Harbin Institute of Technology

About: Harbin Institute of Technology is a education organization based out in Harbin, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 88259 authors who have published 109297 publications receiving 1603393 citations. The organization is also known as: HIT.

Optical manipulation from the microscale to the nanoscale: fundamentals, advances and prospects.

Abstract: Since the invention of optical tweezers, optical manipulation has advanced significantly in scientific areas such as atomic physics, optics and biological science. Especially in the past decade, numerous optical beams and nanoscale devices have been proposed to mechanically act on nanoparticles in increasingly precise, stable and flexible ways. Both the linear and angular momenta of light can be exploited to produce optical tractor beams, tweezers and optical torque from the microscale to the nanoscale. Research on optical forces helps to reveal the nature of light-matter interactions and to resolve the fundamental aspects, which require an appropriate description of momenta and the forces on objects in matter. In this review, starting from basic theories and computational approaches, we highlight the latest optical trapping configurations and their applications in bioscience, as well as recent advances down to the nanoscale. Finally, we discuss the future prospects of nanomanipulation, which has considerable potential applications in a variety of scientific fields and everyday life.

Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network

Abstract: The spatial characteristics of cracks are significant indicators to assess and evaluate the health of existing buildings and infrastructures However, the current manual crack description

Recognizing Named Entities in Tweets

Abstract: The challenges of Named Entities Recognition (NER) for tweets lie in the insufficient information in a tweet and the unavailability of training data. We propose to combine a K-Nearest Neighbors (KNN) classifier with a linear Conditional Random Fields (CRF) model under a semi-supervised learning framework to tackle these challenges. The KNN based classifier conducts pre-labeling to collect global coarse evidence across tweets while the CRF model conducts sequential labeling to capture fine-grained information encoded in a tweet. The semi-supervised learning plus the gazetteers alleviate the lack of training data. Extensive experiments show the advantages of our method over the baselines as well as the effectiveness of KNN and semi-supervised learning.