AstBERT: Enabling Language Model for Financial Code Understanding with Abstract Syntax Trees

Using the pre-trained language model (i.e. BERT) to apprehend source codes has attracted increasing attention from financial institutions owing to the great potential to uncover financial risks. However, there are several challenges in applying these language models to directly solve programming language (PL) related problems. To this end, we propose the AstBERT model, a pre-trained language model aiming to better understand the financial PL using the abstract syntax tree (AST). Specifically, we collect a colossal amount of source codes (both Java and Python) from the Alipay code repository and incorporate both syntactic and semantic code knowledge into our model through the help of code parsers, in which AST information of the source codes can be interpreted and integrated. We evaluate the performance of the proposed model on three tasks, including code question answering, code clone detection and code refinement. Experiment results show that our AstBERT achieves promising performance on three downstream tasks

Short and Long-Term Outcomes of Epidural or Intravenous Analgesia after Esophagectomy: A Propensity-Matched Cohort Study - Fig 3

<p><b>The preoperative and postoperative changes of levels in systolic (A) and diastolic (B) blood pressure between epidural analgesia (EDA) and intravenous analgesia (IVA) groups.</b></p

Perioperative outcomes of epidural and intravenous analgesia groups.

<p>Perioperative outcomes of epidural and intravenous analgesia groups.</p

The selection and matching process of participants.

<p>The selection and matching process of participants.</p

Distribution of patients characteristics of epidural and intravenous analgesia groups, before and after propensity score matching.

<p>Distribution of patients characteristics of epidural and intravenous analgesia groups, before and after propensity score matching.</p

Comparative efficacy of different combinations of acapella, active cycle of breathing technique, and external diaphragmatic pacing in perioperative patients with lung cancer: a randomised controlled trial

Abstract Background Acapella plus active cycle of breathing technique (ACBT), external diaphragm pacemaker (EDP) plus ACBT have been shown to facilitate the recovery of functional capacity and lung function in patients suffering from airway obstruction but the efficacy in perioperative patients with lung cancer has not been proven. Methods We conducted a three-arm, prospective, randomized, assessor-blinded, controlled trial in patients with lung cancer who underwent thoracoscopic lobectomy or segmentectomy in the department of thoracic surgery, China. Patients were randomly assigned (1:1:1) to receive Acapella plus ACBT, EDP plus ACBT, or ACBT group (control group) using SAS software. The primary outcome was functional capacity, measured by the 6-minute walk test (6MWT). Results We recruited 363 participants over 17 months: 123 assigned to the Acapella plus ACBT group, 119 to the EDP plus ACBT group, and 121 to the ACBT group. Statistically significant differences were noted for functional capacity between the EDP plus ACBT and control groups at each follow-up time (1-week follow-up: difference = 47.25 m, 95% CI, 31.56–62.93; P < 0.001; and 1-month follow-up: difference = 49.72 m, 95% CI, 34.04–65.41; P < 0.001), between the Acapella plus ACBT and control groups at postoperative week 1 (difference = 35.23 m, 95% CI, 19.30–51.16; P < 0.001) and postoperative month 1 (difference = 34.96 m, 95% CI, 19.03–50.89; P < 0.001), and between the EDP plus ACBT and Acapella plus ACBT groups at 1-month follow-up (difference = 14.76 m, 95% CI, 1.34–28.19; P = 0.0316). Conclusion EDP plus ACBT and Acapella plus ACBT significantly improved functional capacity and lung function in perioperative patients with lung cancer, compared with single-model ACBT, and the effects of EDP plus ACBT were clearly superior to those of other programs. Trial registration The study was registered in the clinical trial database (clinicaltrials.gov) on June 4, 2021 (No. NCT04914624)