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Institution

Pompeu Fabra University

EducationBarcelona, Spain
About: Pompeu Fabra University is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 8093 authors who have published 23570 publications receiving 858431 citations. The organization is also known as: Universitat Pompeu Fabra & UPF.


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Journal ArticleDOI
TL;DR: The genomes of seven North African populations reveal an extraordinarily complex history of migrations, involving at least five ancestral populations, into North Africa, and a gradient of likely autochthonous Maghrebi ancestry that increases from east to west across northern Africa.
Abstract: North African populations are distinct from sub-Saharan Africans based on cultural, linguistic, and phenotypic attributes; however, the time and the extent of genetic divergence between populations north and south of the Sahara remain poorly understood. Here, we interrogate the multilayered history of North Africa by characterizing the effect of hypothesized migrations from the Near East, Europe, and sub-Saharan Africa on current genetic diversity. We present dense, genome-wide SNP genotyping array data (730,000 sites) from seven North African populations, spanning from Egypt to Morocco, and one Spanish population. We identify a gradient of likely autochthonous Maghrebi ancestry that increases from east to west across northern Africa; this ancestry is likely derived from “back-to-Africa” gene flow more than 12,000 years ago (ya), prior to the Holocene. The indigenous North African ancestry is more frequent in populations with historical Berber ethnicity. In most North African populations we also see substantial shared ancestry with the Near East, and to a lesser extent sub-Saharan Africa and Europe. To estimate the time of migration from sub-Saharan populations into North Africa, we implement a maximum likelihood dating method based on the distribution of migrant tracts. In order to first identify migrant tracts, we assign local ancestry to haplotypes using a novel, principal component-based analysis of three ancestral populations. We estimate that a migration of western African origin into Morocco began about 40 generations ago (approximately 1,200 ya); a migration of individuals with Nilotic ancestry into Egypt occurred about 25 generations ago (approximately 750 ya). Our genomic data reveal an extraordinarily complex history of migrations, involving at least five ancestral populations, into North Africa.

299 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examine whether the publication behavior of academic inventors differs from their colleagues working within similar fields of research and find that inventors publish significantly more than non-inventors.
Abstract: Increasing entrepreneurial activity within academia has raised concerns that the amount of publications added to the scientific commons might become reduced or that academic research would become directed exclusively towards the application-oriented needs of industry. In the case of academic inventions, the potential conflict between public and private oriented considerations seems most salient. With this contribution, we examine whether the publication behaviour of academic inventors (at K.U.Leuven) differs from their colleagues (non-inventors) working within similar fields of research. Our analysis reveals that inventors publish significantly more. Moreover, no empirical evidence was found for the 'skewing problem'. These findings not only suggest the co-existence of both activities; they may actually reinforce one another.

298 citations

Journal ArticleDOI
TL;DR: EEG signals from epileptogenic brain areas are less random, more nonlinear-dependent, and more stationary compared to signals recorded from nonepileptogenicbrain areas.
Abstract: To derive tests for randomness, nonlinear-independence, and stationarity, we combine surrogates with a nonlinear prediction error, a nonlinear interdependence measure, and linear variability measures, respectively. We apply these tests to intracranial electroencephalographic recordings (EEG) from patients suffering from pharmacoresistant focal-onset epilepsy. These recordings had been performed prior to and independent from our study as part of the epilepsy diagnostics. The clinical purpose of these recordings was to delineate the brain areas to be surgically removed in each individual patient in order to achieve seizure control. This allowed us to define two distinct sets of signals: One set of signals recorded from brain areas where the first ictal EEG signal changes were detected as judged by expert visual inspection ("focal signals") and one set of signals recorded from brain areas that were not involved at seizure onset ("nonfocal signals"). We find more rejections for both the randomness and the nonlinear-independence test for focal versus nonfocal signals. In contrast more rejections of the stationarity test are found for nonfocal signals. Furthermore, while for nonfocal signals the rejection of the stationarity test increases the rejection probability of the randomness and nonlinear-independence test substantially, we find a much weaker influence for the focal signals. In consequence, the contrast between the focal and nonfocal signals obtained from the randomness and nonlinear-independence test is further enhanced when we exclude signals for which the stationarity test is rejected. To study the dependence between the randomness and nonlinear-independence test we include only focal signals for which the stationarity test is not rejected. We show that the rejection of these two tests correlates across signals. The rejection of either test is, however, neither necessary nor sufficient for the rejection of the other test. Thus, our results suggest that EEG signals from epileptogenic brain areas are less random, more nonlinear-dependent, and more stationary compared to signals recorded from nonepileptogenic brain areas. We provide the data, source code, and detailed results in the public domain.

298 citations

Journal ArticleDOI
TL;DR: It is shown that different syntactic dependency networks (from Czech, German, and Romanian) share many nontrivial statistical patterns such as the small world phenomenon, scaling in the distribution of degrees, and disassortative mixing.
Abstract: Many languages are spoken on Earth. Despite their diversity, many robust language universals are known to exist. All languages share syntax, i.e., the ability of combining words for forming sentences. The origin of such traits is an issue of open debate. By using recent developments from the statistical physics of complex networks, we show that different syntactic dependency networks (from Czech, German, and Romanian) share many nontrivial statistical patterns such as the small world phenomenon, scaling in the distribution of degrees, and disassortative mixing. Such previously unreported features of syntax organization are not a trivial consequence of the structure of sentences, but an emergent trait at the global scale.

298 citations

Journal ArticleDOI
TL;DR: In this article, the authors reformulate coarse-graining as a supervised machine learning problem and use statistical learning theory to decompose the coarsegraining error and cross-validation to select and compare the performance of different models.
Abstract: Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force-matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show tha...

298 citations


Authors

Showing all 8248 results

NameH-indexPapersCitations
Andrei Shleifer171514271880
Paul Elliott153773103839
Bert Brunekreef12480681938
Philippe Aghion12250773438
Anjana Rao11833761395
Jordi Sunyer11579857211
Kenneth J. Arrow113411111221
Xavier Estivill11067359568
Roderic Guigó108304106914
Mark J. Nieuwenhuijsen10764749080
Jordi Alonso10752364058
Alfonso Valencia10654255192
Luis Serrano10545242515
Vadim N. Gladyshev10249034148
Josep M. Antó10049338663
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202349
2022248
20211,903
20201,930
20191,763
20181,660