Perceptual Models, Gamut Mapping, Display Technology, Visual Engineering

Review: Chronicle - Jihlava 2025
cineuropa.org·2h
📦Digital Cinema Package
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When a clear film wasn’t
rubenerd.com·7h
🔬Projection Optics
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News for October 2025
ptreview.sublinear.info·1d
🧪Property-Based Testing
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Fifteen nostalgic gifts in the US that will take you back to simpler times
theguardian.com·15h
🕹️Retro Gaming
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Feature Stores 2.0: The Next Frontier of Scalable Data Engineering for AI
hackernoon.com·7h
🌀Brotli Internals
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Women perceive sleek and shiny hair as healthier and more youthful, study finds
phys.org·21h
🏛Digital humanities
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The older I get, the better my code becomes
dev.to·9h·
Discuss: DEV
⚙️Build Archaeology
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Enhancing Diffusion-based Restoration Models via Difficulty-Adaptive Reinforcement Learning with IQA Reward
arxiv.org·1d
📊Learned Metrics
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Understanding New-Knowledge-Induced Factual Hallucinations in LLMs: Analysis, Solution, and Interpretation
arxiv.org·7h
📏Linear Logic
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Audience Amplified: Virtual Audiences in Asynchronously Performed AR Theater
arxiv.org·7h
Tactile Computing
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Two-Parameter R\'enyi Information Quantities with Applications to Privacy Amplification and Soft Covering
arxiv.org·7h
📡Information theory
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LongCat-Flash-Omni Technical Report
arxiv.org·1d
🎬WebCodecs
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ZEBRA: Towards Zero-Shot Cross-Subject Generalization for Universal Brain Visual Decoding
arxiv.org·2d
📊Learned Metrics
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Medical Report Generation: A Hierarchical Task Structure-Based Cross-Modal Causal Intervention Framework
arxiv.org·7h
🤖Advanced OCR
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A Cognitive Process-Inspired Architecture for Subject-Agnostic Brain Visual Decoding
arxiv.org·7h
🧠Neural Codecs
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Q-Sat AI: Machine Learning-Based Decision Support for Data Saturation in Qualitative Studies
arxiv.org·7h
🧠Machine Learning
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Neural Transparency: Mechanistic Interpretability Interfaces for Anticipating Model Behaviors for Personalized AI
arxiv.org·1d
🎙️Whisper
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Optimizing Multi-UAV 3D Deployment for Energy-Efficient Sensing over Uneven Terrains
arxiv.org·7h
Information Bottleneck
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Unlock the Power of GANs: Train with Tiny Datasets!
dev.to·17h·
Discuss: DEV
📊Learned Metrics
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