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Multiscale approach for bone remodeling simulation based on finite   element and neural network computation

Multiscale approach for bone remodeling simulation based on finite element and neural network computation

: ๋ณธ ๋…ผ๋ฌธ์€ ๊ณจ ์žฌ๊ตฌ์„ฑ ๊ณผ์ •์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ๋‹ค์ค‘ ๊ทœ๋ชจ ์ ‘๊ทผ ๋ฐฉ์‹, ์ฆ‰ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ FENN(Finite Element and Neural Network) ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ๋ฐฉ๋ฒ•์˜ ํ•ต์‹ฌ์€ ์œ ํ•œ ์š”์†Œ ๋ถ„์„๊ณผ ์ธ๊ณต ์‹ ๊ฒฝ๋ง ๊ณ„์‚ฐ์„ ๊ฒฐํ•ฉํ•˜์—ฌ ๊ณจ ์žฌ๊ตฌ์„ฑ ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋ณต์žกํ•œ ํ˜„์ƒ์„ ํšจ๊ณผ์ ์œผ๋กœ ๋ชจ๋ธ๋งํ•˜๊ณ  ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. 1. ๋‹ค์ค‘ ๊ทœ๋ชจ ์ ‘๊ทผ ๋ฐฉ์‹์˜ ํ•„์š”์„ฑ ๊ณจ ์žฌ๊ตฌ์„ฑ์€ ๋ผˆ์˜ ๋ฏธ์„ธ ๊ตฌ์กฐ๋ถ€ํ„ฐ ๊ฑฐ์‹œ์  ํ–‰๋™๊นŒ์ง€ ๋‹ค์–‘ํ•œ ๊ทœ๋ชจ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋ณต์žกํ•œ ๊ณผ์ •์ด๋‹ค. ์ด ๊ณผ์ •์„ ์ •ํ™•ํ•˜๊ฒŒ ๋ชจ๋ธ๋งํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ฐ ์ˆ˜์ค€์—์„œ์˜ ์ƒํ˜ธ

Quantitative Biology Model Network Physics
Fingerprint recognition using standardized fingerprint model

Fingerprint recognition using standardized fingerprint model

์ง€๋ฌธ ์ธ์‹์€ ์ƒ์ฒด ์ธ์‹ ๊ธฐ์ˆ  ์ค‘ ํ•˜๋‚˜๋กœ, ๊ณ ์œ ํ•œ ํŠน์ง•๊ณผ ์ง€์†์„ฑ ๋•๋ถ„์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•ฉ๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ €ํ’ˆ์งˆ ์ด๋ฏธ์ง€๋‚˜ ์†Œํ˜• ์˜์—ญ์—์„œ ํš๋“๋œ ๋ฐ์ดํ„ฐ๋ฅผ ์ฒ˜๋ฆฌํ•˜๋Š” ๋ฐ ์–ด๋ ค์›€์ด ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ํ‘œ์ค€ํ™”๋œ ์ง€๋ฌธ ํ…œํ”Œ๋ฆฟ ๋ชจ๋ธ์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ง€๋ฌธ ์ธ์‹์˜ ํ•ต์‹ฌ์€ ํ…œํ”Œ๋ฆฟ๊ณผ ์ฟผ๋ฆฌ ์ง€๋ฌธ ๊ฐ„์˜ ๋งค์นญ์ž…๋‹ˆ๋‹ค. ์ด ๊ณผ์ •์—์„œ ๋‹ค์–‘ํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ๊ฐœ๋ฐœ๋˜์—ˆ์ง€๋งŒ, ํŠนํžˆ ์ง€๋ฌธ์ด ํšŒ์ „๋˜๊ฑฐ๋‚˜ ๊ต์ฐจ ๋ถ€๋ถ„์ด ์ž‘์€ ๊ฒฝ์šฐ ์ •ํ™•๋„๊ฐ€ ๋–จ์–ด์ง‘๋‹ˆ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ํ‘œ์ค€ํ™”๋œ ์ง€๋ฌธ ํ…œํ”Œ๋ฆฟ ๋ชจ๋ธ์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. 2. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•

Model Computer Vision Computer Science
The Swift/XRT Catalogue of GRBs

The Swift/XRT Catalogue of GRBs

: ์ด ๋…ผ๋ฌธ์€ ๊ฐ๋งˆ์„  ํญ๋ฐœ(Gamma Ray Burst, GRB)์˜ X ์„  ํ›„๊ด‘์— ๋Œ€ํ•œ ์‹ฌ๋„ ์žˆ๋Š” ๋ถ„์„์„ ์ œ๊ณตํ•˜๋ฉฐ, ์ด๋ฅผ ์œ„ํ•ด ์Šค์œ—(Swift) ์œ„์„ฑ์˜ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜๊ณ  ์žˆ๋‹ค. ์Šค์œ— ์œ„์„ฑ์ด 2010๋…„ ๋ง๊นŒ์ง€ ๊ด€์ธกํ•œ ์ด 658๊ฐœ์˜ ๊ฐ๋งˆ์„  ํญ๋ฐœ ์ค‘์—์„œ ์ ์ƒ‰ํŽธ์ด ๊ฐ’์ด ์ธก์ •๋œ 165๊ฐœ, ๊ธด GRB 414๊ฐœ(์ ์ƒ‰ํŽธ์ด ๊ฐ’์ด ์žˆ๋Š” 153๊ฐœ), ์งง์€ GRB 23๊ฐœ(์ ์ƒ‰ํŽธ์ด ๊ฐ’์ด ์žˆ๋Š” 12๊ฐœ)๋ฅผ ๋ถ„์„ํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” X ์„  ๋ง์›๊ฒฝ(XRT)์„ ํ†ตํ•ด ์ดˆ๊ธฐ ํŠธ๋ฆฌ๊ฑฐ ํ›„ ์•ฝ 80์ดˆ๋ถ€ํ„ฐ ๋ถ€๋“œ๋Ÿฌ์šด X ๋ ˆ์ด ๋ฐด๋“œ์—์„œ ๋ชจ๋‹ˆํ„ฐ๋งํ•œ ๊ฒฐ๊ณผ, GRB์˜ X ์„  ํ›„๊ด‘์€ ์ƒ๋Œ€

Astrophysics
Gravitational constant calculation methodologies

Gravitational constant calculation methodologies

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ์ค‘๋ ฅ ์ƒ์ˆ˜(G)๋Š” ์ค‘๋ ฅ์„ ์ดํ•ดํ•˜๊ณ , ์šฐ์ฃผ์™€ ์ง€๊ตฌ ๋‚ด์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋‹ค์–‘ํ•œ ํ˜„์ƒ์„ ์„ค๋ช…ํ•˜๋Š”๋ฐ ํ•„์ˆ˜์ ์ธ ๋ฌผ๋ฆฌ ์ƒ์ˆ˜์ด๋‹ค. ์ด ๋…ผ๋ฌธ์€ G์˜ ์ •ํ™•ํ•œ ๊ณ„์‚ฐ ๋ฐฉ๋ฒ•์„ ๊ฐœ๋ฐœํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ๊ณผํ•™ ๋ฐ ๊ณตํ•™ ๋ถ„์•ผ์— ์ค‘์š”ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ํƒ์ƒ‰ํ•œ๋‹ค. 2. ์—ฐ๊ตฌ ๋ชฉ์  ๋ณธ ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์„ธ ๊ฐ€์ง€ ์ ‘๊ทผ ๋ฐฉ์‹์„ ์‚ฌ์šฉํ•˜์—ฌ ์ค‘๋ ฅ ์ƒ์ˆ˜(G)๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ๊ฒ€ํ† ํ•˜๋ฉฐ, ํŠนํžˆ ๋น„๊ท ์ผ ๊ฐ€์Šค ํ๋ฆ„์ด G ๊ฐ’์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด G์˜ ์ •ํ™•ํ•œ ๊ฐ’์„ ์–ป๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ๋‹ค. 3. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• ๋ฐฉ๋ฒ• 1: ๋ถˆ์กฐํ™” ์ง„๋™

Physics
Computational Fluid Dynamics In GARUDA Grid Environment

Computational Fluid Dynamics In GARUDA Grid Environment

๋ณธ ๋…ผ๋ฌธ์€ ๊ณ„์‚ฐ ์œ ์ฒด์—ญํ•™(CFD) ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์˜ ํšจ์œจ์ ์ธ ๊ตฌํ˜„๊ณผ ๊ทธ๋ฆฌ๋“œ ์ปดํ“จํŒ… ํ™˜๊ฒฝ์˜ ์ค‘์š”์„ฑ์„ ๊ฐ•์กฐํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, ๊ตญ๊ฐ€ ์ง€์‹ ๋„คํŠธ์›Œํฌ(NKN) ๊ธฐ๋ฐ˜์˜ GARUDA ๊ทธ๋ฆฌ๋“œ ํ™˜๊ฒฝ์„ ํ™œ์šฉํ•˜์—ฌ CFD ๋ชจ๋ธ์„ ๊ตฌํ˜„ํ•จ์œผ๋กœ์จ, ๊ณ ์„ฑ๋Šฅ ์ปดํ“จํŒ…(HPC) ์ž์› ๋ถ€์กฑ๊ณผ ๋ณต์žกํ•œ ๋„๊ตฌ ๊ฒฐํ•ฉ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ ๋‹๋ณด์ž…๋‹ˆ๋‹ค. 1. CFD์™€ HPC์˜ ๊ด€๊ณ„ CFD๋Š” ๋ณต์žกํ•œ ์œ ์ฒด ์—ญํ•™ ๋ฌธ์ œ๋ฅผ ์ˆ˜์น˜์ ์œผ๋กœ ๋ชจ๋ธ๋งํ•˜๊ณ  ๋ถ„์„ํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋˜๋Š” ๊ธฐ์ˆ ๋กœ, ์ด๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋Œ€๊ทœ๋ชจ ๊ณ„์‚ฐ ์ž์›์ด ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค. ์ด์— ๋”ฐ๋ผ CFD๋Š” HPC ๋ฐœ์ „์˜ ํ•ต์‹ฌ ๋™๋ ฅ ์ค‘ ํ•˜๋‚˜์ž…

Physics
QCDMAPT_F: Fortran version of QCDMAPT package

QCDMAPT_F: Fortran version of QCDMAPT package

QCDMAPT F๋Š” ์–‘์ž ์ƒ‰์—ญํ•™(QCD) ๋ถ„์•ผ์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ํ”„๋กœ๊ทธ๋žจ ํŒจํ‚ค์ง€๋กœ, ์ด์ „ ๋ฒ„์ „์ธ Maple ์‹œ์Šคํ…œ์šฉ QCDMAPT๋ฅผ Fortran์œผ๋กœ ๊ตฌํ˜„ํ•œ ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ด ํŒจํ‚ค์ง€๋Š” ๊ฐ•ํ•œ ์ƒํ˜ธ์ž‘์šฉ์˜ ๋ณต์žก์„ฑ์„ ๋‹ค๋ฃจ๋Š” ๋ฐ ์žˆ์–ด ํ•ต์‹ฌ์ ์ธ ๋„๊ตฌ์ด๋ฉฐ, ํŠนํžˆ ์ŠคํŽ™ํŠธ๋Ÿผ ํ•จ์ˆ˜ ๊ณ„์‚ฐ์— ์ค‘์ ์„ ๋‘๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. 1. ํ”„๋กœ๊ทธ๋žจ ๊ฐœ์š” QCDMAPT F๋Š” Fortran 77 ์ด์ƒ์„ ์ง€์›ํ•˜๋Š” ๋ชจ๋“  ์ปดํ“จํ„ฐ์™€ ์šด์˜ ์ฒด์ œ์—์„œ ์‹คํ–‰๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, CERNLIB์˜ MATHLIB ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ๊ฐ€ ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค. ์ด ํŒจํ‚ค์ง€๋Š” ๋ฉ”์ธ ํ”„๋กœ๊ทธ๋žจ(QCDMAPT F.f)๊ณผ ๋‘ ๊ฐœ์˜

HEP-PH HEP-TH Physics
Measuring the Earth-Sun distance during a lunar eclipse

Measuring the Earth-Sun distance during a lunar eclipse

๋ณธ ๋…ผ๋ฌธ์€ ๊ณ ๋Œ€ ์ฒœ๋ฌธํ•™์ž๋“ค์ด ์‚ฌ์šฉํ–ˆ๋˜ ์ง€๊ตฌ ํƒœ์–‘ ๊ฑฐ๋ฆฌ ์ธก์ •๋ฒ•์„ ํ˜„๋Œ€์ ์ธ ๊ด€์ ์—์„œ ์žฌํ•ด์„ํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ์ฒœ๋ฌธ ๋‹จ์œ„์˜ ์ •ํ™•ํ•œ ๊ฐ’์„ ์ถ”์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๋‹ฌ ์‹ ๋™์•ˆ ๋ฐœ์ƒํ•˜๋Š” ํ˜„์ƒ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋ฉฐ, ํŠนํžˆ ์ง€๊ตฌ ๊ทธ๋ฆผ์ž์˜ ๋ชจ์–‘๊ณผ ํฌ๊ธฐ๋ฅผ ๋ถ„์„ํ•˜์—ฌ ์ง€๊ตฌ์™€ ํƒœ์–‘ ์‚ฌ์ด์˜ ๊ฑฐ๋ฆฌ๋ฅผ ์ธก์ •ํ•˜๋Š” ๋ฐ ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. 1. ๋ฐฉ๋ฒ•๋ก  ๋‹ฌ ์‹ ๋™์•ˆ ์ง€๊ตฌ๊ฐ€ ๋‹ฌ์„ ๊ฐ€๋ฆฌ๋Š” ํ˜„์ƒ์„ ๊ด€์ฐฐํ•จ์œผ๋กœ์จ, ์ง€๊ตฌ ๊ทธ๋ฆผ์ž์˜ ๋ชจ์–‘๊ณผ ํฌ๊ธฐ๋ฅผ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Š” ๋‹ฌ์˜ ๊ฐ๋„์™€ ์ง€๊ตฌ ๊ทธ๋ฆผ์ž ๋ฐ˜์ง€๋ฆ„ (RโŠ•,M)๋ฅผ ์ธก์ •ํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋ฉ๋‹ˆ๋‹ค. ์ €์ž๋Š” Hipparcos

Physics Astrophysics
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Misure quantitative del seeing atmosferico

