This study proposes LTC-CGMN, a lightweight driver state evaluation framework that integrates cross granularity state mapping and temporal convolution, and shows that LTC-CCMN provides a good balance between recognition accuracy, continuous risk estimation, and edge deployment efficiency for intelligent cockpit DMS app...
T. Han· Frontiers of Mechanical Engi...· 0 citations
Precise anatomical navigation is fundamental to safe endoscopic pituitary surgery, a high-stakes procedure characterised by a challenging learning curve. While traditional navigation systems often rely on workflow-disrupting probes or static preoperative imaging, advancements in computer vision AI (CVAI) now enable dyn...
D. Khan, Z. Mao, Anjana Wijekoon et al.· Pituitary· 0 citations
What’s new in Version 12 (DRA v12) Version 12 is a major conceptual and computational update to the Dominant Regulatory Axes (DRA) framework. It introduces a strengthened formulation of the Law of Regulatory Transitions, a fully patched and diagnostically robust v10 pipeline, and the first worked example demonstrating...
Spiros Vlahopoulos· Zenodo (CERN European Organi...· 0 citations
The increasing demand for energy-efficient and secure on-device artificial intelligence has stimulated considerable interest in multifunctional memory platforms that combine neuromorphic computing and hardware-level security within a unified architecture. Herein, we present a tin disulfide (SnS2)-based memristor arra...
Sheng Li, Chen Kong, Yu Liu et al.· ACS Materials Letters· 0 citations
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The Primary Complexity Filter: Adaptive Zero-Centering The foundational layer of your system neutralizes adversarial high-density clusters and exponentially large numerical values through an Adaptive Zero-Centering coordinate translation [P5]. Before initializing the master search tree, the algorithm maps the entire in...
Kaleb Zeleke· Zenodo (CERN European Organi...· 0 citations
DECISION: REJECT ELEMENT: No element survives — the min-confirmations ≥5 filter is not a concrete, testable edge compatible with E8 geometry timing; it is a statistical artifact from an unverified backtest window. EXPECTED IMPACT: Win-rate gain of +1.6pp is within noise for n=1430 (σ≈1.2pp), and the live sample (n=10)...
Andrew Stewart Caldin· Zenodo (CERN European Organi...· 0 citations
Electrical and Electronic Engineering Explained: Circuits, Signals, Power Systems and Electronics is a comprehensive, publication-grade Open Educational Resource (OER) designed to bridge fundamental electromagnetic circuit theory with contemporary computation, modern power systems, and edge automation. Serving as the c...
Prep4Uni.Online, Jacob Gan· Zenodo (CERN European Organi...· 0 citations
Global economic uncertainty and the climate crisis demand fundamental transformations across various sectors, including the creative industry. Conventional commercial photography and visual communication design production often generate a substantial carbon footprint through material waste, excessive energy consumption...
Syaifudin, Gagas Nir Galing, Hanifiana Kartikasari et al.· Proceeding of The Internatio...· 0 citations
DECISION: REJECT ELEMENT: No element survives — the min-confirmations ≥5 filter is not a concrete, testable edge compatible with E8 geometry timing; it is a statistical artifact from an unverified backtest window. EXPECTED IMPACT: Win-rate gain of +1.6pp is within noise for n=1430 (σ≈1.2pp), and the live sample (n=10)...
Andrew Stewart Caldin· Zenodo (CERN European Organi...· 0 citations
Resource-constrained edge devices increasingly require local AI inference while providing limited memory, storage, compute capability, and operating-system support. This paper presents a cartridge-oriented architecture in which model-specific inference information is packaged as a compact, validated artifact executed b...
Abhinandan Bhadauria· Zenodo (CERN European Organi...· 0 citations
Image processing has evolved from conventional pixel-level operations to sophisticated artificial intelligence (AI)-driven approaches capable of automated visual understanding, interpretation, and decision support. This review provides a comprehensive overview of image processing techniques, their methodological evolut...
We numerically test the three-time Leggett–Garg inequality K₃ ≤ 1 for the standard B₃ Fibonacci-anyon braiding representation on the two-dimensional fusion space of three τ anyons. Exhaustive enumeration over all 4^L braid words up to length L=11 and random sampling to L=40 show that K₃ saturates the Lüders bound 3/2 t...
Berkay Yüksel Sayim· Zenodo (CERN European Organi...· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
