969 research outputs found
Laplacian Dynamics and Multiscale Modular Structure in Networks
Most methods proposed to uncover communities in complex networks rely on
their structural properties. Here we introduce the stability of a network
partition, a measure of its quality defined in terms of the statistical
properties of a dynamical process taking place on the graph. The time-scale of
the process acts as an intrinsic parameter that uncovers community structures
at different resolutions. The stability extends and unifies standard notions
for community detection: modularity and spectral partitioning can be seen as
limiting cases of our dynamic measure. Similarly, recently proposed
multi-resolution methods correspond to linearisations of the stability at short
times. The connection between community detection and Laplacian dynamics
enables us to establish dynamically motivated stability measures linked to
distinct null models. We apply our method to find multi-scale partitions for
different networks and show that the stability can be computed efficiently for
large networks with extended versions of current algorithms.Comment: New discussions on the selection of the most significant scales and
the generalisation of stability to directed network
A new bound of the ℒ2[0, T]-induced norm and applications to model reduction
We present a simple bound on the finite horizon ℒ2/[0, T]-induced norm of a linear time-invariant (LTI), not necessarily stable system which can be efficiently computed by calculating the ℋ∞ norm of a shifted version of the original operator. As an application, we show how to use this bound to perform model reduction of unstable systems over a finite horizon. The technique is illustrated with a non-trivial physical example relevant to the appearance of time-irreversible phenomena in statistical physics
Estimating development effort in free/open source software projects by mining software repositories: A case study of OpenStack
Because of the distributed and collaborative nature of free/open source software (FOSS) projects, the development effort invested in a project is usually unknown, even after the software has been released. However, this information is becoming of major interest, especially-but not only-because of the growth in the number of companies for which FOSS has become relevant for their business strategy. In this paper we present a novel approach to estimate effort by considering data from source code management repositories. We apply our model to the OpenStack project, a FOSS project with more than 1,000 authors, in which several tens of companies cooperate. Based on data from its repositories and together with the input from a survey answered by more than 100 developers, we show that the model offers a simple, but sound way of obtaining software development estimations with bounded margins of error.Gregorio Robles, Carlos Cervig on and Jes us M. Gonz alez-Barahona, project SobreSale (TIN2011-28110). and The work of Daniel Izquierdo has been funded in part by the Torres Quevedo program (PTQ-12-05577
Observation of magnetization reversal in epitaxial Gd0.67Ca0.33MnO3 thin films
High quality epitaxial thin films of Gd0.67Ca0.33MnO3 have been deposited
onto (100) SrTiO3 substrates by pulsed-laser deposition. Enhanced properties in
comparison with bulk samples were observed. The magnetic transition temperature
(Tc) of the as-grown films is much higher than the corresponding bulk values.
Most interestingly, magnetization measurements performed under small applied
fields, exhibit magnetization reversals below Tc, no matter whether the film is
field-cooled (FC) or zero-field-cooled (ZFC). A rapid magnetization reversal
occurs at 7 K when field cooled, while as for the ZFC process the magnetization
decreases gradually with increasing temperatures, taking negative values above
7 K and changing to positive values again, above 83 K. In higher magnetic
fields the magnetization does not change sign. The reversal mechanism is
discussed in terms of a negative exchange f-d interaction and magnetic
anisotropy, this later enhanced by strain effects induced by the lattice
mismatch between the film and the substrate.Comment: 16 pages, 4 figure
Stability of graph communities across time scales
The complexity of biological, social and engineering networks makes it
desirable to find natural partitions into communities that can act as
simplified descriptions and provide insight into the structure and function of
the overall system. Although community detection methods abound, there is a
lack of consensus on how to quantify and rank the quality of partitions. We
show here that the quality of a partition can be measured in terms of its
stability, defined in terms of the clustered autocovariance of a Markov process
taking place on the graph. Because the stability has an intrinsic dependence on
time scales of the graph, it allows us to compare and rank partitions at each
time and also to establish the time spans over which partitions are optimal.
Hence the Markov time acts effectively as an intrinsic resolution parameter
that establishes a hierarchy of increasingly coarser clusterings. Within our
framework we can then provide a unifying view of several standard partitioning
measures: modularity and normalized cut size can be interpreted as one-step
time measures, whereas Fiedler's spectral clustering emerges at long times. We
apply our method to characterize the relevance and persistence of partitions
over time for constructive and real networks, including hierarchical graphs and
social networks. We also obtain reduced descriptions for atomic level protein
structures over different time scales.Comment: submitted; updated bibliography from v
Using network-flow techniques to solve an optimization problem from surface-physics
The solid-on-solid model provides a commonly used framework for the
description of surfaces. In the last years it has been extended in order to
investigate the effect of defects in the bulk on the roughness of the surface.
