Implicit Discourse Relation Classification via Multi-Task Neural Networks

Without discourse connectives, classifying implicit discourse relations is a challenging task and a bottleneck for building a practical discourse parser. Previous research usually makes use of one kind of discourse framework such as PDTB or RST to improve the classification performance on discourse relations. Actually, under different discourse annotation frameworks, there exist multiple corpora which have internal connections. To exploit the combination of different discourse corpora, we design related discourse classification tasks specific to a corpus, and propose a novel Convolutional Neural Network embedded multi-task learning system to synthesize these tasks by learning both unique and shared representations for each task. The experimental results on the PDTB implicit discourse relation classification task demonstrate that our model achieves significant gains over baseline systems.Comment: This is the pre-print version of a paper accepted by AAAI-1

An improved imaging method for extended targets

The dissertation presents an improved method for the inverse scattering problem to obtain better numerical results. There are two main methods for solving the inverse problem: the direct imaging method and the iterative method. For the direct imaging method, we introduce the MUSIC (MUltiple SIgnal Classification) algorithm, the multi-tone method and the linear sampling method with different boundary conditions in different cases, which are the smooth case, the one corner case, and the multiple corners case. The dissertation introduces the relations between the far field data and the near field data. When we use direct imaging methods for solving inverse scattering problems, we observe artificial lines which make it hard to determine the shape of targets. We try to eliminate those lines in different frequencies, but the artificial lines are still in the results and we are forced to get the shape of the targets. Hence, we try to apply multiple frequency data to obtain better results. There are several reasons to cause the artificial lines. For example, the creation of the response matrix, the error of solving the forward problem and the error of the computation. We propose a signal space test to study the cause of the artificial lines and to use multiple frequency data to reduce the effect from them. Finally, we use the active contour method to further improve the imaging results. This dissertation introduces the active contour method and the level-set algorithm. We use the results of the multiple frequencies to obtain the level-set data by utilizing the active contour method and the level-set algorithm. By using the level-set data, we reconstruct the shape of the targets without artificial lines. In order to demonstrate the robustness of the MUSIC algorithm, we add noise to the response matrix

MED and WPT based technique for bearings fault detection

A new technique is proposed in this work for fault detection in rolling element bearings, which is based on minimum entropy deconvolution (MED), wavelet packet decomposition (WPT) and envelop analysis. Firstly, the collected vibration signal is preprocessed to highlight defect-related impulses, and a new indicator named envelope spectra sparsity (ESS) is proposed to automatically select the filter length of MED. Then the preprocessed signal is decomposed into WPT nodes, and the most sensitive node containing fault-related information are selected from all the nodes to improve the accuracy of the fault detection. Sparsity of wavelet packet nodes signal (SWPN) is proposed in this step as a measure indicator. Lastly the power spectrum is used to highlight the bearing fault characteristic frequencies. The effectiveness of the proposed AMED-WPT technique in feature extraction and analysis is verified by a series of experimental tests corresponding to different bearing conditions

Correlations of BMI-1 expression and telomerase activity in ovarian cancer tissues

Aim: To investigate the correlation between oncoprotein Bmi-1 and telomerase activity in ovarian cancer tissues. Methods: SP immunohistochemistry was adopted to detect the expression of Bmi-1 protein in tissues of 47 ovarian epithelial cancer cases. Modified telomeric repeat amplification protocol (TRAP, silver staining technique) was used to detect the telomerase activity. Results: While in 80.85% (38/47) of ovarian epithelial cancer cases Bmi-1 protein was overexpressed, 46.81% (22/47) had very strong expression level. Bmi-1 expression levels in ovarian carcinoma tissue differ depending on tissue grade (higher for G3 cancer cases — 93.10% than for grade G2 cases — 61.11%) and the stage of the disease (lower for phase II and phase III cases — 66.67% than for phase IV cases — 92.31%). In ovarian epithelial cancer tissues, 87.23% (41/47) demonstrated positive telomerase activity in contrast to zero activity in normal tissues. Majority (90.24%) of specimens with positive telomerase activity possessed high Bmi-1 expression levels. Spearman correlation analysis indicated that expression of Bmi-1 protein was positively correlated with the elevated telomerase activity. Conclusions: Bmi-1 protein is highly expressed in ovarian epithelial cancer tissues, and its expression level correlates with histological grade and clinical phase of the patients. Elevation of Bmi-1 expression is closely correlated to the increased telomerase activity.Цель: изучить корреляцию между экспрессией протеина Bmi-1 и активностью теломеразы при раке яичника. Методы: подобраны оптимальные условия для SP-иммуногистохимии для выявления экспрессии белка Bmi-1 при эпителиальном раке яичника (n = 47). Для определения активности теломеразы был использован усовершенствованый протокол амплификации теломерных повторов (TRAP, методика окрашивания серебром). Результаты: в 80,85% (38/47) случаев рака яичника была выявлена экспрессия белка Bmi-1, в 46,81% (22/47) случаев – на очень высоком уровне. Уровень экспрессии Bmi-1 зависел от степени дифференцировки опухоли (при G3 экспрессия Bmi-1 (93,10%) была выше, чем при G2 (61,11%)) и от стадии заболе- G3 экспрессия 3 экспрессия Bmi-1 (93,10%) была выше, чем при G2 (61,11%)) и от стадии заболе- Bmi-1 (93,10%) была выше, чем при G2 (61,11%)) и от стадии заболе- -1 (93,10%) была выше, чем при -1 (93,10%) была выше, чем при G2 (61,11%)) и от стадии заболе- G2 (61,11%)) и от стадии заболе- G2 (61,11%)) и от стадии заболе- 2 (61,11%)) и от стадии заболевания (уровень экпрессии ниже в стадиях II и III (66,67%), чем в стадии IV (92,31%)). В тканях эпителиального рака яичника в 87,23% (41/47) случаев выявлена положительная теломеразная активность, в отличие от нулевой активности в нормальных тканях. В большинстве исследованных случаев рака яичника (90,24%) при положительной активности теломеразы был отмечен высокий уровень экспрессии Bmi-1. Корреляционный анализ Спирмана показал, что экспрессия белка Bmi-1 положительно коррелирует с повышенной теломеразной активностью. Выводы: белок Bmi-1 экспрессирован на высоком уровне злокачествен- -1 экспрессирован на высоком уровне злокачествен- -1 экспрессирован на высоком уровне злокачественными клетками эпителиального рака яичника, и экспрессия этого белка коррелирует с гистологической градацией и клинической стадией рака. Увеличение экспрессии Bmi-1 коррелировало с повышенной теломеразной активностью

The Impact of Corporate Social Responsibility on the Trust Repair of Brand with Negative Publicity: Mental Account as a Mediator

With the development of internet and popularity of mobile terminals, negative publicity of brand has become more and more widespread. This paper aims to study the impact of corporate social responsibility（CSR） on the trust repair of brand with negative publicity. From Chinese cultural aspect of the differential mode of association, CSR is divided into public morality behavior and private one. The concept of mental account is introduced as a mediating variable and CSR history as a moderate one. By a 2 (CSR type: public VS. private morality behavior) ×2(CSR history: long VS. short) between group experiment, it is found that public morality is more likely to be classified into charity account by consumers, thereby promoting integrity-based trust repair; private morality is more likely to be classified into remedy account, thereby promoting ability-based trust repair. Public morality behavior with long history is more tend to be attributed to charity account by consumers; and CSR including public and private one with short history are more tend to be attributed to remedy account by consumers