Development and validation of a Radiance model for a translucent panel

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We present a method for segmenting cracks in images of masonry buildings damaged by earthquakes. Existing methods of crack detection fail to preserve the continuity of cracks, and their performance deteriorates with imprecise training labels. We address th ...

The training of neural networks by gradient descent methods is a cornerstone of the deep learning revolution. Yet, despite some recent progress, a complete theory explaining its success is still missing. This article presents, for orthogonal input vectors, ...

This dissertation introduces traffic forecasting methods for different network configurations and data availability. Chapter 2 focuses on single freeway cases. Although its topology is simple, the non-linearity of traffic features makes this prediction sti ...

In a turbid medium such as biological tissue, near-infrared optical tomography (NIROT) can image the oxygenation, a highly relevant clinical parameter. To be an efficient diagnostic tool, NIROT has to have high spatial resolution and depth sensitivity, fas ...

Background: Signal processing tools are required to efficiently analyze data collected in body-surface-potential map (BSPM) recordings. A limited number of such tools exist for studying persistent atrial fibrillation (persAF). We propose two novel, spatiot ...

The purpose of this article is to develop and study a decentralized strategy for Pareto optimization of an aggregate cost consisting of regularized risks. Each risk is modeled as the expectation of some loss function with unknown probability distribution, ...

In this paper, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained form ...

Spatially distributed meteorological information at the slope scale is relevant for many processes in complex terrain, yet information at this sub-km spatial resolution is difficult to obtain. While downscaling to kilometer resolutions is well described in ...

Safety-critical navigation applications require that estimation errors be reliably quantified and bounded. Over the last decade, significant effort has been put to guarantee a bounded position estimation by using Global Navigation Satellite Systems (GNSS) ...

A recent study on the glare protection performance of electrochromic (EC) glazing showed that visible transmittance levels lower than 0.6% are necessary to achieve comfortable situations for sun positions, that were close to the central field of view. Howe ...

In this work, we consider the problem of estimating the probability distribution, the quantile or the conditional expectation above the quantile, the so called conditional-value-at-risk, of output quantities of complex random differential models by the MLM ...

We consider increasingly complex models of matrix denoising and dictionary learning in the Bayes-optimal setting, in the challenging regime where the matrices to infer have a rank growing linearly with the system size. This is in contrast with most existin ...

This paper provides a validation of a novel sampling, storage, and evaluation method named raytraverse that can quickly and accurately compute glare and visual comfort metrics including vertical illuminance, Daylight Glare Probability (DGP), and Unified Gl ...

Development and validation of a Radiance model for a translucent panel

Chattez avec Graph Search

TOPO-Loss for continuity-preserving crack detection using deep learning

Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs

Multivariate time series forecasting for freeway networks

Resolution and penetration depth of reflection-mode time-domain near infrared optical tomography using a ToF SPAD camera

Novel spatiotemporal processing tools for body-surface potential map signals for the prediction of catheter ablation outcome in persistent atrial fibrillation

Regularized Diffusion Adaptation via Conjugate Smoothing

Finite Bit Quantization For Decentralized Learning Under Subspace Constraints

A Downscaling Intercomparison Study: The Representation of Slope- and Ridge-Scale Processes in Models of Different Complexity

GNSS/INS Kalman Filter Integrity Monitoring with Uncertain Time Correlated Error Processes

Transmittance thresholds of electrochromic glazing to achieve annual low-glare work environments

Quantifying uncertain system outputs via the multi-level Monte Carlo method --- distribution and robustness measures

Statistical limits of dictionary learning: Random matrix theory and the spectral replica method

Efficient Simulation for Visual Comfort Evaluations