Controlling the transport of an ion: classical and quantum mechanical solutions

The accurate transport of an ion over macroscopic distances represents a challenging control problem due to the different length and time scales that enter and the experimental limitations on the controls that need to be accounted for. Here, we investigate the performance of different control techniques for ion transport in state-of-the-art segmented miniaturized ion traps. We employ numerical optimization of classical trajectories and quantum wavepacket propagation as well as analytical solutions derived from invariant based inverse engineering and geometric optimal control. The applicability of each of the control methods depends on the length and time scales of the transport. Our comprehensive set of tools allows us make a number of observations. We find that accurate shuttling can be performed with operation times below the trap oscillation period. The maximum speed is limited by the maximum acceleration that can be exerted on the ion. When using controls obtained from classical dynamics for wavepacket propagation, wavepacket squeezing is the only quantum effect that comes into play for a large range of trapping parameters. We show that this can be corrected by a compensating force derived from invariant based inverse engineering, without a significant increase in the operation time.

Automated Reasoning About Classical Mechanics

In recent years, researchers in artificial intelligence have become interested in replicating human physical reasoning talents in computers. One of the most important skills in this area is predicting how physical systems will behave. This thesis discusses an implemented program that generates algebraic descriptions of how systems of rigid bodies evolve over time. Discussion about the design of this program identifies a physical reasoning paradigm and knowledge representation approach based on mathematical model construction and algebraic reasoning. This paradigm offers several advantages over methods that have become popular in the field, and seems promising for reasoning about a wide variety of classical mechanics problems.

Notes on PCA, Regularization, Sparsity and Support Vector Machines

We derive a new representation for a function as a linear combination of local correlation kernels at optimal sparse locations and discuss its relation to PCA, regularization, sparsity principles and Support Vector Machines. We first review previous results for the approximation of a function from discrete data (Girosi, 1998) in the context of Vapnik"s feature space and dual representation (Vapnik, 1995). We apply them to show 1) that a standard regularization functional with a stabilizer defined in terms of the correlation function induces a regression function in the span of the feature space of classical Principal Components and 2) that there exist a dual representations of the regression function in terms of a regularization network with a kernel equal to a generalized correlation function. We then describe the main observation of the paper: the dual representation in terms of the correlation function can be sparsified using the Support Vector Machines (Vapnik, 1982) technique and this operation is equivalent to sparsify a large dictionary of basis functions adapted to the task, using a variation of Basis Pursuit De-Noising (Chen, Donoho and Saunders, 1995; see also related work by Donahue and Geiger, 1994; Olshausen and Field, 1995; Lewicki and Sejnowski, 1998). In addition to extending the close relations between regularization, Support Vector Machines and sparsity, our work also illuminates and formalizes the LFA concept of Penev and Atick (1996). We discuss the relation between our results, which are about regression, and the different problem of pattern classification.

Sparse Correlation Kernel Analysis and Reconstruction

This paper presents a new paradigm for signal reconstruction and superresolution, Correlation Kernel Analysis (CKA), that is based on the selection of a sparse set of bases from a large dictionary of class- specific basis functions. The basis functions that we use are the correlation functions of the class of signals we are analyzing. To choose the appropriate features from this large dictionary, we use Support Vector Machine (SVM) regression and compare this to traditional Principal Component Analysis (PCA) for the tasks of signal reconstruction, superresolution, and compression. The testbed we use in this paper is a set of images of pedestrians. This paper also presents results of experiments in which we use a dictionary of multiscale basis functions and then use Basis Pursuit De-Noising to obtain a sparse, multiscale approximation of a signal. The results are analyzed and we conclude that 1) when used with a sparse representation technique, the correlation function is an effective kernel for image reconstruction and superresolution, 2) for image compression, PCA and SVM have different tradeoffs, depending on the particular metric that is used to evaluate the results, 3) in sparse representation techniques, L_1 is not a good proxy for the true measure of sparsity, L_0, and 4) the L_epsilon norm may be a better error metric for image reconstruction and compression than the L_2 norm, though the exact psychophysical metric should take into account high order structure in images.

Bagging Regularizes

Intuitively, we expect that averaging --- or bagging --- different regressors with low correlation should smooth their behavior and be somewhat similar to regularization. In this note we make this intuition precise. Using an almost classical definition of stability, we prove that a certain form of averaging provides generalization bounds with a rate of convergence of the same order as Tikhonov regularization --- similar to fashionable RKHS-based learning algorithms.

