Inferring Types to Eliminate Ownership Checks in an Intentional JavaScript Compiler

Concurrent programs are notoriously difficult to develop due to the non-deterministic nature of thread scheduling. It is desirable to have a programming language to make such development easier. Tscript comprises such a system. Tscript is an extension of JavaScript that provides multithreading support along with intent specification. These intents allow a programmer to specify how parts of the program interact in a multithreaded context. However, enforcing intents requires run-time memory checks which can be inefficient. This thesis implements an optimization in the Tscript compiler that seeks to improve this inefficiency through static analysis. Our approach utilizes both type inference and dataflow analysis to eliminate unnecessary run-time checks

Web and Semantic Web Query Languages

A number of techniques have been developed to facilitate powerful data retrieval on the Web and Semantic Web. Three categories of Web query languages can be distinguished, according to the format of the data they can retrieve: XML, RDF and Topic Maps. This article introduces the spectrum of languages falling into these categories and summarises their salient aspects. The languages are introduced using common sample data and query types. Key aspects of the query languages considered are stressed in a conclusion

Type-Based Detection of XML Query-Update Independence

This paper presents a novel static analysis technique to detect XML query-update independence, in the presence of a schema. Rather than types, our system infers chains of types. Each chain represents a path that can be traversed on a valid document during query/update evaluation. The resulting independence analysis is precise, although it raises a challenging issue: recursive schemas may lead to infer infinitely many chains. A sound and complete approximation technique ensuring a finite analysis in any case is presented, together with an efficient implementation performing the chain-based analysis in polynomial space and time.Comment: VLDB201

The Problem of Mental Action

In mental action there is no motor output to be controlled and no sensory input vector that could be manipulated by bodily movement. It is therefore unclear whether this specific target phenomenon can be accommodated under the predictive processing framework at all, or if the concept of “active inference” can be adapted to this highly relevant explanatory domain. This contribution puts the phenomenon of mental action into explicit focus by introducing a set of novel conceptual instruments and developing a first positive model, concentrating on epistemic mental actions and epistemic self-control. Action initiation is a functionally adequate form of self-deception; mental actions are a specific form of predictive control of effective connectivity, accompanied and possibly even functionally mediated by a conscious “epistemic agent model”. The overall process is aimed at increasing the epistemic value of pre-existing states in the conscious self-model, without causally looping through sensory sheets or using the non-neural body as an instrument for active inference