The determination of the ground state of this model leads to a combinatorial
problem, which is reduced to an uncapacitated, convex minimum-circulation
problem. We will show that the successive shortest path algorithm solves the
problem in polynomial time.Comment: 8 Pages LaTeX, using Elsevier preprint style (macros included
Protein multi-scale organization through graph partitioning and robustness analysis: Application to the myosin-myosin light chain interaction
Despite the recognized importance of the multi-scale spatio-temporal
organization of proteins, most computational tools can only access a limited
spectrum of time and spatial scales, thereby ignoring the effects on protein
behavior of the intricate coupling between the different scales. Starting from
a physico-chemical atomistic network of interactions that encodes the structure
of the protein, we introduce a methodology based on multi-scale graph
partitioning that can uncover partitions and levels of organization of proteins
that span the whole range of scales, revealing biological features occurring at
different levels of organization and tracking their effect across scales.
Additionally, we introduce a measure of robustness to quantify the relevance of
the partitions through the generation of biochemically-motivated surrogate
random graph models. We apply the method to four distinct conformations of
myosin tail interacting protein, a protein from the molecular motor of the
malaria parasite, and study properties that have been experimentally addressed
such as the closing mechanism, the presence of conserved clusters, and the
identification through computational mutational analysis of key residues for
binding.Comment: 13 pages, 7 Postscript figure
Learning spatiotemporal signals using a recurrent spiking network that discretizes time
Learning to produce spatiotemporal sequences is a common task that the brain has to solve. The same neural substrate may be used by the brain to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory biophysical neurons drives a read-out layer: the dynamics of the driver recurrent network is trained to encode time which is then mapped through the read-out neurons to encode another dimension, such as space or a phase. Different spatiotemporal patterns can be learned and encoded through the synaptic weights to the read-out neurons that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on time scales that are behaviourally relevant and we show that the learned sequences are robustly replayed during a regime of spontaneous activity
Forage Quality and the Environment
The influence of environmental factors on forage quality of temperate and tropical grasses has been reviewed by several authors, who summarized how light, temperature, drought and soil nutrients influence chemical composition, and digestibility of forages grown in contrasting areas of the world. The effects of season of the year on forage growth, grazing behavior and animal performance have also been the subject of numerous papers and reviews. However, there are few recent reviews that summarize how changes in climatic and edaphic factors influence forage quality of legumes with variable levels of condensed tannins (CT), which are important secondary compounds in some temperate and tropical legume species adapted to acid infertile soils. In this paper we summarize properties of CT and their positive and negative effects on forage quality of legumes. We also review published work on the effect of temperature, drought, CO2 concentration, season of the year and soil fertility on the accumulation of CT in temperate and tropical legumes. Results from experiments under controlled conditions indicate that high temperature alone can significantly increase the accumulation of CT in some temperate legume species (i.e. Lotus pedunculatus) but not in others (i.e. L. corniculatus). However, the effect of low or high temperature on accumulation of CT is considerably greater when accompanied with other environmental factors such as drought, high CO2 concentration and soil nutrient deficiencies. Soil nutrient deficiencies can have a major effect on elevation of CT concentration and overall feed value of temperate and tropical legumes, but only when deficiencies are such that they affect plant growth. Soil fertility and climatic conditions affect not only the concentration of CT but also their monomer composition and MW (molecular weight), as was observed in a tropical legume species well adapted to acid infertile soils. The nutritional significance of these findings are not all that well understood, but it would seem that CT in forage legumes are not a uniform chemical entity given that they can change with edaphic and climatic factors. Finally we suggest that there is a need to investigate alternatives to enhance the feed value of legumes with tannins adapted to acid soils through selection of genotypes with less CT and /or through manipulation of environmental factors such as soil fertility. For this we need to better understand how edaphic and climatic factors affect not only accumulation of CT but also their chemical structure and biological activity and relate these changes to forage intake, digestibility, N utilization, and, ultimately, to performance of ruminant animals
Laplacian Dynamics and Multiscale Modular Structure in Networks
Most methods proposed to uncover communities in complex networks rely on
their structural properties. Here we introduce the stability of a network
partition, a measure of its quality defined in terms of the statistical
properties of a dynamical process taking place on the graph. The time-scale of
the process acts as an intrinsic parameter that uncovers community structures
at different resolutions. The stability extends and unifies standard notions
for community detection: modularity and spectral partitioning can be seen as
limiting cases of our dynamic measure. Similarly, recently proposed
multi-resolution methods correspond to linearisations of the stability at short
times. The connection between community detection and Laplacian dynamics
enables us to establish dynamically motivated stability measures linked to
distinct null models. We apply our method to find multi-scale partitions for
different networks and show that the stability can be computed efficiently for
large networks with extended versions of current algorithms.Comment: New discussions on the selection of the most significant scales and
the generalisation of stability to directed network
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