Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking

With efficient appearance learning models, Discriminative Correlation Filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions. However, the existing DCF paradigm suffers from two major issues, i.e., spatial boundary effect and temporal filter degradation. To mitigate these challenges, we propose a new DCF-based tracking method. The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensional discriminative manifold. More specifically, we apply structured spatial sparsity constraints to multi-channel filers. Consequently, the process of learning spatial filters can be approximated by the lasso regularisation. To encourage temporal consistency, the filter model is restricted to lie around its historical value and updated locally to preserve the global structure in the manifold. Last, a unified optimisation framework is proposed to jointly select temporal consistency preserving spatial features and learn discriminative filters with the augmented Lagrangian method. Qualitative and quantitative evaluations have been conducted on a number of well-known benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123 and VOT2018. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art approaches

Climatic change controls productivity variation in global grasslands.

Detection and identification of the impacts of climate change on ecosystems have been core issues in climate change research in recent years. In this study, we compared average annual values of the normalized difference vegetation index (NDVI) with theoretical net primary productivity (NPP) values based on temperature and precipitation to determine the effect of historic climate change on global grassland productivity from 1982 to 2011. Comparison of trends in actual productivity (NDVI) with climate-induced potential productivity showed that the trends in average productivity in nearly 40% of global grassland areas have been significantly affected by climate change. The contribution of climate change to variability in grassland productivity was 15.2-71.2% during 1982-2011. Climate change contributed significantly to long-term trends in grassland productivity mainly in North America, central Eurasia, central Africa, and Oceania; these regions will be more sensitive to future climate change impacts. The impacts of climate change on variability in grassland productivity were greater in the Western Hemisphere than the Eastern Hemisphere. Confirmation of the observed trends requires long-term controlled experiments and multi-model ensembles to reduce uncertainties and explain mechanisms

Dynamic changes and convergence of China’s regional green productivity:A dynamic spatial econometric analysis

Low-carbon economic development is at the heart of the post-pandemic green recovery scheme worldwide. It requires economic recovery without compromising on the environment, implying a critical role that green productivity plays in achieving the carbon neutrality goal. Green productivity measures the quality of economic growth with consideration for energy consumption and environmental pollution. This study employs the slacks-based measure directional distance function (SBM-DDF) approach and the Malmquist-Luenberger (ML) index to calculate green productivity and its components of 30 provinces in China between 2001 and 2018. Using a spatial panel data model, we empirically analyzed the conditional β-convergence of China's green productivity. We found that overall, since 2001, China's green productivity has demonstrated a continuous upward trend. When taking into account spatial factors, China's green productivity demonstrates a significant conditional β-convergence. In terms of regional effects, the results indicate that the green productivity of the eastern and western regions demonstrates club convergence, implying a more balanced green economic development. Moreover, the convergence rate of China's green productivity increases with the addition of environmental regulation variable, and so the corresponding convergence time decreases. It indicates that environmental regulations help to facilitate the convergence of China's green productivity, narrowing the gap between the regional green economic development. The findings provide guideline for achieving a low-carbon development and carbon neutrality from a regional green productivity perspective

Efficiency of environmental legislative measures to ICT industry in China with case of Chongqing City - from geographical view

Le résumé en français n'a pas été communiqué par l'auteur.Le résumé en anglais n'a pas été communiqué par l'auteur

Efficiency of environmental legislative measures to ICT industry in China with case of Chongqing City - from geographical view

Le résumé en français n'a pas été communiqué par l'auteur.Le résumé en anglais n'a pas été communiqué par l'auteur

State and market integration in China: A spatial econometrics approach to 'local protectionism'

In the past two decades, controversial evidence has been produced supporting the case for local protectionism in China. This paper overviews the most important contributions and presents a new approach which applies spatial econometrics on prefectural-level data. The main advantage of this method is to rely on a theoretically less biased and internal benchmark for assessing the impact of provincial borders on spatial interdependences, as we compare within province and across province growth spillovers for neighbouring prefectures. We show that provincial borders exert a strong impact on spillovers. Further, we also analyze spillovers of local public expenditures, which could be interpreted as proxies for government interventions. Again, provincial borders matter. Yet, we are cautious in interpreting this as evidence for local protectionism, and propose the notion of 'cellularity' as an alternative explanation. Cellularity results from a confluence of different factors, such as administrative structure, institutional changes and regional culture. --Domestic market integration in China: local protectionism,spatial econometrics,growth spillovers,expenditure spillovers,cellularity

Economic growth across Chinese provinces: in search of innovation-driven gains

In this paper we analyse the impact of R&D on total factor productivity across Chinese provinces. We introduce innovations explicitly into a production function and evaluate their contribution to economic growth in 1993 - 2006. The empirical results highlight the importance and the interaction between local and external research. The evidence indicates that growth in China is not explained simply by factor input accumulation.China; R&D; R&D Spillovers; patents; regional economic growth; semiparametric estimators

Fiscal science and technology expenditure and the spatial convergence of regional innovation efficiency: evidence from China’s province-level data

Narrowing the gap in regional innovation efficiency is conducive to the coordinated development of regional economies. Fiscal science and technology (S&T) expenditure is the government’s primary means of supporting regional innovation. It also plays an essential role in improving the efficiency of regional innovation. This study constructs a spatial convergence economic model based on a dynamic perspective. It also examines the relationship between fiscal S&T expenditure and spatial convergence of regional innovation efficiency. China’s regional innovation efficiency shows a trend of conditional b-convergence. Fiscal S&T expenditure positively affects the spatial convergence of regional innovation efficiency and has an inverted U-shaped, nonlinear relationship as a whole. The transmission mechanism test revealed that the cross-regional flow of research and development (R&D) personnel can enhance this positive effect, and the role of R&D capital is not significant