Ancient DNA from Hunter-Gatherer and Farmer Groups from Northern Spain Supports a Random Dispersion Model for the Neolithic Expansion into Europe

10 p.

The coincidence of the kernel and nucleolus of a convex game: an alternative proof

In 1972, Maschler, Peleg and Shapley proved that in the class of convex the nucleolus and the kernel coincide. The only aim of this note is to provide a shorter, alternative proof of this result.

Sistema konkurrenteak simulatzeko kernel bat: multiprozesadore sistemak

Jarraian, hainbat hilabetetan zehar garatutako proiektuaren deskribapena biltzen duen memoria dugu eskuragarri. Proiektu hau, sistema konkurrenteen simulazioan zentratzen da eta horretarako, mota honetako sistemen arloan hain erabiliak diren Petri Sareak lantzeaz gain, simulatzaile bat programatzeko informazio nahikoa ere barneratzen ditu. Gertaera diskretuko simulatzaile estatistiko batean oinarrituko da proiektuaren garapena, helburua izanik Petri Sareen bidez formalizatzen diren sistemak simulatzeko softwarea osatzea. Proiektuaren helburua da objektuetara zuzendutako hizkuntzaren bidez, Java hizkuntzaren bidez alegia, simulatzailearen programazioa erraztea eta ingurune honen baliabideak erabiltzea, bereziki XML teknologiari lotutakoak. Proiektu hau, bi zati nagusitan banatzen dela esan daiteke. Lehenengo zatiari dagokionez, konputazio munduan simulazioa aurkeztu eta honi buruzko behar adina informazio emango da. Hau, oso erabilgarria izango da programatuko den simulatzailearen nondik norakoak ulertu eta klase desberdinen inplementazioa egin ahal izateko. Horrez gain, zorizko aldagaiak eta hauen simulazioa ere islatzen dira, simulazio prozesu hori ahalik eta era errealean gauzatzeko helburuarekin. Ondoren, Petri Sareak aurkeztuko dira, hauen ezaugarri eta sailkapen desberdinak goraipatuz. Gainera, Petri Sareak definitzeko XML lengoaia erabiliko denez, mota honetako dokumentu eta eskemak aztertuko dira, hauek, garatuko den aplikazioaren oinarri izango direlarik. Bestalde, aplikazioaren muin izango diren klaseen diseinu eta inplementazioak bildu dira azken aurreko kapituluan. Alde batetik, erabili den DOM egituraren inguruko informazioa islatzen da eta bestetik, XML-tik habiatuz lortuko diren PetriNet instantziak maneiatzeko ezinbestekoak diren Java klaseen kodeak erakusten dira. Amaitzeko, egileak ateratako ondorioez gain, proiektuaren garapen prozesuan erabili den bibliografiaren berri ere ematen da.

Contributions to the efficient use of general purpose coprocessors: kernel density estimation as case study

142 p.

Anisotropic expansion and SNIa: An open issue

We review the appropriateness of using SNIa observations to detect potential signatures of anisotropic expansion in the Universe. We focus on Union2 and SNLS3 SNIa datasets and use the hemispherical comparison method to detect possible anisotropic features. Unlike some previous works where nondiagonal elements of the covariance matrix were neglected, we use the full covariance matrix of the SNIa data, thus obtaining more realistic and not underestimated errors. As a matter of fact, the significance of previously claimed detections of a preferred direction in the Union2 dataset completely disappears once we include the effects of using the full covariance matrix. Moreover, we also find that such apreferred direction is aligned with the orthogonal direction of the SDSS observational plane and this suggests a clear indication that the SDSS subsample of the Union2 dataset introduces a significant bias, making the detected preferred direction unphysical. We thus find that current SNIa surveys are inappropriate to test anisotropic features due to their highly non-homogeneous angular distribution in the sky. In addition, after removal of the highest in homogeneous sub-samples, the number of SNIa is too low. Finally, we take advantage of the particular distribution of SNLS SNIa sub- sample in the SNLS3 data set, in which the observations were taken along four different directions. We fit each direction independently and find consistent results at the 1 sigma level. Although the likelihoods peak at relatively different values of Omega(m), the low number of data along each direction gives rise to large errors so that the likelihoods are sufficiently broad as to overlap within 1 sigma. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons. org/licenses/by/4.0/).