A New General Method to Generate Random Modal Formulae for Testing Decision Procedures

The recent emergence of heavily-optimized modal decision procedures has highlighted the key role of empirical testing in this domain. Unfortunately, the introduction of extensive empirical tests for modal logics is recent, and so far none of the proposed test generators is very satisfactory. To cope with this fact, we present a new random generation method that provides benefits over previous methods for generating empirical tests. It fixes and much generalizes one of the best-known methods, the random CNF_[]m test, allowing for generating a much wider variety of problems, covering in principle the whole input space. Our new method produces much more suitable test sets for the current generation of modal decision procedures. We analyze the features of the new method by means of an extensive collection of empirical tests

A New General Method to Generate Random Modal Formulae for Testing Decision Procedures

The recent emergence of heavily-optimized modal decision procedures has highlighted the key role of empirical testing in this domain. Unfortunately, the introduction of extensive empirical tests for modal logics is recent, and so far none of the proposed test generators is very satisfactory. To cope with this fact, we present a new random generation method that provides benefits over previous methods for generating empirical tests. It fixes and much generalizes one of the best-known methods, the random CNF_[]m test, allowing for generating a much wider variety of problems, covering in principle the whole input space. Our new method produces much more suitable test sets for the current generation of modal decision procedures. We analyze the features of the new method by means of an extensive collection of empirical tests

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks

Estimating the Heterogeneity Variance in a Random-Effects Meta-Analysis

In a meta-analysis, differences in the design and conduct of studies may cause variation in effects beyond what is expected from chance alone. This additional variation is commonly known as heterogeneity, which is incorporated into a random-effects model. The heterogeneity variance parameter in this model is commonly estimated by the DerSimonian-Laird method, despite being shown to produce negatively biased estimates in simulated data. Many other methods have been proposed, but there has been less research into their properties. This thesis compares all methods to estimate the heterogeneity variance in both empirical and simulated meta-analysis data. First, methods are compared in 12,894 empirical meta-analyses from the Cochrane Database of Systematic Reviews (CDSR). These results showed high discordance in estimates of the heterogeneity variance between methods, so investigating their properties in simulated meta-analysis data is worthwhile. A systematic review of relevant simulation studies was then conducted and identified 12 studies, but there was little consensus between them and conclusions could only be considered tentative. A new simulation study was conducted in collaboration with other statisticians. Results confirmed that the DerSimonian-Laird method is negatively biased in scenarios where within-study variances are imprecise and/or biased. On the basis of these results, the REML approach to heterogeneity variance estimation is recommended. A secondary analysis combines simulated and empirical meta-analysis data and shows all methods usually have poor properties in practice; only marginal improvements are possible using REML. In conclusion, caution is advised when interpreting estimates of the heterogeneity variance and confidence intervals should always be presented to express its uncertainty. More promisingly, the Hartung-Knapp confidence interval method is robust to poor heterogeneity variance estimates, so sensitivity analysis is not usually required for inference on the mean effect

Estimating the heterogeneity variance in a random-effects meta-analysis [Vol. 1]

In a meta-analysis, differences in the design and conduct of studies may cause variation in effects beyond what is expected from chance alone. This additional variation is commonly known as heterogeneity, which is incorporated into a random-effects model. The heterogeneity variance parameter in this model is commonly estimated by the DerSimonian-Laird method, despite being shown to produce negatively biased estimates in simulated data. Many other methods have been proposed, but there has been less research into their properties. This thesis compares all methods to estimate the heterogeneity variance in both empirical and simulated meta-analysis data. First, methods are compared in 12,894 empirical meta-analyses from the Cochrane Database of Systematic Reviews (CDSR). These results showed high discordance in estimates of the heterogeneity variance between methods, so investigating their properties in simulated meta-analysis data is worthwhile. A systematic review of relevant simulation studies was then conducted and identified 12 studies, but there was little consensus between them and conclusions could only be considered tentative. A new simulation study was conducted in collaboration with other statisticians. Results confirmed that the DerSimonian-Laird method is negatively biased in scenarios where within-study variances are imprecise and/or biased. On the basis of these results, the REML approach to heterogeneity variance estimation is recommended. A secondary analysis combines simulated and empirical meta-analysis data and shows all methods usually have poor properties in practice; only marginal improvements are possible using REML. In conclusion, caution is advised when interpreting estimates of the heterogeneity variance and confidence intervals should always be presented to express its uncertainty. More promisingly, the Hartung-Knapp confidence interval method is robust to poor heterogeneity variance estimates, so sensitivity analysis is not usually required for inference on the mean effect

OCM 2021 - Optical Characterization of Materials

The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field