Asymptotically Locally Optimal Weight Vector Design for a Tighter Correlation Lower Bound of Quasi-Complementary Sequence Sets

A quasi-complementary sequence set (QCSS) refers to a set of two-dimensional matrices with low nontrivial aperiodic auto- and cross-correlation sums. For multicarrier code-division multiple-access applications, the availability of large QCSSs with low correlation sums is desirable. The generalized Levenshtein bound (GLB) is a lower bound on the maximum aperiodic correlation sum of QCSSs. The bounding expression of GLB is a fractional quadratic function of a weight vector w and is expressed in terms of three additional parameters associated with QCSS: the set size K, the number of channels M, and the sequence length N. It is known that a tighter GLB (compared to the Welch bound) is possible only if the condition M ≥ 2 and K ≥ K̅ + 1, where K̅ is a certain function of M and N, is satisfied. A challenging research problem is to determine if there exists a weight vector that gives rise to a tighter GLB for all (not just some) K ≥ K̅ + 1 and M ≥ 2, especially for large N, i.e., the condition is asymptotically both necessary and sufficient. To achieve this, we analytically optimize the GLB which is (in general) nonconvex as the numerator term is an indefinite quadratic function of the weight vector. Our key idea is to apply the frequency domain decomposition of the circulant matrix (in the numerator term) to convert the nonconvex problem into a convex one. Following this optimization approach, we derive a new weight vector meeting the aforementioned objective and prove that it is a local minimizer of the GLB under certain conditions

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Monolingual Sentence Rewriting as Machine Translation: Generation and Evaluation

In this thesis, we investigate approaches to paraphrasing entire sentences within the constraints of a given task, which we call monolingual sentence rewriting. We introduce a unified framework for monolingual sentence rewriting, and apply it to three representative tasks: sentence compression, text simplification, and grammatical error correction. We also perform a detailed analysis of the evaluation methodologies for each task, identify bias in common evaluation techniques, and propose more reliable practices. Monolingual rewriting can be thought of as translating between two types of English (such as from complex to simple), and therefore our approach is inspired by statistical machine translation. In machine translation, a large quantity of parallel data is necessary to model the transformations from input to output text. Parallel bilingual data naturally occurs between common language pairs (such as English and French), but for monolingual sentence rewriting, there is little existing parallel data and annotation is costly. We modify the statistical machine translation pipeline to harness monolingual resources and insights into task constraints in order to drastically diminish the amount of annotated data necessary to train a robust system. Our method generates more meaning-preserving and grammatical sentences than earlier approaches and requires less task-specific data. Once candidate sentences are generated, it is crucial to have reliable evaluation methods. Sentential paraphrases must fulfill a variety of requirements: preserve the meaning of the original sentence, be grammatical, and meet any stylistic or task-specific constraints. We analyze common evaluation practices and propose better methods that more accurately measure the quality of output. Often overlooked, robust automatic evaluation methodology is necessary for improving systems, and this work presents new metrics and outlines important considerations for reliably measuring the quality of the generated text

MapReduce based RDF assisted distributed SVM for high throughput spam filtering

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityElectronic mail has become cast and embedded in our everyday lives. Billions of legitimate emails are sent on a daily basis. The widely established underlying infrastructure, its widespread availability as well as its ease of use have all acted as catalysts to such pervasive proliferation. Unfortunately, the same can be alleged about unsolicited bulk email, or rather spam. Various methods, as well as enabling architectures are available to try to mitigate spam permeation. In this respect, this dissertation compliments existing survey work in this area by contributing an extensive literature review of traditional and emerging spam filtering approaches. Techniques, approaches and architectures employed for spam filtering are appraised, critically assessing respective strengths and weaknesses. Velocity, volume and variety are key characteristics of the spam challenge. MapReduce (M/R) has become increasingly popular as an Internet scale, data intensive processing platform. In the context of machine learning based spam filter training, support vector machine (SVM) based techniques have been proven effective. SVM training is however a computationally intensive process. In this dissertation, a M/R based distributed SVM algorithm for scalable spam filter training, designated MRSMO, is presented. By distributing and processing subsets of the training data across multiple participating computing nodes, the distributed SVM reduces spam filter training time significantly. To mitigate the accuracy degradation introduced by the adopted approach, a Resource Description Framework (RDF) based feedback loop is evaluated. Experimental results demonstrate that this improves the accuracy levels of the distributed SVM beyond the original sequential counterpart. Effectively exploiting large scale, ‘Cloud’ based, heterogeneous processing capabilities for M/R in what can be considered a non-deterministic environment requires the consideration of a number of perspectives. In this work, gSched, a Hadoop M/R based, heterogeneous aware task to node matching and allocation scheme is designed. Using MRSMO as a baseline, experimental evaluation indicates that gSched improves on the performance of the out-of-the box Hadoop counterpart in a typical Cloud based infrastructure. The focal contribution to knowledge is a scalable, heterogeneous infrastructure and machine learning based spam filtering scheme, able to capitalize on collaborative accuracy improvements through RDF based, end user feedback. MapReduce based RDF Assisted Distributed SVM for High Throughput Spam Filterin

