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Academic literature on the topic 'Diffusion cascades'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Diffusion cascades"

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Han, Jinyoung, Daejin Choi, Jungseock Joo, and Chen-Nee Chuah. "Predicting Popular and Viral Image Cascades in Pinterest." Proceedings of the International AAAI Conference on Web and Social Media 11, no. 1 (May 3, 2017): 82–91. http://dx.doi.org/10.1609/icwsm.v11i1.14879.

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The word-of-mouth diffusion has been regarded as an important mechanism to advertise a new idea, image, technology, or product in online social networks (OSNs). This paper studies the prediction of popular and viral image diffusion in Pinterest. We first characterize an image cascade from two perspectives: (i) volume — how large the cascade is, that is, total number of users reached, and (ii) structural virality — how many users in the cascade are responsible for attracting other users. Our model predicts whether an image will be (a) popular in terms of the volume of its cascade, or (b) viral in terms of the structural virality. Our analysis reveals that a popular image is not necessarily viral, and vice versa. This motivates us to investigate whether there are distinctive features for accurately predicting popular or viral image cascades. To predict the popular or viral image cascades, we consider the following feature sets: (i) deep image features, (ii) image meta and poster's information, and (iii) initial propagation pattern. We find that using deep image features alone is not as effective in predicting popular or viral image cascades. We show that image meta and poster's information are strong predictors for predicting popular image cascades while image meta and initial propagation patterns are useful to predict viral image cascades. We believe our exploration can give an important insight for content providers, OSN operators, and marketers in predicting popular or viral image diffusion.
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Sharma, Karishma, Xinran He, Sungyong Seo, and Yan Liu. "Network Inference from a Mixture of Diffusion Models for Fake News Mitigation." Proceedings of the International AAAI Conference on Web and Social Media 15 (May 22, 2021): 668–79. http://dx.doi.org/10.1609/icwsm.v15i1.18093.

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The dissemination of fake news intended to deceive people, influence public opinion and manipulate social outcomes, has become a pressing problem on social media. Moreover, information sharing on social media facilitates diffusion of viral information cascades. In this work, we focus on understanding and leveraging diffusion dynamics of false and legitimate contents in order to facilitate network interventions for fake news mitigation. We analyze real-world Twitter datasets comprising fake and true news cascades, to understand differences in diffusion dynamics and user behaviours with regards to fake and true contents. Based on the analysis, we model the diffusion as a mixture of Independent Cascade models (MIC) with parameters \theta_T , \theta_F over the social network graph; and derive unsupervised inference techniques for parameter estimation of the diffusion mixture model from observed, unlabeled cascades. Users influential in the propagation of true and fake contents are identified using the inferred diffusion dynamics. Characteristics of the identified influential users reveal positive correlation between influential users identified for fake news and their relative appearance in fake news cascades. Identified influential users tend to be related to topics of more viral information cascades than less viral ones; and identified fake news influential users have relatively fewer counts of direct followers, compared to the true news influential users. Intervention analysis on nodes and edges demonstrates capacity of the inferred diffusion dynamics in supporting network interventions for mitigation.
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BALMFORTH, N. J., and W. R. YOUNG. "Diffusion-limited scalar cascades." Journal of Fluid Mechanics 482 (May 10, 2003): 91–100. http://dx.doi.org/10.1017/s0022112003003914.

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Sun, Ling, Yuan Rao, Xiangbo Zhang, Yuqian Lan, and Shuanghe Yu. "MS-HGAT: Memory-Enhanced Sequential Hypergraph Attention Network for Information Diffusion Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4156–64. http://dx.doi.org/10.1609/aaai.v36i4.20334.

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Predicting the diffusion cascades is a critical task to understand information spread on social networks. Previous methods usually focus on the order or structure of the infected users in a single cascade, thus ignoring the global dependencies of users and cascades, limiting the performance of prediction. Current strategies to introduce social networks only learn the social homogeneity among users, which is not enough to describe their interaction preferences, let alone the dynamic changes. To address the above issues, we propose a novel information diffusion prediction model named Memory-enhanced Sequential Hypergraph Attention Networks (MS-HGAT). Specifically, to introduce the global dependencies of users, we not only take advantages of their friendships, but also consider their interactions at the cascade level. Furthermore, to dynamically capture user' preferences, we divide the diffusion hypergraph into several sub graphs based on timestamps, develop Hypergraph Attention Networks to learn the sequential hypergraphs, and connect them with gated fusion strategy. In addition, a memory-enhanced embedding lookup module is proposed to capture the learned user representations into the cascade-specific embedding space, thus highlighting the feature interaction within the cascade. The experimental results over four realistic datasets demonstrate that MS-HGAT significantly outperforms the state-of-the-art diffusion prediction models in both Hits@K and MAP@k metrics.
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Hellmann, M., D. W. Heermann, and M. Weiss. "Enhancing phosphorylation cascades by anomalous diffusion." EPL (Europhysics Letters) 97, no. 5 (February 22, 2012): 58004. http://dx.doi.org/10.1209/0295-5075/97/58004.

