Scalable wavelet-based coding of irregular meshes with interactive region-of-interest support

This paper proposes a novel functionality in wavelet-based irregular mesh coding, which is interactive region-of-interest (ROI) support. The proposed approach enables the user to define the arbitrary ROIs at the decoder side and to prioritize and decode these regions at arbitrarily high-granularity levels. In this context, a novel adaptive wavelet transform for irregular meshes is proposed, which enables: 1) varying the resolution across the surface at arbitrarily fine-granularity levels and 2) dynamic tiling, which adapts the tile sizes to the local sampling densities at each resolution level. The proposed tiling approach enables a rate-distortion-optimal distribution of rate across spatial regions. When limiting the highest resolution ROI to the visible regions, the fine granularity of the proposed adaptive wavelet transform reduces the required amount of graphics memory by up to 50%. Furthermore, the required graphics memory for an arbitrary small ROI becomes negligible compared to rendering without ROI support, independent of any tiling decisions. Random access is provided by a novel dynamic tiling approach, which proves to be particularly beneficial for large models of over 10(6) similar to 10(7) vertices. The experiments show that the dynamic tiling introduces a limited lossless rate penalty compared to an equivalent codec without ROI support. Additionally, rate savings up to 85% are observed while decoding ROIs of tens of thousands of vertices

The role of national medicines agencies on the pharmaceutical innovative production and scope: A comparative case study of Norway and Sweden

This thesis aims to investigate the role of national medicines agencies on the innovative scope and productivity of national pharmaceutical small and medium-sized enterprises (SMEs) through comparative case study of Norway and Sweden. Institutional theory and resource based view are used to investigate the influence of institutional forces and strategic resources on shaping the roles of the agencies. The findings indicate that both factors have influenced the roles of Swedish medical products agency (MPA) and Norwegian medicines agency (NOMA). MPA seems to have an innovation facilitation role whereas NOMA does not. However, the Norwegian SMEs within drug discovery and development seem to perform better regarding innovative scope. The innovative productivity among Norwegian biopharmaceutical SMEs also appears to be on the rise. It is suggested that control variables, such as governmental initiatives on funding and tax benefits, have stronger influence on innovative scope and productivity compared to the role of the national medicines agency. Norwegian SMEs regularly seek guidance at medicines agencies in other countries. It is discussed that NOMA can contribute to an even higher performance of Norwegian SMEs if the agency changes its role towards more innovation-orientation and acts as a supporting organization. The practical implications of this research for NOMA have been elaborated

Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks

Loom (LM), a hardware inference accelerator for Convolutional Neural Networks (CNNs) is presented. In LM every bit of data precision that can be saved translates to proportional performance gains. Specifically, for convolutional layers LM's execution time scales inversely proportionally with the precisions of both weights and activations. For fully-connected layers LM's performance scales inversely proportionally with the precision of the weights. LM targets area- and bandwidth-constrained System-on-a-Chip designs such as those found on mobile devices that cannot afford the multi-megabyte buffers that would be needed to store each layer on-chip. Accordingly, given a data bandwidth budget, LM boosts energy efficiency and performance over an equivalent bit-parallel accelerator. For both weights and activations LM can exploit profile-derived perlayer precisions. However, at runtime LM further trims activation precisions at a much smaller than a layer granularity. Moreover, it can naturally exploit weight precision variability at a smaller granularity than a layer. On average, across several image classification CNNs and for a configuration that can perform the equivalent of 128 16b x 16b multiply-accumulate operations per cycle LM outperforms a state-of-the-art bit-parallel accelerator [1] by 4.38x without any loss in accuracy while being 3.54x more energy efficient. LM can trade-off accuracy for additional improvements in execution performance and energy efficiency and compares favorably to an accelerator that targeted only activation precisions. We also study 2- and 4-bit LM variants and find the the 2-bit per cycle variant is the most energy efficient

