Tootfinder

HypER: Hyperbolic Echo State Networks for Capturing Stretch-and-Fold Dynamics in Chaotic Flows
Pradeep Singh, Sutirtha Ghosh, Ashutosh Kumar, Hrishit B P, Balasubramanian Raman
https://arxiv.org/abs/2508.18196

HypER: Hyperbolic Echo State Networks for Capturing Stretch-and-Fold Dynamics in Chaotic Flows
Forecasting chaotic dynamics beyond a few Lyapunov times is difficult because infinitesimal errors grow exponentially. Existing Echo State Networks (ESNs) mitigate this growth but employ reservoirs whose Euclidean geometry is mismatched to the stretch-and-fold structure of chaos. We introduce the Hyperbolic Embedding Reservoir (HypER), an ESN whose neurons are sampled in the Poincare ball and whose connections decay exponentially with hyperbolic distance. This negative-curvature construction em…

A Spatio-Temporal Feature Fusion EEG Virtual Channel Signal Generation Network and Its Application in Anxiety Assessment
Shangqing Yuan, Wenshuang Zhai, Shengwen Guo
https://arxiv.org/abs/2509.19334

A Spatio-Temporal Feature Fusion EEG Virtual Channel Signal Generation Network and Its Application in Anxiety Assessment
To address the issue of limited channels and insufficient information collection in portable EEG devices, this study explores an EEG virtual channel signal generation network using a novel spatio-temporal feature fusion strategy. Based on the EEG signals from four frontal lobe channels, the network aims to generate virtual channel EEG signals for other 13 important brain regions. The architecture of the network is a two-dimensional convolutional neural network and it includes a parallel module …

FALCON: Autonomous Cyber Threat Intelligence Mining with LLMs for IDS Rule Generation
Shaswata Mitra, Azim Bazarov, Martin Duclos, Sudip Mittal, Aritran Piplai, Md Rayhanur Rahman, Edward Zieglar, Shahram Rahimi
https://arxiv.org/abs/2508.18684

FALCON: Autonomous Cyber Threat Intelligence Mining with LLMs for IDS Rule Generation
Signature-based Intrusion Detection Systems (IDS) detect malicious activities by matching network or host activity against predefined rules. These rules are derived from extensive Cyber Threat Intelligence (CTI), which includes attack signatures and behavioral patterns obtained through automated tools and manual threat analysis, such as sandboxing. The CTI is then transformed into actionable rules for the IDS engine, enabling real-time detection and prevention. However, the constant evolution o…

Contraction, Criticality, and Capacity: A Dynamical-Systems Perspective on Echo-State Networks
Pradeep Singh, Lavanya Sankaranarayanan, Balasubramanian Raman
https://arxiv.org/abs/2507.18467

Contraction, Criticality, and Capacity: A Dynamical-Systems Perspective on Echo-State Networks
Echo-State Networks (ESNs) distil a key neurobiological insight: richly recurrent but fixed circuitry combined with adaptive linear read-outs can transform temporal streams with remarkable efficiency. Yet fundamental questions about stability, memory and expressive power remain fragmented across disciplines. We present a unified, dynamical-systems treatment that weaves together functional analysis, random attractor theory and recent neuroscientific findings. First, on compact multivariate input…

Frozen in Time: Parameter-Efficient Time Series Transformers via Reservoir-Induced Feature Expansion and Fixed Random Dynamics
Pradeep Singh, Mehak Sharma, Anupriya Dey, Balasubramanian Raman
https://arxiv.org/abs/2508.18130

Frozen in Time: Parameter-Efficient Time Series Transformers via Reservoir-Induced Feature Expansion and Fixed Random Dynamics
Transformers are the de-facto choice for sequence modelling, yet their quadratic self-attention and weak temporal bias can make long-range forecasting both expensive and brittle. We introduce FreezeTST, a lightweight hybrid that interleaves frozen random-feature (reservoir) blocks with standard trainable Transformer layers. The frozen blocks endow the network with rich nonlinear memory at no optimisation cost; the trainable layers learn to query this memory through self-attention. The design cu…

Structure-preserving Optimal Kron-based Reduction of Radial Distribution Networks
Omid Mokhtari, Samuel Chevalier, Mads Almassalkhi
https://arxiv.org/abs/2508.15006 https://

Structure-preserving Optimal Kron-based Reduction of Radial Distribution Networks
Network reduction simplifies complex electrical networks to address computational challenges of large-scale transmission and distribution grids. Traditional network reduction methods are often based on a predefined set of nodes or lines to remain in the reduced network. This paper builds upon previous work on Optimal Kron-based Reduction of Networks (Opti-KRON), which was formulated as a mixed-integer linear program (MILP), to enhance the framework in two aspects. First, the scalability is impr…

Vision-Based Perception for Autonomous Vehicles in Off-Road Environment Using Deep Learning
Nelson Alves Ferreira Neto
https://arxiv.org/abs/2509.19378 https://

Vision-Based Perception for Autonomous Vehicles in Off-Road Environment Using Deep Learning
Low-latency intelligent systems are required for autonomous driving on non-uniform terrain in open-pit mines and developing countries. This work proposes a perception system for autonomous vehicles on unpaved roads and off-road environments, capable of navigating rough terrain without a predefined trail. The Configurable Modular Segmentation Network (CMSNet) framework is proposed, facilitating different architectural arrangements. CMSNet configurations were trained to segment obstacles and traf…

AMD bringt den achten Ryzen 5 5600
Sechskerner für die AM4-Plattform haben es AMD angetan. Neu im Portfolio: der Ryzen 5 5600F mit recht niedrigem Takt.
https://www.heise.de/news/AMD-bringt-den-a

Who Let the Diamonds Out?
Vincent Halde, Olivier Bernard, Mathieu Brochu, Laurier Dufresne, Nicolas Fleury, Kayla Johnson, Benjamin Moffet, David Roy-Guay
https://arxiv.org/abs/2509.19179

Perry: A High-level Framework for Accelerating Cyber Deception Experimentation
Brian Singer, Yusuf Saquib, Lujo Bauer, Vyas Sekar
https://arxiv.org/abs/2506.20770

Perry: A High-level Framework for Accelerating Cyber Deception Experimentation
Cyber deception aims to distract, delay, and detect network attackers with fake assets such as honeypots, decoy credentials, or decoy files. However, today, it is difficult for operators to experiment, explore, and evaluate deception approaches. Existing tools and platforms have non-portable and complex implementations that are difficult to modify and extend. We address this pain point by introducing Perry, a high-level framework that accelerates the design and exploration of deception what-if …