Artificial intelligence (AI) has emerged as a powerful accelerator of materials discovery, yet most existing models remain problem-specific, requiring additional data collection and retraining for each new property. Here we introduce and validate GATE (Geometrically Aligned Transfer Encoder) -- a generalizable AI framework that jointly learns 34 physicochemical properties spanning thermal, electrical, mechanical, and optical domains. By aligning these properties within a shared geometric space, GATE captures cross-property correlations that reduce disjoint-property bias -- a key factor causing false positives in multi-criteria screening. To demonstrate its generalizable utility, GATE -- without any problem-specific model reconfiguration -- applied to the discovery of immersion cooling fluids for data centers, a stringent real-world challenge defined by the Open Compute Project (OCP). Screening billions of candidates, GATE identified 92,861 molecules as promising for practical deployment. Four were experimentally or literarily validated, showing strong agreement with wet-lab measurements and performance comparable to or exceeding a commercial coolant. These results establish GATE as a generalizable AI platform readily applicable across diverse materials discovery tasks.

Profiling gamers provides critical insights for adaptive game design, behavioral understanding, and digital well-being. This study proposes an integrated, data-driven framework that combines psychological measures, behavioral analytics, and machine learning to reveal underlying gamer personas. A structured survey of 250 participants, including 113 active gamers, captured multidimensional behavioral, motivational, and social data. The analysis pipeline integrated feature engineering, association-network, knowledge-graph analysis, and unsupervised clustering to extract meaningful patterns. Correlation statistics uses Cramers V, Tschuprows T, Theils U, and Spearmans quantified feature associations, and network centrality guided feature selection. Dimensionality-reduction techniques such as PCA, SVD, t-SNE are coupled with clustering algorithms like K-Means, Agglomerative, Spectral, DBSCAN, evaluated using Silhouette, Calinski Harabasz, and Davies Bouldin indices. The PCA with K-Means with k = 4 model achieved optimal cluster quality with Silhouette = 0.4, identifying four archetypes as Immersive Social Story-Seekers, Disciplined Optimizers, Strategic Systems Navigators, and Competitive Team-Builders. This research contributes a reproducible pipeline that links correlation-driven network insights with unsupervised learning. The integration of behavioral correlation networks with clustering not only enhances classification accuracy but also offers a holistic lens to connect gameplay motivations with psychological and wellness outcomes.

Teleoperation of beyond-human-scale robotic manipulators (BHSRMs) presents unique challenges that differ fundamentally from conventional human-scale systems. As these platforms gain relevance in industrial domains such as construction, mining, and disaster response, immersive interfaces must be rethought to support scalable, safe, and effective human-robot collaboration. This paper investigates the control, cognitive, and interface-level challenges of immersive teleoperation in BHSRMs, with a focus on ensuring operator safety, minimizing sensorimotor mismatch, and enhancing the sense of embodiment. We analyze design trade-offs in haptic and visual feedback systems, supported by early experimental comparisons of exoskeleton- and joystick-based control setups. Finally, we outline key research directions for developing new evaluation tools, scaling strategies, and human-centered safety models tailored to large-scale robotic telepresence.

Geometric representation learning in preserving the intrinsic geometric and topological properties for discrete non-Euclidean data is crucial in scientific applications. Previous research generally mapped non-Euclidean discrete data into Euclidean space during representation learning, which may lead to the loss of some critical geometric information. In this paper, we propose a novel Isometric Immersion Kernel Learning (IIKL) method to build Riemannian manifold and isometrically induce Riemannian metric from discrete non-Euclidean data. We prove that Isometric immersion is equivalent to the kernel function in the tangent bundle on the manifold, which explicitly guarantees the invariance of the inner product between vectors in the arbitrary tangent space throughout the learning process, thus maintaining the geometric structure of the original data. Moreover, a novel parameterized learning model based on IIKL is introduced, and an alternating training method for this model is derived using Maximum Likelihood Estimation (MLE), ensuring efficient convergence. Experimental results proved that using the learned Riemannian manifold and its metric, our model preserved the intrinsic geometric representation of data in both 3D and high-dimensional datasets successfully, and significantly improved the accuracy of downstream tasks, such as data reconstruction and classification. It is showed that our method could reduce the inner product invariant loss by more than 90% compared to state-of-the-art (SOTA) methods, also achieved an average 40% improvement in downstream reconstruction accuracy and a 90% reduction in error for geometric metrics involving isometric and conformal.

