Biomass Production and Carbon Sequestration by Cultivation of Trees under Hyperarid Conditions using Desalinated Seawater (Sewage Water)

As growing economies – in particular in the Gulf region – use extreme and growing amounts of desalinated seawater for municipal purposes the use of produced waste waters is in the focus of science. The fixation of atmospheric carbon-dioxide by a safe cultivation of trees using this slightly salty water sources is of increased importance in times of ongoing climate change. Unfortunately, existing research relies on irrigation of trees in arid lands using ground water, any kind of precipitation, seasonal events like river flooding or a mix of them. To date no data support the biomass or tree production in total absence of natural precipitation and complete lack of ground water. In this study, seven timber and fuelwood tree species, namely, Eucalyptus occidentalis En., Eucalyptus tereticornis Smith, Eucalyptus camaldulensis Dehnh., Eucalyptus gomphocephala DC., Eucalyptus grandis Hybr. Hill ex Maid, Tamarix aphylla (L.) Karst., Tamarix nilotica (Ehrenb.) Bunge were tested for carbon sequestration and biomass-production. Above-soil and sub-soil parts were determined under two levels of drip-irrigation water supply: 25% and 50% of Evapotranspiration (ETo) over a period of two years and four months from planting to harvest. The trees were cultivated under hyper-arid climatic conditions using brackish irrigation water (3.5 dS m-1) on a research and development station in Arava, Israel. Purified waste water from a seawater desalination plant (reverse osmosis) was applied after municipal use. Eucalyptus gomphocephala DC. delivered the highest yields and had the highest water use efficiency, producing 70 t of Dry Matter (DM) /ha/a under the higher irrigation level. Compared with the other species, E. gomphocephala DC. showed a 32% to 65% superior performance . Whereas, lower amounts of saline irrigation water were favoured by E. camaldulensis and T. aphylla – both producing more than 50 t of DM/ha/a. Nevertheless, Tamarix, as a halophyte specialist plant, needed 30 % less water for this growth. Both Eucalyptus varieties mentioned before form a closed tree stand and reached a height of almost 10 m, two years after planting. Regardless of the particular use of the produced timber, about 15 – 25% of the trees’ total DM, approximately equal to the carbon-content, remains in the soil as long-term carbon-storage after harvesting the above ground biomass. Fast growing fuelwood tree species ensure a safe long-term biological fixation of carbon Irrigated with small amounts of saline waste water

ENHANCED GLOBAL NUCLEAR EVENT LOCATION AND ITS UNCERTAINTY ANALYSIS BASED ON VARIOUS ADJOINT ENSEMBLE DISPERSION MODELLING TECHNIQUES

After the detection of treaty-relevant radionuclides in filters or air samples, atmospheric backtracking techniques are employed by the Provisional Technical Secretariat (PTS) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) to trace back the measured substances to their potential areas of origin. In the case of an underground nuclear test, potential sources are co-located with the epicentres of seismic events that may have been triggered by the explosions. Previous studies have shown that predictions or analyses of atmospheric transport can be significantly improved by ensemble techniques. Within the CTBT environment it is important to build confidence in the source-receptor sensitivity (SRS) field based backtracking products issued by the PTS in the case of the occurrence of treaty relevant radionuclides. Therefore the PTS has set up a highly automated response system together with the Regional Specialized Meteorological Centres of the World Meteorological Organization in the field of dispersion modelling. These Centres have committed themselves to provide the PTS with the same standard SRS fields as calculated by their systems for CTBT relevant cases. The SRS field data standard allows for ensemble dispersion modelling. The parametric inter-comparison among ensemble members has been integrated into the decision making software tool WEB-GRAPE (CTBTO Newsletter Spectrum, 7, page 19). In sensitivity studies we varied the choice of LPDM, and the kind and source of wind field utilized to demonstrate the potential of the following two ensemble dispersion modelling (EDM) methods: a) Multi-model EDM in order to improve the accuracy of a global scale source attribution based on joint CTBTO-WMO experiments in January 2005 (Becker et al., 2007) and December 2007 (Wotawa and Becker, 2008). b) Single-model EDM with different lead times of the wind fields utilized in order to estimate the relative error of forecasted source attribution results in comparison to the analyzed ones c) Single-model EDM with different choices of wind field resolutions for the source receptor sensitivity fields of the same station at Schauinsland in order to assess quality of the PTS standard backtracking results based on the rather coarse 1º×1º horizontal resolution

On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning

Improved state space models, such as Recurrent State Space Models (RSSMs), are a key factor behind recent advances in model-based reinforcement learning (RL). Yet, despite their empirical success, many of the underlying design choices are not well understood. We show that RSSMs use a suboptimal inference scheme and that models trained using this inference overestimate the aleatoric uncertainty of the ground truth system. We find this overestimation implicitly regularizes RSSMs and allows them to succeed in model-based RL. We postulate that this implicit regularization fulfills the same functionality as explicitly modeling epistemic uncertainty, which is crucial for many other model-based RL approaches. Yet, overestimating aleatoric uncertainty can also impair performance in cases where accurately estimating it matters, e.g., when we have to deal with occlusions, missing observations, or fusing sensor modalities at different frequencies. Moreover, the implicit regularization is a side-effect of the inference scheme and not the result of a rigorous, principled formulation, which renders analyzing or improving RSSMs difficult. Thus, we propose an alternative approach building on well-understood components for modeling aleatoric and epistemic uncertainty, dubbed Variational Recurrent Kalman Network (VRKN). This approach uses Kalman updates for exact smoothing inference in a latent space and Monte Carlo Dropout to model epistemic uncertainty. Due to the Kalman updates, the VRKN can naturally handle missing observations or sensor fusion problems with varying numbers of observations per time step. Our experiments show that using the VRKN instead of the RSSM improves performance in tasks where appropriately capturing aleatoric uncertainty is crucial while matching it in the deterministic standard benchmarks

