We propose a multimodal 3D scene editing framework MMEditor to create or modify objects within an extant 3D Gaussian Splatting (3DGS) according to text and image prompts. MMEditor employs a multimodal image editing module to iteratively optimize 3D Gaussians in editing regions for delicate and multi-view consistent 3D editing. The key multimodal image editing module can perform editing with accurate appearance and location control, which is achieved by two designs. First, a multimodel adapter block takes the reference image as a foreign language to augment the text prompt, enabling editing results to align with the generic text description and the unique characteristics in the reference image. Second, an attention-based localization block localizes cross-attention with user-defined 3D bounding boxes, thereby ensuring the editing occurs in editing regions. Experiments demonstrate that our method achieves more accurate and controllable results than previous state-of-the-art methods.

Roadside vision-based 3D object detection is vital in many applications, such as autonomous driving. The mainstream methods enhance the accuracy of distance estimation by converting predicted height distribution into depth distribution. However, predicting object’s height in roadside perception is challenging, particularly for distant and small objects. Therefore, this work proposes a series of methods to optimize height prediction. Firstly, we propose depth and height decoding supervision methods to optimize the height network by supervising the decoded depth and height values. Then, a height distribution alignment loss is introduced to optimize the height network by constraining the consistency of height distributions among objects of the same category. Experimental results demonstrate that the proposed optimization methods can effectively improve the accuracy of roadside vision-based 3D object detection. For instance, our methods improve the accuracies by 2.31%, 4.33%, and 4.24% for the cyclist category at easy, medium, and hard levels, respectively.

Emerging technologies, such as cloud computing and artificial intelligence, significantly arouse concern about data security and privacy. Homomorphic encryption (HE) is a promising invention, which enables computation on encrypted data without decrypting it so as to ensure data security and privacy. Nevertheless, computation within homomorphic encryption involves time-consuming operations, e.g., Number Theoretic Transform (NTT). The tremendous computation overhead is the critical obstacle in deploying HE applications widely. Besides, in order to meet the performance and security requirements of different applications, it is pivotal to design parametric NTT architecture. In this paper, we propose a flexible and parametric NTT accelerating scheme based on hybrid-radix butterfly, named FHNTT. Specifically, we construct high radix butterfly units and divide the computation of them into several stages such that every stage can be performed pipelined. The number of required twiddle factors declines with the increase of radix value. In addition, we adopt address offset strategy to reduce memory consumption. We implement FHNTT on FPGA due to its fine-grained parallel computing capabilities and customized architecture. Empirical results show that FHNTT has an improved performance compared with other NTT architectures and supports a wide range of parameters. Concretely, FHNTT achieves up to 1.99 × to 2.78 × improvement in latency over other FPGA implementations and the memory utilization rate is up to 94%. Moreover, the flexibility makes FHNTT applicable to multiple use cases.

Reinforcement Learning (RL) over motion skill space has been verified to generate more diverse behaviors than that over low-level control space, and has exhibited superior autonomous driving performance in complex traffic scenarios. However, the incomplete observations pose challenges in achieving efficient skill exploration under unsupervised conditions, hampering the driving performance and applicability. In this paper, we propose a dynamic-aware RL with hybrid network (Da-HnRL) to develop a pseudo-hierarchical planning framework for better motion skill learning in challenging dense traffics. Based on the semi-POMDP modeling, we construct a hybrid network with skip connections as the RL backbone, facilitating a better understanding of the underlying system dynamics. Then we design an efficiency-oriented reward shaping mechanism to incentivize active skill exploration, promoting enhanced trade-off between exploration and exploitation. Furthermore, we provide a comprehensive scoring mechanism for policy identification, ensuring the near-optimality. We validate the proposed methods on challenging dense-traffic tasks. The results demonstrate the superiority of our approach over previous methods, with improved learning efficiency, driving stability and generalization.

