Los vaivenes de la formación inicial del profesorado. Una reforma siempre inacabada

Se analiza la actual formación inicial del profesorado de infantil y primaria a partir del nuevo plan de estudios de Grado surgido del Espacio Europeo de Educación Superior. Después de una reflexión sobre la temática se expone una investigación sobre el análisis del actu al Grado donde se comprueba, a partir del estudio de la epistemología, la normativa y el currículum, que se continúa formando un perfil de profesorado centrado en su aula, poco implicado en el funcionamiento del centro y menos aún en las relaciones con el entorno escolar. Es decir, la formación inicial del profesorado no ha variado sustancialmente respecto a la de otros planes de estudio anteriores

Evaluation of Clustering Algorithms on HPC Platforms

[EN] Clustering algorithms are one of the most widely used kernels to generate knowledge from large datasets. These algorithms group a set of data elements (i.e., images, points, patterns, etc.) into clusters to identify patterns or common features of a sample. However, these algorithms are very computationally expensive as they often involve the computation of expensive fitness functions that must be evaluated for all points in the dataset. This computational cost is even higher for fuzzy methods, where each data point may belong to more than one cluster. In this paper, we evaluate different parallelisation strategies on different heterogeneous platforms for fuzzy clustering algorithms typically used in the state-of-the-art such as the Fuzzy C-means (FCM), the Gustafson-Kessel FCM (GK-FCM) and the Fuzzy Minimals (FM). The experimental evaluation includes performance and energy trade-offs. Our results show that depending on the computational pattern of each algorithm, their mathematical foundation and the amount of data to be processed, each algorithm performs better on a different platform.This work has been partially supported by the Spanish Ministry of Science and Innovation, under the Ramon y Cajal Program (Grant No. RYC2018-025580-I) and by the Spanish "Agencia Estatal de Investigacion" under grant PID2020-112827GB-I00 /AEI/ 10.13039/501100011033, and under grants RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5, by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by the "Conselleria de Educacion, Investigacion, Cultura y Deporte, Direccio General de Ciencia i Investigacio, Proyectos AICO/2020", Spain, under Grant AICO/2020/302.Cebrian, JM.; Imbernón, B.; Soto, J.; Cecilia-Canales, JM. (2021). Evaluation of Clustering Algorithms on HPC Platforms. Mathematics. 9(17):1-20. https://doi.org/10.3390/math917215612091

