Tootfinder

Multi-Carrier Rydberg Atomic Quantum Receivers with Enhanced Bandwidth Feature for Communication and Sensing
Huizhi Wang, Tierui Gong, Emil Bj\"ornson, Chau Yuen
https://arxiv.org/abs/2510.10473

Multi-Carrier Rydberg Atomic Quantum Receivers with Enhanced Bandwidth Feature for Communication and Sensing
Rydberg atomic quantum receivers (RAQRs) have attracted significant attention in recent years due to their ultra-high sensitivity. Although capable of precisely detecting the amplitude and phase of weak signals, conventional RAQRs face inherent limitations in accurately receiving wideband RF signals, due to the discrete nature of atomic energy levels and their intrinsic instantaneous bandwidth constraints. These limitations hinder their direct application to multi-carrier communication and sens…

Hierarchical Spatial Algorithms for High-Resolution Image Quantization and Feature Extraction
Noor Islam S. Mohammad
https://arxiv.org/abs/2510.08449 https://

Hierarchical Spatial Algorithms for High-Resolution Image Quantization and Feature Extraction
This study introduces a modular framework for spatial image processing, integrating grayscale quantization, color and brightness enhancement, image sharpening, bidirectional transformation pipelines, and geometric feature extraction. A stepwise intensity transformation quantizes grayscale images into eight discrete levels, producing a posterization effect that simplifies representation while preserving structural detail. Color enhancement is achieved via histogram equalization in both RGB and Y…

Neural Feature Geometry Evolves as Discrete Ricci Flow
Moritz Hehl, Max von Renesse, Melanie Weber
https://arxiv.org/abs/2509.22362 https://arxiv.org/pdf/2…

Neural Feature Geometry Evolves as Discrete Ricci Flow
Deep neural networks learn feature representations via complex geometric transformations of the input data manifold. Despite the models' empirical success across domains, our understanding of neural feature representations is still incomplete. In this work we investigate neural feature geometry through the lens of discrete geometry. Since the input data manifold is typically unobserved, we approximate it using geometric graphs that encode local similarity structure. We provide theoretical resul…

Classical feature map surrogates and metrics for quantum control landscapes
Martino Calzavara, Tommaso Calarco, Felix Motzoi
https://arxiv.org/abs/2509.25930 https://

Classical feature map surrogates and metrics for quantum control landscapes
We derive and analyze three feature map representations of parametrized quantum dynamics, which generalize variational quantum circuits. These are (i) a Lie-Fourier partial sum, (ii) a Taylor expansion, and (iii) a finite-dimensional sinc kernel regression representation. The Lie-Fourier representation is shown to have a dense spectrum with discrete peaks, that reflects control Hamiltonian properties, but that is also compressible in typically found symmetric systems. We prove boundedness in th…

D\'esentrelacement Fr\'equentiel Doux pour les Codecs Audio Neuronaux
Beno\^it Gini\`es, Xiaoyu Bie, Olivier Fercoq, Ga\"el Richard
https://arxiv.org/abs/2510.03741

Désentrelacement Fréquentiel Doux pour les Codecs Audio Neuronaux
While neural-based models have led to significant advancements in audio feature extraction, the interpretability of the learned representations remains a critical challenge. To address this, disentanglement techniques have been integrated into discrete neural audio codecs to impose structure on the extracted tokens. However, these approaches often exhibit strong dependencies on specific datasets or task formulations. In this work, we propose a disentangled neural audio codec that leverages spec…

Crosslisted article(s) found for math.DG. https://arxiv.org/list/math.DG/new
[1/1]:
- Neural Feature Geometry Evolves as Discrete Ricci Flow
Moritz Hehl, Max von Renesse, Melanie Weber

JEPA-T: Joint-Embedding Predictive Architecture with Text Fusion for Image Generation
Siheng Wan, Zhengtao Yao, Zhengdao Li, Junhao Dong, Yanshu Li, Yikai Li, Linshan Li, Haoyan Xu, Yijiang Li, Zhikang Dong, Huacan Wang, Jifeng Shen
https://arxiv.org/abs/2510.00974

JEPA-T: Joint-Embedding Predictive Architecture with Text Fusion for Image Generation
Modern Text-to-Image (T2I) generation increasingly relies on token-centric architectures that are trained with self-supervision, yet effectively fusing text with visual tokens remains a challenge. We propose \textbf{JEPA-T}, a unified multimodal framework that encodes images and captions into discrete visual and textual tokens, processed by a joint-embedding predictive Transformer. To enhance fusion, we incorporate cross-attention after the feature predictor for conditional denoising while main…

Boundary on the Table: Efficient Black-Box Decision-Based Attacks for Structured Data
Roie Kazoom, Yuval Ratzabi, Etamar Rothstein, Ofer Hadar
https://arxiv.org/abs/2509.22850 h…

Boundary on the Table: Efficient Black-Box Decision-Based Attacks for Structured Data
Adversarial robustness in structured data remains an underexplored frontier compared to vision and language domains. In this work, we introduce a novel black-box, decision-based adversarial attack tailored for tabular data. Our approach combines gradient-free direction estimation with an iterative boundary search, enabling efficient navigation of discrete and continuous feature spaces under minimal oracle access. Extensive experiments demonstrate that our method successfully compromises nearly …