A Hybrid Approach for Modeling Stochastic Ray Propagation in Stratified Random Lattices

The present contribution deals with ray propagation in semi-innite percolation lattices consisting of a succession of uniform density layers. The problem of analytically evaluating the probability that a single ray penetrates up to a prescribed level before being reected back into the above empty half-plane is addressed. A hybrid approach, exploiting the complementarity of two mathematical models in dealing with uniform congurations, is presented and assessed through numerical ray-tracing-based experiments in order to show improvements upon previous predictions techniques. "The definitive version is available at www3.interscience.wiley.com

A Hybrid Approach Based on PSO and Hadamard Difference Sets for the Synthesis of Square Thinned Arrays

A hybrid approach for the synthesis of planar thinned antenna arrays is presented. The proposed solution exploits and combines the most attractive features of a particle swarm algorithm and those of a combinatorial method based on the noncyclic difference sets of Hadamard type. Numerical experiments validate the proposed solution, showing improvements with respect to previous results. (c) 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Percolation-Based Approaches For Ray-Optical Propagation in Inhomogeneous Random Distribution of Discrete Scatterers

We address the problem of optical ray propagation in an inhomogeneous half�]plane lattice, where each cell can be occupied according to a known one�]dimensional obstacles density distribution. A monochromatic plane wave impinges on the random grid with a known angle and undergoes specular reflections on the occupied cells. We present two different approaches for evaluating the propagation depth inside the lattice. The former is based on the theory of the Martingale random processes, while in the latter ray propagation is modelled in terms of a Markov chain. A numerical validation assesses the proposed solutions, while validation through experimental data shows that the percolation model, in spite of its simplicity, can be applied to model real propagation problems

Stochastic Ray Propagation in Stratified Random Lattices – Comparative Assessment of Two Mathematical Approaches

In this report, ray propagation in stratified semi-infinite percolation lattices consisting of a succession of uniform density layers is considered. The final version of this article is available at the url of the journal PIER: http://www.jpier.org/PIER

Fine-tuning and data augmentation techniques for semantic segmentation of heritage point clouds

This topic of this contribution falls within the broader debate on Digital Humanities. Experiencing and testing an approach that combines geomatics and its production of three-dimensional data of the built cultural heritage (CH) with information technology is the core point. In the digital CH domain, the ever-increasing availability of three-dimensional data, provides the opportunity to rapidly generate detailed 3D scenes to support restoration and conservation activities of built heritage. Concurrently, the recent research trends in geomatics are facing the issue of managing these heritage data to enrich the geometrical representation of the asset, creating a complete informative data collector. HBIM (Historic Building Information Modeling) constitutes a reference, and they typically rely on point clouds to perform the scan-to-BIM processes. These processes are still mostly manually carried out by domain experts, making the workflow very time-consuming, not fully exploiting the potential of point clouds and wasting an uncountable amount of data. In fact, parametric objects can be described through a few relevant points or contours. The use of Artificial Intelligence algorithms, in particular Deep Learning (DL) techniques, for the automatic recognition of architectural elements from point clouds can therefore provide valuable support through the semantic segmentation task. A proposal to tackle this framework was outlined in previous works, and the methodology here proposed constitutes a development of their results. Starting from those former tests obtained with the Dynamic Graph Convolutional Neural Network (DGCNN), close attention is paid to: i) transfer learning techniques, ii) the combination with external classifiers, such as Random Forest (RF), iii) the evaluation of data augmentation techniques on a domain-specific dataset (ArCH dataset). Besides, an investigation on how to make the whole workflow more functional and "friendly" for external users is carried out too. With regard to transfer learning techniques, the fine-tuning approach is proposed to understand if, also in the CH domain, it can lead to performances improvement, introducing a new scene in a pre-trained network. In fact, the peculiarities of each scene do not guarantee certain and definite results, as for other domains. This section is divided into two subsections: a classic fine-tuning and a fine-tuning with the addition of the RF in the final part of the prediction. In the latter case, the choice of adding the RF is due to the results obtained in some stateof-the-art works, where this classifier provides excellent results in a short time and even in the presence of relatively limited data. In this hybrid approach, the network weights are employed as well as in the classic fine-tuning technique. Then, the final part of the DGCNN performing the segmentation of the points is excluded, leading the network to be used as a feature extractor method; afterwards, a scene of the dataset never seen by the network is chosen and divided into one part for training and one for the test. Finally, the features of both parts are extracted, using the feature extractor, and exploited as input for training the RF classifier. Tests conducted on data augmentation show that it does not significantly affect overall performances, but still provide proper support for those categories with fewer points. On the other side, the tests on the fine-tuning have given rise to manifold considerations. Firstly, the standard fine-tuning can achieve performances almost equal to those where only the DGCNN is used, considerably improving some categories. Thus, they confirm that, once the DNN is pre-trained, data processing and prediction times can be significantly reduced (from ca. 48 to 0.5 h), in the case of heritage point clouds too. Then, performances similar to the reference tests are obtained also with the use of the DGCNN as a feature extractor and the RF as a classifier, demonstrating that the final classifier does not affect the prediction

