Gaussian process hyper-parameter estimation using parallel asymptotically independent Markov sampling

Gaussian process emulators of computationally expensive computer codes provide fast statistical approximations to model physical processes. The training of these surrogates depends on the set of design points chosen to run the simulator. Due to computational cost, such training set is bound to be limited and quantifying the resulting uncertainty in the hyper-parameters of the emulator by uni-modal distributions is likely to induce bias. In order to quantify this uncertainty, this paper proposes a computationally efficient sampler based on an extension of Asymptotically Independent Markov Sampling, a recently developed algorithm for Bayesian inference. Structural uncertainty of the emulator is obtained as a by-product of the Bayesian treatment of the hyper-parameters. Additionally, the user can choose to perform stochastic optimisation to sample from a neighbourhood of the Maximum a Posteriori estimate, even in the presence of multimodality. Model uncertainty is also acknowledged through numerical stabilisation measures by including a nugget term in the formulation of the probability model. The efficiency of the proposed sampler is illustrated in examples where multi-modal distributions are encountered. For the purpose of reproducibility, further development, and use in other applications the code used to generate the examples is freely available for download at https://github.com/agarbuno/paims_codesComment: Computational Statistics \& Data Analysis, Volume 103, November 201

Anxious immobilities: an ethnography of coping with contagion (Covid-19) in Macau

In February 2020, Macau became one of the first regions where the pandemic of coronavirus or Covid-19 affected the totality of social and economic life leading to increased anxieties over movement and distance. Although Macau has had very few actual cases of the virus–46 in total–and no deaths from it, the Macau government rapidly instituted a lockdown. The aim of this article is to reflect on how the social experience of being in lockdown can provide insights into understanding the type of experience or condition that we provisionally term ‘anxious immobility.’ Such a condition is characterized by a total disruption of everyday rhythms and specifically anxious waiting for the normalization of activity while being the subject of biosocial narratives of quarantine and socially responsible. The paper is based upon 3 months of ethnographic research conducted by two researchers based in Macau. We also reflect upon some aspects of the politics of mobilities in the light of disruptions and friction points between Hong Kong, Macau, mainland China, and the rest of the world.info:eu-repo/semantics/publishedVersio

Thermoelectric and Magnetothermoelectric Transport Measurements of Graphene

The conductance and thermoelectric power (TEP) of graphene is simultaneously measured using microfabricated heater and thermometer electrodes. The sign of the TEP changes across the charge neutrality point as the majority carrier density switches from electron to hole. The gate dependent conductance and TEP exhibit a quantitative agreement with the semiclassical Mott relation. In the quantum Hall regime at high magnetic field, quantized thermopower and Nernst signals are observed and are also in agreement with the generalized Mott relation, except for strong deviations near the charge neutrality point

A combined Raman lidar for low tropospheric studies

One of the main goals of laser sensing of the atmosphere was the development of techniques and facilities for remote determination of atmospheric meteorological and optical parameters. Of lidar techniques known at present the Raman-lidar technique occupies a specific place. On the one hand Raman lidar returns due to scattering on different molecular species are very simple for interpretation and for extracting the information on the atmospheric parameters sought, but, on the other hand, the performance of these techniques in a lidar facility is overburdened with some serious technical difficulties due to extremely low cross sections of Raman effect. Some results of investigations into this problem is presented which enables the construction of a combined Raman lidar capable of acquiring simultaneously the profiles of atmospheric temperature, humidity, and some optical characteristics in the ground atmospheric layer up to 1 km height. The operation of this system is briefly discussed

Optical models of the molecular atmosphere

The use of optical and laser methods for performing atmospheric investigations has stimulated the development of the optical models of the atmosphere. The principles of constructing the optical models of molecular atmosphere for radiation with different spectral composition (wideband, narrowband, and monochromatic) are considered in the case of linear and nonlinear absorptions. The example of the development of a system which provides for the modeling of the processes of optical-wave energy transfer in the atmosphere is presented. Its physical foundations, structure, programming software, and functioning were considered

Transition from columnar to point pinning in coated conductors: critical currents, that are independent of magnetic field direction

We identify a phase transition in the vortex system of a high-temperature superconductor with nano-columnar stacks of precipitates as strong vortex pinning centers. Above a particular, temperature-dependent field $B_X(T)$ the vortex response is no longer determined by the nano-columns, and is instead determined by point-like pinning. This phase transition leads to the change in the measured critical current density as a function of angle between the applied magnetic field and the nano-columns. Below the unbinding, there is a strong maximum in $J_C$ when field is aligned parallel to the columns. Above the unbinding, there is a minimum for this orientation.Comment: Formatted for AP

Unveiling causal interactions in complex systems

Throughout time, operational laws and concepts from complex systems have been employed to quantitatively model important aspects and interactions in nature and society. Nevertheless, it remains enigmatic and challenging, yet inspiring, to predict the actual interdependencies that comprise the structure of such systems, particularly when the causal interactions observed in real-world phenomena might be persistently hidden. In this article, we propose a robust methodology for detecting the latent and elusive structure of dynamic complex systems. Our treatment utilizes short-term predictions from information embedded in reconstructed state space. In this regard, using a broad class of real-world applications from ecology, neurology, and finance, we explore and are able to demonstrate our method’s power and accuracy to reconstruct the fundamental structure of these complex systems, and simultaneously highlight their most fundamental operations