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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.
Organic thin films are widely used in organic electronics and coatings. Such films often feature film-depth dependent variations of composition and optoelectronic properties. State-of-the-art depth profiling methods such as mass spectroscopy and photoelectron spectroscopy rely on non-intrinsic species (vaporized ions, etching-induced surface defects), which are chemically and functionally different from the original materials. Here we introduce an easily-accessible and generally applicable depth profiling method: film-depth-dependent infrared (FDD-IR) spectroscopy profilometry based on directly measuring the intrinsic material after incremental surface-selective etching by a soft plasma, to study the material variations along the surface-normal direction. This depth profiling uses characteristic vibrational signatures of the involved compounds, and can be used for both conjugated and non-conjugated, neutral and ionic materials. A film-depth resolution of one nanometer is achieved. We demonstrate the application of this method for investigation of device-relevant thin films, including organic field-effect transistors and organic photovoltaic cells, as well as ionized dopant distributions in doped semiconductors.
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.
There is increasing evidence linking the mass-extinction event at the Cretaceous-Paleogene boundary to an asteroid impact near Chicxulub, Mexico. Here we use model simulations to explore the combined effect of sulfate aerosols, carbon dioxide and dust from the impact on the oceans and the marine biosphere in the immediate aftermath of the impact. We find a strong temperature decrease, a brief algal bloom caused by nutrients from both the deep ocean and the projectile, and moderate surface ocean acidification. Comparing the modeled longer-term post-impact warming and changes in carbon isotopes with empirical evidence points to a substantial release of carbon from the terrestrial biosphere. Overall, our results shed light on the decades to centuries after the Chicxulub impact which are difficult to resolve with proxy data.
Plain Language Summary The sudden disappearance of the dinosaurs and many other species during the end-Cretaceous mass extinction 66 million years ago marks one of the most profound events in the history of life on Earth. The impact of a large asteroid near Chicxulub, Mexico, is increasingly recognized as the trigger of this extinction, causing global darkness and a pronounced cooling. However, the links between the impact and the changes in the biosphere are not fully understood. Here, we investigate how life in the ocean reacts to the perturbations in the decades and centuries after the impact. We find a short-lived algal bloom caused by the upwelling of nutrients from the deep ocean and nutrient input from the impactor.
Convolutional neural networks (CNNs) have been used for a wide range of applications in astronomy, including for the restoration of degraded images using a spatially invariant point spread function (PSF) across the field of view. Most existing development techniques use a single PSF in the deconvolution process, which is unrealistic when spatially variable PSFs are present in real observation conditions. Such conditions are simulated in this work to yield more realistic data samples. We propose a method that uses a simulated spatially variable PSF for the T80-South (T80-S) telescope, an 80-cm survey imager at Cerro Tololo (Chile). The synthetic data use real parameters from the detector noise and atmospheric seeing to recreate the T80-S observational conditions for the CNN training. The method is tested on real astronomical data from the T80-S telescope. We present the simulation and training methods, the results from real T80-S image CNN prediction, and a comparison with space observatory Gaia. A CNN can fix optical aberrations, which include image distortion, PSF size and profile, and the field position variation while preserving the source's flux. The proposed restoration approach can be applied to other optical systems and to post-process adaptive optics static residual aberrations in large-diameter telescopes.
We consider the synchrotron emission from electrons out in the Galactic halo bubble region where the Fermi bubble structures reside.
Utilizing a simple analytical expression for the non-thermal electron distribution and a toy magnetic field model, we simulate polarized synchrotron emission maps at a frequency of 30 GHz.
Comparing these maps with the observational data, we obtain constraints on the parameters of our toy Galactic halo bubble magnetic field model.
Utilizing this parameter value range for the toy magnetic field model, we determine the corresponding range of arrival directions and suppression factors of ultra high energy cosmic rays (UHECRs) from potential local source locations.
We find that high levels of flux suppression (down to 2 per cent) and large deflection angles (>= 80 degrees) are possible for source locations whose line of sight pass through the Galactic halo bubble region.
We conclude that the magnetic field out in the Galactic halo bubble region can strongly dominate the level of deflection UHECRs experience whilst propagating from local sources to Earth.
Temperature impacts on hate speech online: evidence from 4 billion geolocated tweets from the USA
(2022)
Background - A link between weather and aggression in the offline world has been established across a variety of societal settings. Simultaneously, the rapid digitalisation of nearly every aspect of everyday life has led to a high frequency of interpersonal conflicts online. Hate speech online has become a prevalent problem that has been shown to aggravate mental health conditions, especially among young people and marginalised groups.
