Develops efficient algorithms for spatial field reconstruction and sensor selection in heterogeneous weather sensor networks.
problem Efficient spatial field reconstruction and query-based sensor set selection in heterogeneous sensor networks.
method Spatial Best Linear Unbiased Estimator (S-BLUE) and Cross Entropy method.
result Efficient algorithms with performance guarantees for spatial field reconstruction and sensor selection.
Method generates dense fields from sparse measurements without needing spatial statistics or examples.
problem Generating dense physical fields from sparse measurements.
method Introduces a differentiable numerical simulator into neural network training.
result Superior results on fluid mechanics problems compared to statistical and neural network methods.
Bayesian approach improves rain field reconstruction using CMLs and DMs.
problem Challenges in accurately reconstructing ground-level rainfall from CML path-integrated measurements.
method Bayesian inverse problem with Diffusion Models as priors.
result Improved performance in rainfall estimation compared to existing methods.
Sparse elasticity reconstruction from local displacements reduces error.
problem Reconstructing elasticity from limited data.
method Sparse elasticity reconstruction theory, local clustering, alternating optimization.
result Higher spatial resolution elasticity distribution estimation.
Non-linear image reconstruction and signal analysis deal with complex inverse problems. To tackle such problems in a systematic way, I present information field theory (IFT) as a means of Bayesian, data based inference on spatially distributed signal fields. IFT is a statistical field theory, which permits the construc…
New method reduces model selection sample complexity for geometric graphs.
problem Model selection in Gaussian Markov fields with sample deficiency.
method Introducing spatial stationarity to geometric graphs, developing information-theoretic bounds and efficient reconstruction techniques.
result Spatial stationarity leads to significant reduction in sample complexity for consistent recovery.
Random Fourier features model reconstructs wind fields from sparse measurements.
problem Reconstructing wind fields from limited data.
method Random Fourier features approximating velocity field with adaptive sampling.
result Random Fourier features model outperforms benchmarks.
Hybrid framework merges data and domain knowledge for better spatial interpolation.
problem Spatial interpolation overlooks domain knowledge and limits to spatial coordinates.
method Integrates data-driven features with rule-assisted spatial dependency function mapping.
result Superior performance in two application scenarios, capturing localized features.
Paper presents a method for robust surface reconstruction from noisy gradients using adaptive dictionary learning.
problem Reconstructing surfaces from noisy photometric stereo normal vector maps.
method Adaptive dictionary learning to sparsely represent spatial patches of the surface, enforcing smoothness constraints.
result The method effectively learns the underlying surface structure and is robust to noise.
Paper proposes a new loss function to improve image reconstruction quality.
problem Blurred images when using pixel loss for convolutional autoencoders.
method Introduces spatial frequency loss (SFL) to mitigate blurring.
result Reduced blurs in reconstructed images using SFL.
Bayesian approach for adaptive radio tomography using SLFs.
problem Accurately modeling spatial loss fields for interference management.
method Variational Bayes framework with hidden Markov random field model.
result Efficient field estimators at reduced complexity.
Paper proposes dp-VAE for preserving spatial context in gene expression data.
problem Inaccessibility of spatial context in single-cell gene expression data.
method Generic representation learning and transfer learning framework with a distance-preserving regularizer.
result dp-VAE effectively reconstructs and imputes spatial context from gene expression data.
Spatial Deconfounder tackles interference and confounding in spatial data.
problem Interference and unmeasured spatial factors confound causal inference in spatial domains.
method Two-stage method using CVAE with spatial prior to reconstruct confounder, then estimate causal effects.
result Nonparametric identification of direct and spillover effects under weak assumptions.
The paper improves GP regression for sparse sensor data in structural mode shape reconstruction.
problem Reconstructing full-field structural mode shapes from sparse sensor data.
method Physics-Constrained Single-Output Gaussian Process (CONS-SOGP) framework.
result The proposed method provides more accurate and reliable mode shapes.
Gaussian processes improved for ocean current reconstruction and divergence identification.
problem Reconstructing ocean currents from sparse buoy data.
method Proposed a Helmholtz decomposition-based approach to Gaussian processes for better physical modeling.
result Improved inference on ocean currents and divergence identification with minimal computational cost.
Jointly estimates flow fields and particle properties from Lagrangian data.
problem Estimating flow fields and particle properties from sparse, noisy Lagrangian data.
method Data assimilation framework coupling Eulerian and Lagrangian models.
result Joint estimation of flow fields and particle properties in various flow regimes.
