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.

Yield estimation model for VLSI artwork evaluation

Abstract: In the letter a model which describes limitations of a manufacturing yield in terms of an IC artwork and a lithography characterisation is proposed. Density and distribution of diameters of defects present in the mask, as well as line-width fluctuations, are taken into account.

Hiding individuals and communities in a social network

Abstract: The Internet and social media have fuelled enormous interest in social network analysis. New tools continue to be developed and used to analyse our personal connections, with particular emphasis on detecting communities or identifying key individuals in a social network. This raises privacy concerns that are likely to exacerbate in the future. With this in mind, we ask the question ‘Can individuals or groups actively manage their connections to evade social network analysis tools?’ By addressing this question, the general public may better protect their privacy, oppressed activist groups may better conceal their existence and security agencies may better understand how terrorists escape detection. We first study how an individual can evade ‘node centrality’ analysis while minimizing the negative impact that this may have on his or her influence. We prove that an optimal solution to this problem is difficult to compute. Despite this hardness, we demonstrate how even a simple heuristic, whereby attention is restricted to the individual’s immediate neighbourhood, can be surprisingly effective in practice; for example, it could easily disguise Mohamed Atta’s leading position within the World Trade Center terrorist network. We also study how a community can increase the likelihood of being overlooked by community-detection algorithms. We propose a measure of concealment—expressing how well a community is hidden—and use it to demonstrate the effectiveness of a simple heuristic, whereby members of the community either ‘unfriend’ certain other members or ‘befriend’ some non-members in a coordinated effort to camouflage their community. Waniek and colleagues show that individuals and communities can disguise themselves from detection online by standard social network analysis tools through simple changes to their social network connections.

Between Sobolev and Poincaré

Abstract: Let a a ∈ [0, 1] and r ∈ [1, 2] satisfy relation r = 2/(2 − a). Let μ(dx)=c r n exp(-(|x1| r +|x2| r +...+|x n | r ))dx1dx2...dx n be a probability measure on the Euclidean space (R n , ‖ · ‖). We prove that there exists a universal constant C such that for any smooth real function f on R n and any p ∈ [1,2) $$E_\mu f^2 - (E_\mu \left| f \right|^p )^{2/p} \leqslant C(2 - p)^a E_\mu \left\| { abla f} \right\|^2$$ . We prove also that if for some probabilistic measure μ on R n the above inequality is satisfied for any p ∈ [1, 2) and any smooth f then for any h : R n → R such that |h(x)-h(y)|≤∥x-y∥ there is E μ |h| < ∞ and $$\mu (h - E_\mu h > \sqrt C \cdot t) \leqslant e^{ - Kt^r }$$ for t > 1, where K > 0 is some universal constant.

NPDock: a web server for protein–nucleic acid docking

Abstract: Protein-RNA and protein-DNA interactions play fundamental roles in many biological processes. A detailed understanding of these interactions requires knowledge about protein-nucleic acid complex structures. Because the experimental determination of these complexes is time-consuming and perhaps futile in some instances, we have focused on computational docking methods starting from the separate structures. Docking methods are widely employed to study protein-protein interactions; however, only a few methods have been made available to model protein-nucleic acid complexes. Here, we describe NPDock (Nucleic acid-Protein Docking); a novel web server for predicting complexes of protein-nucleic acid structures which implements a computational workflow that includes docking, scoring of poses, clustering of the best-scored models and refinement of the most promising solutions. The NPDock server provides a user-friendly interface and 3D visualization of the results. The smallest set of input data consists of a protein structure and a DNA or RNA structure in PDB format. Advanced options are available to control specific details of the docking process and obtain intermediate results. The web server is available at http://genesilico.pl/NPDock.