SpatioTemporal
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
Spatiotemporal Imputation with Graph-Informed Flow Matching
arXiv:2606.06682v1 Announce Type: new Abstract: Missing data is a common challenge in spatiotemporal systems, arising in applications such as air quality monitoring and urban traffic management. Traditional machine learning approaches, like recurrent and graph neural networks, rely on iterative propagation, which tends to accumulate errors over time and space. Recent diffusion-based methods mitigate error propagation but require iterative sampling and often depend on problem-agnostic...
Stimulus-response correlation analysis dissociates spatiotemporal cortical networks supporting speech production
Introduction: Understanding the spatiotemporal distribution of cortical activation during language production is a central question in cognitive neuroscience with broad clinical applications. High spatial/temporal resolution recording over multiple brain regions and specific psycholinguistic manipulations with testable behavioral predictions are necessary to separate neural variance attributable to processing stages. Objective: We combine a delayed naming paradigm with intracranial...
Reaction-transport coupling drives spatiotemporal organization in fuel-driven supramolecular polymerization
Announce Type: replace-cross Abstract: Chemically fueled supramolecular systems provide a versatile platform for generating nonequilibrium structures and dynamical instabilities, including chemical oscillations and traveling waves reminiscent of biological organization. However, a minimal mechanistic framework capable of capturing the emergence of such spatiotemporal order is still lacking. Here, we develop a minimal reaction-transport framework for fuel-driven supramolecular polymerization...
A Hierarchical Spatiotemporal Action Tokenizer for In-Context Imitation Learning in Robotics
Announce Type: replace Abstract: We present a novel hierarchical spatiotemporal action tokenizer for in-context imitation learning. We first propose a hierarchical approach, which consists of two successive levels of vector quantization. In particular, the lower level assigns input actions to fine-grained subclusters, while the higher level further maps fine-grained subclusters to clusters.
MAEPose: Self-Supervised Spatiotemporal Learning for Human Pose Estimation on mmWave Video
arXiv:2605.00242v2 Announce Type: replace Abstract: Millimetre-wave (mmWave) radar offers a more privacy-preserving alternative to RGB-based human pose estimation. However, existing methods typically rely on pre-extracted intermediate representations such as sparse point clouds or spectrogram images, where the rich spatiotemporal information naturally present in radar video streams is discarded for model learning, while such signal processing adds system complexity. In addition, existing...
PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting
Announce Type: replace Abstract: Coupled spatiotemporal forecasting is important for predicting the future evolution of multiple interacting dynamical systems, such as in climate models. However, existing methods are severely constrained by the persistent bottleneck of compounding errors. In coupled systems, errors from each subsystem simulator propagate and amplify one another, a phenomenon we term Reciprocal Error Amplification, leading to a rapid collapse of long-range predictions.
S3Mem: Structured Spatiotemporal Scene-Event Memory for Long-Horizon Interactive Question Answering
arXiv:2605.28831v2 Announce Type: replace Abstract: Long-horizon memory question answering often requires sparse evidence from heterogeneous histories, including events, object states, visual observations, temporal relations, and causal steps. Existing memory interfaces expand reader context, retrieve semantically related chunks, or expose graph neighborhoods, but they are not explicitly designed to select compact evidence for a fixed reader. We propose Structured Spatiotemporal Scene--Event...
AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks
arXiv:2606.01283v1 Announce Type: new Abstract: Modeling spatial dependencies is central to spatiotemporal data analysis using Graph Neural Networks (GNNs). Traditional methods rely on distance-based kernels with predefined parameters, which restricts model capacity. Although generic adaptive mechanisms (e.g., Graph Attention Networks) offer flexibility, they often fail to capture the underlying geometric structure, performing worse than distance-based models in data-sparse scenarios.
HDST-GNN: Heterogeneous Dynamic Spatiotemporal Graph Neural Networks for Multi-Object Tracking in UAV Aerial Imagery
arXiv:2606.05587v1 Announce Type: new Abstract: Multi-object tracking (MOT) from UAV imagery presents unique challenges: altitude varies across sequences, objects are small and densely packed, and frequent occlusion causes identity switches. Existing graph-based trackers assume fixed spatial context and treat all objects uniformly, ignoring the heterogeneous lifecycle states of detections, active tracklets, and lost targets. We propose HDST-GNN, a Heterogeneous Dynamic Spatiotemporal Graph...
From urban runoff to mosquito success : spatiotemporal microbial assembly in larval water habitats under anthropogenic stressors
Urban mosquito habitats are heterogeneous aquatic ecosystems where anthropogenic inputs shape physicochemical conditions and microbial community assembly. However, the combined effects of environmental chemistry and microbial dynamics on mosquito fitness remain poorly understood across space and time. Here, we integrated environmental chemistry, metabarcoding, and experimental assays to investigate how spatiotemporal variation in urban larval habitats influences environmental microbial...