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Search Results: 1 - 10 of 33278 matches for " Min Fan "
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A Perceptual Video Coding Based on JND Model  [PDF]
Qingming Yi, Wenhui Fan, Min Shi
Journal of Computer and Communications (JCC) , 2018, DOI: 10.4236/jcc.2018.64005
Abstract: In view of the fact that the current high efficiency video coding standard does not consider the characteristics of human vision, this paper proposes a perceptual video coding algorithm based on the just noticeable distortion model (JND). The adjusted JND model is combined into the transformation quantization process in high efficiency video coding (HEVC) to remove more visual redundancy and maintain compatibility. First of all, we design the JND model based on pixel domain and transform domain respectively, and the pixel domain model can give the JND threshold more intuitively on the pixel. The transform domain model introduces the contrast sensitive function into the model, making the threshold estimation more precise. Secondly, the proposed JND model is embedded in the HEVC video coding framework. For the transformation skip mode (TSM) in HEVC, we adopt the existing pixel domain called nonlinear additively model (NAMM). For the non-transformation skip mode (non-TSM) in HEVC, we use transform domain JND model to further reduce visual redundancy. The simulation results show that in the case of the same visual subjective quality, the algorithm can save more bitrates.
BRCA1: A New Predictive Genomic Marker for Chemotherapy and Radiotherapy of Non-small Cell Lung Cancer
Min LI,Saijun FAN
Chinese Journal of Lung Cancer , 2012, DOI: 10.3779/j.issn.1009-3419.2012.08.06
Abstract: Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors worldwide. Chemotherapy and radiotherapy are the main methods in therapy of NSCLC. The identification and characterization of predictive markers of radio- and chemo-sensitivity will be beneficial with customized treatment of NSCLC patients. This article will review the evidence regarding breast cancer susceptibility gene 1 (BRCA1) as a new predictive marker for chemotherapy and radiotherapy of NSCLC.
Granular association rules for multi-valued data
Fan Min,William Zhu
Computer Science , 2013,
Abstract: Granular association rule is a new approach to reveal patterns hide in many-to-many relationships of relational databases. Different types of data such as nominal, numeric and multi-valued ones should be dealt with in the process of rule mining. In this paper, we study multi-valued data and develop techniques to filter out strong however uninteresting rules. An example of such rule might be "male students rate movies released in 1990s that are NOT thriller." This kind of rules, called negative granular association rules, often overwhelms positive ones which are more useful. To address this issue, we filter out negative granules such as "NOT thriller" in the process of granule generation. In this way, only positive granular association rules are generated and strong ones are mined. Experimental results on the movielens data set indicate that most rules are negative, and our technique is effective to filter them out.
Cold-start recommendation through granular association rules
Fan Min,William Zhu
Computer Science , 2013,
Abstract: Recommender systems are popular in e-commerce as they suggest items of interest to users. Researchers have addressed the cold-start problem where either the user or the item is new. However, the situation with both new user and new item has seldom been considered. In this paper, we propose a cold-start recommendation approach to this situation based on granular association rules. Specifically, we provide a means for describing users and items through information granules, a means for generating association rules between users and items, and a means for recommending items to users using these rules. Experiments are undertaken on a publicly available dataset MovieLens. Results indicate that rule sets perform similarly on the training and the testing sets, and the appropriate setting of granule is essential to the application of granular association rules.
Mining top-k granular association rules for recommendation
Fan Min,William Zhu
Computer Science , 2013,
Abstract: Recommender systems are important for e-commerce companies as well as researchers. Recently, granular association rules have been proposed for cold-start recommendation. However, existing approaches reserve only globally strong rules; therefore some users may receive no recommendation at all. In this paper, we propose to mine the top-k granular association rules for each user. First we define three measures of granular association rules. These are the source coverage which measures the user granule size, the target coverage which measures the item granule size, and the confidence which measures the strength of the association. With the confidence measure, rules can be ranked according to their strength. Then we propose algorithms for training the recommender and suggesting items to each user. Experimental are undertaken on a publicly available data set MovieLens. Results indicate that the appropriate setting of granule can avoid over-fitting and at the same time, help obtaining high recommending accuracy.
