Labor-Market Volatility in the Search-and-Matching Model: The Role of Investment-Specific Technology Shocks

Shocks to investment-specific technology have been identified as a main source of U.S. aggregate output volatility. In this paper we assess the contribution of these shocks to the volatility of labor market variables, namely, unemployment, vacancies, tightness and the job-finding rate. Thus, our paper contributes to a recent body of literature assessing the ability of the search-and-matching model to account for the large volatility observed in labor market variables. To this aim, we solve a neoclassical economy with search and matching in the labor market, where neutral and investment-specific technologies are subject to shocks. The three key features of our model economy are: i) Firms are large, in the sense that they employ many workers. ii) Adjusting capital and labor is costly. iii) Wages are the outcome of an intra-firm Nash-bargaining problem between the firm and its workers. In our calibrated economy, we find that shocks to investment-specific technology explain 40 percent of the observed volatility in U.S. labor productivity. Moreover, these shocks generate relative volatilities in vacancies and the workers' job finding rate which match those observed in U.S. data. Relative volatilities in unemployment and labor market tightness are 55 and 75 percent of their empirical values, respectively.Business Cycles; Labor Market Fluctuations; Investment-Specific Technical Change; Search and Matching; Adjustment Costs; Wage Bargaining.

Compact Frequency Standard Based on an Intra-cavity Sample Cold Cesium Atoms

We have demonstrated the possibility for a compact frequency standard based on a sample of cold cesium atoms. In a cylindrical microwave cavity, the atoms are cooled and interrogated during a free expansion and then detected. The operation of this experiment is different from conventional atomic fountains, since all the steps are sequentially performed in the same position of space. In this paper we report the analysis of a Ramsey pattern observed to present a 47 Hz linewidth and a stability of 5x10^-13 for an integration time longer than 100s. Some of the main limitations of the standard are analyzed. This present report demonstrates considerable improvement of our previous work (S. T. Muller et al, J. Opt. Soc. Am. B, v.25, p.909, 2008) where the atoms were in a free space and not inside a microwave cavity.Comment: submitted to J. Opt. Soc. Am. B - 15 pages, 10 figure

Ensemble of diluted attractor networks with optimized topology for fingerprint retrieval

The present study analyzes the retrieval capacity of an Ensemble of diluted Attractor Neural Networks for real patterns (i.e., non-random ones), as it is the case of human fingerprints. We explore the optimal number of Attractor Neural Networks in the ensemble to achieve a maximum fingerprint storage capacity. The retrieval performance of the ensemble is measured in terms of the network connectivity structure, by comparing 1D ring to 2D cross grid topologies for the random shortcuts ratio. Given the nature of the network ensemble and the different characteristics of patterns, an optimization can be carried out considering how the pattern subsets are assigned to the ensemble modules. The ensemble specialization splitting into several modules of attractor networks is explored with respect to the activities of patterns and also in terms of correlations of the subsets of patterns assigned to each module in the ensemble network.This work was funded by and UDLA-SIS.MGR.20.01. This research was also funded by the Spanish Ministry of Science and Innovation/FEDER, under the \RETOS" Programme, with grant numbers: TIN2017-84452-R and RTI2018-098019-B-I00; and by the CYTED Network \Ibero-American Thematic Network on ICT Applications for Smart Cities", grant number: 518RT0559

Forecasting Amazon Rain-Forest Deforestation Using a Hybrid Machine Learning Model

The present work aims to carry out an analysis of the Amazon rain-forest deforestation, which can be analyzed from actual data and predicted by means of artificial intelligence algorithms. A hybrid machine learning model was implemented, using a dataset consisting of 760 Brazilian Amazon municipalities, with static data, namely geographical, forest, and watershed, among others, together with a time series data of annual deforestation area for the last 20 years (1999–2019). The designed learning model combines dense neural networks for the static variables and a recurrent Long Short Term Memory neural network for the temporal data. Many iterations were performed on augmented data, testing different configurations of the regression model, for adjusting the model hyper-parameters, and generating a battery of tests to obtain the optimal model, achieving a R-squared score of 87.82%. The final regression model predicts the increase in annual deforestation area (square kilometers), for a decade, from 2020 to 2030, predicting that deforestation will reach 1 million square kilometers by 2030, accounting for around 15% compared with the present 1%, of the between 5.5 and 6.7 millions of square kilometers of the rain-forest. The obtained results will help to understand the impact of man’s footprint on the Amazon rain-forest.This research was funded by DGIV-UDLA grant number SIS.MGR.21.01 and by Spanish Ministry of Science grant number PID2020-114867RB-I00

INVENTÁRIO DO ACERVO ARQUEOLÓGICO DAS FORTIFICAÇÕES CATARINENSES

As fortificações de Florianópolis e arredores foram projetadas, em meados de 1740, por José da Silva Paes – primeiro governador catarinense – com o objetivo de proteger a porção meridional da América Portuguesa. Estrategicamente localizada no caminho entre os portos de Rio de Janeiro/RJ e Buenos Aires, era fundamental para a coroa portuguesa demonstrar a quem pertencia a Ilha de Santa Catarina/SC e protegê-la de viajantes estrangeiros que nela aportavam para reparar e abastecer os navios com víveres

Analise da psicologia do esporte em periódicos de língua inglesa

O objetivo do estudo foi analisar a psicologia do esporte nos periódicos de língua inglesa. Foram selecionados 521 resumos (2002 a 2006), de quatro periódicos. Analisaram-se os temas, amostra e o tipo de estudo utilizado. A amostra mais pesquisada foi de atletas de esporte competitivo (57,2%), seguido por estudos com não atletas (29,3%) e atletas juvenis (12,5%). Em relação ao tipo de estudo, os de revisão tiveram 15,8%, intervenção 15,4% e desenvolvimento de instrumentos 11%, observação e descrição de um fenômeno psicológico obteve 57,9%. Os temas mais pesquisados foram: motivação (15,7%), capacidade visual e mental (11,9%), humor (11,7%) e auto-eficácia (10,6%). São necessários estudos que verifiquem diferenças e semelhanças entre os resultados deste estudo e as pesquisas brasileiras