Resistivity phase diagram of cuprates revisited

The phase diagram of the cuprate superconductors has posed a formidable scientific challenge for more than three decades. This challenge is perhaps best exemplified by the need to understand the normal-state charge transport as the system evolves from Mott insulator to Fermi-liquid metal with doping. Here we report a detailed analysis of the temperature (T) and doping (p) dependence of the planar resistivity of simple-tetragonal HgBa$_2$CuO$_{4+\delta}$ (Hg1201), the single-CuO$_2$-layer cuprate with the highest optimal $T_c$. The data allow us to test a recently proposed phenomenological model for the cuprate phase diagram that combines a universal transport scattering rate with spatially inhomogeneous (de)localization of the Mott-localized hole. We find that the model provides an excellent description of the data. We then extend this analysis to prior transport results for several other cuprates, including the Hall number in the overdoped part of the phase diagram, and find little compound-to-compound variation in (de)localization gap scale. The results point to a robust, universal structural origin of the inherent gap inhomogeneity that is unrelated to doping-related disorder. They are inconsistent with the notion that much of the phase diagram is controlled by a quantum critical point, and instead indicate that the unusual electronic properties exhibited by the cuprates are fundamentally related to strong nonlinearities associated with subtle nanoscale inhomogeneity.Comment: 22 pages, 5 figure

Random projections and the optimization of an algorithm for phase retrieval

Iterative phase retrieval algorithms typically employ projections onto constraint subspaces to recover the unknown phases in the Fourier transform of an image, or, in the case of x-ray crystallography, the electron density of a molecule. For a general class of algorithms, where the basic iteration is specified by the difference map, solutions are associated with fixed points of the map, the attractive character of which determines the effectiveness of the algorithm. The behavior of the difference map near fixed points is controlled by the relative orientation of the tangent spaces of the two constraint subspaces employed by the map. Since the dimensionalities involved are always large in practical applications, it is appropriate to use random matrix theory ideas to analyze the average-case convergence at fixed points. Optimal values of the gamma parameters of the difference map are found which differ somewhat from the values previously obtained on the assumption of orthogonal tangent spaces.Comment: 15 page

The Effect of Smart Contracts on Online Investment Decisions: An Experimental Study in ICOs

The imbalance of internal and external knowledge for investments in Initial Coin Offerings (ICO) leads to an information asymmetry, where issuers may further exploit a moral hazard as a resulting mismatch of time and interest during lock-up situations. The existing regulatory vacuum is mirrored by literature, as scholars deliver insights on effective means of signaling. However, research on smart contracts as immutable mechanisms and effective signals to mitigate risks for online investments remains an untapped subject, whilst market demand for solutions to an existing agency problem remains high. To respond to a pressing research question, this study conducted a randomized between-subjects online experiment with a sample of 391 participants. Results include a significant positive effect of the implementation of smart contracts on investor decisions in a present lock-up situation

