Trip recommendation aims to generate a sequence of points of interest (POIs) under a user's query input. Existing data-driven methods mainly fall into two categories: supervised approaches and self-supervised approaches. The former cannot fully capture the transition patterns among POIs, while the latter fail to comprehensively model user's query intents. Fortunately, privileged knowledge distillation (PKD) provides us an unique opportunity to align user's query intents with its corresponding trip in historical data. However, such knowledge alignment is implicit, which may not directly reflect the query intents. To this end, in this paper, we propose EKD-Trip, an explicit intent-enhanced knowledge distillation framework. EKD-Trip first trains a trajectory encoder (teacher model) and a trip generator jointly in a self-supervised manner. Then, a query encoder (student model) is trained via multi-task learning to extract implicit knowledge by PKD from teacher and explicit knowledge from an auxiliary task, respectively. At inference time, we use the query encoder and the trip generator to recommend trips. Extensive experiments on four real-world datasets demonstrate that EKD-Trip outperforms all baselines over three metrics, with a particularly notable improvement of 13.70% in pairs-F1.

Traffic flow prediction is a typical spatio-temporal prediction problem and has a wide range of applications. The core challenge lies in modeling the underlying complex spatio-temporal dependencies. Various methods have been proposed, and recent studies show that the modeling of dynamics is useful to meet the core challenge. While handling spatial dependencies and temporal dependencies using separate base model structures may hinder the modeling of spatio-temporal correlations, the modeling of dynamics can bridge this gap. Incorporating spatio-temporal heterogeneity also advances the main goal, since it can extend the parameter space and allow more flexibility. Despite these advances, two limitations persist: 1) the modeling of dynamics is often limited to the dynamics of spatial topology (e.g., adjacency matrix changes), which, however, can be extended to a broader scope; 2) the modeling of heterogeneity is often separated for spatial and temporal dimensions, but this gap can also be bridged by the modeling of dynamics. To address the above limitations, we propose a novel framework for traffic prediction, called Meta Dynamic Graph (MetaDG). MetaDG leverages dynamic graph structures of node representations to explicitly model spatio-temporal dynamics. This generates both dynamic adjacency matrices and meta-parameters, extending dynamic modeling beyond topology while unifying the capture of spatio-temporal heterogeneity into a single dimension. Extensive experiments on four real-world datasets validate the effectiveness of MetaDG.

Human mobility prediction is a fundamental technique for many urban applications, e.g., location-based recommendation, traffic scheduling, and travel demand prediction. Over the past decades, many methods, e.g., Markov Model, RNN, Transformer, have been leveraged to tackle the problem. However, existing approaches mainly train a supervised model based on an offline training dataset, which overlooks the phenomenon that the mobility behaviors of humans vary across time, and the trained models may not achieve ideal performance when applied to the testing data. To tackle this challenge, in this paper, we propose AdaMove, an efficient Test-Time Adaptive (TTA) model for human mobility prediction. AdaMove has a Preference-aware Test-Time Adaptation module called PTTA, which can adjust the parameters of a trained model based on the input test trajectory such that the model can generalize to the test distribution. In addition, to address the issue of reduced inference efficiency caused by parameter adjustment during the testing phase, AdaMove is equipped with a Lightweight human Mobility prediction model called LightMob, which only requires the recent trajectory as input to accelerate the inference. It is enhanced by historical trajectory knowledge via contrastive learning during the training time, so it has competitive performance compared with existing models. Extensive experiments on three real-world human mobility datasets demonstrate that AdaMove outperforms the best baseline by 9.3% on average in accuracy, and accelerates the inference speed by 28.5% on average compared with the original TTA - based inference.

Inferring the actual road segment purely based on one positioning point, known as single-point map matching (SMM), is vital for many urban applications, e.g., ride-hailing and geo-tagging. However, it is challenging due to inherent positioning errors and extrinsic heterogeneous environments. Existing methods either overlook the heterogeneity of different regions, or do not exploit the commonality of different matching tasks. In this paper, we treat each region as an individual SMM task to tackle the heterogeneity, and propose Spatial Hierarchical Meta-Learning for SMM (SHSMM) to learn the shared knowledge across tasks. SHSMM is equipped with a Dual-view Map Matcher to perform the matching, which can perceive the knowledge of road segments globally. To learn the task-specific model parameters, SHSMM modulates initial parameters and scales the local update learning rate based on hierarchical geographical and semantic knowledge about spatial tasks. A local update learning rate scheduling strategy is further proposed to facilitate the meta-training. Extensive experiments as well as case studies based on two real-world datasets demonstrate the effectiveness of the proposed method.

Integrated radar sensing and wireless power transfer (ISWPT) is an emerging paradigm that seeks to combine the functionalities of radar sensing and wireless power transfer into a unified system, utilizing a shared hardware platform to maximize resource efficiency. In this paper, we investigate the performance of an ISWPT system that is enhanced by extremely large-scale reconfigurable intelligent surfaces (XL-RIS), which serve to dynamically control the propagation environment and improve both radar sensing and wireless power transfer. Specifically, we consider a system where energy receivers are placed within the near-field region of the XL-RIS and investigate the joint optimization of beamforming at the base station and the XL-RIS reflection phases. The goal is to maximize the efficiency of wireless power transfer while simultaneously enhancing radar sensing performance. The formulated problem, though non-convex in nature, is efficiently addressed through the introduction of an alternating optimization algorithm. Numerical simulations demonstrate the effectiveness of the proposed algorithm, showing substantial improvements in both radar sensing accuracy and wireless power transfer performance compared to existing baseline schemes.

