Phd Research Thesis Computer Science

Molecular Biology studies the composition and interactions of life's agents, namely the various molecules (e.g. DNA, proteins, lipids) sustaining the living process. Traditionally, this study has been performed in wet labs using mostly physicochemical techniques. Such techniques, although precise and detailed, are often cumbersome and time consuming. On top of that, recent advances in sequencing technology have allowed the rapid accumulation of DNA and protein data. As a result a gap has been created (and is constantly being expanded): on the one side there is a rapidly growing collection of data containing all the information upon which life is built; and on the other side we are currently unable to keep up with the study of this data, impaired by the limits of existing analysis tools. It is obvious that alternative analysis techniques are badly needed. In this work we examine how computational methods can help in drilling the information contained in collections of biological data. In particular, we investigate how sequence similarity among various macromolecules (e.g. proteins) can be exploited towards the extraction of biologically useful information.

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Discussion On Ph.D. Thesis Proposals in Computing Science

We first provide new constructions that prove that the "open" Hierarchical Piecewise Constant Derivative (HPCD) subclass is closer to the decidability and undecidability frontiers than was previously understood. After concluding that the HPCD-like classes are unsuitable for modeling chemical reactions, our quest for semi-decidable subclasses leads us to define the "semi-algebraic" subclass. This is the most expressive hybrid automaton subclass amenable to rigorous symbolic temporal reasoning. We begin with the bounded reachability problem, and then show how the dense-time temporal logic Timed Computation Tree Logic (TCTL) can be model-checked by exploiting techniques from real algebraic geometry, primarily real quantifier elimination. We also prove the undecidability of reachability in the Blum-Shub-Smale Turing Machine formalism. We then develop efficient approximation strategies by extending bisimulation partitioning, rectangular grid-based approximation, polytopal approximation and time discretization. We then develop a uniform algebraic framework for modeling biochemical and metabolic networks, also extending flux balance analysis. We present some preliminary results using a prototypical tool Tolque. It is a symbolic algebraic dense time model-checker for semi-algebraic hybrid automata, which uses Qepcad for quantifier elimination.

Thesis research in computer science

In recent years, the increase in the amounts of available genomic as well as gene expression data has provided researchers with the necessary information to train and test various models of gene origin, evolution, function and regulation. In this thesis, we present novel solutions to key problems in computational biology that deal with nucleotide sequences (horizontal gene transfer detection), amino-acid sequences (protein sub-cellular localization prediction), and gene expression data (transcription factor - binding site pair discovery). Different pattern discovery techniques are utilized, such as maximal sequence motif discovery and maximal itemset discovery, and combined with support vector machines in order to achieve significant improvements against previously proposed methods.

This research is an attempt to reason about the control of parallel computation in the world of applicative programming languages.
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Thesis Topics for Computer Science Students - PHD …

This thesis addresses fault tolerance issues in parallel computing on loosely-coupled networks of non-dedicated, heterogeneous workstations. The efficiency of fault tolerance mechanisms is dictated by network and failure characteristics. Traditional approaches to fault tolerance are efficient when network and failure characteristics are identical across workstations, such as in a local area network of homogeneous workstations; however, a loosely coupled network of non-dedicated workstations has non-uniform network and failure characteristics. This thesis presents the design and implementation of a flexible fault tolerance runtime system that allows each process in a parallel application to use one of three rollback recovery mechanisms. Rollback recovery is achieved using a lightweight form of transaction, which performance results show incurs almost no overhead. The system is built on top of the Linda coordination language and runs on Alpha, Linux, Solaris and SGI workstations and Java-enabled browsers. For barrier synchronous parallel applications, a new equi-distant checkpointing interval selection method, the expected maximum heuristic, is presented. The method is applicable to any rollback recovery system in which processes recover from failure independently and communicate through a reliable third party. Simulation results show that the expected maximum heuristic has near optimal performance under a variety of different failure rates and barrier lengths.

22/12/2017 · PhD/MPhil/MSc by Research Computer Science; ..

Phd thesis of computer science ..

The creativity support community has a long history of providing valuable tools to artists and designers. Similarly, creative digital media practice has proven a valuable pedagogical strategy for teaching core computational ideas. Neither strain of research has focused on the domain of literary art however, instead targeting visual, and aural media almost exclusively.

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PhD Dissertation Topics in Computer Science

Designed both for practicing artists and for pedagogy, the research presented directly addresses impediments to participation in the field for a diverse range of users and provides an end-to-end solution for courses attempting to engage the creative faculties of computer science students, and to introduce a wider demographic--from writers, to digital artists, to media and literary theorists --to procedural literacy and computational thinking.