Rok Roskar

Last Name

Roskar

First Name

Rok

ORCID

0000-0001-9962-0389

Organisational unit

02286 - Swiss Data Science Center (SDSC) / Swiss Data Science Center (SDSC)

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Publications 1 - 9 of 9

The effect of stellar feedback on a Milky Way-like galaxy and its gaseous halo
Marasco, Antonino; Debattista, Victor P.; Fraternali, Filippo; et al. (2015)
Monthly Notices of the Royal Astronomical Society
Large-scale network analysis reveals the sequence space architecture of antibody repertoires
Miho, Enkelejda; Roskar, Rok; Greiff, Victor; et al. (2019)
Nature Communications
The architecture of mouse and human antibody repertoires is defined by the sequence similarity networks of the clones that compose them. The major principles that define the architecture of antibody repertoires have remained largely unknown. Here, we establish a high-performance computing platform to construct large-scale networks from comprehensive human and murine antibody repertoire sequencing datasets (>100,000 unique sequences). Leveraging a network-based statistical framework, we identify three fundamental principles of antibody repertoire architecture: reproducibility, robustness and redundancy. Antibody repertoire networks are highly reproducible across individuals despite high antibody sequence dissimilarity. The architecture of antibody repertoires is robust to the removal of up to 50–90% of randomly selected clones, but fragile to the removal of public clones shared among individuals. Finally, repertoire architecture is intrinsically redundant. Our analysis provides guidelines for the large-scale network analysis of immune repertoires and may be used in the future to define disease-associated and synthetic repertoires.
Stellar and gaseous nuclear disks observed in nearby (U)LIRGs
Medling, Anne M.; Vivian, U.; Guedes, Javiera; et al. (2014)
The Astrophysical Journal
The origin of type I profiles in cluster lenticulars: an interplay between ram pressure stripping and tidally induced spiral migration
Clarke, Adam J.; Debattista, Victor P.; Roskar, Rok; et al. (2017)
Monthly Notices of the Royal Astronomical Society. Letters
Using N-body + smooth particle hydrodynamics simulations of galaxies falling into a cluster, we study the evolution of their radial density profiles. When evolved in isolation, galaxies develop a type II (down-bending) profile. In the cluster, the evolution of the profile depends on the minimum cluster-centric radius the galaxy reaches, which controls the degree of ram pressure stripping. If the galaxy falls to ∼50 per cent of the virial radius, then the profile remains type II, but if the galaxy reaches down to ∼20 per cent of the virial radius, the break weakens and the profile becomes more type I like. The velocity dispersions are only slightly increased in the cluster simulations compared with the isolated galaxy; random motion therefore cannot be responsible for redistributing material sufficiently to cause the change in the profile type. Instead, we find that the joint action of radial migration driven by tidally induced spirals and the outside-in quenching of star formation due to ram pressure stripping alters the density profile. As a result, this model predicts a flattening of the age profiles amongst cluster lenticulars with type I profiles, which can be observationally tested.
How to bend galaxy disc profiles: the role of halo spin
Herpich, J.; Stinson, G. S.; Dutton, A. A.; et al. (2015)
Monthly Notices of the Royal Astronomical Society
Renku: a platform for sustainable data science
Roskar, Rok; Ramakrishnan, Chandrasekhar; Volpi, Michele; et al. (2024)
Advances in Neural Information Processing Systems 36
Data and code working together is fundamental to machine learning (ML), but the context around datasets and interactions between datasets and code are in general captured only rudimentarily. Context such as how the dataset was prepared and created, what source data were used, what code was used in processing, how the dataset evolved, and where it has been used and reused can provide much insight, but this information is often poorly documented. That is unfortunate since it makes datasets into black-boxes with potentially hidden characteristics that have downstream consequences. We argue that making dataset preparation more accessible and dataset usage easier to record and document would have significant benefits for the ML community: it would allow for greater diversity in datasets by inviting modification to published sources, simplify use of alternative datasets and, in doing so, make results more transparent and robust, while allowing for all contributions to be adequately credited. We present a platform, Renku, designed to support and encourage such sustainable development and use of data, datasets, and code, and we demonstrate its benefits through a few illustrative projects which span the spectrum from dataset creation to dataset consumption and showcasing.
Internal alignments of red versus blue discs in dark matter haloes
Debattista, Victor P.; Van Den Bosch, Frank C.; Roskar, Rok; et al. (2015)
Monthly Notices of the Royal Astronomical Society
Imprints of radial migration on Milky Way's metallicity distribution functions
Loebman, Sarah R.; Debattista, Victor P.; Nidever, David L.; et al. (2016)
The Astrophysical Journal Letters
Orbital decay of supermassive black hole binaries in clumpy multiphase merger remnants
Roskar, Rok; Fiacconi, Davide; Mayer, Lucio; et al. (2015)
Monthly Notices of the Royal Astronomical Society

Publications 1 - 9 of 9

Rok Roskar

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