1,067 research outputs found

    Towards a robust algorithm to determine topological domains from colocalization data

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    One of the most important tasks in understanding the complex spatial organization of the genome consists in extracting information about this spatial organization, the function and structure of chromatin topological domains from existing experimental data, in particular, from genome colocalization (Hi-C) matrices. Here we present an algorithm allowing to reveal the underlying hierarchical domain structure of a polymer conformation from analyzing the modularity of colocalization matrices. We also test this algorithm on several model polymer structures: equilibrium globules, random fractal globules and regular fractal (Peano) conformations. We define what we call a spectrum of cluster borders, and show that these spectra behave strikingly differently for equilibrium and fractal conformations, allowing us to suggest an additional criterion to identify fractal polymer conformations

    Relative blocking in posets

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    Poset-theoretic generalizations of set-theoretic committee constructions are presented. The structure of the corresponding subposets is described. Sequences of irreducible fractions associated to the principal order ideals of finite bounded posets are considered and those related to the Boolean lattices are explored; it is shown that such sequences inherit all the familiar properties of the Farey sequences.Comment: 29 pages. Corrected version of original publication which is available at http://www.springerlink.com, see Corrigendu

    Some New Bounds For Cover-Free Families Through Biclique Cover

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    An (r,w;d)(r,w;d) cover-free family (CFF)(CFF) is a family of subsets of a finite set such that the intersection of any rr members of the family contains at least dd elements that are not in the union of any other ww members. The minimum number of elements for which there exists an (r,w;d)CFF(r,w;d)-CFF with tt blocks is denoted by N((r,w;d),t)N((r,w;d),t). In this paper, we show that the value of N((r,w;d),t)N((r,w;d),t) is equal to the dd-biclique covering number of the bipartite graph It(r,w)I_t(r,w) whose vertices are all ww- and rr-subsets of a tt-element set, where a ww-subset is adjacent to an rr-subset if their intersection is empty. Next, we introduce some new bounds for N((r,w;d),t)N((r,w;d),t). For instance, we show that for rwr\geq w and r2r\geq 2 N((r,w;1),t)c(r+ww+1)+(r+w1w+1)+3(r+w4w2)logrlog(tw+1), N((r,w;1),t) \geq c{{r+w\choose w+1}+{r+w-1 \choose w+1}+ 3 {r+w-4 \choose w-2} \over \log r} \log (t-w+1), where cc is a constant satisfies the well-known bound N((r,1;1),t)cr2logrlogtN((r,1;1),t)\geq c\frac{r^2}{\log r}\log t. Also, we determine the exact value of N((r,w;d),t)N((r,w;d),t) for some values of dd. Finally, we show that N((1,1;d),4d1)=4d1N((1,1;d),4d-1)=4d-1 whenever there exists a Hadamard matrix of order 4d

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    A max-flow approach to improved lower bounds for quadratic unconstrained binary optimization (QUBO)

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    AbstractThe “roof dual” of a QUBO (Quadratic Unconstrained Binary Optimization) problem has been introduced in [P.L. Hammer, P. Hansen, B. Simeone, Roof duality, complementation and persistency in quadratic 0–1 optimization, Mathematical Programming 28 (1984) 121–155]; it provides a bound to the optimum value, along with a polynomial test of the sharpness of this bound, and (due to a “persistency” result) it also determines the values of some of the variables at the optimum. In this paper we provide a graph-theoretic approach to provide bounds, which includes as a special case the roof dual bound, and show that these bounds can be computed in O(n3) time by using network flow techniques. We also obtain a decomposition theorem for quadratic pseudo-Boolean functions, improving the persistency result of [P.L. Hammer, P. Hansen, B. Simeone, Roof duality, complementation and persistency in quadratic 0–1 optimization, Mathematical Programming 28 (1984) 121–155]. Finally, we show that the proposed bounds (including roof duality) can be applied in an iterated way to obtain significantly better bounds. Computational experiments on problems up to thousands of variables are presented
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