UV R-CNN: Stable and Efficient Dense Human Pose Estimation

Dense pose estimation is a dense 3D prediction task for instance-level human analysis, aiming to map human pixels from an RGB image to a 3D surface of the human body. Due to a large amount of surface point regression, the training process appears to be easy to collapse compared to other region-based human instance analyzing tasks. By analyzing the loss formulation of the existing dense pose estimation model, we introduce a novel point regression loss function, named Dense Points} loss to stable the training progress, and a new balanced loss weighting strategy to handle the multi-task losses. With the above novelties, we propose a brand new architecture, named UV R-CNN. Without auxiliary supervision and external knowledge from other tasks, UV R-CNN can handle many complicated issues in dense pose model training progress, achieving 65.0% $AP_{gps}$ and 66.1% $AP_{gpsm}$ on the DensePose-COCO validation subset with ResNet-50-FPN feature extractor, competitive among the state-of-the-art dense human pose estimation methods.Comment: 9pages, 4 figure

Nanoscale dihydroartemisinin@zeolitic imidazolate frameworks for enhanced antigiardial activity and mechanism analysis

An artificial semisynthetic material can be derived from artemisinin (ART) called dihydroartemisinin (DHA). Although DHA has enhanced antigiardial potential, its clinical application is limited because of its poor selectivity and low solubility. The drug’s absorption has a direct impact on the cell, and mechanism research is limited to its destruction of the cytoskeleton. In this study, we used the zeolitic imidazolate framework-8 and loaded it with DHA (DHA@Zif-8) to improve its antigiardial potential. DHA@Zif-8 can enhance cellular uptake, increase antigiardial proliferation and encystation, and expand the endoplasmic reticulum compared with the DHA-treated group. We used RNA sequencing (RNA-seq) to investigate the antigiardial mechanism. We found that 126 genes were downregulated and 123 genes were upregulated. According to the KEGG and GO pathway analysis, the metabolic functions in G. lamblia are affected by DHA@Zif-8 NPs. We used real-time quantitative reverse transcription polymerase chain reaction to verify our results using the RNA-seq data. DHA@Zif-8 NPs significantly enhanced the eradication of the parasite from the stool in vivo. In addition, the intestinal mucosal injury caused by G. lamblia trophozoites markedly improved in the intestine. This research provided the potential of utilizing DHA@Zif-8 to develop an antiprotozoan drug for clinical applications

Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning

As a comprehensive indicator of mathematical thinking and intelligence, the number sense (Dehaene 2011) bridges the induction of symbolic concepts and the competence of problem-solving. To endow such a crucial cognitive ability to machine intelligence, we propose a dataset, Machine Number Sense (MNS), consisting of visual arithmetic problems automatically generated using a grammar model—And-Or Graph (AOG). These visual arithmetic problems are in the form of geometric figures: each problem has a set of geometric shapes as its context and embedded number symbols. Solving such problems is not trivial; the machine not only has to recognize the number, but also to interpret the number with its contexts, shapes, and relations (e.g., symmetry) together with proper operations. We benchmark the MNS dataset using four predominant neural network models as baselines in this visual reasoning task. Comprehensive experiments show that current neural-network-based models still struggle to understand number concepts and relational operations. We show that a simple brute-force search algorithm could work out some of the problems without context information. Crucially, taking geometric context into account by an additional perception module would provide a sharp performance gain with fewer search steps. Altogether, we call for attention in fusing the classic search-based algorithms with modern neural networks to discover the essential number concepts in future research

Analysis on Dynamic Transmission Accuracy for RV Reducer

By taking rotate vector (RV) reducer as the research object, the factors affecting the transmission accuracy are studied, including the machining errors of the main parts, assembly errors, clearance, micro-displacement, gear mesh stiffness and damping, bearing stiffness. Based on Newton second law, the transmission error mathematical model of RV reducer is set up. Then, the RV reducer transmission error curve is achieved by solving the mathematical model using the Runge-Kutta methods under the combined action of various error factors. Through the analysis of RV reducer transmission test, it can be found that there are similar variation trend and frequency components compared the theoretical research and experimental result. The presented method is useful to the research on dynamic transmission accuracy of RV reducer, and also applies to research the transmission accuracy of other cycloid drive systems

