Traffic prediction is a critical task in spatial-temporal forecasting with broad applications in travel planning and urban management. To model the complex spatial-temporal dependencies in traffic data, Spatial-Temporal Graph Convolutional Networks (STGCNs) have been widely employed, achieving advanced performance. However, when applied to large-scale road networks, the quadratic computational complexity of traditional graph convolution operations severely limits their scalability. Several methods attempt to address this issue through approximation, compression, or spatial partitioning. Nevertheless, these methods often either fail to achieve sufficient computational efficiency or compromise prediction accuracy. To address these challenges, we propose a Regularized Adaptive Graph Convolution (RAGC) model. First, to ensure scalability on large road networks, we develop the Efficient Cosine Operator (ECO), which performs graph convolution based on the cosine similarity of node embeddings with linear time complexity. Second, we introduce a regularized adaptive graph convolution framework that combines Stochastic Shared Embedding (SSE) and adaptive graph convolution through a residual difference mechanism. This design enables the model to learn high-quality node embeddings, thereby improving prediction accuracy while maintaining computational efficiency. Extensive experiments on four large-scale real-world traffic datasets show that RAGC consistently outperforms state-of-the-art methods in terms of prediction accuracy and exhibits competitive computational efficiency. The code is available at: https://github.com/wkq-wukaiqi/RAGC.

Monocular 3D object detection is attractive for autonomous driving due to its low cost, but its performance degrades severely under adverse visual conditions that corrupts appearance cues and destabilizes depth-related representations. We propose Robo-DETR, a robustness-aware depth-guided transformer framework that combines minimal visual-conditioned input preconditioning with detector-internal depth reliability correction. Specifically, a lightweight Condition Recognition and Enhancement Block stabilizes low-level visual cues, while a Condition-aware Depth-guided Block refines depth logits via foreground-aware supervision and structured dynamic/region-level adjustment to suppress visual-induced depth noise. A dual-branch transformer decoder further promotes depth-appearance consistency through modality-specific cross-attention and fusion. Experiments on KITTI-C and the real-world TJ4DRadSet demonstrate consistent improvements over competitive monocular detectors under diverse degradations, and ablations validate the contribution of each component.

Internet-of-Things (IoT) sensing and V2X connectivity are expanding the deployment of electrified unmanned ground vehicles (UGVs) in rugged off-road environments. However, safe and energy-efficient navigation on uneven terrain remains challenging because terrain understanding, vehicle attitude prediction, and trajectory refinement are often handled in isolation. To address this limitation, this paper presents UT-Planner, an integrated energy- and stability-aware planning framework for IoT-enabled UGVs that exploits high-fidelity point-cloud maps. First, a wheel-contact-based attitude prediction module simulates four-wheel interactions on local point-cloud patches to estimate future body attitude and terrain roughness before traversal, thereby forming a predictive traversability layer. Second, an Eco-A∗ search augments Hybrid-A∗ with a Dubins-based energy heuristic that combines path length with terrain-aware energy surrogates to generate short and energy-favorable coarse paths. Third, a multi-objective trajectory optimizer refines the coarse path by jointly minimizing smoothness, gravity-aligned energy expenditure, and attitude deviation. Experiments on four representative uneven-terrain maps show that, relative to strong baselines, UT-Planner shortens the final 3D path length by 9.4%, reduces real energy consumption by 12.4%, improves trajectory smoothness by 13.3%, lowers the maximum tracking error by 19.8%, and also reduces peak body tilt. These results demonstrate a practical route to safe and energy-efficient off-road autonomous navigation.

As autonomous vehicle (AV) technology advance, their ability to navigate uneven terrains, such as mountainous areas, becomes increasingly important. However, current trajectory tracking methods struggle with tracking accuracy and stability due to insufficient consideration of terrain slopes and vehicle kinematics. In this article, we propose a manifold-based model predictive control framework designed for Ackermann steering electrified vehicles on uneven terrains. This method models the trajectory tracking control on manifolds, utilizing acceleration and front-wheel steering angle as control inputs to enhance control stability. To mitigate model inaccuracies and enhance the controller’s adaptability, we dynamically adjust the controller’s objective function weights based on trajectory curvature and integrate a PID feedback mechanism to provide real-time compensation for vehicle speed and steering angle. When fitting the surface terrain equation, we select sparse key points along the reference trajectory, achieving lightweight computation while maintaining high-fitting accuracy. The experimental and simulation results demonstrate that the proposed UT-MPC controller improves tracking performance by 53.74% and 42.68% compared to four baseline methods when tracking a trajectory on different uneven terrain maps. Real-world experiments have also demonstrated the effectiveness of UT-MPC.

