The AstroStat Slog » interdisciplinary http://hea-www.harvard.edu/AstroStat/slog Weaving together Astronomy+Statistics+Computer Science+Engineering+Intrumentation, far beyond the growing borders Fri, 09 Sep 2011 17:05:33 +0000 en-US hourly 1 http://wordpress.org/?v=3.4 Another exciting news (with no use) http://hea-www.harvard.edu/AstroStat/slog/2009/another-news-with-no-use/ http://hea-www.harvard.edu/AstroStat/slog/2009/another-news-with-no-use/#comments Mon, 27 Jul 2009 12:37:45 +0000 hlee http://hea-www.harvard.edu/AstroStat/slog/?p=3178 I wish I could chase all rabbits. Another rabbit I missed came to a realization by a friend, who was sure that I already knew this “call for papers” notice for the special issue of the signal processing magazine (SPM). Although those due dates were mistaken (the white paper due was several months back), my friend thought it would be useful for me and my group just in case I didn’t know about it. Yes, I was very delighted such things were on going. No doubt that I was disappointed when the white paper due was long gone.

It was my fault that I didn’t chase this rabbit for a while. The slog has some SPM article review posts, as you recall. I mentioned a few times that statistics is about information retrieval (uncertainty included but not limited to). Because of this belief, I took courses in signal processing and information theory. I believed that research topics in signal processing is very strongly associated with astronomical data analysis. Nonetheless, fellow EE students saw me like an alien like students in computational physics class. Upon seeing this belated news, I became very delighted because it transfigured me from an alien to a human being.

According to the notice, the topics to be considered cover quite broad areas in astronomy, from which I was afraid that this SPM issue will be unprecedentedly thick. I can say so because the projects that I’m working on cover almost every item listed in the following from the notice.
Topics to be considered are:

  • Calibration (e.g. of large phased arrays in the presence of electronic and atmospheric disturbances)
  • Deconvolution, imaging and data analysis
  • Interference mitigation
  • Image restoration and reconstruction
  • Source separation; inverse problems
  • Data mining and machine learning techniques
  • Classification and feature identification
  • Bayesian techniques

For areas related to:

  • Radio telescopes, e.g. large arrays and focal plane arrays
  • Gamma-ray radio astronomy
  • Cosmological data, Cosmic Microwave Background (CMB) data
  • Optical and IR astronomy; adaptive optics in large telescopes
  • Digital image restoration in optical astronomy (including blind, non-blind, single frame, image sequence,and speckle methods)
  • Analysis of large astronomical databases
  • Stellar imaging and spectroscopy

I’d like to express my sincere gratitude for my friend’s very thoughtful gesture. If I knew it earlier, our group could have written many articles. I’m very curious who would have contributed to this January 2010 issue of SPM. I’m afraid that editors have been greatly challenged by the great volume of white papers submitted by scientists in the fields. Perhaps not, because the SPM is not well recognized by astronomers. I really want to see who have contributed and what topics have been covered in this SPM issue. I should not miss the second chase.

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language barrier http://hea-www.harvard.edu/AstroStat/slog/2008/language-barrier/ http://hea-www.harvard.edu/AstroStat/slog/2008/language-barrier/#comments Wed, 13 Feb 2008 20:41:32 +0000 hlee http://hea-www.harvard.edu/AstroStat/slog/2008/language-barrier/ Last week, I was at Tufts colloquium and happened to have a conversation with a computer scientist about density based clustering. I understood density as probabilistic density and was recollecting a paper by Fraley and Raftery (Model-Based Clustering, Discriminant Analysis, and Density Estimation, JASA, 2002, 97, p.458) and other similar papers I saw in engineering journals like IEEE transactions. For a few moments, I felt uncomfortable and she explained that density meant “how dense observations are.” Density based clustering was meant to be distance based clustering, like k-means, minimum spanning tree, most likely nonparametric approaches.

Although words are same, the first impression and their usage is quite different from society to society (even among statisticians). One word I’m very reluctant to use both to astronomers and statisticians is model. I’m quite confused at the reactions from both sides. To clarify meanings, implications, or intentions, some clever adjectives must accompany these common words; however, once one gets used to these jargons, adjectives are felt redundant to your fellow scientists/colleagues, whereas the other gets lost and seeks explanation of the usage by related examples and backgrounds.

Not only simple words, like model and density, there are more jargons requires inter-disciplinary semantic experts. Yet, patience of explaining and open-mindedness would easily assist to get over language barriers in any interdisciplinary works.

[ Would you mind sharing your experience of any language barrier? ]

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