Uncertainty principle - Wikipedia
The uncertainty principle, also known as Heisenberg's indeterminacy principle, is a fundamental concept in quantum mechanics. It states that there is
Managing Uncertainty in Rule Based Cognitive Models
An experiment replicated and extended recent findings on psychologically realistic ways of modeling propagation of uncertainty in rule based reasoning. Within a single production rule, the antecedent evidence can be summarized by taking the maximum...
Flat UI Elements Attract Less Attention and Cause Uncertainty
Points: 354 | Comments: 171 | Author: nicc
Uncertainty-aware data assimilation through variational inference
Data assimilation, consisting in the combination of a dynamical model with a set of noisy and incomplete observations in order to infer the state of a system over time, involves uncertainty in most settings. Building upon an existing deterministic ma...
Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entro…
The quantification of aleatoric and epistemic uncertainty in terms of conditional entropy and mutual information, respectively, has recently become quite common in machine learning. While the properties of these measures, which are rooted in informat...
ENGINEERING METROLOGY AND MEASUREMENTS
In order to estimate the uncertainty of the measuring process, one needs to consider the systematic and constant errors along with other factors that contribute to the uncertainty due to scatter of results …
ENGINEERING METROLOGY AND MEASUREMENTS
In order to estimate the uncertainty of the measuring process, one needs to consider the systematic and constant errors along with other factors that contribute to the uncertainty due to scatter of results …
The Pattern Method for Incorporating Tidal Uncertainty Into Probabilistic Tsunami Hazard A…
In this paper we describe a general framework for incorporating tidal uncertainty into probabilistic tsunami hazard assessment and propose the Pattern Method and a simpler special case called the $Δt$ Method as effective approaches. The general fram...
FuturePrism: Supporting Adolescence in Collaborative Storytelling to Cope with Future Unce…
FuturePrism is a GenAI-empowered collaborative storytelling system designed to scaffold adolescents to navigate future life challenges. Adolescents often suffer from anxiety related to future uncertainty for lacking the executive function to develop...
ENGINEERING METROLOGY AND MEASUREMENTS
In order to estimate the uncertainty of the measuring process, one needs to consider the systematic and constant errors along with other factors that contribute to the uncertainty due to scatter of results …
Multi-Operator Quantum Uncertainty Relations from New Cauchy-Schwarz Inequalities
We present new generalizations of Cauchy-Schwarz (CS) inequalities to multiple vectors and use them to derive multi-operator quantum uncertainty relations and propose multi-operator squeezing....
QUITE: Quantifying Uncertainty in Natural Language Text in Bayesian Reasoning Scenarios
Reasoning is key to many decision making processes. It requires consolidating a set of rule-like premises that are often associated with degrees of uncertainty and observations to draw conclusions. In this work, we address both the case where premise...
SLURP: Side Learning Uncertainty for Regression Problems
It has become critical for deep learning algorithms to quantify their output uncertainties to satisfy reliability constraints and provide accurate results. Uncertainty estimation for regression has received less attention than classification due to t...
Nassim Nicholas Taleb - Wikipedia
concerns problems of randomness, probability, complexity, and uncertainty. Taleb wrote Incerto, a five-volume work on the nature of uncertainty published
Maybe - definition of maybe by The Free Dictionary
Used to indicate uncertainty or possibility: We should maybe take a different route. Maybe it won't rain. n. Informal. 1. An uncertainty: There are so many maybes involved in playing the stock market. 2. An …
CovarianceNet: Conditional Generative Model for Correct Covariance Prediction in Human Mot…
The correct characterization of uncertainty when predicting human motion is equally important as the accuracy of this prediction. We present a new method to correctly predict the uncertainty associated with the predicted distribution of future trajec...
Improving Metacognition and Uncertainty Communication in Language Models
Large language models (LLMs) are increasingly used in decision-making contexts, but when they present answers without signaling low confidence, users may unknowingly act on erroneous outputs. Prior work shows that LLMs maintain internal uncertainty s...
Predictive Prescription of Unit Commitment Decisions Under Net Load Uncertainty
To take unit commitment (UC) decisions under uncertain net load, most studies utilize a stochastic UC (SUC) model that adopts a one-size-fits-all representation of uncertainty. Disregarding contextual information such as weather forecasts and tempora...
Generalized Uncertainty Principle and Corpuscular Gravity
We show that the implications of the generalized uncertainty principle (GUP) in the black hole physics are consistent with the predictions of the corpuscular theory of gravity, in which a black hole is conceived as a Bose-Einstein condensate of weakl...
Polynomial Chaos-based Input Shaper Design under Time-Varying Uncertainty
The work presented here investigates the application of polynomial chaos expansion toward input shaper design in order to maintain robustness in dynamical systems subject to uncertainty. Furthermore, this work intends to specifically address time-var...