Symmetry Constrained Two Higgs Doublet Models

We study Two-Higgs-Doublet Models (2HDM) where Abelian symmetries have been introduced, leading to a drastic reduction in the number of free parameters in the 2HDM. Our analysis is inspired in BGL models, where, as the result of a symmetry of the Lagrangian, there are tree-level scalar mediated Flavour-Changing-Neutral-Currents, with the flavour structure depending only on the CKM matrix. A systematic analysis is done on the various possible schemes, which are classified in different classes, depending on the way the extra symmetries constrain the matrices of couplings defining the flavour structure of the scalar mediated neutral currents. All the resulting flavour textures of the Yukawa couplings are stable under renormalisation since they result from symmetries imposed at the Lagrangian level. We also present a brief phenomenological analysis of the most salient features of each class of symmetry constrained 2HDM.Comment: 30 pages, 5 Table

Advanced side information creation techniques and framework for Wyner-Ziv video coding

Recently, several distributed video coding (DVC) solutions based on the distributed source coding (DSC) paradigm have appeared in the literature. Wyner-Ziv (WZ) video coding, a particular case of DVC where side information is made available at the decoder, enable to achieve a flexible distribution of the computational complexity between the encoder and decoder, promising to fulfill novel requirements from applications such as video surveillance, sensor networks and mobile camera phones. The quality of the side information at the decoder has a critical role in determining the WZ video coding rate-distortion (RD) performance, notably to raise it to a level as close as possible to the RD performance of standard predictive video coding schemes. Towards this target, efficient motion search algorithms for powerful frame interpolation are much needed at the decoder. In this paper, the RD performance of a Wyner-Ziv video codec is improved by using novel, advanced motion compensated frame interpolation techniques to generate the side information. The development of these type of side information estimators is a difficult problem in WZ video coding, especially because the decoder only has available some reference, decoded frames. Based on the regularization of the motion field, novel side information creation techniques are proposed in this paper along with a new frame interpolation framework able to generate higher quality side information at the decoder. To illustrate the RD performance improvements, this novel side information creation framework has been integrated in a transform domain turbo coding based Wyner-Ziv video codec. Experimental results show that the novel side information creation solution leads to better RD performance than available state-of-the-art side information estimators, with improvements up to 2 dB: moreover, it allows outperforming H.264/AVC Intra by up to 3 dB with a lower encoding complexity

Brazilian fans’ social representations on soccer. (Representaciones sociales de los hinchas brasileños sobre fútbol).

<b>Abstract</b><p align="justify">The study aims at describing the organizing principles and the structure of Brazilian soccer fans’ social representations on soccer. 521 participants, who supported five Brazilian clubs, participated in the study. Data collection took place through an internet form advertised in online communities about soccer. Participants answered open-ended free evocation tasks in which they mentioned the first words that came to their minds when thinking about soccer. Responses were categorized according to their theme and correspondence analysis, prototypical analysis and similarity analysis were employed for data analysis. Results suggest that soccer clubs, fans, emotion and goal constitute the representation’s central core and organize the structure. Correspondence analysis results present contrasts between concrete and symbolic aspects of the sport, while there are also variations in the representational field according to participants’ clubs, age ranges and involvement with fan clubs.</p><b>Resumen</b><p align="justify">El estudio tiene como objetivo describir los principios de organización y la estructura de las representaciones sociales de los hinchas brasileños sobre fútbol. 521 participantes, que apoyaron a cinco clubs brasileños, participaron en el estudio. La colección de datos ocurrió a través de un cuestionario de Internet anunciado en comunidades en on line sobre fútbol. Los participantes contestaron a tareas de evocación libre en las cuales mencionaron las primeras palabras que vinieron a sus mentes al pensar sobre fútbol. Las respuestas fueron categorizadas según su tema y el análisis de correspondencia, el análisis prototípico y el análisis de semejanza fueron empleados para el análisis de datos. Los resultados sugieren que los clubs, los aficionados, la emoción y el gol constituyen la base central de la representación y organizan la estructura. Los resultados del análisis de correspondencia demuestran contrastes entre aspectos simbólicos y concretos del deporte, mientras que hay también variaciones en el campo de representación según los clubs de los participantes, la edad e implicación con los clubes de hinchas.</p

Low complexity intra mode selection for efficient distributed video coding

Motion compensated frame interpolation (MCFI) is one of the most efficient solutions to generate side information (SI) in the context of distributed video coding. However, it creates SI with rather significant motion compensated errors for some frame regions while rather small for some other regions depending on the video content. In this paper, a low complexity Infra mode selection algorithm is proposed to select the most 'critical' blocks in the WZ frame and help the decoder with some reliable data for those blocks. For each block, the novel coding mode selection algorithm estimates the encoding rate for the Intra based and WZ coding modes and determines the best coding mode while maintaining a low encoder complexity. The proposed solution is evaluated in terms of rate-distortion performance with improvements up to 1.2 dB regarding a WZ coding mode only solution

Improving Active Learning Performance through the Use of Data Augmentation

Fonseca, J., & Bacao, F. (2023). Improving Active Learning Performance through the Use of Data Augmentation. International Journal of Intelligent Systems, 2023, 1-17. https://doi.org/10.1155/2023/7941878 --- Funding: This research was supported by three research grants of the Portuguese Foundation for Science and Technology (“Fundação para a Ciencia e a Tecnologia”): SFRH/BD/151473/2021 - MIT Portugal PhD Grant; DSAIPA/DS/0116/2019, and PCIF/SSI/0102/2017.Active learning (AL) is a well-known technique to optimize data usage in training, through the interactive selection of unlabeled observations, out of a large pool of unlabeled data, to be labeled by a supervisor. Its focus is to find the unlabeled observations that, once labeled, will maximize the informativeness of the training dataset, therefore reducing data-related costs. The literature describes several methods to improve the effectiveness of this process. Nonetheless, there is a paucity of research developed around the application of artificial data sources in AL, especially outside image classification or NLP. This paper proposes a new AL framework, which relies on the effective use of artificial data. It may be used with any classifier, generation mechanism, and data type and can be integrated with multiple other state-of-the-art AL contributions. This combination is expected to increase the ML classifier’s performance and reduce both the supervisor’s involvement and the amount of required labeled data at the expense of a marginal increase in computational time. The proposed method introduces a hyperparameter optimization component to improve the generation of artificial instances during the AL process as well as an uncertainty-based data generation mechanism. We compare the proposed method to the standard framework and an oversampling-based active learning method for more informed data generation in an AL context. The models’ performance was tested using four different classifiers, two AL-specific performance metrics, and three classification performance metrics over 15 different datasets. We demonstrated that the proposed framework, using data augmentation, significantly improved the performance of AL, both in terms of classification performance and data selection efficiency (all the codes and preprocessed data developed for this study are available at https://github.com/joaopfonseca/publications/).publishersversionpublishe

a literature review

Fonseca, J., & Bacao, F. (2023). Tabular and latent space synthetic data generation: a literature review. Journal of Big Data, 10, 1-37. [115]. https://doi.org/10.1186/s40537-023-00792-7 --- This research was supported by two research grants of the Portuguese Foundation for Science and Technology (“Fundação para a Ciência e a Tecnologia”), references SFRH/BD/151473/2021 and DSAIPA/DS/0116/2019, and by project UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC).The generation of synthetic data can be used for anonymization, regularization, oversampling, semi-supervised learning, self-supervised learning, and several other tasks. Such broad potential motivated the development of new algorithms, specialized in data generation for specific data formats and Machine Learning (ML) tasks. However, one of the most common data formats used in industrial applications, tabular data, is generally overlooked; Literature analyses are scarce, state-of-the-art methods are spread across domains or ML tasks and there is little to no distinction among the main types of mechanism underlying synthetic data generation algorithms. In this paper, we analyze tabular and latent space synthetic data generation algorithms. Specifically, we propose a unified taxonomy as an extension and generalization of previous taxonomies, review 70 generation algorithms across six ML problems, distinguish the main generation mechanisms identified into six categories, describe each type of generation mechanism, discuss metrics to evaluate the quality of synthetic data and provide recommendations for future research. We expect this study to assist researchers and practitioners identify relevant gaps in the literature and design better and more informed practices with synthetic data.publishersversionpublishe

Geometric SMOTE for imbalanced datasets with nominal and continuous features

Fonseca, J., & Bacao, F. (2023). Geometric SMOTE for imbalanced datasets with nominal and continuous features. Expert Systems with Applications, 234(December), 1-9. [121053]. https://doi.org/10.1016/j.eswa.2023.121053 --- This research was supported by research grants of the Portuguese Foundation for Science and Technology (“Fundação para a Ciência e a Tecnologia”), references SFRH/BD/151473/2021, DSAIPA/DS/0116/2019, and by project UIDB/04152/2020 — Centro de Investigação em Gestão de Informação (MagIC) .Imbalanced learning can be addressed in 3 different ways: Resampling, algorithmic modifications and cost-sensitive solutions. Resampling, and specifically oversampling, are more general approaches when opposed to algorithmic and cost-sensitive methods. Since the proposal of the Synthetic Minority Oversampling TEchnique (SMOTE), various SMOTE variants and neural network-based oversampling methods have been developed. However, the options to oversample datasets with nominal and continuous features are limited. We propose Geometric SMOTE for Nominal and Continuous features (G-SMOTENC), based on a combination of G-SMOTE and SMOTENC. Our method modifies SMOTENC’s encoding and generation mechanism for nominal features while using G-SMOTE’s data selection mechanism to determine the center observation and k-nearest neighbors and generation mechanism for continuous features. G-SMOTENC’s performance is compared against SMOTENC’s along with two other baseline methods, a State-of-the-art oversampling method and no oversampling. The experiment was performed over 20 datasets with varying imbalance ratios, number of metric and non-metric features and target classes. We found a significant improvement in classification performance when using G-SMOTENC as the oversampling method. An open-source implementation of G-SMOTENC is made available in the Python programming language.publishersversionpublishe