Convergence without hard criteria: Does EU soft law affect domestic unemployment protection schemes?

Hervorming Sociale Regelgevin

Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs

Optical coherence tomography angiography (OCTA) can non-invasively image the eye's circulatory system. In order to reliably characterize the retinal vasculature, there is a need to automatically extract quantitative metrics from these images. The calculation of such biomarkers requires a precise semantic segmentation of the blood vessels. However, deep-learning-based methods for segmentation mostly rely on supervised training with voxel-level annotations, which are costly to obtain. In this work, we present a pipeline to synthesize large amounts of realistic OCTA images with intrinsically matching ground truth labels; thereby obviating the need for manual annotation of training data. Our proposed method is based on two novel components: 1) a physiology-based simulation that models the various retinal vascular plexuses and 2) a suite of physics-based image augmentations that emulate the OCTA image acquisition process including typical artifacts. In extensive benchmarking experiments, we demonstrate the utility of our synthetic data by successfully training retinal vessel segmentation algorithms. Encouraged by our method's competitive quantitative and superior qualitative performance, we believe that it constitutes a versatile tool to advance the quantitative analysis of OCTA images

Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs

Optical coherence tomography angiography (OCTA) can non-invasively image the eye's circulatory system. In order to reliably characterize the retinal vasculature, there is a need to automatically extract quantitative metrics from these images. The calculation of such biomarkers requires a precise semantic segmentation of the blood vessels. However, deep-learning-based methods for segmentation mostly rely on supervised training with voxel-level annotations, which are costly to obtain. In this work, we present a pipeline to synthesize large amounts of realistic OCTA images with intrinsically matching ground truth labels; thereby obviating the need for manual annotation of training data. Our proposed method is based on two novel components: 1) a physiology-based simulation that models the various retinal vascular plexuses and 2) a suite of physics-based image augmentations that emulate the OCTA image acquisition process including typical artifacts. In extensive benchmarking experiments, we demonstrate the utility of our synthetic data by successfully training retinal vessel segmentation algorithms. Encouraged by our method's competitive quantitative and superior qualitative performance, we believe that it constitutes a versatile tool to advance the quantitative analysis of OCTA images.Comment: Accepted at MICCAI 202

A skeletonization algorithm for gradient-based optimization

The skeleton of a digital image is a compact representation of its topology, geometry, and scale. It has utility in many computer vision applications, such as image description, segmentation, and registration. However, skeletonization has only seen limited use in contemporary deep learning solutions. Most existing skeletonization algorithms are not differentiable, making it impossible to integrate them with gradient-based optimization. Compatible algorithms based on morphological operations and neural networks have been proposed, but their results often deviate from the geometry and topology of the true medial axis. This work introduces the first three-dimensional skeletonization algorithm that is both compatible with gradient-based optimization and preserves an object's topology. Our method is exclusively based on matrix additions and multiplications, convolutional operations, basic non-linear functions, and sampling from a uniform probability distribution, allowing it to be easily implemented in any major deep learning library. In benchmarking experiments, we prove the advantages of our skeletonization algorithm compared to non-differentiable, morphological, and neural-network-based baselines. Finally, we demonstrate the utility of our algorithm by integrating it with two medical image processing applications that use gradient-based optimization: deep-learning-based blood vessel segmentation, and multimodal registration of the mandible in computed tomography and magnetic resonance images.Comment: Accepted at ICCV 202

Sn-Pb Mixed Perovskites with Fullerene-Derivative Interlayers for Efficient Four-Terminal All-Perovskite Tandem Solar Cells

Interfacial engineering is the key to high-performance perovskite solar cells (PSCs). While a wide range of fullerene interlayers are investigated for Pb-based counterparts with a bandgap of >1.5 eV, the role of fullerene interlayers is barely investigated in Sn-Pb mixed narrow-bandgap (NBG) PSCs. In this work, two novel solution-processed fullerene derivatives are investigated, namely indene-C60-propionic acid butyl ester and indene-C60-propionic acid hexyl ester (IPH), as the interlayers in NBG PSCs. It is found that the devices with IPH-interlayer show the highest performance with a remarkable short-circuit current density of 30.7 mA cm−2 and a low deficit in open-circuit voltage. The reduction in voltage deficit down to 0.43 V is attributed to reduced non-radiative recombination that the authors attribute to two aspects: 1) a higher conduction band offset of ≈0.2 eV (>0 eV) that hampers charge-carrier-back-transfer recombination; 2) a decrease in trap density at the perovskite/interlayer/C60 interfaces that results in reduced trap-assisted recombination. In addition, incorporating the IPH interlayer enhances charge extraction within the devices that results in considerable enhancement in short-circuit current density. Using a NBG device with an IPH interlayer, a respectable power conversion efficiency of 24.8% is demonstrated in a four-terminal all-perovskite tandem solar cell

Transit timing analysis of CoRoT-1b

CoRoT, the pioneer space-based transit search, steadily provides thousands of high-precision light curves with continuous time sampling over periods of up to 5 months. The transits of a planet perturbed by an additional object are not strictly periodic. By studying the transit timing variations (TTVs), additional objects can be detected in the system. A transit timing analysis of CoRoT-1b is carried out to constrain the existence of additional planets in the system. We used data obtained by an improved version of the CoRoT data pipeline (version 2.0). Individual transits were fitted to determine the mid-transit times, and we analyzed the derived $O-C$ diagram. N-body integrations were used to place limits on secondary planets. No periodic timing variations with a period shorter than the observational window (55 days) are found. The presence of an Earth-mass Trojan is not likely. A planet of mass greater than $\sim 1$ Earth mass can be ruled out by the present data if the object is in a 2:1 (exterior) mean motion resonance with CoRoT-1b. Considering initially circular orbits: (i) super-Earths (less than 10 Earth-masses) are excluded for periods less than about 3.5 days, (ii) Saturn-like planets can be ruled out for periods less than about 5 days, (iii) Jupiter-like planets should have a minimum orbital period of about 6.5 days.Comment: 6 pages, accepted at A&

Математическое моделирование распространения загрязнения нефтепродуктами в водной среде

Изучению моделирования аварийных разливов нефти посвящено большое количество научных работ. Но в настоящее время не существует определенной методики или единого подхода по описанию разливов нефти. Существующие математические модели распространения нефтяного загрязнения не включают в себя влияние процессов выветривания (испарение, дисперсия, эмульгирование, седиментация, биоразложение). Необходимо разработать такую модель распространения нефтяного загрязнения, которая будет учитывать максимальное количество параметров, влияющих на ее распространения, из числа возможных. Целью данной работы является разработка двухмерной математической модели и исследование распространения нефтяного пятна в водотоке при разрыве трубопровода.A large number of scientific papers have been devoted to the study of oil spill simulations. But at the moment there is no definite methodology or single approach to describing oil spills. Existing mathematical models of the spread of oil pollution do not include the influence of weathering processes (evaporation, dispersion, emulsification, sedimentation, biodegradation). It is necessary to develop such a model for the spread of oil pollution, which will take into account the maximum number of parameters that affect its distribution, from among possible ones. The aim of this paper is to develop a two-dimensional mathematical model and to study the spread of an oil spill in a watercourse when a pipeline is broken