Quantifying rapid permafrost thaw with computer vision and graph theory

With the Earth’s climate rapidly warming, the Arctic represents one of the most vulnerable regions to environmental change. Permafrost, as a key element of the Arctic system, stores vast amounts of organic carbon that can be microbially decomposed into the greenhouse gases CO2 and CH4 upon thaw. Extensive thawing of these permafrost soils therefore has potentially substantial consequences to greenhouse gas concentrations in the atmosphere. In addition, thaw of ice-rich permafrost lastingly alters the surface topography and thus the hydrology. Fires represent an important disturbance in boreal permafrost regions and increasingly also in tundra regions as they combust the vegetation and upper organic soil layers that usually provide protective insulation to the permafrost below. Field studies and local remote sensing studies suggest that fire disturbances may trigger rapid permafrost thaw, with consequences often already observable in the first years post-disturbance. In polygonal ice-wedge landscapes, this becomes most prevalent through melting ice wedges and degrading troughs. The further these ice wedges degrade; the more troughs will likely connect and build an extensive hydrological network with changing patterns and degrees of connectivity that influences hydrology and runoff throughout large regions. While subsiding troughs over melting ice wedges may host new ponds, an increasing connectivity may also subsequently lead to more drainage of ponds, which in turn can limit further thaw and help stabilize the landscape. Whereas fire disturbances may accelerate the initiation of this process, the general warming of permafrost observed across the Arctic will eventually result in widespread degradation of polygonal landscapes. To quantify the changes in such dynamic landscapes over large regions, remote sensing data offers a valuable resource. However, considering the vast and ever-growing volumes of Earth observation data available, highly automated methods are needed that allow extracting information on the geomorphic state and changes over time of ice-wedge trough networks. In this study, we investigate these changing landscapes and their environmental implications in fire scars in Northern and Western Alaska. We developed a computer vision algorithm to automatically extract ice-wedge polygonal networks and the microtopography of the degrading troughs from high-resolution, airborne laserscanning-based digital terrain models (1 m spatial resolution; full-waveform Riegl Q680i LiDAR sensor). To derive information on the availability of surface water, we used optical and near-infrared aerial imagery at spatial resolutions of up to 5 cm captured by the Modular Aerial Camera System (MACS) developed by DLR. We represent the networks as graphs (a concept from the computer sciences to describe complex networks) and apply methods from graph theory to describe and quantify hydrological network characteristics of the changing landscape. Due to a lack of historical very-high-resolution data, we cannot investigate a dense time series of a single representative study area on the evolution of the microtopographic and hydrologic network, but rather leverage the possibilities of a space-for-time substitution. We thus investigate terrain models and multispectral data from 2019 and 2021 of ten study areas located in ten fire scars of different ages (up to 120 years between date of disturbance and date of data acquisition). With this approach, we can infer past and future states of degradation from the currently prevailing spatial patterns and show how this type of disturbed landscape evolves over time. Representing such polygonal landscapes as graphs and reducing large amounts of data into few quantifiable metrics, supports integration of results into i.e., numerical models and thus largely facilitates the understanding of the underlying complex processes of GHG emissions from permafrost thaw. We highlight these extensive possibilities but also illustrate the limitations encountered in the study that stem from a reduced availability and accessibility to pan-Arctic very-high-resolution Earth observation datasets

Wind field and sex constrain the flight speeds of central-place foraging albatrosses

By extracting energy from the highly dynamic wind and wave fields that typify pelagic habitats, albatrosses are able to proceed almost exclusively by gliding flight. Although energetic costs of gliding are low, enabling breeding albatrosses to forage hundreds to thousands of kilometers from their colonies, these and time costs vary with relative wind direction. This causes albatrosses in some areas to route provisioning trips to avoid headwind flight, potentially limiting habitat accessibility during the breeding season. In addition, because female albatrosses have lower wing loadings than males, it has been argued that they are better adapted to flight in light winds, leading to sexual segregation of foraging areas. We used satellite telemetry and immersion logger data to quantify the effects of relative wind speed, sex, breeding stage, and trip stage on the ground speeds (Vg) of four species of Southern Ocean albatrosses breeding at South Georgia. Vg was linearly related to the wind speed component in the direction of flight (Vwf), its effect being greatest on Wandering Albatrosses Diomedea exulans, followed by Black-browed Albatrosses Thalassarche melanophrys, Light-mantled Sooty Albatrosses Phoebatria palpebrata, and Gray-headed Albatrosses T. chrysostoma. Ground speeds at Vwf = 0 were similar to airspeeds predicted by aerodynamic theory and were higher in males than in females. However, we found no evidence that this led to sexual segregation, as males and females experienced comparable wind speeds during foraging trips. Black-browed, Gray-headed, and Light-mantled Sooty Albatrosses did not engage in direct, uninterrupted bouts of flight on moonless nights, but Wandering Albatrosses attained comparable Vg night and day, regardless of lunar phase. Relative flight direction was more important in determining Vg than absolute wind speed. When birds were less constrained in the middle stage of foraging trips, all species flew predominantly across the wind. However, in some instances, commuting birds encountered headwinds during outward trips and tail winds on their return, with the result that Vg was 1.0–3.4 m/s faster during return trips. This, we hypothesize, could result from constraints imposed by the location of prey resources relative to the colony at South Georgia or could represent an energy optimization strategy

