The existing non‐hybrid image inpainting techniques can be broadly classified into two types. One is the texture‐based inpainting and the other is the structure‐based inpainting. One critical drawback of those techniques is that their inpainting results are not effective for the images with a mixture of texture and structure features in terms of visual quality or processing time. However, the conventional hybrid inpainting algorithms, which aim at inpainting images with texture and structure features, do not effectively deal with the two items: (i) what is the most effective application order of the constituents? and (ii) how can one extract a minimal sub‐image that may contain best candidates of inpainting source? In this study, the authors propose a new hybrid inpainting algorithm to address the two tasks fully and effectively. Precisely, the authors’ algorithm attempts to solve two key ingredients: (i) (right time) determining the best application order for inpainting textural and structural missing regions and (ii) (right place) extracting the sub‐image containing best candidates of source patches to be used to fill in a target region.

Increasing Internet business and computing footprint motivate server consolidation in data centers. Through virtualization technology, server consolidation can reduce physical hosts and provide scalable services. However, the ineffective memory usage among multiple virtual machines (VMs) becomes the bottleneck in server consolidation environment. Because of inaccurate memory usage estimate and the lack of memory resource managements, there is much service performance degradation in data centers, even though they have occupied a large amount of memory. In order to improve this scenario, we first introduce VM's memory division view and VM's free memory division view. Based on them, we propose a hierarchal memory service mechanism. We have designed and implemented the corresponding memory scheduling algorithm to enhance memory efficiency and achieve service level agreement. The benchmark test results show that our implementation can save 30% physical memory with 1% to 5% performance degradation. Based on Xen virtualization platform and balloon driver technology, our works actually bring dramatic benefits to commercial cloud computing center which is providing more than 2,000 VMs' services to cloud computing users.

MOTIVATION: A number of methods have been reported that predict protein-protein interactions (PPIs) with high accuracy using only simple sequence-based features such as amino acid 3mer content. This is surprising, given that many protein interactions have high specificity that depends on detailed atomic recognition between physiochemically complementary surfaces. Are the reported high accuracies realistic? RESULTS: We find that the reported accuracies of the predictions are significantly over-estimated, and strongly dependent on the structure of the training and testing datasets used. The choice of which protein pairs are deemed as non-interactions in the training data has a variable impact on the accuracy estimates, and the accuracies can be artificially inflated by a bias towards dominant samples in the positive data which result from the presence of hub proteins in the protein interaction network. To address this bias, we propose a positive set-specific method to create a 'balanced' negative set maintaining the degree distribution for each protein, leading to the conclusion that simple sequence-based features contain insufficient information to be useful for predicting PPIs, but that protein domain-based features have some predictive value. AVAILABILITY: Our method, named 'BRS-nonint', is available at http://www.bioinformatics.leeds.ac.uk/BRS-nonint/. All the datasets used in this study are derived from publicly available data, and are available at http://www.bioinformatics.leeds.ac.uk/BRS-nonint/PPI_RandomBalance.html CONTACT: [email protected]; [email protected].