It has been found that many real networks, such as power grids and the Internet, are non-robust, i.e., attacking a small set of nodes would cause the paralysis of the entire network. Thus, the Network Enhancement Problem~(NEP), i.e., improving the robustness of a given network by modifying its structure, has attracted increasing attention. Heuristics have been proposed to address NEP. However, a hand-engineered heuristic often has significant performance limitations. A recently proposed model solving NEP by reinforcement learning has shown superior performance than heuristics on in-distribution datasets. However, their model shows considerably inferior out-of-distribution generalization ability when enhancing networks against the degree-based targeted attack. In this paper, we propose a more effective model with stronger generalization ability by incorporating domain knowledge including measurements of local network structures and decision criteria of heuristics. We further design a hierarchical attention model to utilize the network structure directly, where the query range changes from local to global. Finally, we propose neural confined local search~(NCLS) to realize the effective search of a large neighborhood, which exploits a learned model to confine the neighborhood to avoid exhaustive enumeration. We conduct extensive experiments on synthetic and real networks to verify the ability of our models.

Autonomous driving (AD) has experienced rapid development in recent years, and the reinforcement learning (RL) pipeline in trial-and-error manner can surpass human driving ability. However, the poor performance in sample efficiency and generalization limits RL applying in the challenging urban traffic scenarios. In this paper, we build a context-enhanced meta-RL framework with data-reused adaptation for challenging urban AD. At both the meta-learning and adaptation stages, the context-enhanced state representation is designed to reduce the perceptual gap in variant urban scenarios, improving the sample efficiency and robustness. At adaptation stage, the meta-training data with context-enhanced features are reused through propensity estimation to constrain the optimization objective of new tasks, aiming to maintain the good driving performance of meta-trained policy and fast adapt to the new tasks. Extensive experiments are conducted in CARLA simulator with various urban environments and task settings. The learning curves and quantitative comparisons validate the good sample efficiency and generalization of our proposed method, with state-of-the-art driving performance on urban AD benchmarks.

Time-lapse videos can visualize the temporal change of dynamic scenes and present wonderful sights with drastic variance in color appearance and rapid movement that interests people. We propose an aesthetics-driven virtual time-lapse photography framework to explore the automatic generation of time-lapse videos in the virtual world, which has potential applications like artistic creation and entertainment in the virtual space. We first define shooting parameters to parameterize the time-lapse photography process and accordingly propose image, video, and time-lapse aesthetic assessments to optimize these parameters, enabling the process to be autonomous and adaptive. We also build an interactive interface to visualize the shooting process and help users conduct virtual time-lapse photography by personalizing shooting parameters according to their aesthetic preferences. Finally, we present a two-stream time-lapse aesthetic model and a time-lapse aesthetic dataset, which can evaluate the aesthetic quality of time-lapse videos. Experimental results demonstrate our method can automatically generate time-lapse videos comparable to those of professional photographers and is more efficient.

Multi-access Edge Computing (MEC) plays an im-portant role for providing end users with high reliability and low latency services at the edge of mobile network. In the scenario of Internet of Vehicles (IoV), vehicle users continually access nearby base stations to offload real-time tasks for reducing their computing overhead, while the ongoing services on current deployed edge nodes may be far away from users with the vehicles moving, potentially resulting in a high delay of data transmission. To address this challenge, in this paper, we propose a Deep Reinforcement Learning (DRL)-based mobility-aware service migration mechanism for effectively reducing the service delay and migration delay of the network. The proposed technique is adopted by re-calibrating required services at edge locations near the mobile user. Edge network state and user movement information are considered to ensure the generation of real-time service migration decision. Extensive experiments are conducted, and evaluation results demonstrate that our proposed DRL-based technique can effectively reduce the long-term average delay of the MEC system, compared with the state-of-the-art techniques.

In centralized task allocation strategies, real-time status information needs to be collected from distributed edge nodes. Therefore, the overloaded transmission on backbone network appears and leads to devastating decrease in the per-formance of centralized strategies. To address this issue, this paper proposes a multi-agent deep reinforcement learning based online decentralized task allocation mechanism, where each edge node makes task allocation decisions based on local network-state information. A centralized-training distributed-execution method is adopted to decrease data transmission load, and a value decomposition-based technique is applied at training stage for improving long-term performance of task allocation in edge col-laborative networks. Extensive experiments are conducted, and evaluation results demonstrate that our mechanism outperforms other three baseline algorithms in reducing the long-term average system delay and improving request completion rate.