Results 61 to 70 of about 33,561,195 (290)
Kernel Sparse Representation with Hybrid Regularization for On-Road Traffic Sensor Data Imputation
Sensors, 2018 The problem of missing values (MVs) in traffic sensor data analysis is universal in current intelligent transportation systems because of various reasons, such as sensor malfunction, transmission failure, etc. Accurate imputation of MVs is the foundation
Xiaobo Chen +4 more
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Stats, 2022 In this paper, we introduce a kernel-based nonlinear Bayesian model for a right-censored survival outcome data set. Our kernel-based approach provides a flexible nonparametric modeling framework to explore nonlinear relationships between predictors with ...
Sounak Chakraborty +3 more
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Kernel method for corrections to scaling. [PDF]
Physical review. E, Statistical, nonlinear, and soft matter physics, 2014 Scaling analysis, in which one infers scaling exponents and a scaling function in a scaling law from given data, is a powerful tool for determining universal properties of critical phenomena in many fields of science.
K. Harada
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FEBS Open Bio, EarlyView.UiO‐66(Zr) metal–organic frameworks are chemically stable, biocompatible, and highly tunable nanomaterials. Their modular structure enables controlled drug delivery, multimodal bioimaging, and light‐activated photodynamic therapy, supporting integrated diagnostic and therapeutic (theranostic) applications in cancer and biomedical research.
Veronika Huntošová +2 more
wiley +1 more source
MathematicsPrevious studies have shown that hazard ratios between treatment groups estimated with the Cox model are uninterpretable because the unspecified baseline hazard of the model fails to identify temporal change in the risk-set composition due to treatment ...
Takashi Hayakawa, Satoshi Asai
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Generalization Performance of Quantum Metric Learning Classifiers
Biomolecules, 2022 Quantum computing holds great promise for a number of fields including biology and medicine. A major application in which quantum computers could yield advantage is machine learning, especially kernel-based approaches.
Jonathan Kim, Stefan Bekiranov
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CoRRKernel methods approximate nonlinear maps in a data-driven manner by projecting the target map onto a finite-dimensional Hilbert space called the solution space. Traditionally, this space is a subspace of a fixed ambient reproducing kernel Hilbert space (RKHS), determined solely by the chosen kernel and the dataset, whose elements identify the basis ...
Tamás Dózsa +4 more
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Relationship Between Neurologic Symptoms and Signs and FMR1 Genotype in Premutation Carriers
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background and Objectives Fragile X‐associated Tremor/Ataxia Syndrome (FXTAS) is the most severe late‐onset condition caused by a premutation in the FMR1 gene, characterized by expanded CGG triplet repeats of 55–200. Clinical presentations of FXTAS, including gait ataxia, kinetic tremor, cognitive decline, and rare Parkinsonism, are linked to ...
Flora Tassone +8 more
wiley +1 more source
A Kernel-Based Calibration Algorithm for Chromatic Confocal Line Sensors
SensorsIn chromatic confocal line sensors, calibration is usually divided into peak extraction and wavelength calibration. In previous research, the focus was mainly on peak extraction. In this paper, a kernel-based algorithm is proposed to deal with wavelength
Ming Qin +7 more
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MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions
Physics in Medicine and Biology, 2016 Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the ...
P. Novosad, A. Reader
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