skip to main content

Search for: All records

Creators/Authors contains: "Zhou, Feng"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Trust in automation has been mainly studied in the cognitive perspective, though some researchers have shown that trust is also influenced by emotion. Therefore, it is essential to investigate the relationships between emotions and trust. In this study, we explored the pattern of 19 anticipated emotions associated with two levels of trust (i.e., low vs. high levels of trust) elicited from two levels of autonomous vehicles (AVs) performance (i.e., failure and non-failure) from 105 participants from Amazon Mechanical Turk (AMT). Trust was assessed at three layers i.e., dispositional, initial learned, and situational trust. The study was designed to measure how emotions are affected with low and high levels of trust. Situational trust was significantly correlated with emotions that a high level of trust significantly improved participants’ positive emotions, and vice versa. We also identified the underlying factors of emotions associated with situational trust. Our results offered important implications on anticipated emotions associated with trust in AVs.
    Free, publicly-accessible full text available September 1, 2023
  2. Recognizing the attributes of objects and their parts is central to many computer vision applications. Although great progress has been made to apply object-level recognition, recognizing the attributes of parts remains less applicable since the training data for part attributes recognition is usually scarce especially for internet-scale applications. Furthermore, most existing part attribute recognition methods rely on the part annotations which are more expensive to obtain. In order to solve the data insufficiency problem and get rid of dependence on the part annotation, we introduce a novel Concept Sharing Network (CSN) for part attribute recognition. A great advantage of CSN is its capability of recognizing the part attribute (a combination of part location and appearance pattern) that has insufficient or zero training data, by learning the part location and appearance pattern respectively from the training data that usually mix them in a single label. Extensive experiments on CUB, Celeb A, and a newly proposed human attribute dataset demonstrate the effectiveness of CSN and its advantages over other methods, especially for the attributes with few training samples. Further experiments show that CSN can also perform zero-shot part attribute recognition.
  3. Self-replication and exponential growth are ubiquitous in nature but until recently there were few examples of artificial self-replication. Often replication is a templated process where a parent produces a single offspring, doubling the population in each generation. Many species however produce more than one offspring at a time, enabling faster population growth and higher probability of species perpetuation. We have made a system of cross-shaped origami tiles that yields a number of offspring, four to eight or more, depending on the concentration of monomer units to be assembled. The parent dimer template serves as a seed to crystallize a one-dimensional crystal, a ladder. The ladder rungs are then UV–cross-linked and the offspring are then released by heating, to yield a litter of autonomous daughters. In the complement study, we also optimize the growth conditions to speed up the process and yield a 103increase in the growth rate for the single-offspring replication system. Self-replication and exponential growth of autonomous motifs is useful for fundamental studies of selection and evolution as well as for materials design, fabrication, and directed evolution. Methods that increase the growth rate, the primary evolutionary drive, not only speed up experiments but provide additional mechanisms for evolving materialsmore »toward desired functionalities.

    « less