2,047 research outputs found
Kinematics of the Outer Stellar Halo
We have tested whether the simple model for the kinematics of the Galactic
stellar halo (in particular the outer halo) proposed by Sommer-Larsen, Flynn
and Christensen (SLFC) is physically realizable, by directly integrating
particles in a 3-D model of the Galactic potential. We are able to show that
the SLFC solution can be realized in terms of a distribution of particles with
stationary statistical properties in phase-space. Hence, the SLFC model, which
shows a notable change in the anisotropy from markedly radial at the sun to
markedly tangential beyond about Galactocentric radius r=20 kpc, seems a
tenable description of outer halo kinematics.Comment: 13 pages, 6 figures, accepted for publication in MNRAS. also
available at http://astro.utu.fi/~cflynn/papers/fslc4/fslc4.htm
Current-induced forces and hot-spots in biased nano-junctions
We investigate theoretically the interplay of current-induced forces (CIF),
Joule heating, and heat transport inside a current-carrying nano-conductor. We
find that the CIF, due to the electron-phonon coherence, can control the
spatial heat dissipation in the conductor. This yields a significant asymmetric
concentration of excess heating (hot-spot) even for a symmetric conductor. When
coupled to the electrode phonons, CIF drive different phonon heat flux into the
two electrodes. First-principles calculations on realistic biased
nano-junctions illustrate the importance of the effect.Comment: Phys. Rev. Lett. accepted versio
perms: Marginal likelihood estimation for binary Bayesian nonparametric models in Python and R
Binary responses arise in a multitude of statistical problems, including
binary classification, bioassay, current status data problems and sensitivity
estimation. There has been an interest in such problems in the Bayesian
nonparametrics community since the early 1970s, but inference given binary data
is intractable for a wide range of modern simulation-based models, even when
employing MCMC methods. Recently, Christensen (2023) introduced a novel
simulation technique based on counting permutations, which can estimate both
posterior distributions and marginal likelihoods for any model from which a
random sample can be generated. However, the accompanying implementation of
this technique struggles when the sample size is too large (n > 250). Here we
present perms, a new implementation of said technique which is substantially
faster and able to handle larger data problems than the original
implementation. It is available both as an R package and a Python library. The
basic usage of perms is illustrated via two simple examples: a tractable toy
problem and a bioassay problem. A more complex example involving changepoint
analysis is also considered. We also cover the details of the implementation
and illustrate the computational speed gain of perms via a simple simulation
study
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