A Build System for Benchmarking and Comparison of Competing System Implementations

When developing a hardware or software system, the problem at hand may lend itself to multiple solutions. During the implementation process for such systems, it can be helpful to prototype multiple versions that use distinct paradigms, and determine the efficiency of each according to some metric, such as execution time. This paper presents a portable, lightweight build system designed for easy benchmarking and verification of competing implementations of an algorithm. Also presented is a sample project that uses this system to compare the performance and correctness of CPU, GPU, and FPGA implementations of a signal recovery algorith

A Sense of Injustice: Experiences of the Central and Eastern European Roma in Accessing UK Health and Public Services

This study explores how Central and Eastern European Roma communities perceive and experience UK health and public services. The Roma arguably experience poorer health than any other minority ethnic group in the UK, according to a range of indicators, measures and outcomes. Health issues in UK Roma communities are reported to stem from language barriers, difficulties in registering with a GP and mistrust of health professionals. To date, there has been little comprehensive exploration of the influence of social, cultural and political factors on health in Central and Eastern European Roma communities in the UK. Much of the literature provides an overview of barriers to access, yet gives limited attention to the complex social dynamics underlying interactions between the Roma and health service providers. This study investigates the interplay of Roma migration experiences, personal histories of discrimination, barriers to accessing health and benefits systems, and the influence of public policy decisions in shaping UK Roma people’s experiences of health services. This study incorporates data collection through participant observation and interviews, as well as an analysis of national and local policy attention to the health situations of Gypsy, Roma and Traveller (GRT) communities. The policy analysis occurred concurrently with fieldwork, and findings helped to contextualise Roma interviewees’ impressions of health system functioning. Field data was collected over the course of volunteer work carried out at Roma community organisations in London and Luton. Field notes were recorded throughout the course the this fieldwork, and interviews with 27 Roma community members and 10 health professionals began after approximately 5 months of active participant observation. Analysis of field data proceeded according to a strategy that hybridised grounded theory and narrative methodologies. This entailed an initial round of grounded theory analysis, which involved the constant comparison methods of analysing concepts arising from the data, and provided an overarching framework for understanding the social phenomena under observation. Fieldnotes and selected interviews were then subjected to a second round of scrutiny, with particular emphasis on their narrative character. Analysing these individual stories added nuance and depth to the results of grounded theory analysis, and provided insight into the ways in which Roma migrants experience discrimination, unconscious bias and unequal treatment within UK public service environments. The time period in which this study occurred – 2014 to 2018 – was a time of substantial political change in the UK, and the results are best understood in the context of growing public hostility to migrant groups. Within a policy landscape of limited official attention to the needs of the Roma, intra-community development of health-related knowledge, direct contact with health services and engagement in a broader environment of public service provision contribute to the development of power differentials between Roma individuals and service providers. Many participants in this study suffered from long-term ill health, yet intra-community networks of health information-sharing aided participants in better understanding their health conditions and empowering themselves to seek out support. Their contact with health and benefits systems, however, revealed barriers related to limited language support, lack of transparency in administrative procedures, difficulties in navigating complex and unfamiliar systems and cultural disparities between patients and providers. Analysis of these factors in light of participants’ distinct narrative revealed further dimensions of service providers’ unconscious bias, participants’ efforts to assert their right to equitable access to services and the long-term emotional impacts of unequal treatment

The European Language Resources and Technologies Forum: Shaping the Future of the Multilingual Digital Europe

Proceedings of the 1st FLaReNet Forum on the European Language Resources and Technologies, held in Vienna, at the Austrian Academy of Science, on 12-13 February 2009

The DADO Parallel Computer

DADO is a parallel, tree-structured machine designed to provide significant performance improvements in the execution of large production systems. A full-scale production version of the DADO machine would comprise a large (on the order of a hundred thousand) set of processing elements (PE's), each containing its own processor, a small amount (8K bytes, in the current prototype design) of local random access memory, and a specialized I/O switch. The PE's are interconnected to form a complete binary tree. This paper describes the organization of, and programming language for two prototypes of the DADO system. We also detail a general procedure for the parallel execution of production systems on the DADO machine and outline how this procedure can be extended to include commutative and multiple, independent production systems. We then compare this with the RETE machine matching algorithm, and indicate how PROLOG programs may be implemented directly on DADO

