Determinants and Dynamic Adjustment of Capital Structure: Evidence from UK Company Panel Data

This study explores the most important determinants of capital structure and the adjustment speed towards target capital structure. Based on FTSE350, the data of 195 British listed companies for 10 years (2009-2018) are selected for static and dynamic research. In terms of static research, ANOVA analysis shows that capital structures vary across different industries, which proves that industry factor is one of the important factors affecting capital structure. In addition, regression results from the FE (fixed effects) model show that tangibility, profitability, and liquidity have significant effects on total leverage and long-term leverage, while factors that determine short-term leverage levels are size, tangibility, non-debt tax shield, and liquidity. In the aspect of dynamic research, because using irrational estimation method to estimate the adjustment speed will come to erroneous conclusions, the paper compares and analyzes three approaches for estimating the adjustment speed namely OLS, FE and two-step system GMM with a view to providing evidence for relevant research. The study finds that GMM is a relatively reasonable way to estimate the speed of adjustment. And it also shows that the listed companies in the UK have a faster adjustment speed, which indicates that there is a significant mean return phenomenon. Finally, by comparing the adjustment speed of British companies before and after financial risks periods, it is found that the impact of financial crisis on the adjustment speed of capital structure is not obvious

An Unified Search and Recommendation Foundation Model for Cold-Start Scenario

In modern commercial search engines and recommendation systems, data from multiple domains is available to jointly train the multi-domain model. Traditional methods train multi-domain models in the multi-task setting, with shared parameters to learn the similarity of multiple tasks, and task-specific parameters to learn the divergence of features, labels, and sample distributions of individual tasks. With the development of large language models, LLM can extract global domain-invariant text features that serve both search and recommendation tasks. We propose a novel framework called S\&R Multi-Domain Foundation, which uses LLM to extract domain invariant features, and Aspect Gating Fusion to merge the ID feature, domain invariant text features and task-specific heterogeneous sparse features to obtain the representations of query and item. Additionally, samples from multiple search and recommendation scenarios are trained jointly with Domain Adaptive Multi-Task module to obtain the multi-domain foundation model. We apply the S\&R Multi-Domain foundation model to cold start scenarios in the pretrain-finetune manner, which achieves better performance than other SOTA transfer learning methods. The S\&R Multi-Domain Foundation model has been successfully deployed in Alipay Mobile Application's online services, such as content query recommendation and service card recommendation, etc.Comment: CIKM 2023,6 page

Dirac Fermions in Antiferromagnetic FeSn Kagome Lattices with Combined Space Inversion and Time Reversal Symmetry

Symmetry principles play a critical role in formulating the fundamental laws of nature, with a large number of symmetry-protected topological states identified in recent studies of quantum materials. As compelling examples, massless Dirac fermions are jointly protected by the space inversion symmetry $P$ and time reversal symmetry $T$ supplemented by additional crystalline symmetry, while evolving into Weyl fermions when either $P$ or $T$ is broken. Here, based on first-principles calculations, we reveal that massless Dirac fermions are present in a layered FeSn crystal containing antiferromagnetically coupled ferromagnetic Fe kagome layers, where each of the $P$ and $T$ symmetries is individually broken but the combined $PT$ symmetry is preserved. These stable Dirac fermions protected by the combined $PT$ symmetry with additional non-symmorphic $S_{\rm{2z}}$ symmetry can be transformed to either massless/massive Weyl or massive Dirac fermions by breaking the $PT$ or $S_{\rm{2z}}$ symmetry. Our angle-resolved photoemission spectroscopy experiments indeed observed the Dirac states in the bulk and two-dimensional Weyl-like states at the surface. The present study substantially enriches our fundamental understanding of the intricate connections between symmetries and topologies of matter, especially with the spin degree of freedom playing a vital role.Comment: 6 pages, 4 figure

Methyltransferase Dnmt3a upregulates HDAC9 to deacetylate the kinase TBK1 for activation of antiviral innate immunity

The DNA methyltransferase Dnmt3a has high expression in terminally differentiated macrophages; however, its role in innate immunity remains unknown. Here we report that deficiency in Dnmt3a selectively impaired the production of type I interferons triggered by pattern-recognition receptors (PRRs), but not that of the proinflammatory cytokines TNF and IL-6. Dnmt3a-deficient mice exhibited enhanced susceptibility to viral challenge. Dnmt3a did not directly regulate the transcription of genes encoding type I interferons; instead, it increased the production of type I interferons through an epigenetic mechanism by maintaining high expression of the histone deacetylase HDAC9. In turn, HDAC9 directly maintained the deacetylation status of the key PRR signaling molecule TBK1 and enhanced its kinase activity. Our data add mechanistic insight into the crosstalk between epigenetic modifications and post-translational modifications in the regulation of PRR signaling and activation of antiviral innate immune responses

Deep reinforcement learning control of a 2D soft robotic arm

This thesis provides a deep reinforcement learning (DRL) based approach for the development of a control policy for a 2D soft robotic arm. The simulation is based on the SOFA framework, which is a real-time multi-physics simulation package capable of creating models and computing forces for deformable materials. The 2D soft robotic arm is composed of two modules where each module consists of two pneumatic actuators and can extend and bend. Though DRL has been explored in the soft robotics realm, end-to-end training has not been developed. Herein, this thesis presents an end-to-end training from snapshots of the simulation to control policy guiding the soft robotic arm to reach a designated target using DRL, and proofs the validity and stability of this approach. The soft robotic arm is able to reach the target with a 98.1% success rate after approximately 30 epochs of training both for fixed initial position training and varying initial position training. The methodology presented here can be generalized for intelligent motion planning and control of soft robotic systems that are otherwise challenging.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

Novel insights into the role of human hydroxysteroid (17Beta) dehydrogenase 2 (HSD17B2) as revealed by the transgenic mice expressing the human enzym

Siirretty Doriast

Determinants and Dynamic Adjustment of Capital Structure: Evidence from UK Company Panel Data

This study explores the most important determinants of capital structure and the adjustment speed towards target capital structure. Based on FTSE350, the data of 195 British listed companies for 10 years (2009-2018) are selected for static and dynamic research. In terms of static research, ANOVA analysis shows that capital structures vary across different industries, which proves that industry factor is one of the important factors affecting capital structure. In addition, regression results from the FE (fixed effects) model show that tangibility, profitability, and liquidity have significant effects on total leverage and long-term leverage, while factors that determine short-term leverage levels are size, tangibility, non-debt tax shield, and liquidity. In the aspect of dynamic research, because using irrational estimation method to estimate the adjustment speed will come to erroneous conclusions, the paper compares and analyzes three approaches for estimating the adjustment speed namely OLS, FE and two-step system GMM with a view to providing evidence for relevant research. The study finds that GMM is a relatively reasonable way to estimate the speed of adjustment. And it also shows that the listed companies in the UK have a faster adjustment speed, which indicates that there is a significant mean return phenomenon. Finally, by comparing the adjustment speed of British companies before and after financial risks periods, it is found that the impact of financial crisis on the adjustment speed of capital structure is not obvious