Relevant bibliographies by topics / Collective motion

Academic literature on the topic 'Collective motion'

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Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Collective motion"

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Vicsek, Tamás, and Anna Zafeiris. "Collective motion." Physics Reports 517, no. 3-4 (August 2012): 71–140. http://dx.doi.org/10.1016/j.physrep.2012.03.004.

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Rosensteel, G., and J. Troupe. "Nonlinear collective motion." Journal of Physics G: Nuclear and Particle Physics 25, no. 3 (January 1, 1999): 549–56. http://dx.doi.org/10.1088/0954-3899/25/3/007.

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Horiuchi, Noriaki. "Quantum collective motion." Nature Photonics 7, no. 6 (May 30, 2013): 422–23. http://dx.doi.org/10.1038/nphoton.2013.142.

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Charlesworth, Henry J., and Matthew S. Turner. "Intrinsically motivated collective motion." Proceedings of the National Academy of Sciences 116, no. 31 (July 17, 2019): 15362–67. http://dx.doi.org/10.1073/pnas.1822069116.

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Collective motion is found in various animal systems, active suspensions, and robotic or virtual agents. This is often understood by using high-level models that directly encode selected empirical features, such as coalignment and cohesion. Can these features be shown to emerge from an underlying, low-level principle? We find that they emerge naturally under future state maximization (FSM). Here, agents perceive a visual representation of the world around them, such as might be recorded on a simple retina, and then move to maximize the number of different visual environments that they expect to be able to access in the future. Such a control principle may confer evolutionary fitness in an uncertain world by enabling agents to deal with a wide variety of future scenarios. The collective dynamics that spontaneously emerge under FSM resemble animal systems in several qualitative aspects, including cohesion, coalignment, and collision suppression, none of which are explicitly encoded in the model. A multilayered neural network trained on simulated trajectories is shown to represent a heuristic mimicking FSM. Similar levels of reasoning would seem to be accessible under animal cognition, demonstrating a possible route to the emergence of collective motion in social animals directly from the control principle underlying FSM. Such models may also be good candidates for encoding into possible future realizations of artificial “intelligent” matter, able to sense light, process information, and move.
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Felderhof, B. U. "Collective motion in ferrofluids." Journal of Physics: Conference Series 392 (December 11, 2012): 012001. http://dx.doi.org/10.1088/1742-6596/392/1/012001.

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Troupe, J., and G. Rosensteel. "Algebraic Nonlinear Collective Motion." Annals of Physics 270, no. 1 (November 1998): 126–54. http://dx.doi.org/10.1006/aphy.1998.5858.

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Dönau, Friedrich. "Dynamics of collective motion." Nuclear Physics A 520 (December 1990): c437—c449. http://dx.doi.org/10.1016/0375-9474(90)91166-o.

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Bertsch, G. F. "Large amplitude collective motion." Nuclear Physics A 574, no. 1-2 (July 1994): 169–83. http://dx.doi.org/10.1016/0375-9474(94)90044-2.

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Nabeel, Arshed, and Danny Raj Masila. "Disentangling intrinsic motion from neighborhood effects in heterogeneous collective motion." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 6 (June 2022): 063119. http://dx.doi.org/10.1063/5.0093682.

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Most real-world collectives, including animal groups, pedestrian crowds, active particles, and living cells, are heterogeneous. The differences among individuals in their intrinsic properties have emergent effects at the group level. It is often of interest to infer how the intrinsic properties differ among the individuals based on their observed movement patterns. However, the true individual properties may be masked by the nonlinear interactions in the collective. We investigate the inference problem in the context of a bidisperse collective with two types of agents, where the goal is to observe the motion of the collective and classify the agents according to their types. Since collective effects, such as jamming and clustering, affect individual motion, the information in an agent’s own movement is insufficient for accurate classification. A simple observer algorithm, based only on individual velocities, cannot accurately estimate the level of heterogeneity of the system and often misclassifies agents. We propose a novel approach to the classification problem, where collective effects on an agent’s motion are explicitly accounted for. We use insights about the phenomenology of collective motion to quantify the effect of the neighborhood on an agent’s motion using a neighborhood parameter. Such an approach can distinguish between agents of two types, even when their observed motion is identical. This approach estimates the level of heterogeneity much more accurately and achieves significant improvements in classification. Our results demonstrate that explicitly accounting for neighborhood effects is often necessary to correctly infer intrinsic properties of individuals.
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Okolowicz, J., J. M. Irvine, and J. Nemeth. "Nuclear temperatures and collective motion." Journal of Physics G: Nuclear Physics 11, no. 6 (June 1985): 721–34. http://dx.doi.org/10.1088/0305-4616/11/6/009.

