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» A New Constraint for Mining Sets in Sequences.
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ICCAD
2004
IEEE
180views Hardware» more  ICCAD 2004»
14 years 4 months ago
Physical placement driven by sequential timing analysis
Traditional timing-driven placement considers only combinational delays and does not take into account the potential of subsequent sequential optimization steps. As a result, the ...
Aaron P. Hurst, Philip Chong, Andreas Kuehlmann
FIW
1998
130views Communications» more  FIW 1998»
13 years 9 months ago
A Heuristic Approach to Detect Feature Interactions in Requirements
We present a method to systematically detect feature interactions in requirements, which are expressed as constraints on system event traces. We show its application on the lift sy...
Maritta Heisel, Jeanine Souquières
JMLR
2012
11 years 10 months ago
Metric and Kernel Learning Using a Linear Transformation
Metric and kernel learning arise in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over low-dimensional d...
Prateek Jain, Brian Kulis, Jason V. Davis, Inderji...
NAR
2007
113views more  NAR 2007»
13 years 7 months ago
The UCSC genome browser database: update 2007
The University of California, Santa Cruz Genome Browser Database contains, as of September 2006, sequence and annotation data for the genomes of 13 vertebrate and 19 invertebrate ...
Robert M. Kuhn, Donna Karolchik, Ann S. Zweig, Hea...
CIKM
2008
Springer
13 years 9 months ago
SNIF TOOL: sniffing for patterns in continuous streams
Continuous time-series sequence matching, specifically, matching a numeric live stream against a set of predefined pattern sequences, is critical for domains ranging from fire spr...
Abhishek Mukherji, Elke A. Rundensteiner, David C....