229 research outputs found

    A Determinacy Testing Algorithm for Nondeterminate Flat Concurrent Logic Programming Languages

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    39 pagesThis paper describes an algorithm for the code generation of determinacy testing for nondeterminate flat concurrent logic programming languages. Languages such as Andorra and Pandora require that procedure invocations suspend if there is more than one candidate clause potentially satisfying the goal. The algorithm described has been developed specifically for a variant of flat Pandora based on FGHC, although the concepts are general. We have extended Kliger and Shapiro's decision-graph construction algorithm to compile "don't know" procedures which must suspend for nondeterminate goal invocation. The determinacy test is compiled into a decision graph quite different from those of committed-choice procedures, but we argue that in most cases, the same low space complexity is retained

    Probabilistic and Machine Learning Enhancement to CONN Toolbox

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    Clinical depression is a state of mind where the person suffers from persevering and overpowering sorrow. Existing examinations have exhibited that the course of action of arrangement in the brain of patients with clinical depression has a weird framework topology structure. In the earlier decade, resting-state images of the brain have been under the radar a. Specifically, the topological relationship of the brain aligned with graph hypothesis has discovered a strong connection in patients experiencing clinical depression. However, the systems to break down brain networks still have a couple of issues to be unwound. This paper attempts to give a machine-learning answer for the graph-based brain network investigations of resting state graphs analysis. This model attempts to determine a cost function for a given pair of nodes in the brain to check whether they are connected or not. It can be utilized by medicinal experts to treat patients experiencing clinical depression which helps in decision making of whether a node can be hit directly or not to cure the patient

    The Impacts of Machine Learning in Financial Crisis Prediction

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    The most complicated and expected issue to be handled in corporate firms, small-scale businesses, and investors’ even governments are financial crisis prediction. To this effect, it was of interest to us to investigate the current impact of the newly employed technique that is machine learning (ML) to handle this menace in all spheres of business both private and public. The study uses systematic literature assessment to study the impact of ML in financial crisis prediction. From the selected works of literature, we have been able to establish the important role play by this method in the prediction of bankruptcy and creditworthiness that was not handled appropriately by others method. Also, machine learning helps in data handling, data privacy, and confidentiality. This study presents a leading approach to achieving financial growth and plasticity in corporate organizations. We, therefore, recommend a real-time study to investigate the impact of ML in FCP. &nbsp

    Processor Models For Instruction Scheduling using Constraint Programming

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    Instruction scheduling is one of the most important optimisations performed when producing code in a compiler. The problem consists of finding a minimum length schedule subject to latency and different resource constraints. This is a hard problem, classically approached by heuristic algorithms. In the last decade, research interest has shifted from heuristic to potentially optimal methods. When using optimal methods, a lot of compilation time is spent searching for an optimal solution. This makes it important that the problem definition reflects the reality of the processor. In this work, a constraint programming approach was used to study the impact that the model detail has on performance. Several models of a superscalar processor were embedded in LLVM and evaluated using SPEC CPU2000. The result shows that there is substantial performance to be gained, over 5% for some programs. The stability of the improvement is heavily dependent on the accuracy of the model

    A comparative study of structured and un-structured remote data access in distributed computing systems

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    Recently, the use of distributed computing systems has been growing rapidly due to the result of cheap and advanced microelectronic technology. In addition to the decrease in hardware costs, the tremendous development in machine to machine communication interfaces, especially in local area networking, also favours the use of distributed systems. Distributed systems often require remote access to data stored at different sites. Generally, two models of access to remote data storage exist: the un structured and structured models. In the former, data is simply stored as row of bytes, whereas in the latter, data is stored along with the associated access codes. The objective of this thesis is to compare these two models and hence determines the tradeoffs of each model. First of all, an extended review of the field of distributed data access is provided which addressing key issues such as the basic design principles of distributed computing systems, the notions of abstract data types, data inheritance, data type system and data persistence. Secondly, a distributed system is implemented using the persistent programming language PS-algol and the high level language C in conjunction with the remote procedure call facilities available in Unix(^1) 4.2 BSD operating system. This distributed system makes extensive use of Unix's software tools and hence it is called DCSUNIX for Distributed Computing System on UNIX. Thirdly, two specific applications which employ the implemented system will be given so that a comparison can be made between the two remote data access models mentioned above. Finally, the implemented system is compared with the criteria established earlier in the thesis. keywords: abstract data types, class, database management, data persistence, information hiding, inheritance, object oriented programming, programming languages, remote procedure calls, transparency, and type checking

