29 research outputs found
Reducing Spreading Processes on Networks to Markov Population Models
No full textStochastic processes on complex networks, where each node is in one of several compartments, and neighboring nodes interact with each other, can be used to describe a variety of real-world spreading phenomena. However, computational analysis of such processes is hindered by the enormous size of their underlying state space. In this work, we demonstrate that lumping can be used to reduce any epidemic model to a Markov Population Model (MPM). Therefore, we propose a novel lumping scheme based on a partitioning of the nodes. By imposing different types of counting abstractions, we obtain coarse-grained Markov models with a natural MPM representation that approximate the original systems. This makes it possible to transfer the rich pool of approximation techniques developed for MPMs to the computational analysis of complex networks’ dynamics. We present numerical examples to investigate the relationship between the accuracy of the MPMs, the size of the lumped state space, and the type of counting abstraction
Lumping the approximate master equation for multistate processes on complex networks
No full textComplex networks play an important role in human society and in nature. Stochastic multistate processes provide a powerful framework to model a variety of emerging phenomena such as the dynamics of an epidemic or the spreading of information on complex networks. In recent years, mean-field type approximations gained widespread attention as a tool to analyze and understand complex network dynamics. They reduce the model’s complexity by assuming that all nodes with a similar local structure behave identically. Among these methods the approximate master equation (AME) provides the most accurate description of complex networks’ dynamics by considering the whole neighborhood of a node. The size of a typical network though renders the numerical solution of multistate AME infeasible. Here, we propose an efficient approach for the numerical solution of the AME that exploits similarities between the differential equations of structurally similar groups of nodes. We cluster a large number of similar equations together and solve only a single lumped equation per cluster. Our method allows the application of the AME to real-world networks, while preserving its accuracy in computing estimates of global network properties, such as the fraction of nodes in a state at a given time
Analysis of Markov Jump Processes under Terminal Constraints
No full textMany probabilistic inference problems such as stochastic filtering or the computation of rare event probabilities require model analysis under initial and terminal constraints. We propose a solution to this bridging problem for the widely used class of population-structured Markov jump processes. The method is based on a state-space lumping scheme that aggregates states in a grid structure. The resulting approximate bridging distribution is used to iteratively refine relevant and truncate irrelevant parts of the state-space. This way, the algorithm learns a well-justified finite-state projection yielding guaranteed lower bounds for the system behavior under endpoint constraints. We demonstrate the method’s applicability to a wide range of problems such as Bayesian inference and the analysis of rare events
Rejection-Based Simulation of Non-Markovian Agents on Complex Networks
No full textStochastic models in which agents interact with their neighborhood according to a network topology are a powerful modeling framework to study the emergence of complex dynamic patterns in real-world systems. Stochastic simulations are often the preferred—sometimes the only feasible—way to investigate such systems. Previous research focused primarily on Markovian models where the random time until an interaction happens follows an exponential distribution. In this work, we study a general framework to model systems where each agent is in one of several states. Agents can change their state at random, influenced by their complete neighborhood, while the time to the next event can follow an arbitrary probability distribution. Classically, these simulations are hindered by high computational costs of updating the rates of interconnected agents and sampling the random residence times from arbitrary distributions. We propose a rejection-based, event-driven simulation algorithm to overcome these limitations. Our method over-approximates the instantaneous rates corresponding to inter-event times while rejection events counter-balance these over-approximations. We demonstrate the effectiveness of our approach on models of epidemic and information spreading
Sound as Popular Culture:A Research Companion
No full textThe wide-ranging texts in this book take as their premise the idea that sound is a subject through which popular culture can be analyzed in an innovative way. From an infant’s gurgles over a baby monitor to the roar of the crowd in a stadium to the sub-bass frequencies produced by sound systems in the disco era, sound—not necessarily aestheticized as music—is inextricably part of the many domains of popular culture. Expanding the view taken by many scholars of cultural studies, the contributors consider cultural practices concerning sound not merely as semiotic or signifying processes but as material, physical, perceptual, and sensory processes that integrate a multitude of cultural traditions and forms of knowledge.The chapters discuss conceptual issues as well as terminologies and research methods; analyze historical and contemporary case studies of listening in various sound cultures; and consider the ways contemporary practices of sound generation are applied in the diverse fields in which sounds are produced, mastered, distorted, processed, or enhanced. The chapters are not only about sound; they offer a study through sound—echoes from the past, resonances of the present, and the contradictions and discontinuities that suggest the future.ContributorsKarin Bijsterveld, Susanne Binas-Preisendörfer, Carolyn Birdsall, Jochen Bonz, Michael Bull, Thomas Burkhalter, Mark J. Butler, Diedrich Diederichsen, Veit Erlmann, Franco Fabbri, Golo Föllmer, Marta García Quiñones, Mark Grimshaw, Rolf Großmann, Maria Hanáček, Thomas Hecken, Anahid Kassabian, Carla J. Maier, Andrea Mihm, Bodo Mrozek, Carlo Nardi, Jens Gerrit Papenburg, Thomas Schopp, Holger Schulze, Toby Seay, Jacob Smith, Paul Théberge, Peter Wicke, Simon Zagorski-Thoma
Stochastic spreading on complex networks
No full textComplex interacting systems are ubiquitous in nature and society. Computational modeling of these systems is, therefore, of great relevance for science and engineering. Complex networks are common representations of these systems (e.g., friendship networks or road networks). Dynamical processes (e.g., virus spreading, traffic jams) that evolve on these networks are shaped and constrained by the underlying connectivity. This thesis provides numerical methods to study stochastic spreading processes on complex networks. We consider the processes as inherently probabilistic and analyze their behavior through the lens of probability theory. While powerful theoretical frameworks (like the SIS-epidemic model and continuous-time Markov chains) already exist, their analysis is computationally challenging. A key contribution of the thesis is to ease the computational burden of these methods. Particularly, we provide novel methods for the efficient stochastic simulation of these processes. Based on different simulation studies, we investigate techniques for optimal vaccine distribution and critically address the usage of mathematical models during the Covid-19 pandemic. We also provide model-reduction techniques that translate complicated models into simpler ones that can be solved without resorting to simulations. Lastly, we show how to infer the underlying contact data from node-level observations.Komplexe, interagierende Systeme sind in Natur und Gesellschaft allgegenwärtig. Die computergestützte Modellierung dieser Systeme ist daher von immenser Bedeutung für Wissenschaft und Technik. Netzwerke sind eine gängige Art, diese Systeme zu repräsentieren (z. B. Freundschaftsnetzwerke, Straßennetze). Dynamische Prozesse (z. B. Epidemien, Staus), die sich auf diesen Netzwerken ausbreiten, werden durch die spezifische Konnektivität geformt. In dieser Arbeit werden numerische Methoden zur Untersuchung stochastischer Ausbreitungsprozesse in komplexen Netzwerken entwickelt. Wir betrachten die Prozesse als inhärent probabilistisch und analysieren ihr Verhalten nach wahrscheinlichkeitstheoretischen Fragestellungen. Zwar gibt es bereits theoretische Grundlagen und Paradigmen (wie das SIS-Epidemiemodell und zeitkontinuierliche Markov-Ketten), aber ihre Analyse ist rechnerisch aufwändig. Ein wesentlicher Beitrag dieser Arbeit besteht darin, die Rechenlast dieser Methoden zu verringern. Wir erforschen Methoden zur effizienten Simulation dieser Prozesse. Anhand von Simulationsstudien untersuchen wir außerdem Techniken für optimale Impfstoffverteilung und setzen uns kritisch mit der Verwendung mathematischer Modelle bei der Covid-19-Pandemie auseinander. Des Weiteren führen wir Modellreduktionen ein, mit denen komplizierte Modelle in einfachere umgewandelt werden können. Abschließend zeigen wir, wie man von Beobachtungen einzelner Knoten auf die zugrunde liegende Netzwerkstruktur schließt
