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Automatic Multi-GPU Code Generation applied to Simulation of Electrical   Machines

Automatic Multi-GPU Code Generation applied to Simulation of Electrical Machines

: 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ๋ณธ ๋…ผ๋ฌธ์€ ๋ณ‘๋ ฌ ํ”„๋กœ๊ทธ๋ž˜๋ฐ์˜ ์–ด๋ ค์›€์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด, OpenCL ๊ธฐ๋ฐ˜์˜ ํ˜ผ์„ฑ ์•„ํ‚คํ…์ฒ˜(CPU + GPU)์šฉ ์ฝ”๋“œ ์ƒ์„ฑ ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, MDE์™€ MARTE ํ”„๋กœํŒŒ์ผ์„ ํ™œ์šฉํ•˜์—ฌ ๋ณ‘๋ ฌ ํ”„๋กœ๊ทธ๋ž˜๋ฐ์— ์ต์ˆ™ํ•˜์ง€ ์•Š์€ ์‚ฌ๋žŒ๋“ค๋„ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ์‰ฝ๊ฒŒ ๊ตฌํ˜„ํ•  ์ˆ˜ ์žˆ๋„๋ก ์ง€์›ํ•˜๋Š” ๊ฒƒ์ด ์ฃผ์š” ๋ชฉํ‘œ์ž…๋‹ˆ๋‹ค. 2. ๊ธฐ์ˆ ์  ๋ฐฐ๊ฒฝ OpenCL : Khronos ๊ทธ๋ฃน์—์„œ ๊ฐœ๋ฐœํ•œ ๋ณ‘๋ ฌ ์ปดํ“จํŒ… ํ‘œ์ค€์œผ๋กœ, ๋‹ค์–‘ํ•œ ํ•˜๋“œ์›จ์–ด ์•„ํ‚คํ…์ฒ˜๋ฅผ ์ง€์›ํ•ฉ๋‹ˆ๋‹ค. MDE์™€ MARTE ํ”„๋กœํŒŒ์ผ : MDE๋Š” ํ”„๋กœ๊ทธ๋žจ ์‚ฌ์–‘์„ ์ถ”์ƒํ™”ํ•˜๊ณ  ์ž๋™ํ™”๋ฅผ ํ–ฅ์ƒ์‹œ

Distributed Computing Computer Science
Solution to Banff 2 Challenge Based on Likelihood Ratio Test

Solution to Banff 2 Challenge Based on Likelihood Ratio Test

: ๋ณธ ๋…ผ๋ฌธ์€ LHC์˜ ๋ฐœ๊ฒฌ ์ฃผ์žฅ๊ณผ ๊ด€๋ จ๋œ ํ†ต๊ณ„์  ๋ฌธ์ œ์— ๋Œ€ํ•œ ํ•ด๊ฒฐ์ฑ…์„ ์ œ์‹œํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ์–ป์–ด์ง„ ์„ฑ๋Šฅ์„ ๋ถ„์„ํ•ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” 2010๋…„ 7์›” ์บ๋‚˜๋‹ค ์•จ๋ฒ„ํƒ€์ฃผ ๋ฐดํ”„์—์„œ ์—ด๋ฆฐ ํšŒ์˜๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•˜๋ฉฐ, ํ† ๋งˆ์Šค ์ฅฌํฌ๊ฐ€ ์ฃผ๋„ํ•œ ๊ฒฝ์Ÿ์—์„œ ๋‚˜์˜จ ๊ฒฐ๊ณผ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ๋‹ค๋ฃน๋‹ˆ๋‹ค. ๋ฌธ์ œ ์ •์˜์™€ ํ•ด๊ฒฐ ๋ฐฉ์•ˆ ๋…ผ๋ฌธ์€ ๋‘ ๊ฐ€์ง€ ์ฃผ์š” ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃจ๋Š”๋ฐ, ์ฒซ ๋ฒˆ์งธ ๋ฌธ์ œ๋Š” '๋‹ค๋ฅธ ๊ณณ์—์„œ ์ฐพ๊ธฐ' ํšจ๊ณผ๋ฅผ ํ‰๊ฐ€ํ•˜๋Š” ๊ฒƒ์ด๋ฉฐ, ์ด๋Š” ์งˆ๋Ÿ‰ ์ŠคํŽ™ํŠธ๋Ÿผ์—์„œ ๊ฐ€๋Šฅํ•œ ์‹ ํ˜ธ๋ฅผ ๊ฒ€์ƒ‰ํ•˜๋Š” ๋ฌธ์ œ์ž…๋‹ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ ๋ฌธ์ œ๋Š” ๋ฐฐ๊ฒฝ์ด๋‚˜ ์‹ ํ˜ธ์˜ ์•Œ๋ ค์ง„ ๋ถ„ํฌ๊ฐ€ ์—†์„ ๋•Œ ๋ชฌํ…Œ์นด๋ฅผ๋กœ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ์ถ”์ •ํ•ด์•ผ

Physics
Revealing Sub-Optimality Conditions of Strategic Decisions

Revealing Sub-Optimality Conditions of Strategic Decisions

๋ณธ ๋…ผ๋ฌธ์€ ์ „๋žต์  ์˜์‚ฌ๊ฒฐ์ • ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ํ•˜์œ„ ์ตœ์ ์„ฑ ์กฐ๊ฑด์„ ํƒ์ƒ‰ํ•˜๊ณ  ๋ถ„์„ํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์ ํ•ฉ๋„ ํ’๊ฒฝ ์ด๋ก ์„ ๊ธฐ๋ฐ˜์œผ๋กœ, ์˜์‚ฌ๊ฒฐ์ •์ž๊ฐ€ ์™œ ์ง€์—ญ ์ตœ์ ๊ฐ’์— ๋„๋‹ฌํ•˜์ง€ ๋ชปํ•œ ์ฑ„ ํƒ์ƒ‰์„ ์ค‘๋‹จํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ๋†’์ด๊ธฐ ์œ„ํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. 1. ์ ํ•ฉ๋„ ํ’๊ฒฝ ์ด๋ก ์˜ ๋ฐฐ๊ฒฝ ์ ํ•ฉ๋„ ํ’๊ฒฝ ์ด๋ก ์€ ์ง„ํ™” ์ƒ๋ฌผํ•™์—์„œ ์œ ์ „์ž ๊ณต๊ฐ„์˜ ํƒ์ƒ‰ ๊ณผ์ •์„ ์„ค๋ช…ํ•˜๋Š” ๊ฐœ๋…์œผ๋กœ ์‚ฌ์šฉ๋˜์–ด ์™”๋‹ค (Wright, 1932; Gillespie, 1984). ์ด ์ด๋ก ์€ ์กฐ์ง ๋ณ€ํ™”, ์‚ฌํšŒ ๊ตฌ์กฐ์˜ ์ง„ํ™”, ํ˜์‹  ๋„คํŠธ์›Œํฌ ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ํ™œ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ, NK

Statistics
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Variable stars magnitudes estimations exploiting the eye physiology

: ๋ณธ ๋…ผ๋ฌธ์€ ์ฒœ๋ฌธํ•™ ๋ถ„์•ผ์—์„œ ๋ˆˆ์˜ ์–ด๋‘์›€ ์ ์‘ ๊ณผ์ •์„ ํ™œ์šฉํ•˜์—ฌ ๋ณ„์˜ ๋ฐ๊ธฐ๋ฅผ ์ถ”์ •ํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ํŠนํžˆ ๋ง์›๊ฒฝ ํ•œ๊ณ„ ๋ฐ๊ธฐ์— ๊ฐ€๊นŒ์šด ์˜์—ญ์—์„œ ์œ ํšจํ•˜๋ฉฐ, ๋ณ€์„ฑ์„ฑ ๋ณ„์ด๋‚˜ ํŠน์ • ๋ฐ๊ธฐ ๋ฒ”์œ„์— ์žˆ๋Š” ๋ณ„๋“ค์— ์ ์šฉ ๊ฐ€๋Šฅํ•˜๋‹ค. 1. ์„œ๋ก : ๋ณ€์„ฑ์„ฑ ๋ณ„ ๊ด€์ธก ์ฒœ๋ฌธํ•™์ž์™€ ์ฒœ๋ฌธ๋Œ€๋Š” ๋ชจ๋“  ๋ณ€์„ฑ์„ฑ๊ณผ ์‹ ์„ฑ์˜ ๋น› ๊ณก์„ ์„ ์ถ”์ ํ•  ์‹œ๊ฐ„์ด ๋ถ€์กฑํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์ด๋Ÿฌํ•œ ์ž‘์—…์€ ์ฃผ๋กœ ์•„๋งˆ์ถ”์–ด ์ฒœ๋ฌธํ•™์ž๋“ค์— ์˜ํ•ด ์ˆ˜ํ–‰๋œ๋‹ค. ์ด๋“ค์€ ๊ตญ์ œ ๋ณ€์„ฑ์„ฑ ๊ด€์ธก์ž ํ˜‘ํšŒ(AAVSO)์— ์†Œ์†๋˜์–ด ์žˆ์œผ๋ฉฐ, ์ฃผ๋กœ ์œก์•ˆ ๊ด€์ธก์„ ์ด์šฉํ•œ๋‹ค. ์ด๋Š” CCD ์žฅ์น˜์˜ ์ƒ๋Œ€์ ์œผ๋กœ ๋†’์€ ๋น„์šฉ

Physics Astrophysics
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Entropy of Telugu

์ด ๋…ผ๋ฌธ์€ ์ธ๋„์–ด ๋ฌธ์ž์™€ ๊ทธ์— ๋”ฐ๋ฅธ ์ฒ ์ž๋ฒ•์„ ๋ฐ”ํƒ•์œผ๋กœ ํ…”๋ฃจ๊ตฌ์–ด์˜ ์—”ํŠธ๋กœํ”ผ๋ฅผ ๋ถ„์„ํ•˜๋Š” ๋ฐ ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ๋จผ์ €, ์ธ๋„ ๋ฌธ์ž์˜ ๊ธฐ์›๊ณผ ๋ฐœ์ „ ๊ณผ์ •์„ ์‚ดํŽด๋ณด๋ฉด, ๋ธŒ๋ผ๋ฏธ ๋ฌธ์ž๋Š” 3์ฒœ๋…„ ์ „ ์ธ๋”์Šค ๋ฌธ์ž์—์„œ ์ง„ํ™”ํ•œ ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ์œผ๋ฉฐ, ์ดํ›„ ๋‹ค์–‘ํ•œ ์ธ๋„ ํ˜„๋Œ€ ๋ฌธ์ž๋กœ ๋ถ„ํ™”๋˜์—ˆ๋‹ค. ์ด๋“ค ๋ฌธ์ž๋Š” ๊ตฌ์กฐ์ ์œผ๋กœ ๋ฐ€์ ‘ํ•˜๊ฒŒ ์—ฐ๊ด€๋˜์–ด ์žˆ์ง€๋งŒ, ๋ชจ์–‘์€ ๋‹ค์–‘ํ•˜๋‹ค. ์ธ๋„์–ด ์•ŒํŒŒ๋ฒณ์€ ์ž์Œ, ๋ชจ์Œ ๋ฐ ๊ธฐํƒ€ ๊ธฐํ˜ธ๋กœ ๊ตฌ์„ฑ๋˜๋ฉฐ, ํ•œ ์Œ์ ˆ(akshara)์€ 0๊ฐœ์—์„œ 3๊ฐœ์˜ ์ž์Œ๊ณผ ๋ชจ์Œ ๋˜๋Š” ๊ธฐํƒ€ ๊ธฐํ˜ธ๋กœ ์ด๋ฃจ์–ด์ง„๋‹ค. ๊ฐ akshara๋Š” ๋…๋ฆฝ์ ์œผ๋กœ ๋ฐœ์Œ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋ชจ๋“ 

Computer Science NLP
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LISA (Localhost Information Service Agent)

LISA๋Š” MonALISA ํ”„๋กœ์ ํŠธ์—์„œ ๊ฐœ๋ฐœ๋œ ๋ชจ๋‹ˆํ„ฐ๋ง ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์œผ๋กœ์„œ, ๋‹ค์–‘ํ•œ ๋ถ„์‚ฐ ์‹œ์Šคํ…œ ํ™˜๊ฒฝ์—์„œ ๋กœ์ปฌ ์Šคํ…Œ์ด์…˜์˜ ์ƒํƒœ๋ฅผ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜๊ณ  ์ด๋ฅผ ํ†ตํ•ด ๋‹ค๋ฅธ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์˜ ์„ฑ๋Šฅ ์ตœ์ ํ™”์— ๊ธฐ์—ฌํ•ฉ๋‹ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์€ LISA์˜ ๊ตฌ์กฐ์™€ ์ฃผ์š” ํŠน์ง•์„ ์ƒ์„ธํžˆ ์„ค๋ช…ํ•˜๋ฉฐ, ํŠนํžˆ ๊ทธ ์œ ์—ฐ์„ฑ๊ณผ ํ™•์žฅ ๊ฐ€๋Šฅ์„ฑ, ๊ทธ๋ฆฌ๊ณ  ๋†’์€ ํ†ต์‹  ์„ฑ๋Šฅ์„ ๊ฐ•์กฐํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. 1. LISA ํ”„๋ ˆ์ž„์›Œํฌ LISA๋Š” ์šด์˜ ์ฒด์ œ ํ”Œ๋žซํผ์— ๋…๋ฆฝ์ ์œผ๋กœ ์ž‘๋™ํ•˜๋„๋ก ์„ค๊ณ„๋˜์–ด ์žˆ์œผ๋ฉฐ, Java์™€ C ์–ธ์–ด๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๊ฐœ๋ฐœ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด Linux, Windows ๋ฐ MacOS์—์„œ ์‹คํ–‰ ๊ฐ€

Distributed Computing Computer Science
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Competition between hydrogen bonding and electric field in single-file transport of water in carbon nanotubes

๋ณธ ์—ฐ๊ตฌ๋Š” ๋‹จ์ผ ๋ฒฝ ํƒ„์†Œ ๋‚˜๋…ธ ํŠœ๋ธŒ(SWCNTs) ๋‚ด๋ถ€๋ฅผ ํ†ต๊ณผํ•˜๋Š” ๋ฌผ์˜ ์ด๋™์„ MD ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ์กฐ์‚ฌํ•˜๊ณ , ํŠนํžˆ ์ „๊ธฐ์žฅ์ด ์ˆ˜์ง์œผ๋กœ ์ ์šฉ๋˜์—ˆ์„ ๋•Œ์˜ ํšจ๊ณผ์— ์ฃผ๋ชฉํ•ฉ๋‹ˆ๋‹ค. SWCNTs๋Š” ๊ฐ•ํ•œ ์นœ์ˆ˜์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๋ฌผ ๋ถ„์ž๊ฐ€ ์ž๋ฐœ์ ์œผ๋กœ ์ฑ„์›Œ์ง€๋Š” ๋…ํŠนํ•œ ํŠน์„ฑ์„ ๋ณด์ž…๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ์‹œ์Šคํ…œ ๋‚ด์—์„œ ๋ฌผ ๋ถ„์ž์˜ ์ด๋™ ๋ฐฉ์‹์ด ํƒ„์†Œ ๋‚˜๋…ธ ํŠœ๋ธŒ์˜ ์ง€๋ฆ„์— ๋”ฐ๋ผ ์–ด๋–ป๊ฒŒ ๋ณ€ํ™”ํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ํ™•์žฅํ•˜๊ณ ์ž ํ•ฉ๋‹ˆ๋‹ค. 1. ๋ฌผ ๋ถ„์ž์˜ ๋ฐฐ์—ด๊ณผ ์ˆ˜์†Œ ๊ฒฐํ•ฉ ๋„คํŠธ์›Œํฌ ํƒ„์†Œ ๋‚˜๋…ธ ํŠœ๋ธŒ ๋‚ด๋ถ€์—์„œ๋Š” ๋ฌผ ๋ถ„์ž๊ฐ€ 1์ฐจ์› ์ˆœ์„œ๋กœ ๋ฐฐ์—ด๋˜์–ด ์ด๋™ํ•˜๋ฉฐ,