High-throughput fuzzy clustering on heterogeneous architectures

[EN] The Internet of Things (IoT) is pushing the next economic revolution in which the main players are data and immediacy. IoT is increasingly producing large amounts of data that are now classified as "dark data'' because most are created but never analyzed. The efficient analysis of this data deluge is becoming mandatory in order to transform it into meaningful information. Among the techniques available for this purpose, clustering techniques, which classify different patterns into groups, have proven to be very useful for obtaining knowledge from the data. However, clustering algorithms are computationally hard, especially when it comes to large data sets and, therefore, they require the most powerful computing platforms on the market. In this paper, we investigate coarse and fine grain parallelization strategies in Intel and Nvidia architectures of fuzzy minimals (FM) algorithm; a fuzzy clustering technique that has shown very good results in the literature. We provide an in-depth performance analysis of the FM's main bottlenecks, reporting a speed-up factor of up to 40x compared to the sequential counterpart version.This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants TIN2016-78799-P (AEI/FEDER, UE), RTI2018-096384-B-I00, RTI2018-098156-B-C53 and RTC-2017-6389-5.Cebrian, JM.; Imbernón, B.; Soto, J.; García, JM.; Cecilia-Canales, JM. (2020). High-throughput fuzzy clustering on heterogeneous architectures. Future Generation Computer Systems. 106:401-411. https://doi.org/10.1016/j.future.2020.01.022S401411106Waldrop, M. M. (2016). The chips are down for Moore’s law. Nature, 530(7589), 144-147. doi:10.1038/530144aCecilia, J. M., Timon, I., Soto, J., Santa, J., Pereniguez, F., & Munoz, A. (2018). High-Throughput Infrastructure for Advanced ITS Services: A Case Study on Air Pollution Monitoring. IEEE Transactions on Intelligent Transportation Systems, 19(7), 2246-2257. doi:10.1109/tits.2018.2816741Singh, D., & Reddy, C. K. (2014). A survey on platforms for big data analytics. Journal of Big Data, 2(1). doi:10.1186/s40537-014-0008-6Stephens, N., Biles, S., Boettcher, M., Eapen, J., Eyole, M., Gabrielli, G., … Walker, P. (2017). The ARM Scalable Vector Extension. IEEE Micro, 37(2), 26-39. doi:10.1109/mm.2017.35Wright, S. A. (2019). Performance Modeling, Benchmarking and Simulation of High Performance Computing Systems. Future Generation Computer Systems, 92, 900-902. doi:10.1016/j.future.2018.11.020Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering. ACM Computing Surveys, 31(3), 264-323. doi:10.1145/331499.331504Lee, J., Hong, B., Jung, S., & Chang, V. (2018). Clustering learning model of CCTV image pattern for producing road hazard meteorological information. Future Generation Computer Systems, 86, 1338-1350. doi:10.1016/j.future.2018.03.022Pérez-Garrido, A., Girón-Rodríguez, F., Bueno-Crespo, A., Soto, J., Pérez-Sánchez, H., & Helguera, A. M. (2017). Fuzzy clustering as rational partition method for QSAR. Chemometrics and Intelligent Laboratory Systems, 166, 1-6. doi:10.1016/j.chemolab.2017.04.006H.S. Nagesh, S. Goil, A. Choudhary, A scalable parallel subspace clustering algorithm for massive data sets, in: Proceedings 2000 International Conference on Parallel Processing, 2000, pp. 477–484.Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3), 191-203. doi:10.1016/0098-3004(84)90020-7Havens, T. C., Bezdek, J. C., Leckie, C., Hall, L. O., & Palaniswami, M. (2012). Fuzzy c-Means Algorithms for Very Large Data. IEEE Transactions on Fuzzy Systems, 20(6), 1130-1146. doi:10.1109/tfuzz.2012.2201485Flores-Sintas, A., Cadenas, J., & Martin, F. (1998). A local geometrical properties application to fuzzy clustering. Fuzzy Sets and Systems, 100(1-3), 245-256. doi:10.1016/s0165-0114(97)00038-9Soto, J., Flores-Sintas, A., & Palarea-Albaladejo, J. (2008). Improving probabilities in a fuzzy clustering partition. Fuzzy Sets and Systems, 159(4), 406-421. doi:10.1016/j.fss.2007.08.016Timón, I., Soto, J., Pérez-Sánchez, H., & Cecilia, J. M. (2016). Parallel implementation of fuzzy minimals clustering algorithm. Expert Systems with Applications, 48, 35-41. doi:10.1016/j.eswa.2015.11.011Flores-Sintas, A., M. Cadenas, J., & Martin, F. (2001). Detecting homogeneous groups in clustering using the Euclidean distance. Fuzzy Sets and Systems, 120(2), 213-225. doi:10.1016/s0165-0114(99)00110-4Wang, H., Potluri, S., Luo, M., Singh, A. K., Sur, S., & Panda, D. K. (2011). MVAPICH2-GPU: optimized GPU to GPU communication for InfiniBand clusters. Computer Science - Research and Development, 26(3-4), 257-266. doi:10.1007/s00450-011-0171-3Kaltofen, E., & Villard, G. (2005). On the complexity of computing determinants. computational complexity, 13(3-4), 91-130. doi:10.1007/s00037-004-0185-3Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241-254. doi:10.1007/bf02289588Saxena, A., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., … Lin, C.-T. (2017). A review of clustering techniques and developments. Neurocomputing, 267, 664-681. doi:10.1016/j.neucom.2017.06.053Woodley, A., Tang, L.-X., Geva, S., Nayak, R., & Chappell, T. (2019). Parallel K-Tree: A multicore, multinode solution to extreme clustering. Future Generation Computer Systems, 99, 333-345. doi:10.1016/j.future.2018.09.038Kwedlo, W., & Czochanski, P. J. (2019). A Hybrid MPI/OpenMP Parallelization of $K$ -Means Algorithms Accelerated Using the Triangle Inequality. IEEE Access, 7, 42280-42297. doi:10.1109/access.2019.2907885Li, Y., Zhao, K., Chu, X., & Liu, J. (2013). Speeding up k-Means algorithm by GPUs. Journal of Computer and System Sciences, 79(2), 216-229. doi:10.1016/j.jcss.2012.05.004Saveetha, V., & Sophia, S. (2018). Optimal Tabu K-Means Clustering Using Massively Parallel Architecture. Journal of Circuits, Systems and Computers, 27(13), 1850199. doi:10.1142/s0218126618501992Djenouri, Y., Djenouri, D., Belhadi, A., & Cano, A. (2019). Exploiting GPU and cluster parallelism in single scan frequent itemset mining. Information Sciences, 496, 363-377. doi:10.1016/j.ins.2018.07.020Krawczyk, B. (2016). GPU-Accelerated Extreme Learning Machines for Imbalanced Data Streams with Concept Drift. Procedia Computer Science, 80, 1692-1701. doi:10.1016/j.procs.2016.05.509Fang, Y., Chen, Q., & Xiong, N. (2019). A multi-factor monitoring fault tolerance model based on a GPU cluster for big data processing. Information Sciences, 496, 300-316. doi:10.1016/j.ins.2018.04.053Tanweer, S., & Rao, N. (2019). Novel Algorithm of CPU-GPU hybrid system for health care data classification. Journal of Drug Delivery and Therapeutics, 9(1-s), 355-357. doi:10.22270/jddt.v9i1-s.244

