Neural networks predict EV charging station usage from network layout.
problem Designing optimal EV charging station networks.
method Used neural networks to predict usage from station layout.
result Quickly estimates average usage statistics from proposed station placements.
Two approaches detect EV charging patterns at stations.
problem Identify charging patterns at electric vehicle charging stations.
method Two approaches: rule-based and hierarchical clustering.
result Hierarchical clustering revealed unexpected charging patterns.
Challenge forecasts EV charging station usage accurately.
problem Better understanding of EV charging behaviors needed.
method Hierarchical forecasting of EV charging station occupancy.
result Hierarchical forecasting improves EV charging station occupancy predictions.
Develops a combinatorial semi-bandit method for electric vehicle charging station selection.
problem Long-distance navigation for BEVs with unknown charging station availability and performance.
method Combinatorial semi-bandit framework, pre-processing road network, Bayesian modeling, Thompson Sampling, BayesUCB, Epsilon-greedy.
result Demonstrates improved navigation performance on long-distance BEV charging station selection.
A federated graph learning approach improves EV charging demand forecasting while protecting against cyberattacks.
problem Cybersecurity risk and data heterogeneity in EV charging demand forecasting.
method Federated Graph Neural Network (GNN) model with global attention mechanism and credit-based function.
result Enhanced robustness and prediction accuracy in EV charging demand forecasting.
New RL approach for coordinating EV charging stations.
problem Uncertainty in EV customer behavior models.
method Markov decision process (MDP) formulation, batch reinforcement learning.
result Optimal charging policy learned with batch reinforcement learning.
Proposes AtCoR for predicting bike station usage, improving station network reconfiguration.
problem Challenges in predicting new bike stations due to lack of historical data.
method AtCoR algorithm that predicts both existing and new bike stations using station-centered heatmaps and historical correlations.
result AtCoR outperforms existing models in predicting bike station usage.
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.
Predict bike flow at station-level with multi-graph CNNs.
problem Fine-grained bike flow prediction for station-level management.
method Multi-graph convolutional neural network model.
result Reduces 25.1% and 17.0% prediction error in New York City and Chicago.
Model infers street-level air quality using mobile station data.
problem Inferring air quality from limited mobile station data.
method Variational Graph Autoencoder for matrix completion on graph-based data.
result Model outperforms state-of-the-art approaches in air quality inference.
Study models extreme skew surges along French Atlantic coast.
problem Appropriate modelling of extreme skew surges for coastal risk management.
method Peak-over-threshold framework, multivariate generalized Pareto distribution, extreme regression framework.
result Reconstructed historical skew surge time series at stations with limited data.
New RNN model fuses sensor data from multiple stations.
problem Modeling distributed sensor networks for future behavior prediction.
method Multi-Encoder-Decoder RNN architecture with attention mechanism.
result Model improves prediction accuracy on real-world sensor datasets.
This paper examines the charged Riemannian Penrose inequality with and without charged matter.
problem When does the charged Riemannian Penrose inequality hold with charged matter?
method Revisited Jang's proof and constructed counterexamples to explore conditions for the inequality.
result Charged Riemannian Penrose inequality holds with suitable conditions on charged matter, but requires charge density not changing sign.
Study on charged parallel spinors and mass-charge inequalities.
problem Equality case of the spin positive mass theorem with charge.
method Investigation of charged parallel spinors and application to extremal charged manifolds.
result Characterization of the equality case of the mass-charge inequality.
PeakWeather provides Swiss weather station data for machine learning.
problem Accurate weather forecasting for various activities and decision-making.
method High-quality dataset of surface weather observations from 8 years of Swiss stations.
result Dataset supports a wide range of spatiotemporal tasks.
New methods optimize wireless base station tuning in dense networks.
problem Optimizing wireless base station parameters in dense cellular networks.
method Parallel contextual bandit approaches using Thompson sampling and deterministic UCB.
result Thompson sampling outperforms manual tuning and contextual UCB in real base station network data.
Hybrid model improves wind speed prediction accuracy using MLP and WOA.
problem Improving wind speed prediction accuracy for renewable energy control.
method Combining MLP with Whale Optimization Algorithm (WOA) for data preprocessing and model optimization.
result The hybrid MLP-WOA model outperformed standalone MLP model in wind speed prediction accuracy.
