This paper introduces a design framework in which time is treated as an internal ordering structure, represented by a discrete progression index tau that governs the order of state generation. Building on this viewpoint, we present tau-QC (time-ordered computation), an engineering architecture that organizes computation as tau-ordered state generation and consistency checking. Here, "quantum" is used only as structural inspiration ("quantummorphed" formalism), not as a claim about physical qubits, entanglement, or quantum computing hardware. The framework supports deterministic, low-power, continuous information processing by reallocating computation from stochastic sampling and heavy numerical pipelines to structured planning and verification. We illustrate the engineering implications through two case studies: (i) tau-based generative video, which shifts from diffusion-first refinement to symbolic-first generation with bounded diffusion only where needed, enabling smoother scaling with temporal length; and (ii) Rosie SG, a wearable inference profile that applies tau-ordered state verification to continuous physiological signals for early risk prediction under strict constraints on power, latency, and privacy. Overall, this work provides a unified architectural perspective centered on tau for rethinking time-structured computation across media generation, healthcare, and edge intelligence.
Handwriting imitation, a task of generating text images that imitate a target writer's handwriting style, has been widely studied. However, existing methods require a massive amount of real handwriting samples to train a handwriting imitation model, which are impractical to collect. This paper proposes a novel approach that eliminates the need for real handwritten texts as training samples. Instead, we train a handwriting imitation model on digital font text images (DFTI), which are much easier to obtain. To address the limited stylistic variation of DFTI, we introduce a novel Elastic Transform-based data augmentation technique that ensures style consistency across all characters in a single text image. Furthermore, to strengthen the imitation performance, we apply test-time fine-tuning to the trained model, utilizing a target writer's style reference images. Experiments on ten writers selected from the IAM-OnDB dataset demonstrate that our method achieves competitive or superior imitation quality compared to a baseline model trained on a large set of real handwriting samples.
High-resolution coarse integral imaging (CII) utilizes a fine-pitch interleaved Fresnel lens array; however, the smaller lens pitch dramatically increases the number of views to be rendered, hindering real-time performance. Moreover, extending the viewing zone requires elemental images to be generated in real time to match the viewer's eye position, making high frame rates indispensable. To address these challenges, we introduce a cluster-level culling technique that accelerates multi-view rendering for CII and sustains real-time frame rates even for scenes with millions of polygons. We further implement the technique in a prototype system on an iPad and an iPhone, both of which achieve real-time performance, demonstrating that CII can be realized with an exceptionally simple mobile-device setup.
