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A Conversation with Dorothy Gilford

1. ์—ญ์‚ฌ์ ยท์‚ฌํšŒ์  ๋ฐฐ๊ฒฝ ONR์˜ ์„ค๋ฆฝ๊ณผ ์˜๋ฏธ : 1946๋…„ ์„ค๋ฆฝ๋œ ONR์€ ์ „ํ›„ ๋ฏธ๊ตญ ์—ฐ๋ฐฉ ์ •๋ถ€๊ฐ€ ๊ธฐ๋ณธ ์—ฐ๊ตฌ๋ฅผ ์ง€์›ํ•˜๋Š” ์œ ์ผํ•œ ๊ธฐ๊ด€์œผ๋กœ, ์ดํ›„ 1950๋…„๋Œ€ NSF ์„ค๋ฆฝ ์ „๊นŒ์ง€ ๊ณผํ•™ ์—ฐ๊ตฌ ์ž๊ธˆ์˜ ์ค‘์‹ฌ์ด์—ˆ๋‹ค. ๊ธธํฌ๋“œ๊ฐ€ ONR์— ํ•ฉ๋ฅ˜ํ•œ 1955๋…„์€ ๋ƒ‰์ „ ์ดˆ๊ธฐ์ด๋ฉฐ, ๊ตฐ์‚ฌยท๊ธฐ์ˆ  ์—ฐ๊ตฌ์™€ ํ†ต๊ณ„ํ•™์ด ๊ธด๋ฐ€ํžˆ ์—ฐ๊ฒฐ๋˜๋˜ ์‹œ๊ธฐ์ด๋‹ค. ์—ฌ์„ฑ ๊ณผํ•™์ž์˜ ์œ„์น˜ : 1930โ€‘40๋…„๋Œ€ ๋ฏธ๊ตญ ๋Œ€ํ•™์—์„œ ์ˆ˜ํ•™ยทํ†ต๊ณ„ํ•™์„ ์ „๊ณตํ•œ ์—ฌ์„ฑ์€ ๋“œ๋ฌผ์—ˆ๋‹ค. ๊ธธํฌ๋“œ๊ฐ€ ๊ฒช์€ โ€œ์—ฌ์„ฑ์—๊ฒŒ๋Š” ์ˆ˜ํ•™์ด ์ ํ•ฉํ•˜์ง€ ์•Š๋‹คโ€๋Š” ์กฐ์–ธ์€ ๋‹น์‹œ ํ•™๊ณ„ยท์‚ฐ์—…๊ณ„์˜ ์„ฑ๋ณ„ ํŽธ๊ฒฌ์„ ๊ทธ๋Œ€๋กœ ๋ณด์—ฌ์ค€๋‹ค. ๊ทธ๋…€๊ฐ€ ์ด๋Ÿฌํ•œ ์žฅ๋ฒฝ

Statistics
A Family of Generalized Beta Distributions for Income

A Family of Generalized Beta Distributions for Income

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์†Œ๋“๋ถ„ํฌ๋ฅผ ์„ค๋ช…ํ•˜๊ธฐ ์œ„ํ•ด ๊ธฐ์กด์— ๊ฐ๋งˆ, ๋ฒ ํƒ€, Singhโ€‘Maddala, Pareto, Weibull, GB1, GB2 ๋“ฑ ๋‹ค์–‘ํ•œ ํ™•๋ฅ ๋ถ„ํฌ๊ฐ€ ์‚ฌ์šฉ๋ผ ์™”์Œ. ์ผ๋ฐ˜ํ™” ๋ฒ ํƒ€โ€‘F (betaโ€‘F) ๊ณ„์—ด์€ ์ž„์˜์˜ ๋ˆ„์ ๋ถ„ํฌํ•จ์ˆ˜ F(ยท)๋ฅผ ๋ฒ ํƒ€๋ถ„ํฌ์™€ ๊ฒฐํ•ฉํ•ด ๋ฌดํ•œํžˆ ํ’๋ถ€ํ•œ ํ˜•ํƒœ๋ฅผ ์ œ๊ณตํ•œ๋‹ค๋Š” ์ ์—์„œ ๊ธฐ์กด ๋ชจ๋ธ๋“ค์„ ํฌ๊ด„ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด๋Ÿฌํ•œ ์ด๋ก ์  ํ™•์žฅ์„ฑ์„ ์‹ค์ œ ๋ฏธ๊ตญ ๊ฐ€๊ตฌ์†Œ๋“ ๋ฐ์ดํ„ฐ(2003โ€‘2005๋…„)์™€ ์—ฐ๊ฒฐ์‹œ์ผœ, ๋ชจ๋ธ ์„ ํƒ์— ์‹ค์ฆ์  ๊ทผ๊ฑฐ๋ฅผ ์ œ๊ณตํ•œ๋‹ค๋Š” ์ ์—์„œ ์˜๋ฏธ๊ฐ€ ํฌ๋‹ค. 2. ๋ฐฉ๋ฒ•๋ก  ๋ชจ๋ธ ์ •์˜ : ๋ฒ ํƒ€โ€‘F ๋ถ„ํฌ๋Š” (g(

