Income inequality of destination countries and trade patterns: Evidence from Chinese firm-level data

In this paper, we investigate the relation between the export patterns and the income inequality of the destination countries using the Chinese firm-level data. Our empirical analysis finds two main results: (i) export price decreases in the income inequality of the destination countries; while (ii) the exporting firm number and export value will increase in the inequality level. With a conventionally theoretical framework, we discuss the potential influencing mechanism. A higher income inequality leads to higher share of poor consumers in a country, which will lower the quality threshold for Chinese exporters. In this case, the firms with less competitive and producing low quality products are able to enter this market. As a result, we observe that in response to a higher income inequality, more firms enter the market while the exporting price decreases in this market

Income inequality of destination countries and trade patterns: Evidence from Chinese firm-level data

In this paper, we investigate the relation between the export patterns and the income inequality of the destination countries using the Chinese firm-level data. Our empirical analysis finds two main results: (i) export price decreases in the income inequality of the destination countries; while (ii) the exporting firm number and export value will increase in the inequality level. With a conventionally theoretical framework, we discuss the potential influencing mechanism. A higher income inequality leads to higher share of poor consumers in a country, which will lower the quality threshold for Chinese exporters. In this case, the firms with less competitive and producing low quality products are able to enter this market. As a result, we observe that in response to a higher income inequality, more firms enter the market while the exporting price decreases in this market

OCC-VO: Dense Mapping via 3D Occupancy-Based Visual Odometry for Autonomous Driving

Visual Odometry (VO) plays a pivotal role in autonomous systems, with a principal challenge being the lack of depth information in camera images. This paper introduces OCC-VO, a novel framework that capitalizes on recent advances in deep learning to transform 2D camera images into 3D semantic occupancy, thereby circumventing the traditional need for concurrent estimation of ego poses and landmark locations. Within this framework, we utilize the TPV-Former to convert surround view cameras' images into 3D semantic occupancy. Addressing the challenges presented by this transformation, we have specifically tailored a pose estimation and mapping algorithm that incorporates Semantic Label Filter, Dynamic Object Filter, and finally, utilizes Voxel PFilter for maintaining a consistent global semantic map. Evaluations on the Occ3D-nuScenes not only showcase a 20.6% improvement in Success Ratio and a 29.6% enhancement in trajectory accuracy against ORB-SLAM3, but also emphasize our ability to construct a comprehensive map. Our implementation is open-sourced and available at: https://github.com/USTCLH/OCC-VO.Comment: 7pages, 3 figure

Cross-layer design for mission-critical IoT in mobile edge computing systems

In this paper, we establish a cross-layer framework for optimizing user association, packet offloading rates, and bandwidth allocation for mission-critical Internet-of-Things (MC-IoT) services with short packets in mobile edge computing (MEC) systems, where enhanced mobile broadband (eMBB) services with long packets are considered as background services. To reduce communication delay, the fifth generation new radio is adopted in radio access networks. To avoid long queueing delay for short packets from MC-IoT, processor-sharing (PS) servers are deployed at MEC systems, where the service rate of the server is equally allocated to all the packets in the buffer. We derive the distribution of latency experienced by short packets in closed form, and minimize the overall packet loss probability subject to the end-to-end delay requirement. To solve the nonconvex optimization problem, we propose an algorithm that converges to a near optimal solution when the throughput of eMBB services is much higher than MC-IoT services, and extend it into more general scenarios. Furthermore, we derive the optimal solutions in two asymptotic cases: communication or computing is the bottleneck of reliability. The simulation and numerical results validate our analysis and show that the PS server outperforms first-come-first-serve servers

P3OP^{3}O: Transferring Visual Representations for Reinforcement Learning via Prompting

It is important for deep reinforcement learning (DRL) algorithms to transfer their learned policies to new environments that have different visual inputs. In this paper, we introduce Prompt based Proximal Policy Optimization ($P^{3}O$), a three-stage DRL algorithm that transfers visual representations from a target to a source environment by applying prompting. The process of $P^{3}O$ consists of three stages: pre-training, prompting, and predicting. In particular, we specify a prompt-transformer for representation conversion and propose a two-step training process to train the prompt-transformer for the target environment, while the rest of the DRL pipeline remains unchanged. We implement $P^{3}O$ and evaluate it on the OpenAI CarRacing video game. The experimental results show that $P^{3}O$ outperforms the state-of-the-art visual transferring schemes. In particular, $P^{3}O$ allows the learned policies to perform well in environments with different visual inputs, which is much more effective than retraining the policies in these environments.Comment: This paper has been accepted to be presented at the upcoming IEEE International Conference on Multimedia & Expo (ICME) in 202

EdgeCalib: Multi-Frame Weighted Edge Features for Automatic Targetless LiDAR-Camera Calibration

In multimodal perception systems, achieving precise extrinsic calibration between LiDAR and camera is of critical importance. Previous calibration methods often required specific targets or manual adjustments, making them both labor-intensive and costly. Online calibration methods based on features have been proposed, but these methods encounter challenges such as imprecise feature extraction, unreliable cross-modality associations, and high scene-specific requirements. To address this, we introduce an edge-based approach for automatic online calibration of LiDAR and cameras in real-world scenarios. The edge features, which are prevalent in various environments, are aligned in both images and point clouds to determine the extrinsic parameters. Specifically, stable and robust image edge features are extracted using a SAM-based method and the edge features extracted from the point cloud are weighted through a multi-frame weighting strategy for feature filtering. Finally, accurate extrinsic parameters are optimized based on edge correspondence constraints. We conducted evaluations on both the KITTI dataset and our dataset. The results show a state-of-the-art rotation accuracy of 0.086{\deg} and a translation accuracy of 0.977 cm, outperforming existing edge-based calibration methods in both precision and robustness

ProQA: Structural Prompt-based Pre-training for Unified Question Answering

Question Answering (QA) is a longstanding challenge in natural language processing. Existing QA works mostly focus on specific question types, knowledge domains, or reasoning skills. The specialty in QA research hinders systems from modeling commonalities between tasks and generalization for wider applications. To address this issue, we present ProQA, a unified QA paradigm that solves various tasks through a single model. ProQA takes a unified structural prompt as the bridge and improves the QA-centric ability by structural prompt-based pre-training. Through a structurally designed prompt-based input schema, ProQA concurrently models the knowledge generalization for all QA tasks while keeping the knowledge customization for every specific QA task. Furthermore, ProQA is pre-trained with structural prompt-formatted large-scale synthesized corpus, which empowers the model with the commonly-required QA ability. Experimental results on 11 QA benchmarks demonstrate that ProQA consistently boosts performance on both full data fine-tuning, few-shot learning, and zero-shot testing scenarios. Furthermore, ProQA exhibits strong ability in both continual learning and transfer learning by taking the advantages of the structural prompt.Comment: NAACL 202