Rios and Selinger have recently proposed a categorical model for the quantum programming language Proto-Quipper-M, which is an important fragment of the Quipper language. In this work, we describe an extension to their categorical model with the additional property that it is DCPOenriched, bringing us closer to modeling general recursion in the language. Similar to their model, our model exhibi...

data envelopment analysis (dea) is a technique used to evaluate the relative efficiency of comparable decision making units (dmus) with multiple input-output. it computes a scalar measure of efficiency and discriminates between efficient and inefficient dmus. it can also provide reference units for inefficient dmus without consideration of the decision makers’ (dms) preferences. in this paper, ...

This paper employs a three-level hierarchical linear model (HLM) to examine the impacts that the quality of the environment and neighbourhood affluence have on housing prices. The empirical results suggest that there are significant variations in the average housing price for different neighbourhoods and administrative districts. The impact of building characteristics on housing prices is subje...

In this paper we review neglected issues of simultaneous statistical inference and statistical power in survey research applications of the general linear model, and we find that classical hypothesis testing as it is currently applied, is inadequate for the purposes of social research. The intelligent use of statistical inference demands control over the overall level of Type I error and knowle...

SUMMARY We derive the joint distribution of the sequence of estimates of the parameter vector in a normal general linear model when data accumulate over a series of analyses. This sequence of estimates has a remarkably simple covariance structure, even when observations are correlated, allowing standard group sequential tests to be applied in very general settings. If variances and covariances ...

The smooth testing approach described in [2] has been used to develop a test of the distributional assumption for generalized linear models. Application of the test to help assess Poisson and logistic regression models is discussed. Power is compared to other common tests.

It is often asserted that deep networks learn “features”, traditionally expressed by the activations of intermediate nodes. We explore an alternative concept by defining features as partial derivatives of model output with respect to model parameters—extending a simple yet powerful idea from generalized linear models. The resulting features are not equivalent to node activations, and we show th...

This paper describes the SEMAFOR system’s performance in the SemEval 2010 task on linking events and their participants in discourse. Our entry is based upon SEMAFOR 1.0 (Das et al., 2010a), a frame-semantic probabilistic parser built from log-linear models. The extended system models null instantiations, including non-local argument reference. Performance is evaluated on the task data with and...