855 research outputs found
High-pressure study of picosecond exciton dynamics in solid C60
Journal ArticleWe have studied the singlet exciton decay by picosecond photoinduced absorption in films of Qo, under pressures up to 62 kbar. The picosecond decay of excitons excited in the absorption tail continues to be dominated by broad distributions of lifetimes at high pressure. These results suggest that the distributions of lifetimes do not arise from variations in tunneling or hopping rates between molecules as was originally suggested, but arise from distributions of recombination rates at different sites in the sample
Fraglight:shedding light on broken pointcuts in evolving aspect-oriented software
Pointcut fragility is a well-documented problem in Aspect-Oriented Programming; changes to the base-code can lead to join points incorrectly falling in or out of the scope of pointcuts. Deciding which pointcuts have broken due to base-code changes is a daunting venture, especially in large and complex systems. We demonstrate an automated tool called FRAGLIGHT that recommends a set of pointcuts that are likely to require modification due to a particular base-code change. The underlying approach is rooted in harnessing unique and arbitrarily deep structural commonality between program elements corresponding to join points selected by a pointcut in a particular software version. Patterns describing such commonality are used to recommend pointcuts that have potentially broken with a degree of confidence as the developer is typing. Our tool is implemented as an extension to the Mylyn Eclipse IDE plug-in, which maintains focused contexts of entities relevant to a task
Agent inferencing meets the semantic web
We provide all agent; the capability to infer the relations (assertions) entailed by the rules that, describe the formal semantics of art RDFS knowledge-base. The proposed inferencing process formulates each semantic restriction as a rule implemented within a, SPARQL query statement. The process expands the original RDF graph into a fuller graph that. explicitly captures the rule's described semantics. The approach is currently being explored in order to support descriptions that follow the generic Semantic Web Rule Language. An experiment, using the Fire-Brigade domain, a small-scale knowledge-base, is adopted to illustrate the agent modeling method and the inferencing process
Fast Color Quantization Using Weighted Sort-Means Clustering
Color quantization is an important operation with numerous applications in
graphics and image processing. Most quantization methods are essentially based
on data clustering algorithms. However, despite its popularity as a general
purpose clustering algorithm, k-means has not received much respect in the
color quantization literature because of its high computational requirements
and sensitivity to initialization. In this paper, a fast color quantization
method based on k-means is presented. The method involves several modifications
to the conventional (batch) k-means algorithm including data reduction, sample
weighting, and the use of triangle inequality to speed up the nearest neighbor
search. Experiments on a diverse set of images demonstrate that, with the
proposed modifications, k-means becomes very competitive with state-of-the-art
color quantization methods in terms of both effectiveness and efficiency.Comment: 30 pages, 2 figures, 4 table
Accurate, Fast and Scalable Kernel Ridge Regression on Parallel and Distributed Systems
We propose two new methods to address the weak scaling problems of KRR: the
Balanced KRR (BKRR) and K-means KRR (KKRR). These methods consider alternative
ways to partition the input dataset into p different parts, generating p
different models, and then selecting the best model among them. Compared to a
conventional implementation, KKRR2 (optimized version of KKRR) improves the
weak scaling efficiency from 0.32% to 38% and achieves a 591times speedup for
getting the same accuracy by using the same data and the same hardware (1536
processors). BKRR2 (optimized version of BKRR) achieves a higher accuracy than
the current fastest method using less training time for a variety of datasets.
For the applications requiring only approximate solutions, BKRR2 improves the
weak scaling efficiency to 92% and achieves 3505 times speedup (theoretical
speedup: 4096 times).Comment: This paper has been accepted by ACM International Conference on
Supercomputing (ICS) 201
Comprehension of spacecraft telemetry using hierarchical specifications of behavior ⋆
Abstract. A key challenge in operating remote spacecraft is that ground operators must rely on the limited visibility available through spacecraft telemetry in order to assess spacecraft health and operational status. We describe a tool for processing spacecraft telemetry that allows ground operators to impose structure on received telemetry in order to achieve a better comprehension of system state. A key element of our approach is the design of a domain-specific language that allows operators to express models of expected system behavior using partial specifications. The language allows behavior specifications with data fields, similar to other recent runtime verification systems. What is notable about our approach is the ability to develop hierarchical specifications of behavior. The language is implemented as an internal DSL in the Scala programming language that synthesizes rules from patterns of specification behavior. The rules are automatically applied to received telemetry and the inferred behaviors are available to ground operators using a visualization interface that makes it easier to understand and track spacecraft state. We describe initial results from applying our tool to telemetry received from the Curiosity rover currently roving the surface of Mars, where the visualizations are being used to trend subsystem behaviors, in order to identify potential problems before they happen. However, the technology is completely general and can be applied to any system that generates telemetry such as event logs.
A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm
K-means is undoubtedly the most widely used partitional clustering algorithm.
Unfortunately, due to its gradient descent nature, this algorithm is highly
sensitive to the initial placement of the cluster centers. Numerous
initialization methods have been proposed to address this problem. In this
paper, we first present an overview of these methods with an emphasis on their
computational efficiency. We then compare eight commonly used linear time
complexity initialization methods on a large and diverse collection of data
sets using various performance criteria. Finally, we analyze the experimental
results using non-parametric statistical tests and provide recommendations for
practitioners. We demonstrate that popular initialization methods often perform
poorly and that there are in fact strong alternatives to these methods.Comment: 17 pages, 1 figure, 7 table
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