2,679 research outputs found
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
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