    A Survey of Pipelined Workflow Scheduling: Models and Algorithms

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    International audienceA large class of applications need to execute the same workflow on different data sets of identical size. Efficient execution of such applications necessitates intelligent distribution of the application components and tasks on a parallel machine, and the execution can be orchestrated by utilizing task-, data-, pipelined-, and/or replicated-parallelism. The scheduling problem that encompasses all of these techniques is called pipelined workflow scheduling, and it has been widely studied in the last decade. Multiple models and algorithms have flourished to tackle various programming paradigms, constraints, machine behaviors or optimization goals. This paper surveys the field by summing up and structuring known results and approaches

    Streaming-Based Progressive Enhancement of Websites for Slow and Error-Prone Networks

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    This thesis aims to improve the loading times of web pages by streaming the content in a non-render-blocking way. At the beginning of this thesis, a large-scale analysis was performed, spanning all downloadable pages of the top 10.000 web pages according to the Tranco-list. This analysis aimed to gather data about the render-blocking properties of web page resources, including HTML, JavaScript, and CSS. It further gathered data about code coverage, giving insight into how much of the render-blocking code is actually used. Therefore, the structural optimization potential could be determined. Less render-blocking code will, in turn, lead to faster loading times due to requiring less data to display the page. The analysis showed that there is significant optimization potential left. On average, modern web pages are built with a combined 86.7% of JavaScript and CSS, the rest being HTML. Both JavaScript and CSS are loaded mostly render-blocking, with 91.8% of JavaScript and 89.47% of CSS loaded in this way. Furthermore, only 40.8% of JavaScript and 15.9% of CSS is used until render. This shows that, on average, web pages have significant room for improvement. The concept, which is then developed based on the results of this analysis, aims to load web pages in a new way by streaming all render-blocking content. The related work showed that multiple sub-techniques are required first, which were conceptualized next. First, an optimization and splitting tool for CSS is proposed, called Essential. This is followed by an optimization framework concept for JavaScript, consisting of Waiter and AUTRATAC. Lastly, a backward-compatible approach was developed, which allows for splitting HTML and streaming all content to a client. The evaluation showed that the streamed web page loads significantly faster when comparing FCP, content ”Above-the-Fold,” and total transfer time of all render-blocking resources of the document. For example, the case study test determined that the streamed page could reduce the time until FCP by 83.3% at 2 Mbps and the time until the last render-blocking data is transferred by up to 70.4% at 2 Mbps. Furthermore, existing streaming methods were also compared, determining that WebSockets meets the requirements to stream web page content sufficiently. Lastly, an anonymous online user questionnaire showed that 85% of users preferred this new style of loading pages

    Logic programming in the context of multiparadigm programming: the Oz experience

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    Oz is a multiparadigm language that supports logic programming as one of its major paradigms. A multiparadigm language is designed to support different programming paradigms (logic, functional, constraint, object-oriented, sequential, concurrent, etc.) with equal ease. This article has two goals: to give a tutorial of logic programming in Oz and to show how logic programming fits naturally into the wider context of multiparadigm programming. Our experience shows that there are two classes of problems, which we call algorithmic and search problems, for which logic programming can help formulate practical solutions. Algorithmic problems have known efficient algorithms. Search problems do not have known efficient algorithms but can be solved with search. The Oz support for logic programming targets these two problem classes specifically, using the concepts needed for each. This is in contrast to the Prolog approach, which targets both classes with one set of concepts, which results in less than optimal support for each class. To explain the essential difference between algorithmic and search programs, we define the Oz execution model. This model subsumes both concurrent logic programming (committed-choice-style) and search-based logic programming (Prolog-style). Instead of Horn clause syntax, Oz has a simple, fully compositional, higher-order syntax that accommodates the abilities of the language. We conclude with lessons learned from this work, a brief history of Oz, and many entry points into the Oz literature.Comment: 48 pages, to appear in the journal "Theory and Practice of Logic Programming

    B Physics at the Tevatron: Run II and Beyond

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    This report provides a comprehensive overview of the prospects for B physics at the Tevatron. The work was carried out during a series of workshops starting in September 1999. There were four working groups: 1) CP Violation, 2) Rare and Semileptonic Decays, 3) Mixing and Lifetimes, 4) Production, Fragmentation and Spectroscopy. The report also includes introductory chapters on theoretical and experimental tools emphasizing aspects of B physics specific to hadron colliders, as well as overviews of the CDF, D0, and BTeV detectors, and a Summary.Comment: 583 pages. Further information on the workshops, including transparencies, can be found at the workshop's homepage: http://www-theory.lbl.gov/Brun2/. The report is also available in 2-up http://www-theory.lbl.gov/Brun2/report/report2.ps.gz or chapter-by-chapter http://www-theory.lbl.gov/Brun2/report
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