Tootfinder

Validation of collision-free spheres of Stewart-Gough platforms for constant orientations using the Application Programming Interface of a CAD software
Bibekananda Patra, Rajeevlochana G. Chittawadigi, Sandipan Bandyopadhyay
https://arxiv.org/abs/2510.08408

Validation of collision-free spheres of Stewart-Gough platforms for constant orientations using the Application Programming Interface of a CAD software
This paper presents a method of validation of the size of the largest collision-free sphere (CFS) of a 6-6 Stewart-Gough platform manipulator (SGPM) for a given orientation of its moving platform (MP) using the Application Programming Interface (API) of a CAD software. The position of the MP is updated via the API in an automated manner over a set of samples within a shell enclosing the surface of the CFS. For each pose of the manipulator, each pair of legs is investigated for mutual collisions…

A Honest Cross-Validation Estimator for Prediction Performance
Tianyu Pan, Vincent Z. Yu, Viswanath Devanarayan, Lu Tian
https://arxiv.org/abs/2510.07649 https://

A Honest Cross-Validation Estimator for Prediction Performance
Cross-validation is a standard tool for obtaining a honest assessment of the performance of a prediction model. The commonly used version repeatedly splits data, trains the prediction model on the training set, evaluates the model performance on the test set, and averages the model performance across different data splits. A well-known criticism is that such cross-validation procedure does not directly estimate the performance of the particular model recommended for future use. In this paper, w…

Macroeconomic Forecasting and Machine Learning
Ta-Chung Chi (Kevin), Ting-Han Fan (Kevin), Raffaele M. Ghigliazza (Kevin), Domenico Giannone (Kevin), Zixuan (Kevin), Wang
https://arxiv.org/abs/2510.11008

Macroeconomic Forecasting and Machine Learning
We forecast the full conditional distribution of macroeconomic outcomes by systematically integrating three key principles: using high-dimensional data with appropriate regularization, adopting rigorous out-of-sample validation procedures, and incorporating nonlinearities. By exploiting the rich information embedded in a large set of macroeconomic and financial predictors, we produce accurate predictions of the entire profile of macroeconomic risk in real time. Our findings show that regulariza…