# C. A. Rojas - ESO

## Contact Details

NameC. A. Rojas |
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AffiliationESO |
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Location |
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## Pubs By Year |
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## Pub CategoriesComputer Science - Information Theory (9) Mathematics - Information Theory (9) Statistics - Machine Learning (8) Mathematics - Optimization and Control (8) Mathematics - Dynamical Systems (8) Statistics - Theory (5) Mathematics - Statistics (5) Mathematics - Logic (4) Computer Science - Learning (3) Computer Science - Computational Complexity (3) Nonlinear Sciences - Chaotic Dynamics (3) Mathematics - Complex Variables (3) Physics - Soft Condensed Matter (3) Quantum Physics (2) Computer Science - Numerical Analysis (2) Cosmology and Nongalactic Astrophysics (1) Mathematics - Probability (1) Computer Science - Computational Engineering; Finance; and Science (1) Instrumentation and Methods for Astrophysics (1) Physics - Superconductivity (1) Physics - Accelerator Physics (1) High Energy Physics - Experiment (1) Computer Science - Logic in Computer Science (1) Statistics - Computation (1) |

## Publications Authored By C. A. Rojas

This paper discusses algorithms for solving Markov decision processes (MDPs) that have monotone optimal policies. We propose a two-stage alternating convex optimization scheme that can accelerate the search for an optimal policy by exploiting the monotone property. The first stage is a linear program formulated in terms of the joint state-action probabilities. Read More

In this paper, we develop an upper bound for the SPARSEVA (SPARSe Estimation based on a VAlidation criterion) estimation error in a general scheme, i.e., when the cost function is strongly convex and the regularized norm is decomposable for a pair of subspaces. Read More

We discuss computability and computational complexity of conformal mappings and their boundary extensions. As applications, we review the state of the art regarding computability and complexity of Julia sets, their invariant measures and external rays impressions. Read More

We show that there exist real quadratic maps of the interval whose attractors are computationally intractable. This is the first known class of such natural examples. Read More

We show the existence of computable complex numbers $\lambda$ for which the bifurcation locus of the one parameter complex family $f_{b}(z) = \lambda z + b z^{2} + z^{3}$ is not Turing computable. Read More

The Lorenz attractor was introduced in 1963 by E. N. Lorenz as one of the first examples of \emph{strange attractors}. Read More

We consider estimating the transition probability matrix of a finite-state finite-observation alphabet hidden Markov model with known observation probabilities. The main contribution is a two-step algorithm; a method of moments estimator (formulated as a convex optimization problem) followed by a single iteration of a Newton-Raphson maximum likelihood estimator. The two-fold contribution of this letter is, firstly, to theoretically show that the proposed estimator is consistent and asymptotically efficient, and secondly, to numerically show that the method is computationally less demanding than conventional methods - in particular for large data sets. Read More

**Authors:**The CLIC, CLICdp collaborations, :, M. J. Boland, U. Felzmann, P. J. Giansiracusa, T. G. Lucas, R. P. Rassool, C. Balazs, T. K. Charles, K. Afanaciev, I. Emeliantchik, A. Ignatenko, V. Makarenko, N. Shumeiko, A. Patapenka, I. Zhuk, A. C. Abusleme Hoffman, M. A. Diaz Gutierrez, M. Vogel Gonzalez, Y. Chi, X. He, G. Pei, S. Pei, G. Shu, X. Wang, J. Zhang, F. Zhao, Z. Zhou, H. Chen, Y. Gao, W. Huang, Y. P. Kuang, B. Li, Y. Li, J. Shao, J. Shi, C. Tang, X. Wu, L. Ma, Y. Han, W. Fang, Q. Gu, D. Huang, X. Huang, J. Tan, Z. Wang, Z. Zhao, T. Laštovička, U. Uggerhoj, T. N. Wistisen, A. Aabloo, K. Eimre, K. Kuppart, S. Vigonski, V. Zadin, M. Aicheler, E. Baibuz, E. Brücken, F. Djurabekova, P. Eerola, F. Garcia, E. Haeggström, K. Huitu, V. Jansson, V. Karimaki, I. Kassamakov, A. Kyritsakis, S. Lehti, A. Meriläinen, R. Montonen, T. Niinikoski, K. Nordlund, K. Österberg, M. Parekh, N. A. Törnqvist, J. Väinölä, M. Veske, W. Farabolini, A. Mollard, O. Napoly, F. Peauger, J. Plouin, P. Bambade, I. Chaikovska, R. Chehab, M. Davier, W. Kaabi, E. Kou, F. LeDiberder, R. Pöschl, D. Zerwas, B. Aimard, G. Balik, J. -P. Baud, J. -J. Blaising, L. Brunetti, M. Chefdeville, C. Drancourt, N. Geoffroy, J. Jacquemier, A. Jeremie, Y. Karyotakis, J. M. Nappa, S. Vilalte, G. Vouters, A. Bernard, I. Peric, M. Gabriel, F. Simon, M. Szalay, N. van der Kolk, T. Alexopoulos, E. N. Gazis, N. Gazis, E. Ikarios, V. Kostopoulos, S. Kourkoulis, P. D. Gupta, P. Shrivastava, H. Arfaei, M. K. Dayyani, H. Ghasem, S. S. Hajari, H. Shaker, Y. Ashkenazy, H. Abramowicz, Y. Benhammou, O. Borysov, S. Kananov, A. Levy, I. Levy, O. Rosenblat, G. D'Auria, S. Di Mitri, T. Abe, A. Aryshev, T. Higo, Y. Makida, S. Matsumoto, T. Shidara, T. Takatomi, Y. Takubo, T. Tauchi, N. Toge, K. Ueno, J. Urakawa, A. Yamamoto, M. Yamanaka, R. Raboanary, R. Hart, H. van der Graaf, G. Eigen, J. Zalieckas, E. Adli, R. Lillestøl, L. Malina, J. Pfingstner, K. N. Sjobak, W. Ahmed, M. I. Asghar, H. Hoorani, S. Bugiel, R. Dasgupta, M. Firlej, T. A. Fiutowski, M. Idzik, M. Kopec, M. Kuczynska, J. Moron, K. P. Swientek, W. Daniluk, B. Krupa, M. Kucharczyk, T. Lesiak, A. Moszczynski, B. Pawlik, P. Sopicki, T. Wojtoń, L. Zawiejski, J. Kalinowski, M. Krawczyk, A. F. Żarnecki, E. Firu, V. Ghenescu, A. T. Neagu, T. Preda, I-S. Zgura, A. Aloev, N. Azaryan, J. Budagov, M. Chizhov, M. Filippova, V. Glagolev, A. Gongadze, S. Grigoryan, D. Gudkov, V. Karjavine, M. Lyablin, A. Olyunin, A. Samochkine, A. Sapronov, G. Shirkov, V. Soldatov, A. Solodko, E. Solodko, G. Trubnikov, I. Tyapkin, V. Uzhinsky, A. Vorozhtov, E. Levichev, N. Mezentsev, P. Piminov, D. Shatilov, P. Vobly, K. Zolotarev, I. Bozovic Jelisavcic, G. Kacarevic, S. Lukic, G. Milutinovic-Dumbelovic, M. Pandurovic, U. Iriso, F. Perez, M. Pont, J. Trenado, M. Aguilar-Benitez, J. Calero, L. Garcia-Tabares, D. Gavela, J. L. Gutierrez, D. Lopez, F. Toral, D. Moya, A. Ruiz Jimeno, I. Vila, T. Argyropoulos, C. Blanch Gutierrez, M. Boronat, D. Esperante, A. Faus-Golfe, J. Fuster, N. Fuster Martinez, N. Galindo Muñoz, I. García, J. Giner Navarro, E. Ros, M. Vos, R. Brenner, T. Ekelöf, M. Jacewicz, J. Ögren, M. Olvegård, R. Ruber, V. Ziemann, D. Aguglia, N. Alipour Tehrani, A. Andersson, F. Andrianala, F. Antoniou, K. Artoos, S. Atieh, R. Ballabriga Sune, M. J. Barnes, J. Barranco