Statistics
A geometric approach to maximum likelihood estimation of the functional   principal components from sparse longitudinal data

A geometric approach to maximum likelihood estimation of the functional principal components from sparse longitudinal data

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ํฌ์†Œยท๋ถˆ๊ทœ์น™ ๊ด€์ธก : ์žฅ๊ธฐ ์—ฐ๊ตฌ์—์„œ ๊ฐ ํ”ผํ—˜์ž๋Š” ๋ช‡ ๊ฐœ์˜ ์‹œ์ ๋งŒ ์ธก์ •๋˜๋ฉฐ, ์‹œ์ ์€ ๋ฌด์ž‘์œ„ยท๋ถˆ๊ทœ์น™ํ•˜๊ฒŒ ๋ฐฐ์น˜๋œ๋‹ค. ๊ธฐ์กด์˜ FPCA ๋ฐฉ๋ฒ•์€ ๋ฐ€์ง‘ ๊ทธ๋ฆฌ๋“œ ํ˜น์€ ์ถฉ๋ถ„ํ•œ ๊ด€์ธก์„ ์ „์ œ๋กœ ํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์•„, ํฌ์†Œ ๋ฐ์ดํ„ฐ์— ์ง์ ‘ ์ ์šฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๊ณต๋ถ„์‚ฐ ์ปค๋„ ์ถ”์ •์˜ ์ค‘์š”์„ฑ : ๊ณต๋ถ„์‚ฐ ์—ฐ์‚ฐ์ž๋Š” ์–‘์˜ ๋ฐ˜์ •๋ฐ€๋„(positive semiโ€‘definite) ์—ฐ์‚ฐ์ž์ด๋ฉฐ, ์ด๋ฅผ ์ •ํ™•ํžˆ ์ถ”์ •ํ•ด์•ผ ๊ณ ์œ ํ•จ์ˆ˜ยท๊ณ ์œ ๊ฐ’์ด ์˜๋ฏธ๋ฅผ ๊ฐ–๋Š”๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„์  | | | | | | 1 | ๋‚ด์žฌ ๊ธฐํ•˜ํ•™ ํ™œ์šฉ

Data Statistics
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An Improved FPGA Implementation of the Modified Hybrid Hiding Encryption Algorithm (MHHEA) for Data Communication Security

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ์  HHEA ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์€ ํ‰๋ฌธ ๋น„ํŠธ๋ฅผ ๋ฌด์ž‘์œ„ ์ˆจ๊น€ ๋ฒกํ„ฐ์— ์‚ฝ์ž…ํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ, ์ „ํ†ต์ ์ธ ์น˜ํ™˜ยท์ „์น˜ ์—ฐ์‚ฐ์„ ์‚ฌ์šฉํ•˜์ง€ ์•Š๋Š”๋‹ค. ๊ธฐ์กด FPGA ๋งˆ์ดํฌ๋กœ์•„ํ‚คํ…์ฒ˜(

Cryptography and Security Data Computer Science
An Iterative Algorithm for Battery-Aware Task Scheduling on Portable   Computing Platforms

An Iterative Algorithm for Battery-Aware Task Scheduling on Portable Computing Platforms

