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Anomalous diffusion or, more generally, anomalous transport, with nonlinear dependence of the mean-squared displacement on the measurement time, is ubiquitous in nature. It has been observed in processes ranging from microscopic movement of molecules to macroscopic, large-scale paths of migrating birds. Using data from multiple empirical systems, spanning 12 orders of magnitude in length and 8 orders of magnitude in time, we employ a method to detect the individual underlying origins of anomalous diffusion and transport in the data. This method decomposes anomalous transport into three primary effects: long-range correlations (“Joseph effect”), fat-tailed probability density of increments (“Noah effect”), and nonstationarity (“Moses effect”). We show that such a decomposition of real-life data allows us to infer nontrivial behavioral predictions and to resolve open questions in the fields of single-particle tracking in living cells and movement ecology.
Kriminalliteratur gilt als zuverlässiger Seismograph für den inneren Zustand einer Gesellschaft, deren Umgang mit der Abweichung von der Norm zum Indikator sozialer und politischer Verhältnisse wird. Die gemeinsame Vergangenheit eint und trennt die Staaten Ostmittel-, Ost- und Südosteuropas gleichermaßen. Die schicksalhaften Verwerfungen des 20. Jahrhunderts fanden natürlich auch Eingang in die jeweiligen Kriminalliteraturen. So vielgestaltig wie die einzelnen Länder und Regionen sind die im vorliegenden Band untersuchten Texte. Sie ermöglichen einerseits Einblicke in den Herausbildungs- und Etablierungsprozess der Kriminalliteratur der Slavia. Andererseits bilden sie aktuelle Entwicklungen dieses ebenso populären wie zeitlosen Genres ab.
Das literarische Verbrechen hat Prof. Dr. Norbert P. Franz während seines aktiven akademischen Wirkens immer begleitet. Ihm zu Ehren fand im Frühjahr 2017 an der Universität Potsdam eine wissenschaftliche Tagung statt, deren Beiträge in diesem Band zusammengestellt sind.
Die DDR als Chance
(2017)
Motivated by recent epidemic outbreaks, including those of COVID-19, we solve the canonical problem of calculating the dynamics and likelihood of extensive outbreaks in a population within a large class of stochastic epidemic models with demographic noise, including the susceptible-infected-recovered (SIR) model and its general extensions.
In the limit of large populations, we compute the probability distribution for all extensive outbreaks, including those that entail unusually large or small (extreme) proportions of the population infected.
Our approach reveals that, unlike other well-known examples of rare events occurring in discrete-state stochastic systems, the statistics of extreme outbreaks emanate from a full continuum of Hamiltonian paths, each satisfying unique boundary conditions with a conserved probability flux.
The field of American Jewish studies has recently trained its focus on the transnational dimensions of its subject, reflecting in more sustained ways than before about the theories and methods of this approach. Yet, much of the insight to be gained from seeing American Jewry as constitutively entangled in many ways with other Jewries has not yet been realized. Transnational American Jewish studies are still in their infancy.
This issue of PaRDeS presents current research on the multiple entanglements of American with Central European, especially German-speaking Jewries in the 19th and 20th centuries. The articles reflect the wide range of topics that can benefit from a transnational understanding of the American Jewish experience as shaped by its foreign entanglements.
Anomalous diffusion or, more generally, anomalous transport, with nonlinear dependence of the mean-squared displacement on the measurement time, is ubiquitous in nature. It has been observed in processes ranging from microscopic movement of molecules to macroscopic, large-scale paths of migrating birds. Using data from multiple empirical systems, spanning 12 orders of magnitude in length and 8 orders of magnitude in time, we employ a method to detect the individual underlying origins of anomalous diffusion and transport in the data. This method decomposes anomalous transport into three primary effects: long-range correlations (“Joseph effect”), fat-tailed probability density of increments (“Noah effect”), and nonstationarity (“Moses effect”). We show that such a decomposition of real-life data allows us to infer nontrivial behavioral predictions and to resolve open questions in the fields of single-particle tracking in living cells and movement ecology.