Pedestrian trajectory prediction is crucial in various fields, but remains challenging due to complex spatial interactions. While existing pedestrian trajectory prediction methods show promise, they often fail to capture these dynamics effectively. To address this limitation, a Hierarchical Spatial Interaction Graph Convolutional Network (HSIGCN) is proposed to handle both group interactions and spatial interactions. Although previous methods have attempted to model group behaviors, they lack a comprehensive consideration of group interactions and often oversimplify the complex social dynamics in groups. HSIGCN introduces a novel group interaction mechanism that encompasses four types of interactions: all-pedestrian, intra-group, out-group, and inter-group interactions, enhancing the expressiveness in group behavior prediction. Furthermore, current approaches to spatial interaction sparsification either rely solely on prior-based or on learning-based methods. HSIGCN innovatively combines both approaches to form a mixed sparsification mechanism, effectively filtering all-pedestrian and out-group interactions. Additionally, existing prior-based methods fail to consider social factors comprehensively. HSIGCN takes into account the field of view (FOV), collision awareness, and distance factors to establish a more robust prior-based sparse function. Experimental results on ETH and UCY datasets demonstrate that the proposed method significantly outperforms baseline models, showcasing its potential to accurately predict pedestrian trajectories by effectively handling complex spatial interactions.

The angle gathers are the data basis of prestack seismic inversion, and their resolution directly determines the resolution of the inverted elastic parameters. Seismic reflectivity inversion (SRI) is a technique that is able to estimate reflectivity from seismic data, and consequently improve the resolution of seismic data. However, the existing SRI method for angle gathers faces two problems: (1) The assumption that the wavelet is known does not align with reality; (2) Compared with the exact Zoeppritz equation, approximation equations will introduce errors in large angles. To overcome these shortcomings, in this paper, we propose a new blind SRI method for enhancing the resolution of angle gathers. This method can simultaneously build the wavelet and reflectivity of angle gathers without the need for a predefined wavelet, and an angle-based regularization term is constructed to ensure the continuity in angle especially in noisy cases. We use both synthetic experiments on a modified Marmousi model and also a field data experiment using a dataset from the Hampson-Russell software to assess the performance of the proposed method and compare its performance with an existing method. The results clearly validate the effectiveness of the proposed method and its superiority over the existing method in producing high-quality reflectivity models for angle gathers.

The Multi-Agent Path Finding (MAPF) problem is to find a set of collision-free paths for multiple agents within a shared environment while minimizing the total time spent. In MAPF, finding an optimal solution is NP-hard. And in some cases, finding suboptimal solutions is also NP-hard, with severe limitations imposed by the scale of the problem. Anytime multi-agent path planning (MAPF) is a promising method for path optimization in large-scale multi-agent systems. Large Neighborhood Search (LNS) is the foundation of the state-of-the-art anytime MAPF and can even perform well for large-scale issues. It discovers a fast solution at first, and then iteratively plans paths for subsets of agents produced via de-structive heuristic techniques to gradually enhance the quality of the solution until it approaches optimality. In this paper, we propose a subset selection algorithm based on maximum latency and density, which improves upon the subset selection rule of the existing state-of-the-art solver, MAPF-LNS. We conducted tens of thousands of experiments on multiple maps of the MAPF benchmark set with larger numbers of agents. Our experimental results indicate that our solver, MAPF-DD-LNS, significantly outperforms MAPF-LNS in both the quality of the final solution and the speed of resolution.

