ICOS Carbon Data Portal

La infraestructura europea ICOS (Integrated Carbon Observation System), tiene como misión proveer de mediciones de gases de efecto invernadero a largo plazo, lo que ha de permitir estudiar el estado actual y comportamiento futuro del ciclo global del carbono. En este contexto, geomati.co ha desarrollado un portal de búsqueda y descarga de datos que integra las mediciones realizadas en los ámbitos terrestre, marítimo y atmosférico, disciplinas que hasta ahora habían gestionado los datos de forma separada. El portal permite hacer búsquedas por múltiples ámbitos geográficos, por rango temporal, por texto libre o por un subconjunto de magnitudes, realizar vistas previas de los datos, y añadir los conjuntos de datos que se crean interesantes a un “carrito” de descargas. En el momento de realizar la descarga de una colección de datos, se le asignará un identificador universal que permitirá referenciarla en eventuales publicaciones, y repetir su descarga en el futuro (de modo que los experimentos publicados sean reproducibles). El portal se apoya en formatos abiertos de uso común en la comunidad científica, como el formato NetCDF para los datos, y en el perfil ISO de CSW, estándar de catalogación y búsqueda propio del ámbito geoespacial. El portal se ha desarrollado partiendo de componentes de software libre existentes, como Thredds Data Server, GeoNetwork Open Source y GeoExt, y su código y documentación quedarán publicados bajo una licencia libre para hacer posible su reutilización en otros proyecto

Integrating forensic information in a crime intelligence database

Since 2008, Intelligence units of six states of the western part of Switzerland have been sharing a common database for the analysis of high volume crimes. On a daily basis, events reported to the police are analysed, filtered and classified to detect crime repetitions and interpret the crime environment. Several forensic outcomes are integrated in the system such as matches of traces with persons, and links between scenes detected by the comparison of forensic case data. Systematic procedures have been settled to integrate links assumed mainly through DNA profiles, shoemarks patterns and images. A statistical outlook on a retrospective dataset of series from 2009 to 2011 of the database informs for instance on the number of repetition detected or confirmed and increased by forensic case data. Time needed to obtain forensic intelligence in regard with the type of marks treated, is seen as a critical issue. Furthermore, the underlying integration process of forensic intelligence into the crime intelligence database raised several difficulties in regards of the acquisition of data and the models used in the forensic databases. Solutions found and adopted operational procedures are described and discussed. This process form the basis to many other researches aimed at developing forensic intelligence models.

Consumption heterogenity over the business cycle

We study consumption heterogeneity over the business cycle. Using household panel data from 1984 to 2010 in the US we find that the welfare cost of the business cycle is non-negligible, once agents heterogeneity is taken into account, and sums to about 1% of yearly consumption. This is due to the structure of comovements between the different parts of the consumption distribution, in particular the tails are highly volatile and negatively related to each other. We also find that business cycle fluctuations originating from exogenous financial shocks only hit the top end of the consumption distribution and therefore reduce consumption inequality.

A Clinical Decision Support using a Terminology Server to improve Patient Safety.

Clinical Decision Support Systems (CDSS) are software applications that support clinicians in making healthcare decisions providing relevant information for individual patients about their specific conditions. The lack of integration between CDSS and Electronic Health Record (EHR) has been identified as a significant barrier to CDSS development and adoption. Andalusia Healthcare Public System (AHPS) provides an interoperable health information infrastructure based on a Service Oriented Architecture (SOA) that eases CDSS implementation. This paper details the deployment of a CDSS jointly with the deployment of a Terminology Server (TS) within the AHPS infrastructure. It also explains a case study about the application of decision support to thromboembolism patients and its potential impact on improving patient safety. We will apply the inSPECt tool proposal to evaluate the appropriateness of alerts in this scenario.

A multidimensional segmentation evaluation for medical image data.

Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based on a measure that depends on the number of segmented voxels inside and outside of some reference regions that are called gold standards. Although some other measures have been also used, in this work we propose a set of new similarity measures, based on different features, such as the location and intensity values of the misclassified voxels, and the connectivity and the boundaries of the segmented data. Using the multidimensional information provided by these measures, we propose a new evaluation method whose results are visualized applying a Principal Component Analysis of the data, obtaining a simplified graphical method to compare different segmentation results. We have carried out an intensive study using several classic segmentation methods applied to a set of MRI simulated data of the brain with several noise and RF inhomogeneity levels, and also to real data, showing that the new measures proposed here and the results that we have obtained from the multidimensional evaluation, improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.

Inclusive business and poverty: prospects in the Brazilian context

Inclusive business is a term currently used to explain the organizations that aim to solve social problems with efficiency and financial sustainability by means of market mechanisms. It can be said that inclusive businesses are those targeted at generating employment and income for groups with little or no market mobility, in keeping with the standards of so-called "decent jobs" and in a self-sustaining manner, i.e., generating profit for the enterprises, and establishing relationships with typical business organizations as suppliers of products and services or in the distribution of this type of production. This article discusses the different concepts found in the scientific literature on inclusive businesses. It also analyses data from a survey conducted with the audiences of Social Corporate Responsibility seminars held by FIEMG. This analysis reveals that prospects, risks and idealizations similar to those found in inclusive business theories can also be found among individuals that run social corporate responsibility projects, even if this designation is new for them. The connection between companies and poverty, especially in relation to inclusive businesses, seems full of stumbling blocks and traps in the Brazilian context.

Forensic intelligence framework. Part II: study of the main generic building blocks and challenges through the examples of illicit drugs and false identity documents monitoring

The development of forensic intelligence relies on the expression of suitable models that better represent the contribution of forensic intelligence in relation to the criminal justice system, policing and security. Such models assist in comparing and evaluating methods and new technologies, provide transparency and foster the development of new applications. Interestingly, strong similarities between two separate projects focusing on specific forensic science areas were recently observed. These observations have led to the induction of a general model (Part I) that could guide the use of any forensic science case data in an intelligence perspective. The present article builds upon this general approach by focusing on decisional and organisational issues. The article investigates the comparison process and evaluation system that lay at the heart of the forensic intelligence framework, advocating scientific decision criteria and a structured but flexible and dynamic architecture. These building blocks are crucial and clearly lay within the expertise of forensic scientists. However, it is only part of the problem. Forensic intelligence includes other blocks with their respective interactions, decision points and tensions (e.g. regarding how to guide detection and how to integrate forensic information with other information). Formalising these blocks identifies many questions and potential answers. Addressing these questions is essential for the progress of the discipline. Such a process requires clarifying the role and place of the forensic scientist within the whole process and their relationship to other stakeholders.

Business Exits entail greater future levels of entrepreneurship? An empirical analysis at country level

Using Global Entrepreneurship Monitor data for 41 countries this study investigates the impact of business exit on entrepreneurial activity at the country level. The paper distinguishes between two types of entrepreneurial activity according with the motive to start a new business: entrepreneurs driven by opportunity and necessity motives. The findings indicate that exits have a positive impact on future levels of entrepreneurial activity in a country. For each exit in a given year, a larger proportion of entrepreneurial activity the following year. Moreover, this e ffect turns out to be higher for opportunity entrepreneurs. The findings indicate that both types of entrepreneurial activity rates are influenced by the same factors and in the same direction. However, for some factors we find a di fferential impact on the entrepreneurship. The results show some important implications given that business exit may be overcome when there is a necessity motivation. This has important implications for both researchers and policy makers. JEL codes: L26. Keywords: Entrepreneurship, business exit, social values

Are There Classical Business Cycles?

