Zobrazeno 1 - 10
of 22 230
pro vyhledávání: '"A. Salimi"'
Publikováno v:
Advances in Mathematical Physics, Vol 2023 (2023)
In this study, the Fredholm hypersingular integral equation of the first kind with a singular right-hand function on the interval −1,1 is solved. The discontinuous solution on the domain −1,1 is approximated by a piecewise polynomial, and a collo
Externí odkaz:
https://doaj.org/article/4074cdb538b34127923f2aa4b4bd778d
Autor:
Salimi, Salma, Salimpour, Sahar, Queralta, Jorge Peña, Bessa, Wallace Moreira, Westerlund, Tomi
Human pose estimation involves detecting and tracking the positions of various body parts using input data from sources such as images, videos, or motion and inertial sensors. This paper presents a novel approach to human pose estimation using machin
Externí odkaz:
http://arxiv.org/abs/2408.15717
In this short article, using a left-invariant Randers metric $F$, we define a new left-invariant Randers metric $\tilde{F}$. We show that $F$ is of Berwald (Douglas) type if and only if $\tilde{F}$ is of Berwald (Douglas) type. In the case of Berwald
Externí odkaz:
http://arxiv.org/abs/2407.21044
Publikováno v:
Bihdāsht-i Mavādd-i Ghaz̠āyī, Vol 9, Iss 3 (35) پاییز, Pp 1-12 (2019)
Camel milk is considered as the most important sources of nutrition in terms of protein, vitamins and minerals, which is important for health. The aim of this study was to evaluate the chemical properties, microbial profile and the presence of lactic
Externí odkaz:
https://doaj.org/article/7e1bd1a9bdb346d1a4d7cc1a2ccb38c1
We introduce an efficient method for learning linear models from uncertain data, where uncertainty is represented as a set of possible variations in the data, leading to predictive multiplicity. Our approach leverages abstract interpretation and zono
Externí odkaz:
http://arxiv.org/abs/2405.18549
The evolution of Explainable Artificial Intelligence (XAI) has emphasised the significance of meeting diverse user needs. The approaches to identifying and addressing these needs must also advance, recognising that explanation experiences are subject
Externí odkaz:
http://arxiv.org/abs/2405.10446
Conditional independence (CI) constraints are critical for defining and evaluating fairness in machine learning, as well as for learning unconfounded or causal representations. Traditional methods for ensuring fairness either blindly learn invariant
Externí odkaz:
http://arxiv.org/abs/2404.13798
Autor:
Rashnu, Alireza, Salimi-Badr, Armin
Efficient early diagnosis is paramount in addressing the complexities of Parkinson's disease because timely intervention can substantially mitigate symptom progression and improve patient outcomes. In this paper, we present a pioneering deep learning
Externí odkaz:
http://arxiv.org/abs/2404.15335
We address the challenge of inferring causal effects in social network data. This results in challenges due to interference -- where a unit's outcome is affected by neighbors' treatments -- and network-induced confounding factors. While there is exte
Externí odkaz:
http://arxiv.org/abs/2403.11332
Autor:
Pirhadi, Alireza, Moslemi, Mohammad Hossein, Cloninger, Alexander, Milani, Mostafa, Salimi, Babak
Ensuring Conditional Independence (CI) constraints is pivotal for the development of fair and trustworthy machine learning models. In this paper, we introduce \sys, a framework that harnesses optimal transport theory for data repair under CI constrai
Externí odkaz:
http://arxiv.org/abs/2403.02372