Multiple meanings of the middle class in Soweto, South Africa

This study investigated the meanings of middle class amongst those who label themselves middle class. 2559 people were surveyed in Soweto, South Africa biggest township. The study revealed that a diverse number of people call themselves middle class and defined class in terms of ability to afford basic goods. The label middle class seems also to denote self-sufficiency, responsibility and social mobility The paper concludes that studies of the middle class does not seen to focus on how social location affects the scope of people’s social world and their range of comparison

NOVEL TECHNIQUES FOR INTRINSIC DIMENSION ESTIMATION

Since the 1950s, the rapid pace of technological advances allows to measure and record increasing amounts of data, motivating the urgent need to develop dimensionality reduction systems to be applied on datasets comprising high- dimensional points. To this aim, a fundamental information is provided by the intrinsic di- mension (id) defined by Bennet [1] as the minimum number of parameters needed to generate a data description by maintaining the \u201cintrinsic\u201d structure characterizing the dataset, so that the information loss is minimized. More recently, a quite intuitive definition employed by several authors in the past has been reported by Bishop in [2] where the author writes that \u201ca set in D dimensions is said to have an id equal to d if the data lies entirely within a d-dimensional subspace of D \u201d. Though more specific and different id definitions have been proposed in dif- ferent research fieldsthroughout the pattern recognition literature the presently prevailing id definition views a point set as a sample set uniformly drawn from an unknown smooth (or locally smooth) manifold structure, eventually embed- ded in an higher dimensional space through a non-linear smooth mapping; in this case, the id to be estimated is the manifold\u2019s topological dimension. Due to the importance of id in several theoretical and practical application fields, in the last two decades a great deal of research effort has been devoted to the development of effective id estimators. Though several techniques have been proposed in literature, the problem is still open for the following main reasons. 1At first, it must be highlighted that though Lebesgue\u2019s definition of topo- logical dimension (reported by [5]) is quite clear, in practice its estimation is difficult if only a finite set of points is available. Therefore, id estimation tech- niques proposed in literature are either founded on different notions of dimen- sion (e.g. fractal dimensions) approximating the topological one, or on various techniques aimed at preserving the characteristics of data-neighborhood distri- butions, which reflect the topology of the underlying manifold. Besides, the estimated id value markedly changes as the scale used to analyze the input dataset changes, and being the number of available points practically limited, several methods underestimate id when its value is sufficiently high (namely id 10). Other serious problems arise when the dataset is embedded in higher dimensional spaces through a non-linear map. Finally, the too high computa- tional complexity of most estimators makes them unpractical when the need is to process datasets comprising huge amounts of high-dimensional data. The main subject of this thesis work is the development of efficient and ef- fective id estimators. Precisely, two novel estimators, named MiND (Minimum Neighbor Distance estimators of intrinsic dimension, [6]) and DANCo (Dimension- ality from Angle and Norm Concentration, [4]) are described. The aforemen- tioned techniques are based on the exploitation of statistics characterizing the hidden structure of high dimensional spaces, such as the distribution of norms and angles, which are informative of the id and could therefore be exploited for its estimation. A simple practical example to show the informatory power of these features, is the clustering system proposed in [3]; based on the assumption that each class is represented by one manifold, the clustering procedure codes the input data by means of local id estimates and features related to them. This coding allows to obtain reliable results by applying classic and basic clustering algorithms. To evaluate the proposed estimators by objectively comparing them with relevant state-of-the-art techniques, a benchmark framework is proposed. The need of this framework is highlighted by the fact that in literature each method has been assessed on different datasets and by employing different evaluation measures; therefore it is difficult to provide an objective comparison by solely analyzing the results reported by the authors. Based on this observation, the proposed benchmark employs publicly available, synthetic and real, datasets that have been used by several authors in the literature for their interesting, and challenging, peculiarities. Moreover, some synthetic datasets have been added, to more deeply test the estimators\u2019 performance on high dimensional datasets being characterized by similarly high id. The application of this benchmark has shown to provide an objective comparative assessment in terms of robustness w.r.t. parameter settings, high dimensional datasets, datasets being character- ized by an high intrinsic dimension, and noisy datasets. The achieved results show that DANCo provides the most reliable estimates on both synthetic and real datasets. The thesis is organized as follows: in Chapter 1 a brief theoretical description of the various definitions of dimension is presented, along with the problems re- lated to id estimation and interesting application domains profitably exploiting the knowledge of id; in Chapter 2 notable state-of-the-art intrinsic id are sur- veyed, and grouped according to the employed methods; in Chapter 3 MinD, and DANCo are described; in Chapter 4, after summarizing mostly used experimental settings, we propose a benchmark framework and we employ it to objectively assess and compare relevant intrinsic dimensionality estimators; in Chapter 5 conclusions and open research problems are shortly reported. References [1] R. S. Bennett. The Intrinsic Dimensionality of Signal Collections. IEEE Trans. on Information Theory, IT-15(5):517\u2013525, 1969. [2] C. M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, Oxford, 1995. [3] P. Campadelli, E. Casiraghi, C. Ceruti, G. Lombardi, and A. Rozza. Local intrinsic dimensionality based features for clustering. In Alfredo Petrosino, editor, ICIAP (1), volume 8156 of Lecture Notes in Computer Science, pages 41\u201350. Springer, 2013. [4] C. Ceruti, S. Bassis, A Rozza, G. Lombardi, E. Casiraghi, and P. Campadelli. DANCo: an intrinsic Dimensionalty estimator exploiting Angle and Norm Concentration. Pattern recognition, 2014. [5] M. Katetov and P. Simon. Origins of dimension theory. Handbook of the History of General Topology, 1997. [6] A. Rozza, G. Lombardi, C. Ceruti, E. Casiraghi, and P. Campadelli. Novel high intrinsic dimensionality estimators. Machine Learning Journal, 89(1- 2):37\u201365, May 2012

