Results 101 to 110 of about 23,797 (211)

Volatile Memristive Devices With Tunable Temporal Dynamics For Event‐Based Sensing

Advanced Materials, EarlyView.
Tunable volatile memristive devices can serve various neural‐inspired tasks that require different time windows of information retention. The ionic‐based volatility of the presented Pt/a‐STO/TaOx/Ta device stack can be reproducibly and controllably tuned in multiple ways.
Dimitrios Spithouris, Johannes Hellwig, Hugh Greatorex, Clemens Wittberg, Panagiotis Koutsogiannis, César Magén, Elisabetta Chicca, Regina Dittmann   +7 more
wiley   +1 more source

A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient Descent Algorithm. [PDF]

Diagnostics (Basel), 2022
Verma D, Agrawal S, Iwendi C, Sharma B, Bhatia S, Basheer S.   +5 more
europepmc   +1 more source

Toward Pore‐Engineered 3D‐Printed Materials for Sorption Water Harvesting, Interfacial Evaporation, and Radiative Cooling

Advanced Materials, EarlyView.
Water harvesting, radiative cooling, and interfacial solar evaporation are fundamentally governed by coupled heat, mass, and light transport processes. These processes are mediated by pore architecture, including pore size, connectivity, and hierarchical organization.
Dejan J. Trajkovski, Higgins M. Wilson, Abdallah Y. M. Ali, Jiaxing Xu, Ruzhu Wang, Primož Poredoš   +5 more
wiley   +1 more source

An Improvised Sentiment Analysis Model on Twitter Data Using Stochastic Gradient Descent (SGD) Optimization Algorithm in Stochastic Gate Neural Network (SGNN). [PDF]

SN Comput Sci, 2023
Vidyashree KP, Rajendra AB.
europepmc   +1 more source

A Novel Sophia-SPGD (Stochastic Parallel Gradient Descent) Optimization Method for Wavefront Correction in WFS-Less AO (Wavefront Sensorless Adaptive Optics) Systems

Photonics
In wavefront sensorless adaptive optics (WFS-less AO) systems, stochastic parallel gradient descent (SPGD) is the primary optimization method for correcting wavefront distortions. However, as the intensity of atmospheric turbulence interference increases,
Peng Chen, Wenjie Yang, Yongqi Ge, Zhiguang Zhang, Xianshuo Li, Zhengqing Qi, Huizhen Yang   +6 more
doaj   +1 more source

Iterative Selection of DNA Nanostructures for Cellular Uptake

Advanced Materials, EarlyView.
DNA nanostructures are promising cell targeting delivery vehicles for therapeutics, but the targeting behavior is not fully understood. In this study, libraries of DNA nanostructures that can be amplified and sequenced are combined with cellular uptake as a selection pressure to iteratively refine DNA structures taken up in cells to better understand ...
Anjali Rajwar, Lisa Eichhorn, Jakub Palacka, Sandra Ly, Erik Benson   +4 more
wiley   +1 more source

Using the Stochastic Gradient Descent Optimization Algorithm on Estimating of Reactivity Ratios. [PDF]

Materials (Basel), 2021
Fazakas-Anca IS, Modrea A, Vlase S.
europepmc   +1 more source

Non-Iterative Phase-Only Hologram Generation via Stochastic Gradient Descent Optimization

Photonics
In this work, we explored, for the first time, to the best of our knowledge, the potential of stochastic gradient descent (SGD) to optimize random phase functions for application in non-iterative phase-only hologram generation.
Alejandro Velez-Zea, John Fredy Barrera-Ramírez   +1 more
doaj   +1 more source

Descriptors to Dynamics: A Materials and Device Perspective on in‐Materio Physical Reservoir Computing for Neuromorphic Edge Intelligence

Advanced Materials, EarlyView.
Intrinsic material dynamics are harnessed as computational resources for neuromorphic in‐materio physical reservoir computing. Defects, ionic motion, interfaces, percolation, geometry, and biasing shape transient states that provide fading memory, nonlinearity, and high‐dimensional projection for simple readout. A descriptor‐to‐dynamics framework links
Kshitij RB Singh, Alif Syafiq Kamarol Zaman, Muzhen Xu, Yuki Usami, Saptarshi Das, Hirofumi Tanaka   +5 more
wiley   +1 more source

The inverse variance-flatness relation in stochastic gradient descent is critical for finding flat minima. [PDF]

Proc Natl Acad Sci U S A, 2021
Feng Y, Tu Y.
europepmc   +1 more source