On the dimension of orbits of matrix pencils under strict equivalence

We prove that, given two matrix pencils L and M, if M belongs to the closure of the orbit of L under strict equivalence, then the dimension of the orbit of M is smaller than or equal to the dimension of the orbit of M, and the equality is only attained when M belongs to the orbit of L. Our proof uses only the majorization involving the eigenstructures of L and M which characterizes the inclusion relationship between orbit closures, together with the formula for the codimension of the orbit of a pencil in terms of its eigenstruture.The authors thank Andrii Dmytryshyn for suggesting the use of Theorem 6 to prove the main result. This work is part of grant PID2023-147366NB-I00 funded by MICIU/AEI/ 10.13039/501100011033 and FEDER/UE. Also funded by RED2022-134176-T and by the program Excellence Initiative - Research University at the Jagiellonian University in Kraków

Leveraging unlabeled data for lung sound classification through self-supervised contrastive learning

This paper focuses on the classification of lung sounds as can be instrumental in diagnosing respiratory diseases, that are one of the leading causes of death. We address two intrinsic challenges associated with the application of deep learning models to this task. The first one is the need of large databases annotated by expert medical staff. The second one is the presence of a strong class imbalance in the datasets primarily due to the predominance of non-pathological respiratory sounds. For overcoming these issues we propose a deep convolutional network model that is trained using the contrastive self-supervised learning paradigm. This technique is able to generate useful audio data representations from unlabeled data that can be effectively transferred to the target task employing a limited amount of annotated data. We have evaluated the developed systems on the well-known ICBHI dataset that contains respiratory cycles categorized into four different classes. Results show that our approach outperforms the conventional supervised learning model when the size of available labeled data is reduced. With 40% of annotated data, self-supervision achieves a relative improvement with respect to the baseline of 12.1%, 8.6%, 16% and 66.7% in score, accuracy, and sensitivity respectively, while getting a reduction of 1.0% in specificity. Finally, to corroborate our findings, we have also assessed our system on the SPRSound database, confirming the same trends. We believe that the findings in this paper enlightens the path towards the use of unlabeled data in biomedicine alleviating the need of large annotated datasets.The authors acknowledge the support of the Comunidad de Madrid through grants IntCARE-CM and PEJ-2021-AI/TIC-22744 and the Spanish State Research Agency (MICIU/AEI/10.13039/501100011033) and FEDER, UE through projects PID2020-115363RB-I00 and PID2023-146684NB-I00

Influence of coupling symmetries and noise on the critical dynamics of synchronizing oscillator lattices

Recent work has shown that the synchronization process in lattices of self-sustained (phase and limit-cycle) oscillators displays universal scale-invariant behavior previously studied in the physics of surface kinetic roughening. The type of dynamic scaling ansatz which is verified depends on the randomness that occurs in the system, whether it is columnar disorder (quenched noise given by the random assignment of natural frequencies), leading to anomalous scaling, or else time-dependent noise, inducing the more standard Family-Vicsek dynamic scaling ansatz, as in equilibrium critical dynamics. The specific universality class also depends on the coupling function: for a sine function (as in the celebrated Kuramoto model) the critical behavior is that of the Edwards-Wilkinson equation for the corresponding type of randomness, with Gaussian fluctuations around the average growth. In all the other cases investigated, Tracy–Widom fluctuations ensue, associated with the celebrated Kardar–Parisi–Zhang equation for rough interfaces. Two questions remain to be addressed in order to complete the picture, however: (1) Is the atypical scaling displayed by the sine coupling preserved if other coupling functions satisfying the same (odd) symmetry are employed (as suggested by continuum approximations and symmetry arguments)? and (2) how does the competition between both types of randomness (which are expected to coexist in experimental settings) affect the nonequilibrium behavior? We address the latter question by numerically characterizing the crossover between thermal-noise and columnar-disorder criticality, and the former by providing evidence confirming that it is the symmetry of the coupling function that sets apart the sine coupling, among other odd-symmetric couplings, due to the absence of Kardar–Parisi–Zhang fluctuations.This work has been partially supported by Ministerio de Ciencia e Innovación (Spain), by Agencia Estatal de Investigación, Spain (AEI, Spain, 10.13039/501100011033), and by European Regional Development Fund, (ERDF, A way of making Europe) through Grants No. PID2021-123969NB-I00 and No. PID2021-128970OA-I00

From fact-checking to debunking: The case of Elections24Check during the 2024 European Elections

Misleading and false information is an issue in the European public sphere. This article analyzes the verified disinformation by fact-checkers during the 2024 European Parliament elections. From the lens of fact-checking, as a journalism practice to fight against disinformation, this research explores the European initiative Elections24Check, a collaborative fact-checking project associated with the European Fact-Checking Standards Network. The research aims: on the one hand, to demonstrate the prevalence of debunking over fact-checking; and on the other, to dissect the thematic nature, format, typology, and deceitful technique of the hoaxes verified during the last European elections. Using content analysis, the sample comprised 487 publications verified by 32 different fact-checkers across a total of 28 countries for one month related to the 2024 European elections. The results present implications regarding the collaborative fact-checking project that made a greater effort to verify other contextual disinformation issues rather than checking disinformation directly involved in the elections and EU politics. Also, this case study revealed the shift in the European fact-checking movement with the prevalence of debunking activity over scrutinizing public statements. Finally, the verified disinformation underscored the continued dominance of text as the primary format for spreading false information and the predominance of content decontextualization. The results of this study aim to deepen the understanding of fact-checking in the European media landscape.This research was supported by the European Education and Culture Executive Agency (EACEA), belonging to the European Commission, Jean Monnet (Erasmus) Future of Europe Communication in Times of Pandemic Disinformation (FUTEUDISPAN; No: 101083334‐JMO‐2022‐CHAIR). Nevertheless, the authors bear sole responsibility for the content of this article, and the EACEA assumes no liability for the utilization of the disclosed information. This study also belongs to a Spanish National Project of the Ministry of Science, Innovation and Universities (2022). Project reference: PID2022‐142755OB‐I00. Moreover, this research was also funded by Universidad de La Sabana (No: COMCORP‐3–2023), associated with the research group Centro de Investigaciones de la Comunicación Corporativa Organizacional (CICCO). This research was also supported by a University teacher training grant (FPU22/01905), awarded to one of the co‐authors by the Spanish Ministry of UniversitiesThis article draws on the Elections24Check database, to which the EFCSN has granted us access for our research purposes, and for whose collaboration we express our gratitude

Uplink Non-Orthogonal Multiple Access (NOMA) Decoding based on Successive Parzen Windows Interference Cancellation

In this paper, a novel decoding kernel method based on Successive Parzen Windows Interference Cancellation (SPWIC) is proposed for the Non-Orthogonal Multiple Access (NOMA) uplink. The procedure leverages on the diversity in both angle and received power at a 3D antenna combined with a Parzen Windows based decoding to achieve better interference cancellation, providing the decoding process with robustness against multi-user interference and user discrimination. This is specially convenient in vehicular scenarios in crowded cities. We have evaluated SPWIC in various scenarios and concluded that it outperforms the standard Successive Interference Cancellation (SIC) approach even in Multiple-Input Multiple-Output (MIMO) cases such that up to 9 users can be allocated on the same resources -as long as they are not too close to each other-. Although it is proposed for mmWave, it can be directly adapted to lower frequencies.This work was supported in part by project PID2023-147305OB-C31 (SOFIA-AIR) by MICIU/AEI/10.13039/501100011033/ERDF and in part by project PASSIONATE under CHIST-ERA grant CHIST-ERA-22-WAI-04 by AEI PCI2023-145990-2

Authorization models for IoT environments: A survey

Authorization models are pivotal in the Internet of Things (IoT) ecosystem, ensuring secure management of data access and communication. These models function after authentication, determining the specific actions that a device is allowed to perform. This paper aims to provide a comprehensive and comparative analysis of authorization solutions within IoT contexts, based on the requirements identified from the existing literature. We critically assess the functionalities and capabilities of various authorization solutions, particularly those designed for IoT cloud platforms and distributed architectures. Our findings highlight the urgent need for further development of authorization models optimized for the unique demands of IoT environments. Consequently, we address both the persistent challenges and the gaps within this domain. As IoT continues to reshape the technological landscape, the refinement and adaptation of authorization models remain imperative ongoing pursuits.This work was supported by the Spanish Government under the grant TED-2021-130369B-C32, funded by MICIU/AEI/ 10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR” and the grant PID2020-113795RB-C32, fun-ded by MICIU/AEI /10.13039/501100011033. In addition, it was partially supported by project i-SHAPER, which is being carried out within the framework of the Recovery, Transformation, and Resilience Plan funds, funded by the European Union (Next Generation)

Experimental characterization of turbulent boundary layers around a NACA 4412 wing profile

An experimental characterization of the turbulent boundary layers developing around a NACA 4412 wing profile is carried out in the Minimum Turbulence Level (MTL) wind tunnel located at KTH Royal Institute of Technology. The campaign included collecting wall-pressure data via built-in pressure taps, capturing velocity signals in the turbulent boundary layers (TBLs) using hot-wire anemometry (HWA), and conducting direct skin-friction measurements with oil-film interferometry (OFI). The research spanned two chord-based Reynolds numbers ( = 4 × 105 and 106) and four angles of attack (5◦, 8◦, 11◦ and 14◦), encompassing a broad spectrum of flow conditions, from mild to strong adverse-pressure gradients (APGs), including scenarios where the TBL detaches from the wing surface. This dataset offers crucial insights into TBL behavior under varied flow conditions, particularly in the context of APGs. Key features include the quasi-independence of the pressure coefficient distributions from Reynolds number, which aids in distinguishing Reynolds-number effects from those due to APG strengths. The study also reveals changes in TBL dynamics as separation approaches, with energy shifting from the inner to the outer region and the eventual transition to a free-shear flow state post-separation. Additionally, the diagnostic scaling in the outer region under spatial-resolution effects is considered, showing further evidence for its applicability for small +, however with inconsistent results for larger +. The findings and database resulting from this campaign may be of special relevance for the development and validation of turbulence models, especially in the context of aeronautical applications.This research is funded by the Knut and Alice Wallenberg (KAW) Foundation through the Academy Fellow Programme of PS. The high-resolution LES used for the validation in this work were performed on resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at the PDC Center for High-Performance Computing in KTH (Stockholm), by the PRACE, Belgium project nr. 2021250090 on HAWK (Stuttgart) and by the European High-Performance Computing Joint Undertaking (EuroHPC JU) project EHPC-REG-2021R0088 in LUMI (Finland)

Novel computational techniques for decision support through medical imaging

Mención Internacional en el título de doctorClinical experts have to daily confront complex decisions such as disease diagnosis, grading, or treatment determination. To assist them, the computational progress has boosted the creation of Clinical Decision Support Systems (CDSSs). Nowadays, many of these systems are designed with artificial intelligence (AI), giving them the ability to work with all kinds of clinical data, as for example images. Medical imaging has major importance in clinical and pharmacological processes, nevertheless, they are data with high complexity that require intense computation to analyze them. The automation of the image analysis, classification, and examination processes promises to speed up and increase the accuracy of the daily work of the clinical experts, being a current issue. Particularly in lung infection diseases, which are a major concern for the health systems globally, due to the high mortality rates and the spread capability of the infector microorganisms. This propagation produces high infection rates, as in the COVID-19 pandemic, which proved the major importance of fast treatment and isolation of the patient to control incidence rates. Another current concern is the emergence of antimicrobial resistance (AMR), which poses a major challenge in fighting infectious diseases, like tuberculosis, where the AMRs have no response to current treatment schemes. Currently, all these challenges are being addressed thanks to novel computational techniques. This thesis focuses on the proposal of new techniques and analysis for the current CDSSs, exploring the use-case of clinical images for lung infection diseases. This dissertation addresses two main computational streams; image classification systems and automatic image assessment techniques, applying an experimental comparative approach. Previous studies have proved the capability of the CDSSs to support the detection and assessment of diseases with medical images. Despite that, there are many issues to address, especially in a high-precision area like medicine where the systems have to be reliable, robust, and precise. During the dissertation, we addressed different open questions as the decision of the imaging modality during the design of a CDSS, and the implications of applying parallel and distributed learning techniques. In this document, a whole classification system is also proposed. The system is able to differentiate four lung infection diseases and it is researched to maximize the accuracy with deep learning ensemble techniques. This dissertation also presents image assessment techniques. The assessment systems analyze images to provide information without (or with a minimum) human direct interaction. In this area a novel diagnosis system for tuberculosis is proposed, the technique is able to find bacteria in the sputum microscopy images automatically, allowing for increasing the precision of this diagnosis and assessment procedure. Also, a semi-supervised approach to evaluate time-lapse microscopy (TLM) sequences is presented. The system speeds up the evaluation of TLM and the labeling procedure, opening new paths for microbial deep learning future research. To summarize, this thesis includes original contributions to the CDSSs area, proposing ad-hoc techniques, new labeling procedures, and novel analyses to fill literature gaps, increasing the reliability and speed of clinical procedures. The techniques proposed beyond the use-case of lung infection disease are open to being used in other image modalities and diseases, opening new research and application paths.Los expertos clínicos deben enfrentar diariamente decisiones complejas como el diagnóstico de enfermedades, la clasificación o la determinación del tratamiento. Para asistirlos, el progreso computacional ha impulsado la creación de Sistemas de Soporte a la Decisión Clínica (SSDC). Hoy en día, muchos de estos sistemas se diseñan con inteligencia artificial (IA), dándoles la capacidad de trabajar con todo tipo de datos clínicos, como por ejemplo imágenes. La imagenología médica tiene una gran importancia en los procesos clínicos y farmacológicos. Sin embargo, se trata de datos de alta complejidad que requieren una intensa computación para su análisis. La automatización de los procesos de análisis, clasificación y examen de imágenes promete acelerar y aumentar la precisión del trabajo diario de los expertos clínicos, siendo un tema de actualidad. Particularmente, las enfermedades de infección pulmonar son una gran preocupación para los sistemas de salud a nivel mundial, debido a las altas tasas de mortalidad y la capacidad de propagación de los microorganismos infecciosos. Esta propagación produce altas tasas de infección, como en la pandemia de COVID-19, que demostró la gran importancia del tratamiento rápido y el aislamiento del paciente para controlar las tasas de incidencia. Otra preocupación actual es la aparición de la resistencia a los antimicrobianos (RAM), que plantea un gran desafío en la lucha contra enfermedades infecciosas, como la tuberculosis, donde la RAM no responde a los esquemas de tratamiento actuales. Actualmente, todos estos desafíos se están abordando gracias a nuevas técnicas computacionales. Esta tesis se centra en la propuesta de nuevas técnicas y análisis para los SSDC actuales, explorando el caso de uso de imágenes clínicas para enfermedades de infección pulmonar. Esta disertación aborda dos corrientes computacionales principales: sistemas de clasificación de imágenes y técnicas de evaluación automática de imágenes, aplicando un enfoque comparativo experimental. Estudios previos han demostrado la capacidad de los SSDC para apoyar la detección y evaluación de enfermedades con imágenes médicas. A pesar de esto, quedan muchos problemas por abordar, especialmente en un área de alta precisión como la medicina, donde los sistemas deben ser fiables, robustos y precisos. Durante la disertación, abordamos diferentes preguntas abiertas como la decisión de la modalidad de imagenología durante el diseño de un SSDC y las implicaciones de aplicar técnicas de aprendizaje paralelo y distribuido. En este documento, también se propone un sistema de clasificación completo. El sistema es capaz de diferenciar cuatro enfermedades de infección pulmonar y se investiga para maximizar la precisión con técnicas de *ensemble* de aprendizaje profundo. Esta disertación también presenta técnicas de evaluación de imágenes. Los sistemas de evaluación analizan imágenes para proporcionar información sin (o con una mínima) interacción humana directa. En esta área se propone un novedoso sistema de diagnóstico para la tuberculosis; la técnica es capaz de encontrar bacterias en las imágenes de microscopía de esputo automáticamente, lo que permite aumentar la precisión de este procedimiento de diagnóstico y evaluación. Además, se presenta un enfoque semi-supervisado para evaluar secuencias de microscopía de lapso de tiempo (TLM). El sistema acelera la evaluación de TLM y el procedimiento de etiquetado, abriendo nuevos caminos para futuras investigaciones de aprendizaje profundo microbiano. En resumen, esta tesis incluye contribuciones originales al área de los SSDC, proponiendo técnicas *ad-hoc*, nuevos procedimientos de etiquetado y análisis novedosos para llenar vacíos en la literatura, aumentando la fiabilidad y la velocidad de los procedimientos clínicos. Las técnicas propuestas, más allá del caso de uso de las enfermedades de infección pulmonar, están abiertas a ser utilizadas en otras modalidades de imagen y enfermedades, abriendo nuevas vías de investigación y aplicación.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Pedro Ángel Cuenca Castillo.- Secretario: Félix García Carballeira.- Vocal: Fabrizio Marozz

Fast k-medoids and q-Fold Fast k-medoids: New distance-based clustering algorithms for large mixed-type data

In this work new robust efficient clustering algorithms for large datasets of mixedtype data are proposed and implemented in a new Python package called FastKmedoids. Their performance is analyzed through an extensive simulation study, and compared to a wide range of existing clustering alternatives in terms of both predictive power and computational efficiency. MDS is used to visualize clustering results