Nutation damper

A nutation damper for use on a spinning body is disclosed. The damper is positioned parallel to the spin axis of the body and radially displaced therefrom. The damper is partially filled with a fluid and contains a porous media to impede the flow of the fluid induced by nutation

A myth in time: Victor Erice's El sur

En este artículo se estudia tiempo e identidad, mitos y cine en El sur de Victor Erice. Basándose en un análisis de dos escenas, se plantea una relación entre eléxito o fracaso personal, la capacidad para construir mitos, y el transcurso del tiempo. En la primera escena analizada, Estrella aprende a manejar el péndulo en el estudio de su padre, en la segunda, Agustín observa a su ex-amante, Laura, en el melodrama Flor en la sombra. Al principio, tanto Estrella como su padre se confabulan con el mito del otro ? Estrella se deja seducir por la imaginaria omnipotencia de su padre, y Agustín por la identidad cinemática de su antiguo amor. Las mujeres, Estrella y Laura, terminan por rechazar su papel en el drama que es su relación con Agustín, mientras que Agustín , privado de los mitos femeninos que le han soportado e incapaz de cambiar, se suicida, dejando a Estrella el péndulo, símbolo de su perdido poder. Se ve, pues, que la identidad humana (tanto como las identidades cinemáticas) depende de un frágil equilibrio entre el construir mitos y el reconocer que un mito no ofrece mas que una imaginaria estabilidad transitoria: que tendrá que cambiarse con el tiempo, y que tendrá que adaptarse a la continua reconstrucción que exige la paradójica búsqueda de identidad de unos individuos en continua transición

SU(3)-Goodman-de la Harpe-Jones subfactors and the realisation of SU(3) modular invariants

We complete the realisation by braided subfactors, announced by Ocneanu, of all SU(3)-modular invariant partition functions previously classified by Gannon.Comment: 47 pages, minor changes, to appear in Reviews in Mathematical Physic

New observations regarding deterministic, time reversible thermostats and Gauss's principle of least constraint

Deterministic thermostats are frequently employed in non-equilibrium molecular dynamics simulations in order to remove the heat produced irreversibly over the course of such simulations. The simplest thermostat is the Gaussian thermostat, which satisfies Gauss's principle of least constraint and fixes the peculiar kinetic energy. There are of course infinitely many ways to thermostat systems, e.g. by fixing $\sum\limits_i{|{p_i}|^{\mu + 1}}$. In the present paper we provide, for the first time, convincing arguments as to why the conventional Gaussian isokinetic thermostat ($\mu=1$) is unique in this class. We show that this thermostat minimizes the phase space compression and is the only thermostat for which the conjugate pairing rule (CPR) holds. Moreover it is shown that for finite sized systems in the absence of an applied dissipative field, all other thermostats ($\mu=1$) perform work on the system in the same manner as a dissipative field while simultaneously removing the dissipative heat so generated. All other thermostats ($\mu=1$) are thus auto-dissipative. Among all $\mu$-thermostats, only the $\mu=1$ Gaussian thermostat permits an equilibrium state.Comment: 27 pages including 10 figures; submitted for publication Journal of Chemical Physic

Model selection and local geometry

We consider problems in model selection caused by the geometry of models close to their points of intersection. In some cases---including common classes of causal or graphical models, as well as time series models---distinct models may nevertheless have identical tangent spaces. This has two immediate consequences: first, in order to obtain constant power to reject one model in favour of another we need local alternative hypotheses that decrease to the null at a slower rate than the usual parametric $n^{-1/2}$ (typically we will require $n^{-1/4}$ or slower); in other words, to distinguish between the models we need large effect sizes or very large sample sizes. Second, we show that under even weaker conditions on their tangent cones, models in these classes cannot be made simultaneously convex by a reparameterization. This shows that Bayesian network models, amongst others, cannot be learned directly with a convex method similar to the graphical lasso. However, we are able to use our results to suggest methods for model selection that learn the tangent space directly, rather than the model itself. In particular, we give a generic algorithm for learning Bayesian network models

Graphical methods for inequality constraints in marginalized DAGs

We present a graphical approach to deriving inequality constraints for directed acyclic graph (DAG) models, where some variables are unobserved. In particular we show that the observed distribution of a discrete model is always restricted if any two observed variables are neither adjacent in the graph, nor share a latent parent; this generalizes the well known instrumental inequality. The method also provides inequalities on interventional distributions, which can be used to bound causal effects. All these constraints are characterized in terms of a new graphical separation criterion, providing an easy and intuitive method for their derivation.Comment: A final version will appear in the proceedings of the 22nd Workshop on Machine Learning and Signal Processing, 201