Multimodal 3D Object Detection from Simulated Pretraining

The need for simulated data in autonomous driving applications has become increasingly important, both for validation of pretrained models and for training new models. In order for these models to generalize to real-world applications, it is critical that the underlying dataset contains a variety of driving scenarios and that simulated sensor readings closely mimics real-world sensors. We present the Carla Automated Dataset Extraction Tool (CADET), a novel tool for generating training data from the CARLA simulator to be used in autonomous driving research. The tool is able to export high-quality, synchronized LIDAR and camera data with object annotations, and offers configuration to accurately reflect a real-life sensor array. Furthermore, we use this tool to generate a dataset consisting of 10 000 samples and use this dataset in order to train the 3D object detection network AVOD-FPN, with finetuning on the KITTI dataset in order to evaluate the potential for effective pretraining. We also present two novel LIDAR feature map configurations in Bird's Eye View for use with AVOD-FPN that can be easily modified. These configurations are tested on the KITTI and CADET datasets in order to evaluate their performance as well as the usability of the simulated dataset for pretraining. Although insufficient to fully replace the use of real world data, and generally not able to exceed the performance of systems fully trained on real data, our results indicate that simulated data can considerably reduce the amount of training on real data required to achieve satisfactory levels of accuracy.Comment: 12 pages, part of proceedings for the NAIS 2019 symposiu

Investigating the release of coprecipitated uranium from iron oxides

The removal of uranium (VI) from zerovalent iron permeable reactive barriers and wetlands can be explained by its association with iron oxides. The long term stability of immobilized U is yet to be addressed. The present study investigates the remobilization of U(VI) from iron oxides via diverse reaction pathways (acidification, reduction, complex formation). Prior, uranium coprecipitation experiments were conducted under various conditions. The addition of various amounts of a pH-shifting agents (pyrite), an iron complexing agent (EDTA) or iron (III) reduction agent (TiCl3) yielded in uranium remobilization, concentrations above the US EPA allowedmaximum contaminant level(MCL=30 æg/l). This study demonstrates that U(VI) release in nature strongly depends on the conditions and the mechanism of its fixation by geological materials.researc

Coprophagous features in carnivorous Nepenthes plants: a task for ureases

Most terrestrial carnivorous plants are specialized on insect prey digestion to obtain additional nutrients. Few species of the genus Nepenthes developed mutualistic relationships with mammals for nitrogen supplementation. Whether dietary changes require certain enzymatic composition to utilize new sources of nutrients has rarely been tested. Here, we investigated the role of urease for Nepenthes hemsleyana that gains nitrogen from the bat Kerivoula hardwickii while it roosts inside the pitchers. We hypothesized that N. hemsleyana is able to use urea from the bats’ excrements. In fact, we demonstrate that 15N-enriched urea provided to Nepenthes pitchers is metabolized and its nitrogen is distributed within the plant. As ureases are necessary to degrade urea, these hydrolytic enzymes should be involved. We proved the presence and enzymatic activity of a urease for Nepenthes plant tissues. The corresponding urease cDNA from N. hemsleyana was isolated and functionally expressed. A comprehensive phylogenetic analysis for eukaryotic ureases, including Nepenthes and five other carnivorous plants’ taxa, identified them as canonical ureases and reflects the plant phylogeny. Hence, this study reveals ureases as an emblematic example for an efficient, low-cost but high adaptive plasticity in plants while developing a further specialized lifestyle from carnivory to coprophagy

Temporal changes of inorganic ion deposition in the seasonal snow cover for the Austrian Alps (1983–2014)

A long-term record of inorganic ion concentrations in wet and dry deposition sampled from snow packs at two high altitude glaciers was used to assess impacts of air pollution on remote sites in central Europe. Sampling points were located at Wurtenkees and Goldbergkees near the Sonnblick Observatory (3106 m above sea level), a background site for measuring the status of the atmosphere in Austria's Eastern Alps. Sampling was carried out every spring at the end of the winter accumulation period in the years 1983–2014. Concentrations of major ions (NH4+, SO42−, NO3−, Ca2+, Mg2+, K+, Na+ and Cl−) were determined using ion chromatography (IC) as well as atomic absorption spectroscopy (AAS) in the earlier years. Concentration of H+ was calculated via the measured pH of the samples. Trends in deposition and concentration were analysed for all major ions within the period from 1983 to 2014 using Kendall's tau rank correlation coefficient. From 1983 to 2014, total ion concentration declined ∼25%, i.e. solutions became ∼25% more dilute, indicating reduced acidic atmospheric deposition, even at high altitude in winter snow. SO42− and NO3− concentrations decreased significantly by 70% and 30%, respectively, accompanied by a 54% decrease of H+ concentrations. Ionic concentrations in snowpack were dominated by H+ and SO42− in the earliest decade measured, whereas they were dominated by Ca2+ by the most recent decade. SO42− and H+ depositions, i.e. concentrations multiplied by volume, also showed a significant decrease of more than 50% at both sites. This reflects the successful emission reductions of the precursor gases SO2 and NOx. Seasonal values with significantly elevated spring concentrations of NH4+, SO42− and H+ compared to fall snow reflects the beginning of vertical mixing during spring. All other ions do not show any seasonality. Source identification of the ions was performed using a principal component analysis (PCA). One anthropogenic cluster (SO42−, NO3− and NH4+) coming from road traffic or fossil fuel combustion and animal husbandry, one crustal cluster (Ca2+, Mg2+) originating from local geological input or Saharan dust events as well as one cluster of unknown origin with episodic character (Na+, K+ and Cl−) was found

