Velocity-dependent inverse cubic force and solar system gravity tests

Higher mass dimension terms in an effective field theory framework for tests of spacetime symmetries are studied. Using a post-Newtonian expansion method, we derive the spacetime metric and the equations of motion for a binary system. This reveals an inverse cubic force correction to General Relativity that depends on the velocity of the bodies in the system. The results are studied in the context of laboratory and space-based tests including the effects on solar-system ephemeris, laser ranging observations, and gravimeter tests. This work reveals the coefficient combinations for mass dimension 5 operators controlling CPT violation for gravity that can be measured using analysis from these tests. Other tests including light propagation can be used to probe these coefficients. Sensitivity estimates are provided and the results are contrasted with the minimal mass dimension 4 terms in the gravity sector.Comment: 10 pages, matches published versio

Individual variation in plant traits drives species interactions, ecosystem functioning, and responses to global change

Ecologists have long sought to understand the processes that lead to the riotous diversity in communities of organisms that inhabit disparate climates and landscapes. Such a diversity of traits leads to a diversity of interactions among species in natural communities, which in turn generates a diversity of potential responses to ongoing global change. In this dissertation, I do three things: I explore the forces that structure plant communities and the ecosystem functions that they mediate, I describe patterns of variation among communities, species, and individual organisms across environmental contexts, and I disentangle the direct effects of global change from the indirect, cascading effects that result from disruptions of species interactions. I accomplish these goals through the synthesis of global data, the development of statistical and mathematical models, and the manipulation of global change drivers in field experiments. In the first chapter, I present a globe-spanning meta-analysis of plant functional trait patterns along elevational gradients. This meta-analysis shows that the plant traits that drive ecosystem function follow predictable trends with elevation due to climate filtering, and that much of this variation is at the level of the individual organism. In the second chapter, I present simulated data sets and illustrative experimental case studies that quantify how important individual-level variation is for explaining patterns in nature. In the third chapter, I present results from intensive plant sampling across a wide range of mountain environments; even in these harsh environments where only the hardiest species can survive, individual-level variation is so high that it makes predictions based on species identity nearly impossible. The fourth and fifth chapters consist of experimental evidence that ongoing human-caused global change is affecting montane plant communities, that species interactions mediate many of these effects, and that variation in the abiotic environment causes variation in both species interactions and in global change response. I demonstrate this through an experiment that combines nitrogen fertilization with removal of a dominant plant species in a montane meadow, and an experiment replicated at low and high elevations crossing dominant species removal with simulation of global warming

Achieving Sustainability and Scale-Up of Mobile Health Noncommunicable Disease Interventions in Sub-Saharan Africa: Views of Policy Makers in Ghana

Background: A growing body of evidence shows that mobile health (mHealth) interventions may improve treatment and care for the rapidly rising number of patients with noncommunicable diseases (NCDs) in sub-Saharan Africa (SSA). A recent realist review developed a framework highlighting the influence of context factors, including predisposing characteristics, needs, and enabling resources (PNE), for the long-term success of mHealth interventions. The views of policy makers will ultimately determine implementation and scale-up of mHealth interventions in SSA. However, their views about necessary conditions for sustainability and scale-up remain unexplored. Objective: This study aimed to understand the views of policy makers in Ghana with regard to the most important factors for successful implementation, sustainability, and scale-up of mHealth NCD interventions. Methods: Members of the technical working group responsible for Ghana’s national NCD policy were interviewed about their knowledge of and attitude toward mHealth and about the most important factors contributing to long-term intervention success. Using qualitative methods and applying a qualitative content analysis approach, answers were categorized according to the PNE framework. Results: A total of 19 policy makers were contacted and 13 were interviewed. Interviewees had long-standing work experience of an average of 26 years and were actively involved in health policy making in Ghana. They were well-informed about the potential of mHealth, and they strongly supported mHealth expansion in the country. Guided by the PNE framework’s categories, the policy makers ascertained which critical factors would support the successful implementation of mHealth interventions in Ghana. The policy makers mentioned many factors described in the literature as important for mHealth implementation, sustainability, and scale-up, but they focused more on enabling resources than on predisposing characteristics and need. Furthermore, they mentioned several factors that have been rather unexplored in the literature. Conclusions: The study shows that the PNE framework is useful to guide policy makers toward a more systematic assessment of context factors that support intervention implementation, sustainability, and scale-up. Furthermore, the framework was refined by adding additional factors. Policy makers may benefit from using the PNE framework at the various stages of mHealth implementation. Researchers may (and should) use the framework when investigating reasons for success (or failure) of interventions.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Industrial Application of 6D Pose Estimation for Robotic Manipulation in Automotive Internal Logistics

