257 research outputs found
Identification du comportement mécanique dynamique de tubes d'aluminium par un essai d'expansion électromagnétique
National audienceLes vitesses de déformation mises en jeu au cours du magnétoformage sont de l'ordre de 102 à 104 s-1. La maîtrise du procédé exige donc la caractérisation des métaux dans ces conditions de déformation. Cet article présente la mise en place d'une démarche d'identification du comportement dynamique basée sur un essai d'expansion de tube instrumenté à l'aide d'un système de mesure de vitesse par interférométrie doppler-laser. Les simulations numériques sont réalisées à l'aide du code LS-Dyna®, et l'analyse inverse est menée grâce à l'interface d'optimisation LS-Opt®. Après une analyse numérique, des résultats d'identification sur tubes d'aluminium 1050-O sont présentés
A Stochastic Approach to Shortcut Bridging in Programmable Matter
In a self-organizing particle system, an abstraction of programmable matter,
simple computational elements called particles with limited memory and
communication self-organize to solve system-wide problems of movement,
coordination, and configuration. In this paper, we consider a stochastic,
distributed, local, asynchronous algorithm for "shortcut bridging", in which
particles self-assemble bridges over gaps that simultaneously balance
minimizing the length and cost of the bridge. Army ants of the genus Eciton
have been observed exhibiting a similar behavior in their foraging trails,
dynamically adjusting their bridges to satisfy an efficiency trade-off using
local interactions. Using techniques from Markov chain analysis, we rigorously
analyze our algorithm, show it achieves a near-optimal balance between the
competing factors of path length and bridge cost, and prove that it exhibits a
dependence on the angle of the gap being "shortcut" similar to that of the ant
bridges. We also present simulation results that qualitatively compare our
algorithm with the army ant bridging behavior. Our work gives a plausible
explanation of how convergence to globally optimal configurations can be
achieved via local interactions by simple organisms (e.g., ants) with some
limited computational power and access to random bits. The proposed algorithm
also demonstrates the robustness of the stochastic approach to algorithms for
programmable matter, as it is a surprisingly simple extension of our previous
stochastic algorithm for compression.Comment: Published in Proc. of DNA23: DNA Computing and Molecular Programming
- 23rd International Conference, 2017. An updated journal version will appear
in the DNA23 Special Issue of Natural Computin
Individualization as driving force of clustering phenomena in humans
One of the most intriguing dynamics in biological systems is the emergence of
clustering, the self-organization into separated agglomerations of individuals.
Several theories have been developed to explain clustering in, for instance,
multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of
fish, and animal herds. A persistent puzzle, however, is clustering of opinions
in human populations. The puzzle is particularly pressing if opinions vary
continuously, such as the degree to which citizens are in favor of or against a
vaccination program. Existing opinion formation models suggest that
"monoculture" is unavoidable in the long run, unless subsets of the population
are perfectly separated from each other. Yet, social diversity is a robust
empirical phenomenon, although perfect separation is hardly possible in an
increasingly connected world. Considering randomness did not overcome the
theoretical shortcomings so far. Small perturbations of individual opinions
trigger social influence cascades that inevitably lead to monoculture, while
larger noise disrupts opinion clusters and results in rampant individualism
without any social structure. Our solution of the puzzle builds on recent
empirical research, combining the integrative tendencies of social influence
with the disintegrative effects of individualization. A key element of the new
computational model is an adaptive kind of noise. We conduct simulation
experiments to demonstrate that with this kind of noise, a third phase besides
individualism and monoculture becomes possible, characterized by the formation
of metastable clusters with diversity between and consensus within clusters.
When clusters are small, individualization tendencies are too weak to prohibit
a fusion of clusters. When clusters grow too large, however, individualization
increases in strength, which promotes their splitting.Comment: 12 pages, 4 figure
Individual Preferences and Social Interactions Determine the Aggregation of Woodlice
n°e17389.info:eu-repo/semantics/publishe
Automatic Calibration of Artificial Neural Networks for Zebrafish Collective Behaviours using a Quality Diversity Algorithm
During the last two decades, various models have been proposed for fish
collective motion. These models are mainly developed to decipher the biological
mechanisms of social interaction between animals. They consider very simple
homogeneous unbounded environments and it is not clear that they can simulate
accurately the collective trajectories. Moreover when the models are more
accurate, the question of their scalability to either larger groups or more
elaborate environments remains open. This study deals with learning how to
simulate realistic collective motion of collective of zebrafish, using
real-world tracking data. The objective is to devise an agent-based model that
can be implemented on an artificial robotic fish that can blend into a
collective of real fish. We present a novel approach that uses Quality
Diversity algorithms, a class of algorithms that emphasise exploration over
pure optimisation. In particular, we use CVT-MAP-Elites, a variant of the
state-of-the-art MAP-Elites algorithm for high dimensional search space.
Results show that Quality Diversity algorithms not only outperform classic
evolutionary reinforcement learning methods at the macroscopic level (i.e.
group behaviour), but are also able to generate more realistic biomimetic
behaviours at the microscopic level (i.e. individual behaviour).Comment: 8 pages, 4 figures, 1 tabl
Performance-based social comparisons in humans and long-tailed macaques
Social comparisons are a fundamental feature of human thinking and affect self-evaluations and task performance. Little is known about the evolutionary origins of social comparison processes, however. Previous studies that investigated performance-based social comparisons in nonhuman primates yielded mixed results. We report three experiments that aimed (a) to explore how the task type may contribute to performance in monkeys, and (b) how a competitive set-up affects monkeys compared to humans. In a co-action touchscreen task, monkeys were neither influenced by nor interested in the performance of the partner. This may indicate that the experimental set-up was not sufficiently relevant to trigger social comparisons. In a novel co-action foraging task, monkeys increased their feeding speed in competitive and co-active conditions, but not in relation to the degree of competition. In an analogue of the foraging task, human participants were affected by partner performance and experimental context, indicating that the task is suitable to elicit social comparisons in humans.
