Results 111 to 120 of about 2,989,160 (264)

Emerging Post‐CMOS Hardware Neurons for Brain‐Inspired Computing: Devices, Circuits, and System Integration

Advanced Functional Materials, EarlyView.
The physical realization of artificial neurons is a critical challenge for energy‐efficient neuromorphic computing. This review presents a comprehensive analysis of the evolution of artificial neuron implementations from conventional CMOS to emerging post‐CMOS technologies.
Kannan Udaya Mohanan, Hocheon Yoo, Benoît H. Lessard, Ioannis Kymissis, Chang‐Hyun Kim   +4 more
wiley   +1 more source

Bio‐Inspired Artificial Ionic Mechanoreceptor

Advanced Functional Materials, EarlyView.
A skin‐inspired artificial mechanoreceptor based on ionic interactions is presented for biomimetic tactile sensing. Pressure‐driven ionic redistribution within microfluidic channels generates a self‐powered electrical signal without external bias. The generated waveform exhibits mechanoreceptor‐like temporal features, including overshoot and undershoot,
Mohammad Akbari, Jeppe Don, Lasse A. N. Pedersen, John Noee, Rassoul Tabassian   +4 more
wiley   +1 more source

Intrinsic Hamiltonian of Mean Force and Strong-Coupling Quantum Thermodynamics

PRX Quantum
We present a universal thermodynamic framework for quantum systems strongly coupled to thermal reservoirs, identifying the class of initial conditions required for consistent thermodynamic behavior.
Ignacio González, Sagnik Chakraborty, Ángel Rivas   +2 more
doaj   +1 more source

ELEMENTARY PARADIGMATIC CLASSES OF UKRAINIAN MASCULINE HYDRONYMS OF THE 2ND DECLENSION OF THE MIXED GROUP

Lviv Philological Journal, 2023
openaire   +1 more source

Learning from Paradigmatic Information [PDF]

, 2006
Paradigmatic information is information requiring knowledge of morphological identity across words. It consists of the phonological consequences of knowing that a morpheme must have a single phonological underlying form, even if it surfaces differently ...
Tesar, Bruce
core  

Reservoir‐Driven Neuromorphic Computing Based on Composite Rare‐Earth/Transition Metal Oxide Memristor

Advanced Functional Materials, EarlyView.
A defect‐engineered Ag/Gd2O3:Nb2O5/Pt rare earth composite oxide memristor enables stable multilevel reservoir states through pulse driven conductance modulation. Experimentally measured device responses are incorporated into a device aware reservoir computing framework for CIFAR‐100 image classification, highlighting the potential of rare earth ...
Hammad Ghazanfar, Haider Abbas, Muhammad Rabeel, Honggyun Kim, Ghulam Dastgeer, Muhammad Nasim, Furqan Zahoor, Yawar Abbas, Waqas Ahmad, Deok‐kee Kim   +9 more
wiley   +1 more source

Biographical record of the Class of Fifty-eight, Yale College.

Imprint varies: New Britain, Conn.
Yale College. Class of 1858.
core  

Preferential Interfacial Engineering of Hydrogels via Interdigitated Nanoparticle Assembly

Advanced Functional Materials, EarlyView.
Robust surface functionalization of hydrogel surfaces is performed via interdigitated metal nanoparticle assembly, inducing strong affinity with the hydrogel matrix. The partially exposed nanoparticle domains act as interfacial nanobridges that mediate both chemical and physical bonding with diverse materials, including small molecules, fluorous ...
Hyeonjin Kim, Hyeseoung Lim, Ji Min Lee, Heeyeon Kang, Yongkwon Song, Woojae Chang, So‐Jin Park, Jin Yoo, Atsushi Hozumi, Jinhan Cho, Jongkuk Ko, Sanghyuk Wooh   +11 more
wiley   +1 more source

Classification in Music: A Computational Model for Paradigmatic Analysis

, 1997
We present a computational model for the paradigmatic analysis of musical pieces, which is the classification of musical segments into similarity-based categories.
Christina Anagnostopoulou, Gert Westermann   +1 more
core  

Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery

Advanced Functional Materials, EarlyView.
Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang, Qian Wang, Seong‐Hoon Jang, Hao Li   +3 more
wiley   +1 more source