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Publicación Existence of solutions for semilinear nonlocal elliptic problems via a Bolzano theorem(Springer-Verlag, 2013-10-01) Arcoya, David; Leonori, Tommaso; Primo, Ana; https://orcid.org/0000-0002-7284-2413; https://orcid.org/0000-0002-0848-4463; https://orcid.org/0000-0003-1804-3175Publicación W01,1 -solutions for elliptic problems having gradient quadratic lower order terms(Springer Nature, 2013-04-10) David, Arcoya; Boccardo, Lucio; Leonori, Tommaso; https://orcid.org/0000-0002-7284-2413; https://orcid.org/0000-0002-8067-0121; https://orcid.org/0000-0002-0848-4463Publicación Ground states of self-gravitating elastic bodies(Springer Nature, 2014-09) Calogero, Simone; Leonori, Tommaso; https://orcid.org/0000-0003-3802-3665; https://orcid.org/0000-0002-0848-4463Publicación Basic estimates for solutions of a class of nonlocal elliptic and parabolic equations(American Institute of Mathematical Sciences (AIMS), 2015-12) Leonori, Tommaso; Peral, Ireneo; Primo, Ana; Soria, Fernando; https://orcid.org/0000-0002-0848-4463; https://orcid.org/0000-0003-2297-9910; https://orcid.org/0000-0003-1804-3175; https://orcid.org/0000-0001-5753-807XPublicación Evolution of Prospective Secondary Education Economics Teachers' Personal and Emotional Metaphors(Frontiers Media, 2021-03-26) Mellado Bermejo, Lucía; Parte Esteban, Laura; Sánchez Herrera, Susana; Bermejo, María LuisaThis study examines personal and emotional metaphors of prospective economics teachers about the roles they themselves as teachers and their pupils would play by analysing their drawings and responses to open questions. This is a longitudinal study that analyses the evolution of future instructors using two periods: before and after their teaching practicum. Metaphors are categorised into four classes: behaviourist/transmissive, cognitivist/constructivist, situative/socio-historical, and self-referential. The categories for emotions are primary or social and positive, negative, or neutral. The results show that the highest percentage of metaphors for the teacher’s role in both questionnaires were cognitivist/constructivist. Comparison of the findings before and after the teaching practicum revealed no changes in most of the participants’ metaphors and associated models. The analysis also reveals that among those who change, the tendency is to evolve towards more pupil-centred metaphors and associated models. The most common pupil metaphors are behaviourist and cognitivist, increasing after the practicum. Finally, most of the emotions expressed are positive and social, also increasing after the practicum.Publicación Mito, historia y testimonio: Tragedia de la perra vida (1960), de María de la O Lejárraga(Taylor and Francis Group; Routledge, 2021-06-16) Plaza Agudo, InmaculadaEn el presente ensayo, se aborda el estudio de Tragedia de la perra vida, obra que María de la O Lejárraga escribió en 1954, durante su exilio en Buenos Aires (Argentina), ciudad a la que llegó en su ancianidad y en la que desarrolló diversas estrategias para lograr la supervivencia económica, entre las que destacan las traducciones y colaboraciones en diversos medios de prensa y en la Radio Nacional. En nuestro análisis de la pieza, que apareció publicada, junto con otros trabajos, en el volumen Fiesta en el Olimpo y otras diversiones menos olímpicas (1960), indagamos en su importante valor testimonial, en la medida en que es una obra con un trasfondo mitológico y simbólico, que adquiere una dimensión universal, al trazar en ella la autora la biografía de los hombres y mujeres españoles y europeos de su generación, cuyas existencias se desarrollaron en unas circunstancias compartidas, entre las que sobresale la guerra. En el trazado de esta historia generacional, a Lejárraga le interesa, por lo demás, poner énfasis en una serie de cuestiones que habían ocupado un lugar preponderante en su trayectoria como autora teatral y como activista feminista y socialista: la infancia como etapa clave en la conformación de las identidades, la denuncia de las desigualdades sociales y de género, los cambios acaecidos en los roles de las mujeres durante las décadas veinte y treinta, etc.Publicación Modelos de identidad femenina entre la vanguardia y el compromiso en la poesía de Lucía Sánchez Saornil(UNED (Universidad Nacional de Educación a Distancia), 2019) Plaza Agudo, InmaculadaEn el presente ensayo se aborda el estudio de la poesía de Lucía Sánchez Saornil en el periodo comprendido entre 1900 y 1939 desde el punto de vista de los modelos de identidad femenina identificables en su creación. Se explora, así, cómo, en un momento de importantes cambios y transformaciones en los roles de género, tanto en su poesía modernista y vanguardista de juventud como en los textos escritos durante la Guerra Civil (fundamentalmente en el Romancero de Mujeres Libres), la autora llevó a cabo una crítica a los estereotipos sobre las mujeres transmitidos por la tradición literaria, al tiempo que proponía nuevas imágenes de feminidad más acordes a los nuevos tiempos. Se busca, en última instancia, visibilizar el importante papel de Lucía Sánchez Saornil en la lucha por una igualdad real y efectiva entre mujeres y hombres.Publicación Memoria e identidad transnacional en Delirio y Destino (1989), de María Zambrano(Brill, 2017) Plaza Agudo, InmaculadaPublicación Mitos e identidad femenina en el exilio: Circe y los cerdos (1974), de Carlota O'Neill(Brill, 2014) Plaza Agudo, InmaculadaPublicación Deep shared proxy construction hashing for cross-modal remote sensing image fast target retrieval(ELSEVIER, 2024) han, lirong; Paoletti, Mercedes Eugenia; Moreno Álvarez, Sergio; Haut, Juan M.; Plaza, Antonio; https://orcid.org/0000-0002-8613-7037; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0001-6701-961X; https://orcid.org/0000-0002-9613-1659The diversity of remote sensing (RS) image modalities has expanded alongside advancements in RS technologies. A plethora of optical, multispectral, and hyperspectral RS images offer rich geographic class information. The ability to swiftly access multiple RS image modalities is crucial for fully harnessing the potential of RS imagery. In this work, an innovative method, called Deep Shared Proxy Construction Hashing (DSPCH), is introduced for cross-modal hyperspectral scene target retrieval using accessible RS images such as optical and sketch. Initially, a shared proxy hash code is generated in the hash space for each land use class. Subsequently, an end-to-end deep hash network is built to generate hash codes for hyperspectral pixels and accessible RS images. Furthermore, a proxy hash loss function is designed to optimize the proposed deep hashing network, aiming to generate hash codes that closely resemble the corresponding proxy hash code. Finally, two benchmark datasets are established for cross-modal hyperspectral and accessible RS image retrieval, allowing us to conduct extensive experiments with these datasets. Our experimental results validate that the novel DSPCH method can efficiently and effectively achieve RS image cross-modal target retrieval, opening up new avenues in the field of cross-modal RS image retrievalPublicación Large solutions and gradient bounds for quasilinear elliptic equations(Taylor and Francis Group, 2015-10-09) Leonori, Tommaso; Porretta, AlessioPublicación Comparison principles for p-Laplace equations with lower order terms(Springer Nature, 2016-08-06) Leonori, Tommaso; Porretta, Alessio; Riey, GiuseppePublicación Hyperspectral Image Analysis Using Cloud-Based Support Vector Machines(Springer, 2024) Haut, Juan M.; Franco Valiente, José M.; Paoletti, Mercedes Eugenia; Moreno Álvarez, Sergio; Pardo-Diaz, Alfonso; https://orcid.org/0000-0001-6701-961X; https://orcid.org/0000-0002-3880-6697; https://orcid.org/0000-0003-1030-3729Hyperspectral image processing techniques involve time-consuming calculations due to the large volume and complexity of the data. Indeed, hyperspectral scenes contain a wealth of spatial and spectral information thanks to the hundreds of narrow and continuous bands collected across the electromagnetic spectrum. Predictive models, particularly supervised machine learning classifiers, take advantage of this information to predict the pixel categories of images through a training set of real observations. Most notably, the Support Vector Machine (SVM) has demonstrate impressive accuracy results for image classification. Notwithstanding the performance offered by SVMs, dealing with such a large volume of data is computationally challenging. In this paper, a scalable and high-performance cloud-based approach for distributed training of SVM is proposed. The proposal address the overwhelming amount of remote sensing (RS) data information through a parallel training allocation. The implementation is performed over a memory-efficient Apache Spark distributed environment. Experiments are performed on a benchmark of real hyperspectral scenes to show the robustness of the proposal. Obtained results demonstrate efficient classification whilst optimising data processing in terms of training times.Publicación On the comparison principle for unbounded solutions of elliptic equations with first order terms(Elsevier, 2018-01-15) Leonori, Tommaso; Porretta, AlessioPublicación Self-Supervised Learning on Small In-Domain Datasets Can Overcome Supervised Learning in Remote Sensing(IEEE, 2024) Sanchez-Fernandez, Andres J.; Moreno Álvarez, Sergio; Rico Gallego, Juan Antonio; Tabik, Siham; https://orcid.org/0000-0001-6743-3570; https://orcid.org/0000-0002-4264-7473; https://orcid.org/0000-0003-4093-5356The availability of high-resolution satellite images has accelerated the creation of new datasets designed to tackle broader remote sensing (RS) problems. Although popular tasks, such as scene classification, have received significant attention, the recent release of the Land-1.0 RS dataset marks the initiation of endeavors to estimate land-use and land-cover (LULC) fraction values per RGB satellite image. This challenging problem involves estimating LULC composition, i.e., the proportion of different LULC classes from satellite imagery, with major applications in environmental monitoring, agricultural/urban planning, and climate change studies. Currently, supervised deep learning models—the state-of-the-art in image classification—require large volumes of labeled training data to provide good generalization. To face the challenges posed by the scarcity of labeled RS data, self-supervised learning (SSL) models have recently emerged, learning directly from unlabeled data by leveraging the underlying structure. This is the first article to investigate the performance of SSL in LULC fraction estimation on RGB satellite patches using in-domain knowledge. We also performed a complementary analysis on LULC scene classification. Specifically, we pretrained Barlow Twins, MoCov2, SimCLR, and SimSiam SSL models with ResNet-18 using the Sentinel2GlobalLULC small RS dataset and then performed transfer learning to downstream tasks on Land-1.0. Our experiments demonstrate that SSL achieves competitive or slightly better results when trained on a smaller high-quality in-domain dataset of 194 877 samples compared to the supervised model trained on ImageNet-1k with 1 281 167 samples. This outcome highlights the effectiveness of SSL using in-distribution datasets, demonstrating efficient learning with fewer but more relevant data.Publicación Federated learning meets remote sensing(ELSEVIER, 2024-12-01) Moreno Álvarez, Sergio; Paoletti, Mercedes Eugenia; Sanchez Fernandez, Andres J.; Rico Gallego, Juan Antonio; han, lirong; Haut, Juan M.; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0001-6743-3570; https://orcid.org/0000-0002-4264-7473; https://orcid.org/0000-0002-8613-7037; https://orcid.org/0000-0001-6701-961XRemote sensing (RS) imagery provides invaluable insights into characterizing the Earth’s land surface within the scope of Earth observation (EO). Technological advances in capture instrumentation, coupled with the rise in the number of EO missions aimed at data acquisition, have significantly increased the volume of accessible RS data. This abundance of information has alleviated the challenge of insufficient training samples, a common issue in the application of machine learning (ML) techniques. In this context, crowd-sourced data play a crucial role in gathering diverse information from multiple sources, resulting in heterogeneous datasets that enable applications to harness a more comprehensive spatial coverage of the surface. However, the sensitive nature of RS data requires ensuring the privacy of the complete collection. Consequently, federated learning (FL) emerges as a privacy-preserving solution, allowing collaborators to combine such information from decentralized private data collections to build efficient global models. This paper explores the convergence between the FL and RS domains, specifically in developing data classifiers. To this aim, an extensive set of experiments is conducted to analyze the properties and performance of novel FL methodologies. The main emphasis is on evaluating the influence of such heterogeneous and disjoint data among collaborating clients. Moreover, scalability is evaluated for a growing number of clients, and resilience is assessed against Byzantine attacks. Finally, the work concludes with future directions and serves as the opening of a new research avenue for developing efficient RS applications under the FL paradigm. The source code is publicly available at https://github.com/hpc-unex/FLmeetsRS.Publicación Principal eigenvalue of mixed problem for the