US-China trade war and China’s stock market: an event-driven analysis

The US-China trade war, initiated in March 2018, substantially transformed the trading partnership between the two largest economic powers. It directly influenced the profitability of domestic enterprises related to the export chain and harmed the domestic economy in China and its stock market. This study empirically examines the effects of the trade war on China’s stock market based on chronological events and tests whether it is the contagion effect or the present value effect. The empirical study supports the contagion effect because the impact of the US-China trade war differed significantly in different sectors only when the US announced its imposition of more tariffs on US$50 billion worth of Chinese products. However, there is no apparent difference between the industries for other events, nor is there any significant difference between the industries in terms of longterm impact

EgoVM: Achieving Precise Ego-Localization using Lightweight Vectorized Maps

Accurate and reliable ego-localization is critical for autonomous driving. In this paper, we present EgoVM, an end-to-end localization network that achieves comparable localization accuracy to prior state-of-the-art methods, but uses lightweight vectorized maps instead of heavy point-based maps. To begin with, we extract BEV features from online multi-view images and LiDAR point cloud. Then, we employ a set of learnable semantic embeddings to encode the semantic types of map elements and supervise them with semantic segmentation, to make their feature representation consistent with BEV features. After that, we feed map queries, composed of learnable semantic embeddings and coordinates of map elements, into a transformer decoder to perform cross-modality matching with BEV features. Finally, we adopt a robust histogram-based pose solver to estimate the optimal pose by searching exhaustively over candidate poses. We comprehensively validate the effectiveness of our method using both the nuScenes dataset and a newly collected dataset. The experimental results show that our method achieves centimeter-level localization accuracy, and outperforms existing methods using vectorized maps by a large margin. Furthermore, our model has been extensively tested in a large fleet of autonomous vehicles under various challenging urban scenes.Comment: 8 page

A study on the moderating role of national absorptive capacity between institutional quality and FDI inflow: evidence from developing countries

Numerous studies on foreign direct investment (FDI) as a prime element of capital flow and external finance contribute to foreign physical stock of capital, knowledge spillovers, transfer of technology, and recipient countries’ employment. Developing economies need FDI to boost their economic growth. This study explores the moderating role of national absorptive capacity between FDI inflow and institutional quality (control of corruption, government effectiveness, political stability and the absence of violence, regulatory quality, rule of law, voice and accountability) on a panel of 113 developing countries for 2000–2019. Hausman fixed-effect and random-effect estimation are used in the analysis. The results show that national absorptive capacity (AC) moderates the relationship between FDI inflow and institutional quality dimension. To check robustness, we formed an index of institutional quality (OIQ) dimensions through principal component analysis (PCA) and regressed, demonstrating that AC moderates the relationship between OIQ and FDI. Subsequently, taking BRICSþPakistan as a sample, we find that the results hold. This study will help form FDI-friendly policy in developing countries

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Effects of Si/Al Ratios on the Bulk-Type Zeolite Formation Using Synthetic Metakaolin-Based Geopolymer with Designated Composition

In this paper, synthetic metakaolin with fixed composition (Al2O3·2SiO2) was produced by a simple chemosynthetic route. The chemosynthetic metakaolin can eliminate the influence of impurities in metakaolin from natural kaolin minerals. The synthetic metakaolin together with NaOH and SiO2-sol were used to prepare Na-based geopolymer precursors with various molar ratios of Si/Al. The molar ratios of Si/Al from 1 to 2 were tailored by adding different contents of SiO2-sol. Zeolite/geopolymer composites or monolith-type zeolite were successfully fabricated from synthetic metakaolin-based geopolymer through a hydrothermal process. The effects of Si/Al ratios on the phase composition and microstructure of the produced zeolite/geopolymer composites or zeolites were studied. The results proved that the composition of synthetic metakaolin and geopolymer precursors can be facilely tuned, and the monolithic geopolymer precursors can be mostly, or even totally, transformed into zeolite after hydrothermal treatment