Transferencia de técnicas de aprendizaje y mejora del rendimiento en la segmentación semántica profunda de nubes de puntos del patrimonio construido

[EN] The growing availability of three-dimensional (3D) data, such as point clouds, coming from Light Detection and Ranging (LiDAR), Mobile Mapping Systems (MMSs) or Unmanned Aerial Vehicles (UAVs), provides the opportunity to rapidly generate 3D models to support the restoration, conservation, and safeguarding activities of cultural heritage (CH). The so-called scan-to-BIM process can, in fact, benefit from such data, and they can themselves be a source for further analyses or activities on the archaeological and built heritage. There are several ways to exploit this type of data, such as Historic Building Information Modelling (HBIM), mesh creation, rasterisation, classification, and semantic segmentation. The latter, referring to point clouds, is a trending topic not only in the CH domain but also in other fields like autonomous navigation, medicine or retail. Precisely in these sectors, the task of semantic segmentation has been mainly exploited and developed with artificial intelligence techniques. In particular, machine learning (ML) algorithms, and their deep learning (DL) subset, are increasingly applied and have established a solid state-of-the-art in the last half-decade. However, applications of DL techniques on heritage point clouds are still scarce; therefore, we propose to tackle this framework within the built heritage field. Starting from some previous tests with the Dynamic Graph Convolutional Neural Network (DGCNN), in this contribution close attention is paid to: i) the investigation of fine-tuned models, used as a transfer learning technique, ii) the combination of external classifiers, such as Random Forest (RF), with the artificial neural network, and iii) the evaluation of the data augmentation results for the domain-specific ArCH dataset. Finally, after taking into account the main advantages and criticalities, considerations are made on the possibility to profit by this methodology also for non-programming or domain experts.[ES] La creciente disponibilidad de datos tridimensionales (3D), como nubes de puntos, provenientes de la detección de la luz y distancia (LiDAR), sistemas de mapeado móvil (MMS) o vehículos aéreos no tripulados (UAV), brinda la oportunidad de generar rápidamente modelos 3D para apoyar las actividades de restauración, conservación y salvaguardia del patrimonio cultural (CH). El llamado proceso de escaneado-a-BIM puede, de hecho, beneficiarse de dichos datos, y ellos mismos pueden ser una fuente para futuros análisis o actividades sobre el patrimonio arqueológico y el construido. Hay varias formas de explotar este tipo de datos, como el modelado de información de edificios históricos (HBIM), la creación de mallas, la rasterización, la clasificación y la segmentación semántica. Este último, referido a las nubes de puntos, es un tema de máxima actualidad no solo en el dominio del PC sino también en otros campos como la navegación autónoma, la medicina o el comercio minorista. Precisamente en estos sectores, la tarea de la segmentación semántica se ha explotado y desarrollado principalmente con técnicas de inteligencia artificial. En particular, los algoritmos de aprendizaje automático (AA) y su subconjunto de aprendizaje profundo (AP) se aplican cada vez más y han establecido un sólido estado de la técnica en la última media década. Sin embargo, las aplicaciones de las técnicas de AP en las nubes de puntos tradicionales son todavía escasas; por tanto, nos proponemos abordar este marco dentro del ámbito del patrimonio construido. Partiendo de algunas pruebas anteriores con la Red Neural Convolucional de Gráfico Dinámico (DGCNN), en esta contribución se presta atención a: i) la investigación de modelos afinados, utilizados como técnica de aprendizaje por transferencia, ii) la combinación de clasificadores externos, como Random Forest (RF), con la red neuronal artificial, y iii) la evaluación de los resultados de aumentación de datos para el conjunto de datos específico del dominio ArCH. Finalmente, después de tener en cuenta las principales ventajas y criticidades, se hace una consideración sobre la posibilidad de beneficiarse de esta metodología también a expertos no programadores o del campo.Matrone, F.; Martini, M. (2021). Transfer learning and performance enhancement techniques for deep semantic segmentation of built heritage point clouds. Virtual Archaeology Review. 12(25):73-84. https://doi.org/10.4995/var.2021.15318OJS73841225Armeni, I., Sener, O., Zamir, A. 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A Hardware Generator of Multi-point Distributed Random Numbers for Monte Carlo Simulation

