Transductive Multi-label Zero-shot Learning

Zero-shot learning has received increasing interest as a means to alleviate the often prohibitive expense of annotating training data for large scale recognition problems. These methods have achieved great success via learning intermediate semantic representations in the form of attributes and more recently, semantic word vectors. However, they have thus far been constrained to the single-label case, in contrast to the growing popularity and importance of more realistic multi-label data. In this paper, for the first time, we investigate and formalise a general framework for multi-label zero-shot learning, addressing the unique challenge therein: how to exploit multi-label correlation at test time with no training data for those classes? In particular, we propose (1) a multi-output deep regression model to project an image into a semantic word space, which explicitly exploits the correlations in the intermediate semantic layer of word vectors; (2) a novel zero-shot learning algorithm for multi-label data that exploits the unique compositionality property of semantic word vector representations; and (3) a transductive learning strategy to enable the regression model learned from seen classes to generalise well to unseen classes. Our zero-shot learning experiments on a number of standard multi-label datasets demonstrate that our method outperforms a variety of baselines.Comment: 12 pages, 6 figures, Accepted to BMVC 2014 (oral

Attitudes and Behavioral Response Toward Key Tobacco Control Measures from the FCTC among Chinese Urban Residents

BACKGROUND. The Chinese National People's Congress ratified the WHO Framework Convention on Tobacco Control (FCTC) on 27 August 2005, signaling China's commitment to implement tobacco control policies and legislation consistent with the treaty. This study was designed to examine attitudes towards four WHO FCTC measures among Chinese urban residents. METHODS. In a cross-sectional design study, survey data were collected from two Chinese urban cities involving a sample of 3,003 residents aged 15 years or older. Through a face-to-face interview, respondents were asked about attitudes toward four tobacco control measures developed by the WHO FCTC. Data on the four dependent measures were analyzed using multivariate logistic regression analyses. Using descriptive statistics, potential change in smoking behavior that smokers might make in response to increasing cigarette prices is also reported. RESULTS. 81.8% of the respondents in the study sample supported banning smoking in public places, 68.8% favored increasing the cigarette tax, 85.1% supported health warnings on cigarette packages, and 85.7% favored banning tobacco advertising. The likelihood to support these measures was associated with gender, educational level, and personal income. Smokers were less likely to support these measures than non-smokers, with decreased support expressed by daily smokers compared to occasional smokers, and heavy smokers compared to light smokers. The proportion of switching to cheaper cigarette brands, decreasing smoking, and quitting smoking altogether with increased cigarette prices were 29.1%, 30.90% and 40.0% for occasional smokers, respectively; and 30.8%, 32.7% and 36.5% for daily smokers, respectively. CONCLUSION. Results from this study indicate strong public support in key WHO FCTC measures and that increases in cigarette price may reduce tobacco consumption among Chinese urban residents. Findings from this study have implications with respect to policymaking and legislation for tobacco control in China

Finite Element Analysis for the Inhibition of EMAT Lamb Waves Multimode

The guided waves, especially Lamb waves, due to its longer propagation, lower loss and higher efficiency and sensitivity, are widely used in various kinds of thin layer structure test-ing (for example plates, pipelines and tanks). Electromagnetic ultrasonic Lamb waves testing combining the characteristics of Electromagnetic ultrasonic testing and guided waves, which has a better application prospect. Unfortunately, Lamb waves possess the multi-modes char-acteristic: several different modes propagate in the specimen simultaneous. Moreover, all of the modes of lamb waves are dispersive. Both make the received signals so complex and messy that the echo signals of the flaws might be difficult or even impossible to interpret in the practical application. In this paper, according to the characteristics of electromagnetic ul-trasonic excitation and combining with the structure of the double transducer and the method of phase cancellation [1,2], the characteristics of single lamb waves were studied by theory and simulation methods. The simulation results show that the structure of the double transducers can completely eliminates a mode and enhances another to excite single-mode. The single-mode exciting re-duces the difficulty of the subsequent signal analysis and processing, which provides reliable information for the practical application of detecting flaws. This work is supported by National Natural Science Foundation of China (51077036, 51207105, 51307043) and Natural Science Foundation of Hebei Province (E2016202260)

Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks

Deeper and wider Convolutional Neural Networks (CNNs) achieve superior performance but bring expensive computation cost. Accelerating such over-parameterized neural network has received increased attention. A typical pruning algorithm is a three-stage pipeline, i.e., training, pruning, and retraining. Prevailing approaches fix the pruned filters to zero during retraining, and thus significantly reduce the optimization space. Besides, they directly prune a large number of filters at first, which would cause unrecoverable information loss. To solve these problems, we propose an Asymptotic Soft Filter Pruning (ASFP) method to accelerate the inference procedure of the deep neural networks. First, we update the pruned filters during the retraining stage. As a result, the optimization space of the pruned model would not be reduced but be the same as that of the original model. In this way, the model has enough capacity to learn from the training data. Second, we prune the network asymptotically. We prune few filters at first and asymptotically prune more filters during the training procedure. With asymptotic pruning, the information of the training set would be gradually concentrated in the remaining filters, so the subsequent training and pruning process would be stable. Experiments show the effectiveness of our ASFP on image classification benchmarks. Notably, on ILSVRC-2012, our ASFP reduces more than 40% FLOPs on ResNet-50 with only 0.14% top-5 accuracy degradation, which is higher than the soft filter pruning (SFP) by 8%.Comment: Extended Journal Version of arXiv:1808.0686

Moisture content distribution model for the soil wetting body under moistube irrigation

This study investigated the moisture distribution characteristics of a soil wetting body under different influencing factors to inform the design and management of a moistube irrigation system. A mathematical model of soil moisture movement under moistube irrigation was established based on Hydrus-2D software. The suitability of the Hydrus-2D simulation model was verified by laboratory experiments. Numerical simulations were carried out with Hydrus-2D to investigate the influence of soil texture, initial moisture content, moistube specific discharge and irrigation time on the moisture distribution of a soil wetting body. The soil moisture content is highest at the moistube, and its value is related to the moistube-specific discharge and soil texture. The soil moisture content at any point in the wetting body decreased linearly with increasing distance from the wetting front to the moistube in the five set directions (vertical downward, 45° downward, horizontal, 45° upward and vertical upward). This trend is applicable to fine-textured and coarse-textured soil. An estimation model of soil moisture content including soil saturated hydraulic conductivity, initial soil moisture, the specific flow rate of the moistube and the maximum value of the wetting front distance in all directions is proposed. The model estimation is good (root mean square error = 0.008–0.018 cm3·cm−3, close to 0; Nash-Sutcliffe efficiency coefficient = 0.987, close to 1), and it can provide a practical tool for moistube irrigation design and agricultural water management