Optimal Recovery of Elastic Properties for Anisotropic Materials through Ultrasonic Measurements

Full knowledge of material elastic properties is required to facilitate design in many applications. The existence of misorientation between the geometric axes of the part and the material symmetry axes has in particularly created challenged in design of composite structure. In this thesis the potential for optimal identification of material symmetries for a general anisotropic material through a water immersion technique is explored. The concept is extensible to any class of symmetry groups and does not assume a-priori knowledge of the material. Initial experimental results for determining the elastic constants as well as locating the symmetry planes are presented. Many materials have not been investigated completely by a method such as the one described. The specific contribution of this work is to demonstrate this process for experimental data sets. The primary focus is on carbon-carbon composite material. The method is demonstrated using a single crystal with known properties

Chinese Companies Cross-Border Mergers and Acquisitions Performance: Evidence from Inward and Outward Deals

This dissertation focuses on the Chinese cross-border M&As (mergers and acquisitions) market of public companies’ performance. The study precisely identifies short-term performance surrounding a M&A announcement that a public Chinese company is acquiring an overseas firm or is being targeted. The key words of these three chapters are method of payment, public status, and acquirer industry. This study measures short-term performance by investigating CARs (cumulative average abnormal returns). The windows are approximately 2 days and 5 days before and after a M&A announcement. The time span is 15 years (2002–2016) for Chinese public companies’ cross-border transactions and 23 years (1994-2016) for transactions targeting Chinese public companies. The first chapter demonstrates that cash transactions outperform stock transactions although more public Chinese companies chose stock to finance transactions. The second chapter demonstrates that an acquired public overseas target underperforms compared with targeting private companies. In addition, the transaction volume indicates that most bidder companies made the right decision. The third chapter demonstrates that overseas financial institutions are more likely (over 60% of transactions) to acquire Chinese public companies in all industries. These investors do bring abnormal returns to their target companies

Universality for Shape Dependence of Casimir Effects from Weyl Anomaly

We reveal elegant relations between the shape dependence of the Casimir effects and Weyl anomaly in boundary conformal field theories (BCFT). We show that for any BCFT which has a description in terms of an effective action, the near boundary divergent behavior of the renormalized stress tensor is completely determined by the central charges of the theory. These relations are verified by free BCFTs. We test them with holographic models of BCFT and find exact agreement. We propose that these relations between Casimir coefficients and central charges hold for any BCFT. With the holographic models, we reproduce not only the precise form of the near boundary divergent behavior of the stress tensor, but also the surface counter term that is needed to make the total energy finite. As they are proportional to the central charges, the near boundary divergence of the stress tensor must be physical and cannot be dropped by further artificial renormalization.Our results thus provide affirmative support on the physical nature of the divergent energy density near the boundary, whose reality has been a long-standing controversy in the literature.Comment: 19 pages, 1 figure and 3 tables, references added, accepted for publication in JHE

Universality in the Shape Dependence of Holographic R\'enyi Entropy for General Higher Derivative Gravity

We consider higher derivative gravity and obtain universal relations for the shape coefficients $(f_a, f_b, f_c)$ of the shape dependent universal part of the R\'enyi entropy for four dimensional CFTs in terms of the parameters $(c, t_2, t_4)$ of two-point and three-point functions of stress tensors. As a consistency check, these shape coefficients $f_a$ and $f_c$ satisfy the differential relation as derived previously for the R\'enyi entropy. Interestingly, these holographic relations also apply to weakly coupled conformal field theories such as theories of free fermions and vectors but are violated by theories of free scalars. The mismatch of $f_a$ for scalars has been observed in the literature and is due to certain delicate boundary contributions to the modular Hamiltonian. Interestingly, we find a combination of our holographic relations which are satisfied by all free CFTs including scalars. We conjecture that this combined relation is universal for general CFTs in four dimensional spacetime. Finally, we find there are similar universal laws for holographic R\'enyi entropy in general dimensions.Comment: 32 pages,0 figures, references added, appendix added, accepted for publication in JHE

Large scale image classification and object detection

Dissertation supervisor: Dr. Tony X. Han.Includes vita.Significant advancement of research on image classification and object detection has been achieved in the past decade. Deep convolutional neural networks have exhibited superior performance in many visual recognition tasks including image classification, object detection, and scene labeling, due to their large learning capacity and resistance to overfit. However, learning a robust deep CNN model for object recognition is still quite challenging because image classification and object detection is a severely unbalanced large-scale problem. In this dissertation, we aim at improving the performance of image classification and object detection algorithms by taking advantage of deep convolutional neural networks by utilizing the following strategies: We introduce Deep Neural Pattern, a local feature densely extracted from an image with arbitrary resolution using a well trained deep convolutional neural network. We propose a latent CNN framework, which will automatically select the most discriminate region in the image to reduce the effect of irrelevant regions. We also develop a new combination scheme for multiple CNNs via Latent Model Ensemble to overcome the local minima problem of CNNs. In addition, a weakly supervised CNN framework, referred to as Multiple Instance Learning Convolutional Neural Networks is developed to alleviate strict label requirements. Finally, a novel residual-network architecture, Residual networks of Residual networks, is constructed to improve the optimization ability of very deep convolutional neural networks. All the proposed algorithms are validated by thorough experiments and have shown solid accuracy on large scale object detection and recognition benchmarks.Includes bibliographical references (pages 105-119)