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Impromptu crisis mapping to prioritize emergency response
No full textTo visualize post-emergency damage, a crisis-mapping system uses readily available semantic annotators, a machine-learning classifier to analyze relevant tweets, and interactive maps to rank extracted situational information. The system was validated against data from two recent disasters in Italy
Word knowledge and word usage
No full textThis special issue, together with its companion issue to appear in Lingue e Linguaggio, stems from the NetWordS Final Conference Word knowledge and word usage: representations and processes in the mental lexicon.* The conference, held on the 30th and 31st of March, and the 1st of April 2015 in Pisa, concluded the 4-year NetWordS project, the European Network of Word Structure funded by the European Science Foundation within the Research Networking Programme. In line with the highly multidisciplinary profile of NetWordS agenda, the conference offered a comprehensive and inclusive forum focussing on two main lines of lexical inquiry: (i) usage-based approaches to bootstrapping word form and structure (morpho-phonological and morpho-syntactic issues), including: acquisition of lexical categories, emergence of morphological structure, lexical memories, anticipatory prediction-based mechanisms of word recognition, word production, frequency-based models of lexical productivity, word encoding, models of lexical architecture, family-based effects in word processing, word reading and writing; (ii) usage-based approaches to word meanings (lexical semantics and pragmatics in morphologically simple and complex words), including: distributional semantics, compound interpretation, concept composition and coercion, conceptualization of perception and action, time and space in the lexicon, metonymy and metaphor, lexico-semantic relations, perceptual grounding and embodied cognition, context-based and encyclopedic knowledge, semantic association and categorization. The multidisciplinary focus on word knowledge and word usage promoted by the Conference led participants to openly discuss an impressive range of approaches and empirical data: priming and lexical decision in a number of contexts, distributional semantics and models of semantic composition, neural networks, machine learning and mathematical modelling of empirical evidence, as well as their neuro-biological and neuro-functional correlates
Underwater explosions near marine structures: a Dynamics Fluid Structure Domain-Decomposition strategy
No full textHistorically underwater explosions (UEs) have been investigated for their huge military relevance, but they remain an important issue also for civil marine applications. As an example, UEs can occur near or on oil-gas plants due to severe environmental conditions or human errors and this has substantial consequences for the production. Preliminary information about time scales of the phenomenon and knowledge about possible strategies on how to limit the consequences of its interaction with close-by structures is crucial to make the proper decisions. A numerical investigation would in general require a 3D compressible (at least) two-phase hydro-dynamic solver strongly coupled with a suitable model of the involved structure. Because the CPU-time requirements are still too high for reliable and feasible predictions, a Domain Decomposition (DD) strategy has been proposed by Colicchio et al. (2013) and Colicchio et al. (2014) and applied to a fully coupled fluid-structure analysis by Colicchio et al. (2015). Here, the DD is further extended as Dynamic DD (DDD) to overcome limits of applicability in time. The dynamic strategy proposed is not limited to UEs problems and to the two coupled solvers involved. When examining the UE interaction with a marine structure, like a surface ship, one can distinguish basically two stages: the first one, with important compressible effects and local fluid-structure interactions; the second one, with global consequences for the structure, possibly involving large deformations and damages as well as free-surface waves generation. The present research focuses on the first stage but the proposed DDD strategy can be adopted also for the second stage
Problem Solving as Probabilistic Inference with Subgoaling: Explaining Human Successes and Pitfalls in the Tower of Hanoi
No full textHow do humans and other animals face novel problems for which predefined solutions are not available? Human problem solving links to flexible reasoning and inference rather than to slow trial-and-error learning. It has received considerable attention since the early days of cognitive science, giving rise to well known cognitive architectures such as SOAR and ACT-R, but its computational and brain mechanisms remain incompletely known. Furthermore, it is still unclear whether problem solving is a "specialized" domain or module of cognition, in the sense that it requires computations that are fundamentally different from those supporting perception and action systems. Here we advance a novel view of human problem solving as probabilistic inference with subgoaling. In this perspective, key insights from cognitive architectures are retained such as the importance of using subgoals to split problems into subproblems. However, here the underlying computations use probabilistic inference methods analogous to those that are increasingly popular in the study of perception and action systems. To test our model we focus on the widely used Tower of Hanoi (ToH) task, and show that our proposed method can reproduce characteristic idiosyncrasies of human problem solvers: their sensitivity to the "community structure" of the ToH and their difficulties in executing so-called "counterintuitive" movements. Our analysis reveals that subgoals have two key roles in probabilistic inference and problem solving. First, prior beliefs on (likely) useful subgoals carve the problem space and define an implicit metric for the problem at hand-a metric to which humans are sensitive. Second, subgoals are used as waypoints in the probabilistic problem solving inference and permit to find effective solutions that, when unavailable, lead to problem solving deficits. Our study thus suggests that a probabilistic inference scheme enhanced with subgoals provides a comprehensive framework to study problem solving and its deficits
Produzione video per la WebTv del Cnr
No full textProduzione video per la valorizzazione e la divulgazione dei risultati dei progetti di ricerca. Lo scopo ? quello di divulgare le attivit? di ricerca, in primis dell\u27area pisana, ma non solo, e allo stesso tempo raggiungere pi? target possibli, raccontando i progetti in maniera "semplice" ed efficace. I video per la Webtv dello staff dell\u27area pisana della ricerca sono prodotti e interamente realizzati all\u27interno dell\u27Unit? Relazioni esterne, media e comunicazione.http://www.cnrweb.tvProduzione video per la valorizzazione e la divulgazione dei risultati dei progetti di ricerca. Lo scopo ? quello di divulgare le attivit? di ricerca, in primis dell\u27area pisana, ma non solo, e allo stesso tempo raggiungere pi? target possibli, raccontando i progetti in maniera "semplice" ed efficace. I video per la Webtv dello staff dell\u27area pisana della ricerca sono prodotti e interamente realizzati all\u27interno dell\u27Unit? Relazioni esterne, media e comunicazione.http://www.cnrweb.tv?