A framework uses deep learning for spatio-temporal data prediction.
problem Interpolation of continuous spatio-temporal fields on irregular points.
method Decomposes spatio-temporal processes into products of basis functions and spatial coefficients.
result Effectiveness in reconstructing coherent spatio-temporal fields.
New method reconstructs networks from spatiotemporal data.
problem Network reconstruction from spatiotemporal data.
method Multivariate Hawkes processes using both temporal and spatial information.
result Spatiotemporal approach yields improved network reconstruction.
E-LMC improves spatial field prediction accuracy by linearizing complex fields.
problem Predicting complex spatial fields with high accuracy and efficiency.
method Introducing an invertible neural network to linearize nonlinear spatial fields, enabling the use of LMC for nonlinear problems.
result Maximum improvement of about 40% over original LMC, outperforming other models.
KCS improves parametric maps from PET images by reducing noise and variance.
problem Improving the quality of parametric maps from PET images due to noise.
method Kinetic Compressive Sensing (KCS) method based on a hierarchical Bayesian model and novel reconstruction algorithm.
result KCS produces spatially coherent images and parametric maps with lower noise and better contrast.
Kernel Dynamic Mode Decomposition reconstructs dynamical systems using Laplacian kernel.
problem Reconstructing spatial-temporal dynamics of complex systems.
method Kernel Dynamic Mode Decomposition with Laplacian kernel.
result Laplacian kernel allows for the closability of Koopman operators in RKHS, enabling reconstruction.
New algorithm improves sparse-view tomography without needing ground-truth data.
problem Poor image reconstructions with sparse projections and non-uniform sensors.
method Unsupervised deep learning with CNN and STN modules.
result Significantly outperforms filtered backprojection in sparse-view scenarios.
3d-SMRnet speeds up MPI system matrix recovery to 1 minute with high quality.
problem Slow system matrix recovery in MPI due to recalibration.
method 3d-System Matrix Recovery Network using deep learning.
result 3d-SMRnet recovers 3d system matrix with 64x subsampling in 1 minute.
Paper defines spatial risk measures for analyzing extreme events.
problem Risk assessment of extreme environmental events.
method Introduces spatial risk measures and axioms, investigates conditions for asymptotic spatial homogeneity.
result Conditions for spatial risk measures to satisfy asymptotic spatial homogeneity are provided.
CNNs predict spatial fields from sparse data.
problem Predicting complete spatial fields from limited observations.
method Convolutional Neural Networks (CNNs) trained on a single partially observed field.
result CNNs can flexibly capture local spatial patterns without explicit covariance modeling.
Paper speeds up visualization of uncertain data.
problem High computational cost in reconstructing data uncertainties.
method Subdivide data spatially, adaptively reconstructing only necessary values, using GPR kernel and saved data observations to estimate upper bounds for level-crossing probabilities.
result Accurate estimation of value occurrence probabilities with low computation cost.
In this work we study the non-parametric reconstruction of spatio-temporal dynamical Gaussian processes (GPs) via GP regression from sparse and noisy data. GPs have been mainly applied to spatial regression where they represent one of the most powerful estimation approaches also thanks to their universal representing p…
Jointly correct bias fields and reconstruct undersampled MRI images.
problem Recovering fully sampled MRI images from undersampled data while accounting for bias field differences.
method An unsupervised learning-based reconstruction algorithm combined with a N4-based bias field estimation method in a joint optimization scheme.
result The proposed method improves reconstruction quality, both visually and in terms of RMSE.
Image-to-image networks speed up SAR model parameter estimation.
problem Computational infeasibility of MLE for large, non-stationary spatial fields.
method Used image-to-image networks to estimate SAR model parameters.
result Image-to-image networks enable faster and more accurate parameter estimation.
STICC clusters geographic objects considering both spatial contiguity and attributes.
problem Discovering repeated geographic patterns with spatial contiguity.
method Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method.
result STICC significantly outperforms baseline methods in adjusted rand index and macro-F1 score.
Proposes bivariate DeepKriging for efficient wind field prediction.
problem Challenges in predicting large-scale bivariate wind fields with high spatial variability and heterogeneity.
method Spatially dependent deep neural network (DNN) with embedding layer using spatial radial basis functions.
result Outperforms traditional cokriging predictors and reduces computation time.
New variational model preserves image contrasts and features using Weingarten map minimization.
problem Image reconstruction with preservation of contrasts and features.
method Variational model with L1 norm of Weingarten map, ADMM algorithm, gradient descent. result The proposed models preserve image contrasts and features efficiently.
Paper presents a new algorithm for predicting crop yield across fields.
problem Predicting within-field spatial variability of crop yield in complex environments.
method Spatial-temporal Multi-Task Learning algorithm integrating multiple data sources.
result Algorithm outperforms conventional methods in predicting crop yield.
