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Label ranking aims to learn a mapping from instances to rankings over a finite number of predefined labels. Random forest is a powerful and one of the most successful general-purpose machine learning algorithms of modern times. In this paper, we present a powerful random forest label ranking method which uses random decision trees to retrieve nearest neighbors. We have developed a novel two-step rank aggregation strategy to effectively aggregate neighboring rankings discovered by the random forest into a final predicted ranking. Compared with existing methods, the new random forest method has many advantages including its intrinsically scalable tree data structure, highly parallel-able computational architecture and much superior performance. We present extensive experimental results to demonstrate that our new method achieves the highly competitive performance compared with state-of-the-art methods for datasets with complete ranking and datasets with only partial ranking information. [ABSTRACT FROM AUTHOR]

Symmetric positive definite (SPD) matrices have achieved considerable success in numerous computer vision applications including activity recognition, texture classification, and diffusion tensor imaging. Traditional pattern recognition methods developed in Euclidean space are not suitable for direct processing of SPD matrices because they lie in a Riemannian manifold of negative curvature. In this paper, we draw inspiration from neural network based simple competitive learning, which updates the weight of a neural network in Euclidean space, and propose Riemannian competitive learning which updates the weight in the Riemannian weight space, for SPD manifold clustering. This framework is based on the Fisher-Rao metric which is theoretically very good. We further introduce a conscious competition mechanism to enhance the performance of the RCL algorithm and have developed a robust algorithm termed Riemannian Frequency Sensitive Competitive Learning (rFSCL). Compared with existing methods, the advantages are threefold: (1) rFSCL inherits the online nature of competitive learning, which makes it capable of handling very large data sets; (2) rFSCL inherits the advantage of conscious competitive learning, which indicates that it is less sensitive to the initial values of the cluster centers and that all clusters are fully utilized without the “dead unit” problem associated with many clustering algorithms; and (3) As an intrinsic Riemannian clustering method, rFSCL operates along the geodesic on the manifold in an closed-form analytic manner and is completely independent of the choice of local coordinate systems. Extensive experiments on both synthetic data sets and real data sets show its superior performance to that of other state-of-the-art SPD clustering methods. [ABSTRACT FROM AUTHOR]

Digital pathology is set to revolutionise traditional approaches diagnosing and researching diseases. To realise the full potential of digital pathology, accurate and robust computer techniques for automatically detecting biomarkers play an important role. Traditional methods transform the colour histopathology images into a gray scale image and apply a single threshold to separate positively stained tissues from the background. In this paper, we show that the colour distribution of the positive immunohis-tochemical stains varies with the level of luminance and that a single threshold will be impossible to separate positively stained tissues from other tissues, regardless how the colour pixels are transformed. Based on this, we propose two novel luminance adaptive biomarker detection methods. We present experimental results to show that the luminance adaptive approach significantly improves biomarker detection accuracy and that random forest based techniques have the best performances. [ABSTRACT FROM AUTHOR]

Abstract: We have developed an informative sample subspace (ISS) method that is suitable for projecting high-dimensional data onto a low-dimensional subspace for classification purposes. In this paper, we present an ISS algorithm that uses a maximal mutual information criterion to search a labelled training data set directly for the subspace''s projection base vectors. We evaluate the usefulness of the ISS method using synthetic data as well as real world problems. Experimental results demonstrate that the ISS algorithm is effective and can be used as a general method for representing high-dimensional data in a low-dimensional subspace for classification. [Copyright &y& Elsevier]

Abstract: In this paper, we present a learning-based image processing technique. We have developed a novel method to map high dynamic range scenes to low dynamic range images for display in standard (low dynamic range) reproduction media. We formulate the problem as a quantization process and employ an adaptive conscience learning strategy to ensure that the mapped low dynamic range displays not only faithfully reproduce the visual features of the original scenes, but also make full use of the available display levels. This is achieved by the use of a competitive learning neural network that employs a frequency sensitive competitive learning mechanism to adaptively design the quantizer. By optimizing an distortion function, we ensure that the mapped low dynamic images preserve the visual characteristics of the original scenes. By incorporating a frequency sensitive competitive mechanism, we facilitate the full utilization of the limited displayable levels. We have developed a deterministic and practicable learning procedure which uses a single variable to control the display result. We give a detailed description of the implementation procedure of the new learning-based high dynamic range compression method and present experimental results to demonstrate the effectiveness of the method in displaying a variety of high dynamic range scenes. [Copyright &y& Elsevier]

