High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference

We propose a data-driven method for recovering miss-ing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network. The global structure inference network incorporates a long short-term memorized context fusion module (LSTM-CF) that infers the global structure of the shape based on multi-view depth information provided as part of the input. It also includes a 3D fully convolutional (3DFCN) module that further enriches the global structure representation according to volumetric information in the input. Under the guidance of the global structure network, the local geometry refinement network takes as input lo-cal 3D patches around missing regions, and progressively produces a high-resolution, complete surface through a volumetric encoder-decoder architecture. Our method jointly trains the global structure inference and local geometry refinement networks in an end-to-end manner. We perform qualitative and quantitative evaluations on six object categories, demonstrating that our method outperforms existing state-of-the-art work on shape completion.Comment: 8 pages paper, 11 pages supplementary material, ICCV spotlight pape

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

Shotgun metagenome data of a defined mock community using Oxford Nanopore, PacBio and Illumina technologies.

Metagenomic sequence data from defined mock communities is crucial for the assessment of sequencing platform performance and downstream analyses, including assembly, binning and taxonomic assignment. We report a comparison of shotgun metagenome sequencing and assembly metrics of a defined microbial mock community using the Oxford Nanopore Technologies (ONT) MinION, PacBio and Illumina sequencing platforms. Our synthetic microbial community BMock12 consists of 12 bacterial strains with genome sizes spanning 3.2-7.2 Mbp, 40-73% GC content, and 1.5-7.3% repeats. Size selection of both PacBio and ONT sequencing libraries prior to sequencing was essential to yield comparable relative abundances of organisms among all sequencing technologies. While the Illumina-based metagenome assembly yielded good coverage with few misassemblies, contiguity was greatly improved by both, Illumina + ONT and Illumina + PacBio hybrid assemblies but increased misassemblies, most notably in genomes with high sequence similarity to each other. Our resulting datasets allow evaluation and benchmarking of bioinformatics software on Illumina, PacBio and ONT platforms in parallel

The discourse of Olympic security 2012 : London 2012

This paper uses a combination of CDA and CL to investigate the discursive realization of the security operation for the 2012 London Olympic Games. Drawing on Didier Bigo’s (2008) conceptualisation of the ‘banopticon’, it address two questions: what distinctive linguistic features are used in documents relating to security for London 2012; and, how is Olympic security realized as a discursive practice in these documents? Findings suggest that the documents indeed realized key banoptic features of the banopticon: exceptionalism, exclusion and prediction, as well as what we call ‘pedagogisation’. Claims were made for the exceptional scale of the Olympic events; predictive technologies were proposed to assess the threat from terrorism; and documentary evidence suggests that access to Olympic venues was being constituted to resemble transit through national boundarie

Enabling Data-Driven Transportation Safety Improvements in Rural Alaska

Safety improvements require funding. A clear need must be demonstrated to secure funding. For transportation safety, data, especially data about past crashes, is the usual method of demonstrating need. However, in rural locations, such data is often not available, or is not in a form amenable to use in funding applications. This research aids rural entities, often federally recognized tribes and small villages acquire data needed for funding applications. Two aspects of work product are the development of a traffic counting application for an iPad or similar device, and a review of the data requirements of the major transportation funding agencies. The traffic-counting app, UAF Traffic, demonstrated its ability to count traffic and turning movements for cars and trucks, as well as ATVs, snow machines, pedestrians, bicycles, and dog sleds. The review of the major agencies demonstrated that all the likely funders would accept qualitative data and Road Safety Audits. However, quantitative data, if it was available, was helpful

Societal Effects and the Transfer of Business Practices to Britain and France

This paper seeks to reconcile the notion of a 'societal effect' in business organisation with the considerable evidence that competitive pressures continuously lead national producers to emulate the business practices of other nations, which are perceived as providing a basis for superior economic performance. The paper identifies three sources of national specificity in the process of emulation giving rise to 'hybrid' models. First, the fact that a nation's manufacturers have a distinctive knowledge base means that adopting another nation's methods will depend on local learning involving trial and error. The more 'distant' the emulated technology is from the local one, the less likely it is that this learning process will result in an exact replica of the parent model. Second, when there are strong interdependencies between a nation's production methods and its systems of vocational training, there will be strong pressure to adopt new methods in ways that are compatible with existing career structures. Third, the fact each nation has a particular industrial relations legacy involving varying levels of trust between labour and management, means that new practices will be introduced through a distinctive process of negotiation and compromise giving rise to national specific effects.knowledge, learning processes, national specificity