Link Prediction for Wikipedia Articles as a Natural Language Inference Task

Link prediction task is vital to automatically understanding the structure of large knowledge bases. In this paper, we present our system to solve this task at the Data Science and Advanced Analytics 2023 Competition "Efficient and Effective Link Prediction" (DSAA-2023 Competition) with a corpus containing 948,233 training and 238,265 for public testing. This paper introduces an approach to link prediction in Wikipedia articles by formulating it as a natural language inference (NLI) task. Drawing inspiration from recent advancements in natural language processing and understanding, we cast link prediction as an NLI task, wherein the presence of a link between two articles is treated as a premise, and the task is to determine whether this premise holds based on the information presented in the articles. We implemented our system based on the Sentence Pair Classification for Link Prediction for the Wikipedia Articles task. Our system achieved 0.99996 Macro F1-score and 1.00000 Macro F1-score for the public and private test sets, respectively. Our team UIT-NLP ranked 3rd in performance on the private test set, equal to the scores of the first and second places. Our code is publicly for research purposes.Comment: Accepted at the 10th IEEE International Conference On Data Science And Advanced Analytics (DSAA 2023

ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing

English and Chinese, known as resource-rich languages, have witnessed the strong development of transformer-based language models for natural language processing tasks. Although Vietnam has approximately 100M people speaking Vietnamese, several pre-trained models, e.g., PhoBERT, ViBERT, and vELECTRA, performed well on general Vietnamese NLP tasks, including POS tagging and named entity recognition. These pre-trained language models are still limited to Vietnamese social media tasks. In this paper, we present the first monolingual pre-trained language model for Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality and diverse Vietnamese social media texts using XLM-R architecture. Moreover, we explored our pre-trained model on five important natural language downstream tasks on Vietnamese social media texts: emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection. Our experiments demonstrate that ViSoBERT, with far fewer parameters, surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks. Our ViSoBERT model is available only for research purposes.Comment: Accepted at EMNLP'2023 Main Conferenc

ViCGCN: Graph Convolutional Network with Contextualized Language Models for Social Media Mining in Vietnamese

Social media processing is a fundamental task in natural language processing with numerous applications. As Vietnamese social media and information science have grown rapidly, the necessity of information-based mining on Vietnamese social media has become crucial. However, state-of-the-art research faces several significant drawbacks, including imbalanced data and noisy data on social media platforms. Imbalanced and noisy are two essential issues that need to be addressed in Vietnamese social media texts. Graph Convolutional Networks can address the problems of imbalanced and noisy data in text classification on social media by taking advantage of the graph structure of the data. This study presents a novel approach based on contextualized language model (PhoBERT) and graph-based method (Graph Convolutional Networks). In particular, the proposed approach, ViCGCN, jointly trained the power of Contextualized embeddings with the ability of Graph Convolutional Networks, GCN, to capture more syntactic and semantic dependencies to address those drawbacks. Extensive experiments on various Vietnamese benchmark datasets were conducted to verify our approach. The observation shows that applying GCN to BERTology models as the final layer significantly improves performance. Moreover, the experiments demonstrate that ViCGCN outperforms 13 powerful baseline models, including BERTology models, fusion BERTology and GCN models, other baselines, and SOTA on three benchmark social media datasets. Our proposed ViCGCN approach demonstrates a significant improvement of up to 6.21%, 4.61%, and 2.63% over the best Contextualized Language Models, including multilingual and monolingual, on three benchmark datasets, UIT-VSMEC, UIT-ViCTSD, and UIT-VSFC, respectively. Additionally, our integrated model ViCGCN achieves the best performance compared to other BERTology integrated with GCN models

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

Enhancement of polarization property of silane-modified BaTiO3 nanoparticles and its effect in increasing dielectric property of epoxy/BaTiO3 nanocomposites

AbstractThe surface modification of synthesized nano-BaTiO3 particles was carried out using γ-aminopropyl trimethoxy silane (γ-APS) in an ethanol/water solution. The modified particles were characterized by FTIR, TGA, surface charge analysis, and by dielectric constant measurement. The silane molecules were attached to the surface of BaTiO3 particles through SiOBaTiO3 bonds. The γ-APS grafted on BaTiO3 made the dielectric constant of the particles increase at frequencies ≥0.3 kHz in a wide range of temperature (25 °C–140 °C), due to the presence of NH2 groups. The dependence of the polarization vs. electrical field was measured in order to elucidate the dielectric behavior of the silane treated BaTiO3 in comparison to untreated BaTiO3. The nanocomposite based on epoxy resin containing BaTiO3 nanoparticles untreated and treated with γ-APS was also prepared and characterized. The results indicated that the γ-APS-modified BaTiO3 surfaces significantly enhanced the dielectric property of the nanocomposite