This paper presents R-CAGE (Rhythmic Control Architecture for Guarding Ego), a theoretical framework for restructuring emotional output in long-term human-AI interaction. While prior affective computing approaches emphasized expressiveness, immersion, and responsiveness, they often neglected the cognitive and structural consequences of repeated emotional engagement. R-CAGE instead conceptualizes emotional output not as reactive expression but as ethical design structure requiring architectural intervention. The model is grounded in experiential observations of subtle affective symptoms such as localized head tension, interpretive fixation, and emotional lag arising from prolonged interaction with affective AI systems. These indicate a mismatch between system-driven emotion and user interpretation that cannot be fully explained by biometric data or observable behavior. R-CAGE adopts a user-centered stance prioritizing psychological recovery, interpretive autonomy, and identity continuity. The framework consists of four control blocks: (1) Control of Rhythmic Expression regulates output pacing to reduce fatigue; (2) Architecture of Sensory Structuring adjusts intensity and timing of affective stimuli; (3) Guarding of Cognitive Framing reduces semantic pressure to allow flexible interpretation; (4) Ego-Aligned Response Design supports self-reference recovery during interpretive lag. By structurally regulating emotional rhythm, sensory intensity, and interpretive affordances, R-CAGE frames emotion not as performative output but as sustainable design unit. The goal is to protect users from oversaturation and cognitive overload while sustaining long-term interpretive agency in AI-mediated environments.

In augmented reality (AR), where digital content is overlaid onto the real world, realistic thermal feedback has been shown to enhance immersion. Yet current thermal feedback devices, heavily influenced by the needs of virtual reality, often hinder physical interactions and are ineffective for immersion in AR. To bridge this gap, we have identified three design considerations relevant for AR thermal feedback: indirect feedback to maintain dexterity, thermal passthrough to preserve real-world temperature perception, and spatiotemporal rendering for dynamic sensations. We then created a unique and innovative thermal feedback device that satisfies these criteria. Human subject experiments assessing perceptual sensitivity, object temperature matching, spatial pattern recognition, and moving thermal stimuli demonstrated the impact of our design, enabling realistic temperature discrimination, virtual object perception, and enhanced immersion. These findings demonstrate that carefully designed thermal feedback systems can bridge the sensory gap between physical and virtual interactions, enhancing AR realism and usability.

This work reflects upon what Immersion can mean from the perspective of an Artificial Intelligence (AI). Applying the lens of immersive learning theory, it seeks to understand whether this new perspective supports ways for AI participation in cognitive ecologies. By treating AI as a participant rather than a tool, it explores what other participants (humans and other AIs) need to consider in environments where AI can meaningfully engage and contribute to the cognitive ecology, and what the implications are for designing such learning environments. Drawing from the three conceptual dimensions of immersion - System, Narrative, and Agency - this work reinterprets AIs in immersive learning contexts. It outlines practical implications for designing learning environments where AIs are surrounded by external digital services, can interpret a narrative of origins, changes, and structural developments in data, and dynamically respond, making operational and tactical decisions that shape human-AI collaboration. Finally, this work suggests how these insights might influence the future of AI training, proposing that immersive learning theory can inform the development of AIs capable of evolving beyond static models. This paper paves the way for understanding AI as an immersive learner and participant in evolving human-AI cognitive ecosystems.

I remember the first time I watched a tutorial on Blender, a 3D computer graphics software, explaining how metal surfaces have colored reflections, while nonmetal surfaces don't. It was a fascinating art lesson and something I don't think I ever would've noticed if no one had pointed it out. I felt excited to learn about such a cool, if inconsequential detail about how our world looks. While testing out Sony's PlayStation 5 Pro, I experienced that same feeling over and over again. Generally, video game graphics have reached the coveted point of "good enough."

Manifold learning has been proven to be an effective method for capturing the implicitly intrinsic structure of non-Euclidean data, in which one of the primary challenges is how to maintain the distortion-free (isometry) of the data representations. Actually, there is still no manifold learning method that provides a theoretical guarantee of isometry. Inspired by Nash's isometric theorem, we introduce a new concept called isometric immersion learning based on Riemannian geometry principles. Following this concept, an unsupervised neural network-based model that simultaneously achieves metric and manifold learning is proposed by integrating Riemannian geometry priors. What's more, we theoretically derive and algorithmically implement a maximum likelihood estimation-based training method for the new model. In the simulation experiments, we compared the new model with the state-of-the-art baselines on various 3-D geometry datasets, demonstrating that the new model exhibited significantly superior performance in multiple evaluation metrics. Moreover, we applied the Riemannian metric learned from the new model to downstream prediction tasks in real-world scenarios, and the accuracy was improved by an average of 8.8%.

Data for training learning-enabled self-driving cars in the physical world are typically collected in a safe, normal environment. Such data distribution often engenders a strong bias towards safe driving, making self-driving cars unprepared when encountering adversarial scenarios like unexpected accidents. Due to a dearth of such adverse data that is unrealistic for drivers to collect, autonomous vehicles can perform poorly when experiencing such rare events. This work addresses much-needed research by having participants drive a VR vehicle simulator going through simulated traffic with various types of accidental scenarios. It aims to understand human responses and behaviors in simulated accidents, contributing to our understanding of driving dynamics and safety. The simulation framework adopts a robust traffic simulation and is rendered using the Unity Game Engine. Furthermore, the simulation framework is built with portable, light-weight immersive driving simulator hardware, lowering the resource barrier for studies in autonomous driving research. Keywords: Rare Events, Traffic Simulation, Autonomous Driving, Virtual Reality, User Studies