Condensed Matter Physics
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Faire levier sur les architectures logicielles pour guider et verifier le developpement dapplications SCC

์ด ๋…ผ๋ฌธ์€ SCC ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜ ๊ฐœ๋ฐœ์— ์žˆ์–ด ์†Œํ”„ํŠธ์›จ์–ด ์•„ํ‚คํ…์ฒ˜์˜ ์ค‘์š”์„ฑ๊ณผ ์ƒํ˜ธ์ž‘์šฉ ๊ณ„์•ฝ์ด๋ผ๋Š” ์ƒˆ๋กœ์šด ๊ฐœ๋…์„ ์ œ์‹œํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ์•„ํ‚คํ…์ฒ˜์™€ ๊ตฌํ˜„ ๊ฐ„์˜ ์ผ๊ด€์„ฑ์„ ๋ณด์žฅํ•˜๊ณ  ๊ฒ€์ฆ ๊ณผ์ •์„ ๊ฐ•ํ™”ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ํƒ๊ตฌํ•œ๋‹ค. SCC ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์€ ์™ธ๋ถ€ ํ™˜๊ฒฝ๊ณผ ์ƒํ˜ธ์ž‘์šฉํ•˜๋Š” ์‹œ์Šคํ…œ์œผ๋กœ, ํ™ˆ ์˜คํ† ๋ฉ”์ด์…˜, ๋กœ๋ด‡ ๊ณตํ•™, ์ž์œจ ์‹œ์Šคํ…œ ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ํ™œ์šฉ๋œ๋‹ค. 1. ์•„ํ‚คํ…์ฒ˜์™€ ๊ตฌํ˜„์˜ ์—ฐ๊ฒฐ SCC ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์€ ์ •๋ณด ์ˆ˜์ง‘, ์ƒํ™ฉ ์ธ์‹, ์ œ์–ด, ํ–‰๋™์ด๋ผ๋Š” ๋„ค ๊ฐ€์ง€ ์š”์†Œ๋กœ ๊ตฌ์„ฑ๋˜๋ฉฐ, ๊ฐ ์š”์†Œ๋Š” ๊ณ„์ธต์ ์œผ๋กœ ์ƒํ˜ธ์ž‘์šฉํ•œ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๊ณ ์ˆ˜์ค€ ์ถ”์ƒํ™”๋ฅผ ๊ตฌํ˜„์œผ

Computer Science Programming Languages
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An introduction to ML(n)BiCGStab

1. ์„œ๋ก  ๋ฐ ๋ฐฐ๊ฒฝ ML(n)BiCGStab๋Š” BiCGStab ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์ž์—ฐ์Šค๋Ÿฌ์šด ์ผ๋ฐ˜ํ™”๋กœ, Yeung๊ณผ Chan์— ์˜ํ•ด 1999๋…„ ์†Œ๊ฐœ๋˜์—ˆ๋‹ค. ์ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์—ฌ๋Ÿฌ ์‹œ์ž‘ ๋žœํฌ๋กœ์Šค ๊ณผ์ •์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋ฉฐ, van der Vorst์˜ BiCGStab์—์„œ ํŒŒ์ƒ๋˜์—ˆ์ง€๋งŒ ๋” ์•ˆ์ •์ ์ด๊ณ  ํšจ์œจ์ ์ธ ์„ฑ๋Šฅ์„ ์ œ๊ณตํ•œ๋‹ค. Sonneveld์™€ van der Vorst๊ฐ€ CGS์™€ BiCGStab๋ฅผ ๊ตฌ์„ฑํ•˜๋Š” ๋ฐ ์‚ฌ์šฉํ•œ ๊ธฐ๋ฒ•์ด ML(n)BiCGStab์—๋„ ์ ์šฉ๋˜์—ˆ๋‹ค. 2. ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์œ ๋„ ๋ฐ ๊ตฌ์กฐ ML(n)BiCGStab๋Š” ์—ฌ๋Ÿฌ ์‹œ์ž‘ ๋ฒกํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ Kryl

Computer Science Mathematics Numerical Analysis
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Handling uncertainties in SVM classification

๋ณธ ๋…ผ๋ฌธ์€ ๋ถˆํ™•์‹คํ•œ ๋ ˆ์ด๋ธ”์„ ๋‹ค๋ฃจ๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ์„œ ์ƒˆ๋กœ์šด SVM ๋ถ„๋ฅ˜ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ์‹ค์ œ ์ƒํ™œ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ฃผ์„ ๋ฌธ์ œ์— ๋Œ€์‘ํ•˜๋ ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ํ•ต์‹ฌ ์•„์ด๋””์–ด๋Š” ํ™•๋ฅ ์  ๋ ˆ์ด๋ธ”์„ ๋„์ž…ํ•˜์—ฌ ๋ถˆํ™•์‹ค์„ฑ์„ ์ฒ˜๋ฆฌํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. 1. ๋ฌธ์ œ ์ •์˜์™€ ๋ฐฐ๊ฒฝ ์ „ํ†ต์ ์ธ ๊ฐ๋… ํ•™์Šต์—์„œ๋Š” ๋ฐ์ดํ„ฐ ์„ธํŠธ๋ฅผ ์ „๋ฌธ๊ฐ€์—๊ฒŒ ๋ ˆ์ด๋ธ”๋งํ•˜๋Š” ๊ณผ์ •์ด ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Š” ๋งŽ์€ ์‘์šฉ ๋ถ„์•ผ์—์„œ ์–ด๋ ค์›€์„ ๊ฒช๊ฒŒ ๋˜๋ฉฐ, ํŠนํžˆ ์˜๋ฃŒ ์˜์ƒ ๋ถ„์•ผ์—์„œ๋Š” ์ฐธ์กฐ ๋ณ‘๋ฆฌ ์ •๋ณด ์—†์ด ์•…์„ฑ ์กฐ์ง์„ ์œค๊ณฝ์„ ์œผ๋กœ ๊ทธ๋ฆด ๋•Œ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์ ์ด ๋ฐœ์ƒํ•ฉ๋‹ˆ๋‹ค. ์ด๋กœ ์ธํ•ด ํ›ˆ๋ จ ๋ฐ์ดํ„ฐ์— ๋ถ€์ •ํ™•ํ•œ ํด

Machine Learning Computer Science
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Neutrinos and Their Charged Cousins: Are They Secret Sharers?

์ด ๋…ผ๋ฌธ์€ ์ค‘์„ฑ๋ฏธ์ž์™€ ๊ด€๋ จ๋œ ๋‹ค์–‘ํ•œ ์ด๋ก ์  ๋ฐ ์‹คํ—˜์  ์—ฐ๊ตฌ๋ฅผ ์ข…ํ•ฉ์ ์œผ๋กœ ๊ฒ€ํ† ํ•˜๊ณ , ๊ทธ ์ค‘์š”์„ฑ์„ ๊ฐ•์กฐํ•œ๋‹ค. ์ค‘์„ฑ๋ฏธ์ž๋Š” ๋ฌผ๋ฆฌํ•™์˜ ํ•ต์‹ฌ ๊ฐœ๋… ์ค‘ ํ•˜๋‚˜๋กœ, ๊ทธ ํŠน์„ฑ๊ณผ ํ–‰๋™์„ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์€ ํ‘œ์ค€ ๋ชจ๋ธ์˜ ์™„์ „์„ฑ์„ ํ™•๋ฆฝํ•˜๋Š” ๋ฐ ํ•„์ˆ˜์ ์ด๋‹ค. ์—ญ์‚ฌ์  ๋ฐฐ๊ฒฝ ๋…ผ๋ฌธ์€ ์ค‘์„ฑ๋ฏธ์ž์˜ ๋ฐœ๊ฒฌ๋ถ€ํ„ฐ ํ˜„์žฌ๊นŒ์ง€์˜ ๊ณผ์ •์„ ์„ค๋ช…ํ•œ๋‹ค. 1930๋…„๋Œ€์— ์ด๋ฅด๋Ÿฌ, ๋ฒ ํƒ€ ๋ถ•๊ดด์—์„œ ์—๋„ˆ์ง€ ๋ณด์กด ๋ฒ•์น™์ด ์œ„๋ฐ˜๋˜๋Š” ํ˜„์ƒ์— ๋Œ€ํ•œ ํ•ด๊ฒฐ์ฑ…์œผ๋กœ ์ค‘์„ฑ๋ฏธ์ž๊ฐ€ ์ œ์•ˆ๋˜์—ˆ๋‹ค. ์ดํ›„ ๋ ˆ์ด ๋ฐ์ด๋น„์Šค๋Š” ํƒœ์–‘ ์ค‘์„ฑ๋ฏธ์ž๋ฅผ ๊ฐ์ง€ํ–ˆ์ง€๋งŒ, ์˜ˆ์ƒ๋ณด๋‹ค ์ ์€ ์–‘์„ ๊ด€์ธกํ•จ์œผ๋กœ์จ 'ํƒœ์–‘ ์ค‘์„ฑ๋ฏธ์ž ๋ฌธ์ œ'๋ฅผ ์ผ์œผ์ผฐ๋‹ค. ์ค‘์„ฑ๋ฏธ์ž์˜

Physics HEP-PH
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Astronomy in the Church: the Clementine Sundial in Santa Maria degli Angeli, Rome

1. ์—ญ์‚ฌ์  ๋ฐฐ๊ฒฝ ๋…ผ๋ฌธ์€ ๋กœ๋งˆ์˜ ์‚ฐํƒ€ ๋งˆ๋ฆฌ์•„ ๋ฐ๊ธ€๋ฆฌ ์•™์ ค๋ฆฌ ์„ฑ๋‹น์— ์œ„์น˜ํ•œ ์„ฑ ํด๋ ˆ๋ฉ˜ํ‹ด ์ผ๋ฉด์‹œ๊ณ„์˜ ๊ฑด์„ค๊ณผ ๊ทธ ์˜๋ฏธ๋ฅผ ํƒ๊ตฌํ•œ๋‹ค. ์ด ์ผ๋ฉด์‹œ๊ณ„๋Š” ๊ตํ™ฉ ํด๋ ˆ๋ฉ˜์Šค 11์„ธ(1700 1721)๊ฐ€ ํ”„๋ž€์ฒด์Šค์ฝ” ๋น„์•ˆํ‚ค๋‹ˆ์—๊ฒŒ ๋ช…๋ นํ•˜์—ฌ ๊ฑด์„ค๋œ ๊ฒƒ์œผ๋กœ, ์„ฑ๋‹น์€ ๊ณ ๋„ ์ •๋ฐ€ํ•œ ์ฒœ๋ฌธํ•™์  ๊ด€์ธก์„ ์œ„ํ•œ ์ด์ƒ์ ์ธ ์žฅ์†Œ๋กœ ์„ ํƒ๋˜์—ˆ๋‹ค. ์ด ์ผ๋ฉด์‹œ๊ณ„๋Š” 1655๋…„ ๋ณผ๋กœ๋ƒ์˜ ์‚ฐ ํŽ˜ํŠธ๋ก ๋ฆฌ์˜ค์— ๊ฑด์„ค๋œ ์นด์‹œ๋‹ˆ์˜ ์ผ๋ฉด์‹œ๊ณ„๋ฅผ ๋ชจ๋ธ๋กœ ํ•˜์˜€์œผ๋ฉฐ, ๋น„์•ˆํ‚ค๋‹ˆ๋Š” ์ด๋ฅผ ๊ฐœ์„ ํ•˜์—ฌ ๋ณ„์˜ ํ†ต๊ณผ ๊ด€์ฐฐ์ด ๊ฐ€๋Šฅํ•˜๋„๋ก ๋งŒ๋“ค์—ˆ๋‹ค. 2. ๊ณผํ•™์  ํ™œ์šฉ ์„ฑ ํด๋ ˆ๋ฉ˜ํ‹ด ์ผ๋ฉด์‹œ๊ณ„๋Š” ๊ณ ๋„ ์ •๋ฐ€ํ•œ ์ฒœ๋ฌธํ•™์  ์ธก์ •์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ

Physics Astrophysics
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Model of Opinion Spreading in Social Networks

๋ณธ ๋…ผ๋ฌธ์€ ์†Œ์…œ ๋„คํŠธ์›Œํฌ์—์„œ ์ •๋ณด์™€ ์˜๊ฒฌ์ด ์–ด๋–ป๊ฒŒ ํ™•์‚ฐ๋˜๋Š”์ง€์— ๋Œ€ํ•œ ์‹ฌ๋„ ์žˆ๋Š” ๋ถ„์„์„ ์ œ๊ณตํ•œ๋‹ค. ํŠนํžˆ, ๊ธฐ์กด ๋ชจ๋ธ๋“ค์ด ๋…ผ์Ÿ ๊ฐ€๋Šฅํ•œ ์ •๋ณด์˜ ํ™•์‚ฐ ๊ณผ์ •์„ ์ •ํ™•ํžˆ ์„ค๋ช…ํ•˜์ง€ ๋ชปํ•˜๋Š” ํ•œ๊ณ„๋ฅผ ์ง€์ ํ•˜๊ณ , ์ด๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. 1. ๊ธฐ์กด ์—ฐ๊ตฌ์™€์˜ ์ฐจ๋ณ„ํ™” ์ „์—ผ๋ณ‘ ํ™•์‚ฐ ๋ชจ๋ธ : ์ด ๋ชจ๋ธ์€ ์ •๋ณด๋‚˜ ์˜๊ฒฌ์ด ํ™•์‚ฐ๋˜๋Š” ๊ณผ์ •์„ ์งˆ๋ณ‘ ์ „์—ผ๊ณผ ์œ ์‚ฌํ•˜๊ฒŒ ์„ค๋ช…ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ๋…ผ์Ÿ ๊ฐ€๋Šฅํ•œ ์ •๋ณด๋Š” ๋‹จ์ˆœํžˆ ๋…ธ์ถœ์— ์˜ํ•œ ๊ฐ์—ผ๋งŒ์œผ๋กœ ์„ค๋ช…ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๊ธฐ์ค€ ๋ชจ๋ธ (๋ณต์žกํ•œ ์ „์—ผ) : ์ด ๋ชจ๋ธ์€ ๊ฐœ์ธ์˜ ์˜๊ฒฌ ํ˜•์„ฑ์ด ๊ทธ์˜ ์ฃผ๋ณ€ ๋„คํŠธ์›Œํฌ ๊ตฌ์„ฑ์›๋“ค์˜ ์˜ํ–ฅ์„ ๋ฐ›

Model Physics Network Social Networks Computer Science
Rigidity analysis of HIV-1 protease

Rigidity analysis of HIV-1 protease

๋ณธ ๋…ผ๋ฌธ์€ HIV 1 ํ”„๋กœํ…Œ์•„์ œ์˜ ๊ตฌ์กฐ ๊ฐ•๋„๋ฅผ ๋ถ„์„ํ•˜๋Š” ๋ฐ ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. HIV 1 ํ”„๋กœํ…Œ์•„์ œ๋Š” AIDS ๋ฐ”์ด๋Ÿฌ์Šค์˜ ๋ณต์ œ ๊ณผ์ •์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ํšจ์†Œ๋กœ, ์ด ํšจ์†Œ์˜ ๊ตฌ์กฐ์™€ ๊ธฐ๋Šฅ์— ๋Œ€ํ•œ ์ดํ•ด๋Š” ํ•ญ๋ฐ”์ด๋Ÿฌ์Šค ์น˜๋ฃŒ๋ฒ• ๊ฐœ๋ฐœ์— ์žˆ์–ด ํ•ต์‹ฌ์ ์ธ ์š”์†Œ์ž…๋‹ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” FIRST(Flexible and Rigid Clusters) ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ํ™œ์šฉํ•˜์—ฌ HIV 1 ํ”„๋กœํ…Œ์•„์ œ์˜ ๊ฐ•๋„ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ–ˆ์Šต๋‹ˆ๋‹ค. FIRST ์†Œํ”„ํŠธ์›จ์–ด๋Š” ํŽ˜๋ธ” ๊ฒŒ์ž„ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋ฉฐ, ๋‹จ๋ฐฑ์งˆ ๋‚ด์—์„œ ๊ฒฐํ•ฉ์— ์˜ํ•œ ์ œ์•ฝ ์กฐ๊ฑด๊ณผ ์›์ž ์ž์œ ๋„๋ฅผ ๋งค์นญ์‹œ์ผœ ๊ฐ•๊ณ ํ•œ

Analysis Quantitative Biology
Diffusion of Confidential Information on Networks

Diffusion of Confidential Information on Networks

: ๋ณธ ๋…ผ๋ฌธ์€ ๋””์ง€ํ„ธ ์„ธ์ƒ์—์„œ ๊ฐœ์ธ์ •๋ณด์™€ ๋น„๋ฐ€์ •๋ณด๊ฐ€ ์–ด๋–ป๊ฒŒ ํผ์ ธ๋‚˜๊ฐ€๋Š”์ง€๋ฅผ ์ˆ˜ํ•™์ ์œผ๋กœ ๋ชจ๋ธ๋งํ•˜๊ณ  ๋ถ„์„ํ•˜๋Š” ๋ฐ ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ์ด๋Š” ์ •๋ณด ๊ธฐ์ˆ ์˜ ๋ฐœ์ „์œผ๋กœ ์ธํ•ด ์–ธ์ œ ์–ด๋””์„œ๋“  ์ •๋ณด๋ฅผ ๊ณต์œ ํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜๋ฉด์„œ, ๋ฏผ๊ฐํ•œ ์ฝ˜ํ…์ธ ์˜ ๋ณด์•ˆ ์œ ์ง€๊ฐ€ ๋”์šฑ ์ค‘์š”ํ•˜๊ฒŒ ๋œ ์ƒํ™ฉ์„ ๋ฐ˜์˜ํ•œ๋‹ค. ๋„คํŠธ์›Œํฌ ๊ตฌ์กฐ์™€ ๊ฐœ์ธ์ •๋ณด ํ™•์‚ฐ ๋ณธ ๋…ผ๋ฌธ์€ ๋„คํŠธ์›Œํฌ ์ด๋ก ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋‹ค์–‘ํ•œ ์œ ํ˜•์˜ ๋„คํŠธ์›Œํฌ์—์„œ ์ •๋ณด์˜ ํ™•์‚ฐ ํŒจํ„ด์„ ๋ถ„์„ํ•˜๊ณ  ์žˆ๋‹ค. ๋„คํŠธ์›Œํฌ๋Š” ์ (๋…ธ๋“œ)๊ณผ ์„ (์—ฃ์ง€)์œผ๋กœ ๊ตฌ์„ฑ๋˜๋ฉฐ, ๋…ธ๋“œ์™€ ์—ฃ์ง€์˜ ์—ฐ๊ฒฐ ๋ฐฉ์‹์— ๋”ฐ๋ผ ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ํ˜•ํƒœ๋กœ ๊ตฌ๋ถ„๋œ๋‹ค. 1. ์™„์ „ํ•œ ๋„คํŠธ์›Œํฌ : ๋ชจ

Computer Science Physics Social Networks Network
Centre for Mathematical Sciences India (CMS): Professor A.M. Mathais   75th Birthday

Centre for Mathematical Sciences India (CMS): Professor A.M. Mathais 75th Birthday

CMS๋Š” ์ธ๋„ ์ผ€๋ž„๋ผ์ฃผ์—์„œ ์ค‘์š”ํ•œ ์—ฐ๊ตฌ ๋ฐ ๊ต์œก ์„ผํ„ฐ๋กœ, ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ํ™œ๋ฐœํ•œ ํ™œ๋™์„ ํŽผ์น˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ์„ผํ„ฐ๋Š” 1977๋…„ ์„ค๋ฆฝ๋˜์–ด ํ˜„์žฌ๊นŒ์ง€ ์ˆ˜์‹ญ ๋…„ ๋™์•ˆ ์ง€์†์ ์œผ๋กœ ๋ฐœ์ „ํ•ด ์™”์Šต๋‹ˆ๋‹ค. CMS์˜ ์ฃผ์š” ํŠน์ง•๊ณผ ์—ญํ• ์€ ๋‹ค์Œ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค. 1. ์—ฐ๊ตฌ ๋ฐ ๊ต์œก ํ”„๋กœ๊ทธ๋žจ CMS๋Š” ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ํ•™๊ณ„์™€ ์‚ฐ์—…๊ณ„์— ๊ธฐ์—ฌํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ ์ˆ˜ํ•™๊ณผ ํ†ต๊ณ„ํ•™ ๋ถ„์•ผ์—์„œ ๋งŽ์€ ์„ฑ๊ณผ๋ฅผ ๊ฑฐ๋‘๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋Š” CMS์˜ ์ถœํŒ๋ฌผ๊ณผ ๊ฐ•์—ฐ ์‹œ๋ฆฌ์ฆˆ๋ฅผ ํ†ตํ•ด ํ™•์ธํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. CMS๋Š” ๋งค๋…„ 5์ฃผ๊ฐ„ ์ง„ํ–‰๋˜๋Š” SERC ํ•™๊ต๋ผ๋Š” ์—ฐ๊ตฌ ๋ฐฉํ–ฅ์„ฑ ๊ณผ

Mathematics
A short note on the two most recent large EQs of New Zealand (Mw=7.0 on   September 3rd, 2010 and Mw=6.1 on February 21st, 2011). A typical example of   tidally triggered large EQs by the M1 tidal component

A short note on the two most recent large EQs of New Zealand (Mw=7.0 on September 3rd, 2010 and Mw=6.1 on February 21st, 2011). A typical example of tidally triggered large EQs by the M1 tidal component

๋ณธ ๋…ผ๋ฌธ์€ ์กฐ์„ ํŒŒ๋™์ด ํฐ ์ง€์ง„ ๋ฐœ์ƒ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๋ฉฐ, ํŠนํžˆ M1 ์„ฑ๋ถ„์˜ ์—ญํ• ์„ ๊ฐ•์กฐํ•˜๊ณ  ์žˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” 2010๋…„๊ณผ 2011๋…„ ๋‰ด์งˆ๋žœ๋“œ์—์„œ ๋ฐœ์ƒํ•œ ๋‘ ์ฐจ๋ก€์˜ ๋Œ€๊ทœ๋ชจ ์ง€์ง„(Mw 7.0 ๋ฐ Mw 6.1)์„ ํ†ตํ•ด ์กฐ์„ ํŒŒ๋™์ด ์ง€์ง„ ๋ฐœ์ƒ์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š”์ง€ ๊ฒ€์ฆํ•˜๊ณ ์ž ํ•œ๋‹ค. ์„œ๋ก  ์„œ๋ก ์—์„œ๋Š” ๋Œ€๋ถ€๋ถ„์˜ ์ง€์ง„ํ•™๊ณ„๊ฐ€ ํฐ ์ง€์ง„์€ ์‹œ๊ฐ„์ ์œผ๋กœ ์˜ˆ์ธก ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ๋ณด๋Š” ๊ด€์ ์„ ์†Œ๊ฐœํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ†ต๊ณ„์ ์œผ๋กœ ์ž˜ ์•Œ๋ ค์ง„ ๋ฌผ๋ฆฌ์  ๋ฉ”์ปค๋‹ˆ์ฆ˜, ์ฆ‰ ํƒœ์–‘ ๋‹ฌ ๋ฐ ์ง€๊ตฌ ์ค‘๋ ฅ ์ƒํ˜ธ์ž‘์šฉ์œผ๋กœ ์ƒ์„ฑ๋˜๋Š” ์กฐ์„ ํŒŒ๋™์ด ํŠน์ • ์‹œ์ ์— ์ง€์ง„ ๋ฐœ์ƒ์„ ์œ ๋ฐœํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์„

Physics
Preservation of the Borel class under open-$LC$ functions

Preservation of the Borel class under open-$LC$ functions

๋ณธ ๋…ผ๋ฌธ์€ ๋ณด๋  ์ง‘ํ•ฉ๊ณผ ๊ทธ ๋ณด๋  ํด๋ž˜์Šค์— ๋Œ€ํ•œ ์ค‘์š”ํ•œ ์ด๋ก ์  ๊ฒฐ๊ณผ๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ํŠนํžˆ, ๊ฐœ๋ฐฉํ˜• ํด๋กœ์ฆˆ๋“œ(clopen) ํ•จ์ˆ˜๋ผ๋Š” ์ƒˆ๋กœ์šด ๊ฐœ๋…์„ ๋„์ž…ํ•˜์—ฌ, ์ด๋Ÿฌํ•œ ํ•จ์ˆ˜๊ฐ€ ํŠน์ • ์กฐ๊ฑด ํ•˜์—์„œ ๋ณด๋  ์ง‘ํ•ฉ์˜ ๋ณด๋  ํด๋ž˜์Šค๋ฅผ ์œ ์ง€ํ•˜๋Š” ๊ฒƒ์„ ์ฆ๋ช…ํ•˜๊ณ  ์žˆ๋‹ค. 1. ์„œ๋ก  ์„œ๋ก ์—์„œ๋Š” ๋ณด๋  ์ง‘ํ•ฉ๊ณผ ๊ทธ ๋ณด๋  ํด๋ž˜์Šค์— ๋Œ€ํ•œ ๊ธฐ์กด ์—ฐ๊ตฌ๋ฅผ ๊ฐ„๋žตํžˆ ์†Œ๊ฐœํ•œ๋‹ค. ํŠนํžˆ, ๋ณด๋  ์ง‘ํ•ฉ C์˜ ๋ถ€๋ถ„ ์ง‘ํ•ฉ X์™€ ์—ฐ์† ํ•จ์ˆ˜ f: X โ†’ Y๊ฐ€ ์ฃผ์–ด์กŒ์„ ๋•Œ, ๋งŒ์•ฝ ๋ชจ๋“  ํด๋กญ์—” ์„ธํŠธ U์˜ ์ด๋ฏธ์ง€ f(U)๊ฐ€ ์—ด๋ฆฐ ์ง‘ํ•ฉ์ด๊ฑฐ๋‚˜ ๋‹ซํžŒ ์ง‘ํ•ฉ์ด๋ผ๋ฉด, Y๋Š” ๋™์ผํ•œ ๋ณด๋  ํด๋ž˜์Šค๋ฅผ ๊ฐ€์ง„๋‹ค๋Š” ๊ฒฐ๊ณผ๋ฅผ ์–ธ๊ธ‰

Mathematics
A Reformulation of the Arora-Rao-Vazirani Structure Theorem

A Reformulation of the Arora-Rao-Vazirani Structure Theorem

๋งค๋ ฅ์ ์ธ ํ•œ๊ธ€ ์ œ๋ชฉ: ์•„๋กœ๋ผ ๋ผ์˜ค ๋ฐ”์ง€๋ผ๋‹ˆ ๊ตฌ์กฐ ์ •๋ฆฌ์˜ ๊ทธ๋ž˜ํ”„ ์ด๋ก ์  ์žฌํ•ด์„ ์ดˆ๋ก ์ „์ฒด ๋ฒˆ์—ญ ๋ฐ ์ •๋ฆฌ: ๋ณธ ๋…ผ๋ฌธ์€ ์•„๋กœ๋ผ, ๋ผ์˜ค, ๋ฐ”์ง€๋ผ๋‹ˆ(ARV)๊ฐ€ ์ฆ๋ช…ํ•œ ๊ตฌ์กฐ ์ •๋ฆฌ๋ฅผ ํ™•์žฅ๋œ ๊ทธ๋ž˜ํ”„ ๊ฐœ๋…์œผ๋กœ ์žฌํ•ด์„ํ•ฉ๋‹ˆ๋‹ค. ARV๋Š” ๊ท ํ˜• ๋ถ„๋ฆฌ ๋ฌธ์ œ์™€ ๊ท ์ผ ๊ฐ€์žฅ ํฌ๋ฐ• ์ ˆ๋‹จ ๋ฌธ์ œ์— ๋Œ€ํ•ด O(โˆšlog n) ๊ทผ์‚ฌ์น˜๋ฅผ ๋„์ถœํ–ˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋“ค์˜ ๊ฒฐ๊ณผ๋Š” ์‚ผ๊ฐ ๋ถ€๋“ฑ์‹์„ ๋งŒ์กฑํ•˜๋Š” ์  ์ง‘ํ•ฉ์˜ ๊ธฐํ•˜ํ•™์  ์ง„์ˆ ์— ๊ธฐ๋ฐ˜ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ดํ›„ ๊ทผ์‚ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๋ฉ”ํŠธ๋ฆญ ์ž„๋ฒ ๋”ฉ ์—ฐ๊ตฌ์˜ ํ† ๋Œ€๊ฐ€ ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ARV ๊ตฌ์กฐ ์ •๋ฆฌ๋ฅผ ํ™•์žฅ๋œ ๊ทธ๋ž˜ํ”„ GV,ฯต์—์„œ ํฐ ์ง‘ํ•ฉ์˜ ํ™•์žฅ ๊ฐœ๋…์œผ