Behavioral Measures and their Correlation with IPM Iteration Counts on Semi-Definite Programming Problems

We study four measures of problem instance behavior that might account for the observed differences in interior-point method (IPM) iterations when these methods are used to solve semidefinite programming (SDP) problem instances: (i) an aggregate geometry measure related to the primal and dual feasible regions (aspect ratios) and norms of the optimal solutions, (ii) the (Renegar-) condition measure C(d) of the data instance, (iii) a measure of the near-absence of strict complementarity of the optimal solution, and (iv) the level of degeneracy of the optimal solution. We compute these measures for the SDPLIB suite problem instances and measure the correlation between these measures and IPM iteration counts (solved using the software SDPT3) when the measures have finite values. Our conclusions are roughly as follows: the aggregate geometry measure is highly correlated with IPM iterations (CORR = 0.896), and is a very good predictor of IPM iterations, particularly for problem instances with solutions of small norm and aspect ratio. The condition measure C(d) is also correlated with IPM iterations, but less so than the aggregate geometry measure (CORR = 0.630). The near-absence of strict complementarity is weakly correlated with IPM iterations (CORR = 0.423). The level of degeneracy of the optimal solution is essentially uncorrelated with IPM iterations.

Self-organized sociopolitical interactions as the best way to achieve organized patterns in human social systems: going beyond the top-down control of classical political regimes

La monografía presenta la auto-organización sociopolítica como la mejor manera de lograr patrones organizados en los sistemas sociales humanos, dada su naturaleza compleja y la imposibilidad de las tareas computacionales de los regímenes políticos clásico, debido a que operan con control jerárquico, el cual ha demostrado no ser óptimo en la producción de orden en los sistemas sociales humanos. En la monografía se extrapola la teoría de la auto-organización en los sistemas biológicos a las dinámicas sociopolíticas humanas, buscando maneras óptimas de organizarlas, y se afirma que redes complejas anárquicas son la estructura emergente de la auto-organización sociopolítica.

Prevalencia de síntomas osteomusculares en miembros superiores y los probables factores de riesgo asociados en los estudiantes de musica en una institución universitaria de Bogotá,Colombia, 2013

Los músicos por su práctica instrumental tienen una alta demanda de desempeño físico, especialmente de los miembros superiores y están expuestos a varios factores de riesgo biomecánico que pueden resultar en problemas de salud. Objetivo: determinar la prevalencia de sintomatología osteomuscular de miembros superiores y los probables factores de riesgo asociados, en los estudiantes expuestos a la actividad musical durante el segundo semestre del año 2013 en una institución universitaria de Bogotá, Colombia. Método: se realizó un estudio descriptivo de corte trasversal en 134 estudiantes de todos los semestres de música en una institución universitaria. Se aplicó el Cuestionario nórdico estandarizado para análisis de síntomas músculo esqueléticos y una encuesta ad hoc que contemplaba aspectos sociodemográficos y antecedentes académicos, patológicos, factores de exposición y hábitos. Resultados: Las prevalencias generales encontradas en el estudio, son similares a las que refieren algunos estudios revisados que contemplan ciertas variables afines a las que se estudiaron. La prevalencia de síntomas osteomusculares cervico-braquial fue de 77.9%. La prevalencia de molestias en cuello fue mayor en las mujeres (64.3%) que en los hombres (37.4%) (OR=3.02, IC 95%=1.26, 7.18). La prevalencia de síntomas en manos/muñecas que le impidió hacer su trabajo en los últimos 12 meses fue mayor en los estudiantes que refirieron alguna enfermedad (29.4%) que en los que no la manifestaron (10.2%), (OR=3.69, IC 95%=1.34, 10.19). La prevalencia de molestias en cuello que les impidió hacer su trabajo en los últimos 12 meses fue mayor en los estudiantes que practicaron algún pasatiempo con sus brazos (10.4%) versus los que no lo practicaron, cuya frecuencia fue 0.0%. Los instrumentos musicales de mayor práctica fueron cuerda y percusión y se asociaron a prevalencia de síntomas osteomusculares cérvico-braquiales con una distribución por segmentos similar. Los tiempos de práctica semanales y la antigüedad en la práctica, conduce a síntomas cervico-braquiales. Conclusiones: Este estudio coincide con la distribución de las prevalencias encontradas en poblaciones de estudiantes de música revisadas, con respecto a la sintomatología, a los segmentos cervico-braquiales de mayor afectación, a la significancia del género femenino con respecto al masculino, al tipo de instrumentos y a los tiempos de práctica entre otros. Esto plantea la necesidad de educar a nuestros músicos en la detección temprana de síntomas desde su formación de pregrado o quizás mucho antes.

