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The Disp Method for Analysing Large Zenith Angle Gamma-Ray Data

The Disp Method for Analysing Large Zenith Angle Gamma-Ray Data

1. ๋™๊ธฐ์™€ ๋ฐฐ๊ฒฝ Disp ๋ฐฉ๋ฒ•์€ ๋‹จ์ผ ๋ง์›๊ฒฝ ๊ด€์ธก์—์„œ ์ฃผ ๊ฐ๋งˆ์„  ๋ฐฉํ–ฅ ์žฌ๊ตฌ์„ฑ์˜ ๊ธฐ๋ณธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜์–ด ์™”์Šต๋‹ˆ๋‹ค(์˜ˆ: Lessard et al., 2001; Kranich & Stark, 2003; Domingo Santamarรญa et al., 2005). ๊ทธ๋Ÿฌ๋‚˜ ์ƒˆ๋กœ์šด ์„ธ๋Œ€์˜ ์ง€ํ‘œ ๊ธฐ๋ฐ˜ ๊ฐ๋งˆ์„  ๋ง์›๊ฒฝ์€ ๋ฐฐ์—ด ๋ชจ๋“œ๋กœ ์—ฌ๋Ÿฌ ๊ฐœ์˜ ๋ง์›๊ฒฝ์„ ์‚ฌ์šฉํ•˜์—ฌ ๋™์‹œ ๊ด€์ธก์ด ๊ฐ€๋Šฅํ•˜๊ฒŒ ๋˜๋ฉด์„œ ๋ณด๋‹ค ์ •๊ตํ•œ ๋ฐฉํ–ฅ ์žฌ๊ตฌ์„ฑ ๊ธฐ๋ฒ•์ด ํ•„์š”ํ•ด์กŒ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, ํฐ ๊ฐ๋„(LZA)์—์„œ ์ด๋Ÿฌํ•œ ๊ธฐ๋ฒ•๋“ค์€ ์„ฑ๋Šฅ ์ €ํ•˜๋ฅผ ๊ฒช๊ฒŒ ๋ฉ๋‹ˆ๋‹ค. ์ด๋Š” ๊ณต๊ธฐ ์ƒค์›Œ๊ฐ€ IACT ๋ฐฐ์—ด

Data Astrophysics
The Shadow of the Moon in IceCube

The Shadow of the Moon in IceCube

๋ณธ ๋…ผ๋ฌธ์€ ์–ผ์Œ ํ๋ธŒ(IceCube)๋ผ๋Š” ์„ธ๊ณ„ ์ตœ๋Œ€ ๊ทœ๋ชจ์˜ ์ค‘์„ฑ์ž ๋ง์›๊ฒฝ์„ ์ด์šฉํ•˜์—ฌ ๋‹ฌ ๊ทธ๋ฆผ์ž์˜ ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์šฐ์ฃผ์„ (cosmic ray muon)์˜ ๊ฐ์†Œ ํ˜„์ƒ์„ ํ†ตํ•ด ์ค‘์„ฑ์ž ์ฒœ์ฒด์˜ ๋ฐฉํ–ฅ์„ ์ •ํ™•ํ•˜๊ฒŒ ์ธก์ •ํ•˜๋Š” ๋ฐ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋Š” '๋‹ฌ ๊ทธ๋ฆผ์ž' ํšจ๊ณผ์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ์„œ๋ก  ์–ผ์Œ ํ๋ธŒ๋Š” ๊ณ ์—๋„ˆ์ง€ ์ค‘์„ฑ์ž๋ฅผ ์ด์šฉํ•˜์—ฌ ์ฒœ๋ฌธํ•™์ ์ธ ์ ์›์ฒœ์„ ํƒ์ƒ‰ํ•˜๋Š” ๋ง์›๊ฒฝ์ด๋‹ค. ๋‹ค๋ฅธ ๋ง์›๊ฒฝ์€ ํ‘œ์ค€ ๋“ฑ๋ถˆ(์˜ˆ: ๊ฒŒ์ผ๋ผ ๋„ค๋ถ€๋ผ, ๊ฐ๋งˆ์„  ์ฒœ๋ฌธํ•™์—์„œ ์‚ฌ์šฉ)์„ ์‚ฌ์šฉํ•˜์—ฌ ์‹ ํ˜ธ๋ฅผ ๊ต์ •ํ•˜์ง€๋งŒ, ์–ผ์Œ ํ๋ธŒ๋Š” ๊ณ ์—๋„ˆ์ง€ ์ค‘์„ฑ์ž ์›์ฒœ์ด ๋ถ€์กฑํ•œ ์ƒํ™ฉ์—์„œ

Astrophysics
VERITAS observation of Markarian 421 flaring activity

VERITAS observation of Markarian 421 flaring activity

์ด ๋…ผ๋ฌธ์€ VERITAS ๊ด€์ธก๊ธฐ๋ฅผ ํ†ตํ•ด Markarian 421์ด๋ผ๋Š” BL Lac ๊ฐ์ฒด์˜ ๊ธ‰์„ฑ ๋ฐœ๊ด‘ ํ™œ๋™์„ ์—ฐ๊ตฌํ•œ ๋‚ด์šฉ์„ ๋‹ด๊ณ  ์žˆ๋‹ค. Markarian 421์€ ์ ์ƒ‰ํŽธ์ด z 0.031์˜ ๊ณ ์ฃผํŒŒ ํ”ผํฌ(HBL) BL Lac ๊ฐ์ฒด๋กœ, ๊ฐ•๋ ฌํ•˜๊ณ  ๋น ๋ฅธ ๋ณ€๋™์„ฑ์„ ๋ณด์ด๋Š” ํŠน์ง•์ ์ธ ๋ธ”๋ผ์ž๋ฅด์ด๋‹ค. ๋ธ”๋ผ์ž๋ฅด์˜ ํŠน์„ฑ ๋ธ”๋ผ์ž๋ฅด๋Š” ํ™œ๋™์€ํ•˜ํ•ต(AGN)์˜ ํ•˜์œ„ ๋ถ„๋ฅ˜๋กœ, ์ „์ฒด ์ „์ž๊ธฐ ์ŠคํŽ™ํŠธ๋Ÿผ์—์„œ ๋น„์—ด์  ๋ฐฉ์ถœ๊ณผ ๋น ๋ฅธ ๋ณ€๋™์„ฑ์„ ๋ณด์ด๋Š” ํŠน์ง•์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ด๋Š” ๋งค์šฐ ์ƒ๋Œ€๋ก ์ ์ธ ์ œํŠธ ๋‚ด์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๊ด‘์ž๋ฅผ ์‹œ์‚ฌํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ธ”๋ผ์ž๋ฅด์˜ ์ŠคํŽ™ํŠธ๋Ÿผ์€ ์ผ๋ฐ˜์ ์œผ๋กœ ๋‚ฎ์€ ์—๋„ˆ

Astrophysics
t-multiple discrete logarithm problem and solving difficulty

t-multiple discrete logarithm problem and solving difficulty

๋ณธ ๋…ผ๋ฌธ์€ ์–‘์ž ์ปดํ“จํŒ…์˜ ๋ฐœ์ „๊ณผ ํ•จ๊ป˜ ์•”ํ˜ธํ•™์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ์ด์‚ฐ ๋กœ๊ทธ ๋ฌธ์ œ์™€ t ๋‹ค์ค‘ ์ด์‚ฐ ๋กœ๊ทธ ๋ฌธ์ œ(t MDLP)๋ฅผ ํƒ๊ตฌํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, ์‡ผ์–ด์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๊ทธ๋กœ๋ฒ„์˜ ๊ฒ€์ƒ‰ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์–‘์ž ์ปดํ“จํŒ… ํ™˜๊ฒฝ์—์„œ ๊ณต๊ฐœ ํ‚ค ์•”ํ˜ธํ™”์— ๋Œ€ํ•œ ์œ„ํ˜‘์œผ๋กœ ์ž‘์šฉํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์„ ๊ฐ•์กฐํ•ฉ๋‹ˆ๋‹ค. 1. ์ด์‚ฐ ๋กœ๊ทธ ๋ฌธ์ œ์™€ t MDLP ์ด์‚ฐ ๋กœ๊ทธ ๋ฌธ์ œ๋Š” ์ˆœํ™˜ ๊ทธ๋ฃน G์˜ ์›์†Œ ฮฑ์™€ ฮฒ๊ฐ€ ์ฃผ์–ด์กŒ์„ ๋•Œ, ฮฑ^x ฮฒ๋ฅผ ๋งŒ์กฑํ•˜๋Š” x ๊ฐ’์„ ์ฐพ๋Š” ๋ฌธ์ œ์ž…๋‹ˆ๋‹ค. ์ด ๋ฌธ์ œ๋Š” ๊ณ ์ „์  ์ปดํ“จํŒ… ํ™˜๊ฒฝ์—์„œ ํ•˜์œ„ ์ง€์ˆ˜ ์‹œ๊ฐ„ ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ ํ•ด๊ฒฐ๋˜์ง€๋งŒ, ์‡ผ์–ด์˜ ์–‘์ž ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ด๋ฅผ

Information Theory Mathematics Computer Science Cryptography and Security
Optical Emission Lines and the X-Ray Properties of Type 1 Seyfert   Galaxies

Optical Emission Lines and the X-Ray Properties of Type 1 Seyfert Galaxies

: ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํƒ€์ž… 1 ์„ธ์ดํผํŠธ ์€ํ•˜์˜ ๊ด‘ํ•™ ๋ฐฉ์ถœ์„ ๊ณผ X ์„  ํŠน์„ฑ์„ ๋ถ„์„ํ•˜์—ฌ AGN ์ค‘์‹ฌ ์—”์ง„์˜ ๋‚ด์žฌ์  ์†์„ฑ ๋ฐ ๊ทธ์— ๋”ฐ๋ฅธ BLR ํ”Œ๋ผ์ฆˆ๋งˆ์˜ ๋ณ€ํ™”๋ฅผ ์ดํ•ดํ•˜๊ณ ์ž ํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, ์ด ์—ฐ๊ตฌ๋Š” ๊ด‘ํ•™ ๋ฐฉ์ถœ์„ ์˜ ๊ฐ•๋„ ๊ณ„์‚ฐ๋ถ€ํ„ฐ ์‹œ์ž‘ํ•ด, ์ด๋ฅผ ํ†ตํ•ด ์–ป์–ด์ง„ ๋ฐ์ดํ„ฐ์™€ X ์„  ์ŠคํŽ™ํŠธ๋Ÿผ ๋ถ„์„์„ ๊ฒฐํ•ฉํ•˜์—ฌ AGN์˜ ๋ฌผ๋ฆฌ์  ํŠน์„ฑ์„ ์‹ฌ์ธต์ ์œผ๋กœ ํƒ๊ตฌํ•ฉ๋‹ˆ๋‹ค. 1. ๋ฐฉ์ถœ ์„ ์˜ ๊ฐ•๋„ ๊ณ„์‚ฐ ๋ฐฉ์ถœ ์„ ์˜ ๊ฐ•๋„๋Š” ๋ฐฉ์ถœ ์ด์˜จ์˜ ๋ฐ€๋„, ๊ด‘ํ•™ ๊นŠ์ด, ๊ทธ๋ฆฌ๊ณ  ์—๋„ˆ์ง€ ์ค€์œ„ ๊ฐ„ ์ „์ด ํ™•๋ฅ  ๋“ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ๊ณ„์‚ฐ๋ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, ๊ด‘ํ•™์ ์œผ๋กœ ์–‡์€ ํ”Œ๋ผ์ฆˆ๋งˆ์—์„œ๋Š” ์‚ฌํ•˜ ๋ณผ์ธ ๋งŒ ๋ถ„ํฌ๋ฅผ ๋”ฐ๋ฅด๋Š” ๊ฒฝ

Astrophysics
Another elementary proof of $: sum_{n ge 1}{1/{n^2}} = pi^2/6,$ and   a recurrence formula for $,zeta{(2k)}$

Another elementary proof of $: sum_{n ge 1}{1/{n^2}} = pi^2/6,$ and a recurrence formula for $,zeta{(2k)}$