Maintenance in aeronautics in an Industry 4.0 context: The role of Augmented Reality and Additive Manufacturing

The paper broadly addresses how Industry 4.0 program drivers will impact maintenance in aviation. Specifically, Industry 4.0 practices most suitable to aeronautical maintenance are selected, and a detailed exposure is provided. Advantages and open issues are widely discussed and case studies dealing with realistic scenarios are illustrated to support what has been proposed by authors. The attention has been oriented towards Augmented Reality and Additive Manufacturing technologies, which can support maintenance tasks and spare parts production, respectively. The intention is to demonstrate that Augmented Reality and Additive Manufacturing are viable tools in aviation maintenance, and while a strong effort is necessary to develop an appropriate regulatory framework, mandatory before the wide-spread introduction of these technologies in the aerospace systems maintenance process, there has been a great interest and pull from the industry sector

Intrinsic Dimension Estimation: Relevant Techniques and a Benchmark Framework

When dealing with datasets comprising high-dimensional points, it is usually advantageous to discover some data structure. A fundamental information needed to this aim is the minimum number of parameters required to describe the data while minimizing the information loss. This number, usually called intrinsic dimension, can be interpreted as the dimension of the manifold from which the input data are supposed to be drawn. Due to its usefulness in many theoretical and practical problems, in the last decades the concept of intrinsic dimension has gained considerable attention in the scientific community, motivating the large number of intrinsic dimensionality estimators proposed in the literature. However, the problem is still open since most techniques cannot efficiently deal with datasets drawn from manifolds of high intrinsic dimension and nonlinearly embedded in higher dimensional spaces. This paper surveys some of the most interesting, widespread used, and advanced state-of-the-art methodologies. Unfortunately, since no benchmark database exists in this research field, an objective comparison among different techniques is not possible. Consequently, we suggest a benchmark framework and apply it to comparatively evaluate relevant state-of-the-art estimators

