Pilot study assessment of indoor air quality in two dental settings: Particulate matter and volatile organic compounds

Objective: To assess the indoor environment of two different types of dental practices regarding VOCs, PM2.5, and ultrafine particulate concentrations and examine the relationship between specific dental activities and contaminant levels. Method: The indoor environments of two selected dental settings (private practice and community health center) will were assessed in regards to VOCs, PM 2.5, and ultrafine particulate concentrations, as well as other indoor air quality parameters (CO2, CO, temperature, and relative humidity). The sampling duration was four working days for each dental practice. Continuous monitoring and integrated sampling methods were used and number of occupants, frequency, type, and duration of dental procedures or activities recorded. Measurements were compared to indoor air quality standards and guidelines. Results: The private practice had higher CO2, CO, and most VOC concentrations than the community health center, but the community health center had higher PM2.5 and ultrafine PM concentrations. Concentrations of p-dichlorobenzene and PM2.5 exceeded some guidelines. Outdoor concentrations greatly influenced the indoor concentration. There were no significant differences in contaminant levels between the operatory and general area. Indoor concentrations during the working period were not always consistently higher than during the nonworking period. Peaks in particulate matter concentration occurred during root canal and composite procedures.^

Una publicación internacional sobre la estructura productiva mendocina

El objetivo del artículo es presentar el libro “ Agroindustria, competitividad e integración:¿ una fórmula viable para Mendoza, Argentina?" en el cual se pone énfasis en la trama de relaciones y flujos que generan los diferentes circuitos productivos de la provincia y en el impacto que provocan en la organización del espacio neoeconómico. Sobre la base de las conclusiones obtenidas, se desarrolla una propuesta sustentada en la estructuración de un modelo agroindustrial exportador como proceso localizado e integrado vertical y horizontalmente. Se elige como áreas de estudio los Oasis Centro y Sur de Mendoza, en un intento de atenuar los efectos de la excesiva concentración del Oasis Norte y la progresiva marginación de otros lugares no constituidos en oasis. Se trata de un trabajo elaborado sobre la base de la tesis doctoral “ Circuitos económicos urbanos y rurales: posibilidades de integración y diversificación" publicado por Verlag Dr. Markus Hänsel-Hohenhausen, Frankfurt, 1998.

Villefranche MedSeA mesocosm experiment 2013: perturbation studies and field studies

A joint mesocosm experiment took place in February/March 2013 in the bay of Villefranche in France as part of the european MedSeA project. Nine mesocosms (52 m**3) were deployed over a 2 weeks period and 6 different levels of pCO2 and 3 control mesocosms (about 450 µatm), were used, in order to cover the range of pCO2 anticipated for the end of the present century. During this experiment, the potential effects of these perturbations on chemistry, planktonic community composition and dynamics including: eucaryotic and prokaryotic species composition, primary production, nutrient and carbon utilization, calcification, diazotrophic nitrogen fixation, organic matter exudation and composition, micro-layer composition and biogas production were studied by a group of about 25 scientists from 8 institutes and 6 countries. This is one of the first mesocosm experiments conducted in oligotrophic waters. A blog dedicated to this experiment can be viewed at: http://medseavillefranche2013.obs-vlfr.fr.

Stareso MedSeA mesocosm experiment: field and perturbation studies 2012

A joint mesocosm experiment took place in June/July 2012 in Corsica (bay of Calvi, Stareso station;http://www.stareso.com/) as part of the european MedSeA project. Nine mesocosms (52 m**3) were deployed over a 20 days period and 6 different levels of pCO2 and 3 control mesocosms (about 450 µatm), were used, in order to cover the range of pCO2 anticipated for the end of the present century. During this experiment, the potential effects of these perturbations on chemistry, planktonic community composition and dynamics including: eucaryotic and prokaryotic species composition, primary production, nutrient and carbon utilization, calcification, diazotrophic nitrogen fixation, organic matter exudation and composition, micro-layer composition and biogas production were studied by a group of about 25 scientists from 8 institutes and 6 countries. This is one of the first mesocosm experiments conducted in oligotrophic waters. A blog dedicated to this experiment can be viewed at: http://medseastareso2012.wordpress.com/.

Carbon and deuterium isotopes and C ratios in gas hydrates of ODP Leg 204 sites

Sediments at the southern summit of Hydrate Ridge display two distinct modes of gas hydrate occurrence. The dominant mode is associated with active venting of gas exsolved from the accretionary prism and leads to high concentrations (15%-40% of pore space) of gas hydrate in seafloor or near-surface sediments at and around the topographic summit of southern Hydrate Ridge. These near-surface gas hydrates are mainly composed of previously buried microbial methane but also contain a significant (10%-15%) component of thermogenic hydrocarbons and are overprinted with microbial methane currently being generated in shallow sediments. Focused migration pathways with high gas saturation (>65%) abutting the base of gas hydrate stability create phase equilibrium conditions that permit the flow of a gas phase through the gas hydrate stability zone. Gas seepage at the summit supports rapid growth of gas hydrates and vigorous anaerobic methane oxidation. The other mode of gas hydrate occurs in slope basins and on the saddle north of the southern summit and consists of lower average concentrations (0.5%-5%) at greater depths (30-200 meters below seafloor [mbsf]) resulting from the buildup of in situ-generated dissolved microbial methane that reaches saturation levels with respect to gas hydrate stability at 30-50 mbsf. Net rates of sulfate reduction in the slope basin and ridge saddle sites estimated from curve fitting of concentration gradients are 2-4 mmol/m**3/yr, and integrated net rates are 20-50 mmol/m**2/yr. Modeled microbial methane production rates are initially 1.5 mmol/m**3/yr in sediments just beneath the sulfate reduction zone but rapidly decrease to rates of <0.1 mmol/m**3/yr at depths >100 mbsf. Integrated net rates of methane production in sediments away from the southern summit of Hydrate Ridge are 25-80 mmol/m**2/yr. Anaerobic methane oxidation is minor or absent in cored sediments away from the summit of southern Hydrate Ridge. Ethane-enriched Structure I gas hydrate solids are buried more rapidly than ethane-depleted dissolved gas in the pore water because of advection from compaction. With subsidence beneath the gas hydrate stability zone, the ethane (mainly of low-temperature thermogenic origin) is released back to the dissolved gas-free gas phases and produces a discontinuous decrease in the C1/C2 vs. depth trend. These ethane fractionation effects may be useful to recognize and estimate levels of gas hydrate occurrence in marine sediments.

Mass balance measurements on King George Island ice cap in 2007 and 2008

The Antarctic Peninsula has been identified as a region of rapid on-going climate change with impacts on the cryosphere. The knowledge of glacial changes and freshwater budgets resulting from intensified glacier melt is an important boundary condition for many biological and integrated earth system science approaches. We provide a case study on glacier and mass balance changes for the ice cap of King George Island. The area loss between 2000 and 2008 amounted to about 20 km**2 (about 1.6% of the island area) and compares to glacier retreat rates observed in previous years. Measured net accumulation rates for two years (2007 and 2008) show a strong interannual variability with maximum net accumulation rates of 4950 mm w.e./a and 3184 mm w.e./a, respectively. These net accumulation rates are at least 4 times higher than reported mean values (1926-95) from an ice core. An elevation dependent precipitation rate of 343 mm w.e./a (2007) and 432 mm w.e./a (2008) per 100 m elevation increase was observed. Despite these rather high net accumulation rates on the main ice cap, consistent surface lowering was observed at elevations below 270 m above ellipsoid over an 11-year period. These DGPS records reveal a linear dependence of surface lowering with altitude with a maximum annual surface lowering rate of 1.44 m/a at 40 m and -0.20 m/a at 270 m above ellipsoid. These results fit well to observations by other authors and surface lowering rates derived from the ICESat laser altimeter. Assuming that climate conditions of the past 11 years continue, the small ice cap of Bellingshausen Dome will disappear in about 285 years.

Solar radiation over and under sea ice during POLARSTERN cruise PS92 (TRANSSIZ) in 2015

Measurements of solar radiation over and under sea ice have been performed on various stations in the Arctic Ocean during the Polarstern cruise PS92 (TRANSSIZ) between 19 May and 30 June 2015. All radiation measurements have been performed with Ramses spectral radiometers (Trios, Rastede, Germany). All data are given in full spectral resolution interpolated to 1.0 nm, and integrated over the entire wavelength range (broadband, total: 320 to 950 nm). Two sensors were mounted on a Remotely Operated Vehicle (ROV) and one radiometer was installed on the sea ice for surface reference measurements (solar irradiance). On the ROV, one irradiance sensor (cos-collector) for energy budget calculations and one radiance sensor (9° opening angle) to obtain high resolution spatial variability were installed. Along with the radiation measurements, ROV positions were obtained from acoustic USBL-positioning and all parameters of vehicle depth, distance to the ice and attitude recorded. All times are given in UTC.

