Publications by authors named "Jianglin Lu"

Thanks to the efficient retrieval speed and low storage consumption, learning to hash has been widely used in visual retrieval tasks. However, the known hashing methods assume that the query and retrieval samples lie in homogeneous feature space within the same domain. As a result, they cannot be directly applied to heterogeneous cross-domain retrieval.

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Construction land reduction (CLR) is a policy innovation for Shanghai to explore high-quality economic development, but it will also lead to spatial injustice in the implementation process. Although the literature on spatial injustice and CLR is increasing, very little is known about the influence of spatial injustice in CLR on residents' policy acceptance of the economic-social-ecological objectives of CLR. To fill the knowledge gap, this study uses micro-survey data to identify the factors that influence residents' policy acceptance of the economic-social-ecological objectives of CLR.

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Rapid urbanization raises the issue of protecting development interests in net-incremental reduction regions of construction land (NRRCL). Spatial injustice (SI) is one of the key factors for the smooth implementation of construction land reduction (CLR) policies. This study theoretically analyzes the influence of SI on the economic development in CLR saving quota outflow regions, namely, NRRCL, and conducted empirical tests with the difference-in-differences model.

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Waste management is a key challenge the world currently faces. Solid waste imports (SWIs) are counteractive to the construction of ecological civilization. The comprehensive prohibition of "foreign waste" (FW) imports (FWIs) is an iconic measure to promote ecological civilization and ecological environment safety.

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The influential factors of the generation of kitchen solid waste (KSW) are vital to design policies that target effective KSW reductions and achieve sustainable KSW management, but has not been extensively studied. The study aims at the first step of KSW management only, i.e.

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Sparse discriminative projection learning has attracted much attention due to its good performance in recognition tasks. In this article, a framework called generalized embedding regression (GER) is proposed, which can simultaneously perform low-dimensional embedding and sparse projection learning in a joint objective function with a generalized orthogonal constraint. Moreover, the label information is integrated into the model to preserve the global structure of data, and a rank constraint is imposed on the regression matrix to explore the underlying correlation structure of classes.

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The toxic effects of lead on human health and the environment have long been a focus of research. To explore sources of lead in Guangzhou, China, we investigated atmospheric lead-containing particles (LCPs) during wintertime using a single particle aerosol mass spectrometer (SPAMS). Based on mass spectral features, LCPs were classified into eight major particle types, including Pb-Cl and Pb-Cl-Li (coal combustion and waste incineration), Pb-Cl-EC and Pb-Cl-OC (diesel trucks and coal combustion), Pb-Cl-Fe (iron and steel industry), Pb-Cl-AlSi (dust), Pb-Sec (secondary formation), and Pb-Cl-Zn (industrial process); these sources (in parentheses) were identified by comparing atmospheric LCP mass spectra with authentic Pb emission source mass spectra.

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