Lehigh University

About: Lehigh University is a education organization based out in Bethlehem, Pennsylvania, United States. It is known for research contribution in the topics: Catalysis & Fracture mechanics. The organization has 12684 authors who have published 26550 publications receiving 770061 citations.

Does the SOX Definition of an Accounting Expert Matter? The Association between Audit Committee Directors' Accounting Expertise and Accounting Conservatism*

Abstract: The Sarbanes-Oxley Act (SOX) (U.S. Congress 2002) mandates the disclosure of whether the audit committee includes a financial expert. ' However, the operationalization of who is a financial expert was and still is a controversial issue (Plitch and Ceron 2003). Some have argued that effective audit committee members are those who have general management experience rather than those who have an accounting or financial background (Olson 1999). The Securities and Exchange Commission (SEC) initially proposed a narrow definition to include only accounting financial experts — that is, directors with experience as a certified public accountant (CPA), auditor, chief financial officer (CFO), controller, or chief accounting officer. Subsequently, the SEC defined financial expert broadly to include nonaccounting financial experts, such as directors with experience as a chief executive officer (CEO) or president (SEC 2003).2 Was the SEC correct in defining financial experts to include both accounting and nonaccounting experts? Do accounting financial experts enhance the quality of financial reporting more than nonaccounting financial experts? These are important questions because the primary objective of SOX was to restore credibility to the U.S. financial reporting system, which was tarnished by several high-profile accounting scandals. Because the audit committee is the ultimate monitor of the financial reporting process, the audit committee's financial expertise is a key determinant of its effectiveness (Treadway 1987). We contribute to this debate by separately examining the association between the audit committee's accounting financial expertise and nonaccounting financial expertise and several attributes of financial reporting quality.

Uniform polymer particles by dispersion polymerization in alcohol

Abstract: Uniform polystyrene particles in 1–10 μm size range and up to 40% solid contents have been prepared by polymerizing styrene in ethyl alcohol with azo-type initiators and a polymeric stabilizer polyvinylpyrrolidone along with an anionic, nonionic, or comonomeric co-stabilizer. Effects of polymerization parameters, such as monomer concentration, type of co-stabilizer, initiator type and concentration, crosslinking monomer, and diluent on average particle size and size distribution have been studied. Functional groups such as hydroxyl, carboxyl, amine, amide, silane, polydimethylsiloxane, and silacrown have been successfully incorporated onto the particles by copolymerization. A mechanism for particle formation and growth in dispersion polymerization is presented.

StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

Abstract: Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of the object based on given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. Second, an advanced multi-stage generative adversarial network architecture, StackGAN-v2, is proposed for both conditional and unconditional generative tasks. Our StackGAN-v2 consists of multiple generators and discriminators in a tree-like structure; images at multiple scales corresponding to the same scene are generated from different branches of the tree. StackGAN-v2 shows more stable training behavior than StackGAN-v1 by jointly approximating multiple distributions. Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images.