395 research outputs found
Charge mobility determination by current extraction under linear increasing voltages: the case of non-equilibrium charges and field-dependent mobilities
The method of current extraction under linear increasing voltages (CELIV)
allows for the simultaneous determination of charge mobilities and charge
densities directly in thin films as used in organic photovoltaic cells (OPV).
In the past, it has been specifically applied to investigate the interrelation
of microstructure and charge transport properties in such systems. Numerical
and analytical calculations presented in this work show that the evaluation of
CELIV transients with the commonly used analysis scheme is error prone once
charge recombination and, possibly, field dependent charge mobilities are taken
into account. The most important effects are an apparent time-dependence of
charge mobilities and errors in the determined field dependencies. Our results
implicate that reports on time-dependent mobility relaxation in OPV materials
obtained by the CELIV technique should be carefully revisited and confirmed by
other measurement methods.Comment: 15 pages, 9 figure
Behavioral Economics & Machine Learning Expanding the Field Through a New Lens
In this thesis, I investigate central questions in behavioral economics as well as law and economics. I examine well-studied problems through a new methodological lens. The aim is to generate new insights and thus point behavioral scientists to novel analytical tools. To this end, I show how machine learning may be used to build new theories by reducing complexity in experimental economic data. Moreover, I use natural language processing to show how supervised learning can enable the scientific community to expand limited datasets. I also investigate the normative impact of the use of such tools in social science research or decision-making as well as their deficiencies
Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets
In anomaly detection, a prominent task is to induce a model to identify
anomalies learned solely based on normal data. Generally, one is interested in
finding an anomaly detector that correctly identifies anomalies, i.e., data
points that do not belong to the normal class, without raising too many false
alarms. Which anomaly detector is best suited depends on the dataset at hand
and thus needs to be tailored. The quality of an anomaly detector may be
assessed via confusion-based metrics such as the Matthews correlation
coefficient (MCC). However, since during training only normal data is available
in a semi-supervised setting, such metrics are not accessible. To facilitate
automated machine learning for anomaly detectors, we propose to employ
meta-learning to predict MCC scores based on metrics that can be computed with
normal data only. First promising results can be obtained considering the
hypervolume and the false positive rate as meta-features
Modelling Hourly Particulate Matter (PM10) Concentrations at High Spatial Resolution in Germany Using Land Use Regression and Open Data
Air pollution is a major health risk factor worldwide. Regular short- and long-time exposures to ambient particulate matter (PM) promote various diseases and can lead to premature death. Therefore, in Germany, air quality is assessed continuously at approximately 400 measurement sites. However, knowledge about this intermediate distribution is either unknown or lacks a high spatialâtemporal resolution to accurately determine exposure since commonly used chemical transport models are resource intensive. In this study, we present a method that can provide information about the ambient PM concentration for all of Germany at high spatial (100 m Ă 100 m) and hourly resolutions based on freely available data. To do so we adopted and optimised a method that combined land use regression modelling with a geostatistical interpolation technique using ordinary kriging. The land use regression model was set up based on CORINE (Coordination of Information on the Environment) land cover data and the Germany National Emission Inventory. To test the modelâs performance under different conditions, four distinct data sets were used. (1) From a total of 8760 (365 Ă 24) available h, 1500 were randomly selected. From those, the hourly mean concentrations at all stations (ca. 400) were used to run the model (n = 566,326). The leave-one-out cross-validation resulted in a mean absolute error (MAE) of 7.68 ÎŒg mâ3 and a root mean square error (RMSE) of 11.20 ÎŒg mâ3. (2) For a more detailed analysis of how the model performs when an above-average number of high values are modelled, we selected all hourly means from February 2011 (n = 256,606). In February, measured concentrations were much higher than