Sources of Investment Inefficiency: The Case of Fixed-Asset Investment in China

This study attempts to measure the inefficiency associated with aggregate investment in a transitional economy. The inefficiency is decomposed into allocative and production inefficiency based on standard production theory. Allocative inefficiency is measured by disequilibrium investment demand. Institutional factors are then taken into consideration as possible explanatory variables of the disequilibrium. The resulting model is applied to Chinese provincial panel data. The main findings are: Chinese investment demand is strongly receptive to expansionary fiscal policies and inter-provincial network effects; and although there are signs of increasing allocative efficiency, the tendency of over-investment remains, even with improvements in production efficiency.Over-investment, Efficiency, Disequilibrium, Soft-budget constraint

Stress Level of a Classroom Instructors and Its Influence to Their Classroom Performance

In today’s education, teachers face numerous challenges. Among them, teacher stress level has a significant impact on classroom performance. This study aims to explore this influence. Classroom stress refers to the psychological and physiological tension teachers face due to various factors. It affects teachers’ communication with students, classroom management, and teaching quality. Factors contributing to teachers’ stress level include organizational, management, and individual factors. To address this, effective strategies are proposed: organizational interventions like optimizing resource allocation, reducing burdens, and providing psychological counseling; and individual interventions like self-adjustment, improving psychological quality, and teaching skills training. These strategies aim to reduce teacher stress, improve teaching quality, and contribute to education in our country

Feature Grouping and Sparse Principal Component Analysis

Sparse Principal Component Analysis (SPCA) is widely used in data processing and dimension reduction; it uses the lasso to produce modified principal components with sparse loadings for better interpretability. However, sparse PCA never considers an additional grouping structure where the loadings share similar coefficients (i.e., feature grouping), besides a special group with all coefficients being zero (i.e., feature selection). In this paper, we propose a novel method called Feature Grouping and Sparse Principal Component Analysis (FGSPCA) which allows the loadings to belong to disjoint homogeneous groups, with sparsity as a special case. The proposed FGSPCA is a subspace learning method designed to simultaneously perform grouping pursuit and feature selection, by imposing a non-convex regularization with naturally adjustable sparsity and grouping effect. To solve the resulting non-convex optimization problem, we propose an alternating algorithm that incorporates the difference-of-convex programming, augmented Lagrange and coordinate descent methods. Additionally, the experimental results on real data sets show that the proposed FGSPCA benefits from the grouping effect compared with methods without grouping effect.Comment: 21 pages, 5 figures, 2 table

Deciphering Charging Status, Absolute Quantum Efficiency, and Absorption Cross Section of MultiCarrier States in Single Colloidal Quantum Dot

Upon photo- or electrical-excitation, colloidal quantum dots (QDs) are often found in multi-carrier states due to multi-photon absorption and photo-charging of the QDs. While many of these multi-carrier states are observed in single-dot spectroscopy, their properties are not well studied due to random charging/discharging, emission intensity intermittency, and uncontrolled surface defects of single QD. Here we report in-situ deciphering the charging status, and precisely assessing the absorption cross section, and determining the absolute emission quantum yield of mono-exciton and biexciton states for neutral, positively-charged, and negatively-charged single core/shell CdSe/CdS QD. We uncover very different photon statistics of the three charge states in single QD and unambiguously identify their charge sign together with the information of their photoluminescence decay dynamics. We then show their distinct photoluminescence saturation behaviors and evaluated the absolute values of absorption cross sections and quantum efficiencies of monoexcitons and biexcitons. We demonstrate that addition of an extra hole or electron in a QD changes not only its emission properties but also varies its absorption cross section

Targeting Vacuolar H+-ATPases as a New Strategy against Cancer

Abstract Growing evidence suggests a key role of tumor acidic microenvironment in cancer development, progression, and metastasis. As a consequence, the need for compounds that specifically target the mechanism(s) responsible for the low pH of tumors is increasing. Among the key regulators of the tumor acidic microenvironment, vacuolar H+-ATPases (V-ATPases) play an important role. These proteins cover a number of functions in a variety of normal as well as tumor cells, in which they pump ions across the membranes. We discuss here some recent results showing that a molecular inhibition of V-ATPases by small interfering RNA in vivo as well as a pharmacologic inhibition through proton pump inhibitors led to tumor cytotoxicity and marked inhibition of human tumor growth in xenograft models. These results propose V-ATPases as a key target for new strategies in cancer treatment. [Cancer Res 2007;67(22):10627–30

Improving Model Drift for Robust Object Tracking

Discriminative correlation filters show excellent performance in object tracking. However, in complex scenes, the apparent characteristics of the tracked target are variable, which makes it easy to pollute the model and cause the model drift. In this paper, considering that the secondary peak has a greater impact on the model update, we propose a method for detecting the primary and secondary peaks of the response map. Secondly, a novel confidence function which uses the adaptive update discriminant mechanism is proposed, which yield good robustness. Thirdly, we propose a robust tracker with correlation filters, which uses hand-crafted features and can improve model drift in complex scenes. Finally, in order to cope with the current trackers' multi-feature response merge, we propose a simple exponential adaptive merge approach. Extensive experiments are performed on OTB2013, OTB100 and TC128 datasets. Our approach performs superiorly against several state-of-the-art trackers while runs at speed in real time.Comment: 7 pages, 6 figures, 4 table