Tootfinder

Echo State Networks as State-Space Models: A Systems Perspective
Pradeep Singh, Balasubramanian Raman
https://arxiv.org/abs/2509.04422 https://arxiv.org/pd…

Echo State Networks as State-Space Models: A Systems Perspective
Echo State Networks (ESNs) are typically presented as efficient, readout-trained recurrent models, yet their dynamics and design are often guided by heuristics rather than first principles. We recast ESNs explicitly as state-space models (SSMs), providing a unified systems-theoretic account that links reservoir computing with classical identification and modern kernelized SSMs. First, we show that the echo-state property is an instance of input-to-state stability for a contractive nonlinear SSM…

Network-Aware Control of AGVs in an Industrial Scenario: A Simulation Study Based on ROS 2 and Gazebo
Filippo Bragato, Tullia Fontana, Marco Giordani, Malte Schellmann, Josef Eichinger, Michele Zorzi
https://arxiv.org/abs/2509.06451

Network-Aware Control of AGVs in an Industrial Scenario: A Simulation Study Based on ROS 2 and Gazebo
Networked Control System (NCS) is a paradigm where sensors, controllers, and actuators communicate over a shared network. One promising application of NCS is the control of Automated Guided Vehicles (AGVs) in the industrial environment, for example to transport goods efficiently and to autonomously follow predefined paths or routes. In this context, communication and control are tightly correlated, a paradigm referred to as Joint Communication and Control (JCC), since network issues such as del…

Structured Task Solving via Modular Embodied Intelligence: A Case Study on Rubik's Cube
Chongshan Fan, Shenghai Yuan
https://arxiv.org/abs/2507.05607 h…

Structured Task Solving via Modular Embodied Intelligence: A Case Study on Rubik's Cube
This paper presents Auto-RubikAI, a modular autonomous planning framework that integrates a symbolic Knowledge Base (KB), a vision-language model (VLM), and a large language model (LLM) to solve structured manipulation tasks exemplified by Rubik's Cube restoration. Unlike traditional robot systems based on predefined scripts, or modern approaches relying on pretrained networks and large-scale demonstration data, Auto-RubikAI enables interpretable, multi-step task execution with minimal data req…

Radiative Transfer Modeling of a Shadowed Protoplanetary Disk assisted by a Neural Network
Jonathan P. Williams, Myriam Benisty, Christian Ginski, Giuseppe Lodato, Maria Vincent
https://arxiv.org/abs/2509.01937

Radiative Transfer Modeling of a Shadowed Protoplanetary Disk assisted by a Neural Network
We present observations and detailed modeling of a protoplanetary disk around the T Tauri star, V1098 Sco. Millimeter wavelength data from the Atacama Large Millimeter Array (ALMA) show a ring of large dust grains with a central cavity that is filled with molecular gas. Near-infrared data with the Very Large Telescope (VLT) detect the scattered starlight from the disk surface and reveal a large shadow that extends over it's entire southern half. We model the ALMA continuum and line data to dete…

Random Effects Models for Understanding Variability and Association between Brain Functional and Structural Connectivity
Lingyi Peng, Qiaochu Wang, Yaotian Wang, Jie He, Xu Zou, Shuoran Li, Dana L. Tudorascu, David J. Schaeffer, Lauren Schaeffer, Diego Szczupak, Emily S. Rothwell, Stacey J. Sukoff Rizzo, Gregory W. Carter, Afonso C. Silva, Tingting Zhang
https:…

Random Effects Models for Understanding Variability and Association between Brain Functional and Structural Connectivity
The human brain is organized as a complex network, where connections between regions are characterized by both functional connectivity (FC) and structural connectivity (SC). While previous studies have primarily focused on network-level FC-SC correlations (i.e., the correlation between FC and SC across all edges within a predefined network), edge-level correlations (i.e., the correlation between FC and SC across subjects at each edge) has received comparatively little attention. In this study, …

Salt-Rock Creep Deformation Forecasting Using Deep Neural Networks and Analytical Models for Subsurface Energy Storage Applications
Pradeep Kumar Shukla, Tanujit Chakraborty, Mustafa Sari, Joel Sarout, Partha Pratim Mandal
https://arxiv.org/abs/2508.05248

Salt-Rock Creep Deformation Forecasting Using Deep Neural Networks and Analytical Models for Subsurface Energy Storage Applications
This study provides an in-depth analysis of time series forecasting methods to predict the time-dependent deformation trend (also known as creep) of salt rock under varying confining pressure conditions. Creep deformation assessment is essential for designing and operating underground storage facilities for nuclear waste, hydrogen energy, or radioactive materials. Salt rocks, known for their mechanical properties like low porosity, low permeability, high ductility, and exceptional creep and sel…

Dependency Network-Based Portfolio Design with Forecasting and VaR Constraints
Zihan Lin, Haojie Liu, Randall R. Rojas
https://arxiv.org/abs/2507.20039 https://

Dependency Network-Based Portfolio Design with Forecasting and VaR Constraints
This study proposes a novel portfolio optimization framework that integrates statistical social network analysis with time series forecasting and risk management. Using daily stock data from the S&P 500 (2020-2024), we construct dependency networks via Vector Autoregression (VAR) and Forecast Error Variance Decomposition (FEVD), transforming influence relationships into a cost-based network. Specifically, FEVD breaks down the VAR's forecast error variance to quantify how much each stock's shock…

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…

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…

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML
Hendrik Reiter, Ahmad Rzgar Hamid, Florian Schl\"osser, Mikkel Baun Kj{\ae}rgaard, Wilhelm Hasselbring
https://arxiv.org/abs/2507.22702

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML
Edge computing offers significant advantages for realtime data processing tasks, such as object recognition, by reducing network latency and bandwidth usage. However, edge environments are susceptible to various types of fault. A remediator is an automated software component designed to adjust the configuration parameters of a software service dynamically. Its primary function is to maintain the services operational state within predefined Service Level Objectives by applying corrective actions…