Results 151 to 160 of about 128,591 (295)

Nonparametric estimation of concave production technologies by entropic methods [PDF]

An econometric methodology is developed for nonparametric estimation of concave production technologies. The methodology, bases on the priciple of maximum likelihood, uses entropic distance and concvex programming techniques to estimate production ...
Alex Shapiro, Michael Beenstock, Ury Passy, Gad Allon, Steven Hackman   +4 more
core  

Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy

Advanced Intelligent Discovery, EarlyView.
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang, Andrew Bai, Alexandra Bermudez, Yunqi Hong, Zoe Latham, Iris Sloan, Michael Liu, Vishrut Goyal, Cho‐Jui Hsieh, Neil Y. C. Lin   +9 more
wiley   +1 more source

Nonparametric Efficiency Estimation in Stochastic Environments (II)

We consider the issues of noise-to-signal estimation, finite sample performance andhypothesis testing for the nonparametric efficiency estimation technique proposed inCherchye, L., T. Kuosmanen and G. T. Post (2001) 'Nonparametric efficiencyestimation in
Post, G.T., Cherchye, L.
core  

Spatially Informed Feature Selection and Machine Learning in Matrix‐Assisted Laser Desorption/Ionization Imaging for Cohort‐Scale Molecular Tissue Phenomics in Glioblastoma

Advanced Intelligent Discovery, EarlyView.
Matrix‐assisted laser desorption/ionization imaging‐based identification of reliable small molecule markers across heterogeneous glioblastoma cohorts is challenging with intensity‐only methods. We present spatially informed feature selection (SIFS), a spatially informed framework that prioritizes molecules consistently colocalizing with histopathology.
Shad A. Mohammed, Denis Abu Sammour, Dirk C. Hoffmann, Philipp Sievers, Tobias Bausbacher, Melissa Hahn, Yasemin Ucal, Iris Mildenberger, Christiane A. Opitz, Stefan Schmidt, Michael Platten, Oliver Wasenmüller, Wolfgang Wick, Andreas von Deimling, Tobias Kessler, Carsten Hopf   +15 more
wiley   +1 more source

Inference for Deep Neural Network Estimators in Generalized Nonparametric Models. [PDF]

J Am Stat Assoc
Meng X, Li Y.
europepmc   +1 more source

Human‐in‐the‐Loop Object Segmentation for 3D Gaussian Splatting via Finger‐based VR Interface

Advanced Intelligent Systems, EarlyView.
This study introduces a human‐in‐the‐loop segmentation framework for 3D Gaussian Splatting that integrates real‐time optimization with intuitive VR‐based finger prompting. Compared with existing automatic, learning‐based methods, it achieves significantly higher accuracy and reduced segmentation time.
Yongseok Lee, Hyunreal Park, Hyunsu Kim, Harim Ji, Dongho Yee, Dongjun Lee   +5 more
wiley   +1 more source

Kernel embeddings and the separation of measure phenomenon. [PDF]

Proc Natl Acad Sci U S A
Santoro LV, Waghmare KG, Panaretos VM.
europepmc   +1 more source

Adaptive Macroscopic Ensemble Allocation for Robot Teams Monitoring Spatiotemporal Processes

Advanced Intelligent Systems, EarlyView.
We propose an online, environment feedback‐driven macroscopic ensemble approach to adapt robot team task allocation in spatiotemporal environments by controlling robot populations rather than assigning individual robots, all while maintaining robust team performance even for small teams. Our simulation and experimental results show better or comparable
Victoria Edwards, Thales C. Silva, M. Ani Hsieh   +2 more
wiley   +1 more source

Confidence interval construction for multivariable truncated spline logistic model (MTSLM). [PDF]

MethodsX
Suriaslan AS, Budiantara IN, Ratnasari V, Aydin D.   +3 more
europepmc   +1 more source

Nonparametric regression for dependent data in the errors-in-variables problem [PDF]

We consider the nonparametric estimation of the regression functions for dependent data. Suppose that the covariates are observed with additive errors in the data and we employ nonparametric deconvolution kernel techniques to estimate the regression ...
Toshio Honda
core