1,454 research outputs found
DNA multi-bit non-volatile memory and bit-shifting operations using addressable electrode arrays and electric field-induced hybridization.
DNA has been employed to either store digital information or to perform parallel molecular computing. Relatively unexplored is the ability to combine DNA-based memory and logical operations in a single platform. Here, we show a DNA tri-level cell non-volatile memory system capable of parallel random-access writing of memory and bit shifting operations. A microchip with an array of individually addressable electrodes was employed to enable random access of the memory cells using electric fields. Three segments on a DNA template molecule were used to encode three data bits. Rapid writing of data bits was enabled by electric field-induced hybridization of fluorescently labeled complementary probes and the data bits were read by fluorescence imaging. We demonstrated the rapid parallel writing and reading of 8 (23) combinations of 3-bit memory data and bit shifting operations by electric field-induced strand displacement. Our system may find potential applications in DNA-based memory and computations
Geometric characterization on the solvability of regulator equations
The solvability of the regulator equation for a general nonlinear system is discussed in this paper by using geometric method. The ‘feedback’ part of the regulator equation, that is, the feasible controllers for the regulator equation, is studied thoroughly. The concepts of minimal output zeroing control invariant submanifold and left invertibility are introduced to find all the possible controllers for the regulator equation under the condition of left invertibility. Useful results, such as a necessary condition for the output regulation problem and some properties of friend sets of controlled invariant manifolds, are also obtained
Chunk-Based Bi-Scale Decoder for Neural Machine Translation
In typical neural machine translation~(NMT), the decoder generates a sentence
word by word, packing all linguistic granularities in the same time-scale of
RNN. In this paper, we propose a new type of decoder for NMT, which splits the
decode state into two parts and updates them in two different time-scales.
Specifically, we first predict a chunk time-scale state for phrasal modeling,
on top of which multiple word time-scale states are generated. In this way, the
target sentence is translated hierarchically from chunks to words, with
information in different granularities being leveraged. Experiments show that
our proposed model significantly improves the translation performance over the
state-of-the-art NMT model.Comment: Accepted as a short paper by ACL 201
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