Spatio-temporal data proliferates in numerous real-world domains, such as transportation, weather, and energy. Spatio-temporal deep learning models aims to utilize useful patterns in such data to support tasks like prediction, imputation, and anomaly detection. However, previousone-to-onedeep learning models designed for specific tasks typically require separate training for each use case, leading to increased computational and storage costs. To address this issue,one-to-manyspatio-temporal foundation models have emerged, offering a unified framework capable of solving multiple spatio-temporal tasks. These foundation models achieve remarkable success by learning general knowledge with spatio-temporal data or transferring the general capabilities of pre-trained language models. While previous surveys have explored spatio-temporal data and methodologies separately, they have ignored a comprehensive examination of how foundation models are designed, selected, pre-trained, and adapted. As a result, the overall pipeline for spatio-temporal foundation models remains unclear. To bridge this gap, we innovatively provide an up-to-date review of previous spatio-temporal foundation models from the pipeline perspective. The pipeline begins with an introduction to different types of spatio-temporal data, followed by details of data preprocessing and embedding techniques. The pipeline then presents a novel data property taxonomy to divide existing methods according to data sources and dependencies, providing efficient and effective model design and selection for researchers. On this basis, we further illustrate the training objectives of primitive models, as well as the adaptation techniques of transferred models. Overall, our survey provides a clear and structured pipeline to understand the connection between core elements of spatio-temporal foundation models while guiding researchers to get started quickly. Additionally, we introduce emerging opportunities such as multi-objective training in the field of spatio-temporal foundation models, providing valuable insights for researchers and practitioners.

In the realm of multi-behavior sequential recommendation (MBSR), the complexity and heterogeneity of user interactions pose substantial challenges for sequence modeling. Existing studies involve significant efforts in combining different modules to learn more expressive multi-behavior sequence representations or designing strategies to extract user preferences related to the target behavior. Despite their effectiveness, these methods neglect a thorough analysis of how behavioral information shapes the probability distribution for next-item prediction, which is crucial for accurately modeling user preferences. To this end, we first analyze the learning distribution of MBSR, shedding light on the significance of target behavior in next-item prediction. Building upon this insight, we propose a Behavior-Aware Regularization approach for multi-behavior sequential Rec ommendation (BAR4Rec), where we introduce a regularization loss function to preserve the intrinsic constraints of target behavior. In this way, the target probability distribution is extracted from the whole distribution and naturally evolves into a more compatible and tractable form, thus facilitating model design and training. We evaluate the proposed method on three real-world datasets, and the results validate the efficacy of our approach.

Locality sensitive hashing (LSH) is a representative approach for nearest neighbor (NN) search in high-dimensional spaces, which is able to answer c-approximate NN (c-ANN) queries in sublinear time with constant probability. Existing advanced LSH methods leverage a plurality of novel techniques such as query-aware dynamic bucketing, virtual rehashing, and efficient indexing to achieve state-of-the-art performance. However, they rely on similar random LSH functions, which provides distance estimations that are irrelevant to the given data distribution. Therefore, the quality of the searched candi-dates is suboptimal. In this study, we reformulate the c-ANN query from the perspective of data distribution. Specifically, we propose a novel distribution-aware c-ANN query, which can guarantee the quality of searched results from the query distribution perspective. We introduce an accurately unbiased distance estimator into LSH methods, which can provide more precise distance estimations by modeling the data distribution. We also conduct rigorous theoretical analysis to prove that our methods can correctly answer the distribution-aware c-ANN query with at least a constant probability. Experiments on seven real datasets with different sizes and dimensionalities indicate that the proposed method can achieve better performance than existing LSH methods in terms of efficiency and effectiveness.

