Robust rank correlation based screening

Independence screening is a variable selection method that uses a ranking criterion to select significant variables, particularly for statistical models with nonpolynomial dimensionality or "large p, small n" paradigms when p can be as large as an exponential of the sample size n. In this paper we propose a robust rank correlation screening (RRCS) method to deal with ultra-high dimensional data. The new procedure is based on the Kendall \tau correlation coefficient between response and predictor variables rather than the Pearson correlation of existing methods. The new method has four desirable features compared with existing independence screening methods. First, the sure independence screening property can hold only under the existence of a second order moment of predictor variables, rather than exponential tails or alikeness, even when the number of predictor variables grows as fast as exponentially of the sample size. Second, it can be used to deal with semiparametric models such as transformation regression models and single-index models under monotonic constraint to the link function without involving nonparametric estimation even when there are nonparametric functions in the models. Third, the procedure can be largely used against outliers and influence points in the observations. Last, the use of indicator functions in rank correlation screening greatly simplifies the theoretical derivation due to the boundedness of the resulting statistics, compared with previous studies on variable screening. Simulations are carried out for comparisons with existing methods and a real data example is analyzed.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1024 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org). arXiv admin note: text overlap with arXiv:0903.525

Mobility-Aware Caching for Content-Centric Wireless Networks: Modeling and Methodology

As mobile services are shifting from "connection-centric" communications to "content-centric" communications, content-centric wireless networking emerges as a promising paradigm to evolve the current network architecture. Caching popular content at the wireless edge, including base stations (BSs) and user terminals (UTs), provides an effective approach to alleviate the heavy burden on backhaul links, as well as lowering delays and deployment costs. In contrast to wired networks, a unique characteristic of content-centric wireless networks (CCWNs) is the mobility of mobile users. While it has rarely been considered by existing works in caching design, user mobility contains various helpful side information that can be exploited to improve caching efficiency at both BSs and UTs. In this paper, we present a general framework on mobility-aware caching in CCWNs. Key properties of user mobility patterns that are useful for content caching will be firstly identified, and then different design methodologies for mobility-aware caching will be proposed. Moreover, two design examples will be provided to illustrate the proposed framework in details, and interesting future research directions will be identified.Comment: 16 pages, 5 figures, to appear in IEEE Communications Magazin

Backhaul-Aware Caching Placement for Wireless Networks

As the capacity demand of mobile applications keeps increasing, the backhaul network is becoming a bottleneck to support high quality of experience (QoE) in next-generation wireless networks. Content caching at base stations (BSs) is a promising approach to alleviate the backhaul burden and reduce user-perceived latency. In this paper, we consider a wireless caching network where all the BSs are connected to a central controller via backhaul links. In such a network, users can obtain the required data from candidate BSs if the data are pre-cached. Otherwise, the user data need to be first retrieved from the central controller to local BSs, which introduces extra delay over the backhaul. In order to reduce the download delay, the caching placement strategy needs to be optimized. We formulate such a design problem as the minimization of the average download delay over user requests, subject to the caching capacity constraint of each BS. Different from existing works, our model takes BS cooperation in the radio access into consideration and is fully aware of the propagation delay on the backhaul links. The design problem is a mixed integer programming problem and is highly complicated, and thus we relax the problem and propose a low-complexity algorithm. Simulation results will show that the proposed algorithm can effectively determine the near-optimal caching placement and provide significant performance gains over conventional caching placement strategies.Comment: 6 pages, 3 figures, accepted to IEEE Globecom, San Diego, CA, Dec. 201

IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models

This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair. We propose a game theoretical minimax game to iteratively optimise both models. On one hand, the discriminative model, aiming to mine signals from labelled and unlabelled data, provides guidance to train the generative model towards fitting the underlying relevance distribution over documents given the query. On the other hand, the generative model, acting as an attacker to the current discriminative model, generates difficult examples for the discriminative model in an adversarial way by minimising its discrimination objective. With the competition between these two models, we show that the unified framework takes advantage of both schools of thinking: (i) the generative model learns to fit the relevance distribution over documents via the signals from the discriminative model, and (ii) the discriminative model is able to exploit the unlabelled data selected by the generative model to achieve a better estimation for document ranking. Our experimental results have demonstrated significant performance gains as much as 23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of applications including web search, item recommendation, and question answering.Comment: 12 pages; appendix adde

Analytical modeling of surface roughness in precision grinding of particle reinforced metal matrix composites considering nanomechanical response of material

Grinding is usually applied for particle reinforced metal matrix composites (PRMMCs) to achieve high ground surface quality. However, the surface quality especially surface roughness is difficult to predict theoretically due to different mechanical properties of two or more phases inside the PRMMCs. In this study, an analytical model of the surface roughness of ground PRMMCs is developed based on an undeformed chip thickness model with Rayleigh probability distribution by considering the different removal mechanism of metal matrix and reinforcement particles in grinding. GT35, a typical kind of steel based metal matrix composite reinforced with TiC particles is investigated as an example. Nanoindentation experiments are employed for the investigation of nanomechanical properties and cracking behavior of GT35 and the nanoindentation results are integrated in the model. Single factor surface grinding experiments of GT35 are also carried out to understand the material removal mechanism of GT35 and validate this novel surface roughness prediction model. The predicted surface roughness from this model shows good agreement with the experimental results