The effect of study abroad experience and working memory on Chinese-English consecutive interpreting performance

This thesis investigates how study abroad experience (SAE) and working memory (WM) influence interpreting performance. Using a second language (L2) is cognitively demanding because it involves activation of a new language and the inhibition of the first language (L1). This is a general issue with all bilinguals, who have to suppress or control whichever language is currently not in use. As a special group of bilinguals, interpreters are expected to efficiently switch between the two languages by analysing input sound signals, extracting meaning, transforming, storing and retrieving the message in the input language, and then retrieving the lexicon in the target language that will be appropriate for expressing that message, (re)formulating it and finally conveying it in the target language. Moreover, some or all of these operations take place in parallel, and this multi-tasking heavily taxes interpreters’ WM. The quality of interpreting performance is known to correlate with several variables, such as language proficiency, duration of training, and interpreting experience. One factor that has received little research attention is the effect of overseas experience: Does studying in a target-language environment benefit interpreting performance? Language learners, including interpreting students, are often advised to study abroad, but the benefits of this experience, especially for interpreters, is not well understood. Taking an interdisciplinary approach, the present thesis examines the relationship between SAE, WM and interpreting performance. The main research questions examine whether students with SAE outperform those without such an experience in consecutive interpreting (CI), and how WM may be involved. The results show that students with SAE surpassed their non-SAE counterparts in word translation efficiency, L2 fluency and L2 grammatical accuracy. A similar trend was observed in study abroad participants’ overall CI performance from L2 to L1. It is worth noting that the tendency was independent of participants’ WM. Concerning WM, the results indicate that it was strongly correlated with interpreters’ bidirectional CI performance. That is, a larger WM could help achieve a better CI output in both language directions. Taken together, these findings suggest that two factors turn out to significantly influence CI performance, namely, prolonged and effective overseas study, and larger available WM resources. This research illustrates the importance of SAE and WM in interpreting, and sheds light on the relationships between language context, cognitive resources and interpreting performance. A better understanding of these relationships may have implications for future interpreting training and practice

Study concept drift in 150-year english literature

The meaning of a concept or a word changes over time. Such concept drift reects the change of the social consensus as well. Studying concept drift over time is valuable for researchers who are interested in language or culture evolution. Recent word embedding technologies inspire us to automatically detect concept drift in large-scale corpora. However, comparing embeddings generated from different corpora is a complex task. In this paper, we propose to use a simple approach for detecting concept drift based on the change in word contexts from different time periods and apply it to subsequent time periods so that the detailed drift could be detected and visualised. We dive into certain words to track how the meaning of a word changes gradually over a long time span with relevant historical events which demonstrates the effect of our method

Double band inversion in the topological phase transition of Ge1-xSnx alloys

We use first-principles simulation and virtual crystal approximation to reveal the unique double band inversion and topological phase transition in Ge1-xSnx alloys. Wavefunction parity, spatial charge distribution and surface state spectrum analyses suggest that the band inversion in Ge1-xSnx is relayed by its first valence band. As the system evolves from Ge to {\alpha}-Sn, its conduction band moves down, and inverts with the first and the second valence bands consecutively. The first band inversion makes the system nontrivial, while the second one does not change the topological invariant of the system. Both the band inversions yield surface modes spanning the individual inverted gaps, but only the surface mode in the upper gap associates with the nontrivial nature of tensile-strained {\alpha}-Sn.Comment: 5 pages, 6 figure

A Joint Traffic Flow Estimation and Prediction Approach for Urban Networks

Classical methods of traffic flow prediction with missing data are generally implemented in two sequential stages: a) imputing the missing data by certain imputation methods such as kNN, PPCA based methods etc.; b) using parametric or non-parametric methods to predict the future traffic flow with the completed data. However, the errors generated in missing data imputation stage will be accumulated into prediction stage, and thus will negatively influence the prediction performance when missing rate becomes large. To solve this problem, this paper proposes a Joint Traffic Flow Estimation and Prediction (JT-FEP) approach, which considers the missing data as additional unknown network parameters during a deep learning model training process. By updating missing data and the other network parameters via backward propagation, the model training error can generally be evenly distributed across the missing data and future data, thus reducing the error propagation. We conduct extensive experiments for two missing patterns i.e. Completely Missing at Random (CMAR) and Not Missing at Random (NMAR) with various missing rates. The experimental results demonstrate the superiority of JTFEP over existing methods

