Path-integral analysis of passive, graded-index waveguides applicable to integrated optics

The Feynman path integral is used to describe paraxial, scalar wave propagation in weakly inhomogeneous media of the type encountered in passive integrated-optical communication devices. Most of the devices considered in this work are simple models for graded-index waveguide structures, such as tapered and coupled waveguides of a wide variety of geometries. Tapered and coupled graded-index waveguides are the building blocks of waveguide junctions and tapered couplers, and have been mainly studied in the past through numerical simulations. Closed form expressions for the propagator and the coupling efficiency of symmetrically tapered graded-index waveguide sections are presented in this thesis for the first time. The tapered waveguide geometries considered are the general power-law geometry, the linear, parabolic, inverse-square-law, and exponential tapers. Closed form expressions describing the propagation of a centred Gaussian beam in these tapers have also been derived. The approximate propagator of two parallel, coupled graded-index waveguides has also been derived in closed form. An expression for the beat length of this system of coupled waveguides has also been obtained for the cases of strong and intermediate strength coupling. The propagator of two coupled waveguides with a variable spacing was also obtained in terms of an unknown function specified by a second order differential equation with simple boundary conditions. The technique of path integration is finally used to study wave propagation in a number of dielectric media whose refractive index has a random component. A refractive index model of this type is relevant to dielectric waveguides formed using a process of diffusion, and is thus of interest in the study of integrated optical waveguides. We obtained closed form results for the average propagator and the density of propagation modes for Gaussian random media having either zero or infinite refractive-index-inhomogeneity correlation-length along the direction of wave propagation

Data-flow Analysis of Programs with Associative Arrays

Dynamic programming languages, such as PHP, JavaScript, and Python, provide built-in data structures including associative arrays and objects with similar semantics-object properties can be created at run-time and accessed via arbitrary expressions. While a high level of security and safety of applications written in these languages can be of a particular importance (consider a web application storing sensitive data and providing its functionality worldwide), dynamic data structures pose significant challenges for data-flow analysis making traditional static verification methods both unsound and imprecise. In this paper, we propose a sound and precise approach for value and points-to analysis of programs with associative arrays-like data structures, upon which data-flow analyses can be built. We implemented our approach in a web-application domain-in an analyzer of PHP code.Comment: In Proceedings ESSS 2014, arXiv:1405.055

Relational Approach to Logical Query Optimization of XPath

To be able to handle the ever growing volumes of XML documents, effective and efficient data management solutions are needed. Managing XML data in a relational DBMS has great potential. Recently, effective relational storage schemes and index structures have been proposed as well as special-purpose join operators to speed up querying of XML data using XPath/XQuery. In this paper, we address the topic of query plan construction and logical query optimization. The claim of this paper is that standard relational algebra extended with special-purpose join operators suffices for logical query optimization. We focus on the XPath accelerator storage scheme and associated staircase join operators, but the approach can be generalized easily

A model for querying semistructured data through the exploitation of regular sub-structures

Much research has been undertaken in order to speed up the processing of semistructured data in general and XML in particular. Many approaches for storage, compression, indexing and querying exist, e.g. [1, 2]. We do not present yet another such algorithm but a unifying model in which these algorithm can be understood. The key idea behind this research is the assumption, that most practical queries are based on a particular pattern of data that can be deduced from the query and which can then be captured using a regular structure amendable to efficient processing techniques

Robust Grammatical Analysis for Spoken Dialogue Systems

We argue that grammatical analysis is a viable alternative to concept spotting for processing spoken input in a practical spoken dialogue system. We discuss the structure of the grammar, and a model for robust parsing which combines linguistic sources of information and statistical sources of information. We discuss test results suggesting that grammatical processing allows fast and accurate processing of spoken input.Comment: Accepted for JNL

Extracting partition statistics from semistructured data

The effective grouping, or partitioning, of semistructured data is of fundamental importance when providing support for queries. Partitions allow items within the data set that share common structural properties to be identified efficiently. This allows queries that make use of these properties, such as branching path expressions, to be accelerated. Here, we evaluate the effectiveness of several partitioning techniques by establishing the number of partitions that each scheme can identify over a given data set. In particular, we explore the use of parameterised indexes, based upon the notion of forward and backward bisimilarity, as a means of partitioning semistructured data; demonstrating that even restricted instances of such indexes can be used to identify the majority of relevant partitions in the data

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF