A Logical Foundation for Environment Classifiers

Taha and Nielsen have developed a multi-stage calculus {\lambda}{\alpha} with a sound type system using the notion of environment classifiers. They are special identifiers, with which code fragments and variable declarations are annotated, and their scoping mechanism is used to ensure statically that certain code fragments are closed and safely runnable. In this paper, we investigate the Curry-Howard isomorphism for environment classifiers by developing a typed {\lambda}-calculus {\lambda}|>. It corresponds to multi-modal logic that allows quantification by transition variables---a counterpart of classifiers---which range over (possibly empty) sequences of labeled transitions between possible worlds. This interpretation will reduce the "run" construct---which has a special typing rule in {\lambda}{\alpha}---and embedding of closed code into other code fragments of different stages---which would be only realized by the cross-stage persistence operator in {\lambda}{\alpha}---to merely a special case of classifier application. {\lambda}|> enjoys not only basic properties including subject reduction, confluence, and strong normalization but also an important property as a multi-stage calculus: time-ordered normalization of full reduction. Then, we develop a big-step evaluation semantics for an ML-like language based on {\lambda}|> with its type system and prove that the evaluation of a well-typed {\lambda}|> program is properly staged. We also identify a fragment of the language, where erasure evaluation is possible. Finally, we show that the proof system augmented with a classical axiom is sound and complete with respect to a Kripke semantics of the logic

The Morphological Content of Ten EDisCS Clusters at 0.5 < z < 0.8

We describe Hubble Space Telescope (HST) imaging of 10 of the 20 ESO Distant Cluster Survey (EDisCS) fields. Each ~40 square arcminute field was imaged in the F814W filter with the Advanced Camera for Surveys Wide Field Camera. Based on these data, we present visual morphological classifications for the ~920 sources per field that are brighter than I_auto=23 mag. We use these classifications to quantify the morphological content of 10 intermediate-redshift (0.5 < z < 0.8) galaxy clusters within the HST survey region. The EDisCS results, combined with previously published data from seven higher redshift clusters, show no statistically significant evidence for evolution in the mean fractions of elliptical, S0, and late-type (Sp+Irr) galaxies in clusters over the redshift range 0.5 < z < 1.2. In contrast, existing studies of lower redshift clusters have revealed a factor of ~2 increase in the typical S0 fraction between z=0.4 and z=0, accompanied by a commensurate decrease in the Sp+Irr fraction and no evolution in the elliptical fraction. The EDisCS clusters demonstrate that cluster morphological fractions plateau beyond z ~ 0.4. They also exhibit a mild correlation between morphological content and cluster velocity dispersion, highlighting the importance of careful sample selection in evaluating evolution. We discuss these findings in the context of a recently proposed scenario in which the fractions of passive (E,S0) and star-forming (Sp,Irr) galaxies are determined primarily by the growth history of clusters.Comment: 18 pages, 7 figures; To be published in ApJ; minor changes made to table label

Multi-input distributed classifiers for synthetic genetic circuits

For practical construction of complex synthetic genetic networks able to perform elaborate functions it is important to have a pool of relatively simple "bio-bricks" with different functionality which can be compounded together. To complement engineering of very different existing synthetic genetic devices such as switches, oscillators or logical gates, we propose and develop here a design of synthetic multiple input distributed classifier with learning ability. Proposed classifier will be able to separate multi-input data, which are inseparable for single input classifiers. Additionally, the data classes could potentially occupy the area of any shape in the space of inputs. We study two approaches to classification, including hard and soft classification and confirm the schemes of genetic networks by analytical and numerical results

Feature Type Analysis in Automated Genre Classification

In this paper, we compare classifiers based on language model, image, and stylistic features for automated genre classification. The majority of previous studies in genre classification have created models based on an amalgamated representation of a document using a multitude of features. In these models, the inseparable roles of different features make it difficult to determine a means of improving the classifier when it exhibits poor performance in detecting selected genres. By independently modeling and comparing classifiers based on features belonging to three types, describing visual, stylistic, and topical properties, we demonstrate that different genres have distinctive feature strengths.