19 research outputs found
Nonsymmetric Interactions Trigger Collective Swings in Globally Ordered Systems
Many systems in nature, from ferromagnets to flocks of birds, exhibit ordering phenomena on the large scale. In condensed matter systems, order is statistically robust for large enough dimensions, with relative fluctuations due to noise vanishing with system size. Several biological systems, however, are less stable and spontaneously change their global state on relatively short time scales. Here we show that there are two crucial ingredients in these systems that enhance the effect of noise, leading to collective changes of state on finite time scales and off-equilibrium behavior: the nonsymmetric nature of interactions between individuals, and the presence of local heterogeneities in the topology of the network. Our results might explain what is observed in several living systems and are consistent with recent experimental data on bird flocks and other animal groups
Social interactions dominate speed control in driving natural flocks toward criticality
Flocks of birds exhibit a remarkable degree of coordination and collective
response. It is not just that thousands of individuals fly, on average, in the
same direction and at the same speed, but that even the fluctuations around the
mean velocity are correlated over long distances. Quantitative measurements on
flocks of starlings, in particular, show that these fluctuations are
scale-free, with effective correlation lengths proportional to the linear size
of the flock. Here we construct models for the joint distribution of velocities
in the flock that reproduce the observed local correlations between individuals
and their neighbors, as well as the variance of flight speeds across
individuals, but otherwise have as little structure as possible. These
minimally structured, or maximum entropy models provide quantitative,
parameter-free predictions for the spread of correlations throughout the flock,
and these are in excellent agreement with the data. These models are
mathematically equivalent to statistical physics models for ordering in
magnets, and the correct prediction of scale-free correlations arises because
the parameters - completely determined by the data - are in the critical
regime. In biological terms, criticality allows the flock to achieve maximal
correlation across long distances with limited speed fluctuations
Flocking and turning: a new model for self-organized collective motion
Birds in a flock move in a correlated way, resulting in large polarization of
velocities. A good understanding of this collective behavior exists for linear
motion of the flock. Yet observing actual birds, the center of mass of the
group often turns giving rise to more complicated dynamics, still keeping
strong polarization of the flock. Here we propose novel dynamical equations for
the collective motion of polarized animal groups that account for correlated
turning including solely social forces. We exploit rotational symmetries and
conservation laws of the problem to formulate a theory in terms of generalized
coordinates of motion for the velocity directions akin to a Hamiltonian
formulation for rotations. We explicitly derive the correspondence between this
formulation and the dynamics of the individual velocities, thus obtaining a new
model of collective motion. In the appropriate overdamped limit we recover the
well-known Vicsek model, which dissipates rotational information and does not
allow for polarized turns. Although the new model has its most vivid success in
describing turning groups, its dynamics is intrinsically different from
previous ones in a wide dynamical regime, while reducing to the hydrodynamic
description of Toner and Tu at very large length-scales. The derived framework
is therefore general and it may describe the collective motion of any strongly
polarized active matter system.Comment: Accepted for the Special Issue of the Journal of Statistical Physics:
Collective Behavior in Biological Systems, 17 pages, 4 figures, 3 video
GReTA - a novel Global and Recursive Tracking Algorithm in three dimensions
Tracking multiple moving targets allows quantitative measure of the dynamic
behavior in systems as diverse as animal groups in biology, turbulence in fluid
dynamics and crowd and traffic control. In three dimensions, tracking several
targets becomes increasingly hard since optical occlusions are very likely,
i.e. two featureless targets frequently overlap for several frames. Occlusions
are particularly frequent in biological groups such as bird flocks, fish
schools, and insect swarms, a fact that has severely limited collective animal
behavior field studies in the past. This paper presents a 3D tracking method
that is robust in the case of severe occlusions. To ensure robustness, we adopt
a global optimization approach that works on all objects and frames at once. To
achieve practicality and scalability, we employ a divide and conquer
formulation, thanks to which the computational complexity of the problem is
reduced by orders of magnitude. We tested our algorithm with synthetic data,
with experimental data of bird flocks and insect swarms and with public