Computer Science Discrete Mathematics
A Table-top Blast Driven Shock Tube

A Table-top Blast Driven Shock Tube

๋ณธ ๋…ผ๋ฌธ์€ ํ…Œ์ด๋ธ”ํƒ‘ ์ถฉ๊ฒฉ๊ด€์„ ์„ค๊ณ„ํ•˜๊ณ  ์„ฑ๋Šฅ ๋ถ„์„ํ•œ ๋‚ด์šฉ์„ ๋‹ค๋ฃจ๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋Š” ๊ธฐ์กด์˜ ์••์ถ• ๊ณต๊ธฐ ์ถ”์ง„ ๋ฐ ํญ๋ฐœ ์ถ”์ง„ ์ถฉ๊ฒฉ๊ด€๋ณด๋‹ค ๋” ๊ฐ„ํŽธํ•˜๊ณ  ์ ‘๊ทผ ๊ฐ€๋Šฅํ•œ ์‹คํ—˜ ์žฅ์น˜๋ฅผ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, ์ด ์ถฉ๊ฒฉ๊ด€์€ ์†Œ์ด ์ดˆ์ ์— ํญ๋ฐœ์„ฑ ํ˜ผํ•ฉ๋ฌผ์„ ์‚ฌ์šฉํ•˜์—ฌ ์ด์—ด ์ž์ฒด๋ฅผ ์ถฉ๊ฒฉ๊ด€์œผ๋กœ ํ™œ์šฉํ•˜๋Š” ๋ฐฉ์‹์„ ํ†ตํ•ด, ์‹คํ—˜ ๋Œ€์ƒ์˜ ํŠน์ • ์˜์—ญ์— ํญ๋ฐœํŒŒ๋ฅผ ๊ตญ์†Œ์ ์œผ๋กœ ์ ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ๋…์ฐฝ์ ์ž…๋‹ˆ๋‹ค. 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ์ด๋ผํฌ์™€ ์•„ํ”„๊ฐ€๋‹ˆ์Šคํƒ„์—์„œ ๋ฐœ์ƒํ•œ ์™ธ์ƒ์„ฑ ๋‡Œ ์†์ƒ์˜ ์ฆ๊ฐ€๋Š” ํญ๋ฐœํŒŒ์— ๋Œ€ํ•œ ์ƒ๋ฌผํ•™์  ์˜ํ–ฅ๊ณผ ๋‹ค์–‘ํ•œ ์žฌ๋ฃŒ์˜ ํญ๋ฐœํŒŒ ์ „๋‹ฌ ํŠน์„ฑ์„ ์—ฐ๊ตฌํ•˜๋Š” ํ•„์š”์„ฑ์„ ๊ฐ•์กฐํ–ˆ์Šต๋‹ˆ๋‹ค. ๊ธฐ

Physics
5,000,000 Delays -- Some Statistics

5,000,000 Delays -- Some Statistics

: ๋ณธ ๋…ผ๋ฌธ์€ VLBI ์ง€์—ฐ ์‹œ๊ฐ„ ๋ฐ์ดํ„ฐ๋ฅผ ํ†ตํ•ด ์ฒœ๋ฌธํ•™์˜ ์žฅ๊ธฐ์  ๋ฐœ์ „์„ ๋ถ„์„ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. 5,000,000๊ฐœ ์ด์ƒ์˜ VLBI ์ง€์—ฐ ์‹œ๊ฐ„์ด ์ˆ˜์ง‘๋œ ๊ฒƒ์€ ์ฒœ๋ฌธํ•™ ๋ถ„์•ผ์—์„œ ์ค‘์š”ํ•œ ์ด์ •ํ‘œ๋กœ, ์ด๋ฅผ ํ†ตํ•ด ๋‹ค์–‘ํ•œ ํ†ต๊ณ„์™€ ํŒจํ„ด์„ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ๊ด€์ธก ์„ธ์…˜๊ณผ ์—ญ๋Ÿ‰ ๋ถ„์„ ๊ด€์ธก ์„ธ์…˜์€ 24์‹œ๊ฐ„ ์„ธ์…˜์ด ๊ฐ€์žฅ ๋งŽ์ด ์ด๋ฃจ์–ด์กŒ์œผ๋ฉฐ, ์ด๋Š” ์—ฐ๊ตฌ์˜ ์ค‘์š”์„ฑ์„ ๋ฐ˜์˜ํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, 18์‹œ๊ฐ„ ์ด์ƒ ์ง€์†๋œ ์„ธ์…˜ ์ˆ˜๊ฐ€ ์ „์ฒด ์„ธ์…˜ ์ค‘ ์ƒ๋‹น ๋ถ€๋ถ„์„ ์ฐจ์ง€ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์žฅ๊ธฐ ๊ด€์ธก์€ ๋ฐ์ดํ„ฐ์˜ ์ •ํ™•์„ฑ๊ณผ ์‹ ๋ขฐ์„ฑ์„ ๋†’์ด๋Š” ๋ฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•ฉ๋‹ˆ๋‹ค. ์—ญ๋Ÿ‰ ๋ถ„์„์—์„œ

Physics
Primary Initiation of Submarine Canyons

Primary Initiation of Submarine Canyons

1. ์ด๋ก ์  ๋ฐฐ๊ฒฝ๊ณผ ๊ฐ€์„ค ์ €์ž์ธ J. Marvin Herndon๋Š” ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ง€๊ตฌ์˜ ์ดˆ๊ธฐ ๊ธฐ์›์„ ๋ชฉ์„ฑ๊ณผ ์œ ์‚ฌํ•œ ๊ฐ€์Šคํ–‰์„ฑ์œผ๋กœ ์ œ์•ˆํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฐ€์„ค์€ ํƒœ์–‘๊ณ„ ๋‚ด์™ธ์—์„œ ๊ด€์ฐฐ๋˜๋Š” ๋‹ค์–‘ํ•œ ํ–‰์„ฑ ๊ตฌ์กฐ์™€ ์ผ์น˜ํ•˜๋ฉฐ, ํŠนํžˆ ์™ธ๊ณ„ ํ–‰์„ฑ ์ค‘ ์ผ๋ถ€๊ฐ€ ๋ณ„๊ณผ ๊ฐ™์€ ํฌ๊ธฐ์˜ ๊ฐ€์Šคํ–‰์„ฑ์„ ํฌํ•จํ•˜๊ณ  ์žˆ๋‹ค๋Š” ์ ์„ ๊ทผ๊ฑฐ๋กœ ํ•œ๋‹ค. Herndon์€ ์ด ์ดˆ๊ธฐ ์ƒํƒœ์—์„œ ์ง€๊ตฌ๋Š” ์•ฝ 300๊ฐœ์˜ ํ˜„์žฌ ์ง€๊ตฌ ์งˆ๋Ÿ‰์— ํ•ด๋‹นํ•˜๋Š” ์ˆ˜์†Œ์™€ ํ—ฌ๋ฅจ ๋“ฑ ํœ˜๋ฐœ์„ฑ ์›์†Œ๋ฅผ ํฌํ•จํ•œ ๊ฑฐ๋Œ€ํ•œ ๊ฐ€์Šค ํ–‰์„ฑ์ด์—ˆ๋‹ค๊ณ  ์ฃผ์žฅํ•œ๋‹ค. 2. ์ „์ง€๊ตฌ ํƒˆ๊ธฐ ์—ญํ•™ ์ „์ง€๊ตฌ ํƒˆ๊ธฐ ์—ญํ•™์€ ์ด ์ดˆ๊ธฐ ์ƒํƒœ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ค‘์š”ํ•œ ์—ญํ•™์ 

Physics
Ohmic Power of Ideal Pulsars

Ohmic Power of Ideal Pulsars

๋งค๋ ฅ์ ์ธ ํ•œ๊ธ€ ์ œ๋ชฉ: ์ด์ƒ ํŽ„์‚ฌ์˜ ์˜ค๋ฏน ํŒŒ์›Œ ์ดˆ๋ก ์ „์ฒด ๋ฒˆ์—ญ ๋ฐ ์ •๋ฆฌ: ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์ด์ƒ์ ์ธ ํŽ„์‚ฌ ์ž๊ธฐ๊ถŒ์—์„œ ์˜ค๋ฏน ํžˆํŒ…์„ ์˜ˆ์ธกํ•˜๋Š” ์ƒˆ๋กœ์šด ํ•ด๊ฒฐ์ฑ…์„ ์ œ์‹œํ•œ๋‹ค. ํ‘œ์ค€ ์ •์  ํž˜์ž์œ  ๋ฐฉ์ •์‹์„ ์‚ฌ์šฉํ•˜์—ฌ ์ถ•๋Œ€์นญ์ ์ธ ์ด์ƒ ํŽ„์‚ฌ ์ž๊ธฐ๊ถŒ์„ ๊ณ„์‚ฐํ•˜์ง€๋งŒ, ์ ๋„์—์„œ์˜ ์ƒˆ๋กœ์šด ๊ฒฝ๊ณ„ ์กฐ๊ฑด์„ ๋„์ž…ํ•œ๋‹ค. ์ด ์ƒˆ๋กœ์šด ํ•ด๊ฒฐ์ฑ…์— ๋”ฐ๋ฅด๋ฉด, ์•ฝ 50%์˜ ํฌ์ธํŒ… ํ”Œ๋Ÿญ์ด ์ ๋„ ์ „๋ฅ˜์ธต(๋น› ์›ํ†ต ๋ฐ–)์—์„œ ์†์‹ค๋˜๋ฉฐ, ๊ทธ ์ค‘ ์•ฝ 10%๋Š” 1~1.5 ๋น› ์›ํ†ต ๋ฐ˜๊ฒฝ ๋‚ด์—์„œ ์†์‹ค๋œ๋‹ค. ์˜ค๋ฏน ์—ด์€ ๋ณต์‚ฌ, ์Œ์ƒ์„ฑ ๋ฐ ์ „ํ•˜ ๊ฐ€์†์— ํก์ˆ˜๋˜๋Š” ๊ฒƒ์œผ๋กœ ์ถ”์ •๋˜์ง€๋งŒ, ์ •ํ™•ํ•œ ๋น„์œจ์€ ๋ถˆ๋ถ„๋ช…ํ•˜๋‹ค. ์‹ฌ

Astrophysics
Modelling the synchrotron emission from O-star colliding wind binaries

Modelling the synchrotron emission from O-star colliding wind binaries

: ๋ณธ ๋…ผ๋ฌธ์€ Cyg OB2 No. 9๋ผ๋Š” Oํ˜• ๋ณ„ ์ถฉ๋Œ ๋ฐ”๋žŒ ์ด์ค‘์„ฑ ์‹œ์Šคํ…œ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋™์กฐ๋ณต์‚ฌ ๋ฐฉ์ถœ์— ๋Œ€ํ•œ ๋ชจ๋ธ๋ง ์—ฐ๊ตฌ๋ฅผ ๋‹ค๋ฃน๋‹ˆ๋‹ค. ์ด ์‹œ์Šคํ…œ์€ 1984๋…„ ๋น„์—ด์  ๋ฐฉ์ถœ์›์œผ๋กœ ์ฒ˜์Œ ๋ฐœ๊ฒฌ๋˜์—ˆ์œผ๋ฉฐ, Van Loo ์™ธ (2008)์˜ VLA ๊ด€์ธก์„ ํ†ตํ•ด 2.35๋…„ ์ฃผ๊ธฐ๋ฅผ ๊ฐ€์ง„ ๋™์กฐ๋ณต์‚ฌ ๋ฐœ์‚ฐ์„ฑ์„ ํ™•์ธํ•˜์˜€์Šต๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ฃผ๊ธฐ์ ์ธ ๋ณ€๋™์€ ๋ณ„ํ’ ์ถฉ๋Œ์ด ๋น„์—ด์  ๋ผ๋””์˜ค ๋ฐฉ์ถœ์˜ ์›์ธ์ž„์„ ์‹œ์‚ฌํ•˜๋ฉฐ, ์ด๋Š” Eichler & Usov (1993)์™€ Dougherty ์™ธ (2003), Pittard ์™ธ (2006) ๋“ฑ์˜ ์—ฐ๊ตฌ์—์„œ ์ œ์‹œ๋œ ๊ฐ€์„ค๊ณผ ์ผ์น˜ํ•ฉ๋‹ˆ๋‹ค.

Model Astrophysics
Another approach to parametric Bing and Krasinkiewicz maps

Another approach to parametric Bing and Krasinkiewicz maps

๋ณธ ๋…ผ๋ฌธ์€ ๋น„๋‹(Bing) ๋งต๊ณผ ํฌ๋ผ์‹ ํ‚ค์—๋น„์น˜(Krasinkiewicz) ๋งต์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•˜๋ฉฐ, ํŠนํžˆ ํŒŒ๋ผ์ฝคํŒฉํŠธ ๊ณต๊ฐ„์—์„œ ์ด๋Ÿฌํ•œ ๋งต๋“ค์ด ์–ด๋–ป๊ฒŒ ํ•จ์ˆ˜ ๊ณต๊ฐ„ ๋‚ด์—์„œ ๋ฐ€์ง‘ ๋ถ€๋ถ„ ์ง‘ํ•ฉ์„ ํ˜•์„ฑํ•˜๋Š”์ง€ ๋ถ„์„ํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” Pasynkov์˜ ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ, ๋น„๋‹ ๋ฐ ํฌ๋ผ์‹ ํ‚ค์—๋น„์น˜ ๋งต์ด ์–ด๋–ค ์กฐ๊ฑด ํ•˜์—์„œ ํŠน์ • ์„ฑ์งˆ์„ ๊ฐ–๊ฒŒ ๋˜๋Š”์ง€๋ฅผ ํƒ๊ตฌํ•œ๋‹ค. 1. ๋„์ž… ๋ฐ ๋ฐฐ๊ฒฝ ๋…ผ๋ฌธ์€ ๋น„๋‹ ๋งต๊ณผ ํฌ๋ผ์‹ ํ‚ค์—๋น„์น˜ ๋งต์˜ ์ •์˜์™€ ๊ด€๋ จ๋œ ์ด๋ก ์  ๋ฐฐ๊ฒฝ์„ ์„ค๋ช…ํ•œ๋‹ค. ๋น„๋‹ ๋งต์€ ์ปดํŒฉํŠธ ๊ณต๊ฐ„ ์‚ฌ์ด์˜ ๋งต ์ค‘, ๋ชจ๋“  ์„ฌ์œ ๊ฐ€ ๋น„๋‹ ๊ณต๊ฐ„์ธ ๊ฒฝ์šฐ๋ฅผ ๋งํ•˜๋ฉฐ, ์—ฌ๊ธฐ์„œ ๋น„๋‹ ๊ณต

Mathematics
Determination of Different Biological Factors on the Base of Dried Blood   Spot Technology

Determination of Different Biological Factors on the Base of Dried Blood Spot Technology