Monte Carlo simulation of weak approximations of stochastic differential equations constitutes an intensive computational task. In applications such as finance, for instance, to achieve "real time" execution, as often required, one needs highly efficient implementations of the multi-point distributed random number generator underlying the simulations. In this paper a fast and flexible dedicated hardware solution on a field programmable gate array is presented. A comparative performance analysis between a software-only and the proposed hardware solution demonstrates that the hardware solution is bottleneck-free, retains the flexibility of the software solution and significantly increases the computational efficiency. Moreover, simulations in applications such as economics, insurance, physics, population dynamics, epidemiology, structural mechanics, chemistry and biotechnology can benefit from the obtained speedup.random number generators; random bit generators; hardware implementation; field programmable gate arrays (FPGAs); Monte Carlo simulation; weak Taylor schemes; multi-point distributed random variables

Simultaneous matching of dispersion function and Twiss parameters in a transfer line

Dispersion matching in a beam transfer line is an important issue in order to avoid blow-up and luminosity reduction. This is the case for the LHC beam, due to its small emittance and relatively large momentum spread. The dispersion matching can be performed with quadrupoles, but one has to impose the additional constraint of leaving the Twiss parameters unchanged, to preserve the betatron matching. A first order pertubative approach, using the MICADO solver, has been applied to the problem of simultaneous betatron and dispersion matching. A theoretical derivation of the correction matrix as well as simulated and experimental results are presented. (8 refs)

Body image and psychosocial well-being in early adolescent development.

Introduction: Body dissatisfaction is a determining factor in defining psychosocial well being in early adolescence. During this period, young people progressively redefine their aesthetic standards. This new body concept influences how girls evaluate and accept their own appearance and is related to the psychophysical changes they undergo during this developmental phase. The aim of this study is to investigate how this change in body image evolves in a sample of early adolescent girls. Sample: The sample was composed of 2,408 early adolescent females from the Veneto region, subdivided into three age groups (761 11-year-olds, 734 13-year-olds, and 913 15-year-olds). Results: Correspondence analysis reveals how, in the 11-years-old group, feeling unattractive is only related to being overweight; this relation, however changes with increases age, when feeling unattractive is not anymore a synonymous of being overweight, and a new association can be observed in older girls who feel underweight and perceive themselves as attractive. Conclusion: Body image components change considerably during the early adolescence transition