The aim of this paper is to test formally the classical business cycle hypothesis, using data from industrialized countries for the time period since 1960. The hypothesis is characterized by the view that the cyclical structure in GDP is concentrated in the investment series: fixed investment has typically a long cycle, while the cycle in inventory investment is shorter. To check the robustness of our results, we subject the data for 15 OECD countries to a variety of detrending techniques. While the hypothesis is not confirmed uniformly for all countries, there is a considerably high number for which the data display the predicted pattern. None of the countries shows a pattern which can be interpreted as a clear rejection of the classical hypothesis.

GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.

Intégration de méthodes de data mining dans le renseignement criminel : analyse par des structures issues de la théorie des graphes dans le profilage des stupéfiants

Data mining can be defined as the extraction of previously unknown and potentially useful information from large datasets. The main principle is to devise computer programs that run through databases and automatically seek deterministic patterns. It is applied in different fields of application, e.g., remote sensing, biometry, speech recognition, but has seldom been applied to forensic case data. The intrinsic difficulty related to the use of such data lies in its heterogeneity, which comes from the many different sources of information. The aim of this study is to highlight potential uses of pattern recognition that would provide relevant results from a criminal intelligence point of view. The role of data mining within a global crime analysis methodology is to detect all types of structures in a dataset. Once filtered and interpreted, those structures can point to previously unseen criminal activities. The interpretation of patterns for intelligence purposes is the final stage of the process. It allows the researcher to validate the whole methodology and to refine each step if necessary. An application to cutting agents found in illicit drug seizures was performed. A combinatorial approach was done, using the presence and the absence of products. Methods coming from the graph theory field were used to extract patterns in data constituted by links between products and place and date of seizure. A data mining process completed using graphing techniques is called ``graph mining''. Patterns were detected that had to be interpreted and compared with preliminary knowledge to establish their relevancy. The illicit drug profiling process is actually an intelligence process that uses preliminary illicit drug classes to classify new samples. Methods proposed in this study could be used \textit{a priori} to compare structures from preliminary and post-detection patterns. This new knowledge of a repeated structure may provide valuable complementary information to profiling and become a source of intelligence.

International business cycles, financial markets and household production

This paper investigates the properties of an international real business cycle model with household production. We show that a model with disturbances to both market and household technologies reproduces the main regularities of the data and improves existing models in matching international consumption, investment and output correlations without irrealistic assumptions on the structure of international financial markets. Sensitivity analysis shows the robustness of the results to alternative specifications of the stochastic processes for the disturbances and to variations of unmeasured parameters within a reasonable range.

Bridging DSGE models and the raw data

A method to estimate DSGE models using the raw data is proposed. The approachlinks the observables to the model counterparts via a flexible specification which doesnot require the model-based component to be solely located at business cycle frequencies,allows the non model-based component to take various time series patterns, andpermits model misspecification. Applying standard data transformations induce biasesin structural estimates and distortions in the policy conclusions. The proposed approachrecovers important model-based features in selected experimental designs. Twowidely discussed issues are used to illustrate its practical use.

Business cycle measurement with some theory

A method to evaluate cyclical models not requiring knowledge of the DGP and the exact specificationof the aggregate decision rules is proposed. We derive robust restrictions in a class of models; use someto identify structural shocks in the data and others to evaluate the class or contrast sub-models. Theapproach has good properties, even in small samples, and when the class of models is misspecified. Themethod is used to sort out the relevance of a certain friction (the presence of rule-of-thumb consumers)in a standard class of models.

Geographical effects on the accuracy of textile trade data:an international approach for 1913

Foreign trade statistics are the main data source to the study of international trade.However its accuracy has been under suspicion since Morgernstern published hisfamous work in 1963. Federico and Tena (1991) have resumed the question arguing thatthey can be useful in an adequate level of aggregation. But the geographical assignmentproblem remains unsolved. This article focuses on the spatial variable through theanalysis of the reliability of textile international data for 1913. A geographical biasarises between export and import series, but because of its quantitative importance it canbe negligible in an international scale.