A Multimodal Dataset for Mixed Emotion Recognition.

Mixed emotions have attracted increasing interest recently, but existing datasets rarely focus on mixed emotion recognition from multimodal signals, hindering the affective computing of mixed emotions. On this basis, we present a multimodal dataset with four kinds of signals recorded while watching mixed and non-mixed emotion videos. To ensure effective emotion induction, we first implemented a rule-based video filtering step to select the videos that could elicit stronger positive, negative, and mixed emotions. Then, an experiment with 80 participants was conducted, in which the data of EEG, GSR, PPG, and frontal face videos were recorded while they watched the selected video clips. We also recorded the subjective emotional rating on PANAS, VAD, and amusement-disgust dimensions. In total, the dataset consists of multimodal signal data and self-assessment data from 73 participants. We also present technical validations for emotion induction and mixed emotion classification from physiological signals and face videos. The average accuracy of the 3-class classification (i.e., positive, negative, and mixed) can reach 80.96% when using SVM and features from all modalities, which indicates the possibility of identifying mixed emotional states.

PCKRF: Point Cloud Completion and Keypoint Refinement With Fusion Data for 6D Pose Estimation.

Some robust point cloud registration approaches with controllable pose refinement magnitude, such as ICP and its variants, are commonly used to improve 6D pose estimation accuracy. However, the effectiveness of these methods gradually diminishes with the advancement of deep learning techniques and the enhancement of initial pose accuracy, primarily due to their lack of specific design for pose refinement. In this paper, we propose Point Cloud Completion and Keypoint Refinement with Fusion Data (PCKRF), a new pose refinement pipeline for 6D pose estimation. The pipeline consists of two steps. First, it completes the input point clouds via a novel pose-sensitive point completion network. The network uses both local and global features with pose information during point completion. Then, it registers the completed object point cloud with the corresponding target point cloud by our proposed Color supported Iterative KeyPoint (CIKP) method. The CIKP method introduces color information into registration and registers a point cloud around each keypoint to increase stability. The PCKRF pipeline can be integrated with existing popular 6D pose estimation methods, such as the full flow bidirectional fusion network, to further improve their pose estimation accuracy. Experiments demonstrate that our method exhibits superior stability compared to existing approaches when optimizing initial poses with relatively high precision. Notably, the results indicate that our method effectively complements most existing pose estimation techniques, leading to improved performance in most cases. Furthermore, our method achieves promising results even in challenging scenarios involving textureless and symmetrical objects. Our source code is available at https://github.com/zhanhz/KRF.

3D-CariGAN: An End-to-End Solution to 3D Caricature Generation From Normal Face Photos.

Caricature is a type of artistic style of human faces that attracts considerable attention in the entertainment industry. So far a few 3D caricature generation methods exist and all of them require some caricature information (e.g., a caricature sketch or 2D caricature) as input. This kind of input, however, is difficult to provide by non-professional users. In this paper, we propose an end-to-end deep neural network model that generates high-quality 3D caricatures directly from a normal 2D face photo. The most challenging issue for our system is that the source domain of face photos (characterized by normal 2D faces) is significantly different from the target domain of 3D caricatures (characterized by 3D exaggerated face shapes and textures). To address this challenge, we: (1) build a large dataset of 5,343 3D caricature meshes and use it to establish a PCA model in the 3D caricature shape space; (2) reconstruct a normal full 3D head from the input face photo and use its PCA representation in the 3D caricature shape space to establish correspondences between the input photo and 3D caricature shape; and (3) propose a novel character loss and a novel caricature loss based on previous psychological studies on caricatures. Experiments including a novel two-level user study show that our system can generate high-quality 3D caricatures directly from normal face photos.

Bcl-2 interacting protein 3 (BNIP3) promotes tumor growth in breast cancer under hypoxic conditions through an autophagy-dependent pathway.

