Nonlinearity and nonclassicality in a nanomechanical resonator

We address quantitatively the relationship between the nonlinearity of a mechanical resonator and the nonclassicality of its ground state. In particular, we analyze the nonclassical properties of the nonlinear Duffing oscillator (being driven or not) as a paradigmatic example of a nonlinear nanomechanical resonator. We first discuss how to quantify the nonlinearity of this system and then show that the nonclassicality of the ground state, as measured by the volume occupied by the negative part of the Wigner function, monotonically increases with the nonlinearity in all the working regimes addressed in our study. Our results show quantitatively that nonlinearity is a resource to create nonclassical states in mechanical systems.Comment: 6 pages; 7 figures; RevTeX4-

Bilocal Dynamics for Self-Avoiding Walks

We introduce several bilocal algorithms for lattice self-avoiding walks that provide reasonable models for the physical kinetics of polymers in the absence of hydrodynamic effects. We discuss their ergodicity in different confined geometries, for instance in strips and in slabs. A short discussion of the dynamical properties in the absence of interactions is given.Comment: 38 LaTeX2e pages with 9 postscript figure

Inestabilidad de beta de sectores económicos en la Bolsa de Comercio de Buenos Aires (1994-2007)

Si bien el CAPM no requiere que beta sea estable en el tiempo, al trabajar con series de datos y estimar su valor en el contexto del Modelo de Índice Simple, la estabilidad del coeficiente se torna en una condición crucial para su adecuada utilización. Una práctica ampliamente difundida consiste en obtener los valores a través de MCO, asumiendo la estabilidad de dicho coeficiente. El presente trabajo estima los coeficientes beta de portafolios de sectores económicos con oferta pública de acciones en la Bolsa de Comercio de Buenos Aires en el período 1994-2007, introduciendo una metodología de estimación no paramétrica denominada Varying Coefficient Model. El ejercicio muestra la importante volatilidad de los betas, siendo que es por ello altamente recomendable tomar con especial cuidado las estimaciones de betas basadas en datos históricos al querer extrapolarlas en el tiempo. La utilización en esta dirección, puede modificar drásticamente las conclusiones en la práctica de la administración de portafolios de inversión y en la valuación de empresas. Dos ejemplos de estas aplicaciones son mostradas en el anexo.Facultad de Ciencias Económica

Inestabilidad de beta de sectores económicos en la Bolsa de Comercio de Buenos Aires (1994-2007)

Si bien el CAPM no requiere que beta sea estable en el tiempo, al trabajar con series de datos y estimar su valor en el contexto del Modelo de Índice Simple, la estabilidad del coeficiente se torna en una condición crucial para su adecuada utilización. Una práctica ampliamente difundida consiste en obtener los valores a través de MCO, asumiendo la estabilidad de dicho coeficiente. El presente trabajo estima los coeficientes beta de portafolios de sectores económicos con oferta pública de acciones en la Bolsa de Comercio de Buenos Aires en el período 1994-2007, introduciendo una metodología de estimación no paramétrica denominada Varying Coefficient Model. El ejercicio muestra la importante volatilidad de los betas, siendo que es por ello altamente recomendable tomar con especial cuidado las estimaciones de betas basadas en datos históricos al querer extrapolarlas en el tiempo. La utilización en esta dirección, puede modificar drásticamente las conclusiones en la práctica de la administración de portafolios de inversión y en la valuación de empresas. Dos ejemplos de estas aplicaciones son mostradas en el anexo.Facultad de Ciencias Económica

Synchrotron-based visualization and segmentation of elastic lamellae in the mouse carotid artery during quasi-static pressure inflation

This dataset contains images that were obtained during quasi-static pressure inflation of mouse carotid arteries. Images were taken with phase propagation imaging at the X02DA TOMCAT beamline of the Swiss Light Source synchrotron at the Paul Scherrer Institute in Villigen, Switzerland. Scans of n=12 left carotid arteries (n-6 Apoe-deficient mice, n=6 wild-type mice, all on a C57Bl6J background) were taken at pressure levels of 0, 10, 20, 30, 40, 50, 70, 90 and 120 mmHg. For analysis we selected 75 images from the center of each stack (starting at the center of the stack, and skipping 2 of every three images in both cranial and caudal axial directions) for each sample and for each pressure level, resulting in a total of 75 x 12 x 9 = 8100 analyzed images from 108 different scans. Segmentation, 3D visualization and geometric analysis is presented in the corresponding manuscript. Files are uploaded in 16bit .tif format and are named: mouseid_pressurelevel_stacknumber, with mouseid consisting of either Apoe (Apoe-deficient) or Bl (wild-type) and the mouse number, pressurelevel varies from P0 to P120 and stacknumber indicates which image from the stack has been uploaded

Comparison and clustering analysis of the daily electrical load in eight European countries

This paper illustrates and compares the ability of several clustering algorithms to correctly associate a given aggregate daily electrical load curve with its corresponding day of the week. In particular, popular clustering algorithms like the Fuzzy c-Means, Spectral Clustering and Expectation Maximization are compared, and it is shown that the best results are obtained if the daily data are compressed with respect to a single feature, namely the so-called “Morning Slope”. Such a feature-based clustering appears to outperform the clustering results obtained upon using other classic features, and also with respect to using other conventional compression methods, such as the Principal Component Analysis, in all the examined European countries. This result is particularly interesting, as this feature provides a direct physical interpretation that can be used to obtain insights on the structure of the daily load profiles

Spectral Density Classification For Environment Spectroscopy

Spectral densities encode the relevant information characterising the system-environment interaction in an open-quantum system problem. Such information is key to determining the system's dynamics. In this work, we leverage the potential of machine learning techniques to reconstruct the features of the environment. Specifically, we show that the time evolution of a system observable can be used by an artificial neural network to infer the main features of the spectral density. In particular, for relevant example of spin-boson models, we can classify with high accuracy the Ohmicity parameter of the environment as either Ohmic, sub-Ohmic or super-Ohmic, thereby distinguishing between different forms of dissipation.Comment: 11+epsilon pages, 7 figures, RevTeX4-

Unravelling the aortic microstructure : synchrotron-based quasi-static pressure inflation of the mouse carotid artery

The contribution of the aortic microstructure to the mechanical behavior of the aortic wall is poorly understood. Several high-resolution techniques have been proposed to visualize elastic lamellae or collagen fibers, but most have a limited field of view and are challenging to perform in pressurized conditions. In recent experiments we visualized the micro-structure of mouse aortas using phase propagation imaging – a synchrotron-based technique that yielded 3D images on which separate lamellar layers could be identified (unpublished data, manuscript in preparation). In the experimental study that is presented here we used phase propagation imaging to quantify, for the first time, the unfolding of aortic lamellae during quasi-static pressure inflation experiments. Six wild type and six ApoE-/- mice, all male and on a C57Bl6/J background, were used for this study. The left carotid artery was harvested immediately after sacrifice and mounted on a dedicated synchrotron-compatible pressure inflation device. During the experiment pressure was increased quasi-statically with a syringe pump and maintained at a constant level during each imaging step. After two initial loops of 0-120 mmHg to precondition the vessel, scans were taken at pressure levels of 0, 10, 20, 30, 40, 50, 70, 90 and 120 mmHg while the axial stretch was kept at the in vivo value. Phase propagation was performed at 25m source-to-sample distance, 25 cm sample-todetector distance and at 21 keV. A scientific CMOS detector (pco.Edge 5.5) was used in combination with a 4x magnifying visible-light optics and a 20 μm thick scintillator. The effective pixel size was 1.625 x 1.625 μm2. During post-processing the images were skeletonized and a bi-directional graph was generated in Matlab. Using a modified Dijkstra algorithm in which lower weights were assigned to the edges closest to the center of the vessel, we created a Matlab-based algorithm that allows us to automatically segment the main micro-structural features each of the three lamellar layers in the carotid artery. The algorithm exploits the edge connectivity and the shortest path constraints, and weights of edges belonging to the shortest path are subsequently increased order to allow for the detection of subsequent layers. After filtering and de-trending the signal, the undulation of each layer was quantified from the prominence of the peaks in the signal. Both in ApoE-/- and wild type mice we were able to quantify how the increased straightening of the lamellar layers in response to the increasing pressure related to the change in vessel diameter that is quantified in traditional biomechanical experiments. In future work we intend to use the synchrotroncompatible pressure-inflation device in order to experimentally determine the microstructural material properties of aortic lamellae and the interlamellar space

Supervised learning of time-independent Hamiltonians for gate design

We present a general framework to tackle the problem of finding time-independent dynamics generating target unitary evolutions. We show that this problem is equivalently stated as a set of conditions over the spectrum of the time-independent gate generator, thus transforming the task to an inverse eigenvalue problem. We illustrate our methodology by identifying suitable time-independent generators implementing Toffoli and Fredkin gates without the need for ancillae or effective evolutions. We show how the same conditions can be used to solve the problem numerically, via supervised learning techniques. In turn, this allows us to solve problems that are not amenable, in general, to direct analytical solution, providing at the same time a high degree of flexibility over the types of gate-design problems that can be approached. As a significant example, we find generators for the Toffoli gate using only diagonal pairwise interactions, which are easier to implement in some experimental architectures. To showcase the flexibility of the supervised learning approach, we give an example of a nontrivial four-qubit gate that is implementable using only diagonal, pairwise interactions.Comment: updated links and added figure