Hyperspectral sparse unmixing aims at modeling pixels of hyperspectral image as linear combination of a subset of a prior spectral library. Over the past years, spectral library has been constantly expanded, including spectra of the same material with intrinsic variability, which may result in the problem of high correlation. Recently, multi-objective sparse unmixing methods presented promising performance in dealing with sparsity via a non-convex L0 norm, but are insensitive to identifying endmembers with high correlation. In this paper, we propose a multiobjective sparse unmixing method, multi-objective group sparse hyperspectral unmixing (MO-GSU), which integrates a group sparsity structure to address high correlation of the spectral library induced by spectral variability. In order to describe the sparsity within and among groups, MO-GSU develops a mixed norm L0,q instead of the L0 norm. During the optimization, we propose two new search strategies: intra-group local search and group oriented adaptive genetic operator. The intra-group local search strategy is presented in addition to the multi-objective evolutionary algorithm for better exploitation within groups. The group oriented adaptive genetic operator is designed to maintain the inter-group distribution between generations and further ensure the intra-group exploitation. Moreover, we provide theoretical proof for the advantage of the group operators in exploiting the endmembers within group. To verify the efficiency of the proposed method on high correlation situations, MO-GSU is compared with recently proposed endmember bundle based and multi-objective based sparse unmixing methods on synthetic and real data with high correlation libraries.
The relationship between financialization and innovation has become a common focus of academic attention. This paper analyzes the influence of corporate financialization on innovation efficiency based on balanced panel data of listed Chinese pharmaceutical companies from 2015 to 2020. Also, it examines the relationship between corporate financialization and innovation efficiency under different levels of financing constraints and the moderating mechanisms that exist. The results of the study show that corporate financialization negatively affects innovation efficiency and that this effect has a lag; corporate financialization hurts innovation efficiency across the different regions and firm nature, with a less inhibiting effect for eastern firms and non-state-owned firms; further tests of the mechanism of action show that there is a non-linear negative relationship between corporate financialization and innovation efficiency. And the inhibition of corporate financialization on innovation efficiency decreases as the level of financing constraints rises. Based on the above findings, this study provides warnings and recommendations for pharmaceutical companies to finance their innovative activities through financialization.
In 2016, China began to execute the consistency evaluation policy of generic drugs. Many scholars believed that the policy would stimulate pharmaceutical firms to increase R&D investment with a theoretical perspective, but few have conducted empirical studies. Therefore, we conduct a difference-in-differences (DID) model and use panel data of 111 A-share listed pharmaceutical firms from 2012 to 2020 to empirically study the impact of the consistency evaluation policy of generic drugs on pharmaceutical firms' R&D investment intensity. The result shows that the policy has a significant positive impact on the R&D investment intensity of firms with chemical generics, robust under the test for parallel trend test, placebo test, and the propensity score matching and difference-in-differences (PSM-DID) test. In addition, we further analyzed the impact of this policy on the R&D intensity of pharmaceutical firms according to the heterogeneity of enterprise's operational nature, regional distribution and profitability. From the perspective of time changes and the average effect, the R&D investment intensity of private pharmaceutical firms is more affected by the policy than state-owned enterprises; the R&D investment intensity of pharmaceutical firms in the eastern region is more affected by this policy than those in the central and the western; the R&D investment intensity of high-profitability pharmaceutical firms is more affected by the policy than those with low-profitability. The consistency evaluation policy is still being implemented, and its impact on pharmaceutical firms needs to be studied from different empirical research perspectives in the future.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.