Fault Tree Analysis: a survey of the state-of-the-art in modeling, analysis and tools

Fault tree analysis (FTA) is a very prominent method to analyze the risks related to safety and economically critical assets, like power plants, airplanes, data centers and web shops. FTA methods comprise of a wide variety of modelling and analysis techniques, supported by a wide range of software tools. This paper surveys over 150 papers on fault tree analysis, providing an in-depth overview of the state-of-the-art in FTA. Concretely, we review standard fault trees, as well as extensions such as dynamic FT, repairable FT, and extended FT. For these models, we review both qualitative analysis methods, like cut sets and common cause failures, and quantitative techniques, including a wide variety of stochastic methods to compute failure probabilities. Numerous examples illustrate the various approaches, and tables present a quick overview of results

Inter-individual variability in morphological processing: An ERP study on German plurals

Previous studies on the neuro-cognition of language have provided a strong case for systematic inter-individual variability in event-related potentials (ERPs) evoked during language processing. In the present study, we aimed at extending this evidence to the processing of morphologically complex words. We focused on German plural forms and tested two types of morphological violations: overapplications of regular plural morphemes (‘regularizations’) and of irregular plural morphemes (‘irregularizations’). The group-level results showed a biphasic LAN-P600 response for regularizations, and a P600 for irregularizations. In line with previous reports, our analyses of inter-individual variability suggested that biphasic responses consisting of a negativity followed by a positivity are unlikely to exist at the individual level. Importantly, when analyzing the scalp distribution of ERPs elicited in participants supposed to show negativity-dominant responses, we found this to vary as a function of the type of morphological form: regularizations elicited a left-hemisphere response (LAN), while irregularizations a more widespread negativity (N400). Our results are consistent with dual-route accounts of morphological processing that distinguish between rule-based processing for regular inflection and memory retrieval for irregular inflection. At a more general level, our study shows that complementing group-level results with analyses of inter-individual variability can crucially contribute to a more detailed understanding of brain signatures of language

Quantity and Quality: Not a Zero-Sum Game

Quantification of existing theories is a great challenge but also a great chance for the study of language in the brain. While quantification is necessary for the development of precise theories, it demands new methods and new perspectives. In light of this, four complementary methods were introduced to provide a quantitative and computational account of the extended Argument Dependency Model from Bornkessel-Schlesewsky and Schlesewsky. First, a computational model of human language comprehension was introduced on the basis of dependency parsing. This model provided an initial comparison of two potential mechanisms for human language processing, the traditional "subject" strategy, based on grammatical relations, and the "actor" strategy based on prominence and adopted from the eADM. Initial results showed an advantage for the traditional subject" model in a restricted context; however, the "actor" model demonstrated behavior in a test run that was more similar to human behavior than that of the "subject" model. Next, a computational-quantitative implementation of the "actor" strategy as weighted feature comparison between memory units was used to compare it to other memory-based models from the literature on the basis of EEG data. The "actor" strategy clearly provided the best model, showing a better global fit as well as better match in all details. Building upon the success modeling EEG data, the feasibility of estimating free parameters from empirical data was demonstrated. Both the procedure for doing so and the necessary software were introduced and applied at the level of individual participants. Using empirically estimated parameters, the models from the previous EEG experiment were calculated again and yielded similar results, thus reinforcing the previous work. In a final experiment, the feasibility of analyzing EEG data from a naturalistic auditory stimulus was demonstrated, which conventional wisdom says is not possible. The analysis suggested a new perspective on the nature of event-related potentials (ERPs), which does not contradict existing theory yet nonetheless goes against previous intuition. Using this new perspective as a basis, a preliminary attempt at a parsimonious neurocomputational theory of cognitive ERP components was developed

Predictability studies of coastal marine ecosystem behavior

The study presented in this thesis is principally meant to analyze the genericity of a deterministic, comprehensive marine ecosystem model in combination with various refined representations of hydrodynamical processes, and to evaluate the potential predictability skills of this combined modelling system with specific applications in two rather different coastal basins. This objective has been realized by first developing a modular coupling interface between the Princeton Ocean Model (POM) and the European Regional Seas Ecosystem Model (ERSEM), called High-Resolution OpenSESAME POM ERSEM (HiROPE). Secondly, this model framework, embedding a composite of 'complex' conceptual principles of the functioning of the main biogeochemical processes, has been applied to substantially different marine systems, the Baltic proper and the northern Adriatic Sea. The generic biological first principles of the ERSEM ecosystem model have been throughly controlled for consistency, and a suitable mathematical syntax has been defined in order to accomodate the various biogeochemical cycles of the resolved elements. The model has been specifically applied in the chosen basins with different temporal and spatial resolutions: a one-dimensional (vertical, 1D-V), climatological implementation in the northern Adriatic Sea; a 1D-V implementation in the Baltic proper with realistic forcing functions in the period 1979-1991 and a fully three-dimensional, high-frequency realistic implementation in the northern Adriatic Sea (October 1995). General conclusions are that the representation of hydrodynamical variability, the definition and resolution of boundary processes, the introduction of new source terms or the implementation of new biological state variables, affect the predictability of the system behavior more than the utilization of incomplete initial conditions of biological variables in a complex comprehensive ecosystem model

