Study introduces a probabilistic framework for air-sea fluxes using neural networks.
problem Accurately quantifying air-sea fluxes for understanding interactions and improving weather/climate models.
method Gaussian distributions conditioned on input variables, artificial neural networks, eddy-covariance data, minimizing negative log-likelihood loss.
result Trained neural networks provide alternative mean flux estimates and quantify uncertainty.
New model clusters mixed-type data with missing values, improving air quality analysis.
problem Clustering mixed-type data with missing values and regime persistence.
method Statistical jump model incorporating regime persistence and handling missing data.
result Superior performance in inferring persistent air quality regimes compared to traditional methods.
AirRL uses RL to infer urban air quality from selected stations.
problem Inferring fine-grained urban air quality from limited monitoring stations.
method Reinforcement learning model with a dynamic station selector and air quality regressor.
result AirRL achieves highest performance in air quality inference experiments.
Recently, air pollution is one of the most concerns for big cities. Predicting air quality for any regions and at any time is a critical requirement of urban citizens. However, air pollution prediction for the whole city is a challenging problem. The reason is, there are many spatiotemporal factors affecting air pollut…
Study shows reducing anthropogenic emissions significantly lowers PM2.5 levels but has little effect on O3 in Delhi.
problem Understanding and mitigating the effects of anthropogenic emissions on air pollution in Delhi.
method Predictive modeling, causal inference, Gaussian Process modeling, Granger causality analysis.
result Reductions in anthropogenic emissions lead to significant decreases in PM2.5 levels but have little effect on O3. DGPs improve air quality inference from sparse data.
problem Accurate air quality monitoring in unmonitored areas.
method Deep Gaussian Processes with Doubly Stochastic Variational Inference.
result DGPs outperform state-of-the-art models in AQ inference.
The paper optimizes air conditioning setpoints using machine learning.
problem Static setpoints waste energy and increase costs.
method Deep-learning model based on RNNs for predicting future temperatures.
result RNNs outperform state-of-the-art models in prediction accuracy.
Paper uses Gaussian Processes to monitor air quality in Kampala.
problem Monitoring air pollution in cities with limited sensor coverage.
method Gaussian Processes for nowcasting and forecasting air pollution.
result Demonstrates the effectiveness of Gaussian Processes in air quality monitoring.
Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., m…
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…
This paper analyzes air pollution trends in Rwanda using low-cost sensors and machine learning.
problem Lack of reliable air pollution data in Rwanda due to high costs of equipment.
method Analysis of existing data and development of forecasting models using low-cost sensors and machine learning.
result Proposes forecasting models for air pollution data collected by low-cost sensors.
Early fault detection using instrumented sensor data is one of the promising application areas of machine learning in industrial facilities. However, it is difficult to improve the generalization performance of the trained fault-detection model because of the complex system configuration in the target diagnostic system…
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…
AIR-Net adapts low-rank regularization dynamically for better image completion.
problem Fixed low-rank regularization limits adaptability to different images.
method AIR-Net uses adaptive and implicit regularization parameterized by a dynamic Laplacian matrix.
result AIR-Net enhances implicit regularization and outperforms fixed methods in non-uniform missing data scenarios.
The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence e…
In this work we present Discrete Attend Infer Repeat (Discrete-AIR), a Recurrent Auto-Encoder with structured latent distributions containing discrete categorical distributions, continuous attribute distributions, and factorised spatial attention. While inspired by the original AIR model andretaining AIR model's capabi…
GraphSVR forecasts urban air pollution robustly across stations and seasons.
problem Nonlinear, nonstationary, spatiotemporally dependent urban air pollution forecasting challenges.
method Combines graph convolutional learning and support vector regression.
result GraphSVR improves predictive accuracy and maintains stable performance across seasons and outlier-prone episodes.
Engine forecasts NO2, O3, PM2.5, PM10 with high accuracy.
problem Accurate long-term air quality forecasting.
method Convolutional LSTM network trained on grid data.
result 4-day forecasts significantly outperform simple benchmarks.