Despite the advances in robotics a large proportion of the of parts handling tasks in the automotive industry's internal logistics are not automated but still performed by humans. A key component to competitively automate these processes is a 6D pose estimation that can handle a large number of different parts, is adaptable to new parts with little manual effort, and is sufficiently accurate and robust with respect to industry requirements. In this context, the question arises as to the current status quo with respect to these measures. To address this we built a representative 6D pose estimation pipeline with state-of-the-art components from economically scalable real to synthetic data generation to pose estimators and evaluated it on automotive parts with regards to a realistic sequencing process. We found that using the data generation approaches, the performance of the trained 6D pose estimators are promising, but do not meet industry requirements. We reveal that the reason for this is the inability of the estimators to provide reliable uncertainties for their poses, rather than the ability of to provide sufficiently accurate poses. In this context we further analyzed how RGB- and RGB-D-based approaches compare against this background and show that they are differently vulnerable to the domain gap induced by synthetic data.Comment: Accepted for publication at IEEE International Conference on Automation Science and Engineering (CASE 2023

The roles of Eu during the growth of eutectic Si in Al-Si alloys

Controlling the growth of eutectic Si and thereby modifying the eutectic Si from flake-like to fibrous is a key factor in improving the properties of Al-Si alloys. To date, it is generally accepted that the impurity-induced twinning (IIT) mechanism and the twin plane re-entrant edge (TPRE) mechanism as well as poisoning of the TPRE mechanism are valid under certain conditions. However, IIT, TPRE or poisoning of the TPRE mechanism cannot be used to interpret all observations. Here, we report an atomic-scale experimental and theoretical investigation on the roles of Eu during the growth of eutectic Si in Al-Si alloys. Both experimental and theoretical investigations reveal three different roles: (i) the adsorption at the intersection of Si facets, inducing IIT mechanism, (ii) the adsorption at the twin plane re-entrant edge, inducing TPRE mechanism or poisoning of the TPRE mechanism, and (iii) the segregation ahead of the growing Si twins, inducing a solute entrainment within eutectic Si. This investigation not only demonstrates a direct experimental support to the well-accepted poisoning of the TPRE and IIT mechanisms, but also provides a full picture about the roles of Eu atoms during the growth of eutectic Si, including the solute entrainment within eutectic Si

Basin Futures

This unique book brings together 27 chapters from some of the world’s leading practitioners and experts on environmental water, communities, law, economics and governance. Its goal is to understand the many dimensions of water in the Murray- Darling Basin and provide guidance about how to implement a water management plan that addresses the needs of communities, the economy and the environment. The comprehensiveness of topics covered, the expertise of its authors, and the absolute need to take a multidisciplinary approach to resolving the “wicked problem” of governing our scarce water resource makes this volume a must read for all who care about Australian communities and the environment

Language Models for Novelty Detection in System Call Traces

Due to the complexity of modern computer systems, novel and unexpected behaviors frequently occur. Such deviations are either normal occurrences, such as software updates and new user activities, or abnormalities, such as misconfigurations, latency issues, intrusions, and software bugs. Regardless, novel behaviors are of great interest to developers, and there is a genuine need for efficient and effective methods to detect them. Nowadays, researchers consider system calls to be the most fine-grained and accurate source of information to investigate the behavior of computer systems. Accordingly, this paper introduces a novelty detection methodology that relies on a probability distribution over sequences of system calls, which can be seen as a language model. Language models estimate the likelihood of sequences, and since novelties deviate from previously observed behaviors by definition, they would be unlikely under the model. Following the success of neural networks for language models, three architectures are evaluated in this work: the widespread LSTM, the state-of-the-art Transformer, and the lower-complexity Longformer. However, large neural networks typically require an enormous amount of data to be trained effectively, and to the best of our knowledge, no massive modern datasets of kernel traces are publicly available. This paper addresses this limitation by introducing a new open-source dataset of kernel traces comprising over 2 million web requests with seven distinct behaviors. The proposed methodology requires minimal expert hand-crafting and achieves an F-score and AuROC greater than 95% on most novelties while being data- and task-agnostic. The source code and trained models are publicly available on GitHub while the datasets are available on Zenodo.Comment: 12 pages, 7 figures, 3 table