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฐฐํ„ฐ๋ฆฌ ์ œํ•œ : ํœด๋Œ€์šฉ ์ž„๋ฒ ๋””๋“œ ์‹œ์Šคํ…œ์€ ๋ฐฐํ„ฐ๋ฆฌ ์šฉ๋Ÿ‰์ด ์ œํ•œ์ ์ด๋ฏ€๋กœ, ์—๋„ˆ์ง€ ํšจ์œจ์€ ์„ค๊ณ„ ๋ชฉํ‘œ ์ค‘ ์ตœ์šฐ์„ ์ด๋‹ค. ๋น„์„ ํ˜• ๋ฐฉ์ „ ํŠน์„ฑ : ๋ฐฐํ„ฐ๋ฆฌ๋Š” ์ „๋ฅ˜์— ๋”ฐ๋ผ ์šฉ๋Ÿ‰์ด ๊ฐ์†Œํ•˜๋Š” ๋น„์„ ํ˜• ํŠน์„ฑ์„ ๊ฐ€์ง€๋ฉฐ, ๊ธฐ์กด์˜ DVFS(๋™์  ์ „์••ยท์ฃผํŒŒ์ˆ˜ ์Šค์ผ€์ผ๋ง) ๊ธฐ๋ฒ•์„ ๊ทธ๋Œ€๋กœ ์ ์šฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋‹ค์ค‘ ๋””์ž์ธโ€‘ํฌ์ธํŠธ : FPGA์™€ ์ „์••ยท์ฃผํŒŒ์ˆ˜ ์กฐํ•ฉ์„ ํ†ตํ•œ ๋‹ค์–‘ํ•œ ๊ตฌํ˜„ ์˜ต์…˜์ด ์กด์žฌํ•˜๋ฏ€๋กœ, ์ž‘์—…๋ณ„ ์ตœ์  ์„ค๊ณ„ ์„ ํƒ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๊ตฌ๋ถ„ | ๋‚ด์šฉ | | | | | ๋ฐฐํ„ฐ๋ฆฌ ๋ชจ๋ธ ํ™œ์šฉ | Rakhmatov์˜ ๋ณ€์ˆ˜ ๋ถ€ํ•˜ ๋ถ„์„ ๋ชจ๋ธ

Other CS Computer Science
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An iterative algorithm for evaluating approximations to the optimal exercise boundary for a nonlinear Black-Scholes equation

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋น„์„ ํ˜• ๋ณ€๋™์„ฑ ๋ชจ๋ธ์˜ ํ•„์š”์„ฑ ์ „ํ†ต์ ์ธ ๋ธ”๋ž™โ€‘์ˆ„์ฆˆ ๋ชจ๋ธ์€ ๋ฌด๋งˆ์ฐฐยท์™„์ „ ์œ ๋™์„ฑยท์™„์ „ ๋ณต์ œ๋ผ๋Š” ๊ฐ€์ •์„ ์ „์ œ๋กœ ํ•œ๋‹ค. ์‹ค์ œ ์‹œ์žฅ์—์„œ๋Š” ๊ฑฐ๋ž˜ ๋น„์šฉ, ๋Œ€๊ทœ๋ชจ ํŠธ๋ ˆ์ด๋”์˜ ํ”ผ๋“œ๋ฐฑ, ํฌํŠธํด๋ฆฌ์˜ค ์žฌ์กฐ์ • ๋น„์šฉ ๋“ฑ์œผ๋กœ ์ธํ•ด ๋ณ€๋™์„ฑ์ด ์˜ต์…˜ ๊ฐ€๊ฒฉ์˜ 2์ฐจ ๋ฏธ๋ถ„(ฮ“) ํ˜น์€ ์‹œ๊ฐ„ยท์ž์‚ฐ ๊ฐ€๊ฒฉ์— ์˜์กดํ•œ๋‹ค. Leland, Barlesโ€‘Soner, Freyโ€‘Stremme, RAPM ๋“ฑ ๋‹ค์–‘ํ•œ ๋น„์„ ํ˜• ๋ณ€๋™์„ฑ ๋ชจ๋ธ์„ ํ†ตํ•ฉ์ ์œผ๋กœ ๋‹ค๋ฃจ์–ด, ์ด๋ก ์  ํ†ต์ผ์„ฑ์„ ์ œ๊ณตํ•œ๋‹ค. ๋ฏธ๊ตญํ˜• ์˜ต์…˜์˜ ์ž์œ  ๊ฒฝ๊ณ„ ๋ฌธ์ œ ๋ฏธ๊ตญํ˜• ์˜ต์…˜์€ ์กฐ๊ธฐ ํ–‰์‚ฌ ๊ฐ€๋Šฅ์„ฑ ๋•Œ๋ฌธ์— ์ž์œ  ๊ฒฝ๊ณ„ (S

