IS THERE DIVERSIFICATION BENEFIT BETWEEN EMERGING AND DEVELOPED STOCK MARKET: EVIDENCE FROM THE BRIC AND US STOCK MARKET

This paper seeks to investigate the linkage and co-movement relationships between the stock markets of US and BRIC, and determine the degree of diversification benefits among them within the sample period from January 2001 to September 2017. The entire sample period is divided into three phases: pre-crisis, during crisis and post-crisis in order to be more comparative. The empirical results show that there is a strong linkage and co-movement relationship between BRIC and US stock markets, especially after 2007 financial crisis. Also, the upward long run conditional correlations demonstrate that the diversification benefits are weakened substantially. However, there is not any evidence showing the existence of co-integration between BRIC and US market for all three phases, except for the stock market of China during the crisis. Moreover, most of the BRIC stock markets are appeared to have no short term causality to US market

PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation

Panoramic videos contain richer spatial information and have attracted tremendous amounts of attention due to their exceptional experience in some fields such as autonomous driving and virtual reality. However, existing datasets for video segmentation only focus on conventional planar images. To address the challenge, in this paper, we present a panoramic video dataset, PanoVOS. The dataset provides 150 videos with high video resolutions and diverse motions. To quantify the domain gap between 2D planar videos and panoramic videos, we evaluate 15 off-the-shelf video object segmentation (VOS) models on PanoVOS. Through error analysis, we found that all of them fail to tackle pixel-level content discontinues of panoramic videos. Thus, we present a Panoramic Space Consistency Transformer (PSCFormer), which can effectively utilize the semantic boundary information of the previous frame for pixel-level matching with the current frame. Extensive experiments demonstrate that compared with the previous SOTA models, our PSCFormer network exhibits a great advantage in terms of segmentation results under the panoramic setting. Our dataset poses new challenges in panoramic VOS and we hope that our PanoVOS can advance the development of panoramic segmentation/tracking

Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments

Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community. Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based. Despite ubiquity, they share some intrinsic drawbacks of not considering internal argument structures, potentially hindering the model's expressiveness. The key challenge is arguments are flat structures, and there are no determined subtree realizations for words inside arguments. To remedy this, in this paper, we propose to regard flat argument spans as latent subtrees, accordingly reducing SRL to a tree parsing task. In particular, we equip our formulation with a novel span-constrained TreeCRF to make tree structures span-aware and further extend it to the second-order case. We conduct extensive experiments on CoNLL05 and CoNLL12 benchmarks. Results reveal that our methods perform favorably better than all previous syntax-agnostic works, achieving new state-of-the-art under both end-to-end and w/ gold predicates settings.Comment: COLING 202

Mining Word Boundaries in Speech as Naturally Annotated Word Segmentation Data

Inspired by early research on exploring naturally annotated data for Chinese word segmentation (CWS), and also by recent research on integration of speech and text processing, this work for the first time proposes to mine word boundaries from parallel speech/text data. First we collect parallel speech/text data from two Internet sources that are related with CWS data used in our experiments. Then, we obtain character-level alignments and design simple heuristic rules for determining word boundaries according to pause duration between adjacent characters. Finally, we present an effective complete-then-train strategy that can better utilize extra naturally annotated data for model training. Experiments demonstrate our approach can significantly boost CWS performance in both cross-domain and low-resource scenarios.Comment: latest versio

Study on corrosion resistance of Portland cement-calcium sulphoaluminate cement binary system in a sodium chloride environment

Portland cement is widely used in civil engineering. However, Portland cement-based materials are easy to be corroded by seawater in marine environment. Many research show the corrosion resistance of Portland cement mortar can be improved by add appropriate amount of mineral admixture.Sulphoaluminate cement have high strength and good corrosion seawater resistance. However, the short setting time and high hydration heat of sulphoaluminate cement limit its application in civil engineering.Portland cement-sulphoaluminate cement (PC-CSA) not only have high strength and corrosion resistance but also long setting time.In this work, the sulphoaluminate cement was used to partially replace Portland cement. The replacement level of sulphoaluminate cement was 10 %, 20 % and 30 % by weight of Portland cement. Mortar specimens was soaked in sodium chloride solution under standard curing after 28 days. The concentration of sodium chloride solution was 3.5wt %. Mechanical properties , corrosion resistance and setting time of PC-CSA binary system were tested in the research. The hydration behavior of binary system was determined by isothermal calorimetry and X-ray diffraction methods. Microstructure of the binary system at different ages were analyzed by scanning electron microscope. The strength of PC-CSA binary system was tested at different curing ages up to 28 days.The results show when replacement level of sulphoaluminate cement is 20%, the comprehensive strength up to 50MPa and higher than other groups at 28 days soaked in corrosion solution.when replacement level of sulphoaluminate cement is 20%,the corrosion resistance is best,and penetration depth of chloride ions is the least

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Traffic prediction plays an important role in the realization of traffic control and scheduling tasks in intelligent transportation systems. With the diversification of data sources, reasonably using rich traffic data to model the complex spatial-temporal dependence and nonlinear characteristics in traffic flow are the key challenge for intelligent transportation system. In addition, clearly evaluating the importance of spatial-temporal features extracted from different data becomes a challenge. A Double Layer - Spatial Temporal Feature Extraction and Evaluation (DL-STFEE) model is proposed. The lower layer of DL-STFEE is spatial-temporal feature extraction layer. The spatial and temporal features in traffic data are extracted by multi-graph graph convolution and attention mechanism, and different combinations of spatial and temporal features are generated. The upper layer of DL-STFEE is the spatial-temporal feature evaluation layer. Through the attention score matrix generated by the high-dimensional self-attention mechanism, the spatial-temporal features combinations are fused and evaluated, so as to get the impact of different combinations on prediction effect. Three sets of experiments are performed on actual traffic datasets to show that DL-STFEE can effectively capture the spatial-temporal features and evaluate the importance of different spatial-temporal feature combinations.Comment: 39 pages, 14 figures, 5 table

Multi-objective Dwarf Mongoose Optimization Algorithm with Leader Guidance and Dominated Solution Evolution Mechanism

In the face of the increasingly complex multi-objective optimization problems, it is necessary to develop novel multi-objective optimization algorithms to meet the challenges. This paper proposes a multi-objective dwarf mongoose optimization algorithm (MODMO) with leader guidance and dominated solution dynamic reduction evolution mechanism. In the leader guidance mechanism, a dynamic trade-off factor is introduced to regulate the search radius of the scout mongoose exploring the mound. At the same time, an external archive is constructed with a non-inferior solution set and the leader is determined according to the non-dominated ranking level, and then the scout mongoose is guided to advance to the multi-objective frontier to improve the convergence of the algorithm. The dominant solution dynamic reduction evolution strategy is constructed to overcome the redundancy problem in the process of maintaining the external archive of non-inferior solutions. It dynamically selects the dominant solutions based on the dominance relationship and crowding distance and stores them in the external archive. The dominant solution information is integrated into the population evolution to realize the mining of multi-objective potential frontier and enhance the diversity of the algorithm. Compared with five representative algorithms on ZDT, DTLZ and WFG benchmark functions, experimental results show that MODMO algorithm has significant advantages in convergence and diversity