I Congreso Internacional. Nuevas Tendencias en la Formación Permanente del Profesorado

Coordinadors: Maria Teresa Colén i Francisco Imbernon.Amb el suport de la Universitat de Barcelona i FOPID: Grup Formació Docent i Innovació Pedagògica

Enhancing large-scale docking simulation on heterogeneous systems: An MPI vs rCUDA study

[EN] Virtual Screening (VS) methods can considerably aid clinical research by predicting how ligands interact with pharmacological targets, thus accelerating the slow and critical process of finding new drugs. VS methods screen large databases of chemical compounds to find a candidate that interacts with a given target. The computational requirements of VS models, along with the size of the databases, containing up to millions of biological macromolecular structures, means computer clusters are a must. However, programming current clusters of computers is no easy task, as they have become heterogeneous and distributed systems where various programming models need to be used together to fully leverage their resources. This paper evaluates several strategies to provide peak performance to a GPU-based molecular docking application called METADOCK in heterogeneous clusters of computers based on CPU and NVIDIA Graphics Processing Units (GPUs). Our developments start with an OpenMP, MPI and CUDA METADOCK version as a baseline case of cluster utilization. Next, we explore the virtualized GPUs provided by the rCUDA framework in order to facilitate the programming process. rCUDA allows us to use remote GPUs, i.e. installed in other nodes of the cluster, as if they were installed in the local node, so enabling access to them using only OpenMP and CUDA. Finally, several load balancing strategies are analyzed in a search to enhance performance. Our results reveal that the use of middleware like rCUDA is a convincing alternative to leveraging heterogeneous clusters, as it offers even better performance than traditional approaches and also makes it easier to program these emerging clusters.This work is jointly supported by the Fundacion Seneca (Agencia Regional de Ciencia y Tecnologia, Region de Murcia) under grant 18946/JLI/13, and by the Spanish MEC and European Commission FEDER under grants TIN2015-66972-C5-3-R and TIN2016-78799-P (AEI/FEDER, UE). We also thank NVIDIA for hardware donation under GPU Educational Center 2014-2016 and Research Center 2015-2016. Furthermore, researchers from Universitat Politecnica de Valencia are supported by the Generalitat Valenciana under Grant PROMETEO/2017/077. Authors are also grateful for the generous support provided by Mellanox Technologies Inc.Imbernón, B.; Prades Gasulla, J.; Gimenez Canovas, D.; Cecilia, JM.; Silla Jiménez, F. (2018). Enhancing large-scale docking simulation on heterogeneous systems: An MPI vs rCUDA study. Future Generation Computer Systems. 79:26-37. https://doi.org/10.1016/j.future.2017.08.050S26377

Mitofusin 2 in POMC neurons connects ER stress with leptin resistance and energy imbalance

