Relative Select

Motivated by the problem of storing coloured de Bruijn graphs, we show how, if we can already support fast select queries on one string, then we can store a little extra information and support fairly fast select queries on a similar string

Extension of the asymptotic relative efficiency method to select the primary endpoint in a randomized clinical trial

We extend the ARE method proposed in Gómez and Lagakos (2013) devised to decide which primary endpoint to choose when comparing two treatments in a randomized clinical trial. The ARE method is based on the Asymptotic Relative Efficiency (ARE) between two logrank tests to compare two treatments: one is based on a relevant endpoint E1 while the other is based on a composite endpoint E* = E1 ¿ E2, where E2 is an additional endpoint. The ARE depends, besides some intuitive parameters, on the joint law of the times T1 and T2 from randomization to E1 and E2, respectively. Gómez and Lagakos (2013) characterize this joint law by means of Frank’s copula. In our work, several families of copulas can be chosen for the bivariate survival function of (T1, T2) so that different dependence struc- tures between T1 and T2 are feasible. We motivate the problem and show how to apply the method through a real cardiovascular clinical trial. We explore the influence of the copula chosen into the ARE value by means of a simulation study. We conclude that the recommendation on whether or not to use the composite endpoint as the primary endpoint for the investigation is, almost always, independent of the copula chosen.Preprin

Quantum collapse rules from the maximum relative entropy principle

We show that the von Neumann--Lueders collapse rules in quantum mechanics always select the unique state that maximises the quantum relative entropy with respect to the premeasurement state, subject to the constraint that the postmeasurement state has to be compatible with the knowledge gained in the measurement. This way we provide an information theoretic characterisation of quantum collapse rules by means of the maximum relative entropy principle.Comment: v2: some references added, improved presentation, result generalised to cover nonfaithful states; v3: cross-ref to arXiv:1408.3502 added, v4: some small corrections plus reference to a published version adde

Overconfidence Can Improve an Agent's Relative and Absolute Performance in Contests

This paper suggests a potential rationale for the recent empirical finding that overconfident agents tend to self-select into more competitive environments (e.g. Dohmen and Falk, forthcoming). In particular, it shows that moderate overconfidence in a contest can improve the agent's performance relative to an unbiased opponent and can even lead to an advantage for the overconfident agent in absolute terms

Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem

This paper proposes a new method for the K-armed dueling bandit problem, a variation on the regular K-armed bandit problem that offers only relative feedback about pairs of arms. Our approach extends the Upper Confidence Bound algorithm to the relative setting by using estimates of the pairwise probabilities to select a promising arm and applying Upper Confidence Bound with the winner as a benchmark. We prove a finite-time regret bound of order O(log t). In addition, our empirical results using real data from an information retrieval application show that it greatly outperforms the state of the art.Comment: 13 pages, 6 figure

The multilevel trigger system of the DIRAC experiment

The multilevel trigger system of the DIRAC experiment at CERN is presented. It includes a fast first level trigger as well as various trigger processors to select events with a pair of pions having a low relative momentum typical of the physical process under study. One of these processors employs the drift chamber data, another one is based on a neural network algorithm and the others use various hit-map detector correlations. Two versions of the trigger system used at different stages of the experiment are described. The complete system reduces the event rate by a factor of 1000, with efficiency $\geq$95% of detecting the events in the relative momentum range of interest.Comment: 21 pages, 11 figure

SeLeCT: a lexical cohesion based news story segmentation system

In this paper we compare the performance of three distinct approaches to lexical cohesion based text segmentation. Most work in this area has focused on the discovery of textual units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e., distinct news stories from broadcast news programmes. Our approach to news story segmentation (the SeLeCT system) is based on an analysis of lexical cohesive strength between textual units using a linguistic technique called lexical chaining. We evaluate the relative performance of SeLeCT with respect to two other cohesion based segmenters: TextTiling and C99. Using a recently introduced evaluation metric WindowDiff, we contrast the segmentation accuracy of each system on both "spoken" (CNN news transcripts) and "written" (Reuters newswire) news story test sets extracted from the TDT1 corpus