Decorrelation of Neutral Vector Variables: Theory and Applications

In this paper, we propose novel strategies for neutral vector variable decorrelation. Two fundamental invertible transformations, namely serial nonlinear transformation and parallel nonlinear transformation, are proposed to carry out the decorrelation. For a neutral vector variable, which is not multivariate Gaussian distributed, the conventional principal component analysis (PCA) cannot yield mutually independent scalar variables. With the two proposed transformations, a highly negatively correlated neutral vector can be transformed to a set of mutually independent scalar variables with the same degrees of freedom. We also evaluate the decorrelation performances for the vectors generated from a single Dirichlet distribution and a mixture of Dirichlet distributions. The mutual independence is verified with the distance correlation measurement. The advantages of the proposed decorrelation strategies are intensively studied and demonstrated with synthesized data and practical application evaluations

An {\alpha}-Matte Boundary Defocus Model Based Cascaded Network for Multi-focus Image Fusion

Capturing an all-in-focus image with a single camera is difficult since the depth of field of the camera is usually limited. An alternative method to obtain the all-in-focus image is to fuse several images focusing at different depths. However, existing multi-focus image fusion methods cannot obtain clear results for areas near the focused/defocused boundary (FDB). In this paper, a novel {\alpha}-matte boundary defocus model is proposed to generate realistic training data with the defocus spread effect precisely modeled, especially for areas near the FDB. Based on this {\alpha}-matte defocus model and the generated data, a cascaded boundary aware convolutional network termed MMF-Net is proposed and trained, aiming to achieve clearer fusion results around the FDB. More specifically, the MMF-Net consists of two cascaded sub-nets for initial fusion and boundary fusion, respectively; these two sub-nets are designed to first obtain a guidance map of FDB and then refine the fusion near the FDB. Experiments demonstrate that with the help of the new {\alpha}-matte boundary defocus model, the proposed MMF-Net outperforms the state-of-the-art methods both qualitatively and quantitatively.Comment: 10 pages, 8 figures, journal Unfortunately, I cannot spell one of the authors' name coorectl

Real-MFF: A Large Realistic Multi-focus Image Dataset with Ground Truth

Multi-focus image fusion, a technique to generate an all-in-focus image from two or more partially-focused source images, can benefit many computer vision tasks. However, currently there is no large and realistic dataset to perform convincing evaluation and comparison of algorithms in multi-focus image fusion. Moreover, it is difficult to train a deep neural network for multi-focus image fusion without a suitable dataset. In this letter, we introduce a large and realistic multi-focus dataset called Real-MFF, which contains 710 pairs of source images with corresponding ground truth images. The dataset is generated by light field images, and both the source images and the ground truth images are realistic. To serve as both a well-established benchmark for existing multi-focus image fusion algorithms and an appropriate training dataset for future development of deep-learning-based methods, the dataset contains a variety of scenes, including buildings, plants, humans, shopping malls, squares and so on. We also evaluate 10 typical multi-focus algorithms on this dataset for the purpose of illustration

Locally-Enriched Cross-Reconstruction for Few-Shot Fine-Grained Image Classification

Few-shot fine-grained image classification has attracted considerable attention in recent years for its realistic setting to imitate how humans conduct recognition tasks. Metric-based few-shot classifiers have achieved high accuracies. However, their metric function usually requires two arguments of vectors, while transforming or reshaping three-dimensional feature maps to vectors can result in loss of spatial information. Image reconstruction is thus involved to retain more appearance details: the test images are reconstructed by different classes and then classified to the one with the smallest reconstruction error. However, discriminative local information, vital to distinguish sub-categories in fine-grained images with high similarities, is not well elaborated when only the base features from a usual embedding module are adopted for reconstruction. Hence, we propose the novel local content-enriched cross-reconstruction network (LCCRN) for few-shot fine-grained classification. In LCCRN, we design two new modules: the local content-enriched module (LCEM) to learn the discriminative local features, and the cross-reconstruction module (CRM) to fully engage the local features with the appearance details obtained from a separate embedding module. The classification score is calculated based on the weighted sum of reconstruction errors of the cross-reconstruction tasks, with weights learnt from the training process. Extensive experiments on four fine-grained datasets showcase the superior classification performance of LCCRN compared with the state-of-the-art few-shot classification methods. Codes are available at: https://github.com/lutsong/LCCRN

Preparation of supported skeletal Ni catalyst and its catalytic hydrogenation performance of C9 fraction from coking process

Currently, the inferior compressive strength of traditional Raney-Ni catalyst restricts its application in fixed-bed reactor. To approach this problem a series of supported skeletal Ni catalysts were prepared by mixing pseudo boehmite and Ni-Al alloy powder. In the process,the calcination temperature and atmosphere, mass ratio of pseudo boehmite to Ni-Al alloy powder and the sodium hydroxide solution concentration were investigated. The catalysts characterized by intelligent granule intensity tester(IGIT), scanning electron microscopy(SEM), X-ray photoelectron spectroscopy(XPS), X-ray diffraction (XRD),low temperature nitrogen adsorption, temperature programmed reduction of hydrogen (H2-TPR), and thermogravimetric-differential thermal analysis (TG-DTA).The results were shown that the calcination atmosphere had a considerable impact on the compressive strength of the catalyst. Compared with air atmosphere, the compressive strength of the catalyst increased from 12.62 N/mm to 23.96N/mm, obviously, in argon atmosphere, which was almost twice as much as the former.The inherent reason for this was that the argon obviously inhibited the transform of NiAl3 to Ni2Al3 in which the latter was the key factor to improve compressive strength. Additionally, coke-oven C9 hydrogenation was used to evaluate the performance of the catalyst and the results indicated that the conversion of indene, the key component of coke-oven C9, was as high as 90% in 1000h under the optimum reaction conditions:T=220oC, P(H2)=2.5MPa, H2/oil=200(v/v), LHSV=3.0h-1. Our data demonstrated that the supported skeletal Ni catalyst have a good industrial prospect in the fixed-bed reactor in future

Molecular mechanism of fluoroquinolones resistance in Mycoplasma hominis clinical isolates

To evaluate the molecular mechanism of fluoroquinolones resistance in Mycoplasma hominis (MH) clinical strains isolated from urogenital specimens. 15 MH clinical isolates with different phenotypes of resistance to fluoroquinolones antibiotics were screened for mutations in the quinolone resistance-determining regions (QRDRs) of DNA gyrase (gyrA and gyrB) and topoisomerase IV (parC and parE) in comparison with the reference strain PG21, which is susceptible to fluoroquinolones antibiotics. 15 MH isolates with three kinds of quinolone resistance phenotypes were obtained. Thirteen out of these quinolone-resistant isolates were found to carry nucleotide substitutions in either gyrA or parC. There were no alterations in gyrB and no mutations were found in the isolates with a phenotype of resistance to Ofloxacin (OFX), intermediate resistant to Levofloxacin (LVX) and Sparfloxacin (SFX), and those susceptible to all three tested antibiotics. The molecular mechanism of fluoroquinolone resistance in clinical isolates of MH was reported in this study. The single amino acid mutation in ParC of MH may relate to the resistance to OFX and LVX and the high-level resistance to fluoroquinolones for MH is likely associated with mutations in both DNA gyrase and the ParC subunit of topoisomerase IV