ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection

3D object detection is an essential task in autonomous driving and robotics. Though great progress has been made, challenges remain in estimating 3D pose for distant and occluded objects. In this paper, we present a novel framework named ZoomNet for stereo imagery-based 3D detection. The pipeline of ZoomNet begins with an ordinary 2D object detection model which is used to obtain pairs of left-right bounding boxes. To further exploit the abundant texture cues in RGB images for more accurate disparity estimation, we introduce a conceptually straight-forward module -- adaptive zooming, which simultaneously resizes 2D instance bounding boxes to a unified resolution and adjusts the camera intrinsic parameters accordingly. In this way, we are able to estimate higher-quality disparity maps from the resized box images then construct dense point clouds for both nearby and distant objects. Moreover, we introduce to learn part locations as complementary features to improve the resistance against occlusion and put forward the 3D fitting score to better estimate the 3D detection quality. Extensive experiments on the popular KITTI 3D detection dataset indicate ZoomNet surpasses all previous state-of-the-art methods by large margins (improved by 9.4% on APbv (IoU=0.7) over pseudo-LiDAR). Ablation study also demonstrates that our adaptive zooming strategy brings an improvement of over 10% on AP3d (IoU=0.7). In addition, since the official KITTI benchmark lacks fine-grained annotations like pixel-wise part locations, we also present our KFG dataset by augmenting KITTI with detailed instance-wise annotations including pixel-wise part location, pixel-wise disparity, etc.. Both the KFG dataset and our codes will be publicly available at https://github.com/detectRecog/ZoomNet.Comment: Accpeted by AAAI 2020 as Oral presentation; The github page will be updated in March,202

Pre-Treatment with Melatonin Enhances Therapeutic Efficacy of Cardiac Progenitor Cells for Myocardial Infarction

Background/Aims: Melatonin possesses many biological activities such as antioxidant and anti-aging. Cardiac progenitor cells (CPCs) have emerged as a promising therapeutic strategy for myocardial infarction (MI). However, the low survival of transplanted CPCs in infarcted myocardium limits the successful use in treating MI. In the present study, we aimed to investigate if melatonin protects against oxidative stress-induced CPCs damage and enhances its therapeutic efficacy for MI. Methods: TUNEL assay and EdU assay were used to detect the effects of melatonin and miR-98 on H2O2-induced apoptosis and proliferation. MI model was used to evaluate the potential cardioprotective effects of melatonin and miR-98. Results: Melatonin attenuated H2O2-induced the proliferation reduction and apoptosis of c-kit+ CPCs in vitro, and CPCs which pretreated with melatonin significantly improved the functions of post-infarct hearts compared with CPCs alone in vivo. Melatonin was capable to inhibit the increase of miR-98 level by H2O2 in CPCs. The proliferation reduction and apoptosis of CPCs induced by H2O2 was aggravated by miR-98. In vivo, transplantation of CPCs with miR-98 silencing caused the more significant improvement of cardiac functions in MI than CPCs. MiR-98 targets at the signal transducer and activator of the transcription 3 (STAT3), and thus aggravated H2O2-induced the reduction of Bcl-2 protein. Conclusions: Pre-treatment with melatonin protects c-kit+ CPCs against oxidative stress-induced damage via downregulation of miR-98 and thereby increasing STAT3, representing a potentially new strategy to improve CPC-based therapy for MI

Shape Prior Guided Attack: Sparser Perturbations on 3D Point Clouds

Deep neural networks are extremely vulnerable to malicious input data. As 3D data is increasingly used in vision tasks such as robots, autonomous driving and drones, the internal robustness of the classification models for 3D point cloud has received widespread attention. In this paper, we propose a novel method named SPGA (Shape Prior Guided Attack) to generate adversarial point cloud examples. We use shape prior information to make perturbations sparser and thus achieve imperceptible attacks. In particular, we propose a Spatially Logical Block (SLB) to apply adversarial points through sliding in the oriented bounding box. Moreover, we design an algorithm called FOFA for this type of task, which further refines the adversarial attack in the process of breaking down complicated problems into sub-problems. Compared with the methods of global perturbation, our attack method consumes significantly fewer computations, making it more efficient. Most importantly of all, SPGA can generate examples with a higher attack success rate (even in a defensive situation), less perturbation budget and stronger transferability

Helium-induced damage behaviour in hot-rolled Ni-201/Inconel 617 alloy at elevated temperature

Hot-rolled Ni-201/Inconel 617 bimetal composite plate with excellent high-temperature strength has acquired significant attention in molten salt reactors (MSRs). To evaluate its potential applications, this alloy was irradiated by He ions at 650, 750, and 850 °C to study their irradiation performance, especially the hot-rolled interface. The scanning electron microscope (SEM) shows that some precipitates distribute along the micro-scale hot-rolled interface. The transmission electron microscopy (TEM) results indicate that the thickness of alloy elements diffusion layer around the interface increase with the increasing temperature, which can enhance their combination. Additionally, the lower helium swelling values are observed in the hot-rolled interface, which suggests that the hot-rolled interface has better resistance to swelling. The presence of TiN/Cr23C6 has a stronger ability to trap He atoms and inhibit their diffusion to the hot-rolled interface, which can improve the swelling resistance of the interface. The variation of nanohardness around the hot-rolled interface after irradiation reveals that the hot-rolled interface exhibits better resistance to irradiation-induced hardness than Inconel 617 side. Therefore, Ni/Inconel 617 alloy with promising resistance to swelling has a great potential to function for high-temperature MSRs

In silico Study on the deposition and distribution of particles in a realistic airway model with Handilaher®

Effective pulmonary drug delivery plays an essential role in the treatment of diseases. Drug aerosolization and inhalers play an essential role in the therapeutic effect of pulmonary diseases. The main objective of this paper is to evaluate the effect of inhalers, inhalation flow rates, and particle properties on the transport and deposition of 1-19 μm particles in a realistic airway model. Computational fluid dynamics coupled with the discrete phrase model (CFD-DPM) was performed to predict the transport and deposition of inhaled particles. Good agreement in deposition mechanisms was observed with the in vivo published data, which proved the effectiveness of the numerical method in pulmonary drug delivery. Airflow structure as well as deposition pattern showed that differences in turbulence, reverse flow, and vortex formulation between the two different models are determined by the existence of inhaler geometry. Enhancing the air flow rate and particle diameter increases the particle inertial as well as the turbulence level, resulting in an uptrend in deposition fraction (DF) of the mouth-throat (MT) region. In conclusion, this in silico method is valuable to help understand the in vitro - in vivo correlation (IVIVC) of pulmonary drug delivery