Constructing a second language: some final thoughts

All the papers in this special section address issues central to cognitive linguistics research: usage-based models with their focus on frequency; multi-word units and the relationship between lexical and grammatical knowledge; and the nature of lexical meaning, especially construal or “thinking for speaking”. Cognitive Linguistics is thus clearly a useful paradigm for L2 research. The contributors also emphasise that many of the processes operating in L1 acquisition are relevant in L2A as well. In this paper, I discuss the opposite side of the coin: how cognitively-inspired L2 research can inform work on first language learning and theoretical linguistics, focussing in particular on three issues that have been extensively studied in an L2 context but neglected by the other language sciences: transfer of knowledge between constructions, the role of explicit learning, and individual differences in linguistic knowledg

Blurred Boundaries in Ambiguous Space

Publicación exhaustiva de la torre de VPO en Vallecas de entresitio. La revista Space tiene un comité de revisión y selección de artículos formado por arquitectos consagrados de prestigio internacional como son Peter Cook, Peter Eisenman y Arata Isozaki

Characterization of the killer toxin KTCf20 from wickerhamomyces anomalus, a potential biocontrol agent against wine spoilage yeasts

Wickerhamomyces anomalus Cf20 secretes the killer toxin KTCf20 that inhibits several wine spoilage yeasts of the species Pichia guilliermondii, P. membranifaciens, Brettanomyces bruxellensis and Dekkera anomala. KTCf20 binds cell wall extracts from the sensitive target P. guilliermondii Cd6; however, this capacity was lost when cell wall extracts were pre-treated with fungal β-glucanase. Pustulan and laminarin inhibited killer activity, suggesting that β-1,3 and β-1,6-glucans may be the putative binding sites for KTCf20 on the cell wall of sensitive cells. The toxin was produced and showed to be stable and highly active at physicochemical conditions suitable for winemaking process. In addition, the strain Cf20 is compatible with Saccharomyces cerevisiae in co-culture conditions being potential its application in a mixed starter culture. These data suggest that W. anomalus Cf20 and/or KTCf20 are promising biocontrol agents against spoilage yeasts during wine-making process.Fil: Fernandez de Ullivarri, Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Centro de Referencia para Lactobacilos; ArgentinaFil: Mendoza, Lucia Margarita. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Centro de Referencia para Lactobacilos; ArgentinaFil: Raya, Raul Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Centro de Referencia para Lactobacilos; Argentin

Population Growth and Customary Law on Land: The Case of Cordillera Villages in the Philippines

This paper examines how a traditional village deals with the consequences of population growth. The increase in population demands more intensive use of the land which requires the transformation of commonly-owned land into privately-owned land. Customary law contains clear prescriptions about the circumstances under which a couple can privatize land. We estimate this land accumulation rule using date from two villages in the Cordillera Region of the Philippines. In order to study the evolution of the distribution of land, we model the inheritance practices of the community which constitutes another aspect of customary law. Finally, we use the model to show that despite the flexibility of the customary law on land, the present rapid growth of the population given the limited availability of land leads to its breakdown. This could be avoided only if seven out of ten children are able to make a living from occupations other than farming.Population Growth

Decision-Theoretic Planning with Person Trajectory Prediction for Social Navigation

Robots navigating in a social way should reason about people intentions when acting. For instance, in applications like robot guidance or meeting with a person, the robot has to consider the goals of the people. Intentions are inherently nonobservable, and thus we propose Partially Observable Markov Decision Processes (POMDPs) as a decision-making tool for these applications. One of the issues with POMDPs is that the prediction models are usually handcrafted. In this paper, we use machine learning techniques to build prediction models from observations. A novel technique is employed to discover points of interest (goals) in the environment, and a variant of Growing Hidden Markov Models (GHMMs) is used to learn the transition probabilities of the POMDP. The approach is applied to an autonomous telepresence robot

Simulation of Rapidly-Exploring Random Trees in Membrane Computing with P-Lingua and Automatic Programming

Methods based on Rapidly-exploring Random Trees (RRTs) have been widely used in robotics to solve motion planning problems. On the other hand, in the membrane computing framework, models based on Enzymatic Numerical P systems (ENPS) have been applied to robot controllers, but today there is a lack of planning algorithms based on membrane computing for robotics. With this motivation, we provide a variant of ENPS called Random Enzymatic Numerical P systems with Proteins and Shared Memory (RENPSM) addressed to implement RRT algorithms and we illustrate it by simulating the bidirectional RRT algorithm. This paper is an extension of [21]a. The software presented in [21] was an ad-hoc simulator, i.e, a tool for simulating computations of one and only one model that has been hard-coded. The main contribution of this paper with respect to [21] is the introduction of a novel solution for membrane computing simulators based on automatic programming. First, we have extended the P-Lingua syntax –a language to define membrane computing models– to write RENPSM models. Second, we have implemented a new parser based on Flex and Bison to read RENPSM models and produce source code in C language for multicore processors with OpenMP. Finally, additional experiments are presented.Ministerio de Economía, Industria y Competitividad TIN2017-89842-

Radiative damping of standing acoustic waves in solar coronal loops

Context. A detailed understanding of the physical processes that determine the damping timescales of magneto-acoustic waves is essential to interpret diagnostic results from the application of solar magneto-seismology. Aims. The influence of the transition region and the importance of radiative emission, arising from equilibrium and non-equilibrium ionisation balances, for the damping timescale of the fundamental mode standing acoustic wave is investigated. Methods. An extensive numerical study, in the framework of the field-aligned hydrodynamic approximation, is carried out of the damping of the fundamental mode standing wave in a solar coronal loop, for a wide range of loop lengths and apex temperatures. Results. It was found that the radiative emission arising from a non-equilibrium ionisation balance will always act to reduce the damping timescale (in comparison to the equilibrium case) and may do so by up to ~10%. The physics of the transition region is most crucial in determining the magnitude of the reduction of the damping timescale when a non-equilibrium ionisation balance is properly accounted for. Conclusions. The methods of solar magneto-seismology, in particular the tools of coronal seismology, may be used to estimate loop lengths to a reasonable degree of accuracy, although estimates of the apex temperature are significantly less reliable, and one should use alternative (e.g. spectroscopic) diagnostics instead

Distributed Correlation-Based Feature Selection in Spark

CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed version of the CFS algorithm, capable of dealing with the large volumes of data typical of big data applications. Two versions of the algorithm were implemented and compared using the Apache Spark cluster computing model, currently gaining popularity due to its much faster processing times than Hadoop's MapReduce model. We tested our algorithms on four publicly available datasets, each consisting of a large number of instances and two also consisting of a large number of features. The results show that our algorithms were superior in terms of both time-efficiency and scalability. In leveraging a computer cluster, they were able to handle larger datasets than the non-distributed WEKA version while maintaining the quality of the results, i.e., exactly the same features were returned by our algorithms when compared to the original algorithm available in WEKA.Comment: 25 pages, 5 figure