The effect of study-abroad experience on lexical translation among interpreting students

This study investigates the impact of study-abroad experience (SAE) on lexical translation among 50 Chinese (L1)-English (L2) interpreting students. Participants were divided into two groups based on their experience abroad. Both groups consisted of 25 unbalanced L2 learners who were matched in age, working memory, length of interpreting training, and L2 proficiency. Bidirectional word translation recognition tasks, from L1 to L2 and L2 to L1, highlighted several key findings: (1) both groups were significantly more accurate and faster from L2 to L1 than in the reverse direction; (2) the study abroad (SA) group was more inclined to respond quickly at the risk of making errors, whereas the non-study abroad (NSA) group tended to be more cautious, prioritising accuracy over speed; (3) the SA group were more balanced and consistent in their performance across lexical translations in both directions than the NSA group. These results emphasise the potent effect of SAE in resolving bilinguals’ language competition, especially in streamlining language switching, a cognitive process critical for interpreting students engaging daily with dual languages

Clinicopathological features of Bu Gu Zhi-induced liver injury, a long-term follow-up cohort study

Background & Aims Bu Gu Zhi (BGZ) is a Chinese herb consumed mainly for osteoporosis treatment. Only small case series of BGZ-induced liver injury (BGZILI) have been reported. We describe the clinicopathological features and clinical course of BGZILI. Methods Patients diagnosed with drug-induced liver injury (DILI) at Beijing Friendship Hospital from 2005 to 2017 were reviewed. Clinical and follow-up data were analysed. Results Of the 547 DILI patients, 40 cases (7.3%) were attributed to BGZILI. About 34/40 (85.0%) patients were females with a median age of 63 (range, 54-70) years. The median latency period was 45 (range, 29-90) days. Patients commonly presented with loss of appetite (57.5%), dark urine (57.5%) and fatigue (55.0%). The median level of alanine aminotransferase and aspartate aminotransferase at BGZILI onset was 673.5 and 423.0 U/L respectively. Total bilirubin (TB) and direct bilirubin (DB) were 59.0 and 39.4 mu mol/L respectively. The biochemical liver injury pattern was hepatocellular (92.5%), cholestatic (5.0%) and mixed (2.5%). They were categorized into 'mild' (N = 23, 57.5%), 'moderate' (6, 15.0%) or 'severe' (11, 27.5%) according to severity assessment by DILI network. The main histological injury pattern in 9/40 patients with liver biopsy was acute hepatitis with/without cholestasis. Median duration of follow-up was 26.3 months with recovery in 37 patients within 6 months. No patients died or required transplantation. Conclusions BGZ-induced liver injury manifested more often as a hepatocellular injury pattern with mild to moderate hepatocellular damage. Most patients recovered after cessation of BGZ within 6 months, and none developed end-stage liver disease or died

RDAD: A Machine Learning System to Support Phenotype-Based Rare Disease Diagnosis

DNA sequencing has allowed for the discovery of the genetic cause for a considerable number of diseases, paving the way for new disease diagnostics. However, due to the lack of clinical samples and records, the molecular cause for rare diseases is always hard to identify, significantly limiting the number of rare Mendelian diseases diagnosed through sequencing technologies. Clinical phenotype information therefore becomes a major resource to diagnose rare diseases. In this article, we adopted both a phenotypic similarity method and a machine learning method to build four diagnostic models to support rare disease diagnosis. All the diagnostic models were validated using the real medical records from RAMEDIS. Each model provides a list of the top 10 candidate diseases as the prediction outcome and the results showed that all models had a high diagnostic precision (≥98%) with the highest recall reaching up to 95% while the models with machine learning methods showed the best performance. To promote effective diagnosis for rare disease in clinical application, we developed the phenotype-based Rare Disease Auxiliary Diagnosis system (RDAD) to assist clinicians in diagnosing rare diseases with the above four diagnostic models. The system is freely accessible through http://www.unimd.org/RDAD/