in any other month, leading to a slightly higher MAE of 9.77 ÎŒg mâ3 and RMSE of 14.36 ÎŒg mâ3, respectively. (3) To enable better comparability with other studies, the annual mean concentration (n = 413) was modelled with a MAE of 4.82 ÎŒg mâ3 and a RMSE of 6.08 ÎŒg mâ3. (4) To verify the modelâs capability of predicting the exceedance of the daily mean limit value, daily means were modelled for all days in February (n = 10,845). The exceedances of the daily mean limit value of 50 ÎŒg mâ3 were predicted correctly in 88.67% of all cases. We show that modelling ambient PM concentrations can be performed at a high spatialâtemporal resolution for large areas based on open data, land use regression modelling, and kriging, with overall convincing results. This approach offers new possibilities in the fields of exposure assessment, city planning, and governance since it allows more accurate views of ambient PM concentrations at the spatialâtemporal resolution required for such assessments.Peer Reviewe
In-context learning agents are asymmetric belief updaters
We study the in-context learning dynamics of large language models (LLMs)
using three instrumental learning tasks adapted from cognitive psychology. We
find that LLMs update their beliefs in an asymmetric manner and learn more from
better-than-expected outcomes than from worse-than-expected ones. Furthermore,
we show that this effect reverses when learning about counterfactual feedback
and disappears when no agency is implied. We corroborate these findings by
investigating idealized in-context learning agents derived through
meta-reinforcement learning, where we observe similar patterns. Taken together,
our results contribute to our understanding of how in-context learning works by
highlighting that the framing of a problem significantly influences how
learning occurs, a phenomenon also observed in human cognition
Transcriptome-Guided Identification of Drugs for Repurposing to Treat Age-Related Hearing Loss
Age-related hearing loss (ARHL) or presbycusis is a prevalent condition associated with social isolation, cognitive impairment, and dementia. Age-related changes in the cochlea, the auditory portion of the inner ear, are the primary cause of ARHL. Unfortunately, there are currently no pharmaceutical approaches to treat ARHL. To examine the biological processes underlying age-related changes in the cochlea and identify candidate drugs for rapid repurposing to treat ARHL, we utilized bulk RNA sequencing to obtain transcriptomes from the functional substructures of the cochlea-the sensorineural structures, including the organ of Corti and spiral ganglion neurons (OC/SGN) and the stria vascularis and spiral ligament (SV/SL)-in young (6-week-old) and old (2-year-old) C57BL/6 mice. Transcriptomic analyses revealed both overlapping and unique patterns of gene expression and gene enrichment between substructures and with ageing. Based on these age-related transcriptional changes, we queried the protein products of genes differentially expressed with ageing in DrugBank and identified 27 FDA/EMA-approved drugs that are suitable to be repurposed to treat ARHL. These drugs target the protein products of genes that are differentially expressed with ageing uniquely in either the OC/SGN or SV/SL and that interrelate diverse biological processes. Further transcriptomic analyses revealed that most genes differentially expressed with ageing in both substructures encode protein products that are promising drug target candidates but are, nevertheless, not yet linked to approved drugs. Thus, with this study, we apply a novel approach to characterize the druggable genetic landscape for ARHL and propose a list of drugs to test in pre-clinical studies as potential treatment options for ARHL.</p
Correlation from undiluted vitreous cytokines of untreated central retinal vein occlusion with spectral domain optical coherence tomography
Purpose: To correlate inflammatory and proangiogenic key cytokines from undiluted vitreous of treatment-naĂŻve central retinal vein occlusion (CRVO) patients with SD-OCT parameters.
Methods: Thirty-five patients (age 71.1 years, 24 phakic, 30 nonischemic) underwent intravitreal combination therapy, including a single-site 23-gauge core vitrectomy. Twenty-eight samples from patients with idiopathic, non-uveitis floaterectomy served as controls. Interleukin 6 (IL-6), monocyte chemoattractant protein-1 (MCP-1), and vascular endothelial growth factor (VEGF-A) levels were correlated with the visual acuity (logMar), category of CRVO (ischemic or nonischemic) and morphologic parameters, such as central macular thickness-CMT, thickness of neurosensory retina-TNeuro, extent of serous retinal detachment-SRT and disintegrity of the IS/OS and others.