Hypoxia-induced autophagy has been implicated in many cancers. Bcl-2 interacting protein 3 (BNIP3) has been associated with hypoxia, whose aberrant expression is involved in the carcinogenesis of breast cancer (BC). Here, we aim to investigate the role of hypoxia-induced autophagy and the mechanistic actions of the bioinformatically identified BNIP3 in BC. The expression pattern of BNIP3 in BC tissues and cell lines was examined using RT-qPCR and Western blot analyses. The binding affinity among BNIP3, BECN1 and BCL-2 was characterized by co-immunoprecipitation. BNIP3 expression was manipulated to assess its effects on BC cell malignant phenotypes, evaluated by cell counting kit-8, Transwell and wound healing assays, and on BC autophagy under hypoxic conditions. A BC tumor xenografts mouse model was further established to substantiate in vitro findings. Up-regulated expression of BNIP3 was found in BC tissues and cell lines, and BNIP3 expression was positively correlated with hypoxia exposure duration. BNIP3 knockdown restricted BC cell proliferation, invasion, and migration under hypoxic conditions. BNIP3 activated BC cell autophagy by inhibiting the binding between BCL-2 and BECN1 under hypoxic conditions. BNIP3-induced autophagy activation enhanced malignant phenotypes of BC cells, thus accelerating the tumorigenesis of BC cells in vivo. These data collectively supported the tumor-promoting role of BNIP3 in autophagy activation of BC under hypoxic conditions, highlighting a potential therapeutic target against BC.

Automatic Sitting Pose Generation for Ergonomic Ratings of Chairs.

Human poses play a critical role in human-centric product design. Despite considerable researches on pose synthesis and pose-driven product design, most of them adopt the simple stick figure model that captures only skeletons rather than real body geometries and do not link human poses to the environment (e.g., chairs for sitting). This paper focuses on user-tailored ergonomic design and rating of chairs using scanned human geometries. Fully utilizing the anthropometric information of the human models, our method considers more ergonomic guidelines of chair design (such as pressure distribution and support intensity) and links the geometry of 3D chair models and human-to-chair interactions into the pose deformation constraints of the human avatars. The core of our method is a pose generation algorithm which rigs the user's successive poses through coarse- and fine-level pose deformations. We define a non-linear energy function with contact, collision, and joint limit terms, and solve it using a hill-climbing algorithm. The fitting results allow us to quantitatively evaluate the chair model in terms of various ergonomic criteria. Our method is flexible and effective and can be applied to users with varying body shapes and a wide range of chairs. Moreover, the proposed technique can be easily extended to other furniture, such as desk, bed, and cabinet. Extensive evaluations and a user study demonstrate the efficiency and advantages of the proposed virtual fitting method. Given that our method avoids tedious on-site trying, facilitates the exploration/evaluation of various chair products, and provides valuable feedback for the designers and manufacturers to deliver customized products, it is ideal for online shopping of chairs.

Feature-Aware Uniform Tessellations on Video Manifold for Content-Sensitive Supervoxels.

Over-segmenting a video into supervoxels has strong potential to reduce the complexity of downstream computer vision applications. Content-sensitive supervoxels (CSSs) are typically smaller in content-dense regions (i.e., with high variation of appearance and/or motion) and larger in content-sparse regions. In this paper, we propose to compute feature-aware CSSs (FCSSs) that are regularly shaped 3D primitive volumes well aligned with local object/region/motion boundaries in video. To compute FCSSs, we map a video to a 3D manifold embedded in a combined color and spatiotemporal space, in which the volume elements of video manifold give a good measure of the video content density. Then any uniform tessellation on video manifold can induce CSS in the video. Our idea is that among all possible uniform tessellations on the video manifold, FCSS finds one whose cell boundaries well align with local video boundaries. To achieve this goal, we propose a novel restricted centroidal Voronoi tessellation method that simultaneously minimizes the tessellation energy (leading to uniform cells in the tessellation) and maximizes the average boundary distance (leading to good local feature alignment). Theoretically our method has an optimal competitive ratio O(1), and its time and space complexities are O(NK) and O(N+K) for computing K supervoxels in an N-voxel video. We also present a simple extension of FCSS to streaming FCSS for processing long videos that cannot be loaded into main memory at once. We evaluate FCSS, streaming FCSS and ten representative supervoxel methods on four video datasets and two novel video applications. The results show that our method simultaneously achieves state-of-the-art performance with respect to various evaluation criteria.

Intrinsic Manifold SLIC: A Simple and Efficient Method for Computing Content-Sensitive Superpixels.