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White, D. A. "Gas diffusion cascades—properties and optimization." Chemical Engineering Science 45, no. 6 (1990): 1567–70. http://dx.doi.org/10.1016/0009-2509(90)80008-3.

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Huang, Ningbo, Gang Zhou, Mengli Zhang, Meng Zhang, and Ze Yu. "Modelling the Latent Semantics of Diffusion Sources in Information Cascade Prediction." Computational Intelligence and Neuroscience 2021 (September 29, 2021): 1–12. http://dx.doi.org/10.1155/2021/7880215.

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Predicting the information spread tendency can help products recommendation and public opinion management. The existing information cascade prediction models are devoted to extract the chronological features from diffusion sequences but treat the diffusion sources as ordinary users. Diffusion source, the first user in the information cascade, can indicate the latent topic and diffusion pattern of an information item to mine user potential common interests, which facilitates information cascade prediction. In this paper, for modelling the abundant implicit semantics of diffusion sources in information cascade prediction, we propose a Diffusion Source latent Semantics-Fused cascade prediction framework, named DSSF. Specifically, we firstly apply diffusion sources embedding to model the special role of the source users. To learn the latent interaction between users and diffusion sources, we proposed a co-attention-based fusion gate which fuses the diffusion sources’ latent semantics with user embedding. To address the challenge that the distribution of diffusion sources is long-tailed, we develop an adversarial training framework to transfer the semantics knowledge from head to tail sources. Finally, we conduct experiments on real-world datasets, and the results show that modelling the diffusion sources can significantly improve the prediction performance. Besides, this improvement is limited for the cascades from tail sources, and the adversarial framework can help.
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Van Rossum, M., and Y. T. Cheng. "Diffusion in Collision Cascades: a Thermodynamic Viewpoint." Defect and Diffusion Forum 57-58 (January 1988): 1–32. http://dx.doi.org/10.4028/www.scientific.net/ddf.57-58.1.

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Rossi, Francois, and N. V. Doan. "Nonlinear effects of diffusion in displacement cascades." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 61, no. 1 (July 1991): 27–37. http://dx.doi.org/10.1016/0168-583x(91)95556-s.

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SMITH, K. S., G. BOCCALETTI, C. C. HENNING, I. MARINOV, C. Y. TAM, I. M. HELD, and G. K. VALLIS. "Turbulent diffusion in the geostrophic inverse cascade." Journal of Fluid Mechanics 469 (October 15, 2002): 13–48. http://dx.doi.org/10.1017/s0022112002001763.

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Motivated in part by the problem of large-scale lateral turbulent heat transport in the Earth's atmosphere and oceans, and in part by the problem of turbulent transport itself, we seek to better understand the transport of a passive tracer advected by various types of fully developed two-dimensional turbulence. The types of turbulence considered correspond to various relationships between the streamfunction and the advected field. Each type of turbulence considered possesses two quadratic invariants and each can develop an inverse cascade. These cascades can be modified or halted, for example, by friction, a background vorticity gradient or a mean temperature gradient. We focus on three physically realizable cases: classical two-dimensional turbulence, surface quasi-geostrophic turbulence, and shallow-water quasi-geostrophic turbulence at scales large compared to the radius of deformation. In each model we assume that tracer variance is maintained by a large-scale mean tracer gradient while turbulent energy is produced at small scales via random forcing, and dissipated by linear drag. We predict the spectral shapes, eddy scales and equilibrated energies resulting from the inverse cascades, and use the expected velocity and length scales to predict integrated tracer fluxes.When linear drag halts the cascade, the resulting diffusivities are decreasing functions of the drag coefficient, but with different dependences for each case. When β is significant, we find a clear distinction between the tracer mixing scale, which depends on β but is nearly independent of drag, and the energy-containing (or jet) scale, set by a combination of the drag coefficient and β. Our predictions are tested via high- resolution spectral simulations. We find in all cases that the passive scalar is diffused down-gradient with a diffusion coefficient that is well-predicted from estimates of mixing length and velocity scale obtained from turbulence phenomenology.
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Dissertations / Theses on the topic "Diffusion cascades"

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Hobbs, J. E. "The use of radiation-enhanced diffusion to study collision cascades in solids." Thesis, University of Salford, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.356167.

