Can sign language make you better at hand processing?

The languages developed by deaf communities are unique for using visual signs produced by the hand. In the present study, we explored the cognitive effects of employing the hand as articulator. We focused on the arbitrariness of the form-meaning relationship\u2014a fundamental feature of natural languages\u2014and asked whether sign languages change the processing of arbitrary non-linguistic stimulus-response (S-R) associations involving the hand. This was tested using the Simon effect, which specifically requires such type of associations. Differences between signers and speakers (non-signers) only appeared in the Simon task when hand stimuli were shown. Response-time analyses revealed that the distinctiveness of signers\u2019 responses derived from an increased ability to process memory traces of arbitrary S-R pairs related to the hand. These results shed light on the interplay between language and cognition as well as on the effects of sign language acquisition

Innovation in Private Infrastructure Development Effects of the Selection Environment and Modularity

This study investigates how the selection environment and modularity affect innovation in private infrastructure development. Our findings stem from an in-depth empirical study of the extent ten process innovations were implemented in an airport expansion programme. Our findings suggest that developer and customers can each occasionally champion or resist innovations. An innovation succeeds contingent upon the capability of the stakeholder groups to develop collectively a plan to finance and implement the innovation, which reconciles subjective individual assessments. Innovations can be particularly hard to adopt when they require financing from different budgets, or when the developer’s investment pays off only if customers behave in a specified way in the future. We also find that the degrees of novelty and modularity neither represent sufficient or necessary conditions enabling or hindering innovation. Novelty, however, makes the innovation champion’s job harder because it leads to perceptions of downside risk and regulatory changes, whereas modularity helps the champion operationalise ways that moderate resistance to innovate.Innovation; financing; implementation

SolarStat: Modeling Photovoltaic Sources through Stochastic Markov Processes

In this paper, we present a methodology and a tool to derive simple but yet accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources. In particular, we target photovoltaic panels with small form factors, as those exploited by embedded communication devices such as wireless sensor nodes or, concerning modern cellular system technology, by small-cells. Our models are especially useful for the theoretical investigation and the simulation of energetically self-sufficient communication systems including these devices. The Markov models that we derive in this paper are obtained from extensive solar radiation databases, that are widely available online. Basically, from hourly radiance patterns, we derive the corresponding amount of energy (current and voltage) that is accumulated over time, and we finally use it to represent the scavenged energy in terms of its relevant statistics. Toward this end, two clustering approaches for the raw radiance data are described and the resulting Markov models are compared against the empirical distributions. Our results indicate that Markov models with just two states provide a rough characterization of the real data traces. While these could be sufficiently accurate for certain applications, slightly increasing the number of states to, e.g., eight, allows the representation of the real energy inflow process with an excellent level of accuracy in terms of first and second order statistics. Our tool has been developed using Matlab(TM) and is available under the GPL license at[1].Comment: Submitted to IEEE EnergyCon 201

Which types of knowledge-intensive business services firms collaborate with universities for innovation?

Drawing on data from an original survey of UK and US publicly traded knowledge-intensive business services (KIBS) firms, we investigate what types of KIBS firms collaborate with universities and consider the collaboration important for their innovation. First, we find that science-based KIBS firms (those engaged in a science, technology, and innovation [STI] mode of organizational learning), like science-based manufacturing firms, are active collaborators with universities for innovation. This relationship is further enhanced if these firms also provide highly customized services. Second, in contrast to the existing literature suggesting that firms engaged in a doing, using, and interacting (DUI) mode of organizational learning do not regard collaboration with universities as important for their innovation, we find that KIBS firms engaged in a DUI mode of organizational learning and offering highly customized services are active collaborators with universities for innovation, despite the fact that they may not possess highly formalized scientific knowledge. These findings suggest that KIBS firms co-create knowledge with universities differently than manufacturing firms. Moreover, the findings highlight the wide variety of roles that KIBS firms play in innovation networks with universities

How does working on university-industry collaborative projects affect science and engineering doctorates' careers? Evidence from a UK research-based university

