Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications

Developing an intelligent vehicle which can perform human-like actions requires the ability to learn basic driving skills from a large amount of naturalistic driving data. The algorithms will become efficient if we could decompose the complex driving tasks into motion primitives which represent the elementary compositions of driving skills. Therefore, the purpose of this paper is to segment unlabeled trajectory data into a library of motion primitives. By applying a probabilistic inference based on an iterative Expectation-Maximization algorithm, our method segments the collected trajectories while learning a set of motion primitives represented by the dynamic movement primitives. The proposed method utilizes the mutual dependencies between the segmentation and representation of motion primitives and the driving-specific based initial segmentation. By utilizing this mutual dependency and the initial condition, this paper presents how we can enhance the performance of both the segmentation and the motion primitive library establishment. We also evaluate the applicability of the primitive representation method to imitation learning and motion planning algorithms. The model is trained and validated by using the driving data collected from the Beijing Institute of Technology intelligent vehicle platform. The results show that the proposed approach can find the proper segmentation and establish the motion primitive library simultaneously

Dehazed Image Quality Evaluation: From Partial Discrepancy to Blind Perception

Image dehazing aims to restore spatial details from hazy images. There have emerged a number of image dehazing algorithms, designed to increase the visibility of those hazy images. However, much less work has been focused on evaluating the visual quality of dehazed images. In this paper, we propose a Reduced-Reference dehazed image quality evaluation approach based on Partial Discrepancy (RRPD) and then extend it to a No-Reference quality assessment metric with Blind Perception (NRBP). Specifically, inspired by the hierarchical characteristics of the human perceiving dehazed images, we introduce three groups of features: luminance discrimination, color appearance, and overall naturalness. In the proposed RRPD, the combined distance between a set of sender and receiver features is adopted to quantify the perceptually dehazed image quality. By integrating global and local channels from dehazed images, the RRPD is converted to NRBP which does not rely on any information from the references. Extensive experiment results on several dehazed image quality databases demonstrate that our proposed methods outperform state-of-the-art full-reference, reduced-reference, and no-reference quality assessment models. Furthermore, we show that the proposed dehazed image quality evaluation methods can be effectively applied to tune parameters for potential image dehazing algorithms

Reduced-reference quality assessment of point clouds via content-oriented saliency projection

Many dense 3D point clouds have been exploited to represent visual objects instead of traditional images or videos. To evaluate the perceptual quality of various point clouds, in this letter, we propose a novel and efficient Reduced-Reference quality metric for point clouds, which is based on Content-oriented sAliency Projection (RR-CAP). Specifically, we make the first attempt to simplify reference and distorted point clouds into projected saliency maps with a downsampling operation. Through this process, we tackle the issue of transmitting large-volume original point clouds to end-users for quality assessment. Then, motivated by the characteristics of the human visual system (HVS), the objective quality scores of distorted point clouds are produced by combining content-oriented similarity and statistical correlation measurements. Finally, extensive experiments are conducted on SJTU-PCQA and WPC databases. The experiment results demonstrate that our proposed algorithm outperforms existing reduced-reference and no-reference quality metrics, and significantly reduces the performance gap between state-of-the-art full-reference quality assessment methods. In addition, we show the performance variation of each proposed technical component by ablation tests

