Perturbation analysis and condition numbers of symmetric algebraic Riccati equations

This paper is devoted to the perturbation analysis of symmetric algebraic Riccati equations. Based on our perturbation analysis, the upper bounds for the normwise, mixed and componentwise condition numbers are presented. The results are demonstrated by our preliminary numerical experiments.     

SAIL: Structure-aware indexing for effective and progressive top-k keyword search over XML documents

Keyword search in XML documents has recently gained a lot of research attention. Given a keyword query, existing approaches first compute the lowest common ancestors (LCAs) or their variants of XML elements that contain the input keywords, and then identify the subtrees rooted at the LCAs as the answer. In this the paper we study how to use the rich structural relationships embedded in XML documents to facilitate the processing of keyword queries. We develop a novel method, called SAIL, to index such structural relationships for efficient XML keyword search. We propose the concept of minimal-cost trees to answer keyword queries and devise structure-aware indices to maintain the structural relationships for efficiently identifying the minimal-cost trees. For effectively and progressively identifying the top-k answers, we develop techniques using link-based relevance ranking and keyword-pair-based ranking. To reduce the index size, we incorporate a numbering scheme, namely schema-aware dewey code, into our structure-aware indices. Experimental results on real data sets show that our method outperforms state-of-the-art approaches significantly, in both answer quality and search efficiency.     

Online phenotype discovery based on minimum classification error model

Identifying and validating novel phenotypes from images inputting online is a major challenge against high-content RNA interference (RNAi) screening. Newly discovered phenotypes should be visually distinct from existing ones and make biological sense. An online phenotype discovery method featuring adaptive phenotype modeling and iterative cluster merging using improved gap statistics is proposed. Clustering results based on compactness criteria and Gaussian mixture models (GMM) for existing phenotypes iteratively modify each other by multiple hypothesis test and model optimization based on minimum classification error (MCE). The method works well on discovering new phenotypes adaptively when applied to both of synthetic datasets and RNAi high content screen (HCS) images with ground truth labels.     

An order-clique-based approach for mining maximal co-locations

Most algorithms for mining spatial co-locations adopt an Apriori-like approach to generate size-k prevalence co-locations after size-(k − 1) prevalence co-locations. However, generating and storing the co-locations and table instances is costly. A novel order-clique-based approach for mining maximal co-locations is proposed in this paper. The efficiency of the approach is achieved by two techniques: (1) the spatial neighbor relationships and the size-2 prevalence co-locations are compressed into extended prefix-tree structures, which allows the order-clique-based approach to mine candidate maximal co-locations and co-location instances; and (2) the co-location instances do not need to be stored after computing some characteristics of the corresponding co-location, which significantly reduces the execution time and space required for mining maximal co-locations. The performance study shows that the new method is efficient for mining both long and short co-location patterns, and is faster than some other methods (in particular the join-based method and the join-less method).     

Examining the Potentially Confounding Effect of Class Size on the Associations between Object-Oriented Metrics and Change-Proneness

Previous research shows that class size can influence the associations between object-oriented (OO) metrics and fault-proneness and therefore proposes that it should be controlled as a confounding variable when validating OO metrics on fault-proneness. Otherwise, their true associations may be distorted. However, it has not been determined whether this practice is equally applicable to other external quality attributes. In this paper, we use three size metrics, two of which are available during the high-level design phase, to examine the potentially confounding effect of class size on the associations between OO metrics and change-proneness. The OO metrics that are investigated include cohesion, coupling, and inheritance metrics. Our results, based on Eclipse, indicate that: 1) The confounding effect of class size on the associations between OO metrics and change-proneness, in general, exists, regardless of whichever size metric is used; 2) the confounding effect of class size generally leads to an overestimate of the associations between OO metrics and change-proneness; and 3) for many OO metrics, the confounding effect of class size completely accounts for their associations with change-proneness or results in a change of the direction of the associations. These results strongly suggest that studies validating OO metrics on change-proneness should also consider class size as a confounding variable.     

