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新论文 · v1arXiv:2611.13296

A Unified Interactive Framework for Real-Time Three-Dimensional Cellular Automata Simulation in XR

Ashkan Rezaei, Azam Bastanfard, Mohammad Erfan Safabakhsh

原文摘要

Integrating three-dimensional cellular automata (3D CA) into extended reality (XR) requires coordinated simulation, volumetric data management, rendering, and interaction under variable hardware constraints. This paper presents a hybrid study combining a critical review of existing approaches with a unified architecture for real-time 3D CA in XR. The proposed five-layer architecture integrates voxel and rule management, parallel computation, interoperability middleware, visualization, and XR interaction. Its key principles include hardware-aware execution, asynchronous simulation-rendering, adaptive fidelity, and cross-engine interoperability. An analytical evaluation framework is defined to assess performance, latency, scalability, memory efficiency, simulation correctness, and interaction quality. The proposed framework provides a foundation for developing portable and interactive 3D CA systems across desktop and mobile XR platforms.

cs.GR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11382

The comparison of density estimation for transfer learning in Banach, Hilbert, Euclidean spaces

Maryam Alsadat Kazemian, Azam Bastanfard

原文摘要

Domain adaptation or DA methods are indispensable when target labels or computational resources are scarce, yet the domain kernel density and subspace alignment approaches completely assume a Hilbertian geometry. They measure distributional discrepancy with symmetric L2 divergences over Euclidean feature spaces or reproducing kernel Hilbert spaces (RKHS). This preprint summarizes the manuscript Non-Hilbertian Domain Adaptation. Total Variation Alignment via L1 Density Regularization on Feature Manifolds, which proposes Banach Manifold Transfer Learning or BMTL. BMTL makes two coupled design decisions. The first one is that features are embedded on a geodesic manifold rather than treated as points of a flat Euclidean space, and the second one is that the density matching regularizer is formulated in an L1 Banach norm, whose minimization is equivalent, by Scheffe's theorem, to contracting the Total Variation or TV distance between domain densities, a bounded metric that weights all regions of the sample space uniformly. The L2 formulation, by contrast, squares deviations and therefore amplifies errors in the tails, over emphasizing density outliers that do not reflect meaningful distributional shift. Experiments on MNIST-USPS, COIL20, and Office+Caltech10 show consistent gains; the L1 variant beats an otherwise identical L2 ablation on every task, for example 92.44% vs. 89.80% on USPS->MNIST and surpasses the strongest baseline DMDA by +3.68% on average over twelve Office+Caltech transfers. This document situates the contribution with an explicit comparison of Euclidean, Hilbert, and Banach geometries.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01229

Precision Through Play: Enhancing Fundamental Laparoscopic Skill Acquisition Via Gamified XR and Pseudo-Haptics

Bita koohestani, Ashkan Rezaei, Azam Bastanfard

原文摘要

Extended Reality (XR) technologies have shown potential to improve laparoscopic skills acquisition. Among fundamental skills in laparoscopic surgery is accurate instrument handling. Appropriate instrument alignment and a perpendicular path ensure movement efficiency and improved performance. This work introduces an interactive XR platform designed to support an immersive, engaging, and accessible environment that facilitates continuous practice. This platform incorporates a gamification layer enriched by pseudo-haptics that generate a motivational feedback loop, allowing trainees to continuously practice instrument manipulation and angle control. The platform monitors instrument orientation, computes angular deviation from an ideal reference vector, and assigns scores based on user performance. A point-based scoring component reinforces correct technique and penalizes deviations in tool alignment beyond predefined thresholds. Furthermore, a pseudo-haptic feedback mechanism is integrated to provide real-time tactile feedback when deviation exceeds tolerance. Combined with low-cost hardware and accessible deployment, the framework aims to increase training scalability, engagement, and improve skill acquisition. Preliminary evaluations indicate that integrating gamified feedback significantly improves precision, reduces angular variance, and encourages sustained practice.

cs.HC · cs.GR

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arXiv ↗PDF ↗