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Existing vector-based multimedia classification often incurs loss of space-time information and requires generation of high-dimensional vectors. To explore a possible new solution for the problem, we propose a novel tensor-based logistic regression algorithm via Tucker decomposition to complete multimedia classification. In order to strengthen the classification process, ℓ F -norm is used for regularization term. A logistic Tucker regression model is established to achieve effective extraction of principal components out of the tensors, and hence reduce the dimension of inputs to improve the efficiency of multimedia classification. To evaluate the proposed algorithm, we carried out extensive experiments on a number of data sets, including two second-order grayscale image datasets and one third-order video sequence dataset. All the results indicate that our proposed algorithm outperforms the existing state-of-the-arts in relevant areas. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present a novel method for content adaptation and video summarization fully implemented in compressed-domain. Firstly, summarization of generic videos is modeled as the process of extracted human objects under various activities/events. Accordingly, frames are classified into five categories via fuzzy decision including shot changes (cut and gradual transitions), motion activities (camera motion and object motion) and others by using two inter-frame measurements. Secondly, human objects are detected using Haar-like features. With the detected human objects and attained frame categories, activity levels for each frame are determined to adapt with video contents. Continuous frames belonging to same category are grouped to form one activity entry as content of interest (COI) which will convert the original video into a series of activities. An overall adjustable quota is used to control the size of generated summarization for efficient streaming purpose. Upon this quota, the frames selected for summarization are determined by evenly sampling the accumulated activity levels for content adaptation. Quantitative evaluations have proved the effectiveness and efficiency of our proposed approach, which provides a more flexible and general solution for this topic as domain-specific tasks such as accurate recognition of objects can be avoided. [Copyright &y& Elsevier]

Abstract: This paper presents a novel feature selection approach to deal with issues of high dimensionality in biomedical data classification. Extensive research has been performed in the field of pattern recognition and machine learning. Dozens of feature selection methods have been developed in the literature, which can be classified into three main categories: filter, wrapper and hybrid approaches. Filter methods apply an independent test without involving any learning algorithm, while wrapper methods require a predetermined learning algorithm for feature subset evaluation. Filter and wrapper methods have their, respectively, drawbacks and are complementary to each other in that filter approaches have low computational cost with insufficient reliability in classification while wrapper methods tend to have superior classification accuracy but require great computational power. The approach proposed in this paper integrates filter and wrapper methods into a sequential search procedure with the aim to improve the classification performance of the features selected. The proposed approach is featured by (1) adding a pre-selection step to improve the effectiveness in searching the feature subsets with improved classification performances and (2) using Receiver Operating Characteristics (ROC) curves to characterize the performance of individual features and feature subsets in the classification. Compared with the conventional Sequential Forward Floating Search (SFFS), which has been considered as one of the best feature selection methods in the literature, experimental results demonstrate that (i) the proposed approach is able to select feature subsets with better classification performance than the SFFS method and (ii) the integrated feature pre-selection mechanism, by means of a new selection criterion and filter method, helps to solve the over-fitting problems and reduces the chances of getting a local optimal solution. [Copyright &y& Elsevier]

Highlights: • This work proposes a novel deep cascaded structure to exploit the DCT inter-block and spatial correlations in dual domains. • The proposed deep cascaded neural networks estimate the truncated high-frequency DCT coefficients in dual domains. • The performance of the proposed method significantly outperforms the state-of-the-art DCT up-sampling algorithms. Abstract Recent researches show that the high-frequency discrete cosine transform (DCT) coefficients can be estimated from low-frequency DCT coefficients by exploiting the spatial correlations. Hence, images coded by DCT such as JPEG/MJPEG/H.264, etc., can be down-sampled in DCT domain, where the high-frequency information can be accurately restored through image up-sampling. In this letter, we propose a novel deep neural network using the cascaded fully connected layers and convolution layers in dual domains (DCT and spatial domains), in order to restore high-frequency DCT coefficients from observed low-frequency DCT coefficients by exploiting the DCT inter-block and spatial correlations. In the proposed network, many recent techniques are adopted, including residual network in dual domains, batch normalization, denseNet, etc. Experimental results show that the proposed cascaded networks in dual domains significantly outperforms the state-of-the-art DCT up-sampling methods in terms of PSNR (0.63–2.57 dB gain), SSIM values, and subjective evaluations on standard image datasets Set5 and Set14. [ABSTRACT FROM AUTHOR]

Tensors representations are widely used in multimedia applications. As a key step of tensor processing, the rank-1 tensor decomposition (i.e., the CANDECOMP/PARAFAC (CP) decomposition) always requires the estimation of the tensor rank. The ℓ 2 , 1 -norm has been shown to be effective for tensor rank selection. The existing tensor rank selection algorithm force the same columns of the tensor matrices to simultaneously become zero. However, the real sparse columns for different factor matrices may be different. Such strategy does not really uncover the sparse information of each factor matrix. In this paper, we add a separable ℓ 2 , 1 -norm on multiple factor matrices to obtain real sparse results along to different modes. And then different sparse results are assembled into a joint sparse pattern for tensor rank selection. This added separable regularization term has twofold role in enhancing the effect of regularization for each factor matrix and fully utilizing the knowledge of multiple factor matrices to facilitate decision making. In order to effectively exploit the structure information of multimedia data, we propose a model of tensor bag of words (tBOW) as the direct input of our algorithms. In the experiments, we apply the proposed algorithms to three representative tasks of multimedia analysis, i.e., image classification, video action recognition, and head pose estimation. Experimental results on three open benchmark datasets show that our algorithms are effective to multimedia analysis. [ABSTRACT FROM AUTHOR]

