A Molecular-scale Programmable Stochastic Process Based On Resonance Energy Transfer Networks: Modeling And Applications

While molecular and cellular processes are often modeled as stochastic processes, such as Brownian motion, chemical reaction networks and gene regulatory networks, there are few attempts to program a molecular-scale process to physically implement stochastic processes. DNA has been used as a substrate for programming molecular interactions, but its applications are restricted to deterministic functions and unfavorable properties such as slow processing, thermal annealing, aqueous solvents and difficult readout limit them to proof-of-concept purposes. To date, whether there exists a molecular process that can be programmed to implement stochastic processes for practical applications remains unknown.

In this dissertation, a fully specified Resonance Energy Transfer (RET) network between chromophores is accurately fabricated via DNA self-assembly, and the exciton dynamics in the RET network physically implement a stochastic process, specifically a continuous-time Markov chain (CTMC), which has a direct mapping to the physical geometry of the chromophore network. Excited by a light source, a RET network generates random samples in the temporal domain in the form of fluorescence photons which can be detected by a photon detector. The intrinsic sampling distribution of a RET network is derived as a phase-type distribution configured by its CTMC model. The conclusion is that the exciton dynamics in a RET network implement a general and important class of stochastic processes that can be directly and accurately programmed and used for practical applications of photonics and optoelectronics. Different approaches to using RET networks exist with vast potential applications. As an entropy source that can directly generate samples from virtually arbitrary distributions, RET networks can benefit applications that rely on generating random samples such as 1) fluorescent taggants and 2) stochastic computing.

By using RET networks between chromophores to implement fluorescent taggants with temporally coded signatures, the taggant design is not constrained by resolvable dyes and has a significantly larger coding capacity than spectrally or lifetime coded fluorescent taggants. Meanwhile, the taggant detection process becomes highly efficient, and the Maximum Likelihood Estimation (MLE) based taggant identification guarantees high accuracy even with only a few hundred detected photons.

Meanwhile, RET-based sampling units (RSU) can be constructed to accelerate probabilistic algorithms for wide applications in machine learning and data analytics. Because probabilistic algorithms often rely on iteratively sampling from parameterized distributions, they can be inefficient in practice on the deterministic hardware traditional computers use, especially for high-dimensional and complex problems. As an efficient universal sampling unit, the proposed RSU can be integrated into a processor / GPU as specialized functional units or organized as a discrete accelerator to bring substantial speedups and power savings.

NAFTA and the apparel commodity chain: Corporate strategies, interfirm networks, and industrial upgrading

The apparel industry is one of the oldest and largest export industries in the world, with global trade and production networks that connect firms and workers in countries at all levels of economic development. This chapter examines the impact of the North American Free Trade Agreement (NAFTA) as one of the most recent and significant developments to affect patterns of international trade and production in the apparel and textile industries. Tr ade policies are changing the institutional environment in which firms in this industry operate, and companies are responding to these changes with new strategies designed to increase their profitability and strengthen their control over the apparel commodity chain. Our hypothesis is that lead firms are establishing qualitatively different kinds of regional production networks in North America from those that existed prior to NAFTA, and that these networks have important consequences for industrial upgrading in the Mexican textile and apparel industries. Post-NAFTA crossborder production arrangements include full-package networks that link lead firms in the United States with apparel and textile manufacturers, contractors, and suppliers in Mexico. Full-package production is increasing the local value added provided by the apparel commodity chain in Mexico and creating new opportunities for Mexican firms and workers. The chapter is divided into four main sections. The first section uses trade and production data to analyze shifts in global apparel flows, highlighting the emergence and consolidation of a regional trade bloc in North America. The second section discusses the process of industrial upgrading in the apparel industry and introduces a distinction between assembly and full-package production networks. The third section includes case studies based on published industry sources and strategic interviews with several lead companies whose strategies are largely responsible for the shifting trade patterns and NAFTA-inspired cross-border production networks discussed in the previous section. The fourth section considers the implications of these changes for employment in the North American apparel industry. © 2009 by Temple University Press. All rights reserved.