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Blockchain technology (BCT) provides a new way to mitigate the default risks of lease contracts resulting from the information asymmetry in leasing. The conceptual architecture of a consortium blockchain-based leasing platform (CBLP) is first proposed to facilitate information sharing between small and medium-sized enterprises (SMEs, the “lessees”) and leasing firms (LFs, the “lessors”). Then, based on evolutionary game theory (EGT), this study builds a two-party game model and analyzes the influences of four types of factors (i.e., information sharing, credit, incentive–penalty, and risk) on SMEs’ contract compliance or default behaviors with/without blockchain empowerment. The primary findings of this study are as follows: (1) SMEs and LFs eventually evolve to implement the ideal “win–win” strategies of complying with the contract and adopting BCT. (2) The large residual value of the leased asset can tempt SMEs to conduct a default action of unauthorized asset disposal, while leading LFs to access the CBLP to utilize information shared on-chain. (3) When the maintenance service is outsourced instead of being provided by lessors, the maintenance fee is not a core determinant affecting the equilibrium state. (4) There is a critical value concerning the default penalty on-chain to incentivize the involved parties to keep their commitments. (5) The capability of utilizing information, storage overhead, and security risk should all be taken into consideration when deciding on the optimal strategies for SMEs and LFs. This study provides comprehensive insights for designing an incentive mechanism to encourage lessees and lessors to cooperatively construct a sustainable and trustworthy leasing environment.

In cognitive radio networks (CRNs), secondary users (SUs) are permitted to use the available spectrum in opportunity ways to optimize spectrum utilization. As legal spectrum owners, primary users (PUs) can always interrupt SUs’ transmissions when they have transmission needs. To increase SUs’ transmission continuity while guaranteeing PUs’ transmission quality, we propose a preemption backoff mechanism (PBM) with backoff threshold m and backoff parameter f. Under the centralized spectrum sharing structure, we construct a discrete-time queueing model by developing a 3-dimensional Markov-chain (3DMC). Through the model analysis, the system’s steady-state probability distribution is obtained. To assess the comprehensive performance of our proposed mechanism, we deduce various important expressions of performance indices and get the corresponding performance figures. At last, we design a cost function by compromising various performance indices and find the optimum parameter combination under the lowest system cost through the Transient Search Optimization (TSO) algorithm. The numerical and simulation results illustrate our proposed PBM can improve SUs’ transmission continuity. For example, when m=1 and f=0.05, under the parameter settings given by the experiments, the proposed PBM can reduce SU packets’ outage rate and improve throughput by about 60% at most.

Abstract In order to reveal the Spatial distribution characteristics of heavy metal(loid)s health risk in soil on the town-scale, 788 topsoil samples were collected from a town in the hinterland of Chengdu Plain, with 5 subordinate communities and 17 administrative villages as the research sub regions. The USEPA health risk assessment model was used to assess the health risks of heavy metal(loid)s Cd, Hg, As, Cu, Pb, Cr, Zn and Ni in the soil, the health risk analysis method system based on the geographic detector and the optimized rank-size theory model are used to clarify the spatial differentiation and risk level difference of health risk in the study area. The results showed that the average values of Cd, Hg, As, Pb, Cr, Cu, Ni and Zn in the soil of the study area were 0.221, 0.155, 9.76, 32.2, 91.9, 35.2, 37.1 and 108.8 mg/kg, respectively. The health risks of heavy metal(loid)s in soil to adults and children are generally within the acceptable range, but the maximum hazard index of children and the maximum non-carcinogenic risk value of Cr to children are 2.653303 and 1.213098 respectively, which were exceeding the acceptable range. The carcinogenic risk of Cr and As to adults and children and the carcinogenic risk of Cd to children are at 1 × 10–4 to 1 × 10–6, exceeding the 10–6 health threshold. The q-value range of heavy metal(loid)s health risk spatial differentiation of soil in the study area is 0.016–0.425. The spatial differentiation of non-carcinogenic risk of Hg, As, Ni, Pb, Cd and Cr and the spatial differentiation of carcinogenic risk of Cr and As are larger, which needs further attention. The strictly controlled area of heavy metal(loid)s health risk in the soil of the study area (R ≥ 1.1) is mainly concentrated in the central, western and northeast sub regions, and most sub regions belong to the safe utilization area (0.9

The autonomous driving technology based on deep reinforcement learning (DRL) has been confirmed as one of the most cutting-edge research fields worldwide. The agent is enabled to achieve the goal of making independent decisions by interacting with the environment and learning driving strategies based on the feedback from the environment. This technology has been widely used in end-to-end driving tasks. However, this field faces several challenges. First, developing real vehicles is expensive, time-consuming, and risky. To further expedite the testing, verification, and iteration of end-to-end deep reinforcement learning algorithms, a joint simulation development and validation platform was designed and implemented in this study based on VTD–CarSim and the Tensorflow deep learning framework, and research work was conducted based on this platform. Second, sparse reward signals can cause problems (e.g., a low-sample learning rate). It is imperative for the agent to be capable of navigating in an unfamiliar environment and driving safely under a wide variety of weather or lighting conditions. To address the problem of poor generalization ability of the agent to unknown scenarios, a deep deterministic policy gradient (DDPG) decision-making and planning method was proposed in this study in accordance with a multi-task fusion strategy. The main task based on DRL decision-making planning and the auxiliary task based on image semantic segmentation were cross-fused, and part of the network was shared with the main task to reduce the possibility of model overfitting and improve the generalization ability. As indicated by the experimental results, first, the joint simulation development and validation platform built in this study exhibited prominent versatility. Users were enabled to easily substitute any default module with customized algorithms and verify the effectiveness of new functions in enhancing overall performance using other default modules of the platform. Second, the deep reinforcement learning strategy based on multi-task fusion proposed in this study was competitive. Its performance was better than other DRL algorithms in certain tasks, which improved the generalization ability of the vehicle decision-making planning algorithm.