Garcia, H. Bartosik, C. Belver-Aguilar, A. Benot Morell, D. R. Bett, S. Bettoni, G. Blanchot, O. Blanco Garcia, X. A. Bonnin, O. Brunner, H. Burkhardt, S. Calatroni, M. Campbell, N. Catalan Lasheras, M. Cerqueira Bastos, A. Cherif, E. Chevallay, B. Constance, R. Corsini, B. Cure, S. Curt, B. Dalena, D. Dannheim, G. De Michele, L. De Oliveira, N. Deelen, J. P. Delahaye, T. Dobers, S. Doebert, M. Draper, F. Duarte Ramos, A. Dubrovskiy, K. Elsener, J. Esberg, M. Esposito, V. Fedosseev, P. Ferracin, A. Fiergolski, K. Foraz, A. Fowler, F. Friebel, J-F. Fuchs, C. A. Fuentes Rojas, A. Gaddi, L. Garcia Fajardo, H. Garcia Morales, C. Garion, L. Gatignon, J-C. Gayde, H. Gerwig, A. N. Goldblatt, C. Grefe, A. Grudiev, F. G. Guillot-Vignot, M. L. Gutt-Mostowy, M. Hauschild, C. Hessler, J. K. Holma, E. Holzer, M. Hourican, D. Hynds, Y. Inntjore Levinsen, B. Jeanneret, E. Jensen, M. Jonker, M. Kastriotou, J. M. K. Kemppinen, R. B. Kieffer, W. Klempt, O. Kononenko, A. Korsback, E. Koukovini Platia, J. W. Kovermann, C-I. Kozsar, I. Kremastiotis, S. Kulis, A. Latina, F. Leaux, P. Lebrun, T. Lefevre, L. Linssen, X. Llopart Cudie, A. A. Maier, H. Mainaud Durand, E. Manosperti, C. Marelli, E. Marin Lacoma, R. Martin, S. Mazzoni, G. Mcmonagle, O. Mete, L. M. Mether, M. Modena, R. M. Münker, T. Muranaka, E. Nebot Del Busto, N. Nikiforou, D. Nisbet, J-M. Nonglaton, F. X. Nuiry, A. Nürnberg, M. Olvegard, J. Osborne, S. Papadopoulou, Y. Papaphilippou, A. Passarelli, M. Patecki, L. Pazdera, D. Pellegrini, K. Pepitone, E. Perez Codina, A. Perez Fontenla, T. H. B. Persson, M. Petrič, F. Pitters, S. Pittet, F. Plassard, R. Rajamak, S. Redford, Y. Renier, S. F. Rey, G. Riddone, L. Rinolfi, E. Rodriguez Castro, P. Roloff, C. Rossi, V. Rude, G. Rumolo, A. Sailer, E. Santin, D. Schlatter, H. Schmickler, D. Schulte, N. Shipman, E. Sicking, R. Simoniello, P. K. Skowronski, P. Sobrino Mompean, L. Soby, M. P. Sosin, S. Sroka, S. Stapnes, G. Sterbini, R. Ström, I. Syratchev, F. Tecker, P. A. Thonet, L. Timeo, H. Timko, R. Tomas Garcia, P. Valerio, A. L. Vamvakas, A. Vivoli, M. A. Weber, R. Wegner, M. Wendt, B. Woolley, W. Wuensch, J. Uythoven, H. Zha, P. Zisopoulos, M. Benoit, M. Vicente Barreto Pinto, M. Bopp, H. H. Braun, M. Csatari Divall, M. Dehler, T. Garvey, J. Y. Raguin, L. Rivkin, R. Zennaro, A. Aksoy, Z. Nergiz, E. Pilicer, I. Tapan, O. Yavas, V. Baturin, R. Kholodov, S. Lebedynskyi, V. Miroshnichenko, S. Mordyk, I. Profatilova, V. Storizhko, N. Watson, A. Winter, J. Goldstein, S. Green, J. S. Marshall, M. A. Thomson, B. Xu, W. A. Gillespie, R. Pan, M. A Tyrk, D. Protopopescu, A. Robson, R. Apsimon, I. Bailey, G. Burt, D. Constable, A. Dexter, S. Karimian, C. Lingwood, M. D. Buckland, G. Casse, J. Vossebeld, A. Bosco, P. Karataev, K. Kruchinin, K. Lekomtsev, L. Nevay, J. Snuverink, E. Yamakawa, V. Boisvert, S. Boogert, G. Boorman, S. Gibson, A. Lyapin, W. Shields, P. Teixeira-Dias, S. West, R. Jones, N. Joshi, R. Bodenstein, P. N. Burrows, G. B. Christian, D. Gamba, C. Perry, J. Roberts, J. A. Clarke, N. A. Collomb, S. P. Jamison, B. J. A. Shepherd, D. Walsh, M. Demarteau, J. Repond, H. Weerts, L. Xia, J. D. Wells, C. Adolphsen, T. Barklow, M. Breidenbach, N. Graf, J. Hewett, T. Markiewicz, D. McCormick, K. Moffeit, Y. Nosochkov, M. Oriunno, N. Phinney, T. Rizzo, S. Tantawi, F. Wang, J. Wang, G. White, M. Woodley

