1,070 research outputs found
Quantum Monte Carlo scheme for frustrated Heisenberg antiferromagnets
When one tries to simulate quantum spin systems by the Monte Carlo method,
often the 'minus-sign problem' is encountered. In such a case, an application
of probabilistic methods is not possible. In this paper the method has been
proposed how to avoid the minus sign problem for certain class of frustrated
Heisenberg models. The systems where this method is applicable are, for
instance, the pyrochlore lattice and the Heisenberg model. The method
works in singlet sector. It relies on expression of wave functions in dimer
(pseudo)basis and writing down the Hamiltonian as a sum over plaquettes. In
such a formulation, matrix elements of the exponent of Hamiltonian are
positive.Comment: 19 LaTeX pages, 6 figures, 1 tabl
The effect of organelle discovery upon sub-cellular protein localisation.
Prediction of protein sub-cellular localisation by employing quantitative mass spectrometry experiments is an expanding field. Several methods have led to the assignment of proteins to specific subcellular localisations by partial separation of organelles across a fractionation scheme coupled with computational analysis. Methods developed to analyse organelle data have largely employed supervised machine learning algorithms to map unannotated abundance profiles to known proteinâorganelle associations. Such approaches are likely to make association errors if organelle-related groupings present in experimental output are not included in data used to create a proteinâorganelle classifier. Currently, there is no automated way to detect organelle-specific clusters within such datasets. In order to address the above issues we adapted a phenotype discovery algorithm, originally created to filter image-based output for RNAi screens, to identify putative subcellular groupings in organelle proteomics experiments. We were able to mine datasets to a deeper level and extract interesting phenotype clusters for more comprehensive evaluation in an unbiased fashion upon application of this approach. Organelle-related protein clusters were identified beyond those sufficiently annotated for use as training data. Furthermore, we propose avenues for the incorporation of observations made into general practice for the classification of proteinâorganelle membership from quantitative MS experiments. Biological significance Protein sub-cellular localisation plays an important role in molecular interactions, signalling and transport mechanisms. The prediction of protein localisation by quantitative mass-spectrometry (MS) proteomics is a growing field and an important endeavour in improving protein annotation. Several such approaches use gradient-based separation of cellular organelle content to measure relative protein abundance across distinct gradient fractions. The distribution profiles are commonly mapped in silico to known proteinâorganelle associations via supervised machine learning algorithms, to create classifiers that associate unannotated proteins to specific organelles. These strategies are prone to error, however, if organelle-related groupings present in experimental output are not represented, for example owing to the lack of existing annotation, when creating the proteinâorganelle mapping. Here, the application of a phenotype discovery approach to LOPIT gradient-based MS data identifies candidate organelle phenotypes for further evaluation in an unbiased fashion. Software implementation and usage guidelines are provided for application to wider proteinâorganelle association experiments. In the wider context, semi-supervised organelle discovery is discussed as a paradigm with which to generate new protein annotations from MS-based organelle proteomics experiments. This article is part of a Special Issue entitled: New Horizons and Applications for Proteomics [EuPA 2012]
Upon the existence of short-time approximations of any polynomial order for the computation of density matrices by path integral methods
In this article, I provide significant mathematical evidence in support of
the existence of short-time approximations of any polynomial order for the
computation of density matrices of physical systems described by arbitrarily
smooth and bounded from below potentials. While for Theorem 2, which is
``experimental'', I only provide a ``physicist's'' proof, I believe the present
development is mathematically sound. As a verification, I explicitly construct
two short-time approximations to the density matrix having convergence orders 3
and 4, respectively. Furthermore, in the Appendix, I derive the convergence
constant for the trapezoidal Trotter path integral technique. The convergence
orders and constants are then verified by numerical simulations. While the two
short-time approximations constructed are of sure interest to physicists and
chemists involved in Monte Carlo path integral simulations, the present article
is also aimed at the mathematical community, who might find the results
interesting and worth exploring. I conclude the paper by discussing the
implications of the present findings with respect to the solvability of the
dynamical sign problem appearing in real-time Feynman path integral
simulations.Comment: 19 pages, 4 figures; the discrete short-time approximations are now
treated as independent from their continuous version; new examples of
discrete short-time approximations of order three and four are given; a new
appendix containing a short review on Brownian motion has been added; also,
some additional explanations are provided here and there; this is the last
version; to appear in Phys. Rev.
