13,255 research outputs found
Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input
Non-autoregressive translation (NAT) models, which remove the dependence on
previous target tokens from the inputs of the decoder, achieve significantly
inference speedup but at the cost of inferior accuracy compared to
autoregressive translation (AT) models. Previous work shows that the quality of
the inputs of the decoder is important and largely impacts the model accuracy.
In this paper, we propose two methods to enhance the decoder inputs so as to
improve NAT models. The first one directly leverages a phrase table generated
by conventional SMT approaches to translate source tokens to target tokens,
which are then fed into the decoder as inputs. The second one transforms
source-side word embeddings to target-side word embeddings through
sentence-level alignment and word-level adversary learning, and then feeds the
transformed word embeddings into the decoder as inputs. Experimental results
show our method largely outperforms the NAT baseline~\citep{gu2017non} by
BLEU scores on WMT14 English-German task and BLEU scores on WMT16
English-Romanian task.Comment: AAAI 201
Tetra-σ attachment of allyl cyanide on Si(111)−7×7
This is the published version. Copyright 2002 American Physical SocietyThe investigation of allyl cyanide adsorption on Si(111)−7×7 using high-resolution electron-energy-loss spectroscopy (HREELS), x-ray photoelectron spectroscopy (XPS), and ultraviolet photoelectron spectroscopy (UPS) reveals a tetra-σ binding mode through two [2+2]-like cycloaddition reactions. The HREELS spectra of chemisorbed monolayer show the absence of C=C, C≡N, and C(sp2)—H stretching modes coupled with the appearance of C=N stretching mode at ∼1590 cm−1, demonstrating that both the C=C and C≡N of allyl cyanide directly participate in binding with the surface to form C—C and C=N bonds, respectively. This binding configuration was further confirmed in our XPS and UPS studies. The imine-containing skeleton formed on the surface can possibly be employed as a molecular template for a further modification of Si surfaces and syntheses in vacuum
Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation
Non-autoregressive translation (NAT) models remove the dependence on previous
target tokens and generate all target tokens in parallel, resulting in
significant inference speedup but at the cost of inferior translation accuracy
compared to autoregressive translation (AT) models. Considering that AT models
have higher accuracy and are easier to train than NAT models, and both of them
share the same model configurations, a natural idea to improve the accuracy of
NAT models is to transfer a well-trained AT model to an NAT model through
fine-tuning. However, since AT and NAT models differ greatly in training
strategy, straightforward fine-tuning does not work well. In this work, we
introduce curriculum learning into fine-tuning for NAT. Specifically, we design
a curriculum in the fine-tuning process to progressively switch the training
from autoregressive generation to non-autoregressive generation. Experiments on
four benchmark translation datasets show that the proposed method achieves good
improvement (more than BLEU score) over previous NAT baselines in terms of
translation accuracy, and greatly speed up (more than times) the inference
process over AT baselines.Comment: AAAI 202
Computational Design of Wiring Layout on Tight Suits with Minimal Motion Resistance
An increasing number of electronics are directly embedded on the clothing to
monitor human status (e.g., skeletal motion) or provide haptic feedback. A
specific challenge to prototype and fabricate such a clothing is to design the
wiring layout, while minimizing the intervention to human motion. We address
this challenge by formulating the topological optimization problem on the
clothing surface as a deformation-weighted Steiner tree problem on a 3D
clothing mesh. Our method proposed an energy function for minimizing strain
energy in the wiring area under different motions, regularized by its total
length. We built the physical prototype to verify the effectiveness of our
method and conducted user study with participants of both design experts and
smart cloth users. On three types of commercial products of smart clothing, the
optimized layout design reduced wire strain energy by an average of 77% among
248 actions compared to baseline design, and 18% over the expert design.Comment: This work is accepted at SIGGRAPH ASIA 2023(Conference Track
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