: ๋ณธ ๋…ผ๋ฌธ์€ ๋ฆฌ๋งŒ ์ œํƒ€ ํ•จ์ˆ˜ ฮถ(s)์˜ ํŠน๋ณ„ํ•œ ๊ฒฝ์šฐ์ธ ฮถ(2k)์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜๋ฉฐ, ํŠนํžˆ ฮถ(2) ฯ€ยฒ/6์ด๋ผ๋Š” ์ค‘์š”ํ•œ ๊ฒฐ๊ณผ๋ฅผ ๊ฐ„๋‹จํ•˜๊ฒŒ ์ฆ๋ช…ํ•˜๊ณ  ์žฌ๊ท€ ๊ณต์‹์„ ๋„์ถœํ•ฉ๋‹ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์€ Dancs์™€ He (2006)์˜ ์—ฐ๊ตฌ์—์„œ ์‹œ์ž‘ํ•˜์—ฌ, sin(nฯ€) ๋Œ€์‹  cos(nฯ€)๋ฅผ ์‚ฌ์šฉํ•œ ๊ธ‰์ˆ˜ ์ „๊ฐœ ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ฮถ(2k)์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ์ฆ๋ช…๊ณผ ์žฌ๊ท€ ๊ณต์‹์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. 1. ์‹ฌํ”Œํ•œ ์ฆ๋ช…๊ณผ ์žฌ๊ท€ ๊ณต์‹ ๋…ผ๋ฌธ์€ ๋จผ์ € s 1์ผ ๋•Œ ํ•ด๋ฐ€ํ„ด ๊ธ‰์ˆ˜๊ฐ€ ๋ฐœ์‚ฐํ•จ์„ ์–ธ๊ธ‰ํ•˜๊ณ , ์ œ๊ณฑ ะ‘ะตั€ะฝัƒะปะปะธ ์ˆ˜ Bk๋ฅผ z/e^z 1์˜ ํƒ€์ผ๋Ÿฌ ๊ธ‰์ˆ˜ ์ „๊ฐœ์—์„œ z

Mathematics
Computing Correct Truncated Excited State Wavefunctions

Computing Correct Truncated Excited State Wavefunctions

์ด ๋…ผ๋ฌธ์€ ํ—ฌ๋ฅจ ์›์ž์˜ ํฅ๋ถ„ ์ƒํƒœ์— ๋Œ€ํ•œ ํŒŒ๋™ ํ•จ์ˆ˜์˜ ๊ณ„์‚ฐ ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃจ๊ณ  ์žˆ์œผ๋ฉฐ, ํŠนํžˆ ํฐ ์‹œ์Šคํ…œ์—์„œ ์‚ฌ์šฉํ•˜๊ธฐ ์–ด๋ ค์šด ๋Œ€์šฉ๋Ÿ‰ ํŒŒ๋™ ํ•จ์ˆ˜ ํ™•์žฅ์„ ์ œํ•œ๋œ ๊ณต๊ฐ„์—์„œ ํšจ๊ณผ์ ์œผ๋กœ ์‚ฌ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๋…ผ๋ฌธ์€ ์ด๋ก ์  ๋ฐฐ๊ฒฝ๊ณผ ํ•จ๊ป˜ ํ—ฌ๋ฅจ ์›์ž์— ๋Œ€ํ•œ ๊ตฌ์ฒด์ ์ธ ์˜ˆ์ œ๋ฅผ ํ†ตํ•ด ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์˜ ์œ ํšจ์„ฑ์„ ์ž…์ฆํ•ฉ๋‹ˆ๋‹ค. 1. ๋ฌธ์ œ์˜ ๋ฐฐ๊ฒฝ ํŒŒ๋™ ํ•จ์ˆ˜๋Š” ์–‘์ž์—ญํ•™์—์„œ ๋ฌผ์งˆ์˜ ์ƒํƒœ๋ฅผ ํ‘œํ˜„ํ•˜๋Š” ํ•ต์‹ฌ ๊ฐœ๋…์ž…๋‹ˆ๋‹ค. ํŠนํžˆ, ํฐ ์‹œ์Šคํ…œ์„ ๋‹ค๋ฃฐ ๋•Œ ๋Œ€์šฉ๋Ÿ‰ ํŒŒ๋™ ํ•จ์ˆ˜ ํ™•์žฅ์„ ์‚ฌ์šฉํ•˜๋ฉด ๊ณ„์‚ฐ์ด ๋ณต์žกํ•ด์ง€๊ณ  ์‹ค์šฉ์ ์ด์ง€ ์•Š์•„์ง‘๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ž‘๊ณ  ์‹ ๋ขฐํ•  ์ˆ˜ ์žˆ๋Š” ํ™•์žฅ์ด ํ•„์š”

Physics Quantum Physics
No Image

Pseudo Hermitian formulation of Black-Scholes equation

: ์ด ๋…ผ๋ฌธ์€ ๋ธ”๋ž™ ์‡ผ์šธ์Šค(BS) ๋ฐฉ์ •์‹์„ ์–‘์ž์—ญํ•™์  ๊ด€์ ์—์„œ ์žฌํ•ด์„ํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ๊ธˆ์œต ๋ชจ๋ธ๋ง์— ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, BS ํ•จ์ˆ˜์™€ ๊ทธ ์ผ๋ฐ˜ํ™”๋œ ํ˜•ํƒœ๊ฐ€ ฮท ๊ฑฐ์˜ ํ—ˆ๋ฏธํŠธ ์—ฐ์‚ฐ์ž๋กœ ํ‘œํ˜„๋  ์ˆ˜ ์žˆ์Œ์„ ์ฆ๋ช…ํ•จ์œผ๋กœ์จ, ์–‘์ž์—ญํ•™์  ๊ฐœ๋…์ด ๊ธˆ์œต ๋ถ„์•ผ์—์„œ ์–ด๋–ป๊ฒŒ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋Š”์ง€ ํƒ๊ตฌํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๋…ผ๋ฌธ์€ ๋จผ์ € BS ๋ฐฉ์ •์‹์„ ์Šˆ๋ขฐ๋”ฉ๊ฑฐ ์œ ์‚ฌ ๋ฐฉ์ •์‹์œผ๋กœ ๋ณ€ํ™˜ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์„ค๋ช…ํ•ฉ๋‹ˆ๋‹ค. ์ด ๊ณผ์ •์—์„œ BS ํ•จ์ˆ˜ HBS๊ฐ€ ๋น„ํ—ˆ๋ฏธํŠธ์ž„์„ ๊ฐ•์กฐํ•˜๋ฉฐ, ์ด๋ฅผ ฮท ๊ฑฐ์˜ ํ—ˆ๋ฏธํŠธ ์—ฐ์‚ฐ์ž๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ค๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ๋ฒ•์€ BS ๋ฐฉ์ •์‹์˜ ํ•ด์„๊ณผ

Quantitative Finance
No Image

Class-based Rough Approximation with Dominance Principle

์ด ๋…ผ๋ฌธ์€ ๋‹ค์ค‘ ๊ธฐ์ค€ ์˜์‚ฌ๊ฒฐ์ • ๋ถ„์„(MCDA)๊ณผ ์ง€๋ฐฐ ๊ธฐ๋ฐ˜ ๊ฑฐ์นœ ์ง‘ํ•ฉ ์ ‘๊ทผ๋ฒ•(DRSA)์„ ์ค‘์‹ฌ์œผ๋กœ, ๊ณ ์ „์ ์ธ ๊ฑฐ์นœ ์ง‘ํ•ฉ ์ ‘๊ทผ๋ฒ•(CRSA)์˜ ํ•œ๊ณ„์™€ DRSA์˜ ์žฅ์ ์„ ํƒ๊ตฌํ•œ๋‹ค. MCDA๋Š” ๋‹ค์–‘ํ•œ ๊ธฐ์ค€์— ๋”ฐ๋ผ ํ‰๊ฐ€๋œ ๊ฐ์ฒด๋“ค ์ค‘์—์„œ ์ตœ์ ์˜ ๊ฒฐ์ •์„ ๋‚ด๋ฆฌ๋Š” ๋ฐ ์‚ฌ์šฉ๋˜๋ฉฐ, ์ด ๋…ผ๋ฌธ์€ ์ด๋ฅผ ์œ„ํ•œ ํšจ๊ณผ์ ์ธ ์ˆ˜ํ•™์  ๋„๊ตฌ๋กœ์„œ CRSA์™€ DRSA๋ฅผ ๋น„๊ตํ•˜๊ณ  ๋ถ„์„ํ•œ๋‹ค. CRSA๋Š” ๋ถˆ๋ถ„๋ช…์„ฑ ๊ด€๊ณ„๋ฅผ ํ†ตํ•ด ์ง€์‹ ๊ฑฐ์น ๊ธฐ๋ฅผ ๊ตฌ์ถ•ํ•˜๋Š” ๋ฐ˜๋ฉด, DRSA๋Š” ์˜์‚ฌ๊ฒฐ์ • ํ‘œ์—์„œ์˜ ์ง€๋ฐฐ ๊ด€๊ณ„์— ์ดˆ์ ์„ ๋งž์ถ˜๋‹ค. ์ด ๋…ผ๋ฌธ์€ ํŠนํžˆ ์„ ํ˜ธ๋„ ์ˆœ์„œ ๋ฐ์ดํ„ฐ ์ฒ˜๋ฆฌ์— ํ•œ๊ณ„๊ฐ€ ์žˆ๋Š” CRSA์™€

Computational Complexity Computer Science Artificial Intelligence
Two temperature accretion flows around rotating black holes and   determining the kerr parameter of sources

Two temperature accretion flows around rotating black holes and determining the kerr parameter of sources

์ด ๋…ผ๋ฌธ์€ ํšŒ์ „ํ•˜๋Š” ๋ธ”๋ž™ํ™€ ์ฃผ๋ณ€์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋‘ ์˜จ๋„ ์ถ•์  ํ๋ฆ„์„ ์—ฐ๊ตฌํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ๋‹ค์–‘ํ•œ ์ฒœ์ฒด ํ˜„์ƒ์„ ์„ค๋ช…ํ•˜๋ ค๋Š” ๋ชฉํ‘œ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ํŠนํžˆ, Cyg X 1๊ณผ ๊ฐ™์€ ์ €์˜จ ๊ณ ์ฒด ์ƒํƒœ๋ฅผ ๊ฐ€์ง„ ์ฒœ์ฒด์˜ ํŠน์„ฑ์„ ๋‹จ์ˆœํ•œ ์ผ€ํ”Œ๋Ÿฌ์  ์ถ•์  ์›๋ฐ˜ ๋ชจ๋ธ๋กœ๋Š” ์„ค๋ช…ํ•˜๊ธฐ ์–ด๋ ต๋‹ค๋Š” ์ ์„ ๊ฐ•์กฐํ•˜๋ฉฐ, ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋‘ ์˜จ๋„ ํ๋ฆ„ ๋ชจ๋ธ์„ ์ œ์‹œํ•œ๋‹ค. 1. ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์™€ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹ ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ๋Š” Cyg X 1๊ณผ ๊ฐ™์€ ์ฒœ์ฒด์˜ ์ €์˜จ ๊ณ ์ฒด ์ƒํƒœ๋ฅผ ๋‹จ์ˆœํ•œ ์ผ€ํ”Œ๋Ÿฌ์  ์ถ•์  ์›๋ฐ˜ ๋ชจ๋ธ๋กœ๋Š” ์„ค๋ช…ํ•˜๊ธฐ ์–ด๋ ค์› ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด Eardley, Lightma

General Relativity Astrophysics
Confirmation of Lagrange Hypothesis for Twisted Elastic Rod

Confirmation of Lagrange Hypothesis for Twisted Elastic Rod

: ๋ณธ ๋…ผ๋ฌธ์€ ๊ตฌ์กฐ ์ตœ์ ํ™” ๋ถ„์•ผ์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ๋ผ๊ทธ๋ž‘์ฃผ ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃจ๋ฉฐ, ํŠนํžˆ ๊ทธ๋ฆฐํž ๋ถ€๋Ÿฌ์ง ๋ฌธ์ œ์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์–‡์€ ํƒ„์„ฑ ๋ง‰๋Œ€์˜ ์ตœ์  ํ˜•ํƒœ๋ฅผ ์ฐพ๋Š”๋ฐ ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๋ผ๊ทธ๋ž‘์ฃผ ๊ฐ€์„ค์ด ์‹ค์ œ๋กœ ์œ ํšจํ•จ์„ ์ฆ๋ช…ํ•˜๊ณ ์ž ํ•ฉ๋‹ˆ๋‹ค. 1. ์„œ๋ก  ์„œ๋ก ์—์„œ๋Š” ๋ณธ ๋…ผ๋ฌธ์˜ ์ฃผ์š” ๋ชฉํ‘œ์™€ ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ์„ ์„ค๋ช…ํ•ฉ๋‹ˆ๋‹ค. ๊ตฌ์กฐ ์ตœ์ ํ™” ๊ณผํ•™์—์„œ ์ค‘์š”ํ•œ ๋ฌธ์ œ ์ค‘ ํ•˜๋‚˜์ธ ๊ทธ๋ฆฐํž ๋ถ€๋Ÿฌ์ง ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃจ๋ฉฐ, ์ด ๋ฌธ์ œ๋Š” ์–‡์€ ํƒ„์„ฑ ๋ง‰๋Œ€๊ฐ€ ํŠน์ • ๋ชจ๋ฉ˜ํŠธ ํ•˜์ค‘์— ๋Œ€ํ•ด ์–ด๋–ป๊ฒŒ ํœ˜์–ด์ง€๋Š”์ง€๋ฅผ ๋ถ„์„ํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ผ๊ทธ๋ž‘์ฃผ

