Characterisation of spatial network-like patterns from junctions' geometry

We propose a new method for quantitative characterization of spatial network-like patterns with loops, such as surface fracture patterns, leaf vein networks and patterns of urban streets. Such patterns are not well characterized by purely topological estimators: also patterns that both look different and result from different morphogenetic processes can have similar topology. A local geometric cue -the angles formed by the different branches at junctions- can complement topological information and allow to quantify the large scale spatial coherence of the pattern. For patterns that grow over time, such as fracture lines on the surface of ceramics, the rank assigned by our method to each individual segment of the pattern approximates the order of appearance of that segment. We apply the method to various network-like patterns and we find a continuous but sharp dichotomy between two classes of spatial networks: hierarchical and homogeneous. The first class results from a sequential growth process and presents large scale organization, the latter presents local, but not global organization.Comment: version 2, 14 page

X-ray tomographic analysis of the initial structure of the royal chamber and the nest-founding behavior of the drywood termite Incisitermes minor

The nesting biology of the drywood termite, Incisitermes minor, is poorly understood. To date, no published data are available regarding the in situ nest-gallery development of I. minor. Three naturally infested Sitka spruce (Picea sitchensis Bong. Carriere) timbers were analyzed by X-ray computer tomography to observe the structure of the first royal chamber and the termite’s nest-founding behavior. One timber was infested by a group of termites which emerged from their natal nest. The other two timbers were infested by dealate reproductives from the nuptial flight. The study revealed that the drywood termite engages in outside foraging activity and has great foraging flexibility. Computer tomographic images also revealed that I. minor reproductives showed anatomical selectivity in their nest-founding activity. The structure of the initial royal chambers varied to follow the anatomical texture of the timbers, which resembled either a European pear shape or a cashew nut shape

The drivers of heuristic optimization in insect object manufacture and use

Insects have small brains and heuristics or ‘rules of thumb’ are proposed here to be a good model for how insects optimize the objects they make and use. Generally, heuristics are thought to increase the speed of decision making by reducing the computational resources needed for making decisions. By corollary, heuristic decisions are also deemed to impose a compromise in decision accuracy. Using examples from object optimization behavior in insects, we will argue that heuristics do not inevitably imply a lower computational burden or lower decision accuracy. We also show that heuristic optimization may be driven by certain features of the optimization problem itself: the properties of the object being optimized, the biology of the insect, and the properties of the function being optimized. We also delineate the structural conditions under which heuristic optimization may achieve accuracy equivalent to or better than more fine-grained and onerous optimization methods

IMPROVING RESILIENCE OF RAIL-BASED INTERMODAL FREIGHT TRANSPORTATION SYSTEMS

With the increasing natural and human-made disasters, the risk of an event with potential to cause major disruption to our transportation systems and their components also increases. It is of paramount importance that transportation systems could be effectively recovered, thus economic loss due to the disasters can be minimized. This dissertation addresses the optimization problems for transportation system performance measurement, decision-making on pre-disaster preparedness and post-event recovery actions planning and scheduling to achieve the maximum network resilience level. In assessing a network's potential performance given possible future disruptions, one must recognize the contributions of the network's inherent ability to cope with disruption via its topological and operational attributes and potential actions that can be taken in the immediate aftermath of such an event. A two-stage stochastic program is formulated to solve the problem of measuring a network's maximum resilience level and simultaneously determining the optimal set of preparedness and recovery actions necessary to achieve this level under budget and level-of-service constraints. An exact methodology, employing the integer L-shaped method and Monte Carlo simulation, is proposed for its solution. In this dissertation, a nonlinear, stochastic, time-dependent integer program is proposed, from operational perspective, to schedule short-term recovery activities to maximize transportation network resilience. Two solution methods are proposed, both employing a decomposition approach to eliminate nonlinearities of the formulation. The first is an exact decomposition with branch-and-cut methodology, and the second is a hybrid genetic algorithm that evaluates each chromosome's fitness based on optimal objective values to the time-dependent maximum flow subproblem. Algorithm performance is also assessed on a test network. Finally, this dissertation studies the role of network topology in resilience. 17 specific network topologies were selected for network resilience analysis. Simple graph structures with 9~10 nodes and larger network with 100 nodes are assessed. Resilience is measured in terms of throughput and connectivity and average reciprocal distance. The integer L-shaped method is applied again to study the performance of the network structure with respect to all three resilience measures. The relationships between resilience and average degree, diameter, and cyclicity are also investigated

Effects of information quantity and quality on collective decisions in human groups

