Spin correlations: Tevatron vs. LHC

We compare theoretical expectations for the observation of spin correlations in top quark pair production and decay at the Fermilab Tevatron and the CERN Large Hadron Collider (LHC). In particular, we note that the differing top quark pair production mechanisms in the two environments test different aspects of the Standard Model and require different strategies to observe the correlations. At the Tevatron, production is dominated by q qbar --> t tbar and the strategy is to construct a double-decay angle distribution where one decay angle is measured in the t rest frame and the other in the tbar rest frame. The dominant process at the LHC is gg --> t tbar, with a rich spin structure that allows for a second option in observing spin correlations. Here the strategy is to select events where the t tbar pair is produced at relatively low velocity in the zero momentum frame (ZMF). For these events, there are strong azimuthal correlations between t and tbar decay products present. This measurement enjoys the advantage that it can be carried out in the laboratory frame.Comment: 8 pages, talk presented at top2010, Bruges, Belgiu

Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus

The evaluative character of a word is called its semantic orientation. A positive semantic orientation implies desirability (e.g., "honest", "intrepid") and a negative semantic orientation implies undesirability (e.g., "disturbing", "superfluous"). This paper introduces a simple algorithm for unsupervised learning of semantic orientation from extremely large corpora. The method involves issuing queries to a Web search engine and using pointwise mutual information to analyse the results. The algorithm is empirically evaluated using a training corpus of approximately one hundred billion words — the subset of the Web that is indexed by the chosen search engine. Tested with 3,596 words (1,614 positive and 1,982 negative), the algorithm attains an accuracy of 80%. The 3,596 test words include adjectives, adverbs, nouns, and verbs. The accuracy is comparable with the results achieved by Hatzivassiloglou and McKeown (1997), using a complex four-stage supervised learning algorithm that is restricted to determining the semantic orientation of adjectives

Visualization of Big Spatial Data using Coresets for Kernel Density Estimates

The size of large, geo-located datasets has reached scales where visualization of all data points is inefficient. Random sampling is a method to reduce the size of a dataset, yet it can introduce unwanted errors. We describe a method for subsampling of spatial data suitable for creating kernel density estimates from very large data and demonstrate that it results in less error than random sampling. We also introduce a method to ensure that thresholding of low values based on sampled data does not omit any regions above the desired threshold when working with sampled data. We demonstrate the effectiveness of our approach using both, artificial and real-world large geospatial datasets

Measuring praise and criticism: Inference of semantic orientation from association

The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", "superfluous"). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This paper introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8% on the full test set, but the accuracy rises above 95% when the algorithm is allowed to abstain from classifying mild words

The introduction of a learning innovation to enhance the employability of event management students: an action research study.

Curriculum innovation in higher education is often directed at efficiency; however, this paper reports a small change in the curriculum which was designed to enhance student employability. Central to the learning and assessment of an undergraduate Events Management unit is that the students, in groups, organise a real event. In the academic year 2008-09, ‘clients’ were sought for each group, for whom the students could act as consultants in the organisation of an event. Communication skills in relation to consultancy were a particular emphasis of the innovation, which was evaluated using an action research methodology. Data, collected during the year, suggested that just over half of the cohort believed the approach was helping them to obtain a 40 week industrial placement for the following year. Furthermore, about three-quarters of the students felt that it would be beneficial in employment, first, during their placement (30% indicated it would be very useful) and secondly, after graduation. Upon completion of the events, the student group leaders and the clients were each asked to rate the other party and this showed that the clients also had a very favourable opinion of the students. Recommendations for minor modifications to the format were then made for the next academic year

Searching For a Break in GNP

It has been suggested that existing estimates of the long-run impact of a surprise move in income may have a substantial upward bias due to the presence of a trend break in post war U.S. GNP data. This paper shows that the statistical evidence does not warrant abandoning the no trend null hypothesis. A key part of the argument is that conventionally computed significance levels overstate the likelihood of the trend break alternative hypothesis. This is because they do not take into account that, in practice, the break date is chosen based on pre-test examination of the data.

Clinical experience as evidence in evidence-based practice

Background. This paper's starting point is the recognition (descriptive not normative) that, for the vast majority of day-to-day clinical decision-making situations, the 'evidence' for decision-making is experiential knowledge. Moreover, reliance on this knowledge base means that nurses must use cognitive shortcuts or heuristics for handling information when making decisions. These heuristics encourage systematic biases in decision-makers and deviations from the normative rules of 'good' decision-making. Aims. The aim of the paper is to explore three common heuristics and the biases that arise when handling complex information in clinical decision-making (overconfidence, hindsight and base rate neglect) and, in response to these biases, to illustrate some simple techniques for reducing the negative influence of heuristics. Discussion. Nurses face a limited range of types of uncertainty in their clinical decisions and draw primarily on experiential knowledge to handle these uncertainties. This paper argues that experiential knowledge is a necessary but not sufficient basis for clinical decision-making. It illustrates how overconfidence in one's knowledge base, being correct 'after the event' or with the benefit of hindsight, and ignoring the base rates associated with events, conditions or health states, can impact on professional judgements and decisions. The paper illustrates some simple strategies for minimizing the impact of heuristics on the real-life clinical decisions of nurses. Conclusion. The paper concludes that more research knowledge of the impact of heuristics and techniques to combat them in nursing decisions is needed