Predictability studies of coastal marine ecosystem behavior

The study presented in this thesis is principally meant to analyze the genericity of a deterministic, comprehensive marine ecosystem model in combination with various refined representations of hydrodynamical processes, and to evaluate the potential predictability skills of this combined modelling system with specific applications in two rather different coastal basins. This objective has been realized by first developing a modular coupling interface between the Princeton Ocean Model (POM) and the European Regional Seas Ecosystem Model (ERSEM), called High-Resolution OpenSESAME POM ERSEM (HiROPE). Secondly, this model framework, embedding a composite of 'complex' conceptual principles of the functioning of the main biogeochemical processes, has been applied to substantially different marine systems, the Baltic proper and the northern Adriatic Sea. The generic biological first principles of the ERSEM ecosystem model have been throughly controlled for consistency, and a suitable mathematical syntax has been defined in order to accomodate the various biogeochemical cycles of the resolved elements. The model has been specifically applied in the chosen basins with different temporal and spatial resolutions: a one-dimensional (vertical, 1D-V), climatological implementation in the northern Adriatic Sea; a 1D-V implementation in the Baltic proper with realistic forcing functions in the period 1979-1991 and a fully three-dimensional, high-frequency realistic implementation in the northern Adriatic Sea (October 1995). General conclusions are that the representation of hydrodynamical variability, the definition and resolution of boundary processes, the introduction of new source terms or the implementation of new biological state variables, affect the predictability of the system behavior more than the utilization of incomplete initial conditions of biological variables in a complex comprehensive ecosystem model

Ubiquitous Semantic Applications

As Semantic Web technology evolves many open areas emerge, which attract more research focus. In addition to quickly expanding Linked Open Data (LOD) cloud, various embeddable metadata formats (e.g. RDFa, microdata) are becoming more common. Corporations are already using existing Web of Data to create new technologies that were not possible before. Watson by IBM an artificial intelligence computer system capable of answering questions posed in natural language can be a great example. On the other hand, ubiquitous devices that have a large number of sensors and integrated devices are becoming increasingly powerful and fully featured computing platforms in our pockets and homes. For many people smartphones and tablet computers have already replaced traditional computers as their window to the Internet and to the Web. Hence, the management and presentation of information that is useful to a user is a main requirement for today’s smartphones. And it is becoming extremely important to provide access to the emerging Web of Data from the ubiquitous devices. In this thesis we investigate how ubiquitous devices can interact with the Semantic Web. We discovered that there are five different approaches for bringing the Semantic Web to ubiquitous devices. We have outlined and discussed in detail existing challenges in implementing this approaches in section 1.2. We have described a conceptual framework for ubiquitous semantic applications in chapter 4. We distinguish three client approaches for accessing semantic data using ubiquitous devices depending on how much of the semantic data processing is performed on the device itself (thin, hybrid and fat clients). These are discussed in chapter 5 along with the solution to every related challenge. Two provider approaches (fat and hybrid) can be distinguished for exposing data from ubiquitous devices on the Semantic Web. These are discussed in chapter 6 along with the solution to every related challenge. We conclude our work with a discussion on each of the contributions of the thesis and propose future work for each of the discussed approach in chapter 7

Assessing the impact of co-occurrence frequency and diversity in statistical learning accounts of language processing

Language is heralded as one of the foremost human achievements and is vital in scaffolding the successful development of many other skills. Yet, the mechanism by which language is acquired is still poorly understood. One possible account is Statistical Learning Theory, an explanation of language acquisition that has grown in popularity over the past three decades. The central tenet of Statistical Learning Theory is that learners are guided by statistical regularities in their environment and can utilise these to develop an implicit understanding of their natural language. Current theory holds that transitional probabilities are the best predictor of learner performance in statistical learning tasks. However, little has been done to investigate alternative statistical measures. This thesis presents two such metrics: Bigram frequency and bigram diversity and contrasts them with transitional probability in predicting task performance. Through the repurposing of primed lexical decision and sequence learning tasks, I present a novel approach to examining the impact of statistical priming on task performance in a naturalistic dataset. Model comparison using Bayesian multilevel modelling suggests that transitional probability is not as reliable a predictor as was previously believed. Moreover, I demonstrate that bigram frequency may represent a better metric for predicting task performance in these tasks. The current work highlights the importance of considering alternative metrics of statistical regularity when describing the underlying mechanisms of language acquisition and showcases alternative methods of examining statistical learning performance