SpatioTemporal Network
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Related Articles from SNS
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...
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...
Analysis of Multi-Tone, Multi-Conductor, Spatially Discrete Traveling-Wave Modulated Loop Networks
arXiv:2606.08233v1 Announce Type: new Abstract: This work presents a semi-analytical framework for analyzing spatially discrete traveling-wave modulated (SDTWM) loop networks, which exhibit cavity-like behavior and support discrete spatiotemporal modes. We introduce a computationally efficient method, based on the Interpath Relation, to analyze periodic networks using a single unit cell.
On the Effect of Neural Field Reparameterization for 4DVAR
Announce Type: replace-cross Abstract: Four-dimensional variational data assimilation (4DVAR) is a cornerstone of numerical weather prediction, yet it remains computationally intensive and sensitive to initialization due to the non-convexity of its objective function. We propose a neural field-based reformulation of 4DVAR in which the spatiotemporal state is represented as a continuous function parameterized by a neural network.
On the Effect of Neural Field Reparameterization for 4DVAR
Announce Type: replace Abstract: Four-dimensional variational data assimilation (4DVAR) is a cornerstone of numerical weather prediction, yet it remains computationally intensive and sensitive to initialization due to the non-convexity of its objective function. We propose a neural field-based reformulation of 4DVAR in which the spatiotemporal state is represented as a continuous function parameterized by a neural network. We demonstrate that optimizing in parameter space leverages the...
ST-ColoNet: Spatio-Temporal Colon Segment Recognition via Hybrid Attention and Edge-Guided Feature Learning
Announce Type: replace Abstract: Colo-segment recognition in colonoscopy videos is a key requirement for many downstream tasks, but existing automatic recognition methods only use colonoscopy images without fully exploiting the use of temporal information, leading to poor performance. Additionally, relevant public video-based datasets are in scarcity. To tackle this problem, we curate and release a labeled dataset specifically for the task of colo-segment recognition.
ST-ColoNet: Spatio-Temporal Colon Segment Recognition via Hybrid Attention and Edge-Guided Feature Learning
arXiv:2605.28119v3 Announce Type: replace Abstract: Colo-segment recognition in colonoscopy videos is a key requirement for many downstream tasks, but existing automatic recognition methods only use colonoscopy images without fully exploiting the use of temporal information, leading to poor performance. Additionally, relevant public video-based datasets are in scarcity. To tackle this problem, we curate and release a labeled dataset specifically for the task of colo-segment recognition.
DAS-PINNs for high-dimensional partial differential equations: extending deep adaptive sampling to spacetime domains
Announce Type: new Abstract: Time-dependent high-dimensional partial differential equations (PDEs) with spatially localised and dynamically evolving solutions pose a fundamental challenge for physics-informed neural networks (PINNs), as uniform collocation sampling becomes increasingly ineffective in high-dimensional spatiotemporal domains. In this work, a deep adaptive sampling framework for PINNs is extended to the time-dependent setting by treating space and time as a unified domain...
MMTalker: Multiresolution 3D Talking Head Synthesis with Multimodal Feature Fusion
Announce Type: replace Abstract: Speech-driven three-dimensional (3D) facial animation synthesis aims to build a mapping from one-dimensional (1D) speech signals to time-varying 3D facial motion signals. Current methods still face challenges in maintaining lip-sync accuracy and producing realistic facial expressions, primarily due to the highly ill-posed nature of this cross-modal mapping. In this paper, we introduce a novel 3D audio-driven facial animation synthesis method through...