MATH-PH Mathematics Nonlinear Sciences
No Image

Smarandache Curves According to Bishop Frame in Euclidean 3-Space

: ๋ณธ ๋…ผ๋ฌธ์€ ๋น„์ˆ ํ”„๋ ˆ์ž„(Bishop Frame)์„ ์ด์šฉํ•˜์—ฌ ์œ ํด๋ฆฌ๋“œ 3์ฐจ์› ๊ณต๊ฐ„์—์„œ ํŠน์ • ์Šค๋งˆ๋ž€๋‹ค์ฒด ๊ณก์„ (Special Smarandache Curves)์— ๋Œ€ํ•œ ์‹ฌ๋„ ์žˆ๋Š” ๋ถ„์„์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๊ธฐ์กด์˜ ํ”„๋ ˆ๋„คํŠธ ์„ธ๋ฅด๋ ›(Frenet Serret) ํ”„๋ ˆ์ž„์ด ์ œํ•œ๋˜๋Š” ๊ฒฝ์šฐ์—๋„ ๋น„์ˆ ํ”„๋ ˆ์ž„์€ ํšจ๊ณผ์ ์œผ๋กœ ์ ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ์ค‘์š”ํ•œ ์˜๋ฏธ๋ฅผ ๊ฐ€์ง‘๋‹ˆ๋‹ค. 1. ์Šค๋งˆ๋ž€๋‹ค์ฒด ๊ณก์„ ์˜ ์ •์˜์™€ ์ค‘์š”์„ฑ ์Šค๋งˆ๋ž€๋‹ค์ฒด ๊ณก์„ ์€ ๋ฏธ๋ถ„ ๊ธฐํ•˜ํ•™์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ํŠน์ˆ˜ํ•œ ๊ณก์„ ์ž…๋‹ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” T N 1, T N 2, N 1 N 2, ๊ทธ๋ฆฌ๊ณ  T

Mathematics
White Paper: Brief overview of current practices for open consultation

White Paper: Brief overview of current practices for open consultation

๋ณธ ์—ฐ๊ตฌ๋Š” ๊ณต๊ฐœ ์ปจ์„คํ…Œ์ด์…˜ ๊ณผ์ •์—์„œ ์ •๋ณด ๊ธฐ์ˆ (IT)์˜ ์—ญํ• ๊ณผ ํŒŒ๊ดด์  ํ˜์‹  ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•ด ์‹ฌ๋„ ์žˆ๊ฒŒ ๋ถ„์„ํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, ๊ทธ๋ฆฌ์Šค๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ•œ ์‚ฌ๋ก€ ์—ฐ๊ตฌ์™€ SciFY๊ฐ€ ๊ฐœ๋ฐœํ•œ 'DemocracIT' ํ”Œ๋žซํผ์„ ํ†ตํ•ด ๊ณต๊ฐœ ์ปจ์„คํ…Œ์ด์…˜ ๊ณผ์ •์—์„œ IT์˜ ์ค‘์š”์„ฑ์„ ๊ฐ•์กฐํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. 1. ๊ณต๊ฐœ ์ปจ์„คํ…Œ์ด์…˜์˜ ํ˜„ํ™ฉ๊ณผ ๋ฌธ์ œ์  ๊ณต๊ฐœ ์ปจ์„คํ…Œ์ด์…˜์€ ์ •์ฑ… ์ž…์•ˆ ๊ณผ์ •์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜์ง€๋งŒ, ์—ฌ๋Ÿฌ ๋ฌธ์ œ์— ์ง๋ฉดํ•ด ์žˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋ฆฌ์Šค์—์„œ๋Š” ์‹œ๋ฏผ๋“ค์ด ์ด ํ”„๋กœ์„ธ์Šค๋ฅผ ๊ฐ€์น˜ ์žˆ๊ฒŒ ์—ฌ๊ธฐ์ง€๋งŒ ์‹ ๋ขฐ๋„๋Š” ๋‚ฎ๊ณ  ์ฐธ์—ฌ๊ฐ€ ๊ฐ์†Œํ•˜๊ณ  ์žˆ๋‹ค๋Š” ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜์™”์Šต๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ํ˜„์ƒ์€ ๊ณต๊ฐœ

Computer Science Computers and Society
On model of information system for management of information flows

On model of information system for management of information flows

์ด ๋…ผ๋ฌธ์€ ๋ถˆ๊ฐ€๋ฆฌ์•„ ๊ณตํ™”๊ตญ์˜ ๋ฒ•๋ฅ ์— ๊ทผ๊ฑฐํ•œ ๊ณ ๋“ฑ ๊ต์œก ๊ธฐ๊ด€์˜ ํ–‰์ • ์„œ๋น„์Šค๋ฅผ ์ง€์›ํ•˜๋Š” ์ •๋ณด ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ, ๋ฐ•์‚ฌ ๊ณผ์ • ํ•™์ƒ ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•œ ์ •๋ณด ์‹œ์Šคํ…œ ๊ตฌ์ถ•์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋Š” ์œ ์‚ฌํ•œ ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ์—๋„ ์ ์šฉ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์ ์—์„œ ๊ทธ ์ค‘์š”์„ฑ์ด ๊ฐ•์กฐ๋œ๋‹ค. 1. ์ดˆ๊ธฐ ๋‹จ๊ณ„: ๋ฏธ๋ž˜ ์‚ฌ์šฉ์ž์™€์˜ ์ธํ„ฐ๋ทฐ ๋ฐ ์‹œ์Šคํ…œ ๋ชฉ์  ์ •์˜ ์ดˆ๊ธฐ ๋‹จ๊ณ„์—์„œ๋Š” ๋ฏธ๋ž˜ ์‚ฌ์šฉ์ž์™€์˜ ์ธํ„ฐ๋ทฐ๋ฅผ ํ†ตํ•ด ์‹ค์ œ ์š”๊ตฌ์‚ฌํ•ญ์„ ํŒŒ์•…ํ•˜๊ณ , ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์‹œ์Šคํ…œ์˜ ๋ชฉ์ ๊ณผ ์ˆ˜ํ–‰ํ•ด์•ผ ํ•  ์ž‘์—…์„ ๋ช…ํ™•ํžˆ ํ•œ๋‹ค. ์ด๋Š” ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ ํ”„๋กœ์„ธ์Šค์—์„œ ๊ฐ€์žฅ ์ค‘์š”ํ•œ ๋ถ€๋ถ„ ์ค‘

Computer Science System Software Engineering Model
Initialization Errors in Quantum Data Base Recall

Initialization Errors in Quantum Data Base Recall

๋ณธ ๋…ผ๋ฌธ์€ ์–‘์ž ์ปดํ“จํŒ… ๋ถ„์•ผ์—์„œ ์ค‘์š”ํ•œ ๋ฌธ์ œ ์ค‘ ํ•˜๋‚˜์ธ ์ดˆ๊ธฐํ™” ์˜ค๋ฅ˜๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ๊ทธ๋กœ๋ฒ„์˜ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๊ฒ€์ƒ‰ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ฑ๋Šฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์‹ฌ๋„ ์žˆ๊ฒŒ ๋ถ„์„ํ•œ๋‹ค. ์–‘์ž ์ปดํ“จํŒ…์—์„œ๋Š” ์ดˆ๊ธฐ ์ƒํƒœ๊ฐ€ ์ •ํ™•ํ•˜๊ฒŒ ์„ค์ •๋˜์–ด์•ผ ํ•˜๋Š”๋ฐ, ์‹ค์ œ ์‹œ์Šคํ…œ์—์„œ๋Š” ์ด ๊ณผ์ •์—์„œ ์˜ค๋ฅ˜๊ฐ€ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ดˆ๊ธฐํ™” ์˜ค๋ฅ˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ฑ๋Šฅ์— ์ง์ ‘์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฏ€๋กœ, ์ด๋ฅผ ์ตœ์†Œํ™”ํ•˜๋Š” ๋ฐฉ๋ฒ•๊ณผ ๊ทธ๋กœ๋ฒ„ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์–ด๋–ป๊ฒŒ ์ด๋Ÿฐ ์˜ค๋ฅ˜๋ฅผ ์ฒ˜๋ฆฌํ•˜๋Š”์ง€ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ๋…ผ๋ฌธ์€ ๋จผ์ € ์–‘์ž ์ƒํƒœ์—์„œ ๋ฌด์ž‘์œ„์„ฑ์— ๋Œ€ํ•œ ์•„์ด๋””์–ด๋ฅผ ์ œ์‹œํ•œ๋‹ค. ์ดˆ๊ธฐ ๋ฒกํ„ฐ์˜ ์š”์†Œ๋“ค์ด ์‹ค์ˆ˜์ผ ๋•Œ, ๋ถ„

Computer Science Databases Quantum Physics Data
Non-Tiles and Walls - A Variant on the Heesch Problem

Non-Tiles and Walls - A Variant on the Heesch Problem

์ด ๋…ผ๋ฌธ์€ ํžˆ์—์Šˆ ๋ฌธ์ œ์˜ ๋ณ€ํ˜•์œผ๋กœ์„œ '๋ฒฝ'์ด๋ผ๋Š” ๊ฐœ๋…์„ ๋„์ž…ํ•˜์—ฌ ํƒ€์ผ๋ง์ด ๋ถˆ๊ฐ€๋Šฅํ•œ ๋„ํ˜•์ธ ๋น„ํƒ€์ผ(non tile)์„ ๋ถ„์„ํ•˜๊ณ  ์žˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๊ธฐ์กด์˜ ํžˆ์—์Šˆ ์ˆ˜(Heesch number)๋ฅผ ํ™•์žฅํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ, ๋น„ํƒ€์ผ์ด ํ‰๋ฉด์„ ์–ผ๋งˆ๋‚˜ ๊ฐ€๋“ ์ฑ„์šธ ์ˆ˜ ์žˆ๋Š”์ง€๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ์ƒˆ๋กœ์šด ์ฒ™๋„์ธ '๋ฒฝ ๋‘๊ป˜'๋ฅผ ์ œ์•ˆํ•œ๋‹ค. 1. ๋ฒฝ๊ณผ ๋ฒฝ ๋‘๊ป˜์˜ ์ •์˜ ๋…ผ๋ฌธ์€ '๋ฒฝ'์„ ๋‹ค์Œ๊ณผ ๊ฐ™์ด ์ •์˜ํ•œ๋‹ค: ๋‹จ์ˆœ ์—ฐ๊ฒฐ๋œ ํ‰๋ฉด ์˜์—ญ์ด ๋ฌดํ•œํžˆ ๋งŽ์€ ๋ณต์‚ฌ๋ณธ์œผ๋กœ ๊ตฌ์„ฑ๋˜๋ฉด์„œ, ์ด๋“ค ๋ณต์‚ฌ๋ณธ๋“ค์ด ์ •ํ™•ํžˆ ๋‘ ๊ฐœ์˜ ๋‹จ์ˆœ ์—ฐ๊ฒฐ๋œ ์˜์—ญ์œผ๋กœ ๋ถ„ํ• ๋˜๋Š” ๊ฒฝ์šฐ๋ฅผ ๋งํ•˜๋ฉฐ, ์ด ๋‘ ์˜์—ญ์€ ์ผ์ •ํ•œ ๊ฑฐ๋ฆฌ๋กœ

Mathematics
Agent-based model of information spread in social networks

Agent-based model of information spread in social networks

๋ณธ ๋…ผ๋ฌธ์€ ์—์ด์ „ํŠธ ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ์†Œ์…œ ๋„คํŠธ์›Œํฌ์—์„œ์˜ ์ •๋ณด ํ™•์‚ฐ ํŒจํ„ด์„ ๋ถ„์„ํ•˜๊ณ ์ž ํ•˜๋Š” ๋ชฉํ‘œ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ํŠธ์œ„ํ„ฐ ๋ฐ์ดํ„ฐ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ด๋ฃจ์–ด์กŒ์œผ๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ์‹ค์ œ ์‚ฌํšŒ์  ์ƒํ˜ธ์ž‘์šฉ๊ณผ ๊ทธ์— ๋”ฐ๋ฅธ ์ •๋ณด ํ๋ฆ„์„ ๋ชจ๋ธ๋งํ•˜๋ ค๊ณ  ๋…ธ๋ ฅํ–ˆ์Šต๋‹ˆ๋‹ค. 1. ์—์ด์ „ํŠธ ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์˜ ๊ฐœ๋… ๋…ผ๋ฌธ์—์„œ ์ œ์‹œ๋œ ์—์ด์ „ํŠธ ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์€ ๊ฐ ๋ฉ”์‹œ์ง€๊ฐ€ ์—์ด์ „ํŠธ๋กœ ํ‘œํ˜„๋˜๋ฉฐ, ์ด๋“ค ์—์ด์ „ํŠธ๋Š” ์‹œ๊ฐ„์— ๋”ฐ๋ผ ๋‹ค์–‘ํ•œ ๋ฐ˜์‘(์ข‹์•„์š”, ๋ฆฌํŠธ์œ— ๋“ฑ)์„ ํ†ตํ•ด ์ง„ํ™”ํ•ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์—์ด์ „ํŠธ์˜ ์ฃผ์š” ์†์„ฑ ์ค‘ ํ•˜๋‚˜์ธ '์—๋„ˆ์ง€'๋Š” ๋ฉ”์‹œ์ง€์˜ ํ˜„์žฌ ๊ด€๋ จ์„ฑ์„ ๋‚˜ํƒ€๋‚ด๋ฉฐ, ๊ธ์ •์ ์ธ

