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Service robots operating in unstructured environments must effectively recognize and segment unknown objects to enhance their functionality. Traditional supervised learningbased segmentation techniques require extensive annotated datasets, which are impractical for the diversity of objects encountered in real-world scenarios. Unseen Object Instance Segmentation (UOIS) methods aim to address this by training models on synthetic data to generalize to novel objects, but they often suffer from the simulation-to-reality gap. This paper proposes a novel approach (ZISVFM) for solving UOIS by leveraging the powerful zero-shot capability of the segment anything model (SAM) and explicit visual representations from a selfsupervised vision transformer (ViT). The proposed framework operates in three stages: (1) generating object-agnostic mask proposals from colorized depth images using SAM, (2) refining these proposals using attention-based features from the selfsupervised ViT to filter non-object masks, and (3) applying K-Medoids clustering to generate point prompts that guide SAM towards precise object segmentation. Experimental validation on two benchmark datasets and a self-collected dataset demonstrates the superior performance of ZISVFM in complex environments, including hierarchical settings such as cabinets, drawers, and handheld objects. Our source code is available at https://github.com/Yinmlmaoliang/zisvfm.

Service robots are increasingly entering the home to provide domestic tasks for residents. However, when working in an open, dynamic, and unstructured home environment, service robots still face challenges such as low intelligence for task execution and poor long-term autonomy (LTA), which has limited their deployment. As the basis of robotic task execution, environment modeling has attracted significant attention. This integrates core technologies such as environment perception, understanding, and representation to accurately recognize environmental information. This paper presents a comprehensive survey of environmental modeling from a new task-executionoriented perspective. In particular, guided by the requirements of robots in performing domestic service tasks in the home environment, we systematically review the progress that has been made in task-execution-oriented environmental modeling in four respects: 1) localization, 2) navigation, 3) manipulation, and 4) LTA. Current challenges are discussed, and potential research opportunities are also highlighted., Comment: 16 pages, 9 figures; This article has been accepted for publication in a future issue of IEEE/CAA Journal of Automatica Sinica, but has not been fully edited. Content may change prior to final publication

Air-ground collaborative robots have shown great potential in the field of fire and rescue, which can quickly respond to rescue needs and improve the efficiency of task execution. Mapping and navigation, as the key foundation for air-ground collaborative robots to achieve efficient task execution, have attracted a great deal of attention. This growing interest in collaborative robot mapping and navigation is conducive to improving the intelligence of fire and rescue task execution, but there has been no comprehensive investigation of this field to highlight their strengths. In this paper, we present a systematic review of the ground-to-ground cooperative robots for fire and rescue from a new perspective of mapping and navigation. First, an air-ground collaborative robots framework for fire and rescue missions based on unmanned aerial vehicle (UAV) mapping and unmanned ground vehicle (UGV) navigation is introduced. Then, the research progress of mapping and navigation under this framework is systematically summarized, including UAV mapping, UAV/UGV co-localization, and UGV navigation, with their main achievements and limitations. Based on the needs of fire and rescue missions, the collaborative robots with different numbers of UAVs and UGVs are classified, and their practicality in fire and rescue tasks is elaborated, with a focus on the discussion of their merits and demerits. In addition, the application examples of air-ground collaborative robots in various firefighting and rescue scenarios are given. Finally, this paper emphasizes the current challenges and potential research opportunities, rounding up references for practitioners and researchers willing to engage in this vibrant area of air-ground collaborative robots., Comment: 18 pages, 20 figures

By using two-dimensional particle-in-cell simulations, attosecond electron bunches with high density, high energy and small divergence angle can be obtained by p-polarized laser irradiation in conical channel with curved wall. We find that some electrons in the wall are pulled into the channel by the transverse electric field and are directly accelerated. Meanwhile, they move steadily along the conical wall via laser pondermotive force. The results show that the focusing effect of the curved wall conical channel is stronger than that of the traditional flat wall conical channel, and the density of the attosecond electron bunches is increased by nearly 175% as well as the maximum energy is increased by 36%. We also find that the quality of the electron bunches is affected by the geometry of the concial channel wall. Interestingly it is found that the attosecond electron bunches obtained from the specific concial channel with the hyperbolic geometry of the curved wall can keep stable around the maximum electron energy within 10T0 even if they have left the channel.

In the Internet of Vehicles (IoV), Age of Information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although many studies aim to minimize the average AoI of the system through optimized resource scheduling schemes, they often fail to adequately consider the queue characteristics. Moreover, the vehicle mobility leads to rapid changes in network topology and channel conditions, making it difficult to accurately reflect the unique characteristics of vehicles with the calculated AoI under ideal channel conditions. This paper examines the impact of Doppler shifts caused by vehicle speeds on data transmission in error-prone channels. Based on the M/M/1 and D/M/1 queuing theory models, we derive expressions for the Age of Information and optimize the system's average AoI by adjusting the data extraction rates of vehicles (which affect system utilization). We propose an online optimization algorithm that dynamically adjusts the vehicles' data extraction rates based on environmental changes to ensure optimal AoI. Simulation results have demonstrated that adjusting the data extraction rates of vehicles can significantly reduce the system's AoI. Additionally, in the network scenario of this work, the AoI of the D/M/1 system is lower than that of the M/M/1 system., Comment: This paper has been submitted to Sensors. The source code has been released at: https://github.com/qiongwu86/Blockchain-Enabled-Variational-Information-Bottleneck-for-Minimizing-AoI-in-IoV