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Dissertations / Theses on the topic "Collective motion"

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Park, Jinha. "Collective Motion in 3D and Hysteresis." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-156424.

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Many animal groups in nature exhibit collective motion, such as bird flocks and fish schools. The group's internal motion is complex and dynamic, but the group moves cohesively in the same direction. The mechanism behind this collective motion has been studied with various ways. Mathematical modeling provides powerful tools to study this. In particular, individual based, self-propelled particle (SPP) models are among the most popular. Several SPP models have been developed using different local interaction rules between each individual and its neighbours. Among them, the local attraction model is one of the simplest using only attraction as social force. This model is particularly interesting because it can produce a figure of eight pattern in two dimensional space. By studying the local attraction model, I investigate the hysteresis properties in simulated collective motions. I also extend the model to three dimensional space by including additional global attraction forces. In the three dimensional local attraction model, I find an analog of the figure of eight found in the two dimensional model as well as other types of internal dynamics. This present study demonstrates the importance of attraction as a social force and the usefulness of the local attraction model in describing collective motion both in two and three dimensions.
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Strömbom, Daniel. "Attraction Based Models of Collective Motion." Doctoral thesis, Uppsala universitet, Matematiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-205875.

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Animal groups often exhibit highly coordinated collective motion in a variety of situations. For example, bird flocks, schools of fish, a flock of sheep being herded by a dog and highly efficient traffic on an ant trail. Although these phenomena can be observed every day all over the world our knowledge of what rules the individual's in such groups use is very limited. Questions of this type has been studied using so called self-propelled particle (SPP) models, most of which assume that collective motion arises from individuals aligning with their neighbors. Here we introduce and analyze a SPP-model based on attraction alone. We find that it produces all the typical groups seen in alignment-based models and some novel ones. In particular, a group that exhibits collective motion coupled with non-trivial internal dynamics. Groups that have this property are rarely seen in SPP-models and we show that even when a repulsion term is added to the attraction only model such groups are still present. These findings suggest that an interplay between attraction and repulsion may be the main driving force in real flocks and that the alignment rule may be superfluous. We then proceed to model two different experiments using the SPP-model approach. The first is a shepherding algorithm constructed primarily to model experiments where a sheepdog is herding a flock of sheep. We find that in addition to modeling the specific experimental situation well the algorithm has some properties which may make it useful in more general shepherding situations. The second is a traffic model for leaf-cutting ants bridges. Based on earlier experiments a set of traffic rules for ants on a very narrow bridge had been suggested. We show that these are sufficient to produce the observed traffic dynamics on the narrow bridge. And that when extended to a wider bridge by replacing 'Stop' with 'Turn' the new rules are sufficient to produce several key characteristics of the dynamics on the wide bridge, in particular three-lane formation.
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Miller, Adam Morrison. "Simulating collective motion from particles to birds." Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/80148/.

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The main work of this thesis is the construction of a 3D computer model of animal flocking based on vision. The model took an additional input, to those usually considered in tradition models: the projection of all other flock members on to an individual's field of view. Making 2D models is easy (in fact 4 new ones are included in this thesis), but we should be drawing parallels with experimental data for behaviour in animal systems and we should be cautious indeed when drawing conclusions, based on those models. It is common in the literature not to compare model behaviours with measurable quantities of natural flocks. However this work makes a concerted effort to do so in the case of the 3D model. A direct comparison was made in this work between the simulations and an empirical study of starling flocks, of the scaling behaviour of the maximum distance through the flock and the number of flock members, for which the agreement was very good. Other flock properties were compared with the natural flocks, but with less satisfactory results. A careful literature survey was made to investigate and ultimately support the biological plausibility of the 3D projection model. Biological and physiological plausibility is a factor not often considered by computational modellers. A series of novel and related 2D computer flocking models were investigated with hopes to find a single flocking rule that could manifest the most important features of collective motion and thereby be highly parsimonious. The final part of this thesis concerns a 2D computer model of photothermophoresis based on langevin dynamics, which it may be possible to use to find evidence of a density transition found in the continuum model. There was some evidence that a transition from a transparent diffuse state to an opaque compact one may exist for the discrete particle simulation.
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Jiang, Li. "Mechanisms and roles of information processing in collective motion." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30125.