Model Computer Science Physics Social Networks Network
Multi-Level Analysis and Annotation of Arabic Corpora for Text-to-Sign   Language MT

Multi-Level Analysis and Annotation of Arabic Corpora for Text-to-Sign Language MT

๋ณธ ๋…ผ๋ฌธ์€ ์•„๋ž์–ด ํ…์ŠคํŠธ์˜ ์˜๋ฏธ์  ์ฃผ์„๊ณผ ๋ถ„์„์— ์ค‘์ ์„ ๋‘๊ณ , ์ด๋ฅผ ์œ„ํ•œ ๋ฐ˜์ž๋™ํ™”๋œ ์ฃผ์„ ๋„๊ตฌ๋ฅผ ๊ฐœ๋ฐœํ•˜๋Š” ๋ฐ ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ํ˜„๋Œ€ ํ‘œ์ค€ ์•„๋ž์–ด(MSA) ํ…์ŠคํŠธ์—์„œ ํ˜•ํƒœํ•™์ , ๋ฌธ๋ฒ•์ , ๊ทธ๋ฆฌ๊ณ  ์˜๋ฏธ์  ์ˆ˜์ค€์˜ ์„ค๋ช…์„ ๋‹ค๋ฃจ๋ฉฐ, ํŠนํžˆ ํ”„๋ ˆ์ž„ ์˜๋ฏธ๋ก (FS) ์ ‘๊ทผ ๋ฐฉ์‹์ด ์•„๋ž์–ด ํ…์ŠคํŠธ ํ‘œํ˜„ ๋ฐ ๋ถ„์„์— ์ ํ•ฉํ•œ์ง€ ์กฐ์‚ฌํ•œ๋‹ค. ๋˜ํ•œ, ์ด ์—ฐ๊ตฌ๋Š” ์•„๋ž์–ด์™€ ์˜์–ด ๊ฐ„ ์–ดํœ˜ํ™” ํŒจํ„ด์„ ๋น„๊ตํ•˜๋Š” ์ฝ”ํผ์Šค ์ฆ์–ธ ์–ธ์–ด ์ž๋ฃŒ๋ฅผ ์ œ๊ณตํ•˜๊ณ , ์ฃผ์„๋œ ๋ฌธ์žฅ์—์„œ ์ž๋™์ ์œผ๋กœ ๋งคํ•‘ ๊ทœ์น™์„ ๋„์ถœํ•˜์—ฌ ํ˜•ํƒœ์™€ ์˜๋ฏธ ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ๋ฐ ์ดˆ์ ์„ ๋งž์ถ˜๋‹ค. ์•„๋ž์–ด๋Š” ์œ ๋Ÿฝ

Computer Science Analysis NLP
FIT A Fog Computing Device for Speech TeleTreatments

FIT A Fog Computing Device for Speech TeleTreatments

๋ณธ ๋…ผ๋ฌธ์€ ์•ˆ๊ฐœ ์ปดํ“จํŒ…(Fog Computing) ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•˜์—ฌ ํŒŒํ‚จ์Šจ๋ณ‘(PD) ํ™˜์ž๋“ค์˜ ๊ฐ€์ • ๋‚ด ์Œ์„ฑ ๋ฐ์ดํ„ฐ ๋ถ„์„ ์‹œ์Šคํ…œ์ธ 'Fog Computing Interface for TeleTreatments' (FIT)๋ฅผ ์„ค๊ณ„ํ•˜๊ณ  ๊ตฌํ˜„ํ•œ ๋‚ด์šฉ์„ ๋‹ค๋ฃน๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์›๊ฒฉ ์˜๋ฃŒ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋Œ€๋Ÿ‰์˜ ์„ผ์„œ ๋ฐ์ดํ„ฐ ์ฒ˜๋ฆฌ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. 1. ์•ˆ๊ฐœ ์ปดํ“จํŒ…์˜ ํ•„์š”์„ฑ ์›๊ฒฉ ์˜๋ฃŒ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์€ ์Šค๋งˆํŠธ์›Œ์น˜, ECG ํ‹ฐ์…”์ธ , ์„ผ์„œ ํ†ตํ•ฉ ์Šค๋งˆํŠธ ํ™ˆ ๋“ฑ ๋‹ค์–‘ํ•œ ์„ผ์„œ๋ฅผ ํ†ตํ•ด ๋Œ€๋Ÿ‰์˜ ๋ฐ์ดํ„ฐ๋ฅผ ์ƒ์„ฑํ•ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌ

Computer Science Computers and Society
No Image

Contiguity is limited in free recall

๋ณธ ๋…ผ๋ฌธ์€ ์ž์œ  ํšŒ์ƒ์—์„œ '๊ทผ์ ‘์„ฑ ๋ฒ•์น™'์˜ ์ œํ•œ์„ฑ์„ ํƒ๊ตฌํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๋‹จ์ˆœํžˆ ํ•ญ๋ชฉ ๊ฐ„์˜ ์ธ์ ‘์„ฑ์ด ํšŒ์ƒ ํ™•๋ฅ ์„ ๊ฒฐ์ •ํ•˜๋Š” ์œ ์ผํ•œ ์š”์ธ์ด๋ผ๋Š” ๊ธฐ์กด ๊ฐ€์„ค์— ๋„์ „ํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” Murdock (1962)์˜ 40 1 ๋ชฉ๋ก์—์„œ ๋ฐ์ดํ„ฐ๋ฅผ ์ถ”์ถœํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ, ๊ทธ ๊ฒฐ๊ณผ๋Š” ์ž์œ  ํšŒ์ƒ ๊ณผ์ •์—์„œ ํ•ญ๋ชฉ ๊ฐ„ ์ธ์ ‘์„ฑ์˜ ์ค‘์š”์„ฑ์ด ์˜ˆ์ƒ๋ณด๋‹ค ์ œํ•œ์ ์ด๋ผ๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋…ผ๋ฌธ์€ CRP(Conditional Recall Probability) ๊ณก์„ ์„ ํ†ตํ•ด ๊ทผ์ ‘์„ฑ ๋ฒ•์น™์˜ ํ•œ๊ณ„๋ฅผ ์ž…์ฆํ•œ๋‹ค. ํŠนํžˆ, ์ฒซ ๋ฒˆ์งธ ํ•ญ๋ชฉ๊ณผ ๋‘ ๋ฒˆ์งธ ํ•ญ๋ชฉ์— ๋Œ€ํ•œ CRP ๊ณก์„  ๋ถ„์„์—์„œ, ์ด์ „

Quantitative Biology
Direct Evidence Delay with A Task Decreases Working Memory Content in   Free Recall

Direct Evidence Delay with A Task Decreases Working Memory Content in Free Recall

: ๋ณธ ๋…ผ๋ฌธ์€ ์ง€์—ฐ๋œ ๋ฌด์กฐ๊ฑด์  ํšŒ์ƒ ๊ณผ์ œ๊ฐ€ ์ž‘์—… ๊ธฐ์–ต ๋‚ด์šฉ์„ ์–ด๋–ป๊ฒŒ ์ค„์ด๋Š”์ง€๋ฅผ ๋ถ„์„ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” Murdock (1960, 1974)์˜ U์ž ๋ชจ์–‘ ํšŒ์ƒ ํ™•๋ฅ  ๊ณก์„ ๊ณผ Glanzer์™€ Cunitz (1966)์˜ ์ง€์—ฐ ์กฐ์ž‘ ์‹คํ—˜์— ๊ธฐ๋ฐ˜์„ ๋‘๊ณ  ์žˆ์œผ๋ฉฐ, ํŠนํžˆ Howard & Kahana (1999)์˜ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋ถ„์„์„ ์ง„ํ–‰ํ•ฉ๋‹ˆ๋‹ค. 1. ๋ฌด์กฐ๊ฑด์  ํšŒ์ƒ์˜ ์ด๋ก ์  ๋ฐฐ๊ฒฝ ๋ฌด์กฐ๊ฑด์  ํšŒ์ƒ์€ ๋‹จ๊ธฐ ๊ธฐ์–ต ํƒ๊ตฌ์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•ฉ๋‹ˆ๋‹ค. Murdock์˜ ์—ฐ๊ตฌ๋Š” U์ž ๋ชจ์–‘์˜ ํšŒ์ƒ ํ™•๋ฅ  ๊ณก์„ ์„ ์ œ์‹œํ•˜๋ฉฐ, ๋ชฉ๋ก์˜ ์ฒ˜์Œ๊ณผ ๋์— ์žˆ๋Š” ํ•ญ๋ชฉ์ด ๋”

Quantitative Biology
Una tentazione affascinante

Una tentazione affascinante

๋ฌดํ•œ: ์ˆ˜ํ•™์—์„œ ํ•ต์‹ฌ์ ์ธ ๊ฐœ๋… ๋ฌดํ•œ์€ ์ˆ˜ํ•™์˜ ์„ธ๊ณ„์—์„œ ํ•„์ˆ˜์ ์ธ ์š”์†Œ๋กœ, ๊ทธ ์กด์žฌ๋Š” ์ข…์ข… ์ˆ˜ํ•™์  ์—ฐ๊ตฌ๋ฅผ ๋‹จ์ˆœํ™”ํ•˜๊ณ  ๊ฐ•ํ™”ํ•œ๋‹ค. ํŠนํžˆ ๋ฏธ๋ถ„๊ณผ ์ ๋ถ„ ์ด๋ก ์€ ์œ ํ•œ ์ฐจ๋ถ„ ์ด๋ก ๋ณด๋‹ค ๋” ๊ฐ„๊ฒฐํ•˜๊ณ  ๊ฐ•๋ ฅํ•œ ๋„๊ตฌ์ด๋ฉฐ, ์‹ค์ˆ˜ ์ „์ฒด๋ฅผ ์˜์—ญ์œผ๋กœ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ ๋”์šฑ ํšจ๊ณผ์ ์œผ๋กœ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ฌดํ•œ์˜ ์—ญ์‚ฌ์  ๋ฐฐ๊ฒฝ ๋ฌดํ•œ์— ๋Œ€ํ•œ ์ดํ•ด๋Š” ์˜ค๋žœ ์‹œ๊ฐ„ ๋™์•ˆ ์ˆ˜ํ•™์ž๋“ค ์‚ฌ์ด์—์„œ ๋…ผ๋ž€์ด ๋˜์–ด ์™”๋‹ค. ์œ ํด๋ฆฌ๋“œ๋Š” ์ง์„ ์„ '์—ฐ์žฅ ๊ฐ€๋Šฅํ•œ ์„ธ๊ทธ๋จผํŠธ'๋กœ ์ •์˜ํ•˜๋ฉด์„œ ์ž ์žฌ์ ์ธ ๋ฌดํ•œ์„ฑ์„ ์ธ์ •ํ–ˆ์ง€๋งŒ, ํ˜„์žฌ์˜ ์ง์„  ๊ฐœ๋…์—๋Š” ์ ์šฉํ•˜์ง€ ์•Š์•˜๋‹ค. ์ด ๋ชจ์ˆœ์€ ๋ฌดํ•œ์— ๋Œ€ํ•œ ์ˆ˜์šฉ์ด ์ดˆ๋ž˜ํ•˜๋Š” ์—ญ์„ค์—

Mathematics
2.4GHZ Class AB power Amplifier For Healthcare Application

2.4GHZ Class AB power Amplifier For Healthcare Application

๋ฌด์„  ์˜๋ฃŒ ์„ผ์„œ ๋„คํŠธ์›Œํฌ์˜ ์ค‘์š”์„ฑ ๋ฌด์„  ์˜๋ฃŒ ์„ผ์„œ ๋„คํŠธ์›Œํฌ๋Š” ํ™˜์ž์˜ ์ƒ๋ฆฌ์  ์ƒํƒœ๋ฅผ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜๊ณ , ์ด๋ฅผ ์˜์‚ฌ๊ฐ€ ๋ถ„์„ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ํ˜์‹ ์ ์ธ ๊ธฐ์ˆ ์ž…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์‹œ์Šคํ…œ์€ ํŠนํžˆ ๋†์ดŒ ์ง€์—ญ์—์„œ ์˜์‚ฌ ์ ‘๊ทผ์„ฑ์ด ๋ถ€์กฑํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ๋ฐ ํฐ ๋„์›€์ด ๋ฉ๋‹ˆ๋‹ค. ๋…ผ๋ฌธ์—์„œ๋Š” 2.4GHz ํด๋ž˜์Šค AB ์ „๋ ฅ ์ฆํญ๊ธฐ(PA)์˜ ์„ค๊ณ„์™€ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐฉ๋ฒ•์„ ์„ค๋ช…ํ•˜๋ฉฐ, ์ด๋Š” ์ €์ „๋ ฅ ์†Œ๋น„์™€ ์šฐ์ˆ˜ํ•œ ์„ ํ˜•์„ฑ์„ ๊ฐ–์ถ”๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ €์ „๋ ฅ ์„ค๊ณ„์˜ ํ•„์š”์„ฑ ๋ฌด์„  ์„ผ์„œ ๋„คํŠธ์›Œํฌ์—์„œ ๊ฐ ๊ธฐ๊ธฐ๋Š” ์ •๋ณด ์ˆ˜์ง‘, ๊ฐ์ง€ ๋ฐ/๋˜๋Š” ํ‘œ์‹œ ๊ธฐ๋Šฅ์„ ์ˆ˜ํ–‰ํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ ํœด๋Œ€์šฉ ๊ธฐ๊ธฐ์˜ ๋†’