Low-cost sensors improve air quality prediction accuracy significantly.
problem Improving air quality monitoring networks with affordable sensors.
method Developed a high-resolution air quality prediction engine using low-cost sensors and official data.
result The use of low-cost sensors improves prediction accuracy by 25% and 15% for PM2.5 and PM10 respectively in densely monitored areas.
Researchers develop a new method to assess variable importance in spatial machine learning models for air pollution exposure prediction.
problem Understanding the mechanism captured by machine learning models in air pollution studies, especially with spatial correlation.
method Leave-one-out approach for variable importance measure applicable to models with separable mean and covariance components.
result The new method highlights differences in model mechanisms even for similar prediction accuracies.
Air traffic control is a real-time safety-critical decision making process in highly dynamic and stochastic environments. In today's aviation practice, a human air traffic controller monitors and directs many aircraft flying through its designated airspace sector. With the fast growing air traffic complexity in traditi…
Air quality is closely related to public health. Health issues such as cardiovascular diseases and respiratory diseases, may have connection with long exposure to highly polluted environment. Therefore, accurate air quality forecasts are extremely important to those who are vulnerable. To estimate the variation of seve…
DCK improves air quality index prediction with probabilistic spatial models.
problem Non-Gaussian, complex spatial structure of air quality index.
method Deep classifier kriging (DCK) for non-Gaussian, nonlinear spatial prediction.
result DCK outperforms conventional methods in predictive accuracy and uncertainty quantification.
Proposes HOTA-FedGradNorm for faster and robust PFL in noisy channels.
problem Statistical heterogeneity across clients in federated learning.
method Dynamic weighting, OTA aggregation, hierarchical federated learning.
result Training speed improvement and robustness against channel effects.
The economic and social impact of poor air quality in towns and cities is increasingly being recognised, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and t…
DSGD with OAC-MAC scheme achieves better convergence in noisy wireless networks.
problem Performance of decentralized SGD in noisy wireless environments.
method Proposed OAC-MAC scheme for over-the-air computation in DSGD.
result OAC-MAC scheme converges faster with fewer communication rounds.
Work addresses long-term accuracy issues in IoT air quality sensors.
problem Limited accuracy of IoT air quality sensors in long-term field deployments.
method Adaptive machine learning strategies for network calibration.
result Prolongs the validity of multisensor calibration models for continuous learning.
Weather2vec learns representations to adjust for non-local confounding in air pollution studies.
problem Non-local confounding in evaluating environmental policies and climate events on health outcomes.
method weather2vec framework using balancing scores to learn representations of non-local information.
result The framework effectively adjusts for confounding in air pollution studies.
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…
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…
Paper proposes MA-BERT for efficient data-driven ATM models.
problem Long training time and need for large datasets in data-driven ATM models.
method Multi-Agent Bidirectional Encoder Representations from Transformers (MA-BERT) and transfer learning framework.
result MA-BERT saves training time and achieves high performance with little data.
Machine learning is used to compute achievable information rates (AIRs) for a simplified fiber channel. The approach jointly optimizes the input distribution (constellation shaping) and the auxiliary channel distribution to compute AIRs without explicit channel knowledge in an end-to-end fashion.
Tackling air pollution is an imperative problem in South Korea, especially in urban areas, over the last few years. More specially, South Korea has joined the ranks of the world's most polluted countries alongside with other Asian capitals, such as Beijing or Delhi. Much research is being conducted in environmental sci…
RESPIRE calibrates low-cost air-quality sensors for CO levels, resistant to outliers.
problem Calibrating LCAQ sensors against regulatory-grade monitors is expensive and time-consuming.
method PROvably outlier-resistant semi-parametric regression technique.
result RESPIRE offers improved prediction in cross-site, cross-season, and cross-sensor settings.
