The Impact of Foreign Operations and Foreign Ownership on Corporate Tax Avoidance in the Australian Dividend Imputation System

This thesis investigates the impact of foreign operations and foreign ownership on corporate tax avoidance of listed Australian companies and large Australian companies owned by foreign multinational enterprises (MNEs) in the Australian dividend imputation system. With dividend imputation, listed Australian companies can ‘pass’ their corporate income tax to shareholders as a tax credit (franking credit) to offset shareholders’ personal tax liabilities. Therefore, listed Australian companies may not have strong incentives to engage in costly tax avoidance arrangements. However, only domestic income tax payments can be attached to dividends as franking credits, and only domestic shareholders can claim the franking credits received as tax offset. Thus, the corporate tax avoidance-reducing effect of dividend imputation may be undermined by foreign operations (which are subject to foreign taxes) and foreign ownership. Three empirical studies are carried out to investigate the corporate tax avoidance-reducing effect of the dividend imputation system in a comprehensive manner. The first study provides an overview of the impact of franked dividend distributions, foreign operations, and foreign ownership on corporate tax avoidance of listed Australian companies. It is found that companies distributing more franked dividends or having a lower proportion of foreign ownership engage in less corporate tax avoidance. No significant relationship between foreign operations and corporate tax avoidance is found, possibly due to listed Australian companies shifting foreign profits to Australia (inward profit shifting) in order to pay Australian income tax to frank their dividends. The second study focuses on the relationship between foreign operations and corporate tax avoidance. It examines if listed Australian companies with mainly domestic ownership but with foreign subsidiaries take advantage of the tax rate differentials across countries to reduce their worldwide tax liabilities. The results show that companies with subsidiaries in low-tax countries, or high-tax countries, or both, have similar worldwide tax liabilities compared to their counterparts without such subsidiaries. The findings provide further indirect evidence to support the ‘inward profit shifting’ conjecture. The third study focuses on the relationship between foreign ownership and corporate tax avoidance. It examines whether large foreign-owned Australian companies (FOACs) which are subsidiaries of foreign MNEs engage in intra-group transfer pricing and thin capitalisation to avoid Australian tax in comparison with domestic-owned listed Australian companies (DOLACs) which have little incentives to do so. The results show that FOACs use intra-group transfer pricing and pay high interest rates on intra-group debts to shift profits out of Australia to reduce their Australian tax liabilities, which are manifested in their lower gross profit margins and operating profit margins, higher interest expenses but not higher leverage ratios, as well as lower pre-tax profits and income tax expenses in comparison with DOLACs. The thesis contributes to the literature by documenting how foreign operations and foreign ownership shapes the tax avoidance behaviours of large companies in the Australian dividend imputation system. It also has significant policy implications for countries and organisations considering integrating corporate and shareholder taxes and formulating rules and regulations to tackle corporate tax avoidance

Improvement on Gauss circle Problem and Dirichlet divisor Problem

We establish an improvement for both Gauss circle problem and Dirichlet divisor problem, combining a new estimate of the first spacing problem and Huxley's results on the second spacing problem

Model-free screening procedure for ultrahigh-dimensional survival data based on Hilbert-Schmidt independence criterion

How to select the active variables which have significant impact on the event of interest is a very important and meaningful problem in the statistical analysis of ultrahigh-dimensional data. Sure independent screening procedure has been demonstrated to be an effective method to reduce the dimensionality of data from a large scale to a relatively moderate scale. For censored survival data, the existing screening methods mainly adopt the Kaplan--Meier estimator to handle censoring, which may not perform well for scenarios which have heavy censoring rate. In this article, we propose a model-free screening procedure based on the Hilbert-Schmidt independence criterion (HSIC). The proposed method avoids the complication to specify an actual model from a large number of covariates. Compared with existing screening procedures, this new approach has several advantages. First, it does not involve the Kaplan--Meier estimator, thus its performance is much more robust for the cases with a heavy censoring rate. Second, the empirical estimate of HSIC is very simple as it just depends on the trace of a product of Gram matrices. In addition, the proposed procedure does not require any complicated numerical optimization, so the corresponding calculation is very simple and fast. Finally, the proposed procedure which employs the kernel method is substantially more resistant to outliers. Extensive simulation studies demonstrate that the proposed method has favorable exhibition over the existing methods. As an illustration, we apply the proposed method to analyze the diffuse large-B-cell lymphoma (DLBCL) data and the ovarian cancer data

A Three Stage Integrative Pathway Search (TIPS©) framework to identify toxicity relevant genes and pathways

<p>Abstract</p> <p>Background</p> <p>The ability to obtain profiles of gene expressions, proteins and metabolites with the advent of high throughput technologies has advanced the study of pathway and network reconstruction. Genome-wide network reconstruction requires either interaction measurements or large amount of perturbation data, often not available for mammalian cell systems. To overcome these shortcomings, we developed a Three Stage Integrative Pathway Search (<it>TIPS</it>^©) approach to reconstruct context-specific active pathways involved in conferring a specific phenotype, from limited amount of perturbation data. The approach was tested on human liver cells to identify pathways that confer cytotoxicity.</p> <p>Results</p> <p>This paper presents a systems approach that integrates gene expression and cytotoxicity profiles to identify a network of pathways involved in free fatty acid (FFA) and tumor necrosis factor-α (TNF-α) induced cytotoxicity in human hepatoblastoma cells (HepG2/C3A). Cytotoxicity relevant genes were first identified and then used to reconstruct a network using Bayesian network (BN) analysis. BN inference was used subsequently to predict the effects of perturbing a gene on the other genes in the network and on the cytotoxicity. These predictions were subsequently confirmed through the published literature and further experiments.</p> <p>Conclusion</p> <p>The <it>TIPS</it>^©approach is able to reconstruct active pathways that confer a particular phenotype by integrating gene expression and phenotypic profiles. A web-based version of <it>TIPS</it>^©that performs the analysis described herein can be accessed at <url>http://www.egr.msu.edu/tips</url>.</p

