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Obtaining accurate experimental data from Lagrangian tracking and tomographic velocimetry requires an accurate camera calibration consistent over multiple views. Established calibration procedures are often challenging to implement when the length scale of the measurement volume exceeds that of a typical laboratory experiment. Here, we combine tools developed in computer vision and non-linear camera mappings used in experimental fluid mechanics, to successfully calibrate a four-camera setup that is imaging inside a large tank of dimensions ∼ 10 × 25 × 6 m 3 . The calibration procedure uses a planar checkerboard that is arbitrarily positioned at unknown locations and orientations. The method can be applied to any number of cameras. The parameters of the calibration yields direct estimates of the positions and orientations of the four cameras as well as the focal lengths of the lenses. These parameters are used to assess the quality of the calibration. The calibration allows us to perform accurate and consistent linear ray-tracing, which we use to triangulate and track fish inside the large tank. An open-source implementation of the calibration in Matlab is available. [ABSTRACT FROM AUTHOR]

Through combining theoretical models and empirical data, complexity science has increased our understanding of social behavior of animals, in particular of social insects, primates, and fish. What are missing are studies of collective behavior of huge swarms of birds. Recently detailed empirical data have been collected of the swarming maneuvers of large flocks of thousands of starlings (Sturnus vulgaris) at their communal sleeping site (roost). Their flocking maneuvers are of dazzling complexity in their changes in density and flock shape, but the processes underlying them are still a mystery. Recent models show that flocking may arise by self-organization from rules of co-ordination with nearby neighbors, but patterns in these models come nowhere near the complexity of those of the real starlings. The question of this paper, therefore, is whether such complex patterns can emerge by self-organization. In our computer model, called StarDisplay, we combine the usual rules of co-ordination based on separation, attraction, and alignment with specifics of starling behavior: 1) simplified aerodynamics of flight, especially rolling during turning, 2) movement above a “roosting area” (sleeping site), and 3) the low fixed number of interaction neighbors (i.e., the topological range). Our model generates patterns that resemble remarkably not only qualitative but also quantitative empirical data collected in Rome through video recordings and position measurements by stereo photography. Our results provide new insights into the mechanisms underlying complex flocking maneuvers of starlings and other birds. [ABSTRACT FROM PUBLISHER]

Seven long-tailed macaques (Macaca fascicularis) were trained by threats not to drink from a juice nipple as long as an experimenter was facing them. However, they were allowed to drink when the experimenter was standing with his or her back turned. During transfer tests, the monkeys had a choice between 2 juice nipples, one uncovered and the other hidden from the experimenter by a wooden screen, while the experimenter was facing them. We tested whether the monkeys would then prefer to drink behind the screen, thus demonstrating that they transferred knowledge acquired during training. Results did not yield a significant outcome, suggesting that the macaques did not transfer the observable "experimenter's visible open eyes" and that they did not take the experimenter's perspective.