79,602 research outputs found
Nonnegative/binary matrix factorization with a D-Wave quantum annealer
D-Wave quantum annealers represent a novel computational architecture and
have attracted significant interest, but have been used for few real-world
computations. Machine learning has been identified as an area where quantum
annealing may be useful. Here, we show that the D-Wave 2X can be effectively
used as part of an unsupervised machine learning method. This method can be
used to analyze large datasets. The D-Wave only limits the number of features
that can be extracted from the dataset. We apply this method to learn the
features from a set of facial images
Using software visualization technology to help genetic algorithm designers
This work is part of a three year PhD project to examine how Software Visualization(SV) can be applied to support the design and construction of Genetic Algorithms (GAs). A user survey carried out at the start of this project identified a set of key system features required by GA users. A visualization system embodying these features was then designed and a prototype built. This paper describes what genetic algorithms are and how they can be applied. It then reviews some of the survey results and their impact on the design of the visualization system. The paper concludes with an exploration of how the resulting prototype may be evaluated
Logic programming in the context of multiparadigm programming: the Oz experience
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
The future of space exploration: the next 50 years
This repository item contains a single issue of Issues in Brief, a series of policy briefs that began publishing in 2008 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future.This policy brief flows from the 2007 Pardee Center conference on The Future of Space and lays out a set fo critical challenges for the next 50 years of space explorations
A Monitoring Language for Run Time and Post-Mortem Behavior Analysis and Visualization
UFO is a new implementation of FORMAN, a declarative monitoring language, in
which rules are compiled into execution monitors that run on a virtual machine
supported by the Alamo monitor architecture.Comment: In M. Ronsse, K. De Bosschere (eds), proceedings of the Fifth
International Workshop on Automated Debugging (AADEBUG 2003), September 2003,
Ghent. cs.SE/030902
Research on speech understanding and related areas at SRI
Research capabilities on speech understanding, speech recognition, and voice control are described. Research activities and the activities which involve text input rather than speech are discussed
The Locus Algorithm III: A Grid Computing system to generate catalogues of optimised pointings for Differential Photometry
This paper discusses the hardware and software components of the Grid
Computing system used to implement the Locus Algorithm to identify optimum
pointings for differential photometry of 61,662,376 stars and 23,799 quasars.
The scale of the data, together with initial operational assessments demanded a
High Performance Computing (HPC) system to complete the data analysis. Grid
computing was chosen as the HPC solution as the optimum choice available within
this project. The physical and logical structure of the National Grid computing
Infrastructure informed the approach that was taken. That approach was one of
layered separation of the different project components to enable maximum
flexibility and extensibility
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