Infrastructure (Rural Road) Development and Poverty Alleviation in Lao PDR

Rural road in Lao PDR defined as connecting road from village to main road, where it will lead them to market and access to other economic and social service facilities. However, due to mostly rural people accustom with subsistence farming, connecting road seems less important for rural people as their main farming produce is for own consumption rather than markets. After the introduction and implementation of New Economic Mechanism (NEM) since 1986, many rural villages have gradually developed and integrated into market system where people have significantly changed their livelihood with a better system. This progress has significantly contributed in improving income earning of people, better living standard and reduce poverty. The paper aims to illustrate the significant of rural road as connecting road from village to markets or a market access approach of farm produces. It also demonstrates through which approach, rural farmers/people could improve their income earning, develop their farming system, living standard and reduce poverty.

Area-Efficient Linear Regression Architecture for Real-Time Signal Processing on FPGAs

Linear regression is a technique widely used in digital signal processing. It consists on finding the linear function that better fits a given set of samples. This paper proposes different hardware architectures for the implementation of the linear regression method on FPGAs, specially targeting area restrictive systems. It saves area at the cost of constraining the lengths of the input signal to some fixed values. We have implemented the proposed scheme in an Automatic Modulation Classifier, meeting the hard real-time constraints this kind of systems have.

Wake up Radio Architecture for Wireless Sensor Networks Using an Ultra Low Power FPGA

In this paper an implementation of a Wake up Radio(WuR) with addressing capabilities based on an ultra low power FPGA for ultra low energy Wireless Sensor Networks (WSNs) is proposed. The main goal is to evaluate the utilization of very low power configurable devices to take advantage of their speed, flexibility and low power consumption instead of the traditional approaches based on ASICs or microcontrollers, for communication frame decoding and communication data control.

Automatic Inference of Determinacy and Mutual Exclusion for Logic Programs Using Mode and Type Analyses

We propose an analysis for detecting procedures and goals that are deterministic (i.e., that produce at most one solution at most once),or predicates whose clause tests are mutually exclusive (which implies that at most one of their clauses will succeed) even if they are not deterministic. The analysis takes advantage of the pruning operator in order to improve the detection of mutual exclusion and determinacy. It also supports arithmetic equations and disequations, as well as equations and disequations on terms,for which we give a complete satisfiability testing algorithm, w.r.t. available type information. Information about determinacy can be used for program debugging and optimization, resource consumption and granularity control, abstraction carrying code, etc. We have implemented the analysis and integrated it in the CiaoPP system, which also infers automatically the mode and type information that our analysis takes as input. Experiments performed on this implementation show that the analysis is fairly accurate and efficient.

On-chip Monitoring: A Light-Weight Interconnection Network Approach

Current nanometer technologies are subjected to several adverse effects that seriously impact the yield and performance of integrated circuits. Such is the case of within-die parameters uncertainties, varying workload conditions, aging, temperature, etc. Monitoring, calibration and dynamic adaptation have appeared as promising solutions to these issues and many kinds of monitors have been presented recently. In this scenario, where systems with hundreds of monitors of different types have been proposed, the need for light-weight monitoring networks has become essential. In this work we present a light-weight network architecture based on digitization resource sharing of nodes that require a time-to-digital conversion. Our proposal employs a single wire interface, shared among all the nodes in the network, and quantizes the time domain to perform the access multiplexing and transmit the information. It supposes a 16% improvement in area and power consumption compared to traditional approaches.

