エクストリーム・モチベーション / Extreme Motivation

2021年度未踏ジュニアブースト会議LTの発表資料です。目の前のことに夢中になって小さな目標を更新し続けていると、大きな目標を達成できたり軌跡から自分がやりたいことを発見できたりする、という話をしました。

1. ΤΫετϦʔϜɾϞνϕʔγϣϯ ੴؙ
   ᠳ໵
   ະ౿δϡχΞϝϯλʔ
   ະ౿εʔύʔΫϦΤʔλ
2. ͜Μͳ૬ஊ଴ͬͯ·͢ • ػցֶश
   ύλʔϯೝࣝɺ࣌ܥྻσʔλղੳ
   • J04ɾ"OESPJEɾ8FCΞϓϦ։ൃ
   • 6*σβΠϯ
   • ωʔϛϯά
   • ΞΠσΞͷԾઆݕূ
   • ࿦จͷ୳͠ํɾಡΈํ
3. ݚڀऀʹͳΔલ͸δϣʔΫΞϓϦ࡞Ո
   ʁ
   Ͱͨ͠
4. ͜Ε·Ͱʹ։ൃͨ͠ΞϓϦαʔϏεͷ঺հ νίΫΠΠϫέϩϘ ໿ଋͷ࣌ؒʹ஗Εͨͱ͖ͷ
   ݴ͍༁Λݕࡧ͢ΔΞϓϦ 3FTU$BTU ࣍ʹτΠϨʹߦͩ͘Ζ͏
   ࣌ؒΛ༧ใ͢ΔΞϓϦ ,PUPEBNB ৸๥Λޙչͨ͠πΠʔτΛूΊͯ
   ਂ໷ʹͻͨ͢Β౤ߘ͢Δ໎࿭#PU
5. ͜Ε·Ͱʹ։ൃͨ͠ΞϓϦαʔϏεͷ঺հ νίΫΠΠϫέϩϘ ໿ଋͷ࣌ؒʹ஗Εͨͱ͖ͷ
   ݴ͍༁Λݕࡧ͢ΔΞϓϦ 3FTU$BTU ࣍ʹτΠϨʹߦͩ͘Ζ͏
   ࣌ؒΛ༧ใ͢ΔΞϓϦ ,PUPEBNB ৸๥Λޙչͨ͠πΠʔτΛूΊͯ
   ਂ໷ʹͻͨ͢Β౤ߘ͢Δ໎࿭#PU ਓͷߦಈ΍ੈͷதͷৗࣝΛม͑Δ΋ͷ͕޷͖ 
 ୭΋͕ϚΠφεͩͱࢥ͍ͬͯΔ΋ͷΛϓϥεʹͰ͖ͳ͍͔
6. ݱࡏ ະདྷ "* ਓ ਓ "* ݡ͞ ੢ඌହ࿨͞ΜͷൃදࢿྉΛࢀߟʹਤΛ࡞੒ 
 ʮ*5ۀքͷاۀͰಇ͘ʯݚڀऀ͕ߟ͍͑ͯΔ͜ͱ!ट౎େֶ౦ژ 
 IUUQTXXXTMJEFTIBSFOFUOJTIJP
7. ਓ޻஌ೳͱࣗવ஌ೳ %FFQ
   #MVF
    
 νΣε"*͕ਓؒͷϓϩΛഁΔ 
 ਓ͕ؒڭ͑ͨϚχϡΞϧΛ΋ͱʹઓ͏ "MQIB(P
    
 ғޟ"*͕ਓؒͷϓϩع࢜ΛഁΔ 
 ϓϩͷعේͱڧԽֶशΛ΋ͱʹ੒௕ "MQIB(P
   ;FSP
    
 ϧʔϧΛ΋ͱʹֶशͨ͠"*͕"MQIB(Pʹউར 
 ਓؒͷखΛआΓͣʹ੒௕ ਓ͕"*Λ 
 ݡ͘͢Δ "*͕"*Λ 
 ݡ͘͢Δ "*͕ਓΛ 
 ݡ͘͢Δ
   ͱ໘നͦ͏ ࣍͸ͲΜͳ͜ͱ͕ى͖Δʁ
8. ਓΛݡ͘͢Δٕज़ श׳ͷվળ ֶशํ๏ͷ࠷దԽ
9. ֶशํ๏ͷ࠷దԽ ਓΛݡ͘͢Δٕज़ श׳ͷվળ
10. ਭ຾ ௨ֶ तۀΛड͚Δ ຊΛಡΉ ςϨϏΛݟΔ ৯ࣄ ߦಈͷه࿥
    ਎ମతߦಈ͔Βೝ஌తߦಈ·Ͱ ೔ৗ࢖͍Ͱ͖ΔσόΠεΛ࢖ͬͯਓͷೝ஌తߦಈΛه࿥͍ͨ͠ʂ ΢ΣΞϥϒϧσόΠε
    <#VMMJOH
    FU
    BM> εϚʔτΞΠ΢ΣΞ ࢢൢͷΞΫςΟϏςΟτϥοΧ খதߴੜͷ׆ಈ A. Bulling, et al. "Eye movement analysis for activity recognition using electrooculography." IEEE transactions on pattern analysis and machine intelligence 33.4 (2010): 741-753. J. Steil & A. Bulling. "Discovery of everyday human activities from long-term visual behaviour using topic models." Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. 2015. ɾɾɾ
11. +*/4
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12. # 3 - ిۃ ؟ిҐܭଌ ɾਨ௚
    #
    
