Bond Lending and the Law of One Price in China's Treasury Markets

This paper examines how the introduction of bond lending in China's bond market has affected violations of the law of one price, measured by the yield spread between similar treasury bonds. To identify the effect of bond lending, we exploit the fact that in China identical bonds are traded on two segmented markets and bond lending has been introduced in only one of the two markets. We find that the introduction of bond lending has led to a decline in deviations from the law of one price. Consistent with an interpretation based on limits to arbitrage, a significant fraction of the deviations from the law of one price in our sample represent actual profit opportunities and the introduction of bond lending has reduced arbitrage profits

RGBT Tracking via Progressive Fusion Transformer with Dynamically Guided Learning

Existing Transformer-based RGBT tracking methods either use cross-attention to fuse the two modalities, or use self-attention and cross-attention to model both modality-specific and modality-sharing information. However, the significant appearance gap between modalities limits the feature representation ability of certain modalities during the fusion process. To address this problem, we propose a novel Progressive Fusion Transformer called ProFormer, which progressively integrates single-modality information into the multimodal representation for robust RGBT tracking. In particular, ProFormer first uses a self-attention module to collaboratively extract the multimodal representation, and then uses two cross-attention modules to interact it with the features of the dual modalities respectively. In this way, the modality-specific information can well be activated in the multimodal representation. Finally, a feed-forward network is used to fuse two interacted multimodal representations for the further enhancement of the final multimodal representation. In addition, existing learning methods of RGBT trackers either fuse multimodal features into one for final classification, or exploit the relationship between unimodal branches and fused branch through a competitive learning strategy. However, they either ignore the learning of single-modality branches or result in one branch failing to be well optimized. To solve these problems, we propose a dynamically guided learning algorithm that adaptively uses well-performing branches to guide the learning of other branches, for enhancing the representation ability of each branch. Extensive experiments demonstrate that our proposed ProFormer sets a new state-of-the-art performance on RGBT210, RGBT234, LasHeR, and VTUAV datasets.Comment: 13 pages, 9 figure

Learning Target-oriented Dual Attention for Robust RGB-T Tracking

RGB-Thermal object tracking attempt to locate target object using complementary visual and thermal infrared data. Existing RGB-T trackers fuse different modalities by robust feature representation learning or adaptive modal weighting. However, how to integrate dual attention mechanism for visual tracking is still a subject that has not been studied yet. In this paper, we propose two visual attention mechanisms for robust RGB-T object tracking. Specifically, the local attention is implemented by exploiting the common visual attention of RGB and thermal data to train deep classifiers. We also introduce the global attention, which is a multi-modal target-driven attention estimation network. It can provide global proposals for the classifier together with local proposals extracted from previous tracking result. Extensive experiments on two RGB-T benchmark datasets validated the effectiveness of our proposed algorithm.Comment: Accepted by IEEE ICIP 201

Topological States in Twisted Pillared Phononic Plates

In recent years, the advances in topological insulator in the fields of condensed matter have been extended to classical wave systems such as acoustic and elastic waves. However, the quantitative robustness study of topological states which is indispensable in practical realization is rarely reported. In this work, we proposed topologically protected edge states with zigzag, bridge and armchair interfaces in a new twisted phononic plate. The robustness of non-trivial band gap in bulk structure is clearly presented versus twisted angles, revealing a threshold of 5 degrees which is the key fundamental information for the robustness of topological edge states. We further defined a localized displacement ratio as an efficient parameter to characterize edge states. Due to the different orientation of the three interfaces, zigzag and bridge edge states show higher quantitative robustness in their localized displacement ratio. A map of robustness as a function of both frequency and twisted angle highlights the better performance of the topological zigzag edge state. Robustness is evaluated for twisted angle and for all possible types of interfaces for the first time, which benefits for the design and fabrication of solid functional devices with great potential applications