๋ณธ ๋…ผ๋ฌธ์€ ๊ฑด์กฐํ˜ˆ์•ก๋ฐฉ์šธ(DBS) ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•œ ํ˜ˆ๋Ÿ‰ ๊ณ„์‚ฐ ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์‹ฌ๋„ ์žˆ๋Š” ๋ถ„์„์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. DBS๋Š” ํ™˜์ž๊ฐ€ ์ง์ ‘ ํ˜ˆ์•ก ์ƒ˜ํ”Œ์„ ์ฑ„์ทจํ•˜๊ณ  ์ด๋ฅผ ์—ฌ๊ณผ์ง€๋‚˜ ์„ธํฌ์งˆ ์•„์„ธํ…Œ์ดํŠธ ๋ง‰ ๋“ฑ์— ๋ฐฉ์šธ ํ˜•ํƒœ๋กœ ๊ฑด์กฐํ•˜์—ฌ ์‹คํ—˜์‹ค๋กœ ๋ณด๋‚ด๋Š” ๊ธฐ์ˆ ์ž…๋‹ˆ๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ๋Œ€์‚ฌ๋ฌผ์งˆ, ํ˜ธ๋ฅด๋ชฌ, ํ˜ˆ๋‹น์ˆ˜์น˜, ๋ฉด์—ญ ์ฒด๊ณ„ ์ง€ํ‘œ ๋“ฑ์˜ ๋‹ค์–‘ํ•œ ์ƒ๋ฌผํ•™์  ํŠน์„ฑ์„ ๋ถ„์„ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐํšŒ๋ฅผ ์ œ๊ณตํ•˜๋ฉฐ, ํŠนํžˆ DNA ๋ฐ RNA ๋ถ„์„ ๊ฐ€๋Šฅ์„ฑ์œผ๋กœ ์—์ด์ฆˆ๋‚˜ ๊ฐ„์—ผ๊ณผ ๊ฐ™์€ ๊ฐ์—ผ๋ณ‘์˜ ๋Œ€๋Ÿ‰ ์กฐ์‚ฌ๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” DBS ๋ฐฉ์šธ์˜ ์ฒด์ ์„ ๋„์ง• ์žฅ์น˜ ์—†์ด ๊ณ„์‚ฐํ•˜๋Š” ๋ณดํŽธ์ ์ธ ๋ฐฉ๋ฒ•์„ ๊ฐœ๋ฐœํ•˜๊ณ ์ž

Statistics Physics Mathematics
Massive particle production from accelerated sources in high magnetic   fields

Massive particle production from accelerated sources in high magnetic fields

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ๋ณธ ๋…ผ๋ฌธ์€ ๊ณ ๊ฐ•๋„ ์ž๊ธฐ์žฅ ๋‚ด์—์„œ ๊ฐ€์†๋œ ์–‘์„ฑ์ž๊ฐ€ ์ค‘์„ฑ ํ”ผ์˜จ์„ ๋ฐฉ์ถœํ•˜๋Š” ๊ณผ์ •์— ๋Œ€ํ•œ ๋ฐ˜๊ณ ์ „์  ๊ทผ์‚ฌ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•œ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์ด๋Š” ์ž…์ž ๋ฌผ๋ฆฌํ•™์˜ ์ค‘์š”ํ•œ ๋ถ€๋ถ„์œผ๋กœ, ํŠนํžˆ ๊ฐ•๋ ฅํ•œ ์ž๊ธฐ์žฅ์ด ์กด์žฌํ•˜๋Š” ์ฒœ์ฒด๋ฌผ๋ฆฌํ•™์  ํ™˜๊ฒฝ์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ํ˜„์ƒ์„ ์ดํ•ดํ•˜๋Š”๋ฐ ์ค‘์š”ํ•˜๋‹ค. 2. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• ๋ฐ˜๊ณ ์ „์  ๊ทผ์‚ฌ : ์›๋ณธ ์ž…์ž๋Š” ๊ณ ์ „ ์ „๋ฅ˜์™€ ์ง€์ •๋œ ๊ถค์ ์„ ๊ฐ€์ง„ ์ž…์ž๋กœ ๋ฌ˜์‚ฌ๋˜๋ฉฐ, ๋ฐฉ์ถœ๋˜๋Š” ์ž…์ž๋“ค์€ ์™„์ „ํžˆ ์–‘์žํ™”๋œ ํ•„๋“œ๋กœ ๊ฐ„์ฃผ๋œ๋‹ค. ์ž…์ž ์ƒ์„ฑ ์กฐ๊ฑด : ์ž๊ธฐ์žฅ ๋‚ด์—์„œ ์›€์ง์ด๋Š” ๊ฒฝ์šฐ, ๋ฐ˜๊ณ ์ „์  ๊ทผ์‚ฌ์˜ ์œ ํšจ์„ฑ์€ ํŠน์ • ์กฐ๊ฑด์ด ์ถฉ์กฑ๋  ๋•Œ ๋ณด

Astrophysics
Convex Polyhedra Realizing Given Face Areas

Convex Polyhedra Realizing Given Face Areas

: ์ด ๋…ผ๋ฌธ์€ 3์ฐจ์› ๊ณต๊ฐ„์—์„œ ํŠน์ • ๋ฉด์ ์ด ์ฃผ์–ด์กŒ์„ ๋•Œ, ์ด ๋ฉด์ ์ด ๋‹ค๊ฐํ˜•์˜ ๊ฐ ๋ฉด์˜ ๋ฉด์ ์„ ๋‚˜ํƒ€๋‚ด๋Š” ์กฐ๊ฑด์— ๋Œ€ํ•ด ๊นŠ๊ฒŒ ํƒ๊ตฌํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, Aโ‚ โ‰ค i > 1 Aแตข์ธ ๊ฒฝ์šฐ, ์ด ๋ฒกํ„ฐ๋กœ ๊ตฌ์„ฑ๋œ ๋‹ค๊ฐํ˜•์ด ์กด์žฌํ•œ๋‹ค๋Š” ๊ฒƒ์„ ์ฆ๋ช…ํ•ฉ๋‹ˆ๋‹ค. ์ฃผ์š” ๊ฐœ๋…๊ณผ ์ •๋ฆฌ Minkowski์˜ ์ •๋ฆฌ : Minkowski์˜ ์ •๋ฆฌ๋Š” ์ฃผ์–ด์ง„ ๋ฉด์ ๊ณผ ๋‹จ์œ„ ๋ฒ•์„  ๋ฒกํ„ฐ๋ฅผ ํ†ตํ•ด ๊ณ ์œ ํ•œ ๋‹ซํžŒ ๋‹ค๊ฐํ˜•์„ ํ˜•์„ฑํ•  ์ˆ˜ ์žˆ๋Š” ์กฐ๊ฑด์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋ฅผ '์™„์ „ํžˆ ๊ท ํ˜• ์žกํžŒ' ๋ฒกํ„ฐ๋กœ ์žฌํ•ด์„ํ•˜์—ฌ, ๊ฐ ๋ฉด์˜ ๋ฉด์ ์ด ์ฃผ์–ด์กŒ์„ ๋•Œ, ๊ทธ ๋ฉด์ ์„ ๊ฐ€์ง„ ๋‹ค๊ฐํ˜•์ด ์กด์žฌํ•˜๋Š”์ง€ ํŒ๋‹จํ• 

Computer Science Discrete Mathematics
O Algoritmo usado no programa de criptografia PASME

O Algoritmo usado no programa de criptografia PASME

์ด ๋…ผ๋ฌธ์€ PASME ๋„๊ตฌ์˜ ํ•ต์‹ฌ ์•”ํ˜ธํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ƒ์„ธํžˆ ์„ค๋ช…ํ•˜๊ณ  ์žˆ๋‹ค. ์ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ํฐ ์ˆ˜์˜ ์†Œ์ธ์ˆ˜ ๋ถ„ํ•ด๋ผ๋Š” ๊ณ„์‚ฐ์ ์œผ๋กœ ์–ด๋ ค์šด ๋ฌธ์ œ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ฐ์ดํ„ฐ๋ฅผ ์•ˆ์ „ํ•˜๊ฒŒ ์•”ํ˜ธํ™”ํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๊ธฐ์กด ์•”ํ˜ธํ™” ๋ฐฉ๋ฒ•๋“ค์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ์œผ๋ฉฐ, ํŠนํžˆ ๋นˆ๋„ ๋ถ„์„๊ณผ ๊ฐ™์€ ๊ณต๊ฒฉ์— ์ทจ์•ฝํ•œ ๊ณ ์ „์ ์ธ ์•”ํ˜ธํ™” ๋ฐฉ๋ฒ•๋“ค๋ณด๋‹ค ๋ณด์•ˆ์„ฑ์ด ํ–ฅ์ƒ๋˜์—ˆ๋‹ค. I. ์„œ๋ก  ์„œ๋ก ์—์„œ๋Š” ์•”ํ˜ธํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํ•„์š”์„ฑ๊ณผ ๋‹ค์–‘ํ•œ ์•”ํ˜ธํ™” ๊ธฐ๋ฒ•๋“ค์˜ ํ•œ๊ณ„๋ฅผ ์„ค๋ช…ํ•œ๋‹ค. ํŠนํžˆ, ๋ฌธ์ž ์ง‘ํ•ฉ ์ˆœ์„œ ๋ณ€๊ฒฝ์ด๋‚˜ ์ด์ง„ ๋ฉ”์‹œ์ง€์—์„œ ๋น„ํŠธ ์—ญ์ „ ๋“ฑ์˜ ๋ฐฉ๋ฒ•๋“ค์€ ๋นˆ๋„ ๋ถ„์„ ๊ณต๊ฒฉ์— ์ทจ์•ฝํ•˜๋‹ค๋Š” ์ ์„

Mathematics Computer Science Cryptography and Security
The impact of a carbon nanotube on the cholesterol domain localized on a   protein surface

The impact of a carbon nanotube on the cholesterol domain localized on a protein surface

๋งค๋ ฅ์ ์ธ ํ•œ๊ธ€ ์ œ๋ชฉ: ํƒ„์†Œ ๋‚˜๋…ธํŠœ๋ธŒ์˜ ์กด์žฌ๊ฐ€ ์ฝœ๋ ˆ์Šคํ…Œ๋กค ๋„๋ฉ”์ธ ๋ถ„ํฌ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ ์ดˆ๋ก ์ „์ฒด ๋ฒˆ์—ญ ๋ฐ ์ •๋ฆฌ: ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํƒ„์†Œ ๋‚˜๋…ธํŠœ๋ธŒ(CNT)๊ฐ€ ๋‹จ๋ฐฑ์งˆ ํ‘œ๋ฉด์—์„œ ํ˜•์„ฑ๋œ ์ฝœ๋ ˆ์Šคํ…Œ๋กค ๋„๋ฉ”์ธ์˜ ๋ถ„ํฌ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์ž ๋™์—ญํ•™(MD) ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ์กฐ์‚ฌํ–ˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, ํ˜ˆ๊ด€ ๋‚ดํ”ผ ์„ธํฌ์˜ ์–‡์€ ์ธต์„ ํ˜•์„ฑํ•˜๋Š” ๋‹จ๋ฐฑ์งˆ 1LQV๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ, ํƒ„์†Œ ๋‚˜๋…ธํŠœ๋ธŒ๊ฐ€ ์ฝœ๋ ˆ์Šคํ…Œ๋กค ๋ถ„์ž์˜ ๋ถ„ํฌ์™€ ์—ญํ•™์— ์–ด๋–ค ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€ ํ™•์ธํ–ˆ์Šต๋‹ˆ๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ, ํƒ„์†Œ ๋‚˜๋…ธํŠœ๋ธŒ์˜ ์กด์žฌ๋กœ ์ธํ•ด ๋งŽ์€ ์ฝœ๋ ˆ์Šคํ…Œ๋กค์ด ๋‹จ๋ฐฑ์งˆ ํ‘œ๋ฉด์—์„œ ์ด๋™ํ•˜์—ฌ ๋‚˜๋…ธํŠœ๋ธŒ ์ฃผ๋ณ€์œผ๋กœ ์นจ์ฐฉ๋˜๋Š”

Condensed Matter Physics
Information Retrieval of Jumbled Words

Information Retrieval of Jumbled Words

์ด ๋…ผ๋ฌธ์€ ํ˜ผ๋ˆ๋œ ๋‹จ์–ด์˜ ๋…ํ•ด์— ๋Œ€ํ•œ ์‹ฌ๋„ ์žˆ๋Š” ๋ถ„์„์„ ์ œ๊ณตํ•˜๋ฉฐ, ํŠนํžˆ ์ธ๊ฐ„ ๋‡Œ๊ฐ€ ์–ด๋–ป๊ฒŒ ์ด๋Ÿฌํ•œ ํ˜ผ๋ž€์Šค๋Ÿฌ์šด ํ…์ŠคํŠธ๋ฅผ ์ฒ˜๋ฆฌํ•˜๋Š”์ง€์— ๋Œ€ํ•ด ์ƒ์„ธํžˆ ์„ค๋ช…ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์—ฐ๊ตฌ๋Š” ์บ ๋ธŒ๋ฆฌ์ง€ ๋Œ€ํ•™๊ต์˜ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ, ๋‹จ์–ด ๋‚ด ๊ธ€์ž์˜ ์ˆœ์„œ๊ฐ€ ๋ฐ”๋€Œ์–ด๋„ ์ฒซ ๋ฒˆ์งธ์™€ ๋งˆ์ง€๋ง‰ ๊ธ€์ž๊ฐ€ ์˜ฌ๋ฐ”๋ฅธ ์œ„์น˜์— ์žˆ์œผ๋ฉด ๋…ํ•ด์— ํฐ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š๋Š”๋‹ค๋Š” ์‚ฌ์‹ค์„ ๋ฐํ˜€๋ƒ…๋‹ˆ๋‹ค. ์ด๋Š” ์ธ๊ฐ„ ๋‡Œ๊ฐ€ ์ „์ฒด ๋‹จ์–ด๋กœ ์ธ์‹ํ•˜๋Š” ๋Šฅ๋ ฅ ๋•๋ถ„์ด๋ฉฐ, ์ด๋Ÿฌํ•œ ํ˜„์ƒ์€ ์—ฌ๋Ÿฌ ์š”์ธ๋“ค์— ์˜ํ•ด ์„ค๋ช…๋  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. 1. ๋ฌธ๋ฒ• ๊ตฌ์กฐ ๋ณด์กด ํ˜ผ๋ˆ๋œ ํ…์ŠคํŠธ์—์„œ๋„ ๋ฌธ์žฅ์˜ ๋ฌธ๋ฒ•์  ๊ตฌ์กฐ๊ฐ€ ์œ ์ง€๋˜๋ฉด ๋…์ž๋Š” ๋‹ค์Œ ๋‹จ์–ด๋ฅผ

Computer Science Information Retrieval
Status of GDL - GNU Data Language

Status of GDL - GNU Data Language

: GDL์€ ์ฒœ๋ฌธํ•™ ๋ถ„์•ผ์—์„œ IDL์˜ ๋ฌด๋ฃŒ ๋Œ€์ฒด ์†Œํ”„ํŠธ์›จ์–ด๋กœ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ ๋ถ„์„๊ณผ ์‹œ๊ฐํ™” ์ž‘์—…์— ํ™œ์šฉ๋ฉ๋‹ˆ๋‹ค. GDL์˜ ์ฃผ์š” ํŠน์ง• ์ค‘ ํ•˜๋‚˜๋Š” IDL๊ณผ์˜ ์™„๋ฒฝํ•œ ๋ฌธ๋ฒ• ํ˜ธํ™˜์„ฑ์œผ๋กœ, ๊ธฐ์กด IDL ์ฝ”๋“œ๋ฅผ ์‰ฝ๊ฒŒ GDL์—์„œ ์‹คํ–‰ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์ž…๋‹ˆ๋‹ค. ์ด๋กœ ์ธํ•ด ์ฒœ๋ฌธํ•™์ž๋“ค์€ ๋น„์šฉ ๋ถ€๋‹ด ์—†์ด ๊ณ ๊ธ‰ ๋ฐ์ดํ„ฐ ๋ถ„์„ ๋ฐ ์‹œ๊ฐํ™” ์ž‘์—…์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. GDL์€ ๋‹ค์–‘ํ•œ ํ”Œ๋žซํผ์—์„œ ์‹คํ–‰ ๊ฐ€๋Šฅํ•˜๋ฉฐ, Linux, BSD, Mac OSX, OpenSolaris ๋“ฑ ์ฃผ์š” ์šด์˜ ์ฒด์ œ๋ฅผ ์ง€์›ํ•ฉ๋‹ˆ๋‹ค. ๋˜ํ•œ, ์—ฌ๋Ÿฌ ์šด์˜ ์ฒด์ œ์— ๋Œ€ํ•œ ์‚ฌ์ „ ์ปดํŒŒ์ผ