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web 1. INTRODUCTION. LINGUISTIC TOOLS AND ANNOTATIONS: THEIR LIGHTS AND SHADOWS Computational Linguistics is already a consolidated research area. It builds upon the results of other two major ones, namely Linguistics and Computer Science and Engineering, and it aims at developing computational models of human language (or natural language, as it is termed in this area). Possibly, its most well-known applications are the different tools developed so far for processing human language, such as machine translation systems and speech recognizers or dictation programs. These tools for processing human language are commonly referred to as linguistic tools. Apart from the examples mentioned above, there are also other types of linguistic tools that perhaps are not so well-known, but on which most of the other applications of Computational Linguistics are built. These other types of linguistic tools comprise POS taggers, natural language parsers and semantic taggers, amongst others. All of them can be termed linguistic annotation tools. Linguistic annotation tools are important assets. In fact, POS and semantic taggers (and, to a lesser extent, also natural language parsers) have become critical resources for the computer applications that process natural language. Hence, any computer application that has to analyse a text automatically and ‘intelligently’ will include at least a module for POS tagging. The more an application needs to ‘understand’ the meaning of the text it processes, the more linguistic tools and/or modules it will incorporate and integrate. However, linguistic annotation tools have still some limitations, which can be summarised as follows: 1. Normally, they perform annotations only at a certain linguistic level (that is, Morphology, Syntax, Semantics, etc.). 2. They usually introduce a certain rate of errors and ambiguities when tagging. This error rate ranges from 10 percent up to 50 percent of the units annotated for unrestricted, general texts. 3. Their annotations are most frequently formulated in terms of an annotation schema designed and implemented ad hoc. A priori, it seems that the interoperation and the integration of several linguistic tools into an appropriate software architecture could most likely solve the limitations stated in (1). Besides, integrating several linguistic annotation tools and making them interoperate could also minimise the limitation stated in (2). Nevertheless, in the latter case, all these tools should produce annotations for a common level, which would have to be combined in order to correct their corresponding errors and inaccuracies. Yet, the limitation stated in (3) prevents both types of integration and interoperation from being easily achieved. In addition, most high-level annotation tools rely on other lower-level annotation tools and their outputs to generate their own ones. For example, sense-tagging tools (operating at the semantic level) often use POS taggers (operating at a lower level, i.e., the morphosyntactic) to identify the grammatical category of the word or lexical unit they are annotating. Accordingly, if a faulty or inaccurate low-level annotation tool is to be used by other higher-level one in its process, the errors and inaccuracies of the former should be minimised in advance. Otherwise, these errors and inaccuracies would be transferred to (and even magnified in) the annotations of the high-level annotation tool. Therefore, it would be quite useful to find a way to (i) correct or, at least, reduce the errors and the inaccuracies of lower-level linguistic tools; (ii) unify the annotation schemas of different linguistic annotation tools or, more generally speaking, make these tools (as well as their annotations) interoperate. Clearly, solving (i) and (ii) should ease the automatic annotation of web pages by means of linguistic tools, and their transformation into Semantic Web pages (Berners-Lee, Hendler and Lassila, 2001). Yet, as stated above, (ii) is a type of interoperability problem. There again, ontologies (Gruber, 1993; Borst, 1997) have been successfully applied thus far to solve several interoperability problems. Hence, ontologies should help solve also the problems and limitations of linguistic annotation tools aforementioned. Thus, to summarise, the main aim of the present work was to combine somehow these separated approaches, mechanisms and tools for annotation from Linguistics and Ontological Engineering (and the Semantic Web) in a sort of hybrid (linguistic and ontological) annotation model, suitable for both areas. This hybrid (semantic) annotation model should (a) benefit from the advances, models, techniques, mechanisms and tools of these two areas; (b) minimise (and even solve, when possible) some of the problems found in each of them; and (c) be suitable for the Semantic Web. The concrete goals that helped attain this aim are presented in the following section. 2. GOALS OF THE PRESENT WORK As mentioned above, the main goal of this work was to specify a hybrid (that is, linguistically-motivated and ontology-based) model of annotation suitable for the Semantic Web (i.e. it had to produce a semantic annotation of web page contents). This entailed that the tags included in the annotations of the model had to (1) represent linguistic concepts (or linguistic categories, as they are termed in ISO/DCR (2008)), in order for this model to be linguistically-motivated; (2) be ontological terms (i.e., use an ontological vocabulary), in order for the model to be ontology-based; and (3) be structured (linked) as a collection of ontology-based triples, as in the usual Semantic Web languages (namely RDF(S) and OWL), in order for the model to be considered suitable for the Semantic Web. Besides, to be useful for the Semantic Web, this model should provide a way to automate the annotation of web pages. As for the present work, this requirement involved reusing the linguistic annotation tools purchased by the OEG research group (http://www.oeg-upm.net), but solving beforehand (or, at least, minimising) some of their limitations. Therefore, this model had to minimise these limitations by means of the integration of several linguistic annotation tools into a common architecture. Since this integration required the