DeepFDR uses deep learning for better FDR control in neuroimaging data.
problem Spatial dependence among voxel-based tests in neuroimaging data.
method DeepFDR leverages unsupervised deep learning-based image segmentation.
result DeepFDR outperforms existing methods in FDR control and computational efficiency.
fcHMRF-LIS controls FDR in neuroimaging data, improving power and scalability.
problem Complex spatial dependencies and high variability in FDR control methods for neuroimaging data.
method fcHMRF-LIS integrates LIS-based testing with fcHMRF to model spatial structures efficiently.
result fcHMRF-LIS achieves accurate FDR control, lower FNR, and higher true positives compared to existing methods.
Novel SCUSUM detects weak spatial signals more efficiently.
problem Detecting weak clustered signals in spatial data.
method Spatial CUSUM (SCUSUM) using CUSUM procedure and false discovery rate control.
result SCUSUM achieves high classification accuracy for weak spatial signals.
A novel multi-resolution cluster detection (MCD) method is proposed to identify irregularly shaped clusters in space. Multi-scale test statistic on a single cell is derived based on likelihood ratio statistic for Bernoulli sequence, Poisson sequence and Normal sequence. A neighborhood variability measure is defined to …
New method detects and locates changes in spatio-temporal point processes.
problem Detecting and localizing changes in spatio-temporal data.
method Score-based, likelihood-free approach estimating change time and region.
result The method provides theoretical guarantees on detection and localization accuracy.
This paper improves MARL for networked systems through new protocols and discount factors.
problem Improving control in networked systems using multi-agent reinforcement learning.
method Formulated as a spatiotemporal Markov decision process, introduced a spatial discount factor, and proposed NeurComm.
result Appropriate spatial discount factor enhances learning curves of non-communicative MARL algorithms.
Recent work has shown that reinforcement learning (RL) is a promising approach to control dynamical systems described by partial differential equations (PDE). This paper shows how to use RL to tackle more general PDE control problems that have continuous high-dimensional action spaces with spatial relationship among ac…
Survey of robust clustering methods for hotspot detection.
problem Detecting false positives in spatial hotspot mapping.
method Statistically rigorous clustering techniques.
result Survey of models and algorithms for robust clustering.
New method relaxes spatial invariance in locally connected layers, improving accuracy.
problem Improving classification accuracy with locally connected layers.
method Designing a low-rank locally connected layer with varying spatially varying combining weights.
result Relaxing spatial invariance improves classification accuracy over convolution and locally connected layers.
Develops statistical methods for rates of change on Riemannian manifolds.
problem Statistical inference for rates of change in spatial processes over non-Euclidean domains.
method Formalizes smoothness and constructs differential processes for Riemannian manifolds, derives conditions for kernel existence, and develops predictive inference.
result Validates theoretical findings through simulation experiments for derivatives over polyhedral meshes.
Spatially relaxed inference tackles high-dimensional linear models with correlated covariates.
problem Accurate inference is challenging in high-dimensional settings with spatially correlated covariates.
method Proposes ensembled clustered inference algorithms that control the δ δ δ -FWER under standard assumptions. result Ensembled clustered inference algorithms control the δ δ δ -FWER and achieve decent power. New method controls false discoveries in structured hypothesis spaces.
problem Controlling false discoveries in large-scale, interconnected hypothesis spaces.
method Reproducing Kernel Hilbert Space (RKHS) optimization for structured FDR control.
result Unified framework for continuous domains, graphs, and hierarchies.
Sharp Lipschitz bounds for flow-matching and diffusion models with optimal sampling rates.
problem Establishing optimal Lipschitz regularity for flow-matching and diffusion models.
method Sharp Lipschitz regularity theory for flow-matching vector fields and diffusion-model scores.
result Achieves optimal sampling rate of d / N \sqrt{d}/N d / N for Euler-type samplers in dimension d d d . New method improves convergence of spatial filters in neural networks.
problem Poor convergence behavior of spatial filters in neural networks.
method Correlated initialization for spatial filters.
result Uncorrelated initialization leads to poor convergence and slow training of some parameters.
Study on infinitely-wide CNNs and their adaptability to function spatial scales.
problem Understanding how CNNs efficiently learn high-dimensional functions and their adaptability to function spatial scales.
method Study infinitely-wide deep CNNs in the kernel regime, characterizing their spectrum and using generalisation bounds to prove adaptability.
result Deep CNNs adapt to the spatial scale of the target function, with error decay controlled by the effective dimensionality of function subsets.
New video compression algorithm outperforms existing codecs.
problem Efficient video compression with low latency.
method End-to-end learned video coding architecture.
result Significantly outperforms existing codecs across bitrate ranges.
