Results 151 to 160 of about 1,139,102 (303)
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang +4 more
wiley +1 more source
Current guidelines in aviation ophthalmology and challenges: A review. [PDF]
Singh AK +7 more
europepmc +1 more source
Rhode Island Interest: Rhode Island Civil Procedure - Some Problems [PDF]
Kent, Robert B.
core +1 more source
The hydration behavior of C3S in seawater‐relevant solutions is studied based on experiments, boundary nucleation and growth (BNG) modeling, and machine learning. The main ions included in seawater modify hydration mechanisms, with MgCl2 showing the strongest acceleration effect at the same concentration.
Yanjie Sun +6 more
wiley +1 more source
Between Hearts and Gears: Technology at the Service of Life. [PDF]
Duarte Junior PC +2 more
europepmc +1 more source
2001Survey of Rhode Island Law: Cases: Civil Procedure [PDF]
Alves, Michelle M., Taft, Jill A.
core +1 more source
Smart Bioinspired Material‐Based Actuators: Current Challenges and Prospects
This work gathers, in a review style, an extensive and comprehensive literature overview on the development of autonomous actuators based on synthetic materials, bringing together valuable knowledge from several studies. Furthermore, the article identifies the fundamental principles of actuation mechanisms and defines key parameters to address the size
Alejandro Palacios +4 more
wiley +1 more source
Medico-legal cases involving gastroenterologists in Canada between 2017 and 2021. [PDF]
Mostafapour M +7 more
europepmc +1 more source
1999 Survey of Rhode Island Law: Cases: Civil Procedure [PDF]
Beauchesne, Melissa Coulombe +3 more
core +1 more source
Overview of the proposed Gate‐Align‐SED, including two stages of training: (1) Mean‐Teacher SSL Training; and (2) Enhancer Model Training. In complex real‐world environments such as disaster monitoring, effective sound event detection (SED) is often hindered by the presence of noise and limited labeled data.
Jieli Chen +4 more
wiley +1 more source