Quantitative Finance Mathematics
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Arxiv 2511.10693

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์‚ฌํšŒ์  AI์˜ ํ•„์š”์„ฑ : ์˜๋ฃŒยท๊ต์œกยท๊ฐœ์ธ ๋น„์„œ ๋“ฑ ๋ฏผ๊ฐํ•œ ๋ถ„์•ผ์—์„œ ์Œ์„ฑ AI๋Š” ๋‹จ์ˆœ ์ •๋ณด ์ „๋‹ฌ์„ ๋„˜์–ด ์‹ ๋ขฐ์™€ ์ˆ˜์šฉ์„ฑ์„ ํ™•๋ณดํ•ด์•ผ ํ•œ๋‹ค. ์•”๋ฌต์  ๊ทœ๋ฒ” ํƒ์ƒ‰ : ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์–ดํœ˜ยท๋ฌธ๋ฒ•์  ์ •์ค‘์„ฑ์„ ๊ตฌํ˜„ํ–ˆ์ง€๋งŒ, prosody(์–ต์–‘ยท๋ฆฌ๋“ฌยท์†๋„)์™€ ๊ฐ™์€ ๋น„์–ธ์–ด์  ์‹ ํ˜ธ๋Š” ๊ฐ„๊ณผ๋˜์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ โ€œ์†๋„ ๊ฐ์†Œ ์ •์ค‘ํ•จโ€์ด๋ผ๋Š” ๊ตฌ์ฒด์ ์ด๊ณ  ๋น„๊ฐ€์‹œ์ ์ธ ๊ทœ๋ฒ”์„ ํ…Œ์ŠคํŠธํ•จ์œผ๋กœ์จ, AI๊ฐ€ ์–ผ๋งˆ๋‚˜ ๊นŠ๊ฒŒ ์ธ๊ฐ„ ์‚ฌํšŒ ๊ทœ์น™์„ ํ•™์Šตํ–ˆ๋Š”์ง€๋ฅผ ํ‰๊ฐ€ํ•œ๋‹ค. 2. ์‹คํ—˜ ์„ค๊ณ„ | ์š”์†Œ | ๋‚ด์šฉ | | | | | ํ”Œ๋žซํผ | AI Studio (Google

Automatic Methods for Analyzing Non-Repudiation Protocols with an Active   Intruder

Automatic Methods for Analyzing Non-Repudiation Protocols with an Active Intruder

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋น„๋ถ€์ธ์„ฑยท๊ณต์ •์„ฑ ์€ 1990๋…„๋Œ€ ์ธํ„ฐ๋„ทยท์ „์ž๊ฑฐ๋ž˜์˜ ๊ธ‰์ฆ๊ณผ ํ•จ๊ป˜ ๋ณด์•ˆ ์—ฐ๊ตฌ์˜ ์ฃผ์š” ๊ด€์‹ฌ์‚ฌ๋กœ ๋– ์˜ฌ๋ž๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ SVO ๋…ผ๋ฆฌ , CSP , Isabelle , Murฯ• , LTL , APA ๋“ฑ ๋ณต์žกํ•œ ๋…ผ๋ฆฌยท๋ชจ๋ธ์„ ์‚ฌ์šฉํ–ˆ์œผ๋ฉฐ, ์ด๋Š” ๋ชจ๋ธ ์ฒด์ปค์˜ ์ƒํƒœ ํญ๋ฐœ(state explosion) ๋ฌธ์ œ๋ฅผ ์•ผ๊ธฐํ•œ๋‹ค. ํŠนํžˆ ๋‚™๊ด€์ (optimistic) ํ”„๋กœํ† ์ฝœ ์€ TTP(Trusted Third Party)๋ฅผ ์ตœ์†Œํ™”ํ•˜๋ฉด์„œ๋„ ๊ณต์ •์„ฑ์„ ๋ณด์žฅํ•ด์•ผ ํ•˜๋ฏ€๋กœ, ๊ธฐ์กด ์ธ์ฆโ€‘์ค‘์‹ฌ ์ ‘๊ทผ๋ฒ•๋งŒ์œผ๋กœ๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. 2. ๋…ผ๋ฌธ์˜ ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๋ฒˆ

