Results 71 to 80 of about 427,529 (281)
Advanced Robotics Research, EarlyView.Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek +3 more
wiley +1 more source
Machine learning regression models for internal shame
Acta PsychologicaThis study aims to predict Internal Shame (IS) based on childhood trauma, social emotional competence, cognitive flexibility, distress tolerance and alexithymia in an Iranian sample.
Nataša Kovač +3 more
doaj +1 more source
Confidence bands in nonparametric time series regression
, 2008 We consider nonparametric estimation of mean regression and conditional variance (or volatility) functions in nonlinear stochastic regression models.
Wu, Wei Biao, Zhao, Zhibiao
core +2 more sources
Advanced Science, EarlyView.A new class of biohybrid spheroids is engineered through the self‐assembly of adherent cells and extracellular matrix‐mimetic hydrogel microparticles (microgels). By mimicking a snowballing effect, this approach enables scalable formation of porous, millimeter‐scale spheroids with enhanced cell viability and molecular diffusion.
Zaman Ataie +7 more
wiley +1 more source
Forecasting college football game outcomes using modern modeling techniques
Journal of Sports Analytics, 2020 There are many reasons why data scientists and fans of college football would want to forecast the outcome of games – gambling, game preparation and academic research, for example. As advanced statistical methods become more readily accessible, so do the
Charles South, Edward Egros
doaj +1 more source
Truncated Stochastic Approximation with Moving Bounds: Convergence [PDF]
, 2011 In this paper we propose a wide class of truncated stochastic approximation procedures with moving random bounds. While we believe that the proposed class of procedures will find its way to a wider range of applications, the main motivation is to ...
Sharia, Teo
core +1 more source
Stochastic variational inference for large-scale discrete choice models using adaptive batch sizes
, 2015 Discrete choice models describe the choices made by decision makers among alternatives and play an important role in transportation planning, marketing research and other applications. The mixed multinomial logit (MMNL) model is a popular discrete choice
Tan, Linda S. L.
core +1 more source
Advanced Science, EarlyView.We introduce AutomataGPT, a generative pretrained transformer (GPT) trained on synthetic spatiotemporal data from 2D cellular automata to learn symbolic rules. Demonstrating strong performance on both forward and inverse tasks, AutomataGPT establishes a scalable, domain‐agnostic framework for interpretable modeling, paving the way for future ...
Jaime A. Berkovich +2 more
wiley +1 more source
Model Selection with the Loss Rank Principle
, 2010 A key issue in statistics and machine learning is to automatically select the "right" model complexity, e.g., the number of neighbors to be averaged over in k nearest neighbor (kNN) regression or the polynomial degree in regression with polynomials.
Hutter, Marcus, Tran, Minh-Ngoc
core +1 more source
Endocytic Control of Cell‐Autonomous and Non‐Cell‐Autonomous Functions of p53
Advanced Science, EarlyView.NUMB Ex3‐containing isoforms localize to the plasma membrane, where they recruit p53 through SNX9 and direct it to multivesicular bodies and exosomes. Exported p53 is taken up by neighboring cells and activates nuclear programs, revealing an intercellular, exosome‐based pathway that might help establish a tumor‐suppressive microenvironment.
Roberta Cacciatore +20 more
wiley +1 more source