A Comparative Study of Differences and Similarities between English and Chinese Idioms

English idioms and Chinese idioms play very important roles in both languages. They have common characteristics of human language on one hand, but also have great differences in many aspects resulted from national traditions and customs on another hand. The paper probes into the differences and similarities between the idioms in English and Chinese languages and attempts to help people from different cultural background master the usage by comparative study, thus promote international communication. Key words: English and Chinese idioms, differences, similarities, comparison Résumé: Les proverbes occupent une position importante tant dans l’anglais que dans le chinois. Comme un phénomène linguistique, les proverbes anglais et chinois présentent des traits identiques de la langue humaine, et des caractéristiques nationales différentes. L’article présent entreprend une analyse profonde sur les similitudes et différences des proverbes anglais et chinois. L’étude comparative permet aux gens de différent contexte culturel de mieux maîtriser l’emploi respectif des proverbes anglais et chinois, et ainsi, de promouvoir la communication internationale. Mots-clés: proverbes anglais et chinois, similitudes et différences, comparaison 摘要：英、漢成語在各自的語言中占有舉足輕重的地位。作為語言現象，英、漢成語既具有人類語言的一致性，同時又具有不同民族語言的差異性。本文對英、漢成語的異同現象進行了較為深入的分析和探討，通過對這一複雜語言現象的對比研究, 幫助來自不同文化背景的人士了解英、漢成語之間的差異和相似之處，更好地掌握英、漢成語的用法，促進國際交流。關鍵詞：英漢成語；異同；比

Determining layer number of two dimensional flakes of transition-metal dichalcogenides by the Raman intensity from substrate

Transition-metal dichalcogenide (TMD) semiconductors have been widely studied due to their distinctive electronic and optical properties. The property of TMD flakes is a function of its thickness, or layer number (N). How to determine N of ultrathin TMDs materials is of primary importance for fundamental study and practical applications. Raman mode intensity from substrates has been used to identify N of intrinsic and defective multilayer graphenes up to N=100. However, such analysis is not applicable for ultrathin TMD flakes due to the lack of a unified complex refractive index ($\tilde{n}$) from monolayer to bulk TMDs. Here, we discuss the N identification of TMD flakes on the SiO$_2$/Si substrate by the intensity ratio between the Si peak from 100-nm (or 89-nm) SiO$_2$/Si substrates underneath TMD flakes and that from bare SiO$_2$/Si substrates. We assume the real part of $\tilde{n}$ of TMD flakes as that of monolayer TMD and treat the imaginary part of $\tilde{n}$ as a fitting parameter to fit the experimental intensity ratio. An empirical $\tilde{n}$, namely, $\tilde{n}_{eff}$, of ultrathin MoS$_{2}$, WS$_{2}$ and WSe$_{2}$ flakes from monolayer to multilayer is obtained for typical laser excitations (2.54 eV, 2.34 eV, or 2.09 eV). The fitted $\tilde{n}_{eff}$ of MoS$_{2}$ has been used to identify N of MoS$_{2}$ flakes deposited on 302-nm SiO$_2$/Si substrate, which agrees well with that determined from their shear and layer-breathing modes. This technique by measuring Raman intensity from the substrate can be extended to identify N of ultrathin 2D flakes with N-dependent $\tilde{n}$ . For the application purpose, the intensity ratio excited by specific laser excitations has been provided for MoS$_{2}$, WS$_{2}$ and WSe$_{2}$ flakes and multilayer graphene flakes deposited on Si substrates covered by 80-110 nm or 280-310 nm SiO$_2$ layer.Comment: 10 pages, 4 figures. Accepted by Nanotechnolog

Repression of autophagy in diabetic cardiomyopathy via RhoA/ROCK2 signaling pathways

375-380Activated RhoA and ROCK is associated with many cardiovascular diseases (CVD) such as congestive heart failure (CHF), atherosclerosis and hypertension. However, the role of RhoA/ROCK2 signaling pathway in initiating diabetic cardiomyopathy (DCM) has not been fully elucidated. Here, we studied the role of RhoA/ROCK2 signaling pathway in induction of DCM through autophagy suppression in diabetic rat animal models. Broadly, we investigated the potential role and mechanism of diabetes induced myocardial dysfunction in rats. DCM was induced by injections of streptozocin (STZ) in experimental Wistar rats. The experimental rats were randomized to be treated with fasudil and lentivirus carrying the RhoA cDNA. Haemodynamic changes, assessment of cardiac weight index, histopathological examinations, cardiomyocyte autophagy and expression of RhoA and ROCK2 mRNAs were compared between groups. The expression of RhoA and ROCK mRNAs was found significantly increased in cardiac tissues compared with control group. The RhoA overexpression significantly decreased the values of left ventricular ejection fraction (LVEF), ±dp/dtmax and repressed autophagy. RhoA/ROCK2 signaling pathway repressed autophagy in diabetic cardiomyopathy indicating that it may serve as a potential therapeutic target for DCM treatment

Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks

Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics. As a result, these models suffer from accuracy decay over a long time and thus require frequent calibration. In this work, we address this issue by formulating BP estimation as a sequence prediction problem in which both the input and target are temporal sequences. We propose a novel deep recurrent neural network (RNN) consisting of multilayered Long Short-Term Memory (LSTM) networks, which are incorporated with (1) a bidirectional structure to access larger-scale context information of input sequence, and (2) residual connections to allow gradients in deep RNN to propagate more effectively. The proposed deep RNN model was tested on a static BP dataset, and it achieved root mean square error (RMSE) of 3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction respectively, surpassing the accuracy of traditional BP prediction models. On a multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81 mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction, respectively, which outperforms all previous models with notable improvement. The experimental results suggest that modeling the temporal dependencies in BP dynamics significantly improves the long-term BP prediction accuracy.Comment: To appear in IEEE BHI 201

Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation

Different from other sequential data, sentences in natural language are structured by linguistic grammars. Previous generative conversational models with chain-structured decoder ignore this structure in human language and might generate plausible responses with less satisfactory relevance and fluency. In this study, we aim to incorporate the results from linguistic analysis into the process of sentence generation for high-quality conversation generation. Specifically, we use a dependency parser to transform each response sentence into a dependency tree and construct a training corpus of sentence-tree pairs. A tree-structured decoder is developed to learn the mapping from a sentence to its tree, where different types of hidden states are used to depict the local dependencies from an internal tree node to its children. For training acceleration, we propose a tree canonicalization method, which transforms trees into equivalent ternary trees. Then, with a proposed tree-structured search method, the model is able to generate the most probable responses in the form of dependency trees, which are finally flattened into sequences as the system output. Experimental results demonstrate that the proposed X2Tree framework outperforms baseline methods over 11.15% increase of acceptance ratio