A Fully Progressive Approach to Single-Image Super-Resolution

Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality. However, in each case it remains challenging to achieve high quality results for large upsampling factors. To this end, we propose a method (ProSR) that is progressive both in architecture and training: the network upsamples an image in intermediate steps, while the learning process is organized from easy to hard, as is done in curriculum learning. To obtain more photorealistic results, we design a generative adversarial network (GAN), named ProGanSR, that follows the same progressive multi-scale design principle. This not only allows to scale well to high upsampling factors (e.g., 8x) but constitutes a principled multi-scale approach that increases the reconstruction quality for all upsampling factors simultaneously. In particular ProSR ranks 2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge [34]. Compared to the top-ranking team, our model is marginally lower, but runs 5 times faster

Stabilization of Hypersonic Boundary Layers by Porous Coatings

A second-mode stability analysis has been performed for a hypersonic boundary layer on a wall covered by a porous coating with equally spaced cylindrical blind microholes. Massive reduction of the second mode amplification is found to be due to the disturbance energy absorption by the porous layer. This stabilization effect was demonstrated by experiments recently conducted on a sharp cone in the T-5 high-enthalpy wind tunnel of the Graduate Aeronautical Laboratories of the California Institute of Technology. Their experimental confirmation of the theoretical predictions underscores the possibility that ultrasonically absorptive porous coatings may be exploited for passive laminar flow control on hypersonic vehicle surfaces

Efficient Large-scale Approximate Nearest Neighbor Search on the GPU

We present a new approach for efficient approximate nearest neighbor (ANN) search in high dimensional spaces, extending the idea of Product Quantization. We propose a two-level product and vector quantization tree that reduces the number of vector comparisons required during tree traversal. Our approach also includes a novel highly parallelizable re-ranking method for candidate vectors by efficiently reusing already computed intermediate values. Due to its small memory footprint during traversal, the method lends itself to an efficient, parallel GPU implementation. This Product Quantization Tree (PQT) approach significantly outperforms recent state of the art methods for high dimensional nearest neighbor queries on standard reference datasets. Ours is the first work that demonstrates GPU performance superior to CPU performance on high dimensional, large scale ANN problems in time-critical real-world applications, like loop-closing in videos

CaosDB - Research Data Management for Complex, Changing, and Automated Research Workflows

Here we present CaosDB, a Research Data Management System (RDMS) designed to ensure seamless integration of inhomogeneous data sources and repositories of legacy data. Its primary purpose is the management of data from biomedical sciences, both from simulations and experiments during the complete research data lifecycle. An RDMS for this domain faces particular challenges: Research data arise in huge amounts, from a wide variety of sources, and traverse a highly branched path of further processing. To be accepted by its users, an RDMS must be built around workflows of the scientists and practices and thus support changes in workflow and data structure. Nevertheless it should encourage and support the development and observation of standards and furthermore facilitate the automation of data acquisition and processing with specialized software. The storage data model of an RDMS must reflect these complexities with appropriate semantics and ontologies while offering simple methods for finding, retrieving, and understanding relevant data. We show how CaosDB responds to these challenges and give an overview of the CaosDB Server, its data model and its easy-to-learn CaosDB Query Language. We briefly discuss the status of the implementation, how we currently use CaosDB, and how we plan to use and extend it

Structure and motion from scene registration

We propose a method for estimating the 3D structure and the dense 3D motion (scene flow) of a dynamic nonrigid 3D scene, using a camera array. The core idea is to use a dense multi-camera array to construct a novel, dense 3D volumetric representation of the 3D space where each voxel holds an estimated intensity value and a confidence measure of this value. The problem of 3D structure and 3D motion estimation of a scene is thus reduced to a nonrigid registration of two volumes - hence the term ”Scene Registration”. Registering two dense 3D scalar volumes does not require recovering the 3D structure of the scene as a preprocessing step, nor does it require explicit reasoning about occlusions. From this nonrigid registration we accurately extract the 3D scene flow and the 3D structure of the scene, and successfully recover the sharp discontinuities in both time and space. We demonstrate the advantages of our method on a number of challenging synthetic and real data sets

Near-inertial wave scattering by random flows

The impact of a turbulent flow on wind-driven oceanic near-inertial waves is examined using a linearised shallow-water model of the mixed layer. Modelling the flow as a homogeneous and stationary random process with spatial scales comparable to the wavelengths, we derive a transport (or kinetic) equation governing wave-energy transfers in both physical and spectral spaces. This equation describes the scattering of the waves by the flow which results in a redistribution of energy between waves with the same frequency (or, equivalently, with the same wavenumber) and, for isotropic flows, in the isotropisation of the wave field. The time scales for the scattering and isotropisation are obtained explicitly and found to be of the order of tens of days for typical oceanic parameters. The predictions inferred from the transport equation are confirmed by a series of numerical simulations. Two situations in which near-inertial waves are strongly influenced by flow scattering are investigated through dedicated nonlinear shallow-water simulations. In the first, a wavepacket propagating equatorwards as a result from the $\beta$-effect is shown to be slowed down and dispersed both zonally and meridionally by scattering. In the second, waves generated by moving cyclones are shown to be strongly disturbed by scattering, leading again to an increased dispersion.Comment: Accepted for publication in Phys. Rev. Fluid

A rapid magnetic bead-based immunoassay for sensitive determination of diclofenac

Increasing contamination of environmental waters with pharmaceuticals represents an emerging threat for the drinking water quality and safety. In this regard, fast and reliable analytical methods are required to allow quick countermeasures in case of contamination. Here, we report the development of a magnetic bead-based immunoassay (MBBA) for the fast and cost-effective determination of the analgesic diclofenac (DCF) in water samples, based on diclofenac-coupled magnetic beads and a robust monoclonal anti-DCF antibody. A novel synthetic strategy for preparation of the beads resulted in an assay that enabled for the determination of diclofenac with a significantly lower limit of detection (400 ng/L) than the respective enzyme-linked immunosorbent assay (ELISA). With shorter incubation times and only one manual washing step required, the assay demands for remarkably shorter time to result (< 45 min) and less equipment than ELISA. Evaluation of assay precision and accuracy with a series of spiked water samples yielded results with low to moderate intra- and inter-assay variations and in good agreement with LC–MS/MS reference analysis. The assay principle can be transferred to other, e.g., microfluidic, formats, as well as applied to other analytes and may replace ELISA as the standard immunochemical method. Graphical abstractBundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347Indo-German Science and Technology Centre http://dx.doi.org/10.13039/501100018761Bundesanstalt für Materialforschung und -prüfung (BAM) (4232)Peer Reviewe

Hydrothermal, catalyst-free production of a cyclic dipeptide from lysine

The formation of cyclic dipeptides, 2,5-diketopiperazines (DKP), from lysine in aqueous solution was investigated at hydrothermal conditions (250 to 350 °C) without the addition of catalyst. The products obtained were analyzed by GC–MS combined with extensive $^{1}$H,$^{13}$C NMR analysis, after purification via preparative chromatography. The main product of the conversion of lysine, octahydrodipyrido[1,2-a:1\u27,2\u27-d]pyrazine-6,12(2H,6aH)-dione, was successfully isolated and identified. The purification/separation protocol is rapid, environmentally friendly, and highly efficient with excellent selectivity (81 wt%) in the oils obtained from the conversion of lysine at 300 °C. Performing the conversion step at higher temperatures or lysine concentrations led to the formation of complicated side products. Based on the evolution of key compounds during hydrothermal conversion of lysine, we propose a tentative mechanism for the formation of diketopiperazine. The technique presented in this work provides a novel catalyst-free pathway for the synthesis of DKP