Thematic mapper studies of central Andean volcanoes

A series of false color composite images covering the volcanic cordillera was written. Each image is 45 km (1536 x 1536 pixels) and was constructed using bands 7, 4, and 2 of the Thematic Mapper (TM) data. Approximately 100 images were prepared to date. A set of LANDSAT Multispectral Scanner (MSS) images was used in conjunction with the TM hardcopy to compile a computer data base of all volcanic structure in the Central Andean province. Over 500 individual structures were identified. About 75 major volcanoes were identified as active, or potentially active. A pilot study was begun combining Shuttle Imaging Radar (SIR) data with TM for a test area in north Chile and Bolivia

International Experiences of Water Transfers: Relevance to India

Water transfer has and continues to be a complementary water management strategy for promoting socioeconomic development in water-scarce regions. Over 2,500 years ago, the Babylonians, the Roman Empire and the Chinese constructed extensive canal networks, famous aqueducts and the Grand Canal, respectively to support human settlement in water- scarce areas. The Anuradhapura Kingdom of Sri Lanka too, developed major water transfers as far back as 100 AD to support the irrigation civilization needed to feed a growing population (de Silva 2005). In the twentieth century, the phenomenal population growth, economic activities and human settlement in water-scarce regions, advances in science and technology, political will and availability of resources led to the development of many water transfer projects. The global inter-basin water transfer increased from 22 to 56, from 56 to 257 and from 257 to 364 km3 yr-1 during the periods 1900-1940, 1940-1980 and 1980-1986, respectively, and is estimated to increase to 760-1,240 km3 yr-1 by 2020 (Shiklomanov 1999). Most of these transfers took place in Canada, the former USSR, India and the United States of America

Image segmentation with adaptive region growing based on a polynomial surface model

A new method for segmenting intensity images into smooth surface segments is presented. The main idea is to divide the image into flat, planar, convex, concave, and saddle patches that coincide as well as possible with meaningful object features in the image. Therefore, we propose an adaptive region growing algorithm based on low-degree polynomial fitting. The algorithm uses a new adaptive thresholding technique with the L∞ fitting cost as a segmentation criterion. The polynomial degree and the fitting error are automatically adapted during the region growing process. The main contribution is that the algorithm detects outliers and edges, distinguishes between strong and smooth intensity transitions and finds surface segments that are bent in a certain way. As a result, the surface segments corresponding to meaningful object features and the contours separating the surface segments coincide with real-image object edges. Moreover, the curvature-based surface shape information facilitates many tasks in image analysis, such as object recognition performed on the polynomial representation. The polynomial representation provides good image approximation while preserving all the necessary details of the objects in the reconstructed images. The method outperforms existing techniques when segmenting images of objects with diffuse reflecting surfaces

Financial asset returns, direction-of-change forecasting, and volatility dynamics

We consider three sets of phenomena that feature prominently - and separately - in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that: (a) Volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (b) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence; (c) Sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests, because of the special nonlinear nature of sign dependence; (d) Sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; (e) Sign dependence is very much present in actual U.S. equity returns, and its properties match closely our theoretical predictions; (f) The link between volatility forecastability and sign forecastability remains intact in conditionally non-Gaussian environments, as for example with time-varying conditional skewness and/or kurtosis

Financial Asset Returns, Market Timing, and Volatility Dynamics

We consider three sets of phenomena that feature prominently and separately in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs with implications for market timing, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that: (1) Volatility dependence produces sign dependence, so long as expected returns are nonzero. Hence one should expect sign dependence, given the overwhelming evidence of volatility dependence. (2) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence. In particular, sign dependence does not imply market inefficiency. (3) Sign dependence is not likely to be found via analysis of sign autocorrelations, because the nature of sign dependence is highly nonlinear. (4) Sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns. Instead, it is more likely to be found at intermediate return horizons. Nous considérons trois ensembles de phénomènes qui sont souvent - et séparément - discutés dans la littérature d'économie financière, à savoir la dépendance de la moyenne conditionnelle (ou l'absence de dépendance) dans les rendements d'actifs, la dépendance (et donc prévisibilité) des signes de rendements d'actifs ainsi que leurs implications dans le timing du marché, et la dépendance (et donc prévisibilité) dans les volatilités des rendements d'actifs. Nous montrons que ces phénomènes sont étroitement interreliés et nous explorons leurs relations en détail. Entre autres, nous montrons que : 1) la dépendance de la volatilité produit une dépendance du signe tant que les rendements attendus sont non nuls. On devrait par conséquent s attendre à une dépendance du signe, étant donné la présence notoire de dépendance de volatilité; 2) le résultat classique qui ne trouve que peu ou pas de dépendance de la moyenne conditionnelle est parfaitement compatible avec un degré significatif de dépendance de signe et de dépendance de volatilité. En particulier, la dépendance de signe n'implique pas une inefficacité du marché; 3) Il est peu probable qu'une analyse des autocorrélations de signes révèle une dépendance de signe, parce que la nature de la dépendance du signe est fortement non linéaire; 4) il est également peu probable que l'on retrouve une dépendance de signe dans des rendements à très haute fréquence (par exemple quotidiens) ou à très basse fréquence (par exemple annuels). Il est plus probable qu'on la trouve avec des horizons de rendements intermédiaires.Sign prediction, direction of change, volatility timing, investment horizon, prédiction des signes, direction de changement, timing de la volatilité, horizon d'investissement

On the Structure of Lie Pseudo-Groups

We compare and contrast two approaches to the structure theory for Lie pseudo-groups, the first due to Cartan, and the second due to the first two authors. We argue that the latter approach offers certain advantages from both a theoretical and practical standpoint

International experiences of water transfers: relevance to India

River basinsWater scarcityWater transferPlanningCase studiesHistoryWater allocationEnvironmental effectsFood securityPoverty

Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics

We consider three sets of phenomena that feature prominently - and separately - in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that (a) Volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (b) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence; (c) Sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests, because of the special nonlinear nature of sign dependence; (d) Sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; (e) Sign dependence is very much present in actual U.S. equity returns, and its properties match closely our theoretical predictions; (f) The link between volatility forecastability and sign forecastability remains intact in conditionally non-Gaussian environments, as for example with time-varying conditional skewness and/or kurtosis.Conditional Mean Dependence, Conditional Volatility Dependence, Sign Dependence, VIX

Some aspects of the Lewisian geology of Barra and adjacent islands

Imperial Users onl