Childhood Bacterial Meningitis Surveillance in Southern Vietnam: Trends and Vaccination Implications From 2012 to 2021

Background. This retrospective hospital-based surveillance aimed to assess the epidemiology, causative pathogens trend, and serotypes distribution of pneumococcal meningitis among children aged under 5 years with bacterial meningitis in Southern Vietnam after the introduction of pentavalent vaccine in the Expanded Program on Immunization (EPI). Methods. From 2012 to 2021, cerebrospinal fluid samples were collected from children aged under 5 years with suspected bacterial meningitis at Children’s Hospitals 1 and 2 in Ho Chi Minh City. Probable bacterial meningitis (PBM) cases were identified using biochemistry and cytology. Real-time polymerase chain reaction was used to confirm cases of confirmed bacterial meningitis (CBM) caused by Streptococcus pneumoniae, Haemophilus influenzae, or Neisseria meningitidis. Streptococcus pneumoniae serotyping was performed. Results. Of the 2560 PBM cases, 158 (6.2%) were laboratory-confirmed. The CBM proportion decreased during the 10-year study and was associated with age, seasonality, and permanent residence. Streptococcus pneumoniae was the most common pathogen causing bacterial meningitis (86.1%), followed by H influenzae (7.6%) and N meningitidis (6.3%). The case-fatality rate was 8.2% (95% confidence interval, 4.2%–12.2%). Pneumococcal serotypes 6A/B, 19F, 14, and 23F were the most prevalent, and the proportion of pneumococcal meningitis cases caused by the 10-valent pneumococcal conjugate vaccine (PCV) serotypes decreased from 96.2% to 57.1% during the PCV eras. Conclusions. Streptococcus pneumoniae is the most frequent causative agent of bacterial meningitis in children aged under 5 years in Southern Vietnam over the last decade. Policymakers may need to consider introducing PCVs into the EPI to effectively prevent and control bacterial meningitis

Twelve-Month Outcomes of the AFFINITY Trial of Fluoxetine for Functional Recovery After Acute Stroke: AFFINITY Trial Steering Committee on Behalf of the AFFINITY Trial Collaboration

Background and Purpose: The AFFINITY trial (Assessment of Fluoxetine in Stroke Recovery) reported that oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and seizures. After trial medication was ceased at 6 months, survivors were followed to 12 months post-randomization. This preplanned secondary analysis aimed to determine any sustained or delayed effects of fluoxetine at 12 months post-randomization. Methods: AFFINITY was a randomized, parallel-group, double-blind, placebo-controlled trial in adults (n=1280) with a clinical diagnosis of stroke in the previous 2 to 15 days and persisting neurological deficit who were recruited at 43 hospital stroke units in Australia (n=29), New Zealand (4), and Vietnam (10) between 2013 and 2019. Participants were randomized to oral fluoxetine 20 mg once daily (n=642) or matching placebo (n=638) for 6 months and followed until 12 months after randomization. The primary outcome was function, measured by the modified Rankin Scale, at 6 months. Secondary outcomes for these analyses included measures of the modified Rankin Scale, mood, cognition, overall health status, fatigue, health-related quality of life, and safety at 12 months. Results: Adherence to trial medication was for a mean 167 (SD 48) days and similar between randomized groups. At 12 months, the distribution of modified Rankin Scale categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio, 0.93 [95% CI, 0.76–1.14]; P =0.46). Compared with placebo, patients allocated fluoxetine had fewer recurrent ischemic strokes (14 [2.18%] versus 29 [4.55%]; P =0.02), and no longer had significantly more falls (27 [4.21%] versus 15 [2.35%]; P =0.08), bone fractures (23 [3.58%] versus 11 [1.72%]; P =0.05), or seizures (11 [1.71%] versus 8 [1.25%]; P =0.64) at 12 months. Conclusions: Fluoxetine 20 mg daily for 6 months after acute stroke had no delayed or sustained effect on functional outcome, falls, bone fractures, or seizures at 12 months poststroke. The lower rate of recurrent ischemic stroke in the fluoxetine group is most likely a chance finding. REGISTRATION: URL: http://www.anzctr.org.au/ ; Unique identifier: ACTRN12611000774921