Cryptography and Security Logic Computer Science
Bit-interleaved coded modulation in the wideband regime

Bit-interleaved coded modulation in the wideband regime

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ BICM ์€ ๋‹ค์ค‘ ๋ ˆ๋ฒจ ์ฝ”๋”ฉ(Multiโ€‘Level Coding)๋ณด๋‹ค ๊ตฌํ˜„์ด ๊ฐ„๋‹จํ•˜๋ฉด์„œ๋„ ๊ณ ์ฐจ ๋ณ€์กฐ์™€ ๊ฒฐํ•ฉํ•ด ๋†’์€ ์ŠคํŽ™ํŠธ๋Ÿผ ํšจ์œจ์„ ์ œ๊ณตํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ(

Information Theory Mathematics Computer Science
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CANE: The Content Addressed Network Environment

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ | ํ˜„ํ–‰ ์ธํ„ฐ๋„ท์˜ ํ•ต์‹ฌ ๋ฌธ์ œ | ๋…ผ๋ฌธ์—์„œ ์ œ์‹œ๋œ ์›์ธ | | | | | ์‹ ๋ขฐ ๋ถ€์กฑ (์ŠคํŒธยทํ”ผ์‹ฑยท์•…์„ฑ์ฝ”๋“œ) | ๋ฐ์ดํ„ฐยท๋ฉ”์‹œ์ง€์˜ ์ถœ์ฒ˜์™€ ๋ฌด๊ฒฐ์„ฑ์„ ๊ฒ€์ฆํ•  ๋ฉ”์ปค๋‹ˆ์ฆ˜ ๋ถ€์žฌ | | ์›๊ฒฉ ๋ฐ์ดํ„ฐ ์ ‘๊ทผยท์กฐ์ž‘ | ์œ„์น˜ ๊ธฐ๋ฐ˜ ์ฃผ์†Œ ์ฒด๊ณ„๊ฐ€ โ€œ์›๊ฒฉ ๋ณ„๋„โ€ ๋กœ ์ธ์‹๋ผ ์ผ๊ด€๋œ ์ฝยท์“ฐ๊ธฐ ๊ถŒํ•œ ๊ด€๋ฆฌ๊ฐ€ ์–ด๋ ค์›€ | | ์†Œํ”„ํŠธ์›จ์–ด ์„ค์น˜ยท์˜์กด์„ฑ | ๋‹ค์–‘ํ•œ ํ”„๋กœํ† ์ฝœยทํŒจํ‚ค์ง€ ๋งค๋‹ˆ์ €๊ฐ€ ํŒŒํŽธํ™”๋ผ ํ˜ธํ™˜์„ฑยท๋ฒ„์ „ ์ถฉ๋Œ ๋ฐœ์ƒ | | ๋ฐฑ์—…ยท๋ณต๊ตฌยท์•„์นด์ด๋น™ | ํŒŒ์ผ ์‹œ์Šคํ…œ ์ˆ˜์ค€์—์„œ์˜ ์Šค๋ƒ…์ƒทยท๋ฒ„์ „ ๊ด€๋ฆฌ๊ฐ€ ๋ฏธ๋น„ | | ๋„คํŠธ์›Œํฌ ํ˜ผ์žกยท๋น„ํšจ์œจ | ๋™์ผ ํŒŒ์ผ์„ ๋‹ค์ค‘

Cryptography and Security Distributed Computing Computer Science Networking Network
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Capacity of Linear Two-hop Mesh Networks with Rate Splitting, Decode-and-forward Relaying and Cooperation