Abstract: In this work, we are interested in technologies that will allow users to actively browse and navigate large image databases and to retrieve images through interactive fast browsing and navigation. The development of a browsing/navigation-based image retrieval system has at least two challenges. The first is that the system''s graphical user interface (GUI) should intuitively reflect the distribution of the images in the database in order to provide the users with a mental picture of the database content and a sense of orientation during the course of browsing/navigation. The second is that it has to be fast and responsive, and be able to respond to users actions at an interactive speed in order to engage the users. We have developed a method that attempts to address these challenges of a browsing/navigation based image retrieval systems. The unique feature of the method is that we take an integrated approach to the design of the browsing/navigation GUI and the indexing and organization of the images in the database. The GUI is tightly coupled with the algorithms that run in the background. The visual cues of the GUI are logically linked with various parts of the repository (image clusters of various particular visual themes) thus providing intuitive correspondences between the GUI and the database contents. In the backend, the images are organized into a binary tree data structure using a sequential maximal information coding algorithm and each image is indexed by an -bit binary index thus making response to users’ action very fast. We present experimental results to demonstrate the usefulness of our method both as a pre-filtering tool and for developing browsing/navigation systems for fast image retrieval from large image databases. [Copyright &y& Elsevier]

Organizing images into semantic categories can be very useful for searching and browsing through large image repositories. In this work, we use machine learning to associate low level colour representations of digital colour photos with their high level semantic categories. We investigate the redundancy and performance of a number of histogram-based colour image content representations in the context of automatic colour photo categorization using support vector machines. We use principal component analysis to reduce the dimensionality of (high dimensional) histogram based colour descriptors and use support vector machines to learn to classify the images into various high level categories in the histograms subspaces. We present experimental results to demonstrate the usefulness of such an approach to organizing colour photos into semantic categories. Our results show that the colour content descriptors constructed in different ways perform quite differently and the performances are data dependent hence it is difficult to pick a “winning” descriptor. Our results demonstrate conclusively that all descriptors studied in this paper are highly redundant and that regardless of their performances, the dimensionalities of these histogram based colour content descriptors can be significantly reduced without affecting their classification performances. [Copyright &y& Elsevier]

• Bumetanide rescues aquaporin-4 depolarization in a rat retinal I/R injury model. • BU suppresses AQP4 protein/mRNA and β-DG mRNA expression. • BU reduces retinal apoptotic cells and downregulates p-ERK and MMP9 expression. • BU suppresses β-DG cleavage, in part, via the ERK/MMP9 pathway. • BU protects against retinal cytotoxic edema in a rat retinal I/R injury model. Aquaporin-4 (AQP4) regulates retinal water homeostasis and participates in retinal oedema pathophysiology. β-dystroglycan (β-DG) is responsible for AQP4 polarization and can be cleaved by matrix metalloproteinase-9 (MMP9). Retinal oedema induced by ischemia–reperfusion (I/R) injury is an early complication. Bumetanide (BU) has potential efficacy against cytotoxic oedema. Our study investigated the effects of β-DG cleavage on AQP4 and the roles of BU in a rat retinal I/R injury model. The model was induced by applying 110 mm Hg intraocular pressure to the anterior eye chamber. BU and U0126 (a selective ERK inhibitor) were intraperitoneally administered 15 and 30 min, respectively, before I/R induction. Rhodamine isothiocyanate extravasation detection, quantitative real-time PCR, transmission electron microscopy, hematoxylin-eosin staining, immunofluorescence staining, western blotting, and TUNEL staining were performed. AQP4 lost its polarization in the retinal perivascular domain as a result of β-DG cleavage. BU rescued AQP4 depolarization, suppressed AQP4 protein expression, attenuated retinal cytotoxic oedema, and downregulated β-DG and AQP4 mRNA expression. BU suppressed glial responses and mitochondria-mediated apoptotic protein expression, including that of Caspase-3 and Cyto C, raised the Bcl-2/Bax ratio, and lowered the number of apoptotic cells in the retina. Both BU and U0126 downregulated p-ERK and MMP9 expression. Thus, BU treatment suppressed β-DG cleavage, recovered AQP4 polarization partially via inhibiting ERK/MMP9 signaling pathway, and possess potential neuroprotective efficacy in the rat retinal ischemia–reperfusion injury model. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a face-hallucination method, namely face hallucination based on sparse local-pixel structure. In our framework, a high resolution (HR) face is estimated from a single frame low resolution (LR) face with the help of the facial dataset. Unlike many existing face-hallucination methods such as the from local-pixel structure to global image super-resolution method (LPS-GIS) and the super-resolution through neighbor embedding, where the prior models are learned by employing the least-square methods, our framework aims to shape the prior model using sparse representation. Then this learned prior model is employed to guide the reconstruction process. Experiments show that our framework is very flexible, and achieves a competitive or even superior performance in terms of both reconstruction error and visual quality. Our method still exhibits an impressive ability to generate plausible HR facial images based on their sparse local structures. [Copyright &y& Elsevier]

