Bimodality in the transverse fluctuations of a grafted semiflexible polymer and the diffusion-convection analogue: An effective-medium approach

Recent Monte Carlo simulations of a grafted semiflexible polymer in 1+1 dimensions have revealed a pronounced bimodal structure in the probability distribution of the transverse (bending) fluctuations of the free end, when the total contour length is of the order of the persistence length G. Lattanzi , Phys. Rev E 69, 021801 (2004)]. In this paper, we show that the emergence of bimodality is related to a similar behavior observed when a random walker is driven in the transverse direction by a certain type of shear flow. We adapt an effective-medium argument, which was first introduced in the context of the sheared random-walk problem E. Ben-Naim , Phys. Rev. A 45, 7207 (1992)], in order to obtain a simple analytic approximation of the probability distribution of the free-end fluctuations. We show that this approximation captures the bimodality and most of the qualitative features of the free-end fluctuations. We also predict that relaxing the local inextensibility constraint of the wormlike chain could lead to the disappearence of bimodality

Effective Perrin theory for the anisotropic diffusion of a strongly hindered rod

Slender rods in concentrated suspensions constitute strongly interacting systems with rich dynamics: transport slows down drastically and the anisotropy of the motion becomes arbitrarily large. We develop a mesoscopic description of the dynamics down to the length scale of the interparticle distance. Our theory is based on the exact solution of the Smoluchowski-Perrin equation; it is in quantitative agreement with extensive Brownian dynamics simulations in the dense regime. In particular, we show that the tube confinement is characterised by a power law decay of the intermediate scattering function with exponent 1/2.Comment: to appear in EP

Exclusion Processes with Internal States

We introduce driven exclusion processes with internal states that serve as generic transport models in various contexts, ranging from molecular or vehicular traffic on parallel lanes to spintronics. The ensuing non-equilibrium steady states are controllable by boundary as well as bulk rates. A striking polarization phenomenon accompanied by domain wall motion and delocalization is discovered within a mesoscopic scaling. We quantify this observation within an analytic description providing exact phase diagrams. Our results are confirmed by stochastic simulations.Comment: 4 pages, 3 figures. Version as published in Phys. Rev. Let

Instability of spatial patterns and its ambiguous impact on species diversity

Self-arrangement of individuals into spatial patterns often accompanies and promotes species diversity in ecological systems. Here, we investigate pattern formation arising from cyclic dominance of three species, operating near a bifurcation point. In its vicinity, an Eckhaus instability occurs, leading to convectively unstable "blurred" patterns. At the bifurcation point, stochastic effects dominate and induce counterintuitive effects on diversity: Large patterns, emerging for medium values of individuals' mobility, lead to rapid species extinction, while small patterns (low mobility) promote diversity, and high mobilities render spatial structures irrelevant. We provide a quantitative analysis of these phenomena, employing a complex Ginzburg-Landau equation.Comment: 4 pages, 3 figures and supplementary information. To appear in Phys. Rev. Lett

Stochastic effects on biodiversity in cyclic coevolutionary dynamics

Finite-size fluctuations arising in the dynamics of competing populations may have dramatic influence on their fate. As an example, in this article, we investigate a model of three species which dominate each other in a cyclic manner. Although the deterministic approach predicts (neutrally) stable coexistence of all species, for any finite population size, the intrinsic stochasticity unavoidably causes the eventual extinction of two of them.Comment: 5 pages, 2 figures. Proceedings paper of the workshop "Stochastic models in biological sciences" (May 29 - June 2, 2006 in Warsaw) for the Banach Center Publication

Generic principles of active transport

Nonequilibrium collective motion is ubiquitous in nature and often results in a rich collection of intringuing phenomena, such as the formation of shocks or patterns, subdiffusive kinetics, traffic jams, and nonequilibrium phase transitions. These stochastic many-body features characterize transport processes in biology, soft condensed matter and, possibly, also in nanoscience. Inspired by these applications, a wide class of lattice-gas models has recently been considered. Building on the celebrated {\it totally asymmetric simple exclusion process} (TASEP) and a generalization accounting for the exchanges with a reservoir, we discuss the qualitative and quantitative nonequilibrium properties of these model systems. We specifically analyze the case of a dimeric lattice gas, the transport in the presence of pointwise disorder and along coupled tracks.Comment: 21 pages, 10 figures. Pedagogical paper based on a lecture delivered at the conference on "Stochastic models in biological sciences" (May 29 - June 2, 2006 in Warsaw). For the Banach Center Publication

Entropy production of cyclic population dynamics

Entropy serves as a central observable in equilibrium thermodynamics. However, many biological and ecological systems operate far from thermal equilibrium. Here we show that entropy production can characterize the behavior of such nonequilibrium systems. To this end we calculate the entropy production for a population model that displays nonequilibrium behavior resulting from cyclic competition. At a critical point the dynamics exhibits a transition from large, limit-cycle like oscillations to small, erratic oscillations. We show that the entropy production peaks very close to the critical point and tends to zero upon deviating from it. We further provide analytical methods for computing the entropy production which agree excellently with numerical simulations.Comment: 4 pages, 3 figures and Supplementary Material. To appear in Phys. Rev. Lett.

Capturing Value from Data: Exploring Factors Influencing Revenue Model Design for Data-Driven Services

In recent years organizations have started utilizing big data and advanced analytics not just to support decision-making to raise process efficiencies, but also to engage in new data-driven services. These data-driven services complement the current product and service portfolio and create additional value for customers. In order to capture the value created, organizations need to design sustainable revenue models consisting of a revenue(how) and pricing (how much) mechanism. In order to develop a deeper understanding of one part of the decision-making process on revenue models, we apply a qualitative study and analyze the results through the lens of rational choice theory. Based on the interviews, we derived four factors – service characteristics, provider interests, customer interests, and market factors -influencing the design. By this, we contribute to the general understanding of the design of revenue models and enable further investigation into this field of research

Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for Specialized Cyber Threat Intelligence

Gathering cyber threat intelligence from open sources is becoming increasingly important for maintaining and achieving a high level of security as systems become larger and more complex. However, these open sources are often subject to information overload. It is therefore useful to apply machine learning models that condense the amount of information to what is necessary. Yet, previous studies and applications have shown that existing classifiers are not able to extract specific information about emerging cybersecurity events due to their low generalization ability. Therefore, we propose a system to overcome this problem by training a new classifier for each new incident. Since this requires a lot of labelled data using standard training methods, we combine three different low-data regime techniques - transfer learning, data augmentation, and few-shot learning - to train a high-quality classifier from very few labelled instances. We evaluated our approach using a novel dataset derived from the Microsoft Exchange Server data breach of 2021 which was labelled by three experts. Our findings reveal an increase in F1 score of more than 21 points compared to standard training methods and more than 18 points compared to a state-of-the-art method in few-shot learning. Furthermore, the classifier trained with this method and 32 instances is only less than 5 F1 score points worse than a classifier trained with 1800 instances