Quasi-random numbers for copula models

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Multi-frequency and Doppler spectral radars are valuable tools for retrieving information on the microphysics of precipitation, particularly snowfall. However, estimating microphysical properties from radar measurements is an ill-posed problem, i.e., diffe ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

This paper explores data assimilation methods in the context of Bayesian Calibration for fuel performance models. It compares deterministic and stochastic approaches, concentrating on the Generalized Linear Least Squares (GLLS) method and stochastic techni ...

Surrogate-based optimization is widely used for aerodynamic shape optimization, and its effectiveness depends on representative sampling of the design space. However, traditional sampling methods are hard-pressed to effectively sample high-dimensional desi ...

Current cryptographic solutions will become obsolete with the arrival of large-scale universal quantum computers. As a result, the National Institute of Standards and Technology supervises a post-quantum standardization process which involves evaluating ca ...

Purpose: This study was designed and conducted to validate the reference values of hematological parameters for healthy adult male and female residents of Kabul city, Afghanistan. Methodology: In this cross-sectional study, the samples were collected accor ...

Given the patchy nature of gas plumes and the slow response of conventional gas sensors, the use of mobile robots for Gas Source Localization (GSL) tasks presents significant challenges. These aspects increase the difficulties in obtaining gas measurements ...

Two -particle Bose-Einstein momentum correlation functions are studied for charged-hadron pairs in lead -lead collisions at a center -of -mass energy per nucleon pair of .'/sNN = 5.02 TeV. The data sample, containing 4.27 x 109 minimum bias events correspo ...

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

The reported rate of the occurrence of unilateral spatial neglect (USN) is highly variable likely due to the lack of validity and low sensitivity of classical tools used to assess it. Virtual reality (VR) assessments try to overcome these limitations by pr ...

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 ...

We are interested in testing properties of distributions with systematically mislabeled samples. Our goal is to make decisions about unknown probability distributions, using a sample that has been collected by a confused collector, such as a machine-learni ...

The recent geopolitical conflicts in Europe have underscored the vulnerability of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy systems ensuring sustainable energy supply in the future, the ...

Modern digital connectivity has necessitated the creation of robust methods for securely storing and transferring data. At the heart of all security infrastructure is the random number generator (RNG). While random numbers find use in a variety of applicat ...

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

Domain generalization (DG) aims to learn a model from multiple training (i.e., source) domains that can generalize well to the unseen test (i.e., target) data coming from a different distribution. Single domain generalization (SingleDG) has recently emerge ...

Deep-learning-based digital twins (DDT) are a promising tool for data-driven system health management because they can be trained directly on operational data. A major challenge for efficient training however is that industrial datasets remain unlabeled. T ...

Bioaerosols are emitted from various sources into the indoor environment and can positively and negatively impact human health. Humans are the major source of bioaerosol emissions indoors, specifically for bacteria. However, efficient sampling to guarantee ...

The intrinsic random amplitude and phase modulation of 40 distinct lines of a microresonator frequency comb operated in the modulation instability regime are used to realize massively parallel random-modulation continuous-wave light detection and ranging, ...

Quasi-random numbers for copula models

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What can radars tell us about snowfall microphysics? Insights from a Markov chain Monte-Carlo approach

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Extensions of Peer Prediction Incentive Mechanisms

Comparison of Bayesian Model Calibration Techniques for Future Application to Fuel Performance Behavior Models

DiffAirfoil: An Efficient Novel Airfoil Sampler Based on Latent Space Diffusion Model for Aerodynamic Shape Optimization

Side-channel analysis of isogeny-based key encapsulation mechanisms and hash-based digital signatures

Establishment of Reference Intervals of Blood Parameters Among the Healthy Afghan Population

Sense in Motion with Belief Clustering: Efficient Gas Source Localization with Mobile Robots

Two-particle Bose-Einstein correlations and their Lévy parameters in PbPb collisions at √sNN=5.02 TeV

Gibbs sampling the posterior of neural networks

An immersive virtual reality tool for assessing left and right unilateral spatial neglect

Graph generative deep learning models with an application to circuit topologies

Distribution Testing with a Confused Collector

Identification of typical district configurations: A two-step global sensitivity analysis framework

Modelling and design of CMOS SPAD sensors for quantum random number generation

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Center-aware Adversarial Augmentation for Single Domain Generalization

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Comparison of two methods for bioaerosol sampling and characterization in a low-biomass chamber environment

Chaotic microcomb-based parallel ranging