Understanding the truth about subjectivity

Results of two experiments show children’s understanding of diversity in personal preference is incomplete. Despite acknowledging diversity, in Experiment 1(N=108), 6- and 8-year-old children were less likely than adults to see preference as a legitimate basis for personal tastes and more likely to say a single truth could be found about a matter of taste. In Experiment 2 (N=96), 7- and 9-year-olds were less likely than 11- and 13-yearolds to say a dispute about a matter of preference might not be resolved. These data suggest that acceptance of the possibility of diversity does not indicate an adult-like understanding of subjectivity. An understanding of the relative emphasis placed on objective and subjective factors in different contexts continues to develop into adolescence

Measurement of sigma_Total in e+e- Annihilations Below 10.56 GeV

Using the CLEO III detector, we measure absolute cross sections for e+e- -> hadrons at seven center-of-mass energies between 6.964 and 10.538 GeV. R, the ratio of hadronic and muon pair production cross sections, is measured at these energies with a r.m.s. error <2% allowing determinations of the strong coupling alpha_s. Using the expected evolution of alpha_s with energy we find alpha_s(M_Z^2)=0.126 +/- 0.005 ^{+0.015}_{-0.011}, and Lambda=0.31^{+0.09+0.29}_{-0.08-0.21}.Comment: Comments: Presented at "The 2007 Europhysics Conference on High Energy Physics," Manchester, England, 19-25 July 2007, to appear in the proceedings. Three pages, 1 figur

Calculation of compressible turbulent boundary layers with pressure gradients and heat transfer

Calculation of compressible turbulent boundary layers with pressure gradients and heat transfe

Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media

The growing popularity of social media (e.g, Twitter) allows users to easily share information with each other and influence others by expressing their own sentiments on various subjects. In this work, we propose an unsupervised \emph{tri-clustering} framework, which analyzes both user-level and tweet-level sentiments through co-clustering of a tripartite graph. A compelling feature of the proposed framework is that the quality of sentiment clustering of tweets, users, and features can be mutually improved by joint clustering. We further investigate the evolution of user-level sentiments and latent feature vectors in an online framework and devise an efficient online algorithm to sequentially update the clustering of tweets, users and features with newly arrived data. The online framework not only provides better quality of both dynamic user-level and tweet-level sentiment analysis, but also improves the computational and storage efficiency. We verified the effectiveness and efficiency of the proposed approaches on the November 2012 California ballot Twitter data.Comment: A short version is in Proceeding of the 2014 ACM SIGMOD International Conference on Management of dat