The Compact Linear Collider (CLIC) is a multi-TeV high-luminosity linear e+e- collider under development. For an optimal exploitation of its physics potential, CLIC is foreseen to be built and operated in a staged approach with three centre-of-mass energy stages ranging from a few hundred GeV up to 3 TeV. The first stage will focus on precision Standard Model physics, in particular Higgs and top-quark measurements. Read More

$\ell_1$ mean filtering is a conventional, optimization-based method to estimate the positions of jumps in a piecewise constant signal perturbed by additive noise. In this method, the $\ell_1$ norm penalizes sparsity of the first-order derivative of the signal. Theoretical results, however, show that in some situations, which can occur frequently in practice, even when the jump amplitudes tend to $\infty$, the conventional method identifies false change points. Read More

We propose a novel approach to input design for identification of nonlinear state space models. The optimal input sequence is obtained by maximizing a scalar cost function of the Fisher information matrix. Since the Fisher information matrix is unavailable in closed form, it is estimated using particle methods. Read More

We present a magnetic-penetration-depth study on polycrystalline and granular samples of SrPtAs, a pnictide superconductor with a hexagonal structure containing PtAs layers that individually break inversion symmetry (local noncentrosymmetry). Compact samples show a clear-cut s-wave-type BCS behavior, which we consider to be the intrinsic penetration depth of SrPtAs. Granular samples display a sample-dependent second diamagnetic drop, attributed to the intergrain coupling. Read More

In this note we obtain tight bounds on the space-complexity of computing the ergodic measure of a low-dimensional discrete-time dynamical system affected by Gaussian noise. If the scale of the noise is $\varepsilon$, and the function describing the evolution of the system is not by itself a source of computational complexity, then the density function of the ergodic measure can be approximated within precision $\delta$ in space polynomial in $\log 1/\varepsilon+\log\log 1/\delta$. We also show that this bound is tight up to polynomial factors. Read More

Recently, a framework for application-oriented optimal experiment design has been introduced. In this context, the distance of the estimated system from the true one is measured in terms of a particular end-performance metric. This treatment leads to superior unknown system estimates to classical experiment designs based on usual pointwise functional distances of the estimated system from the true one. Read More