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Le déplacement collectif est l'un des phénomènes les plus remarquables de la nature. Il a été observé pour de nombreuses espèces animales, comme les essaims de bactéries, l'agrégation des fourmis, les bancs de poissons, les nuées d'oiseaux ou encore les foules d'humains. Ces comportements collectifs d'animaux ne sont pas seulement des scènes spectaculaires mais sont aussi une source d'intérêt pour explorer les mécanismes sous-jacents dans le but de comprendre les lois et l'évolution des groups biologiques ou même de nous aider à élaborer des essaims auto-organisés de robots.Nous avons étudié différents systèmes de déplacements collectifs, incluant des systèmes avec une seule espèce, comme les bancs de poissons et les foules d'humains et d'autres à plusieurs espèces, comme les systèmes de fuite et poursuite en groupe. Parmi lesquels, nous nous concentrons sur les mécanismes et les rôles du traitement de l'information sur les motifs macroscopiques. De plus, pour palier à la difficulté d'extraire des trajectoires depuis des vidéos expérimentales de qualité médiocre, nous proposons un outil rapide et robuste de suivi du déplacement. Notre contenu de recherche détaillé est le suivant : 1. Nous avons étudié les mécanismes de traitement de l'information dans les déplacements du Nez-rouge dans un dispositive annulaire. Pour la première fois, nous avons définis un comportement particulier aux bancs de poissons : des évènements de demi-tour. En introduisant un délai entre l'interaction entre les poissons, nous avons trouvé qu'à un poisson d'intérêt correspondent seulement un ou deux poissons qui ne sont pas nécessairement les plus proches. De plus, nous avons montré que l'information de tourner pendant un évènement de demi-tour collectif se propage comme des dominos. Enfin, nous avons utilisé le transfert d'entropie pour quantifier les flux d'information dans l'espace et le temps durant les évènements de demi-tour. 2. Nous avons étudié le rôle d'une perturbation dans un système de foule humaine en plaçant des obstacles (les perturbations) dans un flux de fuite de panique. Nous avons trouvé une façon simple et efficace d'augmenter le flux de fuite dans le but de sauver plus de vies dans des situations dangereuses. Nous avons appliqué des algorithmes génétiques pour optimiser l'agencement des piliers dans les simulations puis nous avons testé la qualité de ces résultats contre des expériences avec de vrais humains. Les résultats suggèrent que placer deux piliers le long des deux côtés d'une sortie peut maximiser la vitesse de sortie. 3. Nous avons étudié le rôle des mécanismes de traitement de l'information dans les déplacements collectifs multi-espèces en introduisant différentes strategies pour les proies dans un modèle de poursuite en groupe. Nous proposons trois stratégies d'agrégation : se déplacer vers le centre de masse de toutes les proies (MC), se déplacer vers la proie la plus proche (NN) et minimiser la distance totale entre toutes les proies (MD). Les résultats montrent que l'agrégation augmente grandement la durée de survie du groupe, et ceci même en autorisant les proies à être immortelles. Il y a une transition de phase de t (la durée de survie moyenne) en fonction de M (le nombre de prédateurs). 4. Nous avons développé un nouvel outil de suivi de déplacement pour améliorer les algorithmes de reconnaissance d'image et de suivi actuels afin d'extraire des trajectoires depuis des vidéos de qualité médiocre. Notre outil intègre un filtre de moyenne glissante, la soustraction du bruit de fond, des réseaux de neurones artificiels, du partitionnement en k-moyennes et une fonction d'erreur définie minutieusement. L'outil peut extraire une trajectoire depuis une video de basse qualité qui ne peut être fait que très difficilement par d'autres outils. Il peut suivre plusieurs animaux comme des poissons, des drosophiles, des fourmis, etc. Les performances de notre outil sont meilleures que idTracker et Ctrax
Collective motion is one of the most striking phenomena in nature. It has been observed in a lot of animal species, such as bacteria, ants, fish, flocks of birds and crowds of human. These collective animal behaviors not only show us spectacular scenes, but also attract us to explore the underlying mechanisms in order to understand the laws and evolution of biological groups and even help us design smarter self-organizing robots. We study different collective motion systems including single species systems such as fish school and human crowd; and multi-species group chase and escape system. Among which, we focus on the mechanisms and roles of information processing on macro patterns. Moreover, regarding to the fact that it's very difficult to extract trajectory data from low quality experiment videos, we propose a fast and robust tracking tool. Details are as follows: 1. We study the mechanisms of information processing in the movements of Hemigrammus rhodostomus in a ring-shaped tank. For the first time, we define a special behavior of fish school: U-turn event. By introducing time delay between fish interaction, we find that a focal fish usually corresponds to only 1 or 2 fish which is not necessarily the nearest one. Moreover, we find the turning information during a group U-turn event propagates like domino. In addition, we use transfer entropy to quantify dynamic information flows in space and time across the U-turn events. 2. We study the role of perturbation information in human crowd system by introducing obstacles as perturbation information into a panic escaping flow. We find a useful and simple way to increase the panic flow in order to save more lives under dangerous situation. We apply genetic algorithms to optimize the layout of pillars in the simulations and then test the results with real human experiments. Results show that putting two pillars along the two sides of the exit can maximize the escape velocity. In the end, a tangential momentum theory is proposed to explain the role of the perturbation information. 3. We study the role of information processing mechanisms in multi-species collective motion by introducing different strateg?ies for the prey in a group chase model. We propose three aggregation strategies: moving to mass center of all preys, moving to the nearest prey and minimising the total distance to all preys. Results show that aggregation increase the group survival time greatly, even allowing immortal prey. There is a phase transition of t (average survival time) against M (number of predator). 4. We developed a new tracking tool to improve the current image recognizing and video tracking algorithms so as to extract trajectories from low quality videos. Our tool integrates mean-value filter, background substraction, artificial neural network, K-means clustering and a well defined cost function. It can track low quality videos which can be hardly tracked by other tools. And it can track different animals such as fish, drosophila, ants and so on. The overall tracking performance is better than idTracker and Ctrax
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Menezes, Debora Peres. "Boson mapping techniques and the nuclear collective motion." Thesis, University of Oxford, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.329926.

