Atrial arrhythmias in adult patients with right- versus left-sided congenital heart disease anomalies.

Atrial arrhythmias (AAs) are a common complication in adult patients with congenital heart disease. We sought to compare the lifetime prevalence of AAs in patients with right- versus left-sided congenital cardiac lesions and their effect on the prognosis. A congenital heart disease diagnosis was assigned using the International Disease Classification, Ninth Revision, diagnostic codes in the administrative databases of Quebec, from 1983 to 2005. Patients with AAs were those diagnosed with an International Disease Classification, Ninth Revision, code for atrial fibrillation or intra-atrial reentry tachycardia. To ensure that the diagnosis of AA was new, a washout period of 5 years after entry into the database was used, a period during which the patient could not have received an International Disease Classification, Ninth Revision, code for AA. The cumulative lifetime risk of AA was estimated using the Practical Incidence Estimators method. The hazard ratios (HRs) for mortality, morbidity, and cardiac interventions were compared between those with right- and left-sided lesions after adjustment for age, gender, disease severity, and cardiac risk factors. In a population of 71,467 patients, 7,756 adults developed AAs (isolated right-sided, 2,229; isolated left-sided, 1,725). The lifetime risk of developing AAs was significantly greater in patients with right- sided than in patients with left-sided lesions (61.0% vs 55.4%, p <0.001). The HR for mortality and the development of stroke or heart failure was similar in both groups (HR 0.96, 95% confidence interval [CI] 0.86 to 1.09; HR 0.94, 95% CI 0.80 to 1.09; and HR 1.10, 95% CI 0.98 to 1.23, respectively). However, the rates of cardiac catheterization (HR 0.63, 95% CI 0.55 to 0.72), cardiac surgery (HR 0.40, 95% CI 0.36 to 0.45), and arrhythmia surgery (HR 0.77, 95% CI 0.6 to 0.98) were significantly less for patients with right-sided lesions. In conclusion, patients with right-sided lesions had a greater lifetime burden of AAs. However, their morbidity and mortality were no less than those with left-sided lesions, although the rate of intervention was substantially different.

Multi-source composite kernels for urban image classification

This letter presents advanced classification methods for very high resolution images. Efficient multisource information, both spectral and spatial, is exploited through the use of composite kernels in support vector machines. Weighted summations of kernels accounting for separate sources of spectral and spatial information are analyzed and compared to classical approaches such as pure spectral classification or stacked approaches using all the features in a single vector. Model selection problems are addressed, as well as the importance of the different kernels in the weighted summation.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

On the inefficiency of the restricted maximum likelihood

The restricted maximum likelihood is preferred by many to the full maximumlikelihood for estimation with variance component and other randomcoefficientmodels, because the variance estimator is unbiased. It is shown that thisunbiasednessis accompanied in some balanced designs by an inflation of the meansquared error.An estimator of the cluster-level variance that is uniformly moreefficient than the fullmaximum likelihood is derived. Estimators of the variance ratio are alsostudied.

Axiomatization of the nucl eolus of assignment games

On the domain of general assignment games (with possible reservation prices) the core is axiomatized as the unique solution satisfying two consistency principles: projection consistency and derived consistency. Also, an axiomatic characterization of the nucleolus is given as the unique solution that satisfies derived consistency and equal maximum complaint between groups. As a consequence, we obtain a geometric characterization of the nucleolus. Maschler et al. (1979) provide a geometrical characterization for the intersection of the kernel and the core of a coalitional game, showing that those allocations that lie in both sets are always the midpoint of certain bargaining range between each pair of players. In the case of the assignment game, this means that the kernel can be determined as those core allocations where the maximum amount, that can be transferred without getting outside the core, from one agent to his / her optimally matched partner equals the maximum amount that he / she can receive from this partner, also remaining inside the core. We now prove that the nucleolus of the assignment game can be characterized by requiring this bisection property be satisfied not only for optimally matched pairs but also for optimally matched coalitions. Key words: cooperative games, assignment game, core, nucleolus

Mixed spectral-structural classification of very high resolution images with summation kernels

In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.

ML approaches to channel estimation for pilot-aided multi-rate DS/CDMA systems

This paper analyzes the asymptotic performance of maximum likelihood (ML) channel estimation algorithms in wideband code division multiple access (WCDMA) scenarios. We concentrate on systems with periodic spreading sequences (period larger than or equal to the symbol span) where the transmitted signal contains a code division multiplexed pilot for channel estimation purposes. First, the asymptotic covariances of the training-only, semi-blind conditional maximum likelihood (CML) and semi-blind Gaussian maximum likelihood (GML) channelestimators are derived. Then, these formulas are further simplified assuming randomized spreading and training sequences under the approximation of high spreading factors and high number of codes. The results provide a useful tool to describe the performance of the channel estimators as a function of basicsystem parameters such as number of codes, spreading factors, or traffic to training power ratio.

