Biased landscapes for random Constraint Satisfaction Problems

The typical complexity of Constraint Satisfaction Problems (CSPs) can be investigated by means of random ensembles of instances. The latter exhibit many threshold phenomena besides their satisfiability phase transition, in particular a clustering or dynamic phase transition (related to the tree reconstruction problem) at which their typical solutions shatter into disconnected components. In this paper we study the evolution of this phenomenon under a bias that breaks the uniformity among solutions of one CSP instance, concentrating on the bicoloring of k-uniform random hypergraphs. We show that for small k the clustering transition can be delayed in this way to higher density of constraints, and that this strategy has a positive impact on the performances of Simulated Annealing algorithms. We characterize the modest gain that can be expected in the large k limit from the simple implementation of the biasing idea studied here. This paper contains also a contribution of a more methodological nature, made of a review and extension of the methods to determine numerically the discontinuous dynamic transition threshold.Comment: 32 pages, 16 figure

GenEvA (I): A new framework for event generation

We show how many contemporary issues in event generation can be recast in terms of partonic calculations with a matching scale. This framework is called GenEvA, and a key ingredient is a new notion of phase space which avoids the problem of phase space double-counting by construction and includes a built-in definition of a matching scale. This matching scale can be used to smoothly merge any partonic calculation with a parton shower. The best partonic calculation for a given region of phase space can be determined through physics considerations alone, independent of the algorithmic details of the merging. As an explicit example, we construct a positive-weight partonic calculation for e+e- -> n jets at next-to-leading order (NLO) with leading-logarithmic (LL) resummation. We improve on the NLO/LL result by adding additional higher-multiplicity tree-level (LO) calculations to obtain a merged NLO/LO/LL result. These results are implemented using a new phase space generator introduced in a companion paper [arXiv:0801.4028].Comment: 60 pages, 22 figures, v2: corrected typos, added reference

Lattice Perturbation Theory by Computer Algebra: A Three-Loop Result for the Topological Susceptibility

We present a scheme for the analytic computation of renormalization functions on the lattice, using a symbolic manipulation computer language. Our first nontrivial application is a new three-loop result for the topological susceptibility.Comment: 15 pages + 2 figures (PostScript), report no. IFUP-TH 31/9

Generalized Approximate Message-Passing Decoder for Universal Sparse Superposition Codes

Sparse superposition (SS) codes were originally proposed as a capacity-achieving communication scheme over the additive white Gaussian noise channel (AWGNC) [1]. Very recently, it was discovered that these codes are universal, in the sense that they achieve capacity over any memoryless channel under generalized approximate message-passing (GAMP) decoding [2], although this decoder has never been stated for SS codes. In this contribution we introduce the GAMP decoder for SS codes, we confirm empirically the universality of this communication scheme through its study on various channels and we provide the main analysis tools: state evolution and potential. We also compare the performance of GAMP with the Bayes-optimal MMSE decoder. We empirically illustrate that despite the presence of a phase transition preventing GAMP to reach the optimal performance, spatial coupling allows to boost the performance that eventually tends to capacity in a proper limit. We also prove that, in contrast with the AWGNC case, SS codes for binary input channels have a vanishing error floor in the limit of large codewords. Moreover, the performance of Hadamard-based encoders is assessed for practical implementations

Evaluating Visual Realism in Drawing Areas of Interest on UML Diagrams

Areas of interest (AOIs) are defined as an addition to UML diagrams: groups of elements of system architecture diagrams that share some common property. Some methods have been proposed to automatically draw AOIs on UML diagrams. However, it is not clear how users perceive the results of such methods as compared to human-drawn areas of interest. We present here a process of studying and improving the perceived quality of computer-drawn AOIs. We qualitatively evaluated how users perceive the quality of computer- and human-drawn AOIs, and used these results to improve an existing algorithm for drawing AOIs. Finally, we designed a quantitative comparison for AOI drawings and used it to show that our improved renderings are closer to human drawings than the original rendering algorithm results. The combined user evaluation, algorithmic improvements, and quantitative comparison support our claim of improving the perceived quality of AOIs rendered on UML diagrams.