Our studies indicate that specifics of task and experimental setting are relevant to draw the monkeys’ attention to a co-actor and that, in line with previous research, a competitive element was crucial. We highlight the need to explore what constitutes “relevant” social comparison situations for monkeys as well as nonhuman animals in general, and point out factors that we think are crucial in this respect (e.g. task type, physical closeness, and the species’ ecology). We discuss that early forms of social comparisons evolved in purely competitive environments with increasing social tolerance and cooperative motivations allowing for more fine-grained processing of social information. Competition driven effects on task performance might constitute the foundation for the more elaborate social comparison processes found in humans, which may involve context-dependent information processing and metacognitive monitoring
Group Living Enhances Individual Resources Discrimination: The Use of Public Information by Cockroaches to Assess Shelter Quality
In group-living organisms, consensual decision of site selection results from the interplay between individual responses to site characteristics and to group-members. Individuals independently gather personal information by exploring their environment. Through social interaction, the presence of others provides public information that could be used by individuals and modulates the individual probability of joining/leaving a site. The way that individual's information processing and the network of interactions influence the dynamics of public information (depending on population size) that in turn affect discrimination in site quality is a central question. Using binary choice between sheltering sites of different quality, we demonstrate that cockroaches in group dramatically outperform the problem-solving ability of single individual. Such use of public information allows animals to discriminate between alternatives whereas isolated individuals are ineffective (i.e. the personal discrimination efficiency is weak). Our theoretical results, obtained from a mathematical model based on behavioral rules derived from experiments, highlight that the collective discrimination emerges from competing amplification processes relying on the modulation of the individual sheltering time without shelters comparison and communication modulation. Finally, we well demonstrated here the adaptive value of such decision algorithm. Without any behavioral change, the system is able to shift to a more effective strategy when alternatives are present: the modification of the spatio-temporal distributions of individuals leading to the collective selection of the best resource. This collective discrimination implying such parsimonious and widespread mechanism must be shared by many group living-species
Individual rules for trail pattern formation in Argentine ants (Linepithema humile)
We studied the formation of trail patterns by Argentine ants exploring an
empty arena. Using a novel imaging and analysis technique we estimated
pheromone concentrations at all spatial positions in the experimental arena and
at different times. Then we derived the response function of individual ants to
pheromone concentrations by looking at correlations between concentrations and
changes in speed or direction of the ants. Ants were found to turn in response
to local pheromone concentrations, while their speed was largely unaffected by
these concentrations. Ants did not integrate pheromone concentrations over
time, with the concentration of pheromone in a 1 cm radius in front of the ant
determining the turning angle. The response to pheromone was found to follow a
Weber's Law, such that the difference between quantities of pheromone on the
two sides of the ant divided by their sum determines the magnitude of the
turning angle. This proportional response is in apparent contradiction with the
well-established non-linear choice function used in the literature to model the
results of binary bridge experiments in ant colonies (Deneubourg et al. 1990).
However, agent based simulations implementing the Weber's Law response function
led to the formation of trails and reproduced results reported in the
literature. We show analytically that a sigmoidal response, analogous to that
in the classical Deneubourg model for collective decision making, can be
derived from the individual Weber-type response to pheromone concentrations
that we have established in our experiments when directional noise around the
preferred direction of movement of the ants is assumed.Comment: final version, 9 figures, submitted to Plos Computational Biology
(accepted
Local Enhancement Promotes Cockroach Feeding Aggregations
Communication and learning from each other are part of the success of animal societies. Social insects invest considerable effort into signalling to their nestmates the locations of the most profitable resources in their environment. Growing evidence also indicates that insects glean such information through cues inadvertently provided by their conspecifics. Here, we investigate social information use in the foraging decisions by gregarious cockroaches (Blattella germanica L.). Individual cockroaches given a simultaneous choice in a Y-olfactometer between the odour of feeding conspecifics and the mixed odour of food plus non-feeding conspecifics showed a preference for the arm scented with the odour of feeding conspecifics. Social information (the presence of feeding conspecifics) was produced by cockroaches of all age classes and perceived at short distance in the olfactometer arms, suggesting the use of inadvertently provided cues rather than signals. We discuss the nature of these cues and the role of local enhancement (the selection of a location based on cues associated with the presence of conspecifics) in the formation of feeding aggregations in B. germanica. Similar cue-mediated recruitments could underpin a wide range of collective behaviours in group-living insects
Quality-sensitive foraging by a robot swarm through virtual pheromone trails
Large swarms of simple autonomous robots can be employed to find objects clustered at random locations, and transport them to a central depot. This solution offers system parallelisation through concurrent environment exploration and object collection by several robots, but it also introduces the challenge of robot coordination. Inspired by ants’ foraging behaviour, we successfully tackle robot swarm coordination through indirect stigmergic communication in the form of virtual pheromone trails. We design and implement a robot swarm composed of up to 100 Kilobots using the recent technology Augmented Reality for Kilobots (ARK). Using pheromone trails, our memoryless robots rediscover object sources that have been located previously. The emerging collective dynamics show a throughput inversely proportional to the source distance. We assume environments with multiple sources, each providing objects of different qualities, and we investigate how the robot swarm balances the quality-distance trade-off by using quality-sensitive pheromone trails. To our knowledge this work represents the largest robotic experiment in stigmergic foraging, and is the first complete demonstration of ARK, showcasing the set of unique functionalities it provides
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