interoperation of tools and their annotations, ontologies were proposed as the main technological component to make them effectively interoperate. From the very beginning, it seemed that the formalisation of the elements and the knowledge underlying linguistic annotations within an appropriate set of ontologies would be a great step forward towards the formulation of such a model (henceforth referred to as OntoTag). Obviously, first, to combine the results of the linguistic annotation tools that operated at the same level, their annotation schemas had to be unified (or, preferably, standardised) in advance. This entailed the unification (id. standardisation) of their tags (both their representation and their meaning), and their format or syntax. Second, to merge the results of the linguistic annotation tools operating at different levels, their respective annotation schemas had to be (a) made interoperable and (b) integrated. And third, in order for the resulting annotations to suit the Semantic Web, they had to be specified by means of an ontology-based vocabulary, and structured by means of ontology-based triples, as hinted above. Therefore, a new annotation scheme had to be devised, based both on ontologies and on this type of triples, which allowed for the combination and the integration of the annotations of any set of linguistic annotation tools. This annotation scheme was considered a fundamental part of the model proposed here, and its development was, accordingly, another major objective of the present work. All these goals, aims and objectives could be re-stated more clearly as follows: Goal 1: Development of a set of ontologies for the formalisation of the linguistic knowledge relating linguistic annotation. Sub-goal 1.1: Ontological formalisation of the EAGLES (1996a; 1996b) de facto standards for morphosyntactic and syntactic annotation, in a way that helps respect the triple structure recommended for annotations in these works (which is isomorphic to the triple structures used in the context of the Semantic Web). Sub-goal 1.2: Incorporation into this preliminary ontological formalisation of other existing standards and standard proposals relating the levels mentioned above, such as those currently under development within ISO/TC 37 (the ISO Technical Committee dealing with Terminology, which deals also with linguistic resources and annotations). Sub-goal 1.3: Generalisation and extension of the recommendations in EAGLES (1996a; 1996b) and ISO/TC 37 to the semantic level, for which no ISO/TC 37 standards have been developed yet. Sub-goal 1.4: Ontological formalisation of the generalisations and/or extensions obtained in the previous sub-goal as generalisations and/or extensions of the corresponding ontology (or ontologies). Sub-goal 1.5: Ontological formalisation of the knowledge required to link, combine and unite the knowledge represented in the previously developed ontology (or ontologies). Goal 2: Development of OntoTag’s annotation scheme, a standard-based abstract scheme for the hybrid (linguistically-motivated and ontological-based) annotation of texts. Sub-goal 2.1: Development of the standard-based morphosyntactic annotation level of OntoTag’s scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996a) and also the recommendations included in the ISO/MAF (2008) standard draft. Sub-goal 2.2: Development of the standard-based syntactic annotation level of the hybrid abstract scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996b) and the ISO/SynAF (2010) standard draft. Sub-goal 2.3: Development of the standard-based semantic annotation level of OntoTag’s (abstract) scheme. Sub-goal 2.4: Development of the mechanisms for a convenient integration of the three annotation levels already mentioned. These mechanisms should take into account the recommendations included in the ISO/LAF (2009) standard draft. Goal 3: Design of OntoTag’s (abstract) annotation architecture, an abstract architecture for the hybrid (semantic) annotation of texts (i) that facilitates the integration and interoperation of different linguistic annotation tools, and (ii) whose results comply with OntoTag’s annotation scheme. Sub-goal 3.1: Specification of the decanting processes that allow for the classification and separation, according to their corresponding levels, of the results of the linguistic tools annotating at several different levels. Sub-goal 3.2: Specification of the standardisation processes that allow (a) complying with the standardisation requirements of OntoTag’s annotation scheme, as well as (b) combining the results of those linguistic tools that share some level of annotation. Sub-goal 3.3: Specification of the merging processes that allow for the combination of the output annotations and the interoperation of those linguistic tools that share some level of annotation. Sub-goal 3.4: Specification of the merge processes that allow for the integration of the results and the interoperation of those tools performing their annotations at different levels. Goal 4: Generation of OntoTagger’s schema, a concrete instance of OntoTag’s abstract scheme for a concrete set of linguistic annotations. These linguistic annotations result from the tools and the resources available in the research group, namely • Bitext’s DataLexica (http://www.bitext.com/EN/datalexica.asp), • LACELL’s (POS) tagger (http://www.um.es/grupos/grupo-lacell/quees.php), • Connexor’s FDG (http://www.connexor.eu/technology/machinese/glossary/fdg/), and • EuroWordNet (Vossen et al., 1998). This schema should help evaluate OntoTag’s underlying hypotheses, stated below. Consequently, it should implement, at least, those levels of the abstract scheme dealing with the annotations of the set of tools considered in this implementation. This includes the morphosyntactic, the syntactic and the semantic levels. Goal 5: Implementation of OntoTagger’s configuration, a concrete instance of OntoTag’s abstract architecture for this set of linguistic tools and annotations. This configuration (1) had to use the schema generated in the previous goal; and (2) should help support or refute the hypotheses of this work as well (see the next section). Sub-goal 5.1: Implementation of the decanting processes that facilitate the classification and separation of the results of those linguistic