Research on Multilingual News Clustering Based on Cross-Language Word Embeddings

Classifying the same event reported by different countries is of significant importance for public opinion control and intelligence gathering. Due to the diverse types of news, relying solely on transla-tors would be costly and inefficient, while depending solely on translation systems would incur considerable performance overheads in invoking translation interfaces and storing translated texts. To address this issue, we mainly focus on the clustering problem of cross-lingual news. To be specific, we use a combination of sentence vector representations of news headlines in a mixed semantic space and the topic probability distributions of news content to represent a news article. In the training of cross-lingual models, we employ knowledge distillation techniques to fit two semantic spaces into a mixed semantic space. We abandon traditional static clustering methods like K-Means and AGNES in favor of the incremental clustering algorithm Single-Pass, which we further modify to better suit cross-lingual news clustering scenarios. Our main contributions are as follows: (1) We adopt the English standard BERT as the teacher model and XLM-Roberta as the student model, training a cross-lingual model through knowledge distillation that can represent sentence-level bilingual texts in both Chinese and English. (2) We use the LDA topic model to represent news as a combina-tion of cross-lingual vectors for headlines and topic probability distributions for con-tent, introducing concepts such as topic similarity to address the cross-lingual issue in news content representation. (3) We adapt the Single-Pass clustering algorithm for the news context to make it more applicable. Our optimizations of Single-Pass include ad-justing the distance algorithm between samples and clusters, adding cluster merging operations, and incorporating a news time parameter

The News Delivery Channel Recommendation Based on Granular Neural Network

With the continuous maturation and expansion of neural network technology, deep neural networks have been widely utilized as the fundamental building blocks of deep learning in a variety of applications, including speech recognition, machine translation, image processing, and the creation of recommendation systems. Therefore, many real-world complex problems can be solved by the deep learning techniques. As is known, traditional news recommendation systems mostly employ techniques based on collaborative filtering and deep learning, but the performance of these algorithms is constrained by the sparsity of the data and the scalability of the approaches. In this paper, we propose a recommendation model using granular neural network model to recommend news to appropriate channels by analyzing the properties of news. Specifically, a specified neural network serves as the foundation for the granular neural network that the model is considered to be build. Different information granularities are attributed to various types of news material, and different information granularities are released between networks in various ways. When processing data, granular output is created, which is compared to the interval values pre-set on various platforms and used to quantify the analysis's effectiveness. The analysis results could help the media to match the proper news in depth, maximize the public attention of the news and the utilization of media resources

Using Chinese Version of MYMOP in Chinese Medicine Evaluation: Validity, Responsiveness and Minimally Important Change

<p>Abstract</p> <p>Background</p> <p>Measure Yourself Medical Outcome Profile (MYMOP) is a patient generated outcome instrument applicable in the evaluation of both allopathic and complementary medicine treatment. This study aims to adapt MYMOP into Chinese, and to assess its validity, responsiveness and minimally important change values in a sample of patients using Chinese medicine (CM) services.</p> <p>Methods</p> <p>A Chinese version of MYMOP (CMYMOP) is developed by forward-backward-forward translation strategy, expert panel assessment and pilot testing amongst patients. 272 patients aged 18 or above with subjective symptoms in the past 2 weeks were recruited at a CM clinic, and were invited to complete a set of questionnaire containing CMYMOP and SF-36. Follow ups were performed at 2^ndand 4^thweek after consultation, using the same set of questionnaire plus a global rating of change question. Criterion validity of CMYMOP was assessed by its correlation with SF-36 at baseline, and responsiveness was evaluated by calculating the Cohen effect size (ES) of change at two follow ups. Minimally important difference (MID) values were estimated via anchor based method, while minimally detectable difference (MDC) figures were calculated by distribution based method.</p> <p>Results</p> <p>Criterion validity of CMYMOP was demonstrated by negative correlation between CMYMOP Profile scores and all SF-36 domain and summary scores at baseline. For responsiveness between baseline and 4^thweek follow up, ES of CMYMOP Symptom 1, Activity and Profile reached the moderate change threshold (ES>0.5), while Symptom 2 and Wellbeing reached the weak change threshold (ES>0.2). None of the SF-36 scores reached the moderate change threshold, implying CMYMOP's stronger responsiveness in CM setting. At 2^ndweek follow up, MID values for Symptom 1, Symptom 2, Wellbeing and Profile items were 0.894, 0.580, 0.263 and 0.516 respectively. For Activity item, MDC figure of 0.808 was adopted to estimate MID.</p> <p>Conclusions</p> <p>The findings support the validity and responsiveness of CMYMOP for capturing patient centred clinical changes within 2 weeks in a CM clinical setting. Further researches are warranted (1) to estimate Activity item MID, (2) to assess the test-retest reliability of CMYMOP, and (3) to perform further MID evaluation using multiple, item specific anchor questions.</p

