Within developing countries, a multitude of problems that affect the water supply process can result in the contamination of water taps. While machine learning applications have become popular for attaining efficient water quality predictions, acquiring the necessary data for modelling for developing countries is challenging. This study constructs water quality prediction models by machine learning with a pseudo‐pipeline network to complement the missing data of the water supply process. Using both water source and water tap quality information measured by the Government of Nepal, we apply the three machine learning models: support vector machine (SVM), random forest (RF) and LightGBM. Furthermore, we also apply a traditional statistical method—logistic regression (LR)—to the prediction of the Escherichia coli (E. coli) contamination in water taps. With some input variables (such as the length from the nearest sources) obtained from the pseudo‐pipeline network, the results show that SVM has stable and high accuracy for both the 26 cities (70%) and for the 25 cities except for Kathmandu (79%). LR performed a significantly lower accuracy for all cities (61%) than for 25 cities (79%). Additionally, we show that our method can be applied to other regions where a water quality survey has not yet been conducted.
An aspirational goal for virtual reality (VR) is to bring in a rich diversity of real world objects losslessly. Existing VR applications often convert objects into explicit 3D models with meshes or point clouds, which allow fast interactive rendering but also severely limit its quality and the types of supported objects, fundamentally upperbounding the "realism" of VR. Inspired by the classic "billboards" technique in gaming, we develop Deep Billboards that model 3D objects implicitly using neural networks, where only 2D image is rendered at a time based on the user's viewing direction. Our system, connecting a commercial VR headset with a server running neural rendering, allows real-time high-resolution simulation of detailed rigid objects, hairy objects, actuated dynamic objects and more in an interactive VR world, drastically narrowing the existing real-to-simulation (real2sim) gap. Additionally, we augment Deep Billboards with physical interaction capability, adapting classic billboards from screen-based games to immersive VR. At our pavilion, the visitors can use our off-the-shelf setup for quickly capturing their favorite objects, and within minutes, experience them in an immersive and interactive VR world -with minimal loss of reality. Our project page: https://sites.google.com/view/deepbillboards/
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