Computer Science Data Computational Engineering Astrophysics
Jenius Agent: Towards Experience-Driven Accuracy Optimization in Real-World Scenarios

Jenius Agent: Towards Experience-Driven Accuracy Optimization in Real-World Scenarios

Jeniusโ€‘Agent ๋…ผ๋ฌธ์€ ์ตœ๊ทผ LLMโ€‘๊ธฐ๋ฐ˜ ์—์ด์ „ํŠธ๊ฐ€ ์ง๋ฉดํ•œ ๋‘ ๊ฐ€์ง€ ๊ทผ๋ณธ์ ์ธ ํ•œ๊ณ„๋ฅผ ๋ช…ํ™•ํžˆ ์งš๊ณ  ์žˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” โ€œ์‹คํ–‰ ๊ฐ€์‹œ์„ฑ ๋ถ€์กฑโ€์ด๋‹ค. ๊ธฐ์กด ๋ฒค์น˜๋งˆํฌ๋Š” ์ตœ์ข… ์ถœ๋ ฅ๋งŒ์„ ํ‰๊ฐ€ ์ง€ํ‘œ๋กœ ์‚ผ์•„, ์—์ด์ „ํŠธ๊ฐ€ ๋‚ด๋ถ€์ ์œผ๋กœ ์–ด๋–ค ํ”„๋กฌํ”„ํŠธ๋ฅผ ์ƒ์„ฑํ•˜๊ณ , ์–ด๋–ค ๋„๊ตฌ๋ฅผ ์–ธ์ œ ํ˜ธ์ถœํ–ˆ๋Š”์ง€, ์ƒํƒœ๋ฅผ ์–ด๋–ป๊ฒŒ ์—…๋ฐ์ดํŠธํ–ˆ๋Š”์ง€๋ฅผ ํŒŒ์•…ํ•  ๋ฐฉ๋ฒ•์„ ์ œ๊ณตํ•˜์ง€ ์•Š๋Š”๋‹ค. ์ด๋กœ ์ธํ•ด ๊ฐœ๋ฐœ์ž๋Š” ์˜ค๋ฅ˜ ์›์ธ์„ ์ถ”์ ํ•˜๊ธฐ ์œ„ํ•ด ๋กœ๊ทธ๋ฅผ ์ผ์ผ์ด ์ˆ˜์ž‘์—…์œผ๋กœ ๋ถ„์„ํ•ด์•ผ ํ•˜๋ฉฐ, ์žฌํ˜„์„ฑ๋„ ๋–จ์–ด์ง„๋‹ค. ๋‘ ๋ฒˆ์งธ๋Š” โ€œ์žฅ๊ธฐยท๋„๊ตฌโ€‘๋ณด๊ฐ• ์ž‘์—…์—์„œ์˜ ๋ถˆ์•ˆ์ •์„ฑโ€์ด๋‹ค. LLM์€ ์งง์€ ์ปจํ…์ŠคํŠธ์—์„œ๋Š” ๋›ฐ์–ด๋‚œ

Computer Science Artificial Intelligence
Accelerating Storage-Based Training for Graph Neural Networks

Accelerating Storage-Based Training for Graph Neural Networks

๋ณธ ์—ฐ๊ตฌ๋Š” ๊ทธ๋ž˜ํ”„ ์‹ ๊ฒฝ๋ง(GNN) ํ•™์Šต์„ ๋Œ€๊ทœ๋ชจ ์‹ค์„ธ๊ณ„ ๊ทธ๋ž˜ํ”„์— ์ ์šฉํ•˜๊ธฐ ์œ„ํ•ด ์™ธ๋ถ€ ์Šคํ† ๋ฆฌ์ง€๋ฅผ ํ™œ์šฉํ•˜๋Š” โ€˜์Šคํ† ๋ฆฌ์ง€ ๊ธฐ๋ฐ˜โ€™ ์ ‘๊ทผ๋ฒ•์˜ ๊ทผ๋ณธ์ ์ธ ํ•œ๊ณ„๋ฅผ ์งš์–ด๋‚ธ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ฃผ๋กœ ๋ฉ”๋ชจ๋ฆฌ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด NVMe SSD์™€ ๊ฐ™์€ ๊ณ ์† ์ €์žฅ์†Œ๋ฅผ ์ด์šฉํ–ˆ์ง€๋งŒ, ๊ทธ๋ž˜ํ”„ ๊ตฌ์กฐ๊ฐ€ ๋งค์šฐ ๋ถˆ๊ทœ์น™ํ•˜๊ณ  ์—ฃ์ง€์™€ ๋…ธ๋“œ๊ฐ€ ์ˆ˜๋ฐฑ๋งŒ~์ˆ˜์‹ญ์–ต ์ˆ˜์ค€์œผ๋กœ ๋ถ„์‚ฐ๋ผ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ํ•™์Šต ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ˆ˜์‹ญ๋งŒ~์ˆ˜๋ฐฑ๋งŒ ๊ฐœ์˜ ์ž‘์€ I/O ์š”์ฒญ์„ ํšจ์œจ์ ์œผ๋กœ ๋ณ‘ํ•ฉํ•˜๊ฑฐ๋‚˜ ์Šค์ผ€์ค„๋งํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์ด๋Ÿฌํ•œ โ€˜์†Œํ˜• I/O ํญ์ฃผโ€™๋Š” ์Šคํ† ๋ฆฌ์ง€์˜ ์ˆœ์ฐจ ์ „์†ก ๋Œ€์—ญํญ์„ ํ™œ์šฉํ•˜์ง€ ๋ชปํ•˜๊ฒŒ ๋งŒ๋“ค๊ณ , ๊ฒฐ

Machine Learning Computer Science Network
Data Complexity-aware Deep Model Performance Forecasting

Data Complexity-aware Deep Model Performance Forecasting

์ด ๋…ผ๋ฌธ์€ โ€œ๋ฐ์ดํ„ฐ ๋ณต์žก๋„ ์ธ์‹ ๋”ฅ ๋ชจ๋ธ ์„ฑ๋Šฅ ์˜ˆ์ธกโ€์ด๋ผ๋Š” ์ฃผ์ œ๋กœ, ๋ชจ๋ธ ์„ค๊ณ„ ๋‹จ๊ณ„์—์„œ ์‚ฌ์ „์ ์œผ๋กœ ์„ฑ๋Šฅ์„ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฒฝ๋Ÿ‰ํ™”๋œ ๋‘ ๋‹จ๊ณ„ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ๋‹จ๊ณ„๋Š” ๋ฐ์ดํ„ฐ ์ž์ฒด์˜ ํ†ต๊ณ„์ ยท๊ตฌ์กฐ์  ํŠน์„ฑ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ฒ ์ด์Šค๋ผ์ธ ์„ฑ๋Šฅ์„ ์˜ˆ์ธกํ•œ๋‹ค. ์—ฌ๊ธฐ์—๋Š” ๋ฐ์ดํ„ฐ ๋ถ„์‚ฐ, ํด๋ž˜์Šค ๋ถˆ๊ท ํ˜•, ์ด๋ฏธ์ง€ ํ•ด์ƒ๋„, ํ…์ŠคํŠธ ๊ธธ์ด ๋“ฑ ๋‹ค์–‘ํ•œ ๋ฉ”ํŠธ๋ฆญ์ด ํฌํ•จ๋˜๋ฉฐ, ์ด๋Ÿฌํ•œ ํŠน์„ฑ์€ ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ ๋ชจ๋ธ ๋ณต์žก๋„์™€ ์ง์ ‘์ ์ธ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์ธ ๋ฐ” ์žˆ๋‹ค. ๋‘ ๋ฒˆ์งธ ๋‹จ๊ณ„๋Š” ๋ชจ๋ธ ์•„ํ‚คํ…์ฒ˜(์˜ˆ: ๋ ˆ์ด์–ด ์ˆ˜, ํŒŒ๋ผ๋ฏธํ„ฐ ๊ทœ๋ชจ, ์—ฐ์‚ฐ๋Ÿ‰)์™€ ์ฃผ์š” ํ•˜์ดํผํŒŒ๋ผ๋ฏธํ„ฐ(ํ•™์Šต๋ฅ , ๋ฐฐ์น˜

Computer Science Data Machine Learning Model
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DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving

DrivingGen ๋…ผ๋ฌธ์€ ํ˜„์žฌ ์ž์œจ์ฃผํ–‰ ๋ถ„์•ผ์—์„œ ๊ธ‰์ฆํ•˜๊ณ  ์žˆ๋Š” โ€˜์ƒ์„ฑ ๋น„๋””์˜ค ์„ธ๊ณ„ ๋ชจ๋ธโ€™ ์—ฐ๊ตฌ์˜ ํ‰๊ฐ€ ์ฒด๊ณ„๊ฐ€ ๋ถ€์žฌํ•จ์„ ์ •ํ™•ํžˆ ์งš์–ด๋‚ธ๋‹ค. ๊ธฐ์กด ๋ฒค์น˜๋งˆํฌ๊ฐ€ ์ฃผ๋กœ PSNR, SSIM, LPIPS์™€ ๊ฐ™์€ ์ „ํ†ต์ ์ธ ๋น„๋””์˜ค ํ’ˆ์งˆ ์ง€ํ‘œ์— ์˜์กดํ•˜๋Š”๋ฐ, ์ด๋Ÿฌํ•œ ์ง€ํ‘œ๋Š” ํ”ฝ์…€ ์ˆ˜์ค€์˜ ์ฐจ์ด๋ฅผ ์ •๋Ÿ‰ํ™”ํ•  ๋ฟ, ์ž์œจ์ฃผํ–‰์— ํ•„์ˆ˜์ ์ธ ๋ฌผ๋ฆฌ์  ์ผ๊ด€์„ฑ์ด๋‚˜ ์•ˆ์ „์„ฑ์„ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ์ฐจ๋Ÿ‰์ด ๊ฐ‘์ž๊ธฐ ์‚ฌ๋ผ์ง€๊ฑฐ๋‚˜ ๋ณดํ–‰์ž๊ฐ€ ๋น„ํ˜„์‹ค์ ์ธ ์†๋„๋กœ ์›€์ง์ด๋Š” ๊ฒฝ์šฐ, ์‹œ๊ฐ์ ์œผ๋กœ๋Š” ๋†’์€ SSIM์„ ๊ธฐ๋กํ•˜๋”๋ผ๋„ ์‹ค์ œ ์ฃผํ–‰ ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ๋Š” ์น˜๋ช…์ ์ธ ์˜ค๋ฅ˜๊ฐ€ ๋œ๋‹ค. ๋…ผ๋ฌธ์€ ์ด๋Ÿฌํ•œ

Computer Science Model Computer Vision
EscherVerse: An Open World Benchmark and Dataset for Teleo-Spatial Intelligence with Physical-Dynamic and Intent-Driven Understanding

EscherVerse: An Open World Benchmark and Dataset for Teleo-Spatial Intelligence with Physical-Dynamic and Intent-Driven Understanding

๋ณธ ๋…ผ๋ฌธ์€ ์ธ๊ณต์ง€๋Šฅยท๋กœ๋ณดํ‹ฑ์Šค ๋ถ„์•ผ์—์„œ ์ตœ๊ทผ ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋Š” โ€œํ…”๋ ˆ์˜คโ€‘์ŠคํŽ˜์ด์…œ ์ธํ…”๋ฆฌ์ „์Šค(TSI)โ€๋ผ๋Š” ์ƒˆ๋กœ์šด ๊ฐœ๋…์„ ์ œ์‹œํ•œ๋‹ค. ๊ธฐ์กด์˜ ๋Œ€๋ถ€๋ถ„ ์—ฐ๊ตฌ๋Š” ๋ฌผ์ฒดโ€‘์ค‘์‹ฌ(objectโ€‘centric) ์ ‘๊ทผ์„ ์ทจํ•ด, ๋ฌผ๋ฆฌโ€‘๋™์—ญํ•™(Physicalโ€‘Dynamic) ๋ชจ๋ธ๋ง์— ์ง‘์ค‘ํ•œ๋‹ค. ์˜ˆ์ปจ๋Œ€, ๋ฌผ์ฒด์˜ ์งˆ๋Ÿ‰ยท๋งˆ์ฐฐยท์ถฉ๋Œ ๋ฒ•์น™์„ ์ด์šฉํ•ด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•˜๊ฑฐ๋‚˜, ๋น„์ „ ๊ธฐ๋ฐ˜ ํŠธ๋ž˜ํ‚น์„ ํ†ตํ•ด ์›€์ง์ž„์„ ์˜ˆ์ธกํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•์€ ์ •๋Ÿ‰์  ์ •ํ™•๋„์—์„œ๋Š” ๋›ฐ์–ด๋‚˜์ง€๋งŒ, ์ธ๊ฐ„์ด ๋ฌผ์ฒด๋ฅผ ์กฐ์ž‘ํ•˜๊ฑฐ๋‚˜ ๋ฐฐ์น˜ํ•˜๋Š” ๊ทผ๋ณธ์ ์ธ โ€˜์˜๋„โ€™๋ฅผ ํŒŒ์•…ํ•˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. TSI๋Š” ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ

Computer Vision Computer Science Data
REE-TTT: Highly Adaptive Radar Echo Extrapolation Based on Test-Time Training

