Essays on managerial foreign experience and corporate behaviours in China : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Finance at Massey University, Palmerston North, New Zealand

Managerial foreign experience is a type of resource which allows managers to think globally and act locally. This thesis contributes to the literature on how foreign experienced managers impact corporate behaviour in China, the world’s largest emerging market. The first essay examines how managers with foreign experience influence corporate risk-taking. I find that foreign experienced managers are positively associated with corporate risk-taking. This relationship only robustly exists among private firms rather than state-owned enterprises (SOEs). The excess risk-taking through foreign experienced managers is positively related to Tobin’s Q, indicating that foreign experienced managers increase firm value through value-enhancing projects, which benefits shareholders. The second essay concentrates on the relationship between managerial foreign experience and earnings quality. I find that foreign experienced managers improve corporate earnings quality, and this improvement is more pronounced in private firms. Moreover, I document that the improved earnings quality is an important mechanism for which foreign experienced managers increase stock returns and decrease agency costs. The third essay in the thesis investigates the relationship between foreign experienced managers and corporate labour investment. I find foreign experienced managers are more likely to recruit and retain high skilled employees, which in turn increases labour cost for firms in total. The positive relationship between managerial foreign experience and labour cost is significant in both SOEs and private firms. Foreign experienced managers may focus on employees’ well-being to complete political goals in SOEs while they are more likely to retain and attract high skilled employees to benefit shareholders’ value in private firms. I further document that the increased labour costs through managerial foreign experience can influence firm value positively. However, it also increases the labour stickiness cost. Overall, this thesis documents the benefits and costs of hiring foreign experienced managers in firms

LipLearner: Customizable Silent Speech Interactions on Mobile Devices

Silent speech interface is a promising technology that enables private communications in natural language. However, previous approaches only support a small and inflexible vocabulary, which leads to limited expressiveness. We leverage contrastive learning to learn efficient lipreading representations, enabling few-shot command customization with minimal user effort. Our model exhibits high robustness to different lighting, posture, and gesture conditions on an in-the-wild dataset. For 25-command classification, an F1-score of 0.8947 is achievable only using one shot, and its performance can be further boosted by adaptively learning from more data. This generalizability allowed us to develop a mobile silent speech interface empowered with on-device fine-tuning and visual keyword spotting. A user study demonstrated that with LipLearner, users could define their own commands with high reliability guaranteed by an online incremental learning scheme. Subjective feedback indicated that our system provides essential functionalities for customizable silent speech interactions with high usability and learnability.Comment: Conditionally accepted to the ACM CHI Conference on Human Factors in Computing Systems 2023 (CHI '23

Retailing in College Towns During the Pandemic: Spatial Location and Public Transit

A “college town” is a unique urban landscape in America and displays different features from other urban areas. As a result, retailers located in college towns have faced different situations during the COVID-19 pandemic. In our thesis, we investigated the daily footprint data of 157 retailers in 38 identified college towns from 2018 to 2020. Our study contributes to the research on the economic consequences of college town retailers in the situation of a global pandemic. To be specific, we explore two core research questions: (1) how does the university footprint interact with spatial location and public transit in affecting the retailer footprint? And (2) what moderating effects did government containment and health index exert upon the customer footprints? Our study indicates that the university footprints, distance to university, and public transit percentage had a positive, negligible, and negative impact on store visits, respectively. Furthermore, the positive effect of university visits on store visits decreases as store-university distance increases but has little correlation with percent of public transit. Moreover, we find that the intensity of containment and health index strengthens the negative effect exerted by store-university distance upon the positive correlation between university visits and store visits. In contrast, the effects of the university visits and percent of public transit on store visits do not vary with containment and health index. This research provides essential information that can be utilized by government officials and retail managers to better respond to a global pandemic and prepare for the recovery after pandemic

Generalization Ability of Wide Residual Networks

In this paper, we study the generalization ability of the wide residual network on $\mathbb{S}^{d-1}$ with the ReLU activation function. We first show that as the width $m\rightarrow\infty$, the residual network kernel (RNK) uniformly converges to the residual neural tangent kernel (RNTK). This uniform convergence further guarantees that the generalization error of the residual network converges to that of the kernel regression with respect to the RNTK. As direct corollaries, we then show $i)$ the wide residual network with the early stopping strategy can achieve the minimax rate provided that the target regression function falls in the reproducing kernel Hilbert space (RKHS) associated with the RNTK; $ii)$ the wide residual network can not generalize well if it is trained till overfitting the data. We finally illustrate some experiments to reconcile the contradiction between our theoretical result and the widely observed ``benign overfitting phenomenon''Comment: 28 pages, 3 figure

Statistical Optimality of Deep Wide Neural Networks

In this paper, we consider the generalization ability of deep wide feedforward ReLU neural networks defined on a bounded domain $\mathcal X \subset \mathbb R^{d}$. We first demonstrate that the generalization ability of the neural network can be fully characterized by that of the corresponding deep neural tangent kernel (NTK) regression. We then investigate on the spectral properties of the deep NTK and show that the deep NTK is positive definite on $\mathcal{X}$ and its eigenvalue decay rate is $(d+1)/d$. Thanks to the well established theories in kernel regression, we then conclude that multilayer wide neural networks trained by gradient descent with proper early stopping achieve the minimax rate, provided that the regression function lies in the reproducing kernel Hilbert space (RKHS) associated with the corresponding NTK. Finally, we illustrate that the overfitted multilayer wide neural networks can not generalize well on $\mathbb S^{d}$. We believe our technical contributions in determining the eigenvalue decay rate of NTK on $\mathbb R^{d}$ might be of independent interests

