Following previous series on Looking at People (LAP) competitions [14,13,11,12,2], in 2015 ChaLearn ran two new competitions within the field of Looking at People: (1) age estimation, and (2) cultural event recognition, both in still images. We developed a crowd-sourcing application to collect and label data about the apparent age of people (as opposed to the real age). In terms of cultural event recognition, one hundred categories had to be recognized. These tasks involved scene understanding and human body analysis. This paper summarizes both challenges and data, as well as the results achieved by the participants of the competition. Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/.
Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This paper is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.
Many bacterial species are social, producing costly secreted "public good" molecules that enhance the growth of neighboring cells. The genes coding for these cooperative traits are often propagated via mobile genetic elements and can be virulence factors from a biomedical perspective. Here, we present an experimental framework that links genetic information exchange and the selection of cooperative traits. Using simulations and experiments based on a synthetic bacterial system to control public good secretion and plasmid conjugation, we demonstrate that horizontal gene transfer can favor cooperation. In a well-mixed environment, horizontal transfer brings a direct infectious advantage to any gene, regardless of its cooperation properties. However, in a structured population transfer selects specifically for cooperation by increasing the assortment among cooperative alleles. Conjugation allows cooperative alleles to overcome rarity thresholds and invade bacterial populations structured purely by stochastic dilution effects. Our results provide an explanation for the prevalence of cooperative genes on mobile elements, and suggest a previously unidentified benefit of horizontal gene transfer for bacteria.bacterial cooperation | social evolution | plasmid transfer | gene mobility
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