Second-order parameter estimation

This work provides a general framework for the design of second-order blind estimators without adopting anyapproximation about the observation statistics or the a prioridistribution of the parameters. The proposed solution is obtainedminimizing the estimator variance subject to some constraints onthe estimator bias. The resulting optimal estimator is found todepend on the observation fourth-order moments that can be calculatedanalytically from the known signal model. Unfortunately,in most cases, the performance of this estimator is severely limitedby the residual bias inherent to nonlinear estimation problems.To overcome this limitation, the second-order minimum varianceunbiased estimator is deduced from the general solution by assumingaccurate prior information on the vector of parameters.This small-error approximation is adopted to design iterativeestimators or trackers. It is shown that the associated varianceconstitutes the lower bound for the variance of any unbiasedestimator based on the sample covariance matrix.The paper formulation is then applied to track the angle-of-arrival(AoA) of multiple digitally-modulated sources by means ofa uniform linear array. The optimal second-order tracker is comparedwith the classical maximum likelihood (ML) blind methodsthat are shown to be quadratic in the observed data as well. Simulationshave confirmed that the discrete nature of the transmittedsymbols can be exploited to improve considerably the discriminationof near sources in medium-to-high SNR scenarios.

ML estimator and hybrid beamformer for multipath and interference mitigation in GNSS receivers

This paper addresses the estimation of the code-phase(pseudorange) and the carrier-phase of the direct signal received from a direct-sequence spread-spectrum satellite transmitter. Thesignal is received by an antenna array in a scenario with interferenceand multipath propagation. These two effects are generallythe limiting error sources in most high-precision positioning applications.A new estimator of the code- and carrier-phases is derivedby using a simplified signal model and the maximum likelihood(ML) principle. The simplified model consists essentially ofgathering all signals, except for the direct one, in a component withunknown spatial correlation. The estimator exploits the knowledgeof the direction-of-arrival of the direct signal and is much simplerthan other estimators derived under more detailed signal models.Moreover, we present an iterative algorithm, that is adequate for apractical implementation and explores an interesting link betweenthe ML estimator and a hybrid beamformer. The mean squarederror and bias of the new estimator are computed for a numberof scenarios and compared with those of other methods. The presentedestimator and the hybrid beamforming outperform the existingtechniques of comparable complexity and attains, in manysituations, the Cramér–Rao lower bound of the problem at hand.

Yhteisön langaton palvelualusta

Yhteisön langaton palvelualusta on konsepti langattomien yhteisöllisten verkkopalveluiden tarjoamiseen.Konsepti perustuu langattomaan WLAN-reititinlaitteeseen sekä Linux-käyttöjärjestelmäpohjaiseen laiteohjelmistoon, joiden avulla voidaan tarjota paikallisia, erilaisten pienten yhteisöjen käyttöön tarkoitettuja langattomia verkkopalveluita.Soveltuvia käyttökohteita voivat olla esimerkiksi asuntoyhteisöjen välinen tiedotuskanava tai perheen sisäinen viihdekeskus, jonka kautta voidaan tarjota yhteisön toimintaa helpottavia ja tavoitteita edistäviä palveluita. Yhteisön langattomat palvelut perustuvat vapaisiin ohjelmistoihin, jotka mahdollistavat monipuolisen palveluvalikoiman luomisen. Langattoman palvelualustan avulla pienten yhteisöjen tarvitsemat langattomat palvelut voidaan luoda helposti, edullisesti ja joustavasti, ilman kaupallisten palveluoperaattoreiden asettamia rajoituksia.

Benchmarking Symbian OS smartphones

Diplomityön tarkoituksena oli luoda menetelmä Symbian-käyttöjärjestelmää käyttävien älypuhelinten suorituskyvyn määrittämiseen, jotta laitteiden välisiä eroja voitaisiin mitata. Aluksi Symbian-käyttöjärjestelmää ja älypuhelinlaitteistoja tutkittiin suorituskykyyn ja sen vaihteluun vaikuttavien tekijöiden ja osien löytämiseksi. Tämän jälkeen kehitettiin useita testitapauksia sisältävä testikirjasto, jolla voitiin mitata joidenkin suorituskykyyn vaikuttavien käyttöjärjestelmän rajapintojen suoritusaikoja. Testikirjaston testit ajettiin kolmella eri älypuhelinmallilla, jotta testien toimivuutta voitiin arvioida. Lopuksi testituloksia analysoitiin mahdollisten pullonkaulojen havaitsemiseksi suorituskyvystä. Testikirjaston pystyttiin havaitsemaan eroja laitteiden suorituskyvyssä. Viimeisin, uudella Symbianin EKA2-ytimellä varustettu älypuhelin, Nokia E70, jäi mittauksissa viimeiseksi, koska se pärjäsi huonosti muistinvarauksia ja TRAP-poikkeuksia testaavissa tapauksissa. Muilla mitatuilla osa-alueilla se kuitenkin päihitti selvästi muut testatut puhelimet, Nokia N90:n ja Nokia 6630:n. Näiden kahden muun laitteen tulosten skaalan tasaisuus osoittaa, että kehitetyn testikirjaston avulla saadaan johdonmukaisia ja uskottavia mittaustuloksia.