Functional Slicing-free Inverse Regression via Martingale Difference Divergence Operator

Functional sliced inverse regression (FSIR) is one of the most popular algorithms for functional sufficient dimension reduction (FSDR). However, the choice of slice scheme in FSIR is critical but challenging. In this paper, we propose a new method called functional slicing-free inverse regression (FSFIR) to estimate the central subspace in FSDR. FSFIR is based on the martingale difference divergence operator, which is a novel metric introduced to characterize the conditional mean independence of a functional predictor on a multivariate response. We also provide a specific convergence rate for the FSFIR estimator. Compared with existing functional sliced inverse regression methods, FSFIR does not require the selection of a slice number. Simulations demonstrate the efficiency and convenience of FSFIR

Huge myxoid chondrosarcoma expanded into the thoracic cavity with spinal involvement

En bloc resection is the treatment of choice of myxoid chondrosarcoma. These tumors can produce huge masses. Anatomical constraints limit the possibility to perform en bloc resection in the spine. A very huge myxoid chondrosarcoma (14.2 × 10.8 × 11.4 cm) arising from T2 to T5 and invading the whole higher left pleural cavity was observed. Surgical planning according to WBB staging system was performed. The tumor was successfully submitted to en bloc resection achieving a tumor-free margin as demonstrated by the pathologist's report. A careful planning and a multidisciplinary collaboration make possible to perform en bloc resection even in apparently impossible cases

Temperature Homogenization of Co-Integrated Shape Memory—Silicon Bimorph Actuators

The high work density and beneficial downscaling of shape memory alloy (SMA) actuation performance provide a basis for the development of actuators and systems at microscales. Here, we report a novel monolithic fabrication approach for the co-integration of SMA and Si microstructures to enable SMA-Si bimorph microactuation. Double-beam cantilevers are chosen for the actuator layout to enable electrothermal actuation by Joule heating. The SMA materials under investigation are NiMnGa and NiTi(Hf) films with tunable phase transformation temperatures. We show that Joule heating of the cantilevers generates increasing temperature gradients for decreasing cantilever size, which hampers actuation performance. In order to cope with this problem, a new method for design optimization is presented based on finite element modeling (FEM) simulations. We demonstrate that temperature homogenization can be achieved by the design of additional folded beams in the perpendicular direction to the active beam cantilevers. Thereby, power consumption can be reduced by more than 35 % and maximum deflection can be increased up to a factor of 2 depending on the cantilever geometry

Cross-Ray Neural Radiance Fields for Novel-view Synthesis from Unconstrained Image Collections

Neural Radiance Fields (NeRF) is a revolutionary approach for rendering scenes by sampling a single ray per pixel and it has demonstrated impressive capabilities in novel-view synthesis from static scene images. However, in practice, we usually need to recover NeRF from unconstrained image collections, which poses two challenges: 1) the images often have dynamic changes in appearance because of different capturing time and camera settings; 2) the images may contain transient objects such as humans and cars, leading to occlusion and ghosting artifacts. Conventional approaches seek to address these challenges by locally utilizing a single ray to synthesize a color of a pixel. In contrast, humans typically perceive appearance and objects by globally utilizing information across multiple pixels. To mimic the perception process of humans, in this paper, we propose Cross-Ray NeRF (CR-NeRF) that leverages interactive information across multiple rays to synthesize occlusion-free novel views with the same appearances as the images. Specifically, to model varying appearances, we first propose to represent multiple rays with a novel cross-ray feature and then recover the appearance by fusing global statistics, i.e., feature covariance of the rays and the image appearance. Moreover, to avoid occlusion introduced by transient objects, we propose a transient objects handler and introduce a grid sampling strategy for masking out the transient objects. We theoretically find that leveraging correlation across multiple rays promotes capturing more global information. Moreover, extensive experimental results on large real-world datasets verify the effectiveness of CR-NeRF.Comment: ICCV 2023 Ora

Mesh-MLP: An all-MLP Architecture for Mesh Classification and Semantic Segmentation

With the rapid development of geometric deep learning techniques, many mesh-based convolutional operators have been proposed to bridge irregular mesh structures and popular backbone networks. In this paper, we show that while convolutions are helpful, a simple architecture based exclusively on multi-layer perceptrons (MLPs) is competent enough to deal with mesh classification and semantic segmentation. Our new network architecture, named Mesh-MLP, takes mesh vertices equipped with the heat kernel signature (HKS) and dihedral angles as the input, replaces the convolution module of a ResNet with Multi-layer Perceptron (MLP), and utilizes layer normalization (LN) to perform the normalization of the layers. The all-MLP architecture operates in an end-to-end fashion and does not include a pooling module. Extensive experimental results on the mesh classification/segmentation tasks validate the effectiveness of the all-MLP architecture.Comment: 8 pages, 6 figure