Running Speed Estimation Using Shoe-Worn Inertial Sensors: Direct Integration, Linear, and Personalized Model

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Symbolic Regression holds great potential for uncovering underlying mathematical and physical relationships from observed data. However, the vast combinatorial space of possible expressions poses significant challenges for previous online search methods 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 ...

Background :Sensory reafferents are crucial to correct our posture and movements, both reflexively and in a cognitively driven manner. They are also integral to developing and maintaining a sense of agency for our actions. In cases of compromised reafferen ...

The optimal pricing of goods, especially when they are new and the innovating firm is a monopolist, must proceed without precise knowledge of the demand curve. This paper provides a pricing method with a relative robustness guarantee by maximizing a perfor ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

The main aim of this thesis is to study extreme value theory in a multivariate framework and to propose new methods for modeling and estimating dependence between several variables at extreme levels. We first give a brief overview of the main concepts of e ...

The present invention relates to a compound of the general formula (I), and (II)wherein one of R11 and R12 is hydrogen and the other is -CH2-R70, and one of R13 and R14 is hydrogen and the other is -CH2-R71, or wherein one of R11 and R12 is hydrogen and th ...

The present invention relates to a compound of the general formula (I), (II) and (III), more specifically of formula (Ia), (Ib), (Ic)wherein R11 and R12 or R21 and R22 or R31 and R32 are both hydrogen or form together with CHR50 a cyclic moiety or one of R ...

Purpose: This study aims to evaluate two distinct approaches for fiber radius estimation using diffusion-relaxation MRI data acquired in biomimetic microfiber phantoms that mimic hollow axons. The methods considered are the spherical mean power-law approac ...

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

Parametric oscillators are examples of externally driven systems that can exhibit two stable states with opposite phase depending on the initial conditions. In this work, we propose to study what happens when the external forcing is perturbed by a continuo ...

Language modeling has seen impressive progress over the last few years. The invention of the Transformer architecture sparked a revolution in many fields of machine learning, including breakthroughs in chemistry and biology. In this chapter, we explore how ...

Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set pose ...

Photoplethysmography (PPG) is a widely emerging method to assess vascular health in humans. The origins of the signal of reflective PPG on peripheral arteries have not been thoroughly investigated. We aimed to identify and quantify the optical and biomecha ...

The log law of the wall, joining the inner, near-wall mean velocity profile (MVP) in wall-bounded turbulent flows to the outer region, has been a permanent fixture of turbulence research for over hundred years, but there is still no general agreement on th ...

Years of a fierce competition have naturally selected the fittest deep learning algorithms. Yet, although these models work well in practice, we still lack a proper characterization of why they do so. This poses serious questions about the robustness, trus ...

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

Purpose: The purpose of this study was to describe, validate, and compare the contrast sensitivity functions (CSFs) acquired with the novel quick CSF (qCSF) method from patients with early and intermediate age-related macular degeneration (eAMD and iAMD) a ...

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impa ...

This article presents the outcomes of a large project towards the investigation of the lateral performance of full-scale industrialized light-frame wooden diaphragms. 10 full-scale diaphragms of 3.6 m by 2.4 m were tested under in-plane lateral loading (mo ...

Running Speed Estimation Using Shoe-Worn Inertial Sensors: Direct Integration, Linear, and Personalized Model

Chattez avec Graph Search

Interactive symbolic regression with co-design mechanism through offline reinforcement learning

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

Augmenting locomotor perception by remapping tactile foot sensation to the back

Monopoly Pricing with Unknown Demand

Graph generative deep learning models with an application to circuit topologies

Flexible Statistical Inference for Multivariate Extremes

Biobased surfactant

Biobased surfactant

Pore size estimation in axon-mimicking microfibers with diffusion-relaxation MRI

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Experimental observation of topological transition in linear and nonlinear parametric oscillators

Transformers and Large Language Models for Chemistry and Drug Discovery

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Quantification of the Phenomena Affecting Reflective Arterial Photoplethysmography

The hunt for the Karman 'constant' revisited

The inductive bias of deep learning: Connecting weights and functions

Robust Collaborative Learning with Linear Gradient Overhead

Evaluating contrast sensitivity in early and intermediate age-related Macular Degeneration with the Quick Contrast Sensitivity Function

Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations

Testing and nonlinear modelling of industrialized light-frame wooden diaphragms including optimized nailing and nonstructural sheathing