Gen Z isn’t “the future” audience anymore. They are the growth engine right now. By 2026, the brands that win with Gen Z won’t just market products—they’ll build communities that shape culture and commerce together. As a CMO working with Gen Z–first brands, I’ve seen a consistent pattern emerge: campaigns can still go viral, but the brands that build fandoms, not funnels, grow more efficiently and stick around longer. For years, marketers treated Gen Z like a puzzle: […]

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arXiv:2602.03908v1 Announce Type: new Abstract: Accurate vehicle localization is a critical challenge in urban environments where GPS signals are often unreliable. This paper presents a cooperative multi-sensor and multi-modal localization approach to address this issue by fusing data from vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) systems. Our approach integrates cooperative data with a point cloud registration-based simultaneous localization and mapping (SLAM) algorithm. The system processes point clouds generated from diverse sensor modalities, including vehicle-mounted LiDAR and stereo cameras, as […]

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arXiv:2601.08051v1 Announce Type: new Abstract: We introduce a framework for repurposing error estimators for source problems to compute an estimator for the gap between eigenspaces and their discretizations. Of interest are eigenspaces of finite clusters of eigenvalues of unbounded nonselfadjoint linear operators with compact resolvent. Eigenspaces and eigenvalues of rational functions of such operators are studied as a first step. Under an assumption of convergence of resolvent approximations in the operator norm and an assumption on global reliability […]

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arXiv:2602.07044v1 Announce Type: new Abstract: Pipeline integrity is critical to industrial safety and environmental protection, with Magnetic Flux Leakage (MFL) detection being a primary non-destructive testing technology. Despite the promise of deep learning for automating MFL interpretation, progress toward reliable models has been constrained by the absence of a large-scale public dataset and benchmark, making fair comparison and reproducible evaluation difficult. We introduce textbf{PipeMFL-240K}, a large-scale, meticulously annotated dataset and benchmark for complex object detection in pipeline MFL […]

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arXiv:2603.03412v1 Announce Type: new Abstract: Recent advances in generative image editing have enabled transformative applications, from professional head shot generation to avatar stylization. However, these systems often require uploading high-fidelity facial images to third-party models, raising concerns around biometric privacy, data misuse, and user consent. We propose a privacy-preserving pipeline that supports high-quality editing while keeping users in control over their biometric data in face-centric use cases. Our approach separates identity-sensitive regions from editable image context using on-device […]

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arXiv:2602.10221v1 Announce Type: new Abstract: In this work, we address two major issues in recent Denoising Diffusion Probabilistic Models (DDPM): {bf 1)} geometric key feature extraction and {bf 2)} network equivariance. Since the DDPM prediction network relies on the U-net architecture, which is theoretically only translation equivariant, we introduce a geometric approach combined with an equivariance property of the more general Euclidean group, which includes rotations, reflections, and permutations. We introduce the notion of group morphological convolutions in […]

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This work provides a state-of-the-art survey of continual safe online reinforcement learning (COSRL) methods. We discuss theoretical aspects, challenges, and open questions in building continual online safe reinforcement learning algorithms. We provide the taxonomy and the details of continual online safe reinforcement learning methods based on the type of safe learning mechanism that takes adaptation to nonstationarity into account. We categorize safety constraints formulation for online reinforcement learning algorithms, and finally, we discuss prospects for creating reliable, safe […]

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arXiv:2602.09239v1 Announce Type: new Abstract: The growing adoption of AI-driven smart home devices has introduced new privacy risks for domestic workers (DWs), who are frequently monitored in employers’ homes while also using smart devices in their own households. We conducted semi-structured interviews with 18 UK-based DWs and performed a human-centered threat modeling analysis of their experiences through the lens of Communication Privacy Management (CPM). Our findings extend existing threat models beyond abstract adversaries and single-household contexts by showing […]

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IoT-Infused Pedagogies Empowering Resilient Digital Societies via Next-Gen Smart Campus Innovations presents a transformative vision for education in an interconnected era. This paper explores how Internet of Things (IoT) technologies revolutionize pedagogical approaches within smart campuses, fostering adaptive learning ecosystems that extend beyond classrooms to cultivate robust digital communities. By integrating real-time data from sensors, wearables, and AI-driven analytics, next-generation innovations enable personalized instruction, predictive resource management, and immersive simulations that mirror real-world challenges. Key contributions include conceptual […]

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