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Janes, Jen. "The Texas chainsaw massacre: our collective nightmare." [Denver, Colo.] : Regis University, 2008. http://165.236.235.140/lib/JJanes2008.pdf.

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Zhou, Felix. "Phenotyping cellular motion." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:9fb6a57d-2e16-43c9-92e6-895330353e51.

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In the development of multicellular organisms, tissue development and homeostasis require coordinated cellular motion. For example, in conditions such as wound healing, immune and epithelial cells need to proliferate and migrate. Deregulation of key signalling pathways in pathological conditions causes alterations in cellular motion properties that are critical for disease development and progression, in cancer it leads to invasion and metastasis. Consequently there is strong interest in identifying factors, including drugs that affect the motion and interactions of cells in disease using experimental models suitable for high-content screening. There are two main modes of cell migration; individual and collective migration. Currently analysis tools for robust, sensitive and comprehensive motion characterisation in varying experimental conditions for large extended timelapse acquisitions that jointly considers both modes are limited. We have developed a systematic motion analysis framework, Motion Sensing Superpixels (MOSES) to quantitatively capture cellular motion in timelapse microscopy videos suitable for high-content screening. MOSES builds upon established computer vision approaches to deliver a minimal parameter, robust algorithm that can i) extract reliable phenomena-relevant motion metrics, ii) discover spatiotemporal salient motion patterns and iii) facilitate unbiased analysis with little prior knowledge through unique motion 'signatures'. The framework was validated by application to numerous datasets including YouTube videos, zebrafish immunosurveillance and Drosophila embryo development. We demonstrate two extended applications; the analysis of interactions between two epithelial populations in 2D culture using cell lines of the squamous and columnar epithelia from human normal esophagus, Barrett's esophagus and esophageal adenocarcinoma and the automatic monitoring of 3D organoid culture growth captured through label-free phase contrast microscopy. MOSES found unique boundary formation between squamous and columnar cells and could measure subtle changes in boundary formation due to external stimuli. MOSES automatically segments the motion and shape of multiple organoids even if present in the same field of view. Automated analysis of intestinal organoid branching following treatment agrees with independent RNA-seq results.
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Eriksson, Markus. "Spatial sorting and collective motion in mixed shoals of fish." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-266207.

