Push is Fast on Sparse Random Graphs

We consider the classical push broadcast process on a large class of sparse random multigraphs that includes random power law graphs and multigraphs. Our analysis shows that for every $\varepsilon>0$, whp $O(\log n)$ rounds are sufficient to inform all but an $\varepsilon$-fraction of the vertices. It is not hard to see that, e.g. for random power law graphs, the push process needs whp $n^{\Omega(1)}$ rounds to inform all vertices. Fountoulakis, Panagiotou and Sauerwald proved that for random graphs that have power law degree sequences with $\beta>3$, the push-pull protocol needs $\Omega(\log n)$ to inform all but $\varepsilon n$ vertices whp. Our result demonstrates that, for such random graphs, the pull mechanism does not (asymptotically) improve the running time. This is surprising as it is known that, on random power law graphs with $2<\beta<3$, push-pull is exponentially faster than pull

Detecting seasonal variations in seismic velocities within Los Angeles basin from correlations of ambient seismic noise.

International audienceWe analyze 3 years of continuous seismic records from broadband stations of the Caltech Regional Seismic Network (CI) in vicinity of the Los Angeles basin. Using correlations of ambient seismic noise, relative velocity variations in the order of 0.1 % can be measured between all inter-station pairs. It is the first time that such an extensive study between 861 inter-station pairs over such a large area has been carried out. We perform these measurements using the 'stretching' technique, assuming that one of the two waveforms is merely a stretched version of the other. Obviously this assumption is always violated and the two waveforms are generally decorrelated due to temporal changes in the Earth crust, due to different sources or simply because the cross-correlations are not fully converged. We investigate the stability of these measurements by repeating each measurement over various time-windows of equal length. On average between all inter-station pairs in the Los Angeles basin a seasonal signal in the relative velocity variation is observed, with peaks and troughs during winter and summer time respectively. Generally the observed signal decreases with increasing inter-station distance and relative travel-time perturbations can only be measured up to an inter-station distance of 60 km. Furthermore the travel-time perturbations do not depend on azimuth of station pairs, suggesting that they are not related to seasonal variations of the noise sources. Performing a simple regionalization by laterally averaging measurements over a subset of stations we found the sedimentary basin showing the most consistent signal and conclude that the observed seasonality might be induced either by changes in the ground-water aquifer or thermo-elastic strain variations that persist down to a depth of 15-22 km

Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition

Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0.35% error rate on the famous MNIST handwritten digits benchmark. All we need to achieve this best result so far are many hidden layers, many neurons per layer, numerous deformed training images, and graphics cards to greatly speed up learning.Comment: 14 pages, 2 figures, 4 listing

Genetic subtraction profiling identifies genes essential for Arabidopsis reproduction and reveals interaction between the female gametophyte and the maternal sporophyte

Genetic subtraction and expression profiling of wild-type Arabidopsis and a sporophytic mutant lacking an embryo sac identified 1,260 genes expressed in the embryo sac; a total of 527 genes were identified for their expression in ovules of mutants lacking an embryo sac

Perceptual abstraction and attention

This is a report on the preliminary achievements of WP4 of the IM-CleVeR project on abstraction for cumulative learning, in particular directed to: (1) producing algorithms to develop abstraction features under top-down action influence; (2) algorithms for supporting detection of change in motion pictures; (3) developing attention and vergence control on the basis of locally computed rewards; (4) searching abstract representations suitable for the LCAS framework; (5) developing predictors based on information theory to support novelty detection. The report is organized around these 5 tasks that are part of WP4. We provide a synthetic description of the work done for each task by the partners