Tricorynus rudepunctatus (PIC) (Coleoptera: Anobiidae): Diagnosis and damage

The objective of this research was to identify and study a species of Anobiidae that causes great damage and is a cause of concern as an urban pest in Brazil. This species has been found infesting wood, furniture, doors, books, insect collections, tea, dried fruits, handcrafts, and many other commodities. Inspections were done in houses and storehouses in the city of Curitiba, PR, Brazil in order to collect objects and materials that present signs of anobiid attack. The only species identified was Tricorynus rudepunctatus (Coleoptera: Anobiidae). There is only one reference to this species in the central region of Brazil. Another anobiid, the book pest Tricorynus herbarius has been recorded attacking books and historical documents and Tricorynus sp. attacking forest trees, but it was not recorded in our survey. Usually, the damage caused by T. rudepunctatus is mistaken with damage by termites; and when the insect is collected it is frequently misidentified as T. herbarius or as the cigarette beetle, Lasioderma serricorne or even as Stegobium paniceum, the drugstore beetle. Some morphological characters useful to identify T. rudepunctatus are: oval body about 2.7 mm long; dark brown with smooth hairs all over the body; head concealed under the pronotum; 10-segmented antenna with the three apical segments forming a 3-segmented loose club; elytra with two grooves at the posterior edge; fore femur with a transversal line on its anterior face; pro and mesotibia with two distinct striae; metasternum longitudinally carinate in the middle. Adults and larvae bore inside the materials, forming galleries and producing a coarse powder. Keywords: Anobiids, Insect identification, Morphological characters, Urban pest

Spatial distribution of stored grain insects in a rice storage and processing facility in Brazil

This study describes the spatial distribution of stored product insects captured biweekly using foodbaited cage traps in a large rice storage and processing facility, in the state of Rio Grande do Sul, Brazil. Monitoring started in August 2009 and will be carried out for 1 year, the first 5 months of sampling being presented in this study. From end of August 2009 until the end of December 2009, a total of 9893 insects were captured in the 99 cage traps. The most abundant species were: Carpophilus spp. (76%), Typhaea stercorea (8.6%), Ahasverus advena (5.5%), Tribolium castaneum (2.3%), Sitophilus oryzae (2%), Sitophilus zeamais (1.5%), Ephestia spp. (1.2 %), Cryptolestes ferrugineus (1%), Rhyzopertha dominica (0.64%), Oryzaephilus surinamensis (0.6%), Anthicus floralis (0.4%), Lasioderma serricorne (0.25%). The first two species, which make up for 84.6% of the insects collected, are not considered pests in stored grain, rather are attracted by moldy material present in residues or even in the bait material. The other insects, including primary and secondary species, comprised about 15% of the total trapped. The spatial distribution of the most important species infesting rice grain and of the total insect number was analyzed using Surfer 6.04 (Golden software, Golden, CO, USA) and contour maps were constructed to target areas for sanitation. Except for trap 66, located by the rice hulk storage box, the spatial distribution we observed using the contour maps showed that the greatest number of insects was mostly captured in cages placed in the receiving area, around the dryers, as well as outside of the structure where grain residues frequently accumulate. As indicated on the maps for total number of insects, a few isolated infested spots were detected. The parboiled rice area had the least amount of insects, except for trap 61, placed outside the structure. The population of primary and the most important secondary insect species, as well as the overall number of insects, decreased after sanitation and physical control measures were applied. Our observations confirm that insect monitoring is an essential tool for targeting and evaluating the control measures adopted in the quality program of rice storage and processing facilities. Keywords: Insect monitoring; Spatial distribution; Stored grain pests; Stored ric

Intra and interspecific variation assessment in Psocoptera using near spectoscopy

Several species of Psocoptera are associated with and damage grains and other stored products, books, historical documents, and insect collections. Their small size and lack of expressive morphological variation make it a difficult group for species identification. The spectra of adult males and females of 10 psocid species from the genus Liposcelis were obtained by near infrared spectroscopy (NIRS) and analyzed. Each specimen was placed on a diffuse reflectance accessory of a NIR spectrometer to obtain the respective spectrum, using ten replicates for each species or sex. All spectra were analyzed by combined methods of multivariate analysis using the technique of crossed validation for the multivariate models. The analysis discriminated the species without significant overlapping among the species spectral patterns. The NIRS also revealed variation in the metabolomic profile of males and females; however, it is still possible to distinguish the species using only males or females or even from mixed sex samples. NIRS technique proved to be a powerful tool to discriminate species both at intra and interspecific levels based on dispersion spectral patterns of individual specimens. Keywords: Biological systems, Liposcelididae, stored product pests, Vibrational spectroscopy