Hidden Markov models have successfully been applied as models of discrete time series in many fields. Often, when applied in practice, the parameters of these models have to be estimated. The currently predominating identification methods, such as maximum-likelihood estimation and especially expectation-maximization, are iterative and prone to have problems with local minima. Read More

The aim of this paper is to develop a method to estimate high order FIR and ARX models using least squares with re-weighted nuclear norm regularization. Typically, the choice of the tuning parameter in the reweighting scheme is computationally expensive, hence we propose the use of the SPARSEVA (SPARSe Estimation based on a VAlidation criterion) framework to overcome this problem. Furthermore, we suggest the use of the prediction error criterion (PEC) to select the tuning parameter in the SPARSEVA algorithm. Read More

In this paper, based on a successively accuracy-increasing approximation of the $\ell_0$ norm, we propose a new algorithm for recovery of sparse vectors from underdetermined measurements. The approximations are realized with a certain class of concave functions that aggressively induce sparsity and their closeness to the $\ell_0$ norm can be controlled. We prove that the series of the approximations asymptotically coincides with the $\ell_1$ and $\ell_0$ norms when the approximation accuracy changes from the worst fitting to the best fitting. Read More

The widely used nuclear norm heuristic for rank minimization problems introduces a regularization parameter which is difficult to tune. We have recently proposed a method to approximate the regularization path, i.e. Read More

Suppose that a solution $\widetilde{\mathbf{x}}$ to an underdetermined linear system $\mathbf{b} = \mathbf{A} \mathbf{x}$ is given. $\widetilde{\mathbf{x}}$ is approximately sparse meaning that it has a few large components compared to other small entries. However, the total number of nonzero components of $\widetilde{\mathbf{x}}$ is large enough to violate any condition for the uniqueness of the sparsest solution. Read More

We develop latent variable models for Bayesian learning based low-rank matrix completion and reconstruction from linear measurements. For under-determined systems, the developed methods are shown to reconstruct low-rank matrices when neither the rank nor the noise power is known a-priori. We derive relations between the latent variable models and several low-rank promoting penalty functions. Read More

Substantial improvement in accuracy of identified linear time-invariant single-input multi-output (SIMO) dynamical models is possible when the disturbances affecting the output measurements are spatially correlated. Using an orthogonal representation for the modules composing the SIMO structure, in this paper we show that the variance of a parameter estimate of a module is dependent on the model structure of the other modules, and the correlation structure of the disturbances. In addition, we quantify the variance-error for the parameter estimates for finite model orders, where the effect of noise correlation structure, model structure and signal spectra are visible. Read More

In this paper we study the estimation of changing trends in time-series using $\ell_1$ trend filtering. This method generalizes 1D Total Variation (TV) denoising for detection of step changes in means to detecting changes in trends, and it relies on a convex optimization problem for which there are very efficient numerical algorithms. It is known that TV denoising suffers from the so-called stair-case effect, which leads to detecting false change points. Read More

In computability theory and computable analysis, finite programs can compute infinite objects. Presenting a computable object via any program for it, provides at least as much information as presenting the object itself, written on an infinite tape. What additional information do programs provide? We characterize this additional information to be any upper bound on the Kolmogorov complexity of the object. Read More

We solve the Klein-Gordon equation in the presence of the hyperbolic tangent potential. The scattering solutions are derived in terms of hypergeometric functions. The reflection $R$ and transmission $T$ coefficients are calculated in terms of Gamma function and, superradiance is discussed, when the reflection coefficient $R$ is greater than one. Read More

We solve the Klein-Gordon equation for a step potential with hyperbolic tangent potential. The scattering solutions are derived in terms of hypergeometric functions. The reflection coefficient R and transmission coefficient T are calculated, we observed superradiance and transmission resonances. Read More

