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How predictable is the next move of an animal? Specifically, which factors govern the short- and long-term motion patterns and the overall dynamics of land-bound, plant-eating animals in general and ruminants in particular? To answer this question, we here study the movement dynamics of springbok antelopes Antidorcas marsupialis. We propose several complementary statistical-analysis techniques combined with machine-learning approaches to analyze—across multiple time scales—the springbok motion recorded in long-term GPS tracking of collared springboks at a private wildlife reserve in Namibia. As a result, we are able to predict the springbok movement within the next hour with a certainty of about 20%. The remaining about 80% are stochastic in nature and are induced by unaccounted factors in the modeling algorithm and by individual behavioral features of springboks. We find that directedness of motion contributes approximately 17% to this predicted fraction. We find that the measure for directedeness is strongly dependent on the daily cycle of springbok activity. The previously known daily affinity of springboks to their water points, as predicted from our machine-learning algorithm, overall accounts for only about 3% of this predicted deterministic component of springbok motion. Moreover, the resting points are found to affect the motion of springboks at least as much as the formally studied effects of water points. The generality of these statements for the motion patterns and their underlying behavioral reasons for other ruminants can be examined on the basis of our statistical-analysis tools in the future.
We propose a generalization of the widely used fractional Brownian motion (FBM), memory-multi-FBM (MMFBM), to describe viscoelastic or persistent anomalous diffusion with time-dependent memory exponent α(t ) in a changing environment. In MMFBM the built-in, long-range memory is continuously modulated by α(t ). We derive the essential statistical properties of MMFBM such as its response function, mean-squared displacement (MSD), autocovariance function, and Gaussian distribution. In contrast to existing forms of FBM with time-varying memory exponents but a reset memory structure, the instantaneous dynamic of MMFBM is influenced by the process history, e.g., we show that after a steplike change of α(t ) the scaling exponent of the MSD after the α step may be determined by the value of α(t ) before the change. MMFBM is a versatile and useful process for correlated physical systems with nonequilibrium initial conditions in a changing environment.
We present real-world data processing on measured electron time-of-flight data via neural networks. Specifically, the use of disentangled variational autoencoders on data from a diagnostic instrument for online wavelength monitoring at the free electron laser FLASH in Hamburg. Without a-priori knowledge the network is able to find representations of single-shot FEL spectra, which have a low signal-to-noise ratio. This reveals, in a directly human-interpretable way, crucial information about the photon properties. The central photon energy and the intensity as well as very detector-specific features are identified. The network is also capable of data cleaning, i.e. denoising, as well as the removal of artefacts. In the reconstruction, this allows for identification of signatures with very low intensity which are hardly recognisable in the raw data. In this particular case, the network enhances the quality of the diagnostic analysis at FLASH. However, this unsupervised method also has the potential to improve the analysis of other similar types of spectroscopy data.
In recurrence analysis, the tau-recurrence rate encodes the periods of the cycles of the underlying high-dimensional time series. It, thus, plays a similar role to the autocorrelation for scalar time-series in encoding temporal correlations.
However, its Fourier decomposition does not have a clean interpretation. Thus, there is no satisfactory analogue to the power spectrum in recurrence analysis.
We introduce a novel method to decompose the tau-recurrence rate using an over-complete basis of Dirac combs together with sparsity regularization.
We show that this decomposition, the inter-spike spectrum, naturally provides an analogue to the power spectrum for recurrence analysis in the sense that it reveals the dominant periodicities of the underlying time series.
We show that the inter-spike spectrum correctly identifies patterns and transitions in the underlying system in a wide variety of examples and is robust to measurement noise.
Gravitational waves emitted from the coalescence of neutron star binaries open a new window to probe matter and fundamental physics in unexplored, extreme regimes. To extract information about the supranuclear matter inside neutron stars and the properties of the compact binary systems, robust theoretical prescriptions are required. We give an overview about general features of the dynamics and the gravitational wave signal during the binary neutron star coalescence. We briefly describe existing analytical and numerical approaches to investigate the highly dynamical, strong-field region during the merger. We review existing waveform approximants and discuss properties and possible advantages and shortcomings of individual waveform models, and their application for real gravitational-wave data analysis.