A tale of two cities: effects of air pollution on hospital admissions in Hong Kong and London compared.

The causal interpretation of reported associations between daily air pollution and daily admissions requires consideration of residual confounding, correlation between pollutants, and effect modification. If results obtained in Hong Kong and London--which differ in climate, lifestyle, and many other respects--were similar, a causal association would be supported. We used identical statistical methods for the analysis in each city. Associations between daily admissions and pollutant levels were estimated using Poisson regression. Nonparametric smoothing methods were used to model seasonality and the nonlinear dependence of admissions on temperature, humidity, and influenza admissions. For respiratory admissions (> or = 65 years of age), significant positive associations were observed with particulate matter < 10 microm in aerodynamic diameter (PM(10), nitrogen dioxide, sulfur dioxide, and ozone in both cities. These associations tended to be stronger at shorter lags in Hong Kong and at longer lags in London. Associations were stronger in the cool season in Hong Kong and in the warm season in London, periods during which levels of humidity are at their lowest in each city. For cardiac admissions (all ages) in both cities, significant positive associations were observed for PM(10), NO(2), and SO(2) with similar lag patterns. Associations tended to be stronger in the cool season. The associations with NO(2) and SO(2) were the most robust in two-pollutant models. Patterns of association for pollutants with ischemic heart disease were similar in the two cities. The associations between O(3) and cardiac admissions were negative in London but positive in Hong Kong. We conclude that air pollution has remarkably similar associations with daily cardiorespiratory admissions in both cities, in spite of considerable differences between cities in social, lifestyle, and environmental factors. The results strengthen the argument that air pollution causes detrimental short-term health effects

SOAP3-dp: Fast, Accurate and Sensitive GPU-based Short Read Aligner

To tackle the exponentially increasing throughput of Next-Generation Sequencing (NGS), most of the existing short-read aligners can be configured to favor speed in trade of accuracy and sensitivity. SOAP3-dp, through leveraging the computational power of both CPU and GPU with optimized algorithms, delivers high speed and sensitivity simultaneously. Compared with widely adopted aligners including BWA, Bowtie2, SeqAlto, GEM and GPU-based aligners including BarraCUDA and CUSHAW, SOAP3-dp is two to tens of times faster, while maintaining the highest sensitivity and lowest false discovery rate (FDR) on Illumina reads with different lengths. Transcending its predecessor SOAP3, which does not allow gapped alignment, SOAP3-dp by default tolerates alignment similarity as low as 60 percent. Real data evaluation using human genome demonstrates SOAP3-dp's power to enable more authentic variants and longer Indels to be discovered. Fosmid sequencing shows a 9.1 percent FDR on newly discovered deletions. SOAP3-dp natively supports BAM file format and provides a scoring scheme same as BWA, which enables it to be integrated into existing analysis pipelines. SOAP3-dp has been deployed on Amazon-EC2, NIH-Biowulf and Tianhe-1A.Comment: 21 pages, 6 figures, submitted to PLoS ONE, additional files available at "https://www.dropbox.com/sh/bhclhxpoiubh371/O5CO_CkXQE". Comments most welcom