Image-Based Coral Reef Classification and Thematic Mapping

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos

Covariance Matrix Estimation and Mean-Variance Optimization in application to International Diversification

Tutkimus keskittyy kansainväliseen hajauttamiseen suomalaisen sijoittajan näkökulmasta. Tutkimuksen toinen tavoite on selvittää tehostavatko uudet kovarianssimatriisiestimaattorit minimivarianssiportfolion optimointiprosessia. Tavallisen otoskovarianssimatriisin lisäksi optimoinnissa käytetään kahta kutistusestimaattoria ja joustavaa monimuuttuja-GARCH(1,1)-mallia. Tutkimusaineisto koostuu Dow Jonesin toimialaindekseistä ja OMX-H:n portfolioindeksistä. Kansainvälinen hajautusstrategia on toteutettu käyttäen toimialalähestymistapaa ja portfoliota optimoidaan käyttäen kahtatoista komponenttia. Tutkimusaieisto kattaa vuodet 1996-2005 eli 120 kuukausittaista havaintoa. Muodostettujen portfolioiden suorituskykyä mitataan Sharpen indeksillä. Tutkimustulosten mukaan kansainvälisesti hajautettujen investointien ja kotimaisen portfolion riskikorjattujen tuottojen välillä ei ole tilastollisesti merkitsevää eroa. Myöskään uusien kovarianssimatriisiestimaattoreiden käytöstä ei synnytilastollisesti merkitsevää lisäarvoa verrattuna otoskovarianssimatrisiin perustuvaan portfolion optimointiin.

Does land titling matter? The role of land property rights in the war on illicit crops in Colombia

This paper analyzes the role of formalization of land property rights in the war against illicit crops in Colombia. We argue that as a consequence of the increase of state presence and visibility during the period of 2000 and 2009, municipalities with a higher level of formalization of their land property rights saw a greater reduction in the area allocated to illicit crops. We hypothesize that this is due to the increased cost of growing illicit crops on formal land compared to informal, and due to the possibility of obtaining more benets in the newly in- stalled institutional environment when land is formalized. We exploit the variation in the level of formalization of land property rights in a set of municipalities that had their rst cadastral census collected in the period of 1994-2000; this selection procedure guarantees reliable data and an unbiased source of variation. Using fixed effects estimators, we found a signicant negative relationship between the level of formalization of land property rights and the number of hectares allocated to coca crops per municipality. These results remain robust through a number of sensitivity analyses. Our ndings contribute to the growing body of evidence on the positive effects of formal land property rights, and e ective policies in the war on drugs in Colombia.

Grain yield, carbon isotope discrimination and mineral content in mature kernels of barley under irrigated and rainfed conditions

The relationship between yield, carbon isotope discrimination and ash content in mature kernels was examined for a set of 13 barley (Hordeum vulgare) cultivars. Plants were grown under rainfed and well-irrigated conditions in a Mediterranean area. Water deficit caused a decrease in both grain yield and carbon isotope discrimination (Δ). The yield was positively related to Δ and negatively related to ash content, across genotypes within each treatment. However, whereas the correlation between yield and Δ was higher for the set of genotypes under well-irrigated (r=0.70, P<0.01) than under rainfed (r=0.42) conditions, the opposite occurred when yield and ash content were related, ie r=-0.38 under well-irrigated and r=-0.73, (P<0.01) under rainfed conditions. Carbon isotope discrimination and ash content together account for almost 60% of the variation in yield, in both conditions. There was no significant relationship (r=-0.15) between carbon isotope discrimination and ash content in well-irrigated plants, whereas in rainfed plants, this relationship, although significant (r=-0.54, P< 0.05), was weakly negative. The concentration of several mineral elements was measured in the same kernels. The mineral that correlated best with ash content, yield and A, was K. For yield and Δ, although the relationship with K followed the same pattern as the relationhip with ash content, the correlation coefficients were lower. Thus, mineral accumulation in mature kernels seems to be independent of transpiration efficiency. In fact, filling of grains takes place through the phloem pathway. The ash content in kernels is proposed as a complementary criterion, in addition to kernel Δ, to assess genotype differences in barley grain yield under rainfed conditions.