Spatial Partition
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Related Articles from SNS
Semantic-decoupled Spatial Partition Guided Point-supervised Oriented Object Detection
arXiv:2506.10601v2 Announce Type: replace Abstract: Given its ability to reduce annotation costs, weakly supervised learning based on single-point annotations has emerged as a research focus in oriented object detection. Compared with the classical teacher-student paradigm, the simple model paradigm (e.g., PointOBB-v2) can substantially further reduce resources required for training while ensuring strong performance.
MeshTok: Efficient Multi-Scale Tokenization for Scalable PDE Transformers
arXiv:2606.04366v1 Announce Type: new Abstract: Conventional patchified Transformers operate on uniform spatial partitions, distributing computational effort evenly across the domain irrespective of local features. This inflexible tokenization scheme is inherently limited in its ability to efficiently represent and process solutions to complex PDEs.
Efficient and Privacy-Preserving Distribution Statistics Analytics on Mobile Spatial Data
arXiv:2605.25791v2 Announce Type: replace Abstract: With the rapid development of mobile computing technology, massive amounts of spatial data are continuously generated from various mobile terminals and sensing devices, such as smartphones, connected vehicles, and drones. Performing efficient distributed statistical analysis on this data is crucial for real-time mobile computing applications. However, the constrained and dynamic nature of mobile environments exacerbates the privacy...
Assessing Predictive Models for Fairness Based on Movement Patterns
arXiv:2605.23234v2 Announce Type: replace Abstract: Assessing the spatial fairness of predictive models involves establishing whether they are statistically penalizing (favoring) individuals associated with certain geographical locations. Literature on this topic makes the fundamental assumption that each individual is assigned to a single geographical location (e.g., place of residence). However, fairness with respect to the set of locations where one has been, i.e., their movement patterns...
L-PCN: A Point Cloud Accelerator Exploiting Spatial Locality through Octree-based Islandization
arXiv:2604.10716v3 Announce Type: replace Abstract: Existing Point Cloud Networks (PCNs) have proven to achieve great success in many point cloud tasks such as object part segmentation, shape classification, and so on. The most popular point-based PCNs are usually composed of two sequential steps: Data Structuring (DS) and Feature Computation (FC). In this paper, we first describe an important characteristic of the PCN-specific DS step that has not been addressed in existing PCN...
FMplex: Model Virtualization for Serving Extensible Foundation Models
arXiv:2606.09643v1 Announce Type: new Abstract: Foundation models (FMs) are increasingly used as backbones for downstream tasks across language, vision, time-series, and multimodal applications. Yet existing model-serving systems deploy each customized task as an independent model instance, thereby replicating heavyweight backbones, wasting accelerator memory, and losing opportunities to amortize batching and loading costs. This paper presents FMplex, a serving system that treats FM...
Privilege Risk Evolution for Non-Human Identities: A Temporal Fiber Model for Cloud IAM
arXiv:2606.03289v1 Announce Type: new Abstract: Cloud permission governance implicitly treats permission equivalence as a static relation. We show that for non-human identities (NHIs), equivalence has two irreducible components: structural equivalence, capturing identical permission profiles at a snapshot via graph fibration, and temporal equivalence, capturing recurring permission states via strongly connected components (SCCs) in a fiber transition graph. We call the equivalence classes...
Shifting the Breaking Point of Flow Matching for Multi-Instance Editing
arXiv:2602.08749v3 Announce Type: replace Abstract: Flow matching models have recently emerged as an efficient alternative to diffusion, especially for text-guided image generation and editing, offering faster inference through continuous-time dynamics. However, existing flow-based editors predominantly support global or single-instruction edits and struggle with multi-instance scenarios, where multiple parts of a reference input must be edited independently without semantic interference.
Shifting the Breaking Point of Flow Matching for Multi-Instance Editing
arXiv:2602.08749v4 Announce Type: replace Abstract: Flow matching models have recently emerged as an efficient alternative to diffusion, especially for text-guided image generation and editing, offering faster inference through continuous-time dynamics. However, existing flow-based editors predominantly support global or single-instruction edits and struggle with multi-instance scenarios, where multiple parts of a reference input must be edited independently without semantic interference. We...
Structural gradients and strain partitioning across the mouse Achilles tendon enthesis revealed by in situ X-ray scattering
Announce Type: new Abstract: The enthesis is the insertion site of tendon into bone and exhibits a high mechanical durability despite the large mismatch in material properties between the two tissues. This durability stems from gradients in composition, structure and organization on multiple hierarchical length scales. Despite extensive research on enthesis structure and mechanics, the local deformation mechanisms are poorly understood.