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Development of a method for screening short-lived proteins using green fluorescent protein.
We have developed a screening technology for the identification of short-lived proteins. A green fluorescent protein (GFP)-fusion cDNA library was generated for monitoring degradation kinetics. Cells expressing a subset of the GFP-cDNA expression library were screened to recover those in which the fluorescence signal diminished rapidly when protein synthesis was inhibited. Thirty clones that met the screening criteria were characterized individually. Twenty-three (73%) proved to have a half-life of 4 hours or less
Paraphrase Generation with Deep Reinforcement Learning
Automatic generation of paraphrases from a given sentence is an important yet
challenging task in natural language processing (NLP), and plays a key role in
a number of applications such as question answering, search, and dialogue. In
this paper, we present a deep reinforcement learning approach to paraphrase
generation. Specifically, we propose a new framework for the task, which
consists of a \textit{generator} and an \textit{evaluator}, both of which are
learned from data. The generator, built as a sequence-to-sequence learning
model, can produce paraphrases given a sentence. The evaluator, constructed as
a deep matching model, can judge whether two sentences are paraphrases of each
other. The generator is first trained by deep learning and then further
fine-tuned by reinforcement learning in which the reward is given by the
evaluator. For the learning of the evaluator, we propose two methods based on
supervised learning and inverse reinforcement learning respectively, depending
on the type of available training data. Empirical study shows that the learned
evaluator can guide the generator to produce more accurate paraphrases.
Experimental results demonstrate the proposed models (the generators)
outperform the state-of-the-art methods in paraphrase generation in both
automatic evaluation and human evaluation.Comment: EMNLP 201
Generalized Debye Sources Based EFIE Solver on Subdivision Surfaces
The electric field integral equation is a well known workhorse for obtaining
fields scattered by a perfect electric conducting (PEC) object. As a result,
the nuances and challenges of solving this equation have been examined for a
while. Two recent papers motivate the effort presented in this paper. Unlike
traditional work that uses equivalent currents defined on surfaces, recent
research proposes a technique that results in well conditioned systems by
employing generalized Debye sources (GDS) as unknowns. In a complementary
effort, some of us developed a method that exploits the same representation for
both the geometry (subdivision surface representations) and functions defined
on the geometry, also known as isogeometric analysis (IGA). The challenge in
generalizing GDS method to a discretized geometry is the complexity of the
intermediate operators. However, thanks to our earlier work on subdivision
surfaces, the additional smoothness of geometric representation permits
discretizing these intermediate operations. In this paper, we employ both ideas
to present a well conditioned GDS-EFIE. Here, the intermediate surface
Laplacian is well discretized by using subdivision basis. Likewise, using
subdivision basis to represent the sources, results in an efficient and
accurate IGA framework. Numerous results are presented to demonstrate the
efficacy of the approach
Deep Active Learning for Dialogue Generation
We propose an online, end-to-end, neural generative conversational model for
open-domain dialogue. It is trained using a unique combination of offline
two-phase supervised learning and online human-in-the-loop active learning.
While most existing research proposes offline supervision or hand-crafted
reward functions for online reinforcement, we devise a novel interactive
learning mechanism based on hamming-diverse beam search for response generation
and one-character user-feedback at each step. Experiments show that our model
inherently promotes the generation of semantically relevant and interesting
responses, and can be used to train agents with customized personas, moods and
conversational styles.Comment: Accepted at 6th Joint Conference on Lexical and Computational
Semantics (*SEM) 2017 (Previously titled "Online Sequence-to-Sequence Active
Learning for Open-Domain Dialogue Generation" on ArXiv
A Study on the RAPD and SCAR Molecular Markers of Piper Species
In order to compare the genetic relationships among Kava, Pepper and it’s wild relatives and to distinguish Kava from Pepper and it’s wild relatives, we conducted research on Kava by using RAPD and SCAR molecular markers. 20 random primers selected from 80 random primers were used for RAPD amplification to identify the genetic relationships among Kava, Pepper and it’s wild relatives. Total 170 bands were amplified by 20 random primers, in which 20 bands were polymorphic (12%). Cluster analysis grouped the 28 accessions into six groups at similarity coefficient of 0.36, where 6 materials of Kava formed a group, indicating that Kava was distantly relation to Pepper and its wild relatives. Kava had 562 bp and 355 bp specific fragments amplified by primers OPQ- 02 and OPQ-03, respectively, were recycled for cloning and sequencing analysis, and then converted to SCAR markers. Two pairs of specific SCAR primers for Kava, P4.1 and P4.2, P8.1 and P8.2 were designed. PCR amplification of 28 test materials were performed using the two pairs of the specific primers respectively, the specific bands of 562 bp and 355 bp with expected sizes were amplified in 6 Kava materials but not in other materials. The results showed that primers P4.1 and P4.2, P8.1 and P8.2 might be used as specific SCAR primers for Kava germplasm resources identification. This research provided the basis for selecting rootstocks, molecular identification and the fingerprint construction of Kava
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