์ด ๋…ผ๋ฌธ์€ ๋Œ€๊ธฐ๊ด‘ํ•™ ํ˜„์ƒ, ํŠนํžˆ Seeing๊ณผ ๊ด€๋ จ๋œ ์ด๋ฏธ์ง€ ์šด๋™ ๋ฐ ๋ธ”๋Ÿฌ๋ง์— ๋Œ€ํ•œ ์‹ฌ๋„ ์žˆ๋Š” ๋ถ„์„์„ ์ œ๊ณตํ•œ๋‹ค. ์ด๋Š” ์ฒœ๋ฌธ ๊ด€์ธก์—์„œ ์ค‘์š”ํ•œ ์š”์†Œ๋กœ, ๋ง์›๊ฒฝ์˜ ์„ฑ๋Šฅ๊ณผ ์ง์ ‘์ ์œผ๋กœ ์—ฐ๊ด€๋˜์–ด ์žˆ๋‹ค. 1. ๋Œ€๊ธฐ๊ด‘ํ•™ ํ˜„์ƒ ์ดํ•ด ๋Œ€๊ธฐ๊ด‘ํ•™์€ ๊ด‘์„ ์ด ์ง€๊ตฌ ๋Œ€๊ธฐ๋ฅผ ํ†ต๊ณผํ•˜๋ฉด์„œ ๋ฐœ์ƒํ•˜๋Š” ๋‹ค์–‘ํ•œ ํ˜„์ƒ์„ ๋‹ค๋ฃจ๋ฉฐ, ์ด ์ค‘ Seeing์€ ํŠนํžˆ ์ค‘์š”ํ•˜๋‹ค. Seeing์€ ๋Œ€๊ธฐ์˜ ํˆฌ๋ช…๋„์™€ ๊ด€๋ จ๋œ ๊ฐœ๋…์œผ๋กœ, ๋ง์›๊ฒฝ์„ ํ†ตํ•ด ๋ณผ ์ˆ˜ ์žˆ๋Š” ์ฒœ์ฒด ์ด๋ฏธ์ง€์˜ ์„ ๋ช…๋„๋ฅผ ๊ฒฐ์ •ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” Seeing์ด ์–ด๋–ป๊ฒŒ ๋ฐœ์ƒํ•˜๊ณ , ์ด๋ฅผ ์ธก์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด ์„ค๋ช…ํ•œ๋‹ค. 2. Fried

Astrophysics Physics
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Visual Secret Sharing Scheme using Grayscale Images

: ๋ณธ ๋…ผ๋ฌธ์€ ํšŒ์ƒ‰์กฐ ์ด๋ฏธ์ง€์— ๋Œ€ํ•œ ์‹œ๊ฐ ๋น„๋ฐ€ ๋ถ„๋ฐฐ(VSS) ๋ฐฉ์‹์˜ ๊ฐœ์„ ์ ์„ ์ œ์‹œํ•˜๋ฉฐ, ํŠนํžˆ ํ”ฝ์…€ ํ™•์žฅ ๋ฌธ์ œ์™€ ์žฌ๊ตฌ์„ฑ๋œ ์ด๋ฏธ์ง€์˜ ํ’ˆ์งˆ ์ €ํ•˜๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์†Œ๊ฐœํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๊ธฐ์กด VSS ๋ฐฉ์‹์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ฃผ์š” ๋ฌธ์ œ์ ๋“ค์„ ๊ทน๋ณตํ•˜๊ณ ์ž ํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๋ณด๋‹ค ์•ˆ์ •์ ์ด๊ณ  ํšจ๊ณผ์ ์ธ ๋น„๋ฐ€ ์ •๋ณด ๊ณต์œ  ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. 1. ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ธ์‹ ๋น„๋ฐ€ ๋ถ„๋ฐฐ ๊ธฐ๋ฒ•์€ ๋‹ค์–‘ํ•œ ์‘์šฉ ๋ถ„์•ผ์—์„œ ํ™œ์šฉ๋˜๋ฉฐ, ํŠนํžˆ ์›Œํ„ฐ๋งˆํ‚น๊ณผ ๊ฐ™์€ ์ •๋ณด ์€๋‹‰ ๊ธฐ์ˆ ์— ๋„๋ฆฌ ์‚ฌ์šฉ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋“ค ๋ฐฉ์‹ ์ค‘ ์ผ๋ถ€๋Š” ์žฌ๊ตฌ์„ฑ๋œ ์ด๋ฏธ์ง€์˜ ํ’ˆ์งˆ ์ €ํ•˜์™€ ํ”ฝ์…€ ํ™•์žฅ ๋ฌธ์ œ๋ฅผ ์•ผ

Computer Vision Computer Science Cryptography and Security
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Pose Estimation from a Single Depth Image for Arbitrary Kinematic Skeletons

๋ณธ ๋…ผ๋ฌธ์€ ๊นŠ์ด ์ด๋ฏธ์ง€๋กœ๋ถ€ํ„ฐ ์ž„์˜์˜ ๊ด€์ ˆ ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง„ ๋ฌผ์ฒด์˜ 3D ์ž์„ธ๋ฅผ ์ถ”์ •ํ•˜๋Š” ๋ฌธ์ œ์— ๋Œ€ํ•ด ์ง„ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ™œ์šฉํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๋“ค์ด ์‚ฌ์ „ ํ›ˆ๋ จ์ด๋‚˜ ๋„๋ฉ”์ธ ํŠน์ • ์ง€์‹์— ํฌ๊ฒŒ ์˜์กดํ•˜๊ณ , ์ด๋ฅผ ์ผ๋ฐ˜ํ™”ํ•˜๊ธฐ ์œ„ํ•œ ์ถ”๊ฐ€์ ์ธ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•˜๋‹ค๋Š” ํ•œ๊ณ„์ ์„ ๊ทน๋ณตํ•˜๋ ค๊ณ  ์‹œ๋„ํ•œ๋‹ค. 1. ๋ฌธ์ œ ์ •์˜์™€ ์ ‘๊ทผ ๋ฐฉ์‹ ์ž์„ธ ์ถ”์ •์€ ๋ฌผ์ฒด์˜ ๊ด€์ ˆ ๊ตฌ์กฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๊นŠ์ด ์ด๋ฏธ์ง€์—์„œ ๋ฌผ์ฒด์˜ ์œ„์น˜์™€ ์ž์„ธ๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ๊ณผ์ •์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด ๋ฌธ์ œ๋ฅผ ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ์ถ”์ • ๋ฌธ์ œ๋กœ ์ •์˜ํ•˜๊ณ , ์ง„ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ™œ์šฉํ•˜์—ฌ ์ตœ์ ์˜ ์ž์„ธ ๋งค๊ฐœ๋ณ€์ˆ˜๋ฅผ ์ฐพ

Computer Vision Computer Science Artificial Intelligence Machine Learning
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Stone structures in the Syrian Desert

: ์ด ์—ฐ๊ตฌ๋Š” ์‹œ๋ฆฌ์•„ ์‚ฌ๋ง‰์—์„œ ๋ฐœ๊ฒฌ๋œ ๋‚ ๊ฐœ ๋‹ฌ๋ฆฐ ๊ทธ๋ฌผ(kite)๊ณผ ๊ด€๋ จ๋œ ์„์กฐ ๊ตฌ์กฐ๋ฌผ์„ ์ค‘์‹ฌ์œผ๋กœ ๊ณ ๋Œ€ ์ธ๊ฐ„์˜ ์ƒํ™œ ๋ฐฉ์‹์— ๋Œ€ํ•œ ํ†ต์ฐฐ๋ ฅ์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ์ด๋“ค ๊ตฌ์กฐ๋ฌผ์€ ๋‹จ์ˆœํžˆ ๋™๋ฌผ์„ ํฌํšํ•˜๊ณ  ์‚ดํ•ดํ•˜๋Š” ๋ฐ๋งŒ ์‚ฌ์šฉ๋˜์ง€ ์•Š๊ณ , ์‹๋Ÿ‰ ๋ณด์กด ๋ฐ ์‚ฌ๋ƒฅ๊ฐ ์œ ์ง€์—๋„ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ–ˆ์Šต๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐœ๊ฒฌ์€ ๊ณ ๋Œ€ ์ธ๋ฅ˜๊ฐ€ ์–ด๋–ป๊ฒŒ ์ž์›์„ ๊ด€๋ฆฌํ•˜๊ณ  ์ƒ์กด์— ํ•„์š”ํ•œ ๊ธฐ์ˆ ์„ ๋ฐœ์ „์‹œ์ผฐ๋Š”์ง€๋ฅผ ์ดํ•ดํ•˜๋Š” ๋ฐ ์ค‘์š”ํ•œ ๋‹จ์„œ๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ๋‚ ๊ฐœ ๋‹ฌ๋ฆฐ ๊ทธ๋ฌผ์˜ ์กด์žฌ๋Š” ๋ฐฉ์‚ฌ์„ฑ ํƒ„์†Œ ๋ถ„์„์„ ํ†ตํ•ด ํ™•์ธ๋˜์—ˆ์œผ๋ฉฐ, ๊ฐ€์žฅ ์˜ค๋ž˜๋œ ๊ตฌ์กฐ๋ฌผ์€ ์•ฝ 9์ฒœ ๋…„ ์ „๋ถ€ํ„ฐ 11์ฒœ ๋…„ ์ „๊นŒ์ง€ ๊ฑฐ์Šฌ๋Ÿฌ ์˜ฌ๋ผ๊ฐ‘๋‹ˆ๋‹ค

Physics
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Cryptographic Hardening of d-Sequences

๋ณธ ๋…ผ๋ฌธ์€ d ์‹œํ€€์Šค๋ฅผ ์ด์šฉํ•˜์—ฌ ๋žœ๋ค ์ˆซ์ž ์ƒ์„ฑ๊ธฐ(RNG)์˜ ๋ฌด์ž‘์œ„์„ฑ์„ ๊ฐ•ํ™”ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” RNG์˜ ์ถœ๋ ฅ์— ๋‹ค๋Œ€์ผ ๋งคํ•‘์„ ์ ์šฉํ•จ์œผ๋กœ์จ ์—ญ ๊ณผ์ •์˜ ๋ณต์žก์„ฑ์„ ์ฆ๊ฐ€์‹œํ‚ค๊ณ , ์ด๋ฅผ ํ†ตํ•ด ์‹œํ€€์Šค์˜ ๋ฌด์ž‘์œ„์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ์•„์ด๋””์–ด๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ์ง„ํ–‰๋˜์—ˆ์Šต๋‹ˆ๋‹ค. 1. ๋ฌด์ž‘์œ„์„ฑ ์ธก์ • ๊ธฐ์ค€ ๋ฌด์ž‘์œ„์„ฑ์€ ํ™•๋ฅ ์  ๊ด€์ ๊ณผ ๋ณต์žก์„ฑ ๊ด€์ ์—์„œ ์ธก์ •๋  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ž์ƒ๊ด€ ํ•จ์ˆ˜๋ฅผ ์ฃผ์š”ํ•œ ๋ฌด์ž‘์œ„์„ฑ ์ธก์ • ๊ธฐ์ค€์œผ๋กœ ์‚ฌ์šฉํ•˜์˜€์Šต๋‹ˆ๋‹ค. ์ด๋Š” ์‹œํ€€์Šค์˜ ์ž๊ธฐ ์œ ์‚ฌ์„ฑ์„ ๋‚˜ํƒ€๋‚ด๋ฉฐ, ์ข‹์€ ๋ฌด์ž‘์œ„ ์‹œํ€€์Šค๋Š” ๋Œ€๋žต ์ด์ง„ ๊ฐ’์„ ๊ฐ€์ง€๋Š” ์ž์ƒ๊ด€ ํ•จ์ˆ˜๋ฅผ ๊ฐ€์ง‘๋‹ˆ

Computer Science Cryptography and Security
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Study on radon and radium concentrations in drinking water in west region of Iran

: ๋ณธ ์—ฐ๊ตฌ๋Š” ํˆฌ๋ฅด์‚ฐ ์ง€์—ญ์˜ ์ง€ํ•˜์ˆ˜ ๋‚ด ๋ผ๋ˆ๊ณผ ๋ฐฉ์‚ฌ์šฐ์˜ ๋†๋„๋ฅผ ์ธก์ •ํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ์ธ์ฒด์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ํŠนํžˆ ๋ผ๋ˆ์ด ํ์•” ๋ฐ ์œ„์•” ๋ฐœ๋ณ‘๋ฅ  ์ฆ๊ฐ€์™€ ๊ด€๋ จ์ด ์žˆ๋‹ค๋Š” ์ ์„ ๊ฐ•์กฐํ•˜๋ฉฐ, ๋ฌผ ๊ณต๊ธ‰ ๊ณผ์ •์—์„œ ๊ณ ๊ฐ์—๊ฒŒ ๋„๋‹ฌํ•œ ํ›„, ํก์ž…๊ณผ ์„ญ์ทจ๋ฅผ ํ†ตํ•ด ์ธ๊ฐ„ ๋…ธ์ถœ์ด ์ผ์–ด๋‚˜๋Š” ๊ฒƒ์„ ๋ถ„์„ํ•ฉ๋‹ˆ๋‹ค. ๋ผ๋ˆ์€ ์ง€ํ•˜์ˆ˜ ๋‚ด์— ์กด์žฌํ•˜๋Š” ๋ฐฉ์‚ฌ์„ฑ ๋™์œ„์›์†Œ๋กœ, ์ง€ํ‘œ๋ฉด ์ˆ˜์งˆ๋ณด๋‹ค ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” PRASSI ์‹œ์Šคํ…œ ๋ชจ๋ธ 5S๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋ฌผ ์ƒ˜ํ”Œ ๋‚ด ๋ผ๋ˆ ๋†๋„๋ฅผ ์ธก์ •ํ–ˆ์Šต๋‹ˆ๋‹ค. ์ด ์‹œ์Šคํ…œ์€ ๋†’์€ ๊ฐ๋„์™€ ์งง์€ ์‘๋‹ต ์‹œ๊ฐ„์„

Physics
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Top-quark pair cross-section measurement in the lepton+jets channel

