Results 191 to 200 of about 55,553 (309)

Nonparametric Estimation of Risk-Neutral Densities [PDF]

This chapter deals with nonparametric estimation of the risk neutral density. We present three different approaches which do not require parametric functional assumptions on the underlying asset price dynamics nor on the distributional form of the risk ...
Wolfgang Karl Härdle, Melanie Schienle, Maria Grith   +2 more
core  

De Novo Design of Membrane‐Targeting Antimicrobial Peptides Against Gram‐Negative Bacteria Using a Generative Artificial Intelligence Framework

Advanced Science, EarlyView.
Antimicrobial resistance caused by Gram‐negative bacteria remains difficult to overcome due to the protective outer membrane. To address this challenge, a multi‐condition constrained generative AI framework, GenMTAMP is proposed for de novo membrane‐targeting antimicrobial peptide design by integrating physicochemical and spatial structure descriptors.
Jingxiao Yu, Da‐Wen Sun, QingYi Wei, Yibing Zhang, Jidong Tang, Hongbin Pu   +5 more
wiley   +1 more source

Determination of dengue high-risk areas in the Philippines: a kernel density estimation, inverse distance weighting, and ecological niche modeling. [PDF]

Parasit Vectors
Olana KOA, Thongprachum A, Poprom N, Li W, Punyapornwithaya V.   +4 more
europepmc   +1 more source

Author Correction: An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China. [PDF]

Commun Biol, 2021
Shi W, Tong C, Zhang A, Wang B, Shi Z, Yao Y, Jia P.   +6 more
europepmc   +1 more source

A Two-Stage Plug-In Bandwidth Selection and Its Implementation in Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation [PDF]

The performance of a kernel HAC estimator depends on the accuracy of the estimation of the normalized curvature, an unknown quantity in the optimal bandwidth represented as the spectral density and its derivative.
Hirukawa Masayuki
core  

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

Advanced Science, EarlyView.
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi, Minying Low, Peng Xiu, Kresten Lindorff‐Larsen, Yong Wang   +4 more
wiley   +1 more source

Kernel Density Estimation: a novel tool for visualising training intensity distribution in biathlon. [PDF]

Front Sports Act Living
Staunton CA, Kårström A, Kock H, Laaksonen MS, Björklund G.   +4 more
europepmc   +1 more source

Deconvoluting kernel density estimation and regression for locally differentially private data. [PDF]

Sci Rep, 2020
Farokhi F.
europepmc   +1 more source

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

Advanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio, Wei Xuan Wilson Loo, Yan Shan Loo, Wen Xin Tan, Hui Xuan Lim, Huitao Liu, Chen Seng Ng   +6 more
wiley   +1 more source

Utilizing Kernel Density Estimation and Butterfly Diagram to Characterize the Gait Variability in the Fallers: A Cross-Sectional Study. [PDF]

Health Sci Rep
Mehrlatifan S, Fatahi A, Khezri D.
europepmc   +1 more source