University of Warsaw

About: University of Warsaw is a education organization based out in Warsaw, Poland. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 20832 authors who have published 56617 publications receiving 1185084 citations. The organization is also known as: Uniwersytet Warszawski & Warsaw University.

When Left Is Right: Party Ideology and Policy in Post-Communist Europe

Abstract: According to the classic partisan theory of spending, leftist parties are expected to increase government spending, and rightist parties are expected to decrease it. We argue that this relationship does not hold in post-Communist countries, where in the context of dual transition to democracy and to a market economy, leftist parties have had stronger incentives and better opportunities to enact tighter budgets, whereas rightist parties were compelled to spend more in order to alleviate economic hardships. We illustrate this theoretical argument with case studies from Hungary and Poland. We then test and find support for our theory by considering the influence of cabinet ideology on total, health, and education spending in thirteen post-Communist democracies from 1989 to 2004. We explore various alternative explanations and provide further narratives to support our causal argument.

Upper Limits on the Rates of Binary Neutron Star and Neutron Star-Black Hole Mergers from Advanced LIGO’s First Observing Run

Abstract: We report here the non-detection of gravitational waves from the merger of binary–neutron star systems and neutron star–black hole systems during the first observing run of the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO). In particular, we searched for gravitational-wave signals from binary–neutron star systems with component masses $\in [1,3]\,{M}_{\odot }$ and component dimensionless spins <0.05. We also searched for neutron star–black hole systems with the same neutron star parameters, black hole mass $\in [2,99]\,{M}_{\odot }$, and no restriction on the black hole spin magnitude. We assess the sensitivity of the two LIGO detectors to these systems and find that they could have detected the merger of binary–neutron star systems with component mass distributions of 1.35 ± 0.13 M ⊙ at a volume-weighted average distance of ~70 Mpc, and for neutron star–black hole systems with neutron star masses of 1.4 M ⊙ and black hole masses of at least 5 M ⊙, a volume-weighted average distance of at least ~110 Mpc. From this we constrain with 90% confidence the merger rate to be less than 12,600 Gpc−3 yr−1 for binary–neutron star systems and less than 3600 Gpc−3 yr−1 for neutron star–black hole systems. We discuss the astrophysical implications of these results, which we find to be in conflict with only the most optimistic predictions. However, we find that if no detection of neutron star–binary mergers is made in the next two Advanced LIGO and Advanced Virgo observing runs we would place significant constraints on the merger rates. Finally, assuming a rate of ${10}_{-7}^{+20}$ Gpc−3 yr−1, short gamma-ray bursts beamed toward the Earth, and assuming that all short gamma-ray bursts have binary–neutron star (neutron star–black hole) progenitors, we can use our 90% confidence rate upper limits to constrain the beaming angle of the gamma-ray burst to be greater than $2\buildrel{\circ}\over{.} {3}_{-1.1}^{+1.7}$ ($4\buildrel{\circ}\over{.} {3}_{-1.9}^{+3.1}$).

Cyberbullying among adolescent bystanders: role of the communication medium, form of violence, and empathy

Abstract: The purpose of this study was to understand how adolescents respond as bystanders of cyberbullying and to seek factors that might influence their actions. The study explored the effects of type of contact (online vs. face to face), form of violence (private vs. public), and empathy activation (affective and cognitive) on negative bystander behaviour understood as active participation in victimisation. The influence of experience of cyberbullying as perpetrator and as victim and gender on negative bystander behaviour was also controlled. Three experimental studies were conducted. The results indicate that online contact increases the likelihood of negative bystander behaviour. Private violence was less likely to elicit negative bystander action than was public violence. Previous experience of cyberperpetration was proved to increase the probability of negative bystander behaviour. Neither gender nor cybervictimisation affected the engagement in negative bystander behaviour in any of the studies. The inhibitory effect of empathy activation (both affective and cognitive) on negative bystander behaviour was demonstrated. Both types of cognitive empathy induction, emotion and behaviour focused, diminish the likelihood of negative bystander behaviour. The conclusions of the research are that negative bystander behaviour occurs more often in cyberspace than offline and that forms of intervention involving both affective and cognitive empathy may limit the negative bystander behaviour that supports cyberbullying. Copyright © 2012 John Wiley & Sons, Ltd. Language: en

Robustness of Random Forest-based gene selection methods

Abstract: Gene selection is an important part of microarray data analysis because it provides information that can lead to a better mechanistic understanding of an investigated phenomenon. At the same time, gene selection is very difficult because of the noisy nature of microarray data. As a consequence, gene selection is often performed with machine learning methods. The Random Forest method is particularly well suited for this purpose. In this work, four state-of-the-art Random Forest-based feature selection methods were compared in a gene selection context. The analysis focused on the stability of selection because, although it is necessary for determining the significance of results, it is often ignored in similar studies. The comparison of post-selection accuracy of a validation of Random Forest classifiers revealed that all investigated methods were equivalent in this context. However, the methods substantially differed with respect to the number of selected genes and the stability of selection. Of the analysed methods, the Boruta algorithm predicted the most genes as potentially important. The post-selection classifier error rate, which is a frequently used measure, was found to be a potentially deceptive measure of gene selection quality. When the number of consistently selected genes was considered, the Boruta algorithm was clearly the best. Although it was also the most computationally intensive method, the Boruta algorithm’s computational demands could be reduced to levels comparable to those of other algorithms by replacing the Random Forest importance with a comparable measure from Random Ferns (a similar but simplified classifier). Despite their design assumptions, the minimal optimal selection methods, were found to select a high fraction of false positives.

Information granules: Towards foundations of granular computing,

Abstract: We introduce basic notions related to granular computing, namely the information granule syntax and semantics as well as the inclusion and closeness (similarity) relations of granules. Different information sources (units, agents) are equipped with two kinds of operations on information granules: operations transforming tuples of information granules definable by a given agent into information granules definable by this agent and approximation operations for computing by agents approximations of information granules delivered by other agents. More complex granules are constructed by means of these operations and approximation operations from some input information granules. The construction of information granules is described by expressions called terms. We discuss a problem of synthesis of robust terms, i.e., descriptions of information granules, satisfying a given specification. This is an important problem for granular computing and its applications for spatial reasoning or knowledge discovery and data mining. © 2001 John Wiley & Sons, Inc.