Amr Salem | Searcharxiv

arXiv2026

CIVIC: Cooperative Immersion Via Intelligent Credit-sharing in DRL-Powered Metaverse

The Metaverse faces complex resource allocation challenges due to diverse Virtual Environments (VEs), Digital Twins (DTs), dynamic user demands, and strict immersion needs. This paper introduces CIVIC (Cooperative Immersion Via Intelligent Credit-sharing), a novel framework optimizing resource sharing among multiple Metaverse Service Providers (MSPs) to enhance user immersion. Unlike existing methods, CIVIC integrates VE rendering, DT synchronization, credit sharing, and immersion-aware provisioning within a cooperative multi-MSP model. The resource allocation problem is formulated as two NP-hard challenges: a non-cooperative setting where MSPs operate independently and a cooperative setting utilizing a General Credit Pool (GCP) for dynamic resource sharing. Using Deep Reinforcement Learning (DRL) for tuning resources and managing cooperating MSPs, CIVIC achieves 12-36% higher request completion, 23-70% higher fulfillment rates, 20-60% more served clients, and up to 51% more fairly distributed requests, all with competitive costs. Extensive experiments demonstrate CIVIC's resilience, adaptability, and robust performance under dynamic load conditions and unexpected demand surges, making it suitable for real-world distributed Metaverse infrastructures.

cs.NI↗

arXiv2024

A CNN Approach to Polygenic Risk Prediction of Kidney Stone Formation

Kidney stones are a common and debilitating health issue, and genetic factors play a crucial role in determining susceptibility. While Genome-Wide Association Studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) linked to kidney stone risk, translating these findings into effective clinical tools remains a challenge. In this study, we explore the potential of deep learning techniques, particularly Convolutional Neural Networks (CNNs), to enhance Polygenic Risk Score (PRS) models for predicting kidney stone susceptibility. Using a curated dataset of kidney stone-associated SNPs from a recent GWAS, we apply CNNs to model non-linear genetic interactions and improve prediction accuracy. Our approach includes SNP selection, genotype filtering, and model training using a dataset of 560 individuals, divided into training and testing subsets. We compare our CNN-based model with traditional machine learning models, including logistic regression, random forest, and support vector machines, demonstrating that the CNN outperforms these models in terms of classification accuracy and ROC-AUC. The proposed model achieved a validation accuracy of 62%, with an ROC-AUC of 0.68, suggesting its potential for improving genetic-based risk prediction for kidney stones. This study contributes to the growing field of genomics-driven precision medicine and highlights the promise of deep learning in enhancing PRS models for complex diseases.

q-bio.GN↗