This paper examines the impact of industrial involvement in doctoral projects on the particular nature of the training and careers of doctorates. We draw on an original survey of job histories of doctorates in physical sciences and engineering from a research-based university in the UK. Using multivariate probit analysis and linearised (robust) and resampling (jackknife) variance estimation techniques, we found that projects with industrial involvement are associated with higher degree of socialisation with industry. There is some evidence showing that these projects are also more likely to focus on solving firm-specific technical problems or developing firm-specific specifications/prototypes, rather than exploring high-risk concepts or generating knowledge in the subject areas. Crucially, these projects result in fewer journal publications. Not surprisingly, in line with existing literature, we found that engaging in projects with industrial involvement (in contrast to projects without industrial involvement) confers advantages on careers in the private sector. Nevertheless, there is also a hint that engaging in projects with industrial involvement may have a negative effect on careers in academia or public research organisations. While acknowledging that the modelling results are based on a small sample from a research-based university and that therefore the results need to be treated with caution, we address implications for doctorates, universities and policymakers

Energy sustainability of next generation cellular networks through learning techniques

The trend for the next generation of cellular network, the Fifth Generation (5G), predicts a 1000x increase in the capacity demand with respect to 4G, which leads to new infrastructure deployments. To this respect, it is estimated that the energy consumption of ICT might reach the 51% of global electricity production by 2030, mainly due to mobile networks and services. Consequently, the cost of energy may also become predominant in the operative expenses of a Mobile Network Operator (MNO). Therefore, an efficient control of the energy consumption in 5G networks is not only desirable but essential. In fact, the energy sustainability is one of the pillars in the design of the next generation cellular networks. In the last decade, the research community has been paying close attention to the Energy Efficiency (EE) of the radio communication networks, with particular care on the dynamic switch ON/OFF of the Base Stations (BSs). Besides, 5G architectures will introduce the Heterogeneous Network (HetNet) paradigm, where Small BSs (SBSs) are deployed to assist the standard macro BS for satisfying the high traffic demand and reducing the impact on the energy consumption. However, only with the introduction of Energy Harvesting (EH) capabilities the networks might reach the needed energy savings for mitigating both the high costs and the environmental impact. In the case of HetNets with EH capabilities, the erratic and intermittent nature of renewable energy sources has to be considered, which entails some additional complexity. Solar energy has been chosen as reference EH source due to its widespread adoption and its high efficiency in terms of energy produced compared to its costs. To this end, in the first part of the thesis, a harvested solar energy model has been presented based on accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources. The typical HetNet scenario involves dense deployments with a high level of flexibility, which suggests the usage of distributed control systems rather than centralized, where the scalability can become rapidly a bottleneck. For this reason, in the second part of the thesis, we propose to model the SBS tier as a Multi-agent Reinforcement Learning (MRL) system, where each SBS is an intelligent and autonomous agent, which learns by directly interacting with the environment and by properly utilizing the past experience. The agents implemented in each SBS independently learn a proper switch ON/OFF control policy, so as to jointly maximize the system performance in terms of throughput, drop rate and energy consumption, while adapting to the dynamic conditions of the environment, in terms of energy inflow and traffic demand. However, MRL might suffer the problem of coordination when finding simultaneously a solution among all the agents that is good for the whole system. In consequence, the Layered Learning paradigm has been adopted to simplify the problem by decomposing it in subtasks. In particular, the global solution is obtained in a hierarchical fashion: the learning process of a subtask is aimed at facilitating the learning of the next higher subtask layer. The first layer implements an MRL approach and it is in charge of the local online optimization at SBS level as function of the traffic demand and the energy incomes. The second layer is in charge of the network-wide optimization and it is based on Artificial Neural Networks aimed at estimating the model of the overall network.Con