The Riemannian Geometry of the Space of Positive-Definite Matrices and Its Application to the Regularization of Positive-Definite Matrix-Valued Data

In this paper we present a Riemannian framework for smoothing data that are constrained to live in P(n)\mathcal{P}(n), the space of symmetric positive-definite matrices of order n. We start by giving the differential geometry of P(n)\mathcal{P}(n), with a special emphasis on P(3)\mathcal{P}(3), considered at a level of detail far greater than heretofore. We then use the harmonic map and minimal immersion theories to construct three flows that drive a noisy field of symmetric positive-definite data into a smooth one. The harmonic map flow is equivalent to the heat flow or isotropic linear diffusion which smooths data everywhere. A modification of the harmonic flow leads to a Perona-Malik like flow which is a selective smoother that preserves edges. The minimal immersion flow gives rise to a nonlinear system of coupled diffusion equations with anisotropic diffusivity. Some preliminary numerical results are presented for synthetic DT-MRI data.     

Wormholes supported by chiral fields

We consider static, spherically symmetric solutions of general relativity with a non-linear sigma model (NSM) as a source, i.e., a set of scalar fields Φ = (Φ¹, …, Φ ⁿ ) (so-called chiral fields) parametrizing a target space with a metric h _ab (Φ). For NSM with zero potential V (Φ), it is shown that the space-time geometry is the same as with a single scalar field but depends on h _ab . If the matrix h _ab is positive-definite, we obtain the Fisher metric, originally found for a canonical scalar field with positive kinetic energy; otherwise we obtain metrics corresponding to a phantom scalar field, including singular and nonsingular horizons (of infinite area) and wormholes. In particular, the Schwarzschild metric can correspond to a nontrivial chiral field configuration, which in this case has zero stress-energy. Some explicit examples of chiral field configurations are considered. Some qualitative properties of NSM configurations with nonzero potentials are pointed out.     

On black brane solutions and their fluid analogues

We review spherically symmetric solutions with a horizon in two models: (i) with scalar fields and fields of forms, and (ii) with a multi-component anisotropic fluid. The metrics of the solutions are defined on a manifold that contains a product of n − 1 Ricci-flat “internal” spaces. The solutions are governed by functions H _s obeying nonlinear differential equations with certain boundary conditions. Simulation of black-brane solutions is considered, and the Hawking temperature is calculated. For the fluid solution, the post-Newtonian parameters β and Γ corresponding to the 4-dimensional section of the metric are found.     

Approximate Pattern Matching with the <Emphasis Type="Italic">L</Emphasis><Subscript>1</Subscript>, <Emphasis Type="Italic">L</Emphasis><Subscript>2</Subscript> and <Emphasis Type="Italic">L</Emphasis><Subscript>∞</Subscript> Metrics

Given an alphabet Σ={1,2,…,|Σ|} text string T∈Σ ⁿ and a pattern string P∈Σ ^m , for each i=1,2,…,n−m+1 define L _p (i) as the p-norm distance when the pattern is aligned below the text and starts at position i of the text. The problem of pattern matching with L _p distance is to compute L _p (i) for every i=1,2,…,n−m+1. We discuss the problem for d=1,2,∞. First, in the case of L ₁ matching (pattern matching with an L ₁ distance) we show a reduction of the string matching with mismatches problem to the L ₁ matching problem and we present an algorithm that approximates the L ₁ matching up to a factor of 1+ε, which has an O(\frac1e²nlogmlog|S|)O(\frac{1}{\varepsilon^{2}}n\log m\log|\Sigma|) run time. Then, the L ₂ matching problem (pattern matching with an L ₂ distance) is solved with a simple O(nlog m) time algorithm. Finally, we provide an algorithm that approximates the L _∞ matching up to a factor of 1+ε with a run time of O(\frac1enlogmlog|S|)O(\frac{1}{\varepsilon}n\log m\log|\Sigma|) . We also generalize the problem of String Matching with mismatches to have weighted mismatches and present an O(nlog ⁴ m) algorithm that approximates the results of this problem up to a factor of O(log m) in the case that the weight function is a metric.     

