En el filandero : cuentos de la montaña

Copia digital. Valladolid : Junta de Castilla y León. Consejería de Cultura y Turismo, 201

Modulación de las vías moleculares de ubiquitina y sumo por la infección viral

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid. Facultad de Medicina, Departamento de Bioquímica. Fecha de lectura: 10 de Junio de 201

Fast search of third-order epistatic interactions on CPU and GPU clusters

[Abstract] Genome-Wide Association Studies (GWASs), analyses that try to find a link between a given phenotype (such as a disease) and genetic markers, have been growing in popularity in the recent years. Relations between phenotypes and genotypes are not easy to identify, as most of the phenotypes are a product of the interaction between multiple genes, a phenomenon known as epistasis. Many authors have resorted to different approaches and hardware architectures in order to mitigate the exponential time complexity of the problem. However, these studies make some compromises in order to keep a reasonable execution time, such as limiting the number of genetic markers involved in the interaction, or discarding some of these markers in an initial filtering stage. This work presents MPI3SNP, a tool that implements a three-way exhaustive search for cluster architectures with the aim of mitigating the exponential growth of the run-time. Modern cluster solutions usually incorporate GPUs. Thus, MPI3SNP includes implementations for both multi-CPU and multi-GPU clusters. To contextualize the performance achieved, MPI3SNP is able to analyze an input of 6300 genetic markers and 3200 samples in less than 6 min using 768 CPU cores or 4 min using 8 NVIDIA K80 GPUs. The source code is available at https://github.com/chponte/mpi3snp.Ministerio de Economía y Competitividad and FEDER; TIN2016-75845-PXunta de Galicia and FEDER funds; ED431G/01Consolidation Program of Competitive Research; ED431C 2017/04Ministerio de Educación; FPU16/0133

Rheology of Water-in-water emulsions: Caseinate-pectin and caseinatealginate systems

Conventional models developed for oil-water emulsions do not fit viscosity of caseinate-pectin and caseinatealginate water-in water emulsions, which is always lower than predicted, except for high viscosities of disperse phase. These models do not consider strong deformations, prevented by the high interfacial tension of oil-water interphases. The ultra-low interfacial tension of water-in-water emulsions facilitates the creation of interphase and very elongated droplets. Capron model considers interfacial tension, fitting results when the dispersed phase is the most viscous, but, for other cases, lower experimental values are obtained related to the shear-induced stratification. Even values below the stratification model are observed for some samples, related to the influence of the interphase in the viscosity of the emulsion. A model that takes into account the presence of a relatively thick interphase poor in both polymers is proposed. Intermediate structures between highly elongated and stratified fluids, with influence of interphase viscosity could explain results

Transformer based contextualization of pre-trained word embeddings for irony detection in Twitter

