arXiv subjects

William Woof

Publications and source records attributed to William Woof.

4 recordsLinked to original sources

arXiv2026

The Quantum Cliff: A Critical Proton Tunneling Threshold Determines Clinical Severity in RPE65-Mediated Retinal Disease

Predicting clinical severity from genotype remains a fundamental challenge in molecular medicine, particularly for enzymes whose function depends on sub-atomic-scale geometry. Mutations in the \textit{RPE65} isomerohydrolase cause Leber Congenital Amaurosis (LCA) and related retinal diseases; however, the kinetic mechanisms connecting sub-atomic-scale perturbations to blindness remain unclear. In this study, we demonstrate that mutations in the human visual isomerase RPE65 are governed by a quantum-mechanical threshold effect arising from proton tunneling in the active site. We established a hybrid quantum-classical structure-to-phenotype pipeline combining AlphaFold structure prediction with \textit{ab initio} quantum simulation using the Variational Quantum Eigensolver (VQE) to analyze minimal proton-coupled electron transfer in the visual cycle. Our analysis reveals that many pathogenic mutations do not merely occlude the active site, but rather strongly reduce the quantum probability of proton tunneling. We observed a sharp non-linear effect, termed the "Quantum Cliff," where minute structural changes (below 0.1 \AA) reduce the reaction rate by multiple orders of magnitude. Based on these findings, we introduce a dimensionless Relative Quantum Activity Score (RQAS) that isolates the geometry-controlled exponential sensitivity of the reaction rate and successfully distinguishes between mild and severe patient phenotypes. These results suggest that RPE65 operates near a quantum-critical point, where sub-Angstrom structural perturbations induce a catastrophic loss of function. Furthermore, our findings establish quantum tunneling as a predictive mechanistic link between atomic structure and clinical phenotype, proposing a general framework for quantum-structural disease modeling.

cs.ET↗

arXiv2025

Hierarchical Attention for Sparse Volumetric Anomaly Detection in Subclinical Keratoconus

The detection of weak, spatially distributed anomalies in volumetric medical imaging remains challenging due to the difficulty of integrating subtle signals across non-adjacent regions. This study presents a controlled comparison of sixteen architectures spanning convolutional, hybrid, and transformer families for subclinical keratoconus detection from three-dimensional anterior segment optical coherence tomography (AS-OCT). The results demonstrate that hierarchical architectures achieve 21-23% higher sensitivity and specificity, particularly in the difficult subclinical regime, outperforming both convolutional neural networks (CNNs) and global-attention Vision Transformer (ViT) baselines. Mechanistic analyses indicate that this advantage arises from spatial scale alignment: hierarchical windowing produces effective receptive fields matched to the intermediate extent of subclinical abnormalities, avoiding the excessive locality observed in convolutional models and the diffuse integration characteristic of pure global attention. Attention-distance measurements show that subclinical cases require longer spatial integration than healthy or overtly pathological volumes, with hierarchical models exhibiting lower variance and more anatomically coherent focus. Representational similarity further indicates that hierarchical attention learns a distinct feature space that balances local structure sensitivity with flexible long-range interactions. Auxiliary age and sex prediction tasks demonstrate moderately high cross-task consistency, supporting the generalizability of these inductive principles. The findings provide design guidance for volumetric anomaly detection and highlight hierarchical attention as a principled approach for early pathological change analysis in medical imaging.

cs.CV↗

arXiv2020

A Framework for End-to-End Learning on Semantic Tree-Structured Data

While learning models are typically studied for inputs in the form of a fixed dimensional feature vector, real world data is rarely found in this form. In order to meet the basic requirement of traditional learning models, structural data generally have to be converted into fix-length vectors in a handcrafted manner, which is tedious and may even incur information loss. A common form of structured data is what we term "semantic tree-structures", corresponding to data where rich semantic information is encoded in a compositional manner, such as those expressed in JavaScript Object Notation (JSON) and eXtensible Markup Language (XML). For tree-structured data, several learning models have been studied to allow for working directly on raw tree-structure data, However such learning models are limited to either a specific tree-topology or a specific tree-structured data format, e.g., synthetic parse trees. In this paper, we propose a novel framework for end-to-end learning on generic semantic tree-structured data of arbitrary topology and heterogeneous data types, such as data expressed in JSON, XML and so on. Motivated by the works in recursive and recurrent neural networks, we develop exemplar neural implementations of our framework for the JSON format. We evaluate our approach on several UCI benchmark datasets, including ablation and data-efficiency studies, and on a toy reinforcement learning task. Experimental results suggest that our framework yields comparable performance to use of standard models with dedicated feature-vectors in general, and even exceeds baseline performance in cases where compositional nature of the data is particularly important. The source code for a JSON-based implementation of our framework along with experiments can be downloaded at https://github.com/EndingCredits/json2vec.

cs.LG↗

arXiv2018

Learning to Play General Video-Games via an Object Embedding Network

Deep reinforcement learning (DRL) has proven to be an effective tool for creating general video-game AI. However most current DRL video-game agents learn end-to-end from the video-output of the game, which is superfluous for many applications and creates a number of additional problems. More importantly, directly working on pixel-based raw video data is substantially distinct from what a human player does.In this paper, we present a novel method which enables DRL agents to learn directly from object information. This is obtained via use of an object embedding network (OEN) that compresses a set of object feature vectors of different lengths into a single fixed-length unified feature vector representing the current game-state and fulfills the DRL simultaneously. We evaluate our OEN-based DRL agent by comparing to several state-of-the-art approaches on a selection of games from the GVG-AI Competition. Experimental results suggest that our object-based DRL agent yields performance comparable to that of those approaches used in our comparative study.

cs.LG↗