Regularizing Flows over Lie Groups

In this paper we discuss regularization of images that take their value in matrix Lie groups. We describe an image as a section in a principal bundle which is a fibre bundle where the fiber (the feature space) is a Lie group. Via the scalar product on the Lie algebra, we define a bi-invariant metric on the Lie-group manifold. Thus, the fiber becomes a Riemannian manifold with respect to this metric. The induced metric from the principal bundle to the image manifold is obtained by means of the bi-invariant metric. A functional over the space of sections, i.e., the image manifolds, is defined. The resulting equations of motion generate a flow which evolves the sections in the spatial-Lie-group manifold. We suggest two different approaches to treat this functional and the corresponding PDEs. In the first approach we derive a set of coupled PDEs for the local coordinates of the Lie-group manifold. In the second approach a coordinate-free framework is proposed where the PDE is defined directly with respect to the Lie-group elements. This is a parameterization-free method. The differences between these two methods are discussed. We exemplify this framework on the well-known orientation diffusion problem, namely, the unit-circle S ¹ which is identified with the group of rotations in two dimensions, SO(2). Regularization of the group of rotations in 3D and 4D, SO(3) and SO(4), respectively, is demonstrated as well.

On selecting thek largest with median tests

LetW _itk(n) be the minimax complexity of selecting thek largest elements ofn numbersx ₁,x ₂,...,x _n by pairwise comparisonsx _i :x _j . It is well known thatW ₂(n) =n–2+ [lgn], andW _k (n) = n + (k–1)lg n +O(1) for all fixed k 3. In this paper we studyW _k (n), the minimax complexity of selecting thek largest, when tests of the form Isx _i the median of {x _i ,x _j ,x _t }? are also allowed. It is proved thatW₂(n) =n–2+ [lgn], andW _k (n) =n + (k–1)lg₂ n +O(1) for all fixedk3.This research was supported in part by the National Science Foundation under Grant No. DCR-8308109.     

On Word Equations in One Variable

For a word equation E of length n in one variable x occurring # _x times in E a resolution algorithm of O(n+# _x log n) time complexity is presented here. This is the best result known and for the equations that feature #_x < \fracnlogn\#_{x}<\frac{n}{\log n} it yields time complexity of O(n) which is optimal. Additionally it is proven here that the set of solutions of any one-variable word equation is either of the form F or of the form F∪(uv)⁺ u where F is a set of O(log n) words and u, v are some words such that uv is a primitive word.     

Inversion Problems in theq-Hahn Tableau

If { P_n(x;q)}_nis a family of polynomials belonging to the q -Hahn tableau then each polynomial of this family can be written as P_n(x;q) = ∑_{m = 0}ⁿD_m(n)_m(x) where _m(x) stands for (x;q)_mor x^m. In this paper we solve the corresponding inversion problem, i.e. we find the explicit expression for the coefficients I_m(n) in the expansion _n(x) = ∑_{m = 0}ⁿI_m(n)P_m(x;q).     

Proper Learning Algorithm for Functions of k Terms under Smooth Distributions

In this paper, we introduce a probabilistic distribution, called a smooth distribution, which is a generalization of variants of the uniform distribution such as q-bounded distribution and product distribution. Then, we give an algorithm that, under the smooth distribution, properly learns the class of functions of k terms given as _k ^k_n={g(f₁(v), …, f_k(v)) | g _k, f₁, …, f_{k n}} in polynomial time for constant k, where _k is the class of all Boolean functions of k variables and _n is the class of terms over n variables. Although class _k ^k_n was shown by Blum and Singh to be learned using DNF as the hypothesis class, it has remained open whether it is properly learnable under a distribution-free setting.     

Testing Periodicity

We study the string-property of being periodic and having periodicity smaller than a given bound. Let Σ be a fixed alphabet and let p,n be integers such that p ￡ \fracn2p\leq \frac{n}{2} . A length-n string over Σ, α=(α ₁,…,α _n ), has the property Period(p) if for every i,j∈{1,…,n}, α _i =α _j whenever i≡j (mod p). For an integer parameter g ￡ \fracn2,g\leq \frac{n}{2}, the property Period(≤g) is the property of all strings that are in Period(p) for some p≤g. The property Period( ￡ \fracn2)\mathit{Period}(\leq \frac{n}{2}) is also called Periodicity.     

Improved Bounds for Wireless Localization

We consider a novel class of art gallery problems inspired by wireless localization that has recently been introduced by Eppstein, Goodrich, and Sitchinava. Given a simple polygon P, place and orient guards each of which broadcasts a unique key within a fixed angular range. In contrast to the classical art gallery setting, broadcasts are not blocked by the edges of P. At any point in the plane one must be able to tell whether or not one is located inside P only by looking at the set of keys received. In other words, the interior of the polygon must be described by a monotone Boolean formula composed from the keys. We improve both upper and lower bounds for the general problem where guards may be placed anywhere by showing that the maximum number of guards to describe any simple polygon on n vertices is between roughly \frac35n\frac{3}{5}n and \frac45n\frac{4}{5}n . A guarding that uses at most \frac45n\frac{4}{5}n guards can be obtained in O(nlog n) time. For the natural setting where guards may be placed aligned to one edge or two consecutive edges of P only, we prove that n−2 guards are always sufficient and sometimes necessary.     