A new model predicts bike-sharing station demands using graph neural networks.
problem Predicting hourly bike-sharing station demands in large networks.
method Graph Convolutional Neural Network (GCNN-DDGF) with various adjacency matrices.
result GCNNrec-DDGF model outperforms other models in predicting bike-sharing demands.
Paper proposes ICWT method to balance sewer flow and WWTP capacity.
problem Balancing spatial mismatch of sewer flow and WWTP capacity.
method Hydraulic model and LSTM for multi-step ahead water level prediction.
result LSTM outperforms other models in predicting water levels.
Area-charge inequalities and local rigidity of free boundary MOTS in charged initial data sets
problem Area-charge inequalities and local rigidity of free boundary MOTS in charged initial data sets
method Area-charge inequalities and local rigidity of free boundary MOTS in charged initial data sets
result Prove area-charge inequalities for free boundary MOTS in initial data sets for the Einstein-Maxwell equations with vanishing magnetic fields
In this paper we investigate the extension of the charged Riemannian Penrose inequality to the case where charges are present outside the horizon. We prove a positive result when the charge densities are compactly supported, and present a counterexample when the charges extend to infinity. We also discuss additional ex…
Charge measurements for instantons and gravitational perturbations.
problem Evaluating charges in Hermitian non-Kähler Einstein 4-manifolds and their perturbations.
method Evaluation of charges via Killing spinors and perturbation analysis of gravitational instantons.
result Generic gravitational perturbations admit a closed 2-form measuring the charge change.
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.
FedSpace optimizes ML training on satellites and ground stations.
problem Training machine learning models on satellites with limited bandwidth.
method Federated Learning framework that dynamically schedules model aggregation based on satellite orbits.
result Reduces training time by 1.7 days over state-of-the-art algorithms.
The paper proves a new theorem linking mass and electric charge for certain types of manifolds.
problem Proving a new positive mass theorem for manifolds with charge.
method Using conformal relations and scalar curvature, the authors derive a new theorem.
result The sum of mass is not less than the modulus of total electric charge under certain conditions.
Researchers resolved ambiguities in gravitational radiation charges.
problem Ambiguities in charges related to gravitational radiation.
method Addressed supertranslation ambiguities in classical and extended BMS algebras.
result Proposed and proved an invariant charge free from supertranslation ambiguity.
A framework assesses the quality of crowdsourced weather data.
problem Quality control and assessment of crowdsourced weather data from third-party stations.
method Proposes a simple, scalable, and interpretable AI/Stats/ML framework to assess TPAWS data.
result Demonstrates the performance of the framework using synthetic and real data.
We present a proof of the Riemannian Penrose inequality with charge r ≤ m + m 2 − q 2 r\leq m + \sqrt{m^2-q^2} r ≤ m + m 2 − q 2 , where A = 4 π r 2 A=4πr^2 A = 4 π r 2 is the area of the outermost apparent horizon with possibly multiple connected components, m m m is the total ADM mass, and q q q the total charge of a strongly asymptotically flat initial data set for the Einste…
System predicts ice formation to improve road safety.
problem Improving road safety through accurate ice formation prediction.
method Combining physical models and machine learning for data correction.
result Machine learning effectively corrects incorrect data in forecasts.
Paper proposes Q-learning for efficient aerial BS placement to improve fairness in mobile networks.
problem Optimal placement of aerial base stations to enhance fairness in a dynamic user mobility environment.
method Reinforcement learning approach to solve the NP-hard problem of 3D placement.
result Simulation results show increased fairness among users with a reasonable computing time and solution close to optimal.
Integrating wind power into the grid is challenging because of its random nature. Integration is facilitated with accurate short-term forecasts of wind power. The paper presents a spatio-temporal wind speed forecasting algorithm that incorporates the time series data of a target station and data of surrounding stations…
We show how to reduce the general formulation of the mass-angular momentum-charge inequality, for axisymmetric initial data of the Einstein-Maxwell equations, to the known maximal case whenever a geometrically motivated system of equations admits a solution. It is also shown that the same reduction argument applies to …
Proves Penrose inequality with charge for 2-convex initial data sets.
problem Proving Penrose inequality with charge for 2-convex initial data sets.
method Uses Dong's 2-convexity condition and P-inverse mean curvature flow, modifying monotonicity formula for charge term.
result Establishes Penrose inequality with charge for 2-convex initial data sets.
