This paper considers a network of distributed energy resources (DERs) that intend to optimize their power generation in an open price-based energy management system. Specifically, in the considered problem, the cost function of each DER, depends on not only its own decision variable but also an aggregate of all DERs’ energy decision variables. The DERs are cooperative to minimize their total cost of power generations, thus forming a distributed aggregative optimization problem. In addition, it is considered that the DERs are allowed to frequently arrive in and leave the system, which brings great difficulties in the algorithm design and analysis. To address this problem, a novel distributed optimization algorithm is proposed, by integrating gradient descent algorithms with a multi-level storage-based consensus mechanism. In addition, the performance of the proposed algorithm is evaluated by the dynamic regret, which quantifies the sum of all DERs’ loss between the actual cost and real time optimal cost during its active period length. It is shown that the upper bound of the all active DERs’ dynamic regret over active period length grows sublinearly under diminishing stepsizes. Finally, a numerical simulation is provided to verify the effectiveness of the proposed method.

Depth image denoising is increasingly becoming the hot research topic nowadays, because it reflects the three-dimensional scene and can be applied in various fields of computer vision. But the depth images obtained from depth camera usually contain stains such as noise, which greatly impairs the performance of depth-related applications. In this article, considering that group-based image restoration methods are more effective in gathering the similarity among patches, a group-based nuclear norm and learning graph (GNNLG) model was proposed. For each patch, we find and group the most similar patches within a searching window. The intrinsic low-rank property of the grouped patches is exploited in our model. In addition, we studied the manifold learning method and devised an effective optimized learning strategy to obtain the graph Laplacian matrix, which reflects the topological structure of image, to further impose the smoothing priors to the denoised depth image. To achieve fast speed and high convergence, the alternating direction method of multipliers is proposed to solve our GNNLG. The experimental results show that the proposed method is superior to other current state-of-the-art denoising methods in both subjective and objective criterion.

In embedded processors, the RAM resources required by branch predictor compared to desktop or server processors are far from being reached. Utilizing the limited resources to design superior performance branch predictor has become urgent challenge. In this paper, we exploit the performance of complex TAGE implemented in ultra-small RAM processor. We first define design space exploration problem of the TAGE under the constraints of given RAM size and maximum global history register length. Then, based on the trace-driven simulation, the improved Particle Swarm Optimization algorithm is used to efficiently explore the specific parameters, rewarding design parameters with high prediction accuracy under RAM ranging from 0.125KB to 4KB. We found that, for the traces of this paper, the parameters under the 1.5KB RAM explored by our algorithm can achieve adequate accuracy. The performance loss is considerably small if we reduce the RAM resources from 8KB to 1.5KB. In addition, the misprediction rate of 1.5KB TAGE are reduced by 63.41% compared to 1.5KB Bi-mode. And, 0.25KB TAGE has almost the same accuracy with 4KB GShare.

Besides the accuracy of prediction, chip area occupancy and power consumption also should be taken into account in the design of branch predictors. Many of the previous prediction simulation platforms have either only considered accuracy computed with coarse-grained updating model, or just been the low-speed full system simulators. In this paper, We presents SimpleBP, a lightweight prediction simulator based on trace driven. It leverages the SystemC language to simulate branch predictor at clock cycle granularity. And the CACTI model is introduced to evaluate area and power consumption. The experiment results show that SimpleBP can accurately give multiple evaluations of branch predictors.

In this work, we present the Klout Score, an influence scoring system that assigns scores to 750 million users across 9 different social networks on a daily basis. We propose a hierarchical framework for generating an influence score for each user, by incorporating information for the user from multiple networks and communities. Over 3600 features that capture signals of influential interactions are aggregated across multiple dimensions for each user. The features are scalably generated by processing over 45 billion interactions from social networks every day, as well as by incorporating factors that indicate real world influence. Supervised models trained from labeled data determine the weights for features, and the final Klout Score is obtained by hierarchically combining communities and networks. We validate the correctness of the score by showing that users with higher scores are able to spread information more effectively in a network. Finally, we use several comparisons to other ranking systems to show that highly influential and recognizable users across different domains have high Klout scores.

For many users on social networks, one of the goals when broadcasting content is to reach a large audience. The probability of receiving reactions to a message differs for each user and depends on various factors, such as location, daily and weekly behavior patterns and the visibility of the message. While previous work has focused on overall network dynamics and message flow cascades, the problem of recommending personalized posting times has remained an under-explored topic of research.