Generic model for application driven XML data processing

Abstract XML technology has emerged during recent years as a popular choice for representing and exchanging semi-structured data on the Web. It integrates seamlessly with web- based applications. If data is stored and represented as XML documents, then it should be possible to query the contents of these documents in order to extract, synthesize and analyze their contents. This thesis for experimental study of Web architecture for data processing is based on semantic mapping of XML Schema. The thesis involves complex methods and tools for specification, algorithmic transformation and online processing of semi- structured data over the Web in XML format with persistent storage into relational databases. The main focus of the research is preserving the structure of original data for data reconciliation during database updates and also to combine different technologies for XML data processing such as storing (SQL), transformation (XSL Processors), presenting (HTML), querying (XQUERY) and transporting (Web services) using a common framework, which is both theoretically and technologically well grounded. The experimental implementation of the discussed architecture requires a Web server (Apache), Java container (Tomcat) and object-relational DBMS (Oracle 9) equipped with Java engine and corresponding libraries for parsing and transformation of XML data (Xerces and Xalan). Furthermore the central idea behind the research is to use a single theoretical model of the data to be processed by the system (XML algebra) controlled by one standard metalanguage specification (XML Schema) for solving a class of problems (generic architecture). The proposed work combines theoretical novelty and technological advancement in the field of Internet computing. This thesis will introduce a generic approach since both our model (XML algebra) and our problem solver (the architecture of the integrated system) are XML Schema- driven. Starting with the XML Schema of the data, we first develop domain-specific XML algebra suitable for data processing of the specific data and then use it for implementing the main offline components of the system for data processing

The Leeds Evaluation of Efficacy of Detoxification Study (LEEDS) prisons project pilot study: protocol for a randomised controlled trial comparing dihydrocodeine and buprenorphine for opiate detoxification

Background In the United Kingdom (UK), there is an extensive market for the class 'A' drug heroin. Many heroin users spend time in prison. People addicted to heroin often require prescribed medication when attempting to cease their drug use. The most commonly used detoxification agents in UK prisons are buprenorphine, dihydrocodeine and methadone. However, national guidelines do not state a detoxification drug of choice. Indeed, there is a paucity of research evaluating the most effective treatment for opiate detoxification in prisons. This study seeks to address the paucity by evaluating routinely used interventions amongst drug using prisoners within UK prisons. Methods/Design The Leeds Evaluation of Efficacy of Detoxification Study (LEEDS) Prisons Pilot Study will use randomised controlled trial methodology to compare the open use of buprenorphine and dihydrocodeine for opiate detoxification, given in the context of routine care, within HMP Leeds. Prisoners who are eligible and give informed consent will be entered into the trial. The primary outcome measure will be abstinence status at five days post detoxification, as determined by a urine test. Secondary outcomes during the detoxification and then at one, three and six months post detoxification will be recorded

Gross Domestic Product (GDP) and productivity of schizophrenia trials: an ecological study

The 5000 randomised controlled trials (RCTs) in the Cochrane Schizophrenia Group's database affords an opportunity to research for variables related to the differences between nations of their output of schizophrenia trials. Ecological study – investigating the relationship between four economic/demographic variables and number of schizophrenia RCTs per country. The variable with closest correlation was used to predict the expected number of studies. GDP closely correlated with schizophrenia trial output, with 76% of the total variation about the Y explained by the regression line (r = 0.87, 95% CI 0.79 to 0.92, r2 = 0.76). Many countries have a strong tradition of schizophrenia trials, exceeding their predicted output. All nations with no identified trial output had GDPs that predicted zero trial activity. Several nations with relatively small GDPs are, nevertheless, highly productive of trials. Some wealthy countries seem either not to have produced the expected number of randomised trials or not to have disseminated them to the English-speaking world. This hypothesis-generating study could not investigate causal relationships, but suggests, that for those seeking all relevant studies, expending effort searching the scientific literature of Germany, Italy, France, Brazil and Japan may be a good investment