Climate Changes and Their Elevational Patterns in the Mountains of the World

Quantifying rates of climate change in mountain regions is of considerable interest, not least because mountains are viewed as climate “hotspots” where change can anticipate or amplify what is occurring elsewhere. Accelerating mountain climate change has extensive environmental impacts, including depletion of snow/ice reserves, critical for the world's water supply. Whilst the concept of elevation-dependent warming (EDW), whereby warming rates are stratified by elevation, is widely accepted, no consistent EDW profile at the global scale has been identified. Past assessments have also neglected elevation-dependent changes in precipitation. In this comprehensive analysis, both in situ station temperature and precipitation data from mountain regions, and global gridded data sets (observations, reanalyses, and model hindcasts) are employed to examine the elevation dependency of temperature and precipitation changes since 1900. In situ observations in paired studies (using adjacent stations) show a tendency toward enhanced warming at higher elevations. However, when all mountain/lowland studies are pooled into two groups, no systematic difference in high versus low elevation group warming rates is found. Precipitation changes based on station data are inconsistent with no systematic contrast between mountain and lowland precipitation trends. Gridded data sets (CRU, GISTEMP, GPCC, ERA5, and CMIP5) show increased warming rates at higher elevations in some regions, but on a global scale there is no universal amplification of warming in mountains. Increases in mountain precipitation are weaker than for low elevations worldwide, meaning reduced elevation-dependency of precipitation, especially in midlatitudes. Agreement on elevation-dependent changes between gridded data sets is weak for temperature but stronger for precipitation

The Virtual Teacher (VT) Paradigm: Learning New Patterns of Interpersonal Coordination Using the Human Dynamic Clamp

The Virtual Teacher paradigm, a version of the Human Dynamic Clamp (HDC), is introduced into studies of learning patterns of inter-personal coordination. Combining mathematical modeling and experimentation, we investigate how the HDC may be used as a Virtual Teacher (VT) to help humans co-produce and internalize new inter-personal coordination pattern(s). Human learners produced rhythmic finger movements whilst observing a computer-driven avatar, animated by dynamic equations stemming from the well-established Haken-Kelso-Bunz (1985) and Schöner-Kelso (1988) models of coordination. We demonstrate that the VT is successful in shifting the pattern co-produced by the VT-human system toward any value (Experiment 1) and that the VT can help humans learn unstable relative phasing patterns (Experiment 2). Using transfer entropy, we find that information flow from one partner to the other increases when VT-human coordination loses stability. This suggests that variable joint performance may actually facilitate interaction, and in the long run learning. VT appears to be a promising tool for exploring basic learning processes involved in social interaction, unraveling the dynamics of information flow between interacting partners, and providing possible rehabilitation opportunities

Development of proglacial lakes and evaluation of related outburst susceptibility at the Adygine ice-debris complex, northern Tien Shan

The formation and development of glacial lakes in mountainous regions is one of the consequences of glacier recession. Such lakes may drain partially or completely when the stability of their dams is disturbed or as a consequence of impacts. We present a case study from the Central Asian mountain range of Tien Shan – a north-oriented tributary of the Adygine Valley, where the retreat of a polythermal glacier surrounded by permafrost has resulted in the formation of several generations of lakes. The aim of this study was to analyse the past development of different types of glacial lakes influenced by the same glacier, to project the site's future development, and to evaluate the outburst susceptibility of individual lakes with an outlook for expected future change. We addressed the problem using a combination of methods, namely bathymetric, geodetic and geophysical on-site surveys, satellite images and digital elevation model analysis, and modelling of glacier development. Based on this case of the glacial lakes being of varied age and type, we demonstrated the significance of glacier ice in lake development. Lake 3, which is in contact with the glacier terminus, has changed rapidly over the last decade, expanding both in area and depth and increasing its volume by more than 13 times (7800 to 106&thinsp;000&thinsp;m3). The hydrological connections and routing of glacier meltwater have proved to be an important factor as well, since most lakes in the region are drained by subsurface channels. As the site is at the boundary between continuous and discontinuous permafrost, the subsurface water flow is strongly governed by the distribution of non-frozen zones above, within, or beneath the perennially frozen ground. In the evaluation of lake outburst susceptibility, we have highlighted the importance of field data, which can provide crucial information on lake stability. In our case, an understanding of the hydrological system at the site, and its regime, helped to categorise Lake 2 as having low outburst susceptibility, while Lake 1 and Lake 3 were labelled as lakes with medium outburst susceptibility. Further development of the site will be driven mainly by rising air temperatures and increasingly negative glacier mass balance. All three climate model scenarios predicted a significant glacier areal decrease by 2050, specifically leaving 73.2&thinsp;% (A1B), 62.3&thinsp;% (A2), and 55.6&thinsp;% (B1) of the extent of the glacier in 2012. The glacier retreat will be accompanied by changes in glacier runoff, with the first peak expected around 2020, and the formation of additional lakes.</p

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics