skip to main content
Explore Scholarly Publications and Datasets in the NSF-PAR

  • Home
  • Search Results
  • Page 1 of 5

Search for: All records

Creators/Authors contains: "Ma, J."

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. ; ; ; ; ; ( , IEEE ICDM Social Data Mining in the Post-pandemic Era (SDM) Workshop)
    Free, publicly-accessible full text available December 1, 2022
  2. ; ; ; ; ; ; ; ; ; ; et al ( , Journal of Physics: Condensed Matter)
    Abstract With the motivation to study how non-magnetic ion site disorder affects the quantum magnetism of Ba 3 CoSb 2 O 9 , a spin-1/2 equilateral triangular lattice antiferromagnet, we performed DC and AC susceptibility, specific heat, elastic and inelastic neutron scattering measurements on single crystalline samples of Ba 2.87 Sr 0.13 CoSb 2 O 9 with Sr doping on non-magnetic Ba 2+ ion sites. The results show that Ba 2.87 Sr 0.13 CoSb 2 O 9 exhibits (i) a two-step magnetic transition at 2.7 K and 3.3 K, respectively; (ii) a possible canted 120 degree spin structure at zero field withmore »reduced ordered moment as 1.24 μ B /Co; (iii) a series of spin state transitions for both H ∥ ab -plane and H ∥ c -axis. For H ∥ ab -plane, the magnetization plateau feature related to the up–up–down phase is significantly suppressed; (iv) an inelastic neutron scattering spectrum with only one gapped mode at zero field, which splits to one gapless and one gapped mode at 9 T. All these features are distinctly different from those observed for the parent compound Ba 3 CoSb 2 O 9 , which demonstrates that the non-magnetic ion site disorder (the Sr doping) plays a complex role on the magnetic properties beyond the conventionally expected randomization of the exchange interactions. We propose the additional effects including the enhancement of quantum spin fluctuations and introduction of a possible spatial anisotropy through the local structural distortions.« less
    Free, publicly-accessible full text available March 10, 2023
  3. ; ; ; ; ; ; ; ( , Physical Review B)
    Full Text Available 
  4. ; ; ( , MRS Communications)
    Full Text Available 
  5. ; ; ; ; ; ; ; ; ; ( , Physical Review B)
    Full Text Available 
  6. ; ; ; ; ; ( , Computer supported cooperative work CSCW)
    Crowdworkers depend on Amazon Mechanical Turk (AMT) as an important source of income and it is left to workers to determine which tasks on AMT are fair and worth completing. While there are existing tools that assist workers in making these decisions, workers still spend significant amounts of time finding fair labor. Difficulties in this process may be a contributing factor in the imbalance between the median hourly earnings ($2.00/hour) and what the average requester pays ($11.00/hour). In this paper, we study how novices and experts select what tasks are worth doing. We argue that differences between the two populationsmore »likely lead to the wage imbalances. For this purpose, we first look at workers' comments in TurkOpticon (a tool where workers share their experience with requesters on AMT). We use this study to start to unravel what fair labor means for workers. In particular, we identify the characteristics of labor that workers consider is of "good quality'' and labor that is of "poor quality'' (e.g., work that pays too little.) Armed with this knowledge, we then conduct an experiment to study how experts and novices rate tasks that are of both good and poor quality. Through our research we uncover that experts and novices both treat good quality labor in the same way. However, there are significant differences in how experts and novices rate poor quality labor, and whether they believe the poor quality labor is worth doing. This points to several future directions, including machine learning models that support workers in detecting poor quality labor, and paths for educating novice workers on how to make better labor decisions on AMT.« less
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ; ; ; ; ; et al ( , Journal of Physics: Condensed Matter)
    Full Text Available 
  8. ; ; ( , International Conference on Learning Representations (ICLR))
    Accepted Manuscript Full Text Available
  9. ; ; ; ( , Tribology International)
    Full Text Available 
  10. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Materials)
    Full Text Available