A Crack Defect Detection and Segmentation Method That Incorporates Attention Mechanism and Dimensional Decoupling

In this work, we propose a new crack image detection and segmentation method for addressing the issues regarding the poor detection of crack structures in certain complex background conditions, such as the light and shadow, and the easy-to-lose details in segmentation. This method can be categorized into two phases, where the first one is the coding phase. In this phase, the channel attention mechanism and crack characteristics, using the correlation channel with different scales increasing the network robustness and ability of feature extraction, have been introduced to decouple the channel dimension and space dimension. It also avoids underfitting caused by information redundancy during the jumping connection. In the second stage, i.e., the decoding stage, the spatial attention mechanism has been introduced to capture the crack edge information through the global maximum pooling and global average pooling of the high-dimensional features. Then, the correlation between the space and channel has been recovered through multiscale image information fusion to achieve accurate crack positioning. Furthermore, the Dice loss function has been employed to solve the problem of pixel imbalance between the categories. Finally, the proposed method has been tested and compared with existing methods. The experimental results illustrate that our method has a higher crack segmentation accuracy than existing methods. Furthermore, the mean intersection over the union ratio reaches 87.2% on the public dataset and 83.9% on the self-built dataset, and it has a better segmentation effect and richer details. It can solve the problem of crack image detection and segmentation under a complex background

A Crack Defect Detection and Segmentation Method That Incorporates Attention Mechanism and Dimensional Decoupling

In this work, we propose a new crack image detection and segmentation method for addressing the issues regarding the poor detection of crack structures in certain complex background conditions, such as the light and shadow, and the easy-to-lose details in segmentation. This method can be categorized into two phases, where the first one is the coding phase. In this phase, the channel attention mechanism and crack characteristics, using the correlation channel with different scales increasing the network robustness and ability of feature extraction, have been introduced to decouple the channel dimension and space dimension. It also avoids underfitting caused by information redundancy during the jumping connection. In the second stage, i.e., the decoding stage, the spatial attention mechanism has been introduced to capture the crack edge information through the global maximum pooling and global average pooling of the high-dimensional features. Then, the correlation between the space and channel has been recovered through multiscale image information fusion to achieve accurate crack positioning. Furthermore, the Dice loss function has been employed to solve the problem of pixel imbalance between the categories. Finally, the proposed method has been tested and compared with existing methods. The experimental results illustrate that our method has a higher crack segmentation accuracy than existing methods. Furthermore, the mean intersection over the union ratio reaches 87.2% on the public dataset and 83.9% on the self-built dataset, and it has a better segmentation effect and richer details. It can solve the problem of crack image detection and segmentation under a complex background

Exendin-4 Promotes Beta Cell Proliferation via PI3k/Akt Signalling Pathway

Background/Aims: Prevention of diabetes requires maintenance of a functional beta-cell mass, the postnatal growth of which depends on beta cell proliferation. Past studies have shown evidence of an effect of an incretin analogue, Exendin-4, in promoting beta cell proliferation, whereas the underlying molecular mechanisms are not completely understood. Methods: Here we studied the effects of Exendin-4 on beta cell proliferation in vitro and in vivo through analysing BrdU-incorporated beta cells. We also analysed the effects of Exendin-4 on beta cell mass in vivo, and on beta cell number in vitro. Then, we applied specific inhibitors of different signalling pathways and analysed their effects on Exendin-4-induced beta cell proliferation. Results: Exendin-4 increased beta cell proliferation in vitro and in vivo, resulting in significant increases in beta cell mass and beta cell number, respectively. Inhibition of PI3K/Akt signalling, but not inhibition of either ERK/MAPK pathway, or JNK pathway, significantly abolished the effects of Exendin-4 in promoting beta cell proliferation. Conclusion: Exendin-4 promotes beta cell proliferation via PI3k/Akt signaling pathway