This paper concerns model reduction of dynamical systems using the nuclear norm of the Hankel matrix to make a trade-off between model fit and model complexity. This results in a convex optimization problem where this trade-off is determined by one crucial design parameter. The main contribution is a methodology to approximately calculate all solutions up to a certain tolerance to the model reduction problem as a function of the design parameter. Read More

This article focuses on the problem of reconstructing low-rank matrices from underdetermined measurements using alternating optimization strategies. We endeavour to combine an alternating least-squares based estimation strategy with ideas from the alternating direction method of multipliers (ADMM) to recover structured low-rank matrices, such as Hankel structure. We show that merging these two alternating strategies leads to a better performance than the existing alternating least squares (ALS) strategy. Read More

This paper uses stochastic dominance principles to construct upper and lower sample path bounds for Hidden Markov Model (HMM) filters. Given a HMM, by using convex optimization methods for nuclear norm minimization with copositive constraints, we construct low rank stochastic marices so that the optimal filters using these matrices provably lower and upper bound (with respect to a partially ordered set) the true filtered distribution at each time instant. Since these matrices are low rank (say R), the computational cost of evaluating the filtering bounds is O(XR) instead of O(X2). Read More

In this paper we develop a new Bayesian inference method for low rank matrix reconstruction. We call the new method the Relevance Singular Vector Machine (RSVM) where appropriate priors are defined on the singular vectors of the underlying matrix to promote low rank. To accelerate computations, a numerically efficient approximation is developed. Read More

This paper proposes a set of piecewise Toeplitz matrices as the linear mapping/sensing operator $\mathcal{A}: \mathbf{R}^{n_1 \times n_2} \rightarrow \mathbf{R}^M$ for recovering low rank matrices from few measurements. We prove that such operators efficiently encode the information so there exists a unique reconstruction matrix under mild assumptions. This work provides a significant extension of the compressed sensing and rank minimization theory, and it achieves a tradeoff between reducing the memory required for storing the sampling operator from $\mathcal{O}(n_1n_2M)$ to $\mathcal{O}(\max(n_1,n_2)M)$ but at the expense of increasing the number of measurements by $r$. Read More

In this paper we propose a method for applications oriented input design for linear systems under time-domain constraints on the amplitude of input and output signals. The method guarantees a desired control performance for the estimated model in minimum time, by imposing some lower bound on the information matrix. The problem is formulated as a time domain optimization problem, which is non-convex. Read More

We propose an extended method for experiment design in nonlinear state space models. The proposed input design technique optimizes a scalar cost function of the information matrix, by computing the optimal stationary probability mass function (pmf) from which an input sequence is sampled. The feasible set of the stationary pmf is a polytope, allowing it to be expressed as a convex combination of its extreme points. Read More

We discuss computability of impressions of prime ends of compact sets. In particular, we construct quadratic Julia sets which possess explicitly described non-computable impressions. Read More

In this paper we analyze the asymptotic properties of l1 penalized maximum likelihood estimation of signals with piece-wise constant mean values and/or variances. The focus is on segmentation of a non-stationary time series with respect to changes in these model parameters. This change point detection and estimation problem is also referred to as total variation denoising or l1 -mean filtering and has many important applications in most fields of science and engineering. Read More

In this article a new algorithm for the design of stationary input sequences for system identification is presented. The stationary input signal is generated by optimizing an approximation of a scalar function of the information matrix, based on stationary input sequences generated from prime cycles, which describe the set of finite Markov chains of a given order. This method can be used for solving input design problems for nonlinear systems. Read More

Emulsion Stability Simulations (ESS) of deformable droplets are used to study the influence of the time-dependent adsorption on the coalescence time of a 200-$\mu$m drop of soybean oil pressed by buoyancy against a planar water/oil interface. The interface is represented by a 5000-$\mu$m drop of oil fixed in the space. The movement of the small drop is determined by the interaction forces between the drops, the buoyancy force, and its thermal interaction with the solvent. Read More

