Tootfinder

Replaced article(s) found for nlin.SI. https://arxiv.org/list/nlin.SI/new
[1/1]:
- B\"acklund-Darboux Transformations for Super KdV Type Equations
Lingling Xue, Shasha Wang, Qing Ping Liu

RaGNNarok: A Light-Weight Graph Neural Network for Enhancing Radar Point Clouds on Unmanned Ground Vehicles
David Hunt, Shaocheng Luo, Spencer Hallyburton, Shafii Nillongo, Yi Li, Tingjun Chen, Miroslav Pajic
https://arxiv.org/abs/2507.00937

RaGNNarok: A Light-Weight Graph Neural Network for Enhancing Radar Point Clouds on Unmanned Ground Vehicles
Low-cost indoor mobile robots have gained popularity with the increasing adoption of automation in homes and commercial spaces. However, existing lidar and camera-based solutions have limitations such as poor performance in visually obscured environments, high computational overhead for data processing, and high costs for lidars. In contrast, mmWave radar sensors offer a cost-effective and lightweight alternative, providing accurate ranging regardless of visibility. However, existing radar-base…

Last week, we continued our #ISE2025 lecture on distributional semantics with the introduction of neural language models (NLMs) and compared them to traditional statistical n-gram models.
Benefits of NLMs:
- Capturing Long-Range Dependencies
- Computational and Statistical Tractability
- Improved Generalisation
- Higher Accuracy
@…

Consequences of Non-Gaussian Instrumental Noise in Perturbed Nulling Interferometers
Felix A. Dannert, Philipp A. Huber, Thomas Birbacher, Romain Laugier, Markus J. Bonse, Emily O. Garvin, Adrian M. Glauser, Veronika Oehl, Sascha P. Quanz
https://arxiv.org/abs/2506.20653

Consequences of Non-Gaussian Instrumental Noise in Perturbed Nulling Interferometers
With the astrophysics community working towards the first observations and characterizations of Earth-like exoplanets, interest in space-based nulling interferometry has been renewed. This technique promises unique scientific and technical advantages by enabling direct mid-infrared observations. However, concept studies of nulling interferometers often overlook the impact of systematic noise caused by instrument perturbations. Earlier research introduced analytical and numerical models to addre…

LLM-Based Repair of Static Nullability Errors
Nima Karimipour, Michael Pradel, Martin Kellogg, Manu Sridharan
https://arxiv.org/abs/2507.20674 https://arxi…

LLM-Based Repair of Static Nullability Errors
Modern Java projects increasingly adopt static analysis tools that prevent null-pointer exceptions by treating nullness as a type property. However, integrating such tools into large, existing codebases remains a significant challenge. While annotation inference can eliminate many errors automatically, a subset of residual errors -- typically a mix of real bugs and false positives -- often persist and can only be resolved via code changes. Manually addressing these errors is tedious and error-p…

Collective oscillations in the finite-size Kuramoto model below the critical coupling: shot-noise approach
Sergei Kirillov, Vladimir Klinshov
https://arxiv.org/abs/2506.22160

Collective oscillations in the finite-size Kuramoto model below the critical coupling: shot-noise approach
The Kuramoto model, a paradigmatic framework for studying synchronization, exhibits a transition to collective oscillations only above a critical coupling strength in the thermodynamic limit. However, real-world systems are finite, and their dynamics can deviate significantly from mean-field predictions. Here, we investigate finite-size effects in the Kuramoto model below the critical coupling, where the infinite-size theory predicts complete asynchrony. Using a shot-noise approach, we derive a…

Frequency-Histogram Coarse Graining in Elementary Cellular Automata and 2D CA
Sanyam Jain, Stefano Nichele
https://arxiv.org/abs/2507.18674 https://arxiv.o…

Frequency-Histogram Coarse Graining in Elementary Cellular Automata and 2D CA
Cellular automata and other discrete dynamical systems have long been studied as models of emergent complexity. Recently, neural cellular automata have been proposed as models to investigate the emerge of a more general artificial intelligence, thanks to their propensity to support properties such as self-organization, emergence, and open-endedness. However, understanding emergent complexity in large scale systems is an open challenge. How can the important computations leading to emergent comp…

Analysis of the Chaotic Itinerancy Phenomenon using Entropy and Clustering
Nikodem Mierski, Pawe{\l} Pilarczyk
https://arxiv.org/abs/2507.22643 https://arx…

Analysis of the Chaotic Itinerancy Phenomenon using Entropy and Clustering
We introduce a new methodology for the analysis of the phenomenon of chaotic itinerancy in a dynamical system using the notion of entropy and a clustering algorithm. We determine systems likely to experience chaotic itinerancy by means of local Shannon entropy and local permutation entropy. In such systems, we find quasi-stable states (attractor ruins) and chaotic transition states using a density-based clustering algorithm. Our approach then focuses on examining the chaotic itinerancy dynamics…

This https://arxiv.org/abs/2505.04411 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nli…

Turbulence spreading and anomalous diffusion on combs
This paper presents a simple model for such processes as chaos spreading or turbulence spillover into stable regions. In this simple model the essential transport occurs via inelastic resonant interactions of waves on a lattice. The process is shown to result universally in a subdiffusive spreading of the wave field. The dispersion of this spreading process is found to depend exclusively on the type of the interaction process (three- or four-wave), but not on a particular instability behind. Th…

This https://arxiv.org/abs/2505.05852 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nli…

Attention-Enhanced Reservoir Computing as a Multiple Dynamical System Approximator
Reservoir computing has proven effective for tasks such as time-series prediction, particularly in the context of chaotic systems. However, conventional reservoir computing frameworks often face challenges in achieving high prediction accuracy and adapting to diverse dynamical problems due to their reliance on fixed weight structures. A concept of an attention-enhanced reservoir computer has been proposed, which integrates an attention mechanism into the output layer of the reservoir computing …