283,840 research outputs found

    Super-rigidity for CR embeddings of real hypersurfaces into hyperquadrics

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    Let Q^N_l\subset \bC\bP^{N+1} denote the standard real, nondegenerate hyperquadric of signature ll and M\subset \bC^{n+1} a real, Levi nondegenerate hypersurface of the same signature ll. We shall assume that there is a holomorphic mapping H_0\colon U\to \bC\bP^{N_0+1}, where UU is some neighborhood of MM in \bC^{n+1}, such that H0(M)βŠ‚QlN0H_0(M)\subset Q^{N_0}_l but H(U)βŠ‚ΜΈQlN0H(U)\not\subset Q^{N_0}_l. We show that if N0βˆ’n<lN_0-n<l then, for any Nβ‰₯N0N\geq N_0, any holomorphic mapping H\colon U\to \bC\bP^{N+1} with H(M)βŠ‚QlNH(M)\subset Q^{N}_l and H(U)βŠ‚ΜΈQlN0H(U)\not\subset Q^{N_0}_l must be the standard linear embedding of QlN0Q^{N_0}_l into QlNQ^N_l up to conjugation by automorphisms of QlN0Q^{N_0}_l and QlNQ^N_l

    Lower bounds for identifying subset members with subset queries

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    An instance of a group testing problem is a set of objects \cO and an unknown subset PP of \cO. The task is to determine PP by using queries of the type ``does PP intersect QQ'', where QQ is a subset of \cO. This problem occurs in areas such as fault detection, multiaccess communications, optimal search, blood testing and chromosome mapping. Consider the two stage algorithm for solving a group testing problem. In the first stage a predetermined set of queries are asked in parallel and in the second stage, PP is determined by testing individual objects. Let n=\cardof{\cO}. Suppose that PP is generated by independently adding each x\in \cO to PP with probability p/np/n. Let q1q_1 (q2q_2) be the number of queries asked in the first (second) stage of this algorithm. We show that if q1=o(log⁑(n)log⁑(n)/log⁑log⁑(n))q_1=o(\log(n)\log(n)/\log\log(n)), then \Exp(q_2) = n^{1-o(1)}, while there exist algorithms with q1=O(log⁑(n)log⁑(n)/log⁑log⁑(n))q_1 = O(\log(n)\log(n)/\log\log(n)) and \Exp(q_2) = o(1). The proof involves a relaxation technique which can be used with arbitrary distributions. The best previously known bound is q_1+\Exp(q_2) = \Omega(p\log(n)). For general group testing algorithms, our results imply that if the average number of queries over the course of nΞ³n^\gamma (Ξ³>0\gamma>0) independent experiments is O(n1βˆ’Ο΅)O(n^{1-\epsilon}), then with high probability Ξ©(log⁑(n)log⁑(n)/log⁑log⁑(n))\Omega(\log(n)\log(n)/\log\log(n)) non-singleton subsets are queried. This settles a conjecture of Bill Bruno and David Torney and has important consequences for the use of group testing in screening DNA libraries and other applications where it is more cost effective to use non-adaptive algorithms and/or too expensive to prepare a subset QQ for its first test.Comment: 9 page

    Nonnilpotent subsets in the susuki groups

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    Let G be a group and N be the class of nilpotent groups. A subset A of G is said to be nonnilpotent if for any two distinct elements a and b in A, ha, bi 62 N. If, for any other nonnilpotent subset B in G, |A| ? |B|, then A is said to be a maximal nonnilpotent subset and the cardinality of this subset (if it exists) is denoted by !(NG). In this paper, among other results, we obtain !(NSuz(q)) and !(NPGL(2,q)), where Suz(q) is the Suzuki simple group over the field with q elements and PGL(2, q) is the projective general linear group of degree 2 over the finite field of size q, respectively.Comment: submitte
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