resources that provide annotations at several different levels (on the one hand, LACELL’s tagger operates at the morphosyntactic level and, minimally, also at the semantic level; on the other hand, FDG operates at the morphosyntactic and the syntactic levels and, minimally, at the semantic level as well). Sub-goal 5.2: Implementation of the standardisation processes that allow (i) specifying the results of those linguistic tools that share some level of annotation according to the requirements of OntoTagger’s schema, as well as (ii) combining these shared level results. In particular, all the tools selected perform morphosyntactic annotations and they had to be conveniently combined by means of these processes. Sub-goal 5.3: Implementation of the merging processes that allow for the combination (and possibly the improvement) of the annotations and the interoperation of the tools that share some level of annotation (in particular, those relating the morphosyntactic level, as in the previous sub-goal). Sub-goal 5.4: Implementation of the merging processes that allow for the integration of the different standardised and combined annotations aforementioned, relating all the levels considered. Sub-goal 5.5: Improvement of the semantic level of this configuration by adding a named entity recognition, (sub-)classification and annotation subsystem, which also uses the named entities annotated to populate a domain ontology, in order to provide a concrete application of the present work in the two areas involved (the Semantic Web and Corpus Linguistics). 3. MAIN RESULTS: ASSESSMENT OF ONTOTAG’S UNDERLYING HYPOTHESES The model developed in the present thesis tries to shed some light on (i) whether linguistic annotation tools can effectively interoperate; (ii) whether their results can be combined and integrated; and, if they can, (iii) how they can, respectively, interoperate and be combined and integrated. Accordingly, several hypotheses had to be supported (or rejected) by the development of the OntoTag model and OntoTagger (its implementation). The hypotheses underlying OntoTag are surveyed below. Only one of the hypotheses (H.6) was rejected; the other five could be confirmed. H.1 The annotations of different levels (or layers) can be integrated into a sort of overall, comprehensive, multilayer and multilevel annotation, so that their elements can complement and refer to each other. • CONFIRMED by the development of: o OntoTag’s annotation scheme, o OntoTag’s annotation architecture, o OntoTagger’s (XML, RDF, OWL) annotation schemas, o OntoTagger’s configuration. H.2 Tool-dependent annotations can be mapped onto a sort of tool-independent annotations and, thus, can be standardised. • CONFIRMED by means of the standardisation phase incorporated into OntoTag and OntoTagger for the annotations yielded by the tools. H.3 Standardisation should ease: H.3.1: The interoperation of linguistic tools. H.3.2: The comparison, combination (at the same level and layer) and integration (at different levels or layers) of annotations. • H.3 was CONFIRMED by means of the development of OntoTagger’s ontology-based configuration: o Interoperation, comparison, combination and integration of the annotations of three different linguistic tools (Connexor’s FDG, Bitext’s DataLexica and LACELL’s tagger); o Integration of EuroWordNet-based, domain-ontology-based and named entity annotations at the semantic level. o Integration of morphosyntactic, syntactic and semantic annotations. H.4 Ontologies and Semantic Web technologies (can) play a crucial role in the standardisation of linguistic annotations, by providing consensual vocabularies and standardised formats for annotation (e.g., RDF triples). • CONFIRMED by means of the development of OntoTagger’s RDF-triple-based annotation schemas. H.5 The rate of errors introduced by a linguistic tool at a given level, when annotating, can be reduced automatically by contrasting and combining its results with the ones coming from other tools, operating at the same level. However, these other tools might be built following a different technological (stochastic vs. rule-based, for example) or theoretical (dependency vs. HPS-grammar-based, for instance) approach. • CONFIRMED by the results yielded by the evaluation of OntoTagger. H.6 Each linguistic level can be managed and annotated independently. • REJECTED: OntoTagger’s experiments and the dependencies observed among the morphosyntactic annotations, and between them and the syntactic annotations. In fact, Hypothesis H.6 was already rejected when OntoTag’s ontologies were developed. We observed then that several linguistic units stand on an interface between levels, belonging thereby to both of them (such as morphosyntactic units, which belong to both the morphological level and the syntactic level). Therefore, the annotations of these levels overlap and cannot be handled independently when merged into a unique multileveled annotation. 4. OTHER MAIN RESULTS AND CONTRIBUTIONS First, interoperability is a hot topic for both the linguistic annotation community and the whole Computer Science field. The specification (and implementation) of OntoTag’s architecture for the combination and integration of linguistic (annotation) tools and annotations by means of ontologies shows a way to make these different linguistic annotation tools and annotations interoperate in practice. Second, as mentioned above, the elements involved in linguistic annotation were formalised in a set (or network) of ontologies (OntoTag’s linguistic ontologies). • On the one hand, OntoTag’s network of ontologies consists of − The Linguistic Unit Ontology (LUO), which includes a mostly hierarchical formalisation of the different types of linguistic elements (i.e., units) identifiable in a written text; − The Linguistic Attribute Ontology (LAO), which includes also a mostly hierarchical formalisation of the different types of features that characterise the linguistic units included in the LUO; − The Linguistic Value Ontology (LVO), which includes the corresponding formalisation of the different values that the attributes in the LAO can take; − The OIO (OntoTag’s Integration Ontology), which  Includes the knowledge required to link, combine and unite the knowledge represented in the LUO, the LAO and the LVO;  Can