A Comprehensive Method For Coordinating Distributed Energy Resources In A Power Distribution System

Utilities, faced with increasingly limited resources, strive to maintain high levels of reliability in energy delivery by adopting improved methodologies in planning, operation, construction and maintenance. On the other hand, driven by steady research and development and increase in sales volume, the cost of deploying PV systems has been in constant decline since their first introduction to the market. The increased level of penetration of distributed energy resources in power distribution infrastructure presents various benefits such as loss reduction, resilience against cascading failures and access to more diversified resources. However, serious challenges and risks must be addressed to ensure continuity and reliability of service. By integrating necessary communication and control infrastructure into the distribution system, to develop a practically coordinated system of distributed resources, controllable load/generation centers will be developed which provide substantial flexibility for the operation of the distribution system. On the other hand, such a complex distributed system is prone to instability and black outs due to lack of a major infinite supply and other unpredicted variations in load and generation, which must be addressed. To devise a comprehensive method for coordination between Distributed Energy Resources in order to achieve a collective goal, is the key point to provide a fully functional and reliable power distribution system incorporating distributed energy resources. A road map to develop such comprehensive coordination system is explained and supporting scenarios and their associated simulation results are then elaborated. The proposed road map describes necessary steps to build a comprehensive solution for coordination between multiple agents in a microgrid or distribution feeder.\u2

A simulation analysis of different allocation and pricing policies for cloud computing service providers

The cloud computing is regarded as a paradigm shift in nowadays IT world. As services of cloud computing behave like perishable products, revenue management techniques can be applied to increase cloud service provider's total revenue. In this thesis, we develop various methods for pricing and capacity allocation. We consider three types of instances; subscription, on-demand and spot instances. We introduce three allocation and pricing policies and propose 8 models based on their combinations. First, we establish a queuing mechanism for on-demand instances which are rejected initially by the cloud with a price incentive. Second, we consider an auction based model for spot instances and introduce two types of threshold policies where it is constant or dependent on the remaining capacity. Finally, the criterion for spot instances selection is based on expected revenue or bid of that customer. We simulate these models on several datasets and evaluate the models with di erent capacities. The results we obtain indicate the sensitivity of revenue based on the policies we propose over the studied datasets

The performance evaluation of interpreter based computer systems

PhD ThesisThis thesis explores the problem of making accurate assessments of the performance of high level language interpreter programs which are embedded in some more complex system. The overall system performance will be determined by all the software and hardware components present; but in order either to analyse and improve particular components, or to select between alternative versions of components, the concept of the performance of individual components is important. A model is developed for the abstract behaviour of software components playing the role of an interpreter by considering their interaction with the program code which is being interpreted and with the underlying virtual machine which is, in turn, interpreting them. This model enables a flexible definition of performance by relating the interactions in which an interpreter takes part. A methodology is recommended for assessing experimentally the performances defined within such a framework. The performances of an interesting selection of pseudo-machine and high level interpreter implementations of Lispkit and Prolog are then assessed and conclusions drawn.United Kingdom Science Research Counci

Statistical approaches of gene set analysis with quantitative trait loci for high-throughput genomic studies.

