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Models of the representation of numerosity information used in discrimination tasks are integrated with a diffusion decision model. The representation models assume distributions of numerosity either with means and SD that increase linearly with numerosity or with means that increase logarithmically with constant SD. The models produce coefficients that are applied to differences between two nu...

Changes in global average temperatures and of the seasonal cycle are strongly coupled to the concentration of atmospheric CO2. I estimate transfer functions from changes in atmospheric CO2 and from changes in solar irradiance to hemispheric temperatures that have been corrected for the effects of precession. They show that changes from CO2 over the last century are about three times larger than...

This formalisation contains the proof that there is no anonymous and neutral Social Decision Scheme for at least four voters and alternatives that fulfils both SD-Efficiency and SD-Strategy-Proofness. The proof is a fully structured and quasi-human-redable one. It was derived from the (unstructured) SMT proof of the case for exactly four voters and alternatives by Brandl et al. [1]. Their proof...

In the Göttinger minipig, the reference values of 18 parameters in whole blood or plasma were determined. The results are expressed both as the mean value and +/- 2 SD presuming a symmetrical normal distribution and as the median value with the corresponding range. For most blood constituents, the results of the two modes of calculation were similar. Comparison of the values from young minipigs...

The IDIBELL Cancer Conference (ICC) on Metastasis and Angiogenesis was held in Barcelona, Spain, on May 26-27, 2011. The program content was developed by Dr. Manel Esteller, director of the Cancer Epigenetics and Biology Program (PEBC-IDIBELL), Dr. Oriol Casanovas and Dr. Francesc Viñals Canals of the Catalan Institute of Oncology (ICO-IDIBELL), and Dr. Danny R. Welch from the University of Kan...

SUMMARY Consider a regression problem in which there are many more explanatory variables than data points, i.e., p >> n. Essentially, without reducing the number of variables inference is impossible. So, we group the p explanatory variables into blocks by clustering, evaluate statistics on the blocks and then regress the response on these statistics under a penalized error criterion to obtain e...