89,364 research outputs found
Social capital, poverty and social exclusion in Italy
The paper investigates whether social capital can affect the standard living of the Italian households based on poverty and social exclusion. The analysis is developed at the regional level through cross-sections based in the year 2002 and in the year 2003. The indices of social capital that we use are the associational activity a la Putnam and a new proxy based on the regional density of industrial districts. By using the empirical model advanced by Grootaert (2001) we find that our results confirm the theory of social capital and poverty transition mechanism advanced by Narayan and Woolcock (2000). Moreover we find significant and negative correlation between social capital and the measures of social exclusion. All these results, drive the paper to the conclusion that social capital is positively correlated to higher level of living standard
Graph analysis of functional brain networks: practical issues in translational neuroscience
The brain can be regarded as a network: a connected system where nodes, or
units, represent different specialized regions and links, or connections,
represent communication pathways. From a functional perspective communication
is coded by temporal dependence between the activities of different brain
areas. In the last decade, the abstract representation of the brain as a graph
has allowed to visualize functional brain networks and describe their
non-trivial topological properties in a compact and objective way. Nowadays,
the use of graph analysis in translational neuroscience has become essential to
quantify brain dysfunctions in terms of aberrant reconfiguration of functional
brain networks. Despite its evident impact, graph analysis of functional brain
networks is not a simple toolbox that can be blindly applied to brain signals.
On the one hand, it requires a know-how of all the methodological steps of the
processing pipeline that manipulates the input brain signals and extract the
functional network properties. On the other hand, a knowledge of the neural
phenomenon under study is required to perform physiological-relevant analysis.
The aim of this review is to provide practical indications to make sense of
brain network analysis and contrast counterproductive attitudes
The Community Structure of R&D Cooperation in Europe. Evidence from a social network perspective
The focus of this paper is on pre-competitive R&D cooperation across Europe, as captured by R&D joint ventures funded by the European Commission in the time period 1998-2002, within the 5th Framework Program. The cooperations in this Framework Program give rise to a bipartite network with 72,745 network edges between 25,839 actors (representing organizations that include firms, universities, research organizations and public agencies) and 9,490 R&D projects. With this construction, participating actors are linked only through joint projects.
In this paper we describe the community identification problem based on the concept of modularity, and use the recently introduced label-propagation algorithm to identify communities in the network, and differentiate the identified communities by developing community-specific profiles using social network analysis and geographic visualization techniques. We expect the results to enrich our picture of the European Research Area by providing new insights into the global and local structures of R&D cooperation across Europe
Dynamics of link states in complex networks: The case of a majority rule
Motivated by the idea that some characteristics are specific to the relations
between individuals and not of the individuals themselves, we study a prototype
model for the dynamics of the states of the links in a fixed network of
interacting units. Each link in the network can be in one of two equivalent
states. A majority link-dynamics rule is implemented, so that in each dynamical
step the state of a randomly chosen link is updated to the state of the
majority of neighboring links. Nodes can be characterized by a link
heterogeneity index, giving a measure of the likelihood of a node to have a
link in one of the two states. We consider this link-dynamics model on fully
connected networks, square lattices and Erd \"os-Renyi random networks. In each
case we find and characterize a number of nontrivial asymptotic configurations,
as well as some of the mechanisms leading to them and the time evolution of the
link heterogeneity index distribution. For a fully connected network and random
networks there is a broad distribution of possible asymptotic configurations.
Most asymptotic configurations that result from link-dynamics have no
counterpart under traditional node dynamics in the same topologies.Comment: 9 pages, 13 figure
Cusp Universality for Random Matrices I: Local Law and the Complex Hermitian Case
For complex Wigner-type matrices, i.e. Hermitian random matrices with
independent, not necessarily identically distributed entries above the
diagonal, we show that at any cusp singularity of the limiting eigenvalue
distribution the local eigenvalue statistics are universal and form a Pearcey
process. Since the density of states typically exhibits only square root or
cubic root cusp singularities, our work complements previous results on the
bulk and edge universality and it thus completes the resolution of the
Wigner-Dyson-Mehta universality conjecture for the last remaining universality
type in the complex Hermitian class. Our analysis holds not only for exact
cusps, but approximate cusps as well, where an extended Pearcey process
emerges. As a main technical ingredient we prove an optimal local law at the
cusp for both symmetry classes. This result is also used in the companion paper
[arXiv:1811.04055] where the cusp universality for real symmetric Wigner-type
matrices is proven.Comment: 58 pages, 2 figures. Updated introduction and reference
Intrinsically Dynamic Network Communities
Community finding algorithms for networks have recently been extended to
dynamic data. Most of these recent methods aim at exhibiting community
partitions from successive graph snapshots and thereafter connecting or
smoothing these partitions using clever time-dependent features and sampling
techniques. These approaches are nonetheless achieving longitudinal rather than
dynamic community detection. We assume that communities are fundamentally
defined by the repetition of interactions among a set of nodes over time.
