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3. INVITED AND CONTRIBUTED AUTHORS

Author

Adamson, B., primary, Agnew, H.M., additional, Lin, Bai, additional, Bannister, R.L., additional, Han-chen, Bao, additional, Basile, P.S., additional, Beiting, E.J., additional, Bennett, S.B., additional, Blase, M., additional, Boyle, D.J., additional, Braymer, J., additional, Burcher, E.S., additional, Butler, A.J., additional, Calvin, M., additional, Charters, W.W.S., additional, Shao, Chen Ming, additional, Qi-min, Chen, additional, Cheng, R.J., additional, Clark, P., additional, Clark, R.N., additional, Collares-Pereira, M., additional, Cooper, W., additional, Covell, R.B., additional, Cucolo, B.P., additional, Dambly, B., additional, Damm, J.A., additional, David, E.E., additional, Davis, K.M., additional, Dicks, M., additional, Draper, H.M., additional, Dubin, F.S., additional, Dybbs, A., additional, Edmonds, J.S., additional, Eggers, A.J., additional, Emmerman, P.J., additional, Fan, J.C.C., additional, Ford, D.L., additional, Francis, C.E., additional, Gloyna, E., additional, Gordon, J.M., additional, Goulard, R., additional, Gretz, J., additional, Yu-de, Guan, additional, Bao-sen, Guo, additional, Gan-ci, Guo, additional, Hall, D.O., additional, Harte, J., additional, Hildebrandt, A., additional, Hoffman, K.C., additional, Fa-nan, Jin, additional, Jonguitud, V., additional, Kaczynski, V.W., additional, Keevin, T.M., additional, Kirkpatrick, D.L., additional, Kumar, R.A., additional, LaBounty, J.F., additional, Larrimore, J.A., additional, Lau, T.K., additional, Wei-liang, Le, additional, Leung, P., additional, You-jia, Li, additional, Hao, Lin, additional, Ze-zu, Lin, additional, Liu, L., additional, Zhao-xu, Liu, additional, Qin-kan, Lu, additional, Lyman, R., additional, MacCracken, C.D., additional, Macdonald, R.W.G., additional, McCormick, M.E., additional, McCown, W.R., additional, McGarity, A.E., additional, McKinney, R., additional, Mack, M.R., additional, Masoero, M., additional, Mathieu, S.L., additional, Miller, P.H., additional, Morofsky, E., additional, Murphree, D.L., additional, O'Gallagher, J.J., additional, O'Hara, J., additional, Oliker, I., additional, Guang-yao, Ou, additional, Palmedo, P., additional, Peek, S.C., additional, Phillips, N.A., additional, Pinnoti, M., additional, Plummer, J.L., additional, Rabl, A., additional, Raymond, L.P., additional, Rieber, M., additional, Roedder, C.E., additional, Rosenfeld, A.H., additional, Russell, D.P., additional, Salib, R.A., additional, Santoro, R.J., additional, Sargent, S., additional, Savitz, M., additional, Scheid, H.W., additional, Schultz, E.B., additional, Schweitzer, S., additional, Seamans, R.C., additional, Semerjian, H.G., additional, Jia-yang, Shi, additional, Shih, T.T., additional, Shuldiner, P.W., additional, Silvestri, G.J., additional, Snail, K., additional, Socolow, R.H., additional, Soo, S.L., additional, Sorensen, B., additional, Stickel, R.E., additional, Tabor, H., additional, Hong-guang, Tan, additional, Taylor, T.B., additional, Wei, Tong, additional, Bu-xuan, Wang, additional, Ding-zhu, Wang, additional, Ping-yang, Wang, additional, Huan-chen, Wang, additional, Ying-luo, Wang, additional, Winston, R., additional, Woodcock, L., additional, Wnuk, M.P., additional, Jing, Wu, additional, Mao-feng, Yang, additional, Yao, S.C., additional, Ji-sheng, Ye, additional, Yeager, K.E., additional, Shu-fang, Ying, additional, Yu, Y.Y., additional, Zarmi, Y., additional, Ming-tao, Zhang, additional, Zhen-ming, Zhang, additional, Zhi, Zhang, additional, Dian-wu, Zhao, additional, Lian-sheng, Zhao, additional, Yu-kun, Zhao, additional, Zeng-guong, Zhao, additional, Ding-rong, Zheng, additional, Nai-bai, Zheng, additional, Pei-yuan, Zhou, additional, Ya-jie, Zhu, additional, and Xiao-zhang, Zhu, additional

Published
1982
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4. Query Expansion Based on Query Log and Small World Characteristic.

Author

Cao, Yujuan, Peng, Xueping, Kun, Zhao, Niu, Zhendong, Xu, Gx, and Wang, Weiqiang

Abstract

Automatic query expansion is an effective way to solve the word mismatching and short query problems. This paper presents a novel approach to Expand Queries Based on User log and Small world characteristic of the document (QEBUS). When the query is submitted, the synonymic concept of the query is gotten by searching a synonymic concept dictionary. Then the query log is explored and the key words are extracted from the user clicked documents based on small world network (SWN) characteristic. By analyzing the semantic network of the document based on SWN and exploring the correlations between the key words and the queries based on mutual information, high-quality expansion terms can be gotten. The experiment results show that our technique outperforms some traditional query expansion methods significantly. [ABSTRACT FROM AUTHOR]

Published
2009
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5. Unsupervised and Semi-supervised Lagrangian Support Vector Machines.

Author

Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Rangan, C. Pandu, Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Shi, Yong, van Albada, Geert Dick, Dongarra, Jack, Sloot, Peter M. A., and Kun Zhao

Abstract

Support Vector Machines have been a dominant learning technique for almost ten years, moreover they have been applied to supervised learning problems. Recently two-class unsupervised and semi-supervised classification problems based on Bounded C-Support Vector Machines and Bounded ν-Support Vector Machines are relaxed to semi-definite programming [4][11]. In this paper we will present another version to unsupervised and semi-supervised classification problems based on Lagrangian Support Vector Machines, which trained by convex relaxation of the training criterion: find a labelling that yield a maximum margin on the training data. But the problems have difficulty to compute, we will find their semi-definite relaxations that can approximate them well. Experimental results show that our new unsupervised and semi-supervised classification algorithms often obtain almost the same accurate results as the unsupervised and semi-supervised methods [4][11], while considerably faster than them. [ABSTRACT FROM AUTHOR]

Published
2007
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6. Strong-Field Correlation Imaging: Revealing Molecular Geometries, Orientation and Dynamics.

Author

Castleman, A. W., Toennies, J. P., Zinth, W., Yamanouchi, Kaoru, See Leang Chin, Agostini, Pierre, Ferrante, Gaetano, Hill, Wendell T., Kun Zhao, Elberson, Lee N., and Menkir, Getahun M.

Abstract

A review of several correlation techniques used to extract quantitative information from two-dimensional images obtained with fast-frame CCD cameras is presented. Three examples are discussed that demonstrate the power of these techniques for revealing field-induced dynamics: (1) the relative probability for Coulomb explosions of CO26+ as a function of the angle between the molecular and polarization axes; (2) the relative probability for Coulomb explosion of CO26+ and NO26+ as a function of bond angle and (3) the kinetic energy release and its ability to distinguish enhanced ionization from screening. [ABSTRACT FROM AUTHOR]

Published
2006
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