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Our impression about one person often updates after we see more aspects of him/her and this process keeps iterating given more meetings. We formulate such an intuition into the problem of person re-identification (re-ID), where the representation of a query (probe) image is iteratively updated with new information from the candidates in the gallery. Specifically, we propose a simple attentional aggregation formulation to instantiate this idea and showcase that such a pipeline achieves competitive performance on standard benchmarks including CUHK03, Market-1501 and DukeMTMC. Not only does such a simple method improve the performance of the baseline models, it also achieves comparable performance with latest advanced re-ranking methods. Another advantage of this proposal is its flexibility to incorporate different representations and similarity metrics. By utilizing stronger representations and metrics, we further demonstrate state-of-the-art person re-ID performance, which also validates the general applicability of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes and evaluates a systematic method of scheduling energy storage and conventional generation capacities in a day-ahead frequency regulation market, based on compliance to control performance standard 1 (CPS1), during each hour of the following day. The salient feature of this method is the development of two simple piecewise linear curves that represent the relationship between first, conventional generation and energy storage power capacities, and second, energy storage power and energy capacities, to satisfy CPS1 compliance in the presence of stochastic load variations. These curves are then modeled inside a mixed integer linear program to solve for resource allocations by minimizing total capacity cost. As such, the formulation avoids complex algorithms that are otherwise required to model frequency dynamics inside the optimization problem. To reduce computation time, a scenario reduction algorithm is used to obtain a small set of scenarios to represent the stochastic load. The proposed method is evaluated using comprehensive dynamic simulations of the two-area IEEE reliability test system implemented in PSS/E. The results prove that the proposed method is effective for scheduling capacities to meet CPS1 compliance with minimum cost. [ABSTRACT FROM AUTHOR]

MapReduce has become a major computing model for data intensive applications. Hadoop, an open source implementation of MapReduce, has been adopted by an increasingly growing user community. Cloud computing service providers such as Amazon EC2 Cloud offer the opportunities for Hadoop users to lease a certain amount of resources and pay for their use. However, a key challenge is that cloud service providers do not have a resource provisioning mechanism to satisfy user jobs with deadline requirements. Currently, it is solely the user's responsibility to estimate the required amount of resources for running a job in the cloud. This paper presents a Hadoop job performance model that accurately estimates job completion time and further provisions the required amount of resources for a job to be completed within a deadline. The proposed model builds on historical job execution records and employs Locally Weighted Linear Regression (LWLR) technique to estimate the execution time of a job. Furthermore, it employs Lagrange Multipliers technique for resource provisioning to satisfy jobs with deadline requirements. The proposed model is initially evaluated on an in-house Hadoop cluster and subsequently evaluated in the Amazon EC2 Cloud. Experimental results show that the accuracy of the proposed model in job execution estimation is in the range of 94.97 and 95.51 percent, and jobs are completed within the required deadlines following on the resource provisioning scheme of the proposed model. [ABSTRACT FROM AUTHOR]