PSSA: PCA-domain superpixelwise singular spectral analysis for unsupervised hyperspectral image classification.

Although supervised classification of hyperspectral images (HSI) has achieved success in remote sensing, its applications in real scenarios are often constrained, mainly due to the insufficiently available or lack of labelled data. As a result, unsupervised HSI classification based on data clustering is highly desired, yet it generally suffers from high computational cost and low classification accuracy, especially in large datasets. To tackle these challenges, a novel unsupervised spatial-spectral HSI classification method is proposed. By combining the entropy rate superpixel segmentation (ERS), superpixel-based principal component analysis (PCA), and PCA-domain 2D singular spectral analysis (SSA), both the efficacy and efficiency of feature extraction are improved, followed by the anchor-based graph clustering (AGC) for effective classification. Experiments on three publicly available and five self-collected aerial HSI datasets have fully demonstrated the efficacy of the proposed PCA-domain superpixelwise SSA (PSSA) method, with a gain of 15–20% in terms of the overall accuracy, in comparison to a few state-of-the-art methods. In addition, as an extra outcome, the HSI dataset we acquired is provided freely online

Does the sales seasonality anomaly exist in China?

In this paper, we examine the relationship between sales seasonality and future stock return in the US and Chinese markets. Consistent with Grullon et al. (2020), we find low-sales-season firms tend to significantly outperform high-sales-season firms in the US market. Our empirical results suggest that the sales seasonality anomaly does not exist in the Chinese market

The real effect of partial privatization on corporate innovation: Evidence from China’s split share structure reform

We examine the real effect of partial privatization on corporate innovation. To establish causality, we explore plausibly exogenous variation in the expectation of further partial privatization generated by China’s split share structure reform, which mandatorily converts non-tradable shares into freely tradable shares and opens up the gate to the further privatization of state-owned enterprises. We find that partial privatization prospects have a positive effect on corporate innovation. A better alignment of the interests of government agents with those of private shareholders and improved stock price informativeness appear to be two plausible underlying mechanisms. Our paper sheds new light on the real effects of partial privatization

STEP-Compliant NC Simulation System Modeling

International audienceAlthough some research and commercial software for NC simulation is available, NC simulation modeling is still not matured. Most of them are based on G&M code. STEP-NC is a new data model for computer numerical control (CNC). It provides rich information for CNC machine tools, i.e. "what to do" based on features rather than "how to do" as for G-code. IDEF0 is a method designed to model the decisions, actions, and activities of an organization or system. It helps to organize and analyze a system and to promote a good communications between the analyst and the customer. So in this paper, from the view of system modeling, function modeling of NC simulation system based on STEP-NC is built by IDEF0 method. As a result, NC simulation system can be realized more efficiently

Streamlining and Optimizing Strategies of Electrostatic Parameterization

Accurate characterization of electrostatic interactions is crucial in molecular simulation. Various methods and programs have been developed to obtain electrostatic parameters for additive or polarizable models to replicate electrostatic properties obtained from experimental measurements or theoretical calculations. Electrostatic potentials (ESPs), a set of physically well-defined observables from quantum mechanical (QM) calculations, are well suited for optimization efforts due to the ease of collecting a large amount of conformation-dependent data. However, a reliable set of QM ESP computed at an appropriate level of theory and atomic basis set is necessary. In addition, despite the recent development of the PyRESP program for electrostatic parameterizations of induced dipole-polarizable models, the time-consuming and error-prone input file preparation process has limited the widespread use of these protocols. This work aims to comprehensively evaluate the quality of QM ESPs derived by eight methods, including wave function methods such as Hartree-Fock (HF), second-order Møller-Plesset (MP2), and coupled cluster-singles and doubles (CCSD), as well as five hybrid density functional theory (DFT) methods, used in conjunction with 13 different basis sets. The highest theory levels CCSD/aug-cc-pV5Z (a5z) and MP2/aug-cc-pV5Z (a5z) were selected as benchmark data over two homemade data sets. The results show that the hybrid DFT method, ωB97X-D, combined with the aug-cc-pVTZ (a3z) basis set, performs well in reproducing ESPs while taking both accuracy and efficiency into consideration. Moreover, a flexible and user-friendly program called PyRESP_GEN was developed to streamline input file preparation. The restraining strengths, along with strategies for polarizable Gaussian multipole (pGM) model parameterizations, were also optimized. These findings and the program presented in this work facilitate the development and application of induced dipole-polarizable models, such as pGM models, for molecular simulations of both chemical and biological significance