Professional and Non-Professional Written Travel Texts – Do They Have Common Features? : A Comparative Investigation on Travel Texts on Trysil, Norway

This study aims to investigate possible distinctions between professional and non-professional written travel texts all treating the same destination: the Norwegian ski resort Trysil. The study will investigate to what extent the different texts correlate with the genre of travel texts, as the travel texts are treated as personal narratives, and how they conform to a given structure for narratives and with guidelines for professional writers. Furthermore, the investigation aims to explore to what extent there are similarities and differences between the texts regarding the given structure. The texts will first be analysed and organized separately by macrorules and a news schema that are constructed specifically for these sorts of texts, in order to reveal their discourse structure, and then compared to each other. As the discourse structure of the different texts is revealed, it is seen that there are certain differences between the two different text types. Finally, seen that the text types differ in their structure, this study will show that despite the fact that journalists write stories, and that non-professional written stories are narratives, they do not share the same structure, and are constructed in different ways.

Estimating sectoral cycles using cointegration and common features

This paper investigates the degree of short run and long run co-movement in U.S. sectoral output data by estimating sectoraI trends and cycles. A theoretical model based on Long and Plosser (1983) is used to derive a reduced form for sectoral output from first principles. Cointegration and common features (cycles) tests are performed; sectoral output data seem to share a relatively high number of common trends and a relatively low number of common cycles. A special trend-cycle decomposition of the data set is performed and the results indicate a very similar cyclical behavior across sectors and a very different behavior for trends. Indeed. sectors cyclical components appear as one. In a variance decomposition analysis, prominent sectors such as Manufacturing and Wholesale/Retail Trade exhibit relatively important transitory shocks.

The importance of common cyclical features in VAR analysis: a Monte-Carlo study

Despite the commonly held belief that aggregate data display short-run comovement, there has been little discussion about the econometric consequences of this feature of the data. We use exhaustive Monte-Carlo simulations to investigate the importance of restrictions implied by common-cyclical features for estimates and forecasts based on vector autoregressive models. First, we show that the ìbestî empirical model developed without common cycle restrictions need not nest the ìbestî model developed with those restrictions. This is due to possible differences in the lag-lengths chosen by model selection criteria for the two alternative models. Second, we show that the costs of ignoring common cyclical features in vector autoregressive modelling can be high, both in terms of forecast accuracy and efficient estimation of variance decomposition coefficients. Third, we find that the Hannan-Quinn criterion performs best among model selection criteria in simultaneously selecting the lag-length and rank of vector autoregressions.

The importance of Common-Cyclical Features in VAR analysis: a Monte-Carlo study (Preliminary Version)

Despite the belief, supported byrecentapplied research, thataggregate datadisplay short-run comovement, there has been little discussion about the econometric consequences ofthese data “features.” W e use exhaustive M onte-Carlo simulations toinvestigate theimportance ofrestrictions implied by common-cyclicalfeatures for estimates and forecasts based on vectorautoregressive and errorcorrection models. First, weshowthatthe“best” empiricalmodeldevelopedwithoutcommoncycles restrictions neednotnestthe“best” modeldevelopedwiththoserestrictions, duetothe use ofinformation criteria forchoosingthe lagorderofthe twoalternative models. Second, weshowthatthecosts ofignoringcommon-cyclicalfeatures inV A R analysis may be high in terms offorecastingaccuracy and e¢ciency ofestimates ofvariance decomposition coe¢cients. A lthough these costs are more pronounced when the lag orderofV A R modelsareknown, theyarealsonon-trivialwhenitis selectedusingthe conventionaltoolsavailabletoappliedresearchers. T hird, we…ndthatifthedatahave common-cyclicalfeatures andtheresearcherwants touseaninformationcriterium to selectthelaglength, theH annan-Q uinn criterium is themostappropriate, sincethe A kaike and theSchwarz criteriahave atendency toover- and under-predictthe lag lengthrespectivelyinoursimulations.

Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons

The objective of this article is to study (understand and forecast) spot metal price levels and changes at monthly, quarterly, and annual horizons. The data to be used consists of metal-commodity prices in a monthly frequency from 1957 to 2012 from the International Financial Statistics of the IMF on individual metal series. We will also employ the (relatively large) list of co-variates used in Welch and Goyal (2008) and in Hong and Yogo (2009) , which are available for download. Regarding short- and long-run comovement, we will apply the techniques and the tests proposed in the common-feature literature to build parsimonious VARs, which possibly entail quasi-structural relationships between different commodity prices and/or between a given commodity price and its potential demand determinants. These parsimonious VARs will be later used as forecasting models to be combined to yield metal-commodity prices optimal forecasts. Regarding out-of-sample forecasts, we will use a variety of models (linear and non-linear, single equation and multivariate) and a variety of co-variates to forecast the returns and prices of metal commodities. With the forecasts of a large number of models (N large) and a large number of time periods (T large), we will apply the techniques put forth by the common-feature literature on forecast combinations. The main contribution of this paper is to understand the short-run dynamics of metal prices. We show theoretically that there must be a positive correlation between metal-price variation and industrial-production variation if metal supply is held fixed in the short run when demand is optimally chosen taking into account optimal production for the industrial sector. This is simply a consequence of the derived-demand model for cost-minimizing firms. Our empirical evidence fully supports this theoretical result, with overwhelming evidence that cycles in metal prices are synchronized with those in industrial production. This evidence is stronger regarding the global economy but holds as well for the U.S. economy to a lesser degree. Regarding forecasting, we show that models incorporating (short-run) commoncycle restrictions perform better than unrestricted models, with an important role for industrial production as a predictor for metal-price variation. Still, in most cases, forecast combination techniques outperform individual models.

Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons

The objective of this article is to study (understand and forecast) spot metal price levels and changes at monthly, quarterly, and annual frequencies. Data consists of metal-commodity prices at a monthly and quarterly frequencies from 1957 to 2012, extracted from the IFS, and annual data, provided from 1900-2010 by the U.S. Geological Survey (USGS). We also employ the (relatively large) list of co-variates used in Welch and Goyal (2008) and in Hong and Yogo (2009). We investigate short- and long-run comovement by applying the techniques and the tests proposed in the common-feature literature. One of the main contributions of this paper is to understand the short-run dynamics of metal prices. We show theoretically that there must be a positive correlation between metal-price variation and industrial-production variation if metal supply is held fixed in the short run when demand is optimally chosen taking into account optimal production for the industrial sector. This is simply a consequence of the derived-demand model for cost-minimizing firms. Our empirical evidence fully supports this theoretical result, with overwhelming evidence that cycles in metal prices are synchronized with those in industrial production. This evidence is stronger regarding the global economy but holds as well for the U.S. economy to a lesser degree. Regarding out-of-sample forecasts, our main contribution is to show the benefits of forecast-combination techniques, which outperform individual-model forecasts - including the random-walk model. We use a variety of models (linear and non-linear, single equation and multivariate) and a variety of co-variates and functional forms to forecast the returns and prices of metal commodities. Using a large number of models (N large) and a large number of time periods (T large), we apply the techniques put forth by the common-feature literature on forecast combinations. Empirically, we show that models incorporating (short-run) common-cycle restrictions perform better than unrestricted models, with an important role for industrial production as a predictor for metal-price variation.

Biological features of a puzzling symbiotic association between the hermit crab Dardanus insignis and the porcellanid crab Porcellana Sayana (Crustacea)

The symbiotic lifestyle is widespread among porcellanid crabs, which maintain ecological and co-evolutionary associations with annelid polychaetes, poriferans, cnidarians, echinoderms, gastropod. mollusks, and other crustaceans such as shrimps and hermit crabs, among others. We investigated the ecological association between the hermit crab Dardanus insignis and the porcellanid Porcellana sayana, in southeastern Brazil. Porcellanid crabs, hermit crabs, and available shells were collected monthly from July 2001 to June 2003, with a shrimp boat equipped with two double-rig trawl nets. The majority of P. sayana specimens were collected in shells occupied by D. insignis (96.6%); a few were found in empty shells (3.4%). The catch of both symbionts and hosts increased with increasing depth, with the highest occurrence at 35 m. The F. sayana crabs of various sizes could be found solitary or forming aggregations of up to 14 individuals per host, showing no sex or size segregation. In spite of the high diversity of shell species occupied by the hermit crabs and also available in the field, only a few of them were also utilized by P. sayana. The majority (93%) of shells utilized by P. sayana also hosted other symbiont species, constituting the basis of extensive symbiotic complexes. Thus, the ecological relationship between D. insignis and P. sayana may be classified as a non-obligate and non-specific symbiosis that may also involve other facultative organisms such as sea anemones. (C) 2008 Elsevier B.V. All rights reserved.

