3D MODELING of A COMPLEX BUILDING: From MULTI-VIEW IMAGE FUSION to GOOGLE EARTH PUBLICATION

This paper presents a pipeline that aims at illustrating the procedure to realize a 3D model of a complex building integrating the UAV and terrestrial images and modifying the 3D model in order to publish to Google Earth in an interactive modality so as to provide better available models for visualization and use. The main steps of the procedure are the optimization of the UAV flight, the integration of the different UAV and ground floor images and the optimization of the model to be published to GE. The case study has been identified in a building, The Eremo di Santa Rosalia Convent in Sicily which hash more staggered elevations and located in the hills of the hinterland and of which, the online platform only indicate the position on Google Maps (GM) and Google Earth (GE) with a photo from above and a non-urban road whose GM path is not corresponding with the GE photo. The process highlights the integration of the models and showcases a workflow for the publication of the combined 3D model to the GE platform

Cryo-EM map interpretation and protein model-building using iterative map segmentation.

A procedure for building protein chains into maps produced by single-particle electron cryo-microscopy (cryo-EM) is described. The procedure is similar to the way an experienced structural biologist might analyze a map, focusing first on secondary structure elements such as helices and sheets, then varying the contour level to identify connections between these elements. Since the high density in a map typically follows the main-chain of the protein, the main-chain connection between secondary structure elements can often be identified as the unbranched path between them with the highest minimum value along the path. This chain-tracing procedure is then combined with finding side-chain positions based on the presence of density extending away from the main path of the chain, allowing generation of a Cα model. The Cα model is converted to an all-atom model and is refined against the map. We show that this procedure is as effective as other existing methods for interpretation of cryo-EM maps and that it is considerably faster and produces models with fewer chain breaks than our previous methods that were based on approaches developed for crystallographic maps

Matter inflation with A_4 flavour symmetry breaking

We discuss model building in tribrid inflation, which is a framework for realising inflation in the matter sector of supersymmetric particle physics models. The inflaton is a D-flat combination of matter fields, and inflation ends by a phase transition in which some Higgs field obtains a vacuum expectation value. We first describe the general procedure for implementing tribrid inflation in realistic models of particle physics that can be applied to a wide variety of BSM particle physics models around the GUT scale. We then demonstrate how the procedure works for an explicit lepton flavour model based on an A_4 family symmetry. The model is both predictive and phenomenologically viable, and illustrates how tribrid inflation connects cosmological and particle physics parameters. In particular, it predicts a relation between the neutrino Yukawa coupling and the running of the spectral index alpha_s. We also show how topological defects from the flavour symmetry breaking can be avoided automatically.Comment: 26 pages, 4 figures, v2 matches publication in JCA

Lippmann-Schwinger description of multiphoton ionization

We outline a formalism and develop a computational procedure to treat the process of multiphoton ionization (MPI) of atomic targets in strong laser fields. We treat the MPI process nonperturbatively as a decay phenomenon by solving a coupled set of the integral Lippmann-Schwinger equations. As basic building blocks of the theory we use a complete set of field-free atomic states, discrete and continuous. This approach should enable us to provide both the total and differential cross-sections of MPI of atoms with one or two electrons. As an illustration, we apply the proposed procedure to a simple model of MPI from a square well potential and to the hydrogen atom.Comment: 25 pages, 3 figure

The appraisal of buildable land for property taxation in the adopted general municipal plan

In Italy, tax base for "Imposta Municipale Unica" related to the building area -made such by General Plan or its General Variation adopted but not approved - is the value (of the same building area) depending on the building potential of prediction even if not immediately exercisable. However, the building rights can be exercised only after: (i) the final approval of the General Plan/General Variation; (ii) the approval of the Implementation Plan required by Law; (iii) the issuance of certificates of permission building. This has produced in recent years several disputes between owners and local governments; the law did not give univocal solutions: today (2015) there is a conflict of case law relating to consider this areas absolutely as building areas, as well as it isn't defined what estimating procedures should be used. In this paper, through the application of a model of financial mathematics, an approach that overcomes the conflict law related to the appraisal of the building areas included inGeneral Plans/General Variation adopted but not yet approved, is proposed: the appraisal will be performed in relation to the time and variables between the time of the appraisal and the time (alleged) for the completion of the administrative procedure for obtaining authorizations to build

A Model for Large theta13 Constructed using the Eigenvectors of the S4 Rotation Matrices

A procedure for using the eigenvectors of the elements of the representations of a discrete group in model building is introduced and is used to construct a model that produces a large reactor mixing angle, sin^2(theta13)=2/3 sin^2(pi/16), in agreement with recent neutrino oscillation observations. The model fully constrains the neutrino mass ratios and predicts normal hierarchy with the light neutrino mass, m1~25 meV. Motivated by the model, a new mixing ansatz is postulated which predicts all the mixing angles within 1sigma errors.Comment: 6 pages, 1 figure, contribution to the proceedings of DISCRETE 2012 to appear in the open access Journal of Physics: Conference Series (JPCS), preprint typeset in two-column revtex4 styl

Combining Neuro-Fuzzy Classifiers for Improved Generalisation and Reliability

In this paper a combination of neuro-fuzzy classifiers for improved classification performance and reliability is considered. A general fuzzy min-max (GFMM) classifier with agglomerative learning algorithm is used as a main building block. An alternative approach to combining individual classifier decisions involving the combination at the classifier model level is proposed. The resulting classifier complexity and transparency is comparable with classifiers generated during a single crossvalidation procedure while the improved classification performance and reduced variance is comparable to the ensemble of classifiers with combined (averaged/voted) decisions. We also illustrate how combining at the model level can be used for speeding up the training of GFMM classifiers for large data sets

A Mathematical Programming Approach for Integrated Multiple Linear Regression Subset Selection and Validation

Subset selection for multiple linear regression aims to construct a regression model that minimizes errors by selecting a small number of explanatory variables. Once a model is built, various statistical tests and diagnostics are conducted to validate the model and to determine whether the regression assumptions are met. Most traditional approaches require human decisions at this step. For example, the user adding or removing a variable until a satisfactory model is obtained. However, this trial-and-error strategy cannot guarantee that a subset that minimizes the errors while satisfying all regression assumptions will be found. In this paper, we propose a fully automated model building procedure for multiple linear regression subset selection that integrates model building and validation based on mathematical programming. The proposed model minimizes mean squared errors while ensuring that the majority of the important regression assumptions are met. We also propose an efficient constraint to approximate the constraint for the coefficient t-test. When no subset satisfies all of the considered regression assumptions, our model provides an alternative subset that satisfies most of these assumptions. Computational results show that our model yields better solutions (i.e., satisfying more regression assumptions) compared to the state-of-the-art benchmark models while maintaining similar explanatory power