The Mirror DBMS at TREC-8

The database group at University of Twente participates in TREC8 using the Mirror DBMS, a prototype database system especially designed for multimedia and web retrieval. From a database perspective, the purpose has been to check whether we can get sufficient performance, and to prepare for the very large corpus track in which we plan to participate next year. From an IR perspective, the experiments have been designed to learn more about the effect of the global statistics on the ranking

Syntactic structure and artificial grammar learning : The learnability of embedded hierarchical structures

Embedded hierarchical structures, such as ‘‘the rat the cat ate was brown’’, constitute a core generative property of a natural language theory. Several recent studies have reported learning of hierarchical embeddings in artificial grammar learning (AGL) tasks, and described the functional specificity of Broca’s area for processing such structures. In two experiments, we investigated whether alternative strategies can explain the learning success in these studies. We trained participants on hierarchical sequences, and found no evidence for the learning of hierarchical embeddings in test situations identical to those from other studies in the literature. Instead, participants appeared to solve the task by exploiting surface distinctions between legal and illegal sequences, and applying strategies such as counting or repetition detection. We suggest alternative interpretations for the observed activation of Broca’s area, in terms of the application of calculation rules or of a differential role of working memory. We claim that the learnability of hierarchical embeddings in AGL tasks remains to be demonstrated

Environmental Risk Assessment of Produced Water Discharges on the Dutch Continental Shelf

The OSPAR Offshore Industry Committee (OIC) has decided, in its meeting of 2008, to evaluate the possibility of implementing a risk based approach towards produced water management. Currently, Norway has made most progress in this field as it has fully implemented the Environmental Impact Factor as the basis of their biannual reporting obligations. The Netherlands has for as yet mainly followed a source (immission) based approach, and therefore did not adopt a specific risk based approach. In this study an overview is provided of current approaches to assess the ecological risk of produced water discharges and it is investigated how these approaches can be used in the Dutch situation for produced water management as intended by the OIC

A Study of the Relationship Between Health and Subjective Well-being in Parkinson’s Disease Patients

Objectives: In light of the apparent disconnect between traditional measures of societal well-being such as GDP and reported levels of happiness, governments globally are turning their attention to alternative subjective measures of well-being (SWB) to aid policy decisions. In the context of health, there is therefore growing interest in understanding how measures of health-related quality of life (HRQoL), widely used in health technology appraisal, relates to SWB, and whether SWB could provide a sound basis for resource allocation decisions in health and other sectors in the future. This study investigates the relationship between HRQoL, as measured by EQ-5D, and SWB in Parkinson’s disease (PD) and the extent to which patients’ self-reported health can explain (part of) their SWB. Methods: A paper questionnaire including EQ-5D, four key SWB questions taken from the Office for National Statistics Integrated Household Survey in England and other demographic details was distributed to people with PD in the UK. Responses were used to estimate multiple regression models explaining SWB using each of the EQ-5D Index (UK weights), EQ-5D dimensions and EQ-VAS and patient socio-demographic characteristics. Results: 276 questionnaires were distributed and 183 responses received. The EQ-5D Index was a moderate predictor of SWB (adjusted R2 range 0.19-0.38 in OLS models), but EQ-VAS performed better (adjusted R2 range 0.32-0.49). Combining EQ-VAS and EQ-5D dimensions, especially anxiety/depression and mobility, and household status in some cases, yielded the best-fitting models (adjusted R2 range 0.40-0.52). Conclusions: The findings imply that EQ-VAS and some dimensions of the EQ-5D, together with key demographic data, could potentially be used to predict SWB, e.g. via mapping. However, further empirical research into the relationship between SWB and EQ-5D longitudinally, and in different disease areas, is required to corroborate these findings, and further standardisation of SWB measures is recommended

Runtime Optimizations for Prediction with Tree-Based Models

Tree-based models have proven to be an effective solution for web ranking as well as other problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, given an already-trained model. Although exceedingly simple conceptually, most implementations of tree-based models do not efficiently utilize modern superscalar processor architectures. By laying out data structures in memory in a more cache-conscious fashion, removing branches from the execution flow using a technique called predication, and micro-batching predictions using a technique called vectorization, we are able to better exploit modern processor architectures and significantly improve the speed of tree-based models over hard-coded if-else blocks. Our work contributes to the exploration of architecture-conscious runtime implementations of machine learning algorithms

The SIKS/BiGGrid Big Data Tutorial

The School for Information and Knowledge Systems SIKS and the Dutch e-science grid BiG Grid organized a new two-day tutorial on Big Data at the University of Twente on 30 November and 1 December 2011, just preceding the Dutch-Belgian Database Day. The tutorial is on top of some exciting new developments in large-scale data processing and data centers, initiated by Google, and followed by many others such as Yahoo, Amazon, Microsoft, and Facebook. The course teaches how to process terabytes of data on large clusters, and discusses several core computer science topics adapted for big data, such as new file systems (Google File System and Hadoop FS), new programming paradigms (MapReduce), new programming languages and query languages (Sawzall, Pig Latin), and new 'noSQL' databases (BigTable, Cassandra and Dynamo)

Three computer programs for n-body trajector- ies and interplanetary trajectories

Input and operating instructions, and sample problems for IBM 7094 computer programs - interplanetary trajectory program, n-body trajectory program, and sensitivity coefficient

Unsupervised image segmentation with neural networks

The segmentation of colour images (RGB), distinguishing clusters of image points, representing for example background, leaves and flowers, is performed in a multi-dimensional environment. Considering a two dimensional environment, clusters can be divided by lines. In a three dimensional environment by planes and in an n-dimensional environment by n-1 dimensional structures. Starting with a complete data set the first neural network, represents an n-1 dimensional structure to divide the data set into two subsets. Each subset is once more divided by an additional neural network: recursive partitioning. This results in a tree structure with a neural network in each branching point. Partitioning stops as soon as a partitioning criterium cannot be fulfilled. After the unsupervised training the neural system can be used for the segmentation of images