Results 241 to 250 of about 2,817,119 (330)

A deep learning-enriched framework for analyzing brain functional connectivity. [PDF]

Sci Rep
Borra D, Magosso E.
europepmc   +1 more source

Unveiling Phonon Contributions to Thermal Conductivity and the Applicability of the Wiedemann—Franz Law in Ruthenium and Tungsten Thin Films

Advanced Functional Materials, EarlyView.
Thermal transport in Ru and W thin films is studied using steady‐state thermoreflectance, ultrafast pump–probe spectroscopy, infrared‐visible spectroscopy, and computations. Significant Lorenz number deviations reveal strong phonon contributions, reaching 45% in Ru and 62% in W.
Md. Rafiqul Islam, Pravin Karna, Niraj Bhatt, Sandip Thakur, Helge Heinrich, Daniel M. Hirt, Saman Zare, Christopher Jezewski, Rinus T.P. Lee, Kandabara Tapily, John T. Gaskins, Colin D. Landon, Sean W. King, Ashutosh Giri, Patrick E. Hopkins   +14 more
wiley   +1 more source

Histopathological evaluation of abdominal aortic aneurysms with deep learning. [PDF]

Diagn Pathol
Kolbinger FR, El Nahhas OSM, Nackenhorst MC, Brostjan C, Eilenberg W, Busch A, Kather JN.   +6 more
europepmc   +1 more source

Deep Learning by Scattering

, 2013
Stéphane Mallat
openalex   +2 more sources

Toward the 3rd Generation of Smart Farming: Materials, Devices, and Systems for E‐Plant Technologies

Advanced Functional Materials, EarlyView.
This review explores the latest developments in e‐plant technologies, which are revolutionizing smart farming by enabling real‐time monitoring of plant and environmental conditions. It covers the design, applications, and systems of e‐plant devices, detailing how they integrate data analytics to optimize agricultural practices, enhance crop yields, and
Daegun Kim, Jonghyun Won, Hyeongmin Park, Jun‐Gyu Choi, Sungjun Park, Giwon Lee   +5 more
wiley   +1 more source

DeepProtein: deep learning library and benchmark for protein sequence learning. [PDF]

Bioinformatics
Xie J, Li Y, Fu T.
europepmc   +1 more source

Gaussian‐Sigmoid Reinforcement Transistors: Resolving Exploration‐Exploitation Trade‐Off Through Gate Voltage‐Controlled Activation Functions

Advanced Functional Materials, EarlyView.
The characteristics of a vertical n–p–i–p heterostructure transistor device, which exhibits a voltage‐tunable transition between Gaussian and sigmoid functions, are investigated. The mixed state of the transfer curve enables the utilization of both exploitation and exploration, improving computational performance in reinforcement learning tasks ...
Jisoo Park, Juhyung Seo, Ryun‐Han Koo, Dinithi Jayasuriya, Nethmi Jayasinghe, Wonjun Shin, Amit R. Trivedi, Hocheon Yoo   +7 more
wiley   +1 more source

Multifidelity deep learning modeling of spatiotemporal lung mechanics. [PDF]

Front Physiol
Barahona Yáñez J, Hurtado DE.
europepmc   +1 more source

Learning Paired-associate Images with An Unsupervised Deep Learning Architecture

, 2013
Ti Wang, Daniel Silver
openalex   +2 more sources

SMaRT Stacking: A Methodology to Produce Optimally Layered EMI Shields with Maximal Green Index Using Fused Deposition Modeling

Advanced Functional Materials, EarlyView.
Electromagnetic interference (EMI) shields consisting of polylactic acid (PLA) in layers with different concentrations of multiwalled carbon nanotubes (MWCNT) are produced using additive manufacturing. The permittivity function of layers with different filler concentrations is learned using data of homogeneous and randomly ordered shields.
Stijn De Smedt, Brecht Evens, Pramod Ravichandran, Panagiotis Patrinos, Frederik Van Loock, Ruth Cardinaels   +5 more
wiley   +1 more source