Zobrazeno 1 - 10
of 3 839
pro vyhledávání: '"Ercan E"'
Modeling brain connectivity dynamics in functional magnetic resonance imaging via particle filtering
Autor:
Pierfrancesco Ambrosi, Mauro Costagli, Ercan E. Kuruoğlu, Laura Biagi, Guido Buonincontri, Michela Tosetti
Publikováno v:
Brain Informatics, Vol 8, Iss 1, Pp 1-12 (2021)
Abstract Interest in the studying of functional connections in the brain has grown considerably in the last decades, as many studies have pointed out that alterations in the interaction among brain areas can play a role as markers of neurological dis
Externí odkaz:
https://doaj.org/article/1b6fbd0a43b8440da2d68c83a213a2a9
In the presence of impulsive noise, and missing observations, accurate online prediction of time-varying graph signals poses a crucial challenge in numerous application domains. We propose the Adaptive Least Mean $p^{th}$ Power Graph Neural Networks
Externí odkaz:
http://arxiv.org/abs/2405.04111
Efficient and robust prediction of graph signals is challenging when the signals are under impulsive noise and have missing data. Exploiting graph signal processing (GSP) and leveraging the simplicity of the classical adaptive sign algorithm, we prop
Externí odkaz:
http://arxiv.org/abs/2405.04107
Autor:
Yan, Yi, Kuruoglu, Ercan E.
Graph Neural Networks have a limitation of solely processing features on graph nodes, neglecting data on high-dimensional structures such as edges and triangles. Simplicial Convolutional Neural Networks (SCNN) represent higher-order structures using
Externí odkaz:
http://arxiv.org/abs/2405.04098
Publikováno v:
Clinical Interventions in Aging, Vol Volume 13, Pp 1225-1230 (2018)
Ayse Ferdane Oguzoncul,1 Emel Ercan,2 Evrim Celebi3 1Department of Public Health, Faculty of Medicine, Firat University, Elazig, Turkey; 2Department of Public Health, Faculty of Medicine, Firat University Hospital, Elazig, Turkey; 3Faculty of Health
Externí odkaz:
https://doaj.org/article/bdf427a23854407bb1e422ed8b917611
Autor:
Kara, Sinancan, Plšek, Tomáš, Protušová, Klaudia, Breuer, Jean-Paul, Werner, Norbert, Mernier, François, Ercan, E. Nihal
The chemical enrichment of X-ray-emitting hot atmospheres has hitherto been primarily studied in galaxy clusters. These studies revealed relative abundances of heavy elements that are remarkably similar to Solar. Here, we present measurements of the
Externí odkaz:
http://arxiv.org/abs/2401.04709
The problem of how to take the right actions to make profits in sequential process continues to be difficult due to the quick dynamics and a significant amount of uncertainty in many application scenarios. In such complicated environments, reinforcem
Externí odkaz:
http://arxiv.org/abs/2310.00642
Autor:
Zhang, Hengxi, Shi, Zhendong, Hu, Yuanquan, Ding, Wenbo, Kuruoglu, Ercan E., Zhang, Xiao-Ping
Quantitative markets are characterized by swift dynamics and abundant uncertainties, making the pursuit of profit-driven stock trading actions inherently challenging. Within this context, reinforcement learning (RL), which operates on a reward-centri
Externí odkaz:
http://arxiv.org/abs/2303.11959
We report the observation of a possible optical counterpart to the recently discovered X-ray source NuSTAR J053449+2126.0 (J0534 in short). We observed the source location using the 1.5-m Telescope (RTT150) to search for an optical counterpart, and d
Externí odkaz:
http://arxiv.org/abs/2302.08994
The adaptive estimation of coexisting temporal vertex (node) and edge signals on graphs is a critical task when a change in edge signals influences the temporal dynamics of the vertex signals. However, the current Graph Signal Processing algorithms m
Externí odkaz:
http://arxiv.org/abs/2211.06533