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    -
    
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13. +*/4
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14. +*/4
    .&.&
    %FNP
15. ສޠܭ
    ؟ٿӡಈ͔ΒಡॻྔΛܭଌ า਺ͷ୅ΘΓʹޠ਺ΛՄࢹԽ ಡॻΛଅ͢ϑΟʔυόοΫ read! Nice condition. Read one paragraph more! There are interesting articles for you. S. Ishimaru et al. "The Wordometer 2.0: Estimating the Number of Words You Read in Real Life using Commercial EOG Glasses”. UbiComp '16 Adjunct, pp. 293–296, 2016.
16. ͳͥಡॻྔͷه࿥͕ඞཁͳͷ͔ ϕωοηڭҭ૯߹ݚڀॴͷௐࠪ݁Ռ
    
    ʹΑΔͱ 
 ͨ͘͞ΜಡॻΛ͍ͯ͠ΔࢠͲ΋΄Ͳֶྗ͕޲্͢Δɻ 
 ಡॻྔ͸ಛʹʮࢉ਺ʯͰภࠩ஋ͷมԽͷ͕ࠩେ͖͍ͱ͍͏ɻ 
 খֶੜɺ೥ϲ݄ͷௐࠪɺಡॻগͳ͍
    ʙ࡭
    ଟ͍
    ࡭Ҏ্
17. ೔ʑͷߦಈྔͱ৺ͷঢ়ଶʹ͸૬ޓͷ૬͕ؔ͋Δ ೖྗ ग़ྗ ؾ෼ ػ 
 ց 
 ֶ 
 श ਎ମߦಈྔ ೝ஌ߦಈྔ ࣾձߦಈྔ ਭ຾ ಛ௃நग़ ׆ྗ iPhone, AppleWatch Fitbit JINS MEME Twitter, Facebook RescueTime ਪఆ
18. ೥౓ະ౿ࣄۀʮ৺Թܭʯ ηϯαͰه࿥ͨ͠೔ʑͷߦಈϩά͔Β 
 ৺ͷঢ়ଶΛఆྔԽͯ͠දࣔ͢ΔΞϓϦ
19. श׳ͷվળ ਓΛݡ͘͢Δٕज़ ֶशํ๏ͷ࠷దԽ
20. ໨͸ޱ΄Ͳʹ෺Λݴ͏
    ͜͜ͰσϞ
21. ࢹઢ͔Βओ؍తߴ೉қ౓୯ޠ Θ͔Βͳ͍୯ޠ Λਪఆ ܭଌ͞Εͨࢹઢ
    ԁͷେ͖͞
    ஫ࢹ࣌ؒ ೉͍͠ͱײͨ͡୯ޠͷਪఆ
    ΦϨϯδਖ਼ղ৘ใ
    ਫ৭ਪఆ݁Ռ େࣾ et al. "ࢹ఺৘ใΛ༻͍ͨओ؍తߴ೉қ౓୯ޠͷਪఆ". ిࢠ৘ใ௨৴ֶձٕज़ݚڀใࠂ, vol. 115, no. 517, PRMU2015-189, pp. 149-153, 2016.
22. ࢹઢ͔Βӳޠྗ 50&*$είΞ Λਪఆ ӳޠจॻΛͭಡΉͱϓϥεϚΠφε఺ͰਪఆՄೳ ౻޷ et al. "ࢹ఺৘ใΛ༻͍ͨӳޠशख़౓ਪఆ๏ͷ࣮ݧతݕ౼". ిࢠ৘ใ௨৴ֶձٕज़ݚڀใࠂ, vol. 115, no. 517, PRMU2015-195, pp. 185-190, 2016.
