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Related Articles from SNS
The relative strength of hierarchical structure and statistics differs across the measures in naturalistic reading
Announce Type: replace Abstract: The hierarchical syntactic structure and non-hierarchical, statistical, or sequential factors have long been framed as rival theories in accounting for online comprehension. A lot of evidence has shown that both hierarchical and non-hierarchical factors can shape comprehension and the more open question is when, and how strongly, hierarchy exerts its influence in comprehension. We addressed the question with co-registered EEG and eye-tracking, treating...
Hierarchical RBF-KAN and RBF-SKAN Architectures for Multidimensional Function Approximation and Random Field Learning
arXiv:2606.02936v1 Announce Type: new Abstract: In this manuscript, we propose and analyze hierarchical Kolmogorov--Arnold neural network architectures employing radial basis functions as activation functions for approximating deterministic functions and random field models. Specifically, we develop a hierarchical radial-basis-function Kolmogorov--Arnold network (hierarchical RBF-KAN) for multidimensional deterministic function approximation and a hierarchical radial-basis-function...
Non-Supervised Community Detection and Hierarchical Modularity Estimation in Complex Networks
arXiv:2606.04972v1 Announce Type: new Abstract: The present work extends to complex networks a recently described methodology (A. Benatti and L. da F Costa, Detecting Hierarchical Clusters and Estimating their Modularity Directly from Dendrograms, May 2026) for non-supervised hierarchical cluster detection and hierarchical modularity estimation. First, the edge betweenness centrality of a given complex network (or graph) is estimated, and a dendrogram is obtained from these values by using...
Contract-based hierarchical control using predictive feasibility value functions
Announce Type: replace Abstract: Today's control systems are often characterized by modularity and safety requirements to handle complexity, resulting in the use of hierarchical control structures. Although hierarchical model predictive control offers favorable properties, achieving a provably safe, yet modular design remains a challenge. This paper introduces a contract-based hierarchical control strategy to improve the performance of control systems facing challenges related to model...
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.
Detecting Hierarchical Clusters and Estimating their Modularity Directly from Dendrograms
arXiv:2605.26268v2 Announce Type: replace Abstract: Identifying possible clusters in datasets and estimating their hierarchical modularity are central tasks in pattern recognition. In the present work, concepts and methodologies are described for performing these tasks while considering only the density of mergings obtained from hierarchical representations (dendrograms) of data inter-relationship along a scale variable. More specifically, the mergings of subclusters along the scale variable...
Hierarchical Object Representation for Spatial Robot Perception: Points, Meshes, and Superquadrics
Announce Type: new Abstract: Hierarchical 3D Scene Graphs (3DSG) have emerged as an actionable and scalable representation for long-term autonomy incorporating metric, semantic, and topological information in the scene. However, the question of geometric representation of objects in 3DSG has been overlooked as most methods use simplified geometric models such as partial point clouds or 3D bounding boxes. In this work, we introduce a hierarchical object representation that can be leveraged...
Coarse-to-fine Hierarchical Architecture with Sequential Mamba for Brain Reconstruction
Announce Type: new Abstract: Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience. While modern vision models achieve strong performance in image recognition, their correspondence with the hierarchical organization of the human visual cortex remains an open question. In this study, we propose CHASMBrain, a novel hierarchical two-stage framework for image-to-fMRI encoding.
Enhancing Protein-Protein Interaction Prediction with Hierarchical Motif-based Multimodal Protein Embedding
Announce Type: cross Abstract: Protein-protein interactions (PPIs) are essential for many biological processes. However, existing PPI prediction approaches suffer from two major limitations: they overlook the hierarchical organization of proteins, particularly meso-scale motifs that critically regulate PPIs, and fail to effectively integrate sequence, structure, and function modalities. To address these limitations, we propose MMM-PPI, a Hierarchical Motif-based Multi-Modal protein Encoder...
HiRQA: Hierarchical Ranking and Quality Alignment for Opinion-Unaware Image Quality Assessment
arXiv:2508.15130v3 Announce Type: replace Abstract: Despite significant progress in no-reference image quality assessment (NR-IQA), dataset biases and reliance on subjective labels continue to hinder their generalization performance. We propose HiRQA (Hierarchical Ranking and Quality Alignment), a self-supervised, opinion-unaware framework that offers a hierarchical, quality-aware embedding through a combination of ranking and contrastive learning. Unlike prior approaches that depend on...