1,657 research outputs found
Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification
Connectionist temporal classification (CTC) is a powerful approach for
sequence-to-sequence learning, and has been popularly used in speech
recognition. The central ideas of CTC include adding a label "blank" during
training. With this mechanism, CTC eliminates the need of segment alignment,
and hence has been applied to various sequence-to-sequence learning problems.
In this work, we applied CTC to abstractive summarization for spoken content.
The "blank" in this case implies the corresponding input data are less
important or noisy; thus it can be ignored. This approach was shown to
outperform the existing methods in term of ROUGE scores over Chinese Gigaword
and MATBN corpora. This approach also has the nice property that the ordering
of words or characters in the input documents can be better preserved in the
generated summaries.Comment: Accepted by Interspeech 201
On Investigating the Conservative Property of Score-Based Generative Models
Existing Score-based Generative Models (SGMs) can be categorized into
constrained SGMs (CSGMs) or unconstrained SGMs (USGMs) according to their
parameterization approaches. CSGMs model probability density functions as
Boltzmann distributions, and assign their predictions as the negative gradients
of some scalar-valued energy functions. On the other hand, USGMs employ
flexible architectures capable of directly estimating scores without the need
to explicitly model energy functions. In this paper, we demonstrate that the
architectural constraints of CSGMs may limit their modeling ability. In
addition, we show that USGMs' inability to preserve the property of
conservativeness may lead to degraded sampling performance in practice. To
address the above issues, we propose Quasi-Conservative Score-based Generative
Models (QCSGMs) for keeping the advantages of both CSGMs and USGMs. Our
theoretical derivations demonstrate that the training objective of QCSGMs can
be efficiently integrated into the training processes by leveraging the
Hutchinson trace estimator. In addition, our experimental results on the
CIFAR-10, CIFAR-100, ImageNet, and SVHN datasets validate the effectiveness of
QCSGMs. Finally, we justify the advantage of QCSGMs using an example of a
one-layered autoencoder
Circuit Simulation for Solar Power Maximum Power Point Tracking with Different Buck-Boost Converter Topologies
[[sponsorship]]MDPI[[conferencetype]]國際[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]http://sciforum.net/conference/ece-
Incorporating Evolutionary Information and Functional Domains for Identifying RNA Splicing Factors in Humans
Regulation of pre-mRNA splicing is achieved through the interaction of RNA sequence elements and a variety of RNA-splicing related proteins (splicing factors). The splicing machinery in humans is not yet fully elucidated, partly because splicing factors in humans have not been exhaustively identified. Furthermore, experimental methods for splicing factor identification are time-consuming and lab-intensive. Although many computational methods have been proposed for the identification of RNA-binding proteins, there exists no development that focuses on the identification of RNA-splicing related proteins so far. Therefore, we are motivated to design a method that focuses on the identification of human splicing factors using experimentally verified splicing factors. The investigation of amino acid composition reveals that there are remarkable differences between splicing factors and non-splicing proteins. A support vector machine (SVM) is utilized to construct a predictive model, and the five-fold cross-validation evaluation indicates that the SVM model trained with amino acid composition could provide a promising accuracy (80.22%). Another basic feature, amino acid dipeptide composition, is also examined to yield a similar predictive performance to amino acid composition. In addition, this work presents that the incorporation of evolutionary information and domain information could improve the predictive performance. The constructed models have been demonstrated to effectively classify (73.65% accuracy) an independent data set of human splicing factors. The result of independent testing indicates that in silico identification could be a feasible means of conducting preliminary analyses of splicing factors and significantly reducing the number of potential targets that require further in vivo or in vitro confirmation
Gold-Catalyzed Aminoalkenylation of β-Amino-1,n -Diynols to Cycloalkyl-, Piperidinyl- and Pyranyl-Fused Pyrroles
A synthetic method to prepare cycloalkyl-, piperidinyl- and pyranyl-fused pyrroles efficiently by gold(I)-catalyzed dehydrative aminoalkenylation of β-amino-1,n-diynols under mild conditions at room temperature is described
High-fidelity, broadband stimulated-Brillouin-scattering-based slow light using fast noise modulation
We demonstrate a 5-GHz-broadband tunable slow-light device based on
stimulated Brillouin scattering in a standard highly-nonlinear optical fiber
pumped by a noise-current-modulated laser beam. The noise modulation waveform
uses an optimized pseudo-random distribution of the laser drive voltage to
obtain an optimal flat-topped gain profile, which minimizes the pulse
distortion and maximizes pulse delay for a given pump power. Eye-diagram and
signal-to-noise ratio (SNR) analysis show that this new broadband slow-light
technique significantly increases the fidelity of a delayed data sequence,
while maintaining the delay performance. A fractional delay of 0.81 with a SNR
of 5.2 is achieved at the pump power of 350 mW using a 2-km-long highly
nonlinear fiber with the fast noise-modulation method, demonstrating a 50%
increase in eye-opening and a 36% increase in SNR compared to a previous
slow-modulation method
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