Three Concepts: Information

Posters

A part of the course (25 % of the grade) is to prepare a poster presentation for the joint poster session. The posters will be prepared individually or in pairs .

A poster session is an occasion where you present your topic to the public with the help of posters on a bulletin board. Your role is to stand nearby your poster, explain details and answer questions to the public. Because everyone is doing this at the same time, you do not need to give a lecture, and the public can wander around the poster session room and concentrate on posters whose topics interest them. The session is meant to be relaxed and rather informal.

Poster report

After the poster session, each student has to write a poster report containing a short (one paragraph) evaluation of each of the posters (execpt his/her own). The poster reports are to be done individually, not in pairs. In your evaluation, pay attention to both the poster layout (How well does the material explain the poster topic? Does it support the verbal poster presentation well?) and to the verbal presentation (Did the presenter/presenters seem to know their business? Were they capable of explaining the material well?). Please give the name or names of the people who were presenting the poster to you. Give also an overall score of the poster using the scale A- (below average), A (average) and A+ (above average). Note that this means that each student has to visit all the posters during the poster session!. I strongly advice you to take notes during the session, as this will be a great help when writing the poster report. The deadline for the poster reports is Monday, 04.12. (at noon).

Guidelines for poster presentation

Some guidelines for designing the poster can be found from the following addresses:

» Henry's slides on posters (Post Script)
» Poster preparation guidelines in Chemistry
» SIAM guidelines for preparing posters
» ... and many, many more from different search engines.

The poster area is 95 cm x 115 cm. Copies of the poster material should be delivered to the instructors after the poster session. The poster session will be held at the Course Seminar at the end of the course. Participation to this seminar is necessary in order to pass the course.

Language: Prepare the posters in English, but it is OK to speak in Finnish during the session, except that please be prepared to use English for potential poster session guests who do not understand Finnish.

Poster topics

You may choose freely a poster topic for yourself from the following list. The topics that have been already assigned have the name(s) of the presentator(s) after the topic. In case there is only one name, please contact either Tomi or Petri or the person in question directly in order to find out whether it is possible to prepare the poster in pairs.

The topics are not ordered by their difficultness, for example - they are in random order. If needed, more topics will be added after the course has started.

Note: You may also do the poster from a topic of your own. Ask Petri whether your topic needs improving or whether it's ok.

1. Kolmogorov Complexity
   • Presenters: Marja Hassinen & Joonas Kukkonen
   • Source material: Li M., Vitanyi P., An Introduction to Kolmogorov Complexity and its Applications. Springer-Verlag, New York, 2nd Edition, 1997, Ch. 2.
2. Algorithmic Statistics
   • Presenters:
   • Source material: Gacs P., Tromp J., Vitanyi P., Algorithmic Statistics, IEEE Transactions on Information Theory, 47, 2001, pp. 2443-2463.
3. Quantum Algorithmic Information Theory
   • Presenters: Hannu Maksimainen & Topi Musto
   • Source material: Svozil K., Quantum algorithmic information theory. Journal of Universal Computer Science, 2, 1996, pp. 311-346.
4. Randomness and Mathematical Proof
   • Presenters: Ilkka Kosunen & Kristian Ovaska
   • Source material: Chaitin G. J., Randomness and Mathematical Proof. Scientific American, 232, May 1975, pp. 47-52. In Chaitin G. J., Information Randomness and Incompleteness. Papers on Algorithmich Information Theory. World Scientific Publishing Co. Pte. Ltd., Singapore, 1987, pp. 3-13.
5. Predictive Minimum Description Length Principle
   • Presenters: Timo Koskennieni & ?
   • Source material: Kuusisto S., Application of the PMDL Principle to the Induction of Classification Trees. Tampere University of Technology, Publications 233, 1998, Ch. 3.
6. Minimum Message Length Principle
   • Presenters: Joel Linden & Kimmo Holm
   • Source material: Baxter R., Minimum Message Length Inductive Inference: Theory and Applications, Ph.D. Thesis, Department of Computer Science, Monash University, 1996, Ch. 1.
7. Adaptive Dictionary Encoders: Ziv-Lempel Coding
   • Presenters: Jani Rautiainen & Pekka Maksimainen
   • Source material: Bell T., Cleary J., Witten I., Text Compression. Prentice Hall, New Jersey, 1990, Ch. 8.3.
8. Dynamic Huffman Codes
   • Presenters: Kliment Yanev & Tomi Jylhä-Ollila
   • Source material: Vitter J. S., Dynamic Huffman coding, ACM Trans. Mathematical Software, and Collected Algorithms of ACM.
9. "Bits-back" Encoding
   • Presenters: Lauri Peltonen & Tomi Korkki
   • Source material:
     • Hinton G. E. and Zemel R. S. Autoencoders, Minimum Description Length, and Helmholtz Free Energy. Advances in Neural Information Processing Systems 6. Cowan J. D., Tesauro G. and Alspector J. (Eds.), Morgan Kaufmann, San Mateo, CA, 1994.
     • Zemel R. S. and Hinton G. E., Learning Population Codes by Minimizing Description Length. Neural Computation, 7, 1995, pp. 549-564.
     • Hinton G. E., Dayan P., Frey B. J. and Neal R., The wake-sleep algorithm for unsupervised Neural Networks. Science, 268, 1995, pp. 1158-1161.
     • Dayan P., Hinton G. E., Neal R., and Zemel R. S., The Helmholtz Machine. Neural Computation, 7, 1995, pp. 1022-1037.
10. Constrained Maximum Entropy Tomography
    • Presenters:
    • Source material:
      • Ozan Öktem, A short overview of the COMET (COnstrained Maximum Entropy Tomography) methodology for solving inverse problems (PDF)
      • Ulf Skoglund, Lars-Göran Öfverstedt, Roger M. Burnett, and Gérard Bricogne, Maximum-Entropy Three-Dimensional Reconstruction with Deconvolution of the Contrast Transfer Function: A Test Application with Adenovirus, Journal of Structural Biology 117(1996), 3.
11. Gene Expression Data Classification via MDL
    • Presenters: Mika Viljanen & Tero Kallioinen
    • Source material: Rebecka Jörnsten and Bin Yu, Simultaneous Gene Clustering and Subset Selection for Classification via MDL, submitted, available at http://www.stat.rutgers.edu/~rebecka/
12. Information Bottleneck
• Presenters: Mikko T Jormalainen & Heikki Aitakangas
• Source material: Tishby, Pereira, Bialek: The Information Bottleneck Method.
13. Turbo codes
    • Presenters: Jaakko Santala & Juhani Bergström
    • Source material: McEliece, Mackay, Cheng: Turbo Decoding as an Instance of Pearl's Belief Propagation Algorithm.
14. Normalized Compression Distance / Normalized Google Distance
    • Presenters: Päivi Suomalainen & Teppo E. Ahonen
    • Source material:
      • Cilibrasi, Vitanyi: Clustering by Compression
      • Cilibrasi, Vitanyi: Automatic Meaning Discovery using Google
15. Information Rate and Game Theory
    • Presenters:
    • Source material: Kelly: A New Interpretation of Information Rate
16. Quantum Teleportation
    • Presenters: Matti Vuorinen & Matti Jokipii

Three Concepts: Information	2006