Rule-Enhanced Active Learning for Semi-Automated Weak Supervision.

A major bottleneck preventing the extension of deep learning systems to new domains is the prohibitive cost of acquiring sufficient training labels. Alternatives such as weak supervision, active learning, and fine-tuning of pretrained models reduce this burden but require substantial human input to select a highly informative subset of instances or to curate labeling functions. REGAL (Rule-Enhanced Generative Active Learning) is an improved framework for weakly supervised text classification that performs active learning over labeling functions rather than individual instances.

Tests of a Noncontact Piezoelectric Motor Modulated by an Electromagnetic Field.

For improving the service life of piezoelectric motors and the performance of noncontact piezoelectric motors, a novel noncontact piezoelectric motor modulated by the electromagnetic field is proposed. This type of piezoelectric motor uses an electromagnetic field to modulate the reciprocating vibrations of a piezoelectric drive mechanism to form the step motion of the rotor. The working principle and structural design of the piezoelectric motor are described in detail.