背包类问题的并行O(25n/6)时间-空间-处理机折衷

将串行动态二表算法应用于并行三表算法的设计中,提出一种求解背包、精确的可满足性和集覆盖等背包类NP完全问题的并行三表六子表算法.基于EREW-PRAM模型,该算法可使用O(2^n/8)的处理机在O(2^7n/16)的时间和O(2^13n/48)的空间求解n维背包类问题,其时间-空间-处理机折衷为O(2^5n/6).与现有文献的性能对比分析表明,该算法极大地提高了并行求解背包类问题的时间-空间-处理机折衷性能.由于该算法能够破解更高维数的背包类公钥和数字水印系统,其结论在密钥分析领域具有一定的理论和实际意义.     

Exact 3-satisfiability is decidable in time O(20.16254n)

Let F = C ₁ C _m be a Boolean formula in conjunctive normal form over a set V of n propositional variables, s.t. each clause C _i contains at most three literals l over V. Solving the problem exact 3-satisfiability (X3SAT) for F means to decide whether there is a truth assignment setting exactly one literal in each clause of F to true (1). As is well known X3SAT is NP-complete [6]. By exploiting a perfect matching reduction we prove that X3SAT is deterministically decidable in time O(2^0.18674n ). Thereby we improve a result in [2,3] stating X3SAT O(2^0.2072n ) and a bound of O(2^0.200002n ) for the corresponding enumeration problem #X3SAT stated in a preprint [1]. After that by a more involved deterministic case analysis we are able to show that X3SAT O(2^0.16254n ).An extended abstract of this paper was presented at the Fifth International Symposium on the Theory and Applications of Satisfiability Testing (SAT 2002).     

Necessary and sufficient conditions on Q ( = C( I + PC)) for stabilization of the linear feedback system S(P, C)

We find the following necessary and sufficient conditions for Q (:=C(I+PC)⁻¹) to -stabilize the standard linear time-invariant unity feedback system S(P, C) where P has the l.c.f. (D_pl, N_pl) and the r.c.f. (N_pr, D_pr); and is a principal ideal domain. (i) Q must have elements in (ii) (resp. (iii)) Q must factorize in with D_pr, (resp. D_pl) as a left (resp. right) factor and (iv) (I – QP) must factor in with D_pr, as a left factor.     

Optimal piecewise linear motion of an object among obstacles

We present an algorithm for determining the shortest restricted path motion of a polygonal object amidst polygonal obstacles. The class of motions which are allowed can be described as follows: a designated vertex,P, of the polygonal object traverses a piecewise linear path, whose breakpoints are restricted to the vertices of the obstacles. The distance measure being minimized is the length of the path traversed byP. Our algorithm runs in timeO(n ⁴kogn). We also discuss a variation of this algorithm which minimizes any positive linear combination of length traversed byP and angular rotation of the ladder aboutP. This variation requiresO(n ₅) time.Research partially supported by a grant from the Hughes Artificial Intelligence Center, part of Hughes Aircraft.     

On the performance of Dijkstra’s third self-stabilizing algorithm for mutual exclusion and related algorithms

In Dijkstra (Commun ACM 17(11):643–644, 1974) introduced the notion of self-stabilizing algorithms and presented three such algorithms for the problem of mutual exclusion on a ring of n processors. The third algorithm is the most interesting of these three but is rather non intuitive. In Dijkstra (Distrib Comput 1:5–6, 1986) a proof of its correctness was presented, but the question of determining its worst case complexity—that is, providing an upper bound on the number of moves of this algorithm until it stabilizes—remained open. In this paper we solve this question and prove an upper bound of 3\frac1318 n² + O(n){3\frac{13}{18} n^2 + O(n)} for the complexity of this algorithm. We also show a lower bound of 1\frac56 n² - O(n){1\frac{5}{6} n^2 - O(n)} for the worst case complexity. For computing the upper bound, we use two techniques: potential functions and amortized analysis. We also present a new-three state self-stabilizing algorithm for mutual exclusion and show a tight bound of \frac56 n² + O(n){\frac{5}{6} n^2 + O(n)} for the worst case complexity of this algorithm. In Beauquier and Debas (Proceedings of the second workshop on self-stabilizing systems, pp 17.1–17.13, 1995) presented a similar three-state algorithm, with an upper bound of 5\frac34n²+O(n){5\frac{3}{4}n^2+O(n)} and a lower bound of \frac18n²-O(n){\frac{1}{8}n^2-O(n)} for its stabilization time. For this algorithm we prove an upper bound of 1\frac12n² + O(n){1\frac{1}{2}n^2 + O(n)} and show a lower bound of n ²−O(n). As far as the worst case performance is considered, the algorithm in Beauquier and Debas (Proceedings of the second workshop on self-stabilizing systems, pp 17.1–17.13, 1995) is better than the one in Dijkstra (Commun ACM 17(11):643–644, 1974) and our algorithm is better than both.     

