University of Trento

About: University of Trento is a education organization based out in Trento, Italy. It is known for research contribution in the topics: Population & Context (language use). The organization has 10527 authors who have published 30978 publications receiving 896614 citations. The organization is also known as: Universitá degli Studi di Trento & Universita degli Studi di Trento.

Semisupervised Classification of Hyperspectral Images by SVMs Optimized in the Primal

Abstract: This paper addresses classification of hyperspectral remote sensing images with kernel-based methods defined in the framework of semisupervised support vector machines (S3VMs). In particular, we analyzed the critical problem of the nonconvexity of the cost function associated with the learning phase of S3VMs by considering different (S3VMs) techniques that solve optimization directly in the primal formulation of the objective function. As the nonconvex cost function can be characterized by many local minima, different optimization techniques may lead to different classification results. Here, we present two implementations, which are based on different rationales and optimization methods. The presented techniques are compared with S3VMs implemented in the dual formulation in the context of classification of real hyperspectral remote sensing images. Experimental results point out the effectiveness of the techniques based on the optimization of the primal formulation, which provided higher accuracy and better generalization ability than the S3VMs optimized in the dual formulation

Search for physics beyond the standard model in dilepton mass spectra in proton-proton collisions at √s=8 TeV

Abstract: Dimuon and dielectron mass spectra, obtained from data resulting from proton-proton collisions at 8 TeV and recorded by the CMS experiment, are used to search for both narrow resonances and broad deviations from standard model predictions. The data correspond to an integrated luminosity of 20.6 (19.7) fb^(−1) for the dimuon (dielectron) channel. No evidence for non-standard-model physics is observed and 95% confidence level limits are set on parameters from a number of new physics models. The narrow resonance analyses exclude a Sequential Standard Model Z'_(SSM) resonance lighter than 2.90 TeV, a superstring-inspired Z'_ψ lighter than 2.57 TeV, and Randall-Sundrum Kaluza-Klein gravitons with masses below 2.73, 2.35, and 1.27 TeV for couplings of 0.10, 0.05, and 0.01, respectively. A notable feature is that the limits have been calculated in a model-independent way to enable straightforward reinterpretation in any model predicting a resonance structure. The observed events are also interpreted within the framework of two non-resonant analyses: one based on a large extra dimensions model and one based on a quark and lepton compositeness model with a left-left isoscalar contact interaction. Lower limits are established on MS, the scale characterizing the onset of quantum gravity, which range from 4.9 to 3.3 TeV, where the number of additional spatial dimensions varies from 3 to 7. Similarly, lower limits on Λ, the energy scale parameter for the contact interaction, are found to be 12.0 (15.2) TeV for destructive (constructive) interference in the dimuon channel and 13.5 (18.3) TeV in the dielectron channel.

${{\rm E}^{2}}{\rm LMs}$ : Ensemble Extreme Learning Machines for Hyperspectral Image Classification

Abstract: Extreme learning machine (ELM) has attracted attentions in pattern recognition field due to its remarkable advantages such as fast operation, straightforward solution, and strong generalization. However, the performance of ELM for high-dimensional data, such as hyperspectral image, is still an open problem. Therefore, in this paper, we introduce ELM for hyperspectral image classification. Furthermore, in order to overcome the drawbacks of ELM caused by the randomness of input weights and bias, two new algorithms of ensemble extreme learning machines (Bagging-based and AdaBoost-based ELMs) are proposed for the classification task. In order to illustrate the performance of the proposed algorithms, support vector machines (SVMs) are used for evaluation and comparison. Experimental results with real hyperspectral images collected by reflective optics spectrographic image system (ROSIS) and airborne visible/infrared imaging spectrometer (AVIRIS) indicate that the proposed ensemble algorithms produce excellent classification performance in different scenarios with respect to spectral and spectral-spatial feature sets.

Sub-micrometer vaterite containers: synthesis, substance loading, and release.

Abstract: Promising candidates for the development of universal nanoscale delivery systems are porous inorganic nanoparticles. Recently, the applications of porous silicon in a multistage delivery system, in polymer coated nanocarriers, and of porous silica as core material for lipid bilayers have attracted great attention. A system with similarly high potential, but less studied so far, is porous calcium carbonate in the form of polycrystalline vaterite spheres. It has been shown to exhibit various beneficial properties such as biocompatibility, high drug loading capacity, and preservation of the loaded drugs properties. However, all these works on CaCO3 were performed with micrometer-sized particles, since the fabrication of nanocontainers turned out to be a big challenge. The common synthesis method of mixing salt solutions allowed producing container sizes of 3 to 15 mm, while the best reproducibility was reached for sizes of about 4 mm with a porosity of 40%. Yet, the most promising applications demand sub-micrometer size containers, for example, active coating or drug delivery, since smaller sizes favor efficient and homogeneous distribution and give access to micrometer-sized structures such as cells or tissue. Herein we report, for the first time, the fabrication of submicron porous vaterite containers, their loading with a probe payload, and its release. Their synthesis is based on crystal growth of polycrystalline, spherical vaterite particles, precipitated from concentrated solutions of CaCl2 and Na2CO3. [10] The nucleation and growth rate of the vaterite spheres is determined by the supersaturation level of the dissolved amorphous CaCO3. [11] The final size of the vaterite particles depends strongly on the concentration of the reagents, the solubility of the salts, the reaction time, and the rotation during mixing. It was shown that increasing the concentration of the salts up to 1m, the rotation speed up to 1500 rpm, and the reaction time to 2 min allowed reducing the vaterite particle size to 3 mm. A particle size reduction beyond these values has so far not been achieved, since vaterite was found to become unstable in water below this critical size, leading to a rapid recrystallization to the calcite phase. This recrystallization is due to the growing surface-to-volume ratio and enhanced solubility with decreasing particle size. We resolved this problem by adding ethylene glycol (EG) as a solvent, offering an enhanced density and reduced solubility of CaCO3. This diminished the molecular diffusion, reducing the crystal growth rate and the probability of nucleation, which finally stabilized the vaterite crystals. Experimental evidence of these effects is presented in Figure 1, where the average of the vaterite particle size distribution is shown as a function the reaction time for