์ด ๋…ผ๋ฌธ์€ LHC ์ดˆ๊ธฐ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ๊ธฐ๊ฐ„ ๋™์•ˆ tt ์Œ์˜ ๊ต์ฐจ ์ ˆ๋‹จ(cross section) ์ธก์ •์— ๋Œ€ํ•œ ์‹ฌ๋„ ์žˆ๋Š” ๋ถ„์„์„ ์ œ๊ณตํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ํผํŠธ๋ฅด๋ธŒ QCD ์˜ˆ์ธก์˜ ์ •ํ™•์„ฑ์„ ๊ฒ€์ฆํ•˜๊ณ , ์ดˆ์›” ํ‘œ์ค€ ๋ชจ๋ธ ๋ฌผ๋ฆฌํ•™ ํƒ์ƒ‰์—์„œ ์ค‘์š”ํ•œ ๋ฐฐ๊ฒฝ์„ ํ˜•์„ฑํ•˜๋ฉฐ, ์ƒˆ๋กœ์šด ๋ฌผ๋ฆฌํ•™ ํƒ์‚ฌ์˜ ์ฒซ๊ฑธ์Œ์ด ๋˜๋Š” ์ธก์ •์— ์ค‘์ ์„ ๋‘”๋‹ค. 1. ์ด๋ก ์  ๋ฐฐ๊ฒฝ ๋ฐ ์ค‘์š”์„ฑ tt ์Œ์˜ ๊ต์ฐจ ์ ˆ๋‹จ์€ pp ์ถฉ๋Œ์—์„œ ์œ„ ์ฟผํฌ์™€ ๋ฐ˜์œ„ ์ฟผํฌ๊ฐ€ ์ƒ์„ฑ๋˜๋Š” ํ™•๋ฅ ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. ์ด ์ธก์ •์€ ํผํŠธ๋ฅด๋ธŒ QCD ์˜ˆ์ธก๊ณผ ์ง์ ‘ ๋น„๊ตํ•˜์—ฌ ๊ทธ ์ •ํ™•๋„๋ฅผ ๊ฒ€์ฆํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋œ๋‹ค. ๋˜ํ•œ, tt ์ƒ์‚ฐ์€ ์ดˆ์›” SM ๋ฌผ

Physics HEP-EX
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Abusir: from Pliny the Elder to Google Maps

: ๋ณธ ๋…ผ๋ฌธ์€ ์ด์ง‘ํŠธ ์•„๋ถ€์‹œ๋ฅด ์œ ์ ์ง€์˜ ๋ฐœ๊ตด๊ณผ ์—ฐ๊ตฌ ๊ณผ์ •์„ ์ค‘์‹ฌ์œผ๋กœ, ํ˜„๋Œ€ ๊ธฐ์ˆ ์ธ ๊ตฌ๊ธ€ ๋งต ์œ„์„ฑ ์ด๋ฏธ์ง€ ์ฒ˜๋ฆฌ๋ฅผ ํ†ตํ•ด ๊ณ ๊ณ ํ•™์  ๋ฐœ๊ฒฌ์ด ์–ด๋–ป๊ฒŒ ์ด๋ฃจ์–ด์งˆ ์ˆ˜ ์žˆ๋Š”์ง€๋ฅผ ๊นŠ์ด ์žˆ๊ฒŒ ๋ถ„์„ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, ์ฒด์ฝ” ์›์ •๋Œ€์™€ ์ดํƒˆ๋ฆฌ์•„ ํ•™์ž๋“ค์˜ ์—ญํ• ์— ์ดˆ์ ์„ ๋งž์ถ”์–ด ์•„๋ถ€์‹œ๋ฅด ํ”ผ๋ผ๋ฏธ๋“œ ๋ณตํ•ฉ์ฒด์˜ ์—ญ์‚ฌ์  ๋ฐฐ๊ฒฝ๊ณผ ํ˜„๋Œ€ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๋ก ์„ ๋‹ค๋ฃจ๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๋…ผ๋ฌธ์€ ์•„๋ถ€์‹œ๋ฅด ์œ ์ ์ง€๊ฐ€ 14๊ฐœ์˜ ํ”ผ๋ผ๋ฏธ๋“œ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ์œผ๋ฉฐ, ์ด๋“ค ๊ตฌ์กฐ๋ฌผ์˜ ํ’ˆ์งˆ์ด ์ œ4์™•์กฐ๋ณด๋‹ค ์—ด๋“ฑํ•˜๋‹ค๋Š” ์ ์„ ๊ฐ•์กฐํ•ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ฐจ์ด๋Š” ์™•๊ถŒ์˜ ์•ฝํ™” ๋˜๋Š” ๊ฒฝ์ œ ์ƒํ™ฉ์˜ ์•…ํ™”์™€ ๊ด€๋ จ์ด ์žˆ์„ ๊ฒƒ์œผ๋กœ ์ถ”์ •๋ฉ๋‹ˆ๋‹ค

Physics
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Zen Puzzle Garden is NP-complete

๋ณธ ๋…ผ๋ฌธ์€ '์   ํผ์ฆ ๊ฐ€๋“ (ZPG)' ๊ฒŒ์ž„์˜ ๋ณต์žก์„ฑ ๋ถ„์„์„ ํ†ตํ•ด, ์ด ๊ฒŒ์ž„์ด NP ์™„์ „ํ•œ ๋ฌธ์ œ๋ผ๋Š” ๊ฒƒ์„ ์ฆ๋ช…ํ•˜๊ณ  ์žˆ๋‹ค. ZPG๋Š” 2์ฐจ์› ๊ฒฉ์ž ์ƒ์—์„œ ์ง„ํ–‰๋˜๋Š” ๋‹จ์ถ• ํ”Œ๋ ˆ์ด์–ด ํผ์ฆ ๊ฒŒ์ž„์œผ๋กœ, ๋ชจ๋ž˜, ๋ฐ”์œ„ ๋˜๋Š” ํ†ตํ–‰ ๊ฐ€๋Šฅํ•œ ์‚ฌ๊ฐํ˜•๋“ค๋กœ ๊ตฌ์„ฑ๋œ ์ •์‚ฌ๊ฐํ˜• ๊ทธ๋ฆฌ๋“œ ์œ„์—์„œ ์›€์ง์ด๋Š” ์ˆ˜๋„์Šน ์บ๋ฆญํ„ฐ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ•œ๋‹ค. ๊ฒŒ์ž„์˜ ๋ชฉํ‘œ๋Š” ๋ชจ๋“  ๋ชจ๋ž˜ ์‚ฌ๊ฐํ˜•์„ ํ†ต๊ณผ์‹œํ‚ค๋Š” ๊ฒƒ์ด๋‹ค. ZPG์˜ ๋ณต์žก์„ฑ ๋ถ„์„์€ NP ์™„์ „์„ฑ ์ฆ๋ช…์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์ž…๋ฐฉํ˜• ๊ทธ๋ž˜ํ”„์— ๋Œ€ํ•œ ํ•˜๋ฐ€ํ„ด ํšŒ๋กœ ๋ฌธ์ œ์™€ ZPG ๊ฐ„์˜ ๊ฐ์†Œ๋ฅผ ํ†ตํ•ด NP ์™„์ „์„ฑ์„ ์ฆ๋ช…ํ•œ๋‹ค. ์ž…๋ฐฉํ˜• ๊ทธ๋ž˜ํ”„

Computational Complexity Computer Science
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The Curious Case of Lemaitres Equation No. 24

์ด ๋…ผ๋ฌธ์€ ์ฒœ์ฒด๋ฌผ๋ฆฌํ•™ ์—ญ์‚ฌ์ƒ ์ค‘์š”ํ•œ ๋ฐœ๊ฒฌ ์ค‘ ํ•˜๋‚˜์ธ ์šฐ์ฃผ์˜ ํŒฝ์ฐฝ์— ๋Œ€ํ•œ ์ดˆ๊ธฐ ์—ฐ๊ตฌ๋ฅผ ๋‹ค๋ฃน๋‹ˆ๋‹ค. ํŠนํžˆ, ์ด ๋…ผ๋ฌธ์€ ๋ ˆ๋งˆํŠธ์˜ 1927๋…„ ๋…ผ๋ฌธ๊ณผ ๊ทธ ํ›„์˜ ์˜์–ด ๋ฒˆ์—ญ๋ณธ ๊ฐ„์˜ ์ฐจ์ด์ ์„ ์ง‘์ค‘์ ์œผ๋กœ ๋ถ„์„ํ•ฉ๋‹ˆ๋‹ค. ๋ ˆ๋งˆํŠธ๋Š” ์„ฑ์ง์ž๋กœ์„œ ์šฐ์ฃผ์˜ ์ฐฝ์กฐ์— ๋Œ€ํ•œ ์ž์‹ ์˜ ํŽธํ–ฅ์„ ์ธ์ •ํ•˜๋ฉด์„œ, ์šฐ์ฃผ ํŒฝ์ฐฝ์˜ ๋ฐœ๊ฒฌ์€ ๊ทธ์—๊ฒŒ ํŠน๋ณ„ํ•œ ์˜๋ฏธ๋ฅผ ๊ฐ€์กŒ์Šต๋‹ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ ๋ ˆ๋งˆํŠธ๋Š” 42๊ฐœ์˜ ์€ํ•˜๋“ค์˜ ๊ฑฐ๋ฆฌ์™€ ๋ฐฉ์‚ฌ์†๋„ ๋ฐ์ดํ„ฐ๋ฅผ ๋ถ„์„ํ•˜์—ฌ ์šฐ์ฃผ์˜ ํŒฝ์ฐฝ์„ ์ตœ์ดˆ๋กœ ์ œ์‹œํ–ˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ๋ ˆ๋งˆํŠธ์˜ ๋ฐœ๊ฒฌ์€ ์—๋“œ์œˆ ํ—ˆ๋ธ”์ด 1929๋…„์— ๋ฐœํ‘œํ•œ ์—ฐ๊ตฌ๋ณด๋‹ค ๋‘ ํ•ด ์•ž์„ฐ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋„๋ฆฌ ์•Œ๋ ค์ง€์ง€

Astrophysics Physics
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Harmonic analysis of spherical sampling in diffusion MRI

๋ณธ ๋…ผ๋ฌธ์€ ํ™•์‚ฐ MRI์—์„œ ๋‹ค์ค‘ ์…ธ ๊ตฌํ˜• ์ƒ˜ํ”Œ๋ง์˜ ํšจ์œจ์„ฑ์„ ์ด๋ก ์ ์œผ๋กœ ๋ถ„์„ํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด HYDI์™€ ์œ ์‚ฌํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ์ฆ์ง„์‹œํ‚ค๋ฉฐ ํ–ฅํ›„ ์ตœ์ ํ™” ์ „๋žต์„ ์ œ์‹œํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ์—ฐ๊ตฌ๋Š” ํ™•์‚ฐ MRI์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ๋ฐฉํ–ฅ ๋ถ„ํฌ ํ•จ์ˆ˜(ODF)์˜ ๊ณ„์‚ฐ ๋ฐฉ๋ฒ•๊ณผ ๊ทธ ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๋ฌธ์ œ์ ๋“ค์„ ์ง‘์ค‘์ ์œผ๋กœ ๋‹ค๋ฃฌ๋‹ค. 1. ODF์™€ ๊ตฌ๋ฉด ์กฐํ™” ๋ณ€ํ™˜(SH) ODF๋Š” ํ™•์‚ฐ ์‹ ํ˜ธ๋ฅผ ๋ฐฉํ–ฅ๋ณ„๋กœ ๋ถ„์„ํ•˜์—ฌ ๋‡Œ ๋‚ด ๋ฏธ์„ธ๊ตฌ์กฐ์˜ ๋ฐฉํ–ฅ์„ฑ์„ ํŒŒ์•…ํ•˜๋Š” ๋ฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ODF๋ฅผ ๊ณ„์‚ฐํ•˜๊ธฐ ์œ„ํ•ด ๊ตฌ๋ฉด ์กฐํ™” ๋ณ€ํ™˜(SH)์„ ์‚ฌ

Quantitative Biology Analysis Physics
A Note on the Group-theoretic Approach to Fast Matrix Multiplication

A Note on the Group-theoretic Approach to Fast Matrix Multiplication

: ์ด ๋…ผ๋ฌธ์€ ํ–‰๋ ฌ ๊ณฑ์…ˆ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๋ณต์žก๋„๋ฅผ ์ค„์ด๋Š” ๋ฐ ์žˆ์–ด ๊ทธ๋ฃน ์ด๋ก ์  ์ ‘๊ทผ ๋ฐฉ์‹์˜ ์ค‘์š”์„ฑ์— ๋Œ€ํ•ด ํƒ๊ตฌํ•˜๊ณ  ์žˆ๋‹ค. ์ „ํ†ต์ ์ธ ํ–‰๋ ฌ ๊ณฑ์…ˆ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ O(nยณ) ์‹œ๊ฐ„ ๋ณต์žก๋„๋ฅผ ๊ฐ€์ง„๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ๋ณด๋‹ค ํšจ์œจ์ ์ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์กด์žฌํ•˜๋ฉฐ, ๊ฐ€์žฅ ๋น ๋ฅธ ์•Œ๋ ค์ง„ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ Don Coppersmith์™€ Shmuel Winograd์— ์˜ํ•ด ์ œ์‹œ๋œ O(nยฒ.376) ์‹œ๊ฐ„ ๋ณต์žก๋„์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด๋‹ค. ๋…ผ๋ฌธ์—์„œ๋Š” ํ–‰๋ ฌ ๊ณฑ์…ˆ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋ฐ ์žˆ์–ด ๊ทธ๋ฃน ์ด๋ก ์  ์ ‘๊ทผ ๋ฐฉ์‹์˜ ์ค‘์š”์„ฑ์„ ๊ฐ•์กฐํ•œ๋‹ค. ํŠนํžˆ, 2003๋…„์— ์ฝ”ํ—จ(Cohn)๊ณผ ์šฐ๋งŒ์Šค(Umans)์ด

Symbolic Computation Mathematics Computer Science
Toda tau functions with quantum torus symmetries

Toda tau functions with quantum torus symmetries

: ๋ณธ ๋…ผ๋ฌธ์€ ์œ„์ƒ ๋ฌธ์ž์—ด์˜ ๋…น๋Š” ๊ฒฐ์ • ๋ชจ๋ธ๊ณผ 5์ฐจ์› ๊ฒŒ์ด์ง€ ์ด๋ก  ๊ฐ„์˜ ํ†ตํ•ฉ ๊ฐ€๋Šฅ์„ฑ์„ ํƒ๊ตฌํ•œ๋‹ค. ๋…น๋Š” ๊ฒฐ์ • ๋ชจ๋ธ์€ ๋ฌผ๋ฆฌํ•™์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ๋ชจ๋ธ๋กœ, ํŠนํžˆ ์œ„์ƒ ๋ฌธ์ž์—ด๊ณผ ๊ด€๋ จ๋œ ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃจ๋Š”๋ฐ ํ™œ์šฉ๋œ๋‹ค. ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋ชจ๋ธ์˜ ๋ถ„ํ•  ํ•จ์ˆ˜๊ฐ€ ํŠน์ • ์™ธ๋ถ€ ์ž ์žฌ๋ ฅ์— ์˜ํ•ด ๋ณ€ํ˜•๋  ๋•Œ Toda ๊ณ„์ธต์˜ ํƒ€์šฐ ํ•จ์ˆ˜์™€ ๋ณธ์งˆ์ ์œผ๋กœ ๋™์ผํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค. Toda ๊ณ„์ธต์€ ํ†ต์ƒ์ ์ธ ํŽ˜๋ฅด๋ฏธ์˜จ ์‹œ์Šคํ…œ์—์„œ ํŒŒ์ƒ๋œ ์ค‘์š”ํ•œ ์ˆ˜ํ•™์  ๊ตฌ์กฐ๋กœ, ์ด๋ก  ๋ฌผ๋ฆฌํ•™๊ณผ ์ˆ˜ํ•™์—์„œ ๋„๋ฆฌ ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ๋…ผ๋ฌธ์—์„œ๋Š” Toda ํƒ€์šฐ ํ•จ์ˆ˜์™€ ๋ณ€ํ˜•๋œ ๋ถ„ํ•  ํ•จ์ˆ˜ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ์–‘์ž ํ† 

HEP-TH Nonlinear Sciences Mathematics MATH-PH
Liber Mathematicae: A Web-Based Documentation and Collaboration Project   for Mathematics

Liber Mathematicae: A Web-Based Documentation and Collaboration Project for Mathematics

: ๋ณธ ๋…ผ๋ฌธ์€ ๊ธฐ์กด์˜ ์ˆ˜ํ•™ ์ถœํŒ ๋ฐฉ์‹์— ๋Œ€ํ•œ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ณ ์ž ํ•˜๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ „ํ†ต์ ์œผ๋กœ ์ˆ˜ํ•™ ์ง€์‹์€ ์ฑ…์ด๋‚˜ ํ•™์ˆ ์ง€์— ์ธ์‡„ ๋งค์ฒด๋กœ ์ถœํŒ๋˜์–ด ์™”์ง€๋งŒ, ์ธํ„ฐ๋„ท์ด ๋“ฑ์žฅํ•จ์— ๋”ฐ๋ผ ์ƒˆ๋กœ์šด ์ถœํŒ ๋ฐฉ์‹์˜ ๊ฐ€๋Šฅ์„ฑ์ด ์—ด๋ ธ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๋Œ€๋ถ€๋ถ„์˜ ์˜จ๋ผ์ธ ์ˆ˜ํ•™ ์ถœํŒ๋ฌผ์€ ์—ฌ์ „ํžˆ ๋ฏธ๋””์–ด์˜ ์ž ์žฌ๋ ฅ์„ ์ถฉ๋ถ„ํžˆ ํ™œ์šฉํ•˜์ง€ ๋ชปํ•˜๊ณ  ์žˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, arXiv๋Š” PDF ํ˜•์‹์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ฌธ์„œ๋ฅผ ํ˜ธ์ŠคํŒ…ํ•˜๋Š”๋ฐ, ์ด๋Š” ์ธ์‡„์šฉ ๋ฌธ์„œ ์ „์†ก ๋ฐ ํ˜•์‹ ์œ ์ง€์— ์ตœ์ ํ™”๋˜์–ด ์žˆ์ง€๋งŒ, ์ˆ˜ํ•™์  ํ‘œํ˜„์„ ํ™•์žฅ ๊ฐ€๋Šฅํ•˜๊ฑฐ๋‚˜ ๋ณต์‚ฌ ๊ฐ€๋Šฅํ•œ ํ˜•ํƒœ๋กœ ํ‘œ์‹œํ•˜์ง€๋Š” ๋ชปํ•œ๋‹ค. ์œ„ํ‚คํ”ผ๋””์•„์™€ ๊ฐ™

Digital Libraries Mathematics Computer Science
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The Stochastic Universe: Professor A.M. Mathais 75th Birthday

A.M. ๋งˆํƒ€์ด ๊ต์ˆ˜๋Š” 1970๋…„๋Œ€๋ถ€ํ„ฐ ํ™•๋ฅ  ๋ฐ ํ†ต๊ณ„๋ฅผ ํ™œ์šฉํ•œ ๋ฌผ๋ฆฌํ•™ ์—ฐ๊ตฌ ํ”„๋กœ๊ทธ๋žจ์„ ์‹œ์ž‘ํ•˜์—ฌ, ์ •๋ณด ์ด๋ก ๊ณผ ์—”ํŠธ๋กœํ”ผ ํ•จ์ˆ˜, ์ •๊ทœ ๋ถ„ํฌ, ๊ทธ๋ฆฌ๊ณ  ์ผ๋ฐ˜ํ™”๋œ ํ•˜์ดํผ์ง€์ˆ˜ ํ•จ์ˆ˜ ๋“ฑ์„ ๋‹ค๋ฃฌ ์„ธ ๊ถŒ์˜ ๋‹จํ–‰๋ณธ์„ ์ถœํŒํ–ˆ์Šต๋‹ˆ๋‹ค. ๊ทธ์˜ ์—ฐ๊ตฌ๋Š” ์ˆ˜ํ•™๊ณผ ๋ฌผ๋ฆฌํ•™ ๊ฐ„์˜ ๊นŠ์€ ์—ฐ๊ด€์„ฑ์„ ๊ฐ•์กฐํ•˜๋ฉฐ, ํŠนํžˆ ํ†ต๊ณ„๋ฌผ๋ฆฌํ•™์—์„œ ์•„์ง ํ˜•์‹์  ๊ณต๋ฆฌ๊ณ„๊ฐ€ ๊ฐœ๋ฐœ๋˜์ง€ ๋ชปํ•œ ๋ถ€๋ถ„์— ์ฃผ๋ชฉํ•ฉ๋‹ˆ๋‹ค. ์ˆ˜ํ•™๊ณผ ๋ฌผ๋ฆฌํ•™์˜ ์ƒํ˜ธ์ž‘์šฉ์€ ์˜ค๋žœ ์—ญ์‚ฌ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ์ผ๋ฐ˜ ์ƒ๋Œ€์„ฑ์ด๋ก ์€ ๋ฆฌ๋งŒ ๊ธฐํ•˜ํ•™์„ ๋ฐ”ํƒ•์œผ๋กœ ํ•˜๋ฉฐ, ์–‘์ž์—ญํ•™์€ ํ•จ์ˆ˜ ๋ถ„์„์— ํฌ๊ฒŒ ์˜์กดํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์•„์ธ์Šˆํƒ€์ธ์€ 1915๋…„์—

Physics
$sigma$-homogeneity of Borel sets

$sigma$-homogeneity of Borel sets

๋ณธ ๋…ผ๋ฌธ์€ ๋ณด๋  ์ง‘ํ•ฉ์˜ ๋™์งˆ์„ฑ์— ๋Œ€ํ•œ ๊นŠ์ด ์žˆ๋Š” ์ดํ•ด๋ฅผ ์ œ๊ณตํ•˜๋ฉฐ, ํŠนํžˆ ์นธํ† ์–ด ์ง‘ํ•ฉ C ๋‚ด์—์„œ ๋ณด๋  ์ง‘ํ•ฉ์˜ ๊ตฌ์กฐ์™€ ๊ทธ ์„ฑ์งˆ์„ ํƒ๊ตฌํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” h ๋™์งˆ ๊ณต๊ฐ„์ด๋ผ๋Š” ๊ฐœ๋…์„ ์ค‘์‹ฌ์œผ๋กœ ์ง„ํ–‰๋˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๋ณด๋  ์ง‘ํ•ฉ์˜ ๋™์งˆ์„ฑ์„ ๋ถ„์„ํ•˜๊ณ  ์žˆ๋‹ค. 1. ๊ธฐ๋ณธ ๊ฐœ๋… ๋ฐ ์ •์˜ ๋…ผ๋ฌธ์—์„œ๋Š” ๋จผ์ € ๋ช‡ ๊ฐ€์ง€ ์ค‘์š”ํ•œ ์šฉ์–ด๋ฅผ ์ •์˜ํ•œ๋‹ค: ์นธํ† ์–ด ์ง‘ํ•ฉ (C) : ์ด์‚ฐ์  ์ˆ˜์™€ ํ•ฉ๋ฆฌ์  ์ˆ˜๋ฅผ ๊ฐ๊ฐ P, Q๋กœ ํ‘œ๊ธฐํ•˜๋ฉฐ, ์‹ค์ˆ˜ R์€ P์™€ Q์˜ ํ•ฉ์ง‘ํ•ฉ์œผ๋กœ ํ‘œํ˜„๋œ๋‹ค. h ๋™์งˆ ๊ณต๊ฐ„ : 0์ฐจ์› ํ† ํด๋กœ์ง€ ๊ณต๊ฐ„ X๊ฐ€ ๋ชจ๋“  ๋น„๊ณตํ—ˆ ํด๋กœํ”„ ์—ด๋ถ„ U์— ๋Œ€ํ•ด U์™€ X๊ฐ€ ํ™ˆ๋ชจ๋ฅดํ”ฝ(h

Mathematics
Echo-based measurement of the speed of sound

Echo-based measurement of the speed of sound

์ด ๋…ผ๋ฌธ์€ ๊ฐ„๋‹จํ•œ ์‹คํ—˜ ๋ฐฉ๋ฒ•์œผ๋กœ ์Œ์†์„ ์ธก์ •ํ•˜๋Š” ๊ณผ์ •๊ณผ ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ์ƒ์„ธํžˆ ์„ค๋ช…ํ•˜๊ณ  ์žˆ๋‹ค. ์—ฐ๊ตฌ์˜ ํ•ต์‹ฌ์€ ํ’์„ ์ด ํ„ฐ์ง€๋Š” ์ˆœ๊ฐ„๋ถ€ํ„ฐ ์—์ฝ”๊ฐ€ ๋„์ฐฉํ•  ๋•Œ๊นŒ์ง€์˜ ์‹œ๊ฐ„ ์ฐจ์ด๋ฅผ ํ†ตํ•ด ์Œ์†์„ ๊ณ„์‚ฐํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์‹คํ—˜ ๋ฐฉ๋ฒ• ๋ฐ ์žฅ๋น„ ์‹คํ—˜์—์„œ๋Š” Audacity๋ผ๋Š” ์˜ค๋””์˜ค ํŽธ์ง‘ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํ’์„ ์ด ํ„ฐ์ง€๋Š” ์ˆœ๊ฐ„๊ณผ ์—์ฝ”๊ฐ€ ๋„์ฐฉํ•œ ์‹œ์ ์„ ์ •ํ™•ํžˆ ์ธก์ •ํ•˜์˜€๋‹ค. ํ’์„ ์€ ํฐ ํ‰ํ‰ํ•œ ๋ฒฝ์—์„œ 45.72m ๋–จ์–ด์ง„ ๊ณณ์—์„œ ํ„ฐ์กŒ์œผ๋ฉฐ, ์‹คํ—˜ ํ™˜๊ฒฝ์˜ ์˜จ๋„๋Š” 18.3ยฐC, ์Šต๋„๋Š” 16.1%, ํ˜„์ง€ ๊ณ ๋„๋Š” 360m์˜€๋‹ค. ๊ฒฐ๊ณผ ๋ถ„์„ ์‹คํ—˜์—์„œ๋Š” ์„ธ ๊ฐœ์˜ ํ’์„ ์„ ์‚ฌ์šฉํ•˜์—ฌ ์ธก์ •์„

Physics
Upside Down Magic, Bimagic, Palindromic Squares and Pythagoras Theorem   on a Palindromic Day - 11.02.2011

Upside Down Magic, Bimagic, Palindromic Squares and Pythagoras Theorem on a Palindromic Day - 11.02.2011

๋ณธ ๋…ผ๋ฌธ์€ ์ˆ˜ํ•™์  ๊ตฌ์กฐ์™€ ์˜ˆ์ˆ ์  ๋””์ž์ธ ์‚ฌ์ด์˜ ํฅ๋ฏธ๋กœ์šด ์—ฐ๊ฒฐ ๊ณ ๋ฆฌ๋ฅผ ์ œ์‹œํ•˜๋ฉฐ, ํŠนํžˆ ๋งˆ์ˆ  ์ •์‚ฌ๊ฐํ˜•๊ณผ ํ”ผํƒ€๊ณ ๋ผ์Šค ์ •๋ฆฌ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ํƒ๊ตฌํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” 2011๋…„ 2์›” 11์ผ(11.02.2011)์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜์—ฌ, ์ด ๋‚ ์งœ์—์„œ ์‚ฌ์šฉ๋˜๋Š” ์ˆซ์ž๋“ค(0, 1, 2)๋งŒ์„ ํ™œ์šฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ ํฌ๊ธฐ์˜ ๋งˆ์ˆ  ์ •์‚ฌ๊ฐํ˜•๊ณผ ๋น„๋งˆ์ˆ  ์ •์‚ฌ๊ฐํ˜•์„ ์ƒ์„ฑํ•˜์˜€์Šต๋‹ˆ๋‹ค. ํ”ผํƒ€๊ณ ๋ผ์Šค ์ •๋ฆฌ์™€์˜ ์—ฐ๊ด€์„ฑ ๋…ผ๋ฌธ์—์„œ๋Š” ํŠนํžˆ 3x3, 4x4, 5x5 ํฌ๊ธฐ์˜ ๋งˆ์ˆ  ์ •์‚ฌ๊ฐํ˜•์ด ํ”ผํƒ€๊ณ ๋ผ์Šค ์ •๋ฆฌ๋ฅผ ๋งŒ์กฑํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ–ˆ์Šต๋‹ˆ๋‹ค. ๊ฐ ๋ธ”๋ก์˜ ํ•ฉ์€ S1 33 (3x3), S14ร—4 444

Mathematics
On the impossibility of non-static quantum bit commitment between two   parties

On the impossibility of non-static quantum bit commitment between two parties

๋ณธ ๋…ผ๋ฌธ์€ ์–‘์ž ๋น„ํŠธ ์ปค๋ฐ‹๋จผํŠธ(QBC) ํ”„๋กœํ† ์ฝœ์˜ ๋ณด์•ˆ์„ฑ๊ณผ ํ•œ๊ณ„์— ๋Œ€ํ•ด ์‹ฌ๋„ ์žˆ๊ฒŒ ๋ถ„์„ํ•ฉ๋‹ˆ๋‹ค. QBC๋Š” ์•”ํ˜ธํ™”์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š”๋ฐ, ์ด๋Š” ์ „์†ก์ž๊ฐ€ ์ˆ˜์‹ ์ž์—๊ฒŒ 0 ๋˜๋Š” 1 ์ค‘ ํ•˜๋‚˜์˜ ๋น„ํŠธ๋ฅผ ์•ฝ์†ํ•˜๋Š” ๊ณผ์ •์ž…๋‹ˆ๋‹ค. ์ด ํ”„๋กœ์„ธ์Šค๋Š” ๋ฐ”์ธ๋”ฉ ์†์„ฑ๊ณผ ๊ฐ์ถ”๋Š” ์†์„ฑ์„ ๋™์‹œ์— ์ถฉ์กฑํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. ๋ฐ”์ธ๋”ฉ ์†์„ฑ์€ ์ „์†ก์ž๊ฐ€ ์ปค๋ฐ‹๋จผํŠธ ๋‹จ๊ณ„ ์ดํ›„์— ์•ฝ์†ํ•œ ๋น„ํŠธ ๊ฐ’์„ ๋ณ€๊ฒฝํ•  ์ˆ˜ ์—†์Œ์„ ์˜๋ฏธํ•˜๋ฉฐ, ๊ฐ์ถ”๋Š” ์†์„ฑ์€ ์ˆ˜์‹ ์ž์—๊ฒŒ ์ปค๋ฐ‹๋จผํŠธ ๋‹จ๊ณ„ ์ด์ „์—๋Š” ์•ฝ์†๋œ ๋น„ํŠธ ๊ฐ’์ด ์•Œ๋ ค์ง€์ง€ ์•Š์Œ์„ ๋‚˜ํƒ€๋ƒ…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋‘ ๊ฐ€์ง€ ์†์„ฑ์„ ๋™์‹œ์— ์ถฉ์กฑํ•˜๋Š” ๊ณ ์ „์  ๋น„ํŠธ ์ปค๋ฐ‹๋จผํŠธ ํ”„๋กœํ† ์ฝœ

Quantum Physics Computer Science Cryptography and Security
A Short Note on Fuzzy Characteristic Interior Ideals of   Po-Gamma-Semigroups

A Short Note on Fuzzy Characteristic Interior Ideals of Po-Gamma-Semigroups

: ๋ณธ ๋…ผ๋ฌธ์€ ๋ถˆํ™•์‹คํ•œ ๋ถ€๋ถ„ ์ˆœ์„œ ฮ“ ๋ฐ˜๊ตฐ(Po ฮ“ semigroup)์ด๋ผ๋Š” ๋ณต์žกํ•œ ์ˆ˜ํ•™ ๊ตฌ์กฐ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜๋ฉฐ, ํŠนํžˆ ์ด ๊ตฌ์กฐ์—์„œ์˜ ํŠน์„ฑ ์ด์ƒ์ (fuzzy characteristic ideal) ๊ฐœ๋…์„ ํƒ๊ตฌํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๋Š” ์ถ”์ƒ ๋Œ€์ˆ˜ํ•™๊ณผ ๋ถˆํ™•์‹ค์„ฑ ์ด๋ก  ๊ฐ„์˜ ๊ต์ฐจ์ ์—์„œ ์ค‘์š”ํ•œ ์˜๋ฏธ๋ฅผ ๊ฐ–๋Š”๋‹ค. 1. ๊ธฐ๋ณธ ์ •์˜์™€ ๋ฐฐ๊ฒฝ ๋…ผ๋ฌธ์€ ์‹œ์ž‘ ๋ถ€๋ถ„์—์„œ ฮ“ ๋ฐ˜๊ตฐ์ด๋ผ๋Š” ๊ฐœ๋…์„ ์†Œ๊ฐœํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด Po ฮ“ semigroups์˜ ๊ธฐ์ดˆ์ ์ธ ์„ฑ์งˆ๋“ค์„ ์„ค๋ช…ํ•œ๋‹ค. ์—ฌ๊ธฐ์„œ ์ฃผ๋ชฉํ•  ์ ์€, ์ด๋“ค ๊ตฌ์กฐ๊ฐ€ ์ผ๋ฐ˜์ ์ธ ๋ฐ˜๊ตฐ๋ณด๋‹ค ๋” ๋ณต์žกํ•œ ์—ฐ์‚ฐ ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง„๋‹ค๋Š” ๊ฒƒ์ด

Mathematics
Finding Supernova Ia Progenitors with the Chandra X-ray Observatory

Finding Supernova Ia Progenitors with the Chandra X-ray Observatory

: ๋ณธ ์—ฐ๊ตฌ๋Š” Ia ์ดˆ์‹ ์„ฑ์˜ ์›์ฒœ ํƒ์ƒ‰์ด๋ผ๋Š” ๋ณต์žกํ•œ ๊ณผ์ œ๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด ์ฐจ๋“œ๋ผ X ๋ ˆ์ด ๊ด€์ธก์†Œ์˜ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์˜€์Šต๋‹ˆ๋‹ค. Ia ์ดˆ์‹ ์„ฑ์€ ์šฐ์ฃผ ๊ฑฐ๋ฆฌ ๊ณ„์ธก์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ๋ฐ์€ ํ•ญ์„ฑ ํญ๋ฐœ๋กœ, ์ฒ ์˜ ์ฃผ์š” ๊ณต๊ธ‰์›์œผ๋กœ์„œ ์€ํ•˜ ํ˜•์„ฑ๊ณผ ์ง„ํ™”์— ๋Œ€ํ•œ ์ดํ•ด์—๋„ ํ•„์ˆ˜์ ์ž…๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ์ค‘์š”ํ•œ ์ฒœ์ฒด๋“ค์˜ ์›์ฒœ์ธ ๋ฐฑ์ƒ‰์™œ์„ฑ์˜ ์ •ํ™•ํ•œ ํŠน์„ฑ์€ ์—ฌ์ „ํžˆ ์ˆ˜์ˆ˜๊ป˜๋ผ๋กœ ๋‚จ์•„ ์žˆ์Šต๋‹ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‘ ๊ฐ€์ง€ ์ฃผ๋œ ์‹œ๋‚˜๋ฆฌ์˜ค, ์ฆ‰ ๋‹จ์ผ ๋ฐฑ์ƒ‰์™œ์„ฑ(SD) ์‹œ๋‚˜๋ฆฌ์˜ค์™€ ์ด์ค‘ ๋ฐฑ์ƒ‰์™œ์„ฑ(DD) ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๋น„๊ตํ•˜์˜€์Šต๋‹ˆ๋‹ค. SD ์‹œ๋‚˜๋ฆฌ์˜ค๋Š” ๋ฐฑ์ƒ‰์™œ์„ฑ์ด ๋™๋ฐ˜์„ฑ๋ณ„๋กœ๋ถ€ํ„ฐ ๋ฌผ์งˆ์„ ํก

Astrophysics
Pulsar Magnetosphere

Pulsar Magnetosphere

: ์ด ๋…ผ๋ฌธ์€ ์ค‘์„ฑ์ž ๋ณ„ ์ฃผ๋ณ€์˜ ์ž๊ธฐ๊ถŒ ํ˜•์„ฑ์„ ์ดํ•ดํ•˜๋Š”๋ฐ ์žˆ์–ด ์ค‘์š”ํ•œ ์ด๋ก ์  ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ํŠนํžˆ, ํšŒ์ „ํ•˜๋Š” ์ „๋„ ๊ตฌ๊ฐ€ ์ง„๊ณต์—์„œ ์–ด๋–ป๊ฒŒ ์ž๊ธฐ๊ถŒ์„ ํ˜•์„ฑํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ๊ธฐ์กด ๋ชจ๋ธ์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ณ ์ž ํ•œ๋‹ค. 1. ์ด๋ก ์  ๋ฐฐ๊ฒฝ Schwinger Field์™€ ์–‘์ž ์ „์ž ์—ญํ•™(QED) ๊ณผ์ • : ์ด ๋…ผ๋ฌธ์€ ํšŒ์ „ํ•˜๋Š” ์ž๊ธฐํ™”๋œ ์ „๋„ ๊ตฌ๊ฐ€ ์ง„๊ณต์—์„œ ์ „ํ•˜๋ฅผ ์ƒ์„ฑํ•˜๋Š” ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์„ค๋ช…ํ•œ๋‹ค. ์ด๋Š” Schwinger field์— ๋„๋‹ฌํ•˜๊ธฐ ์ „์˜ 1์ฐจ ์ˆ˜์ค€ QED ๊ณผ์ •์ด๋‚˜ ํ‘œ๋ฉด์—์„œ ์ „ํ•˜ ์ž…์ž๋ฅผ ๋Œ์–ด๋‹น๊ธฐ๋Š” ํ˜„์ƒ์œผ๋กœ ์ธํ•ด ๋ฐœ์ƒํ•  ์ˆ˜

Astrophysics
Two Applications of Desargues Theorem

Two Applications of Desargues Theorem

1. ๋ฐ์‚ฌ๋ฅด๊ทธ์˜ ์ •๋ฆฌ์™€ ๊ทธ ์ฆ๋ช… ๋ฐ์‚ฌ๋ฅด๊ทธ์˜ ์ •๋ฆฌ๋Š” ๋‘ ์‚ผ๊ฐํ˜•์ด ์›์ ์— ๋Œ€ํ•ด ์œ„์น˜ํ•  ๋•Œ, ๋Œ€์‘ํ•˜๋Š” ๋ณ€์˜ ๊ต์ฐจ์ ์ด ์ผ์ง์„  ์œ„์— ๋†“์ธ๋‹ค๋Š” ๊ฒƒ์„ ๋งํ•œ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ์ •๋ฆฌ๋ฅผ ์ฆ๋ช…ํ•˜๊ธฐ ์œ„ํ•ด ๋ฉ”๋„ค๋ผ์šฐ์Šค ์ •๋ฆฌ์™€ ํ•จ๊ป˜ ์‚ฌ์šฉํ•œ๋‹ค. ์ฆ๋ช… ๊ณผ์ •์—์„œ ์ฃผ์–ด์ง„ ๋‘ ์‚ผ๊ฐํ˜• ABC์™€ A1B1C1์˜ ๋ณ€๋“ค์ด ๊ต์ฐจํ•˜๋Š” ์  N, M, P๊ฐ€ ์ผ์ง์„  ์œ„์— ๋†“์ด๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋ฐ์‚ฌ๋ฅด๊ทธ์˜ ์ •๋ฆฌ๊ฐ€ ์„ฑ๋ฆฝํ•จ์„ ์ฆ๋ช…ํ•œ๋‹ค. 2. ํ˜ธํ•™์  ์‚ผ๊ฐํ˜•๊ณผ ๊ทธ ์‘์šฉ ํ˜ธํ•™์ ์ธ ์‚ผ๊ฐํ˜•์€ ์„œ๋กœ ๋Œ€์‘ํ•˜๋Š” ๋ณ€๋“ค์ด ์ผ์ง์„  ์œ„์— ๊ต์ฐจ์ ์ด ์žˆ๋Š” ์‚ผ๊ฐํ˜•๋“ค์„ ๋งํ•œ๋‹ค. ๋…ผ๋ฌธ์—์„œ๋Š” ์™„์ „ํ•œ ์‚ฌ๊ฐ

Mathematics
Reply to Comment on `Formation of bound states of electrons in   spherically symmetric oscillations of plasma

Reply to Comment on `Formation of bound states of electrons in spherically symmetric oscillations of plasma

: ์ด ๋…ผ๋ฌธ์€ ํ”Œ๋ผ์ฆˆ๋งˆ ๋ฌผ๋ฆฌํ•™์—์„œ ์ค‘์š”ํ•œ ๋ฌธ์ œ์ธ ์•ˆ์ •์ ์ธ ์ž์—ฐ ํ”Œ๋ผ์ฆˆ๋งˆ ๊ตฌ์กฐ์˜ ์ด๋ก ์  ๋ชจ๋ธ ๋ถ€์กฑ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์‹œํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, ํ”Œ๋ผ์ฆˆ๋ชจ์ด๋“œ๋ผ๋Š” ๊ตฌ์กฐ์—์„œ ์ „์ž ๊ธฐ์ฒด์˜ ๋ฐฉ์‚ฌํ˜• ์ง„๋™์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ํ˜„์ƒ์€ ์–‘์ž ๋ฐ ๊ณ ์ „ ํ”„๋ ˆ์ž„์›Œํฌ ๋ชจ๋‘์—์„œ ๋‹ค๋ฃจ์–ด์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๋…ผ๋ฌธ์˜ ํ•ต์‹ฌ ๋‚ด์šฉ ์ค‘ ํ•˜๋‚˜๋Š” ์ด์˜จ ์ŒํŒŒ ๊ตํ™˜์„ ํ†ตํ•ด ์ „์ž์˜ ๊ฒฐํ•ฉ ์ƒํƒœ ํ˜•์„ฑ ๊ฐ€๋Šฅ์„ฑ์„ ๋ถ„์„ํ•œ ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ €์ž๋Š” ์ด๋ฅผ ๊ณ ์ „ ์ „์ž๊ธฐํ•™์˜ ํ”„๋ ˆ์ž„์›Œํฌ์—์„œ ๋‹ค๋ฃจ์—ˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์€ ํ”Œ๋ผ์ฆˆ๋งˆ ๋ฌผ๋ฆฌํ•™์—์„œ ์ค‘์š”ํ•œ ํ˜„์ƒ์„ ์ดํ•ดํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋ 

Physics
Bandwidth and pathwidth of three-dimensional grids

Bandwidth and pathwidth of three-dimensional grids

๋ณธ ๋…ผ๋ฌธ์€ ๊ทธ๋ž˜ํ”„ ์ด๋ก ์—์„œ ์ค‘์š”ํ•œ ๋‘ ๊ฐ€์ง€ ํŒŒ๋ผ๋ฏธํ„ฐ์ธ ๋ฐด๋“œ ๋„ˆ๋น„์™€ ๊ฒฝ๋กœ ๋„ˆ๋น„์— ๋Œ€ํ•œ ์‹ฌ๋„ ์žˆ๋Š” ์—ฐ๊ตฌ๋ฅผ ์ œ๊ณตํ•˜๋ฉฐ, ํŠนํžˆ 3์ฐจ์› ๊ทธ๋ฆฌ๋“œ์˜ ๊ฒฝ์šฐ์— ๋Œ€ํ•ด ์ง‘์ค‘์ ์œผ๋กœ ๋‹ค๋ฃฌ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์ปดํ“จํ„ฐ ๊ณผํ•™ ๋ถ„์•ผ์—์„œ ๋„๋ฆฌ ํ™œ์šฉ๋˜๋Š” ๊ทธ๋ž˜ํ”„ ์ด๋ก ์˜ ์ค‘์š”ํ•œ ์ธก๋ฉด์„ ํƒ๊ตฌํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ๊ทธ ๊ฒฐ๊ณผ๋Š” VLSI ๋ ˆ์ด์•„์›ƒ๊ณผ ๋ณ‘๋ ฌ ์ปดํ“จํŒ… ๋“ฑ ๋‹ค์–‘ํ•œ ์‘์šฉ ๋ถ„์•ผ์— ์ ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ฐด๋“œ ๋„ˆ๋น„์™€ ๊ฒฝ๋กœ ๋„ˆ๋น„์˜ ์ •์˜ ๋ฐ ๊ด€๋ จ์„ฑ ๋ฐด๋“œ ๋„ˆ๋น„์™€ ๊ฒฝ๋กœ ๋„ˆ๋น„๋Š” ๊ทธ๋ž˜ํ”„ ์ด๋ก ์—์„œ ์ค‘์š”ํ•œ ๊ฐœ๋…์œผ๋กœ, ๊ฐ๊ฐ ๊ทธ๋ž˜ํ”„์˜ ๊ตฌ์กฐ๋ฅผ ์ธก์ •ํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋œ๋‹ค. ๋ฐด๋“œ ๋„ˆ๋น„๋Š” ๊ทธ๋ž˜ํ”„์˜ ์ •์  ์ง‘ํ•ฉ์„ ์ผ๋ ฌ๋กœ ๋ฐฐ์—ดํ–ˆ์„

Computer Science Discrete Mathematics
DARC: Drum accompaniment generation with fine-grained rhythm control

DARC: Drum accompaniment generation with fine-grained rhythm control

DARC๋Š” ํ˜„์žฌ ์Œ์•… ์ƒ์„ฑ ์—ฐ๊ตฌ์—์„œ ๋‘๋“œ๋Ÿฌ์ง„ ๋ฌธ์ œ์ , ์ฆ‰ โ€˜๊ตฌ์กฐ์  ์ œ์–ด์™€ ์Šคํƒ€์ผ์  ์ž์œ  ์‚ฌ์ด์˜ ํŠธ๋ ˆ์ด๋“œ์˜คํ”„โ€™๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ํ•ด์†Œํ•œ๋‹ค๋Š” ์ ์—์„œ ์˜๋ฏธ๊ฐ€ ํฌ๋‹ค. ๊ธฐ์กด ์Šคํ…œโ€‘ํˆฌโ€‘์Šคํ…œ ์ ‘๊ทผ๋ฒ•์€ ์•…๊ธฐ ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ์„ ๋ฐ˜์˜ํ•ด ์กฐํ™”๋กœ์šด ๋ฐ˜์ฃผ๋ฅผ ๋งŒ๋“ค ์ˆ˜ ์žˆ์ง€๋งŒ, ๋ฆฌ๋“ฌ ํŒจํ„ด์„ ์‚ฌ์šฉ์ž๊ฐ€ ์ง์ ‘ ์ง€์ •ํ•˜๊ฑฐ๋‚˜ ๋ฏธ์„ธ ์กฐ์ •ํ•˜๊ธฐ๋Š” ์–ด๋ ค์› ๋‹ค. ๋ฐ˜๋Œ€๋กœ timbreโ€‘transfer ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์€ ์‚ฌ์šฉ์ž๊ฐ€ ์ง์ ‘ ๋งŒ๋“  ๋น„ํŠธ๋ฐ•์‹ฑยทํƒญํ•‘ ๊ฐ™์€ ๋ฆฌ๋“ฌ ์‹œํ€€์Šค๋ฅผ ์ž…๋ ฅ์œผ๋กœ ๋ฐ›์•„๋“ค์ผ ์ˆ˜ ์žˆ์ง€๋งŒ, ๊ทธ ๋ฆฌ๋“ฌ์ด ํ˜„์žฌ ์ง„ํ–‰ ์ค‘์ธ ํ™”์„ฑยท๋ฉœ๋กœ๋””์™€ ์–ผ๋งˆ๋‚˜ ์ž˜ ๋งž๋Š”์ง€๋Š” ๋ณด์žฅ๋˜์ง€ ์•Š๋Š”๋‹ค. DARC๋Š” ์ด ๋‘

Computer Science Sound
The Machine Learning Canvas: Empirical Findings on Why Strategy Matters More Than AI Code Generation

The Machine Learning Canvas: Empirical Findings on Why Strategy Matters More Than AI Code Generation

๋ณธ ๋…ผ๋ฌธ์€ ํ˜„์žฌ AI ๊ธฐ๋ฐ˜ ์ฝ”๋”ฉ ์–ด์‹œ์Šคํ„ดํŠธ๊ฐ€ ๊ฐœ๋ฐœ ํ˜„์žฅ์—์„œ ๋„๋ฆฌ ํ™œ์šฉ๋˜๋Š” ์ƒํ™ฉ์—์„œ, ์™œ ์—ฌ์ „ํžˆ ML ํ”„๋กœ์ ํŠธ์˜ ์„ฑ๊ณต๋ฅ ์ด ๋‚ฎ์€์ง€๋ฅผ ์ฒด๊ณ„์ ์œผ๋กœ ํƒ๊ตฌํ•œ๋‹ค. ์—ฐ๊ตฌ์ž๋Š” ๋จผ์ € ๊ธฐ์กด ๋ฌธํ—Œ์„ ๊ฒ€ํ† ํ•˜์—ฌ ์„ฑ๊ณต ์š”์ธ์„ ์ „๋žต, ํ”„๋กœ์„ธ์Šค, ์ƒํƒœ๊ณ„, ์ง€์› ๋„ค ๊ฐ€์ง€ ์ฐจ์›์œผ๋กœ ๊ตฌ๋ถ„ํ•˜๊ณ , ์ด๋ฅผ ์‹œ๊ฐํ™”ํ•œ โ€˜๋จธ์‹ ๋Ÿฌ๋‹ ์บ”๋ฒ„์Šคโ€™๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์„ค๋ฌธ ์„ค๊ณ„๋Š” ๊ฐ ์ฐจ์›์„ ์ •๋Ÿ‰ํ™”ํ•  ์ˆ˜ ์žˆ๋Š” ํ•ญ๋ชฉ๋“ค์„ ํฌํ•จํ–ˆ์œผ๋ฉฐ, 150๋ช…์˜ ๋ฐ์ดํ„ฐ ๊ณผํ•™์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ 6๊ฐœ์›”์— ๊ฑธ์ณ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜์˜€๋‹ค. ํ†ต๊ณ„ ๋ถ„์„์—๋Š” ๊ตฌ์กฐ๋ฐฉ์ •์‹ ๋ชจ๋ธ๋ง(SEM)์„ ํ™œ์šฉํ•ด ์š”์ธ ๊ฐ„ ์ธ๊ณผ๊ด€๊ณ„๋ฅผ ๊ฒ€์ฆํ–ˆ์œผ๋ฉฐ, ฮฒ๊ฐ’๊ณผ p๊ฐ’์„

Learning
No Image

Yukthi Opus: A Multi-Chain Hybrid Metaheuristic for Large-Scale NP-Hard Optimization

Yukthi Opus(YO)๋Š” ๋ฉ”ํƒ€ํœด๋ฆฌ์Šคํ‹ฑ ์„ค๊ณ„์—์„œ ํ”ํžˆ ๋ฐœ์ƒํ•˜๋Š” โ€˜์ „์—ญ ํƒ์ƒ‰๊ณผ ์ง€์—ญ ํƒ์ƒ‰์˜ ๊ท ํ˜•โ€™ ๋ฌธ์ œ๋ฅผ ์„ธ ๋‹จ๊ณ„ ๊ตฌ์กฐ๋กœ ๋ช…ํ™•ํžˆ ๊ตฌ๋ถ„ํ•จ์œผ๋กœ์จ ํ•ด๊ฒฐ์ฑ…์„ ์ œ์‹œํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ๋ ˆ์ด์–ด์ธ MCMC ๊ธฐ๋ฐ˜ ์ „์—ญ ํƒ์ƒ‰์€ ํ™•๋ฅ ์  ์ „์ด ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์ด์šฉํ•ด ํƒ์ƒ‰ ๊ณต๊ฐ„์„ ๊ท ๋“ฑํ•˜๊ฒŒ ์ƒ˜ํ”Œ๋งํ•˜๊ณ , ์ดˆ๊ธฐ ๋ฒˆ์ธ ๋‹จ๊ณ„์—์„œ ์ถฉ๋ถ„ํžˆ ๋‹ค์–‘ํ•œ ์˜์—ญ์„ ๋ฐฉ๋ฌธํ•˜๋„๋ก ์„ค๊ณ„๋˜์—ˆ๋‹ค. ์ด๋•Œ ๋ธ”๋ž™๋ฆฌ์ŠคํŠธ๋ฅผ ๋„์ž…ํ•ด ์ด๋ฏธ ๋‚ฎ์€ ํ’ˆ์งˆ์„ ๋ณด์ธ ์ง€์—ญ์„ ์žฌ๋ฐฉ๋ฌธํ•˜์ง€ ์•Š๊ฒŒ ํ•จ์œผ๋กœ์จ ๋ถˆํ•„์š”ํ•œ ๊ณ„์‚ฐ์„ ํฌ๊ฒŒ ์ ˆ๊ฐํ•œ๋‹ค๋Š” ์ ์ด ๋ˆˆ์— ๋ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ ๋ ˆ์ด์–ด๋Š” ์ „ํ†ต์ ์ธ ํƒ์š•์  ๋กœ์ปฌ ์„œ์น˜๋ฅผ ์ ์šฉํ•ด ํ˜„์žฌ ์œ„์น˜์—์„œ ๊ฐ€

Neural Computing Computer Science
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A construction of an optimal base for conditional attribute and attributional condition implications in triadic contexts

์‚ผ์ค‘ ์ปจํ…์ŠคํŠธ๋Š” ์ „ํ†ต์ ์ธ ์ดํ•ญ ๊ด€๊ณ„๋ฅผ ๋„˜์–ด ๊ฐ์ฒดโ€‘์†์„ฑโ€‘์กฐ๊ฑด์ด๋ผ๋Š” ์„ธ ์ฐจ์›์„ ๋™์‹œ์— ๊ณ ๋ คํ•˜๋Š” ๋ฐ์ดํ„ฐ ๋ชจ๋ธ๋กœ, ์ง€์‹ ๋ฐœ๊ฒฌ ๋ฐ ์˜๋ฏธ๋ก ์  ๋ถ„์„์— ์žˆ์–ด ๊ฐ•๋ ฅํ•œ ํ‘œํ˜„๋ ฅ์„ ์ œ๊ณตํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ๋‹ค์ฐจ์› ๊ตฌ์กฐ์—์„œ๋Š” ๊ธฐ์กด์˜ ์ดํ•ญ ์ปจํ…์ŠคํŠธ์—์„œ ์‚ฌ์šฉ๋˜๋Š” ํ•จ์ถ•(implication) ๊ธฐ๋ฐ˜ ์ถ”๋ก  ๊ธฐ๋ฒ•์„ ๊ทธ๋Œ€๋กœ ์ ์šฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ํŠนํžˆ ์กฐ๊ฑด ์†์„ฑ(conditional attribute)๊ณผ ๊ท€์† ์กฐ๊ฑด(attributional condition)์ด๋ผ๋Š” ๋‘ ์ข…๋ฅ˜์˜ ํ•จ์ถ•์ด ๋™์‹œ์— ์กด์žฌํ•  ๊ฒฝ์šฐ, ์„œ๋กœ ์–ฝํžˆ๋Š” ์ „์ œ์™€ ๊ฒฐ๋ก  ์‚ฌ์ด์˜ ์ค‘๋ณต ๋ฐ ๋ถˆํ•„์š”ํ•œ ํ•จ์ถ•์ด ๊ธ‰์ฆํ•˜์—ฌ ํšจ์œจ

Computer Science Artificial Intelligence
Exposing Hidden Interfaces: LLM-Guided Type Inference for Reverse Engineering macOS Private Frameworks

Exposing Hidden Interfaces: LLM-Guided Type Inference for Reverse Engineering macOS Private Frameworks

MOTIF๋Š” ํฌ๊ฒŒ ๋‘ ๊ฐ€์ง€ ํ•ต์‹ฌ ์š”์†Œ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” Objectiveโ€‘C ๋Ÿฐํƒ€์ž„ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•ด ๋ฉ”์„œ๋“œ ํ˜ธ์ถœ ๊ด€๊ณ„์™€ ํด๋ž˜์Šค ๊ณ„์ธต ๊ตฌ์กฐ๋ฅผ ์ถ”์ถœํ•˜๋Š” โ€˜๋ฉ”ํƒ€๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ๋ชจ๋“ˆโ€™์ด๋‹ค. ์ด ๋ชจ๋“ˆ์€ dyld shared cache์™€ Machโ€‘O ๋ฐ”์ด๋„ˆ๋ฆฌ๋ฅผ ๋™์ ์œผ๋กœ ๋กœ๋“œํ•˜๊ณ , objc getClass, method getImplementation ๋“ฑ์˜ ๋Ÿฐํƒ€์ž„ API๋ฅผ ํ˜ธ์ถœํ•ด ์‹ค์ œ ๋ฉ”๋ชจ๋ฆฌ ์ฃผ์†Œ์™€ ์‹ฌ๋ณผ ์ •๋ณด๋ฅผ ์ˆ˜์ง‘ํ•œ๋‹ค. ์ˆ˜์ง‘๋œ ๋ฐ์ดํ„ฐ๋Š” ๊ทธ๋ž˜ํ”„ ํ˜•ํƒœ๋กœ ์ •๊ทœํ™”๋˜์–ด ์ดํ›„ ๋‹จ๊ณ„์— ์ „๋‹ฌ๋œ๋‹ค. ๋‘ ๋ฒˆ์งธ๋Š” ํŒŒ์ธํŠœ๋‹๋œ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM)์ด๋‹ค. ์—ฐ๊ตฌํŒ€์€

Computer Science Framework Cryptography and Security
No Image

FALCON: Few-Shot Adversarial Learning for Cross-Domain Medical Image Segmentation

FALCON ๋…ผ๋ฌธ์€ ํ˜„์žฌ ์˜๋ฃŒ ์˜์ƒ ๋ถ„ํ•  ๋ถ„์•ผ๊ฐ€ ์ง๋ฉดํ•œ ์„ธ ๊ฐ€์ง€ ํ•ต์‹ฌ ๋ฌธ์ œโ€”๋ผ๋ฒจ ๋ฐ์ดํ„ฐ์˜ ํฌ์†Œ์„ฑ, ํ™˜์ž ๊ฐ„ ํ•ด๋ถ€ํ•™์  ๋ณ€์ด, ๊ทธ๋ฆฌ๊ณ  ์‹ค์‹œ๊ฐ„ ์ž„์ƒ ์ ์šฉ์„ ์œ„ํ•œ ์—ฐ์‚ฐ ํšจ์œจ์„ฑโ€”๋ฅผ ๋™์‹œ์— ํ•ด๊ฒฐํ•˜๋ ค๋Š” ์‹œ๋„๋กœ ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ฐ€์žฅ ๋ˆˆ์— ๋„๋Š” ์ ์€ โ€œ๋ฉ”ํƒ€โ€‘ํ•™์Šต โ†’ ์ ๋Œ€์  ํŒŒ์ธํŠœ๋‹ โ†’ ๊ฒฝ๊ณ„ ์ธ์‹ ํ•™์Šตโ€์ด๋ผ๋Š” 3๋‹จ๊ณ„ ์ „์ด ์ „๋žต์ด๋‹ค. ์ฒซ ๋‹จ๊ณ„์—์„œ ์ž์—ฐ ์ด๋ฏธ์ง€(์˜ˆ: PASCALโ€‘5โฑ, COCOโ€‘20โฑ)๋กœ ๋ฉ”ํƒ€โ€‘๋Ÿฌ๋‹์„ ์ˆ˜ํ–‰ํ•จ์œผ๋กœ์จ, ๋‹ค์–‘ํ•œ ํด๋ž˜์Šค์™€ ๋ณต์žกํ•œ ๋ฐฐ๊ฒฝ์„ ๊ฐ€์ง„ ์ƒํ™ฉ์—์„œ๋„ ๋น ๋ฅด๊ฒŒ ์ƒˆ๋กœ์šด ํƒœ์Šคํฌ์— ์ ์‘ํ•  ์ˆ˜ ์žˆ๋Š” โ€˜ํ•™์Šตโ€‘ํ•™์Šตโ€™ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๊ตฌ์ถ•ํ•œ๋‹ค.