Opportunistic channel selection approach under collision probability constraint in cognitive radio systems

In this work, we consider a cognitive radio system with multiple primary channels and one secondary user, and then we introduce a channel-usage pattern model and some basic concepts in this system. Based on this system model and the basic concepts, we propose two opportunistic channel selection algorithms to optimize the throughput of the secondary user: minimum collision rate channel selection algorithm and minimum handoff rate channel selection algorithm. According to the two algorithms, we, respectively, present the channel selection scheme based on minimum collision rate algorithm (CSS-MCRA) and the channel selection scheme based on minimum handoff rate algorithm (CSS-MHRA) under the constraint that the collision probability is bounded below collision tolerable level. Theoretical analysis and simulation results both show that, on one hand, both CSS-MCRA scheme and CSS-MHRA can follow the constraint of collision tolerable level; on the other hand, the performance of CSS-MCRA scheme is better than that of CSS-MHRA scheme if handoff latency is zero or very low, while the performance of CSS-MHRA scheme is better than that of CSS-MCRA scheme if handoff latency is long enough.     

Fast,Sub‐pixel Antialiased Shadow Maps

Solving aliasing artifacts is an essential problem in shadow mapping approaches. Many works have been proposed, however, most of them focused on removing the texel‐level aliasing that results from the limited resolution of shadow maps. Little work has been done to solve the pixel‐level shadow aliasing that is produced by the rasterization on the screen plane. In this paper, we propose a fast, sub‐pixel antialiased shadowing algorithm to solve the pixel aliasing problem. Our work is based on the alias‐free shadow maps, which is capable of computing accurate per‐pixel shadow, and only incurs little cost to extend to sub‐pixel accuracy. Instead of direct supersampling the screen space, we take facets to approximate pixels in shadow testing. The shadowed area of one facet is rapidly evaluated by projecting blocker geometry onto a supersampled 2D occlusion mask with bitmasks fusion. It provides a sub‐pixel occlusion sampling so as to capture fine shadow details and features. Furthermore, we introduce the silhouette mask map that limits visibility evaluation to pixels only on the silhouette, which greatly reduces the computation cost. Our algorithm runs entirely on the GPU, achieving real‐time performance and is an order of magnitude faster than the brute‐force supersampling method to produce comparable 32× antialiased shadows.     

Efficient and provably secure password-based group key agreement protocol

This paper considers the issue on authenticated group key agreement protocol among n users broadcasting communication over an insecure public network. Many authenticated group Diffie-Hellman key agreement protocols have been proposed to meet the challenges. However, existing protocols are either limited by the use of public key infrastructure or by their scalability, requiring O(n) rounds. To overcome these disadvantages, we propose an efficient password-based group key agreement protocol resistant to the dictionary attacks by adding password-authentication services to a non-authenticated multi-party key agreement protocol proposed by Horng. The proposed protocol is very efficient since it only requires constant rounds to agree upon a session key, and each user broadcasts a constant number of messages and only requires four exponentiations. Under the Decisional Diffie-Hellman assumption, we will show the proposed protocol is provably secure in both the ideal-cipher model and the random-oracle model.     

Generalized predictive control of linear systems with actuator arrearage faults

Arrearage phenomenon commonly exists in actuators. With arrearages, the actuator cannot achieve the target suggested by the controller, so the control performance degrades. Deadzones and stuck faults can be considered to be two special classes of arrearages, which are studied widely; however, more general arrearage phenomenon cannot be described and treated by these forms. In this paper, a general formulation is introduced for this phenomenon, which is referred as actuator arrearage faults. Based on this formulation, an improved generalized predictive control scheme is proposed for a class of single-input single-output linear systems. With arrearage faults, the system becomes a stochastic process. Hence, a sufficient condition, which guarantees the output tracking error to be uniformly ultimately bounded in the mean-square sense, is presented in this paper. Simulations are given to illustrate the feasibility and effectiveness of the proposed scheme.