The "movements of the squares" involved first and foremost an awakening or re-discovering of the radical imagination both in the square encampments, and in later projects created with the movements' decentralizations. The new alternative projects born after the square have materialized the movements' radical imaginaries in urban environments, extending and deepening concerns of broad political change over everyday life. Based on ethnographic work on the Indignados' movement in the city of Barcelona, this paper delves more particularly into three Indignant urban projects. It untangles three common and interlinked radical imaginaries both embodied and actualized in participants' social practices, and further orienting their future visions: commons, autonomy and ecologism. Scrutinizing their meaning, it also sheds light on connected issues such new ways of interfacing with local state authorities and redefining the boundaries between the public and the common. It shows that the ecologism imaginary cannot be properly grasped if disconnected from the other two imaginaries, and argues that a transformative eco-politics can only be claimed as such if it is able to articulate such an integrated vision typical of "socio-environmental movements". / Series: SRE - Discussion Papers

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Partial Least Squares (PLS) is an important statistical technique with multipleand diverse applications, used as an effective regression method for correlated orcollinear datasets or for datasets that are not full rank for other reasons. A shorthistory of PLS is followed by a review of the publications where the issues with theapplication PLS that have been discussed. The theoretical basis of PLS is developedfrom the single value decomposition of the covariance, so that the strong links between principal components analysis and within the various PLS algorithms appear as a natural consequence. Latent variable selection by crossvalidation, permutation and information criteriaare examined. A method for plotting crossvalidation results is proposed that makeslatent variable selection less ambiguous than conventional plots. Novel and practicalmethods are proposed to extend published methods for latent variable selection byboth permutation and information criteria from univariate PLS1 models to PLS2 multivariate cases. The numerical method proposed for information criteria is also more general than the algebraic methods for PLS1 that have been recently published as it does not assume any particular form for the PLS regression coefficients. All of these methods have been critically assessed using a number of datasets, selected specifically to represent a diverse set of dimensions and covariance structures. Methods for simulating multivariate datasets were developed that allow controlof correlation and collinearity in both regressors and responses independently. Thisdevelopment also allows control over the variate distributions. Statistical design ofexperiments was used to generate plans for the simulation that allowed the factorsthat infuence PLS model fit and latent variable selection. It was found that all thelatent variable selection methods in the simulation tend to overfit and the feature inthe simulation that causes overfitting has been identified.

This thesis designs new methods for Q tomography and Q-compensated prestack depth migration when the recorded seismic data suffer from strong attenuation. A motivation of this work is that the presence of gas clouds or mud channels in overburden structures leads to the distortion of amplitudes and phases in seismic waves propagating inside the earth. If the attenuation parameter Q is very strong, i.e., Q<30, ignoring the anelastic effects in imaging can lead to dimming of migration amplitudes and loss of resolution. This, in turn, adversely affects the ability to accurately predict reservoir properties below such layers. To mitigate this problem, I first develop an anelastic least-squares reverse time migration (Q-LSRTM) technique. I reformulate the conventional acoustic least-squares migration problem as a viscoacoustic linearized inversion problem. Using linearized viscoacoustic modeling and adjoint operators during the least-squares iterations, I show with numerical tests that Q-LSRTM can compensate for the amplitude loss and produce images with better balanced amplitudes than conventional migration. To estimate the background Q model that can be used for any Q-compensating migration algorithm, I then develop a wave-equation based optimization method that inverts for the subsurface Q distribution by minimizing a skeletonized misfit function ε. Here, ε is the sum of the squared differences between the observed and the predicted peak/centroid-frequency shifts of the early-arrivals. Through numerical tests on synthetic and field data, I show that noticeable improvements in the migration image quality can be obtained from Q models inverted using wave-equation Q tomography. A key feature of skeletonized inversion is that it is much less likely to get stuck in a local minimum than a standard waveform inversion method. Finally, I develop a preconditioning technique for least-squares migration using a directional Gabor-based preconditioning approach for isotropic, anisotropic or anelastic least-squares migration. During the least-squares iterations, I impose sparsity constraints on the inverted reflectivity model in the local Radon domain. The forward and the inverse mapping of the reflectivity to the local Radon domain is done through 3D Fourier-based discrete Radon transform operators. Using numerical tests on synthetic and 3D field data, I demonstrate that the proposed preconditioning approach can discriminate against artifacts in the image resulting from irregular or insufficient acquisition and can produce images with improved signal-to-noise ratio when compared with standard migration.

Optimal control design and implementation for nonlinear systems is a topic of much interest. However, unlike for linear systems, for nonlinear systems explicit analytical solution for optimal feedback control is not available. Numerical techniques, on the other hand, can be used to approximate the solution of the HJB equation to find the optimal control. In this research, a computational approach is developed for finding the optimal control for nonlinear systems with polynomial vector fields based on sum of squares technique. In this research, a numerical technique is developed for optimal control of polynomial nonlinear systems. The approach follows a four-step procedure to obtain both local and approximate global optimality. In the first step, local optimal control is found by using the linearization method and solving the Algebraic Riccati equation with respect to the quadratic part of a given performance index. Next, we utilize the density function method to find a globally stabilizing polynomial nonlinear control for the nonlinear system. In the third step, we find a corresponding Lyapunov function for the designed control in the previous steps based on the Hamilton Jacobi inequality by using semidefinite programming. Finally, to achieve global optimality, we iteratively update the pair of nonlinear control and Lyapunov function based on a state-dependent polynomial matrix inequality. Numerical examples illustrate the effectiveness of the design approach.

The purpose of this paper is to examine the interactions between two plant species endemic to Florida and develop a model for the growth of one of the plant species. An equation for the growth of Hypericum cumulicola is developed through analyzing how the distance to and the height of the nearest Ceratiola ericoides (Florida rosemary) affects the growth of Hypericum cumulicola. The hypericums were separated into five separate regions according to the distance to the nearest rosemary plant. The parameters for a basic growth equation were obtained in each of the five regions and compared to each other along with the average deviations in each of the five regions. Analysis of the five separate regions aided in the creation of different growth equations that each encompassed all of the regions together. Four different growth equations are developed and then compared and analyzed for their accuracy.

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