Highlights • Deep multi-scale intensity and depth feature fusion scheme increases model capacity. • Modified edge preserving loss function can protect the edge information in depth map. • Theoretical time complexity of the proposed DIG-Net is only 50% of IG-Net. Abstract In this paper, we propose an deep intensity guidance based compression artifacts reduction model (denoted as DIG-Net) for depth map. The proposed DIG-Net model can learn an end-to-end mapping from the color image and distorted depth map to the uncompressed depth map. To eliminate undesired artifacts such as discontinuities around object boundary, the proposed model is with three branches, which extracts the high frequency information from color image and depth maps as priors. Based on the modified edge preserving loss function, the deep multi-scale guidance information are learned and fused in the model to make the edge of depth map sharper. Experimental results show the effectiveness and superiority of our proposed model compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Tumor metastasis is the main reason of death for hepatocellular carcinoma (HCC) patients. Cell migration and invasion are two prerequisites for tumor metastasis, in which TRPM7 and MMPs play an important role. In our study, we found that bradykinin (BK) could upregulate the expression of TRPM7 and dynamically regulate the phosphorylation of non-muscle myosin IIA heavy chain (NMHC-IIA) Ser-1943 in HepG2 cells. The influx of Ca 2+ via TRPM7 was necessary for elevating the activity of m-calpain and μ-calpain. Additionally, we observed that BK stimulated HepG2 cells to secrete more MMP2 but not MMP9. Src was critical in the process of MMP2 secretion and invadopodia formation. The heat map showed that BDKRB2, TRPM7 and MMP2 had higher overexpression proportions in 25 HCC cell lines. Some clinical specimens of HCC also indicated that BDKRB2 and MMP2 were overexpressed. In conclusion, BK promoted migration and invasion of HCC cells through TRPM7 and MMP2. [ABSTRACT FROM AUTHOR]

Pathological cardiac hypertrophy is a maladaptive response in a variety of organic heart disease (OHD), which is characterized by mitochondrial dysfunction that results from disturbed energy metabolism. SIRT3, a mitochondria-localized sirtuin, regulates global mitochondrial lysine acetylation and preserves mitochondrial function. However, the mechanisms by which SIRT3 regulates cardiac hypertrophy remains to be further elucidated. In this study, we firstly demonstrated that expression of SIRT3 was decreased in Angiotension II (Ang II)-treated cardiomyocytes and in hearts of Ang II-induced cardiac hypertrophic mice. In addition, SIRT3 overexpression protected myocytes from hypertrophy, whereas SIRT3 silencing exacerbated Ang II-induced cardiomyocyte hypertrophy. In particular, SIRT3-KO mice exhibited significant cardiac hypertrophy. Mechanistically, we identified NMNAT3 (nicotinamide mononucleotide adenylyltransferase 3), the rate-limiting enzyme for mitochondrial NAD biosynthesis, as a new target and binding partner of SIRT3. Specifically, SIRT3 physically interacts with and deacetylates NMNAT3, thereby enhancing the enzyme activity of NMNAT3 and contributing to SIRT3-mediated anti-hypertrophic effects. Moreover, NMNAT3 regulates the activity of SIRT3 via synthesis of mitochondria NAD. Taken together, these findings provide mechanistic insights into the negative regulatory role of SIRT3 in cardiac hypertrophy. [ABSTRACT FROM AUTHOR]

Phase correlation is a well-established frequency domain method to estimate rigid 2-D translational motion between pairs of images. However, it suffers from interference terms such as noise and non-overlapped regions. In this paper, a novel variant of the phase correlation approach is proposed, in which 2-D translation is estimated by projection-based subspace phase correlation (SPC). Conventional wisdom has suggested that such an approach can only amount to a compromise solution between accuracy and efficiency. In this work, however, we prove that the original SPC and the further introduced gradient-based SPC can provide robust solution to zero-mean and non-zero-mean noise, and the latter is also used to model the interference term of non-overlapped regions. Comprehensive results from synthetic data and MRI images have fully validated our methodology. Due to its substantially lower computational complexity, the proposed method offers additional advantages in terms of efficiency and can lend itself to very fast implementations for a wide range of applications where speed is at a premium. [ABSTRACT FROM AUTHOR]

Abstract: Peroxisome proliferator-activated receptors (PPARα, β/δ and γ) are nuclear receptors and PPARγ activation was previously reported to inhibit collagen expression in the heart, but whether PPARβ/δ also regulates collagen expression in the heart remains unclear. In this study, we investigated the effect of PPARβ/δ activation on angiotensin II (Ang II)-induced collagen type I expression in adult rat cardiac fibroblasts. The results showed that PPARβ/δ was expressed at the moderate level in cardiac fibroblasts. GW501516, a selective PPARβ/δ agonist, depressed Ang II-stimulated collagen type I expression and collagen synthesis in cardiac fibroblasts in a concentration-dependent manner. Furthermore, these inhibitory effects of GW501516 were completely reversed by the knockdown of PPARβ/δ via RNA interference. In summary, we find that PPARβ/δ is present in cardiac fibroblasts and PPARβ/δ activation inhibits Ang II-induced collagen type I expression at least in part via decreasing collagen synthesis. PPARβ/δ may be a promising therapeutic target for myocardial fibrosis. [Copyright &y& Elsevier]