Dans cette thèse, nous nous sommes intéressés à l'impact de la quantité et de la qualité de l'information échangée entre individus d'un groupe sur leurs performances collectives dans deux types de tâches bien spécifiques. Dans une première série d'expériences, les sujets devaient estimer des quantités séquentiellement, et pouvaient réviser leurs estimations après avoir reçu comme information sociale l'estimation moyenne d'autres sujets. Nous contrôlions cette information sociale à l'aide de participants virtuels (dont nous contrôlions le nombre) donnant une information (dont nous contrôlions la valeur), à l'insu des sujets. Nous avons montré que lorsque les sujets ont peu de connaissance préalable sur une quantité à estimer, (les logarithmes de) leurs estimations suivent une distribution de Laplace. La médiane étant un bon estimateur du centre d'une distribution de Laplace, nous avons défini la performance collective comme la proximité de la médiane (du logarithme) des estimations à la vraie valeur. Nous avons trouvé qu'après influence sociale, et lorsque les agents virtuels fournissent une information correcte, la performance collective augmente avec la quantité d'information fournie (fraction d'agents virtuels). Nous avons aussi analysé la sensibilité à l'influence sociale des sujets, et trouvé que celle-ci augmente avec la distance entre l'estimation personnelle et l'information sociale. Ces analyses ont permis de définir 5 traits de comportement : garder son opinion, adopter celle des autres, faire un compromis, amplifier l'information sociale ou au contraire la contredire. Nos résultats montrent que les sujets qui adoptent l'opinion des autres sont ceux qui améliorent le mieux leur performance, car ils sont capables de bénéficier de l'information apportée par les agents virtuels. Nous avons ensuite utilisé ces analyses pour construire et calibrer un modèle d'estimation collective, qui reproduit quantitativement les résultats expérimentaux et prédit qu'une quantité limitée d'information incorrecte peut contrebalancer un biais cognitif des sujets consistant à sous-estimer les quantités, et ainsi améliorer la performance collective. D'autres expériences ont permis de valider cette prédiction. Dans une seconde série d'expériences, des groupes de 22 piétons devaient se séparer en clusters de la même "couleur", sans indice visuel (les couleurs étaient inconnues), après une courte période de marche aléatoire. Pour les aider à accomplir leur tâche, nous avons utilisé un système de filtrage de l'information disponible (analogue à un dispositif sensoriel tel que la rétine), prenant en entrée l'ensemble des positions et couleurs des individus, et retournant un signal sonore aux sujets (émit par des tags attachés à leurs épaules) lorsque la majorité de leurs k plus proches voisins était de l'autre couleur que la leur. La règle consistait à s'arrêter de marcher lorsque le signal stoppait. Nous avons étudié l'impact de diverses valeurs de k sur le temps et la qualité de la ségrégation, définie comme le nombre de clusters à l'instant final, par analogie avec les phénomènes de séparation de phase (une ségrégation "parfaite" correspondant à la formation de deux clusters bien distincts). Nous avons trouvé que le temps de ségrégation est optimisé pour k = 7 ~ 9, et que la qualité de la ségrégation augmente avec k jusqu'à k = 7 ~ 9 également, valeur au-delà de laquelle elle sature. Notre dispositif nous a également permis d'enregistrer les positions des piétons durant les expériences, ce qui nous a permis de caractériser et modéliser les interactions des piétons avec le bord de l'arène et entre eux durant la marche aléatoire. À l'aide d'une procédure de minimisation d'erreur, nous avons reconstruit les formes fonctionnelles précises des interactions et construit un modèle fin de mouvement collectif de piétons.In this thesis, we were interested in the impact of the quantity and quality of information ex- changed between individuals in a group on their collective performance in two very specific types of tasks. In a first series of experiments, subjects had to estimate quantities sequentially, and could revise their estimates after receiving the average estimate of other subjects as social information. We controlled this social information through virtual participants (which number we controlled) giving information (which value we controlled), unknowingly to the subjects. We showed that when subjects have little prior knowledge about a quantity to estimate, (the loga- rithms of) their estimates follow a Laplace distribution. Since the median is a good estimator of the center of a Laplace distribution, we defined collective performance as the proximity of the median (log) estimate to the true value. We found that after social influence, and when the information provided by the virtual agents is correct, the collective performance increases with the amount of information provided (fraction of virtual agents). We also analysed subjects' sensitivity to social influence, and found that it increases with the distance between personal estimate and social information. These analyses made it possible to define five behavioral traits: to keep one's opinion, to adopt that of others, to compromise, to amplify social information or to contradict it. Our results showed that the subjects who adopt the opinion of others are the ones who best improve their performance because they are able to benefit from the infor- mation provided by the virtual agents. We then used these analyses to construct and calibrate a model of collective estimation, which quantitatively reproduced the experimental results and predicted that a limited amount of incorrect information can counterbalance a cognitive bias that makes subjects underestimate quantities, and thus improve collective performance. Further experiments have validated this prediction. In a second series of experiments, groups of 22 pedestrians had to segregate into clusters of the same "color", without visual cue (the colors were unknown), after a short period of random walk. To help them accomplish their task, we used an information filtering system (analogous to a sensory device such as the retina), taking all the positions and colors of individuals in input, and returning an acoustic signal to the subjects (emitted by tags attached to their shoulders) when the majority of their k nearest neighbors was of a different color from theirs. The rule was to stop walking when the signal stopped. We studied the impact of various values of k on segregation time and quality, defined as the number of clusters at final time, by analogy with phase separation phenomena (a segregation was considered "perfect" when two distinct clusters were formed). We found that segregation time is optimized for k = 7 ~ 9, and that segregation quality increases with k up to k = 7 ~ 9 as well, value beyond which it saturates. Our device has also allowed us to record the positions of the pedestrians during the experiments, which allowed us to characterize and model the interactions of pedestrians with the border of the arena and between them during the random walk phase. Using an error minimization procedure, we were able to reconstruct the precise functional forms of the interactions and built a fine model of collective pedestrian motion

The topological fortress of termites

appeared in LNCS 5151: Bio-Inspired Computing and Communication, First Workshop on Bio-Inspired Design of Networks, BIOWIRE 2007, Cambridge, UK, April 2-5, 2007, Revised Selected Papers. Eds: P Lió, E Crowcroft, D C Verma. Springer Verlag.International audienceTermites are known for building some of the most elaborate architectures observed in the animal world. We here analyse some topological properties of three dimensional networks of galleries built by termites of the genus Cubitermes. These networks are extremely sparse, in spite of the fact that there is no building cost associated with higher connectivity. In addition, more “central” vertices (in term of betweenness or degree) are preferentially localised at spatial positions far from the external nest walls (more than in a null network model calibrated to exactly the same spatial arrangement of vertices). We argue that both sparseness and the particular spatial location of “central” vertices may be adaptive, because they provide an ecological advantage for nest defence against the attacks from other insects