ENHANCED GLOBAL NUCLEAR EVENT LOCATION AND ITS UNCERTAINTY ANALYSIS BASED ON VARIOUS ADJOINT ENSEMBLE DISPERSION MODELLING TECHNIQUES

After the detection of treaty-relevant radionuclides in filters or air samples, atmospheric backtracking techniques are employed by the Provisional Technical Secretariat (PTS) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) to trace back the measured substances to their potential areas of origin. In the case of an underground nuclear test, potential sources are co-located with the epicentres of seismic events that may have been triggered by the explosions. Previous studies have shown that predictions or analyses of atmospheric transport can be significantly improved by ensemble techniques. Within the CTBT environment it is important to build confidence in the source-receptor sensitivity (SRS) field based backtracking products issued by the PTS in the case of the occurrence of treaty relevant radionuclides. Therefore the PTS has set up a highly automated response system together with the Regional Specialized Meteorological Centres of the World Meteorological Organization in the field of dispersion modelling. These Centres have committed themselves to provide the PTS with the same standard SRS fields as calculated by their systems for CTBT relevant cases. The SRS field data standard allows for ensemble dispersion modelling. The parametric inter-comparison among ensemble members has been integrated into the decision making software tool WEB-GRAPE (CTBTO Newsletter Spectrum, 7, page 19). In sensitivity studies we varied the choice of LPDM, and the kind and source of wind field utilized to demonstrate the potential of the following two ensemble dispersion modelling (EDM) methods: a) Multi-model EDM in order to improve the accuracy of a global scale source attribution based on joint CTBTO-WMO experiments in January 2005 (Becker et al., 2007) and December 2007 (Wotawa and Becker, 2008). b) Single-model EDM with different lead times of the wind fields utilized in order to estimate the relative error of forecasted source attribution results in comparison to the analyzed ones c) Single-model EDM with different choices of wind field resolutions for the source receptor sensitivity fields of the same station at Schauinsland in order to assess quality of the PTS standard backtracking results based on the rather coarse 1º×1º horizontal resolution

Versatile Inverse Reinforcement Learning via Cumulative Rewards

Inverse Reinforcement Learning infers a reward function from expert demonstrations, aiming to encode the behavior and intentions of the expert. Current approaches usually do this with generative and uni-modal models, meaning that they encode a single behavior. In the common setting, where there are various solutions to a problem and the experts show versatile behavior this severely limits the generalization capabilities of these methods. We propose a novel method for Inverse Reinforcement Learning that overcomes these problems by formulating the recovered reward as a sum of iteratively trained discriminators. We show on simulated tasks that our approach is able to recover general, high-quality reward functions and produces policies of the same quality as behavioral cloning approaches designed for versatile behavior

Expected Information Maximization: Using the I-Projection for Mixture Density Estimation

Modelling highly multi-modal data is a challenging problem in machine learning. Most algorithms are based on maximizing the likelihood, which corresponds to the M(oment)-projection of the data distribution to the model distribution. The M-projection forces the model to average over modes it cannot represent. In contrast, the I(nformation)-projection ignores such modes in the data and concentrates on the modes the model can represent. Such behavior is appealing whenever we deal with highly multi-modal data where modelling single modes correctly is more important than covering all the modes. Despite this advantage, the I-projection is rarely used in practice due to the lack of algorithms that can efficiently optimize it based on data. In this work, we present a new algorithm called Expected Information Maximization (EIM) for computing the I-projection solely based on samples for general latent variable models, where we focus on Gaussian mixtures models and Gaussian mixtures of experts. Our approach applies a variational upper bound to the I-projection objective which decomposes the original objective into single objectives for each mixture component as well as for the coefficients, allowing an efficient optimization. Similar to GANs, our approach employs discriminators but uses a more stable optimization procedure, using a tight upper bound. We show that our algorithm is much more effective in computing the I-projection than recent GAN approaches and we illustrate the effectiveness of our approach for modelling multi-modal behavior on two pedestrian and traffic prediction datasets

Rolf Taubert

Nachruf der Braunschweigischen Wissenschaftlichen Gesellschaft, vorgetragen in der Plenarsitzung am 12. 11. 1976 in Braunschwei

Joint Representations for Reinforcement Learning with Multiple Sensors

Combining inputs from multiple sensor modalities effectively in reinforcement learning (RL) is an open problem. While many self-supervised representation learning approaches exist to improve performance and sample complexity for image-based RL, they usually neglect other available information, such as robot proprioception. However, using this proprioception for representation learning can help algorithms to focus on relevant aspects and guide them toward finding better representations. In this work, we systematically analyze representation learning for RL from multiple sensors by building on Recurrent State Space Models. We propose a combination of reconstruction-based and contrastive losses, which allows us to choose the most appropriate method for each sensor modality. We demonstrate the benefits of joint representations, particularly with distinct loss functions for each modality, for model-free and model-based RL on complex tasks. Those include tasks where the images contain distractions or occlusions and a new locomotion suite. We show that combining reconstruction-based and contrastive losses for joint representation learning improves performance significantly compared to a post hoc combination of image representations and proprioception and can also improve the quality of learned models for model-based RL