Computer Vision Computer Science Learning
HanoiWorld : A Joint Embedding Predictive Architecture BasedWorld Model for Autonomous Vehicle Controller

HanoiWorld : A Joint Embedding Predictive Architecture BasedWorld Model for Autonomous Vehicle Controller

๋ณธ ๋…ผ๋ฌธ์€ ์ž์œจ์ฃผํ–‰ ์ œ์–ด์— ์žˆ์–ด ๊ฐ•ํ™”ํ•™์Šต(RL)์ด ์ง๋ฉดํ•œ ๊ทผ๋ณธ์ ์ธ ํ•œ๊ณ„๋ฅผ ์งš์–ด๋‚ธ ๋’ค, JEPA ๊ธฐ๋ฐ˜ ์ž๊ธฐ์ง€๋„ ํ•™์Šต์ด ์ œ๊ณตํ•  ์ˆ˜ ์žˆ๋Š” ๋Œ€์•ˆ์„ ์ œ์‹œํ•œ๋‹ค. ๊ธฐ์กด RL ์ ‘๊ทผ๋ฒ•์€ ๋ณด์ƒ ์„ค๊ณ„๊ฐ€ ๋ณต์žกํ•˜๊ณ , ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํ˜น์€ ์‹ค์ œ ๋„๋กœ์—์„œ ์ˆ˜๋ฐฑ๋งŒ ๋‹จ๊ณ„์— ๋‹ฌํ•˜๋Š” ๋ฐ์ดํ„ฐ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ํŠนํžˆ ํ”ฝ์…€ ์ˆ˜์ค€์˜ ์žฌ๊ตฌ์„ฑ ์†์‹ค์„ ์ตœ์†Œํ™”ํ•˜๋ ค๋Š” ๋ชฉํ‘œ๋Š” ๊ณ ์ฐจ์› ์ž…๋ ฅ ๊ณต๊ฐ„์—์„œ ์žก์Œ(๋…ธ์ด์ฆˆ) ํŠน์„ฑ์„ ๊ณผ๋„ํ•˜๊ฒŒ ํ•™์Šตํ•˜๊ฒŒ ๋งŒ๋“ค๋ฉฐ, ์ด๋Š” ์ •์ฑ…์˜ ๋ถˆ์•ˆ์ •์„ฑ๊ณผ ์•ˆ์ „์„ฑ ์ €ํ•˜๋กœ ์ด์–ด์ง„๋‹ค. ์ธ๊ฐ„์ด ์ƒˆ๋กœ์šด ๊ธฐ์ˆ ์„ ์Šต๋“ํ•  ๋•Œ โ€˜์ƒ์ƒโ€™๊ณผ โ€˜์˜ˆ์ธกโ€™ ๊ณผ์ •์„ ํ†ตํ•ด ์ ์€ ๊ฒฝํ—˜๋งŒ์œผ๋กœ๋„ ์ผ๋ฐ˜ํ™”๋œ ๋ชจ๋ธ์„ ๊ตฌ์ถ•

Computer Science Robotics Model
KGCE: Knowledge-Augmented Dual-Graph Evaluator for Cross-Platform Educational Agent Benchmarking with Multimodal Language Models

KGCE: Knowledge-Augmented Dual-Graph Evaluator for Cross-Platform Educational Agent Benchmarking with Multimodal Language Models

KGCE ๋…ผ๋ฌธ์€ ํ˜„์žฌ ๊ต์œก์šฉ AI ์—์ด์ „ํŠธ ํ‰๊ฐ€ ์ฒด๊ณ„๊ฐ€ ์ง๋ฉดํ•œ ๋‘ ๊ฐ€์ง€ ํ•ต์‹ฌ ํ•œ๊ณ„๋ฅผ ์ฒด๊ณ„์ ์œผ๋กœ ์ง„๋‹จํ•˜๊ณ , ์ด๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•œ ์„ค๊ณ„ ์›์น™์„ ๋ช…ํ™•ํžˆ ์ œ์‹œํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ํ•œ๊ณ„๋Š” โ€˜ํ”„๋ผ์ด๋น— ๋„๋ฉ”์ธ ์†Œํ”„ํŠธ์›จ์–ดโ€™์— ๋Œ€ํ•œ ๊ตฌ์กฐ์  ์ดํ•ด ๋ถ€์กฑ์ด๋‹ค. XiaoYa Intelligent Assistant๋‚˜ HuaShi XiaZi์™€ ๊ฐ™์€ ํ•™๊ต ์ „์šฉ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์€ UI ํ๋ฆ„, API ํ˜ธ์ถœ ๋ฐฉ์‹, ๋ฐ์ดํ„ฐ ํฌ๋งท ๋“ฑ์ด ์ผ๋ฐ˜ ์ƒ์šฉ ์†Œํ”„ํŠธ์›จ์–ด์™€ ํฌ๊ฒŒ ๋‹ค๋ฅด๋‹ค. ๊ธฐ์กด ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ LLM ๊ธฐ๋ฐ˜ ์—์ด์ „ํŠธ๋Š” ์‚ฌ์ „ ํ•™์Šต ๋ฐ์ดํ„ฐ์— ์ด๋Ÿฌํ•œ ํŠน์ˆ˜ ์‚ฌ๋ก€๊ฐ€ ๊ฑฐ์˜ ํฌํ•จ๋˜์ง€ ์•Š์•„, ์‹ค์ œ ์‹คํ–‰

Computer Science Artificial Intelligence Model
Length-Aware Adversarial Training for Variable-Length Trajectories: Digital Twins for Mall Shopper Paths

Length-Aware Adversarial Training for Variable-Length Trajectories: Digital Twins for Mall Shopper Paths

๋ณธ ๋…ผ๋ฌธ์€ ๊ฐ€๋ณ€ ๊ธธ์ด ์‹œํ€€์Šค, ํŠนํžˆ ์‡ผํ•‘๋ชฐ ๋‚ด ๊ณ ๊ฐ์˜ ์ด๋™ ๊ฒฝ๋กœ์™€ ๊ฐ™์€ ๋ณตํ•ฉ์ ์ธ ์‹œ๊ฐ„ยท๊ณต๊ฐ„ ๋ฐ์ดํ„ฐ๋ฅผ ์ƒ์„ฑํ•˜๋Š” ๋ฌธ์ œ์— ์ดˆ์ ์„ ๋งž์ถ˜๋‹ค. ์ „ํ†ต์ ์ธ ๋ฏธ๋‹ˆ๋ฐฐ์น˜ ํ•™์Šต์—์„œ๋Š” ๋™์ผ ๋ฐฐ์น˜ ๋‚ด์— ๊ธธ์ด๊ฐ€ ํฌ๊ฒŒ ๋‹ค๋ฅธ ์‹œํ€€์Šค๊ฐ€ ์„ž์ด๊ฒŒ ๋˜๋Š”๋ฐ, ์ด๋Š” ๊ทธ๋ž˜๋””์–ธํŠธ์˜ ์Šค์ผ€์ผ ์ฐจ์ด๋ฅผ ์•ผ๊ธฐํ•˜๊ณ  ์ตœ์ ํ™” ๊ณผ์ •์—์„œ ๋ถˆ์•ˆ์ •์„ฑ์„ ์ดˆ๋ž˜ํ•œ๋‹ค. ํŠนํžˆ GAN ๊ธฐ๋ฐ˜ ์ƒ์„ฑ ๋ชจ๋ธ์€ ํŒ๋ณ„์ž๊ฐ€ โ€œ๊ธธ์ด ์ž์ฒดโ€๋ฅผ ์‰ฝ๊ฒŒ ๊ตฌ๋ถ„ํ•˜๋Š” ๋‹จ์ถ•(shortcut) ์ „๋žต์„ ์ทจํ•  ์œ„ํ—˜์ด ์žˆ๋Š”๋ฐ, ์ด๋Š” ์‹ค์ œ ์˜๋ฏธ ์žˆ๋Š” ํŒจํ„ด(์˜ˆ: ๋ฐฉ๋ฌธ ์ˆœ์„œ, ์ฒด๋ฅ˜ ์‹œ๊ฐ„)๋ณด๋‹ค ๊ธธ์ด ์ฐจ์ด์—๋งŒ ๋ฏผ๊ฐํ•˜๊ฒŒ ๋ฐ˜์‘ํ•˜๊ฒŒ ๋งŒ๋“ ๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ ์ƒ์„ฑ

Machine Learning Computer Science
Logics-STEM: Empowering LLM Reasoning via Failure-Driven Post-Training and Document Knowledge Enhancement

Logics-STEM: Empowering LLM Reasoning via Failure-Driven Post-Training and Document Knowledge Enhancement

Logicsโ€‘STEM ๋…ผ๋ฌธ์€ ์ตœ๊ทผ LLM(Large Language Model) ๋ถ„์•ผ์—์„œ ๊ฐ€์žฅ ๋œจ๊ฑฐ์šด ์ด์Šˆ์ธ โ€œ์ถ”๋ก  ๋Šฅ๋ ฅ ๊ฐ•ํ™”โ€์— ๋Œ€ํ•ด ๋ฐ์ดํ„ฐ์™€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๋™์‹œ์— ์ตœ์ ํ™”ํ•˜๋Š” ์ „๋žต์„ ์ œ์‹œํ•œ๋‹ค. ๋จผ์ € ๋ฐ์ดํ„ฐ ์ธก๋ฉด์„ ์‚ดํŽด๋ณด๋ฉด, ์ €์ž๋“ค์€ 7.2 M ๊ทœ๋ชจ์˜ SFT( supervised fineโ€‘tuning ) ๋ฐ์ดํ„ฐ์…‹์„ ๊ตฌ์ถ•ํ•˜๊ธฐ ์œ„ํ•ด 5๋‹จ๊ณ„ ํŒŒ์ดํ”„๋ผ์ธ์„ ์ ์šฉํ–ˆ๋‹ค. ์ฃผ์„ ๋‹จ๊ณ„์—์„œ๋Š” ์ธ๊ฐ„ ์ „๋ฌธ๊ฐ€๊ฐ€ ์žฅ๊ธฐ ์‚ฌ๊ณ  ์‚ฌ์Šฌ(chainโ€‘ofโ€‘thought) ํ˜•ํƒœ์˜ ๋‹ต๋ณ€์„ ์ง์ ‘ ์ž‘์„ฑํ•˜๋„๋ก ํ•˜์—ฌ, ๋ชจ๋ธ์ด ๋‹จ์ˆœํžˆ ์ •๋‹ต์„ ๋งž์ถ”๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ์‚ฌ๊ณ  ๊ณผ์ •์„ ํ•™์Šตํ•˜๋„๋ก

Computer Science Artificial Intelligence
Online Estimation and Manipulation of Articulated Objects

Online Estimation and Manipulation of Articulated Objects

์ด ๋…ผ๋ฌธ์€ ์„œ๋น„์Šค ๋กœ๋ด‡์ด ์ผ์ƒ ๊ฐ€์ • ํ™˜๊ฒฝ์—์„œ ๋งˆ์ฃผ์น˜๋Š” ๋‹ค์–‘ํ•œ ๊ด€์ ˆํ˜• ๊ฐ์ฒด(์˜ˆ: ์„œ๋ž, ๋ฌธ, ํšŒ์ „์‹ ์†์žก์ด ๋“ฑ)๋ฅผ ์‚ฌ์ „ ์ง€์‹ ์—†์ด๋„ ์‹ ์†ํžˆ ํŒŒ์•…ํ•˜๊ณ  ์กฐ์ž‘ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ํ†ตํ•ฉ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ํ•ต์‹ฌ ์•„์ด๋””์–ด๋Š” โ€˜์‹œ๊ฐโ€‘์ด‰๊ฐ ์œตํ•ฉโ€™์ด๋‹ค. ๊ธฐ์กด ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ์–ดํฌ๋˜์Šค ์˜ˆ์ธก์€ ํ’๋ถ€ํ•œ ์ด๋ฏธ์ง€ ๋ฐ์ดํ„ฐ๋กœ๋ถ€ํ„ฐ ๊ฐ์ฒด์˜ ์ž ์žฌ์  ์›€์ง์ž„ ์ถ•์ด๋‚˜ ์ œํ•œ์„ ์ถ”์ •ํ•˜์ง€๋งŒ, ์‹ค์ œ ๋ฌผ๋ฆฌ์  ์ œ์•ฝ(๋งˆ์ฐฐ, ์Šคํ”„๋ง ์ €ํ•ญ ๋“ฑ)์„ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ๋ฐ˜๋ฉด, ๊ด€์ ˆ ์šด๋™์„ ์ง์ ‘ ๊ด€์ฐฐํ•ด ์ถ”์ •ํ•˜๋Š” ์ „ํ†ต์  ๋ฐฉ๋ฒ•์€ ์‹ค์ œ ์ ‘์ด‰์ด ์„ ํ–‰๋ผ์•ผ ํ•˜๋ฏ€๋กœ ์ดˆ๊ธฐ ํƒ์ƒ‰ ๋น„์šฉ์ด ํฌ๋‹ค. ์ €์ž๋“ค์€ ์ด ๋‘

Computer Science Robotics
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The Optimal Sample Complexity of Linear Contracts

์ด ๋…ผ๋ฌธ์€ ๋””์ง€ํ„ธ ์Œ์•… ํ”Œ๋žซํผ์—์„œ ์ƒˆ๋กœ์šด ๋กœ์—ดํ‹ฐ ๋ชจ๋ธ ๋„์ž…์— ๋Œ€ํ•œ ํ•ต์‹ฌ ๋ฌธ์ œ, ์ฆ‰ ๋ถˆํ™•์‹คํ•œ ์•„ํ‹ฐ์ŠคํŠธ ํŠน์„ฑ์˜ ํ•™์Šต์„ ๋‹ค๋ฃจ๋ฉฐ, ์ด๋ฅผ ์œ„ํ•œ ์ตœ์  ์ƒ˜ํ”Œ ๋ณต์žก๋„๋ฅผ ๋ถ„์„ํ•˜๊ณ  ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ๋…ผ๋ฌธ์€ (Dรผtting ๋“ฑ, 2025)์˜ ํ‘œ๋ณธ ๊ธฐ๋ฐ˜ ํ•™์Šต ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ํ™•์žฅํ•˜์—ฌ ์„ ํ˜• ๊ณ„์•ฝ์ด๋ผ๋Š” ํŠน์ • ๊ณ„์•ฝ ํด๋ž˜์Šค์— ๋Œ€ํ•œ ์ตœ์  ํ•™์Šต์„ ์œ„ํ•œ ์ƒ˜ํ”Œ ๋ณต์žก๋„๋ฅผ ๋ถ„์„ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๊ธฐ์ˆ ์  ํ˜์‹ ์„ฑ: ๋…ผ๋ฌธ์€ ๋””์ง€ํ„ธ ์Œ์•… ํ”Œ๋žซํผ์ด ์ƒˆ๋กœ์šด ๋กœ์—ดํ‹ฐ ๋ชจ๋ธ์„ ๋„์ž…ํ•  ๋•Œ ์ง๋ฉดํ•˜๋Š” ํ•ต์‹ฌ ๋ฌธ์ œ์ธ ๋ถˆํ™•์‹คํ•œ ์•„ํ‹ฐ์ŠคํŠธ ํŠน์„ฑ์˜ ํ•™์Šต์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, ์ด ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆ

Computer Science Game Theory
Data-Driven Assessment of Concrete Mixture Compositions on Chloride Transport via Standalone Machine Learning Algorithms

Data-Driven Assessment of Concrete Mixture Compositions on Chloride Transport via Standalone Machine Learning Algorithms

์ด ๋…ผ๋ฌธ์€ ์ฝ˜ํฌ๋ฆฌํŠธ ๊ตฌ์กฐ๋ฌผ ๋‚ด์—์„œ ์—ผํ™”๋ฌผ์˜ ์‹œ๊ฐ„ ๊ฒฝ๊ณผ ๋ณ€ํ™”์— ๋ฏธ์น˜๋Š” ํ˜ผํ•ฉ๋ฌผ ์กฐ์„ฑ์˜ ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๋Š”๋ฐ ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ๋จธ์‹ ๋Ÿฌ๋‹ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ฐ์ดํ„ฐ ์ฃผ๋„์  ์ ‘๊ทผ๋ฒ•์„ ์ ์šฉํ•œ๋‹ค. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๋ก ์€ ๊ฐ„๋‹จํ•œ ์„ ํ˜• ํšŒ๊ท€, k ์ตœ๊ทผ์ ‘ ์ด์›ƒ ํšŒ๊ท€, ์ปค๋„ ๋ฆฌ์ง€ ํšŒ๊ท€์™€ ๋ณต์žกํ•œ ์„œํฌํŠธ ๋ฒกํ„ฐ ํšŒ๊ท€, ๊ฐ€์šฐ์‹œ์•ˆ ํ”„๋กœ์„ธ์Šค ํšŒ๊ท€, ๋‹ค์ธต ํผ์…‰ํŠธ๋ก (MLP), ๊ฒŒ์ดํŠธ ์ˆœํ™˜ ์œ ๋‹›(GRU)์„ ํฌํ•จํ•œ๋‹ค. ๊ฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ฑ๋Šฅ์€ ๋‹ค์–‘ํ•œ ์ง€ํ‘œ๋ฅผ ํ†ตํ•ด ํ‰๊ฐ€๋˜๋ฉฐ, ํŠนํžˆ GPR ๋ชจ๋ธ์€ ๋ช…ํ™•ํ•˜๊ณ  ์„ค๋ช… ๊ฐ€๋Šฅํ•œ ์ถ”์„ธ๋ฅผ ์ œ๊ณตํ•˜์—ฌ ์ˆจ๊ฒจ์ง„ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋“œ๋Ÿฌ๋‚ด๋Š” ๋ฐ ํšจ๊ณผ

Computer Science Learning Data Machine Learning
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EgoGrasp: World-Space Hand-Object Interaction Estimation from Egocentric Videos

EgoGrasp ๋…ผ๋ฌธ์€ ์ด๋จธ์‹œ๋ธŒ(egocentric) ์˜์ƒ์—์„œ ์„ธ๊ณ„ ์ขŒํ‘œ๊ณ„์˜ ์†โ€‘๋ฌผ์ฒด ์ƒํ˜ธ์ž‘์šฉ์„ ๋ณต์›ํ•œ๋‹ค๋Š” ๋งค์šฐ ์•ผ์‹ฌ์ฐฌ ๋ชฉํ‘œ๋ฅผ ์„ค์ •ํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ํฌ๊ฒŒ ๋‘ ๊ฐ€์ง€ ํ•œ๊ณ„์— ๋ด‰์ฐฉํ•œ๋‹ค. ์ฒซ์งธ, ์ •์  ์ด๋ฏธ์ง€ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•์€ ์‹œ๊ฐ„์  ์—ฐ์†์„ฑ์„ ํ™œ์šฉํ•˜์ง€ ๋ชปํ•ด ๋™์ ์ธ ์†๋™์ž‘๊ณผ ๋ฌผ์ฒด ๋ณ€ํ™”๋ฅผ ์ •ํ™•ํžˆ ํฌ์ฐฉํ•˜์ง€ ๋ชปํ•œ๋‹ค. ๋‘˜์งธ, ์นด๋ฉ”๋ผ ์ขŒํ‘œ๊ณ„์— ๊ตญํ•œ๋œ ์ ‘๊ทผ๋ฒ•์€ ์ „์—ญ์ ์ธ ์œ„์น˜์™€ ์ž์„ธ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜์ง€ ๋ชปํ•œ๋‹ค. ํŠนํžˆ ์ผ์ƒ ์ƒํ™œ์—์„œ ์ดฌ์˜๋˜๋Š” ์ด๋จธ์‹œ๋ธŒ ์˜์ƒ์€ ๊ธ‰๊ฒฉํ•œ ์นด๋ฉ”๋ผ ์›€์ง์ž„, ์กฐ๋ช… ๋ณ€ํ™”, ๊ทธ๋ฆฌ๊ณ  ์†๊ณผ ๋ฌผ์ฒด์˜ ๋นˆ๋ฒˆํ•œ ๊ฐ€๋ฆผ ํ˜„์ƒ์ด ํŠน์ง•์ด๋‹ค. ์ด๋Ÿฌํ•œ ํ™˜๊ฒฝ์—์„œ๋Š” 2D

Computer Vision Computer Science
Learning from Historical Activations in Graph Neural Networks

Learning from Historical Activations in Graph Neural Networks

HISTOGRAPH๋Š” ๊ทธ๋ž˜ํ”„ ์‹ ๊ฒฝ๋ง(GNN) ๋ถ„์•ผ์˜ ์ค‘์š”ํ•œ ๋ฐœ์ „์„ ์ด๋Œ์–ด๋‚ด๋ฉฐ, ํŠนํžˆ ๋…ธ๋“œ ์ž„๋ฒ ๋”ฉ์˜ ์ง„ํ™”์™€ ๊ณต๊ฐ„์  ์ƒํ˜ธ์ž‘์šฉ์„ ๋™์‹œ์— ๋ชจ๋ธ๋งํ•˜๋Š” ๋ฐ ์ค‘์ ์„ ๋‘๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ๋ฒ•์€ ๊ธฐ์กด GNN ์•„ํ‚คํ…์ฒ˜๋ฅผ ๊ฐœ์„ ํ•˜๊ณ , ๋‹ค์–‘ํ•œ ๊ทธ๋ž˜ํ”„ ๊ด€๋ จ ์ž‘์—…์—์„œ ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋‹ฌ์„ฑํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ๋ฐฉํ–ฅ์„ฑ์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ๊ธฐ์ˆ ์  ํ˜์‹ ์„ฑ: HISTOGRAPH์˜ ํ•ต์‹ฌ ํ˜์‹  ์ค‘ ํ•˜๋‚˜๋Š” ์—ญ์‚ฌ์  ํ™œ์„ฑํ™” ์ถ”์  ์ž…๋‹ˆ๋‹ค. ์ด ์ ‘๊ทผ๋ฒ•์€ GNN์ด ๋ฉ”์‹œ์ง€ ์ „๋‹ฌ๊ณผ ์ง‘๊ณ„๋ฅผ ํ†ตํ•ด ๋…ธ๋“œ ์ž„๋ฒ ๋”ฉ์„ ์ƒ์„ฑํ•˜๋Š” ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•œ ์‹œ๊ฐ„์  ๊ฒฝ๋กœ๋ฅผ ๊ณ ๋ คํ•ฉ๋‹ˆ๋‹ค. ๊ฐ ๋…ธ๋“œ์˜ ์ธต๋ณ„ ํ‘œํ˜„์„ ์‹œํ€€์Šค

Computer Science Network Learning Machine Learning
Multi-Dimensional Prompt Chaining to Improve Open-Domain Dialogue Generation

Multi-Dimensional Prompt Chaining to Improve Open-Domain Dialogue Generation

๋ณธ ๋…ผ๋ฌธ์€ ์ตœ๊ทผ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM)์ด ๋Œ€ํ™” ์‹œ์Šคํ…œ์—์„œ ๋ณด์—ฌ์ฃผ๋Š” ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ๊ณผ๋Š” ๋‹ฌ๋ฆฌ, ์†Œํ˜• ์–ธ์–ด ๋ชจ๋ธ(SLM)์ด ๊ฐ–๋Š” ๋ฐฐํฌยท์šด์˜์ƒ์˜ ์žฅ์ ์„ ์‚ด๋ฆฌ๋ฉด์„œ๋„ ํ’ˆ์งˆ ๊ฒฉ์ฐจ๋ฅผ ๋ฉ”์šฐ๊ธฐ ์œ„ํ•œ ์‹ค์šฉ์ ์ธ ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ํ•ต์‹ฌ ์•„์ด๋””์–ด๋Š” โ€˜ํ”„๋กฌํ”„ํŠธ ์ฒด์ด๋‹(prompt chaining)โ€™์ด๋ผ๋Š” ๊ธฐ๋ฒ•์„ ๋‹ค์ฐจ์›์ ์œผ๋กœ ํ™•์žฅํ•˜์—ฌ, ๊ฐ๊ฐ์˜ ๋Œ€ํ™” ํ’ˆ์งˆ ์š”์†Œ์ธ ์ž์—ฐ์Šค๋Ÿฌ์›€(Naturalness), ์ผ๊ด€์„ฑ(Coherence), ํฅ๋ฏธ์„ฑ(Engagingness)์„ ๋…๋ฆฝ์ ์œผ๋กœ ๊ฐ•ํ™”ํ•˜๊ณ , ์ตœ์ข… ์‘๋‹ต์—์„œ ์ด๋“ค์„ ์กฐํ™”๋กญ๊ฒŒ ๊ฒฐํ•ฉํ•˜๋„๋ก ์„ค๊ณ„ํ•œ ๊ฒƒ์ด๋‹ค. 1. ํ”„๋ ˆ์ž„์›Œํฌ ์„ค๊ณ„ N

Computer Science NLP
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Warp-Cortex: An Asynchronous, Memory-Efficient Architecture for Million-Agent Cognitive Scaling on Consumer Hardware

๋ณธ ๋…ผ๋ฌธ์€ ์›Œํ”„ ์ฝ”๋ฅดํ…์Šค ์•„ํ‚คํ…์ฒ˜๋ฅผ ํ†ตํ•ด ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM)์˜ ๋ณ‘๋ ฌ ์ถ”๋ก  ๋Šฅ๋ ฅ์„ ํฌ๊ฒŒ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ด ์•„ํ‚คํ…์ฒ˜๋Š” ์—์ด์ „ํŠธ๊ฐ€ ๋…๋ฆฝ์ ์ธ ํ”„๋กœ์„ธ์Šค๊ฐ€ ์•„๋‹Œ ๋น„๋™๊ธฐ ์Šค๋ ˆ๋“œ๋กœ์„œ ๋™์ž‘ํ•˜๋„๋ก ์„ค๊ณ„๋˜์–ด, ๊ฐ€์ค‘์น˜์™€ ์ปจํ…์ŠคํŠธ ๋ฉ”๋ชจ๋ฆฌ์˜ ๋ณต์žก๋„๋ฅผ ํฌ๊ฒŒ ์ค„์ธ๋‹ค. ํŠนํžˆ, ํ† ํด๋กœ์ง€ ๋ฐ์ดํ„ฐ ๋ถ„์„(TDA)์—์„œ ์œ ๋ž˜ํ•œ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ๋žœ๋“œ๋งˆํฌ ๊ธฐ๋ฒ•์„ ํ†ตํ•ด KV ์บ์‹œ๋ฅผ ์  ํด๋ผ์šฐ๋“œ๋กœ ์ทจ๊ธ‰ํ•˜๊ณ , ์ฆ์ธ ๋ณต์žก์ฒด์— ์˜๊ฐ์„ ๋ฐ›์€ ํฌ๋ฐ•ํ™” ๋ฐฉ๋ฒ•์„ ์ ์šฉํ•˜์—ฌ ์ปจํ…์ŠคํŠธ์˜ ์ง€์†์  ํ˜ธ๋ชจ๋กœ์ง€ ํŠน์„ฑ์„ ์œ ์ง€ํ•˜๋ฉด์„œ ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰์„ ํฌ๊ฒŒ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค. ๋…ผ๋ฌธ์—์„œ ์ œ์‹œ๋œ ์›Œํ”„ ์ฝ”๋ฅดํ…

Machine Learning Computer Science

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