Noncircular Sources-Based Sparse Representation Algorithm for Direction of Arrival Estimation in MIMO Radar with Mutual Coupling

In this paper, a reweighted sparse representation algorithm based on noncircular sources is proposed, and the problem of the direction of arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar with mutual coupling is addressed. Making full use of the special structure of banded symmetric Toeplitz mutual coupling matrices (MCM), the proposed algorithm firstly eliminates the effect of mutual coupling by linear transformation. Then, a reduced dimensional transformation is exploited to reduce the computational complexity of the proposed algorithm. Furthermore, by utilizing the noncircular feature of signals, the new extended received data matrix is formulated to enlarge the array aperture. Finally, based on the new received data, a reweighted matrix is constructed, and the proposed method further designs the joint reweighted sparse representation scheme to achieve the DOA estimation by solving the l 1 -norm constraint minimization problem. The proposed method enlarges the array aperture due to the application of signal noncircularity, and in the presence of mutual coupling, the proposed algorithm provides higher resolution and better angle estimation performance than ESPRIT-like, l 1 -SVD and l 1 -SRDML (sparse representation deterministic maximum likelihood) algorithms. Numerical experiment results verify the effectiveness and advantages of the proposed method

Analysis on Dynamic Transmission Accuracy for RV Reducer

By taking rotate vector (RV) reducer as the research object, the factors affecting the transmission accuracy are studied, including the machining errors of the main parts, assembly errors, clearance, micro-displacement, gear mesh stiffness and damping, bearing stiffness. Based on Newton second law, the transmission error mathematical model of RV reducer is set up. Then, the RV reducer transmission error curve is achieved by solving the mathematical model using the Runge-Kutta methods under the combined action of various error factors. Through the analysis of RV reducer transmission test, it can be found that there are similar variation trend and frequency components compared the theoretical research and experimental result. The presented method is useful to the research on dynamic transmission accuracy of RV reducer, and also applies to research the transmission accuracy of other cycloid drive systems

The Anti-Cancer Potency and Mechanism of a Novel Tumor-Activated Fused Toxin, DLM

Melittin, which acts as a membrane-disrupting lytic peptide, is not only cytotoxic to tumors, but also vital to normal cells. Melittin had low toxicity when coupled with target peptides. Despite significant research development with the fused toxin, a new fused toxin is needed which has a cleavable linker such that the fused toxin can release melittin after protease cleavage on the tumor cell surface. We describe a novel fused toxin, composed of disintegrin, uPA (urokinase-type plasminogen activator)-cleavable linker, and melittin. Disintegrin is a single strand peptide (73 aa) isolated from Gloydius Ussuriensis venom. The RGD (Arg-Gly-Asp) site of disintegrin dominates its interaction with integrins on the surface of the tumor cells. uPA is over-expressed and plays an important role in tumor cell invasiveness and metastatic progression. The DLM (disintegrin-linker-melittin) linker is uPA-cleavable, enabling DLM to release melittin. We compared binding activity of our synthesized disintegrin with native disintegrin and report that DLM had less binding activity than the native form. uPA-cleavage was evaluated in vitro and the uPA-cleavable linker released melittin. Treating tumors expressing uPA with DLM enhanced tumor cell killing as well as reduced toxicity to erythrocytes and other non-cancerous normal cells. The mechanism behind DLM tumor cell killing was tested using a DNA ladder assay, fluorescent microscopy, flow cytometry, and transmission electron microscopy. Data revealed tumor cell necrosis as the mechanism of cell death, and the fused DLM toxin with an uPA-cleavable linker enhanced tumor selectivity and killing ability