Yellow pages search is a popular service that provides a means for finding businesses close to particular locations. The efficient search of yellow pages is becoming a rapidly evolving research area. The underlying data maintained in yellow pages search engines are typically labeled according to Standard Industry Classification (SIC) categories, and users can search yellow pages with categories according to their interests. Categorizing yellow pages queries into a subset of topical categories can help to improve search experience and quality. However, yellow pages queries are usually short and ambiguous. In addition, a yellow pages query taxonomy is typically organized by a hierarchy of a fairly large number of categories. These characteristics make automatic yellow pages query categorization difficult and challenging. In this article, we propose a flexible yellow pages query categorization approach. The proposed technique is built based on a TF-IDF similarity taxonomy matching scheme that is able to provide more accurate query categorization than previous keyword-based matching schemes. To further improve the categorization performance, we design several filtering schemes. Through extensive experimentation, we demonstrate encouraging results. We obtain F1 measures of about 0.5 and 0.3 for categorizing yellow pages queries into 19 coarse categories and 244 finer categories, respectively. We investigate different components in the proposed approach and also demonstrate the superiority of our approach over a hierarchical support vector machine classifier.

In this poster, we introduce a web image search reranking approach with exploring multiple modalities. Diff erent from the conventional methods that build graph with one feature set for reranking, our approach integrates multiple feature sets that describe visual content from different aspects. We simultaneously integrate the learning of relevance scores, the weighting of different feature sets, the distance metric and the scaling for each feature set into a unified scheme. Experimental results on a large data set that contains more than 1,100 queries and 1 million images demonstrate the effectiveness of our approach.

Given a geographic query that is composed of query keywords and a location, a geographic search engine retrieves documents that are the most textually and spatially relevant to the query keywords and the location, respectively, and ranks the retrieved documents according to their joint textual and spatial relevances to the query. The lack of an efficient index that can simultaneously handle both the textual and spatial aspects of the documents makes existing geographic search engines inefficient in answering geographic queries. In this paper, we propose an efficient index, called IR-tree, that together with a top-k document search algorithm facilitates four major tasks in document searches, namely, 1) spatial filtering, 2) textual filtering, 3) relevance computation, and 4) document ranking in a fully integrated manner. In addition, IR-tree allows searches to adopt different weights on textual and spatial relevance of documents at the runtime and thus caters for a wide variety of applications. A set of comprehensive experiments over a wide range of scenarios has been conducted and the experiment results demonstrate that IR-tree outperforms the state-of-the-art approaches for geographic document searches.

Map search engines, such as Google Maps, Yahoo! Maps, and Microsoft Live Maps, allow users to explicitly specify a target geographic location, either in keywords or on the map, and to search businesses, people, and other information of that location. In this article, we report a first study on a million-entry map search log. We identify three key attributes of a map search record—the keyword query, the target location and the user location, and examine the characteristics of these three dimensions separately as well as the associations between them. Comparing our results with those previously reported on logs of general search engines and mobile search engines, including those for geographic queries, we discover the following unique features of map search: (1) People use longer queries and modify queries more frequently in a session than in general search and mobile search; People view fewer result pages per query than in general search; (2) The popular query topics in map search are different from those in general search and mobile search; (3) The target locations in a session change within 50 kilometers for almost 80% of the sessions; (4) Queries, search target locations and user locations (both at the city level) all follow the power law distribution; (5) One third of queries are issued for target locations within 50 kilometers from the user locations; (6) The distribution of a query over target locations appears to follow the geographic location of the queried entity.

It has been observed that many queries submitted to search engines are location-sensitive. Traditional search techniques fail to interpret the significance of such geographical clues and as such are unable to return highly relevant search results. Although there have been efforts in the literature to support location-aware information retrieval, critical challenges still remain in terms of search result quality and data scalability. In this paper, we propose an innovative probabilistic ranking framework for domain information retrieval where users are interested in a set of location-sensitive topics. Our proposed method recognizes the geographical distribution of topic influence in the process of ranking documents and models it accurately using probabilistic Gaussian Process classifiers. Additionally, we demonstrate the effectiveness of the proposed ranking framework by implementing it in a Web search service for NBA news. Extensive performance evaluation is performed on real Web document collections, which confirms that our proposed mechanism works significantly better (around 29.7% averagely using DCG20 measure) than other popular location-aware information retrieval techniques in ranking quality.