Time varying fiscal policy in the U.S.

To investigate the time heterogeneity effects of fiscal policy in the U.S., we use a non-recursive, Blanchard and Perotti-like structural VAR with time-varying parameters, estimated through Bayesian simulation over the 1965:2-2009:2 period. Our evidence suggests that fiscal policy has lost some capacity to stimulate output but that this trend is more pronounced for taxes net of transfers than for government expenditure, whose effectiveness declines only slightly. Fiscal multipliers keep conventional signs throughout. An investigation of changes in fiscal policy conduct indicates an increase in the countercyclical activism of net taxes over time, which appears to have reached a maximum during the 2008-09 recession.Fiscal policy, Bayesian estimation, Structural change, Macroeconomic stabilization

Services Innovation and Economic Performance An analysis at the firm level

We present a model that links innovation effort to economic performance, along the lines of the Crépon et al (1998) model. However, in contrast to Crépon et al, that analyze R&D intensive manufacturing sectors, the present application examines the relationship between innovation and performance for services sectors. This is relevant since much effort has been made to explore that relationship for manufacturing but very little is known about it in the case of services sectors. In trying to fulfill this gap the paper uses firm-level data from the Second Community Innovation Survey to estimate a simultaneous equations model for firms in ten services sectors in Portugal. The present model also differs from former approaches by the specific explanatory structure proposed to estimate the complex relationship between innovation and economic performance. Instead of estimating a direct link between innovation and labor productivity, three specific relationships were put forward. The first of them explains the innovation effort intensity (an input in the innovation process). The second one relates service innovation (an output of the innovation process) to effort intensity and to other explanatory variables. Finally, the third relationship links labor productivity to both service innovation and effort intensity considering also some other influences. Sensitivity analysis of the results to alternative estimation techniques was performed.Innovation and performance; Innovation in services; Technology; Service sectors; Labor productivity; CIS

Interlanguage pragmatics and instructional pragmatics: two vibrant and illuminating research fields

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Control of Pain and Dyspnea in Non-Pharmacological Interventions: Patients with Oncologic Disease in Acute Care

To identify non-pharmacological strategies in the control of pain and dyspnea, in patient with oncological disease, in acute care. The gold standard to an adequate symptom control is a systematized assessment. Non-pharmacological measures: psycho-emotional support, hypnosis, counseling/ training/ instruction, therapeutic adherence, music therapy, massage, relaxation techniques, telephone support, functional and respiratory re-education increase health gains. The control of oncologic pain and dyspnea require a comprehensive and multimodal approach

Multi-Armed Bandits for Intelligent Tutoring Systems

We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two algorithms that rely on the empirical estimation of the learning progress, RiARiT that uses information about the difficulty of each exercise and ZPDES that uses much less knowledge about the problem. The system is based on the combination of three approaches. First, it leverages recent models of intrinsically motivated learning by transposing them to active teaching, relying on empirical estimation of learning progress provided by specific activities to particular students. Second, it uses state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the exploration/exploitation challenge of this optimization process. Third, it leverages expert knowledge to constrain and bootstrap initial exploration of the MAB, while requiring only coarse guidance information of the expert and allowing the system to deal with didactic gaps in its knowledge. The system is evaluated in a scenario where 7-8 year old schoolchildren learn how to decompose numbers while manipulating money. Systematic experiments are presented with simulated students, followed by results of a user study across a population of 400 school children

Sex Education in School: Contexts for Change

Sex education is, nowadays, one of the priority intervention areas in our country and occupies a position of interest in the development of educative and public health politics in the European Union. Several studies have been developed, adressing the sexuality in younger people issues(1,2,3,4), which verify the precocity of starting sexual relations amongst teenagers, the inexistence of a regular sexual partner and the reduced systematic use of condoms during situations of increased risk. In this context, sex education’s interventions headed to individuals at this stage of life become relevant. Especially because its unquestionable the crucial role that sexuality plays in adolescent growth and development, in the interpersonal relationship, respect, communication, self-esteem and in the assertiveness and self-confidence. In the view of the above, we chose the research-action methodology. During the diagnostic phase we applied a set of instruments of which we highlight the questionnaire : assessment of teachers' attitudes towards sexual education (QAAPES)(5) ; conceptions and practices in relation to sex education - version for teachers(6). The presented results relate only to the first phase of the process. These allowed us to note the recognition of sex education in school as an explicit necessity. In the opinion of teachers this concerns all teachers and should be addressed preferably in the teaching component. For these, the main purpose of sex education is to develop the students' skills so that they can live their sexuality in a healthier way

Considerações sobre o ordenamento do território

O ordenamento do território é, fundamentalmente, a gestão da interacção homem/espaço natural. Consiste no planeamento das ocupações, no potenciar do aproveitamento das infra-estruturas existentes e no assegurar da preservação de recursos limitados. Os diferentes planos, para serem eficazes, têm que ser enquadráveis a diversas escalas de análise, dependendo a efectividade de todos eles da coerência dos restantes. Um plano nacional de ordenamento do território tem que se basear na lógica dos planos das diferentes regiões; estes, por sua vez, têm por base planos municipais que definem o uso dos solos e estabelecem princípios para a gestão das cidades e das aldeias do local; os aglomerados deverão ser organizados por planos operativos que regulem e ordenem a sua estrutura construída, os seus edifícios, e que definam coerências para a localização das diferentes funções que neles coexistem – a indústria, o comércio, a habitação ou a agricultura. São os Planos de Urbanização, os de Pormenor ou de Salvaguarda que, e mais uma vez a escalas diversas, delimitam e desenham as malhas que estruturam e definem a urbe.

Machine Learning for Interactive Systems: Challenges and Future Trends

National audienceMachine learning has been introduced more than 40 years ago in interactive systems through speech recognition or computer vision. Since that, machine learning gained in interest in the scientific community involved in human- machine interaction and raised in the abstraction scale. It moved from fundamental signal processing to language understanding and generation, emotion and mood recogni- tion and even dialogue management or robotics control. So far, existing machine learning techniques have often been considered as a solution to some problems raised by inter- active systems. Yet, interaction is also the source of new challenges for machine learning and offers new interesting practical but also theoretical problems to solve. In this paper, we address these challenges and describe why research in machine learning and interactive systems should converge in the future