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Related Articles from SNS
CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting
arXiv:2606.05413v1 Announce Type: new Abstract: As urban environments continue to evolve rapidly, accurately modeling the dynamic behaviour of Points of Interest is essential for supporting data-driven urban planning and commercial decision-making. While recent advancements in spatio-temporal graph learning have improved POI forecasting, most methods rely on proximity-based graphs and correlation-driven modeling, which overlook the functional dependencies between POIs and fail to capture the...
Mobility-Embedded POIs: Learning What A Place Is and How It Is Used from Human Movement
arXiv:2601.21149v3 Announce Type: replace Abstract: Recent progress in geospatial foundation models highlights the importance of learning general-purpose representations for real-world locations, particularly points-of-interest (POIs) where human activity concentrates. Existing approaches, however, focus primarily on place identity derived from static textual metadata, or learn representations tied to trajectory context, which capture movement regularities rather than how places are actually...
Ghost: Plausible Yet Unlearnable Trajectories via On-Manifold Substitution for Next-POI Privacy
arXiv:2606.03711v1 Announce Type: new Abstract: A publisher who releases check-in trajectories inadvertently publishes a strong predictor of every user's future locations. We address this risk by generating unlearnable trajectories, perturbed sequences that yield victim models with degraded next-Point-of-Interest (next-POI) accuracy on clean test inputs. Direct ports of image-domain unlearnable examples fail on two counts.
Contrastive Training with LLM-generated Near-Misses for Robust Code-Switching Speech Recognition
new Abstract: Code-switching (CS), the alternation between multiple languages within a single utterance, remains challenging for Automatic Speech Recognition (ASR). To address this issue, we propose a Point-of-Interest (POI)-aware contrastive training framework that improves recognition at CS-critical regions. We first identify CS spans by adopting POI detection method from literature, then construct acoustically plausible near-miss hypotheses by perturbing POIs in ASR N-best outputs and...
Think Before You Act: Intention-Guided Reasoning for LLM-Based Location Prediction
Announce Type: new Abstract: Predicting a user's next Point-of-Interest (POI) based on their historical check-in records is a fundamental task in location-based services. While recent methods incorporating large language models have shown strong reasoning capabilities and promising results, they typically formulate the prediction task as a one-step trajectory-to-location mapping problem, making predictions prone to shallow trajectory correlations and historical frequency bias. We argue that...
Enhancing the Socioeconomic Understanding of Foundation Models with Urban Mobility
Announce Type: new Abstract: Foundation models have recently been applied to urban socioeconomic prediction using POI text, satellite imagery, and geospatial descriptions. However, these models mostly rely on static attributes of individual places, while ignoring the mobility patterns that reveal how places are functionally connected. To address this gap, we explore whether mobility networks can elicit the geospatial capabilities of foundation models by explicitly encoding connectivity among...
Are Hong Kong civil servants being punished for Tai Po fire with flat 2% pay rise?
Are Hong Kong civil servants being punished for Tai Po fire with flat 2% pay rise? Unions argue that an across-the-board pay rise of 2 per cent will not offset inflation or reflect overall performance Human resources experts, however, said public sentiment had to be considered as pay adjustments involved allocating taxpayers’ money, while the modest rise would have very limited impact on the private sector. The backlash from unions came ahead of a meeting on Wednesday between Secretary for...
UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations
arXiv:2510.13774v2 Announce Type: replace Abstract: Forecasting urban phenomena such as housing prices and public health indicators requires the effective integration of various geospatial data. Current methods primarily utilize task-specific models, while recent generic models for spatial representations often support only limited modalities and lack multimodal fusion capabilities. To overcome these challenges, we present UrbanFusion, a spatial representation model that features Stochastic...
Using large scale GPS data to reveal EV driver activity patterns beyond charging sessions
new Abstract: Accurate insights into electric vehicle (EV) driver behavior are essential for long-term infrastructure planning, grid management, and understanding downstream economic impacts, yet individual level data on EV mobility remains limited. Here, we develop a scalable framework to infer EV ownership and charging behavior from passively collected, high-resolution mobility traces covering over 760,000 drivers across four major U.S. metropolitan areas. We identify likely EV drivers...
GroupTravelBench: Benchmarking LLM Agents on Multi-Person Travel Planning
arXiv:2605.25200v2 Announce Type: replace Abstract: Travel planning in the real world is overwhelmingly a \textit{group} activity, yet existing LLM travel-planning benchmarks reduce it to a single user, where the field is approaching saturation. This single-user assumption sidesteps what makes group planning hard for an agent: discovering private preferences across multiple users, surfacing conflicts, and balancing utility against fairness. To bring the task back to its multi-user reality,...