Non-parametric estimation of a Langevin model driven by correlated noise

Langevin models are frequently used to model various stochastic processes in different fields of natural and social sciences. They are adapted to measured data by estimation techniques such as maximum likelihood estimation, Markov chain Monte Carlo methods, or the non-parametric direct estimation method introduced by Friedrich et al. The latter has the distinction of being very effective in the context of large data sets. Due to their $\delta$-correlated noise, standard Langevin models are limited to Markovian dynamics. A non-Markovian Langevin model can be formulated by introducing a hidden component that realizes correlated noise. For the estimation of such a partially observed diffusion a different version of the direct estimation method was introduced by Lehle et al. However, this procedure includes the limitation that the correlation length of the noise component is small compared to that of the measured component. In this work we propose another version of the direct estimation method that does not include this restriction. Via this method it is possible to deal with large data sets of a wider range of examples in an effective way. We discuss the abilities of the proposed procedure using several synthetic examples

Efficient Bayesian estimation of the generalized Langevin equation from data

The generalized Langevin equation (GLE) overcomes the limiting Markov approximation of the Langevin equation by an incorporated memory kernel and can be used to model various stochastic processes in many fields of science ranging from climate modeling over neuroscience to finance. Generally, Bayesian estimation facilitates the determination of both suitable model parameters and their credibility for a measured time series in a straightforward way. In this work we develop a realization of this estimation technique for the GLE in the case of white noise. We assume piecewise constant drift and diffusion functions and represent the characteristics of the data set by only a few coefficients, which leads to a numerically efficient procedure. The kernel function is an arbitrary time-discrete function with a fixed length $K$. We show how to determine a reasonable value of $K$ based on the data. We illustrate the abilities of both the method and the model by an example from turbulence

On the Impact of the Degree of Fluorination on the ORR Limiting Processes within Iron Based Catalysts: A Model Study on Symmetrical Films of Barium Ferrate

In this study, symmetrical films of BaFeO$_{2.67}$, BaFeO$_{2.33}$F$_{0.33}$ and BaFeO$_{2}$F were synthesized and the oxygen uptake and conduction was investigated by high temperature impedance spectroscopy under an oxygen atmosphere. The data were analyzed on the basis of an impedance model designed for highly porous mixed ionic electronic conducting (MIEC) electrodes. Variable temperature X-ray diffraction experiments were utilized to estimate the stability window of the oxyfluoride compounds, which yielded a degradation temperature for BaFeO$_{2.33}$F$_{0.33}$ of 590 °C and a decomposition temperature for BaFeO$_{2}$F of 710 °C. The impedance study revealed a significant change of the catalytic behavior in dependency of the fluorine content. BaFeO$_{2.67}$ revealed a bulk-diffusion limited process, while BaFeO$_{2.33}$F$_{0.33}$ appeared to exhibit a fast bulk diffusion and a utilization region δ larger than the electrode thickness L (8 μm). In contrast, BaFeO$_{2}$F showed very area specific resistances due to the lack of oxygen vacancies. The activation energy for the uptake and conduction process of oxygen was found to be 0.07/0.29 eV (temperature range-dependent), 0.33 eV and 0.67 eV for BaFeO$_{2.67}$, BaFeO$_{2.33}$F$_{0.33}$ and BaFeO$_{2}$F, respectively

Loneliness increased significantly among people in middle and older adulthood during the Covid-19 Pandemic

After March 2020, Corona virus containment measures significantly impaired the social relationships of many people. Against this background, this chapter examines how the perception of loneliness of people aged 46 to 90 changed during the first lockdown. The results are compared with those of 2014 and 2017

A compliance-centric view of grasping

We advocate the central importance of compliance for grasp performance and demonstrate that grasp algorithms can achieve robust performance by explicitly considering and exploiting mechanical compliance of the grasping hand. Specifically, we consider the problem of robust grasping in the absence of a priori object models, focusing on object capture and grasp stability under variations of object shape for a given robotic hand. We present a simple characterization of the relationship between hand compliance, object shape, and grasp success. Based on this hypothesis, we devise a compliance-centric grasping algorithm. Real-world experiments show that this algorithm outperforms compliance-agnostic grasping, eliminates the need for explicit contact state planning, and simplifies the perceptual requirements when no a priori information about the environment is available.EC/FP7/248258/EU/Flexible Skill Acquisition and Intuitive Robot Tasking for Mobile Manipulation in the Real World/FIRST-M

Taste the rainbow: A review of color abnormalities affecting the herpetofauna of the British Isles