be viewed as a knowledge representation ontology that describes the most elementary vocabulary used in the area of annotation. • On the other hand, OntoTag’s ontologies incorporate the knowledge included in the different standards and recommendations for linguistic annotation released so far, such as those developed within the EAGLES and the SIMPLE European projects or by the ISO/TC 37 committee: − As far as morphosyntactic annotations are concerned, OntoTag’s ontologies formalise the terms in the EAGLES (1996a) recommendations and their corresponding terms within the ISO Morphosyntactic Annotation Framework (ISO/MAF, 2008) standard; − As for syntactic annotations, OntoTag’s ontologies incorporate the terms in the EAGLES (1996b) recommendations and their corresponding terms within the ISO Syntactic Annotation Framework (ISO/SynAF, 2010) standard draft; − Regarding semantic annotations, OntoTag’s ontologies generalise and extend the recommendations in EAGLES (1996a; 1996b) and, since no stable standards or standard drafts have been released for semantic annotation by ISO/TC 37 yet, they incorporate the terms in SIMPLE (2000) instead; − The terms coming from all these recommendations and standards were supplemented by those within the ISO Data Category Registry (ISO/DCR, 2008) and also of the ISO Linguistic Annotation Framework (ISO/LAF, 2009) standard draft when developing OntoTag’s ontologies. Third, we showed that the combination of the results of tools annotating at the same level can yield better results (both in precision and in recall) than each tool separately. In particular, 1. OntoTagger clearly outperformed two of the tools integrated into its configuration, namely DataLexica and FDG in all the combination sub-phases in which they overlapped (i.e. POS tagging, lemma annotation and morphological feature annotation). As far as the remaining tool is concerned, i.e. LACELL’s tagger, it was also outperformed by OntoTagger in POS tagging and lemma annotation, and it did not behave better than OntoTagger in the morphological feature annotation layer. 2. As an immediate result, this implies that a) This type of combination architecture configurations can be applied in order to improve significantly the accuracy of linguistic annotations; and b) Concerning the morphosyntactic level, this could be regarded as a way of constructing more robust and more accurate POS tagging systems. Fourth, Semantic Web annotations are usually performed by humans or else by machine learning systems. Both of them leave much to be desired: the former, with respect to their annotation rate; the latter, with respect to their (average) precision and recall. In this work, we showed how linguistic tools can be wrapped in order to annotate automatically Semantic Web pages using ontologies. This entails their fast, robust and accurate semantic annotation. As a way of example, as mentioned in Sub-goal 5.5, we developed a particular OntoTagger module for the recognition, classification and labelling of named entities, according to the MUC and ACE tagsets (Chinchor, 1997; Doddington et al., 2004). These tagsets were further specified by means of a domain ontology, namely the Cinema Named Entities Ontology (CNEO). This module was applied to the automatic annotation of ten different web pages containing cinema reviews (that is, around 5000 words). In addition, the named entities annotated with this module were also labelled as instances (or individuals) of the classes included in the CNEO and, then, were used to populate this domain ontology. • The statistical results obtained from the evaluation of this particular module of OntoTagger can be summarised as follows. On the one hand, as far as recall (R) is concerned, (R.1) the lowest value was 76,40% (for file 7); (R.2) the highest value was 97, 50% (for file 3); and (R.3) the average value was 88,73%. On the other hand, as far as the precision rate (P) is concerned, (P.1) its minimum was 93,75% (for file 4); (R.2) its maximum was 100% (for files 1, 5, 7, 8, 9, and 10); and (R.3) its average value was 98,99%. • These results, which apply to the tasks of named entity annotation and ontology population, are extraordinary good for both of them. They can be explained on the basis of the high accuracy of the annotations provided by OntoTagger at the lower levels (mainly at the morphosyntactic level). However, they should be conveniently qualified, since they might be too domain- and/or language-dependent. It should be further experimented how our approach works in a different domain or a different language, such as French, English, or German. • In any case, the results of this application of Human Language Technologies to Ontology Population (and, accordingly, to Ontological Engineering) seem very promising and encouraging in order for these two areas to collaborate and complement each other in the area of semantic annotation. Fifth, as shown in the State of the Art of this work, there are different approaches and models for the semantic annotation of texts, but all of them focus on a particular view of the semantic level. Clearly, all these approaches and models should be integrated in order to bear a coherent and joint semantic annotation level. OntoTag shows how (i) these semantic annotation layers could be integrated together; and (ii) they could be integrated with the annotations associated to other annotation levels. Sixth, we identified some recommendations, best practices and lessons learned for annotation standardisation, interoperation and merge. They show how standardisation (via ontologies, in this case) enables the combination, integration and interoperation of different linguistic tools and their annotations into a multilayered (or multileveled) linguistic annotation, which is one of the hot topics in the area of Linguistic Annotation. And last but not least, OntoTag’s annotation scheme and OntoTagger’s annotation schemas show a way to formalise and annotate coherently and uniformly the different units and features associated to the different levels and layers of linguistic annotation. This is a great scientific step ahead towards the global standardisation of this area, which is the aim of ISO/TC 37 (in particular, Subcommittee 4, dealing with the standardisation of linguistic annotations and resources).