The rapid development of smart devices has fostered the growth of Spatial Crowdsourcing (SC), where workers complete spatial tasks by traveling to specific locations. Task assignment is a key issue in SC due to the inherent complexity of matching workers with these spatial tasks efficiently. Previous studies on task assignment have primarily focused on optimizing worker-task matching within a single, centralized area, often ignoring scenarios that involve multiple independent service centers across an area. To address this gap, we introduce a collaborative multi-center task assignment problem, which focuses on scenarios where an SC platform manages multiple independent service centers within an area, shifting the focus from worker-level cooperation to exploring the solutions specific to multi-center coordination. We target the imbalances between available workers and unassigned tasks among different centers, aiming to maximize the total number of assigned tasks and minimize unfairness in inter-center collaboration. In particular, we propose an Iterative Multi-center Task Assignment and Optimization (IMTAO) framework. IMTAO operates in two phases: (1) center-independent task assignment based on an efficient sequential task assignment algorithm, and (2) inter-center workforce transfer based on a game-theoretic multi-center collaboration algorithm that ensures fair collaboration through bi-directional optimization. Extensive experiments demonstrate the efficiency and effectiveness of IMTAO in enhancing task assignment and improving collaboration fairness compared to baseline methods.

Approximate nearest neighbor search is fundamental in information retrieval. Previous partition-based methods enhance search efficiency by probing partial partitions, yet they face two common issues. In the query phase, a common strategy is to probe partitions based on the distance ranks of a query to partition centroids, which inevitably probes irrelevant partitions as it ignores data distribution. In the partition construction phase, all partition-based methods face the boundary problem that separates a query's nearest neighbors to multiple partitions, resulting in a long-tailed kNN distribution and degrading the optimal nprobe (i.e., the number of probing partitions). To address this gap, we propose LIRA, a LearnIng-based queRy-aware pArtition framework. Specifically, we propose a probing model to directly probe the partitions containing the kNN of a query, which can reduce probing waste and allow for query-aware probing with nprobe individually. Moreover, we incorporate the probing model into a learning-based redundancy strategy to mitigate the adverse impact of the long-tailed kNN distribution on search efficiency. Extensive experiments on real-world vector datasets demonstrate the superiority of LIRA in the trade-off among accuracy, latency, and query fan-out. The codes are available at https://github.com/SimoneZeng/LIRA-ANN-search.

High-dimensional approximate K nearest neighbor search (AKNN) is a fundamental task for various applications, including information retrieval. Most existing algorithms for AKNN can be decomposed into two main components, i.e., candidate generation and distance comparison operations (DCOs). While different methods have unique ways of generating candidates, they all share the same DCO process. In this study, we focus on accelerating the process of DCOs that dominates the time cost in most existing AKNN algorithms. To achieve this, we propose an Data-Aware Distance Estimation approach, called DADE , which approximates the exact distance in a lower-dimensional space. We theoretically prove that the distance estimation in DADE is unbiased in terms of data distribution. Furthermore, we propose an optimized estimation based on the unbiased distance estimation formulation. In addition, we propose a hypothesis testing approach to adaptively determine the number of dimensions needed to estimate the exact distance with sufficient confidence. We integrate DADE into widely-used AKNN search algorithms, e.g., IVF and HNSW , and conduct extensive experiments to demonstrate the superiority.

Autonomous driving systems (ADSs) have the potential to revolutionize transportation by improving traffic safety and efficiency. As the core component of ADSs, maneuver decision aims to make tactical decisions to accomplish road following, obstacle avoidance, and efficient driving. In this work, we consider a typical but rarely studied task, called Target-Lane-Entering (TLE), where an autonomous vehicle should enter a target lane before reaching an intersection to ensure a smooth transition to another road. For navigation-assisted autonomous driving, a maneuver decision module chooses the optimal timing to enter the target lane in each road section, thus avoiding rerouting and reducing travel time. To achieve the TLE task, we propose a ruLe-aided reINforcement lEarning framework, called LINE, which combines the advantages of RL-based policy and rule-based strategy, allowing the autonomous vehicle to make target-oriented maneuver decisions. Specifically, an RL-based policy with a hybrid reward function is able to make safe, efficient, and comfortable decisions while considering the factors of target lanes. Then a strategy of rule revision aims to help the policy learn from intervention and block the risk of missing target lanes. Extensive experiments based on the SUMO simulator confirm the effectiveness of our framework. The results show that LINE achieves state-of-the-art driving performance with over 95% task success rate.