Massive-star-associated models for production of elements heavier than Fe are reviewed. The important roles of neutrinos in many of these models are discussed along with uncertainties in the relevant neutrino physics. Data on elemental abundances in...
It is well known that the accuracy of a calibration depends strongly on the choice of camera poses from which images of a calibration object are acquired. We present a system -- Calibration Wizard -- that interactively guides a user towards taking op...
3D hand pose estimation is a fundamental task in understanding human hands. However, accurately estimating 3D hand poses remains challenging due to the complex movement of hands, self-similarity, and frequent occlusions. In this work, we address two...
From a theoretical point of view, result-based agri-environmental payments are clearly preferable to action-based payments. However, they suffer from two major practical disadvantages: costs of measuring the results and payment uncertainty for the pa...
Understanding model uncertainty is important for many applications. We propose Bootstrap Your Own Variance (BYOV), combining Bootstrap Your Own Latent (BYOL), a negative-free Self-Supervised Learning (SSL) algorithm, with Bayes by Backprop (BBB), a B...
Covert communication can prevent the adversary from knowing that a wireless transmission has occurred. In the additive white Gaussian noise channels, a square root law is obtained and the result shows that Alice can reliably and covertly transmit $\m...
This book is about conformal prediction and related inferential techniques that build on permutation tests and exchangeability. These techniques are useful in a diverse array of tasks, including hypothesis testing and providing uncertainty quantifica...
In a supervised learning problem, given a predicted value that is the output of some trained model, how can we quantify our uncertainty around this prediction? Distribution-free predictive inference aims to construct prediction intervals around this...
The popularity of online surveys has increased the prominence of using weights that capture units' probabilities of inclusion for claims of representativeness. Yet, much uncertainty remains regarding how these weights should be employed in the analys...
Yeah, ooh, ooh, oh That hit's for you I usually have an answer to the question But this time I'm gon' be quiet (this time) Ain't nothing like the feeling of uncertainty, the eeriness of silence ...
Machine unlearning for LLMs aims to remove sensitive or copyrighted data from trained models. However, the true efficacy of current unlearning methods remains uncertain. Standard evaluation metrics rely on benign queries that often mistake superficia...
The rapid integration of variable energy sources (VRES) into power grids increases variability and uncertainty of the net demand, making the power system operation challenging. Operating reserve is used by system operators to manage and hedge against...
Product information labels can help users understand complex information leading them to make better decisions. One area where consumers are particularly prone to make costly decision-making errors is long-term saving, which requires understanding of...
Wearable devices are widely used for heart rate (HR) monitoring, yet their accuracy across diverse body compositions and skin tones remains uncertain. This study evaluated four wrist worn devices (Apple, Fitbit, Samsung, Garmin) in 58 Hispanic adults...
Parity violation experiments involving only two nucleons provide a way to study the non-leptonic, strangeness conserving part of the weak interaction in a clean measurement free of nuclear structure uncertainties. Although simple in principle, the...
The identification of strategic business partnerships can potentially provide competitive advantages for businesses; however, due to the dynamics and uncertainty present in business environments, this task could be challenging. To help businesses in...
The Parallel C++ Statistical Library for the Quantification of Uncertainty for Estimation, Simulation and Optimization, Queso, is a collection of statistical algorithms and programming constructs supporting research into the quantification of uncerta...
Physics based numerical climate models serve as critical tools for evaluating the effects of climate change and projecting future climate scenarios. However, the reliance on numerical simulations of physical equations renders them computationally int...
1. dangerous, risky, hazardous, threatening, uncertain, unstable, insecure, unreliable, precarious, treacherous, perilous, unsound Critics claim the trucks are unsafe. 2. vulnerable, threatened, …
During the COVID-19 pandemic, different countries, regions, and communities constructed various epidemic models to evaluate spreading behaviors and assist in making mitigation policies. Model uncertainties, introduced by complex transmission behavior...