An uncued brain-computer interface using reservoir computing

Brain-Computer Interfaces are an important and promising avenue for possible next-generation assistive devices. In this article, we show how Reservoir Comput- ing – a computationally efficient way of training recurrent neural networks – com- bined with a novel feature selection algorithm based on Common Spatial Patterns can be used to drastically improve performance in an uncued motor imagery based Brain-Computer Interface (BCI). The objective of this BCI is to label each sample of EEG data as either motor imagery class 1 (e.g. left hand), motor imagery class 2 (e.g. right hand) or a rest state (i.e., no motor imagery). When comparing the re- sults of the proposed method with the results from the BCI Competition IV (where this dataset was introduced), it turns out that the proposed method outperforms the winner of the competition

An inordinate fondness?: the number, distributions, and origins of diatom species

The number of extant species of diatoms is estimated here to be at least 30,000 and probably ca. 100,000, by extrapolation from an eclectic sample of genera and species complexes. Available data, although few, indicate that the pseudocryptic species being discovered in many genera are not functionally equivalent. Molecular sequence data show that some diatom species are ubiquitously dispersed. A good case can be made that at least some diatom species and even a few genera are endemics, but many such claims are still weak. The combination of very large species numbers and relatively rapid dispersal in diatoms is inconsistent with some versions of the ubiquity hypothesis of protist biogeography, and appears paradoxical. However, population genetic data indicate geographical structure in all the (few) marine and freshwater species that have been examined in detail, sometimes over distances of a few tens of kilometres. The mode of speciation may often be parapatric, in the context of a constantly shifting mosaic of temporarily isolated (meta) populations, but if our intermediate dispersal hypothesis is true (that long-distance dispersal is rare, but not extremely rare), allopatric speciation could also be maximized

A probabilistic extension of UML statecharts: specification and verification

This paper is the extended technical report that corresponds to a published paper [14]. This paper introduces means to specify system randomness within UML statecharts, and to verify probabilistic temporal properties over such enhanced statecharts which we call probabilistic UML statecharts. To achieve this, we develop a general recipe to extend a statechart semantics with discrete probability distributions, resulting in Markov decision processes as semantic models. We apply this recipe to the requirements-level UML semantics of [8]. Properties of interest for probabilistic statecharts are expressed in PCTL, a probabilistic variant of CTL for processes that exhibit both non-determinism and probabilities. Verification is performed using the model checker Prism. A model checking example shows the feasibility of the suggested approach

Epidemics on random intersection graphs

In this paper we consider a model for the spread of a stochastic SIR (Susceptible $\to$ Infectious $\to$ Recovered) epidemic on a network of individuals described by a random intersection graph. Individuals belong to a random number of cliques, each of random size, and infection can be transmitted between two individuals if and only if there is a clique they both belong to. Both the clique sizes and the number of cliques an individual belongs to follow mixed Poisson distributions. An infinite-type branching process approximation (with type being given by the length of an individual's infectious period) for the early stages of an epidemic is developed and made fully rigorous by proving an associated limit theorem as the population size tends to infinity. This leads to a threshold parameter $R_*$, so that in a large population an epidemic with few initial infectives can give rise to a large outbreak if and only if $R_*>1$. A functional equation for the survival probability of the approximating infinite-type branching process is determined; if $R_*\le1$, this equation has no nonzero solution, while if $R_*>1$, it is shown to have precisely one nonzero solution. A law of large numbers for the size of such a large outbreak is proved by exploiting a single-type branching process that approximates the size of the susceptibility set of a typical individual.Comment: Published in at http://dx.doi.org/10.1214/13-AAP942 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Cycling around bends: the effect of cycling speed on steering and gaze behavior

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Revealing velocity dispersion as the best indicator of a galaxy's color, compared to stellar mass, surface mass density or morphology

Using data of nearby galaxies from the Sloan Digital Sky Survey we investigate whether stellar mass, central velocity dispersion, surface mass density, or the Sersic n parameter is best correlated with a galaxy's rest-frame color. Specifically, we determine how the mean color of galaxies varies with one parameter when another is fixed. When the stellar mass is fixed we see that strong trends remain with all other parameters, whereas residual trends are weaker when surface mass density, n, or velocity dispersion are fixed. Overall velocity dispersion is the best indicator of a galaxy's typical color, showing the largest residual color dependence when any of the other three parameters are fixed, and stellar mass is the poorest. Other studies have indicated that both the halo and black hole properties are better correlated with velocity dispersion than with stellar mass, surface mass density or Sersic n. Therefore, our results are consistent with a picture where a galaxy's star formation history and present star formation rate are determined to some significant degree by the current properties and assembly history of its dark matter halo and/or the feedback from its central super massive black hole.Comment: 7 pages, 5 figures, submitted to ApJ Letter

Readable and efficient HEP data analysis with bamboo

With the LHC continuing to collect more data and experimental analyses becoming increasingly complex, tools to efficiently develop and execute these analyses are essential. The bamboo framework defines a domain-specific language, embedded in python, that allows to concisely express the analysis logic in a functional style. The implementation based on ROOT's RDataFrame and cling C++ JIT compiler approaches the performance of dedicated native code. Bamboo is currently being used for several CMS Run 2 analyses that rely on the NanoAOD data format, which will become more common in Run 3 and beyond, and for which many reusable components are included, but it provides many possibilities for customisation, which allow for straightforward adaptation to other formats and workflowsComment: Updated version, taking into account feedback from the vCHEP2021 reviewe