Ponedeljkov seminar računalništva in informatike - Arhiv
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ponedeljek, 27. maj 2024 Nedim ŠIŠIĆ: Comparison of brain MRI segmentation pipelines
V ponedeljek, 27. maja 2024, bo ob 16.00 uri izvedeno
predavanje v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE
Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM.
ČAS/PROSTOR: 27. maj 2024 ob 16.00 v FAMNIT-VP2.
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PREDAVATELJ: Nedim ŠIŠIĆ
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Nedim Šišić is a second-year PhD student at UP FAMNIT under the mentorship of Assist. Prof. Peter Rogelj. His PhD research concerns neural networks in medical imaging, specifically in segmentation of brain MRI.
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NASLOV: Comparison of brain MRI segmentation pipelines
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POVZETEK:
Magnetic resonance imaging (MRI) is frequently used in clinical trials to estimate longitudinal brain volume changes in order to asses treatment effect in neurological disorders such as Alzheimer's Disease and Multiple Sclerosis. A standard pre-processing step in volume estimation involves segmentation of MRI into distinct brain structures, and a large number of automated brain segmentation tools/pipelines have thus been developed. The seminar will present a comparison of six segmentation pipelines frequently used in clinical trials: FreeSurfer, SamSeg, FastSurfer, SIENAX, SPM12, and CAT12 on healthy controls and patients with Alzheimer’s disease and multiple sclerosis. The project was conducted as part of an internship at F. Hoffmann-La Roche, a pharmaceutical company currently developing drugs for various neurological diseases.
Seminar bo potekal v angleškem jeziku v FAMNIT-VP2 s pričetkom ob 16:00 uri.
Vabljeni.
petek, 17. maj 2024 Zorica STANIMIROVIĆ: A GVNS-based solution approach to the Uncapacitated Single Allocation p-hub Maximal Covering Problem
V ponedeljek, 20. maja 2024, bo ob 16.00 uri izvedeno
predavanje v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE
Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM.
ČAS/PROSTOR: 20. maj 2024 ob 16.00 v FAMNIT-VP2.
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PREDAVATELJICA: Zorica STANIMIROVIĆ
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Zorica Stanimirović PhD, is a Full Professor at the Department for Numerical Mathematics and Optimization, Faculty of Mathematics, University of Belgrade and Coordinator for International Cooperation of the same institution. She graduated from the Faculty of Mathematics in 2000, received a master's degree in 2004, and PhD in 2007 at the same institution. She has been the local coordinator for several international projects and a member of program or organizational boards of several national and international conferences. From 2010 to 2015, she was Vice-Dean for Science and Research at the Faculty of Mathematics. Her research areas include Mathematical Modeling, Combinatorial Optimization, Metaheuristics, and Hybrid Optimization Methods. Up to now, she has published over 120 publications that have been cited 1182 times, her h-index is 19 and her i10 index is 32. More information and a list of publications are available at http://www.matf.bg.ac.rs/p/zoricast/pocetna/.
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NASLOV: A GVNS-based solution approach to the Uncapacitated Single Allocation p-hub Maximal Covering Problem
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POVZETEK:
Hub covering problems represent extensions of classical covering location problems and they are widely studied in the literature, due to their theoretical and practical importance in location science. This study considers the Uncapacitated Single Allocation p-hub Maximal Covering Problem (USApHMCP) with binary coverage criterion. The USApHMCP considers a complete symmetric graph G=(N,E), where N represents a set of nodes, while E denotes a set of edges. Transportation costs per unit of flow and the flow demand for each origin-destination (O-D) pair i-j are given (i,j ϵ N). The goal of USApHMCP is to choose locations of p hubs from the set H ⊆ N, and to allocate non-hub nodes to hubs, such that the total covered flow between O-D pairs is maximized. The flow between an O-D pair is considered "covered" if the transportation costs are within the given maximum service distance (coverage radius). Each non-hub node is assigned to exactly one hub and the incoming and outgoing flows are sent only through that hub (single allocation scheme). A mixed integer formulation of the USApHMCP is presented and used within the framework of CPLEX solver on hub instances from the literature. As USApHMCP belongs to the class of NP-hard optimization problems, a solution approach based on General Variable Neighborhood Search (GVNS) heuristic is developed to tackle instances of larger problem dimensions. Two variants of GVNS heuristics are proposed, which use different procedures in the solution improvement phase: Sequential Variable Neighborhood Descent and Nested Variable Neighborhood Descent. The impact of these two procedures on overall GVNS performance is investigated through extensive computational experiments on standard hub instances from the literature. The obtained results indicate the efficiency of both GVNS variants, however, the one with Nested Variable Neighborhood Descent procedure was more successful with respect to both solution quality and running times. The results of GVNS on large-scale problem instances are also presented, showing the potential of GVNS when solving USApHMCP on realistic-size hub networks.
Seminar bo potekal v angleškem jeziku v FAMNIT-VP2 s pričetkom ob 16:00 uri.
Vabljeni.
ponedeljek, 6. maj 2024 Domen VAKE: URŠKA - Univerzitetne rešitve: Študentski Komunikacijski Agent
V ponedeljek, 6. maja 2024, bo ob 16.00 uri izvedeno
predavanje v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE
Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM.
ČAS/PROSTOR: 6. maj 2024 ob 16.00 v FAMNIT-VP2.
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PREDAVATELJ: Domen VAKE
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Domen Vake is a third-year PhD student at UP FAMNIT under the mentorship of Assoc. Prof. Branko Kavšek and Assoc. Prof. Jernej Vičič. His research interests and projects mostly concern machine learning, more specifically natural language processing with large language models.
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NASLOV: URŠKA - Univerzitetne rešitve: Študentski Komunikacijski Agent
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POVZETEK:
This presentation will provide an overview of large language models (LLMs), focusing on their architecture, capabilities, and the underlying technologies that power them. We will then explore a practical application of these models through a case study on URŠKA, a Retrieval-Augmented Generation (RAG) based communication agent. URŠKA is designed to assist university students by answering frequently asked questions about deadlines, academic policies, and more. The talk will highlight the implementation challenges, the integration of RAG with URŠKA, and its plans.
Seminar bo potekal v angleškem jeziku v FAMNIT-VP2 s pričetkom ob 16:00 uri.
Vabljeni.