Tropical cyclones range among the costliest of all meteorological events worldwide and planetary scale warming provides more energy and moisture to these storms. Modelling the national and global economic repercussions of 2017's Hurricane Harvey, we find a qualitative change in the global economic response in an increasingly warmer world.
While the United States were able to balance regional production failures by the original 2017 hurricane, this option becomes less viable under future warming.
In our simulations of over 7000 regional economic sectors with more than 1.8 million supply chain connections, the US are not able to offset the losses by use of national efforts with intensifying hurricanes under unabated warming.
At a certain warming level other countries have to step in to supply the necessary goods for production, which gives US economic sectors a competitive disadvantage. In the highly localized mining and quarrying sector-which here also comprises the oil and gas production industry-this disadvantage emerges already with the original Hurricane Harvey and intensifies under warming.
Eventually, also other regions reach their limit of what they can offset.
While we chose the example of a specific hurricane impacting a specific region, the mechanism is likely applicable to other climate-related events in other regions and other sectors.
It is thus likely that the regional economic sectors that are best adapted to climate change gain significant advantage over their competitors under future warming.
Diffusive search for a static target is a common problem in statistical physics with numerous applications in chemistry and biology.
We look at this problem from a different perspective and investigate the statistics of encounters between the diffusing particle and the target. While an exact solution of this problem was recently derived in the form of a spectral expansion over the eigenbasis of the Dirichlet-to-Neumann operator, the latter is generally difficult to access for an arbitrary target.
In this paper, we present three complementary approaches to approximate the probability density of the rescaled number of encounters with a small target in a bounded confining domain. In particular, we derive a simple fully explicit approximation, which depends only on a few geometric characteristics such as the surface area and the harmonic capacity of the target, and the volume of the confining domain.
We discuss the advantages and limitations of three approaches and check their accuracy.
We also deduce an explicit approximation for the distribution of the first-crossing time, at which the number of encounters exceeds a prescribed threshold. Its relations to common first-passage time problems are discussed.
First-passage times in conical varying-width channels biased by a transverse gravitational force
(2022)
We study the crossing time statistic of diffusing point particles between the two ends of expanding and narrowing two-dimensional conical channels under a transverse external gravitational field.
The theoretical expression for the mean first-passage time for such a system is derived under the assumption that the axial diffusion in a two-dimensional channel of smoothly varying geometry can be approximately described as a one-dimensional diffusion in an entropic potential with position-dependent effective diffusivity in terms of the modified Fick-Jacobs equation.
We analyze the channel crossing dynamics in terms of the mean first-passage time, combining our analytical results with extensive two-dimensional Brownian dynamics simulations, allowing us to find the range of applicability of the one-dimensional approximation.
We find that the effective particle diffusivity decreases with increasing amplitude of the external potential.
Remarkably, the mean first-passage time for crossing the channel is shown to assume a minimum at finite values of the potential amplitude.
It is often claimed that the entropy of a network's degree distribution is a proxy for its robustness. Here, we clarify the link between degree distribution entropy and giant component robustness to node removal by showing that the former merely sets a lower bound to the latter for randomly configured networks when no other network characteristics are specified. Furthermore, we show that, for networks of fixed expected degree that follow degree distributions of the same form, the degree distribution entropy is not indicative of robustness. By contrast, we show that the remaining degree entropy and robustness have a positive monotonic relationship and give an analytic expression for the remaining degree entropy of the log-normal distribution. We also show that degree-degree correlations are not by themselves indicative of a network's robustness for real networks. We propose an adjustment to how mutual information is measured which better encapsulates structural properties related to robustness.
We discuss the coherent splitting and recombining of a nanoparticle in a mesoscopic "closed-loop" Stern-Gerlach interferometer in which the observable is the spin of a single impurity embedded in the particle.
This spin, when interacting with a pulsed magnetic gradient, generates the force on the particle.
We calculate the internal decoherence, which arises as the displaced impurity excites internal degrees of freedom (phonons) that may provide WelcherWeg information and preclude interference.
We estimate the constraints this decoherence channel puts on future interference experiments with massive objects. We find that for a wide range of masses, forces, and temperatures, phonons do not inhibit Stern-Gerlach interferometry with micro-scale objects.
However, phonons do constitute a fundamental limit on the splitting of larger macroscopic objects if the applied force induces phonons.