266 research outputs found
Enriching Rare Word Representations in Neural Language Models by Embedding Matrix Augmentation
The neural language models (NLM) achieve strong generalization capability by
learning the dense representation of words and using them to estimate
probability distribution function. However, learning the representation of rare
words is a challenging problem causing the NLM to produce unreliable
probability estimates. To address this problem, we propose a method to enrich
representations of rare words in pre-trained NLM and consequently improve its
probability estimation performance. The proposed method augments the word
embedding matrices of pre-trained NLM while keeping other parameters unchanged.
Specifically, our method updates the embedding vectors of rare words using
embedding vectors of other semantically and syntactically similar words. To
evaluate the proposed method, we enrich the rare street names in the
pre-trained NLM and use it to rescore 100-best hypotheses output from the
Singapore English speech recognition system. The enriched NLM reduces the word
error rate by 6% relative and improves the recognition accuracy of the rare
words by 16% absolute as compared to the baseline NLM.Comment: 5 pages, 2 figures, accepted to INTERSPEECH 201
Constrained Output Embeddings for End-to-End Code-Switching Speech Recognition with Only Monolingual Data
The lack of code-switch training data is one of the major concerns in the
development of end-to-end code-switching automatic speech recognition (ASR)
models. In this work, we propose a method to train an improved end-to-end
code-switching ASR using only monolingual data. Our method encourages the
distributions of output token embeddings of monolingual languages to be
similar, and hence, promotes the ASR model to easily code-switch between
languages. Specifically, we propose to use Jensen-Shannon divergence and cosine
distance based constraints. The former will enforce output embeddings of
monolingual languages to possess similar distributions, while the later simply
brings the centroids of two distributions to be close to each other.
Experimental results demonstrate high effectiveness of the proposed method,
yielding up to 4.5% absolute mixed error rate improvement on Mandarin-English
code-switching ASR task.Comment: 5 pages, 3 figures, accepted to INTERSPEECH 201
Research on the Experimental Teaching Method of Vibration Damping Fastener for Undergraduates Majoring in Rail Transit
Experiment is an important teaching link in talent training. Aiming at the current situation and problems of the experimental teaching of rail transit major, taking the experimental teaching of vibration damping fastener drop weight for railway engineering major of Central South University as an example, the specific methods of the new experimental teaching mode for undergraduates majoring in rail transit are expounded: Improve the subject experimental system, build an open experimental platform, and improve the school-enterprise resource sharing system, etc. This model is conducive to the reform and development of the experimental teaching model for rail transit majors and related science and engineering majors
Research on Practical Teaching of Railway Engineering Specialty Based on Temperature Test of Rubber Sleepers
Experimental teaching plays an important role in cultivating college students' innovative ability. This paper takes the practical teaching of the temperature test of the new rubber sleeper as an example to analyze the current situation and problems of the practical teaching of railway engineering. The specific measures of the new system of practical teaching of railway engineering are put forward: Build a practical teaching curriculum system, improve the practical teaching evaluation mechanism, and promote the sharing of school-enterprise resources, so as to cultivate outstanding railway engineering talents with engineering ability and innovative spirit
Fast Super-Resolution Imaging with Ultra-High Labeling Density Achieved by Joint Tagging Super-Resolution Optical Fluctuation Imaging (JT-SOFI)
Previous stochastic localization-based super-resolution techniques are
largely limited by the labeling density and the fidelity to the morphology of
specimen. We report on an optical super-resolution imaging scheme implementing
joint tagging using multiple fluorescent blinking dyes associated with
super-resolution optical fluctuation imaging (JT-SOFI), achieving ultra-high
labeling density super-resolution imaging. To demonstrate the feasibility of
JT-SOFI, quantum dots with different emission spectra were jointly labeled to
the tubulin in COS7 cells, creating ultra-high density labeling. After
analyzing and combining the fluorescence intermittency images emanating from
spectrally resolved quantum dots, the microtubule networks are capable of being
investigated with high fidelity and remarkably enhanced contrast at
sub-diffraction resolution. The spectral separation also significantly
decreased the frame number required for SOFI, enabling fast super-resolution
microscopy through simultaneous data acquisition. As the joint-tagging scheme
can decrease the labeling density in each spectral channel, we can faithfully
reflect the continuous microtubule structure with high resolution through
collection of only 100 frames per channel. The improved continuity of the
microtubule structure is quantitatively validated with image skeletonization,
thus demonstrating the advantage of JT-SOFI over other localization-based
super-resolution methods.Comment: 19 pages, 4 figures, with S
Independent language modeling architecture for end-to-end ASR
The attention-based end-to-end (E2E) automatic speech recognition (ASR)
architecture allows for joint optimization of acoustic and language models
within a single network. However, in a vanilla E2E ASR architecture, the
decoder sub-network (subnet), which incorporates the role of the language model
(LM), is conditioned on the encoder output. This means that the acoustic
encoder and the language model are entangled that doesn't allow language model
to be trained separately from external text data. To address this problem, in
this work, we propose a new architecture that separates the decoder subnet from
the encoder output. In this way, the decoupled subnet becomes an independently
trainable LM subnet, which can easily be updated using the external text data.
We study two strategies for updating the new architecture. Experimental results
show that, 1) the independent LM architecture benefits from external text data,
achieving 9.3% and 22.8% relative character and word error rate reduction on
Mandarin HKUST and English NSC datasets respectively; 2)the proposed
architecture works well with external LM and can be generalized to different
amount of labelled data
Graphene Oxide Quantum Dots Covalently Functionalized PVDF Membrane with Significantly-Enhanced Bactericidal and Antibiofouling Performances
Covalent bonding of graphene oxide quantum dots (GOQDs) onto amino modified polyvinylidene fluoride (PVDF) membrane has generated a new type of nano-carbon functionalized membrane with significantly enhanced antibacterial and antibiofouling properties. A continuous filtration test using E. coli containing feedwater shows that the relative flux drop over GOQDs modified PVDF is 23%, which is significantly lower than those over pristine PVDF (86%) and GO-sheet modified PVDF (62%) after 10 h of filtration. The presence of GOQD coating layer effectively inactivates E. coli and S. aureus cells, and prevents the biofilm formation on the membrane surface, producing excellent antimicrobial activity and potentially antibiofouling capability, more superior than those of previously reported two-dimensional GO sheets and one-dimensional CNTs modified membranes. The distinctive antimicrobial and antibiofouling performances could be attributed to the unique structure and uniform dispersion of GOQDs, enabling the exposure of a larger fraction of active edges and facilitating the formation of oxidation stress. Furthermore, GOQDs modified membrane possesses satisfying long-term stability and durability due to the strong covalent interaction between PVDF and GOQDs. This study opens up a new synthetic avenue in the fabrication of efficient surface-functionalized polymer membranes for potential waste water treatment and biomolecules separation
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