We examine the effect of temperature on the occurrence of hate speech on the social media platform Twitter and interpret the results in the context of the interlinkage between climate change, human behaviour, and mental health.
Methods - In this quantitative empirical study, we used a supervised machine learning approach to identify hate speech in a dataset containing around 4 billion geolocated tweets from 773 cities across the USA between May 1, 2014 and May 1, 2020.
We statistically evaluated the changes in daily hate tweets against changes in local temperature, isolating the temperature influence from confounding factors using binned panel-regression models.
Findings - The prevalence of hate tweets was lowest at moderate temperatures (12 to 21?) and marked increases in the number of hate tweets were observed at hotter and colder temperatures, reaching up to 12middot5% (95% CI 8middot0-16middot5) for cold temperature extremes (-6 to -3?) and up to 22middot0% (95% CI 20middot5-23middot5) for hot temperature extremes (42 to 45?). Outside of the moderate temperature range, the hate tweets also increased as a proportion of total tweeting activity. The quasi-quadratic shape of the temperature-hate tweet curve was robust across varying climate zones, income quartiles, religious and political beliefs, and both city-level and state-level aggregations.
However, temperature ranges with the lowest prevalence of hate tweets were centred around the local temperature mean and the magnitude of the increases in hate tweets for hot and cold temperatures varied across the climate zones.
Interpretation - Our results highlight hate speech online as a potential channel through which temperature alters interpersonal conflict and societal aggression. We provide empirical evidence that hot and cold temperatures can aggravate aggressive tendencies online. The prevalence of the results across climatic and socioeconomic subgroups points to limitations in the ability of humans to adapt to temperature extremes.
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.
The radiation model is a parameter-free model of human mobility that has been applied primarily for short-distance moves, such as commuting. When applied to migration, it underestimates the number of long-range moves, such as between different US states. Here we show that it additionally suffers from a conceptual inconsistency that can have substantial numerical effects on long-distance moves.
We propose a modification of the radiation model that introduces a dependence on the angle between any two alternative potential destinations, accounting for the possibility that migrants may have preferences about the approximate direction of their move.
We demonstrate that this modification mitigates the conceptual inconsistency and improves the model fit to observational migration data, without introducing any fitting parameters.
Contour scanning and process gas type are process parameters typically considered achieving second order effects compared to first order factors such as laser power and scanning speed.
The present work highlights that contour scanning is crucial to ensure geometrical accuracy and thereby the high performance under uniaxial compression of complex Alloy 718 lattice structures.
Studies of X-ray computed tomography visualizations of as-built and compression-strained structures reveal the continuous and smooth bending and compression of the walls, and the earlier onset of internal contact appearance in the denser lattices printed with contour. In contrast, the effect of addition of He to the Ar process gas appears to have limited influence on the mechanical response of the lattices and their microstructure as characterized by electron backscattered diffraction.
However, the addition of He proved to significantly enhance the cooling rate and to reduce the amount of the generated spatters as evidenced by in situ monitoring of the process emissions, which is very promising for the process stability and powder reusability during laser powder bed fusion.
In this work, we investigate the photo-aquation reaction of the ferrocyanide anion with multi-edge picosecond soft X-ray spectroscopy.
Combining the information of the iron L-edge with nitrogen and oxygen K-edges, we carry out a complete characterization of the bonding channels in the [Fe(CN)(5)(H2O)](3-) photo-product.
We observe clear spectral signatures of covalent bonding between water and the metal, reflecting the mixing of the Fe d(z)(2) orbital with the 3a(1) and 4a(1) orbitals of H2O. Additional fingerprints related to the symmetry reduction and the resulting loss in orbital degeneracy are also reported.
The implications of the elucidated fingerprints in the context of future ultra-fast experiments are also discussed.
In the Universe, matter outside of stars and compact objects is mostly composed of collisionless plasma.
The interaction of a supersonic plasma flow with an obstacle results in collisionless shocks that are often associated with intense nonthermal radiation and the production of cosmic ray particles.
Motivated by simulations of non-relativistic high-Mach-number shocks in supernova remnants, we investigate the instabilities excited by relativistic electron beams in the extended foreshock of oblique shocks.
The phase-space distributions in the inner and outer foreshock regions are derived with a particle-in-cell simulation of the shock and used as initial conditions for simulations with periodic boundary conditions to study their relaxation toward equilibrium.
We find that the observed electron-beam instabilities agree very well with the predictions of a linear dispersion analysis: the electrostatic electron-acoustic instability dominates in the outer region of the foreshock, while the denser electron beams in the inner foreshock drive the gyroresonant oblique-whistler instability.