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Grove, Darren V. "Experimental and numerical investigation of second-generation, controlled-diffusion, compressor blades in cascade." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA333407.

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Fitoussi, Thomas. "Les cascades électromagnétiques cosmologiques comme sondes du milieu intergalactique." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30235/document.

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Cette thèse vise à étudier le phénomène dit de " cascades électromagnétiques cosmologiques ". Ces cascades sont typiquement générées dans le milieu intergalactique par l'absorption de rayons gamma sur les photons du fond optique / UV et par la production de paires électron / positron associés. Ces leptons eux-mêmes interagissent avec les photons du fond diffus cosmologique via diffusion inverse Compton pour produire de nouveaux rayons gamma qui eux même peuvent s'annihiler, générant à partir d'un unique photon primaire toute une gerbe de photons et de particules secondaires. D'un point de vue observationnel, le développement de cette cascade introduit trois effets : une déformation du spectre à haute énergie, un retard temporel dans l'arrivée des rayons gamma et une extension de la taille apparente de la source. Les cascades électromagnétiques cosmologiques ont commencé à être étudiées dans les années soixante. Mais ce n'est qu'à partir des années 2010 avec l'arrivée du satellite Fermi (entre autres) et des observations dans la bande au GeV et au TeV que la discipline a explosé. Le phénomène est particulièrement important. D'une part il altère le spectre observé des sources rendant difficile la compréhension de la physique de ces dernières. D'autre part les cascades se développant dans le milieu extragalactique, elles sont très sensibles à la composition de ce dernier (fond diffus de photons, champ magnétique). Or ce milieu étant très ténu, il est difficile à étudier. Les cascades deviennent alors une formidable sonde pour accéder à sa compréhension et pouvoir en comprendre l'origine qui remonte au commencement de l'Univers. Pourtant les cascades cosmologiques sont un phénomène complexe faisant intervenir des interactions difficiles à modéliser (sections efficaces complexes) et le transport de particules dans un Univers en expansion (cosmologie). Face à cette complexité les expressions analytiques sont vite limitées et le passage au numérique devient inévitable. Dans le cadre de cette thèse un code de simulation Monte Carlo a donc été développé visant à reproduire aussi précisément que possible le phénomène des cascades. Ce code a été testé et validé en le confrontant aux expressions analytiques. Grâce à ce code, le rôle des différents paramètres physiques impactant le développement de la cascade a été étudié de manière systématique. Cette étude a permis de mieux comprendre la physique du phénomène. En particulier, l'impact des propriétés du milieu extragalactique (fond diffus extragalactique, champ magnétique extragalactique) sur les observables a été mis en évidence. Finalement, une seconde étude a été menée pour mesurer la contribution des cascades au fond gamma extragalactique. Des travaux récents montrent qu'une grande partie de l'émission diffuse à très haute énergie provient de sources ponctuelles non résolues (blazars en particulier). Ces sources gamma (résolues et non résolues) doivent en principe initier des cascades qui peuvent contribuer au fond diffus. En partant d'une modélisation de l'émission des blazars à différents redshifts, l'absorption et la contribution des cascades ont alors été calculées à l'aide du code Monte Carlo. Les résultats montrent que la contribution des cascades au fond gamma extragalactique pourrait violer les limites Fermi mais l'excès doit encore être confirmé
This thesis aims at studying "cosmological electromagnetic cascades". These cascades are initiated by the absorption of very high energy gamma-rays through gamma-gamma annihilation with optical / UV background photons of the intergalactic medium. In this interaction, electron/positron pairs are produced. The newly created leptons interact with photons of the Cosmological Microwave Background producing new gamma-rays through inverse Compton scattering which can also annihilate producing a cascade of secondary particles from a single primary photon. Observationally, the development of this cascade has three effects : the observed high energy spectrum is altered, observed photons arrive with a time delay with respect to primary photons and the source appears extended. Cosmological electromagnetic cascades start to being studied in the early sixties. But it is during the 2010's with the Fermi satellite and GeV to TeV observations that the field has really started to being explored. In the fast evolving backgound of gamma-ray astronomy, understanding the cascade physics has become a crucial stake. First the observed spectrum from a distant source is altered, which directly affects the modelling of high energy sources. Secondly, the cascades develop in the extragalactic medium and are very sensitive to its composition (background light, magnetic field). This medium is hard to study because it is extremely thin. Hence the cosmological cascades are a formidable probe to access its comprehension and its origin coming from the very beginning of our Universe. Yet the cosmological cascades are a complex phenomenon which involves complicated interactions (complex cross sections) and transport of particles in an expanding Universe. Analytical expressions are rapidly limited and numerical computations are required. In this thesis a Monte Carlo simulation code has been developed aiming at reproducing the cosmological cascades. This code has been tested and validated against analytical expressions. With the simulation code, a systematic study of the parameters impacting the development of the cascade has been led. This study allows a better understanding of the cascade physics. Especially, the impact of the intergalactic medium properties (extragalactic background light, extragalactic magnetic field) on the observables has been highlighted. Finally, a second study has been done to measure the contribution of cascades to the extragalactic gamma ray background. Recent works show that a great part of the diffuse emission at very high energy is explained by unresolved sources (blazars in particular). These gamma sources (resolved and unresolved) must in principle initiate cosmological cascades which can also contribute to the extragalactic gamma ray background. Starting from a modeling of the blazars at different redshifts, absorption and contribution of the cascades have been estimated with the simulation code. The results show that the contribution of the cascades might violate the Fermi limits but the excess must be confirmed
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Faria, Bernardes Daniel. "Information Diffusion in Complex Networks : Measurement-Based Analysis Applied to Modelling." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2014. http://tel.archives-ouvertes.fr/tel-01001763.

