Results 151 to 160 of about 16,833 (261)

Physics‐Grounded Probabilistic Bits for Hardware‐Efficient Intelligent Inference and Optimization

open access: yesAdvanced Intelligent Systems, EarlyView.
Si–SiNx interface traps are harnessed as a complementary metal–oxide–semiconductor‐compatible source of controllable randomness for probabilistic bits. Pulse‐width‐programmed stochastic capture converts nanoscale defect dynamics into Boltzmann‐consistent binary outputs, while a physics‐based Simulation Program with Integrated Circuit Emphasis model ...
Dokyoung Lee   +3 more
wiley   +1 more source

Energy‐Aware Perturbation Optimization for Memristor‐Array Convolutional Neural Networks

open access: yesAdvanced Intelligent Systems, EarlyView.
Memristor‐array inference becomes more energy efficient when layer inputs are reshaped before computation. Sinusoidal perturbation encoding with dual‐threshold screening reduces active voltage pulses and contracts ADC input‐current ranges, jointly lowering crossbar and peripheral energy while preserving accuracy across hardware MNIST validation, deep ...
Ao Xu   +6 more
wiley   +1 more source

Vernacular Futurism: How Persian Language Users Imagine AI

open access: yesAI &Innovation, EarlyView.
ABSTRACT Public discourse about artificial intelligence increasingly unfolds through compressed forecasts, moral warnings, and everyday speculation circulating at platform speed. This study examines how Persian language users on X construct and contest AI futures, analyzing a corpus of 4741 posts collected between January 2023 and December 2025, with ...
Arthur Asa Berger, Ehsan Shahghasemi
wiley   +1 more source

The multiple doctrines of legitimate expectations

open access: yes, 2016
Legitimate expectations arise in a variety of different ways and yet ‘the doctrine of legitimate expectations’ is still regarded as one amorphous doctrine. Any underlying theory which attempts to unify diverse case law will thus inevitably be fairly general.
openaire   +1 more source

Who Owns the Output? Authorship, Creative Labour, and Innovation Capability in Human‐AI Collaboration

open access: yesAI &Innovation, EarlyView.
ABSTRACT Generative AI is radically transforming how creative authorship is understood, attributed, and governed across the world’s cultural and creative industries. As AI systems increasingly produce outputs that organisations and audiences recognise as creative, foundational assumptions about who authors creative work, who receives credit for it, and
Ololade A. Shonubi
wiley   +1 more source

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