Ricerche sull\u27evoluzione del Lago Maggiore. Aspetti limnologici. Programma triennale 2013-2015. Campagna 2015 e rapporto triennale 2013-2015
No full textNot availableRicerche sull\u27evoluzione del Lago Maggiore. Aspetti limnologici. Programma triennale 2013-2015. Campagna 2015 e rapporto triennale 2013-201
Indagini su DDT e sostanze pericolose nell\u27ecosistema del Lago Maggiore. Programma 2013-2015. Rapporto annuale 2015 e finale 2013-2015
No full textNot availableIndagini su DDT e sostanze pericolose nell\u27ecosistema del Lago Maggiore. Programma 2013-2015. Rapporto annuale 2015 e finale 2013-201
Towards operational detection of forest ecosystem changes in protected areas
No full textThis paper discusses the application of the Cross-Correlation Analysis (CCA) technique to multi-spatial resolution Earth Observation (EO) data for detecting and quantifying changes in forest ecosystems in two different protected areas, located in Southern Italy and Southern India. The input data for CCA investigation were elaborated from the forest layer extracted from an existing Land Cover/Land Use (LC/LU) map (time T1) and a more recent (T2, with T2 > T1) single date image. The latter consist of a High Resolution (HR) Landsat 8 OLI image and a Very High Resolution (VHR) Worldview-2 image, which were analysed separately. For the Italian site, the forest layer (1:5000) was first compared to the HR Landsat 8 OLI image and then to the VHR Worldview-2 image. For the Indian site, the forest layer (1:50,000) was compared to the Landsat 8 OLI image then the changes were interpreted using Worldview-2. The changes detected through CCA, at HR only, were compared against those detected by applying a traditional NDVI image differencing technique of two Landsat scenes at T1 and T2. The accuracy assessment, concerning the change maps of the multi-spatial resolution outputs, was based on stratified random sampling. The CCA technique allowed an increase in the value of the overall accuracy: from 52% to 68% for the Italian site and from 63% to 82% for the Indian site. In addition, a significant reduction of the error affecting the stratified changed area estimation for both sites was obtained. For the Italian site, the error reduction became significant at VHR (?2 ha) in respect to HR (?32 ha) even though both techniques had comparable overall accuracy (82%) and stratified changed area estimation. The findings obtained support the conclusions that CCA technique can be a useful tool to detect and quantify changes in forest areas due to both legal and illegal interventions, including relatively inaccessible sites (e.g., tropical forest) with costs remaining rather low. The data obtained through CCA intervention could not only support the commitments undertaken by the European Habitats Directive (92/43/EEC) and the Convention of Biological Diversity (CBD) but also satisfy UN Sustainable Development Goals (SDG)
Earth observation for maritime spatial planning: Measuring, observing and modeling marine environment to assess potential aquaculture sites
No full textPhysical, chemical and biological characteristics of seawaters are primary descriptors for understanding environmental patterns and improving maritime spatial planning for potential aquaculture uses. By analyzing these descriptors in spatial and temporal dimensions, it is possible to characterize the potential productivity performances of different locations for specific aquaculture species. We developed a toolbox that, starting from the actual competing uses of the maritime space, aims at: (a) identifying sites with conditions feasible for aquaculture fish growth (feasibility scenario); and (b) assessing their different productivity performances in terms of potential fish harvest (suitability scenario). The toolbox is being designed in the Mediterranean, northern Adriatic Sea, but because of its modularity/multi-stage process, it can be easily adapted to other areas, or scaled to larger areas. The toolbox, representing a pre-operational Copernicus downstreaming service that integrates data and products from different sources (in situ, Earth Observation and modeling), is innovative because it is based more on parameters relevant for fish vitality than on those oriented to farm functioning. Stakeholders and farmers involved in the maritime spatial planning can use resulting scenarios for decision-making and market-trading processes
Final Report
No full textThis document constitutes the final publishable report for the Learn PAd project. It includes a first part with a sketch of objectives and a summary of main achievements. Then it discusses potential impact and summarizes the project dissemination and exploitation strategy. Detailed tables report dissemination and exploitation items