Ratio Rule Mining from Multiple Data Sources

Abstract. Both multiple source data mining and streaming data mining problems have attracted much attention in the past decade. In contrast to traditional association-rule mining, to capture the quantitative association knowledge, a new paradigm called Ratio Rule (RR) was proposed recently. We extend this framework to mining ratio rules from multiple source data streams which is a novel and challenging problem. The traditional techniques used for ratio rule mining is an eigen-system analysis which can often fall victim to noises. The multiple data sources impose additional constraints for the mining procedure to be robust in the presence of noise, because it is difficult to clean all the data sources in real time in real-world tasks. In addition, the traditional batch methods for ratio rules cannot cope with data streams. In this paper, we propose an integrated method to mining ratio rules from data streams from multiple data sources, by first mining the ratio rules from each data source respectively through a novel robust and adaptive one-pass algorithm (which is called Robust and Adaptive Ratio Rule (RARR)), and then integrating the rules of each data source in a simple probabilistic model with a rule-clustering procedure. In this way, we can acquire the global rules from all the local information sources incrementally. We show that the RARR can converge to a fixed point and i

A Comparison Study of the Nutrient Fluxes in a Newly Impounded Riverine Lake (Longjing Lake): Model Calculation and Sediment Incubation

Diffusion flux is an essential tool to estimate the contribution of internal nitrogen and phosphorus in eutrophic lakes. There are mainly two methods, i.e., model calculation based on in-situ porewater sampling and water quality monitoring in laboratory incubation. The results obtained by the two methods are rarely compared, decreasing the validity of internal contribution and following management strategies. In this study, sediment samples were collected from a lake in China, then the fluxes were estimated by model calculation and laboratory incubation. The results show that there is an order of magnitude difference in the fluxes measured by these two methods. The mean values of ammonia (NH4+-N) and soluble reactive phosphate (SRP) obtained from the model calculations were 24.4 and 1.30, respectively. The mean values of NH4+-N and SRP obtained in the undisturbed group of sediment incubation were 7.84 and 5.47, respectively, and in the disturbed group of sediment incubation were 16.2 and 4.06, respectively. Sediment incubation is a combination of multiple influencing factors to obtain fluxes, while porewater model is based on molecular diffusion as the theoretical basis for obtaining fluxes. According to the different approaches of the two methods, sediment incubation is recommended as a research tool in lake autochthonous release management when the main objective is to remove pollution, while the porewater model is recommended as a research tool when the main objective is to control pollution. When assessing the diffusive flux of nitrogen, it is recommended to choose the stable form of total dissolved nitrogen to discuss the flux results

Conjugate and natural gradient rules for BYY harmony learning on Gaussian mixture with automated model selection

Under the Bayesian Ying–Yang (BYY) harmony learning theory, a harmony function has been developed on a BI-directional architecture of the BYY system for Gaussian mixture with an important feature that, via its maximization through a general gradient rule, a model selection can be made automatically during parameter learning on a set of sample data from a Gaussian mixture. This paper further proposes the conjugate and natural gradient rules to efficiently implement the maximization of the harmony function, i.e. the BYY harmony learning, on Gaussian mixture. It is demonstrated by simulation experiments that these two new gradient rules not only work well, but also converge more quickly than the general gradient ones. Keywords: Bayesian Ying–Yang learning; Gaussian mixture; automated model selection; conjugate gradient; natural gradient

Face Alignment Using View-Based Direct Appearance Models

Accurate face alignment is the prerequisite for many computer vision problems, such as face recognition, synthesis and 3-D face modeling. In this paper, a novel appearance model, called Direct Appearance Model (DAM), is proposed and its extended view-based models are applied for multiview face alignment. Similar to the active appearance model (AAM), DAM also makes ingenious use of both shape and texture constraints; however, it doesn't combine them as in AAM, texture information is used directly to predict the shape and estimate the position and appearance (hence the name DAM). The way that DAM models shapes and tex- tures has the following advantages as compared with AAM: (1) DAM subspaces include admissible appearances previously unseen in AAM, (2) It can converge more quickly and has higher accuracy, and (3) the memory requirement is cut down to a large extent. Extensive experiments are presented to evaluate the DAM alignment in comparison with AAM. I

Heterologous Replicase from Cucumoviruses can Replicate Viral RNAs, but is Defective in Transcribing Subgenomic RNA4A or Facilitating Viral Movement

Interspecific exchange of RNA1 or RNA2 between the cucumoviruses cucumber mosaic virus (CMV) and tomato aspermy virus (TAV) was reported to be non-viable in plants previously. Here we investigated viability of the reassortants between CMV and TAV in Nicotiana benthamiana plants by Agrobacterium-mediated viral inoculation. The reassortants were composed of CMV RNA1 and TAV RNA2 plus RNA3 replicated in the inoculated leaves, while they were defective in viral systemic movement at the early stage of infection. Interestingly, the reassortant containing TAV RNA1 and CMV RNA2 and RNA3 infected plants systemically, but produced RNA4A (the RNA2 subgenome) at an undetectable level. The defect in production of RNA4A was due to the 1a protein encoded by TAV RNA1, and partially restored by replacing the C-terminus (helicase domain) in TAV 1a with that of CMV 1a. Collectively, exchange of the replicase components between CMV and TAV was acceptable for viral replication, but was defective in either directing transcription of subgenomic RNA4A or facilitating viral long-distance movement. Our finding may shed some light on evolution of subgenomic RNA4A in the family Bromoviridae

Diverse Topic Phrase Extraction from Text Collection

Keyword extraction is an efficient approach to managing an explosion of online text on the Web. Traditionally, an abstraction of the online text is constructed though keywords, which are extracted according to a certain importance measure. One such measure is their occurrence frequency. However, previous work has not considered another important factor: the diversity of th

Face Alignment Using Texture-Constrained Active Shape Models

In this paper, we propose a texture-constrained active shape model (TC-ASM) to localize a face in an image. TC-ASM effectively incorporates not only the shape prior and local appearance around each landmark, but also the global texture constraint over the shape. Therefore, it performs stable to initialization, accurate in shape localization and robust to illumination variation, with low computational cost. Extensive experiments are provided to demonstrate our algorithm