Technische Universität Darmstadt

About: Technische Universität Darmstadt is a education organization based out in Darmstadt, Germany. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 17316 authors who have published 40619 publications receiving 937916 citations. The organization is also known as: Darmstadt University of Technology & University of Darmstadt.

Small antibody-like proteins with prescribed ligand specificities derived from the lipocalin fold.

Abstract: We demonstrate that the ligand pocket of a lipocalin from Pieris brassicae, the bilin-binding protein (BBP), can be reshaped by combinatorial protein design such that it recognizes fluorescein, an established immunological hapten. For this purpose 16 residues at the center of the binding site, which is formed by four loops on top of an eight-stranded β-barrel, were subjected to random mutagenesis. Fluorescein-binding BBP variants were then selected from the mutant library by bacterial phage display. Three variants were identified that complex fluorescein with high affinity, exhibiting dissociation constants as low as 35.2 nM. Notably, one of these variants effects almost complete quenching of the ligand fluorescence, similarly as an anti-fluorescein antibody. Detailed ligand-binding studies and site-directed mutagenesis experiments indicated (i) that the molecular recognition of fluorescein is specific and (ii) that charged residues at the center of the pocket are responsible for tight complex formation. Sequence comparison of the BBP variants directed against fluorescein with the wild-type protein and with further variants that were selected against several other ligands revealed that all of the randomized amino acid positions are variable. Hence, a lipocalin can be used for generating molecular pockets with a diversity of shapes. We term this class of engineered proteins “anticalins.” Their one-domain scaffold makes them a promising alternative to antibodies to create a stable receptor protein for a ligand of choice.

Chaos Synchronization and Spontaneous Symmetry-Breaking in Symmetrically Delay-Coupled Semiconductor Lasers

Abstract: We present experimental and numerical investigations of the dynamics of two device-identical, optically coupled semiconductor lasers exhibiting a delay in the coupling. Our results give evidence for subnanosecond coupling-induced synchronized chaotic dynamics in conjunction with a spontaneous symmetry-breaking: we find a well-defined time lag between the dynamics of the two lasers, and an asymmetric physical role of the subsystems. We demonstrate that the leading laser synchronizes its lagging counterpart, whereas the synchronized lagging laser drives the coupling-induced instabilities.

UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss.

Abstract: In the era of end-to-end deep learning, many advances in computer vision are driven by large amounts of labeled data. In the optical flow setting, however, obtaining dense per-pixel ground truth for real scenes is difficult and thus such data is rare. Therefore, recent end-to-end convolutional networks for optical flow rely on synthetic datasets for supervision, but the domain mismatch between training and test scenarios continues to be a challenge. Inspired by classical energy-based optical flow methods, we design an unsupervised loss based on occlusion-aware bidirectional flow estimation and the robust census transform to circumvent the need for ground truth flow. On the KITTI benchmarks, our unsupervised approach outperforms previous unsupervised deep networks by a large margin, and is even more accurate than similar supervised methods trained on synthetic datasets alone. By optionally fine-tuning on the KITTI training data, our method achieves competitive optical flow accuracy on the KITTI 2012 and 2015 benchmarks, thus in addition enabling generic pre-training of supervised networks for datasets with limited amounts of ground truth.