arXiv subjects

László Kovács

Publications and source records attributed to László Kovács.

8 recordsLinked to original sources

arXiv2026

Particle species dependence of femtoscopic source parameters in high-energy nuclear collisions

High-energy nuclear physics explores the properties of strongly interacting matter created in relativistic collisions of nuclei. Femtoscopy, a subfield of high-energy physics, utilizes quantum-statistical correlations of particles to characterize the space-time geometry of the particle-emitting source. Recent measurements and phenomenological investigations indicated that the shape of the source for identical pions can be well-described by Lévy-stable distributions. The significant power-law tail of the pion source observed both in experiment and in simulations has been shown to originate from the process of Lévy walk during the hadronic scattering phase of the collisions. To better understand the physical processes behind the formation of such power laws, an important next step is to investigate particle species dependence, especially the source shape of identical kaon and proton pairs. As a direct continuation of our previous studies, in this Letter, we present a detailed three-dimensional investigation of the two-particle source shape in simulations of Au+Au collisions at 200 GeV per nucleon pair collision energy using the EPOS3 model. We show the dependence of the extracted femtoscopic source parameters on particle species, centrality and average transverse mass. We find that the scale parameters do not show a clear transverse mass scaling between particle species, as there are systematic differences in the overlapping regions. The power-law exponents of pion and kaon pairs are compatible, while for protons it is higher, closer to the Gaussian limit. When new experimental measurements of kaon and proton correlations become available, these results will provide the basis of a data-model comparison.

hep-ph↗

arXiv2024

Neural Networks for Vehicle Routing Problem

The Vehicle Routing Problem is about optimizing the routes of vehicles to meet the needs of customers at specific locations. The route graph consists of depots on several levels and customer positions. Several optimization methods have been developed over the years, most of which are based on some type of classic heuristic: genetic algorithm, simulated annealing, tabu search, ant colony optimization, firefly algorithm. Recent developments in machine learning provide a new toolset, the rich family of neural networks, for tackling complex problems. The main area of application of neural networks is the area of classification and regression. Route optimization can be viewed as a new challenge for neural networks. The article first presents an analysis of the applicability of neural network tools, then a novel graphical neural network model is presented in detail. The efficiency analysis based on test experiments shows the applicability of the proposed NN architecture.

cs.AI↗

arXiv2024

Navigating Process Mining: A Case study using pm4py

Process-mining techniques have emerged as powerful tools for analyzing event data to gain insights into business processes. In this paper, we present a comprehensive analysis of road traffic fine management processes using the pm4py library in Python. We start by importing an event log dataset and explore its characteristics, including the distribution of activities and process variants. Through filtering and statistical analysis, we uncover key patterns and variations in the process executions. Subsequently, we apply various process-mining algorithms, including the Alpha Miner, Inductive Miner, and Heuristic Miner, to discover process models from the event log data. We visualize the discovered models to understand the workflow structures and dependencies within the process. Additionally, we discuss the strengths and limitations of each mining approach in capturing the underlying process dynamics. Our findings shed light on the efficiency and effectiveness of road traffic fine management processes, providing valuable insights for process optimization and decision-making. This study demonstrates the utility of pm4py in facilitating process mining tasks and its potential for analyzing real-world business processes.

cs.AI↗

arXiv2024

Graphs Unveiled: Graph Neural Networks and Graph Generation

One of the hot topics in machine learning is the field of GNN. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. This paper represents a survey, providing a comprehensive overview of Graph Neural Networks (GNNs). We discuss the applications of graph neural networks across various domains. Finally, we present an advanced field in GNNs: graph generation.

cs.LG↗

arXiv2023

Charged Kaon Femtoscopy with Lévy Sources in $\sqrt{s_{\text{NN}}}$ = 200 GeV Au+Au Collisions at PHENIX

The PHENIX experiment measured Bose-Einstein quantum-statistical correlations of charged kaons in Au+Au collisions at $\sqrt{s_{\text{NN}}}$ = 200 GeV. The correlation functions are parametrized assuming that the source emitting the particles has a Lévy shape, characterized by the Lévy exponent $α$ and the Lévy scale $R$. By introducing the intercept parameter $λ$, we account for the core-halo fraction. The parameters are investigated as a function of transverse mass. The comparison of the parameters measured for kaon-kaon with those measured from pion-pion correlation may clarify the connection of Lévy parameters to physical processes.

nucl-ex↗

arXiv2020

Notes on the lattice of fuzzy rough sets with crisp reference sets

Since the theory of rough sets was introduced by Zdzislaw Pawlak, several approaches have been proposed to combine rough set theory with fuzzy set theory. In this paper, we examine one of these approaches, namely fuzzy rough sets with crisp reference sets, from a lattice-theoretic point of view. We connect the lower and upper approximations of a fuzzy relation $R$ to the approximations of the core and support of $R$. We also show that the lattice of fuzzy rough sets corresponding to a fuzzy equivalence relation $R$ and the crisp subsets of its universe is isomorphic to the lattice of rough sets for the (crisp) equivalence relation $E$, where $E$ is the core of $R$. We establish a connection between the exact (fuzzy) sets of $R$ and the exact (crisp) sets of the support of $R$.

math.GM↗

arXiv2019

Defining rough sets using tolerances compatible with an equivalence

We consider tolerances $T$ compatible with an equivalence $E$ on $U$, meaning that the relational product $E \circ T$ is included in $T$. We present the essential properties of $E$-compatible tolerances and study rough approximations defined by such $E$ and $T$. We consider rough set pairs $(X_E,X^T)$, where the lower approximation $X_E$ is defined as is customary in rough set theory, but $X^T$ allows more elements to be possibly in $X$ than $X^E$. Motivating examples of $E$-compatible tolerances are given, and the essential lattice-theoretical properties of the ordered set of rough sets $\{ (X_E,X^T) \mid X \subseteq U\}$ are established.

math.CO↗

arXiv2009

Service-oriented Context-aware Framework

Location- and context-aware services are emerging technologies in mobile and desktop environments, however, most of them are difficult to use and do not seem to be beneficial enough. Our research focuses on designing and creating a service-oriented framework that helps location- and context-aware, client-service type application development and use. Location information is combined with other contexts such as the users' history, preferences and disabilities. The framework also handles the spatial model of the environment (e.g. map of a room or a building) as a context. The framework is built on a semantic backend where the ontologies are represented using the OWL description language. The use of ontologies enables the framework to run inference tasks and to easily adapt to new context types. The framework contains a compatibility layer for positioning devices, which hides the technical differences of positioning technologies and enables the combination of location data of various sources.

cs.SE↗