Dehazed image quality evaluation: from partial discrepancy to blind perception

Nowadays, vision oriented intelligent vehicle systems such as autonomous driving or transportation assistance can be optimized by enhancing the visual visibility of images acquired in bad weather conditions. The presence of haze in such visual scenes is a critical threat. Image dehazing aims to restore spatial details from hazy images. There have emerged a number of image dehazing algorithms, designed to increase the visibility of those hazy images. However, much less work has been focused on evaluating the visual quality of dehazed images. In this paper, we propose a Reduced-Reference dehazed image quality evaluation approach based on Partial Discrepancy (RRPD) and then extend it to a No-Reference quality assessment metric with Blind Perception (NRBP). Specifically, inspired by the hierarchical characteristics of the human perceiving dehazed images, we introduce three groups of features: luminance discrimination, color appearance, and overall naturalness. In the proposed RRPD, the combined distance between a set of sender and receiver features is adopted to quantify the perceptually dehazed image quality. By integrating global and local channels from dehazed images, the RRPD is converted to NRBP which does not rely on any information from the references. Extensive experiment results on both synthetic and real dehazed image quality databases demonstrate that our proposed methods outperform state-of-the-art full-reference, reduced-reference, and no-reference quality assessment models. Furthermore, we show that the proposed dehazed image quality evaluation methods can be effectively applied to tune parameters for image dehazing algorithms and have the potential to be deployed in real transportation systems

Effect of Ethylene Diamine Phosphate on the Sulfidization Flotation of Chrysocolla

In this study, ethylene diamine phosphate (EDP) was employed as an activator to improve the sulfidization and flotation of chrysocolla. The micro-flotation experiment results indicated that EDP could greatly increase the flotation recovery of chrysocolla. BET and TEM measurements confirmed that the porous structure of the chrysocolla’s surface would lead to large amounts of the reagents. ICP-AES analysis revealed that the addition of EDP caused more active Cu sites formed on the chrysocolla’s surface, enhancing the adsorption of S2− on its surface. Meanwhile, a redox reaction occurred between the S2− and [Cu(en)2]2+ ions causing the Cu, S, and N in the solution to counter-adsorb onto the chrysocolla’s surface by forming new complexes. During this reaction, the Cu(II) species reduced to Cu(I) species and the sulfide ions in the form of S2−, S22−, Sn2−, and SO42− appeared on the mineral surface. The zeta potential measurements further revealed that the EDP-activated chrysocolla surfaces adsorbed more sulfide species and xanthate species, thereby improving the floatability of the chrysocolla

The Hippo signaling pathway: from multiple signals to the hallmarks of cancers

Evolutionarily conserved, the Hippo signaling pathway is critical in regulating organ size and tissue homeostasis. The activity of this pathway is tightly regulated under normal circumstances, since its physical function is precisely maintained to control the rate of cell proliferation. Failure of maintenance leads to a variety of tumors. Our understanding of the mechanism of Hippo dysregulation and tumorigenesis is becoming increasingly precise, relying on the emergence of upstream inhibitor or activator and the connection linking Hippo target genes, mutations, and related signaling pathways with phenotypes. In this review, we summarize recent reports on the signaling network of the Hippo pathway in tumorigenesis and progression by exploring its critical mechanisms in cancer biology and potential targeting in cancer therapy

Basic Characteristics of Hemimorphite and Its Transformation Mechanism with Na2CO3

The crystal of hemimorphite is a non-conductor. The Si–O bond in the crystal is strong, whereas the Zn–O bond is weak. These properties lead to the easy breakage of the Zn–O bond in the crushing process of hemimorphite. Thus, the interaction between minerals and polar water molecules is strong, and natural floatability of ores is poor. This study systematically investigated the characteristics of hemimorphite and its action mechanism with Na2CO3. Results of SEM-EDS showed that the surface of hemimorphite dissolved after interacting with Na2CO3, and the contents of Si and O decreased, whereas Zn and C increased. XPS analysis showed that the carbonate group was detected. The interaction between CO32− and hemimorphite was calculated using the first principles calculation based on density functional theory. The results indicate that an O atom in CO32− interacted with Zn2+ from the (100) plane of hemimorphite. The interaction between Zn and O atoms was not strong, and the Zn atoms were not completely displaced, which was proven by density of state analysis and the EDS and XPS results. The Mulliken population showed that the O–Zn bond was the atomic bonding of CO32− with Zn2+ and exhibited properties of ionic bonds. Thus, hemimorphite transformed to smithsonite-like mineral (ZnCO3) when acting with CO32−