14,789 research outputs found
On the Fueling of Massive Black Holes and the Properties of their Host Spheroids
We study the relation between nuclear massive black holes and their host
spheroid gravitational potential. Using simple models, we analyze how gas is
expected to be transported in the nuclear regions of galaxies. When we couple
it with the expected gas lifetime given by the Kennicutt-Schmidt Law, this
naturally leads to the `M_BH - M_virial' and `M_BH - sigma' relations. We also
numerically test, using AMR simulations, our simple models for the mass
transport with satisfactory results.Comment: Reduced version that fit the 4-page limit. In press ApJ Letter
Comments on "State equation for the three-dimensional system of 'collapsing' hard spheres"
A recent paper [I. Klebanov et al. \emph{Mod. Phys. Lett. B} \textbf{22}
(2008) 3153; arXiv:0712.0433] claims that the exact solution of the
Percus-Yevick (PY) integral equation for a system of hard spheres plus a step
potential is obtained. The aim of this paper is to show that Klebanov et al.'s
result is incompatible with the PY equation since it violates two known cases:
the low-density limit and the hard-sphere limit.Comment: 4 pages; v2: title chang
On the equivalence between the energy and virial routes to the equation of state of hard-sphere fluids
The energy route to the equation of state of hard-sphere fluids is
ill-defined since the internal energy is just that of an ideal gas and thus it
is independent of density. It is shown that this ambiguity can be avoided by
considering a square-shoulder interaction and taking the limit of vanishing
shoulder width. The resulting hard-sphere equation of state coincides exactly
with the one obtained through the virial route. Therefore, the energy and
virial routes to the equation of state of hard-sphere fluids can be considered
as equivalent.Comment: 2 page
Bayesian Analysis of ODE's: solver optimal accuracy and Bayes factors
In most relevant cases in the Bayesian analysis of ODE inverse problems, a
numerical solver needs to be used. Therefore, we cannot work with the exact
theoretical posterior distribution but only with an approximate posterior
deriving from the error in the numerical solver. To compare a numerical and the
theoretical posterior distributions we propose to use Bayes Factors (BF),
considering both of them as models for the data at hand. We prove that the
theoretical vs a numerical posterior BF tends to 1, in the same order (of the
step size used) as the numerical forward map solver does. For higher order
solvers (eg. Runge-Kutta) the Bayes Factor is already nearly 1 for step sizes
that would take far less computational effort. Considerable CPU time may be
saved by using coarser solvers that nevertheless produce practically error free
posteriors. Two examples are presented where nearly 90% CPU time is saved while
all inference results are identical to using a solver with a much finer time
step.Comment: 28 pages, 6 figure
Explicit Soliton for the Laplacian Co-Flow on a Solvmanifold
We apply the general Ansatz in geometric flows on homogeneous spaces proposed
by Jorge Lauret for the Laplacian co-flow of invariant -structures on a
Lie group, finding an explicit soliton on a particular almost Abelian
-manifold.Comment: Minor corrections, proof's Lemma 4.1 modified. To appear in the S\~ao
Paulo Journal of mathematical scienc
Full-vector analysis of a realistic photonic crystal fiber
We analyze the guiding problem in a realistic photonic crystal fiber using a
novel full-vector modal technique, a biorthogonal modal method based on the
nonselfadjoint character of the electromagnetic propagation in a fiber.
Dispersion curves of guided modes for different fiber structural parameters are
calculated along with the 2D transverse intensity distribution of the
fundamental mode. Our results match those achieved in recent experiments, where
the feasibility of this type of fiber was shown.Comment: 3 figures, submitted to Optics Letter
Biomechanical analysis of a cranial Patient Specific Implant on the interface with the bone using the Finite Element Method
- New advance technologies based on reverse engineering , design and additive
manufacturing, have expanded design capabilities for biomedical applications to
include Patient Specific Implants (PSI). This change in design paradigms needs
advanced tools to assess the mechanical performance of the product, and
simulate the impact on the patient. In this work, we perform a structural
analysis on the interface of a cranial PSI under static loading conditions.
Based on those simulations, we have identified the regions with high stress and
strain and checked the failure criteria both in the implant and the skull. We
evaluate the quality of the design of the implant and determine their response
given different materials, in order to ensure optimality of the final product
to be manufactured
Can Network Analysis Techniques help to Predict Design Dependencies? An Initial Study
The degree of dependencies among the modules of a software system is a key
attribute to characterize its design structure and its ability to evolve over
time. Several design problems are often correlated with undesired dependencies
among modules. Being able to anticipate those problems is important for
developers, so they can plan early for maintenance and refactoring efforts.
However, existing tools are limited to detecting undesired dependencies once
they appeared in the system. In this work, we investigate whether module
dependencies can be predicted (before they actually appear). Since the module
structure can be regarded as a network, i.e, a dependency graph, we leverage on
network features to analyze the dynamics of such a structure. In particular, we
apply link prediction techniques for this task. We conducted an evaluation on
two Java projects across several versions, using link prediction and machine
learning techniques, and assessed their performance for identifying new
dependencies from a project version to the next one. The results, although
preliminary, show that the link prediction approach is feasible for package
dependencies. Also, this work opens opportunities for further development of
software-specific strategies for dependency prediction.Comment: Accepted at ICSA 201
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