23. ࢹઢ͔Βબ୒ࢶ໰୊ʹ͓͚Δ֬৴౓Λਪఆ ར༻ऀͷֶशσʔλ͕͋Ε͹ͷਫ਼౓ͰਪఆͰ͖Δ ֬৴Λ΋ͨͣʹ౴͑ͯؒҧ͑ͨ໰୊ͷྫ ֬৴Λ΋ͬͯ౴͑ͯؒҧ͑ͨ໰୊ͷྫ S. Ishimaru et al. "Confidence-Aware Learning Assistant". In arXiv preprint arXiv:2102.07312, 2021.
24. ͳͥ֬৴౓ͷਪఆ͕ඞཁͳͷ͔ तۀڭՊॻ ԋश ෮श Ͳͷ໰୊Λ෮श͢΂͖͔ʁൈ͚࿙Εɾޮ཰ͷτϨʔυΦϑ
25. ͳͥ֬৴౓ͷਪఆ͕ඞཁͳͷ͔ ཧղ͍ͯ͠Δ͜ͱΛ 
 ཧղ͍ͯ͠Δ ཧղ͍ͯ͠ͳ͍͜ͱΛ 
 ཧղ͍ͯ͠Δ ۮવʁ ཧղ͍ͯ͠ͳ͍͜ͱΛ 
 ཧղ͍ͯ͠ͳ͍ ୒໰୊ʹ͓͚Δ੒੷ͱճ౴ͷ֬৴౓ͷ෼෍ ੜె໊ ੒੷ ఺ຬ఺ ճ౴΁ͷ֬৴౓ ͔Β
27. ηϯαͷ૊Έ߹ΘͤͰֶशऀͷ৺తঢ়ଶΛਪఆ ڵຯ ओ؍٬؍త
    ཧղ౓ ೝ஌ෛՙ ΞΠτϥοΧ αʔϞΧϝϥ ಡΈฦ͠ إԹ౓ ஫ࢹ࣌ؒ
28. )ZQFS.JOE
    %FNP
29. ಡॻࢧԉٕज़ͷ"MQIBCFO
    υΠπͰىۀ
30. ϝοηʔδ
31. ఺Λܨ͛Δ :PV
    DBOU
    DPOOFDU
    UIF
    EPUT
    MPPLJOH
    GPSXBSE
    :PV
    DBO
    POMZ
    DPOOFDU
    UIFN
    MPPLJOH
    CBDLXBSET
    TP
    ZPV
    IBWF
    UP
    USVTU
    UIBU
    UIF
    EPUT
    XJMM
    TPNFIPX
    DPOOFDU
    JO
    ZPVS
    GVUVSF
    ະདྷʹ޲͔ͬͯ఺Λܨ͛Δ͜ͱ͸Ͱ͖·ͤΜɻաڈΛ ৼΓฦͬͯ఺Λܨ͛ΒΕΔ͚ͩͰ͢ɻ͔ͩΒɺࠓ΍ͬ ͍ͯΔ͜ͱ͕ɺকདྷͲ͔͜ʹܨ͕Δͱ৴ͯ͡Լ͍͞ɻ
    εςΟʔϒɾδϣϒζ͕೥ελϯϑΥʔυେֶ ͷଔۀࣜͰߦͬͨεϐʔνΑΓ
32. ΤΫετϦʔϜɾϞνϕʔγϣϯ ΢ΥʔλʔϑΥʔϧ։ൃ ΞδϟΠϧ։ൃ 
 ΤΫετϦʔϜɾϓϩάϥϛϯά ܭըੑΑΓॊೈੑ 
 ྫ͑͹σβΠφʔ
    ˠ
    ΤϯδχΞ
    ˠ
    ݚڀऀ΁ͱస৬ͨ͠Γɺ 
 ໨ͷલͷখ͞ͳ໨ඪΛߋ৽͠ଓ͚Δ͜ͱͰେ͖ͳ໨ඪΛୡ੒͢Δɻ
33. ·ͱΊ • ΍Γ͍ͨ͜ͱ͸มΘͬͯ΋͍͍ʂ
    • ͦΕͧΕͷ׆ಈ͕ܨ͕Δॠ͕ؒདྷΔ
    • ܭըੑΑΓॊೈੑ
    • ະ౿δϡχΞ͸ΰʔϧ͡Όͳ͍
    • ະ౿δϡχΞͷϲ݄ؒΛશྗͰָ͠ΜͰ͍ͩ͘͞ʂ
    • ԿΛୡ੒͢Ε͹ϓϩδΣΫτ͸੒ޭͱ͍͑Δ͔
    • ࡞ͬͨ΋ͷ͕͋ͳͨͷ໊ࢗʹͳΔ