Structural features of the epididymal region of the domestic duck (Anas plathyrynchos)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

New features of Cuban endemic Pinguicula filifolia (Lentibulariaceae) and considerations on its habitat and ecology

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Microscopic features of tick-bite lesions in anteaters and armadillos - Emas National Park and the Pantanal region of Brazil

The naturally occurring wildlife host associations between ticks and tick-borne pathogens found in the neotropics are poorly described. Understanding tick-bite lesions is important as these are the site of host reaction to and pathogen delivery by ticks. As part of a comprehensive study concerning established and emerging tick-host relationships. The present work describes some aspects of tick-bite lesions in anteaters and armadillos captured at the Emas National Park and the Pantanal region of Brazil. Biopsies were of skin were taken and examine. Tick feeding sites of all animals displayed an eosinophilic homogeneous mass, the cement cone, and, occasionally, a feeding cavity underneath the tick attachment site. At these locations the epidermis was usually thickened due to keratinocyte hyperplasia. The main dermal changes included tissue infiltration with a varying number of inflammatory cells, edema, hemorrhage. and vascular dilatation. Cellular infiltration of the dermis was predominantly composed of mononuclear cells, neutrophils. and eosinophils. Mast cells were also seen in both non-parasitized and parasitized skin but were found in higher numbers at perivascular sites and in parasitized skin. Basophils were not seen at tick attachment sites of anteaters or armadillos.

Análise e síntese de antenas e superfícies seletivas de frequência utilizando computação evolucionária e inteligência de enxames

The frequency selective surfaces, or FSS (Frequency Selective Surfaces), are structures consisting of periodic arrays of conductive elements, called patches, which are usually very thin and they are printed on dielectric layers, or by openings perforated on very thin metallic surfaces, for applications in bands of microwave and millimeter waves. These structures are often used in aircraft, missiles, satellites, radomes, antennae reflector, high gain antennas and microwave ovens, for example. The use of these structures has as main objective filter frequency bands that can be broadcast or rejection, depending on the specificity of the required application. In turn, the modern communication systems such as GSM (Global System for Mobile Communications), RFID (Radio Frequency Identification), Bluetooth, Wi-Fi and WiMAX, whose services are highly demanded by society, have required the development of antennas having, as its main features, and low cost profile, and reduced dimensions and weight. In this context, the microstrip antenna is presented as an excellent choice for communications systems today, because (in addition to meeting the requirements mentioned intrinsically) planar structures are easy to manufacture and integration with other components in microwave circuits. Consequently, the analysis and synthesis of these devices mainly, due to the high possibility of shapes, size and frequency of its elements has been carried out by full-wave models, such as the finite element method, the method of moments and finite difference time domain. However, these methods require an accurate despite great computational effort. In this context, computational intelligence (CI) has been used successfully in the design and optimization of microwave planar structures, as an auxiliary tool and very appropriate, given the complexity of the geometry of the antennas and the FSS considered. The computational intelligence is inspired by natural phenomena such as learning, perception and decision, using techniques such as artificial neural networks, fuzzy logic, fractal geometry and evolutionary computation. This work makes a study of application of computational intelligence using meta-heuristics such as genetic algorithms and swarm intelligence optimization of antennas and frequency selective surfaces. Genetic algorithms are computational search methods based on the theory of natural selection proposed by Darwin and genetics used to solve complex problems, eg, problems where the search space grows with the size of the problem. The particle swarm optimization characteristics including the use of intelligence collectively being applied to optimization problems in many areas of research. The main objective of this work is the use of computational intelligence, the analysis and synthesis of antennas and FSS. We considered the structures of a microstrip planar monopole, ring type, and a cross-dipole FSS. We developed algorithms and optimization results obtained for optimized geometries of antennas and FSS considered. To validate results were designed, constructed and measured several prototypes. The measured results showed excellent agreement with the simulated. Moreover, the results obtained in this study were compared to those simulated using a commercial software has been also observed an excellent agreement. Specifically, the efficiency of techniques used were CI evidenced by simulated and measured, aiming at optimizing the bandwidth of an antenna for wideband operation or UWB (Ultra Wideband), using a genetic algorithm and optimizing the bandwidth, by specifying the length of the air gap between two frequency selective surfaces, using an optimization algorithm particle swarm