benchmark datasets, and show that our system yields high quality trajectories
for hundreds of moving targets with severe overlap. The results obtained on
very heterogeneous data show the potential applicability of our method to the
most diverse experimental situations.Comment: 13 pages, 6 figures, 3 tables. Version 3 was slightly shortened, and
new comprative results on the public datasets (thermal infrared videos of
flying bats) by Z. Wu and coworkers (2014) were included. in A. Attanasi et
al., "GReTA - A Novel Global and Recursive Tracking Algorithm in Three
Dimensions", IEEE Trans. Pattern Anal. Mach. Intell., vol.37 (2015
Flocking and turning: a new model for self-organized collective motion
Birds in a flock move in a correlated way, resulting in large polarization of velocities. A good understanding of this collective behavior exists for linear motion of the flock. Yet observing actual birds, the center of mass of the group often turns giving rise to more complicated dynamics, still keeping strong polarization of the flock. Here we propose novel dynamical equations for the collective motion of polarized animal groups that account for correlated turning including solely social forces. We exploit rotational symmetries and conservation laws of the problem to formulate a theory in terms of generalized coordinates of motion for the velocity directions akin to a Hamiltonian formulation for rotations. We explicitly derive the correspondence between this formulation and the dynamics of the individual velocities, thus obtaining a new model of collective motion. In the appropriate overdamped limit we recover the well-known Vicsek model, which dissipates rotational information and does not allow for polarized turns. Although the new model has its most vivid success in describing turning groups, its dynamics is intrinsically different from previous ones in a wide dynamical regime, while reducing to the hydrodynamic description of Toner and Tu at very large length-scales. The derived framework is therefore general and it may describe the collective motion of any strongly polarized active matter system.Instituto de Investigaciones FisicoquÃmicas Teóricas y Aplicada
Real world applications using parallel computing techniques in dynamic traffic assignment and shortest path search
As the range of applications for Intelligent Transport Systems (ITS) grows wider, the efficiency of the underlying tools for Big Data Analytics becomes of crucial importance. Smart Cities are able to monitor, forecast and (possibly) control the pulse of collective interactions involving networks and environment (such as traffic and pollution) by means of key performance indicators. Technology-guided solutions can proactively support the sustainable development and the optimal management of infrastructures and services, improving the quality of life for both city dwellers and commuters. This requires processing huge amounts of data, continuously streaming in from a variety of fixed sensors (e.g. loops, cameras) and mobile devices (GPS trajectories). In particular, Mobility Control Centres need effective software solutions and fast algorithms to deal with two major problems: Traffic Forecasting and Route Guidance. This paper presents real world examples of large scale applications where both tasks are addressed by implementing parallel computing algorithms, achieving high performances and allowing real time management operations and end-user services. The first test case examines the performance of a routing platform covering the entire Austria region, while the second concerns large instances of dynamic traffic assignment for real-time forecasting
Collective Behaviour without Collective Order in Wild Swarms of Midges
Collective behaviour is a widespread phenomenon in biology, cutting through a huge span of scales, from cell colonies up to bird flocks and fish schools. The most prominent trait of collective behaviour is the emergence of global order: individuals synchronize their states, giving the stunning impression that the group behaves as one. In many biological systems, though, it is unclear whether global order is present. A paradigmatic case is that of insect swarms, whose erratic movements seem to suggest that group formation is a mere epiphenomenon of the independent interaction of each individual with an external landmark. In these cases, whether or not the group behaves truly collectively is debated. Here, we experimentally study swarms of midges in the field and measure how much the change of direction of one midge affects that of other individuals. We discover that, despite the lack of collective order, swarms display very strong correlations, totally incompatible with models of non-interacting particles. We find that correlation increases sharply with the swarm's density, indicating that the interaction between midges is based on a metric perception mechanism. By means of numerical simulations we demonstrate that such growing correlation is typical of a system close to an ordering transition. Our findings suggest that correlation, rather than order, is the true hallmark of collective behaviour in biological systems