arXiv subjects

Yefeng Wang

Publications and source records attributed to Yefeng Wang.

3 recordsLinked to original sources

arXiv2025

Effects of Early-Universe Inhomogeneity on Bubble Formation: Primordial Black Holes as an Extreme Case

Our early Universe is not perfectly homogeneous and it may contain some inhomogeneous sources, which might distort the local spacetime and modify the bubble nucleation rate. Taking the primordial black hole as an extreme example, we investigate the bubble nucleation rate of a first-order phase transition in the vicinity of primordial black holes or other primordial gravitational sources. Our analysis reveals that the presence of primordial black holes can reduce the effective action and might modify the nucleation rate due to their gravitational effects, potentially altering the dynamics of the phase transition in the early universe and producing new gravitational wave signals since the gravitational effects of the primordial black hole or other possible inhomogeneous sources could lead to nucleation of non-spherical symmetric bubbles.

hep-ph↗

arXiv2021

Deep Learning Models in Detection of Dietary Supplement Adverse Event Signals from Twitter

Objective: The objective of this study is to develop a deep learning pipeline to detect signals on dietary supplement-related adverse events (DS AEs) from Twitter. Material and Methods: We obtained 247,807 tweets ranging from 2012 to 2018 that mentioned both DS and AE. We annotated biomedical entities and relations on 2,000 randomly selected tweets. For the concept extraction task, we compared the performance of traditional word embeddings with SVM, CRF and LSTM-CRF classifiers to BERT models. For the relation extraction task, we compared GloVe vectors with CNN classifiers to BERT models. We chose the best performing models in each task to assemble an end-to-end deep learning pipeline to detect DS AE signals and compared the results to the known DS AEs from a DS knowledge base (i.e., iDISK). Results: In both tasks, the BERT-based models outperformed traditional word embeddings. The best performing concept extraction model is the BioBERT model that can identify supplement, symptom, and body organ entities with F1-scores of 0.8646, 0.8497, and 0.7104, respectively. The best performing relation extraction model is the BERT model that can identify purpose and AE relations with F1-scores of 0.8335 and 0.7538, respectively. The end-to-end pipeline was able to extract DS indication and DS AEs with an F1-score of 0.7459 and 0,7414, respectively. Comparing to the iDISK, we could find both known and novel DS-AEs. Conclusion: We have demonstrated the feasibility of detecting DS AE signals from Twitter with a BioBERT-based deep learning pipeline.

cs.CL↗

arXiv2019

Analyzing Social Media Data to Understand Consumers' Information Needs on Dietary Supplements

Despite the high consumption of dietary supplements (DS), there are not many reliable, relevant, and comprehensive online resources that could satisfy information seekers. The purpose of this research study is to understand consumers' information needs on DS using topic modeling and to evaluate its accuracy in correctly identifying topics from social media. We retrieved 16,095 unique questions posted on Yahoo! Answers relating to 438 unique DS ingredients mentioned in sub-section, "Alternative medicine" under the section, "Health". We implemented an unsupervised topic modeling method, Correlation Explanation (CorEx) to unveil the various topics consumers are most interested in. We manually reviewed the keywords of all the 200 topics generated by CorEx and assigned them to 38 health-related categories, corresponding to 12 higher-level groups. We found high accuracy (90-100%) in identifying questions that correctly align with the selected topics. The results could be used to guide us to generate a more comprehensive and structured DS resource based on consumers' information needs.

cs.CY↗