Over the years, the terminology in regards to the abnormal coloration of reptiles and amphibians has become more complex with not all authors in agreement regarding the different terms. This, combined with the diversity of chromatic abnormalities, has led to some confusion, particularly between hobbyists and conservationists who tend to use different technical jargon. In this review, we aim to tackle this issue by explaining how color within the skin of amphibians and reptiles arises, and evaluating which terminology should be used. This information will then be used to explore each of the known chromatic abnormalities observed in amphibians and reptiles before summarizing the known cases throughout the British Isles. Finally, we also present a number of previously unrecorded instances of color abnormalities in the hope that it promotes further examples to be recorded

Evaluation of Growth Simulators for Forest Management in Terms of Functionality and Software Structure Using AHP

A range of computer models exist for simulating forest growth, with different model functions, spatial resolutions and regional calibration specifications. Choosing a suitable simulator is difficult due to its abundance and complexity. The aim of the project is to evaluate a simulator that could be adapted to conditions in Switzerland and used to support decision‐making processes in both forest enterprises and scientific contexts. Fourteen potentially suitable forest growth simulators were identified through a literature review, which was then narrowed down to four: BWINPro, SILVA, MOSES and PrognAus. In the second phase, these were systematically evaluated in terms of functionality and software structure using AHP, in order to identify a suitable simulator. The AHP evaluation entailed: (1) determining the decision criteria and hierarchy, (2) performing pairwise comparisons and calculating the utility values and (3) conducting a sensitivity analysis. AHP was found to provide a transparent, verifiable evaluation process for simulator selection. This enabled a critical argumentation and assessment of the simulators. In the third phase, not covered by this article, the selected simulator will be parametrised for Swiss conditions and incorporated into an overarching decision‐support system for forest planning and management

Emerging Radionuclides in a Regulatory Framework for Medicinal Products – How Do They Fit?

Recent years have seen the establishment of several radionuclides as medicinal products in particular in the setting of theranostics and PET. [177Lu]Lutetium Chloride or [64Cu]Copper Chloride have received marketing authorization as radionuclide precursor, [68Ga]Gallium Chloride has received regulatory approval in the form of different 68Ge/68Ga generators. This is a formal requirement by the EU directive 2001/83, even though for some of these radionuclide precursors no licensed kit is available that can be combined to obtain a final radiopharmaceuticals, as it is the case for Technetium-99m. In view of several highly promising, especially metallic radionuclides for theranostic applications in a wider sense, the strict regulatory environment poses the risk of slowing down development, in particular for radionuclide producers that want to provide innovative radionuclides for clinical research purposes, which is the basis for their further establishment. In this paper we address the regulatory framework for novel radionuclides within the EU, the current challenges in particular related to clinical translation and potential options to support translational development within Europe and worldwide

Femtosecond holography in lithium niobate crystals

Spatial gratings are recorded holographically by two femtosecond pump pulses at 388 nm in lithium niobate (LiNbO3) crystals and read out by a Bragg-matched, temporally delayed probe pulse at 776 nm. We claim, to our knowledge, the first holographic pump-probe experiments with subpicosecond temporal resolution for LiNbO3. An instantaneous grating that is due mostly to the Kerr effect as well as a long-lasting grating that results mainly from the absorption caused by photoexcited carriers was observed. The Kerr coefficient of LiNbO3 for our experimental conditions, i.e., pumped and probed at different wavelengths, was approximately 1.0×10^-5 cm²/GW

Optimisation of manufacturing process parameters for variable component geometries using reinforcement learning

Tailoring manufacturing processes to optimum part quality often requires numerous resource-intensive trial experiments in practice. Physics-based process simulations in combination with general-purpose optimisation algorithms allow for an a priori process optimisation and help concentrate costly trials on the most promising variants. However, considerable computation times are a significant barrier, especially for iterative optimisation. Surrogate-based optimisation often helps reduce the computational effort but surrogate models are typically case-specific and cannot adapt to different manufacturing situations. Consequently, even minor problem variations e.g. geometry adaptions invalidate the surrogate and require resampling of data and retraining of the surrogate. Reinforcement Learning aims at inferring optimal actions in variable situations. In this work, it is used to train a neural network to estimate optimal process parameters (“actions”) for variable component geometries (“situations”). The use case is fabric forming in which pressure pads are positioned to optimise the material intake. After training, the network is found to give meaningful parameter estimations even for new geometries not considered during training. Thus, it extracts reusable information from generic process samples and successfully applies it to new, non-generic components. Since data is reused rather than resampled, the approach is deemed a promising option for lean part and process development