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Refers to: Gené-Mola, J. [et al.]. Looking behind occlusions: a study on amodal segmentation for robust on-tree apple fruit size estimation. "Computers and electronics in agriculture", Juny 2023, vol. 209, article 107854. https://doi.org/10.1016/j.compag.2023.107854 http://hdl.handle.net/2117/387035, The present dataset comprises a collection of RGB-D apple tree images that can be used to train and test computer vision-based fruit detection and sizing methods. This dataset encompasses two distinct sets of data obtained from a Fuji and an Elstar apple orchards. The Fuji apple orchard sub-set consists of 3925 RGB-D images containing a total of 15,335 apples annotated with both modal and amodal apple segmentation masks. Modal masks denote the visible portions of the apples, whereas amodal masks encompass both visible and occluded apple regions. Notably, this dataset is the first public resource to incorporate on-tree fruit amodal masks. This pioneering inclusion addresses a critical gap in existing datasets, enabling the development of robust automatic fruit sizing methods and accurate fruit visibility estimation, particularly in the presence of partial occlusions. Besides the fruit segmentation masks, the dataset also includes the fruit size (calliper) ground truth for each annotated apple. The second sub-set comprises 2731 RGB-D images capturing five Elstar apple trees at four distinct growth stages. This sub-set includes mean diameter information for each tree at every growth stage and serves as a valuable resource for evaluating fruit sizing methods trained with the first sub-set. The present data was employed in the research paper titled “Looking behind occlusions: a study on amodal segmentation for robust on-tree apple fruit size estimation” [1]., This work was partly funded by the Departament de Recerca i Universitats de la Generali- tat de Catalunya (grant 2021 LLAV 0 0 088 ), the Spanish Ministry of Science, Innovation and Uni- versities (grants RTI2018-094222-B-I00 [PAgFRUIT project], PID2021-126648OB-I00 [PAgPROTECT project] and PID2020-117142GB-I00 [DeeLight project] by MCIN/AEI/10.13039/50110 0 011033 and by “ERDF, a way of making Europe”, by the European Union). Data presented in this paper is also part of a Public Private Partnership project Precisie Tuinbouw, WP Fruit 4.0 (PPS KV 1604-025) and financed by Topsector Tuinbouw & Uitgangsmateriaal and various private companies. The work of Jordi GenéMola was supported by the Spanish Ministry of Universities through a Mar- garita Salas postdoctoral grant funded by the European Union - NextGenerationEU., Peer Reviewed, Postprint (published version)

Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging., This research was funded by H2020-EU.1.3.—EXCELLENT SCIENCE—Marie Skłodowska-Curie Actions, grant number H2020-MSCA-ITN-2018-813120; Proyectos de investigación en salud 2020, grant number PI20/00064. PID2019-104551RB-I00; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN (http://www.ciber-bbn.es/en, accessed on 3 November 2023), CB06/01/0010), an initiative of the Instituto de Salud Carlos III (Spain) co-funded by EU Fondo Europeo de Desarrollo Regional (FEDER); Spanish Agencia Española de Investigación (AEI) PID2022-143299OB-I00 grant; XartecSalut 2021-XARDI-00021. Carla Pitarch is a fellow of Eurecat’s “Vicente López” Ph.D. grant program., Peer Reviewed, Postprint (published version)

The risk factors for post-COVID-19 cognitive impairment have been poorly described. This study aimed to identify the sociodemographic, clinical, and lifestyle characteristics that characterize a group of post-COVID-19 condition (PCC) participants with neuropsychological impairment. The study sample included 426 participants with PCC who underwent a neurobehavioral evaluation. We selected seven mental speed processing and executive function variables to obtain a data-driven partition. Clustering algorithms were applied, including K-means, bisecting K-means, and Gaussian mixture models. Different machine learning algorithms were then used to obtain a classifier able to separate the two clusters according to the demographic, clinical, emotional, and lifestyle variables, including logistic regression with least absolute shrinkage and selection operator (LASSO) (L1) and Ridge (L2) regularization, support vector machines (linear/quadratic/radial basis function kernels), and decision tree ensembles (random forest/gradient boosting trees). All clustering quality measures were in agreement in detecting only two clusters in the data based solely on cognitive performance. A model with four variables (cognitive reserve, depressive symptoms, obesity, and change in work situation) obtained with logistic regression with LASSO regularization was able to classify between good and poor cognitive performers with an accuracy and a weighted averaged precision of 72%, a recall of 73%, and an area under the curve of 0.72. PCC individuals with a lower cognitive reserve, more depressive symptoms, obesity, and a change in employment status were at greater risk for poor performance on tasks requiring mental processing speed and executive function., Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research was supported by the Agency for Management of University and Research Grants (AGAUR) from the Generalitat de Catalunya (Pandemies, 2020PANDE00053), the La Marató de TV3 Foundation (202111–30-31–32), the Ministerio de Ciencia e Innovación (TED2021-130409B-C55)., Peer Reviewed, Membres del NAUTILUS-Project Collaborative Group: Jose A. Bernia, Servei d’Anestesia Reanimació i Clinica del Dolor, Consorci Sanitari de Terrassa (CST) (Terrassa, Barcelona, Spain). Vanesa Arauzo, Servei de Medicina Intensiva. Consorci Sanitari de Terrassa (CST) (Terrassa, Barcelona, Spain). Marta Balague Marmaña, Hospital Sant Joan Despí Moisès Broggi, Consorci Sanitari Integral (Sant Joan Despí, Spain). Pérez-Pellejero, Cristian, Hospital Sant Joan Despí Moisès Broggi, Consorci Sanitari Integral (Sant Joan Despí, Barcelona, Spain). Silvia Cañizares. Hospital Clinic de Barcelona (Barcelona, Spain). Jose Antonio Lopez Muñoz. Occupational Health Care Service, Hospital Clínic (Barcelona, Spain). Jesús Caballero, Hospital Universitari Arnau de Vilanova (Lleida, Spain). Anna Carnes-Vendrell, Hospital Universitari de Santa Maria (Lleida, Spain). Gerard Piñol-Ripoll, Hospital Universitari de Santa Maria (Lleida, Spain). Ester Gonzalez-Aguado, Consorci Sanitari Alt Penedès-Garraf (Vilafranca de Penedés, Barcelona, Spain). Mar Riera-Pagespetit, Consorci Sanitari Alt Penedès-Garraf (Vilafranca de Penedés, Barcelona, Spain). Eva Forcadell-Ferreres, Hospital Verge de la Cinta, (Tortosa, Tarragona, Spain). Silvia Reverte-Vilarroya, Hospital Verge de la Cinta, (Tortosa, Tarragona, Spain). Susanna Forné, Fundació Sant Hospital de la Seu d’Urgell (La Seu d’Urgell, Lleida, Spain). Jordina Muñoz-Padros, Consorci Hospitalari de Vic (Vic, Barcelona, Spain). Anna Bartes-Plan, Consorci Hospitalari de Vic (Vic, Barcelona, Spain). Jose A. Muñoz-Moreno, Servei de Malalties Infeccioses, Fundació Lluita contra les Infeccions—Hospital Universitari Germans Trias i Pujol (Badalona, Barcelona, Spain). Anna Prats-Paris, Servei de Malalties Infeccioses, Fundació Lluita contra les Infeccions—Hospital Universitari Germans Trias i Pujol (Badalona, Barcelona, Spain). Inmaculada Rico Pons, Hospital Universitari de Bellvitge (L’Hospitalet de Llobregat, Barcelona, Spain). Judit Martínez Molina, Hospital, Postprint (published version)

Text escrit a principis de 2024 a partir de la ponència presentada per l’autor a la Taula Interuniversitària “Per a repensar l’experiència d’aprenentatge a la universitat a Catalunya”, celebrada a la UPC el dia 13 de juliol de 2023., Es presenten de forma planera els elements bàsics de la intel·ligència artificial (IA) generativa. Sobretot es descriuen els models de llenguatge de gran capacitat, que són la base d’eines com el xatbot ChatGPT. A continuació es repassen les potencialitats i també les limitacions més rellevants dels sistemes basats en IA generativa, així com els riscs que comporta el seu ús. Finalment, es fa un intent de resposta parcial de la pregunta sobre el seu impacte en les competències d’aprenentatge a la universitat., Preprint

The objective of this work is to explore the impact of the data in the automatic generation of social housing layouts with different deep learning models. The design of Social Housing is a subfield of architecture that necessitates, from the architect, a high degree of precision in refining layouts to effectively address both the programmatic requirements and the aspiration to attain the utmost architectural quality to guarantee inhabitants comfort. This dual challenge stands to gain significantly from the incorporation of data-driven generative processes bolstered by machine learning techniques in general and deep learning ones in particular. The most widely used dataset for data-driven layout generation with deep learning methods is RPLAN, a generic floor plan dataset available online. Although it contains a significant amount of plans, it lacks any information about the architectural quality criteria used for the selection of the data. We conducted an initial analysis of the architectural consistency and key quality features of RPLAN. Upon finding several issues that raise doubts about its suitability for training social housing generation models, we proceeded to create PUBLICPLAN, a new dataset based on proposals submitted and evaluated in public architecture competitions in Spain over the past three years. It comprises 2446 distinct layouts extracted from 1279 proposals in Catalonia and the Balearic Islands. All of these layouts demonstrate high architectural standards. Then, we retrained the three most advanced existing models for generating interior plans using PUBLICPLAN and compared their performance to the same models trained with different subsets of RPLAN. Once the models were trained, we conducted several experiments comparing different models and data subsets and evaluating the impact of the quantity and quality of the data on the models' performance. Those experiments involved over two hundred architects and architecture students. We also examined the, Postprint (published version)

In the context of climate change and increasing water scarcity, adopting water demand-side management (DSM) policies has become necessary. This study advocates for utilizing agent-based modelling (ABM) as a robust simulation tool to assess the impact of nonprice (nudges) and price (changes in increasing block tariff) measures on urban water use. Overcoming challenges posed by insufficient high-quality data, the research integrates four sociocognitive profiles and diverse household income levels to reflect the variability in DSM policy effectiveness based on socioeconomic characteristics. Through 125 simulated scenarios combining various increasing block tariffs with nonpricing measures, the study reveals an average monthly demand reduction ranging from 8.1% to 15.6%. Significantly, nonprice measures prove more effective in curbing water use than pricing measures, attributed to the prioritization of environmental concerns in conservation efforts. Higher-income households exhibit less-pronounced reductions in water consumption. Emphasizing the reliability of ABM for ex ante evaluations of DSM policies, this research underscores the importance of a balanced approach, incorporating both nonprice and price-based measures, to effectively address water scarcity challenges., Pol Vidal-Lamolla acknowledges support from the LEQUIA Research Group, Aigües de Barcelona and the Industrial Doctorate Programme of the Catalan Agency for Management of University and Research Grants (AGAUR), Spain (Ref 2021 DI 85). LEQUIA has been recognized as a consolidated research group by AGAUR (Ref 2021 SGR 1352)., Peer Reviewed, Postprint (published version)

In this study, we train and compare three types of machine learning algorithms for Survival Analysis: Random Survival Forest, DeepSurv and DeepHit, using the SEER database to model cutaneous malignant melanoma. Additionally, we employ SurvLIMEpy library, a Python package designed to provide explainability for survival machine learning models, to analyse feature importance. The results demonstrate that machine learning algorithms outperform the Cox Proportional Hazards Model. Our work underscores the importance of explainability methods for interpreting black-box models and provides insights into important features related to melanoma prognosis., This research was supported by the Spanish Research Agency (AEI) under projects PID2020-116294GB-I00 and PID2020-116907RB-I00 of the call MCIN/ AEI /10.13039/501100011033, the project 718/C/2019 with id 201923-31 funded by Fundació la Marato de TV3 and the grant 2020 FI SDUR 306 funded by AGAUR., Peer Reviewed, Postprint (author's final draft)

One of the major problems when designing control and supervision systems for environmental systems is the need to be adapted to the particularities of each system. The use of Artificial Intelligence (AI) techniques instead of classical control approaches have been used in recent years in the design of Intelligent Decision Support Systems (IDSS). A static Case-Based Reasoning (CBR) approach for the control and supervision of environmental systems has been proposed in previous works, providing a general efficient methodology to allow scalability to further types of systems. However, the dynamic nature of environmental processes suggests temporary dependencies between cases, hence the use of a temporal CBR (TCBR) approach could provide better performance. The main contribution of this research is to propose a new TCBR method providing improved retrieval process, leading to improved overall performance by considering the dependency of consecutive cases. Thus, the retrieval process is addressed considering not only a single case but a set of consecutive cases named episodes. Our proposal is based on using fixed-length episodes. The approach presented has been tested in a real facility within the ambit of a local water administration in the area of Barcelona. The results indicated that the TCBR approach improved the accuracy of the obtained solutions and case diagnosis with respect to the single-case CBR approach. A tuning process with different episode lengths is performed in order to find a good trade-off between performance and computing time., This research was partially funded by the Industrial Doctorate Program from the Catalan Agency of University and Research Grants Management (AGAUR), grant number 2017-DI-006, and by the Research Consolidated Groups/Centers Grant from AGAUR, grant numbers 2017 SGR 574 and 2017 SGR 482., Peer Reviewed, Postprint (author's final draft)

La flora marina, en especial las praderas marinas y praderas de algas, posee un rol esencial en la sostenibilidad de los ecosistemas costeros. Por esto, existe una elevada necesidad en desarrollar herramientas que permitan monitorizar de forma precisa la flora marina. La teledetección por satélite es una tecnología eficiente y económica para poder elaborar mapas de hábitats bentónicos o de tipos de fondos, permitiendo caracterizar los fondos a lo largo del tiempo. Para la generación de estos mapas, en la actualidad típicamente se implementan técnicas basadas en píxel, como modelos Machine Learning convencionales, y otras técnicas basadas en objetos, como el OBIA. No obstante, el avance de las técnicas Deep Learning permite entrenar modelos que aprovechen mejor las características de los fondos marinos para la generación de mapas de elevada precisión. En este trabajo se expone el uso del modelo U-Net, basado en Redes Neuronales Convoluciones (CNN), para la generación de mapas de hábitats bentónicos a partir de imágenes satelitales multiespectrales. La zona de estudio es El Río, estrecho entre las islas de La Graciosa y Lanzarote, islas Canarias, donde existe una elevada biodiversidad con la presencia de multitud de especies como praderas de Cymodocea nodosa, Caulerpa prolifera y algas rojas filamentosas, entre otras especies., Marine flora, especially seagrass and seaweed meadows, plays an essential role in the sustainability of marine ecosystems. Therefore, there is a great need to develop tools to accurately monitor the marine flora. Satellite remote sensing is an efficient and economical technology for mapping benthic habitats or bottom types, allowing the characterization of the seabed over time. For the generation of these maps, techniques pixel-based techniques, such as conventional Machine Learning models, and object-based techniques, such as OBIA, are currently typically implemented. However, the advancement of Deep Learning techniques allows training models that take better advantage of seabed features for the generation of high accurate maps. In this paper we present the use of the U-Net model, based on Convolutional Neural Networks (CNN), for the generation of benthic habitat maps based on multispectral satellite imagery. The study area is El Río, a strait between the islands of La Graciosa and Lanzarote, Canary Islands, where there is a high biodiversity with the presence of a multitude of species such as Cymodocea nodosa meadows, Caulerpa prolifera and red filamentous algae, among other species., Esta investigación ha sido parcialmente subvencionada por el Ministerio para la Transición Ecológica y el Reto Demográfico mediante el proyecto TARA (Ref. SPIP2022-02897)., Peer Reviewed, Postprint (published version)

Emerging from the symbiotic combination of nanotechnology and communications, the field of nanonetworking has come a long way since its inception more than fifteen years ago. Significant progress has been achieved in several key communication technologies as enablers of the paradigm, as well as in the multiple application areas that it opens. In this paper, the focus is placed on the electromagnetic nanonetworking paradigm, providing an overview of the advances made in wireless nanocommunication technology from microwave through terahertz to optical bands. The characteristics and potential of the compared technologies are then confronted with the requirements and challenges of the broad set of nanonetworking applications in the Internet of NanoThings (IoNT) and on-chip networks paradigms, including quantum computing applications for the first time. Finally, a selection of cross-cutting issues and possible directions for future work are given, aiming to guide researchers and practitioners towards the next generation of electromagnetic nanonetworks., S. A. acknowledges support from the EU’s Horizon Europe program through the European Research Council (ERC) under grant agreement 101042080 (WINC) and through the European Innovation Council (EIC) PATHFINDER scheme, grant agreement No 101099697 (QUADRATURE). C. H. acknowledges the in-part support from the National Natural Science Foundation of China (NSFC) – European Research Council (ERC) Research Program under Project No. 62311530342, and from Alexander von Humboldt Foundation. The EU partially supported the activity of L. G. under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on “Telecommunications of the Future” (PE0000001 - program “RESTART”). I. F. A. is supported by Rannis (Icelandic Research Fund) for the Grant of Excellence program at the University of Iceland. J. M. J. acknowledges the support of the US National Science Foundation through Award CNS-1955004, Award CNS-2011411, Award CNS-2225590, and CBET-2039189., Peer Reviewed, Postprint (author's final draft)

© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM., In some contexts, like geriatric hospitals, the number of patients requiring assistance with feeding is very high and robots may be an effective tool for caregivers to provide better assistance. This article introduces NYAM, a robot designed to aid in the feeding process for individuals. Our robot is equipped with a mechanism to effectively recapture the person's attention whenever necessary. The mechanism is easily adjustable by the caregivers, allowing the straightforward customisation of the feeding service. The approach was evaluated, within a geriatric hospital, with 9 patients who used the robot for 5 consecutive days. We argue that incorporating enhanced social aspects into the robot is imperative to enhance the effectiveness and acceptance of this solution. © 2024 Copyright held by the owner/author(s), This work was supported by the project ROB-IN PLEC2021-007859 funded by MCIN/ AEI /10.13039/501100011033 and by the "European Union NextGenerationEU/PRTR"; project CHLOE-GRAPH PID2020-118649RB-I00 funded by MCIN/ AEI /10.13039/501100011033; and project PIONEER from PSPV., Peer Reviewed, Postprint (published version)

Self-supervised learning is gaining considerable attention as a solution to avoid the requirement of extensive annotations in representation learning on graphs. Current algorithms are based on contrastive learning, which is computation an memory expensive, and the assumption of invariance under certain graph augmentations. However, graph transformations such as edge sampling may modify the semantics of the data so that the invariance assumption may be incorrect. We introduce Regularized Graph Infomax (RGI), a simple yet effective framework for node level self-supervised learning that trains a graph neural network encoder by maximizing the mutual information between output node embeddings and their propagation through the graph, which encode the nodes’ local and global context, respectively. RGI do not use graph data augmentations but instead generates self-supervision signals with feature propagation, is non-contrastive and does not depend on a two branch architecture. We run RGI on both transductive and inductive settings with popular graph benchmarks and show that it can achieve state-of-the-art performance regardless of its simplicity., This work has been supported by the Spanish Research Agency (AEI) under project PID2020-116907RB-I00 of the call MCIN/AEI/ 10.13039/501100011033 and the FI-AGAUR 2021-FI-B-00845 grant funded by Secretaria d’Universitats i Recerca de la Generalitat de Catalunya and the European Social Fund (ESF)., Peer Reviewed, Postprint (published version)

This paper presents a set of experiments on fine-tuning LLMs to produce high-precision semantic representations for the NLU component of a dialog system front-end. The aim of this research is threefold. First, we want to explore the capabilities of LLMs on real, industry-based use cases that involve complex data and strict requirements on results. Since the LLM output should usable by the application backend, the produced semantic representation must satisfy strict format and consistency requirements. Second, we also want to assess the language scalability of the LLMs in this kind of applications; specifically, whether a multilingual model is able to cast patterns learnt from one language to other ones –with special attention to underresourced languages–, thus reducing required training data and computation costs. Finally, we want to evaluate the cost-benefit of open-source LLMs, that is, the feasibility of running this kind of models in machines affordable to small-medium enterprises (SMEs), in order to assess how far this organizations can go without depending on the large players controlling the market, and with a moderate use of computation resources. This work was carried out within an R&D context of assisting a real company in defining its NLU model strategy, and thus the results have a practical, industry-level focus., This research has been funded by the Spanish Science and Innovation Ministry, via grant TED2021-131257B-I00, supported by Next Generation European Funds., Peer Reviewed, Postprint (published version)

Advancements in dermatological artificial intelligence research require high-quality and comprehensive datasets that mirror real-world clinical scenarios. We introduce a collection of 18,946 dermoscopic images spanning from 2010 to 2016, collated at the Hospital Clínic in Barcelona, Spain. The BCN20000 dataset aims to address the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions in hard-to-diagnose locations such as those found in nails and mucosa, large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. Our dataset covers eight key diagnostic categories in dermoscopy, providing a diverse range of lesions for artificial intelligence model training. Furthermore, a ninth out-of-distribution (OOD) class is also present on the test set, comprised of lesions which could not be distinctively classified as any of the others. By providing a comprehensive collection of varied images, BCN20000 helps bridge the gap between the training data for machine learning models and the day-to-day practice of medical practitioners. Additionally, we present a set of baseline classifiers based on state-of-the-art neural networks, which can be extended by other researchers for further experimentation., We acknowledge the support of the International Skin Imaging Collaboration (ISIC). This research was supported by the Spanish Research Agency (AEI) under project PID2020-116907RB-I00 of the call MCIN/ AEI /10.13039/501100011033 and the project 718/C/2019 with id 201923-30 and 201923-31, funded by Fundació la Marató de TV3, iTOBOs grant from the European Union’s Horizon 2020 research and innovation programme num 965221. Other funding sources include the Melanoma Research Alliance Young Investigator Award 614197. This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748., Peer Reviewed, Postprint (published version)

The statistical study of speech production in aphasiology poses the challenge that there is usually little data to validate models and linguistic laws. On the other hand, there is still a lack of consensus as to which model offers a more robust explanation of bilingual language processing, mainly between declarative-procedural, shared brain network models or, recently, implicit-statistical learning models. In this context, in this work we will study the statistical patterns derived from the oral production of bilingual Catalan-Spanish people with aphasia. We compare the discursive ability in their initial language (L1) and in their second language (L2). For this purpose, the Western Aphasia Battery has been adapted to Catalan and Spanish, so that the traditional analysis of aphasic production has been compared with the computational analysis. In the results section, the comparative data will be presented according to the sets of discourse analysis in L1 and L2. The discussion will address aspects related to speech complexity, as well as other statistical indicators, with regard to bilingual language processing models. We finally focus on the possibilities offered by statistical indicators for diagnosis and automatic patient assessment considering the type of aphasia., This work was supported by the project PRO2023-S03-HERNANDEZ by the Secció de Ciències i Tecnologia de l’Institut d’Estudis Catalans, recognition 2021-SGR-Cat (01266 LQMC) from AGAUR (Generalitat de Catalunya) and grants AGRUPS-2022 and AGRUPS-2023 from Universitat Politècnica de Catalunya, and by Institut de Ciències de l’Educació (UPC). Maite Zaragoza-Cortés was supported by grant PREDOCS UB-2020. The project was approved by the CSI IDIBELL and by the Ethics Committee of the Universitat Politecnica de Catalunya (UPC)., Peer Reviewed, Objectius de Desenvolupament Sostenible::3 - Salut i Benestar, Objectius de Desenvolupament Sostenible::17 - Aliança per a Aconseguir els Objetius, Postprint (published version)

The emergence of Large Language Models (LLMs) like ChatGPT has significantly transformed both theoretical and applied linguistics, raising a profound debate within linguistics. These models, such as GPT (Generative Pre-trained Transformer) series, have revolutionized the way linguists approach language analysis and comprehension. In contrast to traditional Quantitative Linguistics (QL) and conventional linguistic laws like Zipf's laws (Zipf, 1949), LLMs leverage massive datasets to generate linguistic patterns, syntactic structures, and semantic nuances in a comprehensive manner. In light of this, we recently introduced a novel quantitative concept called ”semanticity” which establishes a connection between a word’s potential meanings and its position within the linguistic network. To explore this notion, we conduct a comprehensive analysis of Catalan using extensive written corpora, leveraging the resources of the official dictionary (DIEC2). Our findings reveal that the semanticity of words provides a straightforward and quantitative classification for content and function words and for other word types in Catalan, allowing for the integration of both their semantic and syntactic attributes into this single quantitative parameter., Peer Reviewed, Postprint (published version)

La conservación de las zonas naturales es esencial para mantener el equilibrio ecológico de nuestro planeta y preservar la biodiversidad. Estas áreas desempeñan un papel fundamental en la regulación del clima y la provisión de hábitats para innumerables especies. En este contexto, la teledetección se ha convertido en una herramienta valiosa para la gestión sostenible de estos espacios. Específicamente, este trabajo aborda la evaluación del estado de conservación de un bosque de laurisilvas a partir de datos multiplataforma (satélite, avión y dron) de alta y muy alta resolución. Se ha realizado un detallado estudio y evaluación de índices de vegetación, se han aplicado los correspondientes procesados, usando modelos avanzados de corrección y clasificación y se ha generado información relativa al estado de salud del ecosistema, a la estructura del dosel arbóreo y a la distribución de los sistemas naturales de vegetación predominantes (bosques de laurisilva y de fayal-brezal). Los resultados de este trabajo están siendo utilizados para el seguimiento ecológico del Parque y para la identificación de las zonas de desvitalización y de pérdida de vigor vegetal., The conservation of natural areas is essential to maintain the ecological balance of our planet and preserve biodiversity. These areas play a critical role in regulating the climate and providing habitats for countless species. In this context, remote sensing has become a valuable tool for the sustainable management of these spaces. Specifically, this work addresses the evaluation of the conservation status of a laurel forest based on high and very high resolution multiplatform data (satellite, plane and drone). Basically, a detailed study and evaluation of vegetation indices has been carried out, the corresponding processing has been applied, using advanced correction and classification models, and information has been generated regarding the state of health of the ecosystem, the structure of the tree canopy and the distribution of the predominant natural vegetation systems (laurisilva and fayal-brezal forests). These results are being used for the ecological monitoring of the Park and for the identification of areas of devitalization and loss of plant vigor., Este trabajo ha sido financiado por el Organismo Autónomo Parques Nacionales (Proyecto SPIP2022-02897)., Peer Reviewed, Postprint (published version)

© 2024 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, Traditional dynamic energy management methods optimize the energy usage in wireless sensor nodes adjusting their behavior to the operating conditions. However, this comes at the cost of losing the predictability in the operation of the sensor nodes. This loss of predictability is particularly problematic for the battery life, as it determines when the nodes need to be serviced. In this paper, we propose an energy and relevance-aware monitoring method, which leverages the principles of self-awareness to address this challenge. On one hand, the relevance-aware behavior optimizes how the monitoring efforts are allocated to maximize the monitoring accuracy; while on the other hand, the power-aware behavior adjusts the overall energy consumption of the node to achieve the target battery life. The proposed method is able to balance both behaviors so as to achieve the target battery life, at the same time is able to exploit variations in the collected data to maximize the monitoring accuracy. Furthermore, the proposed method coordinates two different adaptive schemes, a dynamic sampling period scheme, and a dual prediction scheme, to adjust the behavior of the sensor node. The evaluation results show that the proposed method consistently meets its battery lifetime goal, even when the operating conditions are artificially changed, and is able to improve the mean square error of the collected signal by up to 20% with respect to the same method with the relevance-aware behavior disabled, and of up to 16% with respect the same algorithm with just the adaptive sampling period or the dual prediction scheme enabled. Consequently showing the ability of the proposed method of making appropriate decisions to balance the competing interest of its two behaviors and coordinate the two adaptive schemes to improve their performance., This study was supported by the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR 2019 DI 075 to David Arnaiz). The founder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript., Peer Reviewed, Postprint (published version)

The standard treatment in glioblastoma includes maximal safe resection followed by concomitant radiotherapy plus chemotherapy and adjuvant temozolomide. The first follow-up study to evaluate treatment response is performed 1¿month after concomitant treatment, when contrast-enhancing regions may appear that can correspond to true progression or pseudoprogression. We retrospectively evaluated 31 consecutive patients at the first follow-up after concomitant treatment to check whether the metabolic pattern assessed with multivoxel MRS was predictive of treatment response 2¿months later. We extracted the underlying metabolic patterns of the contrast-enhancing regions with a blind-source separation method and mapped them over the reference images. Pattern heterogeneity was calculated using entropy, and association between patterns and outcomes was measured with Cramér's V. We identified three distinct metabolic patterns—proliferative, necrotic, and responsive, which were associated with status 2¿months later. Individually, 70% of the patients showed metabolically heterogeneous patterns in the contrast-enhancing regions. Metabolic heterogeneity was not related to the regions' size and only stable patients were less heterogeneous than the rest. Contrast-enhancing regions are also metabolically heterogeneous 1¿month after concomitant treatment. This could explain the reported difficulty in finding robust pseudoprogression biomarkers., "Funding information: H2020-EU.1.3. EXCELLENT SCIENCE—Marie Skłodowska-Curie Actions, grant number H2020-MSCA-ITN-2018-813120. Instituto de Salud Carlos III (ISCIII), Proyectos de investigación en salud 2020, grant numbers PI20/00064 and PI20/00360. Spanish Ministerio de Economía y Competitividad, SAF2014-52332-R. Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN, http://www.ciber-bbn.es/en, accessed December 29, 2023), CB06/01/0010, an initiative of the Instituto de Salud Carlos III (Spain) co-funded by EU Fondo Europeo de Desarrollo Regional (FEDER). Spanish AEI PID2019-104551RB-I00 grant. Generalitat de Catalunya, Xartecsalut, 2018 XARDI 00016 and 2021 XARDI 00021.", Peer Reviewed, Postprint (published version)

Reconfigurable intelligent surfaces (RISs) are considered a key enabler of highly energy-efficient 6G and beyond networks. This property arises from the absence of power amplifiers in the structure, in contrast to active nodes, such as small cells and relays. However, a certain amount of power is still required for RIS operation. To improve their energy efficiency further, we propose the notion of perpetual RISs, which secure the power needed to supply their functionalities through wireless energy harvesting (EH) of impinging transmitted electromagnetic (EM) signals. Toward this, we initially explain the rationale behind such RIS capability and proceed with a presentation of the main RIS controller architecture that can realize this vision under an in-band EH consideration. Furthermore, we present a typical EH architecture, followed by two harvesting protocols. Subsequently, we study the performance of the two protocols under a typical communications scenario. Finally, we elaborate on the main research challenges governing the realization of large-scale networks with perpetual RISs., This work was supported by the Luxembourg National Research Fund (FNR)–RISOTTI Project under Grant 14773976., Peer Reviewed, Postprint (published version)

Objective: A long-term decline in health-related quality of life (HRQoL) has been reported after coronavirus disease 2019 (COVID-19). Studies with people with persistent symptoms showed inconsistent outcomes. Cognition and emotion are important determinants in HRQoL, but few studies have examined their prognostic significance for HRQoL and functionality in post-COVID patients with persisting symptoms. We aimed to describe QoL, HRQoL, and functioning in individuals post-COVID with varying COVID-19 severities and to investigate the predictive value of cognitive and emotional variables for QoL, HRQoL, and functioning. Methods: In total, 492 participants (398 post-COVID and 124 healthy controls) underwent a neurobehavioral examination that included assessments of cognition, mood, QoL/HRQoL (WHOQOL-BREF, EQ-5D), and functioning (WHODAS-II). Analysis of covariance and linear regression models were used to study intergroup differences and the relationship between cognitive and emotional variables and QoL and functioning. Results: The Physical and Psychological dimensions of WHOQoL, EQ-5D, and WHODAS Cognition, Mobility, Life Activities, and Participation dimensions were significantly lower in post-COVID groups compared with a control group. Regression models explaining 23.9%–53.9% of variance were obtained for the WHOQoL-BREF dimensions and EQ-5D, with depressive symptoms, post-COVID symptoms, employment status, income, and mental speed processing as main predictors. For the WHODAS, models explaining 17%–60.2% of the variance were obtained. Fatigue, depressive symptoms, mental speed processing, and post-COVID symptoms were the main predictors. Interpretation: QoL/HRQoL and functioning after COVID-19 in individuals with persistent symptoms were lower than in non-affected persons. Depressive symptoms, fatigue, and slower mental processing speed were predictors of lower QoL/HRQoL and functioning., This research was supported by the Agency for Management of University and Research Grants (AGAUR) from the Generalitat de Catalunya (Pandemies, 202PANDE00053) and La Marató de TV3 Foundation (202111-30-31-32)., Peer Reviewed, Membres del NAUTILUS-Project Collaborative Group: Jose A. Bernia, Servei d’Anestesia Reanimacio i Clinica del Dolor, Consorci Sanitari de Terrassa (CST) (Terrassa,Barcelona, Spain); Vanesa Arauzo, Servei de Medicina Intensiva, Consorci Sanitari de Terrassa (CST) (Terrassa, Barcelona, Spain); Marta Balague-Marmaña, Hospital Sant Joan Despı Moises Broggi, Consorci Sanitari Integral (Sant Joan Despı, Spain); Cristian Perez-Pellejero, Hospital Sant Joan Despı Moises Broggi, Consorci Sanitari Integral (Sant Joan Despı, Barcelona, Spain); Silvia Cañizares, Hospital Clinic de Barcelona (Barcelona, Spain); Jose Antonio Lopez Muñoz, Occupational Health CareService, Hospital Clınic (Barcelona, Spain); Jesus Caballero, Hospital Universitari Arnau de Vilanova (Lleida, Spain); Anna Carnes-Vendrell, Hospital Universitari de Santa Maria (Lleida, Spain); Gerard Piñol-Ripoll, Hospital Universitari de Santa Maria (Lleida, Spain); Ester Gonzalez-Aguado, Consorci Sanitari Alt Penedes-Garraf (Vilafranca de Penedes, Barcelona, Spain); Carme Tayo-Juli, Consorci Sanitari Alt Penedes-Garraf (Vilafranca de Penedes, Barcelona, Spain); Eva Forcadell-Ferreres, Hospital Verge de la Cinta,(Tortosa, Tarragona, Spain); Silvia Reverte-Vilarroya, Hospital Verge de la Cinta, (Tortosa, Tarragona, Spain); Susanna Forne, Fundacio Sant Hospital de la Seu d’Urgell (La Seu d’Urgell, Lleida, Spain); Jordina Muñoz-Padros, Consorci Hospitalari de Vic (Vic, Barcelona, Spain); Anna Bartes-Plan, Consorci Hospitalari de Vic (Vic,Barcelona, Spain); Jose A. Muñoz-Moreno, Servei de Malalties Infeccioses, Fundacio Lluita contra les Infeccions - Hospital Universitari Germans Trias i Pujol (Badalona, Barcelona, Spain); Anna Prats-Paris, Servei de Malalties Infeccioses, Fundacio Lluita contra les Infeccions - Hospital Universitari Germans Trias i Pujol (Badalona, Barcelona, Spain); Anna Gasa-Roque, Hospital Universitari de Bellvitge (L’Hospitalet de Llobregat, Barcelona, Spain); Laura Casas Valls, Hospital Universitari d, Postprint (published version)

The study aimed to assess sleep quality in PCC patients and its predictors by analysing its relationship with emotional, cognitive and functional variables, as well as possible differences based on COVID-19 severity. We included 368 individuals with PCC and 123 healthy controls (HCs) from the NAUTILUS Project (NCT05307549 and NCT05307575). We assessed sleep quality (Pittsburgh Sleep Quality Index, PSQI), anxiety (Generalized Anxiety Disorder, GAD-7), depression (Patient Health Questionnaire, PHQ-9), global cognition (Montreal Cognitive Assessment, MoCA), everyday memory failures (Memory Failures of Everyday Questionnaire, MFE-30), fatigue (Chadler Fatigue Questionnaire, CFQ), quality of life (European Quality of Life-5 Dimensions, EQ-5D), and physical activity levels (International Physical Activity Questionnaire, IPAQ). 203 were nonhospitalized, 83 were hospitalized and 82 were admitted to the intensive care unit (ICU). We found statistically significant differences in the PSQI total score between the PCC and HC groups (p < 0.0001), but there were no differences among the PCC groups. In the multiple linear regressions, the PHQ-9 score was a predictor of poor sleep quality for mild PCC patients (p = 0.003); GAD-7 (p = 0.032) and EQ-5D (p = 0.011) scores were predictors of poor sleep quality in the hospitalized PCC group; and GAD-7 (p = 0.045) and IPAQ (p = 0.005) scores were predictors of poor sleep quality in the group of ICU-PCC. These results indicate that worse sleep quality is related to higher levels of depression and anxiety, worse quality of life and less physical activity. Therapeutic strategies should focus on these factors to have a positive impact on the quality of sleep., This research was supported by: Grants from the Agency for Management of University and Research Grants (AGAUR) from the Generalitat de Catalunya (Pandemies, 202PANDE00053) and La Marató de TV3 Foundation (202111-30-31-32) to MG.- Grants from the Instituto de Salud Carlos III de Madrid (PI22/01687, ISCIII) and Agency for Management of University and Research Grants (2021SGR 00761) to GPR., Peer Reviewed, Membres del NAUTILUS-Project Collaborative Group: Vanesa Arauzo j, Jose A. Bernia j, Marta Balague-Marmaña k, Berta Valles-Pauls k, Ester Gonzalez-Aguado l, Carme Tayó-Juli l, Eva Forcadell-Ferreres m, Silvia Reverte-Vilarroya m, Susanna Forné n, Anna Bartes-Plans o, Jordina Muñoz-Padros o, Jose A. Muñoz-Moreno p, Anna Prats-Paris p, Inmaculada Rico q, Nuria Sabé q, Marta Almeria r, Laura Casas r, Maria José Ciudad s, Anna Ferré s, Tamar Garzon t, Marta Cullell u, Sonia Vega u, Sílvia Alsina v, Maria J. Maldonado-Belmonte w, Susana Vazquez-Rivera w, Eva Baillès x, Sandra Navarro x / j Consorci Sanitari de Terrassa (CST), Terrassa, Spain; k Hospital Sant Joan Despí Moisès Broggi, Consorci Sanitari Integral Jesús Caballero, Hospital Universitari Arnau de Vilanova, Lleida, Spain; l Consorci Sanitari Alt Penedès-Garraf, Vilafranca de Penedés, Barcelona, Spain; m Hospital Verge de la Cinta, Tortosa, Tarragona, Spain; n Fundació Sant Hospital de la Seu d’Urgell, La Seu d’Urgell, Lleida, Spain; o Consorci Hospitalari de Vic, Vic, Barcelona, Spain; p Servei de Malalties Infeccioses, Fundació Lluita contra les Infeccions – Hospital Universitari Germans Trias i Pujol, Badalona, Barcelona, Spain; q Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain; r Hospital Universitari Mútua Terrassa, Terrassa, Barcelona, Spain; s Badalona Serveis Assistencials, Badalona, Barcelona, Spain; t Manuela Lozano, Institut d’Assistència Sanitària, Girona, Spain; u Fundació Salut Empordà, Figueres, Girona, Spain; v Fundació Hospital de Puigcerdà, Puigcerdà, Girona, Spain; w Hospital Universitario Central de la Cruz Roja San José y Santa Adela, Madrid, Spain; x Servei Andorrà d’Atenció Sanitària (SAAS), Andorra, Postprint (published version)

The adoption of algorithms based on Artificial Intelligence (AI) has been rapidly increasing during the last few years. However, some aspects of AI techniques are under heavy scrutiny. For instance, in many use cases, it is not clear whether the decisions of an algorithm are well informed and conforming to human understanding. Having ways to address these concerns is crucial in many domains, especially whenever humans and intelligent (physical or virtual) agents must cooperate in a shared environment. In this paper, we apply an explainability method based on the creation of a Policy Graph (PG) based on discrete predicates that represent and explain a trained agent’s behaviour in a multi-agent cooperative environment. We show that from these policy graphs, policies for surrogate interpretable agents can be automatically generated. These policies can be used to measure the reliability of the explanations enabled by the PGs through a fair behavioural comparison between the original opaque agent and the surrogate one. The contributions of this paper represent the first use case of policy graphs in the context of explaining agent behaviour in cooperative multi-agent scenarios and present experimental results that sets this kind of scenario apart from previous implementations in single-agent scenarios: when requiring cooperative behaviour, predicates that allow representing observations about the other agents are crucial to replicate the opaque agent’s behaviour and increase the reliability of explanations., This work has been partially supported by the H2020 knowlEdge European project (Grant agreement ID: 957331)., Peer Reviewed, Postprint (published version)

Ultra-high-density single-atom catalysts (UHD-SACs) present unique opportunities for harnessing cooperative effects between neighboring metal centers. However, the lack of tools to establish correlations between the density, type, and arrangement of the isolated metal atoms with the support surface properties hinders efforts to engineer advanced material architectures. Here, we precisely describe the metal center organization in various mono- and multimetallic UHD-SACs based on nitrogen-doped carbon (NC) supports by coupling transmission electron microscopy with tailored machine-learning methods (released as a user-friendly web app) and density functional theory simulations. Our approach quantifies the non-negligible presence of multimers with increasing atom density, characterizes the size and shape of these low-nuclearity clusters, and identifies surface atom density criteria to ensure isolation. Further, it provides previously inaccessible experimental insights into coordination site arrangements in the NC host, uncovering a repulsive interaction that influences the disordered distribution of metal centers in UHD-SACs. This observation holds in multimetallic systems, where chemically-specific analysis quantifies the degree of intermixing. These fundamental insights into the materials chemistry of single-atom catalysts are crucial for designing catalytic systems with superior reactivity., This publication was created as part of NCCR Catalysis (grant number 180544), a National Centre of Competence in Research funded by the Swiss National Science Foundation. A. R.-F. acknowledges funding from the Generalitat de Catalunya and the European Union under Grant 2023 FI-3 00027. N.L. acknowledges support from the Ministerio de Ciencia e Innovación, ref. no. RTI2018-101394-B-100, and the Severo Ochoa Grant, MCIN/AEI/10.13039/501100011033-CEX2019-000925-S. The authors thank BSC-RES for generously providing computational resources., Peer Reviewed, Postprint (published version)

The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems., The research leading to these results has received funding from Horizon 2020 and the European Union’s Framework Programme for Research and Innovation (H2020/2014-2020) under grant agreement no. 957331., Peer Reviewed, Article signat per 20 autors/es: Enrico Alberti 1, Sergio Alvarez-Napagao 2, Victor Anaya 3, Marta Barroso 2, Cristian Barrué 4, Christian Beecks 5, Letizia Bergamasco 6, Sisay Adugna Chala 7, Victor Gimenez-Abalos 2, Alexander Graß 7, Daniel Hinjos 2, Maike Holtkemper 5, Natalia Jakubiak 4, Alexandros Nizamis 8, Edoardo Pristeri 6, Miquel Sànchez-Marrè 4, Georg Schlake 5, Jona Scholz 5, Gabriele Scivoletto 1, Stefan Walter 9 / 1 Nextworks Srl, Via Livornese 1027, 56122 Pisa, Italy; 2 Barcelona Supercomputing Center, Plaça Eusebi Güell 1-3, 08034 Barcelona, Spain; 3 Information Catalyst SL, Cl Reina 27, 4-7, 46800 Xativa, Spain; 4 Department of Computer Science, IDEAI Research Centre, Universitat Politècnica de Catalunya (UPC), Carrer Jordi Girona 1-3, 08034 Barcelona, Spain; 5 Department of Data Science, University of Hagen, 58097 Hagen, Germany; 6 LINKS Foundation, Via Pier Carlo Boggio 61, 10138 Torino, Italy; 7 Fraunhofer Institute for Applied Information Technology (FIT), Schloss Birlinghoven, 53757 Sankt Augustin, Germany; 8 Centre for Research and Technology Hellas-Information Technologies Institute (CERTH/ITI), Charilaou-Thermis, 57001 Thessaloniki, Greece; 9 VTT Technical Research Centre of Finland Ltd., Tekniikantie 21, 02150 Espoo, Finland, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura, Objectius de Desenvolupament Sostenible::12 - Producció i Consum Responsables, Postprint (published version)