[EN] Human communication using natural language, specially in social media, is influenced by the use of figurative language like irony. Recently, several workshops are intended to explore the task of irony detection in Twitter by using computational approaches. This paper describes a model for irony detection based on the contextualization of pre-trained Twitter word embeddings by means of the Transformer architecture. This approach is based on the same powerful architecture as BERT but, differently to it, our approach allows us to use in-domain embeddings. We performed an extensive evaluation on two corpora, one for the English language and another for the Spanish language. Our system was the first ranked system in the Spanish corpus and, to our knowledge, it has achieved the second-best result on the English corpus. These results support the correctness and adequacy of our proposal. We also studied and interpreted how the multi-head self-attention mechanisms are specialized on detecting irony by means of considering the polarity and relevance of individual words and even the relationships among words. This analysis is a first step towards understanding how the multi-head self-attention mechanisms of the Transformer architecture address the irony detection problem.This work has been partially supported by the Spanish Ministerio de Ciencia, Innovacion y Universidades and FEDER founds under project AMIC (TIN2017-85854-C4-2-R) and the GiSPRO project (PROMETEU/2018/176). Work of Jose-Angel Gonzalez is financed by Universitat Politecnica de Valencia under grant PAID-01-17.González-Barba, JÁ.; Hurtado Oliver, LF.; Pla Santamaría, F. (2020). Transformer based contextualization of pre-trained word embeddings for irony detection in Twitter. Information Processing & Management. 57(4):1-15. https://doi.org/10.1016/j.ipm.2020.102262S115574Farías, D. I. H., Patti, V., & Rosso, P. (2016). Irony Detection in Twitter. ACM Transactions on Internet Technology, 16(3), 1-24. doi:10.1145/2930663Greene, R., Cushman, S., Cavanagh, C., Ramazani, J., & Rouzer, P. (Eds.). (2012). The Princeton Encyclopedia of Poetry and Poetics. doi:10.1515/9781400841424Van Hee, C., Lefever, E., & Hoste, V. (2018). We Usually Don’t Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter. Computational Linguistics, 44(4), 793-832. doi:10.1162/coli_a_00337Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735Joshi, A., Bhattacharyya, P., & Carman, M. J. (2017). Automatic Sarcasm Detection. ACM Computing Surveys, 50(5), 1-22. doi:10.1145/3124420Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019). Albert: A lite bert for self-supervised learning of language representations.Mohammad, S. M., & Turney, P. D. (2012). CROWDSOURCING A WORD-EMOTION ASSOCIATION LEXICON. Computational Intelligence, 29(3), 436-465. doi:10.1111/j.1467-8640.2012.00460.xMuecke, D. C. (1978). Irony markers. Poetics, 7(4), 363-375. doi:10.1016/0304-422x(78)90011-6Potamias, R. A., Siolas, G., & Stafylopatis, A. (2019). A transformer-based approach to irony and sarcasm detection. arXiv:1911.10401.Rosso, P., Rangel, F., Farías, I. H., Cagnina, L., Zaghouani, W., & Charfi, A. (2018). A survey on author profiling, deception, and irony detection for the Arabic language. Language and Linguistics Compass, 12(4), e12275. doi:10.1111/lnc3.12275Sulis, E., Irazú Hernández Farías, D., Rosso, P., Patti, V., & Ruffo, G. (2016). Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not. Knowledge-Based Systems, 108, 132-143. doi:10.1016/j.knosys.2016.05.035Wilson, D., & Sperber, D. (1992). On verbal irony. Lingua, 87(1-2), 53-76. doi:10.1016/0024-3841(92)90025-eYus, F. (2016). Propositional attitude, affective attitude and irony comprehension. Pragmatics & Cognition, 23(1), 92-116. doi:10.1075/pc.23.1.05yusZhang, S., Zhang, X., Chan, J., & Rosso, P. (2019). Irony detection via sentiment-based transfer learning. Information Processing & Management, 56(5), 1633-1644. doi:10.1016/j.ipm.2019.04.00

Self-attention for Twitter sentiment analysis in Spanish

[EN] This paper describes our proposal for Sentiment Analysis in Twitter for the Spanish language. The main characteristics of the system are the use of word embedding specifically trained from tweets in Spanish and the use of self-attention mechanisms that allow to consider sequences without using convolutional nor recurrent layers. These self-attention mechanisms are based on the encoders of the Transformer model. The results obtained on the Task 1 of the TASS 2019 workshop, for all the Spanish variants proposed, support the correctness and adequacy of our proposal.This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC (TIN2017-85854-C4-2-R) and the GiSPRO project (PROMETEU/2018/176). Work of Jose-Angel Gonzalez is financed by Universitat Politecnica de Valencia under grant PAID-01-17.González-Barba, JÁ.; Hurtado Oliver, LF.; Pla Santamaría, F. (2020). Self-attention for Twitter sentiment analysis in Spanish. Journal of Intelligent & Fuzzy Systems. 39(2):2165-2175. https://doi.org/10.3233/JIFS-179881S21652175392Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.173

