On Inference Stability for Diffusion Models

Denoising Probabilistic Models (DPMs) represent an emerging domain of generative models that excel in generating diverse and high-quality images. However, most current training methods for DPMs often neglect the correlation between timesteps, limiting the model's performance in generating images effectively. Notably, we theoretically point out that this issue can be caused by the cumulative estimation gap between the predicted and the actual trajectory. To minimize that gap, we propose a novel \textit{sequence-aware} loss that aims to reduce the estimation gap to enhance the sampling quality. Furthermore, we theoretically show that our proposed loss function is a tighter upper bound of the estimation loss in comparison with the conventional loss in DPMs. Experimental results on several benchmark datasets including CIFAR10, CelebA, and CelebA-HQ consistently show a remarkable improvement of our proposed method regarding the image generalization quality measured by FID and Inception Score compared to several DPM baselines. Our code and pre-trained checkpoints are available at \url{https://github.com/VinAIResearch/SA-DPM}.Comment: Oral presentation at AAAI 202

Health impact of exposure to arsenic-contaminated drinking water in Vietnam

The public health situation of the population of Hanam Province in Vietnam is of great concern, as it is exposed daily to arsenic-contaminated drinking water. Optimising arsenic (As) removal efficiency of current sand filters at household level or switching to cleaner or As-free water sources is crucial to prevent or reduce community health risks

Damage detection for a cable-stayed Bridge under the effect of moving loads using Transmissibility and Artificial Neural Network

Artificial Neural Network (ANN) has been widely used for Structural Health Monitoring (SHM) in the last decades. To detect damage in the structure, ANN often uses input data consisting of natural frequencies or mode shapes. However, this data is not sensitive enough to accurately identify minor structural defects. Therefore, in this study, we propose to use transmissibility to generate input data for the input layer of ANN. Transmissibility uses output signals exclusively to preserve structural dynamic properties and is sensitive to damage characteristics. To evaluate the efficiency of the proposed approach, a cable-stayed bridge with a wide variety of damage scenarios is employed. The results show that the combination of transmissibility and ANN not only accurately detect damages but also outperforms natural frequencies-based ANN in terms of accuracy and computational cost

Damage detection for a cable-stayed Bridge under the effect of moving loads using Transmissibility and Artificial Neural Network

Artificial Neural Network (ANN) has been widely used for Structural Health Monitoring (SHM) in the last decades. To detect damage in the structure, ANN often uses input data consisting of natural frequencies or mode shapes. However, this data is not sensitive enough to accurately identify minor structural defects. Therefore, in this study, we propose to use transmissibility to generate input data for the input layer of ANN. Transmissibility uses output signals exclusively to preserve structural dynamic properties and is sensitive to damage characteristics. To evaluate the efficiency of the proposed approach, a cable-stayed bridge with a wide variety of damage scenarios is employed. The results show that the combination of transmissibility and ANN not only accurately detect damages but also outperforms natural frequencies-based ANN in terms of accuracy and computational cost

Burden of diarrheal diseases from biogas wastewater exposure among smallholder farmers in Ha Nam province, Vietnam

Livestock production has developed rapidly in Vietnam in recent years, particularly at the small-scale which account for 65% of the total livestock production. Biogas systems are commonly used to treat livestock waste, however, the health risks from biogas wastewater exposure at smallholder farms are not yet well understood. A quantitative microbial risk assessment approach was applied to estimate the burden of diarrheal diseases from biogas wastewater exposure among 451 smallholder farmers using biogas systems in Ha Nam province. A total of 150 biogas wastewater samples were collected and analysed for E. coli, Giardia, and Cryptosporidium. The study showed that farmers faced diarrheal disease risks due to exposure to biogas wastewater at different exposure scenarios. The calculated annual risk of diarrheal disease by E. coli ranked from 0.15 to 0.21; by Giardia ranked from 0.022 to 0.095; and by Cryptosporidium ranked from 0.006 to 0.015. The estimated diarrheal diseases burden from pathogens in all exposure scenarios largely exceeded the reference level of health outcome target of 10-6DALYs loss per person per year recommended by WHO. The results suggest the importance in reducing concentrations of pathogens in biogas wastewater before use in the fields as a means for mitigating public health impacts

Policy Response, Social Media and Science Journalism for the Sustainability of the Public Health System Amid the COVID-19 Outbreak: The Vietnam Lessons

Vietnam, with a geographical proximity and a high volume of trade with China, was the first country to record an outbreak of the new Coronavirus disease (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 or SARS-CoV-2. While the country was expected to have a high risk of transmission, as of April 4, 2020—in comparison to attempts to contain the disease around the world—responses from Vietnam are being seen as prompt and effective in protecting the interests of its citizens, with 239 confirmed cases and no fatalities. This study analyzes the situation in terms of Vietnam’s policy response, social media and science journalism. A self-made web crawl engine was used to scan and collect official media news related to COVID-19 between the beginning of January and April 4, yielding a comprehensive dataset of 14,952 news items. The findings shed light on how Vietnam—despite being under-resourced—has demonstrated political readiness to combat the emerging pandemic since the earliest days. Timely communication on any developments of the outbreak from the government and the media, combined with up-to-date research on the new virus by the Vietnamese science community, have altogether provided reliable sources of information. By emphasizing the need for immediate and genuine cooperation between government, civil society and private individuals, the case study offers valuable lessons for other nations concerning not only the concurrent fight against the COVID-19 pandemic but also the overall responses to a public health crisis

On how religions could accidentally incite lies and violence: folktales as a cultural transmitter

Folklore has a critical role as a cultural transmitter, all the while being a socially accepted medium for the expressions of culturally contradicting wishes and conducts. In this study of Vietnamese folktales, through the use of Bayesian multilevel modeling and the Markov chain Monte Carlo technique, we offer empirical evidence for how the interplay between religious teachings (Confucianism, Buddhism, and Taoism) and deviant behaviors (lying and violence) could affect a folktale’s outcome. The findings indicate that characters who lie and/or commit violent acts tend to have bad endings, as intuition would dictate, but when they are associated with any of the above Three Teachings, the final endings may vary. Positive outcomes are seen in cases where characters associated with Confucianism lie and characters associated with Buddhism act violently. The results supplement the worldwide literature on discrepancies between folklore and real-life conduct, as well as on the contradictory human behaviors vis-à-vis religious teachings. Overall, the study highlights the complexity of human decision-making, especially beyond the folklore realm