Modern autonomous hardware is trapped between two flawed computational paradigms: power-hungry, data-dependent Deep Learning (AI) networks that lack physical predictability, and rigid Classical Control loops (Calculus) that fail when encountering unmodeled environmental dynamics. The APDA bridges this chasm by operating natively on low-power edge microcontrollers using continuous-time calculus for real-time operations, mapping unmodeled environmental dynamics via an on-demand Neuromorphic Processing Unit (NPU) subroutine, and hibernating the AI once the unknown physics have been distilled into mathematical equations.
## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Transformation in Metallurgical Engineering: From Microstructure Analysis to Smart Manufacturing and Sustainable Production"** ### Alternative Title 2 (Process-Focused)**"Machine Learning and Deep Learning…
Abstract **Background** Modulating endogenously silenced regenerative pathways—such as BMP derepression through Anti-SOST and Anti-GDF8 therapies—presents significant safety challenges in bio-electric tissue engineering. Uncontrolled signaling carries severe risks of tissue hyper…
This repository contains the complete execution pipeline for the study: "Leakage-Safe Evaluation of Stochastic Channel Masking for Sensor-Failure Robustness in Dynamic Gas Mixture Quantification." The code provides an end-to-end reproducible workflow for processing the UCI Gas Se…
Overview AutoML-Lite is a powerful, user-friendly desktop application designed to democratize machine learning by automating the entire modeling pipeline. Built with Python and PyQt6, it provides a comprehensive GUI-based environment for data preprocessing, feature engineering, m…
Overview & Core Problem The document presents a comprehensive tutorial on the Topological Governor, a novel architectural component designed to solve the 35-year-old problem of catastrophic forgetting in deep learning. Catastrophic forgetting is defined as the abrupt degradation…
Feedforward neural networks deployed autoregressively act as deterministic oscillators, their period and stability governed by a linear Companion Matrix built from the network’s origin-linearization. This paper stress-tests a serial-baseline “Pyramid Up” topology on a challenging…