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Shoaling behaviour arises when fish respond to the movements and positions of nearby neighbours. The dynamic patterns of shoaling fish has been studied by the Mathematical department in Uppsala University. In this project experimental data collected for groups with two sizes of fish are analysed. An existing model was modified to reproduce the dynamic patterns in the fish shoal, this was done by comparing visual and statistical properties from the simulations with experimental observations. By analysing the impact of the parameters in the model it was found out that introducing limitations in the vision of the smaller fish are essential to be able to reproduce the behaviour of the mixed sized fish shoal. The limitations in the vision are speculated to be a representation of physiological limitations in the coordination of mechano-sensoric activities and visual information.
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Oyama, Norihiro. "Direct Numerical Calculation on the Collective Motion of Model Microswimmers." 京都大学 (Kyoto University), 2017. http://hdl.handle.net/2433/225640.

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Cohen, Joanna (Joanna Renee). "Models and simulations of collective motion in biomimetic robots and bacteria." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/39872.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.
Includes bibliographical references (p. 119-124).
In nature, one finds many examples of collective motion, from flocking birds to swarming bees. Any one organism makes its decisions based solely on local information; either it can sense what its close neighbors are doing, or in the case of a single-celled organism, it can sense some local property of its environment. Yet complex global behaviors arise from these local interactions, and these large-scale patterns have neither a leader nor any other centralized control system. In this thesis, two specific cases of collective motion are studied: fish schooling and bacteria swimming across a surface. When fish swim in schools, they swim in the same direction as each other at approximately the same speed. Previous studies of fish have discovered three primary behaviors that, together, lead to large-scale coordination and schooling in the animals. This thesis demonstrates that the same algorithms can be applied to a group of identical underwater robots. If the robots need to coordinate with each other, they can use biomimetic control laws and adopt the interaction algorithms used by fish. A series of simulations are run to see what possible group behaviors can come from these control laws. At a smaller scale, prior experiments have revealed that bacteria and other small organisms also show collective motion.
(cont.) Unlike fish, bacteria cannot see their neighbors; the individual can only sense the bulk contribution of its neighbors to the flow at its location. The single-celled organisms are small and swim slowly, so they have very small Reynolds numbers. They are modeled in this work in a Stokes flow regime; the model is built bottom-up starting from the hydrodynamic field created by one organism and then superimposing these fields on top of each other. Different possible control policies are tested where each organism has an instantaneous desired direction based on some local property of the flow. While simulations of the current model do not yield results that fully emulate real bacteria, they have some similarities and provide insight into the complex hydrodynamic interactions between low Reynolds number swimmers.
Joanna Cohen.
S.M.
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Books on the topic "Collective motion"

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Nuclear collective motion: Models and theory. New Jersey: World Scientific, 2010.

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Melhuish, C. R. Strategies for collective minimalist mobile robots. London: Professional Engineering, 2001.

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Matti, Krusius, ed. Collective motion of quantized vortex lines in rotating superfluid ³He-B. Otaniemi [Finland]: Helsinki University of Technology, Low Temperature Laboratory, 1992.

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Řehořová, Irena. Kulturní paměť a film: Jak se měnil obraz poválečného odsunu v české filmové tvorbě. Praha: SLON, Sociologické nakladatelství, 2018.

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Film és kollektív emlékezet: Magyar múltfilmek a rendszerváltozás után. Szombathely: Savaria University Press, 2008.

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L'art des foules: Théories de la réception filmique comme phénomène collectif en France. Villeneuve-d'Ascq: Presses universitaires du Septentrion, 2011.