Abstract: This paper proposes to employ the visual saliency for moving object detection via direct analysis from videos. Object saliency is represented by an information saliency map (ISM), which is calculated from spatio-temporal volumes. Both spatial and temporal saliencies are calculated and a dynamic fusion method developed for combination. We use dimensionality reduction and kernel density estimation to develop an efficient information theoretic based procedure for constructing the ISM. The ISM is then used for detecting foreground objects. Three publicly available visual surveillance databases, namely CAVIAR, PETS and OTCBVS-BENCH are selected for evaluation. Experimental results show that the proposed method is robust for both fast and slow moving object detection under illumination changes. The average detection rates are and while the false detection rates are and in CAVIAR (INRIA entrance hall and shopping center) dataset and OTCBVS-BENCH database, respectively. The average processing speed is 6.6fps with frame resolution in a typical Pentium IV computer. [Copyright &y& Elsevier]

• Multiple-origin fatigue fracture occurred on the 17-4PH feed water pump shaft. • Crack origin were located on stress concentration area where matched lock nut. • The failure mechanism was revealed systematically in micro-level. • The materials at microscopic level determines the intrinsic physical mechanism of macroscopic behavior. The failure of feed water pump shaft failure uesed in HRSG (heat recovery steam generator) of 9F class gas turbine generator unit has been systematically investigated in terms of visual inspection, fractographic, chemical component, micro structure and mechanical properties analysis using optical microscope (OM), scanning electron microscope (SEM) equipped with energy-dispersive X-ray (EDX) analysis, transmission electron microscope (TEM), elemental analyzer and universal tensile testing machine, respectively. Results suggest that the failure of feed water pump shaft was typical multiple-origin fatigue fracture resulting from torsion stress, owing to the lower mechanical properties caused by the less alloying element and alloy strengthening phase (ε-Cu, η-Ni). A detailed analysis of the micro structure revealed the existence of severe microsegregation and coarse M 23 C 6 carbide at the grain boundaries, which accelerated the nucleation of micro holes and led to the formation of microcracks at the junction of micro holes. This failure analysis can help identification of root cause and its elimination with corrective measures undertaken. [ABSTRACT FROM AUTHOR]

Abstract We present a new method for improving the performances of variational autoencoder (VAE). In addition to enforcing the deep feature consistent principle thus ensuring the VAE output and its corresponding input images to have similar deep features, we also implement a generative adversarial training mechanism to force the VAE to output realistic and natural images. We present experimental results to show that the VAE trained with our new method outperforms state of the art in generating face images with much clearer and more natural noses, eyes, teeth, hair textures as well as reasonable backgrounds. We also show that our method can learn powerful embeddings of input face images, which can be used to achieve facial attribute manipulation. Moreover we propose a multi-view feature extraction strategy to extract effective image representations, which can be used to achieve state of the art performance in facial attribute prediction. [ABSTRACT FROM AUTHOR]

• We propose a novel attribute-guided latent space optimization approach to control various facial attributes in the latent space of GANs. • We propose a semantic-aware encoder for GAN inversion, which can not only faithfully reconstruct the input images, but also ensure that the inverted codes are semantically meaningful. • We demonstrate the usefulness of synthesized face images for augmenting training data to improve weight initialization and accelerate the learning process for deep networks, leading to state-of-the-art performance for face attribute prediction. Previous works on generative adversarial networks (GANs) mainly focus on how to synthesize high-fidelity images. In this paper, we present a framework to leverage the knowledge learned by GANs for semantic face manipulation. In particular, we propose to control the semantics of synthesized faces by adapting the latent codes with an attribute prediction model. Moreover, in order to achieve a more accurate estimation of different facial attributes, we propose to pretrain the attribute prediction model by inverting the synthesized face images back to the GAN latent space. As a result, our method explicitly considers the semantics encoded in the latent space of a pretrained GAN and is able to faithfully edit various attributes like eyeglasses, smiling, bald, age, mustache and gender for high-resolution face images. Extensive experiments show that our method has superior performance compared to state of the art for both face attribute prediction and semantic face manipulation. [ABSTRACT FROM AUTHOR]

