An ontology-based approach to the optimization of non-binary (2,v)-regular LDPC codes

A non-binary (2,v)-regular LDPC code is defined by a parity-check matrix with column weight 2 and row weight v. In this report, we give an ontology-based approach to the optimization for this class of codes. All possible inter-connected cycle patterns that lead to low symbol-weight codewords are identified to put together the ontology. The optimization goal is to improve the distance property of equivalent binary images. Using the proposed method, the estimation and optimization of bit-distance spectrum becomes easily handleable. Three codes in the CCSDS recommendation are analyzed and several codes with good minimum bit-distance are designed.Comment: Technical Repor

Distribution of Coefficients of Modular Forms and the Partition Function

Let $\ell\ge5$ be an odd prime and $j, s$ be positive integers. We study the distribution of the coefficients of integer and half-integral weight modular forms modulo odd positive integer $M$. As a consequence, we prove that for each integer $1\le r\le\ell^j$, $$\sharp\{1\le n\le X\ |\ p(n)\equiv r\pmod{\ell^j}\}\gg_{s,r,\ell^j}\frac{\sqrt X}{\log X}(\log\log X)^s.$$Comment: 8page

Symmetry Partition Sort

In this paper, we propose a useful replacement for quicksort-style utility functions. The replacement is called Symmetry Partition Sort, which has essentially the same principle as Proportion Extend Sort. The maximal difference between them is that the new algorithm always places already partially sorted inputs (used as a basis for the proportional extension) on both ends when entering the partition routine. This is advantageous to speeding up the partition routine. The library function based on the new algorithm is more attractive than Psort which is a library function introduced in 2004. Its implementation mechanism is simple. The source code is clearer. The speed is faster, with O(n log n) performance guarantee. Both the robustness and adaptivity are better. As a library function, it is competitive

Darknet-Based Inference of Internet Worm Temporal Characteristics

Internet worm attacks pose a significant threat to network security and management. In this work, we coin the term Internet worm tomography as inferring the characteristics of Internet worms from the observations of Darknet or network telescopes that monitor a routable but unused IP address space. Under the framework of Internet worm tomography, we attempt to infer Internet worm temporal behaviors, i.e., the host infection time and the worm infection sequence, and thus pinpoint patient zero or initially infected hosts. Specifically, we introduce statistical estimation techniques and propose method of moments, maximum likelihood, and linear regression estimators. We show analytically and empirically that our proposed estimators can better infer worm temporal characteristics than a naive estimator that has been used in the previous work. We also demonstrate that our estimators can be applied to worms using different scanning strategies such as random scanning and localized scanning

Characterizing Internet Worm Infection Structure

Internet worm infection continues to be one of top security threats and has been widely used by botnets to recruit new bots. In this work, we attempt to quantify the infection ability of individual hosts and reveal the key characteristics of the underlying topology formed by worm infection, i.e., the number of children and the generation of the worm infection family tree. Specifically, we first apply probabilistic modeling methods and a sequential growth model to analyze the infection tree of a wide class of worms. We analytically and empirically find that the number of children has asymptotically a geometric distribution with parameter 0.5. As a result, on average half of infected hosts never compromise any vulnerable host, over 98% of infected hosts have no more than five children, and a small portion of infected hosts have a large number of children. We also discover that the generation follows closely a Poisson distribution and the average path length of the worm infection family tree increases approximately logarithmically with the total number of infected hosts. Next, we empirically study the infection structure of localized-scanning worms and surprisingly find that most of the above observations also apply to localized-scanning worms. Finally, we apply our findings to develop bot detection methods and study potential countermeasures for a botnet (e.g., Conficker C) that uses scan-based peer discovery to form a P2P-based botnet. Specifically, we demonstrate that targeted detection that focuses on the nodes with the largest number of children is an efficient way to expose bots. For example, our simulation shows that when 3.125% nodes are examined, targeted detection can reveal 22.36% bots. However, we also point out that future botnets may limit the maximum number of children to weaken targeted detection, without greatly slowing down the speed of worm infection