Um método para determinação da profundidade combinando visão estéreo e autocalibração para aplicação em robótica móvel

This work proposes a method to determine the depth of objects in a scene using a combination between stereo vision and self-calibration techniques. Determining the rel- ative distance between visualized objects and a robot, with a stereo head, it is possible to navigate in unknown environments. Stereo vision techniques supply a depth measure by the combination of two or more images from the same scene. To achieve a depth estimates of the in scene objects a reconstruction of this scene geometry is necessary. For such reconstruction the relationship between the three-dimensional world coordi- nates and the two-dimensional images coordinates is necessary. Through the achievement of the cameras intrinsic parameters it is possible to make this coordinates systems relationship. These parameters can be gotten through geometric camera calibration, which, generally is made by a correlation between image characteristics of a calibration pattern with know dimensions. The cameras self-calibration allows the achievement of their intrinsic parameters without using a known calibration pattern, being possible their calculation and alteration during the displacement of the robot in an unknown environment. In this work a self-calibration method based in the three-dimensional polar coordinates to represent image features is presented. This representation is determined by the relationship between images features and horizontal and vertical opening cameras angles. Using the polar coordinates it is possible to geometrically reconstruct the scene. Through the proposed techniques combination it is possible to calculate a scene objects depth estimate, allowing the robot navigation in an unknown environment

Antenas de microfita com patch quase-fractal para aplicações em Redes WPAN/WLAN

The microstrip antennas are in constant evidence in current researches due to several advantages that it presents. Fractal geometry coupled with good performance and convenience of the planar structures are an excellent combination for design and analysis of structures with ever smaller features and multi-resonant and broadband. This geometry has been applied in such patch microstrip antennas to reduce its size and highlight its multi-band behavior. Compared with the conventional microstrip antennas, the quasifractal patch antennas have lower frequencies of resonance, enabling the manufacture of more compact antennas. The aim of this work is the design of quasi-fractal patch antennas through the use of Koch and Minkowski fractal curves applied to radiating and nonradiating antenna s edges of conventional rectangular patch fed by microstrip inset-fed line, initially designed for the frequency of 2.45 GHz. The inset-fed technique is investigated for the impedance matching of fractal antennas, which are fed through lines of microstrip. The efficiency of this technique is investigated experimentally and compared with simulations carried out by commercial software Ansoft Designer used for precise analysis of the electromagnetic behavior of antennas by the method of moments and the neural model proposed. In this dissertation a study of literature on theory of microstrip antennas is done, the same study is performed on the fractal geometry, giving more emphasis to its various forms, techniques for generation of fractals and its applicability. This work also presents a study on artificial neural networks, showing the types/architecture of networks used and their characteristics as well as the training algorithms that were used for their implementation. The equations of settings of the parameters for networks used in this study were derived from the gradient method. It will also be carried out research with emphasis on miniaturization of the proposed new structures, showing how an antenna designed with contours fractals is capable of a miniaturized antenna conventional rectangular patch. The study also consists of a modeling through artificial neural networks of the various parameters of the electromagnetic near-fractal antennas. The presented results demonstrate the excellent capacity of modeling techniques for neural microstrip antennas and all algorithms used in this work in achieving the proposed models were implemented in commercial software simulation of Matlab 7. In order to validate the results, several prototypes of antennas were built, measured on a vector network analyzer and simulated in software for comparison

Seleção de features guiada por atenção visual em imagens com fóvea

Visual attention is a very important task in autonomous robotics, but, because of its complexity, the processing time required is significant. We propose an architecture for feature selection using foveated images that is guided by visual attention tasks and that reduces the processing time required to perform these tasks. Our system can be applied in bottom-up or top-down visual attention. The foveated model determines which scales are to be used on the feature extraction algorithm. The system is able to discard features that are not extremely necessary for the tasks, thus, reducing the processing time. If the fovea is correctly placed, then it is possible to reduce the processing time without compromising the quality of the tasks outputs. The distance of the fovea from the object is also analyzed. If the visual system loses the tracking in top-down attention, basic strategies of fovea placement can be applied. Experiments have shown that it is possible to reduce up to 60% the processing time with this approach. To validate the method, we tested it with the feature algorithm known as Speeded Up Robust Features (SURF), one of the most efficient approaches for feature extraction. With the proposed architecture, we can accomplish real time requirements of robotics vision, mainly to be applied in autonomous robotics