Enhancing water resource management in rural areas by means of simulation tools

Conjunctive use of ground- and surface-water in agriculture is of paramount importance in many rural areas of Europe, where freshwater resource is facing growing pressure, due to both human impacts and climate changes. In this framework, the development of open source public domain GIS-integrated, fully distributed and numerically-based simulation platforms may provide powerful tools to support planning, management and monitoring activities. The HORIZON 2020 FREEWAT (FREE and open source software tools for WATer resource management, www.freewat.eu) implements the Farm Process (FMP) embedded in MODFLOW-OWHM to simulate conjunctive water use in rural areas under demand-driven and supply-constrained conditions, taking also into account constraints on well abstraction and water-rights ranking of water accounting units. The choice to integrate the FMP, after a careful review of the available codes, is related to the rigorous approach in dealing with the groundwater component. Thus the FMP allows to dynamically integrate infiltration, surface runoff and deep percolation components, to effectively balance crop water demand and supply from both sources of water

Water planning and management in the Cornia river plain by means of the gis-integrated freewat platform

Coastal aquifer, Cornia valley, Tuscany, Managed Aquifer Recharge, FREEWAT platform

ICT tools for enhancing sustainable water management in rural environments

Numerical models are relevant tools to achieve the proper application of the Water Framework Directive (WFD, EU 2000). Their major advantage is to foster a full characterization of the involved flow terms and contaminant transport pathways. Thanks to their predictive function, numerical models can help also to address planning and management activities. Many hydrological codes developed so far have faced the problem of tackling multiscale territorial planning (e.g., Bergez et al. 2012). We introduce here the GIS-integrated FREEWAT platform aimed at providing a unique modeling environment to simulate multiple hydrological processes, with a focus on the sustainable management of conjunctive use of surface- and ground-water resources in rural environments. FREEWAT (FREE and open source software tools for WATer resource management; Rossetto et al., 2015) is an EU HORIZON 2020 project, whose main goal is to simplify the application of EU water-related Directives. It aims at integrating a simulation platform in a Geographic Information System (GIS), coupling the power of GIS geo-processing and post-processing tools in spatial data analysis to that of simulation codes. The FREEWAT platform is being developed within the QGIS free open source software package and fosters the simulation of the whole hydrological cycle using open source numerical codes mainly belonging to the USGS MODFLOW family

Aprendizajes y prácticas educativas en las actuales condiciones de época: COVID-19

“Esta obra colectiva es el resultado de una convocatoria a docentes, investigadores y profesionales del campo pedagógico a visibilizar procesos investigativos y prácticas educativas situadas en el marco de COVI-19. La misma se inscribe en el trabajo llevado a cabo por el equipo de Investigación responsable del Proyecto “Sentidos y significados acerca de aprender en las actuales condiciones de época: un estudio con docentes y estudiantes de la educación secundarias en la ciudad de Córdoba” de la Facultad de Filosofía y Humanidades. Universidad Nacional de Córdoba. El momento excepcional que estamos atravesando, pero que también nos atraviesa, ha modificado la percepción temporal a punto tal que habitamos un tiempo acelerado y angustiante que nos exige la producción de conocimiento provisorio. La presente publicación surge como un espacio para detenernos a documentar lo que nos acontece y, a su vez, como oportunidad para atesorar y resguardar las experiencias educativas que hemos construido, inventado y reinventando en este contexto. En ella encontrarán pluralidad de voces acerca de enseñar y aprender durante la pandemia. Este texto es una pausa para reflexionar sobre el hacer y las prácticas educativas por venir”.Fil: Beltramino, Lucia (comp.). Universidad Nacional de Córdoba. Facultad de Filosofía y Humanidades. Escuela de Archivología; Argentina

A Comparative Study of Models for Answer Sentence Selection

Answer Sentence Selection is one of the steps typically involved in Question Answering. Question Answering is considered a hard task for natural language processing systems, since full solutions would require both natural language understanding and inference abilities. In this paper, we explore how the state of the art in answer selection has improved recently, comparing two of the best proposed models for tackling the problem: the Cross-attentive Convolutional Network and the BERT model. The experiments are carried out on two datasets, WikiQA and SelQA, both created for and used in open-domain question answering challenges. We also report on cross domain experiments with the two datasets

Relevance Transformer: Generating Concise Code Snippets with Relevance Feedback

Tools capable of automatic code generation have the potential to augment programmer's capabilities. While straightforward code retrieval is incorporated into many IDEs, an emerging area is explicit code generation. Code generation is currently approached as a Machine Translation task, with Recurrent Neural Network (RNN) based encoder-decoder architectures trained on code-description pairs. In this work we introduce and study modern Transformer architectures for this task. We further propose a new model called the Relevance Transformer that incorporates external knowledge using pseudo-relevance feedback. The Relevance Transformer biases the decoding process to be similar to existing retrieved code while enforcing diversity. We perform experiments on multiple standard benchmark datasets for code generation including Django, Hearthstone, and CoNaLa. The results show improvements over state-of-the-art methods based on BLEU evaluation. The Relevance Transformer model shows the potential of Transformer-based architectures for code generation and introduces a method of incorporating pseudo-relevance feedback during inference

Cross Attention for Selection-based Question Answering

Answer Sentence Selection (ASS) is one of the steps typically involved in Question Answering, a hard task for natural language processing since full solutions would require both natural language understanding and world knowledge. We present a new approach to tackle ASS, based on a Cross-Attentive Convolutional Neural Network. The approach was designed for competing in the Fujitsu AI-NLP challenge Fujitsu [4], which evaluates systems on their performance on the SelQA[7] dataset. This dataset was created on purpose as a benchmark to stress the ability of systems to go beyond simple word co-occurrence criteria. Our submission achieved the top score in the challenge