Choosing the right loss function for multi-label Emotion Classification

[EN] Natural Language Processing problems has recently been benefited for the advances in Deep Learning. Many of these problems can be addressed as a multi-label classification problem. Usually, the metrics used to evaluate classification models are different from the loss functions used in the learning process. In this paper, we present a strategy to incorporate evaluation metrics in the learning process in order to increase the performance of the classifier according to the measure we are interested to favor. Concretely, we propose soft versions of the Accuracy, micro-F-1, and macro-F-1 measures that can be used as loss functions in the back-propagation algorithm. In order to experimentally validate our approach, we tested our system in an Emotion Classification task proposed at the International Workshop on Semantic Evaluation, SemEval-2018. Using a Convolutional Neural Network trained with the proposed loss functions we obtained significant improvements both for the English and the Spanish corpora.This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC (TIN2017-85854-C4-2-R) and the GiSPRO project (PROMETEU/2018/176). Work of Jose-Angel Gonzalez is also financed by Universitat Politecnica de Valencia under grant PAID-01-17.Hurtado Oliver, LF.; González-Barba, JÁ.; Pla Santamaría, F. (2019). Choosing the right loss function for multi-label Emotion Classification. 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D., Martínez-Cámara, E., Martín-Valdivia, M.-T., & Perea-Ortega, J. M. (2013). Semantic orientation for polarity classification in Spanish reviews. Expert Systems with Applications, 40(18), 7250-7257. doi:10.1016/j.eswa.2013.06.076Nair V. and Hinton G.E. , Rectified linear units improve restricted boltzmann machines, In Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML’10, USA, 2010, pp. 807–814. Omnipress.NielsenF.Å., AFINN, 2011.Pastor-Pellicer J. , Zamora-Martínez F. , España Boquera S. and Castro Bleda M.J. , F-Measure as the Error Function to Train Neural Networks, In IWANN Proceedings, 2013.Pennebaker J. , Chung C. , Ireland M. , Gonzales A. and Booth R. , The development and psychological properties of liwc2007, 2014.Pla, F., & Hurtado, L.-F. (2016). Language identification of multilingual posts from Twitter: a case study. 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ELiRF-UPV at TASS 2020: TWilBERT for Sentiment Analysis and Emotion Detection in Spanish Tweets

[EN] This paper describes the participation of the ELiRF research group of the Universitat Politècnica de València in the TASS 2020 Workshop, framed within the XXXVI edition of the International Conference of the Spanish Society for the Processing of Natural Language (SEPLN). We present the approach used for the Monolingual Sentiment Analysis and Emotion Detection tasks of the workshop, as well as the results obtained. Our participation has focused mainly on employing an adaptation of BERT for text classification on the Twitter domain and the Spanish language. This system, that we have called TWilBERT, shown systematic improvements of the state of the art in almost all the tasks framed in the SEPLN conference of previous years, and also obtains the most competitive performance in the tasks addressed in this work.This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC (TIN2017-85854-C4-2-R) and by the GiSPRO project (PROMETEU/2018/176). Work of José-Ángel González is financed by Universitat Politècnica de València under grant PAID-01-17.González-Barba, JÁ.; Arias-Moncho, J.; Hurtado Oliver, LF.; Pla Santamaría, F. (2020). ELiRF-UPV at TASS 2020: TWilBERT for Sentiment Analysis and Emotion Detection in Spanish Tweets. CEUR. 179-186. http://hdl.handle.net/10251/17855817918

Synthesis of mechanically strong waterborne poly(urethane-urea)s capable of self-healing at elevated temperatures

Although various chemistries have been introduced into polyurethanes in order to obtain self-healing abilities, implementing these materials in applications requiring high strength is challenging as strong materials imply a limited molecular motion, but without movement of polymer chains self-healing is not possible. Here, waterborne poly(urethane-urea)s (PU(U)s) based on aromatic disulfide compounds are developed which balance these contradictory requirements by presenting good mechanical properties at room temperature, while showing the mobility necessary for healing when moderately heated. The influence of hard monomers on the stability and mobility of the materials is investigated by scratch closure, cut healing and rheological measurements, so that the limits of the readily available aromatic disulfide compounds, bis(4-aminophenyl)- and bis(4-hydroxyphenyl)disulfide, can be determined. Subsequently, a modified aromatic disulfide compound, bis[4-(3'-hydroxypropoxy)phenyl]disulfide, with increased reactivity, solubility and flexibility is synthesized and incorporated into the PU backbone, so that materials with more attractive mechanical properties, reaching ultimate tensile strengths up to 23 MPa, and self-healing abilities at elevated temperatures could be obtained.The European Union’s Horizon 2020 research and innovation programme is accredited for the financial support through Project TRACKWAY-ITN 642514 under the Marie Sklodowska-Curie grant agreement. N.B. acknowledges the financial support obtained through the Post-Doctoral fellowship Juan de la Cierva - Incorporación (IJCI-2016-28442), from the Ministry of Economy and Competitiveness of Spai