Insider Ownership and Bank Performance: Evidence from the Financial Crisis of 2007-2009

This paper examines the relation between insider ownership and bank performance in the United States before and during the recent financial crisis of 2007 – 2009. For the period before this crisis, we find a curvilinear relation between insider ownership and bank performance. Bank performance first increases, then decreases, and finally increases again with the rise of insider ownership. During the financial crisis, we find an inverted-U shaped relation between insider ownership and bank performance. Overall, our results are consistent with the notion that managers with higher ownership are better aligned the interests of shareholders (Jensen and Meckling 1976). Managers adopt effective strategies on the bank performance before the crisis, but those make a negative impact during the financial crisis

10,000+ Times Accelerated Robust Subset Selection (ARSS)

Subset selection from massive data with noised information is increasingly popular for various applications. This problem is still highly challenging as current methods are generally slow in speed and sensitive to outliers. To address the above two issues, we propose an accelerated robust subset selection (ARSS) method. Specifically in the subset selection area, this is the first attempt to employ the $\ell_{p}(0<p\leq1)$-norm based measure for the representation loss, preventing large errors from dominating our objective. As a result, the robustness against outlier elements is greatly enhanced. Actually, data size is generally much larger than feature length, i.e. $N\gg L$. Based on this observation, we propose a speedup solver (via ALM and equivalent derivations) to highly reduce the computational cost, theoretically from $O(N^{4})$ to $O(N{}^{2}L)$. Extensive experiments on ten benchmark datasets verify that our method not only outperforms state of the art methods, but also runs 10,000+ times faster than the most related method

Danshen-Chuanxiong-Honghua Ameliorates Cerebral Impairment and Improves Spatial Cognitive Deficits after Transient Focal Ischemia and Identification of Active Compounds.

Previously, we only apply a traditional Chinese medicine (TCM) Danshen-Chuanxiong-Honghua (DCH) for cardioprotection via anti-inflammation in rats of acute myocardial infarction by occluding coronary artery. Presently, we select not only DCH but also its main absorbed compound ferulic acid (FA) for cerebra protection via similar action of mechanism above in animals of the transient middle cerebral artery occlusion (tMCAO). We investigated whether oral administration of DCH and FA could ameliorate MCAO-induced brain lesions in animals. By using liquid chromatography-tandem mass spectrometry (LC-MS/MS), we analyzed four compounds, including tanshinol, salvianolic acid B, hydroxysafflor yellow A and especially FA as the putative active components of DCH extract in the plasma, cerebrospinal fluid and injured hippocampus of rats with MCAO. In our study, it was assumed that FA played a similar neuroprotective role to DCH. We found that oral pretreatment with DCH (10 or 20 g/kg) and FA (100 mg/kg) improved neurological function and alleviated the infarct volume as well as brain edema in a dose-dependent manner. These changes were accompanied by improved ischemia-induced apoptosis and decreased the inflammatory response. Additionally, chronic treatment with DCH reversed MCAO-induced spatial cognitive deficits in a manner associated with enhanced neurogenesis and increased the expression of brain-derived neurotrophic factor in lesions of the hippocampus. These findings suggest that DCH has the ability to recover cognitive impairment and offer neuroprotection against cerebral ischemic injury via inhibiting microenvironmental inflammation and triggering of neurogenesis in the hippocampus. FA could be one of the potential active compounds

A aquisição dos artigos por aluno chineses de PLE

Mestrado em Línguas, Literaturas e CulturasA presente dissertação apresenta os resultados duma investigação sobre a aprendizagem dos artigos em português por alunos chineses de PLE. Com base num inquérito feito a alunos universitários chineses a estudar em Portugal, analisamos os principais problemas com que um aprendente chinês de PLE se depara no processo de aquisição do uso do artigo. No final, depois do estudo e análise dos resultados, propomos sugestões para melhorar o ensino-aprendizagem do artigo por estes aprendentes de PLE.This paper shows the results of investigation about the Portuguese article learners of student PLE. Based on the questionnaire survey for those Chinese students that study in Portugal, we analyze the main problems of the Chinese students in the process of learning and using the article. In the end, after the study and analyze the results, we come up with advices in improving the teaching-studying of article for the students of PLE

Giant edge state splitting at atomically precise zigzag edges

Zigzag edges of graphene nanostructures host localized electronic states that are predicted to be spin-polarized. However, these edge states are highly susceptible to edge roughness and interaction with a supporting substrate, complicating the study of their intrinsic electronic and magnetic structure. Here, we focus on atomically precise graphene nanoribbons whose two short zigzag edges host exactly one localized electron each. Using the tip of a scanning tunneling microscope, the graphene nanoribbons are transferred from the metallic growth substrate onto insulating islands of NaCl in order to decouple their electronic structure from the metal. The absence of charge transfer and hybridization with the substrate is confirmed by scanning tunneling spectroscopy (STS), which reveals a pair of occupied / unoccupied edge states. Their large energy splitting of 1.9 eV is in accordance with ab initio many-body perturbation theory calculations and reflects the dominant role of electron-electron interactions in these localized states.Comment: 14 pages, 4 figure

Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation

Stance detection is typically framed as predicting the sentiment in a given text towards a target entity. However, this setup overlooks the importance of the source entity, i.e., who is expressing the opinion. In this paper, we emphasize the need for studying interactions among entities when inferring stances. We first introduce a new task, entity-to-entity (E2E) stance detection, which primes models to identify entities in their canonical names and discern stances jointly. To support this study, we curate a new dataset with 10,619 annotations labeled at the sentence-level from news articles of different ideological leanings. We present a novel generative framework to allow the generation of canonical names for entities as well as stances among them. We further enhance the model with a graph encoder to summarize entity activities and external knowledge surrounding the entities. Experiments show that our model outperforms strong comparisons by large margins. Further analyses demonstrate the usefulness of E2E stance detection for understanding media quotation and stance landscape, as well as inferring entity ideology.Comment: EMNLP'22 Main Conferenc