512 research outputs found
Service Life Prediction of Basalt Fiber Reinforced Concrete under Salt Freeze-thaw Cycles
To address the reduced durability of concrete structures under salt freeze-thaw erosion in Northwest China, basalt fiber reinforced concrete and common concrete with different mixing amounts were selected to predict their service life in three freeze-thaw conditions. Results showed that the damage on concrete under fresh water freeze-thaw condition is lower than that caused by salt freeze-thaw erosion, the addition of basalt fiber can effectively slow down the degradation of mechanical properties of concrete under salt freeze-thaw erosion, and the lowest degradation rate is reached when the content of basalt fiber is 0.15%. Fiber hinders the expansion of cracks and reduces the pores, and in turn improves the frost resistance durability of concrete. The service life prediction results obtained with Gray Model and Weibull Model are roughly similar, among which, Gray Model needs less sample volume, while Weibull Model presents more accurate prediction results
Joint Training for Neural Machine Translation Models with Monolingual Data
Monolingual data have been demonstrated to be helpful in improving
translation quality of both statistical machine translation (SMT) systems and
neural machine translation (NMT) systems, especially in resource-poor or domain
adaptation tasks where parallel data are not rich enough. In this paper, we
propose a novel approach to better leveraging monolingual data for neural
machine translation by jointly learning source-to-target and target-to-source
NMT models for a language pair with a joint EM optimization method. The
training process starts with two initial NMT models pre-trained on parallel
data for each direction, and these two models are iteratively updated by
incrementally decreasing translation losses on training data. In each iteration
step, both NMT models are first used to translate monolingual data from one
language to the other, forming pseudo-training data of the other NMT model.
Then two new NMT models are learnt from parallel data together with the pseudo
training data. Both NMT models are expected to be improved and better
pseudo-training data can be generated in next step. Experiment results on
Chinese-English and English-German translation tasks show that our approach can
simultaneously improve translation quality of source-to-target and
target-to-source models, significantly outperforming strong baseline systems
which are enhanced with monolingual data for model training including
back-translation.Comment: Accepted by AAAI 201
Regularizing Neural Machine Translation by Target-bidirectional Agreement
Although Neural Machine Translation (NMT) has achieved remarkable progress in
the past several years, most NMT systems still suffer from a fundamental
shortcoming as in other sequence generation tasks: errors made early in
generation process are fed as inputs to the model and can be quickly amplified,
harming subsequent sequence generation. To address this issue, we propose a
novel model regularization method for NMT training, which aims to improve the
agreement between translations generated by left-to-right (L2R) and
right-to-left (R2L) NMT decoders. This goal is achieved by introducing two
Kullback-Leibler divergence regularization terms into the NMT training
objective to reduce the mismatch between output probabilities of L2R and R2L
models. In addition, we also employ a joint training strategy to allow L2R and
R2L models to improve each other in an interactive update process. Experimental
results show that our proposed method significantly outperforms
state-of-the-art baselines on Chinese-English and English-German translation
tasks.Comment: Accepted by AAAI 201
Budgeted Policy Learning for Task-Oriented Dialogue Systems
This paper presents a new approach that extends Deep Dyna-Q (DDQ) by
incorporating a Budget-Conscious Scheduling (BCS) to best utilize a fixed,
small amount of user interactions (budget) for learning task-oriented dialogue
agents. BCS consists of (1) a Poisson-based global scheduler to allocate budget
over different stages of training; (2) a controller to decide at each training
step whether the agent is trained using real or simulated experiences; (3) a
user goal sampling module to generate the experiences that are most effective
for policy learning. Experiments on a movie-ticket booking task with simulated
and real users show that our approach leads to significant improvements in
success rate over the state-of-the-art baselines given the fixed budget.Comment: 10 pages, 7 figures, ACL 201
QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model
Security has always been a critical issue in machine learning (ML)
applications. Due to the high cost of model training -- such as collecting
relevant samples, labeling data, and consuming computing power --
model-stealing attack is one of the most fundamental but vitally important
issues. When it comes to quantum computing, such a quantum machine learning
(QML) model-stealing attack also exists and is even more severe because the
traditional encryption method, such as homomorphic encryption can hardly be
directly applied to quantum computation. On the other hand, due to the limited
quantum computing resources, the monetary cost of training QML model can be
even higher than classical ones in the near term. Therefore, a well-tuned QML
model developed by a third-party company can be delegated to a quantum cloud
provider as a service to be used by ordinary users. In this case, the QML model
will likely be leaked if the cloud provider is under attack. To address such a
problem, we propose a novel framework, namely QuMoS, to preserve model
security. We propose to divide the complete QML model into multiple parts and
distribute them to multiple physically isolated quantum cloud providers for
execution. As such, even if the adversary in a single provider can obtain a
partial model, it does not have sufficient information to retrieve the complete
model. Although promising, we observed that an arbitrary model design under
distributed settings cannot provide model security. We further developed a
reinforcement learning-based security engine, which can automatically optimize
the model design under the distributed setting, such that a good trade-off
between model performance and security can be made. Experimental results on
four datasets show that the model design proposed by QuMoS can achieve
competitive performance while providing the highest security than the
baselines
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