Tootfinder

F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding
Jianxin Sun, David Lenz, Hongfeng Yu, Tom Peterka
https://arxiv.org/abs/2507.03836

F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding
Interactive time-varying volume visualization is challenging due to its complex spatiotemporal features and sheer size of the dataset. Recent works transform the original discrete time-varying volumetric data into continuous Implicit Neural Representations (INR) to address the issues of compression, rendering, and super-resolution in both spatial and temporal domains. However, training the INR takes a long time to converge, especially when handling large-scale time-varying volumetric datasets. …

A Short-Term Integrated Wind Speed Prediction System Based on Fuzzy Set Feature Extraction
Yijun Geng, Jianzhou Wang, Jinze Li, Zhiwu Li
https://arxiv.org/abs/2507.05761

A Short-Term Integrated Wind Speed Prediction System Based on Fuzzy Set Feature Extraction
Wind energy has significant potential owing to the continuous growth of wind power and advancements in technology. However, the evolution of wind speed is influenced by the complex interaction of multiple factors, making it highly variable. The nonlinear and nonstationary nature of wind speed evolution can have a considerable impact on the overall power system. To address this challenge, we propose an integrated multiframe wind speed prediction system based on fuzzy feature extraction. This sys…

Black-box Adversarial Attacks on CNN-based SLAM Algorithms
Maria Rafaela Gkeka, Bowen Sun, Evgenia Smirni, Christos D. Antonopoulos, Spyros Lalis, Nikolaos Bellas
https://arxiv.org/abs/2505.24654

Black-box Adversarial Attacks on CNN-based SLAM Algorithms
Continuous advancements in deep learning have led to significant progress in feature detection, resulting in enhanced accuracy in tasks like Simultaneous Localization and Mapping (SLAM). Nevertheless, the vulnerability of deep neural networks to adversarial attacks remains a challenge for their reliable deployment in applications, such as navigation of autonomous agents. Even though CNN-based SLAM algorithms are a growing area of research there is a notable absence of a comprehensive presentati…

A General Approach to the Shape Transition of Run-and-Tumble Particles: The 1D PDMP Framework for Invariant Measure Regularity
Leo Hahn
https://arxiv.org/abs/2506.02274

A General Approach to the Shape Transition of Run-and-Tumble Particles: The 1D PDMP Framework for Invariant Measure Regularity
Run-and-tumble particles (RTPs) have emerged as a paradigmatic example for studying nonequilibrium phenomena in statistical mechanics. The invariant measure of a wide class of RTPs subjected to a potential possesses a density, which is continuous when tumble rates are high and discontinuous when they are low. This key feature is known as shape transition. By comparison with the Boltzmann distribution characteristic of thermodynamic equilibrium, this constitutes a qualitative indicator of the re…

Good Morning #Canada
Before our move to #Innisfil, we spent 16 years in Caledon living on top of the Niagara Escarpment. We were fortunate to take advantage of the hiking trails and scenic beauty of the area with the Bruce Trail, Forks of The Credit Road, and numerous parks within a few kilometers. More Canadians should be aware of this approximately 1,050-kilometre geological feature that today is protected in Ontario as a continuous corridor.
#CanadaIsAwesome
https://youtu.be/5V5DIgF2yag?si=Z4tTs4k-3htyv6V8

This https://arxiv.org/abs/2505.13017 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Optimal Scalogram for Computational Complexity Reduction in Acoustic Recognition Using Deep Learning
The Continuous Wavelet Transform (CWT) is an effective tool for feature extraction in acoustic recognition using Convolutional Neural Networks (CNNs), particularly when applied to non-stationary audio. However, its high computational cost poses a significant challenge, often leading researchers to prefer alternative methods such as the Short-Time Fourier Transform (STFT). To address this issue, this paper proposes a method to reduce the computational complexity of CWT by optimizing the length o…

This https://arxiv.org/abs/2504.19992 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Towards Non-Abelian Quantum Signal Processing: Efficient Control of Hybrid Continuous- and Discrete-Variable Architectures
Robust quantum control is crucial for achieving operations below the quantum error correction threshold. Quantum Signal Processing (QSP) transforms a unitary parameterized by $θ$ into one governed by a polynomial function $f(θ)$, a feature that underpins key quantum algorithms. Originating from composite pulse techniques in NMR, QSP enhances robustness against systematic control errors. We extend QSP to a new class, non-abelian QSP, which utilizes non-commuting control parameters, $\hatθ_1, …

A Novel Signal Processing Strategy for Short-Range Laser Feedback Interferometry Sensors
Alexander Zimmer, Johannes Meyer, Enkelejda Kasneci
https://arxiv.org/abs/2506.10705

A Novel Signal Processing Strategy for Short-Range Laser Feedback Interferometry Sensors
The rapid evolution of wearable technologies, such as AR glasses, demands compact, energy-efficient sensors capable of high-precision measurements in dynamic environments. Traditional Frequency-Modulated Continuous Wave (FMCW) Laser Feedback Interferometry (LFI) sensors, while promising, falter in applications that feature small distances, high velocities, shallow modulation, and low-power constraints. We propose a novel sensor-processing pipeline that reliably extracts distance and velocity me…