Recovering Causal Structures from Low-Order Conditional Independencies

One of the common obstacles for learning causal models from data is that high-order conditional independence (CI) relationships between random variables are difficult to estimate. Since CI tests with conditioning sets of low order can be performed accurately even for a small number of observations, a reasonable approach to determine casual structures is to base merely on the low-order CIs. Recent research has confirmed that, e.g. in the case of sparse true causal models, structures learned even from zero- and first-order conditional independencies yield good approximations of the models. However, a challenging task here is to provide methods that faithfully explain a given set of low-order CIs. In this paper, we propose an algorithm which, for a given set of conditional independencies of order less or equal to $k$, where $k$ is a small fixed number, computes a faithful graphical representation of the given set. Our results complete and generalize the previous work on learning from pairwise marginal independencies. Moreover, they enable to improve upon the 0-1 graph model which, e.g. is heavily used in the estimation of genome networks

New lower and upper bounds for the competitive ratio of transmission protocols

Transmission protocols like TCP are usually divided into a time scheduling and a data selection policy. We consider on-line algorithms of data selection policies for any time scheduling policy and any routing behavior in a network. For the model introduced by Adler et al. [Proc. 5th Israel Symp. on Theory of Computing Systems, 1997, pp. 64–72], we improve both the lower and the upper bound on the competitive ratio making them asymptotically tight. Furthermore, we present a lower bound that depends on the size of the buffers that are available both to the sender and to the receiver. We obtain a constant lower bound for the competitive ratio for constant buffer size

Generalized Shortest Path Kernel on Graphs

We consider the problem of classifying graphs using graph kernels. We define a new graph kernel, called the generalized shortest path kernel, based on the number and length of shortest paths between nodes. For our example classification problem, we consider the task of classifying random graphs from two well-known families, by the number of clusters they contain. We verify empirically that the generalized shortest path kernel outperforms the original shortest path kernel on a number of datasets. We give a theoretical analysis for explaining our experimental results. In particular, we estimate distributions of the expected feature vectors for the shortest path kernel and the generalized shortest path kernel, and we show some evidence explaining why our graph kernel outperforms the shortest path kernel for our graph classification problem.Comment: Short version presented at Discovery Science 2015 in Banf

Algorithms for finding the maximal length quasiplateau interval of the experimental curve

There exist some problems related to the determination of the quasiplateau region inexperimental sciences. The paper describes two approaches to this matter which lead to twoalgorithms for finding the maximal length quasiplateau interval for discrete data. Numerical testsare presented

Dynamical system analysis of unstable flow phenomena in centrifugal blower

Methods of dynamical system analysis were employed to analyze unsteady phenomena in a centrifugal blower. Pressure signals gathered at different control points were decomposed into their Principal Components (PCs) by means of Singular Spectrum Analysis (SSA). Certain number of PCs was considered in the analysis based on their statistical correlation. Projection of the original signal onto its PCs allowed to draw the phase trajectory that clearly separated non-stable blower working conditions from its regular operation

Dlaczego w polskiej armii nie ma kobiet-generałów?

Publikacja recenzowana / Peer-reviewed publicationAutorka w artykule przeanalizowała sytuację w polskiej armii, by znaleźć odpowiedź na pytanie o powody braku w Wojsku Polskim kobiety w stopniu generalskim. W tekście została opisana geneza udziału kobiet w armii oraz pokazane determinanty służby kobiet- żołnierzy. Poruszona została w artykule także miedzy innymi problematyka występowania szklanego sufitu w polskiej armii. W drugiej części tekstu autorka przedstawia swoje tezy dotyczące przyczyn braku kobiety-żołnierza w stopniu generała, wskazując na uwarunkowania historyczne, polityczne, kulturowe oraz na politykę personalną.The author in the paper has analyzed the situation in the Polish army to answer the question: Why are there no female generals in The Polish Army? The paper presented the history of women's participation in the Polish army, since the Second World War to the present. The author presented also the statistics showing the determinants of female soldiers work. In this article autor refers to the occurrence of glass ceiling in The Polish Army, between the rank of the colonel and general. At the end the autor presents her thesis about the reasons of the lack of women soldiers in the rank of general, pointing to historical events, personnel policy and blocking access to the highest military positions by men.Анализируя ситуацию в Вооружённых силах Польши, автор ищет ответ на вопрос: почему в Польской Армии нет женщин в звании генерала? В статье представлена история присутствия женщин в Польской Армии от Второй мировой войны до сегодняшнего дня. В частности затронута проблема так называемого «стеклянного потолка» в Вооружённых силах Польши. Представлены некоторые статистические данные касающиеся прохождения воинской службы женщинами. Подводя итоги, автор, указывая на историческую обусловленность кадровой политики блокирования доступа женщинам к высоким воинским должностям и званиям, дает ответ на поставленный в заглавии статьи вопрос

