1,203 research outputs found
Seasonal variation in the Indian birth-rate
This article does not have an abstract
Magnetic Properties of Undoped
The Heisenberg antiferromagnet, which arises from the large Hubbard
model, is investigated on the molecule and other fullerenes. The
connectivity of leads to an exotic classical ground state with
nontrivial topology. We argue that there is no phase transition in the Hubbard
model as a function of , and thus the large solution is relevant for
the physical case of intermediate coupling. The system undergoes a first order
metamagnetic phase transition. We also consider the S=1/2 case using
perturbation theory. Experimental tests are suggested.Comment: 12 pages, 3 figures (included
Synchronization time in a hyperbolic dynamical system with long-range interactions
We show that the threshold of complete synchronization in a lattice of
coupled non-smooth chaotic maps is determined by linear stability along the
directions transversal to the synchronization subspace. We examine carefully
the sychronization time and show that a inadequate observation of the system
evolution leads to wrong results. We present both careful numerical experiments
and a rigorous mathematical explanation confirming this fact, allowing for a
generalization involving hyperbolic coupled map lattices.Comment: 22 pages (preprint format), 4 figures - accepted for publication in
Physica A (June 28, 2010
Effect of randomness and anisotropy on Turing patterns in reaction-diffusion systems
We study the effect of randomness and anisotropy on Turing patterns in
reaction-diffusion systems. For this purpose, the Gierer-Meinhardt model of
pattern formation is considered. The cases we study are: (i)randomness in the
underlying lattice structure, (ii)the case in which there is a probablity p
that at a lattice site both reaction and diffusion occur, otherwise there is
only diffusion and lastly, the effect of (iii) anisotropic and (iv) random
diffusion coefficients on the formation of Turing patterns. The general
conclusion is that the Turing mechanism of pattern formation is fairly robust
in the presence of randomness and anisotropy.Comment: 11 pages LaTeX, 14 postscript figures, accepted in Phys. Rev.
Slow Switching in Globally Coupled Oscillators: Robustness and Occurrence through Delayed Coupling
The phenomenon of slow switching in populations of globally coupled
oscillators is discussed. This characteristic collective dynamics, which was
first discovered in a particular class of the phase oscillator model, is a
result of the formation of a heteroclinic loop connecting a pair of clustered
states of the population. We argue that the same behavior can arise in a wider
class of oscillator models with the amplitude degree of freedom. We also argue
how such heteroclinic loops arise inevitably and persist robustly in a
homogeneous population of globally coupled oscillators. Although the
heteroclinic loop might seem to arise only exceptionally, we find that it
appears rather easily by introducing the time-delay in the population which
would otherwise exhibit perfect phase synchrony. We argue that the appearance
of the heteroclinic loop induced by the delayed coupling is then characterized
by transcritical and saddle-node bifurcations. Slow switching arises when the
system with a heteroclinic loop is weakly perturbed. This will be demonstrated
with a vector model by applying weak noises. Other types of weak
symmetry-breaking perturbations can also cause slow switching.Comment: 10 pages, 14 figures, RevTex, twocolumn, to appear in Phys. Rev.
Balancing Minimum Spanning and Shortest Path Trees
This paper give a simple linear-time algorithm that, given a weighted
digraph, finds a spanning tree that simultaneously approximates a shortest-path
tree and a minimum spanning tree. The algorithm provides a continuous
trade-off: given the two trees and epsilon > 0, the algorithm returns a
spanning tree in which the distance between any vertex and the root of the
shortest-path tree is at most 1+epsilon times the shortest-path distance, and
yet the total weight of the tree is at most 1+2/epsilon times the weight of a
minimum spanning tree. This is the best tradeoff possible. The paper also
describes a fast parallel implementation.Comment: conference version: ACM-SIAM Symposium on Discrete Algorithms (1993
Mixtures of independent component analyzers for EEG prediction
This paper presents a new application of independent component analysis mixture modeling (ICAMM) for prediction of electroencephalographic (EEG) signals. Demonstrations in prediction of missing EEG data in a working memory task using classic methods and an ICAMM-based algorithm are included. The performance of the methods is measured by using four error indicators: signal-to-interference (SIR) ratio, Kullback-Leibler divergence, correlation at lag zero and mean structural similarity index. The results show that the ICAMM-based algorithm outperforms the classical spherical splines method which is commonly used in EEG signal processing. Hence, the potential of using mixtures of independent component analyzers (ICAs) to improve prediction, as opposed on estimating only one ICA is demonstrated.This work has been supported by Generalitat Valenciana under
