Results 111 to 120 of about 1,764,639 (264)

FEWT: Frequency‐Enhanced Wavelet‐Based Transformer for Multimodal Wheeled Bimanual Manipulation

SmartBot, EarlyView.
An embodied mobile bimanual robot learns dexterous manipulation skills through imitation learning with multimodal perception. The integration of vision, proprioception, and tactile sensing establishes a comprehensive perception–action loop, enabling the robot to continuously understand, interact with, and adapt to complex physical environments.
Jiaxin Huang, Hanyu Liu, Yunsheng Ma, Jian Shen, Yilin Zheng, Jiayi Wen, Zhigong Song   +6 more
wiley   +1 more source

Applied aspects of modern non-blind image deconvolution methods

Компьютерная оптика
The focus of this paper is the study of modern non-blind image deconvolution methods and their application to practical tasks. The aim of the study is to determine the current state-of-the-art in non-blind image deconvolution and to identify the ...
O.B. Chaganova, A.S. Grigoryev, D.P. Nikolaev, I.P. Nikolaev   +3 more
doaj   +1 more source

Imaging Ionic Displacements Across Inclined Ferroelectric Domains by a Reciprocity Approach to 4D‐STEM

Small, EarlyView.
Four‐dimensional STEM comprises a vast portfolio of electron microscopy methods in one data set, conventional plane‐wave illumination TEM being one of them. Here, the principle of reciprocity is employed to produce true structural projections of misoriented ferroelectric domains at atomic resolution. By reciprocal TEM imaging from four‐dimensional STEM
Jean Felix Dushimineza, Max Leo Leidl, Sebastian Sturm, Thibaud Denneulin, Knut Müller‐Caspary   +4 more
wiley   +1 more source

Locally linear approximation for Kernel methods : the Railway Kernel [PDF]

, 2008
In this paper we present a new kernel, the Railway Kernel, that works properly for general (nonlinear) classification problems, with the interesting property that acts locally as a linear kernel.
González, Javier, Muñoz, Alberto
core   +1 more source

Double Kernel estimation of sensitivities [PDF]

This paper adresses the general issue of estimating the sensitivity of the expectation of a random variable with respect to a parameter characterizing its evolution.
Romuald Elie
core  

Artificial intelligence empowers targeted protein degradation: Core technological innovations, multi‐scenario applications, and translational prospects

Smart Molecules, EarlyView.
AI is transforming TPD by improving the design, prediction, and optimization of degraders such as PROTACs, molecular glues, and LYTACs. This review summarizes key AI‐driven advances, highlights applications across drug discovery stages, and discusses remaining challenges and future directions for accelerating the development of therapies against ...
Shuanglin Qin, Rui Peng, Guangshuai Zhang, Si Yan, Jin Xiao, Zishu Liu, Ziyu Tan, Qingqing Xie, Shangjie Li, Wei Luo, Xiaohe Xiao   +10 more
wiley   +1 more source

Blind Image Deblurring via Bayesian Estimation Using Expected Loss

IEEE Access
This paper introduces a new approach to single image blind deblurring via Bayesian estimation using expected loss, diverging from traditional maximum a posteriori (MAP) estimation methods that are limited by the delta kernel problem-a phenomenon where ...
Jinook Lee, Moon Gi Kang
doaj   +1 more source

Transformation kernel density estimation of actuarial loss functions [PDF]

A transformation kernel density estimator that is suitable for heavy-tailed distributions is discussed. Using a truncated Beta transformation, the choice of the bandwidth parameter becomes straightforward.
Montserrat Guillen (Universitat de Barcelona), Jens Perch Nielsen (City University London), Catalina Bolance (Universitat de Barcelona)   +2 more
core  

Denoise Stepwise Signals by Diffusion Model‐Based Approach

Small Methods, EarlyView.
This work presents SSDM, a diffusion‐model‐based framework for denoising stepwise signals in single‐molecule measurements. By learning the statistical structure of state transitions and noise, SSDM reconstructs signal levels and identifies transition time points more accurately than conventional approaches, enabling robust analysis of signals across ...
Xingdi Tong, Chenyu Wen
wiley   +1 more source

Nonparametric Beta kernel estimator for long memory time series [PDF]

The paper introduces a new nonparametric estimator of the spectral density that is given in smoothing the periodogram by the probability density of Beta random variable (Beta kernel).
VAN BELLEGEM, Sébastien, BOUEZMARNI, Taoufik   +1 more
core   +2 more sources