Notion Of Adjacency Research Articles

Geographical queries reformulation using a parallel association rules generator to build spatial taxonomies

Geographical queries need a special process of reformulation by information retrieval systems (IRS) due to their specificities and hierarchical structure. This fact is ignored by most of web search engines. In this paper, we propose an automatic approach for building a spatial taxonomy, that models’ the notion of adjacency that will be used in the reformulation of the spatial part of a geographical query. This approach exploits the documents that are in top of the retrieved list when submitting a spatial entity, which is composed of a spatial relation and a noun of a city. Then, a transactional database is constructed, considering each document extracted as a transaction that contains the nouns of the cities sharing the country of the submitted query’s city. The algorithm frequent pattern growth (FP-growth) is applied to this database in his parallel version (parallel FP-growth: PFP) in order to generate association rules, that will form the country’s taxonomy in a Big Data context. Experiments has been conducted on Spark and their results show that query reformulation using the taxonomy constructed based on our proposed approach improves the precision and the effectiveness of the IRS.

Adjacent LBP and LTP based background modeling with mixed-mode learning for foreground detection

Detection of video objects under bad weather and poor illumination condition is a challenging task. We address this issue using the notion of background modeling. LBP-based background modeling and model learning has been used to detect the video objects but performance degrades in the above complex background. We propose the adjacency- and reinforced adjacency-based variants of LBP for complex real-world background modeling and model learning for object detection. In this regard, we have proposed the four variants; (1) enhanced adjacent local binary pattern, (2) enhanced reinforced adjacent local binary pattern, (3) enhanced adjacent local ternary pattern, and (4) enhanced reinforced adjacent local ternary pattern. Besides, we have embedded the Gabor and LBP features to obtain an embedded feature, which is subsequently used with the notions of adjacency. Unlike the background learning approach where the model learns only the background, our model learning algorithm learns the background together with the foreground objects and hence named as mixed-mode learning strategy. These models together with the new learning strategy are tested with CD2014 (snowfall and blizzard) and PETS (2014 and 2016) data sets, and the performance of the proposed models has been compared with LBP-based methods and other state-of-the-art methods.

Vertical Indexing for Moving Objects in Multifloor Environments

The indexing and tracking of objects moving in indoor spaces has increasingly become an important area of research, which presents a fundamentally different challenge. There are two main reasons for why indoor should be treated as cellular space. Firstly, an indoor space has entities, such as rooms and walls, that constrain the movement of the moving objects. Secondly, the relevant notion of locations of an object is cell based rather than an exact Euclidean coordinate. As a solution, in our earlier works, we proposed a cell-based indexing structure, called the C-tree, for indexing objects moving in indoor space. In this paper, we extend the C-tree to solve another interesting problem. It can be observed that many indoor spaces (such as shopping centers) contain wings/sections. For such a space, there are queries for which the wing/section location of an object, rather than the cellular location, is the relevant answer (e.g., “the object is in the east wing”). In this paper, we propose a new index structure, called the GMI-tree (“GMI” stands for “Graph-based Multidimensional Index”). The GMI-tree is based on two notions of distance, or equivalently, two notions of adjacency: one represents horizontal adjacency and the other represents vertical adjacency.

Graphs of Morphisms of Graphs

This is an account for the combinatorially minded reader of various categories of directed and undirected graphs, and their analogies with the category of sets. As an application, the endomorphisms of a graph are in this context not only composable, giving a monoid structure, but also have a notion of adjacency, so that the set of endomorphisms is both a monoid and a graph. We extend Shrimpton's (unpublished) investigations on the morphism digraphs of reflexive digraphs to the undirected case by using an equivalence between a category of reflexive, undirected graphs and the category of reflexive, directed graphs with reversal. In so doing, we emphasise a picture of the elements of an undirected graph, as involving two types of edges with a single vertex, namely 'bands' and 'loops'. Such edges are distinguished by the behaviour of morphisms with respect to these elements.

Spatial heterogeneity of the risk of BSE in France following the ban of meat and bone meal in cattle feed

