Effective Aesthetics Prediction with Multi-level Spatially Pooled Features

We propose an effective deep learning approach to aesthetics quality assessment that relies on a new type of pre-trained features, and apply it to the AVA data set, the currently largest aesthetics database. While previous approaches miss some of the information in the original images, due to taking small crops, down-scaling or warping the originals during training, we propose the first method that efficiently supports full resolution images as an input, and can be trained on variable input sizes. This allows us to significantly improve upon the state of the art, increasing the Spearman rank-order correlation coefficient (SRCC) of ground-truth mean opinion scores (MOS) from the existing best reported of 0.612 to 0.756. To achieve this performance, we extract multi-level spatially pooled (MLSP) features from all convolutional blocks of a pre-trained InceptionResNet-v2 network, and train a custom shallow Convolutional Neural Network (CNN) architecture on these new features.Comment: To appear in CVPR 201

CPA-free amplification of sub-10 ps pulses in Ho:YLF to the mJ-level at 2 μm wavelength

The generation of sub-10 ps pulses around a wavelength of 2 μm with pulse energy at millijoule-level in a compact CPA-free amplifier chain is presented. This laser source covers a broad range of pulse repetition frequencies from 1 to 100 kHz with a pulse peak power from 136 to 17MW, respectively. We used highly doped Ho:YLF crystals to achieve an overall amplification factor of almost 52 dB. A characterization of these crystals regarding upconversion losses and attainable small-signal gain supports this work. © 2019 SPIE

Adaptive modelling of coupled hydrological processes with application in water management

This paper presents recent results of a network project aiming at the modelling and simulation of coupled surface and subsurface flows. In particular, a discontinuous Galerkin method for the shallow water equations has been developed which includes a special treatment of wetting and drying. A robust solver for saturated-unsaturated groundwater flow in homogeneous soil is at hand, which, by domain decomposition techniques, can be reused as a subdomain solver for flow in heterogeneous soil. Coupling of surface and subsurface processes is implemented based on a heterogeneous nonlinear Dirichlet-Neumann method, using the dune-grid-glue module in the numerics software Dune

The effect of dielectric and thermal properties of plastic mold materials on the high frequency welding of three‐dimensional foam components

Abstract High‐frequency heating processes are widely used in industry, for example, for food processing, wood drying, or plastic welding. A recent development in this field uses radio‐frequency heating to fuse particle foam beads to complex components. The aim of using the modified process is to reduce the energy consumption of particle foam fusion. Within this process, the molds used must be electrically insulating to ensure the building of an electrical field. For this purpose, mold made of two different plastics were analyzed. Additional to the dielectric, mechanical, and thermal expansion, the thermal conductivity of the mold materials are of great interest. Although welding is finished quickly, cooling to the demolding temperature is comparably long. To achieve shorter cooling and therefore shorter process times, the mold materials were modified by two different thermal conductive but electrically insulating fillers. By the use of the thermally optimized mold materials, a reduction in cooling time of up to 40% was possible. At the same time, the heating step was hardly affected. Mechanical properties were enhanced while thermal expansion was reduced

Principles Of Heliophysics: a textbook on the universal processes behind planetary habitability

This textbook gives a perspective of heliophysics in a way that emphasizes universal processes from a perspective that draws attention to what provides Earth (and similar (exo-)planets) with a relatively stable setting in which life as we know it can thrive. The book is intended for students in physical sciences in later years of their university training and for beginning graduate students in fields of solar, stellar, (exo-)planetary, and planetary-system sciences.Comment: 419 pages, 119 figures, and 200 "activities" in the form of problems, exercises, explorations, literature readings, and "what if" challenge

Effective Aesthetics Prediction with Multi-level Spatially Pooled Features

We propose an effective deep learning approach to aesthetics quality assessment that relies on a new type of pre-trained features, and apply it to the AVA data set, the currently largest aesthetics database. While previous approaches miss some of the information in the original images, due to taking small crops, down-scaling or warping the originals during training, we propose the first method that efficiently supports full resolution images as an input, and can be trained on variable input sizes. This allows us to significantly improve upon the state of the art, increasing the Spearman rank-order correlation coefficient (SRCC) of ground-truth mean opinion scores (MOS) from the existing best reported of 0.612 to 0.756. To achieve this performance, we extract multi-level spatially pooled (MLSP) features from all convolutional blocks of a pre-trained InceptionResNet-v2 network, and train a custom shallow Convolutional Neural Network (CNN) architecture on these new features.publishe

Safety Assessment of Windshield Washing Technologies

We present a new assessment method for driver visibility based on reaction time measurement and workload in real driving situations from the most relevant accident scenario involving pedestrians. The procedure was validated in a balanced trial to compare a wet flatblade windshield washing system to a conventional Fluidic nozzles system. The test cohort comprised 204 subjects who form a representative sample of German driving license holders. The average reaction time gain of wet flatblade over Fluidic nozzles is 315 ms for pedestrian detection and 270 ms for the recognition of critical traffic situations