Computer Science Other CS
End-to-end evaluation of research organizations

End-to-end evaluation of research organizations

: ๋ณธ ๋…ผ๋ฌธ์€ ์•„์น˜๋ฉ”๋ฐ์Šค์˜ ๋ฐ€๋„ ๊ฐœ๋…์„ ํ†ตํ•ด ํฌ๊ธฐ ์˜์กด์  ๋ฐ ๋…๋ฆฝ์ ์ธ ์š”์†Œ์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ์ œ๊ณตํ•˜๋ฉฐ, ์ด๋ฅผ ๋ฌธํ—Œ ์ธก์ •์— ์ ์šฉํ•˜์—ฌ ์—ฐ๊ตฌ ์„ฑ๊ณผ๋ฅผ ํ‰๊ฐ€ํ•˜๋Š” ์ฒด๊ณ„๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ด๋Š” ๊ณผํ•™ ๋ถ„์•ผ์—์„œ ์—ฐ๊ตฌ ์„ฑ๊ณผ๋ฅผ ์ข…ํ•ฉ์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•œ ์ค‘์š”ํ•œ ๋ฐฉ๋ฒ•๋ก ์  ์ ‘๊ทผ์ด๋‹ค. ๋…ผ๋ฌธ์€ ์•„์น˜๋ฉ”๋ฐ์Šค์˜ ๋ฐ€๋„ ๊ฐœ๋…์„ ํ†ตํ•ด ํฌ๊ธฐ ์˜์กด์  ๋ฐ ๋…๋ฆฝ์ ์ธ ์š”์†Œ์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ๋ฐ€๋„ ฯ๋Š” ๋ถ€ํ”ผ V์™€ ๋ฌด๊ฒŒ W ์‚ฌ์ด์˜ ๊ด€๊ณ„์—์„œ ํฌ๊ธฐ ๋…๋ฆฝ์ ์ธ ํ•ญ์œผ๋กœ, ์ด๋ฅผ ํ†ตํ•ด ๋ฌด๊ฒŒ W๊ฐ€ ํฌ๊ธฐ ์˜์กด์  ๋ฐ ๋…๋ฆฝ์  ์š”์ธ์„ ๋ชจ๋‘ ํฌํ•จํ•˜๋Š” 2์ฐจ ์ง€ํ‘œ๋กœ ๊ฒฐํ•ฉ๋  ์ˆ˜ ์žˆ์Œ์„ ์„ค๋ช…ํ•œ๋‹ค. ์ด ๊ฐœ๋…์€ ๋ฌธํ—Œ

Computer Science Digital Libraries
Formation of subject area and the co-authors network by sounding of   Google Scholar Citations service

Formation of subject area and the co-authors network by sounding of Google Scholar Citations service

๋ณธ ๋…ผ๋ฌธ์€ Google Scholar Citations ์„œ๋น„์Šค๋ฅผ ํ™œ์šฉํ•˜์—ฌ ํŠน์ • ์ฃผ์ œ ์˜์—ญ๊ณผ ๊ณต๋™ ์ €์ž ๋„คํŠธ์›Œํฌ๋ฅผ ํ˜•์„ฑํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์•ˆํ•˜๊ณ  ์žˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๊ณผํ•™ ํ˜‘๋ ฅ์˜ ํŒจํ„ด์„ ํƒ์ƒ‰ํ•˜๊ณ , ํšจ๊ณผ์ ์ธ ํ˜‘์—… ๊ธฐํšŒ๋ฅผ ์ œ๊ณตํ•˜๋ฉฐ, ์ •๋ณด ๊ณผํ•™ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์˜ ํ™œ์šฉ์„ ํ†ตํ•ด ์—ฐ๊ตฌ์ž๋“ค์˜ ํ™œ๋™ ์ถ”์  ๋ฐ ์„ฑ๊ณผ ์ธก์ •์— ๋„์›€์„ ์ค€๋‹ค. 1. ์ฃผ์ œ ์˜์—ญ ํ˜•์„ฑ ๋ฐฉ๋ฒ•๋ก  ๋ณธ ๋…ผ๋ฌธ์€ Google Scholar Citations ์„œ๋น„์Šค์—์„œ ์ถ”์ถœํ•œ ์ง€์‹ ๋งˆ์ปค(ํƒœ๊ทธ)๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํŠน์ • ์ฃผ์ œ ์˜์—ญ์„ ์ •์˜ํ•˜๊ณ , ๊ณต๋™ ์ €์ž ๋„คํŠธ์›Œํฌ๋ฅผ ํ˜•์„ฑํ•˜๋Š” ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ๊ณผ์ •์€ ๋‹ค์Œ๊ณผ ๊ฐ™

Computer Science Digital Libraries Social Networks Network
A Step from Probabilistic Programming to Cognitive Architectures

A Step from Probabilistic Programming to Cognitive Architectures

: ์ด ๋…ผ๋ฌธ์€ ์ธ๊ณต ์ผ๋ฐ˜ ์ง€๋Šฅ(AGI) ์‹œ์Šคํ…œ ์„ค๊ณ„์— ๋Œ€ํ•œ ์‹ฌ๋„ ์žˆ๋Š” ๋ถ„์„์„ ์ œ๊ณตํ•˜๋ฉฐ, ํŠนํžˆ ํ™•๋ฅ  ํ”„๋กœ๊ทธ๋ž˜๋ฐ ์–ธ์–ด(PPL)์˜ ์—ญํ• ๊ณผ ๊ทธ ํ™œ์šฉ ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•ด ์ง‘์ค‘์ ์œผ๋กœ ๋‹ค๋ฃฌ๋‹ค. AGI๋Š” ์ง€์‹ ํ‘œํ˜„, ํ•™์Šต ๋ฐฉ๋ฒ•, ์ถ”๋ก  ๋ฐฉ๋ฒ•์ด๋ผ๋Š” ์„ธ ๊ฐ€์ง€ ํ•ต์‹ฌ ๊ตฌ์„ฑ ์š”์†Œ๋ฅผ ํ•„์š”๋กœ ํ•˜๋Š”๋ฐ, ์ด ๋…ผ๋ฌธ์€ ์ด๋Ÿฌํ•œ ์š”์†Œ๋“ค์ด ์–ด๋–ป๊ฒŒ ํ†ตํ•ฉ๋˜์–ด์•ผ ํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ๊นŠ์ด ์žˆ๋Š” ๊ณ ์ฐฐ์„ ์ œ๊ณตํ•œ๋‹ค. ๋…ผ๋ฌธ์˜ ์ฃผ์š” ๋‚ด์šฉ ์ค‘ ํ•˜๋‚˜๋Š” ํ™•๋ฅ  ํ”„๋กœ๊ทธ๋ž˜๋ฐ ์–ธ์–ด(PPL)๊ฐ€ AGI ์‹œ์Šคํ…œ ์„ค๊ณ„์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์ด๋‹ค. PPL์€ ์กฐ๊ฑด๋ถ€ ์ถ”๋ก ์„ ์ˆ˜ํ–‰ํ•˜๊ณ , ์ƒ์„ฑ ๋ชจ๋ธ์„ ํ”„๋กœ๊ทธ๋žจ ํ˜•ํƒœ

Computer Science Artificial Intelligence
Obstacle evasion using fuzzy logic in a sliding blades problem   environment

Obstacle evasion using fuzzy logic in a sliding blades problem environment

๋ณธ ๋…ผ๋ฌธ์€ ๋“œ๋ก ์ด ๋ณต์žกํ•œ ํ™˜๊ฒฝ์—์„œ ์•ˆ์ „ํ•˜๊ฒŒ ๋น„ํ–‰ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ์ƒˆ๋กœ์šด ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ฐœ๋ฐœ์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ํŠนํžˆ, ๊ธฐ์กด์˜ ์žฅ์• ๋ฌผ ํšŒํ”ผ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์ •์ ์ธ ์žฅ์• ๋ฌผ์—๋Š” ํšจ๊ณผ์ ์ด์ง€๋งŒ ์ด๋™ํ•˜๋Š” ์žฅ์• ๋ฌผ์„ ํ”ผํ•˜๋Š”๋ฐ๋Š” ์ œํ•œ์ ์ด๋ผ๋Š” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ํ‘ธ์ฆˆ ๋…ผ๋ฆฌ์™€ ์ตœ๋‹จ ๊ฒฝ๋กœ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฒฐํ•ฉํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์‹œํ•˜๊ณ  ์žˆ๋‹ค. 1. ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๊ธฐ์กด์— ์‚ฌ์šฉ๋˜์—ˆ๋˜ ๊ฐ€์ƒ ํž˜์žฅ(VFF) ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ •์ ์ธ ์žฅ์• ๋ฌผ์„ ํ”ผํ•˜๋Š”๋ฐ ํšจ๊ณผ์ ์ด์ง€๋งŒ, ์ด๋™ํ•˜๋Š” ์žฅ็ข็‰ฉๆ—ถ๏ผŒๆ•ˆๆžœๆœ‰้™ใ€‚ๅ› ๆญค๏ผŒKyongsๅผ€ๅ‘ไบ†ๆ”น่ฟ›็š„่™šๆ‹ŸๅŠ›ๅœบ็ฎ—ๆณ•๏ผˆMVFF๏ผ‰๏ผŒๅฏไปฅๆ›ดๅฅฝๅœฐๅค„็†็งปๅŠจ้šœ็ข็‰ฉ็Žฏๅขƒใ€‚็„ถ่€Œ

Computer Science Artificial Intelligence Robotics
Simple2Complex: Global Optimization by Gradient Descent

Simple2Complex: Global Optimization by Gradient Descent

๋ณธ ๋…ผ๋ฌธ์€ ์‹ฌ์ธต ํ•™์Šต์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด '๋‹จ์ˆœ์—์„œ ๋ณต์žก์œผ๋กœ(Simple2Complex)' ์ ‘๊ทผ๋ฒ•์„ ์ œ์•ˆํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ์ข…๋‹จ(end to end) ํ•™์Šต๊ณผ๋Š” ๋‹ฌ๋ฆฌ, ๋‹จ์ˆœํ•œ ๋ชจ๋ธ๋กœ๋ถ€ํ„ฐ ์‹œ์ž‘ํ•˜์—ฌ ์ ์ง„์ ์œผ๋กœ ๋ณต์žก์„ฑ์„ ์ถ”๊ฐ€ํ•˜๋Š” ๋ฐฉ์‹์ž…๋‹ˆ๋‹ค. 1. ๋ฌธ์ œ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์‹ฌ์ธต ์‹ ๊ฒฝ๋ง์€ ๋น„์ „, ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ, ๊ฒŒ์ž„ ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ํ˜์‹ ์ ์ธ ์„ฑ๊ณผ๋ฅผ ์ด๋Œ์—ˆ์ง€๋งŒ, ์ข…๋‹จ ํ•™์Šต์˜ ํ•œ๊ณ„๋กœ ์ธํ•ด ๊ตญ์†Œ ์ตœ์ ํ™”์— ๋น ์ง€๋Š” ๋ฌธ์ œ๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, ์‹ ๊ฒฝ๋ง์ด ๊นŠ์–ด์งˆ์ˆ˜๋ก ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋Š” ๋”์šฑ ์‹ฌ๊ฐํ•ด์ง‘๋‹ˆ๋‹ค. 2. Simple2Complex ์ ‘๊ทผ๋ฒ• Simple

Computer Science Machine Learning Neural Computing
A method to Implement the Kerberos User Authentication and the secured   Internet Service

A method to Implement the Kerberos User Authentication and the secured Internet Service

