Results 81 to 90 of about 28,952 (292)

Thermally Pre‐Formed Reconfigurable Resistive Random‐Access Memory Crossbar Arrays: A Dual‐Mode Platform for Robust Physically Unclonable Functions and In‐Memory Computing

open access: yesAdvanced Functional Materials, EarlyView.
A reconfigurable RRAM platform utilizing thermally pre‐formed filaments (TPFs) is developed to realize robust hardware security. By exploiting the thermodynamic stochasticity of TPFs, exceptionally reliable physically unclonable functions (PUFs) are achieved.
Seongbin Kwon   +4 more
wiley   +1 more source

Balancing Privacy and Utility in Artificial Intelligence-Based Clinical Decision Support: Empirical Evaluation Using De-Identified Electronic Health Record Data

open access: yesApplied Sciences
The secondary use of electronic health records is essential for developing artificial intelligence-based clinical decision support systems. However, even after direct identifiers are removed, de-identified electronic health records remain vulnerable to ...
Jungwoo Lee, Kyu Hee Lee
doaj   +1 more source

Time‐Resolved Magnetization Switching Dynamics Driven by Orbital Torques

open access: yesAdvanced Functional Materials, EarlyView.
Du et al. reveal nanosecond magnetization switching driven by orbital currents using time‐resolved Hall detection. The measurements separate domain nucleation from domain wall propagation and show that Joule heating strongly assists switching by lowering energy barriers.
Ao Du   +4 more
wiley   +1 more source

A note on diffusion limits for stochastic gradient descent [PDF]

open access: yes
In the machine learning literature stochastic gradient descent has recently been widely discussed for its purported implicit regularization properties. Much of the theory, that attempts to clarify the role of noise in stochastic gradient algorithms, has ...
Lanconelli, Alberto   +1 more
core   +1 more source

Mesoscale Domain Evolution Mechanism during Alternating Current (AC) Poling of Relaxor Ferroelectrics

open access: yesAdvanced Functional Materials, EarlyView.
Ferroelectric domain variants that are energetically equivalent are expected to remain preserved during polarization reversal. However, phase‐field simulations reveal that inclined domain walls in relaxor ferroelectrics can undergo irreversible elimination during alternating current poling through a proximity effect driven by long‐range elastic ...
Yuan‐Jie Sun   +2 more
wiley   +1 more source

Bioinspired Adaptive Sensors: A Review on Current Developments in Theory and Application

open access: yesAdvanced Materials, EarlyView.
This review comprehensively summarizes the recent progress in the design and fabrication of sensory‐adaptation‐inspired devices and highlights their valuable applications in electronic skin, wearable electronics, and machine vision. The existing challenges and future directions are addressed in aspects such as device performance optimization ...
Guodong Gong   +12 more
wiley   +1 more source

Thermal‐Driven Diode Polarity Switching From Competing Helical Superconducting States in WTe2/α‐Fe2O3 Heterostructures

open access: yesAdvanced Materials, EarlyView.
A Nb‐proximitized Josephson junction based on a WTe2/α‐Fe2O3 heterostructure exhibits a robust superconducting diode effect with programmable polarity. The diode direction can be trained by magnetic fields and switched by temperature cycling, revealing tunable finite‐momentum pairing states and competing superconducting states in symmetry‐broken ...
Enze Zhang   +9 more
wiley   +1 more source

Revisiting Stochastic Approximation and Stochastic Gradient Descent

open access: yesCoRR
31 ...
Rajeeva Laxman Karandikar   +2 more
openaire   +2 more sources

Federated Accelerated Stochastic Gradient Descent

open access: yesCoRR, 2020
Accepted to NeurIPS 2020. Best paper in International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020 (FL-ICML'20).
Honglin Yuan, Tengyu Ma 0001
openaire   +3 more sources

Asynchronous parallel stochastic gradient descent

open access: yes, 2022
The implementation of a vast majority of machine learning (ML) algorithms boils down to solving a numerical optimization problem. In this context, Stochastic Gradient Descent (SGD) methods have long proven to provide good results, both in terms of ...
Pfreundt, Franz-Josef, Keuper, Janis
core  

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