Bits from Photons : Oversampled Binary Image Acquisition

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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 ...

We prove global in time well-posedness for perturbations of the 2D stochastic Navier-Stokes equations partial derivative( t)u + u center dot del u = Delta u - del p + sigma + xi, u(0, center dot ) = u(0),div (u) = 0, driven by additive space-time white noi ...

There is a bias in the inference pipeline of most diffusion models. This bias arises from a signal leak whose distribution deviates from the noise distribution, creating a discrepancy between training and inference processes. We demonstrate that this signa ...

The p-Laplacian problem -del & sdot; ((mu + |del u|(p-2))del u) = f is considered, where mu is a given positive number. An anisotropic a posteriori residual-based error estimator is presented. The error estimator is shown to be equivalent, up to higher ord ...

Electrochemical Impedance Spectroscopy (EIS) and Equivalent Circuit Models (ECMs) are widely used to characterize the impedance and estimate parameters of electrochemical systems such as batteries. We use a generic ECM with ten parameters grouped to model ...

Applications demanding imaging at low-light conditions at near-infrared (NIR) and short-wave infrared (SWIR) wavelengths, such as quantum information science, biophotonics, space imaging, and light detection and ranging (LiDAR), have accelerated the develo ...

The space industry has experienced substantial growth in recent years, leading to rapid advancements in space exploration and space-based technologies. Consequently, the study of electronics and sensor performance in extreme environments has become crucial ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Dynamic downscaling of atmospheric forcing data to the hectometer resolution has shown increases in accuracy for landsurface models, but at great computational cost. Here we present a validation of a novel intermediate complexity atmospheric model, HICAR, ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

The invention relates to an Imaging sensor device in a stacked arrangement comprising: - a pixel array tier comprising a plurality of pixel segments each having a plurality of pixels for photon detection each providing a digital pixel output; - a processin ...

Activity-based models offer the potential for a far deeper understanding of daily mobility behaviour than trip-based models. Based on the fundamental assumption that travel demand is derived from the need to do activities, they are flexible tools that aim ...

Orthogonal group synchronization is the problem of estimating n elements Z(1),& mldr;,Z(n) from the rxr orthogonal group given some relative measurements R-ij approximate to Z(i)Z(j)(-1). The least-squares formulation is nonconvex. To avoid its local minim ...

The different receptors in human skin show not only diversity in the stimuli to which they respond, but also variable sensitivity and directionality. This is often determined by their location or morphology, and can play an important role in filtering or a ...

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 ...

Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions ...

A user’s benefit from the energy stored in a battery over its lifetime depends on the time-varying characteristics of the battery, which are in turn affected by the chosen usage behavior. Both the capacity shrinkage and the number of lifetime cycles are st ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

We develop new tools to study landscapes in nonconvex optimization. Given one optimization problem, we pair it with another by smoothly parametrizing the domain. This is either for practical purposes (e.g., to use smooth optimization algorithms with good g ...

Bits from Photons : Oversampled Binary Image Acquisition

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Spectral Estimators for High-Dimensional Matrix Inference

Global existence for perturbations of the 2D stochastic Navier-Stokes equations with space-time white noise

Exploiting the Signal-Leak Bias in Diffusion Models

Anisotropic Adaptive Finite Elements for a p-Laplacian Problem

On the use of Cramér-Rao Lower Bound for least-variance circuit parameters identification of Li-ion cells

Silicon CMOS and InGaAs(P)/InP SPADs for NIR/SWIR detection

Single-Photon Avalanche Diode Image Sensors for Harsh Radiation Environments

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Partial discharge localization in power transformer tanks using machine learning methods

Intermediate complexity atmospheric modeling in complex terrain: is it right?

Aggregating Spatial and Photometric Context for Photometric Stereo

Imaging sensor device using an array of single-photon avalanche diode photodetectors

OASIS: An integrated optimisation framework for activity scheduling

BENIGN LANDSCAPES OF LOW-DIMENSIONAL RELAXATIONS FOR ORTHOGONAL SYNCHRONIZATION ON GENERAL GRAPHS

Morphological and Material Programability of a Hall-Effect Based Soft Tactile Sensors

Boosting likelihood learning with event reweighting

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Optimal Depth of Discharge for Electric Batteries with Robust Capacity-Shrinkage Estimator

Random matrix methods for high-dimensional machine learning models

The effect of smooth parametrizations on nonconvex optimization landscapes