Estudio de las interacciones intra-específicas (la competición, la cooperación y la depredación) en las organizaciones: revisión de literatura

Las organizaciones son un ente social y económico que según la teoría clásica debe ser vista como una estructura administrativa. Para Mooney (1947) existen principios los cuales baso en la teoría organización, en donde busca establecer relaciones entre principios, procesos y efectos. Por lo que el satisfacer las necesidades de una población debe ser un proceso que involucre intereses particulares .En este caso la organización debe ser estudiada y evaluada como un sistema que tiene diferentes jerarquías y agentes que interactúan entre si, para cumplir un objetivo. Las relaciones que se generan dentro de una organización, y en este caso una organización privada tienden a dar respuesta a muchas necesidades que como procesos se presentan en el diario vivir; es así como se habla del éxito de las organizaciones basado en la competitividad que generan las habilidades y actitudes del personal, el número de variables, fenómenos y efectos, que busca que los empleados asuman actitudes que conlleven a tomar buenas decisiones (Garcia, 2009 ). Es importante resaltar en las organizaciones, los fenómenos de liderazgo, poder e influencia, cada concepto independiente pero visto conjuntamente desde un panorama organizacional, donde toda relación intraespecifica deben determinarse en los diferentes comportamientos, conductas y culturas del personal de la empresa (Fuentes, 2004). La depredación, competencia y cooperación, son conceptos que indudablemente están presentes en todos los procesos de la organización y serán determinantes en la perdurabilidad de la misma. Por último, la presente investigación pretende establecer los efectos de los fenómenos sociales de liderazgo, poder e influencia sobre las interacciones intra-especificas (competencia, cooperación y depredación) al interior de una organización, de tal manera que se logre explicar el efecto y la correlación que existe entre unos y otros fenómenos (Castro, 2012).

Dynamic conditional correlation in Latin-American asset markets

In this paper we reviewed the models of volatility for a group of five Latin American countries, mainly motivated by the recent periods of financial turbulence. Our results based on high frequency data suggest that Dynamic multivariate models are more powerful to study the volatilities of asset returns than Constant Conditional Correlation models. For the group of countries included, we identified that domestic volatilities of asset markets have been increasing; but the co-volatility of the region is still moderate.

Governance and change in higher education : the debate between classical political sociology, new institutionalism and critical theories

Resumen tomado de la publicaci??n

Classical and causal inference approaches to statistical mediation analysis

Resumen tomado de la publicaci??n

Sample size planning for multiple correlation : reply to Shieh (2013)

Resumen tomado de la publicaci??n

The mapping of the local contributions of Fermi and Coulomb correlation into intracule and extracule density distributions

The contributions of the correlated and uncorrelated components of the electron-pair density to atomic and molecular intracule I(r) and extracule E(R) densities and its Laplacian functions ∇2I(r) and ∇2E(R) are analyzed at the Hartree-Fock (HF) and configuration interaction (CI) levels of theory. The topologies of the uncorrelated components of these functions can be rationalized in terms of the corresponding one-electron densities. In contrast, by analyzing the correlated components of I(r) and E(R), namely, IC(r) and EC(R), the effect of electron Fermi and Coulomb correlation can be assessed at the HF and CI levels of theory. Moreover, the contribution of Coulomb correlation can be isolated by means of difference maps between IC(r) and EC(R) distributions calculated at the two levels of theory. As application examples, the He, Ne, and Ar atomic series, the C2-2, N2, O2+2 molecular series, and the C2H4 molecule have been investigated. For these atoms and molecules, it is found that Fermi correlation accounts for the main characteristics of IC(r) and EC(R), with Coulomb correlation increasing slightly the locality of these functions at the CI level of theory. Furthermore, IC(r), EC(R), and the associated Laplacian functions, reveal the short-ranged nature and high isotropy of Fermi and Coulomb correlation in atoms and molecules