Results 21 to 30 of about 1,004,442 (74)

The Derivation of Deser's Formula and Electromagnetic Corrections to the Pionium Life Time [PDF]

open access: yesarXiv, 1998
We give the modern derivation of Deser's formula using analytic continuation of the scattering amplitude as a function of momentum. The electromagnetic corrections to the pionium lifetime are given as evaluated in a potential model.
arxiv  

A Joint Motion Model for Human-Like Robot-Human Handover [PDF]

open access: yesarXiv, 2018
In future, robots will be present in everyday life. The development of these supporting robots is a challenge. A fundamental task for assistance robots is to pick up and hand over objects to humans. By interacting with users, soft factors such as predictability, safety and reliability become important factors for development.
arxiv  

Bias-Reduction in Variational Regularization [PDF]

open access: yesarXiv, 2016
The aim of this paper is to introduce and study a two-step debiasing method for variational regularization. After solving the standard variational problem, the key idea is to add a consecutive debiasing step minimizing the data fidelity on an appropriate set, the so-called model manifold.
arxiv  

Maximum likelihood estimation in the sparse Rasch model [PDF]

open access: yesarXiv
The Rasch model has been widely used to analyse item response data in psychometrics and educational assessments. When the number of individuals and items are large, it may be impractical to provide all possible responses. It is desirable to study sparse item response experiments.
arxiv  

Random pairing MLE for estimation of item parameters in Rasch model [PDF]

open access: yesarXiv
The Rasch model, a classical model in the item response theory, is widely used in psychometrics to model the relationship between individuals' latent traits and their binary responses on assessments or questionnaires. In this paper, we introduce a new likelihood-based estimator -- random pairing maximum likelihood estimator ($\mathsf{RP\text{-}MLE ...
arxiv  

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