Results: The mean IL-6 was 64.7pg/ml (SD ± 115.8), MCP-1 1015.7 ( ± 970.1), and VEGF-A 278.4 ( ± 512.8), which was significantly higher than the control IL-6 6.2 ± 3.4pg/ml (P=0.06), MCP-1 253.2 ± 73.5 (P<0.0000001) and VEGF-A 7.0 ± 4.9 (P<0.0006). All cytokines correlated highly with one another (correlation coefficient r=0.82 for IL-6 and MCP-1; r=0.68 for Il-6 and VEGF-A; r=0.64 for MCP-1 and VEGF-A). IL-6 correlated significantly with CMT, TRT, SRT, dIS/OS, and dELM. MCP-1 correlated significantly with SRT, dIS/OS, and dELM. VEGF-A correlated not with changes in SD-OCT, while it had a trend to be higher in the ischemic versus the nonischemic CRVO group (P=0.09).
Conclusions: The inflammatory cytokines were more often correlated with morphologic changes assessed by SD-OCT, whereas VEGF-A did not correlate with CRVO-associated changes in SD-OCT. VEGF inhibition alone may not be sufficient in decreasing the inflammatory response in CRVO therapy
Optically manipulated micromirrors for precise excitation of WGM microlasers
Whispering gallery mode microlasers are highly sensitive refractive index
sensors widely explored for biophotonic and biomedical applications. Microlaser
excitation and collection of the emitted light typically utilize microscope
objectives at normal incidence, limiting the choice of the oscillation plane of
the modes. Here, we present a platform that enables the excitation of
microlasers from various directions using an optically manipulated micromirror.
The scheme enables precise sensing of the environment surrounding the
microlasers along different well-controlled planes. We further demonstrate the
capability of the platform to perform a time-resolved experiment of dynamic
sensing using a polystyrene probe bead orbiting the microlaser
Selective metalâcomplexation on polymeric templates and their investigation via isothermal titration calorimetry
Selective complexation of metal ions represents a powerful tool for the development of versatile supramolecular architectures. While research in the field of molecular devices and machinery is sophisticated, the selective formation of metal complexes is not prevalent in polymer chemistry. Thus, the implementation of orthogonal binding concepts into a polymeric matrix is presented. In this context, an endâfunctionalized poly( N âisopropylacrylamide) (PNIPAm) carrying zincâporphyrin (ZnTPP) as well as a terpyridine (tpy) ligand side by side is utilized. With these binding sites, the polymer can simultaneously interact with a pyridine moiety via a ZnTPP interaction and a terpyridine unit by the formation of a bisâterpyridine complex. The complexation behavior of this polymer and different model compounds is intensively investigated by isothermal titration calorimetry. The obtained results indicate that the reported orthogonality of these two systems is successfully transferred into a functional polymeric architecture
Red-shifted excitation and two-photon pumping of biointegrated GaInP/AlGaInP quantum well microlasers
This work received financial support from the Leverhulme Trust (RPG-2017-231), European Unionâs Horizon 2020 Framework Programme (FP/2014-2020)/ERC grant agreement no. 640012 (ABLASE), EPSRC (EP/P030017/1), and the Humboldt Foundation (Alexander von Humboldt professorship). MS acknowledges funding by the Royal Society (Dorothy Hodgkin Fellowship, DH160102; Research Grant, RGF\R1\180070; Enhancement Award, RGF\EA\180051). ADF acknowledges support from European Research Council (ERC) under the European Union Horizon 2020 research and innovation program (Grant Agreement No. 819346).Biointegrated intracellular microlasers have emerged as an attractive and versatile tool in biophotonics. Different inorganic semiconductor materials have been used for the fabrication of such biocompatible microlasers, but often operate at visible wavelengths ill-suited for imaging through tissue. Here, we report on whispering gallery mode microdisk lasers made from a range of GaInP/AlGaInP multi-quantum well structures with compositions tailored to red-shifted excitation and emission. The selected semiconductor alloys show minimal toxicity and allow fabrication of lasers with stable single-mode emission in the NIR (675 â 720 nm) and sub-pJ thresholds. The microlasers operate in the first therapeutic window under direct excitation by a conventional diode laser and can also be pumped in the second therapeutic window using two-photon excitation at pulse energies compatible with standard multiphoton microscopy. Stable performance is observed under cell culturing conditions for five days without any device encapsulation. With their bio-optimized spectral characteristics, low lasing threshold and compatibility with two-photon pumping, AlGaInP-based microlasers are ideally suited for novel cell tagging and in vivo sensing applications.Publisher PDFPeer reviewe
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