The role of winding numbers in quantum Monte Carlo simulations
We discuss the effects of fixing the winding number in quantum Monte Carlo
simulations. We present a simple geometrical argument as well as strong
numerical evidence that one can obtain exact ground state results for periodic
boundary conditions without changing the winding number. However, for very
small systems the temperature has to be considerably lower than in simulations
with fluctuating winding numbers. The relative deviation of a calculated
observable from the exact ground state result typically scales as ,
where the exponent is model and observable dependent and the prefactor
decreases with increasing system size. Analytic results for a quantum rotor
model further support our claim.Comment: 5 pages, 5 figure
Chaos, containment and change: responding to persistent offending by young people
This article reviews policy developments in Scotland concerning 'persistent young offenders' and then describes the design of a study intended to assist a local planning group in developing its response. The key findings of a review of casefiles of young people involved in persistent offending are reported. It emerges that youth crime and young people involved in offending are more complex and heterogeneous than is sometimes assumed. This, along with a review of some literature about desistance from offending, reaffirms the need for properly individualised interventions. Studies of 'desisters' suggest the centrality of effective and engaging working relationships in this process. However, these studies also re-assert the significance of the social contexts of workersâ efforts to bring 'change' out of 'chaos'. We conclude therefore that the 'new correctionalism' must be tempered with appreciation of the social exclusion of young people who offend
Brownian Dynamics Simulation of Polydisperse Hard Spheres
Standard algorithms for the numerical integration of the Langevin equation
require that interactions are slowly varying during to the integration
timestep. This in not the case for hard-body systems, where there is no
clearcut between the correlation time of the noise and the timescale of the
interactions. Starting from a short time approximation of the Smoluchowsky
equation, we introduce an algorithm for the simulation of the overdamped
Brownian dynamics of polydisperse hard-spheres in absence of hydrodynamics
interactions and briefly discuss the extension to the case of external drifts
Quantum Monte Carlo in the Interaction Representation --- Application to a Spin-Peierls Model
A quantum Monte Carlo algorithm is constructed starting from the standard
perturbation expansion in the interaction representation. The resulting
configuration space is strongly related to that of the Stochastic Series
Expansion (SSE) method, which is based on a direct power series expansion of
exp(-beta*H). Sampling procedures previously developed for the SSE method can
therefore be used also in the interaction representation formulation. The new
method is first tested on the S=1/2 Heisenberg chain. Then, as an application
to a model of great current interest, a Heisenberg chain including phonon
degrees of freedom is studied. Einstein phonons are coupled to the spins via a
linear modulation of the nearest-neighbor exchange. The simulation algorithm is
implemented in the phonon occupation number basis, without Hilbert space
truncations, and is exact. Results are presented for the magnetic properties of
the system in a wide temperature regime, including the T-->0 limit where the
chain undergoes a spin-Peierls transition. Some aspects of the phonon dynamics
are also discussed. The results suggest that the effects of dynamic phonons in
spin-Peierls compounds such as GeCuO3 and NaV2O5 must be included in order to
obtain a correct quantitative description of their magnetic properties, both
above and below the dimerization temperature.Comment: 23 pages, Revtex, 11 PostScript figure
Thermodynamic and diamagnetic properties of weakly doped antiferromagnets
Finite-temperature properties of weakly doped antiferromagnets as modeled by
the two-dimensional t-J model and relevant to underdoped cuprates are
investigated by numerical studies of small model systems at low doping. Two
numerical methods are used: the worldline quantum Monte Carlo method with a
loop cluster algorithm and the finite-temperature Lanczos method, yielding
consistent results. Thermodynamic quantities: specific heat, entropy and spin
susceptibility reveal a sizeable perturbation induced by holes introduced into
a magnetic insulator, as well as a pronounced temperature dependence. The
diamagnetic susceptibility introduced by coupling of the magnetic field to the
orbital current reveals an anomalous temperature dependence, changing character
from diamagnetic to paramagnetic at intermediate temperatures.Comment: LaTeX, 10 pages, 10 figures, submitted to Phys. Rev.
Quantum Monte Carlo Loop Algorithm for the t-J Model
We propose a generalization of the Quantum Monte Carlo loop algorithm to the
t-J model by a mapping to three coupled six-vertex models. The autocorrelation
times are reduced by orders of magnitude compared to the conventional local
algorithms. The method is completely ergodic and can be formulated directly in
continuous time. We introduce improved estimators for simulations with a local
sign problem. Some first results of finite temperature simulations are
presented for a t-J chain, a frustrated Heisenberg chain, and t-J ladder
models.Comment: 22 pages, including 12 figures. RevTex v3.0, uses psf.te
Case management and Think First completion
âThe final, definitive version of this article has been published in the Journal, Probation Journal, Vol 53 Issue 3, 2006, Copyright The Trade Union and Professional Association for Family Court and Probation Staff, by SAGE Publications Ltd at: http://prb.sagepub.com/ " DOI: 10.1177/0264550506066771This article considers the findings of a small-scale study of the practice of case managers supervising offenders required to attend the Think First Group. It explores the interface between one-to-one and group-based work within multi-modal programmes of supervision and seeks to identify those practices that support individuals in completing a group.Peer reviewe
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