la llegada de la nueva generación de redes móviles, la quinta generación (5G), se predice un aumento por un factor 1000 en la demanda de capacidad respecto a la 4G, con la consecuente instalación de nuevas infraestructuras. Se estima que el gasto energético de las tecnologías de la información y la comunicación podría alcanzar el 51% de la producción mundial de energía en el año 2030, principalmente debido al impacto de las redes y servicios móviles. Consecuentemente, los costes relacionados con el consumo de energía pasarán a ser una componente predominante en los gastos operativos (OPEX) de las operadoras de redes móviles. Por lo tanto, un control eficiente del consumo energético de las redes 5G, ya no es simplemente deseable, sino esencial. En la última década, la comunidad científica ha enfocado sus esfuerzos en la eficiencia energética (EE) de las redes de comunicaciones móviles, con particular énfasis en algoritmos para apagar y encender las estaciones base (BS). Además, las arquitecturas 5G introducirán el paradigma de las redes heterogéneas (HetNet), donde pequeñas BSs, o small BSs (SBSs), serán desplegadas para ayudar a las grandes macro BSs en satisfacer la gran demanda de tráfico y reducir el impacto en el consumo energético. Sin embargo, solo con la introducción de técnicas de captación de la energía ambiental, las redes pueden alcanzar los ahorros energéticos requeridos para mitigar los altos costes de la energía y su impacto en el medio ambiente. En el caso de las HetNets alimentadas mediante energías renovables, la naturaleza errática e intermitente de esta tipología de energías constituye una complejidad añadida al problema. La energía solar ha sido utilizada como referencia debido a su gran implantación y su alta eficiencia en términos de cantidad de energía producida respecto costes de producción. Por consiguiente, en la primera parte de la tesis se presenta un modelo de captación de la energía solar basado en un riguroso modelo estocástico de Markov que representa la energía capturada por paneles solares para exteriores. El escenario típico de HetNet supondrá el despliegue denso de SBSs con un alto nivel de flexibilidad, lo cual sugiere la utilización de sistemas de control distribuidos en lugar de aquellos que están centralizados, donde la adaptabilidad podría convertirse rápidamente en un reto difícilmente gestionable. Por esta razón, en la segunda parte de la tesis proponemos modelar las SBSs como un sistema multiagente de aprendizaje automático por refuerzo, donde cada SBS es un agente inteligente y autónomo que aprende interactuando directamente con su entorno y utilizando su experiencia acumulada. Los agentes en cada SBS aprenden independientemente políticas de control del apagado y encendido que les permiten maximizar conjuntamente el rendimiento y el consumo energético a nivel de sistema, adaptándose a condiciones dinámicas del ambiente tales como la energía renovable entrante y la demanda de tráfico. No obstante, los sistemas multiagente sufren problemas de coordinación cuando tienen que hallar simultáneamente una solución de forma distribuida que sea buena para todo el sistema. A tal efecto, el paradigma de aprendizaje por niveles ha sido utilizado para simplificar el problema dividiéndolo en subtareas. Más detalladamente, la solución global se consigue de forma jerárquica: el proceso de aprendizaje de una subtarea está dirigido a ayudar al aprendizaje de la subtarea del nivel superior. El primer nivel contempla un sistema multiagente de aprendizaje automático por refuerzo y se encarga de la optimización en línea de las SBSs en función de la demanda de tráfico y de la energía entrante. El segundo nivel se encarga de la optimización a nivel de red del sistema y está basado en redes neuronales artificiales diseñadas para estimar el modelo de todas las BSsPostprint (published version

Analisi dell'errore nell'acquisizione dell'italiano in un contesto LS e in contesti L2.

L'influenza del contesto nell'acquisizione del linguaggio \ue8 stata analizzata confrontando il contesto L2 e il contesto LS. La prima parte include un quadro teorico riguardante l'analisi dell'errore e le sue applicazioni, i diversi tipi di errore e le possibili fonti degli stessi e alcune differenze tra l'italiano come seconda lingua e l'italiano come lingua straniera. Sulla base di questi studi, sono stati elaborati un'autobiografia sociolinguistica e due esercizi (un riempimento di spazi vuoti e una produzione scritta) che gli studenti hanno dovuto completare e svolgere. Gli studenti seguivano corsi di livello A1 e A2 nelle citt\ue0 di Padova, Venezia, Rovigo e Londra. L'analisi dei dati \ue8 stata condotta seguendo cinque fasi: raccolta dei dati, identificazione degli errori, classificazione, spiegazione e valutazione degli stessi. Per ogni livello sono stati evidenziati gli errori pi\uf9 frequenti ma non sono emerse differenze sostanziali tra i due contesti. Partendo dagli errori sono state create delle attivit\ue0 per aiutare gli studenti a superare gli stessi errori