16 skills found
PiXeL16 / RevealingSplashViewA Splash view that animates and reveals its content, inspired by Twitter splash
mxrch / RevealinUncover the full name of a target on Linkedin.
sovata8 / RevealingTableViewCellRevealingTableViewCell is a UITableViewCell that can be swiped to reveal content underneath its main view
jagar2 / Revealing Ferroelectric Switching Character Using Deep Recurrent Neural NetworksThe ability to manipulate domains and domain walls underpins function in a range of next-generation applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automation. Here, using a deep sequence-to-sequence autoencoder we automate the extraction of features of nanoscale ferroelectric switching from multichannel hyperspectral band-excitation piezoresponse force microscopy of tensile-strained PbZr0.2Ti0.8O3 with a hierarchical domain structure. Using this approach, we identify characteristic behavior in the piezoresponse and cantilever resonance hysteresis loops, which allows for the classification and quantification of nanoscale-switching mechanisms. Specifically, we are able to identify elastic hardening events which are associated with the nucleation and growth of charged domain walls. This work demonstrates the efficacy of unsupervised neural networks in learning features of the physical response of a material from nanoscale multichannel hyperspectral imagery and provides new capabilities in leveraging multimodal in operando spectroscopies and automated control for the manipulation of nanoscale structures in materials.
datar001 / Revealing Vulnerabilities In Stable Diffusion Via Targeted AttacksNo description available
Jeremy1189 / Revealing The Crucial Role Of Rough Energy Landscape On Self Diffusion In High Entropy AlloysMost of the outstanding functional and structural performance in high-entropy alloys (HEAs) relates to their sluggish diffusion properties under the rough potential energy landscape (PEL) induced by intrinsic chemical disorder. Due to the highly rugged and multi-dimensional nature of PEL, it is challenging to describe how the diffusion process is controlled by the PEL in HEAs. Here we develop machine learning (ML) models to accurately represent the local atomic environment dependence of PEL in HEAs. By combining the ML model with the kinetic Monte Carlo (kMC) method, we reveal that self-diffusion in HEAs is predominantly governed by the PEL roughness, as characterized by the elemental-specific site energies and migration barriers. Comparisons with previously-proposed simplified models for self-diffusion in HEAs elucidate that the species-averaged model may be a suitable alternative method to rapidly assess diffusion properties, though the correlation effects may be underestimated. Aided by theoretical analysis, we show that the atomic concentrations of fast-diffusing elements and the differences in the averaged migration barriers for different species are the dominant factors influencing sluggish diffusion in HEAs.
vpaliy / Floating Button Revealing Animation[Deprecated]An API for revealing a FAB.
XinleCao / Frequency Revealing AttackThe code repository of Paper: Frequency-revealing attacks against Frequency-hiding Order-preserving Encryption.
RyanBurnell / Revealing LLM CapabilitiesCode and data for the paper Revealing the structure of language model capabilities
kpatsakis / OrderRevealingEncryptionThis is a simple implementation in Python of the Order Revealing scheme of Chenette et al. For more details refer to the original work:
dulacp / Revealing.jsReveal your text with a delightful letter by letter animation. Inspired by the Secret app.
HowProgrammingWorks / RevealingConstructorRevealing Constructor Pattern
jnotnull / Revealing Magic JavaScript原生JS实现的框架功能
Aptiva / RevealingTableViewRevealingTableView
tiagomatosweb / Password RevealingSimple Vue.js component to reveal password input field
0AnonymousSite0 / Mining Social Media Data For Revealing Public Attitudes Toward Mobile Cabin HospitalsThis repository aims at providing the codes and data regarding the paper entitled “……” for the public, and it is developed by University of XXX in UK, The University of XXX in Hong Kong SAR, and XXX University in China.
