1,012 research outputs found
3-manifolds which are spacelike slices of flat spacetimes
We continue work initiated in a 1990 preprint of Mess giving a geometric
parameterization of the moduli space of classical solutions to Einstein's
equations in 2+1 dimensions with cosmological constant 0 or -1 (the case +1 has
been worked out in the interim by the present author). In this paper we make a
first step toward the 3+1-dimensional case by determining exactly which closed
3-manifolds M^3 arise as spacelike slices of flat spacetimes, and by finding
all possible holonomy homomorphisms pi_1(M^3) to ISO(3,1).Comment: 10 page
Efficient Behavior of Small-World Networks
We introduce the concept of efficiency of a network, measuring how
efficiently it exchanges information. By using this simple measure small-world
networks are seen as systems that are both globally and locally efficient. This
allows to give a clear physical meaning to the concept of small-world, and also
to perform a precise quantitative a nalysis of both weighted and unweighted
networks. We study neural networks and man-made communication and
transportation systems and we show that the underlying general principle of
their construction is in fact a small-world principle of high efficiency.Comment: 1 figure, 2 tables. Revised version. Accepted for publication in
Phys. Rev. Let
Modularity produces small-world networks with dynamical time-scale separation
The functional consequences of local and global dynamics can be very
different in natural systems. Many such systems have a network description that
exhibits strong local clustering as well as high communication efficiency,
often termed as small-world networks (SWN). We show that modular organization
in otherwise random networks generically give rise to SWN, with a
characteristic time-scale separation between fast intra-modular and slow
inter-modular processes. The universality of this dynamical signature, that
distinguishes modular networks from earlier models of SWN, is demonstrated by
processes as different as spin-ordering, synchronization and diffusion.Comment: 6 pages, 7 figures. Published version, Results and figures of
additional dynamics have been include
APJ1 and GRE3 Homologs Work in Concert to Allow Growth in Xylose in a Natural Saccharomyces sensu stricto Hybrid Yeast
Creating Saccharomyces yeasts capable of efficient fermentation of pentoses such as xylose remains a key challenge in the production of ethanol from lignocellulosic biomass. Metabolic engineering of industrial Saccharomyces cerevisiae strains has yielded xylose-fermenting strains, but these strains have not yet achieved industrial viability due largely to xylose fermentation being prohibitively slower than that of glucose. Recently, it has been shown that naturally occurring xylose-utilizing Saccharomyces species exist. Uncovering the genetic architecture of such strains will shed further light on xylose metabolism, suggesting additional engineering approaches or possibly even enabling the development of xylose-fermenting yeasts that are not genetically modified. We previously identified a hybrid yeast strain, the genome of which is largely Saccharomyces uvarum, which has the ability to grow on xylose as the sole carbon source. To circumvent the sterility of this hybrid strain, we developed a novel method to genetically characterize its xylose-utilization phenotype, using a tetraploid intermediate, followed by bulk segregant analysis in conjunction with high-throughput sequencing. We found that this strain’s growth in xylose is governed by at least two genetic loci, within which we identified the responsible genes: one locus contains a known xylose-pathway gene, a novel homolog of the aldo-keto reductase gene GRE3, while a second locus contains a homolog of APJ1, which encodes a putative chaperone not previously connected to xylose metabolism. Our work demonstrates that the power of sequencing combined with bulk segregant analysis can also be applied to a nongenetically tractable hybrid strain that contains a complex, polygenic trait, and identifies new avenues for metabolic engineering as well as for construction of nongenetically modified xylose-fermenting strains
Nonlocal mechanism for cluster synchronization in neural circuits
The interplay between the topology of cortical circuits and synchronized
activity modes in distinct cortical areas is a key enigma in neuroscience. We
present a new nonlocal mechanism governing the periodic activity mode: the
greatest common divisor (GCD) of network loops. For a stimulus to one node, the
network splits into GCD-clusters in which cluster neurons are in zero-lag
synchronization. For complex external stimuli, the number of clusters can be
any common divisor. The synchronized mode and the transients to synchronization
pinpoint the type of external stimuli. The findings, supported by an
information mixing argument and simulations of Hodgkin Huxley population
dynamic networks with unidirectional connectivity and synaptic noise, call for
reexamining sources of correlated activity in cortex and shorter information
processing time scales.Comment: 8 pges, 6 figure
Effect of resonant magnetic perturbations on low collisionality discharges in MAST and a comparison with ASDEX Upgrade
Sustained ELM mitigation has been achieved on MAST and AUG using RMPs with a
range of toroidal mode numbers over a wide region of low to medium
collisionality discharges. The ELM energy loss and peak heat loads at the
divertor targets have been reduced. The ELM mitigation phase is typically
associated with a drop in plasma density and overall stored energy. In one
particular scenario on MAST, by carefully adjusting the fuelling it has been
possible to counteract the drop in density and to produce plasmas with
mitigated ELMs, reduced peak divertor heat flux and with minimal degradation in
pedestal height and confined energy. While the applied resonant magnetic
perturbation field can be a good indicator for the onset of ELM mitigation on
MAST and AUG there are some cases where this is not the case and which clearly
emphasise the need to take into account the plasma response to the applied
perturbations. The plasma response calculations show that the increase in ELM
frequency is correlated with the size of the edge peeling-tearing like response
of the plasma and the distortions of the plasma boundary in the X-point region.Comment: 31 pages, 28 figures. This is an author-created, un-copyedited
version of an article submitted for publication in Nuclear Fusion. IoP
Publishing Ltd is not responsible for any errors or omissions in this version
of the manuscript or any version derived from i
From modular to centralized organization of synchronization in functional areas of the cat cerebral cortex
Recent studies have pointed out the importance of transient synchronization
between widely distributed neural assemblies to understand conscious
perception. These neural assemblies form intricate networks of neurons and
synapses whose detailed map for mammals is still unknown and far from our
experimental capabilities. Only in a few cases, for example the C. elegans, we
know the complete mapping of the neuronal tissue or its mesoscopic level of
description provided by cortical areas. Here we study the process of transient
and global synchronization using a simple model of phase-coupled oscillators
assigned to cortical areas in the cerebral cat cortex. Our results highlight
the impact of the topological connectivity in the developing of
synchronization, revealing a transition in the synchronization organization
that goes from a modular decentralized coherence to a centralized synchronized
regime controlled by a few cortical areas forming a Rich-Club connectivity
pattern.Comment: 24 pages, 8 figures. Final version published in PLoS On
Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation
Cine cardiac magnetic resonance (CMR) has become the gold standard for the
non-invasive evaluation of cardiac function. In particular, it allows the
accurate quantification of functional parameters including the chamber volumes
and ejection fraction. Deep learning has shown the potential to automate the
requisite cardiac structure segmentation. However, the lack of robustness of
deep learning models has hindered their widespread clinical adoption. Due to
differences in the data characteristics, neural networks trained on data from a
specific scanner are not guaranteed to generalise well to data acquired at a
different centre or with a different scanner. In this work, we propose a
principled solution to the problem of this domain shift. Domain-adversarial
learning is used to train a domain-invariant 2D U-Net using labelled and
unlabelled data. This approach is evaluated on both seen and unseen domains
from the M\&Ms challenge dataset and the domain-adversarial approach shows
improved performance as compared to standard training. Additionally, we show
that the domain information cannot be recovered from the learned features.Comment: Accepted at the STACOM workshop at MICCAI 202
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