32,114 research outputs found
Transversals in -Uniform Hypergraphs
Let be a -regular -uniform hypergraph on vertices. The
transversal number of is the minimum number of vertices that
intersect every edge. Lai and Chang [J. Combin. Theory Ser. B 50 (1990),
129--133] proved that . Thomass\'{e} and Yeo [Combinatorica
27 (2007), 473--487] improved this bound and showed that .
We provide a further improvement and prove that , which is
best possible due to a hypergraph of order eight. More generally, we show that
if is a -uniform hypergraph on vertices and edges with maximum
degree , then , which proves a known
conjecture. We show that an easy corollary of our main result is that the total
domination number of a graph on vertices with minimum degree at least~4 is
at most , which was the main result of the Thomass\'{e}-Yeo paper
[Combinatorica 27 (2007), 473--487].Comment: 41 page
An Enhanced Perturbational Study on Spectral Properties of the Anderson Model
The infinite- single impurity Anderson model for rare earth alloys is
examined with a new set of self-consistent coupled integral equations, which
can be embedded in the large expansion scheme ( is the local spin
degeneracy). The finite temperature impurity density of states (DOS) and the
spin-fluctuation spectra are calculated exactly up to the order . The
presented conserving approximation goes well beyond the -approximation
({\em NCA}) and maintains local Fermi-liquid properties down to very low
temperatures. The position of the low lying Abrikosov-Suhl resonance (ASR) in
the impurity DOS is in accordance with Friedel's sum rule. For its shift
toward the chemical potential, compared to the {\em NCA}, can be traced back to
the influence of the vertex corrections. The width and height of the ASR is
governed by the universal low temperature energy scale . Temperature and
degeneracy -dependence of the static magnetic susceptibility is found in
excellent agreement with the Bethe-Ansatz results. Threshold exponents of the
local propagators are discussed. Resonant level regime () and intermediate
valence regime () of the model are thoroughly
investigated as a critical test of the quality of the approximation. Some
applications to the Anderson lattice model are pointed out.Comment: 19 pages, ReVTeX, no figures. 17 Postscript figures available on the
WWW at http://spy.fkp.physik.th-darmstadt.de/~frithjof
Decomposing labeled interval orders as pairs of permutations
We introduce ballot matrices, a signed combinatorial structure whose
definition naturally follows from the generating function for labeled interval
orders. A sign reversing involution on ballot matrices is defined. We show that
matrices fixed under this involution are in bijection with labeled interval
orders and that they decompose to a pair consisting of a permutation and an
inversion table. To fully classify such pairs, results pertaining to the
enumeration of permutations having a given set of ascent bottoms are given.
This allows for a new formula for the number of labeled interval orders
Dendritic Actin Filament Nucleation Causes Traveling Waves and Patches
The polymerization of actin via branching at a cell membrane containing
nucleation-promoting factors is simulated using a stochastic-growth
methodology. The polymerized-actin distribution displays three types of
behavior: a) traveling waves, b) moving patches, and c) random fluctuations.
Increasing actin concentration causes a transition from patches to waves. The
waves and patches move by a treadmilling mechanism which does not require
myosin II. The effects of downregulation of key proteins on actin wave behavior
are evaluated.Comment: 10 pages, 4 figure
A Bayesian Heteroscedastic GLM with Application to fMRI Data with Motion Spikes
We propose a voxel-wise general linear model with autoregressive noise and
heteroscedastic noise innovations (GLMH) for analyzing functional magnetic
resonance imaging (fMRI) data. The model is analyzed from a Bayesian
perspective and has the benefit of automatically down-weighting time points
close to motion spikes in a data-driven manner. We develop a highly efficient
Markov Chain Monte Carlo (MCMC) algorithm that allows for Bayesian variable
selection among the regressors to model both the mean (i.e., the design matrix)
and variance. This makes it possible to include a broad range of explanatory
variables in both the mean and variance (e.g., time trends, activation stimuli,
head motion parameters and their temporal derivatives), and to compute the
posterior probability of inclusion from the MCMC output. Variable selection is
also applied to the lags in the autoregressive noise process, making it
possible to infer the lag order from the data simultaneously with all other
model parameters. We use both simulated data and real fMRI data from OpenfMRI
to illustrate the importance of proper modeling of heteroscedasticity in fMRI
data analysis. Our results show that the GLMH tends to detect more brain
activity, compared to its homoscedastic counterpart, by allowing the variance
to change over time depending on the degree of head motion
Nonlinear control synthesis by convex optimization
A stability criterion for nonlinear systems, recently derived by the third author, can be viewed as a dual to Lyapunov's second theorem. The criterion is stated in terms of a function which can be interpreted as the stationary density of a substance that is generated all over the state-space and flows along the system trajectories toward the equilibrium. The new criterion has a remarkable convexity property, which in this note is used for controller synthesis via convex optimization. Recent numerical methods for verification of positivity of multivariate polynomials based on sum of squares decompositions are used
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