Early Stages of Precipitation In Aluminum Alloys by First-Principles and Machine-Learning Atomistic Simulations

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The microstructure evolution associated with the cold forming sequence of an Fe-14Cr-1W-0.3Ti-0.3Y2O3 grade ferritic stainless steel strengthened by dispersion of nano oxides (ODS) was investigated. The material, initially hot extruded at 1100 degrees C an ...

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 ...

Automatic speech recognition (ASR) systems are well known to perform poorly on dysarthric speech. Previous works have addressed this by speaking rate modification to reduce the mismatch with typical speech. Unfortunately, these approaches rely on transcrib ...

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

Given a ground-level query image and a geo-referenced aerial image that covers the query’s local surroundings, fine-grained cross-view localization aims to estimate the location of the ground camera inside the aerial image. Recent works have focused on dev ...

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

A Finite Element Model produced from a real WC-10wt.%Co sample is used to study the mechanical behavior of cemented carbides in compression tests. The model is obtained by slicing the sample by Focused Ion Beam and reconstructing it. This model is used to ...

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 ...

Over the last two decades, Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) has been developed as a powerful MR imaging modality that allows estimating non-invasively the microscopic structure of biological tissues by exploiting the natural motion of ...

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

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 ...

Interface stress is a fundamental descriptor for interphase boundaries and is defined in strict relation to the interface energy. In nanomultilayers with their intrinsically high interface density, the functional properties are dictated by the interface st ...

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

Early Stages of Precipitation In Aluminum Alloys by First-Principles and Machine-Learning Atomistic Simulations

Chattez avec Graph Search

Dislocation Hardening in a New Manufacturing Route of Ferritic Oxide Dispersion-Strengthened Fe-14Cr Cladding Tube

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

Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR

Boosting likelihood learning with event reweighting

Adapting Fine-Grained Cross-View Localization to Areas Without Fine Ground Truth

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Cyclic loading of WC-Ni by FEM with a realistic 3D morphology

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

Simulation-based Microstructure Imaging: Towards Realistic White Matter Modeling using Monte Carlo Methods in DW-MRI

Gibbs sampling the posterior of neural networks

Incorporating Projective Geometry into Deep Learning

Experimental and ab initio derivation of interface stress in nanomultilayered coatings: Application to immiscible Cu/W system with variable in-plane stress

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Driving and suppressing the human language network using large language models

Generalization of Scaled Deep ResNets in the Mean-Field Regime