The paper proves positive energy-momentum theorems for charged AdS initial data sets.
problem Proving positive energy-momentum theorems for charged asymptotically AdS initial data sets.
method Introducing a charged energy-momentum functional and establishing positive theorems under a dominant energy condition.
result The charged energy-momentum functional is non-negative on a natural real cone.
Study of charged scalar fields on Reissner-Nordström spacetimes via energy estimates.
problem Understanding the behavior and stability of charged scalar fields on near-extremal Reissner-Nordström spacetimes.
method Global integrated energy decay and boundedness estimates for solutions to the charged scalar field equation.
result Established global, weighted integrated energy decay and boundedness estimates for solutions on (near-)extremal Reissner-Nordström(--de Sitter) spacetimes.
Paper proposes efficient UAV placement for aerial base stations.
problem Efficient placement of UAVs as aerial base stations to serve varying traffic demand.
method Modeling UAV deployment as a non-cooperative game and using a learning-based algorithm to update UAV locations.
result Significant performance gains up to 52% and 74% in terms of throughput and dropped users compared to an optimized baseline algorithm.
Two oppositely charged droplets of (say) water in e.g. oil or air will tend to drift together under the influence of their charges. As they make contact, one might expect them to coalesce and form one large droplet, and this indeed happens when the charge difference is sufficiently small. However, Ristenpart et al disc…
Survey of reinforcement learning for sustainable energy challenges.
problem Sequential decision-making challenges in sustainable energy.
method Reinforcement learning applied to energy production, storage, transmission, and consumption.
result Survey identifies various reinforcement learning themes and potential future directions.
Paper presents a simple method for accurate user localization in urban areas.
problem Poor GPS performance in dense urban environments.
method Localization based on pathloss using base station signal strengths and RadioUNet approximations.
result Accurate user location extraction in urban environments.
Extends Young integral to Hölder differential forms in arbitrary dimensions.
problem Extending the Young integral to Hölder differential forms in arbitrary dimensions.
method Introducing a complex of cochains, α-fractional charges, and defining the exterior product between them.
result The exterior product between α-fractional and β-fractional charges is defined when α + β > 1.
A universal inequality that bounds the charge of a body by its size is presented, and is proven as a consequence of the Einstein equations in the context of initial data sets which satisfy an appropriate energy condition. We also present a general sufficient condition for the formation of black holes due to concentrati…
Classifies charge-3 monopoles with symmetry, identifying new spectral curves.
problem Classifying charge-3 monopoles with symmetry.
method Constructing Nahm data and identifying spectral curves with elliptic quotients.
result New monopole spectral curves with D 6 D_6 D 6 and V 4 V_4 V 4 symmetry identified. Proof confirms cosmic censorship for charged gravitational collapse.
problem Cosmic censorship for Einstein-Maxwell-Charged Scalar Field system.
method Systematic approach to incorporate charge and complex scalar field, new trapped surface criterion, modified BV area estimates, and new instability theorems.
result Cosmic censorship holds for charged gravitational collapse.
Researchers prove charged Penrose inequality and positive mass theorem for specific manifold types.
problem Proving inequalities for charged initial data sets with cylindrical ends.
method Doubling argument and application of existing results by Weinstein, Yamada, and Khuri, Weinstein, Yamada.
result Established charged Penrose inequality and positive mass theorem for time symmetric initial data sets with cylindrical ends.
Second part of series studying charged scalar fields on Reissner--Nordström spacetimes.
problem Analyzing late-time behavior and stability of charged scalar fields on black hole backgrounds.
method Purely physical-space based methods, energy estimates, inverse-power laws.
result First pointwise decay estimates for charged scalar fields on black hole backgrounds.
Study shows how charged MOTS restrict spacetime configurations.
problem Understanding charged initial data sets and their rigidity.
method Analyzes marginally outer trapped surfaces with charge considerations.
result New rigidity results for charged initial data sets.
Positive energy theorems for spin initial data with charge in higher dimensions.
problem Establishing positive energy theorems for spin initial data with charge in dimensions n ≥ 4 n \geq 4 n ≥ 4 . method Using a dominant energy condition and asymptotically flat ends, extending classical theorems.
result Extending classical positive energy theorems to spin initial data with charge.
Gaussian Process Regression accurately models daily pan evaporation in humid climates.
problem Precise estimation of pan evaporation in humid climates using data-based methods.
method Gaussian Process Regression and other machine learning techniques were used to estimate pan evaporation.
result GPR models with specific meteorological parameters performed best in estimating pan evaporation.