Engineering Principles Of Photosystems And Their Practical Application As Long-Lived Charge Separation In Maquettes

Light-activated electron transfer reactions between cofactors embedded in proteins serve as the central mechanism underlying numerous biological processes essential to the survival and prosperity of most organisms on this planet. These processes range from navigation, to DNA repair, to metabolism, and to solar energy conversion. The proper functioning of these processes relies on the creation of a charge-separated states lasting for a necessary length of time, from tens of nanoseconds to hundreds of milliseconds, by the arrays of cofactors in photosystems. In spite of decades of experiments and theoretical frameworks providing detailed and extensive description of the behavior of the photosystems, coherent and systematic understanding is lacking regarding the underlying structural and chemical engineering principles that govern the performance of charge-separation in photosystems, evaluated by the fraction of the input energy made available by the photosystem for its intended function. This thesis aims to establish a set of engineering principles of natural and man-made photosystems based on the fundamental theories of electron transfer and the biophysical and biochemical constraints imposed by the protein environment, and then to apply these engineering principles to design and construct man-made photosystems that can excel in charge-separation while incurring minimal cost in their construction. Using the fundamental theories of electron transfer, this thesis develops an efficient computational algorithm that returns a set of guidelines for engineering optimal light-driven charge-separation in cofactor-based photosystems. This thesis then examines the validity of these guidelines in natural photosystems, discovering significant editing and updating of these guidelines imposed by the biological environment in which photosystems are engineered by nature. This thesis then organizes the two layers of engineering principles into a concise set of rules and demonstrates that they can be applied as guidelines to the practical construction of highly efficient man-made photosystems. To test these engineering guidelines in practice, the first ever donor-pigment-acceptor triad is constructed in a maquette and successfully separates charges stably for \u3e300ms, establishing the world record in a triad. Finally, this work looks ahead to the engineering of the prescribed optimal tetrads in maquettes, identifying what’s in place and what challenges yet remain

Relaxed Majorization-Minimization for Non-smooth and Non-convex Optimization

We propose a new majorization-minimization (MM) method for non-smooth and non-convex programs, which is general enough to include the existing MM methods. Besides the local majorization condition, we only require that the difference between the directional derivatives of the objective function and its surrogate function vanishes when the number of iterations approaches infinity, which is a very weak condition. So our method can use a surrogate function that directly approximates the non-smooth objective function. In comparison, all the existing MM methods construct the surrogate function by approximating the smooth component of the objective function. We apply our relaxed MM methods to the robust matrix factorization (RMF) problem with different regularizations, where our locally majorant algorithm shows advantages over the state-of-the-art approaches for RMF. This is the first algorithm for RMF ensuring, without extra assumptions, that any limit point of the iterates is a stationary point.Comment: AAAI1

Silicon Metasurface Embedded Fabry-Perot Cavity Enables High Quality Transmission Structural Color

While nanoscale color generations have been studied for years, high performance transmission structural colors, simultaneously equipped with large gamut, high resolution, low loss and optical multiplexing abilities, still remain as a hanging issue. Here, beneficial from metasurfaces, we demonstrate a silicon metasurface embedded Fabry-P\'erot cavity (meta-FP cavity), with polydimethylsiloxanes (PDMS) surrounding media and silver film mirrors. By changing the planar geometries of the embedded nanopillars, the meta-FP cavity provides transmission colors with ultra large gamut of 194% sRGB and ultrahigh resolution of 141111 DPI, along with considerably average transmittance of 43% and more than 300% enhanced angular tolerance. Such high density allows two-dimensional color mixing at diffraction limit scale. The color gamut and the resolution can be flexibly tuned and improved by modifying the silver film thickness and the lattice period. The polarization manipulation ability of the metasurface also enables arbitrary color arrangement between cyan and red for two orthogonal linear polarization states, at deep subwavelength scale. Our proposed cavities can be used in filters, printings, optical storages and many other applications in need of high quality and density colors.Comment: Keywords: metasurfaces; structural colors; optical storage; multiplexing. 30 pages, 9 figure

Automatic Curriculum Learning With Over-repetition Penalty for Dialogue Policy Learning

Dialogue policy learning based on reinforcement learning is difficult to be applied to real users to train dialogue agents from scratch because of the high cost. User simulators, which choose random user goals for the dialogue agent to train on, have been considered as an affordable substitute for real users. However, this random sampling method ignores the law of human learning, making the learned dialogue policy inefficient and unstable. We propose a novel framework, Automatic Curriculum Learning-based Deep Q-Network (ACL-DQN), which replaces the traditional random sampling method with a teacher policy model to realize the dialogue policy for automatic curriculum learning. The teacher model arranges a meaningful ordered curriculum and automatically adjusts it by monitoring the learning progress of the dialogue agent and the over-repetition penalty without any requirement of prior knowledge. The learning progress of the dialogue agent reflects the relationship between the dialogue agent's ability and the sampled goals' difficulty for sample efficiency. The over-repetition penalty guarantees the sampled diversity. Experiments show that the ACL-DQN significantly improves the effectiveness and stability of dialogue tasks with a statistically significant margin. Furthermore, the framework can be further improved by equipping with different curriculum schedules, which demonstrates that the framework has strong generalizability