Ultralight Hierarchical FeO/MoS/rGO/TiCT MXene Composite Aerogels for High-Efficiency Electromagnetic Wave Absorption.

Aerogel-based composites, renowned for their three-dimensional (3D) network architecture, are gaining increasing attention as lightweight electromagnetic (EM) wave absorbers. However, attaining high reflection loss, broad effective absorption bandwidth (EAB), and ultrathin thickness concurrently presents a formidable challenge, owing to the stringent demands for precise structural regulation and incorporation of magnetic/dielectric multicomponents with synergistic loss mechanisms within the 3D networks. In this study, we successfully synthesized a 3D hierarchical porous FeO/MoS/rGO/TiCT MXene (FMGM) composite aerogel via directional freezing and subsequent heat treatment processes.

LollipopE: Bi-centered lollipop embedding for complex logic query on knowledge graph.

Answering complex First-Order Logic (FOL) query plays a vital role in multi-hop knowledge graph (KG) reasoning. Geometric methods have emerged as a promising category of approaches in this context. However, existing best-performing geometric query embedding (QE) model is still up against three-fold potential problems: (i) underutilization of embedding space, (ii) overreliance on angle information, (iii) uncaptured hierarchy structure.

Tackling higher-order relations and heterogeneity: Dynamic heterogeneous hypergraph network for spatiotemporal activity prediction.

Spatiotemporal activity prediction aims to predict user activities at a particular time and location, which is applicable in city planning, activity recommendations, and other domains. The fundamental endeavor in spatiotemporal activity prediction is to model the intricate interaction patterns among users, locations, time, and activities, which is characterized by higher-order relations and heterogeneity. Recently, graph-based methods have gained popularity due to the advancements in graph neural networks.