Research and Practice of Postgraduate Training Mode of Financial Professional Degree From the Perspective of Online Education

In 2009, the Ministry of Education expanded the enrollment scale of full-time professional degree postgraduates, mainly fresh undergraduates, to meet the large demand for high-level applied talents in the society. Compared with foreign countries, the training time of professional degree postgraduates in my country is not long. Although a lot of useful experience has been obtained, there are still some problems in the training of professional degree graduate students.Although a lot of useful experience has been obtained, there are still problems such as the lack of distinctive characteristics of professional degree postgraduate training, the lack of professionalism and professionalism, the lack of outstanding practicality of the curriculum system, and the lack of rich curriculum learning form and content. How to improve the training quality of professional degree postgraduates and form a distinctive training model is an important issue in current postgraduate education. This article intends to carry out some discussions from the perspective of online education promoting the training of postgraduates in finance majors

Artificial intelligence innovation and stock price crash risk

This study examines the association between artificial intelligence innovation (AII) and stock price crash risk (SPCR). AII serves as a governance mechanism that can bolster strength in internal controls, leading to increased financial transparency and thereby reducing the likelihood of future SPCR. The results hold after accounting for possible endogeneity issues Further, we find that monitoring through corporate governance mechanisms, level of following by equity analysts, and the reduced information asymmetry constitute important channels that mediate the association between AII and SPCR. Additionally, the relationship between AII and SPCR varies across corporate life cycle stages and workplace culture

What accounts for the effect of sustainability engagement on stock price crash risk during the COVID-19 pandemic—agency theory or legitimacy theory?

In this study, we conduct a textual analysis of the third-party disclosure of corporate sustainability news focused on the Standard and Poor\u27s 500 firms in the United States market during the first and second quarter of 2020. We find a positive relationship between corporate sustainability news release and firm-specific stock price crash risk. This finding is surprising, but it indeed aligns with agency theory. It indicates that the coronavirus disease (COVID-19) pandemic exacerbated the tendency of managers under increasing financial pressure to use the sustainability information release as a mechanism to mask and withhold bad news for extended periods at the expense of shareholders. This tendency results in high stock price crash risk. Our results are robust to alternative empirical specifications, estimation methods, and tests for endogeneity. Moreover, additional evidence reveals that agency theory dominates legitimacy theory in explaining the effect of sustainability on this risk during the COVID-19 pandemic

Principled Architecture-aware Scaling of Hyperparameters

Training a high-quality deep neural network requires choosing suitable hyperparameters, which is a non-trivial and expensive process. Current works try to automatically optimize or design principles of hyperparameters, such that they can generalize to diverse unseen scenarios. However, most designs or optimization methods are agnostic to the choice of network structures, and thus largely ignore the impact of neural architectures on hyperparameters. In this work, we precisely characterize the dependence of initializations and maximal learning rates on the network architecture, which includes the network depth, width, convolutional kernel size, and connectivity patterns. By pursuing every parameter to be maximally updated with the same mean squared change in pre-activations, we can generalize our initialization and learning rates across MLPs (multi-layer perception) and CNNs (convolutional neural network) with sophisticated graph topologies. We verify our principles with comprehensive experiments. More importantly, our strategy further sheds light on advancing current benchmarks for architecture design. A fair comparison of AutoML algorithms requires accurate network rankings. However, we demonstrate that network rankings can be easily changed by better training networks in benchmarks with our architecture-aware learning rates and initialization

CartiMorph: a framework for automated knee articular cartilage morphometrics

We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient $\rho \in [0.82,0.97]$), surface area ($\rho \in [0.82,0.98]$) and volume ($\rho \in [0.89,0.98]$) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.Comment: To be published in Medical Image Analysi

MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals

Brain signals are important quantitative data for understanding physiological activities and diseases of human brain. Most existing studies pay attention to supervised learning methods, which, however, require high-cost clinical labels. In addition, the huge difference in the clinical patterns of brain signals measured by invasive (e.g., SEEG) and non-invasive (e.g., EEG) methods leads to the lack of a unified method. To handle the above issues, we propose to study the self-supervised learning (SSL) framework for brain signals that can be applied to pre-train either SEEG or EEG data. Intuitively, brain signals, generated by the firing of neurons, are transmitted among different connecting structures in human brain. Inspired by this, we propose MBrain to learn implicit spatial and temporal correlations between different channels (i.e., contacts of the electrode, corresponding to different brain areas) as the cornerstone for uniformly modeling different types of brain signals. Specifically, we represent the spatial correlation by a graph structure, which is built with proposed multi-channel CPC. We theoretically prove that optimizing the goal of multi-channel CPC can lead to a better predictive representation and apply the instantaneou-time-shift prediction task based on it. Then we capture the temporal correlation by designing the delayed-time-shift prediction task. Finally, replace-discriminative-learning task is proposed to preserve the characteristics of each channel. Extensive experiments of seizure detection on both EEG and SEEG large-scale real-world datasets demonstrate that our model outperforms several state-of-the-art time series SSL and unsupervised models, and has the ability to be deployed to clinical practice

Social capital and cost of debt: Evidence from Chinese CEO network centrality

Using a unique dataset comprising 6313 firm-year observations for Chinese listed firms between 2008 and 2017, we investigate the impact of CEO social capital on cost of debt. Our results show that CEO social capital is negatively related to cost of debt, and the impact of CEO social capital in environments with a low degree of marketization or social trust is more pronounced than in environments with a high degree of marketization or social trust. Moreover, our results reveal that two potential mechanisms, discretionary accruals and information disclosure quality, mediate the impact of CEO social capital on cost of debt

BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning

Epilepsy is one of the most serious neurological diseases, affecting 1-2% of the world's population. The diagnosis of epilepsy depends heavily on the recognition of epileptic waves, i.e., disordered electrical brainwave activity in the patient's brain. Existing works have begun to employ machine learning models to detect epileptic waves via cortical electroencephalogram (EEG). However, the recently developed stereoelectrocorticography (SEEG) method provides information in stereo that is more precise than conventional EEG, and has been broadly applied in clinical practice. Therefore, we propose the first data-driven study to detect epileptic waves in a real-world SEEG dataset. While offering new opportunities, SEEG also poses several challenges. In clinical practice, epileptic wave activities are considered to propagate between different regions in the brain. These propagation paths, also known as the epileptogenic network, are deemed to be a key factor in the context of epilepsy surgery. However, the question of how to extract an exact epileptogenic network for each patient remains an open problem in the field of neuroscience. To address these challenges, we propose a novel model (BrainNet) that jointly learns the dynamic diffusion graphs and models the brain wave diffusion patterns. In addition, our model effectively aids in resisting label imbalance and severe noise by employing several self-supervised learning tasks and a hierarchical framework. By experimenting with the extensive real SEEG dataset obtained from multiple patients, we find that BrainNet outperforms several latest state-of-the-art baselines derived from time-series analysis

BCS-BEC crossover in atomic Fermi gases in quasi-two-dimensional Lieb lattices: Effects of flat band and finite temperature

We investigate the finite-temperature superfluid behavior of ultracold atomic Fermi gases in quasi-two-dimensional Lieb lattices with a short-range attractive interaction, using a pairing fluctuation theory within the BCS-BEC crossover framework. We find that the presence of a flat band, along with van Hove singularities, leads to exotic quantum phenomena. As the Fermi level enters the flat band, both the gap and the superfluid transition temperature $T_c$ as a function of interaction change from a conventional exponential behavior into an unusual power law, and the evolution of superfluid densities with temperature also follows a power law even at weak interactions. The quantum geometric effects, manifested by an enhanced effective pair hopping integral, may contribute significantly to both $T_c$ and the superfluidities. As the chemical potential crosses the van Hove singularities in the weak interaction regime, the nature of pairing changes between particle-like and hole-like. A pair density wave state emerges at high densities with a relatively strong interaction strength.Comment: 10 pages, 6 figures. arXiv admin note: text overlap with arXiv:2310.1294