This contribution considers one central aspect of experiment design in system identification. When a control design is based on an estimated model, the achievable performance is related to the quality of the estimate. The degradation in control performance due to errors in the estimated model is measured by an application cost function. Read More

The fundamental task of a digital receiver is to decide the transmitted symbols in the best possible way, i.e., with respect to an appropriately defined performance metric. Read More

In this paper, the problem of training optimization for estimating a multiple-input multiple-output (MIMO) flat fading channel in the presence of spatially and temporally correlated Gaussian noise is studied in an application-oriented setup. So far, the problem of MIMO channel estimation has mostly been treated within the context of minimizing the mean square error (MSE) of the channel estimate subject to various constraints, such as an upper bound on the available training energy. We introduce a more general framework for the task of training sequence design in MIMO systems, which can treat not only the minimization of channel estimator's MSE, but also the optimization of a final performance metric of interest related to the use of the channel estimate in the communication system. Read More

Conformal Riemann mapping of the unit disk onto a simply-connected domain $W$ is a central object of study in classical Complex Analysis. The first complete proof of the Riemann Mapping Theorem given by P. Koebe in 1912 is constructive, and theoretical aspects of computing the Riemann map have been extensively studied since. Read More

In this article, we analyze the SPICE method developed in [1], and establish its connections with other standard sparse estimation methods such as the Lasso and the LAD-Lasso. This result positions SPICE as a computationally efficient technique for the calculation of Lasso-type estimators. Conversely, this connection is very useful for establishing the asymptotic properties of SPICE under several problem scenarios and for suggesting suitable modifications in cases where the naive version of SPICE would not work. Read More

The coalescence of liquid drops induces a higher level of complexity compared to the classical studies about the aggregation of solid spheres. Yet, it is commonly believed that most findings on solid dispersions are directly applicable to liquid mixtures. Here, the state of the art in the evaluation of the flocculation rate of these two systems is reviewed. Read More

Computation plays a key role in predicting and analyzing natural phenomena. There are two fundamental barriers to our ability to computationally understand the long-term behavior of a dynamical system that describes a natural process. The first one is unaccounted-for errors, which may make the system unpredictable beyond a very limited time horizon. Read More

We study the computational content of the Radon-Nokodym theorem from measure theory in the framework of the representation approach to computable analysis. We define computable measurable spaces and canonical representations of the measures and the integrable functions on such spaces. For functions f,g on represented sets, f is W-reducible to g if f can be computed by applying the function g at most once. Read More

The phase-integral approximation devised by Fr\"oman and Fr\"oman, is used for computing cosmological perturbations in the quartic chaotic inflationary model. The phase-integral formulas for the scalar power spectrum are explicitly obtained up to fifth order of the phase-integral approximation. As in previous reports [1-3], we point out that the accuracy of the phase-integral approximation compares favorably with the numerical results and those obtained using the slow-roll and uniform approximation methods. Read More

This paper addresses the problem of segmenting a time-series with respect to changes in the mean value or in the variance. The first case is when the time data is modeled as a sequence of independent and normal distributed random variables with unknown, possibly changing, mean value but fixed variance. The main assumption is that the mean value is piecewise constant in time, and the task is to estimate the change times and the mean values within the segments. Read More

