Learning from text-based close call data

A key feature of big data is the variety of data sources that are available; which include not just numerical data but also image or video data or even free text. The GB railways collects a large volume of free text data daily from railway workers describing close call hazard reports: instances where an accident could have – but did not – occur. These close call reports contain valuable safety information which could be useful in managing safety on the railway, but which can be lost in the very large volume of data – much larger than is viable for a human analyst to read. This paper describes the application of rudimentary natural language processing (NLP) techniques to uncover safety information from close calls. The analysis has proven that basic information extraction is possible using the rudimentary techniques, but has also identified some limitations that arise using only basic techniques. Using these findings further research in this area intends to look at how the techniques that have been proven to date can be improved with the use of more advanced NLP techniques coupled with machine-learning

Global Search for New Physics with 2.0/fb at CDF

Data collected in Run II of the Fermilab Tevatron are searched for indications of new electroweak-scale physics. Rather than focusing on particular new physics scenarios, CDF data are analyzed for discrepancies with the standard model prediction. A model-independent approach (Vista) considers gross features of the data, and is sensitive to new large cross-section physics. Further sensitivity to new physics is provided by two additional algorithms: a Bump Hunter searches invariant mass distributions for "bumps" that could indicate resonant production of new particles; and the Sleuth procedure scans for data excesses at large summed transverse momentum. This combined global search for new physics in 2.0/fb of ppbar collisions at sqrt(s)=1.96 TeV reveals no indication of physics beyond the standard model.Comment: 8 pages, 7 figures. Final version which appeared in Physical Review D Rapid Communication

Observation of the Y(4140)Y(4140) structure in the J/ψ ϕJ/\psi\,\phi Mass Spectrum in B±→J/ψ ϕKB^\pm\to J/\psi\,\phi K cays

The observation of the $Y(4140)$ structure in $B^\pm\rightarrow J/\psi\,\phi K^\pm$ decays produced in $\bar{p} p$ collisions at \sqrt{s}=1.96~\TeV is reported with a statistical significance greater than 5 standard deviations. A fit to the $J/\psi\,\phi$ mass spectrum is performed assuming the presence of a Breit-Wigner resonance. The fit yields a signal of $19^{+6}_{-5}$ resonance events, and resonance mass and width of 4143.4^{+2.9}_{-3.0}(\mathrm{stat})\pm0.6(\mathrm{syst})~\MeVcc and 15.3^{+10.4}_{-6.1}(\mathrm{stat})\pm2.5(\mathrm{syst})~\MeVcc respectively. The parameters of this resonance-like structure are consistent with values reported from an earlier CDF analysis.Comment: 7 pages, 2 figures, submited to Phys. Rev. Let

Search for charged Higgs bosons in decays of top quarks in p-pbar collisions at sqrt(s) = 1.96 TeV

7 pages, 2 figuresWe report the recent charged Higgs search in top quark decays in 2.2/fb CDF data. This is the first attempt to search for charged Higgs using fully reconstructed mass assuming H->c-sbar in small tan beta region. No evidence of a charged Higgs is observed in the CDF data, hence 95% upper limits are placed at B(t->H+b)We report on the first direct search for charged Higgs bosons decaying into cs̅ in tt̅ events produced by pp̅ collisions at √s=1.96  TeV. The search uses a data sample corresponding to an integrated luminosity of 2.2  fb-1 collected by the CDF II detector at Fermilab and looks for a resonance in the invariant mass distribution of two jets in the lepton+jets sample of tt̅ candidates. We observe no evidence of charged Higgs bosons in top quark decays. Hence, 95% upper limits on the top quark decay branching ratio are placed at B(t→H+b)< 0.1 to 0.3 for charged Higgs boson masses of 60 to 150  GeV/c2 assuming B(H+→cs̅ )=1.0. The upper limits on B(t→H+b) are also used as model-independent limits on the decay branching ratio of top quarks to generic scalar charged bosons beyond the standard model.Peer reviewe

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe