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[EN] We present a corpus of Spanish tweets of 15 Twitter accounts of politicians of the main five parties (PSOE, PP, Cs, UP and VOX) covering the campaign of the Spanish election of 10th November 2019 (10N Spanish Election). We perform a semi-automatic annotation of domainspecific topics using a mixture of keyword-based and supervised techniques. In this preliminary study we extracted the tweets of few politicians of each party with the aim to analyse their official communication strategy. Moreover, we analyse sentiments and emotions employed in the tweets. Although the limited size of the Twitter corpus due to the very short time span, we hope to provide with some first insights on the communication dynamics of social network accounts of these five Spanish political parties. ; The work of the authors from the Universitat Politecnica de Valencia was funded by the Spanish MICINN under the research project MISMISFAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). ; Sánchez-Junquera, J.; Ponzetto, SP.; Rosso, P. (2020). A Twitter Political Corpus of the 2019 10N Spanish Election. Springer. 41-49. https://doi.org/10.1007/978-3-030-58323-1_4 ; S ; 41 ; 49 ; Abercrombie, G., Nanni, F., Batista-Navarro, R., Ponzetto, S.P.: Policy preference detection in parliamentary debate motions. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), Hong Kong, China, pp. 249–259. Association for Computational Linguistics, November 2019 ; Ekman, P., et al.: Universals and cultural differences in the judgments of facial expressions of emotion. J. Pers. Soc. Psychol. 53(4), 712 (1987) ; Gao, W., Sebastiani, F.: Tweet sentiment: from classification to quantification. In: 2015 IEEE/ACM International Conference on ASONAM, pp. 97–104. IEEE (2015) ; Glavaš, G., Nanni, F., Ponzetto, S.P.: Computational analysis of political texts: bridging research efforts across communities. In: Proceedings of the 57th Annual Meeting of the Association for ...

Stereotypes about immigrants are a type of social bias increasingly present in the human interaction in social networks and political speeches. This challenging task is being studied by computational linguistics because of the rise of hate messages, offensive language, and discrimination that many people receive. In this work, we propose to identify stereotypes about immigrants using two different explainable approaches: a deep learning model based on Transformers; and a text masking technique that has been recognized by its capabilities to deliver good and human-understandable results. Finally, we show the suitability of the two models for the task and offer some examples of their advantages in terms of explainability. ; Los estereotipos sobre inmigrantes son un tipo de sesgo social cada vez más presente en la interacción humana en redes sociales y en los discursos políticos. Esta desafiante tarea está siendo estudiada por la lingüística computacional debido al aumento de los mensajes de odio, el lenguaje ofensivo, y la discriminación que reciben muchas personas. En este trabajo, nos proponemos identificar estereotipos sobre inmigrantes utilizando dos enfoques diametralmente opuestos prestando atención a la explicabilidad de los mismos: un modelo de aprendizaje profundo basado en Transformers; y una técnica de enmascaramiento de texto que ha sido reconocida por su capacidad para ofrecer buenos resultados a la vez que comprensibles para los humanos. Finalmente, mostramos la idoneidad de los dos modelos para la tarea, y ofrecemos algunos ejemplos de sus ventajas en términos de explicabilidad. ; The work of the authors from the Universitat Politècnica of València was funded by the Spanish Ministry of Science and Innovation under the research project MISMIS-FAKEnHATE on MISinformation and MIScommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). Experiments were carried out on the GPU cluster at PRHLT thanks to the PROMETEO/2019/121 (DeepPattern) research project funded by the Generalitat Valenciana.

[EN] Stereotype is a type of social bias massively present in texts that computational models use. There are stereotypes that present special difficulties because they do not rely on personal attributes. This is the case of stereotypes about immigrants, a social category that is a preferred target of hate speech and discrimination. We propose a new approach to detect stereotypes about immigrants in texts focusing not on the personal attributes assigned to the minority but in the frames, that is, the narrative scenarios, in which the group is placed in public speeches. We have proposed a fine-grained social psychology grounded taxonomy with six categories to capture the different dimensions of the stereotype (positive vs. negative) and annotated a novel StereoImmigrants dataset with sentences that Spanish politicians have stated in the Congress of Deputies. We aggregate these categories in two supracategories: one is Victims that expresses the positive stereotypes about immigrants and the other is Threat that expresses the negative stereotype. We carried out two preliminary experiments: first, to evaluate the automatic detection of stereotypes; and second, to distinguish between the two supracategories of immigrants¿ stereotypes. In these experiments, we employed state-of-the-art transformer models (monolingual and multilingual) and four classical machine learning classifiers. We achieve above 0.83 of accuracy with the BETO model in both experiments, showing that transformers can capture stereotypes about immigrants with a high level of accuracy. ; The work of the authors from the Universitat Politecnica de Valencia was funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). The work of Paolo Rosso was done also in the framework of the research project PROMETEO/2019/121 (DeepPattern) funded by the Generalitat Valenciana. ; Sánchez-Junquera, J.; Chulvi-Ferriols, MA.; Rosso, P.; Ponzetto, SP. ...

35 pages, 8 figures.-- Printed version published on Mar 25, 2002.-- ArXiv pre-print available at: http://arxiv.org/abs/cond-mat/0111138 ; We have developed and implemented a self-consistent density functional method using standard norm-conserving pseudopotentials and a flexible, numerical linear combination of atomic orbitals basis set, which includes multiple-zeta and polarization orbitals. Exchange and correlation are treated with the local spin density or generalized gradient approximations. The basis functions and the electron density are projected on a real-space grid, in order to calculate the Hartree and exchange-correlation potentials and matrix elements, with a number of operations that scales linearly with the size of the system. We use a modified energy functional, whose minimization produces orthogonal wavefunctions and the same energy and density as the Kohn-Sham energy functional, without the need for an explicit orthogonalization. Additionally, using localized Wannier-like electron wavefunctions allows the computation time and memory required to minimize the energy to also scale linearly with the size of the system. Forces and stresses are also calculated efficiently and accurately, thus allowing structural relaxation and molecular dynamics simulations. ; This work was supported by the Fundación Ramón Areces and by Spain's MCyT grant BFM2000-1312. JDG would like to thank the Royal Society for a University Research Fellowship and EPSRC for the provision of computer facilities. DSP acknowledges support from the Basque Government (Programa de Formación de Investigadores). ; Peer reviewed

We have uncovered a giant gyrotropic magneto-optical response for doped ferromagnetic manganite La2/3Ca1/3MnO3 around the near room-temperature paramagnetic-to-ferromagnetic transition. At odds with current wisdom, where this response is usually assumed to be fundamentally fixed by the electronic band structure, we point to the presence of small polarons as the driving force for this unexpected phenomenon. We explain the observed properties by the intricate interplay of mobility, Jahn-Teller effect and spin-orbit coupling of small polarons. As magnetic polarons are ubiquitously inherent to many strongly correlated systems, our results provide an original, general pathway towards the generation of magnetic-responsive gigantic gyrotropic responses that may open novel avenues for magnetoelectric coupling beyond the conventional modulation of magnetization. ; This work was supported by the Spanish Government through Projects No. MAT2014-56063-C2-1-R, No. FIS2012-37549-C05-04, No. FIS2015-64886-C5-2- P, No. MAT2012-37776, and the Severo Ochoa SEV-2015-0496 grant and the Generalitat de Catalunya (2014 SGR 734 project). B. C. acknowledges Grant No. FPI BES- 2012-059023, R. C. acknowledges support from CNPq— Brazil, and P. G. F. recognizes support from Ramón and Cajal Grant No. RYC-2013-12515. ; Peer reviewed

A review of the present status, recent enhancements, and applicability of the Siesta program is presented. Since its debut in the mid-1990s, Siesta's flexibility, efficiency, and free distribution have given advanced materials simulation capabilities to many groups worldwide. The core methodological scheme of Siesta combines finite-support pseudo-atomic orbitals as basis sets, norm-conserving pseudopotentials, and a real-space grid for the representation of charge density and potentials and the computation of their associated matrix elements. Here, we describe the more recent implementations on top of that core scheme, which include full spin-orbit interaction, non-repeated and multiple-contact ballistic electron transport, density functional theory (DFT)+U and hybrid functionals, time-dependent DFT, novel reduced-scaling solvers, density-functional perturbation theory, efficient van der Waals non-local density functionals, and enhanced molecular-dynamics options. In addition, a substantial effort has been made in enhancing interoperability and interfacing with other codes and utilities, such as wannier90 and the second-principles modeling it can be used for, an AiiDA plugin for workflow automatization, interface to Lua for steering Siesta runs, and various post-processing utilities. Siesta has also been engaged in the Electronic Structure Library effort from its inception, which has allowed the sharing of various low-level libraries, as well as data standards and support for them, particularly the PSeudopotential Markup Language definition and library for transferable pseudopotentials, and the interface to the ELectronic Structure Infrastructure library of solvers. Code sharing is made easier by the new open-source licensing model of the program. This review also presents examples of application of the capabilities of the code, as well as a view of on-going and future developments. ; SIESTA development has been historically supported by different Spanish National Plan projects: MEC-DGESPB95-0202, MCyT-BFM2000-1312, MEC-BFM2003-03372,FIS2006-12117, FIS2009-12721, FIS2012-37549, FIS2015- 64886-P, and RTC-2016-5681-7, the latter one together with Simune Atomistics Ltd. Currently, we thank financial support from the Spanish Ministry of Science, Innovation and Universities through the grant No. PGC2018-096955-B. We acknowledge the Severo Ochoa Centers of Excellence Program under Grants No. SEV-2015-0496 (ICMAB), and SEV-2017-0706 (ICN2), the GenCat Grant No.2017SGR1506, and the European Union MaX Center of Excellence (EU-H2020 Grant No. 824143). P.G.-F. acknowledges support from Ramón y Cajal Grant No. RyC-2013-12515. J.I.C acknowledges RTI2018-097895-B-C41. R.C. acknowledges to the European Union's Horizon 2020 research and innovation program under the Marie Skłodoswka–Curie grant agreement no. 665919. D.S.P, P.K, and P.B acknowledge MAT2016-78293-C6, FET-Open No. 863098, and UPV-EHU Grant IT1246-19. V. Yu was supported by a MolSSI fellowship (U.S. NSF award 1547580), and the ELSI development (V.B.,V.Yu) by NSF award 1450280. We also acknowledge Honghui Shang and Xinming Qin for giving us access to the HONPAS code, where a preliminary version of the hybrid functionals support described here was implemented. We are indebted to other contributors to the SIESTA project, whose names can be seen in the file in the Docs/Contributors.txt file of the SIESTA distribution, and we thank those, too many to list, contributing fixes, comments, clarifications, and documentation for the code. The data that support the findings of this study are available from the corresponding author upon reasonable request. ; Peer Reviewed ; Postprint (author's final draft)