**Authors:**Jean-Baptiste Le Bouquin

^{1}, J. -P. Berger

^{2}, B. Lazareff

^{3}, G. Zins

^{4}, P. Haguenauer

^{5}, L. Jocou

^{6}, P. Kern

^{7}, R. Millan-Gabet

^{8}, W. Traub

^{9}, O. Absil

^{10}, J. -C. Augereau

^{11}, M. Benisty

^{12}, N. Blind

^{13}, X. Bonfils

^{14}, P. Bourget

^{15}, A. Delboulbe

^{16}, P. Feautrier

^{17}, M. Germain

^{18}, P. Gitton

^{19}, D. Gillier

^{20}, M. Kiekebusch

^{21}, J. Kluska

^{22}, J. Knudstrup

^{23}, P. Labeye

^{24}, J. -L. Lizon

^{25}, J. -L. Monin

^{26}, Y. Magnard

^{27}, F. Malbet

^{28}, D. Maurel

^{29}, F. Menard

^{30}, M. Micallef

^{31}, L. Michaud

^{32}, G. Montagnier

^{33}, S. Morel

^{34}, T. Moulin

^{35}, K. Perraut

^{36}, D. Popovic

^{37}, P. Rabou

^{38}, S. Rochat

^{39}, C. Rojas

^{40}, F. Roussel

^{41}, A. Roux

^{42}, E. Stadler

^{43}, S. Stefl

^{44}, E. Tatulli

^{45}, N. Ventura

^{46}

**Affiliations:**

^{1}IPAG,

^{2}ESO,

^{3}IPAG,

^{4}IPAG,

^{5}ESO,

^{6}IPAG,

^{7}IPAG,

^{8}NExSI,

^{9}IPAG,

^{10}IPAG,

^{11}IPAG,

^{12}MPIA,

^{13}IPAG,

^{14}IPAG,

^{15}ESO,

^{16}IPAG,

^{17}IPAG,

^{18}IPAG,

^{19}ESO,

^{20}IPAG,

^{21}IPAG,

^{22}IPAG,

^{23}LETI,

^{24}LETI,

^{25}IPAG,

^{26}IPAG,

^{27}IPAG,

^{28}IPAG,

^{29}IPAG,

^{30}IPAG,

^{31}IPAG,

^{32}IPAG,

^{33}ESO,

^{34}ESO,

^{35}IPAG,

^{36}IPAG,

^{37}IPAG,

^{38}IPAG,

^{39}IPAG,

^{40}ESO,

^{41}IPAG,

^{42}IPAG,

^{43}IPAG,

^{44}ESO,

^{45}IPAG,

^{46}IPAG

PIONIER stands for Precision Integrated-Optics Near-infrared Imaging ExpeRiment. It combines four 1.8m Auxilliary Telescopes or four 8m Unit Telescopes of the Very Large Telescope Interferometer (ESO, Chile) using an integrated optics combiner. Read More

The paper considers quantitative versions of different randomness notions: algorithmic test measures the amount of non-randomness (and is infinite for non-random sequences). We start with computable measures on Cantor space (and Martin-Lof randomness), then consider uniform randomness (test is a function of a sequence and a measure, not necessarily computable) and arbitrary constructive metric spaces. We also consider tests for classes of measures, in particular Bernoulli measures on Cantor space, and show how they are related to uniform tests and original Martin-Lof definition. Read More

We survey an area of recent development, relating dynamics to theoretical computer science. We discuss the theoretical limits of simulation and computation of interesting quantities in dynamical systems. We will focus on central objects of the theory of dynamics, as invariant measures and invariant sets, showing that even if they can be computed with arbitrary precision in many interesting cases, there exists some cases in which they can not. Read More

In a previous report [10] it was shown that emulsion stability simulations are able to reproduce the lifetime of micrometer-size drops of hexadecane pressed by buoyancy against a planar water-hexadecane interface. It was confirmed that small drops (ri<10 {\mu}m) stabilized with {\beta}-casein behave as nondeformable particles, moving with a combination of Stokes and Taylor tensors as they approach the interface. Here, a similar methodology is used to parametrize the potential of interaction of drops of soybean oil stabilized with bovine serum albumin. Read More

Brolin-Lyubich measure $\lambda_R$ of a rational endomorphism $R:\riem\to\riem$ with $\deg R\geq 2$ is the unique invariant measure of maximal entropy $h_{\lambda_R}=h_{\text{top}}(R)=\log d$. Its support is the Julia set $J(R)$. We demonstrate that $\lambda_R$ is always computable by an algorithm which has access to coefficients of $R$, even when $J(R)$ is not computable. Read More