Results 211 to 220 of about 594,136 (249)
[Digital, DICOM, diagnostics-unity over chaos : Data communication in digital pathology]. [PDF]
Blattgerste C, Legnar M, Weis CA.
europepmc +1 more source
A volatile‐switching compact model of electrochemical metallization memory cells for neuromorphic architecture is developed and validated by reliable reproduction of device characterization measurements: I−V sweeps, SET kinetics, relaxation dynamics.
Rana Walied Ahmad +4 more
wiley +1 more source
[Improving record linkage for health research-how to overcome deficiencies?] [PDF]
Intemann T +25 more
europepmc +1 more source
Quantization‐aware training creates resource‐efficient structured state space sequential S4(D) models for ultra‐long sequence processing in edge AI hardware. Including quantization during training leads to efficiency gains compared to pure post‐training quantization.
Sebastian Siegel +5 more
wiley +1 more source
New healthcare insights in ophthalmology: using a data integration center (DIC) to analyze the care of patients with corneal ulceration during the COVID-19 pandemic. [PDF]
Stolze G +5 more
europepmc +1 more source
A Flexible and Energy‐Efficient Compute‐in‐Memory Accelerator for Kolmogorov–Arnold Networks
This article presents KA‐CIM, a compute‐in‐memory accelerator for Kolmogorov–Arnold Networks (KANs). It enables flexible and efficient computation of arbitrary nonlinear functions through cross‐layer co‐optimization from algorithm to device. KA‐CIM surpasses CPU, ASIC, VMM‐CIM, and prior KAN accelerators by 1–3 orders of magnitude in energy‐delay ...
Chirag Sudarshan +6 more
wiley +1 more source
T1w/T2w Ratio Identifies the Basolateral Amygdala as a Preferential Target in Autoimmune Limbic Encephalitis. [PDF]
Dadarwal R +12 more
europepmc +1 more source
Comparing the Latent Features of Universal Machine‐Learning Interatomic Potentials
This study quantitatively assesses how universal machine‐learning interatomic potentials encode the chemical space into latent features, showing unique model‐specific representations with high cross‐model reconstruction errors. It explores how training datasets, protocols, and targets affect these encodings.
Sofiia Chorna +5 more
wiley +1 more source

