Faculty leadership practices in graduate hybrid education

Hybrid education is an instructional delivery format that includes both online learning and traditional classroom learning and is often seen as the best of two worlds. It is one of the fastest-growing trends in higher education because of its countless educational benefits. Studies reviewed about hybrid learning focused on various areas, including student engagement, student attitudes, faculty experiences, learning effectiveness, and supporting technology tools. However, a review of the literature revealed scant studies focused on faculty leadership, especially graduate-level education faculty\u27s leadership strategies and their influence on teaching. Faculty leadership is a critical component that is directly related to effective teaching and school success (Berry et al., 2010). Therefore, the purpose of this study was to discover and identify faculty leadership practices used in graduate hybrid courses at a U.S.-based nonprofit university. Qualitative data related to faculty experiences were gathered to offer insights about leadership practices in graduate-level hybrid education. Aligned with the theoretical framework of this paper - Kouzes and Posner\u27s (2017) 5 exemplary leadership disciplines: model the way, inspire a shared vision, challenge the process, enable others to act, and encourage the heart, 21 significant leadership practices of faculty applying in graduate-level hybrid education were discovered. These findings indicated that leadership practices could be considered in graduate-level hybrid education to further the concept of teaching and learning

The economic impact of credit default swap on credit markets

This study conducts a comprehensive analysis of the economic benefits and costs of credit default swap (CDS) in credit markets since its inception. Consistent with its role of insuring credit risk, the introduction of CDS reduces illiquidity and liquidity risk more for speculative grade bonds with high credit risk than investment grade ones. More importantly, CDS significantly improves the price convergence between investment grade bonds and CDS spreads through a popular trading strategy—CDS-bond basis arbitrage in normal period. In the recent crisis, however, CDS fails to reduce the prolonged price divergence between the two markets plausibly due to the lack of arbitrage. Overall, the economic impact of CDS is dependent on the prevailing trading strategies in the credit markets

Rethinking AI Explainability and Plausibility

Setting proper evaluation objectives for explainable artificial intelligence (XAI) is vital for making XAI algorithms follow human communication norms, support human reasoning processes, and fulfill human needs for AI explanations. In this article, we examine explanation plausibility, which is the most pervasive human-grounded concept in XAI evaluation. Plausibility measures how reasonable the machine explanation is compared to the human explanation. Plausibility has been conventionally formulated as an important evaluation objective for AI explainability tasks. We argue against this idea, and show how optimizing and evaluating XAI for plausibility is sometimes harmful, and always ineffective to achieve model understandability, transparency, and trustworthiness. Specifically, evaluating XAI algorithms for plausibility regularizes the machine explanation to express exactly the same content as human explanation, which deviates from the fundamental motivation for humans to explain: expressing similar or alternative reasoning trajectories while conforming to understandable forms or language. Optimizing XAI for plausibility regardless of the model decision correctness also jeopardizes model trustworthiness, as doing so breaks an important assumption in human-human explanation namely that plausible explanations typically imply correct decisions, and violating this assumption eventually leads to either undertrust or overtrust of AI models. Instead of being the end goal in XAI evaluation, plausibility can serve as an intermediate computational proxy for the human process of interpreting explanations to optimize the utility of XAI. We further highlight the importance of explainability-specific evaluation objectives by differentiating the AI explanation task from the object localization task

Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?

Being able to explain the prediction to clinical end-users is a necessity to leverage the power of artificial intelligence (AI) models for clinical decision support. For medical images, a feature attribution map, or heatmap, is the most common form of explanation that highlights important features for AI models' prediction. However, it is unknown how well heatmaps perform on explaining decisions on multi-modal medical images, where each image modality or channel visualizes distinct clinical information of the same underlying biomedical phenomenon. Understanding such modality-dependent features is essential for clinical users' interpretation of AI decisions. To tackle this clinically important but technically ignored problem, we propose the modality-specific feature importance (MSFI) metric. It encodes clinical image and explanation interpretation patterns of modality prioritization and modality-specific feature localization. We conduct a clinical requirement-grounded, systematic evaluation using computational methods and a clinician user study. Results show that the examined 16 heatmap algorithms failed to fulfill clinical requirements to correctly indicate AI model decision process or decision quality. The evaluation and MSFI metric can guide the design and selection of XAI algorithms to meet clinical requirements on multi-modal explanation.Comment: AAAI 202

CDS-bond basis and bond return predictability

We examine the predictive power of the CDS-bond basis for future corporate bond returns. We find that residual basis, the part of the CDS-bond basis that cannot be explained by a wide range of market frictions such as counterparty risk, funding risk, and liquidity risk, strongly negatively predicts excess returns. Controlling for systematic risk factors, including credit risk and liquidity risk, we find that a bond portfolio formed on the residual basis generates a significant abnormal bond return of 1.79% at the 20-day horizon. The abnormal returns due to the residual basis reflect mispricing rather than missing systematic risk factors. These results are robust to different horizons and sample periods and to the various characteristics of bonds. Overall, our results imply a beneficial role of CDS in the bond market as the existence of mispricing between CDS and bonds results in a subsequent price convergence in bonds

The CDS-bond basis arbitrage and the cross section of corporate bond returns

We provide a comprehensive empirical analysis on the implication of CDS-Bond basis arbitrage for the pricing of corporate bonds. Basis arbitrageurs introduce new risks such as funding liquidity and counterparty risk into the corporate bond market, which was dominated by passive investors before the existence of credit default swap (CDS). We show that a basis factor, constructed as the return differential between LOW and HIGH quintile basis portfolios, is a superior empirical proxy that captures the new risks. In the cross section of investment grade bond returns, the basis factor carries an annual risk premium of about 3% in normal periods

An Analysis of the Principles in Formulation and Implementation of University Constitution from the Perspective of the Spirit of Law

Under the background of university constitution construction,the formulation and implementation of the university constitution need to be divided into three parts, the legal effect,the regulatory mechanism,the power inside and outside of the university and the legal relationship, these three areas need further improvement. This paper will analyze the principles of university constitution from three aspects: constitution formulation, rights and interests protection and procedure implementation conditions