Natural resource or market seeking motive of China’s FDI in asia? New evidence at income and sub-regional level

Asia is a heterogeneous region including countries with distinct features in quite a few facets. This study is designed to unravel the motivations of Chinese FDI in 30 Asian countries (For list of countries see Appendix 1.) during 2003–2016. For estimation, we utilised the Random effect (RE), Fixed effect (FE) and System-GMM (SGMM) methodologies. We transpired that both market and natural resource (mineral richness) seeking motives of Chinese FDI in the whole sample analysis. With respect to income group, we confirmed the market seeking FDI in both high and middle- income countries whereas, mineral richness is priority for Chinese FDI in middle-income group. Thus, Chinese firms targeted middle income developing economies to acquire non-fuel natural resources. Analogously, on the regional basis, the results show that in all regression models, GDP is positive and significant predictor, characterising market seeking FDI by Chinese firms in West, East and South East Asia. In resource seeking motive, among the two types of natural resources, mineral richness affect Chinese FDI positively in East & South East Asia. In a nutshell, seeking market is the common motive for Chinese FDI in the entire sample, whereas the resource seeking motive varies across the income groups and regions

Free Cash Flows and Price Momentum

This study investigates the role of free cash flows and (cross-sectional and time-series) price momentum in predicting future stock returns. Past returns and free cash flows each positively predict future stock returns after controlling for the other, suggesting that cash flows and momentum both contain valuable and distinctive information about future stock returns. A strategy of buying past winners with high free cash flows and shorting past losers with low free cash flows significantly outperforms the traditional momentum trading strategy. The enhanced performance is not sensitive to investor sentiment, time variations, or transaction costs. Further analysis shows that the incremental cash flow effects are largely attributable to net distributions to equity/debt holders. Overall, our findings shed light on the role of corporate fundamentals in technical trading strategies

Eigenvalues of Sturm-Liouville problems with eigenparameter dependent boundary and interface conditions

In this paper, a regular discontinuous Sturm-Liouville problem which contains eigenparameter in both boundary and interface conditions is investigated. Firstly, a new operator associated with the problem is constructed, and the self-adjointness of the operator in an appropriate Hilbert space is proved. Some properties of eigenvalues are discussed. Finally, the continuity of eigenvalues and eigenfunctions is investigated, and the differential expressions in the sense of ordinary or Fréchet of the eigenvalues concerning the data are given

Identification of Somatic Genetic Alterations Using Whole-Exome Sequencing of Uterine Leiomyosarcoma Tumors

BackgroundThe genomic abnormalities associated with uterine leiomyosarcoma (uLMS) have not been fully elucidated to date.ObjectiveTo understand the pathogenesis of uLMS and to identify driver mutations and potential therapeutic targets in uLMS.MethodsThree matched tumor-constitutional DNA pairs from patients with recurrent uLMS were subjected to whole-exome capture and next-generation sequencing. The role of the selected gene SHARPIN in uLMS was analyzed by the CCK-8 assay and colony formation assay after specific siRNA knockdown.ResultsWe identified four genes with somatic SNVs, namely, SLC39A7, GPR19, ZNF717, and TP53, that could be driver mutations. We observed that 30.7% (4/13) of patients with uLMS had TP53 mutations as analyzed by direct sequencing. Analysis of somatic copy number variants (CNVs) showed regions of chromosomal gain at 1q21-23, 19p13, 17q21, and 17q25, whereas regions of chromosomal loss were observed at 2q35, 2q37, 1p36, 10q26, 6p22, 8q24, 11p15, 11q12, and 9p21. The SHARPIN gene was amplified in two patients and mutated in another (SHARPIN: NM_030974: exon2: c.G264C, p.E88D). Amplification of the SHARPIN gene was associated with shorter PFS and OS in soft tissue sarcoma, as shown by TCGA database analysis. Knockdown of SHARPIN expression was observed to decrease cell growth and colony formation in uterine sarcoma cell lines.ConclusionsExome sequencing revealed mutational heterogeneity of uLMS. The SHARPIN gene was amplified in uLMS and could be a candidate oncogene