Highlights: [•] Bottom-up saliency can be considered as a binary classification problem between centre and surround classes. [•] Discriminant power for classification is mutual information between image features and corresponding classes distributions. [•] A multi-scale structure is integrated into the framework by employing discrete wavelet features and Hidden Markov Tree (HMT). [•] Standard quantitative tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating the proposed method. [•] It takes less than 0.4s to process each frame provided that MDIS is run on Intel Xenon Duo-core 2.53 GHz workstation. [Copyright &y& Elsevier]

Abstract: In this paper, we present novel histogram adjustment methods for displaying high dynamic range image. We first present a global histogram adjustment based tone mapping operator, which well reproduces global contrast for high dynamic range images. We then segment images and carry out adaptive contrast adjustment using our global tone mapping operator in the local regions to reproduce local contrast and ensure better quality. We demonstrate that our methods are fast, easy to use and a fixed set of parameter values produce good results for a wide variety of images. [Copyright &y& Elsevier]

• There is evidence of prefrontal abnormalities in Alzheimer's disease, but the relationship to hippocampal damage is unknown. • Injection of Aβ 25−35 into the hippocampi lead to changes in the P35/P25-CDK5 pathway in the prefrontal cortex. • This is one potential mechanism for prefrontal-mediated cognitive impairment and psychiatric symptoms of AD. Alzheimer's disease (AD) is one of the common neurodegenerative illnesses in aging populations around the world. Recently, psychiatric symptoms are becoming increasingly important in recognizing the manifestations of AD in addition to cognitive impairment. Some studies suggest that the prefrontal cortex (PFC) is closely related to apathy/depression, and a network may exist between the CA1 of hippocampus and PFC. However, whether the injection of Aβ 2535 into hippocampi may result in PFC abnormalities in AD model rats is unclear. In this study, it was investigated the changes in the PFCs after the hippocampal injection via the P35/P25 - Cyclin-dependent kinase5 (CDK5) - Tau hyperphosphorylation signaling pathway. Our results demonstrated that rats injected with Aβ 25−35 showed decreased learning and memory ability, and increased depression-like behaviors compared with uninjected controls and saline-injected shams. P35/P25, CDK5, Tau[pS199], and Tau[pS202] are significantly elevated in the PFCs and hippocampi after Aβ 25−35 was injected into the hippocampi. Furthermore, P35/P25-CDK5 complexes were detected in vivo by immunofluorescence and co-immunoprecipitation. Therefore, the relative expression of proteins associated with the P35/P25-CDK5 pathway showed the same changes in the hippocampi and PFCs after Aβ 25−35 injection. These findings demonstrate a potential mechanism for prefrontal-mediated cognitive impairment and the psychiatric symptoms of AD. [ABSTRACT FROM AUTHOR]

This paper presents a simple yet effective and efficient method for feature-preserving surface smoothing. Through analyzing the differential property of surfaces, we show that the conventional discrete Laplacian operator with uniform weights is not applicable to feature points at which the surface is non-differentiable and the second order derivatives do not exist. To overcome this difficulty, we propose a Half-kernel Laplacian Operator (HLO) as an alternative to the conventional Laplacian. Given a vertex v , HLO first finds all pairs of its neighboring vertices and divides each pair into two subsets (called half windows); then computes the uniform Laplacians of all such subsets and subsequently projects the computed Laplacians to the full-window uniform Laplacian to alleviate flipping and degeneration. The half window with least regularization energy is then chosen for v. We develop an iterative approach to apply HLO for surface denoising. Our method is conceptually simple and easy to use because it has a single parameter, i.e., the number of iterations for updating vertices. We show that our method can preserve features better than the popular uniform Laplacian-based denoising and it significantly alleviates the shrinkage artifact. Extensive experimental results demonstrate that HLO is better than or comparable to state-of-the-art techniques both qualitatively and quantitatively and that it is particularly good at handling meshes with high noise. Also, it outperforms all other compared methods in terms of computational time. We make our executable program publicly available. • Mathematical analysis of the differential property at feature points. • A half-window algorithm to generate multiple half windows (subsets) for each vertex. • A half-kernel Laplacian operator for feature-preserving surface smoothing. [ABSTRACT FROM AUTHOR]