A Computational Model of Creative Design as a Sociocultural Process Involving the Evolution of Language

The aim of this research is to investigate the mechanisms of creative design within the context of an evolving language through computational modelling. Computational Creativity is a subfield of Artificial Intelligence that focuses on modelling creative behaviours. Typically, research in Computational Creativity has treated language as a medium, e.g., poetry, rather than an active component of the creative process. Previous research studying the role of language in creative design has relied on interviewing human participants, limiting opportunities for computational modelling. This thesis explores the potential for language to play an active role in computational creativity by connecting computational models of the evolution of artificial languages and creative design processes. Multi-agent simulations based on the Domain-Individual-Field-Interaction framework are employed to evolve artificial languages with features that may support creative designing including ambiguity, incongruity, exaggeration and elaboration. The simulation process consists of three steps: (1) constructing representations associating topics, meanings and utterances; (2) structured communication of utterances and meanings through the playing of “language games”; and (3) evaluation of design briefs and works. The use of individual agents with different evaluation criteria, preferences and roles enriches the scope and diversity of the simulations. The results of the experiments conducted with artificial creative language systems demonstrate the expansion of design spaces by generating compositional utterances representing novel concepts among design agents using language features and weighted context free grammars. They can be used to computationally explore the roles of language in creative design, and possibly point to computational applications. Understanding the evolution of artificial languages may provide insights into human languages, especially those features that support creativity

Efficient Change Management of XML Documents

XML-based documents play a major role in modern information architectures and their corresponding work-flows. In this context, the ability to identify and represent differences between two versions of a document is essential. A second important aspect is the merging of document versions, which becomes crucial in parallel editing processes. Many different approaches exist that meet these challenges. Most rely on operational transformation or document annotation. In both approaches, the operations leading to changes are tracked, which requires corresponding editing applications. In the context of software development, however, a state-based approach is common. Here, document versions are compared and merged using external tools, called diff and patch. This allows users for freely editing documents without being tightened to special tools. Approaches exist that are able to compare XML documents. A corresponding merge capability is still not available. In this thesis, I present a comprehensive framework that allows for comparing and merging of XML documents using a state-based approach. Its design is based on an analysis of XML documents and their modification patterns. The heart of the framework is a context-oriented delta model. I present a diff algorithm that appears to be highly efficient in terms of speed and delta quality. The patch algorithm is able to merge document versions efficiently and reliably. The efficiency and the reliability of my approach are verified using a competitive test scenario

Journal of Telecommunications and Information Technology, 2007, nr 4

kwartalni

Proceedings of the 26th International Symposium on Theoretical Aspects of Computer Science (STACS'09)

The Symposium on Theoretical Aspects of Computer Science (STACS) is held alternately in France and in Germany. The conference of February 26-28, 2009, held in Freiburg, is the 26th in this series. Previous meetings took place in Paris (1984), Saarbr¨ucken (1985), Orsay (1986), Passau (1987), Bordeaux (1988), Paderborn (1989), Rouen (1990), Hamburg (1991), Cachan (1992), W¨urzburg (1993), Caen (1994), M¨unchen (1995), Grenoble (1996), L¨ubeck (1997), Paris (1998), Trier (1999), Lille (2000), Dresden (2001), Antibes (2002), Berlin (2003), Montpellier (2004), Stuttgart (2005), Marseille (2006), Aachen (2007), and Bordeaux (2008). ..

Essays on contract design in behavioral and development economics

This thesis consists of three chapters that fall under the broad banner of contract theory, applied to topics in behavioral and development economics. Empirically, labor contracts that financially penalize failure induce higher effort provision than economically identical contracts presented as paying a bonus for success. This is puzzling, as penalties are infrequently used in practice. The most obvious explanation is selection: loss averse agents are unwilling to accept such contracts. In the first chapter, I formalize and experimentally test this intuition. Surprisingly, I find that workers were 25 percent more likely to accept penalty contracts, with no evidence of adverse selection. Penalty contracts also increased performance on the job by 0.2 standard deviations. Finally, I outline extensions to the basic theory that are consistent with the main results. The second chapter analyzes the effect of market structure in microfinance on borrower welfare and the types of contracts used. We find that market power can have severe implications for borrower welfare, while despite information frictions, competition delivers similar borrower welfare to non-profit lending. We also find that for-profit lenders are less likely to use joint liability than non-profits, which is consistent with some empirical stylized facts suggesting a decline in use of joint liability. We simulate the model to evaluate quantitatively the importance of market structure for borrower welfare. The third chapter contrasts individual liability lending with and without groups to joint liability lending, motivated by an apparent shift away from joint liability lending. We show under what conditions individual liability can deliver welfare improvements over joint liability, conditions that depend on the joint income distribution and social capital. We then show that lower transaction costs that mechanically favor group lending may also encourage the creation of social capital. Finally, we again simulate the model to quantify our welfare conclusions