The unusually large population of Blazhko variables in the globular cluster NGC 5024 (M53)

We report the discovery of amplitude and phase modulations typical of the Blazhko effect in 22 RRc and 9 RRab type RR Lyrae stars in NGC 5024 (M53). This brings the confirmed Blazhko variables in this cluster to 23 RRc and 11 RRab, that represent 66% and 37% of the total population of RRc and RRab stars in the cluster respectively, making NGC 5024 the globular cluster with the largest presently known population of Blazhko RRc stars. We place a lower limit on the overall incidence rate of the Blazhko effect among the RR Lyrae population in this cluster of 52%. New data have allowed us to refine the pulsation periods. The limitations imposed by the time span and sampling of our data prevents reliable estimations of the modulation periods. The amplitudes of the modulations range between 0.02 and 0.39 mag. The RRab and RRc are neatly separated in the CMD, and the RRc Blazhko variables are on averge redder than their stable couterparts; these two facts may support the hypothesis that the HB evolution in this cluster is towards the red and that the Blazhko modulations in the RRc stars are connected with the pulsation mode switch.Comment: ACCEPTED IN MNRAS 14 pages, 9 figures and 6 table

Quantization and Compressive Sensing

Quantization is an essential step in digitizing signals, and, therefore, an indispensable component of any modern acquisition system. This book chapter explores the interaction of quantization and compressive sensing and examines practical quantization strategies for compressive acquisition systems. Specifically, we first provide a brief overview of quantization and examine fundamental performance bounds applicable to any quantization approach. Next, we consider several forms of scalar quantizers, namely uniform, non-uniform, and 1-bit. We provide performance bounds and fundamental analysis, as well as practical quantizer designs and reconstruction algorithms that account for quantization. Furthermore, we provide an overview of Sigma-Delta ($\Sigma\Delta$) quantization in the compressed sensing context, and also discuss implementation issues, recovery algorithms and performance bounds. As we demonstrate, proper accounting for quantization and careful quantizer design has significant impact in the performance of a compressive acquisition system.Comment: 35 pages, 20 figures, to appear in Springer book "Compressed Sensing and Its Applications", 201

Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem

lp l _{ p } -Multiresolution Analysis: How to Reduce Ringing and Sparsify the Error

We propose to design the reduction operator of an image pyramid so as to minimize the approximation error in the $l _{ p }$-sense (not restricted to the usual p=2), where p can take non-integer values. The underlying image model is specified using shift-invariant basis functions, such as B-splines. The solution is well-defined and determined by an iterative optimization algorithm based on digital filtering. Its convergence is accelerated by the use of first and second order derivatives. For p close to 1, we show that the ringing is reduced and that the histogram of the detail image is sparse as compared with the standard case, where p=2

Least-Squares Image Resizing Using Finite Differences

We present an optimal spline-based algorithm for the enlargement or reduction of digital images with arbitrary (noninteger) scaling factors. This projection-based approach can be realized thanks to a new finite difference method that allows the computation of inner products with analysis functions that are B-splines of any degree n. A noteworthy property of the algorithm is that the computational complexity per pixel does not depend on the scaling factor a. For a given choice of basis functions, the results of our method are consistently better than those of the standard interpolation procedure; the present scheme achieves a reduction of artifacts such as aliasing and blocking and a significant improvement of the signal-to-noise ratio. The method can be generalized to include other classes of piecewise polynomial functions, expressed as linear combinations of B-splines and their derivatives

Wavelet-Based Multi-Resolution Statistics for Optical Imaging Signals: Application to Automated Detection of Odour Activated Glomeruli in the Mouse Olfactory Bulb

Optical imaging techniques offer powerful solutions to capture brain networks processing in animals, especially when activity is distributed in functionally distinct spatial domains. Despite the progress in imaging techniques, the standard analysis procedures and statistical assessments for this type of data are still limited. In this paper, we perform two in vivo non-invasive optical recording techniques in the mouse olfactory bulb, using a genetically expressed activity reporter fluorescent protein (synaptopHfluorin) and intrinsic signals of the brain. For both imaging techniques, we show that the odour-triggered signals can be accurately parameterized using linear models. Fitting the models allows us to extract odour specific signals with a reduced level of noise compared to standard methods. In addition, the models serve to evaluate statistical significance, using a wavelet-based framework that exploits spatial correlation at different scales. We propose an extension of this framework to extract activation patterns at specific wavelet scales. This method is especially interesting to detect the odour inputs that segregate on the olfactory bulb in small spherical structures called glomeruli. Interestingly, with proper selection of wavelet scales, we can isolate significantly activated glomeruli and thus determine the odour map in an automated manner. Comparison against manual detection of glomeruli shows the high accuracy of the proposed method. Therefore, beyond the advantageous alternative to the existing treatments of optical imaging signals in general, our framework propose an interesting procedure to dissect brain activation patterns on multiple scales with statistical control

Fences and profanations: Questioning the sacredness of urban design

Adopting an impure and contingent conception of urban design as a biopolitical apparatus, along the theme of urban informal squatter-occupied spatialities, this paper searches for an alternative narrative of urban design. It presents a theoretical and analytical framework developed around Michel Foucault's and Giorgio Agamben's spatial ontology and political aesthetics as an aggregate source toward recalibrating the approach to urban design research, pedagogy and practice, integrating the debate around the dispositif and its profanation. Critically engaging with the complexity and contradictions of the current neoliberal urban design practice—articulated as a complex urban apparatus instrumental to regimes of security and control—the paper explores the conceptual tool of profanation as a potential antidote to the sacred production of the neoliberal city. The act of profaning the urban realm, of ‘returning it to the free use of men’, is approached through the lens of a design research initiative in a squatter-occupied space in Rome, Italy. The narrative that emerges from this theoretically inspired action research points to an alternative practice that can be read as a site of resistance in reclaiming the intellectual productivity of urban design theory and research