Pompeu Fabra University

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

Human Capital, the Structure of Production and Growth

Abstract: We document that countries with higher initial education levels experienced faster value-added and employment growth in schooling-intensive industries in the 1980s and 1990s. This effect is robust to controls for other determinants of international specialization and becomes stronger when we focus on economies open to international trade. Our finding is consistent with schooling fostering the adoption of new technologies if such technologies are skilled-labor augmenting, as was the case in the 1980s and the 1990s. In line with international specialization theory, we also find that countries where education levels increased rapidly experienced stronger shifts in production toward schooling-intensive industries.

Overcoming catastrophic forgetting with hard attention to the task

Abstract: Catastrophic forgetting occurs when a neural network loses the information learned in a previous task after training on subsequent tasks. This problem remains a hurdle for artificial intelligence systems with sequential learning capabilities. In this paper, we propose a task-based hard attention mechanism that preserves previous tasks' information without affecting the current task's learning. A hard attention mask is learned concurrently to every task, through stochastic gradient descent, and previous masks are exploited to condition such learning. We show that the proposed mechanism is effective for reducing catastrophic forgetting, cutting current rates by 45 to 80%. We also show that it is robust to different hyperparameter choices, and that it offers a number of monitoring capabilities. The approach features the possibility to control both the stability and compactness of the learned knowledge, which we believe makes it also attractive for online learning or network compression applications.

Chromosomal speciation revisited: rearranging theory with pieces of evidence.

Abstract: The suggestion that chromosomal rearrangements play a role in speciation resulted from the observation that heterokaryotypes are often infertile. However, the first chromosomal speciation models were unsatisfactory and data available to test them was scarce. Recently, large amounts of data have become available and new theoretical models have been developed explaining how rearrangements facilitate speciation in the face of gene flow. Here, we re-examine theoretical predictions and revisit different sources of data. Although rearrangements are often associated with increased levels of divergence, unequivocal demonstration that their role in suppressing recombination results in speciation is often lacking. Finally, we question some previous predictions and suggest new empirical and theoretical approaches to understanding the relevance of rearrangements in the origin of species.

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries.

Abstract: Social data in digital form—including user-generated content, expressed or implicit relations between people, and behavioral traces—are at the core of popular applications and platforms, driving the research agenda of many researchers. The promises of social data are many, including understanding “what the world thinks” about a social issue, brand, celebrity, or other entity, as well as enabling better decision-making in a variety of fields including public policy, healthcare, and economics. Many academics and practitioners have warned against the naive usage of social data. There are biases and inaccuracies occurring at the source of the data, but also introduced during processing. There are methodological limitations and pitfalls, as well as ethical boundaries and unexpected consequences that are often overlooked. This paper recognizes the rigor with which these issues are addressed by different researchers varies across a wide range. We identify a variety of menaces in the practices around social data use, and organize them in a framework that helps to identify them. “For your own sanity, you have to remember that not all problems can be solved. Not all problems can be solved, but all problems can be illuminated.” –Ursula Franklin1