Kernel Spectral Clustering and applications

In this chapter we review the main literature related to kernel spectral clustering (KSC), an approach to clustering cast within a kernel-based optimization setting. KSC represents a least-squares support vector machine based formulation of spectral clustering described by a weighted kernel PCA objective. Just as in the classifier case, the binary clustering model is expressed by a hyperplane in a high dimensional space induced by a kernel. In addition, the multi-way clustering can be obtained by combining a set of binary decision functions via an Error Correcting Output Codes (ECOC) encoding scheme. Because of its model-based nature, the KSC method encompasses three main steps: training, validation, testing. In the validation stage model selection is performed to obtain tuning parameters, like the number of clusters present in the data. This is a major advantage compared to classical spectral clustering where the determination of the clustering parameters is unclear and relies on heuristics. Once a KSC model is trained on a small subset of the entire data, it is able to generalize well to unseen test points. Beyond the basic formulation, sparse KSC algorithms based on the Incomplete Cholesky Decomposition (ICD) and $L_0$, $L_1, L_0 + L_1$, Group Lasso regularization are reviewed. In that respect, we show how it is possible to handle large scale data. Also, two possible ways to perform hierarchical clustering and a soft clustering method are presented. Finally, real-world applications such as image segmentation, power load time-series clustering, document clustering and big data learning are considered.Comment: chapter contribution to the book "Unsupervised Learning Algorithms

Energy-based predictions in Lorenz system by a unified formalism and neural network modelling

In the framework of a unified formalism for Kolmogorov-Lorenz systems, predictions of times of regime transitions in the classical Lorenz model can be successfully achieved by considering orbits characterised by energy or Casimir maxima. However, little uncertainties in the starting energy usually lead to high uncertainties in the return energy, so precluding the chance of accurate multi-step forecasts. In this paper, the problem of obtaining good forecasts of maximum return energy is faced by means of a neural network model. The results of its application show promising results

Depositional age and exhumation of Tethyan Sedimentary rocks intruded by Oligo-Miocene granite

Abstract HKT-ISTP 2013 A

Immunodepletion in xenotransplantation

Xenograft transplantation is perhaps the most immunologically difficult problem in transplantation today. An overwhelming hyperacute rejection reaction (HAR) occurs within minutes of organ implantation. Preformed antibodies are thought to initiate this process. We used a pig-to-dog renal xenograft transplant model and investigated methods of decreasing the severity of hyperacute rejection. Female pigs weighing 15-20 kg were used as donors. Recipients were mongrel dogs weighing 15-25 kg. Experimental dogs were all given a number of treatments of IgG depletion using an antibody removal system (Dupont-Excorim). This machine immunoadsorbs plasma against a column containing immobilized staphylococcal protein A, which is known to bind the IgG Fc receptor. An 84% reduction in the IgG levels and a 71% reduction in IgM levels was achieved. Postoperative assessment was made of urine output, time to onset of HAR, and histopathological examination of the rejected kidneys. Although cross-matches between donor lymphocytes and recipient sera remained strongly positive in the treated dogs, there was a two- to fourfold reduction in the titers. The time to onset of HAR was prolonged in the experimental group, and the urine output was increased slightly. The histopathologic changes in the experimental group generally showed signs of HAR, but of less intensity than in the nonimmunodepleted control group. © 1990 Informa UK Ltd All rights reserved: reproduction in whole or part not permitted

Reconstructing Bioinvasion Dynamics Through Micropaleontologic Analysis Highlights the Role of Temperature Change as a Driver of Alien Foraminifera Invasion

Invasive alien species threaten biodiversity and ecosystem structure and functioning, but incomplete assessments of their origins and temporal trends impair our ability to understand the relative importance of different factors driving invasion success. Continuous time-series are needed to assess invasion dynamics, but such data are usually difficult to obtain, especially in the case of small-sized taxa that may remain undetected for several decades. In this study, we show how micropaleontologic analysis of sedimentary cores coupled with radiometric dating can be used to date the first arrival and to reconstruct temporal trends of foraminiferal species, focusing on the alien Amphistegina lobifera and its cryptogenic congener A. lessonii in the Maltese Islands. Our results show that the two species had reached the Central Mediterranean Sea several decades earlier than reported in the literature, with considerable implications for all previous hypotheses of their spreading patterns and rates. By relating the population dynamics of the two foraminifera with trends in sea surface temperature, we document a strong relationship between sea warming and population outbreaks of both species. We conclude that the micropaleontologic approach is a reliable procedure for reconstructing the bioinvasion dynamics of taxa having mineralized remains, and can be added to the toolkit for studying invasions

Garnets from Val d’Ala Rodingites, Piedmont, Italy: An Investigation of Their Gemological, Spectroscopic and Crystal Chemical Properties

In Val d\u2019Ala (Piedmont,Western Alps, Italy), the more interesting rocks for the mineralogical research are represented by rodingites (rich in mineralized veins and fractures) associated with serpentinites in the eclogitized oceanic crust of Piemonte Zone, south of Gran Paradiso Massif. Among the vein-filling minerals, garnets are the most appreciated as mineral specimens and, in less degree despite their vivid and rich colors, for their potential as gem-quality materials. This study provides a complete gemological characterization of five faceted samples and others new information by means of Synchrotron X-ray computed micro-tomography imaging gem features. Electron-probe microanalysis (EMPA) and laser ablation\u2013inductively coupled plasma\u2013mass spectrometry (LA\u2013ICP\u2013MS) established that the chemical composition of garnets from different localities, resulted both close to pure andradite, enriched in light rare earth elements (LREE) with a positive Eu anomaly, and grossular-andradite solid solution (grandite), enriched in heavy rare earth elements (HREE). X-ray powder diffraction analyses indicate the possible coexistence of almost pure grossular and andradite. A spectroscopic approach, commonly used with gem-like material, by Raman and diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy, completes the characterization of the samples. The new data on the textural and geochemical features of the grandite and andradite garnets suggest local growth processes under various chemical and oxidation conditions of metasomatic and metamorphic fluids interacting with the host-rocks. Garnets represent long-lasting mineral records of the complex geological history of the Val d\u2019Ala rodingitic dikes during their oceanic- and subduction-related metamorphic evolution