Traditional voxel-level multiple testing procedures in neuroimaging, mostly p p p -value based, often ignore the spatial correlations among neighboring voxels and thus suffer from substantial loss of power. We extend the local-significance-index based procedure originally developed for the hidden Markov chain models, whic…
New method recovers relative rates in spatial compositional data from IMS.
problem Challenges in analyzing spatial data from IMS due to competitive sampling.
method Hierarchical Variational Graph Fused Lasso using heavy-tailed graphical lasso prior and automatic differentiation variational inference.
result Our method outperforms state-of-the-practice point estimate methodologies in IMS and has superior posterior coverage.
Future stability of FLRW solutions in expanding 3D space is shown for compact perturbations.
problem Future stability of expanding FLRW solutions with spatial topology R^3.
method Nonlinear stability analysis of spherically symmetric perturbations.
result Decay rates of energy momentum tensor components compared to Minkowski space.
Proposes a deep neural network for spatial data regression.
problem Regression of spatial data using deep neural networks.
method Localized two-layer deep neural network for spatial data, proving consistency and asymptotic convergence.
result Asymptotic convergence rate is faster than existing methods, demonstrating effectiveness on temperature estimation.
New model estimates species population trends from citizen science data.
problem Interannual confounding in citizen science data.
method Double Machine Learning framework to estimate population change and propensity scores for confounding adjustment.
result Spatially detailed trend estimates from citizen science data with low error rates.
Uniform consistency proven for spatial distribution and depth estimators in any dimension.
problem Uniform consistency of spatial distribution and depth estimators in arbitrary dimensions.
method Proof of uniform L 1 L^1 L 1 -consistency using sample size n n n as the only dependency. result Consistency rate is independent of dimension d d d and sample size n n n . An accurate assessment of the risk of extreme environmental events is of great importance for populations, authorities and the banking/insurance/reinsurance industry. Koch (2017) introduced a notion of spatial risk measure and a corresponding set of axioms which are well suited to analyze the risk due to events having …
DFIV uses deep features for IV regression, achieving optimal rates.
problem Optimal IV regression with deep features for complex target functions.
method Two-stage approach: deep feature learning followed by IV regression.
result DFIV achieves minimax optimal learning rate under certain conditions.
Develops spatial uncertainty guarantees for image segmentation models.
problem Ensuring reliable segmentation predictions for biomedical images.
method Adapting conformal inference to imaging, using transformed logit scores and calibration datasets.
result Confidence sets provide spatial uncertainty guarantees with desired probability.
Novel approach uses Gaussian processes to estimate conflict trends.
problem Estimating temporal and spatial patterns of violent conflict.
method Highly disaggregated conflict event data with Gaussian processes.
result Powerful conflict forecasts and insights into conflict dynamics.
Method predicts spatial values with few data using GP framework.
problem Few data limit predictive performance in spatial regression.
method Trains neural network to infer task representation from small data, uses GP framework to predict spatial values.
result Proposed method achieves better predictive performance than meta-learning methods.
Gradient boosting for spatial regression models improves prediction accuracy.
problem Spatial data with autoregressive disturbances.
method Model-based gradient boosting algorithm for spatial regression models.
result Improves prediction accuracy on out-of-sample spatial data.
Develops a new method for pricing GMWBs with jumps and stochastic interest rates.
problem Pricing guaranteed minimum withdrawal benefits (GMWBs) with jumps and stochastic interest rates.
method Combines semi-Lagrangian method with Fourier pricing and Green's function.
result Mathematically demonstrates convergence to the viscosity solution of the HJB-QVI.
Deep nets with massive data learn spatially sparse functions.
problem Understanding the importance of massive data in deep learning.
method Established a sampling theorem and proved optimal learning rates.
result Massive data is crucial for deep nets to learn spatially sparse functions.
The study introduces measures of collective mobility from aggregated OD data.
problem Understanding large-scale mobility patterns from aggregated data.
method Developed a framework using synthetic and real data to interpret network-level mobility.
result Aggregated mobility measures reveal network structure and flow constraints.
Framework for precise recall control in spatial conflation tasks.
problem Precise recall control in large-scale spatial conflation tasks to avoid downstream analytics failures and excessive manual review.
method End-to-end framework using equigrid bounding-box filter, CSR representation, neural ranker, and inverse-variance weighted ensemble of threshold estimators.
result Achieves exact recall with sub-percent variance over tens of millions of geometry pairs, runs on a single TPU v3 core.
Deep-learning method solves BSVIEs and coupled systems.
problem High-dimensional, time-inconsistent stochastic control problems.
method Trains a neural network to approximate solution fields directly.
result Non-asymptotic error bound and scalable performance.