Vortex information of scalar pair production in circularly polarized field is investigated in the multiphoton regime. We find that vortex orientation is related to the intrinsic orbital angular momentum of created particles associating with the helicity of absorbed photons, while the magnitude of the orbital angular momentum, i.e., the topology charge is determined by the number of absorbed photons. Moreover, the properties of particle creation and vortices formation can be understood by analyzing the pair production process in quasiparticle representation. This study provides new insights into the angular momentum transfer from field to particle in the scalar pair production process. It is expected that there are similar findings about vortex features for different spin alignment in electron-positron pair production in strong fields via the topology charge as a new freedom., Comment: 7 pages, 4 figures

The Internet of Vehicles (IoV) network can address the issue of limited computing resources and data processing capabilities of individual vehicles, but it also brings the risk of privacy leakage to vehicle users. Applying blockchain technology can establish secure data links within the IoV, solving the problems of insufficient computing resources for each vehicle and the security of data transmission over the network. However, with the development of the IoV, the amount of data interaction between multiple vehicles and between vehicles and base stations, roadside units, etc., is continuously increasing. There is a need to further reduce the interaction volume, and intelligent data compression is key to solving this problem. The VIB technique facilitates the training of encoding and decoding models, substantially diminishing the volume of data that needs to be transmitted. This paper introduces an innovative approach that integrates blockchain with VIB, referred to as BVIB, designed to lighten computational workloads and reinforce the security of the network. We first construct a new network framework by separating the encoding and decoding networks to address the computational burden issue, and then propose a new algorithm to enhance the security of IoV networks. We also discuss the impact of the data extraction rate on system latency to determine the most suitable data extraction rate. An experimental framework combining Python and C++ has been established to substantiate the efficacy of our BVIB approach. Comprehensive simulation studies indicate that the BVIB consistently excels in comparison to alternative foundational methodologies., Comment: This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/BVIB-for-Data-Extraction-Based-on Mutual-Information-in-the-IoV

Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead of local data. The gradients of vehicles' local models are usually large for the vehicular artificial intelligence (AI) applications, thus transmitting such large gradients would cause large per-round latency. Gradient quantization has been proposed as one effective approach to reduce the per-round latency in FL enabled VEC through compressing gradients and reducing the number of bits, i.e., the quantization level, to transmit gradients. The selection of quantization level and thresholds determines the quantization error, which further affects the model accuracy and training time. To do so, the total training time and quantization error (QE) become two key metrics for the FL enabled VEC. It is critical to jointly optimize the total training time and QE for the FL enabled VEC. However, the time-varying channel condition causes more challenges to solve this problem. In this paper, we propose a distributed deep reinforcement learning (DRL)-based quantization level allocation scheme to optimize the long-term reward in terms of the total training time and QE. Extensive simulations identify the optimal weighted factors between the total training time and QE, and demonstrate the feasibility and effectiveness of the proposed scheme., Comment: This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/Distributed-Deep-Reinforcement-Learning-Based-Gradient Quantization-for-Federated-Learning-Enabled-Vehicle-Edge-Computing

In vehicle edge computing (VEC), asynchronous federated learning (AFL) is used, where the edge receives a local model and updates the global model, effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles, renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model, and the vehicle may also be affected by Byzantine attacks, leading to the deterioration of the vehicle data. However, based on deep reinforcement learning (DRL), we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL. At the same time, when aggregating AFL, we can focus on those vehicles with better performance to improve the accuracy and safety of the system. In this paper, we proposed a vehicle selection scheme based on DRL in VEC. In this scheme, vehicle s mobility, channel conditions with temporal variations, computational resources with temporal variations, different data amount, transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model., Comment: This paper has been accepted by China Communications.The source code has been released at:https://github.com/giongwu86/By-AFLDDPG

We present the generation of well-collimated $\gamma$-photons and pairs with extrinsic transverse orbital angular momentum (TOAM) through the head-on collision of an intense spatiotemporal optical vortex (STOV) pulse carrying intrinsic TOAM with a high-energy electron beam. It is found that the TOAM of STOV pulse remains almost unchanged, and the TOAM is conserved in the center-of-mass frame (CMF). Moreover, there exhibits duality for particles TOAM in the CMF and laboratory frame (LF) when the initial location of high-energy electron beam is different. Furthermore, the TOAM of $\gamma$-photons in the CMF increases while that of positrons decreases as the topological charge of STOV pulse increases, whereas in the LF, the TOAM of both $\gamma$-photons and positrons decreases. And the result under the same pulse intensity is better than that under the same pulse energy. The increase in the initial energy of high-energy electrons leads to an enhancement of the TOAM for both $\gamma$-photons and positrons in both frames. $\gamma$-photons and electrons/positrons with TOAM as a new degree of freedom maybe have an extensive applications in optical communication, astrophysics and nanomaterials and so on., Comment: 18 pages, 9 figures

The Wigner D-matrix is essential in the course of angular momentum techniques. We here derive the new even- and odd-orthogonality properties of the Wigner D-matrix which was yet to be demonstrated in textbooks and also apply them to identifying optimal measurements for linear phase estimation based on two-mode optical interferometry with two specific quantum states., Comment: 16 pages,2 figures