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Ben-Moshe, Yael. Hitler konstruieren: Die Darstellung Adolf Hitlers in deutschen und amerikanischen Spielfilmen 1945-2009 : eine Analyse zur Formung kollektiver Erinnerung. Leipzig: Leipziger Universitätsverlag, 2012.

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Plasseraud, Emmanuel. L'art des foules: Théories de la réception filmique comme phénomène collectif en France. Villeneuve-d'Ascq: Presses universitaires du Septentrion, 2011.

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Nosa hyŏbyak ihu yŏnghwa chejak hyŏnjang pyŏnhwa mit kaesŏn panghyang yŏn'gu. Sŏul T'ŭkpyŏlsi: Yŏnghwa Chinhŭng Wiwŏnhoe, 2008.

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L'attrait de l'oubli. Crisnée, Belgique: Yellow Now, 2017.

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Book chapters on the topic "Collective motion"

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Kamimura, Atsushi, and Toru Ohira. "Collective Motion." In Theoretical Biology, 25–41. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1731-0_3.

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Brink, D. M. "Nuclear Collective Motion." In Nuclear Physics at the Borderlines, 15–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-84708-0_2.

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Patel, Darshana Chandrakant. "Theory of Collective Motion." In A Study of the Isoscalar Giant Monopole Resonance, 17–26. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22207-3_2.

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Greiner, Walter, and Joachim A. Maruhn. "Large-Amplitude Collective Motion." In Nuclear Models, 317–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-60970-1_9.

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Ren, Wei, and Yongcan Cao. "Collective Periodic Motion Coordination." In Communications and Control Engineering, 45–75. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-169-1_3.

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Nazarewicz, Witold. "The nuclear collective motion." In An Advanced Course in Modern Nuclear Physics, 102–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44620-6_4.

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Li, Z. P., and D. Vretenar. "Model for Collective Motion." In Handbook of Nuclear Physics, 1–33. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-15-8818-1_11-1.

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Chaté, Hugues, and Guillaume Grégoire. "Forms Emerging from Collective Motion." In Morphogenesis, 211–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13174-5_12.

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Sepulchre, Rodolphe, Derek Paley, and Naomi Leonard. "Collective Motion and Oscillator Synchronization." In Cooperative Control, 189–205. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-31595-7_11.

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Nishiguchi, Daiki. "Standard Models on Collective Motion." In Springer Theses, 9–43. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-32-9998-6_2.

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Conference papers on the topic "Collective motion"

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Raduta, A. A., D. S. Delion, and I. I. Ursu. "COLLECTIVE MOTION AND NUCLEAR DYNAMICS." In Predeal lnternational Summer School. WORLD SCIENTIFIC, 1996. http://dx.doi.org/10.1142/9789814531542.

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Chazelle, Bernard, and Kritkorn Karntikoon. "Quick Relaxation in Collective Motion." In 2022 IEEE 61st Conference on Decision and Control (CDC). IEEE, 2022. http://dx.doi.org/10.1109/cdc51059.2022.9992475.

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Nam, Woochul, and Bogdan I. Epureanu. "Collective Transport by Multiple Molecular Motors." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71226.

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Kinesin is a processive molecular motor that transports various cellular cargoes by converting chemical energy into mechanical movements. Although the motion of a single molecule has been characterized in several studies, the dynamics of collective transports remains unresolved. Since the fluctuating load acting on each motor is an important factor in the collective transport, the relation between the varying force and the chemical reaction of kinesin is considered using a stochastic mechanistic model. Several metrics are developed to measure the correlation among the motion of the motors, the force distribution, and the power loss. It is shown that both large external load and stiff cargo linkers cause highly correlated motions of motors. However, these correlated motions do not lead to faster collective transport.
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Klein, D. J., and K. A. Morgansen. "Controlled collective motion for trajectory tracking." In 2006 American Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/acc.2006.1657560.

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Paley, D., N. E. Leonard, and R. Sepulchre. "Collective motion: bistability and trajectory tracking." In 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601). IEEE, 2004. http://dx.doi.org/10.1109/cdc.2004.1430330.

