Superluminal Motion-Assisted Four-Dimensional Light-in-Flight Imaging

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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 ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

Time-resolved non-line-of-sight (NLOS) imaging based on single-photon avalanche diode (SPAD) detectors have demonstrated impressive results in recent years. To acquire adequate number of indirect photons from a hidden scene in the presence of overwhelming ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Central aortic diastolic pressure decay time constant ( ) is according to the two-element Windkessel model equal to the product of total peripheral resistance (R) times total arterial compliance (C ). As such, it is related to arterial stiffness, which has ...

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 ...

In this thesis we study stability from several viewpoints. After covering the practical importance, the rich history and the ever-growing list of manifestations of stability, we study the following. (i) (Statistical identification of stable dynamical syste ...

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, ...

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 ...

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 ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

This paper studies kernel ridge regression in high dimensions under covariate shifts and analyzes the role of importance re-weighting. We first derive the asymptotic expansion of high dimensional kernels under covariate shifts. By a bias-variance decomposi ...

This paper proposes a data-driven control design method for nonlinear systems that builds upon the Koopman operator framework. In particular, the Koopman operator is used to lift the nonlinear dynamics to a higher-dimensional space where the so-called obse ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

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 ...

We describe a novel method to compute the components of dynamo tensors from direct magnetohydrodynamic (MHD) simulations. Our method relies upon an extension and generalization of the standard H & ouml;gbom CLEAN algorithm widely used in radio astronomy to ...

The theory underlying robust distributed learning algorithms, designed to resist adversarial machines, matches empirical observations when data is homogeneous. Under data heterogeneity however, which is the norm in practical scenarios, established lower bo ...

With the capabilities such as single-photon detection, time stamping and high-speed acquisition, time-resolved imaging based on single-photon avalanche diode (SPAD) detectors has found significant applications across diverse domains, including but not limi ...

Purpose: To develop a self-navigated motion compensation strategy for 3D radial MRI that can compensate for continuous head motion by measuring rigid body motion parameters with high temporal resolution from the central k-space acquisition point (self-enco ...

Context. We report the exploitation of a sample of Solar System observations based on data from the third Gaia Data Release (Gaia DR3) of nearly 157 000 asteroids. It extends the epoch astrometric solution over the time coverage planned for the Gaia DR4, w ...

Superluminal Motion-Assisted Four-Dimensional Light-in-Flight Imaging

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Random matrix methods for high-dimensional machine learning models

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

A Gradient-Gated SPAD Array for Non-Line-of-Sight Imaging

Robust machine learning for neuroscientific inference

Novel theory and potential applications of central diastolic pressure decay time constant

Aggregating Spatial and Photometric Context for Photometric Stereo

Stability: a search for structure

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

Spectral Estimators for High-Dimensional Matrix Inference

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

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Understanding generalization and robustness in modern deep learning

Boosting likelihood learning with event reweighting

Iterative removal of sources to model the turbulent electromotive force

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity

Time-resolved imaging with SPAD detectors

Rapid motion estimation and correction using self-encoded FID navigators in 3D radial MRI

Gaia Focused Product Release: Asteroid orbital solution: Properties and assessment