Recently, gene set analysis has become the first choice for gaining insights into the underlying complex biology of diseases through high-throughput genomic studies, such as Microarrays, bulk RNA-Sequencing, single cell RNA-Sequencing, etc. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Further, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. Hence, a comprehensive overview of the available gene set analysis approaches used for different high-throughput genomic studies is provided. The analysis of gene sets is usually carried out based on gene ontology terms, known biological pathways, etc., which may not establish any formal relation between genotype and trait specific phenotype. Further, in plant biology and breeding, gene set analysis with trait specific Quantitative Trait Loci data are considered to be a great source for biological knowledge discovery. Therefore, innovative statistical approaches are developed for analyzing, and interpreting gene expression data from Microarrays, RNA-sequencing studies in the context of gene sets with trait specific Quantitative Trait Loci. The utility of the developed approaches is studied on multiple real gene expression datasets obtained from various Microarrays and RNA-sequencing studies. The selection of gene sets through differential expression analysis is the primary step of gene set analysis, and which can be achieved through using gene selection methods. The existing methods for such analysis in high-throughput studies, such as Microarrays, RNA-sequencing studies, suffer from serious limitations. For instance, in Microarrays, most of the available methods are either based on relevancy or redundancy measures. Through these methods, the ranking of genes is done on single Microarray expression data, which leads to the selection of spuriously associated, and redundant gene sets. Therefore, newer, and innovative differential expression analytical methods have been developed for Microarrays, and single-cell RNA-sequencing studies for identification of gene sets to successfully carry out the gene set and other downstream analyses. Furthermore, several methods specifically designed for single-cell data have been developed in the literature for the differential expression analysis. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to review the performance of the existing methods. Hence, a comprehensive overview, classification, and comparative study of the available single-cell methods is hereby undertaken to study their unique features, underlying statistical models and their shortcomings on real applications. Moreover, to address one of the shortcomings (i.e., higher dropout events due to lower cell capture rates), an improved statistical method for downstream analysis of single-cell data has been developed. From the users’ point of view, the different developed statistical methods are implemented in various software tools and made publicly available. These methods and tools will help the experimental biologists and genome researchers to analyze their experimental data more objectively and efficiently. Moreover, the limitations and shortcomings of the available methods are reported in this study, and these need to be addressed by statisticians and biologists collectively to develop efficient approaches. These new approaches will be able to analyze high-throughput genomic data more efficiently to better understand the biological systems and increase the specificity, sensitivity, utility, and relevance of high-throughput genomic studies

A framework for the design of a medical tutoring system for the instruction of undergraduates in general practice.

One of the difficulties in teaching clinical medicine is the lack of opportunity a student has to acquire techniques for solving clinical problems. By using a computer to simulate a General Practice environment where patients with sets of symptoms are presented, a student can gain experience of diagnostic techniques and treatment management for any medical condition. Such an approach should enhance a student's development of properly structured clinical algorithms for interrogating a patient and arriving at an appropriate management plan. The intelligent tutoring system developed at the Department of Computer Science with the collaboration of the Department of General Practice aims not only to simulate this environment but also to formulate the basis for a general interactive learning environment for all subject domains with similar problem-solving model. In this system, a student may question, examine and provide treatment plans for a patient whilst constantly being monitored by the system:. Using Artificial Intelligence techniques, the tutor is able to assess the progress of a student throughout the tutorial session and produce tutoring interventions at appropriate stages, according to the student's ability. The system's knowledge base consists of disease profiles and population parameters which are created and updated by a separate system - the Medical Editor. The manipulation of this database allows tailoring of the system to simulate any clinical situation in Primary Care. This research considers in detail the current teaching/tutoring strategies adopted by all medical computer-assisted learning systems. It identifies the main areas of difficulty for using such systems in the Primary Care undergraduate course and discusses the consultation model used in this system with full comparison of the models used in Secondary Care. The research also discusses the main design issues which forms the framework for building learning environments based on intelligent tutoring systems

Financial information extraction using pre-defined and user-definable templates in the Lolita system

Financial operators have today access to an extremely large amount of data, both quantitative and qualitative, real-time or historical and can use this information to support their decision-making process. Quantitative data are largely processed by automatic computer programs, often based on artificial intelligence techniques, that produce quantitative analysis, such as historical price analysis or technical analysis of price behaviour. Differently, little progress has been made in the processing of qualitative data, which mainly consists of financial news articles from financial newspapers or on-line news providers. As a result the financial market players are overloaded with qualitative information which is potentially extremely useful but, due to the lack of time, is often ignored. The goal of this work is to reduce the qualitative data-overload of the financial operators. The research involves the identification of the information in the source financial articles which is relevant for the financial operators' investment decision making process and to implement the associated templates in the LOLITA system. The system should process a large number of source articles and extract specific templates according to the relevant information located in the source articles. The project also involves the design and implementation in LOLITA of a user- definable template interface for allowing the users to easily design new templates using sentences in natural language. This allows user-defined information extraction from source texts. This differs from most of existing information extraction systems which require the developers to code the templates directly in the system. The results of the research have shown that the system performed well in the extraction of financial templates from source articles which would allow the financial operator to reduce his qualitative data-overload. The results have also shown that the user-definable template interface is a viable approach to user-defined information extraction. A trade-off has been identified between the ease of use of the user-definable template interface and the loss of performance compared to hand- coded templates