The epidemiology of COVID-19 cases and the successful containment strategy in Hong Kong-January to May 2020

BACKGROUND: Hong Kong, a Special Administrative Region of China, recorded its first confirmed coronavirus disease 2019 (COVID-19) case on 23 January 2020. We reviewed the case epidemiology and the various public health measures implemented from January to May 2020. METHODS: The epidemiological and clinical characteristics of the cases recorded in different phases of the epidemic were described and compared, and the effectiveness of the public health measures implemented were reviewed using the changes in the daily number of confirmed cases and the interval from symptom onset to hospital admission. RESULTS: Between January and May 2020, 1084 confirmed COVID-19 cases were reported, about 70% of which had a history of travel during the incubation period. The case fatality ratio was 0.4%. The local epidemic progressed through four phases: (1) preparedness and imported infection from mainland China, (2) local transmission, (3) imported infection from overseas countries associated with local transmission, and (4) controlled imported infection with limited local transmission, with an eventual reduction of the daily case number and minimization of the onset-to-admission interval. Various public health measures, including enhanced surveillance, border control, and social distancing, were introduced in phases in response to the prevailing local and global situations. DISCUSSION: The overall containment strategy in Hong Kong led to a stabilization of the number of cases and the absence of a community-wide outbreak during the 4.5 m after the first case was reported. This strategy of containment might serve as an example for future planning of preparedness and response against novel infectious agents

Avian Influenza Risk Perception, Hong Kong

A telephone survey of 986 Hong Kong households determined exposure and risk perception of avian influenza from live chicken sales. Householders bought 38,370,000 live chickens; 11% touched them when buying, generating 4,220,000 exposures annually; 36% (95% confidence interval [CI] 33%–39%) perceived this as risky, 9% (7%–11%) estimated >50% likelihood of resultant sickness, whereas 46% (43%–49%) said friends worried about such sickness. Recent China travel (adjusted odds ratio 0.35; CI 0.13–0.91), traditional beliefs (1.20, 1.06–1.13), willingness to change (0.29, 0.11–0.81) and believing cooking protects against avian influenza (8.66, 1.61-46.68) predicted buying. Birth in China (2.79, 1.43–5.44) or overseas (4.23, 1.43–12.53) and unemployment (3.87, 1.24–12.07) predicted touching. Age, avian influenza contagion worries, husbandry threat, avian influenza threat, and avian influenza anxiety predicted perceived sickness risk. High population exposures to live chickens and low perceived risk are potentially important health threats in avian influenza

Modification by Influenza on Health Effects of Air Pollution in Hong Kong

Background: Both influenza viruses and air pollutants have been well documented as major hazards to human health, but few epidemiologic studies have assessed effect modification of influenza on health effects of ambient air pollutants. Objectives: We aimed to assess modifying effects of influenza on health effects of ambient air pollutants. Methods: We applied Poisson regression to daily numbers of hospitalizations and mortality to develop core models after adjustment for potential time-varying confounding variables. We assessed modification of influenza by adding variables for concentrations of single ambient air pollutants and proportions of influenza-positive specimens (influenza intensity) and their cross-product terms. Results: We found significant effect modification of influenza (p < 0.05) for effects of ozone. When influenza intensity is assumed to increase from 0% to 10%, the excess risks per 10-μg/m 3 increase in concentration of O 3 increased 0.24% and 0.40% for hospitalization of respiratory disease in the all-ages group and ≥ 65 year age group, respectively; 0.46% for hospitalization of acute respiratory disease in the all-ages group; and 0.40% for hospitalization of chronic obstructive pulmonary disease in the ≥ 65 group. The estimated increases in the excess risks for mortality of respiratory disease and chronic obstructive pulmonary disease in the all-ages group were 0.59% and 1.05%, respectively. We found no significant modification of influenza on effects of other pollutants in most disease outcomes under study. Conclusions: Influenza activity could be an effect modifier for the health effects of air pollutants particularly for O 3 and should be considered in the studies for short-term effects of air pollutants on health.published_or_final_versio