OR Residual Connection Achieving Comparable Accuracy to ADD Residual Connection in Deep Residual Spiking Neural Networks

Spiking Neural Networks (SNNs) have garnered substantial attention in brain-like computing for their biological fidelity and the capacity to execute energy-efficient spike-driven operations. As the demand for heightened performance in SNNs surges, the trend towards training deeper networks becomes imperative, while residual learning stands as a pivotal method for training deep neural networks. In our investigation, we identified that the SEW-ResNet, a prominent representative of deep residual spiking neural networks, incorporates non-event-driven operations. To rectify this, we introduce the OR Residual connection (ORRC) to the architecture. Additionally, we propose the Synergistic Attention (SynA) module, an amalgamation of the Inhibitory Attention (IA) module and the Multi-dimensional Attention (MA) module, to offset energy loss stemming from high quantization. When integrating SynA into the network, we observed the phenomenon of "natural pruning", where after training, some or all of the shortcuts in the network naturally drop out without affecting the model's classification accuracy. This significantly reduces computational overhead and makes it more suitable for deployment on edge devices. Experimental results on various public datasets confirmed that the SynA enhanced OR-Spiking ResNet achieved single-sample classification with as little as 0.8 spikes per neuron. Moreover, when compared to other spike residual models, it exhibited higher accuracy and lower power consumption. Codes are available at https://github.com/Ym-Shan/ORRC-SynA-natural-pruning.Comment: 16 pages, 8 figures and 11table

Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks

Spiking neural networks (SNNs) are emerging as an energy-efficient alternative to traditional artificial neural networks (ANNs) due to their unique spike-based event-driven nature. Coding is crucial in SNNs as it converts external input stimuli into spatio-temporal feature sequences. However, most existing deep SNNs rely on direct coding that generates powerless spike representation and lacks the temporal dynamics inherent in human vision. Hence, we introduce Gated Attention Coding (GAC), a plug-and-play module that leverages the multi-dimensional gated attention unit to efficiently encode inputs into powerful representations before feeding them into the SNN architecture. GAC functions as a preprocessing layer that does not disrupt the spike-driven nature of the SNN, making it amenable to efficient neuromorphic hardware implementation with minimal modifications. Through an observer model theoretical analysis, we demonstrate GAC's attention mechanism improves temporal dynamics and coding efficiency. Experiments on CIFAR10/100 and ImageNet datasets demonstrate that GAC achieves state-of-the-art accuracy with remarkable efficiency. Notably, we improve top-1 accuracy by 3.10\% on CIFAR100 with only 6-time steps and 1.07\% on ImageNet while reducing energy usage to 66.9\% of the previous works. To our best knowledge, it is the first time to explore the attention-based dynamic coding scheme in deep SNNs, with exceptional effectiveness and efficiency on large-scale datasets.Comment: 12 pages, 7 figure

Correlation analysis of gamma-glutamyl transferase to lymphocyte ratio and patients with acute aortic syndrome in China: a propensity score-matched analysis

Background and objectivesAcute aortic syndrome (AAS) is a life-threatening condition in which there is a fracture in the integrity of the aortic wall. gamma-glutamyl transferase to lymphocyte ratio (GLR) is recognized as a risk factor for liver cirrhosis, fibrosis, and hepatocellular carcinoma. However, there are no clinical reports of GLR and AAS. We attempted to determine whether GLR level is associated with AAS in patients from the Chaoshan region of southern China.MethodsA total of 2,384 patients were recruited in this study and were divided into AAS and no-AAS groups according to the results of CT angiography of the thoracoabdominal aorta. Univariate and multivariate logistic regression was performed to identify risk factors for the occurrence of AAS. ROC was applied to assess the value of D-Dimer, GLR alone, or in combination for the diagnosis of AAS. And a 1:1 propensity score-matched analysis was performed.ResultsMultivariate logistics regression analysis indicated that male, age, hypertension, diabetes, creatinine, D-dimer, and GLR were independent risk factors of AAS patients in the before propensity score-matching cohort. After propensity score-matching, it showed that D-dimer, GLR [OR 3.558(1.891, 6.697); p &lt; 0.001] were independent risk factors of AAS patients. Before propensity score-matching, the area under the curve (AUC) was 0.822 of GLR and 0.767 of D-dimer. When both clinical backgrounds were adjusted, the AUC was 0.773 of GLR and 0.631 of D-dimer. GLR showed high specificity (80.5% and 77.1%), and D-dimer showed high sensitivity (84.7% and 73.6%) in the before and after propensity score-matching cohort.ConclusionGLR and D-dimer were independent risk factors of acute aortic syndrome. D-dimer in combination with GLR is more valuable than a single indicator for diagnosing acute aortic syndrome