This article presents a novel concept termed integrated imaging and wireless power transfer (IWPT), wherein the integration of imaging and wireless power transfer functionalities is achieved on a unified hardware platform. IWPT leverages a transmitting array to efficiently illuminate a specific Region of Interest (ROI), enabling the extraction of ROI’s scattering coefficients while concurrently providing wireless power to nearby users. The integration of IWPT offers compelling advantages, including notable reductions in power consumption and spectrum utilization, pivotal for the optimization of future 6G wireless networks. As an initial investigation, we explore two antenna architectures: 1) a fully digital array and 2) a digital/analog hybrid array. Our goal is to characterize the fundamental tradeoff between imaging and wireless power transfer by optimizing the illumination signal. With imaging operating in the near-field, we formulate the illumination signal design as an optimization problem that minimizes the condition number of the equivalent channel. To address this optimization problem, we propose an semi-definite relaxation-based approach for the fully digital array and an alternating optimization algorithm for the hybrid array. Finally, numerical results verify the effectiveness of our proposed solutions and demonstrate the tradeoff between imaging and wireless power transfer.

Parametric Query Optimization (PQO) is an important problem in database systems, yet existing approaches suffer from high training costs, sensitivity to estimation errors, and vulnerability to severe performance regressions. This paper introduces PAR 2 QO (PARametric Penalty-Aware Robust Query Optimization), a system that integrates robust query optimization into PQO. PAR 2 QO strategically obtains plans from a well-balanced set of probe locations informed by the workload, and caches them as plan-penalty profiles. At runtime, PAR 2 QO selects the plan with the lowest expected penalty, explicitly accounting for selectivity uncertainties. Extensive experiments show that PAR 2 QO delivers significant speedups over existing methods while ensuring robustness against performance degradation. Additionally, we introduce CARVER , a workload generator aimed at covering possible cardinalities of subqueries. Not only does CARVER provide a more comprehensive way to evaluate PQO methods, but when used for training learned methods, it can also enhance their generalizability and stability.

Query optimizers rely heavily on selectivity estimates to choose efficient execution plans, but inaccuracies in these estimates often result in poor query performance. We introduce Hint-QPT ( Hint s for Robust Q uery P erformance T uning), an interactive tool designed to help users diagnose and improve query performance. Hint-QPT proactively recommends robust plans that are resilient to uncertainty in selectivity estimates, identifies sensitive subqueries for which selectivity estimation errors greatly affect plan quality, and provides intuitive interfaces for targeted selectivity adjustments. Users can either choose the recommended robust plans for execution, or acquire additional statistics on the identified sensitive subqueries to tune query performance. Moreover, Hint-QPT visualizes the alternative execution plans and their costs under uncertainty, helping users to better understand their robustness.

Intelligent logistics relies on accurately predicting the service time, which is a part of time cost in the last-mile delivery. However, service time prediction (STP) is non-trivial given complex delivery circumstances, location heterogeneity, and skewed observations in space, which are not well-handled by existing solutions. In our prior work, we treat STP at each location as a learning task to keep the location heterogeneity, propose a prior knowledge-enhanced meta-learning to tackle skewed observations, and introduce a Transformer-based representation module to encode complex delivery circumstances. Maintaining the design principles of prior work, in this extended paper, we propose MetaSTP+. In addition to fusing the prior knowledge after the meta-learning process, MetaSTP+also injects the prior knowledge before and during the meta-learning process to better tackle skewed observations. More specifically, MetaSTP+completes the support set of tasks with scarce samples from other tasks based on prior knowledge and is equipped with a prior knowledge-aware historical observation encoding module to achieve those purposes accordingly. Experiments show MetaSTP+outperforms the best baseline by 11.2% and 8.4% on two real-world datasets. Finally, an intelligent waybill assignment system based on MetaSTP+is deployed in JD Logistics.

Sixth generation (6G) cellular communications are expected to support enhanced wireless localization capabilities. The widespread deployment of large arrays and high-frequency bandwidths give rise to new considerations for localization applications. Emerging antenna architectures, such as dynamic metasurface antennas (DMAs), are expected to be frequently utilized thanks to the achievable high angular resolution and low hardware complexity. Further, wireless localization is likely to take place in the radiating near-field (Fresnel) region, which provides new degrees of freedom, because of the adoption of arrays with large apertures. While current studies mostly focus on the use of costly fully-digital antenna arrays, in this paper we investigate how DMAs can be applied for near-field localization of a single user. We use a direct positioning estimation method based on curvature-of-arrival of the impinging wavefront to obtain the user location, and characterize the effects of DMA tuning on the estimation accuracy. Next, we propose an algorithm for configuring the DMA to optimize near-field localization, by first tuning the adjustable DMA coefficients to minimize the estimation error using postulated knowledge of the actual user position. Finally, we propose a sub-optimal iterative algorithm that does not rely on such knowledge. Simulation results show that the DMA-based near-field localization accuracy could approach that of fully-digital arrays at lower cost.