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NL-MambaXCT: Self-Supervised Nested-Learning Mamba for Nomex Honeycomb X-ray CT Defect Classification
Announce Type: replace-cross Abstract: X-ray computed tomography (XCT) is widely used for non-destructive testing of Nomex honeycomb structures in aerospace manufacturing, but industrial inspection still relies heavily on manual interpretation and supervised models trained on limited labeled data. This work introduces NL-MambaXCT, a Mamba-based framework that combines self-supervised masked image modelling with a Nested Learning (NL) formulation for automated, label-efficient defect...
RobustModelMaker: Coupling Bootstrap Stability Selection with Leakage-Safe Nested Cross-Validation for Scientific Machine Learning
arXiv:2606.01566v1 Announce Type: new Abstract: Small-to-medium scientific datasets place machine learning pipelines under two compounding pressures. Single-run feature selection produces feature sets that change substantially under small perturbations of the training data, and any procedure that uses the same data for selection, tuning, and evaluation produces optimistically biased performance estimates. The two failure modes are routinely treated as separable, but in the regimes where...
MIPIC: Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining
arXiv:2604.24374v2 Announce Type: replace Abstract: Representation learning is fundamental to NLP, but building embeddings that work well at different computational budgets is challenging. Matryoshka Representation Learning (MRL) offers a flexible inference paradigm through nested embeddings; however, learning such structures requires explicit coordination of how information is arranged across embedding dimensionality and model depth. In this work, we propose MIPIC (Matryoshka Representation...
Action Motifs: Self-Supervised Hierarchical Representation of Human Body Movements
arXiv:2604.28173v2 Announce Type: replace Abstract: Effective human behavior modeling requires a representation of the human body movement that capitalizes on its compositionality. We propose a hierarchical representation consisting of Action Atoms that capture the atomic joint movements and Action Motifs that are formed by their temporal compositions and encode similar body movements found across different overall human actions. We derive A4Mer, a nested latent Transformer to learn this...
Action Motifs: Self-Supervised Hierarchical Representation of Human Body Movements
arXiv:2604.28173v3 Announce Type: replace Abstract: Effective human behavior modeling requires a representation of the human body movement that capitalizes on its compositionality. We propose a hierarchical representation consisting of Action Atoms that capture the atomic joint movements and Action Motifs that are formed by their temporal compositions and encode similar body movements found across different overall human actions. We derive A4Mer, a nested latent Transformer to learn this...
MIC: Maximizing Informational Capacity in Adaptive Representations via Isotropic Subspace Alignment
arXiv:2605.29987v2 Announce Type: replace Abstract: Although multi-scales representation learning enables elastic-dimension embeddings, nested subspaces often suffer from dimensional redundancy and spectral collapse. To address this, we introduce MIC, a framework that optimizes the geometric landscape of multi-granular embeddings through isotropic subspace alignment.
Emergent control of ant learning walks using the mismatch between path-integration and visual cues
Despite their small brains, desert ants can safely navigate back to their nest after travelling hundreds of metres to find food. This ability relies on visual memories, gathered on initial Learning Walks (LWs), during which ants slowly explore the surroundings of the nest. LWs follow a clear progression: early walks are short, spiraling, and interspersed with pirouettes: brief stops during which the ant perform a visual scan.
Home Depot Promo Codes: 50% Off in June 2026
The company pretty much invented the hardware superstore when it began in 1978, just by being so big. They inflated the neighborhood tool shop into a whole city of lumber, hammers, caulk, power saws, and big rolls of wire. I would know I’m in a Home Depot blindfolded, because of a distinct quality to the air—crisp and particulate, smelling like wood dust and paint and the oiled metal of power tools.
NestRL: A Nested Training Regime for Mutual Adaptation in Human-AI Teaming
arXiv:2602.17737v2 Announce Type: replace Abstract: Mutual adaptation is a central challenge in human-AI teaming, as humans naturally adjust their strategies in response to an AI agent's behavior. Existing approaches attempt to approximate human behavior by diversifying training partners; however, these partners are typically static and fail to capture the adaptive nature of human teammates. When agents are trained jointly in standard multi-agent settings, they often converge to opaque...
AdaTok: Self-Budgeting Image Tokenization with Quality-Preserving Dynamic Tokens
Announce Type: new Abstract: Image tokenizers, from 2D grids to recent 1D sequences, typically encode every image with the same fixed number of tokens. Yet visual complexity is highly heterogeneous, so a uniform budget overspends on simple inputs and underserves complex ones. Existing elastic tokenizers expose variable-length reconstructions, but often leave token length as a deployment-time operating point, a search target, or an external prediction rather than an output of the tokenizer...