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新论文 · v1arXiv:2611.15313

Influence of Assortativity on the SICP Spreading Process in Hypergraphs

Jelmer Stolwijk, Renze Dijkhuizen, H. A., Peters, Huijuan Wang

原文摘要

Many real-world systems involve group interactions that cannot be captured by pairwise graphs. They can be better represented by hypergraphs where each hyperedge could connect an arbitrary number of nodes. The Susceptible-Infected Contact Process (SICP) has been proposed to model the spreading of information and epidemics on hypergraphs. However, how higher-order topological properties of a hypergraph influence the SICP spreading process remains understudied. In this paper, we examine the impact of hypergraph assortativity based on the Neighborhood Coefficient (NC)-the average size of hyperedges incident to a node-on the SICP spreading process. By introducing a low-complexity measure of hypergraph assortativity grounded in analysis of variance, we apply hyperdegree-preserving rewirings to real-world networks and synthetic hypergraph models to tune their assortativity. We find and explain why lower NC assortativity yields higher average epidemic prevalence and lower prevalence variance across different seed selections. This phenomenon is evident when hyperdegree-preserving rewirings are applied to real-world networks with heterogeneous topological structures, but it is largely absent when comparing synthetic random hypergraphs to their rewired counterparts.

cs.SI · physics.soc-ph

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.15551

Characterizing Learning in Deep Neural Networks using Tractable Algorithmic Complexity Analysis

Pedram Bakhtiarifard, Sophia N. Wilson, Mahmoud H. A. Afifi, Jonathan Wenshøj, Raghavendra Selvan

原文摘要

Training large-scale deep neural networks (DNNs) is resource-intensive, making model compression a practical necessity. The widely accepted ''learning as compression'' hypothesis posits that training induces structure in network weights, which enables compression. Measuring this structure through Kolmogorov-Chaitin-Solomonoff (KCS) complexity is appealing, but existing estimators based on the Coding Theorem Method (CTM) and the Block Decomposition Method (BDM) are limited to small binary objects and do not scale to modern DNNs. We introduce the Quantized Block Decomposition method (QuBD), which extends algorithmic complexity estimation to any $k$-ary object. QuBD first quantizes the network weights to a finite alphabet, then estimates the KCS complexity by aggregating per bit-plane CTM estimates. We show theoretically that QuBD yields a tighter upper-bound on the estimation gap for ideal prefix KCS complexity under bit-plane independence than binarization-based methods. Using QuBD, we study how the algorithmic complexity of neural network weights evolves during training, showing that it decreases as models learn, scales with data budget, increases during overfitting, follows the delayed generalization observed during grokking, and correlates with generalization performance. We further show that algorithmic information resides predominantly in the most significant bit-planes, which can serve as a practical diagnostic for determining appropriate post-training quantization levels. This work offers novel insights into learning mechanisms in DNNs by providing the first scalable, tractable estimates of KCS complexity for large, non-binary objects such as DNN weights.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10182

ATLAS: A Multi-Procedure Benchmark and Context-Aware Model for Surgical Anatomy Segmentation

Ronald L. P. D. de Jong, Tim J. M. Jaspers, Raf A. H. Vervoort, Aron F. H. A. Bakker, Anna L. Dijk, Yiping Li, Jip L. Tolenaar, Jelle P. Ruurda 等 13 位作者

原文摘要

Recognizing anatomical structures during minimally invasive surgery (MIS) could support intraoperative navigation and improve patient safety, but progress is limited by small, single-procedure datasets and by segmentation methods developed for natural scenes. This work addresses both limitations through a new dataset and model. First, we introduce ATLAS-120k, a clip-level semantic segmentation dataset of 121,018 annotated frames from 100 surgical videos, covering 14 laparoscopic and robot-assisted procedures and 41 classes. Annotations were created with a pipeline that combines manual labeling, tracker-based propagation, and review by surgeons. Second, we propose ATLAS (Anatomy Recognition with Context Learning using Foundation Representations), a video semantic segmentation model for surgical anatomy recognition. ATLAS builds on in-domain foundation-model embeddings and adds context queries for the procedure and surgical phase, together with query propagation between frames for short-term memory. ATLAS outperforms state-of-the-art natural-image and in-domain foundation models in both segmentation accuracy and temporal consistency, achieving a Dice score of 0.70 with ViT-L while running in real time at 76.2 FPS. Ablations show that the context queries improve accuracy and that query propagation improves temporal consistency. ATLAS also generalizes well, matching a dedicated model zero-shot on CholecSeg8k and improving phase recognition on AutoLaparo over SurgeNet initialization. The model weights, dataset annotations, and annotation tool are publicly available at https://github.com/TimJaspers0801/ATLAS.

cs.CV

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arXiv ↗PDF ↗