fractional Laplacian: Moving the boundary conditions(Elsevier, 2018-07-15) Leonori, Tommaso; Medina, Maria; Peral, Ireneo; Primo, Ana; Soria, FernandoPublicación Hashing for Retrieving Long-Tailed Distributed Remote Sensing Images(IEEE, 2024) han, lirong; Paoletti, Mercedes Eugenia; Moreno Álvarez, Sergio; Haut, Juan M.; Pastor Vargas, Rafael; Plaza, Antonio; https://orcid.org/0000-0002-8613-7037; https://orcid.org/0000-0003-1030-3729; https://orcid.org/0000-0001-6701-961X; https://orcid.org/0000-0002-4089-9538; https://orcid.org/0000-0002-9613-1659The widespread availability of remotely sensed datasets establishes a cornerstone for comprehensive image retrieval within the realm of remote sensing (RS). In response, the investigation into hashing-driven retrieval methods garners significance, enabling proficient image acquisition within such extensive data magnitudes. Nevertheless, the used datasets in practical applications are invariably less desirable and with long-tailed distribution. The primary hurdle pertains to the substantial discrepancy in class volumes. Moreover, commonly utilized RS datasets for hashing tasks encompass approximately two–three dozen classes. However, real-world datasets exhibit a randomized number of classes, introducing a challenging variability. This article proposes a new centripetal intensive attention hashing (CIAH) mechanism based on intensive attention features for long-tailed distribution RS image retrieval. Specifically, an intensive attention module (IAM) is adopted to enhance the significant features to facilitate the subsequent generation of representative hash codes. Furthermore, to deal with the inherent imbalance of long-tailed distributed datasets, the utilization of a centripetal loss function is introduced. This endeavor constitutes the inaugural effort toward long-tailed distributed RS image retrieval. In pursuit of this objective, a collection of long-tail datasets is meticulously curated using four widely recognized RS datasets, subsequently disseminated as benchmark datasets. The selected fundamental datasets contain 7, 25, 38, and 45 land-use classes to mimic different real RS datasets. Conducted experiments demonstrate that the proposed methodology attains a performance benchmark that surpasses currently existing methodologies.Publicación Comparison results for unbounded solutions for a parabolic Cauchy-Dirichlet problem with superlinear gradient growth(American Institute of Mathematical Sciences (AIMS), 2019-05) Leonori, Tommaso; Magliocca, MartinaPublicación Cloud-Based Analysis of Large-Scale Hyperspectral Imagery for Oil Spill Detection(IEEE, 2024) Haut, Juan M.; Moreno Álvarez, Sergio; Pastor Vargas, Rafael; Pérez García, Ámbar; Paoletti, Mercedes Eugenia; https://orcid.org/0000-0001-6701-961X; https://orcid.org/0000-0002-4089-9538; https://orcid.org/0000-0002-2943-6348; https://orcid.org/0000-0003-1030-3729Spectral indices are of fundamental importance in providing insights into the distinctive characteristics of oil spills, making them indispensable tools for effective action planning. The normalized difference oil index (NDOI) is a reliable metric and suitable for the detection of coastal oil spills, effectively leveraging the visible and near-infrared (VNIR) spectral bands offered by commercial sensors. The present study explores the calculation of NDOI with a primary focus on leveraging remotely sensed imagery with rich spectral data. This undertaking necessitates a robust infrastructure to handle and process large datasets, thereby demanding significant memory resources and ensuring scalability. To overcome these challenges, a novel cloud-based approach is proposed in this study to conduct the distributed implementation of the NDOI calculation. This approach offers an accessible and intuitive solution, empowering developers to harness the benefits of cloud platforms. The evaluation of the proposal is conducted by assessing its performance using the scene acquired by the airborne visible infrared imaging spectrometer (AVIRIS) sensor during the 2010 oil rig disaster in the Gulf of Mexico. The catastrophic nature of the event and the subsequent challenges underscore the importance of remote sensing (RS) in facilitating decision-making processes. In this context, cloud-based approaches have emerged as a prominent technological advancement in the RS field. The experimental results demonstrate noteworthy performance by the proposed cloud-based approach and pave the path for future research for fast decision-making applications in scalable environments.