SPADE4: Sparsity and Delay Embedding based Forecasting of Epidemics

Predicting the evolution of diseases is challenging, especially when the data availability is scarce and incomplete. The most popular tools for modelling and predicting infectious disease epidemics are compartmental models. They stratify the population into compartments according to health status and model the dynamics of these compartments using dynamical systems. However, these predefined systems may not capture the true dynamics of the epidemic due to the complexity of the disease transmission and human interactions. In order to overcome this drawback, we propose Sparsity and Delay Embedding based Forecasting (SPADE4) for predicting epidemics. SPADE4 predicts the future trajectory of an observable variable without the knowledge of the other variables or the underlying system. We use random features model with sparse regression to handle the data scarcity issue and employ Takens' delay embedding theorem to capture the nature of the underlying system from the observed variable. We show that our approach outperforms compartmental models when applied to both simulated and real data.Comment: 24 pages, 13 figures, 2 table

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

Ranking load in microgrid based on fuzzy analytic hierarchy process and technique for order of preference by similarity to ideal solution algorithm for load shedding problem

This paper proposes a method to rank the loads in the microgrid by means of a weight that combines the criteria together in terms of both technical and economic aspects. The fuzzy analytic hierarchy process technique for order of preference by similarity to ideal solution (fuzzy AHP TOPSIS) algorithm is used to calculate this combined weight. The criteria to be considered are load importance factor (LIF), voltage electrical distance (VED) and voltage sensitivity index (VSI). The fuzzy algorithm helps to fuzzy the judgment matrix of the analytic hierarchy process (AHP) method, making it easier to compare objects with each other and remove the uncertainty of the AHP method. The technique for order of preference by similarity to ideal solution (TOPSIS) algorithm is used to normalize the decision matrix, determine the positive and negative ideal solutions to calculate the index of proximity to the ideal solution, and finally rank all the alternatives. The combination of fuzzy AHP and TOPSIS algorithms is the optimal combination for decision making and ranking problems in a multi-criteria environment. The 19-bus microgrid system is applied to calculate and demonstrate the effectiveness of the proposed method

F2SD: A dataset for end-to-end group detection algorithms

The lack of large-scale datasets has been impeding the advance of deep learning approaches to the problem of F-formation detection. Moreover, most research works on this problem rely on input sensor signals of object location and orientation rather than image signals. To address this, we develop a new, large-scale dataset of simulated images for F-formation detection, called F-formation Simulation Dataset (F2SD). F2SD contains nearly 60,000 images simulated from GTA-5, with bounding boxes and orientation information on images, making it useful for a wide variety of modelling approaches. It is also closer to practical scenarios, where three-dimensional location and orientation information are costly to record. It is challenging to construct such a large-scale simulated dataset while keeping it realistic. Furthermore, the available research utilizes conventional methods to detect groups. They do not detect groups directly from the image. In this work, we propose (1) a large-scale simulation dataset F2SD and a pipeline for F-formation simulation, (2) a first-ever end-to-end baseline model for the task, and experiments on our simulation dataset.Comment: Accepted at ICMV 202

A community participatory intervention model to reduce the health risks from biogas wastewater in Hanam Province, Vietnam

In Vietnam, using biogas to treat livestock waste is common, in particular on small holder farms. However, most small holder farms do not know how to use biogas correctly and wastewater can affect health and the environment. Using a participatory approach with farmers and other stakeholders we developed and implemented a set of interventions in Hanam province to reduce health risks from biogas wastewater. Twenty-four pig farmers were selected as a "core group" to be instrumental in developing the interventions and training other farmers to correctly use biogas. The intervention model was piloted for 6 months. Several outputs were obtained including i) approval and enforcement of a "huong uoc - village law" on environmental protection; ii) training of 24 farmers from the core group in communication skills to share information on using biogas; iii) development of a 6-step program of pig cage cleaning to limit waste loaded to biogas to improve the efficiency of biogas production; iv) a health monitoring books for humans and animals for use by families in the community. The results provided evidence that applying the participatory approach can lead to improved knowledge and practices of farmer using biogas and can reduce the health risks from biogas wastewater

Global Evolution of Obesity Research in Children and Youths: Setting Priorities for Interventions and Policies

Background: Childhood obesity has become a major global epidemic that causes substantial social and health burdens worldwide. The effectiveness of childhood obesity control and prevention depends largely on understanding the issue, including its current development and associated factors in a contextualized perspective. Objectives: Our study aimed to gauge this kind of understanding. Methods: We systematically searched the Web of Science database for studies concerning child obesity published up to 2017 and analyzed the volume of publications, growth rates, impact scores, collaborations, authors, affiliations, and journals. A total of 57,444 research papers were included. Results: The three subject categories with the highest number of papers (over 3,000) were (1) nutrition and dietetics, (2) pediatrics, and (3) public, environmental, and occupational health. We found a dramatic increase in the amount of scientific literature on childhood obesity in the past one or two decades, led by scholars from the USA – ranking at the top regarding the total number of papers (23,965 papers; 30.8%) and total number of citations (859,793 citations) – and multiple Western countries where the obesity epidemic is prevalent. Conclusions: The findings highlight the need for improving international and local research capacities and collaboration to accelerate knowledge production and translation into contextualized and effective childhood obesity prevention

Using Patch Testing to Improve Therapeutic Outcome in the Treatment of Hand Eczema in Vietnamese Patients

BACKGROUND: Hand eczema is a common chronic and relapsing skin disease with various clinical features. Hand eczema aetiology can be allergic contact dermatitis (ACD), irritant contact dermatitis (ICD), atopic dermatitis (AD) and unknown or combination causes. If the causative agents are not detected treatment of hand eczema will be a failure. A patch test can be useful to detect causative agents in suspected allergic contact hand eczema. Then patients will avoid contacting them. This results in the improvement of hand eczema. In Vietnam, patch test has not been used before, so we conduct this study. AIM: To identify causative allergens by using patch test with 28 standard allergens in consecutive patients. METHODS: A group of 300 HE patients from the National Hospital of Dermatology and Venereology (NHDV) in Vietnam were enrolled in this study. They were divided into 4 groups-ACD, ICD, AD and unknown aetiology. The patient was patch tested with 28 standard allergens to identify the causative agents. RESULTS: Among the 300 HE enrolled patients, ACD accounted for 72.7%, AD and ICD had the same rate of 12.7%. 39.3% of the patients had a positive patch test. Reaction to nickel sulfate was the most common (10.3%), followed by potassium dichromate (9.7%), cobalt (4%) and fragrance mix (3.1%). About one-third of the cases had relevant clinical reactions correlated with the contact agents and clinical history. Males reacted to cement, thiuram mix and formaldehyde more than females, while females reacted to a nickel more than males. CONCLUSIONS: Hand eczema has variable clinical features and diverse aetiology. ACD is an important cause of hand eczema that can be managed with a patch test to detect causative allergens. Nearly 40% of HE cases had positive patch test. Relevant patch test reactions were seen in one-third of the patients. We propose using patch test detect causative agents in suspected allergic contact hand eczema. Then patients will avoid contacting them. This results in the improvement of hand eczema