Search results for "retrieval"

On the use of multi-temporal series of COSMO-SkyMed data for LANDcover classification and surface parameter retrieval over agricultural sites

2011

The objective of this paper is to report on the activities carried out during the first year of the Italian project “Use of COSMO-SkyMed data for LANDcover classification and surface parameters retrieval over agricultural sites” (COSMOLAND), funded by the Italian Space Agency. The project intends to contribute to the COSMO-SkyMed mission objectives in the agriculture and hydrology application domains.

Component search in a metaCASE environment

2001

Cognitive Linguistics as the Underlying Framework for Semantic Annotation

2012

In recent years many attempts have been made to design suitable sets of rules aimed at extracting the semantic meaning from plain text, and to achieve annotation, but very few approaches make extensive use of grammars. Current systems are mainly focused on extracting the semantic role of the entities described in the text. This approach has limitations: in such applications the semantic role is conceived merely as the meaning of the involved entities without considering their context. As an example, current semantic annotators often specify a date entity without any annotation regarding the kind of the date itself i.e. a birth date, a book publication date, and so on. Moreover, these system…

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data.

2022

The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval models are of interest to run in these platforms as they combine the advantages of physically-based radiative transfer models (RTM) with the flexibility of machine learning regression algorithms. Previous research with GEE primarily relied on processing bottom-of-atmosphere (BOA) reflectance data, which requires atmospheric correction. In the present study, we implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C)…

Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion

2020

In this study, we compare six different machine learning methods in the inversion of a stochastic model for light propagation in layered media, and use the inverse models to estimate four parameters of the skin from the simulated data: melanin concentration, hemoglobin volume fraction, and thicknesses of epidermis and dermis. The aim of this study is to determine the best methods for stochastic model inversion in order to improve current methods in skin related cancer diagnostics and in the future develop a non-invasive way to measure the physical parameters of the skin based partially on the results of the study. Of the compared methods, which are convolutional neural network, multi-layer …

Il ruolo della corteccia prefrontale dorsolaterale nella memoria di riconoscimento: uso dellla Stimolazione Magnetica Transcranica per il trattamento…

Il ruolo della corteccia prefrontale dorsolaterale (DLPFC) in compiti di memoria è stato largamente documentato. Una questione ancora molto dibattuta è il grado di lateralizzazione anatomica-funzionale dei processi di controllo esecutivo della DLPFC coinvolti nella memoria di riconoscimento. Il lavoro di tesi indaga le connessioni tra DLPFC e ippocampo mediante l’applicazione di tecniche di neuromodulazione dell’eccitabilità della DLPFC che consentano di accrescere l’attività ippocampale durante compiti di memoria di riconoscimento. I primi tre studi valutano gli effetti inibitori ed eccitatori della rTMS applicata alla DLPFC sulla performance in compiti di memoria di riconoscimento, in div…

Phase retrieval of vitreous floaters: simulation experiment

2020

Knowledge of the structure of vitreous floaters is crucial to evaluate the need for surgical removal of these floaters. We simulated the phase retrieval of microstructures simulating vitreous floaters by an algorithm PhaseLift and investigate the effects of various parameters on the retrieved phase. The object under test was modulated and the coded diffraction patterns were calculated. Next, PhaseLift was used to retrieve the phase. In the current study, we simulate the effect of Gaussian and Poison noise on the phase retrieval of pure phase objects. We apply an iterative algorithm PhaseLift for phase retrieval as this algorithm requires a very few modulating masks and is able to retrieve t…

Generalizability and Simplicity as Criteria in Feature Selection: Application to Mood Classification in Music

2011

Classification of musical audio signals according to expressed mood or emotion has evident applications to content-based music retrieval in large databases. Wrapper selection is a dimension reduction method that has been proposed for improving classification performance. However, the technique is prone to lead to overfitting of the training data, which decreases the generalizability of the obtained results. We claim that previous attempts to apply wrapper selection in the field of music information retrieval (MIR) have led to disputable conclusions about the used methods due to inadequate analysis frameworks, indicative of overfitting, and biased results. This paper presents a framework bas…

A Cooperative Coevolution Framework for Parallel Learning to Rank

2015

We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed sub-problems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. We implement CCRank with three EA-based learning to rank algorithms for demonstration. E…

Investigating serendipity in recommender systems based on real user feedback

2018

Over the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-addressed question. According to the most common definition, serendipity consists of three components: relevance, novelty and unexpectedness, where each component has multiple variations. In this paper, we looked at eight different definitions of serendipity and asked users how they perceived them in the context of movie recommendations. We surveyed 475 users of the movie recommender system, MovieLens regarding 2146 movies in total and compared tho…