Epidemiology Of Giant Cell Arteritis Related Hospital Admissions In The United States From 2007-2016

The primary objective of this retrospective cross-sectional study is to investigate the national and regional incidence, epidemiology, and clinical characteristics of Giant Cell Arteritis (GCA) related hospital admissions in the United States (US) from 2007 to 2016. The secondary objectives are to investigate the rate of systemic complications, ocular involvement, resource utilization, and predictors of mortality in GCA. The Nationwide Inpatient Sample was queried to identify all patients hospitalized with an ICD 9 or ICD 10 code for GCA between 2007-2016. Incidence was calculated using US Census data, and risk factors for in-hospital mortality were analyzed with logistic regression. A weighted total of 200,533 GCA related hospital admissions were included. The overall national incidence of GCA related hospital admissions was 6.42 per 100,000 population and 19.81 per 100,000 population for those ≥50 years. The median age was 80 years. The incidence was 3 times higher in women than men (3.43 vs. 1.33 per 100,000 population) and 2 times higher in Caucasians than African Americans (7.52 vs. 3.75 per 100,000 population). The most common systemic comorbidity was hypertension (73.2%), followed by hyperlipidemia (42.0%), and diabetes mellitus (33.2%). Autoimmune disorders were common: 23% of patients had thyroid disease, 14.6% had polymyalgia rheumatica, and 5.2% had rheumatoid arthritis. 18% of GCA patients had ocular involvement, 8.6% had stroke or cerebral arteritis, and 2.87% had aortic dissection/aneurysm or myocarditis. The in-hospital mortality was 2.7%. Age \u3e75 years (OR, 1.99; 95% CI, 1.85 – 2.13; p \u3c0.001), stroke (OR, 1.83; 95% CI, 1.68 – 1.98; p \u3c0.0001), and aortic compromise (OR, 1.76; 95% CI, 1.54 – 1.99; p \u3c0.0001) were significant predictors of mortality. Notably, there was no increase in mortality in patients with ocular involvement or autoimmune disease. In the US, Giant Cell Arteritis preferentially affects older individuals, females, and Caucasians. Approximately one fifth of cases had ocular involvement during the same hospital admission. Stroke, aortic compromise, and increased age are associated with higher mortality risk

Learning Volatility Surfaces using Generative Adversarial Networks

In this paper, we propose a generative adversarial network (GAN) approach for efficiently computing volatility surfaces. The idea is to make use of the special GAN neural architecture so that on one hand, we can learn volatility surfaces from training data and on the other hand, enforce no-arbitrage conditions. In particular, the generator network is assisted in training by a discriminator that evaluates whether the generated volatility matches the target distribution. Meanwhile, our framework trains the GAN network to satisfy the no-arbitrage constraints by introducing penalties as regularization terms. The proposed GAN model allows the use of shallow networks which results in much less computational costs. In our experiments, we demonstrate the performance of the proposed method by comparing with the state-of-the-art methods for computing implied and local volatility surfaces. We show that our GAN model can outperform artificial neural network (ANN) approaches in terms of accuracy and computational time.Comment: This is a working draf

Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning

Interacting with the actual environment to acquire data is often costly and time-consuming in robotic tasks. Model-based offline reinforcement learning (RL) provides a feasible solution. On the one hand, it eliminates the requirements of interaction with the actual environment. On the other hand, it learns the transition dynamics and reward function from the offline datasets and generates simulated rollouts to accelerate training. Previous model-based offline RL methods adopt probabilistic ensemble neural networks (NN) to model aleatoric uncertainty and epistemic uncertainty. However, this results in an exponential increase in training time and computing resource requirements. Furthermore, these methods are easily disturbed by the accumulative errors of the environment dynamics models when simulating long-term rollouts. To solve the above problems, we propose an uncertainty-aware sequence modeling architecture called Environment Transformer. It models the probability distribution of the environment dynamics and reward function to capture aleatoric uncertainty and treats epistemic uncertainty as a learnable noise parameter. Benefiting from the accurate modeling of the transition dynamics and reward function, Environment Transformer can be combined with arbitrary planning, dynamics programming, or policy optimization algorithms for offline RL. In this case, we perform Conservative Q-Learning (CQL) to learn a conservative Q-function. Through simulation experiments, we demonstrate that our method achieves or exceeds state-of-the-art performance in widely studied offline RL benchmarks. Moreover, we show that Environment Transformer's simulated rollout quality, sample efficiency, and long-term rollout simulation capability are superior to those of previous model-based offline RL methods.Comment: ICRA202

An assessment of dual audit effect and contagious effect on the audit quality of non-Big N CPA firms for Chinese companies in different markets

External auditor is an independent agent to provide assurance about the validity of financial statements prepared by management to enhance the reliability of information in financial reports. As such, audit quality has long been a concern for all stakeholders and is a topic of on-going research interest. In China, the dual audit requirement for AB share companies and AH share companies started in 2001 was abolished in 2007 and 2010 respectively. This study attempts to examine whether there are dual audit effect and contagious effect on the audit quality of non-Big N audit firms for A share companies in different markets. I focus on non-Big N audit firms since the audit quality of these firms are of greater concern. Using data from 2001 to 2012, I compare the audit quality of A share companies that also have B (or H) shares ((AB/H) with the audit quality of pure A share companies to test whether there is a dual audit effect on the audit quality of A-share financial statements. I also compare AB/H share companies which hire only non-Big N auditors with those ABIH share companies who hire non-Big N domestic auditors and Big N international auditors to test the existence of contagious effect on the audit quality of A-share companies. My findings indicate that dual audit does improve the audit quality of non-Big N audit firms for A share companies. However, there was mixed evidences on the contagious effect using different measures of audit quality. This study contributes to the literature on enhancing our understanding of the determinants of audit quality in China. It can also provide policy makers in emerging economies some useful evidence on ways to improve audit quality

EquiDiff: A Conditional Equivariant Diffusion Model For Trajectory Prediction

Accurate trajectory prediction is crucial for the safe and efficient operation of autonomous vehicles. The growing popularity of deep learning has led to the development of numerous methods for trajectory prediction. While deterministic deep learning models have been widely used, deep generative models have gained popularity as they learn data distributions from training data and account for trajectory uncertainties. In this study, we propose EquiDiff, a deep generative model for predicting future vehicle trajectories. EquiDiff is based on the conditional diffusion model, which generates future trajectories by incorporating historical information and random Gaussian noise. The backbone model of EquiDiff is an SO(2)-equivariant transformer that fully utilizes the geometric properties of location coordinates. In addition, we employ Recurrent Neural Networks and Graph Attention Networks to extract social interactions from historical trajectories. To evaluate the performance of EquiDiff, we conduct extensive experiments on the NGSIM dataset. Our results demonstrate that EquiDiff outperforms other baseline models in short-term prediction, but has slightly higher errors for long-term prediction. Furthermore, we conduct an ablation study to investigate the contribution of each component of EquiDiff to the prediction accuracy. Additionally, we present a visualization of the generation process of our diffusion model, providing insights into the uncertainty of the prediction

Preliminary Exploring the Influence of Person-Organization Fit on Counterproductive Work Behavior

Counterproductive work behavior, an importance work performance, exists widely in organization and hurts the organization seriously. The past research about counterproductive work behavior often based on the model that the behavior is influence by the perception, whereas this paper centers on the influence of person-organization fit on counterproductive work behavior based on the principle that behavior can be influenced by the value. The research frame which includes organizational commitment as a mediating variable and locus of control as a moderating variable is constructed by paper study and may throw light on the further research in future.Key words: Counterproductive work behavior; Person-organization fit; Influenc