In France, meat-and-bone meal (MBM) has been prohibited for cattle feeding since 1990, but bovine spongiform encephalopathy (BSE) cases, called ‘NAIF’, appeared in animals born after this feed ban. Furthermore, in 1996 a new measure was taken: removal of cadavers and specified risk materials (SRM) from the processing of MBM dedicated to animal feed. Nevertheless, BSE cases (called ‘super-NAIF’) appeared in cattle born after this measure was in force. We analysed the spatial distribution of 445 ‘NAIF’ and 58 ‘super-NAIF’ cases detected in France from July 1, 2001 to July 31, 2003. The detection of BSE was based both on the mandatory reporting system (MRS) and the systematic test screening of cattle at the abattoir and at the fallen-animal plant with rapid tests. The background population was based on the adult-cow census. The disease mapping of the BSE risk was based on the standardised incidence ratio (stochastic Poisson process). A spatial component, which takes into account the spatial dependence between the geographical units by a notion of adjacency was used to eliminate the over-dispersion in the risk assessment. The geographical units were defined by hexagons with a side of 23 km (France had 1264 hexagons). The parameters were estimated by a Metropolis Gibbs sampling algorithm using the Markov-chain Monte Carlo methods. The BSE cases were not randomly distributed. Furthermore, the areas at risk for the ‘super-NAIF’ matched part of the areas at risk for the ‘NAIF’ cases—which suggests that it might be a common source of contamination.

A type theory for memory allocation and data layout

Ordered type theory is an extension of linear type theory in which variables in the context may be neither dropped nor re-ordered. This restriction gives rise to a natural notion of adjacency . We show that a language based on ordered types can use this property to give an exact account of the layout of data in memory. The fuse constructor from ordered logic describes adjacency of values in memory, and the mobility modal describes pointers into the heap. We choose a particular allocation model based on a common implementation scheme for copying garbage collection and show how this permits us to separate out the allocation and initialization of memory locations in such a way as to account for optimizations such as the coalescing of multiple calls to the allocator.

Barrier Trees on Poset-Valued Landscapes

Fitness landscapes have proved to be a valuable concept in evolutionary biology, combinatorial optimization, and the physics of disordered systems. Usually, a fitness landscape is considered as a mapping from a configuration space equipped with some notion of adjacency, nearness, distance, or accessibility, into the real numbers. In the context of multi-objective optimization problems this concept can be extended to poset-valued landscapes. In a geometric analysis of such a structure, local Pareto points take on the role of local minima. We show that the notion of saddle points, barriers, and basins can be extended to the poset-valued case in a meaningful way and describe an algorithm that efficiently extracts these features from an exhaustive enumeration of a given generalized landscape.

Combinatorial Landscapes

Fitness landscapes have proven to be a valuable concept in evolutionary biology, combinatorial optimization, and the physics of disordered systems. A fitness landscape is a mapping from a configuration space into the real numbers. The configuration space is equipped with some notion of adjacency, nearness, distance, or accessibility. Landscape theory has emerged as an attempt to devise suitable mathematical structures for describing the ``static'' properties of landscapes as well as their influence on the dynamics of adaptation. In this review we focus on the connections of landscape theory with algebraic combinatorics and random graph theory, where exact results are available.

Fiat and Bona Fide Boundaries

There is a basic distinction, in the realm of spatial boundaries, between bona fide boundaries on the one hand, and fiat boundaries on the other. The former are just the physical boundaries of old. The latter are exemplified especially by boundaries induced through human demarcation, for example, in the geographic domain. The classical metaphysical problems connected with the notions of adjacency, contact, separation, and division can be resolved in an intuitive way by recognizing this two-sorted ontology of boundaries. Bona fide boundaries yield a notion of contact that is effectively modeled by classical topology; the analogue of contact involving fiat boundaries calls, however, for a different account, based on the intuition that fiat boundaries do not support the open/closed distinction on which classical topology is based. In the presence of this twosorted ontology it then transpires that mereotopology-typology erected on a mereological basis-is more than a trivial formal variant of classical point-set topology.

Fuzzy Adjacency between Image Objects

The notion of adjacency has a strong interest for image processing and pattern recognition, since it denotes an important relationship between objects or regions in an image, widely used as a feature in model-based pattern recognition. A crisp definition of adjacency often leads to low robustness in the presence of noise, imprecision, or segmentation errors. We propose two approaches to cope with spatial imprecision in image processing applications, both based on the framework of fuzzy sets. These approaches lead to two completely different classes of definitions of a degree of adjacency. In the first approach, we introduce imprecision as a property of the adjacency relation, and consider adjacency between two (crisp) objects to be a matter of degree. We represent adjacency by a fuzzy relation whose value depends on the distance between the objects. In the second approach, we introduce imprecision (in particular spatial imprecision) as a property of the objects, and consider objects to be fuzzy subsets of the image space. We then represent adjacency by a relation between fuzzy sets. This approach is, in our opinion, more powerful and general. We propose several ways for extending adjacency to fuzzy sets, either by using α-cuts, or by using a formal translation of binary equations into fuzzy ones. Since set equations are more easily translated into fuzzy terms, we shall privilege set representations of adjacency, particularly in the framework of fuzzy mathematical morphology. Finally, we give some hints on how to compare degrees of adjacency, typically for applications in model-based pattern recognition.