24,173 research outputs found
Equivalence of particle-particle random phase approximation correlation energy and ladder-coupled-cluster doubles
We present an analytical proof and numerical demonstrations of the
equivalence of the correlation energy from particle-particle random phase
approximation (pp-RPA) and ladder-couple-cluster-doubles (ladder-CCD). These
two theories reduce to the identical algebraic matrix equation and correlation
energy expressions, under the assumption that the pp-RPA equation is stable.
The numerical examples illustrate that the correlation energy missed by pp-RPA
in comparison with couple-cluster single and double is largely canceled out
when considering reaction energies. This theoretical connection will be
beneficial to future pp-RPA studies based on the well established couple
cluster theory
On the spectrum of the Wilson-Dirac lattice operator in topologically non-trivial background configurations
We study characteristic features of the eigenvalues of the Wilson-Dirac
operator in topologically non-trivial gauge field configurations by examining
complete spectra of the fermion matrix. In particular we discuss the role of
eigenvectors with real eigenvalues as the lattice equivalents of the continuum
zero-modes. We demonstrate, that those properties of the spectrum which
correspond to non-trivial topology are stable under adding fluctuations to the
gauge fields. The behavior of the spectrum in a fully quantized theory is
discussed using QED_2 as an example.Comment: Revised version, to appear in Nuclear Physics B; introductory part
rewritten and shortened, references update
Survivability of Deterministic Dynamical Systems
The notion of a part of phase space containing desired (or allowed) states of
a dynamical system is important in a wide range of complex systems research. It
has been called the safe operating space, the viability kernel or the sunny
region. In this paper we define the notion of survivability: Given a random
initial condition, what is the likelihood that the transient behaviour of a
deterministic system does not leave a region of desirable states. We
demonstrate the utility of this novel stability measure by considering models
from climate science, neuronal networks and power grids. We also show that a
semi-analytic lower bound for the survivability of linear systems allows a
numerically very efficient survivability analysis in realistic models of power
grids. Our numerical and semi-analytic work underlines that the type of
stability measured by survivability is not captured by common asymptotic
stability measures.Comment: 21 pages, 6 figure
Multi-scale Modularity in Complex Networks
We focus on the detection of communities in multi-scale networks, namely
networks made of different levels of organization and in which modules exist at
different scales. It is first shown that methods based on modularity are not
appropriate to uncover modules in empirical networks, mainly because modularity
optimization has an intrinsic bias towards partitions having a characteristic
number of modules which might not be compatible with the modular organization
of the system. We argue for the use of more flexible quality functions
incorporating a resolution parameter that allows us to reveal the natural
scales of the system. Different types of multi-resolution quality functions are
described and unified by looking at the partitioning problem from a dynamical
viewpoint. Finally, significant values of the resolution parameter are selected
by using complementary measures of robustness of the uncovered partitions. The
methods are illustrated on a benchmark and an empirical network.Comment: 8 pages, 3 figure
Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies
In motion analysis and understanding it is important to be able to fit a
suitable model or structure to the temporal series of observed data, in order
to describe motion patterns in a compact way, and to discriminate between them.
In an unsupervised context, i.e., no prior model of the moving object(s) is
available, such a structure has to be learned from the data in a bottom-up
fashion. In recent times, volumetric approaches in which the motion is captured
from a number of cameras and a voxel-set representation of the body is built
from the camera views, have gained ground due to attractive features such as
inherent view-invariance and robustness to occlusions. Automatic, unsupervised
segmentation of moving bodies along entire sequences, in a temporally-coherent
and robust way, has the potential to provide a means of constructing a
bottom-up model of the moving body, and track motion cues that may be later
exploited for motion classification. Spectral methods such as locally linear
embedding (LLE) can be useful in this context, as they preserve "protrusions",
i.e., high-curvature regions of the 3D volume, of articulated shapes, while
improving their separation in a lower dimensional space, making them in this
way easier to cluster. In this paper we therefore propose a spectral approach
to unsupervised and temporally-coherent body-protrusion segmentation along time
sequences. Volumetric shapes are clustered in an embedding space, clusters are
propagated in time to ensure coherence, and merged or split to accommodate
changes in the body's topology. Experiments on both synthetic and real
sequences of dense voxel-set data are shown. This supports the ability of the
proposed method to cluster body-parts consistently over time in a totally
unsupervised fashion, its robustness to sampling density and shape quality, and
its potential for bottom-up model constructionComment: 31 pages, 26 figure
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