19,258 research outputs found
Simulated Tempering: A New Monte Carlo Scheme
We propose a new global optimization method ({\em Simulated Tempering}) for
simulating effectively a system with a rough free energy landscape (i.e. many
coexisting states) at finite non-zero temperature. This method is related to
simulated annealing, but here the temperature becomes a dynamic variable, and
the system is always kept at equilibrium. We analyze the method on the Random
Field Ising Model, and we find a dramatic improvement over conventional
Metropolis and cluster methods. We analyze and discuss the conditions under
which the method has optimal performances.Comment: 12 pages, very simple LaTeX file, figures are not included, sorr
Optimized cross-slot flow geometry for microfluidic extension rheometry
A precision-machined cross-slot flow geometry with a shape that has been optimized by numerical simulation of the fluid kinematics is fabricated and used to measure the extensional viscosity of a dilute polymer solution. Full-field birefringence microscopy is used to monitor the evolution and growth of macromolecular anisotropy along the stagnation point streamline, and we observe the formation of a strong and uniform birefringent strand when the dimensionless flow strength exceeds a critical Weissenberg number Wicrit 0:5. Birefringence and bulk pressure drop measurements provide self consistent estimates of the planar extensional viscosity of the fluid over a wide range of deformation rates (26 s1 "_ 435 s1) and are also in close agreement with numerical simulations performed by using a finitely extensible nonlinear elastic dumbbell model
Bridging the gap between cluster and grid computing
The Internet computing model with its ubiquitous networking and computing infrastructure is driving a new class of interoperable applications that benefit both from high computing power and multiple Internet connections. In this context, grids are promising computing platforms that allow to aggregate distributed resources such as workstations and clusters to solve large-scale problems. However, because most parallel programming tools were primarily developed for MPP and cluster computing, to exploit the new environment higher abstraction and cooperative interfaces are required. Rocmeμ is a platform originally designed to support the operation of multi-SAN clusters that integrates application modeling and resource allocation. In this paper we show how the underlying resource oriented computation model provides the necessary abstractions to accommodate the migration from cluster to multicluster grid enabled computing
Biased Random Access Codes
A Random Access Code (RAC) is a communication task in which the sender
encodes a random message into a shorter one to be decoded by the receiver so
that a randomly chosen character of the original message is recovered with some
probability. Both the message and the character to be recovered are assumed to
be uniformly distributed. In this paper, we extend this protocol by allowing
more general distributions of these inputs, which alters the encoding and
decoding strategies optimizing the protocol performance, either with classical
or quantum resources. We approach the problem of optimizing the performance of
these biased RACs with both numerical and analytical tools. On the numerical
front, we present algorithms that allow a numerical evaluation of the optimal
performance over both classical and quantum strategies and provide a Python
package designed to implement them, called RAC-tools. We then use this
numerical tool to investigate single-parameter families of biased RACs in the
and scenarios. For RACs in the
scenario, we derive a general upper bound for the cases in which the inputs are
not correlated, which coincides with the quantum value for and, in some
cases for . Moreover, it is shown that attaining this upper bound
self-tests pairs or triples of rank-1 projective measurements, respectively. An
analogous upper bound is derived for the value of RACs in the
scenario which is shown to be always attainable using mutually unbiased
measurements if the distribution of input strings is unbiased
Rugged Metropolis Sampling with Simultaneous Updating of Two Dynamical Variables
The Rugged Metropolis (RM) algorithm is a biased updating scheme, which aims
at directly hitting the most likely configurations in a rugged free energy
landscape. Details of the one-variable (RM) implementation of this
algorithm are presented. This is followed by an extension to simultaneous
updating of two dynamical variables (RM). In a test with Met-Enkephalin in
vacuum RM improves conventional Metropolis simulations by a factor of about
four. Correlations between three or more dihedral angles appear to prevent
larger improvements at low temperatures. We also investigate a multi-hit
Metropolis scheme, which spends more CPU time on variables with large
autocorrelation times.Comment: 8 pages, 5 figures. Revisions after referee reports. Additional
simulations for temperatures down to 220
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