Enhancing density-based clustering: Parameter reduction and outlier detection

Clustering is a widely used unsupervised data mining technique. It allows to identify structures in collections of objects by grouping them into classes, named clusters, in such a way that similarity of objects within any cluster is maximized and similarity of objects belonging to different clusters is minimized. In density-based clustering, a cluster is defined as a connected dense component and grows in the direction driven by the density. The basic structure of density-based clustering presents some common drawbacks: (i) parameters have to be set; (ii) the behavior of the algorithm is sensitive to the density of the starting object; and (iii) adjacent clusters of different densities could not be properly identified. In this paper, we address all the above problems. Our method, based on the concept of space stratification, efficiently identifies the different densities in the dataset and, accordingly, ranks the objects of the original space. Next, it exploits such a knowledge by projecting the original data into a space with one more dimension. It performs a density based clustering taking into account the reverse-nearest-neighbor of the objects. Our method also reduces the number of input parameters by giving a guideline to set them in a suitable way. Experimental results indicate that our algorithm is able to deal with clusters of different densities and outperforms the most popular algorithms DBSCAN and OPTICS in all the standard benchmark datasets.     

An effective and efficient parallel approach for random graph generation over GPUs

The widespread usage of random graphs   has been highlighted in the context of database applications for several years. This because such data structures turn out to be very useful in a large family of database applications ranging from simulation to sampling, from analysis of complex networks to study of randomized algorithms, and so forth. Amongst others, Erd?s–Rényi _Γv,p${\Gamma }_{v,p}$ is the most popular model to obtain and manipulate random graphs. Unfortunately, it has been demonstrated that classical algorithms for generating Erd?s–Rényi based random graphs do not scale well in large instances and, in addition to this, fail to make use of the parallel processing capabilities of modern hardware. Inspired by this main motivation, in this paper we propose and experimentally assess a novel parallel algorithm for generating random graphs under the Erd?s–Rényi model that is designed and implemented in a Graphics Processing Unit (GPU), called PPreZER. We demonstrate the nice amenities due to our solution via a succession of several intermediary algorithms, both sequential and parallel, which show the limitations of classical approaches and the benefits due to the PPreZER algorithm. Finally, our comprehensive experimental assessment and analysis brings to light a relevant average speedup gain of PPreZER over baseline algorithms.     

Biomarkers for cardiovascular risk assessment in autoimmune diseases

Autoimmune diseases, such as antiphospholipid syndrome, systemic lupus erythematosus, and rheumatoid arthritis, are characterized by a high prevalence of cardiovascular (CV) disease (CVD), which constitutes the leading causes of morbidity and mortality among such patients. Although such effects are partly explained by a higher prevalence of traditional CV risk factors, many studies indicate that such factors do not fully explain the enhanced CV risk in these patients. In addition, risk stratification algorithms based upon traditional CV risk factors are not as predictive in autoimmune diseases as in the general population. For these reasons, the timely and accurate assessment of CV risk in these high-risk populations still remains an unmet clinical need. An enhanced contribution of different inflammatory components of the immune response, as well as autoimmune elements (e.g. autoantibodies, autoantigens, and cellular response), has been proposed to underlie the incremental CV risk observed in these populations. Recent advances in proteomic tools have contributed to the discovery of proteins involved in CVDs, including some that may be suitable to be used as biological markers. In this review we summarize the main markers in the field of CVDs associated with autoimmunity, as well as the recent advances in proteomic technology and their application for biomarker discovery in autoimmune disease.     

Social coordination in toddler's word learning: interacting systems of perception and action

We measured turn-taking in terms of hand and head movements and asked if the global rhythm of the participants' body activity relates to word learning. Six dyads composed of parents and toddlers (M = 18 months) interacted in a tabletop task wearing motion-tracking sensors on their hands and head. Parents were instructed to teach the labels of 10 novel objects and the child was later tested on a name-comprehension task. Using dynamic time warping, we compared the motion data of all body-part pairs, within and between partners. For every dyad, we also computed an overall measure of the quality of the interaction, that takes into consideration the state of interaction when the parent uttered an object label and the overall smoothness of the turn-taking. The overall interaction quality measure was correlated with the total number of words learned.In particular, head movements were inversely related to other partner's hand movements, and the degree of bodily coupling of parent and toddler predicted the words that children learned during the interaction. The implications of joint body dynamics to understanding joint coordination of activity in a social interaction, its scaffolding effect on the child's learning and its use in the development of artificial systems are discussed.     

Carbon loss from <Emphasis Type="Italic">Brachystegia spiciformis</Emphasis> leaf litter in the sandy soils of southern Mozambique

