Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1

This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines

Supersymmetric gauge theories, Coulomb gases and Chern-Simons matrix models

We develop Coulomb gas pictures of strong and weak coupling regimes of supersymmetric Yang-Mills theory in five and four dimensions. By relating them to the matrix models that arise in Chern-Simons theory, we compute their free energies in the large N limit and establish relationships between the respective gauge theories. We use these correspondences to rederive the N^3 behaviour of the perturbative free energy of supersymmetric gauge theory on certain toric Sasaki-Einstein five-manifolds, and the one-loop thermal free energy of N=4 supersymmetric Yang-Mills theory on a spatial three-sphere.Comment: 17 pages; v2: reference adde

Measuring Weak Sustainability for the future: Calculating Genuine Saving with population change by an integrated assessment model

This paper presents a future figure of Genuine Saving with population growth (GSn). This was enabled by using an integrated assessment model, similar to the RICE model by Nordhaus. The model consists of sub-models that evaluate various kinds of mineral resources and environmental impacts. Results indicates that GSn is positive i) in OECD during the 21st century, ii) in World and the former Soviet Union and East Europe after 2030, and iii) in Asia and the Middle East and Africa after 2050. GSn is negative in Latin America during the 21st century.Genuine Saving, population change, sustainability, integrated assessment model, impact assessment model, growth model

The postcranium of the carnivorous cynodont Chiniquodon from the Middle Triassic of Namibia and the palaeo-environment of the Upper Omingonde Formation

A Dissertation submitted to the Faculty of Science, University of the Witwatersrand, in fulfilment of the requirements for the Degree of Master of Science Johannesburg, 2015The Chiniquodontidae is a family of Triassic carnivorous cynodonts well represented in the Middle-Upper Triassic of Argentina and Brazil. Chiniquodontids were more recently discovered in Madagascar and central Namibia, representing the only record of the family outside South America. The Namibian specimen was discovered in the Upper Omingonde Formation and is represented by the skull and a partial skeleton. The new chiniquodontid was identified as Chiniquodon and is diagnosed by the postcranial characteristics identified; a strong bend in the proximal portion of thoracic ribs, reduced curvature of the clavicle, although this may be due to deformation, robustness of the neck of the ilium, differences in the angulation between the edge of the posterior lamina of the ilium and the margin of the neck, and a large ischium, which is more than twice the size of the pubic plate. The postcranial material of the chiniquodontid from Namibia is described and compared with that of South American chiniquodontids. Chiniquodontids lack costal plates on ribs, show a tall and slender scapular blade, a large acromion process positioned well above the scapular neck and absence of disc-like phalanges in the autopodium. The Namibian Chiniquodon provides the first evidence of elements from the pes in chiniquodontids, and one of the few for non-mammaliaform cynodonts. Sedimentological studies confirm that the Upper Omingonde Formation of Namibia represents fluvial deposits of braided and meandering rivers formed in a predominately arid climatic regime during the Middle Triassic

Measuring Weak Sustainability for the future: Calculating Genuine Saving with population change by an integrated assessment model

This paper presents a future figure of Genuine Saving with population growth (GSn). This was enabled by using an integrated assessment model, similar to the RICE model by Nordhaus. The model consists of sub-models that evaluate various kinds of mineral resources and environmental impacts. Results indicates that GSn is positive i) in OECD during the 21st century, ii) in World and the former Soviet Union and East Europe after 2030, and iii) in Asia and the Middle East and Africa after 2050. GSn is negative in Latin America during the 21st century

Dynamic Bayesian belief network to model the development of walking and cycling schemes

This paper aims to describe a model which represents the formulation of decision-making processes (over a number of years) affecting the step-changes of walking and cycling (WaC) schemes. These processes can be seen as being driven by a number of causal factors, many of which are associated with the attitudes of a variety of factors, in terms of both determining whether any scheme will be implemented and, if it is implemented, the extent to which it is used. The outputs of the model are pathways as to how the future might unfold (in terms of a number of future time steps) with respect to specific pedestrian and cyclist schemes. The transitions of the decision making processes are formulated using a qualitative simulation method, which describes the step-changes of the WaC scheme development. In this article a Bayesian belief network (BBN) theory is extended to model the influence between and within factors in the dynamic decision making process

iSOM-GSN: An Integrative Approach for Transforming Multi-omic Data into Gene Similarity Networks via Self-organizing Maps

Deep learning models are currently applied in diverse domains, including image recognition, text generation, and event prediction. With the advent of new high-throughput sequencing technologies, a multitude of genomic data has been generated and made available. The representation of such data using deep neural networks, or for that matter, application of differential analysis has, however, not been able to match the growth of that data. One of the main challenges in applying convolutional neural networks on gene interaction data is the lack of understanding of the vector space domain to which they belong and also the inherent difficulties involved in representing those interactions on a significantly lower dimension viz Euclidean spaces. These challenges become more prevalent when dealing with various types of omics data with different forms. In this regard, we introduce a systematic, and generalized method, called iSOM-GSN, used to transform multi-omic genomic data with higher-dimensions into a two-dimensional grid. Afterwards, we apply a convolutional neural network (CNN) to predict disease states of various types. Based on the idea of the Kohonen\u27s self-organizing map (SOM), we generate a two-dimensional grid for each sample for a given set of genes that represent a gene similarity network (GSN). The set of genes that are significantly highly mutated across the whole genome, are related to each other based on functional interactions. We then test the model to predict breast and prostate cancer stages using gene expression, DNA methylation, and copy number alteration, yielding accuracies in the 94-98% range for tumor stages of breast cancer and calculated Gleason scores of prostate cancer with just 14 input genes for both cases. To our knowledge, this is the first attempt to use self-organizing maps and convolutional neural networks on integrating high-dimensional multi-omics data. The scheme not only outputs nearly perfect classification accuracy, but also provides an enhanced scheme for visualization, dimensionality reduction, and interpretation of the results