The rate of quasiparticle recombination probes the onset of coherence in cuprate superconductors

The condensation of an electron superfluid from a conventional metallic state at a critical temperature $T_c$ is described well by the BCS theory. In the underdoped copper-oxides, high-temperature superconductivity condenses instead from a nonconventional metallic "pseudogap" phase that exhibits a variety of non-Fermi liquid properties. Recently, it has become clear that a charge density wave (CDW) phase exists within the pseudogap regime, appearing at a temperature $T_{CDW}$ just above $T_c$. The near coincidence of $T_c$ and $T_{CDW}$, as well the coexistence and competition of CDW and superconducting order below $T_c$, suggests that they are intimately related. Here we show that the condensation of the superfluid from this unconventional precursor is reflected in deviations from the predictions of BSC theory regarding the recombination rate of quasiparticles. We report a detailed investigation of the quasiparticle (QP) recombination lifetime, $\tau_{qp}$, as a function of temperature and magnetic field in underdoped HgBa$_{2}$CuO$_{4+\delta}$ (Hg-1201) and YBa$_{2}$Cu$_{3}$O$_{6+x}$ (YBCO) single crystals by ultrafast time-resolved reflectivity. We find that $\tau_{qp}(T)$ exhibits a local maximum in a small temperature window near $T_c$ that is prominent in underdoped samples with coexisting charge order and vanishes with application of a small magnetic field. We explain this unusual, non-BCS behavior by positing that $T_c$ marks a transition from phase-fluctuating SC/CDW composite order above to a SC/CDW condensate below. Our results suggest that the superfluid in underdoped cuprates is a condensate of coherently-mixed particle-particle and particle-hole pairs

The use of social network analysis to describe the effect of immune activation on group dynamics in pigs

The immune system can influence social motivation with potentially dire consequences for group-housed production animals, such as pigs. The aim of this study was to test the effect of a controlled immune activation in group-housed pigs, through an injection with lipopolysaccharide (LPS) and an intervention with ketoprofen on centrality parameters at the individual level. In addition, we wanted to test the effect of time relative to the injection on general network parameters in order to get a better understanding of changes in social network structures at the group level. 52 female pigs (11-12 weeks) were allocated to four treatments, comprising two injections: ketoprofen-LPS (KL), ketoprofen-saline (KS), saline-LPS (SL) and saline-saline (SS). Social behaviour with a focus on damaging behaviour was observed continuously in 10 x 15 min bouts between 0800 am and 1700 pm 1 day before (baseline) and two subsequent days after injection. Activity was scan-sampled every 5 min for 6 h after the last injection in the pen. Saliva samples were taken for cortisol analysis at baseline and at 4, 24, 48, 72 h after the injections. A controlled immune activation affected centrality parameters for ear manipulation networks at the individual level. Lipopolysaccharide-injected pigs had a lower in-degree centrality, thus, received less interactions, 2 days after the challenge. Treatment effects on tail manipulation and fighting networks were not observed at the individual level. For networks of manipulation of other body parts, in-degree centrality was positively correlated with cortisol response at 4 h and lying behaviour in the first 6 h after the challenge in LPS-injected pigs. Thus, the stronger the pigs reacted to the LPS, the more interactions they received in the subsequent days. The time in relation to injection affected general network parameters for ear manipulation and fighting networks at the group level. For ear manipulation networks, in -degree centralisation was higher on the days following injection, thus, certain individuals in the pen received more interactions than the rest of the group compared to baseline. For fighting networks, betweenness decreased on the first day after injection compared to baseline, indicating that network connectivity increased after the challenge. Networks of tail manipulation and manipulation of other body parts did not change on the days after injection at the group level. Social network analysis is a method that can potentially provide important insights into the effects of sickness on social behaviour in group-housed pigs. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of The Animal Consortium. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe