Shuai Pang

Shuai Pang, Junlong Zhang, Yu Liu et al.

Urban space is highly heterogeneous, with economic and population activities concentrating in localized centers. However, the global organization of such intra-urban centers remains poorly understood due to the lack of consistent, comparable data. Here we develop a scalable geospatial framework using nighttime light observations to identify over 15,000 intra-urban centers worldwide. We uncover a robust regularity: despite differences in city size, geography, and development context, total urban area scales linearly with the number of centers, implying a roughly constant spatial footprint per center. This macroscopic regularity is underpinned by two independent sublinear scaling laws -- center number and urban area both scale with population at closely matched rates -- whose ratio cancels to produce the observed linear relationship. At the within-city level, this constancy manifests as a characteristic Voronoi coverage area per center that is consistent across regions, and centers are more regularly spaced than spatial null models predict. As a consequence, polycentric cities maintain stable accessibility as they expand. These findings provide a new empirical foundation for understanding the spatial organization of urban growth.

Jianqiang Ma, Zeyu Yan, Shuai Pang et al.

On the WikiSQL benchmark, state-of-the-art text-to-SQL systems typically take a slot-filling approach by building several dedicated models for each type of slots. Such modularized systems are not only complex butalso of limited capacity for capturing inter-dependencies among SQL clauses. To solve these problems, this paper proposes a novel extraction-linking approach, where a unified extractor recognizes all types of slot mentions appearing in the question sentence before a linker maps the recognized columns to the table schema to generate executable SQL queries. Trained with automatically generated annotations, the proposed method achieves the first place on the WikiSQL benchmark.