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Location-Based Social Network (LBSN) check-in trajectory data are important for many practical applications like POI recommendation, advertising, and pandemic intervention. However, the high collection costs and ever-increasing privacy concerns prevent us from accessing large-scale LBSN trajectory data. The recent advances in synthetic data generation provide us with a new opportunity to achieve this, which utilizes generative AI to generate synthetic data that preserves the characteristics of real data while ensuring privacy protection. However, generating synthetic LBSN check-in trajectories remains challenging due to their spatially discrete, temporally irregular nature and the complex spatio-temporal patterns caused by sparse activities and uncertain human mobility. To address this challenge, we propose GeoGen, a two-stage coarse-to-fine framework for large-scale LBSN check-in trajectory generation. In the first stage, we reconstruct spatially continuous, temporally regular latent movement sequences from the original LBSN check-in trajectories and then design a Sparsity-aware Spatio-temporal Diffusion model (S^2TDiff) with an efficient denosing network to learn their underlying behavioral patterns. In the second stage, we design Coarse2FineNet, a Transformer-based Seq2Seq architecture equipped with a dynamic context fusion mechanism in the encoder and a multi-task hybrid-head decoder, which generates fine-grained LBSN trajectories based on coarse-grained latent movement sequences by modeling semantic relevance and behavioral uncertainty. Extensive experiments on four real-world datasets show that GeoGen excels state-of-the-art models for both fidelity and utility evaluation, e.g., it increases over 69% and 55% in distance and radius metrics on the FS-TKY dataset. 

Recent advances in large language models (LLMs) have transformed software development by automatically generating code from natural language. Yet challenges remain in generating fully correct code that aligns with user intent. Our study reveals that LLMs tend to pay less attention to user prompts as more code tokens are generated. We hypothesize that this attention dilution issue is an important reason for code generation errors. To mitigate this issue, we propose Selective Prompt Anchoring (SPA) to guide code LLMs to pay more attention to user intent when generating code. We evaluate SPA using six base LLMs across six benchmarks. Our results demonstrate that SPA enhances Pass@1 by up to 12.9%, consistently outperforming SOTA methods in all settings. Our code is available at https://github.com/magic-YuanTian/Selective- Prompt-Anchoring. 

Structurally stabilized composites are promising for using phase change materials in high‐temperature thermal energy storage (TES). However, conventional skeleton materials, which typically comprise 30–50 wt% of the composite, mainly provide sensible heat storage and contribute minimally to overall energy density. This study introduces a new class of redox‐active oxide‐molten salt (ROMS) composites that overcome this limitation by combining sensible, latent, and thermochemical heat storage in a single particle. Specifically, porous, redox‐active Ca₂AlMnO_5+δ(CAM) complex oxide particles were demonstrated as a suitable support matrix, with the pores filled by eutectic NaCl/CaCl₂salt. X‐ray diffraction confirms excellent phase compatibility between CAM and the salt. Scanning electron microscopy/energy dispersive X‐ray spectroscopy and nano X‐ray tomography show good salt infiltration and wettability within the CAM pores. Thermogravimetric analysis reveals that a 60 wt% CAM/40 wt% salt composite achieves an energy density of 267 kJ kg⁻¹over a narrow 150 °C window, with ≈50 kJ kg⁻¹from thermochemical storage. Additionally, the composite shows higher thermal conductivity than salt alone, enabling faster energy storage and release. ROMS composites thus represent a novel and efficient solution for high‐performance TES. 

City-wide free WiFi is one of the most common initiatives of smart city infrastructures. While city-wide free WiFi services are not subject to privacy-focused regulations and appeal to a broader demographic, how users perceive privacy in such services is unknown. This study explores perspectives of users in the United States regarding the privacy practices of such services as well as their expectations. We conducted surveys with 199 participants of US, consisting of those who had used such services (i.e., experienced users, n=99) and those who had not (i.e., potential users, n=100), assessing their satisfaction with the services, perceptions regarding data privacy practices of city-wide free WiFi services, and general expectations of privacy. We identify 14 key findings by analyzing the responses from participants. We found that participants are aware of the data collection and data sharing by the WiFi services and are uncomfortable with both but are still inclined to use the services as the need for WiFi outweighs privacy, as well as because of the significant trust they place in the services due to their non-profit and government-run nature. Our analysis provides actionable takeaways for researchers and practitioners, arguing for long-term privacy gains through a regulatory approach that treats city-wide WiFi as a utility, given the trust consumers place in it, and the overall tendency of consumers to trade-off privacy for WiFi access in this context.