Results 71 to 80 of about 845,859 (278)

Operating System’s Kernel Hooking Methods (Study Case of Linux Kernel)

Безопасность информационных технологий, 2014
The article presents an overview of dynamic integration in the kernel Linux, allowed to modify (add, change) its functionality. Traditional methods of integration based on changing in the kernel code (patching), and methods based on using system ...
Ilya Vladimirovich Matveychikov
doaj  

Understanding Decoherence of the Boron Vacancy Center in Hexagonal Boron Nitride

Advanced Functional Materials, EarlyView.
State‐of‐the‐art computations unravel the intricate decoherence dynamics of the boron vacancy center in hexagonal boron nitride across magnetic fields from 0 to 3 T. Five distinct regimes emerge, dominated by nuclear spin interactions, revealing optimal coherence times of 1–20 µs in the 180–350 mT range for isotopically pure samples.
András Tárkányi, Viktor Ivády
wiley   +1 more source

Locally-Scaled Kernels and Confidence Voting

Machine Learning and Knowledge Extraction
Classification, the task of discerning the class of an unlabeled data point using information from a set of labeled data points, is a well-studied area of machine learning with a variety of approaches.
Elizabeth Hofer, Martin v. Mohrenschildt
doaj   +1 more source

Exact heat kernel on a hypersphere and its applications in kernel SVM

, 2017
Many contemporary statistical learning methods assume a Euclidean feature space. This paper presents a method for defining similarity based on hyperspherical geometry and shows that it often improves the performance of support vector machine compared to ...
Song, Jun S., Zhao, Chenchao
core   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Crack‐Growing Interlayer Design for Deep Crack Propagation and Ultrahigh Sensitivity Strain Sensing

Advanced Functional Materials, EarlyView.
A crack‐growing semi‐cured polyimide interlayer enabling deep cracks for ultrahigh sensitivity in low‐strain regimes is presented. The sensor achieves a gauge factor of 100 000 at 2% strain and detects subtle deformations such as nasal breathing, highlighting potential for minimally obstructive biomedical and micromechanical sensing applications ...
Minho Kim, Taewi Kim, Jieun Park, Insic Hong, Myungrae Hong, Seonggyeong Park, Beomjin Kim, Sehyuk Ahn, Siwoo Jang, Seungyong Han, Je‐Sung Koh, Daeshik Kang   +11 more
wiley   +1 more source

Sharp analysis of low-rank kernel matrix approximations [PDF]

, 2013
We consider supervised learning problems within the positive-definite kernel framework, such as kernel ridge regression, kernel logistic regression or the support vector machine.
Bach, Francis
core   +4 more sources

Local Thermal Conductivity Patterning in Rotating Lattice Crystals of Anisotropic Sb2S3

Advanced Functional Materials, EarlyView.
Microscale control of thermal conductivity in Sb2S3 is demonstrated via laser‐induced rotating lattice crystals. Thermal conductivity imaging reveals marked thermal transport anisotropy, with the c axis featuring amorphous‐like transport, whereas in‐plane directions (a, b) exhibit 3.5x and 1.7x larger thermal conductivity.
Eleonora Isotta, Rosemary Wynnychenko, Binayak Mukherjee, Jack Kaman, Hrushikesh Sahasrabuddhe, Jiongzhi Zheng, Anubhav Jain, Geoffroy Hautier, Alexandra Zevalkink, Evan Musterman, Volkmar Dierolf, Himanshu Jain, G. Jeffrey Snyder, Oluwaseyi Balogun   +13 more
wiley   +1 more source

Thickness‐Dependent Skyrmion Evolution in Fe3GeTe2 During Magnetization Reversal

Advanced Functional Materials, EarlyView.
Thickness‐ and field‐dependent magnetic domain behavior in 2D van der Waals Fe3GeTe2 is studied using Lorentz TEM and micromagnetic simulations. A patch‐like domain phase evolves from skyrmions during magnetization reversal, and step edges between thickness regions act as pinning sites.
Jennifer Garland, John Fullerton, PeiYu Cai, Rabindra Basnet, Santosh Karki Chhetri, Jin Hu, Elton J. G. Santos, Yue Li, Charudatta Phatak, Amanda Petford‐Long   +9 more
wiley   +1 more source

Kernel methods for in silico chemogenomics

, 2007
Predicting interactions between small molecules and proteins is a crucial ingredient of the drug discovery process. In particular, accurate predictive models are increasingly used to preselect potential lead compounds from large molecule databases, or to
Jacob, Laurent, Vert, Jean-Philippe
core   +1 more source