arXiv research

Study characterizes PM2.5 dynamics in Bujumbura using low-cost sensors.

Artificial Neural Network predicts PM2.5 pollution with low-cost sensors.

Study compares geostatistical and machine learning models for PM2.5 prediction.

This work investigates the framework and performance issues of the composite neural network, which is composed of a collection of pre-trained and non-instantiated neural network models connected as a rooted directed acyclic graph for solving complicated applications. A pre-trained neural network model is generally well…

The study corrects measurement error in evaluating health effects of multiple pollutants.

We prove boundedness and polynomial decay statements for solutions to the spin $\pm2$ generalized Teukolsky system on a Reissner-Nordström background with small charge. The first equation of the system is the generalization of the standard Teukolsky equation in Schwarzschild for the extreme component of the curvature $…

Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this paper, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality rel…

Low-cost sensors improve air quality prediction accuracy significantly.

Team aims to predict particulate matter levels on ISS using Bi-GRU.

Characterizes a specific type of alternating knot.

Study prenatal PM2.5 exposure and 4th grade reading scores, identifying critical windows of susceptibility.

Study estimates personalized effects of maternal PM2.5 exposure on birth weight.

DeepKriging uses DNNs to predict spatial data with improved accuracy and scalability.

Engine forecasts NO2, O3, PM2.5, PM10 with high accuracy.

Causal discovery is a fundamental problem in statistics and has wide applications in different fields. Transfer Entropy (TE) is a important notion defined for measuring causality, which is essentially conditional Mutual Information (MI). Copula Entropy (CE) is a theory on measurement of statistical independence and is …

This paper analyzes air pollution trends in Rwanda using low-cost sensors and machine learning.

Mobile and ubiquitous sensing of urban air quality has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. This paper proposes a machine learning based mobile air pollution sensing framework, called Deep-MAPS, and demons…

Trisections are obtained by regluing surface-knots in 4-manifolds.

Scattering theory developed for linearised gravity near Schwarzschild black hole.

Photo-identification (photo-id) of dolphin individuals is a commonly used technique in ecological sciences to monitor state and health of individuals, as well as to study the social structure and distribution of a population. Traditional photo-id involves a laborious manual process of matching each dolphin fin photogra…

In this paper, we prove the linear stability to gravitational and electromagnetic perturbations of the Reissner-Nordström family of charged black holes with small charge. Solutions to the linearized Einstein-Maxwell equations around a Reissner-Nordström solution arising from regular initial data remain globally bounded…

In epidemiology, identifying the effect of exposure variables in relation to a time-to-event outcome is a classical research area of practical importance. Incorporating propensity score in the Cox regression model, as a measure to control for confounding, has certain advantages when outcome is rare. However, in situati…

Study uses open data to improve traffic emissions estimation.

Develops a method for causal inference in recurrent event data with terminal failure.

Scattering theory for linearised gravity on Schwarzschild black hole exterior.

This paper presents an engine able to predict jointly the real-time concentration of the main pollutants harming people's health: nitrogen dioxyde (NO2), ozone (O3) and particulate matter (PM2.5 and PM10, which are respectively the particles whose size are below 2.5 um and 10 um). The engine covers a large part of the …

We prove boundedness and polynomial decay statements for solutions to the spin $\pm1$ Teukolsky-type equation projected to the $\ell=1$ spherical harmonic on Reissner-Nordström spacetime. The equation is verified by a gauge-invariant quantity which we identify and which involves the electromagnetic and curvature tensor…

REGAIN learns optimal auxiliary directions for forecast reconciliation.

Many real-world applications involve multivariate, geo-tagged time series data: at each location, multiple sensors record corresponding measurements. For example, air quality monitoring system records PM2.5, CO, etc. The resulting time-series data often has missing values due to device outages or communication errors. …

DeepKriging uses neural networks for spatio-temporal interpolation and forecasting.

The dynamic nature of air quality chemistry and transport makes it difficult to identify the mixture of air pollutants for a region. In this study of air quality in the Houston metropolitan area we apply dynamic principal component analysis (DPCA) to a normalized multivariate time series of daily concentration measurem…

High levels of air pollution may seriously affect people's living environment and even endanger their lives. In order to reduce air pollution concentrations, and warn the public before the occurrence of hazardous air pollutants, it is urgent to design an accurate and reliable air pollutant forecasting model. However, m…

Bayesian model tackles spatial count data issues with flexible non-parametric techniques.

Researchers develop a new method to assess variable importance in spatial machine learning models for air pollution exposure prediction.