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Understanding information diffusion on complex networks is a key issue from a theoretical and applied perspective. Epidemiology-inspired SIR models have been proposed to model information diffusion. Recent papers have analyzed this question from a data-driven perspective. We complement these findings investigating if epidemic models calibrate with a systematic procedure are capable of reproducing key spreading cascade properties. We first identify a large-scale, rich dataset from which we can reconstruct the diffusion trail and the underlying network. Secondly, we examine the simple SIR model as a baseline model and conclude that it was unable to generate structurally realistic spreading cascades. We found the same result examining model extensions to which take into account heterogeneities observed in the data. In contrast, other models which take into account time patterns available in the data generate qualitatively more similar cascades. Although one key property was not reproduced in any model, this result highlights the importance of taking time patterns into account. We have also analyzed the impact of the underlying network structure on the models examined. In our data the observed cascades were constrained in time, so we could not rely on the theoretical results relating the asymptotic behavior of the epidemic and network topological features. Performing simulations we assessed the impact of these common topological properties in time-bounded epidemic and identified that the distribution of neighbors of seed nodes had the most impact among the investigated properties in our context. We conclude discussing identifying perspectives opened by this work.
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Lemonnier, Rémi. "Application des processus stochastiques aux enchères en temps réel et à la propagation d'information dans les réseaux sociaux." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLN068/document.

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Dans cette thèse, nous étudions deux applications des processus stochastiques au marketing internet. Le premier chapitre s’intéresse au scoring d’internautes pour les enchères en temps réel. Ce problème consiste à trouver la probabilité qu’un internaute donné réalise une action d’intérêt, appelée conversion, dans les quelques jours suivant l’affichage d’une bannière publicitaire. Nous montrons que les processus de Hawkes constituent une modélisation naturelle de ce phénomène mais que les algorithmes de l’état de l’art ne sont pas applicables à la taille des données typiquement à l’œuvre dans des applications industrielles. Nous développons donc deux nouveaux algorithmes d’inférence non-paramétrique qui sont plusieurs ordres de grandeurs plus rapides que les méthodes précédentes. Nous montrons empiriquement que le premier a de meilleures performances que les compétiteurs de l’état de l’art, et que le second permet une application à des jeux de données encore plus importants sans payer un prix trop important en terme de pouvoir de prédiction. Les algorithmes qui en découlent ont été implémentés avec de très bonnes performances depuis plusieurs années à 1000 mercis, l’agence marketing d’avant-garde étant le partenaire industriel de cette thèse CIFRE, où ils sont devenus un actif important pour la production. Le deuxième chapitre s’intéresse aux processus diffusifs sur les graphes qui constituent un outil important pour modéliser la propagation d’une opération de marketing viral sur les réseaux sociaux. Nous établissons les premières bornes théoriques sur le nombre total de nœuds atteint par une contagion dans le cadre de graphes et dynamiques de diffusion quelconques, et montrons l’existence de deux régimes bien distincts : le régime sous-critique où au maximum $O(sqrt{n})$ nœuds seront infectés, où $n$ est la taille du réseau, et le régime sur-critique ou $O(n)$ nœuds peuvent être infectés. Nous étudions également le comportement par rapport au temps d’observation $T$ et mettons en lumière l’existence de temps critiques en-dessous desquels une diffusion, même sur-critique sur le long terme, se comporte de manière sous-critique. Enfin, nous étendons nos travaux à la percolation et l’épidémiologie, où nous améliorons les résultats existants
In this thesis, we study two applications of stochastic processes in internet marketing. The first chapter focuses on internet user scoring for real-time bidding. This problem consists in finding the probability for a given user to perform an action of interest, called conversion, in the next few days. We show that Hawkes processes are well suited for modelizing this phenomena but that state-of-the-art algorithms are not applicable to the size of datasets involved. We therefore develop two new algorithms able to perform nonparametric multivariate Hawkes process inference orders of magnitude faster than previous methods. We show empirically that the first one outperforms state-of-the-art competitors, and the second one scales to very large datasets while keeping very high prediction power. The resulting algorithms have been implemented with very good performances for several years in 1000mercis, a pioneering marketing agency being the industrial partner of this CIFRE PhD, where they became an important business asset. The second chapter focuses on diffusion processes graphs, an important tool for modelizing the spread of a viral marketing operation over social networks. We derive the first theoretical bounds for the total number of nodes reached by a contagion for general graphs and diffusion dynamics, and show the existence of two well distinct regimes: the sub-critical one where at most $O(sqrt{n})$ nodes are infected, where $n$ is the size of the network, and the super-critical one where $O(n)$ nodes can be infected. We also study the behavior wrt to the observation time $T$ and reveals the existence of critical times under which a long-term super-critical diffusion process behaves sub-critically. Finally, we extend our works to different application fields, and improve state-of-the-art results in percolation and epidemiology
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Hansen, Dennis J. "Investigation of second generation controlled-diffusion compressor blades in cascade." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1995. http://handle.dtic.mil/100.2/ADA304897.

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Thesis (M.S. in Aeronautical Engineering) Naval Postgraduate School, September 1995.
Thesis advisor(s): Garth V. Hobson. "September 1995." Includes bibliographical references. Also available online.
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Nicholls, Jennifer L. "Investigation of flow over second generation controlled-diffusion blades in a linear cascade." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1999. http://handle.dtic.mil/100.2/ADA370892.

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Thesis (M.S. in Aeronautical Engineering) Naval Postgraduate School, September 1999.
"September 1999". Thesis advisor(s): Garth V. Hobson. Includes bibliographical references (p. 99). Also Available online.
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Wakefield, Bryce Edwin. "Hotwire measurements of the turbulent flow into a cascade of controlled-diffusion compressor blades." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA277297.

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Ganaim, Rickel Humberto Javier. "Laser-Doppler velocimeter measurements in a cascade of controlled diffusion compressor blades at stall." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA283416.

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Fitzgerald, Kevin D. "Examination of flow around second-generation controlled diffusion compressor blades in cascade at stall." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Jun%5FFitzgerald.pdf.

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Books on the topic "Diffusion cascades"

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Grove, Darren V. Experimental and numerical investigation of second-generation, controlled-diffusion, compressor blades in cascade. Monterey, Calif: Naval Postgraduate School, 1997.

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Hobbs, Jacqueline Elizabeth. The use of radiation-enhanced diffusion to study collision cascades in solids. Salford: Universityof Salford, 1985.

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Elazar, Yekutiel. A mapping of the viscous flow behavior in a controlled diffusion compressor cascade using laser doppler velocimetry and preliminary evaluation of codes for the prediction of stall. Monterey, California: Naval Postgraduate School, 1988.

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Schnorenberg, David G. Investigation of the effect of Reynolds number on laminar separation bubbles on controlled-diffusion compressor blades in cascade. Monterey, Calif: Naval Postgraduate School, 1996.

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Dober, David Michael. Three-dimensional fiber-optical LDV measurements in the endwall region of a linear cascade of controlled-diffusion stator blades. Monterey, Calif: Naval Postgraduate School, 1993.

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A compendium of controlled diffusion blades generated by an automated inverse design procedure. [Washington, DC]: National Aeronautics and Space Administration, 1989.

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Nikerov, V. A. Fast Particle Degradation Diffusion Cascade (Physics Reviews). Taylor & Francis, 2000.

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Bianconi, Ginestra. Multilayer Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.001.0001.

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Multilayer networks are formed by several networks that interact with each other and co-evolve. Multilayer networks include social networks, financial markets, transportation systems, infrastructures and molecular networks and the brain. The multilayer structure of these networks strongly affects the properties of dynamical and stochastic processes defined on them, which can display unexpected characteristics. For example, interdependencies between different networks of a multilayer structure can cause cascades of failure events that can dramatically increase the fragility of these systems; spreading of diseases, opinions and ideas might take advantage of multilayer network topology and spread even when its single layers cannot sustain an epidemic when taken in isolation; diffusion on multilayer transportation networks can significantly speed up with respect to diffusion on single layers; finally, the interplay between multiplexity and controllability of multilayer networks is a problem with major consequences in financial, transportation, molecular biology and brain networks. This field is one of the most prosperous recent developments of Network Science and Data Science. Multilayer networks include multiplex networks, multi-slice temporal networks, networks of networks, interdependent networks. Multilayer networks are characterized by having a highly correlated multilayer network structure, providing a significant advantage for extracting information from them using multilayer network measures and centralities and community detection methods. The multilayer network dynamics (including percolation, epidemic spreading, diffusion, synchronization, game theory and control) is strongly affected by the multilayer network topology. This book will present a comprehensive account of this emerging field.
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Prescott, Tony J., and Leah Krubitzer. Evo-devo. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0008.

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This chapter explores how principles underlying natural evo-devo (evolution and development) continue to inspire the design of artificial systems from models of cell growth through to simulated three-dimensional evolved creatures. Research on biological evolvability shows that phenotypic outcomes depend on multiple interactions across different organizational levels—the adult organism is the outcome of a series of genetic cascades modulated in time and space by the wider embryological, bodily, and environmental context. This chapter reviews evo-devo principles discovered in biology and explores their potential for improving the evolvability of artificial systems. Biological topics covered include adaptive, selective, and generative mechanisms, and the role of epigenetic processes in creating phenotypic diversity. Modeling approaches include L-systems, Boolean networks, reaction-diffusion processes, genetic algorithms, and artificial embryogeny. A particular focus is on the evolution and development of the mammalian brain and the possibility of designing, using synthetic evo-devo approaches, brain-like control architectures for biomimetic robots.
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Investigation of Flow Over Second Generation Controlled-Diffusion Bladesin a Linear Cascade. Storming Media, 1999.

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Book chapters on the topic "Diffusion cascades"

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Wang, Meng, and Kan Li. "Predicting Information Diffusion Cascades Using Graph Attention Networks." In Communications in Computer and Information Science, 104–12. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63820-7_12.

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della Porta, Donatella. "1. Riding the wave. Protest cascades, and what we can learn from them." In Global Diffusion of Protest, edited by Donatella della Porta, 9–30. Amsterdam: Amsterdam University Press, 2017. http://dx.doi.org/10.1515/9789048531356-002.

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Panagopoulos, George, Fragkiskos D. Malliaros, and Michalis Vazirgiannis. "DiffuGreedy: An Influence Maximization Algorithm Based on Diffusion Cascades." In Studies in Computational Intelligence, 392–404. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05411-3_32.

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Wang, Senzhang, Honghui Zhang, Jiawei Zhang, Xiaoming Zhang, Philip S. Yu, and Zhoujun Li. "Inferring Diffusion Networks with Sparse Cascades by Structure Transfer." In Database Systems for Advanced Applications, 405–21. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18120-2_24.

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Dou, Peng, Sizhen Du, and Guojie Song. "Inferring Diffusion Network on Incomplete Cascade Data." In Web-Age Information Management, 325–37. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39937-9_25.

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Porto de Oliveira, Osmany. "The Cascade: From the Tipping Point to Mass Diffusion." In International Policy Diffusion and Participatory Budgeting, 129–65. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-43337-0_5.

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Lv, Ruilin, Chengxi Zang, Wai Kin (Victor) Chan, and Wenwu Zhu. "Analyzing WeChat Diffusion Cascade: Pattern Discovery and Prediction." In Smart Service Systems, Operations Management, and Analytics, 379–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30967-1_34.