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Lu, Wanting, Hui Gao, and Mingxiang Dai. "Collective four-group antagonistic formation motion." In 2014 33rd Chinese Control Conference (CCC). IEEE, 2014. http://dx.doi.org/10.1109/chicc.2014.6896815.

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Chen, Ge. "Small noise may diversify collective motion." In 2015 34th Chinese Control Conference (CCC). IEEE, 2015. http://dx.doi.org/10.1109/chicc.2015.7259827.

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Mwaffo, Violet, David McLeod, Enrico Fonda, and Maurizio Porfiri. "Poster: Visualization of a collective motion." In 69th Annual Meeting of the APS Division of Fluid Dynamics. American Physical Society, 2016. http://dx.doi.org/10.1103/aps.dfd.2016.gfm.p0053.

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Frauendorf, S. "Open questions on nuclear collective motion." In THERMOPHYSICS 2016: 21st International Meeting. Author(s), 2016. http://dx.doi.org/10.1063/1.4955342.

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Yamaji, Shuhei. "Microscopic description of damped collective motion." In Tours symposium on nuclear physics IV. AIP, 2001. http://dx.doi.org/10.1063/1.1372816.

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Reports on the topic "Collective motion"

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Ghose, Debasish. Control Strategies for Guided Collective Motion. Fort Belvoir, VA: Defense Technical Information Center, January 2015. http://dx.doi.org/10.21236/ada617908.

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Dr. Boris Fain. Collective motion sampling in proteins and DNA. Office of Scientific and Technical Information (OSTI), February 2000. http://dx.doi.org/10.2172/765120.

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Kartavenko, V. G. Large amplitude collective nuclear motion and soliton concept. Office of Scientific and Technical Information (OSTI), December 1993. http://dx.doi.org/10.2172/10127753.

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Klein, A. Theoretical research in nuclear structure and nuclear collective motion. Office of Scientific and Technical Information (OSTI), January 1990. http://dx.doi.org/10.2172/7142155.

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Klein, A. Theoretical research in nuclear structure and nuclear collective motion. Office of Scientific and Technical Information (OSTI), January 1992. http://dx.doi.org/10.2172/5658778.

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Oberacker, V. E., A. S. Umar, J. C. Wells, M. R. Strayer, J. A. Maruhn, and P. G. Reinhard. Prompt muon-induced fission: A probe for nuclear friction in large-amplitude collective motion. Office of Scientific and Technical Information (OSTI), January 1998. http://dx.doi.org/10.2172/634104.

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Klein, A. Theoretical research in nuclear structure and nuclear collective motion. Progress report, March 1, 1991--February 29, 1992. Office of Scientific and Technical Information (OSTI), January 1992. http://dx.doi.org/10.2172/10132652.

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Mazzoni, Silvia, Nicholas Gregor, Linda Al Atik, Yousef Bozorgnia, David Welch, and Gregory Deierlein. Probabilistic Seismic Hazard Analysis and Selecting and Scaling of Ground-Motion Records (PEER-CEA Project). Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/zjdn7385.

