Browsing by Author "Pan, Z."

Now showing items 1-7 of 7

    • CHIMPS2 : Survey description and $^{12}$CO emission in the Galactic Centre 

      Eden, D. J.; Moore, T. J. T.; Currie, M. J.; Rigby, A. J.; Rosolowsky, E.; Su, Y.; Kim, Kee-Tae; Parsons, H.; Morata, O.; Chen, H. -R.; Minamidani, T.; Park, Geumsook; Ragan, S. E.; Urquhart, J. S.; Rani, R.; Tahani, K.; Billington, S. J.; Deb, S.; Figura, C.; Fujiyoshi, T.; Joncas, G.; Liao, L. W.; Liu, T.; Ma, H.; Tuan-Anh, P.; Yun, Hyeong-Sik; Zhang, S.; Zhu, M.; Henshaw, J. D.; Longmore, S. N.; Kobayashi, M. I. N.; Thompson, M. A.; Ao, Y.; Campbell-White, J.; Ching, T. -C.; Chung, E. J.; Duarte-Cabral, A.; Fich, M.; Gao, Y.; Graves, S. F.; Jiang, X. -J.; Kemper, F.; Kuan, Y. -J.; Kwon, W.; Lee, C. W.; Lee, J. -E.; Liu, M.; Penaloza, C. H.; Peretto, N.; Phuong, N. T.; Pineda, J. E.; Plume, R.; Puspitaningrum, E.; Samal, M. R.; Soam, A.; Sun, Y.; Tang, X. D.; Traficante, A.; White, G. J.; Yan, C. -H.; Yang, A.; Yuan, J.; Yue, N.; Bemis, A.; Brunt, C. M.; Chen, Z.; Cho, J.; Clark, P. C.; Cyganowski, C. J.; Friberg, P.; Fuller, G. A.; Han, I.; Hoare, M. G.; Izumi, N.; Kim, H. -J.; Kim, J.; Kim, S.; Koch, E. W.; Kuno, N.; Lacialle, K. M.; Lai, S. -P.; Lee, H. Lee Y. -H.; Li, D. L.; Liu, S. -Y.; Mairs, S.; Oka, T.; Pan, Z.; Qian, L.; Scicluna, P.; Shi, C. -S.; Shi, H.; Srinivasan, S.; Tan, Q. -H.; Thomas, H. S.; Torii, K.; Trejo, A.; Umemoto, T.; Violino, G.; Wallstrom, S.; Wang, B.; Wu, Y.; Yuan, L.; Zhang, C.; Zhang, M.; Zhou, C.; Zhou, J. J. (2020-09-10)
      The latest generation of Galactic-plane surveys is enhancing our ability to study the effects of galactic environment upon the process of star formation. We present the first data from CO Heterodyne Inner Milky Way Plane ...

    • A Genomic Regulatory Network for Development 

      Davidson, E.H.; Rast, J.P.; Oliveri, P.; Ransick, A.; Calestani, C.; Yuh, C.H.; Minokawa, T.; Amore, G.; Hinman, V.; Arenas-Mena, C.; Otim, A.; Brown, C.T.; Livi, C.B.; Lee, P.Y.; Revilla, R.; Rust, A.G.; Pan, Z.; Schilstra, M.; Clarke, P.J.C.; Arnone, M.I.; Rowen, L.; Cameron, R.A.; McClay, D.R.; Hood, L.; Bolouri, H. (2002)
      Development of the body plan is controlled by large networks of regulatory genes. A gene regulatory network that controls the specification of endoderm and mesoderm in the sea urchin embryo is summarized here. The network ...

    • Image redundancy reduction for neural network classification using discrete cosine transforms 

      Pan, Z.; Rust, A.G.; Bolouri, H. (2000)
      High information redundancy and strong correlations in face images result in inefficiencies when such images are used directly in recognition tasks. In this paper, discrete cosine transforms (DCT) are used to reduce image ...

    • Image redundancy reduction for neural network classification using discrete cosine transforms 

      Pan, Z.; Rust, A.G.; Bolouri, H. (Institute of Electrical and Electronics Engineers (IEEE), 2000)
      High information redundancy and strong correlations in face images result in inefficiencies when such images are used directly in recognition tasks. In this paper, discrete cosine transforms (DCT) are used to reduce image ...

    • New Computational Approaches for Analysis of cis-Regulatory Networks 

      Brown, C.T.; Rust, A.G.; Clarke, P.J.C.; Pan, Z.; Schilstra, M.; De Buysscher, T.; Griffin, G.; Wold, B.J.; Cameron, R.A.; Davidson, E.H.; Bolouri, H. (2002)

    • A provisonal regulatory gene network for specification of endomesoderm in the sea urchin embryo 

      Davidson, E.H.; Rast, J.P.; Oliveri, P.; Ransick, A.; Calestani, C.; Yuh, C.H.; Minokawa, T.; Amore, G.; Hinman, V.; Arenas-Mena, C.; Otim, O.; Brown, C.T.; Livi, C.B.; Lee, P.Y.; Revilla, R.; Schilstra, M.; Clarke, P.J.C.; Rust, A.G.; Pan, Z.; Arnone, M.I.; Rowen, L.; Cameron, R.A.; McClay, D.R.; Hood, L.; Bolouri, H. (2002)
      We present the current form of a provisional DNA sequence-based regulatory gene network that explains in outline how endomesodermal specification in the sea urchin embryo is controlled. The model of the network is in a ...

    • Staged training of Neocognitron by evolutionary algorithms 

      Pan, Z.; Sabisch, T.; Adams, R.G.; Bolouri, H. (Institute of Electrical and Electronics Engineers (IEEE), 1999)
      The Neocognitron, inspired by the mammalian visual system, is a complex neural network with numerous parameters and weights which should be trained in order to utilise it for pattern recognition. However, it is not easy ...