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MNEMONIC

MNEMONIC. Amol, Deepak, Nilesh, Parag & Sumit. MNEMONIC. a N AMING E xpert, M ostly O ffensive. N evertheless I ncredibly C lever. NAMING. N AMING is A M NEMONIC I nterface for N ew G rads. MNEMONIC. a N AMING E xpert, M ostly O ffensive. N evertheless I ncredibly C lever.

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MNEMONIC

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  1. MNEMONIC Amol, Deepak, Nilesh, Parag & Sumit.

  2. MNEMONIC a NAMINGExpert, Mostly Offensive. Nevertheless Incredibly Clever.

  3. NAMING NAMING is AMNEMONICInterface for New Grads

  4. MNEMONIC a NAMINGExpert, Mostly Offensive. Nevertheless Incredibly Clever.

  5. Problem Definition • Goals of the NAMING system: • Easy to Remember (and short) • Nationality Preserving • Peculiarity Preserving • Offensive (not given in the order of importance) • Relaxation • Can be probably approximate.

  6. System Model • We will follow the OOOPS model for our system: • Obscene • Offensive • Equal Opportunity discrimination based on color, caste, sex, region and religion • Peer-to-Peer (we want to be buzz-word complete) • Something else (as if we really care about this mnemonic thingy)

  7. Hard Problem • Theoretical results • Few Chinese were left out. • By Chinese Remainder Theorem[3], the proof follows trivially • Practically hard • Krzysztof Gajos (double “z”, c’mon) • Gary Yngve (For God sake, gimme vowels) • Annamalai Muthsukaruppan (A new grad)

  8. Failed Algo-1 (ALGO-US) /* N[0] = “tom”; N[1] = “dick”; N[2] = “harry”; */ N[0] = “steve”; N[1] = “andrew”; N[2] = “david”; i = random(); return N[i % 3]; • Pros : • There is 0.33 chance of getting names right • Satisfy our “O” criterion • Cons : • There is 0.66 chance of not getting them right

  9. Failed Algo-2 (ALGO-IND) ALGO-2() { Randomly Sample a small substring of characters. } • Handles long names like • Annamalai Muthsukaruppan (A new grad) • Venkatesanguruswami (A new faculty)

  10. ALGO-IND (Continued) • Example: • Venkatesanguruswami => Ng • Annamalai Muthsukaruppan => Hsu • Cons: • Are not nationality preserving

  11. Failed Algo-3 (ALGO-汉语 ) • We define two name spaces: • Lin: Names whose root can be traced back to chinese origin in non-deterministic polynomial time • Co-lin: The complementary space • Assign names to people based on the name space where they belong • Cons: Same problems as in ALGO-US

  12. Failed Algo-4(ALGO-NATIVE) Rachel Pottinger David Notkin Mike Ernst Dances with wolves Chief running beard Eagle with no feathers

  13. Results • We have found a truly remarkable algorithm (a polynomial time algo, one of whose uninteresting corollary in LIN = CO-LIN), but this slide is too small to contain it.

  14. References [1] Amol, Nilesh and Sumit. “Naming Expert. A Vision Paper”. In the proceedings of International Conference on Mnemonics. Worst Paper Award. [2] Douglas Zongker. “Chicken Chicken Chicken”. In the the proceedings of Potentially Computer Science, PoCSci 2002. [3] South Park. http://www.southpark.com/ [4] Amol, Nilesh 和Sumit 。的专家。视觉纸? 。在国际会议记录关于歌诀的。最坏的纸褒奖.

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