New method controls error in low-dimensional marginals of spatial models.
problem Inaccurate approximation of low-dimensional marginals in spatial models.
method Stein's method with δ-locality condition for spatial models.
result Uniform error bound for marginals of approximate distributions.
Gradient boosting algorithm for spatial panel models improves estimation in high-dimensional settings.
problem Estimation failure in high-dimensional spatial panel models.
method Model-based gradient boosting algorithm for spatial panel models with random and fixed effects.
result Feasibility and interpretability in both low- and high-dimensional settings.
Latent FxLMS accelerates ANC by adapting along low-dimensional filter weights.
problem Improving active noise control with neural adaptive filters.
method Training an auto-encoder on filter coefficients, constraining weights to latent variables, and updating in latent space.
result Latent FxLMS converges in fewer steps with comparable error to standard FxLMS.
Paper presents a method for geographic ratemaking using spatial embeddings.
problem Lack of historical loss data in areas with high exposures.
method Construct spatial features within a complex representation model and use them as inputs to a predictive model.
result Predictions have smaller bias and variance than other spatial interpolation models.
New CH covariance class improves spatial statistics by balancing differentiability and tail behavior.
problem Lack of control over mean-square differentiability and tail behavior in Matérn covariance functions.
method Developed a new Confluent Hypergeometric (CH) covariance class using a scale mixture of Matérn and polynomial covariances.
result The CH class offers improved theoretical properties and better performance in extrapolative settings.
This study prioritizes temporal resolution over spatial in energy systems models due to higher influence.
problem The impact of spatial and temporal resolution on energy system models.
method Global sensitivity analysis to compare structural aspects, spatial, and temporal resolution.
result Temporal resolution has a higher influence on all results parameters compared to spatial resolution.
Hybrid model integrates GATv2 and geostatistics for better spatial prediction and uncertainty.
problem Accurate spatial prediction and uncertainty quantification in epidemiology and risk analysis.
method Integrates Graph Attention Network (GATv2) with model-based geostatistics (MBG) to capture relational and spatial dependencies.
result Hybrid model improves predictive accuracy and uncertainty quantification compared to standalone models.
SX-GeoTree improves spatially coherent explanations in geospatial regression trees.
problem Capturing spatial dependence and producing robust explanations in tabular prediction models.
method Integrates three objectives: impurity reduction, spatial residual control, and explanation robustness via modularity maximization on a consensus similarity network.
result Improves residual spatial evenness and doubles attribution consensus (modularity: Fujian 0.19 vs 0.09; Seattle 0.10 vs 0.05).
A new framework enhances IDW models for complex industrial datasets.
problem Low performance of IDW models in complex industrial datasets.
method Deep reinforcement learning network to enhance IDW models and learn hyperparameters.
result The proposed framework achieves differential spatial prediction and is more accurate than current IDW models.
The paper develops online methods to control familywise error rate in growing hypothesis testing sequences.
problem Controlling familywise error rate in a growing sequence of hypotheses over time.
method Unified algorithmic concepts for offline and online FWER control, including new adaptive online algorithms.
result Substantial gains in power demonstrated and formally proved in a Gaussian sequence model.
Develops control and observer methods for complex systems.
problem Controlling and observing infinite-dimensional systems with boundary actuation.
method Energy-Casimir method and port-Hamiltonian system representation.
result Control law and observer designed for Kirchhoff-Love plate example.
Deep learning method for semiparametric regression of spatial data.
problem Estimating relationships between response and covariates in spatially dependent data.
method A sparsely connected deep neural network with ReLU activation function.
result The method is consistent and can handle large datasets.
Study controls error rates of binary classifiers using hypothesis testing.
problem Traditional binary classifiers have uncontrolled error rates.
method Combines binary classification with statistical hypothesis testing.
result Trained classifiers can be made to meet target error rate thresholds.
GeoConformal predicts spatial uncertainty without relying on specific models.
problem Measuring uncertainty in spatial predictions to enhance model credibility.
method GeoConformal Prediction integrates geographical weighting into conformal prediction.
result GeoConformal achieves higher coverage rates in uncertainty assessment compared to Bootstrap methods.
The paper tackles ride-hailing fleet repositioning with a calibrated demand approach.
problem Repositioning idle supply before future demand is observed in ride-hailing.
method A predict-then-optimize approach using calibrated demand regimes, a similarity gate, and spatial queue-regret decomposition.
result The spatial gate reduces mean wait time to 82.3s compared to 85.3s for a hand-tuned similarity gate and 85.8s for a distributional-only baseline.