Abstract With the increasing of requirements from many aspects, various queries and analyses arise focusing on social network. Time is a common and necessary dimension in various types of social networks. Social networks with time information are called temporal social networks, in which time information can be the time when a user sends message to another user. Keywords search in temporal social networks consists of finding relationships between a group users that has a set of query labels and is valid within the query time interval. It provides assistance in social network analysis, classification of social network users, community detection, etc. However, the existing methods have limitations in solving temporal social network keyword search problems. We propose a basic algorithm, the discrete timestamp algorithm, with the intention of turning the problem into a traditional keyword search on social networks. We also propose an approximative algorithm based on the discrete timestamp algorithm, but it still suffers from the traditional algorithms’ low efficiency. To further improve the performance, we propose a new algorithm based on dynamic programming to solve the keyword search in temporal social network. The main idea is to extend a vertex into a solution by edge-growth operation and tree-merger operation. We also propose two powerful pruning techniques to reduce the intermediate results during the extension. Additionally, all of the algorithms we proposed are capable of handling a variety of ranking functions, and all of them can be made to conform to top-N keyword querying. The efficiency and effectiveness of the proposed algorithms are verified through extensive empirical studies.

The computation of a group Steiner tree (GST) in various types of graph networks, such as social network and transportation network, is a fundamental graph problem in graphs, with important applications. In these graphs, time is a common and necessary dimension, for example, time information in social network can be the time when a user sends a message to another user. Graphs with time information can be called temporal graphs. However, few studies have been conducted on GST in terms of temporal graphs. This study analyzes the computation of GST for temporal graphs, i.e., the computation of temporal GST (TGST), which is shown to be an NP‐hard problem. We propose an efficient solution based on a dynamic programming algorithm for our problem. This study adopts new optimization techniques, including graph simplification, state pruning, and A∗ search, are adopted to dramatically reduce the algorithm search space. Moreover, we consider three extensions for our problem, namely the TGST with unspecified tree root, the progressive search of TGST, and the top‐N search of TGST. Results of the experimental study performed on real temporal networks verify the efficiency and effectiveness of our algorithms.

k-Regret Querying is a well studied problem to query a dataset$D$for a small subset$S$of size$k$with the minimal regret ratio for unknown utility functions. In this paper, we point out some issues in$k$-Regret Querying, including the assumption of non-negative dataset and the lack of shift invariance. Known algorithms for$k$-Regret Querying are limited in scope and result quality, and are based on the assumption of non-negative data. We introduce a new problem definition called$k$-pleased querying for dealing with the shift variance issue, and propose a strategy of random sampling of the utility functions. This strategy is based on a study of the theoretical guarantee of the sampling approach. We also introduce a dimensionality reduction strategy, an improved greedy algorithm, and a study of other utility function sampling methods. All of our solutions can handle negative data. Theoretically, we derive a guarantee on the approximation attained by our sampling algorithm. Experimental results on numerous real datasets show that our proposed method is effective even with a small number of samples and small values of$k$.

$k$-Regret Querying is a well studied problem to query a dataset$D$for a small subset$S$of size$k$with the minimal regret ratio for unknown utility functions. In this paper, we point out some issues in$k$- Regret Querying, including the assumption of non-negative dataset and the lack of shift invariance. Known algorithms for$k$- Regret Querying are limited in scope and result quality, and are based on the assumption of non-negative data. We introduce a new problem definition called k-pleased querying for dealing with the shift variance issue, and propose a strategy of random sampling of the utility functions. This strategy is based on a study of the theoretical guarantee of the sampling approach. We also introduce a dimensionality reduction strategy, an improved greedy algorithm, and a study of other utility function sampling methods. All of our solutions can handle negative data. Theoretically, we derive a guarantee on the approximation attained by our sampling algorithm. Experimental results on numerous real datasets show that our proposed method is effective even with a small number of samples and small values of$k$.

The most efficient known approach for shortest distance querying on road networks is via a tree decomposition based 2-hop labeling index. A major challenge here is how to reduce the query time by reducing the label size. To this end, we propose P2H with the novel ideas of projected vertex separators and optimized selection of vertex separators. We also introduce mechanisms for index maintenance for edge weight updating. Our experiments on multiple real road networks show that P2H can greatly reduce the effective label sizes and query time over existing algorithms. For larger datasets, P2H is around twice as efficient as the best known algorithm.

Museum exhibitions on traditional Chinese paintings are gaining popularity for educational and cultural value. Chinese paintings are characterized by a long history and implicit emotional expression, and it is challenging for non-professional and non-Chinese visitors to understand. To enhance museum visitors’ interest and comprehension of Chinese artworks, we design an EEG-based interactive installation. The installation simulates the process of creation of a work of art, in this case a painting. Visitors can control the generation of lines, colors, and movements of characters by wearing a commercial EEG headset. Our interactive design contributes a novel experience of ’painting with your mind’ and at the same time transform the exhibition into an enjoyable game experience.