Granular association rule mining through parametric rough sets for cold start recommendation
Fan Min,William Zhu
Computer Science , 2012,
Abstract: Granular association rules reveal patterns hide in many-to-many relationships which are common in relational databases. In recommender systems, these rules are appropriate for cold start recommendation, where a customer or a product has just entered the system. An example of such rules might be "40% men like at least 30% kinds of alcohol; 45% customers are men and 6% products are alcohol." Mining such rules is a challenging problem due to pattern explosion. In this paper, we propose a new type of parametric rough sets on two universes to study this problem. The model is deliberately defined such that the parameter corresponds to one threshold of rules. With the lower approximation operator in the new parametric rough sets, a backward algorithm is designed for the rule mining problem. Experiments on two real world data sets show that the new algorithm is significantly faster than the existing sandwich algorithm. This study indicates a new application area, namely recommender systems, of relational data mining, granular computing and rough sets.
Multivariate two-sample extended empirical likelihood
Fan Wu,Min Tsao
Statistics , 2013,
Abstract: Jing (1995) and Liu et al. (2008) studied the two-sample empirical likelihood and showed it is Bartlett correctable for the univariate and multivariate cases, respectively. We expand its domain to the full parameter space and obtain a two-sample extended empirical likelihood which is more accurate and can also achieve the second-order accuracy of the Bartlett correction.
Extended empirical likelihood for general estimating equations
Min Tsao,Fan Wu
Statistics , 2013,
Abstract: We derive an extended empirical likelihood for parameters defined by estimating equations which generalizes the original empirical likelihood for such parameters to the full parameter space. Under mild conditions, the extended empirical likelihood has all asymptotic properties of the original empirical likelihood. Its contours retain the data-driven shape of the latter. It can also attain the second order accuracy. The first order extended empirical likelihood is easy-to-use yet it is substantially more accurate than other empirical likelihoods, including second order ones. We recommend it for practical applications of the empirical likelihood method.
Empirical likelihood on the full parameter space
Min Tsao,Fan Wu
Statistics , 2013, DOI: 10.1214/13-AOS1143
Abstract: We extend the empirical likelihood of Owen [Ann. Statist. 18 (1990) 90-120] by partitioning its domain into the collection of its contours and mapping the contours through a continuous sequence of similarity transformations onto the full parameter space. The resulting extended empirical likelihood is a natural generalization of the original empirical likelihood to the full parameter space; it has the same asymptotic properties and identically shaped contours as the original empirical likelihood. It can also attain the second order accuracy of the Bartlett corrected empirical likelihood of DiCiccio, Hall and Romano [Ann. Statist. 19 (1991) 1053-1061]. A simple first order extended empirical likelihood is found to be substantially more accurate than the original empirical likelihood. It is also more accurate than available second order empirical likelihood methods in most small sample situations and competitive in accuracy in large sample situations. Importantly, in many one-dimensional applications this first order extended empirical likelihood is accurate for sample sizes as small as ten, making it a practical and reliable choice for small sample empirical likelihood inference.
Two-sample extended empirical likelihood for estimating equations
Min Tsao,Fan Wu
Statistics , 2014,
Abstract: We propose a two-sample extended empirical likelihood for inference on the difference between two p-dimensional parameters defined by estimating equations. The standard two-sample empirical likelihood for the difference is Bartlett correctable but its domain is a bounded subset of the parameter space. We expand its domain through a composite similarity transformation to derive the two-sample extended empirical likelihood which is defined on the full parameter space. The extended empirical likelihood has the same asymptotic distribution as the standard one and can also achieve the second order accuracy of the Bartlett correction. We include two applications to illustrate the use of two-sample empirical likelihood methods and to demonstrate the superior coverage accuracy of the extended empirical likelihood confidence regions.
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