๋ณธ ๋…ผ๋ฌธ์€ Kerberos์™€ IPSec์ด๋ผ๋Š” ๋‘ ๊ฐ€์ง€ ๋„คํŠธ์›Œํฌ ๋ณด์•ˆ ํ”„๋กœํ† ์ฝœ์„ ๊ฒฐํ•ฉํ•˜์—ฌ ์•ˆ์ „ํ•œ ์ธํ„ฐ๋„ท ์„œ๋น„์Šค ๊ตฌํ˜„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€์Šต๋‹ˆ๋‹ค. Kerberos๋Š” MIT์—์„œ ๊ฐœ๋ฐœ๋œ ๋„คํŠธ์›Œํฌ ์ธ์ฆ ํ”„๋กœํ† ์ฝœ๋กœ์„œ, ๋Œ€์นญํ‚ค ์•”ํ˜ธํ™”๋ฅผ ์‚ฌ์šฉํ•ด ํด๋ผ์ด์–ธํŠธ์™€ ์„œ๋ฒ„ ๊ฐ„์˜ ์‹ ๋ขฐ์„ฑ์„ ๋ณด์žฅํ•ฉ๋‹ˆ๋‹ค. ๋ฐ˜๋ฉด IPSec์€ TCP/IP ํ”„๋กœํ† ์ฝœ์˜ ๋„คํŠธ์›Œํฌ ๊ณ„์ธต์—์„œ ๊ฐ€์ƒ ์‚ฌ์„ค๋ง(VPN)์„ ๊ตฌ์ถ•ํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋˜๋Š” ์ผ๋ฐ˜์ ์ธ ๋ณด์•ˆ ํ”„๋กœํ† ์ฝœ์ž…๋‹ˆ๋‹ค. ๋…ผ๋ฌธ์—์„œ๋Š” Kerberos์™€ IPSec์„ ๊ฒฐํ•ฉํ•˜์—ฌ ์•ˆ์ „ํ•œ ์ธํ„ฐ๋„ท ์„œ๋น„์Šค๋ฅผ ๊ตฌํ˜„ํ•  ๋•Œ ๋ฐœ์ƒํ•˜๋Š” ๋ฌธ์ œ์ ๊ณผ ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•

Computer Science Cryptography and Security
Improved IKE Key Exchange Protocol Combined with Computer Security USB   Key Device

Improved IKE Key Exchange Protocol Combined with Computer Security USB Key Device

๋ณธ ๋…ผ๋ฌธ์€ ๊ธฐ์กด IKE์™€ IKEv2 ํ”„๋กœํ† ์ฝœ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋‹ค์–‘ํ•œ ๋ณด์•ˆ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์ปดํ“จํ„ฐ ๋ณด์•ˆ USB ํ‚ค ์žฅ์น˜๋ฅผ ํ™œ์šฉํ•œ ๊ฐ•ํ™”๋œ IKE 1๋‹จ๊ณ„ ๋ณด์•ˆ ํ˜‘์ƒ ๊ณผ์ •์„ ์ œ์‹œํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Š” ํŠนํžˆ DoS ๊ณต๊ฒฉ ๋ฐ MITM ๊ณต๊ฒฉ์— ๋Œ€ํ•œ ๋ฐฉ์–ด๋ ฅ์„ ๋†’์ด๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๋„คํŠธ์›Œํฌ ํ†ต์‹ ์˜ ์‹ ๋ขฐ์„ฑ์„ ํฌ๊ฒŒ ํ–ฅ์ƒ์‹œํ‚ค๊ณ ์ž ํ•œ๋‹ค. 1๋‹จ๊ณ„ ๋ณด์•ˆ ํ˜‘์ƒ ๊ณผ์ • ๊ฐœ์„  IKE์™€ IKEv2๋Š” ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๋Š” ์ธํ„ฐ๋„ท ํ‚ค ๊ตํ™˜ ํ”„๋กœํ† ์ฝœ๋“ค์ด๋‹ค. ์ด๋“ค์€ SA(Security Association) ๋ฐ KE(Key Exchange) ํŽ˜์ด๋กœ๋“œ๋ฅผ ํ†ตํ•ด ๋‘

Computer Science Cryptography and Security
Image Colorization Using a Deep Convolutional Neural Network

Image Colorization Using a Deep Convolutional Neural Network

์ด ๋…ผ๋ฌธ์€ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฒ•, ํŠนํžˆ ์ปจ๋ณผ๋ฃจ์…˜ ์‹ ๊ฒฝ๋ง(CNN)์„ ํ™œ์šฉํ•˜์—ฌ ํšŒ์ƒ‰์กฐ ์ด๋ฏธ์ง€๋ฅผ ์ƒ‰์ƒํ™”ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜๊ณ  ์žˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์ด๋ฏธ์ง€ ์ƒ‰์ƒํ™”์˜ ๋‘ ๊ฐ€์ง€ ์ฃผ์š” ์ ‘๊ทผ๋ฒ• ์ค‘ ํ•˜๋‚˜์ธ '์œ ์‚ฌํ•œ ์ฝ˜ํ…์ธ ๋ฅผ ๊ฐ€์ง„ ์ƒ‰์ƒ ์ด๋ฏธ์ง€์—์„œ ๊ฐ ํ”ฝ์…€์˜ ์ƒ‰์ƒ์„ ํ•™์Šต'ํ•˜๋Š” ๋ฐฉ์‹์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. 1. ์ด๋ฏธ์ง€ ์ƒ‰์ƒํ™”์˜ ๋ฐฐ๊ฒฝ ์ด๋ฏธ์ง€ ์ƒ‰์ƒํ™”๋Š” ํšŒ์ƒ‰์กฐ ์ด๋ฏธ์ง€๋ฅผ ์ƒ‰์ƒ์œผ๋กœ ์ฑ„์›Œ์„œ ์‹œ๊ฐ์  ํ‘œํ˜„๋ ฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๊ณผ์ •์ด๋‹ค. ์ด ์ž‘์—…์€ ์ข…์ข… ์‚ฌ์ „ ์ง€์‹์ด๋‚˜ ์ˆ˜๋™ ์กฐ์ • ์—†์ด๋„ ์ •๊ตํ•œ ๊ฒฐ๊ณผ๋ฅผ ์–ป๊ธฐ ์œ„ํ•ด ๋ณต์žกํ•œ ๊ธฐ์ˆ ์„ ํ•„์š”๋กœ ํ•œ๋‹ค. ๋˜ํ•œ, ๋ฌผ์ฒด๊ฐ€ ๋‹ค์–‘ํ•œ ์ƒ‰์ƒ์„ ๊ฐ€์งˆ ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์—

Machine Learning Computer Vision Computer Science Network Neural Computing
Procedural Generation of Angry Birds Levels using Building Constructive   Grammar with Chinese-Style and/or Japanese-Style Models

Procedural Generation of Angry Birds Levels using Building Constructive Grammar with Chinese-Style and/or Japanese-Style Models

: ๋ณธ ๋…ผ๋ฌธ์˜ ํ•ต์‹ฌ ๋ชฉํ‘œ๋Š” ์•ต๊ทธ๋ฆฌ ๋ฒ„๋“œ ๊ฒŒ์ž„ ๋‚ด์—์„œ ์ค‘๊ตญ๊ณผ ์ผ๋ณธ์˜ ๊ฑด์ถ• ์Šคํƒ€์ผ์„ ๋ฐ˜์˜ํ•œ ๋ ˆ๋ฒจ์„ ์ ˆ์ฐจ์ ์œผ๋กœ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด, 2D Building Construction Grammar (2D BCG)๋ผ๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ ์‚ฌ์šฉ๋œ ๋ชจ๋ธ ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์™€ ์‚ฌ์ „ ์ •์˜๋œ ๋งค๊ฐœ๋ณ€์ˆ˜๋ฅผ ํ™œ์šฉํ•œ ๋ฐฉ๋ฒ•๊ณผ๋Š” ๋‹ฌ๋ฆฌ, ๊ฑด๋ฌผ ๊ตฌ์กฐ๋ฅผ ์ƒ์„ฑ ๊ทœ์น™์„ ํ†ตํ•ด ์ž๋™ํ™”ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. 2D BCG๋Š” ๊ฑด๋ฌผ์˜ ๊ธฐ๋ณธ ๊ตฌ์„ฑ ์š”์†Œ์ธ ๋ฐ”๋‹ฅ, ์ฃผ์ฒด, ์ง€๋ถ•์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋ฉฐ, ๊ฐ ๋ถ€๋ถ„์€ ๋ฒฝ, ์ฐฝ๋ฌธ, ๋ฌธ ๋“ฑ ๋‹ค์–‘ํ•œ ์š”์†Œ๋“ค๋กœ ์„ธ๋ถ„ํ™”๋œ๋‹ค

Computer Science Artificial Intelligence HCI Model
Entities as topic labels: Improving topic interpretability and   evaluability combining Entity Linking and Labeled LDA

Entities as topic labels: Improving topic interpretability and evaluability combining Entity Linking and Labeled LDA

๋ณธ ๋…ผ๋ฌธ์€ ํ…์ŠคํŠธ ๋งˆ์ด๋‹ ๊ธฐ๋ฒ• ์ค‘ ํ•˜๋‚˜์ธ LDA(Latent Dirichlet Allocation) ํ† ํ”ฝ ๋ชจ๋ธ๋ง์˜ ํ•œ๊ณ„์ ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ์ ‘๊ทผ๋ฒ•์€ ์—”ํ‹ฐํ‹ฐ ๋งํ‚น(Entity Linking)๊ณผ ๋ผ๋ฒจ๋ง๋œ LDA๋ฅผ ๊ฒฐํ•ฉํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ, ๋Œ€๊ทœ๋ชจ ์ฝ”ํผ์Šค์—์„œ ์ฃผ์ œ ๋ถ„์„์˜ ํ•ด์„์„ฑ๊ณผ ํ‰๊ฐ€ ๊ฐ€๋Šฅ์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. 1. ๊ธฐ์กด ๋ฌธ์ œ์  LDA ํ† ํ”ฝ ๋ชจ๋ธ๋ง์€ ๋””์ง€ํ„ธ ์ธ๋ฌธํ•™ ์—ฐ๊ตฌ์—์„œ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜์ง€๋งŒ, ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ํ•ด์„ํ•˜๋Š” ๋ฐ ์–ด๋ ค์›€์ด ์žˆ๋‹ค. ๊ฐ ์ฃผ์ œ๊ฐ€ ๋‹จ์ˆœํžˆ ๋‹จ์–ด ๋ชฉ๋ก์œผ๋กœ ์ œ์‹œ๋˜๊ธฐ ๋•Œ๋ฌธ์—, ์—ฐ๊ตฌ์ž๊ฐ€ ์ง์ ‘ ์ด ๋‹จ์–ด๋“ค์„

Computer Science NLP
Bursting Money Bins, the ice and water structure

Bursting Money Bins, the ice and water structure

: ์„œ๋ก : ๋…ผ๋ฌธ์€ ์นด๋ฅด Barks์˜ ๋งŒํ™” 'ํ‚ฌ๋ชจํ„ฐ ์–ธ๋•์˜ ํฐ ๋ˆ ์ƒ์ž'์—์„œ ์‹œ์ž‘ํ•œ๋‹ค. ์Šคํฌ๋ฃจ์ง€ ์•„์ €์”จ๋Š” ์ž์‹ ์˜ ๋ˆ์„ ๋ณดํ˜ธํ•˜๊ธฐ ์œ„ํ•ด ๋น„๊ธ€ ๋ณด์ด์ฆˆ์—๊ฒŒ ๋งž์„œ ๋ฌผ๋กœ ์ƒ์ž๋ฅผ ์ฑ„์šด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ถ”์šด ๊ฒจ์šธ ๋ฐค์— ์ด ๋ฌผ์€ ์–ผ์–ด๋ถ™๊ณ , ๊ทธ ๊ณผ์ •์—์„œ ๋ถ€ํ”ผ๊ฐ€ ์ฆ๊ฐ€ํ•˜์—ฌ ์ƒ์ž๊ฐ€ ํ„ฐ์ง€๋ฉด์„œ ๋‹ฌ๋Ÿฌ๋“ค์ด ์–ธ๋• ์•„๋ž˜๋กœ ๋ฏธ๋„๋Ÿฌ์ ธ ๋‚ด๋ ค๊ฐ„๋‹ค. ๋ฌผ ๋ถ„์ž์˜ ๊ตฌ์กฐ: ๋ฌผ ๋ถ„์ž๋Š” ํŠน์ดํ•œ ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ˆ˜์†Œ ์›์ž์™€ ์‚ฐ์†Œ ์›์ž๋ฅผ ์—ฐ๊ฒฐํ•˜๋Š” ๊ณต์œ  ๊ฒฐํ•ฉ์€ ๊ฐ๊ฐ์˜ ์ˆ˜์†Œ ์›์ž๊ฐ€ ์‚ฐ์†Œ ์›์ž์— ๋Œ€ํ•ด 104.5๋„์˜ ๊ฐ๋„๋กœ ๋ฐฐ์น˜๋˜์–ด ์žˆ์–ด, ๋ฌผ ๋ถ„์ž๋Š” ๋ฏธํ‚ค ๋งˆ์šฐ์Šค์˜ ๋จธ๋ฆฌ์ฒ˜๋Ÿผ ์ƒ๊ฒผ๋‹ค. ์ด ๊ตฌ์กฐ๋Š” ๋ฉ”

Physics
No Image

Delicious ice Creams in Plain Awful. Why does salt thaw ice?