REE-TTT: Highly Adaptive Radar Echo Extrapolation Based on Test-Time Training

๋ณธ ๋…ผ๋ฌธ์€ ๋ ˆ์ด๋” ์—์ฝ” ์™ธ์‚ฝ(Radar Echo Extrapolation, REE) ๋ถ„์•ผ์—์„œ ๊ฐ€์žฅ ์‹œ๊ธ‰ํ•œ ๋ฌธ์ œ์ธ ๋„๋ฉ”์ธ ์ „์ด์™€ ๋ฐ์ดํ„ฐ ๋ถ„ํฌ ๋ณ€ํ™” ์— ๋Œ€ํ•œ ํ•ด๊ฒฐ์ฑ…์„ ์ œ์‹œํ•œ๋‹ค. ๊ธฐ์กด์˜ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ REE ๋ชจ๋ธ์€ ๋Œ€๊ทœ๋ชจ ๋ ˆ์ด๋” ์‹œํ€€์Šค๋ฅผ ํ•™์Šตํ•ด ๋‹จ๊ธฐ ๊ฐ•์ˆ˜ ์˜ˆ๋ณด๋ฅผ ์ˆ˜ํ–‰ํ•˜์ง€๋งŒ, ํ•™์Šต ๋‹จ๊ณ„์—์„œ ์‚ฌ์šฉ๋œ ์ง€์—ญยท์‹œ์ฆŒยท๊ธฐํ›„ ์กฐ๊ฑด์— ๊ฐ•ํ•˜๊ฒŒ ์˜์กดํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์ƒˆ๋กœ์šด ์ง€์—ญ์ด๋‚˜ ๊ทนํ•œ ๊ฐ•์ˆ˜ ์ƒํ™ฉ์— ์ ์šฉํ•˜๋ฉด ์„ฑ๋Šฅ์ด ๊ธ‰๊ฒฉํžˆ ์ €ํ•˜๋˜๋Š” ๊ฒƒ์ด ์ผ๋ฐ˜์ ์ด๋‹ค. 1. ํ•ต์‹ฌ ์•„์ด๋””์–ด์™€ ๊ธฐ์—ฌ ํ…Œ์ŠคํŠธโ€‘์‹œ์  ํ•™์Šต(Testโ€‘Time Training, TTT) ์ ์šฉ : ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ

Computer Science Machine Learning
SwinIFS: Landmark Guided Swin Transformer For Identity Preserving Face Super Resolution

SwinIFS: Landmark Guided Swin Transformer For Identity Preserving Face Super Resolution

SwinIFS๋Š” ์–ผ๊ตด ์ดˆํ•ด์ƒ๋„(Face Superโ€‘Resolution, FSR) ๋ถ„์•ผ์—์„œ ๋‘ ๊ฐ€์ง€ ํ•ต์‹ฌ ๋ฌธ์ œ, ์ฆ‰ โ€˜์„ธ๋ฐ€ํ•œ ๊ตฌ์กฐ ์†์‹คโ€™๊ณผ โ€˜์•„์ด๋ดํ‹ฐํ‹ฐ ๋ณ€ํ˜•โ€™์— ๋™์‹œ์— ๋Œ€์‘ํ•˜๋ ค๋Š” ์‹œ๋„๋กœ ๋ˆˆ์— ๋ˆ๋‹ค. ๊ธฐ์กด CNN ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•๋“ค์€ ์ง€์—ญ์ ์ธ ํŠน์ง•์„ ์ž˜ ํฌ์ฐฉํ•˜์ง€๋งŒ, ์žฅ๊ฑฐ๋ฆฌ ์˜์กด์„ฑ์„ ๋ชจ๋ธ๋งํ•˜๊ธฐ์—” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ๋ฐ˜๋ฉด Vision Transformer(ViT) ๊ณ„์—ด์€ ์ „์—ญ ์ •๋ณด๋ฅผ ํšจ์œจ์ ์œผ๋กœ ํ•™์Šตํ•˜์ง€๋งŒ, ์–ผ๊ตด๊ณผ ๊ฐ™์ด ๊ณ ์ •๋œ ๊ตฌ์กฐ์  ํŒจํ„ด์„ ๋‹ค๋ฃฐ ๋•Œ๋Š” ์‚ฌ์ „ ์ง€์‹์ด ๋ถ€์กฑํ•ด ์„ธ๋ถ€ ๋””ํ…Œ์ผ ๋ณต์›์— ์•ฝ์ ์ด ์žˆ๋‹ค. SwinIFS๋Š” ์ด๋Ÿฌํ•œ ๋”œ๋ ˆ๋งˆ๋ฅผ โ€˜๋žœ๋“œ๋งˆํฌ ๊ฐ€์ด๋“œ

Computer Science Computer Vision
Correctness isnt Efficiency: Runtime Memory Divergence in LLM-Generated Code

Correctness isnt Efficiency: Runtime Memory Divergence in LLM-Generated Code

์ด ๋…ผ๋ฌธ์€ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM)์—์„œ ์ƒ์„ฑ๋œ ์ฝ”๋“œ์˜ ์‹คํ–‰ ์•ˆ์ •์„ฑ์— ์ค‘์ ์„ ๋‘๊ณ , ํŠนํžˆ ๋ฉ”๋ชจ๋ฆฌ ๋™์—ญํ•™ ์ธก๋ฉด์—์„œ ๊ทธ ์ค‘์š”์„ฑ์„ ๊ฐ•์กฐํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ๋Š” ์ฃผ๋กœ LLM์˜ ์ถœ๋ ฅ ์ •ํ™•๋„์™€ ๋‹ค์–‘์„ฑ์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์ง€๋งŒ, ์ด ๋…ผ๋ฌธ์€ ์ด๋Ÿฌํ•œ ์ •ํ™•ํ•œ ๊ฒฐ๊ณผ๊ฐ€ ๋ฐ˜๋“œ์‹œ ์‹คํ–‰ ์‹œ๊ฐ„ ๋ฉ”๋ชจ๋ฆฌ ํ”„๋กœํŒŒ์ผ๋ง์—์„œ๋„ ์•ˆ์ •์ ์ธ ์„ฑ๋Šฅ์„ ๋ณด์žฅํ•˜์ง€ ์•Š๋Š”๋‹ค๋Š” ์ ์„ ์ง€์ ํ•œ๋‹ค. ๋…ผ๋ฌธ์—์„œ ์ œ์‹œ๋œ ์ฃผ์š” ๊ธฐ์—ฌ ์ค‘ ํ•˜๋‚˜๋Š” Monotonic Peak Profile (MPP) ์™€ Dynamic Time Warping (DTW) ๋ฅผ ํ™œ์šฉํ•œ ์‹คํ–‰ ์‹œ๊ฐ„ ๋ฉ”๋ชจ๋ฆฌ ํ”„๋กœํŒŒ์ผ๋ง ๋ฐฉ๋ฒ•์ด๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ์• ํ”Œ

Computer Science Software Engineering
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Improved Object-Centric Diffusion Learning with Registers and Contrastive Alignment

๋ณธ ๋…ผ๋ฌธ์€ ๊ฐ์ฒด ์ค‘์‹ฌ ํ•™์Šต(Object centric Learning, OCL) ๋ถ„์•ผ์—์„œ ์ค‘์š”ํ•œ ๊ธฐ์ˆ ์  ํ˜์‹ ์„ ์ œ์‹œํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. CODA(Contrastive Object centric Diffusion Alignment)๋Š” ์‚ฌ์ „ ํ•™์Šต๋œ ๋””ํ“จ์ „ ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜์—ฌ ์Šฌ๋กฏ ์—ฎ์ž„๊ณผ ์•ฝํ•œ ์ •๋ ฌ์ด๋ผ๋Š” ์ฃผ์š” ๋„์ „ ๊ณผ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์ž…๋‹ˆ๋‹ค. ๊ธฐ์ˆ ์  ํ˜์‹ ์„ฑ: 1. ๋“ฑ๋ก ์Šฌ๋กฏ(Register Slots): ๋“ฑ๋ก ์Šฌ๋กฏ์€ ๋…๋ฆฝ์ ์ธ ์ž…๋ ฅ ๋ฐ์ดํ„ฐ๋กœ ์ถ”๊ฐ€๋˜์–ด ์ž”์—ฌ ์ฃผ์˜๋ฅผ ํก์ˆ˜ํ•˜๊ณ  ๊ฐ์ฒด ์Šฌ๋กฏ ๊ฐ„์˜ ๊ฐ„์„ญ์„ ์ค„์ด๋Š” ์—ญํ• ์„ ํ•ฉ๋‹ˆ๋‹ค. ์ด๋Š” ์Šฌ๋กฏ ์—ฎ์ž„ ๋ฌธ

Computer Science Learning Computer Vision
RovoDev Code Reviewer: A Large-Scale Online Evaluation of LLM-based Code Review Automation at Atlassian

RovoDev Code Reviewer: A Large-Scale Online Evaluation of LLM-based Code Review Automation at Atlassian

RovoDev ์ฝ”๋“œ ๋ฆฌ๋ทฐ์–ด๋Š” LLM ๊ธฐ๋ฐ˜์˜ ์ฝ”๋“œ ๊ฒ€ํ†  ์ž๋™ํ™” ๋„๊ตฌ๋กœ์„œ, ํ˜„๋Œ€ ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ ํ”„๋กœ์„ธ์Šค์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์ด ๋…ผ๋ฌธ์€ RovoDev์ด ์–ด๋–ป๊ฒŒ ๋ฐ์ดํ„ฐ ํ”„๋ผ์ด๋ฒ„์‹œ์™€ ๋ณด์•ˆ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ณ , ๋ฆฌ๋ทฐ ๊ฐ€์ด๋“œ๋ผ์ธ์— ๋”ฐ๋ฅธ ์ •ํ™•ํ•œ ์ฝ”๋“œ ๊ฒ€ํ† ๋ฅผ ์ œ๊ณตํ•˜๋ฉฐ, ์ƒˆ๋กœ์šด ํ”„๋กœ์ ํŠธ์—์„œ๋„ ํšจ๊ณผ์ ์œผ๋กœ ์ž‘๋™ํ•˜๋Š”์ง€ ์„ค๋ช…ํ•œ๋‹ค. ๊ธฐ์ˆ ์  ํ˜์‹ ์„ฑ: RovoDev์˜ ํ•ต์‹ฌ ๊ธฐ์ˆ ์€ ์ œ๋กœ์ƒท ์ปจํ…์ŠคํŠธ ์ธ์‹ ๋ฆฌ๋ทฐ ๋Œ“๊ธ€ ์ƒ์„ฑ, ์‚ฌ์‹ค์  ์ •ํ™•์„ฑ ํ’ˆ์งˆ ๊ฒ€์‚ฌ, ๊ทธ๋ฆฌ๊ณ  ํ–‰๋™์„ฑ ํ’ˆ์งˆ ๊ฒ€์‚ฌ๋ฅผ ํ†ตํ•œ ์œ ํšจํ•˜๊ณ  ํ–‰๋™ ๊ฐ€๋Šฅํ•œ ๋ฆฌ๋ทฐ ๋Œ“๊ธ€ ์ถ”์ฒœ์ด๋‹ค. ์ด ์ค‘ ํŠนํžˆ ์ œ๋กœ์ƒท ์ ‘๊ทผ ๋ฐฉ์‹์€ ๊ณ ๊ฐ

Computer Science Software Engineering
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CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge

CoCoโ€‘Fed๊ฐ€ ์ œ์‹œํ•˜๋Š” ๋‘ ๊ฐ€์ง€ ํ•ต์‹ฌ ํ˜์‹ ์€ โ€˜์ด์ค‘ ์ฐจ์› ๋‹ค์šดํ”„๋กœ์ ์…˜โ€™๊ณผ โ€˜์ง๊ต ๋ถ€๋ถ„๊ณต๊ฐ„ ์ดˆ์ค‘์ฒฉ ์ „์†กโ€™์ด๋‹ค. ์ฒซ ๋ฒˆ์งธ ๋‹จ๊ณ„์—์„œ๋Š” ๊ธฐ์กด ์—ฐํ•ฉ ํ•™์Šต์—์„œ ๊ฐ gNB๊ฐ€ ์ „์ฒด ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ์— ๋Œ€ํ•œ ๊ทธ๋ž˜๋””์–ธํŠธ๋ฅผ ์ €์žฅยท์ „์†กํ•ด์•ผ ํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•œ๋‹ค. ์ €์ž๋“ค์€ ๊ทธ๋ž˜๋””์–ธํŠธ๋ฅผ ๋จผ์ € ์ฑ„๋„ ์ฐจ์›(์˜ˆ: ์ž…๋ ฅ ํ”ผ์ฒ˜)๊ณผ ๋ชจ๋ธ ์ฐจ์›(์˜ˆ: ์ถœ๋ ฅ ํ”ผ์ฒ˜) ๋‘ ์ถ•์—์„œ ๊ฐ๊ฐ ์ €โ€‘๋žญํฌ ํ–‰๋ ฌ๋กœ ๊ทผ์‚ฌํ•œ๋‹ค. ์ด๋•Œ ์‚ฌ์šฉ๋˜๋Š” ํˆฌ์‚ฌ ํ–‰๋ ฌ์€ ์‚ฌ์ „์— ํ•™์Šต๋œ ๊ณ ์ • ์ •๊ทœ ์ง๊ต ํ–‰๋ ฌ์ด๊ฑฐ๋‚˜, ๊ฐ ๋ผ์šด๋“œ๋งˆ๋‹ค ๋žœ๋คํ•˜๊ฒŒ ์ƒ์„ฑ๋œ ์Šค์ผ€์น˜ ํ–‰๋ ฌ์ผ ์ˆ˜ ์žˆ๋‹ค. ์ด๋ ‡๊ฒŒ ํ•˜๋ฉด ๋ฉ”๋ชจ๋ฆฌ ์š”๊ตฌ๋Ÿ‰์ด O(rankยท

Computer Science Learning Information Theory Framework
Detecting Performance Degradation under Data Shift in Pathology Vision-Language Model

Detecting Performance Degradation under Data Shift in Pathology Vision-Language Model

๋ณธ ๋…ผ๋ฌธ์€ ์ตœ๊ทผ ์˜๋ฃŒ ์˜์ƒ ๋ถ„์•ผ์—์„œ ๊ฐ๊ด‘๋ฐ›๊ณ  ์žˆ๋Š” ๋น„์ „โ€‘์–ธ์–ด ๋ชจ๋ธ(VLM)์˜ ์‹ค์ œ ์šด์˜ ๋‹จ๊ณ„์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” โ€˜๋ฐ์ดํ„ฐ ์‹œํ”„ํŠธ(data shift)โ€™ ๋ฌธ์ œ์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ๋‹ค. ๋ฐ์ดํ„ฐ ์‹œํ”„ํŠธ๋Š” ํฌ๊ฒŒ ๋‘ ๊ฐ€์ง€ ์ฐจ์›์œผ๋กœ ๋‚˜๋‰œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ์ž…๋ ฅ ๋ฐ์ดํ„ฐ ์ž์ฒด๊ฐ€ ํ›ˆ๋ จ ์‹œ์™€ ๋‹ค๋ฅธ ๋ถ„ํฌ๋ฅผ ๋ณด์ด๋Š” ๊ฒฝ์šฐ์ด๋ฉฐ, ๋‘ ๋ฒˆ์งธ๋Š” ๋ชจ๋ธ์ด ์ถœ๋ ฅํ•˜๋Š” ์˜ˆ์ธก๊ฐ’์ด๋‚˜ ์‹ ๋ขฐ๋„(confidence)๊ฐ€ ๋ณ€ํ•˜๋Š” ๊ฒฝ์šฐ์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ฃผ๋กœ ์ž…๋ ฅ ์ฐจ์›์˜ ํ†ต๊ณ„์  ๋ณ€ํ™”๋ฅผ ํƒ์ง€ํ•˜๋Š” ๋ฐฉ๋ฒ•์— ์˜์กดํ–ˆ์ง€๋งŒ, ์ด๋Ÿฌํ•œ ๋ณ€๋™์ด ๋ฐ˜๋“œ์‹œ ๋ชจ๋ธ ์„ฑ๋Šฅ ์ €ํ•˜์™€ ์ง๊ฒฐ๋˜์ง€ ์•Š๋Š”๋‹ค๋Š” ์ ์„ ๊ฐ„๊ณผํ•˜๊ณ  ์žˆ์—ˆ๋‹ค.

