Zobrazeno 1 - 10
of 378
pro vyhledávání: '"Stipanović, P."'
We explore ultradilute Bose-Bose liquid droplets squeezed by an external harmonic potential in one spatial direction. Our theoretical study is based on a functional that is built using quantum Monte Carlo results of the bulk phase and incorporates fi
Externí odkaz:
http://arxiv.org/abs/2311.05244
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 24, Iss , Pp 126-135 (2024)
Mirror therapy is a standard technique of rehabilitation for recovering motor and vision abilities of stroke patients, especially in the case of asymmetric limb function. To enhance traditional mirror therapy, robotic mirror therapy (RMT) has been pr
Externí odkaz:
https://doaj.org/article/7b492999d62a4ab296ea94d9cb1aba04
Publikováno v:
Scientific Reports 12, 10368 (2022)
A universal relationship between scaled size and scaled energy is explored in five-body self-bound quantum systems. The ground-state binding energy and structure properties are obtained by means of the diffusion Monte Carlo method. We use pure estima
Externí odkaz:
http://arxiv.org/abs/2206.11946
Autor:
Danijel Bursać, Jasminka Stipanović, Jasenka Zmijanac Partl, Dejana Lučić, Daria Hadžić, Diana Culej Bošnjak, Željko Duić
Publikováno v:
European Journal of Obstetrics & Gynecology and Reproductive Biology: X, Vol 22, Iss , Pp 100306- (2024)
Vein of Galen aneurysmal malformation (VGAM) is a rare vascular anomaly originating during embryonic development, specifically between the 6th and 11th weeks of gestation. This malformation results from abnormal arteriovenous connections between prim
Externí odkaz:
https://doaj.org/article/94f96aac5dfe4e4abfd28340f978daf6
This paper presents a family of algorithms for decentralized convex composite problems. We consider the setting of a network of agents that cooperatively minimize a global objective function composed of a sum of local functions plus a regularizer. Th
Externí odkaz:
http://arxiv.org/abs/2204.06380
Large-scale optimization problems require algorithms both effective and efficient. One such popular and proven algorithm is Stochastic Gradient Descent which uses first-order gradient information to solve these problems. This paper studies almost-sur
Externí odkaz:
http://arxiv.org/abs/2110.12634
In this work, multiplicative stochasticity is applied to the learning rate of stochastic optimization algorithms, giving rise to stochastic learning-rate schemes. In-expectation theoretical convergence results of Stochastic Gradient Descent equipped
Externí odkaz:
http://arxiv.org/abs/2110.10710
In a multi-agent network, we consider the problem of minimizing an objective function that is expressed as the sum of private convex and smooth functions, and a (possibly) non-differentiable convex regularizer. We propose a novel distributed second-o
Externí odkaz:
http://arxiv.org/abs/2109.14243
We consider a class of distributed optimization problem where the objective function consists of a sum of strongly convex and smooth functions and a (possibly nonsmooth) convex regularizer. A multi-agent network is assumed, where each agent holds a p
Externí odkaz:
http://arxiv.org/abs/2109.14804
Biological evidence shows that animals are capable of evading eminent collision without using depth information, relying solely on looming stimuli. In robotics, collision avoidance among uncooperative vehicles requires measurement of relative distanc
Externí odkaz:
http://arxiv.org/abs/2103.12239