Linear Statistics of Matrix Ensembles in Classical Background

Given a joint probability density function of $N$ real random variables, $\{x_j\}_{j=1}^{N},$ obtained from the eigenvector-eigenvalue decomposition of $N\times N$ random matrices, one constructs a random variable, the linear statistics, defined by the sum of smooth functions evaluated at the eigenvalues or singular values of the random matrix, namely, $\sum_{j=1}^{N}F(x_j).$ For the jpdfs obtained from the Gaussian and Laguerre ensembles, we compute, in this paper the moment generating function $\mathbb{E}_{\beta}({\rm exp}(-\lambda\sum_{j}F(x_j))),$ where $\mathbb{E}_{\beta}$ denotes expectation value over the Orthogonal ($\beta=1$) and Symplectic ($\beta=4)$ ensembles, in the form one plus a Schwartz function, none vanishing over $\mathbb{R}$ for the Gaussian ensembles and $\mathbb{R}^+$ for the Laguerre ensembles. These are ultimately expressed in the form of the determinants of identity plus a scalar operator, from which we obtained the large $N$ asymptotic of the linear statistics from suitably scaled $F(\cdot).

Dealing With 4-Variables by Resolution: An Improved MaxSAT Algorithm

We study techniques for solving the Maximum Satisfiability problem (MaxSAT). Our focus is on variables of degree 4. We identify cases for degree-4 variables and show how the resolution principle and the kernelization techniques can be nicely integrated to achieve more efficient algorithms for the MaxSAT problem. As a result, we present an algorithm of time $O^*(1.3248^k)$ for the MaxSAT problem, improving the previous best upper bound $O^*(1.358^k)$ by Ivan Bliznets and Alexander

On the variance of linear statistics of Hermitian random matrices

Linear statistics, a random variable build out of the sum of the evaluation of functions at the eigenvalues of a N times N random matrix,sum[j=1 to N]f(xj) or tr f(M), is an ubiquitous statistical characteristics in random matrix theory. Hermitian random matrix ensembles, under the eigenvalue-eigenvector decomposition give rise to the joint probability density functions of N random variables. We show that if f(.) is a polynomial of degree K, then the variance of trf(M), is of the form,sum[n=1 to K] n(d[n])square, and d[n] is related to the expansion coefficients c[n] of the polynomial f(x) =sum[n=0 to K] c[n] b Pn(x), where Pn(x) are polynomials of degree n, orthogonal with respect to the weights 1/[(b-x)(x-a)]^(1/2), [(b -x)(x -a)]^(1/2), [(b-x)(x-a)]^(1/2)/x; (0 < a < x < b), [(b-x)(x-a)]^(1/2)/[x(1-x)] ; (0 < a < x < b < 1), respectively.Comment: 17 pages, 0 figures, variance of linear statistic

Gap Probability Distribution of the Jacobi Unitary Ensemble: An Elementary Treatment, from Finite nn to Double Scaling

In this paper, we study the gap probability problem of the (symmetric) Jacobi unitary ensemble of Hermitian random matrices, namely the probability that the interval $(-a,a)\:(0<a<1)$ is free of eigenvalues. Using the ladder operator technique for orthogonal polynomials and the associated supplementary conditions, we derive three quantities instrumental in the gap probability, denoted by $H_{n}(a)$, $R_{n}(a)$ and $r_{n}(a)$. We find that each one satisfies a second order differential equation. We show that after a double scaling, the large second order differential equation in the variable $a$ with $n$ as parameter satisfied by $H_{n}(a)$, can be reduced to the Jimbo-Miwa-Okamoto $\sigma$ form of the Painlev\'{e} V equation.Comment: 20 page

Weighted inequlities for a generalized dyadic maximal operator involving the infinite product

We define a generalized dyadic maximal operator involving the infinite product and discuss weighted inequalities for the operator. A formulation of the Carleson embedding theorem is proved. Our results depend heavily on a generalized H\"{o}lder's inequalities.Comment: 17pages. arXiv admin note: text overlap with arXiv:1401.143