Exploring entropy measures in polymer graphs using logarithmic regression model. [PDF]
Irfan M+5 more
europepmc +1 more source
THE EFFECT OF MENTAL ARITHMETIC ON CEREBRAL CIRCULATION AND METABOLISM 1
Louis Sokoloff+4 more
openalex +1 more source
Improving Long‐Term Glucose Prediction Accuracy with Uncertainty‐Estimated ProbSparse‐Transformer
Wearable devices collect blood glucose and other physiological data, which serve as inputs to the prediction model. After data embedding, a structure utilizing ProbSparse self‐attention and a one‐step generative head within a Transformer‐based model is introduced, which is concurrently designed for deployment on edge devices, enabling real‐time ...
Wei Huang+5 more
wiley +1 more source
Radiographic Risk Factors for Excessive Joint Line Obliquity After Knee Osteotomy for Medial Osteoarthritis: A Phenotype-Based Approach. [PDF]
Ryu J, Lee BS, Kim JM, Song JH, Kim HY.
europepmc +1 more source
Symbolic Reservoir Computing within Memristive Crossbar Arrays as a Cellular Automata
In quest of a neuro‐symbolic system with both strong intelligent computing capability and better explainability, a memristor crossbar array‐based cellular automata (symbolic model) for reservoir computing (neural network) is proposed and experimentally demonstrated using an algorithm–hardware codesign approach.
Yunpeng Guo+8 more
wiley +1 more source
Non-isomorphic abelian varieties with the same arithmetic. [PDF]
Bell J.
europepmc +1 more source
Kolmogorov–Arnold Network for Transistor Compact Modeling
This work introduces Kolmogorov–Arnold network (KAN) for the transistor—an architecture that integrates interpretability with high precision in physics‐based function modeling. The results reveal that despite achieving superior prediction accuracy for critical figures of merit, KAN demonstrates unique inherent challenges for transistor modeling ...
Rodion Novkin, Hussam Amrouch
wiley +1 more source
Impossibility of restoring unique factorization in a hypercomplex arithmetic [PDF]
L. E. Dickson
openalex +1 more source
Memory‐Reduced Convolutional Neural Network for Fast Phase Hologram Generation
This article reports a lightweight convolutional neural network framework using INT8 quantization to efficiently generate 3D computer‐generated holograms from a single 2D image. The quantized model reduces memory usage and computational cost, accelerates inference speed, and maintains high output quality, enabling real‐time holographic display on low ...
Chenliang Chang+6 more
wiley +1 more source