Combining static analysis and profiling for estimating execution times

Effective static analyses have been proposed which infer bounds on the number of resolutions. These have the advantage of being independent from the platform on which the programs are executed and have been shown to be useful in a number of applications, such as granularity control in parallel execution. On the other hand, in distributed computation scenarios where platforms with different capabilities come into play, it is necessary to express costs in metrics that include the characteristics of the platform. In particular, it is specially interesting to be able to infer upper and lower bounds on actual execution times. With this objective in mind, we propose an approach which combines compile-time analysis for cost bounds with a one-time profiling of a given platform in order to determine the valúes of certain parameters for that platform. These parameters calibrate a cost model which, from then on, is able to compute statically time bound functions for procedures and to predict with a significant degree of accuracy the execution times of such procedures in that concrete platform. The approach has been implemented and integrated in the CiaoPP system.

Determinacy analysis for logic programs using mode and type information

We propose an analysis for detecting procedures and goals that are deterministic (i.e. that produce at most one solution), or predicates whose clause tests are mutually exclusive (which implies that at most one of their clauses will succeed) even if they are not deterministic (because they cali other predicates that can produce more than one solution). Applications of such determinacy information include detecting programming errors, performing certain high-level program transformations for improving search efñciency, optimizing low level code generation and parallel execution, and estimating tighter upper bounds on the computational costs of goals and data sizes, which can be used for program debugging, resource consumption and granularity control, etc. We have implemented the analysis and integrated it in the CiaoPP system, which also infers automatically the mode and type information that our analysis takes as input. Experiments performed on this implementation show that the analysis is fairly accurate and efncient.