According to this definition, analyzing the data by considering successive
snapshots induces a significant loss of information: we suggest that it blurs
essentially dynamic phenomena - such as communities based on repeated
inter-temporal interactions, nodes switching from a community to another across
time, or the possibility that a community survives while its members are being
integrally replaced over a longer time period. We propose a formalism which
aims at tackling this issue in the context of time-directed datasets (such as
citation networks), and present several illustrations on both empirical and
synthetic dynamic networks. We eventually introduce intrinsically dynamic
metrics to qualify temporal community structure and emphasize their possible
role as an estimator of the quality of the community detection - taking into
account the fact that various empirical contexts may call for distinct
`community' definitions and detection criteria.Comment: 27 pages, 11 figure
Multiscale Topological Properties Of Functional Brain Networks During Motor Imagery After Stroke
In recent years, network analyses have been used to evaluate brain
reorganization following stroke. However, many studies have often focused on
single topological scales, leading to an incomplete model of how focal brain
lesions affect multiple network properties simultaneously and how changes on
smaller scales influence those on larger scales. In an EEG-based experiment on
the performance of hand motor imagery (MI) in 20 patients with unilateral
stroke, we observed that the anatomic lesion affects the functional brain
network on multiple levels. In the beta (13-30 Hz) frequency band, the MI of
the affected hand (Ahand) elicited a significantly lower smallworldness and
local efficiency (Eloc) versus the unaffected hand (Uhand). Notably, the
abnormal reduction in Eloc significantly depended on the increase in
interhemispheric connectivity, which was in turn determined primarily by the
rise in regional connectivity in the parieto-occipital sites of the affected
hemisphere. Further, in contrast to the Uhand MI, in which significantly high
connectivity was observed for the contralateral sensorimotor regions of the
unaffected hemisphere, the regions that increased in connection during the
Ahand MI lay in the frontal and parietal regions of the contralaterally
affected hemisphere. Finally, the overall sensorimotor function of our
patients, as measured by Fugl-Meyer Assessment (FMA) index, was significantly
predicted by the connectivity of their affected hemisphere. These results
increase our understanding of stroke-induced alterations in functional brain
networks.Comment: Neuroimage, accepted manuscript (unedited version) available online
19-June-201
The density matrix renormalization group for ab initio quantum chemistry
During the past 15 years, the density matrix renormalization group (DMRG) has
become increasingly important for ab initio quantum chemistry. Its underlying
wavefunction ansatz, the matrix product state (MPS), is a low-rank
decomposition of the full configuration interaction tensor. The virtual
dimension of the MPS, the rank of the decomposition, controls the size of the
corner of the many-body Hilbert space that can be reached with the ansatz. This
parameter can be systematically increased until numerical convergence is
reached. The MPS ansatz naturally captures exponentially decaying correlation
functions. Therefore DMRG works extremely well for noncritical one-dimensional
systems. The active orbital spaces in quantum chemistry are however often far
from one-dimensional, and relatively large virtual dimensions are required to
use DMRG for ab initio quantum chemistry (QC-DMRG). The QC-DMRG algorithm, its
computational cost, and its properties are discussed. Two important aspects to
reduce the computational cost are given special attention: the orbital choice
and ordering, and the exploitation of the symmetry group of the Hamiltonian.
With these considerations, the QC-DMRG algorithm allows to find numerically
exact solutions in active spaces of up to 40 electrons in 40 orbitals.Comment: 24 pages; 10 figures; based on arXiv:1405.1225; invited review for
European Physical Journal
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