Multi-dimensional boundary effects and regional economic integration: Evidence from the Yangtze River Economic Belt

While the challenges posed by multi-dimensional boundary effects to global economic integration are studied widely, regional economic integration within a sovereign country requires additional analysis. The Yangtze River Economic Belt (YREB), a super-scale interprovincial area including three nested urban alliances, is a meaningful vision of regional economic integration in China. After building the producer services-based urban corporate network, this study investigates the influence of multi-dimensional boundary effects on regional economic integration by social network analysis and the exponential random graph model. The findings show that the fragmented reality of YREB’s economy is significantly different from the vision of the Chinese central government. More specifically, although the natural boundary restraints represented by distance have disappeared, multi-dimensional barriers to regional economic integration are still posed by administrative, policy, economic, and cultural boundaries. The estimation results pass the robustness test of the grouping sample of producer services. Therefore, we confirm that the multi-dimensional boundary effects, particularly the intangible ones, significantly impact regional economic integration even within a country with a top-down ‘strong’ governance.

A Novel Anti-Drift Visual Object Tracking Algorithm Based on Sparse Response and Adaptive Spatial-Temporal Context-Aware

Correlation filter (CF) based trackers have gained significant attention in the field of visual single-object tracking, owing to their favorable performance and high efficiency; however, existing trackers still suffer from model drift caused by boundary effects and filter degradation. In visual tracking, long-term occlusion and large appearance variations easily cause model degradation. To remedy these drawbacks, we propose a sparse adaptive spatial-temporal context-aware method that effectively avoids model drift. Specifically, a global context is explicitly incorporated into the correlation filter to mitigate boundary effects. Subsequently, an adaptive temporal regularization constraint is adopted in the filter training stage to avoid model degradation. Meanwhile, a sparse response constraint is introduced to reduce the risk of further model drift. Furthermore, we apply the alternating direction multiplier method (ADMM) to derive a closed-solution of the object function with a low computational cost. In addition, an updating scheme based on the APEC-pool and Peak-pool is proposed to reveal the tracking condition and ensure updates of the target’s appearance model with high-confidence. The Kalam filter is adopted to track the target when the appearance model is persistently unreliable and abnormality occurs. Finally, extensive experimental results on OTB-2013, OTB-2015 and VOT2018 datasets show that our proposed tracker performs favorably against several state-of-the-art trackers.

Learning Spatial–Temporal Background-Aware Based Tracking

Discriminative correlation filter (DCF) based tracking algorithms have obtained prominent speed and accuracy strengths, which have attracted extensive attention and research. However, some unavoidable deficiencies still exist. For example, the circulant shifted sampling process is likely to cause repeated periodic assumptions and cause boundary effects, which degrades the tracker’s discriminative performance, and the target is not easy to locate in complex appearance changes. In this paper, a spatial–temporal regularization module based on BACF (background-aware correlation filter) framework is proposed, which is performed by introducing a temporal regularization to deal effectively with the boundary effects issue. At the same time, the accuracy of target recognition is improved. This model can be effectively optimized by employing the alternating direction multiplier (ADMM) method, and each sub-problem has a corresponding closed solution. In addition, in terms of feature representation, we combine traditional hand-crafted features with deep convolution features linearly enhance the discriminative performance of the filter. Considerable experiments on multiple well-known benchmarks show the proposed algorithm is performs favorably against many state-of-the-art trackers and achieves an AUC score of 64.4% on OTB-100.