Computer Science Model Data Computer Vision
ElecTwit: A Framework for Studying Persuasion in Multi-Agent Social Systems

ElecTwit: A Framework for Studying Persuasion in Multi-Agent Social Systems

ElecTwit์ด ์ œ์‹œํ•˜๋Š” ๊ฐ€์žฅ ํฐ ํ˜์‹ ์€ ์„ค๋“ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ์‹คํ—˜ ํ™˜๊ฒฝ์„ โ€˜๊ฒŒ์ž„โ€™์ด๋ผ๋Š” ์ธ์œ„์  ์„ค์ •์—์„œ ๋ฒ—์–ด๋‚˜ ์‹ค์ œ ์†Œ์…œ ๋ฏธ๋””์–ด ํ”Œ๋žซํผ์˜ ๊ตฌ์กฐ์™€ ํ–‰ํƒœ๋ฅผ ๋ชจ์‚ฌํ•œ ์ ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ €์ž๋“ค์€ ํŠธ์œ„ํ„ฐ์™€ ์œ ์‚ฌํ•œ ๊ฒŒ์‹œยท๋ฆฌํŠธ์œ—ยทํŒ”๋กœ์šฐ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๊ตฌํ˜„ํ•˜๊ณ , ์ •์น˜์  ์„ ๊ฑฐ๋ผ๋Š” ๊ตฌ์ฒด์  ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์„ค์ •ํ•จ์œผ๋กœ์จ ์—์ด์ „ํŠธ ๊ฐ„์˜ ์ •๋ณด ํ๋ฆ„, ์˜๊ฒฌ ํ˜•์„ฑ, ๊ทธ๋ฆฌ๊ณ  ์„ค๋“ ์ „๋žต์˜ ์ „๊ฐœ ๊ณผ์ •์„ ์ •๋ฐ€ํ•˜๊ฒŒ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํŠนํžˆ 25๊ฐ€์ง€ ์„ค๋“ ๊ธฐ๋ฒ•์„ ์‚ฌ์ „ ์ •์˜ํ•˜๊ณ , ๊ฐ LLM์ด ์ด๋ฅผ ์–ด๋–ป๊ฒŒ ์„ ํƒยท์กฐํ•ฉํ•˜๋Š”์ง€๋ฅผ ์ •๋Ÿ‰ํ™”ํ•œ ๋ฐฉ๋ฒ•๋ก ์€ ์„ค๋“ ๊ธฐ์ˆ ์˜ โ€˜๋ ˆ์‹œํ”ผโ€™๋ฅผ ๋น„๊ต ๋ถ„์„ํ•˜๋Š” ๋ฐ

Computer Science Artificial Intelligence Framework System
No Image

LLM Agents for Combinatorial Efficient Frontiers: Investment Portfolio Optimization

๋ณธ ๋…ผ๋ฌธ์€ ์–ธ์–ด ๋ชจ๋ธ(LLM)์„ ํ™œ์šฉํ•œ ์—์ด์ „ํŠธ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ํ†ตํ•ด ๋ณต์žกํ•œ ์กฐํ•ฉ ์ตœ์ ํ™” ๋ฌธ์ œ์— ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์‹œํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ํ•ต์‹ฌ ๊ฐ€์น˜๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ธฐ์ˆ ์  ํ˜์‹ ์„ฑ, ๋ฐฉ๋ฒ•๋ก , ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ๋ถ„์„ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. 1. ๊ธฐ์ˆ ์  ํ˜์‹ ์„ฑ: ๋ณธ ๋…ผ๋ฌธ์€ LLM์„ ํ™œ์šฉํ•œ ์—์ด์ „ํŠธ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์‹œํ•จ์œผ๋กœ์จ, ๊ธฐ์กด์˜ ํ•™๋ฌธ์ ์ธ ์ตœ์ ํ™” ์ ‘๊ทผ๋ฒ•์—์„œ ๋ฒ—์–ด๋‚˜ ์‹ค์ œ ์ƒํ™œ ๋ฌธ์ œ์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, NPhardํ•œ ์กฐํ•ฉ ์ตœ์ ํ™” ๋ฌธ์ œ ํ•ด๊ฒฐ์— ์žˆ์–ด LLM์„ ํ™œ์šฉํ•˜๋Š” ๊ฒƒ์€ ์ด์ „ ์—ฐ๊ตฌ์—์„œ๋Š” ์ฐพ์•„๋ณด๊ธฐ ์–ด๋ ค์šด ํ˜์‹ ์ ์ธ ์‹œ๋„์ž…๋‹ˆ๋‹ค. MOCO

Computer Science Computational Engineering
LOFA: Online Influence Maximization under Full-Bandit Feedback using Lazy Forward Selection

LOFA: Online Influence Maximization under Full-Bandit Feedback using Lazy Forward Selection

๋ณธ ๋…ผ๋ฌธ์ด ๋‹ค๋ฃจ๋Š” ์˜จ๋ผ์ธ ์˜ํ–ฅ๋ ฅ ์ตœ๋Œ€ํ™”(Online Influence Maximization, OIM) ๋ฌธ์ œ๋Š” ์ „ํ†ต์ ์ธ ์ •์  IM๊ณผ ๋‹ฌ๋ฆฌ ์‹œ๊ฐ„์— ๋”ฐ๋ผ ์‹œ๋“œ ์ง‘ํ•ฉ์„ ๋™์ ์œผ๋กœ ์„ ํƒํ•ด์•ผ ํ•˜๋Š” ์ƒํ™ฉ์„ ์ „์ œ๋กœ ํ•œ๋‹ค. ์—ฌ๊ธฐ์„œ โ€˜์ „์ฒด ๋ฐด๋”ง(fullโ€‘bandit) ํ”ผ๋“œ๋ฐฑโ€™์ด๋ผ๋Š” ๊ฐ€์ •์€ ์—์ด์ „ํŠธ๊ฐ€ ๋งค ๋ผ์šด๋“œ๋งˆ๋‹ค ์„ ํƒํ•œ ์‹œ๋“œ ์ง‘ํ•ฉ์— ์˜ํ•ด ์‹ค์ œ๋กœ ๋ฐœ์ƒํ•œ ํ™•์‚ฐ ๊ทœ๋ชจ(์ฆ‰, ์ „์ฒด ํ™œ์„ฑํ™”๋œ ๋…ธ๋“œ ์ˆ˜)๋งŒ์„ ๊ด€์ฐฐํ•œ๋‹ค๋Š” ์˜๋ฏธ์ด๋ฉฐ, ์ด๋Š” ๊ฐ ๋…ธ๋“œ๋ณ„ ํ™œ์„ฑํ™” ์—ฌ๋ถ€๋‚˜ ๋„คํŠธ์›Œํฌ์˜ ์ธ์ ‘ ํ–‰๋ ฌ ๋“ฑ ์ถ”๊ฐ€์ ์ธ ๊ตฌ์กฐ์  ์ •๋ณด๋ฅผ ์ „ํ˜€ ์ œ๊ณตํ•˜์ง€ ์•Š๋Š”๋‹ค. ์ด๋Ÿฌํ•œ ์ œํ•œ๋œ ํ”ผ๋“œ๋ฐฑ ํ™˜๊ฒฝ์—์„œ๋Š”

Machine Learning Computer Science
An AI Monkey Gets Grapes for Sure -- Sphere Neural Networks for Reliable Decision-Making

An AI Monkey Gets Grapes for Sure -- Sphere Neural Networks for Reliable Decision-Making

๋ณธ ์—ฐ๊ตฌ๋Š” ํ˜„์žฌ ์ธ๊ณต์ง€๋Šฅ ์ถ”๋ก  ๋ถ„์•ผ์—์„œ ๊ฐ€์žฅ ๋…ผ์Ÿ์ด ๋˜๋Š” ์„ธ ๊ฐ€์ง€ ์ ‘๊ทผ๋ฒ•์„ ์ฒด๊ณ„์ ์œผ๋กœ ๋น„๊ตํ•จ์œผ๋กœ์จ, โ€œ์‹ ๋ขฐ์„ฑโ€์ด๋ผ๋Š” ๊ด€์ ์—์„œ ์ค‘์š”ํ•œ ํ†ต์ฐฐ์„ ์ œ๊ณตํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ๋ฒ”์ฃผ์ธ ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ(LLM) ๊ธฐ๋ฐ˜ ์ถ”๋ก ์€ ๋ฐฉ๋Œ€ํ•œ ํ…์ŠคํŠธ ์ฝ”ํผ์Šค๋ฅผ ์‚ฌ์ „ ํ•™์Šตํ•จ์œผ๋กœ์จ ์–ธ์–ด์  ์œ ์—ฐ์„ฑ์„ ์–ป์ง€๋งŒ, ๋…ผ๋ฆฌ์  ์ผ๊ด€์„ฑ ์œ ์ง€์™€ ๊ฐ™์€ ์—„๊ฒฉํ•œ ํŒ๋‹จ์—์„œ๋Š” ์—ฌ์ „ํžˆ ๋ถˆ์•ˆ์ •ํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ธ๋‹ค. ์ด๋Š” LLM์ด ํ†ต๊ณ„์  ํŒจํ„ด์— ๊ธฐ๋ฐ˜ํ•œ ์˜ˆ์ธก์„ ์ˆ˜ํ–‰ํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ๋ช…์‹œ์ ์ธ ๋…ผ๋ฆฌ ๊ทœ์น™์„ ๋‚ด์žฌํ™”ํ•˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ๊ทผ๋ณธ์ ์ธ ํ•œ๊ณ„์™€ ์—ฐ๊ฒฐ๋œ๋‹ค. ๋‘ ๋ฒˆ์งธ์ธ ๊ฐ๋… ํ•™์Šต ๊ธฐ๋ฐ˜ ์ถ”๋ก ์€ ํŠน์ • ๋…ผ๋ฆฌ ๊ณผ์ œ์— ๋Œ€ํ•ด ๋ผ

Network Computer Science Artificial Intelligence
An Empirical Evaluation of LLM-Based Approaches for Code Vulnerability Detection: RAG, SFT, and Dual-Agent Systems

An Empirical Evaluation of LLM-Based Approaches for Code Vulnerability Detection: RAG, SFT, and Dual-Agent Systems

๋ณธ ์—ฐ๊ตฌ๋Š” LLM์„ ํ™œ์šฉํ•œ ์ฝ”๋“œ ์ทจ์•ฝ์  ํƒ์ง€์˜ ์‹ค์šฉ์„ฑ์„ ์ •๋Ÿ‰์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ์„ธ ๊ฐ€์ง€ ์ ‘๊ทผ๋ฒ•์„ ์ฒด๊ณ„์ ์œผ๋กœ ๋น„๊ตํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ ์ ‘๊ทผ๋ฒ•์ธ Retrievalโ€‘Augmented Generation(RAG)์€ ์‚ฌ์ „ ํ•™์Šต๋œ LLM์— ์™ธ๋ถ€ ์ง€์‹ ๋ฒ ์ด์Šค๋ฅผ ๋™์ ์œผ๋กœ ์—ฐ๊ฒฐํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, MITRE CWE ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์™€ ์ตœ์‹  ์›น ๊ฒ€์ƒ‰ ๊ฒฐ๊ณผ๋ฅผ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๊ฐ€์ ธ์™€ ํ”„๋กฌํ”„ํŠธ์— ์‚ฝ์ž…ํ•จ์œผ๋กœ์จ ๋ชจ๋ธ์ด ์ฝ”๋“œ ์กฐ๊ฐ์„ ํ•ด์„ํ•  ๋•Œ ์ตœ์‹  ๋ณด์•ˆ ํŒจํ„ด๊ณผ CWE ์ •์˜๋ฅผ ์ฐธ์กฐํ•˜๋„๋ก ์„ค๊ณ„๋˜์—ˆ๋‹ค. ์ด ๊ณผ์ •์€ ๋ฒกํ„ฐ ๊ฒ€์ƒ‰ ์—”์ง„(FAISS)๊ณผ ํ…์ŠคํŠธ ์ž„๋ฒ ๋”ฉ์„ ํ™œ์šฉํ•ด ๊ด€๋ จ ๋ฌธ์„œ๋ฅผ

System Computer Science Software Engineering Detection
Benchmarking Preprocessing and Integration Methods in Single-Cell Genomics

Benchmarking Preprocessing and Integration Methods in Single-Cell Genomics

๋ณธ ๋…ผ๋ฌธ์€ ๋‹จ์ผ์„ธํฌ ๋‹ค์ค‘๋ชจ๋‹ฌ ๋ฐ์ดํ„ฐ ๋ถ„์„ ํŒŒ์ดํ”„๋ผ์ธ์˜ ํ•ต์‹ฌ ๋‹จ๊ณ„์ธ ์ •๊ทœํ™”, ํ†ตํ•ฉ, ์ฐจ์› ์ถ•์†Œ๋ฅผ ์ฒด๊ณ„์ ์œผ๋กœ ๋น„๊ตํ•จ์œผ๋กœ์จ ํ˜„์žฌ ์‚ฌ์šฉ๋˜๋Š” ๋ฐฉ๋ฒ•๋“ค์˜ ์‹ค์ œ ์ ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ํ‰๊ฐ€ํ•œ๋‹ค. ๋จผ์ € ์ •๊ทœํ™” ๋‹จ๊ณ„์—์„œ๋Š” ์ด 7๊ฐ€์ง€ ๋ฐฉ๋ฒ•์„ ์‹œํ—˜ํ–ˆ๋Š”๋ฐ, ์ด๋Š” ์ „ํ†ต์ ์ธ ๋กœ๊ทธ ๋ณ€ํ™˜, SCTransform, CPM, TPM ๋“ฑ๋ถ€ํ„ฐ ์ตœ์‹ ์˜ ๋ฒ ์ด์ง€์•ˆ ๊ธฐ๋ฐ˜ ์ •๊ทœํ™”๊นŒ์ง€ ํฌ๊ด„ํ•œ๋‹ค. ์ •๊ทœํ™”๋Š” ๊ฐ ์„ธํฌ์˜ ๊ธฐ์ˆ ์  ๋ณ€๋™์„ฑ์„ ์ตœ์†Œํ™”ํ•˜๊ณ , ์„œ๋กœ ๋‹ค๋ฅธ ๋ชจ๋‹ฌ๋ฆฌํ‹ฐ ๊ฐ„์˜ ์Šค์ผ€์ผ ์ฐจ์ด๋ฅผ ์กฐ์ •ํ•˜๋Š” ๋ฐ ํ•„์ˆ˜์ ์ด๋ฉฐ, ๋…ผ๋ฌธ์€ ์ •๊ทœํ™” ์„ ํƒ์ด ํ†ตํ•ฉ ๊ฒฐ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ •๋Ÿ‰์ ์œผ๋กœ ์ œ์‹œํ•œ๋‹ค. ํ†ตํ•ฉ ๋‹จ๊ณ„์—์„œ

Quantitative Biology

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