Learning Personalized End-to-End Goal-Oriented Dialog

Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues. In this paper, we present a personalized end-to-end model in an attempt to leverage personalization in goal-oriented dialogs. We first introduce a Profile Model which encodes user profiles into distributed embeddings and refers to conversation history from other similar users. Then a Preference Model captures user preferences over knowledge base entities to handle the ambiguity in user requests. The two models are combined into the Personalized MemN2N. Experiments show that the proposed model achieves qualitative performance improvements over state-of-the-art methods. As for human evaluation, it also outperforms other approaches in terms of task completion rate and user satisfaction.Comment: Accepted by AAAI 201

Iteratively Optimized Patch Label Inference Network for Automatic Pavement Disease Detection

We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement diseases that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classification approaches in automatic pavement disease detection. The source codes of IOPLIN are released on \url{https://github.com/DearCaat/ioplin}.Comment: Revision on IEEE Trans on IT

Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning

Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions. The primary difficulty in designing effective learning models to solve such problems lies in how to bridge the gap between the source and target distributions. In this paper, we provide comprehensive analysis of feature learning algorithms used in conjunction with linear classifiers for domain adaptation. Our analysis shows that in order to achieve good adaptation performance, the second moments of the source domain distribution and target domain distribution should be similar. Based on our new analysis, a novel extremely easy feature learning algorithm for domain adaptation is proposed. Furthermore, our algorithm is extended by leveraging multiple layers, leading to a deep linear model. We evaluate the effectiveness of the proposed algorithms in terms of domain adaptation tasks on the Amazon review dataset and the spam dataset from the ECML/PKDD 2006 discovery challenge.Comment: ijca

Piezoelectric Accelerometer with Improved Temperature Stability

Piezoceramic materials like PZT allow the manufacturing of piezoelectric sensors with advantages including high sensitivity, low price, and easy to shape. However, it is also featured with the pyroelectric effect, which brings extra charge generation with temperature variations. Those charges caused by the thermal effect contribute to errors in the sensor measurement result. Theoretically, the appropriate configuration of the sensor would neutralize the thermal effect. In this thesis, a triple layer piezoelectric sensor with a parallel connection would be used to check its thermal stability at elevated temperatures. The thesis begins with reviewing the fundamental concepts of piezoelectricity. The following section contains the analysis of the relationship between the different external inputs and the output of a triple layer sensor. The experiment is designed to put the triple layer sensor in a chamber with a temperature control system to test its performance at around 35 ℃ with sinusoidal excitation input. A unimorph sensor would be set as the reference group, so that the result of the triple layer sensor could have a comparison with. The cancellation of the temperature effect in the triple layer sensor successfully reduces the output deviation to an acceptable level. Meanwhile, the unimorph structure sensor exhibits obvious instability under the same conditions

Analysis of corrections to the eikonal approximation

Various corrections to the eikonal approximations are studied for two- and three-body nuclear collisions with the goal to extend the range of validity of this approximation to beam energies of 10 MeV/nucleon. Wallace's correction does not improve much the elastic-scattering cross sections obtained at the usual eikonal approximation. On the contrary, a semiclassical approximation that substitutes the impact parameter by a complex distance of closest approach computed with the projectile-target optical potential efficiently corrects the eikonal approximation. This opens the possibility to analyze data measured down to 10 MeV/nucleon within eikonal-like reaction models.Comment: 10 pages, 8 figure

Linking ethylene to nitrogen-dependent leaf longevity of grass species in a temperate steppe

Author's manuscript made available in accordance with the publisher's policy.Background and Aims Leaf longevity is an important plant functional trait that often varies with soil nitrogen supply. Ethylene is a classical plant hormone involved in the control of senescence and abscission, but its role in nitrogen-dependent leaf longevity is largely unknown. Methods Pot and field experiments were performed to examine the effects of nitrogen addition on leaf longevity and ethylene production in two dominant plant species, Agropyron cristatum and Stipa krylovii, in a temperate steppe in northern China. Key Results Nitrogen addition increased leaf ethylene production and nitrogen concentration but shortened leaf longevity; the addition of cobalt chloride, an ethylene biosynthesis inhibitor, reduced leaf nitrogen concentration and increased leaf longevity. Path analysis indicated that nitrogen addition reduced leaf longevity mainly through altering leaf ethylene production. Conclusions These findings provide the first experimental evidence in support of the involvement of ethylene in nitrogen-induced decrease in leaf longevity