Adaptive Approximation of Shapes

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In this work, we analyze space-time reduced basis methods for the efficient numerical simulation of haemodynamics in arteries. The classical formulation of the reduced basis (RB) method features dimensionality reduction in space, while finite difference sc ...

Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equ ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

By the addition of entropic regularization, multimarginal optimal transport problems can be trans-formed into tensor scaling problems, which can be solved numerically using the multimarginal Sinkhorn algorithm. The main computational bottleneck of this alg ...

We consider the defocusing nonlinear wave equation ❑u D jujp ⠀1u in R3 ⠂ & UOELIG;0; 1/. We prove that for any initial datum with a scaling-subcritical norm bounded by M0 the equation is globally well-posed for p D 5 C i, where i 2 .0; ...

Speech recognition-based applications upon the advancements in artificial intelligence play an essential role to transform most aspects of modern life. However, speech recognition in real-life conditions (e.g., in the presence of overlapping speech, varyin ...

To bring educational robots to classrooms, we need to consider teachers' self-efficacy and challenges in managing a robot-mediated classroom, and how to support them in overcoming these challenges. Orchestration tools are designed to support teachers by pr ...

In the current era of big data, aggregation queries on high-dimensional datasets are frequently utilized to uncover hidden patterns, trends, and correlations critical for effective business decision-making. Data cubes facilitate such queries by employing p ...

Reduced-order models are indispensable for multi-query or real-time problems. However, there are still many challenges to constructing efficient ROMs for time-dependent parametrized problems. Using a linear reduced space is inefficient for time-dependent n ...

In a groundbreaking work, Duplantier, Miller and Sheffield showed that subcritical Liouville quantum gravity (LQG) coupled with Schramm-Loewner evolutions (SLE) can be obtained by gluing together a pair of Brownian motions. In this paper, we study the coun ...

When learning from data, leveraging the symmetries of the domain the data lies on is a principled way to combat the curse of dimensionality: it constrains the set of functions to learn from. It is more data efficient than augmentation and gives a generaliz ...

We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...

We study the phenomenon of intransitivity in models of dice and voting. First, we follow a recent thread of research for n-sided dice with pairwise ordering induced by the probability, relative to 1/2, that a throw from one die is higher than the other. We ...

The objective of this series is to study metric geometric properties of disjoint unions of Cayley graphs of amenable groups by group properties of the Cayley accumulation points in the space of marked groups. In this Part II, we prove that a disjoint union ...

Collective Remote Attestation (CRA) is a well-established approach where a single Verifier attests the integrity of multiple devices in a single execution of the challenge-response protocol. Current CRA solutions are well-suited for Internet of Things (IoT ...

The human hand is a versatile and complex system with dexterous manipulation capabilities. For the transfer of human grasping capabilities to humanoid robotic and prosthetic hands, an understanding of the dynamic characteristics of grasp motions is fundame ...

Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from a noisy image only is a challenging task. Here, we study the case when paired noisy and noise-free samples are accessible. N ...

Integrating functions on discrete domains into neural networks is key to developing their capability to reason about discrete objects. But, discrete domains are (I) not naturally amenable to gradient-based optimization, and (II) incompatible with deep lear ...

In the context of next-generation radio interferometers, we are facing a big challenge of how to economically process data. The classical dimensionality reduction technique, averaging visibilities on time, may dilute fast radio transients (FRT). We propose ...

Deep learning algorithms are responsible for a technological revolution in a variety oftasks including image recognition or Go playing. Yet, why they work is not understood.Ultimately, they manage to classify data lying in high dimension – a feat generical ...

Adaptive Approximation of Shapes

Chattez avec Graph Search

SPACE-TIME REDUCED BASIS METHODS FOR PARAMETRIZED UNSTEADY STOKES EQUATIONS

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

Spectral Estimators for High-Dimensional Matrix Inference

Low-Rank Tensor Approximations for Solving Multimarginal Optimal Transport Problems\ast

Global Regularity For The Nonlinear Wave Equation With Slightly Supercritical Power

Sparse Autoencoders for Speech Modeling and Recognition

Supporting Teachers' Orchestration in Robot-mediated Classrooms

Interactive-time Exploration, Querying, and Analysis of Large High-dimensional Datasets

Non-intrusive data-driven reduced-order modeling for time-dependent parametrized problems

Brownian half-plane excursion and critical Liouville quantum gravity

Leveraging topology, geometry, and symmetries for efficient Machine Learning

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

The probability of intransitivity in dice and close elections

Group Approximation in Cayley Topology and Coarse Geometry, Part II: Fibred Coarse Embeddings

HolA: Holistic and Autonomous Attestation for IoT Networks

On Force Synergies in Human Grasping Behavior

PoGaIN: Poisson-Gaussian Image Noise Modeling From Paired Samples

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions

Fourier dimensionality reduction for fast radio transients

Landscape and training regimes in deep learning