We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable deep generative model for videos of moving objects. It can reliably discover and track objects throughout the sequence of frames, and can also generate future frames conditioning on the current frame, thereby simulating expected motion of objects. Th…
Compact models for methane/air combustion reduce complexity without sacrificing accuracy.
problem Creating accurate, computationally efficient models for methane combustion.
method Data-oriented three-step methodology: 1) Remove non-essential species, 2) Numerically optimize to key species profiles, 3) Machine learning to refine parameters.
result Produced 19 and 15 species compact models that outperform current state-of-the-art models in accuracy and range of conditions.
New method combines neural networks and data assimilation for indoor air quality prediction.
problem Accurate and fast prediction of indoor air quality using real-time data.
method Combines Data Assimilation and Machine Learning using a Convolutional neural network and Long-Short-Term-Memory.
result Improved accuracy of dynamic system representation by integrating real data.
Optimizing over-the-air convex optimization, analog schemes are nearly optimal at low SNR.
problem Optimizing over-the-air convex optimization with coded gradients.
method Analyzes coded gradients over an additive Gaussian noise channel, considers analog coding schemes.
result Analog coding schemes nearly match the optimal convergence rate at low SNR, but a slowdown is inevitable.
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 …
Predicts local AQI using mobile sensor data, improving accuracy by 71.654 MSE.
problem Inaccurate AQI data from sparse sensors in developing countries.
method Spatio-temporal GNNs for fine-grained AQI forecasting.
result Significant improvement in AQI prediction accuracy (71.654 MSE reduction).
New framework aligns latent representations over-the-air using intelligent metasurfaces.
problem Heterogeneous transmitter-receiver models produce misaligned latent representations in semantic communication.
method Intelligent metasurfaces (SIM) emulate supervised and zero-shot semantic aligners directly in the wave domain.
result SIMs achieve up to 90% task accuracy in high SNR regimes, robust to low SNR.
New algorithm tracks subspaces with missing and corrupted data, simpler and federated.
problem Subspace tracking with missing and corrupted data.
method Proposes a novel algorithm that does not assume piecewise constant subspace changes and is simpler.
result Guarantees for both subspace tracking with missing data and outliers.
China's rapid economic growth resulted in serious air pollution, which caused substantial losses to economic development and residents' health. In particular, the road transport sector has been blamed to be one of the major emitters. During the past decades, fluctuation in the international oil prices has imposed signi…
Interpretable additive models outperform complex DL and hybrid pipelines for air quality forecasting.
problem Accurate forecasting of urban air pollution for public health and policy guidance.
method Investigated lightweight additive models (FBP, NP) vs. deep learning and hybrid pipelines on Beijing PM2.5 and PM10 data.
result Facebook Prophet consistently outperformed NeuralProphet and traditional models, achieving high R2 values. Modeling air pollutants using data-driven techniques and sparse identification of nonlinear dynamics.
problem Predicting concentrations of air pollutants using hidden physical laws.
method Sparse identification of nonlinear dynamics (SINDy) for parsimonious systems of ordinary differential equations.
result More than half of the critical points are saddle points, indicating system instability.
A new framework uses deep reinforcement learning to improve aircraft separation in busy airspace.
problem Improving aircraft separation in high-density, dynamic airspace constrained by human controllers.
method Proximal Policy Optimization with an attention network for distributed vehicle autonomy.
result The framework significantly reduces offline training time and increases performance.
New deep learning model optimizes energy use in buildings.
problem Optimizing energy use and comfort in large buildings.
method Transformer-based metamodel trained with simulation and sensor data, calibrated with CMA-ES, optimized with multi-objective algorithms.
result Optimal settings reduce energy loads while maintaining thermal comfort and air quality.
Study improves PM concentration forecasting using MCCR loss.
problem Forecasting particulate matter concentration in South Korea.
method Used MCCR loss for regression analysis of air pollution and weather data.
result MCCR loss is more effective for extreme value forecasting.