Berezin Kernels and Analysis on Makarevich Spaces

Following ideas of van Dijk and Hille we study the link which exists between maximal degenerate representations and Berezin kernels. We consider the conformal group ${\rm Conf}(V)$ of a simple real Jordan algebra $V$. The maximal degenerate representations $\pi_s$ ($s\in {\mathbb C}$) we shall study are induced by a character of a maximal parabolic subgroup $\bar P$ of ${\rm Conf}(V)$. These representations $\pi_s$ can be realized on a space $I_s$ of smooth functions on $V$. There is an invariant bilinear form ${\mathfrak B}_s$ on the space $I_s$. The problem we consider is to diagonalize this bilinear form ${\mathfrak B}_s$, with respect to the action of a symmetric subgroup $G$ of the conformal group ${\rm Conf}(V)$. This bilinear form can be written as an integral involving the Berezin kernel $B_{\nu}$, an invariant kernel on the Riemannian symmetric space $G/K$, which is a Makarevich symmetric space in the sense of Bertram. Then we can use results by van Dijk and Pevzner who computed the spherical Fourier transform of $B_{\nu}$. From these, one deduces that the Berezin kernel satisfies a remarkable Bernstein identity : $$D(\nu)B_{\nu} =b(\nu)B_{\nu +1},$$ where $D(\nu)$ is an invariant differential operator on $G/K$ and $b(\nu)$ is a polynomial. By using this identity we compute a Hua type integral which gives the normalizing factor for an intertwining operator from $I_{-s}$ to $I_s$. Furthermore we obtain the diagonalization of the invariant bilinear form with respect to the action of the maximal compact group $U$ of the conformal group ${\rm Conf}(V)$

On a Speculated Relation Between Chv\'atal-Sankoff Constants of Several Sequences

It is well known that, when normalized by n, the expected length of a longest common subsequence of d sequences of length n over an alphabet of size sigma converges to a constant gamma_{sigma,d}. We disprove a speculation by Steele regarding a possible relation between gamma_{2,d} and gamma_{2,2}. In order to do that we also obtain new lower bounds for gamma_{sigma,d}, when both sigma and d are small integers.Comment: 13 pages. To appear in Combinatorics, Probability and Computin

Symmetric Spaces and Star representations II : Causal Symmetric Spaces

We construct and identify star representations canonically associated with holonomy reducible simple symplectic symmetric spaces. This leads the a non-commutative geometric realization of the correspondence between causal symmetric spaces of Cayley type and Hermitian symmetric spaces of tube type.Comment: 13 page

Sum-of-squares lower bounds for planted clique

Finding cliques in random graphs and the closely related "planted" clique variant, where a clique of size k is planted in a random G(n, 1/2) graph, have been the focus of substantial study in algorithm design. Despite much effort, the best known polynomial-time algorithms only solve the problem for k ~ sqrt(n). In this paper we study the complexity of the planted clique problem under algorithms from the Sum-of-squares hierarchy. We prove the first average case lower bound for this model: for almost all graphs in G(n,1/2), r rounds of the SOS hierarchy cannot find a planted k-clique unless k > n^{1/2r} (up to logarithmic factors). Thus, for any constant number of rounds planted cliques of size n^{o(1)} cannot be found by this powerful class of algorithms. This is shown via an integrability gap for the natural formulation of maximum clique problem on random graphs for SOS and Lasserre hierarchies, which in turn follow from degree lower bounds for the Positivestellensatz proof system. We follow the usual recipe for such proofs. First, we introduce a natural "dual certificate" (also known as a "vector-solution" or "pseudo-expectation") for the given system of polynomial equations representing the problem for every fixed input graph. Then we show that the matrix associated with this dual certificate is PSD (positive semi-definite) with high probability over the choice of the input graph.This requires the use of certain tools. One is the theory of association schemes, and in particular the eigenspaces and eigenvalues of the Johnson scheme. Another is a combinatorial method we develop to compute (via traces) norm bounds for certain random matrices whose entries are highly dependent; we hope this method will be useful elsewhere

A Computational Method for the Rate Estimation of Evolutionary Transpositions

Genome rearrangements are evolutionary events that shuffle genomic architectures. Most frequent genome rearrangements are reversals, translocations, fusions, and fissions. While there are some more complex genome rearrangements such as transpositions, they are rarely observed and believed to constitute only a small fraction of genome rearrangements happening in the course of evolution. The analysis of transpositions is further obfuscated by intractability of the underlying computational problems. We propose a computational method for estimating the rate of transpositions in evolutionary scenarios between genomes. We applied our method to a set of mammalian genomes and estimated the transpositions rate in mammalian evolution to be around 0.26.Comment: Proceedings of the 3rd International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO), 2015. (to appear

A New Simulated Annealing Algorithm for the Multiple Sequence Alignment Problem: The approach of Polymers in a Random Media

We proposed a probabilistic algorithm to solve the Multiple Sequence Alignment problem. The algorithm is a Simulated Annealing (SA) that exploits the representation of the Multiple Alignment between $D$ sequences as a directed polymer in $D$ dimensions. Within this representation we can easily track the evolution in the configuration space of the alignment through local moves of low computational cost. At variance with other probabilistic algorithms proposed to solve this problem, our approach allows for the creation and deletion of gaps without extra computational cost. The algorithm was tested aligning proteins from the kinases family. When D=3 the results are consistent with those obtained using a complete algorithm. For $D>3$ where the complete algorithm fails, we show that our algorithm still converges to reasonable alignments. Moreover, we study the space of solutions obtained and show that depending on the number of sequences aligned the solutions are organized in different ways, suggesting a possible source of errors for progressive algorithms.Comment: 7 pages and 11 figure