Czeczeński ślad na Ukrainie

Publikacja recenzowana / Peer-reviewed publicationW czasie konfliktu ukraińskiego wielokrotnie pojawiały się prasowe doniesienia o ekstremistach, znajdujących się zarówno po stronie prozachodnich Ukraińców, jak i prorosyjskich separatystów. Autorka niniejszego artykułu skupiła się jedynie na tzw. „czeczeńskim śladzie na Ukrainie". Analizując informacje medialne i wzbogacając je ciekawostkami związanymi z kulturą i historią czeczeńską, starała się odnaleźć odpowiedź na pytanie o motywy udziału kaukaskich najemników w konflikcie ukraińskim.During the Ukrainian conflict the information about extremists fighting on the side of pro-Western Ukrainians, as well as the ones operating on the side of pro-Russian separatists, appeared several times in the press. The author of this article has focused only on the 'Chechen trace in Ukraine'. Analysing the press releases and enriching them with tidbits associated with the Chechen culture and history, she was trying to find an answer to the question of motives for the Caucasian mercenaries' participation in the Ukrainian conflict.Во время украинского конфликта неоднократно появлялись газетные отчеты об экстремистах, действующих как на стороне прозападных украинцев, так и на стороне пророссийских сепаратистов. Автор статьи сосредотачивает внимание только на так называемом «чеченском следе в Украине». Анализируя донесения СМИ и сопоставляя их с интересными фактами, связанными с чеченской культурой и историей, исследовательница пытается дать ответ на вопрос о мотивах участия кавказских наемников в украинском конфликте

Separators and Adjustment Sets in Causal Graphs: Complete Criteria and an Algorithmic Framework

Principled reasoning about the identifiability of causal effects from non-experimental data is an important application of graphical causal models. This paper focuses on effects that are identifiable by covariate adjustment, a commonly used estimation approach. We present an algorithmic framework for efficiently testing, constructing, and enumerating $m$-separators in ancestral graphs (AGs), a class of graphical causal models that can represent uncertainty about the presence of latent confounders. Furthermore, we prove a reduction from causal effect identification by covariate adjustment to $m$-separation in a subgraph for directed acyclic graphs (DAGs) and maximal ancestral graphs (MAGs). Jointly, these results yield constructive criteria that characterize all adjustment sets as well as all minimal and minimum adjustment sets for identification of a desired causal effect with multivariate exposures and outcomes in the presence of latent confounding. Our results extend several existing solutions for special cases of these problems. Our efficient algorithms allowed us to empirically quantify the identifiability gap between covariate adjustment and the do-calculus in random DAGs and MAGs, covering a wide range of scenarios. Implementations of our algorithms are provided in the R package dagitty.Comment: 52 pages, 20 figures, 12 table

ChIP — Does it work correctly? The optimization steps of chromatin immunoprecipitation

The proteins interaction with DNA is one of the key regulatory elements of many biological processes; including gene transcription, epigenetic modification or cell differentiation. Immunoprecipitation of chromatin; ChIP; is a method used to assess the interaction of the protein with a DNA sequence, and determines the localization of specific locus in the genome. The main steps of this method are fixation, sonication, immunoprecipitation and analysis of DNA. Although the immunoprecipitation assay is a multipurpose tool applied in biochemistry and biotechnology, it requires optimization. This paper describes several critical parameters that should be taken into account when immunoprecipitation assay is applied