Dynamic structure of stock communities: A comparative study between stock returns and turnover rates

The detection of community structure in stock market is of theoretical and practical significance for the study of financial dynamics and portfolio risk estimation. We here study the community structures in Chinese stock markets from the aspects of both price returns and turnover rates, by using a combination of the PMFG and infomap methods based on a distance matrix. We find that a few of the largest communities are composed of certain specific industry or conceptional sectors and the correlation inside a sector is generally larger than the correlation between different sectors. In comparison with returns, the community structure for turnover rates is more complex and the sector effect is relatively weaker. The financial dynamics is further studied by analyzing the community structures over five sub-periods. Sectors like banks, real estate, health care and New Shanghai take turns to compose a few of the largest communities for both returns and turnover rates in different sub-periods. Several specific sectors appear in the communities with different rank orders for the two time series even in the same sub-period. A comparison between the evolution of prices and turnover rates of stocks from these sectors is conducted to better understand their differences. We find that stock prices only had large changes around some important events while turnover rates surged after each of these events relevant to specific sectors, which may offer a possible explanation for the complexity of stock communities for turnover rates

Graphene-Based Terahertz Holographic Conformal Antenna

In this paper, a conformal graphene holographic antenna designed for terahertz (THz) band is proposed. The radiation principle of the proposed pattern reconfigurable antenna is based on the holographic technology. The surface reactance modulation and pattern steering capability can be easily facilitated by a tunable DC-biased graphene patch array. Thanks to the super thin structure and excellent mechanical property of graphene, the proposed THz graphene holographic antenna can be designed conformal to required platforms easily. Besides, the equal size as well as same spacing of graphene patches make it easy to modeling and manufacture. To verify the proposed idea, an antenna conformal to a cylinder is designed and simulated. The results of full wave simulation software HFSS shown that the conformal antenna has great performance

Synthesis of Three-Dimensional Nanocarbon Hybrids by Chemical Vapor Deposition

Carbon nanomaterials such as graphene, carbon nanotube (CNT), and carbon nanofiber (CNF) have received tremendous attentions in the past two decades due to their extraordinary mechanical strength and thermal and electrical properties. Recently, it indicates that three-dimensional (3D) nanocarbon hybrids overcome the weakness of individual low-dimensional nanocarbon materials and exhibit unique properties among carbon nanomaterials. Efforts have thus been made to acquire synergistic integration of one-dimensional (1D) and two-dimensional (2D) carbon nanomaterials. Meanwhile, chemical vapor deposition (CVD) is a widespread and effective method of fabricating three-dimensional nanocarbon hybrids compared with other synthetic methods. In this case, a number of 3D nanocarbon hybrids are synthesized by using different precursors at diverse temperature, and the nanocarbon hybrids are expected to be a promising choice for various application areas in the future

Tree-based Text-Vision BERT for Video Search in Baidu Video Advertising

The advancement of the communication technology and the popularity of the smart phones foster the booming of video ads. Baidu, as one of the leading search engine companies in the world, receives billions of search queries per day. How to pair the video ads with the user search is the core task of Baidu video advertising. Due to the modality gap, the query-to-video retrieval is much more challenging than traditional query-to-document retrieval and image-to-image search. Traditionally, the query-to-video retrieval is tackled by the query-to-title retrieval, which is not reliable when the quality of tiles are not high. With the rapid progress achieved in computer vision and natural language processing in recent years, content-based search methods becomes promising for the query-to-video retrieval. Benefited from pretraining on large-scale datasets, some visionBERT methods based on cross-modal attention have achieved excellent performance in many vision-language tasks not only in academia but also in industry. Nevertheless, the expensive computation cost of cross-modal attention makes it impractical for large-scale search in industrial applications. In this work, we present a tree-based combo-attention network (TCAN) which has been recently launched in Baidu's dynamic video advertising platform. It provides a practical solution to deploy the heavy cross-modal attention for the large-scale query-to-video search. After launching tree-based combo-attention network, click-through rate gets improved by 2.29\% and conversion rate get improved by 2.63\%.Comment: This revision is based on a manuscript submitted in October 2020, to ICDE 2021. We thank the Program Committee for their valuable comment

Grease film evolution in rolling elastohydrodynamic lubrication contacts

Although most rolling element bearings are grease lubricated, the underlying mechanisms of grease lubrication has not been fully explored. This study investigates grease film evolution with glass disc revolutions in rolling elastohydrodynamic lubrication (EHL) contacts. The evolution patterns of the grease films were highly related to the speed ranges and grease structures. The transference of thickener lumps, film thickness decay induced by starvation, and residual layer were recognized. The formation of an equilibrium film determined by the balance of lubricant loss and replenishment was analyzed. The primary mechanisms that dominate grease film formation in different lubricated contacts were clarified. © 2020, The Author(s)

Bilateral Propagation Network for Depth Completion

Depth completion aims to derive a dense depth map from sparse depth measurements with a synchronized color image. Current state-of-the-art (SOTA) methods are predominantly propagation-based, which work as an iterative refinement on the initial estimated dense depth. However, the initial depth estimations mostly result from direct applications of convolutional layers on the sparse depth map. In this paper, we present a Bilateral Propagation Network (BP-Net), that propagates depth at the earliest stage to avoid directly convolving on sparse data. Specifically, our approach propagates the target depth from nearby depth measurements via a non-linear model, whose coefficients are generated through a multi-layer perceptron conditioned on both \emph{radiometric difference} and \emph{spatial distance}. By integrating bilateral propagation with multi-modal fusion and depth refinement in a multi-scale framework, our BP-Net demonstrates outstanding performance on both indoor and outdoor scenes. It achieves SOTA on the NYUv2 dataset and ranks 1st on the KITTI depth completion benchmark at the time of submission. Experimental results not only show the effectiveness of bilateral propagation but also emphasize the significance of early-stage propagation in contrast to the refinement stage. Our code and trained models will be available on the project page.Comment: Accepted by CVPR 202