An Impact Study of the Economic Partnership Agreements (EPAs) in the Six ACP Regions
No full textThis article intends to present a very detailed analysis of the trade-related aspects of Economic Partnership Agreements (EPAs) negotiations. We use a dynamic partial equilibrium model – focusing on the demand side – at the HS6 level (covering 5,113 HS6 products). Two alternative lists of sensitive products are constructed, one giving priority to the agricultural sectors, the other focusing on tariff revenue preservation. In order to be WTO compatible, EPAs must translate into 90 percent of bilateral trade fully liberalised. We use this criterion to simulate EPAs for each negotiating regional block. ACP exports to the EU are forecast to be 10 percent higher with the EPAs than under the GSP/EBA option. On average ACP countries are forecast to lose 70 percent of tariff revenues on EU imports in the long run. Yet imports from other regions of the world will continue to provide tariff revenues. Thus when tariff revenue losses are computed on total ACP imports, losses are limited to 26 percent on average in the long run and even 19 percent when the product lists are optimised. The final impact on the economy depends on the importance of tariffs in government revenue and on potential compensatory effects. However this long term and less visible effect will mainly depend on the capacity of each ACP country to reorganise its fiscal base.Preferential Trade Agreements, Africa, EPAs, Partial Equilibrium Simulations, International Relations/Trade,
Arc-Flags Meet Trip-Based Public Transit Routing
No full textWe present Arc-Flag TB, a journey planning algorithm for public transit
networks which combines Trip-Based Public Transit Routing (TB) with the
Arc-Flags speedup technique. Compared to previous attempts to apply Arc-Flags
to public transit networks, which saw limited success, our approach uses
stronger pruning rules to reduce the search space. Our experiments show that
Arc-Flag TB achieves a speedup of up to two orders of magnitude over TB,
offering query times of less than a millisecond even on large countrywide
networks. Compared to the state-of-the-art speedup technique Trip-Based Public
Transit Routing Using Condensed Search Trees (TB-CST), our algorithm achieves
similar query times but requires significantly less additional memory. Other
state-of-the-art algorithms which achieve even faster query times, e.g., Public
Transit Labeling, require enormous memory usage. In contrast, Arc-Flag TB
offers a tradeoff between query performance and memory usage due to the fact
that the number of regions in the network partition required by our algorithm
is a configurable parameter. We also identify an issue in the transfer
precomputation of TB that affects both TB-CST and Arc-Flag TB, leading to
incorrect answers for some queries. This has not been previously recognized by
the author of TB-CST. We provide discussion on how to resolve this issue in the
future. Currently, Arc-Flag TB answers 1-6% of queries incorrectly, compared to
over 20% for TB-CST on some networks
On the copyright hermeneutics of the original
No full textThe original is not only an aesthetic term but also part of copyright terminology. As such, the original is subject to legal interpretation – and the object of conceptual controversy. These disputes interact with definitions and functions of the original in art. With the inception of modern copyright, the artistic idea of originality has always been influenced by the law.<p></p>
The term of the original connects the work of art with its origin, i.e. with its author. Historically, this concept emerged in the 17th century; the author’s rights of the French Revolution legally imposed the idea of the genius. Ever since, an original is a work expressing the individual creative personality of the artist (with some differences mainly between the Continental and the Anglo-American legal cultures).<p></p>
Throughout the 18th and 19th centuries, artists have engaged for the recognition of their artistic copyright, on the one hand in order to secure use rights such as reproduction rights, on the other hand to protect themselves against plagiarists. The discussion on copyright protection for photographs around 1900, which was also conducted before the judicial courts, contributed substantially to refine the terminological distinction between original and reproduction.<p></p>
