Results 51 to 60 of about 686,580 (270)
High‐Entropy Magnetism of Murunskite
Advanced Functional Materials, EarlyView.The study of murunskite (K2FeCu3S4) reveals that its magnetic and orbital order emerges in a simple I4/mmm crystal structure with complete disorder in the transition metal positions. Mixed‐valence Fe ions randomly occupy 1/4 of the tetrahedral sites, with the remaining 3/4 being filled by non‐magnetic Cu+ ions.
Davor Tolj +18 more
wiley +1 more source
Forgetting Exceptions is Harmful in Language Learning [PDF]
, 1998 We show that in language learning, contrary to received wisdom, keeping exceptional training instances in memory can be beneficial for generalization accuracy.
Bosch, Antal van den +2 more
core +5 more sources
Flow‐Induced Vascular Remodeling on‐Chip: Implications for Anti‐VEGF Therapy
Advanced Functional Materials, EarlyView.Flow‐induced vascular remodeling plays a critical role in network stabilization and function. Using a vasculature‐on‐chip system, this study reveals how physiological VEGF levels and flow affect vascular remodeling and provides insights into tumor vessel normalization.
Fatemeh Mirzapour‐Shafiyi +6 more
wiley +1 more source
On Learning Decision Trees with Large Output Domains [PDF]
Algorithmica, 1998 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nader H. Bshouty +2 more
openaire +2 more sources
Advanced Functional Materials, EarlyView.Herein, a comprehensive framework that enabled the optimization of colloidal solubility within a high‐dimensional parameter space and study of reversible assembly processes is developed. This data‐driven workflow integrated innovations including the robotic platform for automated AuNPs functionalization, machine learning for predicting and revealing ...
Yueyang Gao +5 more
wiley +1 more source
Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning [PDF]
, 2014 A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and
Kurbatsky, Victor +5 more
core +2 more sources
, 2019 One obstacle that so far prevents the introduction of machine learning models primarily in critical areas is the lack of explainability. In this work, a practicable approach of gaining explainability of deep artificial neural networks (NN) using an ...
Huber, Marco F. +2 more
core +1 more source
End-to-End Learning of Decision Trees and Forests [PDF]
International Journal of Computer Vision, 2019 Abstract Conventional decision trees have a number of favorable properties, including a small computational footprint, interpretability, and the ability to learn from little training data. However, they lack a key quality that has helped fuel the deep learning revolution: that of being end-to-end trainable. Kontschieder et al.
Thomas M. Hehn +2 more
openaire +3 more sources
Machine Learning‐Enabled Polymer Discovery for Enhanced Pulmonary siRNA Delivery
Advanced Functional Materials, EarlyView.This study provides an efficient approach to train a machine learning model by merging heterogeneous literature data to predict suitable polymers for siRNA delivery. Without the need for extensive laboratory synthesis, the machine learning enabled a virtual screening and successfully predicted a polymer that is validated for effective gene silencing in
Felix Sieber‐Schäfer +10 more
wiley +1 more source
Advanced Functional Materials, EarlyView.By integrating machine learning into flux‐regulated crystallization (FRC), accurate prediction of solvent evaporation rates in real time, improving crystallization control and reducing crystal growth variability by over threefold, is achieved. This enhances the reproducibility and quality of perovskite single crystals, leading to reproducible ...
Tatiane Pretto +8 more
wiley +1 more source