Expressiveness of Generic Process Shape Types

Shape types are a general concept of process types which work for many process calculi. We extend the previously published Poly* system of shape types to support name restriction. We evaluate the expressiveness of the extended system by showing that shape types are more expressive than an implicitly typed pi-calculus and an explicitly typed Mobile Ambients. We demonstrate that the extended system makes it easier to enjoy advantages of shape types which include polymorphism, principal typings, and a type inference implementation.Comment: Submitted to Trustworthy Global Computing (TGC) 2010

Generic process shape types and the Poly* system

Shape types are a general concept of process types which allows verification of various properties of processes from various calculi. The key property is that shape types “look like processes”, that is, they resemble process structure and content. PolyV, originally designed by Makholm and Wells, is a type system scheme which can be instantiated to a shape type system for many calculi. Every PolyV instantiation has desirable properties including subject reduction, polymorphism, the existence of principal typings, and a type inference algorithm. In the first part of this thesis, we fix and describe inconsistencies found in the original PolyV system, we extend the system to support name restriction, and we provide a detailed proof of the correctness of the system. In the second part, we present a description of the type inference algorithm which we use to constructively prove the existence of principal typings. In the third part, we present various applications of shape types which demonstrate their advantages. Furthermore we prove that shape types can provide the same expressive power as and also strictly superior expressive power than predicates of three quite dissimilar analysis systems from the literature, namely, (1) an implicitly typed π-calculus, (2) an explicitly typed Mobile Ambients, (3) and a flow analysis system for BioAmbients.Engineering and Physical Sciences Research Council (EPSRC) EP/C013573/

Shape Types for Labeling Natural Polygon Features with Maplex

The article presents information on a methodology used for describing natural polygons, which enables feature label placement rules to be derived for Maplex. Maplex is a cartographic label placement extension for ArcGIS, the complete Geographic Information Systems (GIS). In cartographic label placement one need to find the shape of the polygon and how large the polygon is within the context of the space required to place text. The methodology being is the polygon\u27s minimum bounding rectangle (MBR). MBRs have also been used for automated text placement algorithms. This method could prove to be very effective in labeling soil type, vegetation type, and surface geology type

Исследование форм, видов и условий построения системы экологического страхования

Досліджено форми, види, правила компенсації екологічних ризиків для формування системи екологічного страхування, яка забезпечує мотивацію підприємств - потенційних забруднювачів до зниження екологічних ризиків.The shape, types, rules of compensation for environmental risks for the formation of an environmental insurance, which provides motivation for enterprises - potential polluters to reduce environmental risks

Dendritic Spine Shape Analysis: A Clustering Perspective

Functional properties of neurons are strongly coupled with their morphology. Changes in neuronal activity alter morphological characteristics of dendritic spines. First step towards understanding the structure-function relationship is to group spines into main spine classes reported in the literature. Shape analysis of dendritic spines can help neuroscientists understand the underlying relationships. Due to unavailability of reliable automated tools, this analysis is currently performed manually which is a time-intensive and subjective task. Several studies on spine shape classification have been reported in the literature, however, there is an on-going debate on whether distinct spine shape classes exist or whether spines should be modeled through a continuum of shape variations. Another challenge is the subjectivity and bias that is introduced due to the supervised nature of classification approaches. In this paper, we aim to address these issues by presenting a clustering perspective. In this context, clustering may serve both confirmation of known patterns and discovery of new ones. We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem. We use histogram of oriented gradients (HOG), disjunctive normal shape models (DNSM), morphological features, and intensity profile based features for cluster analysis. We use x-means to perform cluster analysis that selects the number of clusters automatically using the Bayesian information criterion (BIC). For all features, this analysis produces 4 clusters and we observe the formation of at least one cluster consisting of spines which are difficult to be assigned to a known class. This observation supports the argument of intermediate shape types.Comment: Accepted for BioImageComputing workshop at ECCV 201

Anomalous couplings in Higgs-boson pair production at approximate NNLO QCD

We combine NLO predictions with full top-quark mass dependence with approximate NNLO predictions for Higgs-boson pair production in gluon fusion, including the possibility to vary coupling parameters within a non-linear Effective Field Theory framework containing five anomalous couplings for this process. We study the impact of the anomalous couplings on various observables, and present Higgs-pair invariant-mass distributions at seven benchmark points characterising different m$_{hh}$ shape types. We also provide numerical coefficients for the approximate NNLO cross section as a function of the anomalous couplings at $\sqrt{s}$ = 14 TeV

Data mining Mandarin tone contour shapes

In spontaneous speech, Mandarin tones that belong to the same tone category may exhibit many different contour shapes. We explore the use of data mining and NLP techniques for understanding the variability of tones in a large corpus of Mandarin newscast speech. First, we adapt a graph-based approach to characterize the clusters (fuzzy types) of tone contour shapes observed in each tone n-gram category. Second, we show correlations between these realized contour shape types and a bag of automatically extracted linguistic features. We discuss the implications of the current study within the context of phonological and information theory