While most approaches to semantic reasoning have focused on improving performance, in this paper we argue that computational times are very important in order to enable real time applications such as autonomous driving. Towards this goal, we present an approach to joint classification, detection and semantic segmentation via a unified architecture where the encoder is shared amongst the three tasks. Our approach is very simple, can be trained end-toend and performs extremely well in the challenging KITTI dataset, outperforming the state-of-the-art in the road segmentation task. Our approach is also very efficient, allowing us to perform inference at more then 23 frames per second.Training scripts and trained weights to reproduce our results can be found here: https://github.com/ MarvinTeichmann/MultiNet

Pit membranes in bordered pits of tracheary elements of angiosperm xylem represent primary cell walls that undergo structural and chemical modifications, not only during cell death but also during and after their role as safety valves for water transport between conduits. Cellulose microfibrils, which are typically grouped in aggregates with a diameter between 20 to 30 nm, make up their main component. While it is clear that pectins and hemicellulose are removed from immature pit membranes during hydrolysis, recent observations of amphiphilic lipids and proteins associated with pit membranes raise important questions about drought-induced embolism formation and spread via air-seeding from gas-filled conduits. Indeed, mechanisms behind air-seeding remain poorly understood, which is due in part to little attention paid to the three-dimensional structure of pit membranes in earlier studies. Based on perfusion experiments and modelling, pore constrictions in fibrous pit membranes are estimated to be well below 50 nm, and typically smaller than 20 nm. Together with the low dynamic surface tensions of amphiphilic lipids at air-water interfaces in pit membranes, 5 to 20 nm pore constrictions are in line with the observed xylem water potentials values that generally induce spread of embolism. Moreover, pit membranes appear to show ideal porous medium properties for sap flow to promote hydraulic efficiency and safety due to their very high porosity (pore volume fraction), with highly interconnected, non-tortuous pore pathways, and the occurrence of multiple pore constrictions within a single pore. This three-dimensional view of pit membranes as mesoporous media may explain the relationship between pit membrane thickness and embolism resistance, but is largely incompatible with earlier, two-dimensional views on air-seeding. It is hypothesised that pit membranes enable water transport under negative pressure by producing stable, surfactant coated nanobubbles while preventing the entry of large bubbles that would cause embolism.

Embolism spreading in angiosperm xylem occurs via mesoporous pit membranes between vessels. Here, we investigate how the size of pore constrictions in pit membranes is related to pit membrane thickness and embolism resistance.Pit membranes were modelled as multiple layers to investigate how pit membrane thickness and the number of intervessel pits per vessel determine pore constriction sizes, the probability of encountering large pores, and embolism resistance. These estimations were complemented by measurements of pit membrane thickness, embolism resistance, and number of intervessel pits per vessel in stem xylem (n = 31, 31 and 20 species, respectively).The modelled constriction sizes in pit membranes decreased with increasing membrane thickness, explaining the measured relationship between pit membrane thickness and embolism resistance. The number of pits per vessel affected constriction size and embolism resistance much less than pit membrane thickness. Moreover, a strong relationship between modelled and measured embolism resistance was observed.Pore constrictions provide a mechanistic explanation for why pit membrane thickness determines embolism resistance, which suggests that hydraulic safety can be uncoupled from hydraulic efficiency. Although embolism spreading remains puzzling and encompasses more than pore constriction sizes, angiosperms are unlikely to have leaky pit membranes, which enables tensile transport of water.

Abstract-Vehicular networks are a very promising technology to increase traffic safety and efficiency and to enable numerous other applications in the domain of vehicular communication. Proposed applications for VANETs have very diverse properties and often require non-standard communication protocols. Moreover, the dynamics of the network due to vehicle movements further complicates the design of an appropriate, comprehensive communication system. In this work, we collect and categorize envisioned applications from various sources and classify the unique network characteristics of vehicular networks. Based on this analysis, we propose five distinct communication patterns that form the basis of almost all VANET applications. Both the analysis and the communication patterns shall deepen the understanding of VANETs and simplify further development of VANET communication systems.

Pit membranes between xylem vessels play a major role in angiosperm water transport. Yet, their three‐dimensional (3D) structure as fibrous porous media remains unknown, largely due to technical challenges and sample preparation artefacts. Here, we applied a modelling approach based on thickness measurements of fresh and fully shrunken pit membranes of seven species. Pore constrictions were also investigated visually by perfusing fresh material with colloidal gold particles of known sizes. Based on a shrinkage model, fresh pit membranes showed tiny pore constrictions of ca. 20 nm, but a very high porosity (i.e. pore volume fraction) of on average 0.81. Perfusion experiments showed similar pore constrictions in fresh samples, well below 50 nm based on transmission electron microscopy. Drying caused a 50% shrinkage of pit membranes, resulting in much smaller pore constrictions. These findings suggest that pit membranes represent a mesoporous medium, with the pore space characterized by multiple constrictions. Constrictions are much smaller than previously assumed, but the pore volume is large and highly interconnected. Pores do not form highly tortuous, bent, or zigzagging pathways. These insights provide a novel view on pit membranes, which is essential to develop a mechanistic, 3D understanding of air‐seeding through this porous medium.

Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most large-scale bioacoustic archives contain only a small percentage of animal vocalizations and a large amount of environmental noise, which makes it extremely difficult to manually retrieve sufficient vocalizations for further analysis – particularly important for species with advanced social systems and complex vocalizations. In this study deep neural networks were trained on 11,509 killer whale ( Orcinus orca ) signals and 34,848 noise segments. The resulting toolkit ORCA-SPOT was tested on a large-scale bioacoustic repository – the Orchive – comprising roughly 19,000 hours of killer whale underwater recordings. An automated segmentation of the entire Orchive recordings (about 2.2 years) took approximately 8 days. It achieved a time-based precision or positive-predictive-value (PPV) of 93.2% and an area-under-the-curve (AUC) of 0.9523. This approach enables an automated annotation procedure of large bioacoustics databases to extract killer whale sounds, which are essential for subsequent identification of significant communication patterns. The code will be publicly available in October 2019 to support the application of deep learning to bioaoucstic research. ORCA-SPOT can be adapted to other animal species.

Virtual keyboards of different smartphone platforms seem quite similar at first glance, but the transformation from a physical to a virtual keyboard on a small-scale display results in user experience variations that cause significant differences in usability as well as shoulder surfing susceptibility, i.e., the risk of a bystander observing what is being typed. In our work, we investigate the impact of both aspects on the security of text-based password entry on mobile devices. In a between subjects study with 80 participants, we analyzed usability and shoulder surfing susceptibility of password entry on different mobile platforms (iOS, Android, Windows Phone, Symbian, MeeGo). Our results show significant differences in the usability of password entry (required password entry time, typing accuracy) and susceptibility to shoulder surfing. Our results provide insights for security-aware design of on-screen keyboards and for password composition strategies tailored to entry on smartphones.

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