: ์ด ๋…ผ๋ฌธ์€ ๋ฌผ๋ฆฌํ•™์  ์›๋ฆฌ๋ฅผ ๋งŒํ™”๋ฅผ ํ†ตํ•ด ์žฌ๋ฏธ์žˆ๊ฒŒ ์ „๋‹ฌํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ, ์†Œ๊ธˆ์ด ์–ผ์Œ์„ ๋…น์ด๋Š” ํ˜„์ƒ์— ๋Œ€ํ•ด ์‹ฌ๋„ ์žˆ๊ฒŒ ๋ถ„์„ํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Š” ๋‹จ์ˆœํžˆ ๊ณผํ•™์ ์ธ ์„ค๋ช…์„ ๋„˜์–ด์„œ, ์ผ์ƒ์ƒํ™œ์—์„œ์˜ ์‘์šฉ๊ณผ ์—ญ์‚ฌ์  ๋ฐฐ๊ฒฝ๊นŒ์ง€ ํฌ๊ด„์ ์œผ๋กœ ๋‹ค๋ฃจ๊ณ  ์žˆ์–ด ๋…์ž๋“ค์˜ ์ดํ•ด๋ฅผ ๋†’์ผ ์ˆ˜ ์žˆ๋Š” ๋‚ด์šฉ์ด ํ’๋ถ€ํ•˜๋‹ค. 1. ๋งŒํ™”๋ฅผ ํ†ตํ•œ ๋ฌผ๋ฆฌํ•™์  ๊ฐœ๋… ์ „๋‹ฌ ๋…ผ๋ฌธ์€ ์นด๋ฅด ๋ฐ”ํฌ์Šค์™€ ๋„๋„๋“œ ๋ˆ ๋กœ์‚ฌ์˜ ๋งŒํ™”๋ฅผ ํ†ตํ•ด ๋ฌผ์˜ ์ด์ƒ ํ˜„์ƒ, ํŠนํžˆ ์†Œ๊ธˆ์ด ์–ผ์Œ์„ ๋…น์ด๋Š” ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์„ค๋ช…ํ•œ๋‹ค. ์ด๋Š” ๋‹จ์ˆœํžˆ ๊ณผํ•™์ ์ธ ๋‚ด์šฉ๋งŒ์„ ๋‹ค๋ฃจ๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ, ๋งŒํ™”๋ผ๋Š” ๋งค์ฒด๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ณต์žกํ•œ ๊ฐœ๋…์„ ์‰ฝ๊ฒŒ ์ดํ•ดํ• 

Physics
Interpretation of the Omori Law

Interpretation of the Omori Law

: ๋ณธ ๋…ผ๋ฌธ์€ ์˜ค๋ชจ๋ฆฌ ๋ฒ•์น™์„ ์ƒˆ๋กœ์šด ๊ด€์ ์—์„œ ๋ถ„์„ํ•˜๊ณ  ํ•ด์„ํ•จ์œผ๋กœ์จ ์ง€์ง„ ์—ฌ์ง„ ํ˜„์ƒ์— ๋Œ€ํ•œ ๊นŠ์ด ์žˆ๋Š” ์ดํ•ด๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์ฃผ์š” ์ง€์ง„ ๋ฐœ์ƒ ํ›„ ์—ฌ์ง„ ํ™œ๋™์˜ ์ง„ํ™” ๊ณผ์ •์„ ๋น„์„ ํ˜• ๊ตฌ์กฐ ์‹œ์Šคํ…œ์˜ ๊ด€์ ์—์„œ ์„ค๋ช…ํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๊ธฐ์กด์˜ ์˜ค๋ชจ๋ฆฌ ๋ฒ•์น™์„ ์žฌํ•ด์„ํ•˜๊ณ  ํ™•์žฅํ•ฉ๋‹ˆ๋‹ค. 1. ์˜ค๋ชจ๋ฆฌ ๋ฒ•์น™๊ณผ ๊ทธ ํ•ด์„ ์˜ค๋ชจ๋ฆฌ ๋ฒ•์น™์€ ๊ฐ•๋ ฅํ•œ ์ง€์ง„ ๋ฐœ์ƒ ํ›„ ์—ฌ์ง„์˜ ๋นˆ๋„๊ฐ€ ์‹œ๊ฐ„์— ๋”ฐ๋ผ ์ง€์ˆ˜ํ•จ์ˆ˜์ ์œผ๋กœ ๊ฐ์†Œํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๋‚˜ํƒ€๋ƒ…๋‹ˆ๋‹ค. ์ด๋Š” ์ง€์ง„ ๋ฐœ์›์ง€๊ฐ€ ์„œ์„œํžˆ ์•ˆ์ •ํ™”๋˜๋Š” ๊ณผ์ •์„ ๋ฐ˜์˜ํ•˜๋ฉฐ, ์ผ๋ฐ˜์ ์ธ ๊ท ์ผํ•œ ๋ฌผ์ฒด์˜ ๋ƒ‰๊ฐ๊ณผ๋Š” ๋‹ค๋ฅธ ๊ณ ๋„๋กœ ๋น„์„ ํ˜•์ ์ธ ํ˜„์ƒ์ž…๋‹ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ

Physics
Miquel point and isogonal conjugation

Miquel point and isogonal conjugation

๋ณธ ๋…ผ๋ฌธ์€ ๊ธฐํ•˜ํ•™์  ๋ถ„์„์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ๋ฏธ์ผˆ ์ ๊ณผ ๊ด€๋ จ๋œ ์ด๋ก ์„ ์ฒด๊ณ„์ ์œผ๋กœ ํƒ๊ตฌํ•˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ, ์ฃผ์–ด์ง„ ์‚ผ๊ฐํ˜• ๋‚ด๋ถ€์˜ ํŠน์ • ์œ„์น˜์— ์žˆ๋Š” ์ ๋“ค์ด ์–ด๋–ป๊ฒŒ ๋‹ค๋ฅธ ์œ ์‚ฌํ•œ ํ˜•ํƒœ๋ฅผ ๊ฐ–๋Š” ๋ฏธ์ผˆ ์‚ผ๊ฐํ˜•๊ณผ ์—ฐ๊ฒฐ๋˜๋Š”์ง€๋ฅผ ๋ถ„์„ํ•œ๋‹ค. 1. ๋ฏธ์ผˆ ์ ๊ณผ ์‚ผ๊ฐํ˜•์˜ ์œ ์‚ฌ์„ฑ ๋ฏธ์ผˆ ์ ์€ ์ฃผ์–ด์ง„ ์‚ผ๊ฐํ˜•์˜ ๋‚ด๋ถ€์—์„œ ํŠน์ • ์กฐ๊ฑด์„ ๋งŒ์กฑํ•˜๋Š” ์ ์œผ๋กœ, ์ด ์ ๋“ค์„ ์ด์šฉํ•ด ํ˜•์„ฑ๋œ ๋ชจ๋“  ๋ฏธ์ผˆ ์‚ผ๊ฐํ˜•์ด ์„œ๋กœ ์œ ์‚ฌํ•œ ํ˜•ํƒœ๋ฅผ ๊ฐ–๋Š”๋‹ค๋Š” ๊ฒƒ์ด ํ•ต์‹ฌ ๊ฐœ๋…์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ธฐ๋ณธ ์‚ผ๊ฐํ˜• ABC์— ๋Œ€ํ•ด, ํŠนํžˆ ๋ณ€ AB, BC, CA ์ค‘ ์–ด๋А ํ•˜๋‚˜์—๋„ ์†ํ•˜์ง€ ์•Š๋Š” ๋น„์ž๋ช… ์‚ฌ๋ก€์ธ ์ 

Mathematics
Liquid money or hard cash? Drowning into granular material

Liquid money or hard cash? Drowning into granular material

ํ”„๋ž‘์ฝ” ๋ฐ”๋‡ฐ๋ฆฌ์˜ ๋…ผ๋ฌธ '์•ก์ฒด์™€ ํ˜„๊ธˆ: ๋ณต์žกํ•œ ๋™์—ญํ•™ ์—ฐ๊ตฌ'๋Š” ๋ˆ์˜ ๋‘ ๊ฐ€์ง€ ๊ฐœ๋…, ์ฆ‰ ์•ก์ฒด์™€ ํ˜„๊ธˆ ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ํƒ๊ตฌํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๋ˆ์˜ ๋ณธ์งˆ๊ณผ ๊ทธ์— ๋”ฐ๋ฅธ ๋ฌผ๋ฆฌ์  ํ–‰๋™์„ ์ดํ•ดํ•˜๋ ค๊ณ  ํ•œ๋‹ค. ๋…ผ๋ฌธ์€ ๋‹ค์–‘ํ•œ ์–ธ์–ด์—์„œ ๋ˆ์ด ์–ด๋–ป๊ฒŒ ํ‘œํ˜„๋˜๋Š”์ง€๋ถ€ํ„ฐ ์‹œ์ž‘ํ•˜์—ฌ, ์ดํƒˆ๋ฆฌ์•„์–ด์—์„œ๋Š” '์•ก์ฒด ๋ˆ'์ด๋ผ๋Š” ๋ฌธํ•™์ ์ธ ํ‘œํ˜„์„ ์‚ฌ์šฉํ•˜๊ณ  ํ”„๋ž‘์Šค์–ด์—์„œ๋Š” 'argent'๋กœ ์„ค๋ช…ํ•œ๋‹ค. ๋…ผ๋ฌธ์˜ ํ•ต์‹ฌ ๊ฐœ๋… ์ค‘ ํ•˜๋‚˜๋Š” ๋ˆ์ด ๋‹จ๋‹จํ•œ ํ˜„๊ธˆ์ธ์ง€, ์•„๋‹ˆ๋ฉด ์•ก์ฒด์ ์ธ ํŠน์„ฑ์„ ๊ฐ€์ง„ ๊ฒƒ์ธ์ง€๋ฅผ ํƒ๊ตฌํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋งŒํ™”๋ฅผ ํ†ตํ•ด ๋ˆ์˜ ํ–‰๋™์„ ์‹œ๊ฐ์ ์œผ๋กœ ํ‘œํ˜„ํ•˜๋ฉฐ, ํŠนํžˆ ์Šคํฌ๋ฃจ์ง€ ์•„์ €์”จ

Physics
A note on the ranking of earthquake forecasts

A note on the ranking of earthquake forecasts

์ง€์ง„ ์˜ˆ์ธก์€ ์ž์—ฐ์žฌํ•ด ๊ด€๋ฆฌ์™€ ์•ˆ์ „์— ์žˆ์–ด ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•ฉ๋‹ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์€ ์ง€์ง„ ๋ฐœ์ƒ๋ฅ ์˜ ์˜ˆ์ธก๊ณผ ๊ทธ ํ‰๊ฐ€ ๋ฐฉ๋ฒ•๋ก ์— ๋Œ€ํ•ด ๊นŠ์ด ์žˆ๊ฒŒ ๋ถ„์„ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, RELM ํ”„๋กœ์ ํŠธ๋ฅผ ํ†ตํ•ด ๋‹ค์–‘ํ•œ ๋ชจ๋ธ ๊ฐ„์˜ ๋น„๊ต์™€ ์ˆœ์œ„ ๊ฒฐ์ • ๋ฐฉ๋ฒ•๋ก ์„ ํƒ๊ตฌํ•ฉ๋‹ˆ๋‹ค. ๋…ผ๋ฌธ์—์„œ ์ฃผ๋ชฉํ•  ๋งŒํ•œ ์ ์€ ๊ธฐ์กด์˜ ์Œ๋ณ„ ๊ฐ€์„ค ๊ฒ€์ • ์ ‘๊ทผ ๋ฐฉ์‹๊ณผ ํ•จ๊ป˜ ์ƒˆ๋กœ์šด ์˜ˆ์ธก ํ’ˆ์งˆ ์ธก์ •์ธ R์„ ์‚ฌ์šฉํ•œ ๋ชจ๋ธ ํ‰๊ฐ€ ๋ฐฉ๋ฒ•์ž…๋‹ˆ๋‹ค. ์ด R ์ ์ˆ˜๋Š” ์ง€์ง„ ๋ฐœ์ƒ๋ฅ  ์˜ˆ์ธก์˜ ์ •ํ™•์„ฑ์„ ํ‰๊ฐ€ํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋˜๋ฉฐ, ํŠนํžˆ N์ด ํฐ ๊ฒฝ์šฐ์— ๊ทธ ์„ฑ๋Šฅ์„ ๋ถ„์„ํ•ฉ๋‹ˆ๋‹ค. R ์ ์ˆ˜๋ฅผ ํ†ตํ•ด ๊ฐ ๋ชจ๋ธ์˜ ์ˆœ์œ„๋ฅผ ๋งค๊ธฐ๋Š” ๊ฒƒ์€ ์ค‘์š”ํ•œ ์ž‘์—…