Reoptimization of constraint satisfaction problems with approximation resistant predicates

If k = O(log n) and a predicate P is approximation resistant for the reoptimization of the Max-EkCSP-P problem, then, after inserting a truth-value into the predicate and imposing some constraint, there exists a polynomial algorithm with the approximation ratio q(P) = \frac12 - d(P) q(P) = \frac{1}{{2 - d(P)}} , where d(P) = 2 ^{- k}| P ^{- 1}(1) | d(P) = {2^{ - k}}\left| {{P^{ - 1}}(1)} \right| is a “random” threshold approximation ratio of the predicate P. The ratio q(P) is a threshold approximation ratio.     

On generalized language equations

A system of generalized language equations over an alphabet A is a set of n equations in n variables: X_i = G_i(X₁,..., X_n), i = 1,...,n, where the G_i are functions from [P(A^*)]ⁿ into P(A^*), i=1,..., n, P(A^*) denoting the set of all languages over A. Furthermore the G_i are expressible in terms of set-operations, concatenations, and stars which involve the variable X_i as well as certain mixed languages. In this note we investigate existence and uniqueness of solutions of a certain subclass of generalized language equations. Furthermore we show that a solution is regular if all fixed languages are regular.     

The Baire Partial Quasi-Metric Space: A?Mathematical Tool for Asymptotic Complexity Analysis in Computer Science

In 1994, S.G. Matthews introduced the notion of partial metric space in order to obtain a suitable mathematical tool for program verification (Ann. N.Y. Acad. Sci. 728:183–197, 1994). He gave an application of this new structure to parallel computing by means of a partial metric version of the celebrated Banach fixed point theorem (Theor. Comput. Sci. 151:195–205, 1995). Later on, M.P. Schellekens introduced the theory of complexity (quasi-metric) spaces as a part of the development of a topological foundation for the asymptotic complexity analysis of programs and algorithms (Electron. Notes Theor. Comput. Sci. 1:211–232, 1995). The applicability of this theory to the asymptotic complexity analysis of Divide and Conquer algorithms was also illustrated by Schellekens. In particular, he gave a new proof, based on the use of the aforenamed Banach fixed point theorem, of the well-known fact that Mergesort algorithm has optimal asymptotic average running time of computing. In this paper, motivated by the utility of partial metrics in Computer Science, we discuss whether the Matthews fixed point theorem is a suitable tool to analyze the asymptotic complexity of algorithms in the spirit of Schellekens. Specifically, we show that a slight modification of the well-known Baire partial metric on the set of all words over an alphabet constitutes an appropriate tool to carry out the asymptotic complexity analysis of algorithms via fixed point methods without the need for assuming the convergence condition inherent to the definition of the complexity space in the Schellekens framework. Finally, in order to illustrate and to validate the developed theory we apply our results to analyze the asymptotic complexity of Quicksort, Mergesort and Largesort algorithms. Concretely we retrieve through our new approach the well-known facts that the running time of computing of Quicksort (worst case behaviour), Mergesort and Largesort (average case behaviour) are in the complexity classes O(n²)\mathcal{O}(n^{2}), O(nlog₂(n))\mathcal{O}(n\log_{2}(n)) and O(2(n-1)-log₂(n))\mathcal{O}(2(n-1)-\log_{2}(n)), respectively.     

Injective Colorings of Graphs with Low Average Degree

Let mad (G) denote the maximum average degree (over all subgraphs) of G and let χ _i (G) denote the injective chromatic number of G. We prove that if Δ≥4 and mad(G) < \frac145\mathrm{mad}(G)<\frac{14}{5}, then χ _i (G)≤Δ+2. When Δ=3, we show that mad(G) < \frac3613\mathrm{mad}(G)<\frac{36}{13} implies χ _i (G)≤5. In contrast, we give a graph G with Δ=3, mad(G)=\frac3613\mathrm{mad}(G)=\frac{36}{13}, and χ _i (G)=6.