General audio tagging with ensembling convolutional neural network and statistical features

Audio tagging aims to infer descriptive labels from audio clips. Audio tagging is challenging due to the limited size of data and noisy labels. In this paper, we describe our solution for the DCASE 2018 Task 2 general audio tagging challenge. The contributions of our solution include: We investigated a variety of convolutional neural network architectures to solve the audio tagging task. Statistical features are applied to capture statistical patterns of audio features to improve the classification performance. Ensemble learning is applied to ensemble the outputs from the deep classifiers to utilize complementary information. a sample re-weight strategy is employed for ensemble training to address the noisy label problem. Our system achieves a mean average precision (mAP@3) of 0.958, outperforming the baseline system of 0.704. Our system ranked the 1st and 4th out of 558 submissions in the public and private leaderboard of DCASE 2018 Task 2 challenge. Our codes are available at https://github.com/Cocoxili/DCASE2018Task2/.Comment: Submitted to ICASS

Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering

Generative question answering (QA) models generate answers to questions either solely based on the parameters of the model (the closed-book setting) or additionally retrieving relevant evidence (the open-book setting). Generative QA models can answer some relatively complex questions, but the mechanism through which they do so is still poorly understood. We perform several studies aimed at better understanding the multi-hop reasoning capabilities of generative QA models. First, we decompose multi-hop questions into multiple corresponding single-hop questions, and find marked inconsistency in QA models' answers on these pairs of ostensibly identical question chains. Second, we find that models lack zero-shot multi-hop reasoning ability: when trained only on single-hop questions, models generalize poorly to multi-hop questions. Finally, we demonstrate that it is possible to improve models' zero-shot multi-hop reasoning capacity through two methods that approximate real multi-hop natural language (NL) questions by training on either concatenation of single-hop questions or logical forms (SPARQL). In sum, these results demonstrate that multi-hop reasoning does not emerge naturally in generative QA models, but can be encouraged by advances in training or modeling techniques.Comment: COLING 202

Research and Engineering Implementation of Automatic Fundus Photography Algorithm for Ultra-Wide Angle Confocal Laser Fundus Scanning System

To achieve automatic photo capture of fundus images in an ultra-wide-angle laser-focused fundus scanning system, a pupil detection and positioning algorithm based on confocal laser technology was proposed and deployed. First, image preprocessing methods such as automatic laser intensity enhancement and contrast adjustment were applied to reduce the impact of noise and interference on subsequent processing. Next, the pupil was quickly and accurately located using the proposed pupil detection and positioning algorithm based on confocal laser technology. Finally, the movement information was calculated based on the position of the pupil center and image center, and the three-axis stepper motor was controlled in a rapid closed- loop manner to achieve three-dimensional automatic tracking of the pupil. Once the shooting conditions were met, the photo was automatically captured. To validate the effectiveness and timeliness of this solution, automatic photo capture tests were conducted on different subjects using a prototype. The success rate of automatic photo capture was 95.6%, with an average time of about 8 seconds per single-eye capture. The fundus images captured by successful automatic photos all met the requirements for automatic capture

Recent progresses in outcome-dependent sampling with failure time data

An outcome-dependent sampling (ODS) design is a retrospective sampling scheme where one observes the primary exposure variables with a probability that depends on the observed value of the outcome variable. When the outcome of interest is failure time, the observed data are often censored. By allowing the selection of the supplemental samples depends on whether the event of interest happens or not and oversampling subjects from the most informative regions, ODS design for the time-to-event data can reduce the cost of the study and improve the efficiency. We review recent progresses and advances in research on ODS designs with failure time data. This includes researches on ODS related designs like case–cohort design, generalized case–cohort design, stratified case–cohort design, general failure-time ODS design, length-biased sampling design and interval sampling design

Co-occurrence of fecal incontinence with constipation or irritable bowel syndrome indicates the need for personalized treatment

Background: This study aimed to compare the prevalence and symptoms of fecal incontinence (FI) in relation to irritable bowel syndrome (IBS-associated FI), constipation (constipation-associated FI), and isolation (isolated FI). Methods: Data were analyzed from 3145 respondents without organic comorbidities known to influence defecation function from the general Chinese population who filled in the online Groningen Defecation and Fecal Continence questionnaire. FI, IBS, and constipation were evaluated with the Rome IV criteria. Key Results: The prevalence of FI was 10.5% (n = 329) in the non-comorbidity group. After multivariable logistic regression analysis, IBS (odds ratio [OR]: 12.55, 95% confidence interval [CI]: 9.06–17.36) and constipation (OR: 4.38, 95% CI: 3.27–5.85) were the most significant factors contributing to FI. Based on this finding, 106/329 (32.2%) had IBS-associated FI, 119/329 (36.2%) had constipation-associated FI, and 104/329 (31.6%) had isolated FI. Among the 329 FI respondents, there was a high prevalence of IBS and constipation-related symptoms, including abdominal pain (81.5%) and abdominal bloating (77.8%) for IBS and straining during defecation (75.4%), incomplete defecation (72.3%), defecation blockage (63.2%), anal pain during defecation (59.3%), and hard stools (24%) for constipation. The patients with IBS-associated FI asked for specialists' help less frequently than those with isolated FI. Interestingly, among the patients with constipation-associated FI, 56.3% used anti-diarrhea medicine. Conclusions and Inferences: The prevalence of IBS-associated FI, constipation-associated FI, and isolated FI is comparably high. It is important to diagnose and target the cause of FI to provide personalized and cause-targeting care instead of treating only the FI symptoms.</p