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Moroz, Anna, Sergei Pashakhin, and Sergei Koltsov. "Modeling Cascade Growth: Predicting Content Diffusion on VKontakte." In Lecture Notes in Networks and Systems, 180–95. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64877-0_12.

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Ho, Thi Kim Thoa, Quang Vu Bui, and Marc Bui. "Homophily Independent Cascade Diffusion Model Based on Textual Information." In Computational Collective Intelligence, 134–45. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98443-8_13.

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Wang, Ding, Lingwei Wei, Chunyuan Yuan, Yinan Bao, Wei Zhou, Xian Zhu, and Songlin Hu. "Cascade-Enhanced Graph Convolutional Network for Information Diffusion Prediction." In Database Systems for Advanced Applications, 615–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00123-9_50.

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Conference papers on the topic "Diffusion cascades"

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Chen, Xueqin, Fan Zhou, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Fengli Zhang. "Information Diffusion Prediction via Recurrent Cascades Convolution." In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 2019. http://dx.doi.org/10.1109/icde.2019.00074.

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Zhou, Fan, Xovee Xu, Kunpeng Zhang, Goce Trajcevski, and Ting Zhong. "Variational Information Diffusion for Probabilistic Cascades Prediction." In IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. IEEE, 2020. http://dx.doi.org/10.1109/infocom41043.2020.9155349.

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Shi, Chaoyi, Qi Zhang, and Tianguang Chu. "Provenance Identification in Diffusion Networks with Incomplete Cascades." In 2018 37th Chinese Control Conference (CCC). IEEE, 2018. http://dx.doi.org/10.23919/chicc.2018.8483866.

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Wang, Zhitao, and Wenjie Li. "Hierarchical Diffusion Attention Network." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/531.

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A series of recent studies formulated the diffusion prediction problem as a sequence prediction task and proposed several sequential models based on recurrent neural networks. However, non-sequential properties exist in real diffusion cascades, which do not strictly follow the sequential assumptions of previous work. In this paper, we propose a hierarchical diffusion attention network (HiDAN), which adopts a non-sequential framework and two-level attention mechanisms, for diffusion prediction. At the user level, a dependency attention mechanism is proposed to dynamically capture historical user-to-user dependencies and extract the dependency-aware user information. At the cascade (i.e., sequence) level, a time-aware influence attention is designed to infer possible future user's dependencies on historical users by considering both inherent user importance and time decay effects. Significantly higher effectiveness and efficiency of HiDAN over state-of-the-art sequential models are demonstrated when evaluated on three real diffusion datasets. The further case studies illustrate that HiDAN can accurately capture diffusion dependencies.
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Yang, Ming-Hao, Chung-Kuang Chou, and Ming-Syan Chen. "Cluster cascades: Infer multiple underlying networks using diffusion data." In 2014 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM 2014). IEEE, 2014. http://dx.doi.org/10.1109/asonam.2014.6921597.

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Zhang, Bo, Qiong Wu, Xu Chen, and Liang Chen. "Information Cascades over Diffusion-Restricted Social Network: A Data-Driven Analysis." In IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2019. http://dx.doi.org/10.1109/infcomw.2019.8845264.

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Wang, Yongqing, Huawei Shen, Shenghua Liu, Jinhua Gao, and Xueqi Cheng. "Cascade Dynamics Modeling with Attention-based Recurrent Neural Network." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/416.

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An ability of modeling and predicting the cascades of resharing is crucial to understanding information propagation and to launching campaign of viral marketing. Conventional methods for cascade prediction heavily depend on the hypothesis of diffusion models, e.g., independent cascade model and linear threshold model. Recently, researchers attempt to circumvent the problem of cascade prediction using sequential models (e.g., recurrent neural network, namely RNN) that do not require knowing the underlying diffusion model. Existing sequential models employ a chain structure to capture the memory effect. However, for cascade prediction, each cascade generally corresponds to a diffusion tree, causing cross-dependence in cascade---one sharing behavior could be triggered by its non-immediate predecessor in the memory chain. In this paper, we propose to an attention-based RNN to capture the cross-dependence in cascade. Furthermore, we introduce a \emph{coverage} strategy to combat the misallocation of attention caused by the memoryless of traditional attention mechanism. Extensive experiments on both synthetic and real world datasets demonstrate the proposed models outperform state-of-the-art models at both cascade prediction and inferring diffusion tree.
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Zhao, Yunwei, Can Wang, Chi-Hung Chi, Kwok-Yan Lam, and Sen Wang. "A Comparative Study of Transactional and Semantic Approaches for Predicting Cascades on Twitter." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/169.