Optimal location querying (OLQ) in road networks is important for various applications. Existing work assumes no labels for servers and that a client only visits the nearest server. These assumptions are not realistic and it renders the existing work not useful in many cases. In this paper, we introduce the KOLQ problem which considers the k nearest servers of clients and labeled servers. We also proposed algorithms for the problem. Extensive experiments on the real road networks illustrate the efficiency of our proposed solutions.

Online social network (OSN) analysis has attracted much attention in recent years. One important distinguishing feature of OSNs is that every user provides his/her personal profile online, which can be regarded as the labels or attributes of this user. Knowing the number of nodes or edge with a particular label will give us deeper insight of the OSNs and can provide valuable information in many real-world applications such as web marketing and advertising. For many OSNs, one can only access parts of the network using the application programming interfaces (APIs). In such cases, conventional algorithms become infeasible. In this paper, we introduce efficient algorithms for estimating the number of edges with target labels in OSNs based on random walk. We also derive theoretical bounds on the sample size and the number of APIs calls needed in our algorithms for a probabilistic accuracy guarantee. We ran experiments on several publicly available real-world networks and the results demonstrate the effectiveness of our algorithms.

We study the problem of optimal location querying for location-based services in road networks, which aims to find locations for new servers or facilities. The existing optimal solutions on this problem consider only the cases with one new server. When two or more new servers are to be set up, the problem with minmax cost criteria, MinMax, becomes NP-hard. In this work we identify some useful properties about the potential locations for the new servers, from which we derive a novel algorithm for MinMax, and show that it is efficient when the number of new servers is small. When the number of new servers is large, we propose an efficient 3-approximate algorithm. We verify with experiments on real road networks that our solutions are effective and attain significantly better result quality compared to the existing greedy algorithms.

In this article, we study an optimal location query based on a road network. Specifically, given a road network containing clients and servers, an optimal location query finds a location on the road network such that when a new server is set up at this location, a certain cost function computed based on the clients and servers (including the new server) is optimized. Two types of cost functions, namely, MinMax and MaxSum, have been used for this query. The optimal location query problem with MinMax as the cost function is called the MinMax query, which finds a location for setting up a new server such that the maximum cost of a client being served by his/her closest server is minimized. The optimal location query problem with MaxSum as the cost function is called the MaxSum query, which finds a location for setting up a new server such that the sum of the weights of clients attracted by the new server is maximized. The MinMax query and the MaxSum query correspond to two types of optimal location query with the objectives defined from the clients' perspective and from the new server's perspective, respectively. Unfortunately, the existing solutions for the optimal query problem are not efficient. In this article, we propose an efficient algorithm, namely, MinMax-Alg ( MaxSum-Alg ), for the MinMax (MaxSum) query, which is based on a novel idea of nearest location component . We also discuss two extensions of the optimal location query, namely, the optimal multiple-location query and the optimal location query on a 3D road network. Extensive experiments were conducted, showing that our algorithms are faster than the state of the art by at least an order of magnitude on large real benchmark datasets. For example, in our largest real datasets, the state of the art ran for more than 10 (12) hours while our algorithm ran within 3 (2) minutes only for the MinMax (MaxSum) query, that is, our algorithm ran at least 200 (600) times faster than the state of the art.

In this paper, we study the optimal location query problem based on road networks. Specifically, we have a road network on which some clients and servers are located. Each client finds the server that is closest to her for service and her cost of getting served is equal to the (network) distance between the client and the server serving her multiplied by her weight or importance. The optimal location query problem is to find a location for setting up a new server such that the maximum cost of clients being served by the servers (including the new server) is minimized. This problem has been studied before, but the state-of-the-art is still not efficient enough. In this paper, we propose an efficient algorithm for the optimal location query problem, which is based on a novel idea of \emph{nearest location component}. We also discuss three extensions of the optimal location query problem, namely the optimal multiple-location query problem, the optimal location query problem on 3D road networks, and the optimal location query problem with another objective. Extensive experiments were conducted which showed that our algorithms are faster than the state-of-the-art by at least an order of magnitude on large real benchmark datasets. For example, on our largest real datasets, the state-of-the-art ran for more than 10 hours but our algorithm ran within 3 minutes only (i.e., >200 times faster).