No abstract

In December 1993 a study of contaminated sites at Casey Station, Wilkes Land, Antarctica, was undertaken. The preliminary assessment of these contaminated sites is presented here. A register of contaminated sites for Casey Station was developed, based on a survey of past Antarctic expeditioners, relevant literature, and in-house reports relating to site usage and history. On this basis a sampling strategy was devised for the highest priority, potentially contaminated sites at Casey Station. Samples were collected from the refuse disposal site (tip site) at Thala Valley and the mechanical workshop/powerhouse areas of ‘Old’ Casey. The results indicated that copper, lead, and zinc were leaching from the tip site into adjacent Brown Bay, with ‘hot spots’ of high petroleum hydrocarbon levels. The mechanical workshop/powerhouse area was also shown to be contaminated with petroleum hydrocarbons, polycyclic aromatic hydrocarbons, and metals such as copper, lead, and zinc. Several recommendations were made, including the removal of rubbish mixed with soil at the bottom end of Thala Valley in such a manner as to prevent any further release of contaminants, with subsequent site monitoring to verify effective removal of contaminants to acceptable environmental levels. It was also recommended that further investigations be carried out on the ecosystem of Brown Bay, the large fuel spill site, the upper and lower fuel-storage areas, and the area around the incinerator. There is scope for monitoring the natural breakdown and migration of contaminants at the mechanical workshop/powerhouse site. This should include studies on bioremediation of hydrocarbon-contaminated soils. Mechanisms for arresting surface migration should be investigated in those sites identified. In addition to meeting some of the international obligations of the Protocol on Environmental Protection to the Antarctic Treaty, this work also contributes towards the development of an approach to assessment and management of contaminated sites that is uniquely adapted to the Antarctic environment and could be applied at other Antarctic stations. to the assessment and management of contaminated sites that is uniquely adapted to the Antarctic environment. This approach could be utilised for site assessments at other Antarctic stations.

Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a black box model due to their multilayer nonlinear structure, Deep Neural Networks are often criticized as being non-transparent and their predictions not traceable by humans. Furthermore, the models learn from artificially generated datasets, which often do not reflect reality. By basing decision-making algorithms on Deep Neural Networks, prejudice and unfairness may be promoted unknowingly due to a lack of transparency. Hence, several so-called explanators, or explainers, have been developed. Explainers try to give insight into the inner structure of machine learning black boxes by analyzing the connection between the input and output. In this survey, we present the mechanisms and properties of explaining systems for Deep Neural Networks for Computer Vision tasks. We give a comprehensive overview about the taxonomy of related studies and compare several survey papers that deal with explainability in general. We work out the drawbacks and gaps and summarize further research ideas.

SummaryWe report on results on the preparation of thin (<100 nm) aluminum oxide (Al2O3) films on silicon substrates using thermal atomic layer deposition (T-ALD) and plasma enhanced atomic layer deposition (PE-ALD) in the SENTECH SI ALD LL system. The T-ALD Al2O3 layers were deposited at 200 °C, for the PE-ALD films we varied the substrate temperature range between room temperature (rt) and 200 °C. We show data from spectroscopic ellipsometry (thickness, refractive index, growth rate) over 4” wafers and correlate them to X-ray photoelectron spectroscopy (XPS) results. The 200 °C T-ALD and PE-ALD processes yield films with similar refractive indices and with oxygen to aluminum elemental ratios very close to the stoichiometric value of 1.5. However, in both also fragments of the precursor are integrated into the film. The PE-ALD films show an increased growth rate and lower carbon contaminations. Reducing the deposition temperature down to rt leads to a higher content of carbon and CH-species. We also find a decrease of the refractive index and of the oxygen to aluminum elemental ratio as well as an increase of the growth rate whereas the homogeneity of the film growth is not influenced significantly. Initial state energy shifts in all PE-ALD samples are observed which we attribute to a net negative charge within the films.

No abstract

HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

In recent years, there is a shift from modeling the tracking problem based on Bayesian formulation towards using deep neural networks. Towards this end, in this paper the effectiveness of various deep neural networks for predicting future pedestrian paths are evaluated. The analyzed deep networks solely rely, like in the traditional approaches, on observed tracklets without human-human interaction information. The evaluation is done on the publicly available TrajNet benchmark dataset [39], which builds up a repository of considerable and popular datasets for trajectory prediction. We show how a Recurrent-Encoder with a Dense layer stacked on top, referred to as RED-predictor, is able to achieve toprank at the TrajNet 2018 challenge compared to elaborated models. Further, we investigate failure cases and give explanations for observed phenomena, and give some recommendations for overcoming demonstrated shortcomings.

No abstract