Towards Strengthening Deep Learning-based Side Channel Attacks with Mixup

In recent years, various deep learning techniques have been exploited in side channel attacks, with the anticipation of obtaining more appreciable attack results. Most of them concentrate on improving network architectures or putting forward novel algorithms, assuming that there are adequate profiling traces available to train an appropriate neural network. However, in practical scenarios, profiling traces are probably insufficient, which makes the network learn deficiently and compromises attack performance. In this paper, we investigate a kind of data augmentation technique, called mixup, and first propose to exploit it in deep-learning based side channel attacks, for the purpose of expanding the profiling set and facilitating the chances of mounting a successful attack. We perform Correlation Power Analysis for generated traces and original traces, and discover that there exists consistency between them regarding leakage information. Our experiments show that mixup is truly capable of enhancing attack performance especially for insufficient profiling traces. Specifically, when the size of the training set is decreased to 30% of the original set, mixup can significantly reduce acquired attacking traces. We test three mixup parameter values and conclude that generally all of them can bring about improvements. Besides, we compare three leakage models and unexpectedly find that least significant bit model, which is less frequently used in previous works, actually surpasses prevalent identity model and hamming weight model in terms of attack results

PD-1 expression on peripheral CD8+ TEM/TEMRA subsets closely correlated with HCV viral load in chronic hepatitis C patients

<p>Abstract</p> <p>Background</p> <p>Tight correlation between host circulating CD8+ T cell-mediated immune response and control of viral replication is classical characteristic of long-term HCV infection. CD8+ T cell maturation/activation markers are expected to be associated with viral replication and disease progression in chronic HCV infection. The aim of the present study was to explore novel markers on CD8+ T cells with ability to evaluate HCV viral replication and disease progression.</p> <p>Methods</p> <p>PBMCs were isolated from 37 chronic HCV-infected patients and 17 healthy controls. Distributed pattern of CD8+ T cells subsets and expression of PD-1, CD38, HLA-DR and CD127 were analyzed by flow cytometry. The correlation between expression of surface markers and HCV viral load or ALT was studied.</p> <p>Results</p> <p>Declined naïve and increased TEMRA CD8+ T subsets were found in HCV-infected individuals compared with healthy controls. Percentage and MFI of PD-1, CD38 and HLA-DR on all CD8+ T cell subsets were higher in HCV-infected patients than healthy controls. In contrast, CD127 expression on CD8+ TCM showed an opposite trend as PD-1, CD38 and HLA-DR did. In chronic HCV infection, MFI of PD-1 on CD8+ TEM (p < 0.0001) and TEMRA (p = 0.0015) was positively correlated with HCV viral load while HLA-DR expression on non-naive CD8+ T cell subsets (p < 0.05) was negatively correlated with HCV viral load.</p> <p>Conclusion</p> <p>PD-1 level on peripheral CD8+ TEM/TEMRA was highly correlated with HCV viral load in chronic HCV-infected patients, which made PD-1 a novel indicator to evaluate HCV replication and disease progression in chronic hepatitis C patients.</p

Onfocus detection:Identifying individual-camera eye contact from unconstrained images

Onfocus detection aims at identifying whether the focus of the individual captured by a camera is on the camera or not. Based on the behavioral research, the focus of an individual during face-to-camera communication leads to a special type of eye contact, i.e., the individual-camera eye contact, which is a powerful signal in social communication and plays a crucial role in recognizing irregular individual status (e.g., lying or suffering mental disease) and special purposes (e.g., seeking help or attracting fans). Thus, developing effective onfocus detection algorithms is of significance for assisting the criminal investigation, disease discovery, and social behavior analysis. However, the review of the literature shows that very few efforts have been made toward the development of onfocus detector due to the lack of large-scale public available datasets as well as the challenging nature of this task. To this end, this paper engages in the onfocus detection research by addressing the above two issues. Firstly, we build a large-scale onfocus detection dataset, named as the OnFocus Detection In the Wild (OFDIW). It consists of 20,623 images in unconstrained capture conditions (thus called ``in the wild'') and contains individuals with diverse emotions, ages, facial characteristics, and rich interactions with surrounding objects and background scenes. On top of that, we propose a novel end-to-end deep model, i.e., the eye-context interaction inferring network (ECIIN), for onfocus detection, which explores eye-context interaction via dynamic capsule routing. Finally, comprehensive experiments are conducted on the proposed OFDIW dataset to benchmark the existing learning models and demonstrate the effectiveness of the proposed ECIIN. The project (containing both datasets and codes) is at https://github.com/wintercho/focus