Mitofusin 2 (MFN2) plays critical roles in both mitochondrial fusion and the establishment of mitochondria-endoplasmic reticulum (ER) interactions. Hypothalamic ER stress has emerged as a causative factor for the development of leptin resistance, but the underlying mechanisms are largely unknown. Here, we show that mitochondria-ER contacts in anorexigenic pro-opiomelanocortin (POMC) neurons in the hypothalamus are decreased in diet-induced obesity. POMC-specific ablation of Mfn2 resulted in loss of mitochondria-ER contacts, defective POMC processing, ER stress-induced leptin resistance, hyperphagia, reduced energy expenditure, and obesity. Pharmacological relieve of hypothalamic ER stress reversed these metabolic alterations. Our data establish MFN2 in POMC neurons as an essential regulator of systemic energy balance by fine-tuning the mitochondrial-ER axis homeostasis and function. This previously unrecognized role for MFN2 argues for a crucial involvement in mediating ER stress-induced leptin resistance

La enseñanza de la lengua de signos en el Espacio Europeo de Educación Superior: Adaptaciones al Marco Común Europeo de Referencia para las Lenguas

La red de investigación que, desde la Universidad de Alicante, analiza la enseñanza de la lengua de signos española (LSE) en el marco del Espacio Europeo de Educación Superior (EEES), se ha articulado en torno a tres ejes: la propia enseñanza de la lengua y la cultura de la Comunidad Sorda española en el ámbito universitario y, más concretamente, en la Universidad de Alicante; el aprendizaje por parte del alumnado que ha cursado esta materia, y el papel de las intérpretes de lengua de signos que desarrollan su labor en este contexto. En los tres casos, presentamos la investigación que hemos llevado a cabo desde la red gracias a la colaboración de profesorado, alumnado e intérpretes, y que supone una completa panorámica de la situación actual de la LSE en el EEES. Una de las principales conclusiones es la necesidad de armonizar su enseñanza bajo las directrices europeas de enseñanza de idiomas que dicta el Marco Común Europeo de Referencia para las Lenguas (MCER, 2001), lo cual presupone una estandarización lingüística de la LSE que aún está en vías de desarrollo

Mitochondrial cristae-remodeling protein OPA1 in POMC neurons couples Ca2+ homeostasis with adipose tissue lipolysis

© 2021 The Authors.Appropriate cristae remodeling is a determinant of mitochondrial function and bioenergetics and thus represents a crucial process for cellular metabolic adaptations. Here, we show that mitochondrial cristae architecture and expression of the master cristae-remodeling protein OPA1 in proopiomelanocortin (POMC) neurons, which are key metabolic sensors implicated in energy balance control, is affected by fluctuations in nutrient availability. Genetic inactivation of OPA1 in POMC neurons causes dramatic alterations in cristae topology, mitochondrial Ca2+ handling, reduction in alpha-melanocyte stimulating hormone (α-MSH) in target areas, hyperphagia, and attenuated white adipose tissue (WAT) lipolysis resulting in obesity. Pharmacological blockade of mitochondrial Ca2+ influx restores α-MSH and the lipolytic program, while improving the metabolic defects of mutant mice. Chemogenetic manipulation of POMC neurons confirms a role in lipolysis control. Our results unveil a novel axis that connects OPA1 in POMC neurons with mitochondrial cristae, Ca2+ homeostasis, and WAT lipolysis in the regulation of energy balance.This work was supported by Agencia Estatal de Investigación y Fondo Social Europeo, Proyecto BFU2016-76973-R FEDER (C.V.A.); AG052005, AG052986, AG051459, DK111178 from NIH and NKFI-KKP-126998 from Hungarian National Research, Development and Innovation Office (T.L.H.); MR/P009824/2 from Medical Research Council UK (G.D.); and Ayudas Fundación BBVA a Investigadores y Creadores Culturales (2015), European Research Council (ERC) under the European Union’s Horizon 2020 Research And Innovation Program (grant agreement 725004) and CERCA Programme/Generalitat de Catalunya (M.C.). A.O. is supported by a Miguel Servet contract (CP19/00083) from Instituto de Salud Carlos III and co-financed by FEDER