Investigates transfer learning in spatial statistics.
problem Applying transfer learning to spatial statistics.
method Simple MLP models for spatial data.
result Potential of transfer learning in spatial statistics.
We consider the traffic data reconstruction problem. Suppose we have the traffic data of an entire city that are incomplete because some road data are unobserved. The problem is to reconstruct the unobserved parts of the data. In this paper, we propose a new method to reconstruct incomplete traffic data collected from …
SaR-SVM-STV improves hyperspectral image classification with shape-adaptive reconstruction and denoising.
problem Classifying hyperspectral images with limited labeled data.
method Shape-adaptive Reconstruction (SaR) for pixel preprocessing, SVM for probability estimation, and Smoothed Total Variation (STV) for denoising.
result SaR-SVM-STV outperforms SVM-STV with fewer labeled data.
Diffusion MRI (dMRI) provides the ability to reconstruct neuronal fibers in the brain, in vivo, by measuring water diffusion along angular gradient directions in q-space. High angular resolution diffusion imaging (HARDI) can produce better estimates of fiber orientation than the popularly used diffusion tens…
New model reconstructs flow from sparse data with uncertainty quantification.
problem Reconstructing nonlinear flow from limited observations.
method Semi-Conditional Variational Autoencoder (SCVAE) for probabilistic flow reconstruction.
result SCVAE improves reconstruction accuracy compared to Gappy Proper Orthogonal Decomposition (GPOD).
Capsule networks improve classification accuracy on complex data.
problem CNN's failure to account for spatial hierarchies and lack of rotational invariance.
method Dynamic routing and reconstruction regularization to create capsules that are both rotation invariant and spatially aware.
result Capsule networks achieve a state-of-the-art 0.25% test error on MNIST without data augmentation.
Spatial Adapter adds structured spatial representation to frozen predictors.
problem Efficiently adding spatial structure to pre-trained models.
method Structured spatial decomposition and closed-form covariance for residual fields.
result Adapter improves spatial prediction and uncertainty quantification.
Reconstructing signature features from randomized vector fields in differential equations.
problem Reconstructing signature features from controlled differential equations with random vector fields.
method Using controlled ordinary differential equations driven by continuous bounded variation curves, the study explores the extent to which signature features can be reconstructed from the non-linear flow of these equations.
result The number of signature features that can be reconstructed from the non-linear flow of controlled ordinary differential equations with random vector fields is exponential in the hidden dimension, under certain conditions.
A new compressed sensing system speeds up PSTE detection.
problem Accurately detecting fast, small optical events (PSTEs) is challenging.
method Developed a novel compressed sensing algorithm for rolling shutter systems.
result Accurately recovered PSTEs with spatial undersampling, showing speed and quality advantages.
Neural net reconstructs dark matter density from halo velocities.
problem Reconstructing local dark matter density from halo velocities.
method Hybrid architecture combining U-Net and DeepSets.
result Hybrid network recovers density amplitudes and phases better than U-Net.
Study quantifies risk of extreme wind events using spatial risk measures.
problem Assessing risk of impacts from extreme wind events.
method Spatial risk measure axioms, Brown-Resnick max-stable random fields, powers of max-stable random fields.
result Spatial risk measures associated with extreme wind speeds satisfy risk measure axioms.
Holographic reconstruction over local fields using Radon transform.
problem Holographic reconstruction of quantum fields in anti-de Sitter space over local fields.
method Novel construction of Radon transform and its inverse on anti-de Sitter space over local fields.
result Holographic bulk reconstruction can be formulated as the inverse Radon transform.
Paper analyzes learning from ghost imaging without reconstruction bottleneck.
problem High-speed cell classification bottleneck in ghost cytometry.
method Theoretical analysis of learning from ghost imaging without reconstruction.
result Theoretical analysis supports learning from ghost imaging without reconstruction.
Study on future stability of FLRW spacetime solutions with decelerated expansion.
problem Stability of solutions to Einstein equations coupled with a nonlinear scalar field.
method Decomposition of metric and scalar field perturbations into spatial averages and oscillatory remainders.
result Future-stability of FLRW spacetime solutions for 1/3<p<1. The article recovers tensor fields from partial data using weighted divergent ray transforms.
problem Recovering tensor fields from partial data.
method Weighted divergent ray transforms, unique continuation property of fractional Laplacian, explicit reconstruction formulas.
result Recovery of symmetric m-tensor fields and unique continuation for vector fields and symmetric 2-tensor fields.