Ahmed Sheta

Ahmed Sheta

Online multiplayer games are population-dependent systems whose playability depends on the continued presence of an active player base. We propose a formal framework for reasoning about viability collapse in such systems under explicit scope conditions. The framework introduces a conditional Critical Mass Threshold $Φ$, below which queue times, match quality, or role balance render a game operationally non-viable under a fixed operational profile; an uninhabited runtime taxonomy spanning pre-launch and post-decline states; and a Nostalgia Inversion Point $ψ$, at which cultural memory exceeds active participation. We model post-peak decline using a threshold-sensitive hazard model and show how games in the modeled class can cross below viability under finite official-service horizons or bounded novelty under continuing exposure. Case studies based on public concurrent-player data are used illustratively rather than as formal validation. The contribution of the paper is not a universal law, but a formal vocabulary, a collapse model, and an empirical agenda for studying online game decline, preservation risk, and uninhabited virtual worlds.

Ahmed Sheta, Mathias Zinnen, Aline Sindel et al.

Finding smell references in historic artworks is a challenging problem. Beyond artwork-specific challenges such as stylistic variations, their recognition demands exceptionally detailed annotation classes, resulting in annotation sparsity and extreme class imbalance. In this work, we explore the potential of synthetic data generation to alleviate these issues and enable accurate detection of smell-related objects. We evaluate several diffusion-based augmentation strategies and demonstrate that incorporating synthetic data into model training can improve detection performance. Our findings suggest that leveraging the large-scale pretraining of diffusion models offers a promising approach for improving detection accuracy, particularly in niche applications where annotations are scarce and costly to obtain. Furthermore, the proposed approach proves to be effective even with relatively small amounts of data, and scaling it up provides high potential for further enhancements.