five human senses. Here, we propose tele-perception to enhance human perception and cognition beyond these conventional noncontact sensors. Our bionic multi-receptor skin employs structured ...
Understanding materials properties depends largely on the ability to determine its components, and in particular its mineral phases. Powder X-ray diffraction (XRD) is a powerful tool for such purposes. This paper presents a Transformerbased vision model (ViT) for mineral phase identification, and proportion inference to quantify the mineral phases ...
Published in bioRxiv : the preprint server for biology
We introduce a novel framework BEATRICE to identify putative causal variants from GWAS statistics. Identifying causal variants is challenging due to their sparsity and high correlation in the nearby regions. To account for these challenges, we rely on a hierarchical Bayesian model that imposes a binary concrete prior on the set of causal variants. ...