Due to the complementary characteristics of visual and LiDAR information, these two modalities have been fused to facilitate many vision tasks. However, current studies of learning-based odometries mainly focus on either the visual or LiDAR modality, leaving visual–LiDAR odometries (VLOs) under-explored. This work proposes a new method to implement an unsupervised VLO, which adopts a LiDAR-dominant scheme to fuse the two modalities. We, therefore, refer to it as unsupervised vision-enhanced LiDAR odometry (UnVELO). It converts 3D LiDAR points into a dense vertex map via spherical projection and generates a vertex color map by colorizing each vertex with visual information. Further, a point-to-plane distance-based geometric loss and a photometric-error-based visual loss are, respectively, placed on locally planar regions and cluttered regions. Last, but not least, we designed an online pose-correction module to refine the pose predicted by the trained UnVELO during test time. In contrast to the vision-dominant fusion scheme adopted in most previous VLOs, our LiDAR-dominant method adopts the dense representations for both modalities, which facilitates the visual–LiDAR fusion. Besides, our method uses the accurate LiDAR measurements instead of the predicted noisy dense depth maps, which significantly improves the robustness to illumination variations, as well as the efficiency of the online pose correction. The experiments on the KITTI and DSEC datasets showed that our method outperformed previous two-frame-based learning methods. It was also competitive with hybrid methods that integrate a global optimization on multiple or all frames.

Semantic segmentation has always been a fundamental and critical task to scene understanding. Current deep convolutional neural networks (DCNN) are able to successfully learn context from very large receptive fields due to convolutions with deep layers. However, current convolutions in DCNNs does not consider local object boundaries that are the borders among different semantic regions. Convolution with equal contribution on the pixels across the boundary may lead to inferior segmentation results. In this paper, a novel boundary-aware convolution is proposed. It is able to effectively fuse features by adaptively assigning contributions from pixels within receptive fields according to the boundary similarity map. A new semantic segmentation network based on classical FCN8S is then designed by employing multi-scale boundary-aware convolution. The whole network is implemented end-to-end and evaluated with heterogeneous RGB and depth input. Experiments conducted on multiple datasets show that our boundary-aware CNN can effectively improve the semantic segmentation performance.

LHCb has productive studies of quarkonia and $b$-hadrons which provide essential inputs to study quantum chromodynamics (QCD). In this proceeding, seven recent results are reported: the observation of a new excited $\Xi_b^0$ resonance in $\Xi_b^- \pi^+$ final states, the new excited $B_s^0$ state is observed in $B^+ K^-$ mass spectrum, the first observation of suppressed semileptonic $B_s^0 \rightarrow K^- \mu^+ \nu_{\mu}$ decay, the first observation of the decay $B^0 \rightarrow D^0 \bar{D}^0 K^+ \pi^-$, the first observation of the Cabibbo-suppressed decay $\Lambda_b^0 \rightarrow \chi_{c1} p \pi^-$, the first observation of $\Lambda_b^0 \rightarrow \Lambda_c^+ K^+ K^- \pi^-$ decay, and end this report with the first search for the doubly heavy baryon $\Omega_{bc}^{0}$ and a search for $\Xi_{bc}^0$ baryon.

Lane mark detection plays an important role in autonomous driving under structural environments. Many deep learning-based lane mark detection methods have been put forward in recent years. However, most of current methods limit their solutions within one single image and do not make use of the de facto successive image input during the driving scene, which may lead to inferior performance in some challenging scenarios such as occlusion, shadows, and lane mark degradation. To address the issue, we propose a novel lane mark detection network which takes pre-aligned multiple successive frames as inputs to produce more stable predictions. A Spatial-Temporal Attention Module (STAM) is designed in the network to adaptively aggregate the feature information of history frames to the current frame. Various structure of the STAM is also studied to ensure the best performance. Experiments on Tusimple and ApolloScape datasets show that our method can effectively improve lane mark detection and achieve state-of-the-art performance.

As the core task of scene understanding, semantic segmentation and depth completion play a vital role in lots of applications such as robot navigation, AR/VR and autonomous driving. They are responsible for parsing scenes from the angle of semantics and geometry, respectively. While great progress has been made in both tasks through deep learning technologies, few works have been done on building a joint model by deeply exploring the inner relationship of the above tasks. In this paper, semantic segmentation and depth completion are jointly considered under a multi-task learning framework. By sharing a common encoder part and introducing boundary features as inner constraints in the decoder part, the two tasks can properly share the required information from each other. An extra boundary detection sub-task is responsible for providing the boundary features and constructing cross-task joint loss functions for network training. The entire network is implemented end-to-end and evaluated with both RGB and sparse depth input. Experiments conducted on synthesized and real scene datasets show that our proposed multi-task CNN model can effectively improve the performance of every single task.

A novel and practical self-calibration method for misaligned non-central catadioptric cameras is proposed in this paper. Without the aid of any special calibration patterns in the scene, the developed method is able to automatically estimate the pose parameters of the mirror with respect to the perspective camera. First it uses the ellipse corresponding to the mirror boundary in the image to compute the possible solutions for mirror postures. With two pose candidates, thereafter we propose a novel selection method to find the correct solution by using the image of the lens boundary. The whole calibration process is automatic and convenient to carry out since nothing more than a single image acquired from the catadioptric camera is needed. Experimental results both on synthesized and real images prove our success.