Balázs Kacskovics
Supervisor: Mátyás Vasúth

Wigner RCP

Abstract: Although gravitational waves have been detected in 2016 with GW150914, but there are still many exotic cases to discover, e.g. binaries with Zoom-Whirl orbits, Super Massive Black Holes, etc. To examine such cases the Wigner RCP Gravitational group developed a software called CBwaves , that we would like to upgrade with the fourth terms of the post-Newtonian formalism and with its Hamiltonian formalism. Also we would like to enchance the runime of our code using parallelism.

Richárd Forster (2017.01.01-2018.12.31)

CERN

Publication: Parallel Louvain Community Detection Optimized for GPUs

Abstract: Network analysis became a fundamental tool in understanding the structural and functional organization of the brain. The connectome of the cerebral cortex, the most complex part of the brain, is best known at the large scale, which, as a network, represents the connectivity between the different cortical areas or sub-regions. However, in reality brain areas and sub-regions are connected via numerous parallel pathways formed by populations of neurons residing within the areas, sub-regions. This mesoscale, or so called columnar network architecture of the cerebral cortex is not known, largely because of serious experimental limitations. However, graph theory provides some tools to approach the blueprint of the mesoscale cortical network.

The goal of the Neuroscience project is the application of graph theory and network analytic tools to explore the hidden structure of the large scale cortical network relevant to the mesoscale organization. To this end we study the network of the combinations of the incoming and outgoing edges of the different areas, which represent the interactions within the cortical network forming the interaction network. Considering cortical functioning, interactions or transfer between the inputs and the outputs is the basic operation of the areas. The interaction network is the two-hop representation of the network, which can be computed by graph derivations. Importantly, the derived network preserves the topological features of the original network but increases the size by orders of magnitude. The collaboration has been focusing on the exploration of the architecture of the interaction network. We aim to understand both the global organization of the interaction network as well as the role of the particular areas in regard to the specific pathways of the cortical signal flow.

collspotting.cern.ch (Accessible only with a CERN account)

István Csabai PhD (2018.08.01-2018.12.31)

ELTE, The Department of Physics of Complex Systems

Publication: StePS: A Multi-GPU Cosmological N-body Code for Compactified Simulations

Abstract: Although the LCDM model has achieved remarkable success, however, in recent years the accuracy of the measurements has reached the limit where parameter estimates from various observations, such as the Hubble constant determined from both CMB and supernovae, are incompatible with it. Recently we have developed a model, based on N-body simulations, which is able to resolve this tension by taking better account of complex structure formation and without introducing dark energy. During the project we will develop a new type of $N$-body simulation algorithm ,,StePS" that overcomes limitations of current methods through mapping the infinite spatial extent of the universe onto a compact manifold. Specifically, we use stereographic projection onto the surface of a four dimensional sphere. The discretization of this surface leads to a systematic multi-resolution simulation with unprecedented dynamic range for given computational resources and perfect consistency with the Newtonian force law. Our approach retains the best features of multipole solvers and AMR simulations through a continuous, mathematically consistent refinement of scales toward the center of the simulations and constant angular resolution of distant fluctuations. The algorithm is ideal for GPUs, harnessing a recent cost effective numerical hardware revolution. A prototype of our algorithm has been successfully tested against GADGET, the early version of the code is open source, and available on GitHub, and the paper on the preliminary results R\'acz et al. (2018) has been submitted to MNRAS.

Sándor Zsebők (2018.03.01-2019.03.31)

Department of Systematic Zoology and Ecology

Kivonat: In the last two decades, many bioacoustic projects were conducted in the Department of Systematic Zoology and Ecology (Eötvös Loránd University). The works mainly related to the study of the structure and function of birdsong, the echolocation of bats and the development of passive acoustic methods to measure biological diversity in nature.
In our current research, we study the cultural evolution of the song of collared flycatcher based on large amount of acoustic data recorded in the nature. For that, first we have to find the songs in the recordings, then we have to segment the smallest units in the song (so called syllables), and at the end we have to cluster the syllables resulting a universal syllable library. This is a very time-consuming process that is intended to be done by computers. For that we would like to teach Deep Neural Networks. We possess hundreds of hours of recordings, thousands of songs and more than 150.000 manually segmented and clustered syllables. We would like to teach convolutional networks to detect the songs and syllables on raw recordings based on their spectrographic representations. This teaching process needs large capacity GPUs to be effectively done through parameter tuning. We would like to use the ready models for predicting songs and syllables in new recordings. Both this prediction and the clustering of syllables can be done on personal computers, so the application would only call for the training of the deep neural nets. The developing these models would facilitate all the projects that can be done only on large amount of collared flycatcher songs, and so it would open up new scientific directions to answer questions related to the evolution of animal acoustic communication.

Michał Bejger (2017.08.01-2018.12.31)

Nicolaus Copernicus Astronomical Center, Observatoire de Paris

Publication: Astronomical Distance Determination in the Space Age. Secondary distance indicators

Abstract: The aim of this project is to develop a production-ready version of the data-analysis pipeline to search for gravitational-wave signals from the network of Advanced Era LIGO and Virgo interferometric detectors. The algorithm developed by the Polish Virgo-POLGRAW group aims at finding almost-monochromatic gravitational-wave signals from rotating, non-axisymmetric, isolated neutron stars. The detection of such signals will open an exciting possibility of studying the physics of neutron-stars’ interiors, its elastic properties and structure of the crust. Joint project within the Hungarian high-performance computing experts and gravitational-wave experts will be beneficial for both sides and will initiate long-term collaboration in this field.

Hegedűs Tamás (2018.03.01-2019.03.31)

MTA-SE Molecular Biophysics Research Group, Hungarian Academy of Sciences

Publication: Quantitative comparison of ABC membrane protein type I exporter structures in a standardized way

Abstract: Cystic fibrosis is a fatal inherited monogenic recessive disease affecting several organs in our body (1:3000 prevalence in the Caucasian population). The disease is associated to the absence of functional CFTR (Cystic Fibrosis Transmembrane Conductance Regulator) chloride channel from the apical membrane of epithelial cells. Over 2,000 mutations are known covering all regions of the protein and the most frequent mutation is the deletion of F508 (ΔF508). This position is located in the N-terminal nucleotide binding domain (NBD1) resulting in the misfolding of this segment. We employ molecular dynamics simulations to detect differences between the dynamics of the wild type, ΔF508, and other mutant forms of NBD1. The simulations will be accelerated by GPU technologies that allow more and longer simulations than before, thus a more exhaustive characterization of the NBD1 conformational space. Our results will contribute to both drug development and understanding the effects of mutations at the atomic level.