Conventional gang scheduling has the disadvantage that when processes perform I/O or blocking communication, their processors remain idle, because alternative processes cannot be run independently of their own gangs. To alleviate this problem we suggest a slight relaxation of this rule: match gangs

Identifying criminal gangs and monikers is one of the most important tasks for graffiti analysis in low enforcement. In current practice, this is typically performed manually by the law enforcement officers, which is not only time-consuming but also results in limited identification performance

speaker to represent different parts of the characteristic space and fuse the scores to fit the test data on the models with the purpose of increasing the matching degree between training models and test utterance. Experimental results showed that the proposed method achieved a relative EER reduction

trees. The final face representation is a histogram of the quantized descriptors. The location expansion constrains the quantization regions to be localized not just in feature space but also in image space, allowing us to achieve an implicit elastic matching for face images. Our experiments

not match the number of available processors. We show that in DHC this effect can be reduced by reclaiming the leftover processors when the gang size is smaller than the allocated block of processors, and by adjusting the scheduling time quantum to control the adverse effect of badly-matched gangs

Abstract-- A 2.4GHz power amplifier is implemented with standard thin-oxide transistors in a 1.2V, 0.13 µm CMOS process. The output matching network is fully integrated on chip. The PA transmits up to 24dBm linear power with 25% drain efficiency at-1dB compression point. When driven into saturation

related to Watson (1993) and Diebold, Ohanian and Berkowitz (1995) to test whether the second moments of the actual macroeconomic time series data are matched by the model's time series. Several models are explored. The overall results are that sensible param-eter estimates may be obtained when

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The spread of youth gangs to nonmetropolitan counties in the 1990s has been widely cited but difficult to document empirically nd interpret theo-retically. Using merged ata from the 1996, 1997, and 1998 National Youth Gang Surveys, and matching the combined National Youth Gang Surveys

deception. In this study we propose a probabilistic relational model (PRM) based approach to match identities in databases. By exploring a database relational structure, we derive three categories of features, namely personal identity features, social activity features, and social relationship features