WPU-Net: Boundary Learning by Using Weighted Propagation in Convolution Network

Deep learning has driven a great progress in natural and biological image processing. However, in material science and engineering, there are often some flaws and indistinctions in material microscopic images induced from complex sample preparation, even due to the material itself, hindering the detection of target objects. In this work, we propose WPU-net that redesigns the architecture and weighted loss of U-Net, which forces the network to integrate information from adjacent slices and pays more attention to the topology in boundary detection task. Then, the WPU-net is applied into a typical material example, i.e., the grain boundary detection of polycrystalline material. Experiments demonstrate that the proposed method achieves promising performance and outperforms state-of-the-art methods. Besides, we propose a new method for object tracking between adjacent slices, which can effectively reconstruct 3D structure of the whole material. Finally, we present a material microscopic image dataset with the goal of advancing the state-of-the-art in image processing for material science.Comment: technical repor

Lost in Translation: Cross-Country Differences in Hotel Guest Satisfaction

With the global expansion of the hotel industry and greater mobility of international travelers, awareness of international differences in guests’ attitudes about their travel experiences is important. As a consequence, most multinational hotel chains currently invest significant resources in implementing large-scale measurement programs to track, compare, and benchmark guest satisfaction across their various international markets. They do so for two related reasons. First, most hoteliers understand that highly satisfied guests are much more likely to return to that property and spend more during future stays than guests who are indifferent or displeased.1 More important, successful hoteliers understand that simply tracking performance is not enough. What is required is using the results of tracking programs to guide day-to-day management decisions and, ultimately, long-term operational strategies

A case report on ATP6V0A4 gene mutation: Forecast of familial deafness by genetic investigation in a patient with autosomal recessive distal renal tubular acidosis

The autosomal recessive form of distal renal tubular acidosis (dRTA), a condition associated with the systemic accumulation of acid owing to its reduced elimination through kidneys, is caused by ATP6V0A4 mutation, which is typically related to either late-onset sensorineural hearing loss (SNHL) or normal hearing. This article reports dRTA in seven year old boy, born to a Chinese couple who have family history of deafness. The patient does not have hearing impairment. ATP6V0A4 gene sequencing demonstrated that there were 2 heterozygous mutations, c.1376C>T and c.1029+5G>A, in gene ATP6V0A4. c.1376C > T (p.Ser459Phe) is a kind of missense mutation in gene ATP6V0A4. c.1029+5G>A is a kind of intragenic mutation near the cutting area of gene ATP6V0A4. ATP6V0A4 gene mutation study substantiated the autosomal recessive dRTA without hearing impairment in the patient. This case report emphasizes the significance of early diagnosis and genetic screening of recessive forms of dRTA independent of hearing status and offer suitable intervention to treat dRTA as well as diminish the influence of SNHL on the child’s learning and communication in daily life.Keywords: Renal tubular acidosis, Homeostasis, Electrolytes, Hearing impairment, ATP6V0A4 gene, Mutatio

Scientific chart image recognition and interpretation

Ph.DDOCTOR OF PHILOSOPH

Beyond triplet loss: a deep quadruplet network for person re-identification

Person re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on the training set. It still suffers from a weaker generalization capability from the training set to the testing set, thus resulting in inferior performance. In this paper, we design a quadruplet loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability and can achieve a higher performance on the testing set. In particular, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID. In extensive experiments, the proposed network outperforms most of the state-of-the-art algorithms on representative datasets which clearly demonstrates the effectiveness of our proposed method.Comment: accepted to CVPR201

A Multi-task Deep Network for Person Re-identification

Person re-identification (ReID) focuses on identifying people across different scenes in video surveillance, which is usually formulated as a binary classification task or a ranking task in current person ReID approaches. In this paper, we take both tasks into account and propose a multi-task deep network (MTDnet) that makes use of their own advantages and jointly optimize the two tasks simultaneously for person ReID. To the best of our knowledge, we are the first to integrate both tasks in one network to solve the person ReID. We show that our proposed architecture significantly boosts the performance. Furthermore, deep architecture in general requires a sufficient dataset for training, which is usually not met in person ReID. To cope with this situation, we further extend the MTDnet and propose a cross-domain architecture that is capable of using an auxiliary set to assist training on small target sets. In the experiments, our approach outperforms most of existing person ReID algorithms on representative datasets including CUHK03, CUHK01, VIPeR, iLIDS and PRID2011, which clearly demonstrates the effectiveness of the proposed approach.Comment: Accepted by AAAI201

Acquiring Knowledge from Pre-trained Model to Neural Machine Translation

Pre-training and fine-tuning have achieved great success in the natural language process field. The standard paradigm of exploiting them includes two steps: first, pre-training a model, e.g. BERT, with a large scale unlabeled monolingual data. Then, fine-tuning the pre-trained model with labeled data from downstream tasks. However, in neural machine translation (NMT), we address the problem that the training objective of the bilingual task is far different from the monolingual pre-trained model. This gap leads that only using fine-tuning in NMT can not fully utilize prior language knowledge. In this paper, we propose an APT framework for acquiring knowledge from the pre-trained model to NMT. The proposed approach includes two modules: 1). a dynamic fusion mechanism to fuse task-specific features adapted from general knowledge into NMT network, 2). a knowledge distillation paradigm to learn language knowledge continuously during the NMT training process. The proposed approach could integrate suitable knowledge from pre-trained models to improve the NMT. Experimental results on WMT English to German, German to English and Chinese to English machine translation tasks show that our model outperforms strong baselines and the fine-tuning counterparts