grants PROMETEO/2010/040 and ISIC/2012/006Safont Armero, G.; Salazar Afanador, A.; Vergara Domínguez, L.; Gonzalez, A.; Vidal Maciá, AM. (2012). Mixtures of independent component analyzers for EEG prediction. En Green and smart technology with sensor applications. Springer Verlag (Germany). 338:328-335. doi:10.1007/978-3-642-35251-5_46S328335338Common, P., Jutten, C.: Handbook of Blind Source Separation: Independent Component Analysis and Applications. Academic Press, USA (2010)Salazar, A., Vergara, L., Serrano, A., Igual, J.: A general procedure for learning mixtures of independent component analyzers. Pattern Recognition 43(1), 69–85 (2010)Lee, T.W., Lewicki, M.S., Sejnowski, T.J.: ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1078–1089 (2000)Salazar, A., Vergara, L.: ICA mixtures applied to ultrasonic nondestructive classification of archaeological ceramics. Eurasip Journal on Advances in Signal Processing 2010, article ID 125201, 11 pages (2010), doi:10.1155/2010/125201Klein, C., Feige, B.: An independent component analysis (ICA) approach to the study of developmental differences in the saccadic contingent negative variation. Biological Psychology 70, 105–114 (2005)Makeig, S., Westerfield, M., Jung, T.P., Covington, J., Townsend, J., Sejnowski, T.J., Courchesne, E.: Functionally Independent Components of the Late Positive Event-Related Potential during Visual Spatial Attention. Journal of Neuroscience 19(7), 2665–2680 (1999)Wibral, M., Turi, G., Linden, D.E.J., Kaiser, J., Bledowski, C.: Decomposition of working memory-related scalp ERPs: Crossvalidation of fMRI-constrained source analysis and ICA. Internt J. of Psychol. 67, 200–211 (2008)Castellanos, N.P., Makarov, V.A.: Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis. Journal of Neuroscience Methods 158, 300–312 (2006)Salazar, A., Vergara, L., Miralles, R.: On including sequential dependence in ICA mixture models. Signal Processing 90, 2314–2318 (2010)Dayan, P., Abbot, L.F.: Theoretical neuroscience: computational and mathematical modeling of neural systems. The MIT Press (2001)Sternberg, S.: High-speed scanning in human memory. Science 153(3736), 652–654 (1966)Raghavachari, S., Lisman, J.E., Tully, M., Madsen, J.R., Bromfield, E.B., Kahana, M.J.: Theta oscillations in human cortex during a working-memory task: evidence for local generators. J. of Neurophys. 95, 1630–1638 (2006)Gorriz, J.M., Puntonet, C.G., Salmeron, G., Lang, E.W.: Time series prediction using ICA algorithms. In: Proc. of 2nd IEEE Internat. W. on Intellig Data Acquisition and Advanc. Comp. Systems: Tech. and App., pp. 226–230 (2003)Lin, C.-T., Cheng, W.-C., Liang, S.-F.: An On-line ICA-Mixture-Model-Based Self-Constructing Fuzzy Neural Network. IEEE Transactions on Circuits and Systems I: Regular Papers 52(1), 207–221 (2005)Lee, T.W., Girolami, M., Sejnowski, T.J.: Independent component analysis using an extended InfoMax algorithm for mixed sub-gaussian and super-gaussian sources. Neural Computation 11(2), 417–441 (1999)Perrin, F., Pernier, J., Bertrand, D., Echallier, J.F.: Spherical splines for scalp potential and current density matching. Electroencep. and Clin. Neurophys. 72, 184–187 (1989)Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004
Analytical results for coupled map lattices with long-range interactions
We obtain exact analytical results for lattices of maps with couplings that
decay with distance as . We analyze the effect of the coupling
range on the system dynamics through the Lyapunov spectrum. For lattices whose
elements are piecewise linear maps, we get an algebraic expression for the
Lyapunov spectrum. When the local dynamics is given by a nonlinear map, the
Lyapunov spectrum for a completely synchronized state is analytically obtained.
The critical lines characterizing the synchronization transition are determined
from the expression for the largest transversal Lyapunov exponent. In
particular, it is shown that in the thermodynamical limit, such transition is
only possible for sufficiently long-range interactions, namely, for , where is the lattice dimension.Comment: 4 pages, 2 figures, corrections included. Phys. Rev. E 68, 045202(R)
(2003); correction in pres
Expression of Regulatory Platelet MicroRNAs in Patients with Sickle Cell Disease
Background: Increased platelet activation in sickle cell disease (SCD) contributes to a state of hypercoagulability and confers a risk of thromboembolic complications. The role for post-transcriptional regulation of the platelet transcriptome by microRNAs (miRNAs) in SCD has not been previously explored. This is the first study to determine whether platelets from SCD exhibit an altered miRNA expression profile. Methods and Findings: We analyzed the expression of miRNAs isolated from platelets from a primary cohort (SCD = 19, controls = 10) and a validation cohort (SCD = 7, controls = 7) by hybridizing to the Agilent miRNA microarrays. A dramatic difference in miRNA expression profiles between patients and controls was noted in both cohorts separately. A total of 40 differentially expressed platelet miRNAs were identified as common in both cohorts (p-value 0.05, fold change>2) with 24 miRNAs downregulated. Interestingly, 14 of the 24 downregulated miRNAs were members of three families - miR-329, miR-376 and miR-154 - which localized to the epigenetically regulated, maternally imprinted chromosome 14q32 region. We validated the downregulated miRNAs, miR-376a and miR-409-3p, and an upregulated miR-1225-3p using qRT-PCR. Over-expression of the miR-1225-3p in the Meg01 cells was followed by mRNA expression profiling to identify mRNA targets. This resulted in significant transcriptional repression of 1605 transcripts. A combinatorial approach using Meg01 mRNA expression profiles following miR-1225-3p overexpression, a computational prediction analysis of miRNA target sequences and a previously published set of differentially expressed platelet transcripts from SCD patients, identified three novel platelet mRNA targets: PBXIP1, PLAGL2 and PHF20L1. Conclusions: We have identified significant differences in functionally active platelet miRNAs in patients with SCD as compared to controls. These data provide an important inventory of differentially expressed miRNAs in SCD patients and an experimental framework for future studies of miRNAs as regulators of biological pathways in platelets. © 2013 Jain et al
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