Vehicle-to-everything (V2X) technology has emerged as a key enabler of intelligent transportation systems, while the terahertz (THz) band offers abundant spectrum resources to support ultra-high-speed and low-latency V2X communications. This paper investigates the in-vehicle wireless channel at the 300 GHz band using ray-tracing (RT) simulations. A digital twin (DT) of the vehicle is constructed from high-resolution point cloud data and a measurement-based material property database. The DT is integrated into an open-source RT simulator, Sionna, to model multipath propagation. The simulation results are analyzed and compared with the measurement data, showing strong agreement and validating the feasibility of THz channel simulation. Leveraging the validated model, further wireless planning is carried out, including signal-to-interference-plus-noise ratio (SINR) analysis, coverage probability evaluation, and optimal transmitter placement. These findings provide valuable insights for the design and deployment of future THz in-vehicle communication systems.

Recent progress in information extraction has shown how to automatically build large ontologies from high-quality sources like Wikipedia. But knowledge evolves over time; facts have associated validity intervals. Therefore, ontologies should include time as a first-class dimension. In this paper, we introduce Timely YAGO, which extends our previously built knowledge base YAGO with temporal aspects. This prototype system extracts temporal facts from Wikipedia infoboxes, categories, and lists in articles, and integrates these into the Timely YAGO knowledge base. We also support querying temporal facts, by temporal predicates in a SPARQL-style language. Visualization of query results is provided in order to better understand of the dynamic nature of knowledge.

Modern web search engines, while indexing billions of web pages, are expected to process queries and return results in a very short time. Many approaches have been proposed for efficiently computing top-k query results, but most of them ignore one key factor in the ranking functions of commercial search engines - term-proximity, which is the metric of the distance between query terms in a document. When term-proximity is included in ranking functions, most of the existing top-k algorithms will become inefficient. To address this problem, in this paper we propose to build a compact phrase index to speed up the search process when incorporating the term-proximity factor. The compact phrase index can help more accurately estimate the score upper bounds of unknown documents. The size of the phrase index is controlled by including a small portion of phrases which are possibly helpful for improving search performance. Phrase index has been used to process phrase queries in existing work. It is, however, to the best of our knowledge, the first time that phrase index is used to improve the performance of generic queries. Experimental results show that, compared with the state-of-the-art top-k computation approaches, our approach can reduce average query processing time to 1/5 for typical setttings.

Modern web search engines are expected to return top-k results efficiently given a query. Although many dynamic index pruning strategies have been proposed for efficient top-k computation, most of them are prone to ignore some especially important factors in ranking functions, e.g. term proximity (the distance relationship between query terms in a document). The inclusion of term proximity breaks the monotonicity of ranking functions and therefore leads to additional challenges for efficient query processing. This paper studies the performance of some existing top-k computation approaches using term-proximity-enabled ranking functions. Our investigation demonstrates that, when term proximity is incorporated into ranking functions, most existing index structures and top-k strategies become quite inefficient. According to our analysis and experimental results, we propose two index structures and their corresponding index pruning strategies: Structured and Hybrid, which performs much better on the new settings. Moreover, the efficiency of index building and maintenance would not be affected too much with the two approaches.

This paper examines the problem of utilizing pseudo-anchor text to help ranking Web objects in vertical search. We adopt a machine learning based approach to extract pseudo-anchor text for a vertical object from its candidate anchor blocks. Experiments in academic search domain indicate that our approach is able to dramatically improve search performance.