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The availability of massive social media data has enabled the prediction of people’s future behavioral trends at an unprecedented large scale. Information cascades study on Twitter has been an integral part of behavior analysis. A number of methods based on the transactional features (such as keyword frequency) and the semantic features (such as sentiment) have been proposed to predict the future cascading trends. However, an in-depth understanding of the pros and cons of semantic and transactional models is lacking. This paper conducts a comparative study of both approaches in predicting information diffusion with three mechanisms: retweet cascade, url cascade, and hashtag cascade. Experiments on Twitter data show that the semantic model outperforms the transactional model, if the exterior pattern is less directly observable (i.e. hashtag cascade). When it becomes more directly observable (i.e. retweet and url cascades), the semantic method yet delivers approximate accuracy (i.e. url cascade) or even worse accuracy (i.e. retweet cascade). Further, we demonstrate that the transactional and semantic models are not independent, and the performance gets greatly enhanced when combining both.
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Nagase, Takuya, and Sho Tsuguwa. "Effectiveness of a Data-based Influence Maximization Algorithm Using Information Diffusion Cascades." In 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2021. http://dx.doi.org/10.1109/compsac51774.2021.00062.

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Williams, Richard, Grant Ingram, and David Gregory-Smith. "Large Tip Clearance Flows in Two Compressor Cascades." In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-22952.

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Large tip clearances in the region of six percent span exist in the high pressure stages of compressors of industrial gas turbines. The over tip clearance flow causes significant blockage and accounts for the largest proportion of loss in the high pressure compressor. This paper examines large tip clearances in two different compressor cascades. The first was a cascade of controlled-diffusion blades and the second cascade had geometry more representative of modern engine practise. The second cascade also featured realistic inlet boundary layer conditions delivered by an upstream injection system. Increasing the understanding of such flows will allow for improvements in the design of such compressors. The key conclusions of this paper are: a) At large tip clearances the tip clearance is the primary variable influencing the flow pattern, blade geometry is a secondary consideration; b) The blade geometry has a significant influence on loss generation through the cascade; c) With increasing tip clearance the loss generation through the cascade can diminish but the flow turning is much reduced; and d) The blade loading at the tip can increase at large tip clearances.
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Reports on the topic "Diffusion cascades"

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Simmons, D. W. An introduction to technetium in the gaseous diffusion cascades. Office of Scientific and Technical Information (OSTI), September 1996. http://dx.doi.org/10.2172/469160.

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Mansur, L. K., A. D. Brailsford, and W. A. Coghlan. Cascade diffusion theory of sink capture fluctuations during irradiation of a solid. Office of Scientific and Technical Information (OSTI), January 1985. http://dx.doi.org/10.2172/6108807.

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Dr. Ala Qubbaj. CONTROL OF POLLUTANT EMISSIONS IN NATURAL GAS DIFFUSION FLAMES BY USING CASCADE BURNERS. Office of Scientific and Technical Information (OSTI), December 2001. http://dx.doi.org/10.2172/810444.

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Ala Qubbaj. CONTROL OF POLLUTANT EMISSIONS IN NATURAL GAS DIFFUSION FLAMES BY USING CASCADE BURNERS. Office of Scientific and Technical Information (OSTI), March 2001. http://dx.doi.org/10.2172/833196.

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Huffer, J. E. Nuclear criticality safety aspects of gaseous uranium hexafluoride (UF{sub 6}) in the diffusion cascade. Office of Scientific and Technical Information (OSTI), April 1997. http://dx.doi.org/10.2172/459892.

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P. L. Kerr, D. A. Close, W. S. Johnson, R. M. Kandarian, C. E. Moss, and C. D. Romero. IAEA Verification Experiment at the Portsmouth Gaseous Diffusion Plant: Report on the Cascade Header Enrichment Monitor. Office of Scientific and Technical Information (OSTI), March 1999. http://dx.doi.org/10.2172/8200.

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Negron, S. B., R. W. Jr Tayloe, and M. C. Dobelbower. Analysis of criticality alarm system response to an accidental criticality outside the cascade process buildings at the Portsmouth Gaseous Diffusion Plant. Office of Scientific and Technical Information (OSTI), July 1994. http://dx.doi.org/10.2172/10190511.

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