Tootfinder

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…

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…

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…