Physics
Churn analysis using deep convolutional neural networks and autoencoders

Churn analysis using deep convolutional neural networks and autoencoders

์ด ๋…ผ๋ฌธ์€ ํ†ต์‹  ์‚ฐ์—…์—์„œ์˜ ๊ณ ๊ฐ ์ดํƒˆ(churn) ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์‹ฌ์ธต ํ•™์Šต, ํŠนํžˆ ์ปจ๋ณผ๋ฃจ์…˜ ์‹ ๊ฒฝ๋ง(CNN)๊ณผ ์˜คํ† ์ธ์ฝ”๋”๋ฅผ ํ™œ์šฉํ•œ ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์—ฐ๊ตฌ๋Š” ๊ณ ๊ฐ ํ–‰๋™ ํŒจํ„ด์„ ์ด๋ฏธ์ง€๋กœ ํ‘œํ˜„ํ•˜์—ฌ ์ด๋ฅผ CNN ๋ชจ๋ธ์— ์ž…๋ ฅํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ ์ดํƒˆ ์—ฌ๋ถ€๋ฅผ ์˜ˆ์ธกํ•˜๋Š”๋ฐ ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ์ ‘๊ทผ๋ฒ•์ด ๊ธฐ์กด์˜ CHAID ๊ฒฐ์ • ํŠธ๋ฆฌ ๋ชจ๋ธ๋ณด๋‹ค ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค๋Š” ๊ฒƒ์„ ์ž…์ฆํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. 1. ๊ณ ๊ฐ ํ–‰๋™ ํŒจํ„ด์˜ ์ด๋ฏธ์ง€ํ™” ๋…ผ๋ฌธ์€ ๊ฐ ๊ณ ๊ฐ์˜ ์ผ๋ณ„ ๋ฐ์ดํ„ฐ๋ฅผ ํ–‰์œผ๋กœ, ๋‹ค์–‘ํ•œ ํ–‰๋™ ์œ ํ˜•(๋ฐ์ดํ„ฐ ์‚ฌ์šฉ๋Ÿ‰, ์ถฉ์ „ ๊ธˆ์•ก ๋“ฑ)์„ ์—ด๋กœ ๊ตฌ์„ฑํ•˜๋Š”

Machine Learning Analysis Computer Science Statistics Network Neural Computing
Fixing the shadows while moving the gnomon

Fixing the shadows while moving the gnomon

๋ณธ ๋…ผ๋ฌธ์€ ์ „ํ†ต์ ์ธ ํƒœ์–‘์‹œ๊ณ„์˜ ๊ณ ์ •๋œ ์ง€์นจํŒ(gnomon) ๋Œ€์‹  ๊ทธ๋ฆผ์ž๋ฅผ ์›€์ง์ด๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ๊ธฐ์กด์˜ ๊ด€์ ์„ ๋’ค์ง‘์–ด, ์œ„๋„์™€ ํƒœ์–‘์˜ ์œ„์น˜์— ๋”ฐ๋ฅธ ๊ทธ๋ฆผ์ž์˜ ๋ณ€ํ™”๋ฅผ ๋” ๋ช…ํ™•ํ•˜๊ฒŒ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•œ๋‹ค. 1. ๊ธฐ์กด ์ง€์‹๊ณผ ์ƒˆ๋กœ์šด ๊ด€์  ๊ธฐ์กด์—๋Š” ์ˆ˜ํ‰์ ์ธ ์ง€์นจํŒ์„ ๊ณ ์ •ํ•˜๊ณ  ๊ทธ ์œ„๋กœ ๋–จ์–ด์ง€๋Š” ๊ทธ๋ฆผ์ž๋ฅผ ๊ด€์ฐฐํ•˜์—ฌ ํ˜„์ง€ ํƒœ์–‘ ์‹œ๊ฐ„์„ ์ธก์ •ํ•˜๊ณ  ๊ณ„์ ˆ์„ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์ด ์ผ๋ฐ˜์ ์ด์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๊ด€์ ์„ ๋’ค์ง‘์–ด, ๊ทธ๋ฆผ์ž๋ฅผ ๊ณ ์ •ํ•˜๊ณ  ์ง€์นจํŒ์„ ์›€์ง์ด๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ด๋Š” ์œ„๋„์™€ ํƒœ์–‘์˜ ์œ„์น˜์— ๋”ฐ๋ฅธ ๊ทธ๋ฆผ์ž์˜ ๋ณ€ํ™”๋ฅผ ๋”

Physics Astrophysics
Implementing OpenSHMEM for the Adapteva Epiphany RISC Array Processor

Implementing OpenSHMEM for the Adapteva Epiphany RISC Array Processor

๋งค๋ ฅ์ ์ธ ํ•œ๊ธ€ ์ œ๋ชฉ: ์—ํ”ผํŒŒ๋‹ˆ ํ”„๋กœ์„ธ์„œ๋ฅผ ์œ„ํ•œ ์˜คํ”ˆSHMEM ๊ตฌํ˜„ ๋ฐ ์„ฑ๋Šฅ ์ตœ์ ํ™” ์ดˆ๋ก ์ „์ฒด ๋ฒˆ์—ญ ๋ฐ ์ •๋ฆฌ: Adapteva์˜ ์—ํ”ผํŒŒ๋‹ˆ MIMD ์•„ํ‚คํ…์ฒ˜๋Š” ์ €๋ ดํ•œ Parallella ํ”Œ๋žซํผ์— ๊ตฌํ˜„๋˜์–ด ์žˆ์ง€๋งŒ, ์ œํ•œ๋œ ์ฝ”์–ด ๋ฉ”๋ชจ๋ฆฌ์™€ ๋‚ฎ์€ ์˜คํ”„์นฉ ๋Œ€์—ญํญ ๋“ฑ์œผ๋กœ ์ธํ•ด ํ”„๋กœ๊ทธ๋ž˜๋ฐ์ด ์–ด๋ ต์Šต๋‹ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์—ํ”ผํŒŒ๋‹ˆ ์•„ํ‚คํ…์ฒ˜์˜ ํŠน์ง•๊ณผ OpenSHMEM API๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ด๋ฅผ ๊ทน๋ณตํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์„ค๋ช…ํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, ์ œํ•œ๋œ ๋ฉ”๋ชจ๋ฆฌ์™€ ํšจ์œจ์ ์ธ ํ†ต์‹  ์‹คํ–‰์ด๋ผ๋Š” ์ฃผ์š” ๋„์ „ ๊ณผ์ œ์— ๋Œ€ํ•ด ๋‹ค๋ฃน๋‹ˆ๋‹ค. OpenSHMEM๋Š” ๋ฐ์ดํ„ฐ ์ฐธ์กฐ ์„ธ๋งˆํ‹ฑ์Šค๊ฐ€ ๊ฐœ์„ ๋˜๊ณ  ์ธํ„ฐ

Computer Science Distributed Computing
Cryptompress: A Symmetric Cryptography algorithm to deny Bruteforce   Attack

Cryptompress: A Symmetric Cryptography algorithm to deny Bruteforce Attack

1. ์•”ํ˜ธ์••์ถ•(Cryptompress)์˜ ํ•ต์‹ฌ ๊ฐœ๋… ์•”ํ˜ธ์••์ถ•์€ ๋ธŒ๋ฃจํŠธํฌ์Šค ๊ณต๊ฒฉ์„ ๋ฐฉ์–ดํ•˜๊ธฐ ์œ„ํ•œ ๋Œ€์นญ ์•”ํ˜ธํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด๋‹ค. ์ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๋ฐ์ดํ„ฐ๋ฅผ ์••์ถ•ํ•˜๋Š” ๊ณผ์ •์—์„œ ์•”ํ˜ธํ™”๋ฅผ ์ˆ˜ํ–‰ํ•จ์œผ๋กœ์จ, ํ•ด๋…์ด ๋”์šฑ ์–ด๋ ค์›Œ์ง€๋„๋ก ์„ค๊ณ„๋˜์—ˆ๋‹ค. ํŠนํžˆ, ์—ฐ์†์ ์ธ ์œ ์‚ฌํ•œ ์ด์ง„ ์Œ์„ ํšจ๊ณผ์ ์œผ๋กœ ์ฒ˜๋ฆฌํ•˜์—ฌ ์••์ถ•๋ฅ ์„ ๋†’์ด๋Š” ๊ฒƒ์ด ํ•ต์‹ฌ์ด๋‹ค. 2. ์••์ถ• ๋ฐ ์•”ํ˜ธํ™” ๊ณผ์ • ์•”ํ˜ธ์••์ถ•์˜ ์ฒซ ๋‹จ๊ณ„๋Š” ๋ฐ์ดํ„ฐ๋ฅผ ์••์ถ•ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด, 30๋น„ํŠธ ๋ธ”๋ก ์ฝ”๋“œ๊ฐ€ ์‚ฌ์šฉ๋˜๋ฉฐ, ์ด ์ค‘ ๋‘ ๊ฐœ์˜ ์—ฐ์†์ ์ธ ๋น„ํŠธ(00, 01, 10, 11)๋Š” ๊ฐ๊ฐ ์ฒซ ๋„ค ๊ฐœ์˜ ์–‘์˜ ์†Œ์ˆ˜(2, 3, 5, 7

Computer Science Cryptography and Security
SciChallenge: Using Student-Generated Content and Contests to Enhance   the Interest for Science Education and Careers

SciChallenge: Using Student-Generated Content and Contests to Enhance the Interest for Science Education and Careers

1. STEM ๊ต์œก์˜ ์ค‘์š”์„ฑ๊ณผ SciChallenge์˜ ๋ฐฐ๊ฒฝ 21์„ธ๊ธฐ์—๋Š” ๊ณผํ•™ ๋ฐ ๊ธฐ์ˆ  ํ˜์‹ ์ด ๊ฒฝ์ œ์  ๋ฒˆ์˜๊ณผ ์„ธ๊ณ„์  ๊ฒฝ์Ÿ๋ ฅ์— ํ•„์ˆ˜์ ์ธ ์—ญํ• ์„ ํ•˜๊ณ  ์žˆ๋‹ค. National Science Foundation์€ ์ƒˆ๋กœ์šด ์ •๋ณดํ™” ์‹œ๋Œ€์™€ ๊ณ ๋„๋กœ ๊ธฐ์ˆ ํ™”๋œ ์‹œ๋Œ€์—์„œ ์„ฑ๊ณตํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ํ•™์ƒ๋“ค์ด STEM ์—ญ๋Ÿ‰์„ ๊ฐœ๋ฐœํ•ด์•ผ ํ•œ๋‹ค๊ณ  ๊ฐ•์กฐํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ STEM ๊ด€๋ จ ํ•™์œ„ ํ”„๋กœ๊ทธ๋žจ์˜ ์ˆ˜๊ฐ•๋ฅ ์ด ๋‚ฎ์•„ ์‚ฐ์—… ํ˜„์žฅ์—์„œ ์ธ๋ ฅ ๋ถ€์กฑ ๋ฌธ์ œ๋ฅผ ์•ผ๊ธฐํ•˜๊ณ  ์žˆ๋‹ค. SciChallenge ํ”„๋กœ์ ํŠธ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์ ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด, ์ Š์€์ด๋“ค์ด ๊ณผํ•™ ๊ต์œก๊ณผ ๊ฒฝ๋ ฅ ์ถ”๊ตฌ์— ๋Œ€ํ•œ ๋™๊ธฐ๋ฅผ

Computer Science Computers and Society
Unsupervised Measure of Word Similarity: How to Outperform Co-occurrence   and Vector Cosine in VSMs

Unsupervised Measure of Word Similarity: How to Outperform Co-occurrence and Vector Cosine in VSMs

๋ณธ ๋…ผ๋ฌธ์€ ๋‹จ์–ด ์œ ์‚ฌ๋„๋ฅผ ๋น„๊ฐ๋…์ ์œผ๋กœ ์ธก์ •ํ•˜๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•, APSyn์„ ์ œ์•ˆํ•˜๊ณ  ๊ทธ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” NLP ๋ถ„์•ผ์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ๋‹จ์–ด ์œ ์‚ฌ๋„ ํƒ์ง€์˜ ์ค‘์š”์„ฑ์„ ๊ฐ•์กฐํ•˜๋ฉฐ, ์ด๋ฅผ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•๋ก ์ด ์‚ฌ์šฉ๋˜๊ณ  ์žˆ์Œ์„ ์„ค๋ช…ํ•œ๋‹ค. 1. ๋‹จ์–ด ์œ ์‚ฌ๋„ ์ธก์ •์˜ ํ•„์š”์„ฑ ๋‹จ์–ด ์œ ์‚ฌ๋„๋Š” ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ(NLP)์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•˜๋ฉฐ, ํŒจ๋Ÿฌํ”„๋ ˆ์ด์ง•, ์ฟผ๋ฆฌ ํ™•์žฅ, ์ž๋™ ์‚ฌ์ „ ์ƒ์„ฑ ๋“ฑ ๋‹ค์–‘ํ•œ ์‘์šฉ ๋ถ„์•ผ์— ํ™œ์šฉ๋œ๋‹ค. ๊ธฐ์กด ๋ฐฉ๋ฒ•๋ก ์€ ๋ ‰์‹œ์ฝ˜ ๊ธฐ๋ฐ˜๊ณผ ์ฝ”ํผ์Šค ๊ธฐ๋ฐ˜ ์ ‘๊ทผ๋ฒ•์œผ๋กœ ๋‚˜๋ˆŒ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ํŠนํžˆ ์ฝ”ํผ์Šค ๊ธฐ๋ฐ˜ ์ ‘๊ทผ๋ฒ•์€ ๋ถ„ํฌ์  ๊ฐ€์„ค์„ ๋ฐ”ํƒ•์œผ๋กœ

Computer Science NLP

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