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This report is one of a series of reports documenting the methods and findings of a multi-year, multi-disciplinary project coordinated by the Pacific Earthquake Engineering Research Center (PEER) and funded by the California Earthquake Authority (CEA). The overall project is titled “Quantifying the Performance of Retrofit of Cripple Walls and Sill Anchorage in Single-Family Wood-Frame Buildings,” henceforth referred to as the “PEER–CEA Project.” The overall objective of the PEER–CEA Project is to provide scientifically based information (e.g., testing, analysis, and resulting loss models) that measure and assess the effectiveness of seismic retrofit to reduce the risk of damage and associated losses (repair costs) of wood-frame houses with cripple wall and sill anchorage deficiencies as well as retrofitted conditions that address those deficiencies. Tasks that support and inform the loss-modeling effort are: (1) collecting and summarizing existing information and results of previous research on the performance of wood-frame houses; (2) identifying construction features to characterize alternative variants of wood-frame houses; (3) characterizing earthquake hazard and ground motions at representative sites in California; (4) developing cyclic loading protocols and conducting laboratory tests of cripple wall panels, wood-frame wall subassemblies, and sill anchorages to measure and document their response (strength and stiffness) under cyclic loading; and (5) the computer modeling, simulations, and the development of loss models as informed by a workshop with claims adjustors. This report is a product of Working Group 3 (WG3), Task 3.1: Selecting and Scaling Ground-motion records. The objective of Task 3.1 is to provide suites of ground motions to be used by other working groups (WGs), especially Working Group 5: Analytical Modeling (WG5) for Simulation Studies. The ground motions used in the numerical simulations are intended to represent seismic hazard at the building site. The seismic hazard is dependent on the location of the site relative to seismic sources, the characteristics of the seismic sources in the region and the local soil conditions at the site. To achieve a proper representation of hazard across the State of California, ten sites were selected, and a site-specific probabilistic seismic hazard analysis (PSHA) was performed at each of these sites for both a soft soil (Vs30 = 270 m/sec) and a stiff soil (Vs30=760 m/sec). The PSHA used the UCERF3 seismic source model, which represents the latest seismic source model adopted by the USGS [2013] and NGA-West2 ground-motion models. The PSHA was carried out for structural periods ranging from 0.01 to 10 sec. At each site and soil class, the results from the PSHA—hazard curves, hazard deaggregation, and uniform-hazard spectra (UHS)—were extracted for a series of ten return periods, prescribed by WG5 and WG6, ranging from 15.5–2500 years. For each case (site, soil class, and return period), the UHS was used as the target spectrum for selection and modification of a suite of ground motions. Additionally, another set of target spectra based on “Conditional Spectra” (CS), which are more realistic than UHS, was developed [Baker and Lee 2018]. The Conditional Spectra are defined by the median (Conditional Mean Spectrum) and a period-dependent variance. A suite of at least 40 record pairs (horizontal) were selected and modified for each return period and target-spectrum type. Thus, for each ground-motion suite, 40 or more record pairs were selected using the deaggregation of the hazard, resulting in more than 200 record pairs per target-spectrum type at each site. The suites contained more than 40 records in case some were rejected by the modelers due to secondary characteristics; however, none were rejected, and the complete set was used. For the case of UHS as the target spectrum, the selected motions were modified (scaled) such that the average of the median spectrum (RotD50) [Boore 2010] of the ground-motion pairs follow the target spectrum closely within the period range of interest to the analysts. In communications with WG5 researchers, for ground-motion (time histories, or time series) selection and modification, a period range between 0.01–2.0 sec was selected for this specific application for the project. The duration metrics and pulse characteristics of the records were also used in the final selection of ground motions. The damping ratio for the PSHA and ground-motion target spectra was set to 5%, which is standard practice in engineering applications. For the cases where the CS was used as the target spectrum, the ground-motion suites were selected and scaled using a modified version of the conditional spectrum ground-motion selection tool (CS-GMS tool) developed by Baker and Lee [2018]. This tool selects and scales a suite of ground motions to meet both the median and the user-defined variability. This variability is defined by the relationship developed by Baker and Jayaram [2008]. The computation of CS requires a structural period for the conditional model. In collaboration with WG5 researchers, a conditioning period of 0.25 sec was selected as a representative of the fundamental mode of vibration of the buildings of interest in this study. Working Group 5 carried out a sensitivity analysis of using other conditioning periods, and the results and discussion of selection of conditioning period are reported in Section 4 of the WG5 PEER report entitled Technical Background Report for Structural Analysis and Performance Assessment. The WG3.1 report presents a summary of the selected sites, the seismic-source characterization model, and the ground-motion characterization model used in the PSHA, followed by selection and modification of suites of ground motions. The Record Sequence Number (RSN) and the associated scale factors are tabulated in the Appendices of this report, and the actual time-series files can be downloaded from the PEER Ground-motion database Portal (https://ngawest2.berkeley.edu/)(link is external).
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Briggs, Michael J., Stephen T. Maynord, Charles R. Nickles, and Terry N. Waller. Charleston Harbor Ship Motion Data Collection and Squat Analysis. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada457976.

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Darbha, Swaroop, Sivakumar Rathinam, and K. R. Rajagopal. Combinatorial Motion Planning Algorithms for a Heterogeneous Collection of Unmanned Vehicles. Fort Belvoir, VA: Defense Technical Information Center, October 2013. http://dx.doi.org/10.21236/ada590747.

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