Leaves of Brachystegia spiciformis represent a substantial fraction of the total aboveground litter in bush fallow fields with sandy soils in southern Mozambique, where annual rainfall exceeds 600 mm. This species is one of the most important in the miombo woodland that is the natural vegetation of the region. Proper knowledge of the decomposition of its litter is therefore crucial for understanding processes responsible for natural build-up of fertility in agricultural soils abandoned to bush fallow during shifting cultivation. This study investigated the effects of soil water content and soil temperature on loss of organic carbon (C) from decomposing leaves in litterbags with 1 mm mesh size. The litterbags were buried 50 mm deep in recently abandoned agricultural fields cleared of any vegetation (Bare) and in more than 15-year-old bush fallow fields (Fallow) of sites covering a climatic transect with annual rainfall from <400 mm to >1,000 mm. Two patterns of C loss were observed, one in coastal and wetter agroecosystems (rainfall >600 mm) and the other in inland and drier agroecosystems (rainfall <600 mm). In the wetter agroecosystems C loss was faster, whereas in drier agroecosystems it was more sensitive to rainfall pulses. Similarly, C loss was faster in fallow fields than in bare fields. During summer, bare fields reached soil temperatures higher than the estimated upper boundary favourable for C loss from decomposing leaf litter at all sites. A simple dynamic decomposition model describing the C fraction remaining in the litterbags was developed. Coefficients of determination (R ²) for the individual experimental units varied between 0.79 and 0.97. The general model for all sites and fields improved explanation of total variation from 81% to 86% when measured soil temperature and soil water content were used as modifiers of decomposition rate, compared with the standard negative exponential model. Root-mean-square error and systematic bias were 9.7% and 0.5% of initial C, respectively. Decomposition was more strongly affected by soil water content than by soil temperature and explained 75% of total variation. Thus, rainfall is the main driver of C loss from leaf litter in these agroecosystems.     

Direct numerical simulation of turbulent heat transfer modulation in micro-dispersed channel flow

Summary The objective of this paper is to study the influence of dispersed micrometer size particles on turbulent heat transfer mechanisms in wall-bounded flows. The strategic target of the current research is to set up a methodology to size and design new-concept heat transfer fluids with properties given by those of the base fluid modulated by the presence of dynamically-interacting, suitably-chosen, discrete micro- and nano-particles. We ran direct numerical simulations for hydrodynamically fully developed, thermally developing turbulent channel flow at shear Reynolds number Re _τ = 150 and Prandtl number Pr = 3, and we tracked two large swarms of particles, characterized by different inertia and thermal inertia. Preliminary results on velocity and temperature statistics for both phases show that, with respect to single-phase flow, heat transfer fluxes at the walls increase by roughly 2% when the flow is laden with the smaller particles, which exhibit a rather persistent stability against non-homogeneous distribution and near-wall concentration. An opposite trend (slight heat transfer flux decrease) is observed when the larger particles are dispersed into the flow. These results are consistent with previous experimental findings and are discussed in the frame of the current research activities in the field. Future developments are also outlined. Dedicated to Professor Franz Ziegler on the occasion of his 70th birthday It is our great pleasure to take part in this Festschrift Issue dedicated to the celebration of the 70th birthday of Professor Franz Ziegler. To honour his activity and his scientific achievements, we prepared this paper, crafted with friendship and respect. We wish Franz many more productive, enjoyable and happy years and a solid and long collaboration as Editors of Acta Mechanica.     

On the Contrast in Visual Cryptography Schemes

A visual cryptography scheme is a method to encode a secret image SI into shadow images called shares such that certain qualified subsets of shares enable the ``visual' recovery of the secret image. The ``visual' recovery consists of xeroxing the shares onto transparencies, and then stacking them. The shares of a qualified set will reveal the secret image without any cryptographic computation. In this paper we analyze the contrast of the reconstructed image in k out of n visual cryptography schemes. (In such a scheme any k shares will reveal the image, but no set of k-1 shares gives any information about the image.) In the case of 2 out of n threshold schemes we give a complete characterization of schemes having optimal contrast and minimum pixel expansion in terms of certain balanced incomplete block designs. In the case of k out of n threshold schemes with we obtain upper and lower bounds on the optimal contrast. Received 27 September 1996 and revised 13 February 1998     

Adhesion of Listeria monocytogenes to materials commonly found in domestic kitchens

The aim of this work was to investigate the adhesion of Listeria monocytogenes ATCC 15313 to glass, granite, marble, polypropylene from a bowl (PPb), polypropylene from a cutting board (PPcb) and stainless steel (SS), which are materials commonly used in kitchens. Marble and granite were chosen because they are applied as kitchen bench covers and pavements in many countries and there are no literature reports on their behaviour in terms of microbial adhesion. The effect of surface hydrophobicity and roughness on the adhesion process was also analysed. The results showed that the highest extent of adhesion of L. monocytogenes occurred to stainless steel, followed by glass and in less extent to the other materials studied. However, it was not possible to establish a correlation between surface hydrophobicity or roughness and the extent of adhesion of L. monocytogenes . The adherence of L. monocytogenes should be dependent on other factors, like the presence of exopolymers and surface charge.     

Development of operational strategies to remove carbon dioxide in photobioreactors

The objective of this work was to evaluate different operational strategies for photobioreactors to remove carbon dioxide using the cyanobacteria, Aphanothece microscopica Nägeli. Two types of reactor configuration, bubble column and airlift were evaluated under three different operational conditions to treat air containing 15% carbon dioxide: simple operation, air recirculation and two sequential reactors. The results obtained showed that the reactor configuration and the operational mode were both determinant criteria for the performance of photobioreactors in the biological conversion of carbon dioxide. Operations with air recirculation showed possibilities for use in small-scale operations, but two-stage sequential photobioreactors (elimination capacity and removal efficiency of 12,217 g_carbon/m³_reactor day and 52.5%, respectively) were shown to be the operational mode with greatest potential for application on an industrial scale by the increased removal efficiency.