Superpixels are perceptually meaningful atomic regions that can effectively capture image features. Among various methods for computing uniform superpixels, simple linear iterative clustering (SLIC) is popular due to its simplicity and high performance. In this paper, we extend SLIC to compute content-sensitive superpixels, i.e., small superpixels in content-dense regions with high intensity or colour variation and large superpixels in content-sparse regions. Rather than using the conventional SLIC method that clusters pixels in , we map the input image to a 2-dimensional manifold , whose area elements are a good measure of the content density in . We propose a simple method, called intrinsic manifold SLIC (IMSLIC), for computing a geodesic centroidal Voronoi tessellation (GCVT)-a uniform tessellation-on , which induces the content-sensitive superpixels in . In contrast to the existing algorithms, IMSLIC characterizes the content sensitivity by measuring areas of Voronoi cells on . Using a simple and fast approximation to a closed-form solution, the method can compute the GCVT at a very low cost and guarantees that all Voronoi cells are simply connected. We thoroughly evaluate IMSLIC and compare it with eleven representative methods on the BSDS500 dataset and seven representative methods on the NYUV2 dataset. Computational results show that IMSLIC outperforms existing methods in terms of commonly used quality measures pertaining to superpixels such as compactness, adherence to boundaries, and achievable segmentation accuracy. We also evaluate IMSLIC and seven representative methods in an image contour closure application, and the results on two datasets, WHD and WSD, show that IMSLIC achieves the best foreground segmentation performance.

A Robust Divide and Conquer Algorithm for Progressive Medial Axes of Planar Shapes.

The medial axis is an important shape representation that finds a wide range of applications in shape analysis. For large-scale shapes of high resolution, a progressive medial axis representation that starts with the lowest resolution and gradually adds more details is desired. In this paper, we propose a fast and robust geometric algorithm that computes progressive medial axes of a large-scale planar shape. The key ingredient of our method is a novel structural analysis of merging medial axes of two planar shapes along a shared boundary. Our method is robust by separating the analysis of topological structure from numerical computation. Our method is also fast and we show that the time complexity of merging two medial axes is O(n lognv) , where n is the number of total boundary generators, nv is strictly smaller than n and behaves as a small constant in all our experiments. Experiments on large-scale polygonal data and comparison with state-of-the-art methods show the efficiency and effectiveness of the proposed method.

Effects of cyclin-dependent kinase 8 specific siRNA on the proliferation and apoptosis of colon cancer cells.

BACKGROUND: To investigate the expression of cyclin-dependent kinase 8 (CDK8) and ß-catenin in colon cancer and evaluate the role of CDK8 in the proliferation, apoptosis and cell cycle progression of colon cancer cells, especially in HCT116 cell line. METHODS: Colon cancer cell line HCT116 was transfected with small interfering RNA (siRNA) targeting on CDK8. After CDK8-siRNA transfection, mRNA and protein expression levels of CDK8 and ß-catenin were determined by reverse transcriptase-polymerase chain reaction (RT-PCR) and Western blot assay in HCT116 cells. Cell proliferation was measured by 3-(4, 5-Dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide Methylthiazolyl tetrazolium (MTT) assay, and cell cycle distribution and apoptosis were analyzed by flow cytometry analysis (FACS). CDK8 and ß-catenin protein levels were also examined by real-time PCR and immunohistochemistry (IHC) in colon cancer tissues and adjacent normal tissues. RESULTS: After CDK8 specific siRNA transfection, mRNA and protein expression levels of CDK8 and ß-catenin in HCT116 cells were noticeably decreased (P < 0.05). CDK8 specific siRNA transfection inhibited HCT116 cells' proliferation and facilitated their apoptosis significantly (P < 0.05). In addition, the proportion of HCT116 cells in the G0/G1 phase was remarkably increased after CDK8-siRNA transfection (P < 0.05). The expression levels of CDK8 and ß-catenin in adjacent normal tissues were lower than in tumor tissues (P < 0.05). Moreover, the expression of CDK8 was correlated with the expression of ß-catenin in both tumor and adjacent normal tissues (P < 0.05). CONCLUSIONS: CDK8 and ß-catenin were expressed in colon cancer at a high frequency. CDK8 specific siRNA transfection down-regulated the expression of CDK8 in colon cancer cells, which was also associated with a decrease in the expression of ß-catenin Moreover, CDK8 specific siRNA inhibited the proliferation of colon cancer cells, promoted their apoptosis and arrested these cells in the G0/G1 phase. Interference of CDK8 might be an effective strategy through ß-catenin regulation of colon cancer.