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ๋ฉ”์‰ฌ ๋„คํŠธ์›Œํฌ ๋Š” ์ธํ”„๋ผ(์…€๋ฃฐ๋Ÿฌ)์™€ ๋ฉ€ํ‹ฐโ€‘ํ™‰(์• ๋“œํ˜น) ๋„คํŠธ์›Œํฌ์˜ ์žฅ์ ์„ ๊ฒฐํ•ฉํ•˜๋ ค๋Š” ์ตœ์‹  ํ†ต์‹  ํŒจ๋Ÿฌ๋‹ค์ž„์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ๋‹จ์ผ ๋ ˆ์ดํŠธ ์ „์†ก ํ˜น์€ AF/DF ๋ฆด๋ ˆ์ด ์— ์ดˆ์ ์„ ๋งž์ท„์œผ๋ฉฐ, ์ธ์ ‘ ์…€ ๊ฐ„ ๊ฐ„์„ญ์„ ๋‹จ์ˆœํžˆ ์žก์Œ์œผ๋กœ ์ทจ๊ธ‰ํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋‘ ๊ฐœ์˜ ๊ฐ„์„ญ ์ฑ„๋„ (์ฒซ ๋ฒˆ์งธ ํ™‰, ๋‘ ๋ฒˆ์งธ ํ™‰)์„ ์—ฐ์†์ ์œผ๋กœ ์—ฐ๊ฒฐ๋œ ํ˜•ํƒœ๋กœ ๋ชจ๋ธ๋งํ•˜๊ณ , ๋ ˆ์ดํŠธ ์Šคํ”Œ๋ฆฌํŒ… ์ด๋ผ๋Š” ๋‹ค์ค‘์ ‘๊ทผ(MAC) ๊ธฐ๋ฒ•์„ ์ ์šฉํ•ด ๊ฐ„์„ญ์„ ์œ ์šฉํ•œ ์ •๋ณด ๋กœ ์ „ํ™˜ํ•œ๋‹ค๋Š” ์ ์ด ํ•ต์‹ฌ์ด๋‹ค. 2. ์‹œ์Šคํ…œ ๋ชจ๋ธ ๋ฐ ๊ฐ€์ • | ์š”์†Œ | ์„ค๋ช… | | | |

Network Information Theory Mathematics Computer Science
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CMSSM Spectroscopy in light of PAMELA and ATIC

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์•”ํ‘๋ฌผ์งˆ ๋ฌธ์ œ : ์šฐ์ฃผ ์—๋„ˆ์ง€ ๋ฐ€๋„์˜ ์•ฝ 23 %๊ฐ€ ๋น„๋ฐœ๊ด‘ ์•”ํ‘๋ฌผ์งˆ์ด๋ฉฐ, ํ‘œ์ค€๋ชจํ˜•(SM)์—๋Š” ์ ์ ˆํ•œ ํ›„๋ณด๊ฐ€ ์—†๋‹ค. CMSSM์˜ ๋งค๋ ฅ : Rโ€‘ํŒจ๋ฆฌํ‹ฐ ๋ณด์กด ํ•˜์— ๊ฐ€์žฅ ๊ฐ€๋ฒผ์šด ์ดˆ์ž…์ž(LSP)์ธ ์ค‘์„ฑ๋ฏธ์ž๋Š” ์ „ํ˜•์ ์ธ WIMP ํ›„๋ณด์ด๋ฉฐ, ์งˆ๋Ÿ‰์ด 100 GeVโ€“1 TeV ์ •๋„์ด๋ฉด ์—ด์—ญํ•™์  ์ž”๋ฅ˜๋ฐ€๋„์™€ ๊ด€์ธก๊ฐ’์ด ์ผ์น˜ํ•œ๋‹ค. PAMELAยทATIC ๊ด€์ธก : PAMELA๋Š” 10โ€“100 GeV ๊ตฌ๊ฐ„์—์„œ ์–‘์ „์ž ๋น„์œจ์ด ์˜ˆ์ƒ๋ณด๋‹ค ํฌ๊ฒŒ ์ƒ์Šนํ–ˆ์ง€๋งŒ ๋ฐ˜์–‘์„ฑ์ž ์ฆ๊ฐ€๋Š” ์—†์—ˆ๋‹ค. ATIC์€ 100โ€“800 GeV ๊ตฌ๊ฐ„์—์„œ ์ „์žยท์–‘์ „์ž ์ด ํ”Œ๋Ÿญ์Šค๊ฐ€

Astrophysics HEP-PH
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Common Beliefs and Public Announcements in Strategic Games with Arbitrary Strategy Sets

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ „๋žต ๊ฒŒ์ž„์˜ ์ธ์‹๋ก ์  ๋ถ„์„ ์€ ํ”Œ๋ ˆ์ด์–ด๋“ค์ด ์„œ๋กœ์˜ ํ–‰๋™์— ๋Œ€ํ•ด ์–ด๋–ค ๋ฏฟ์Œ(belief) ํ˜น์€ ์ง€์‹(knowledge)์„ ๊ฐ€์ง€๊ณ  ์žˆ๋Š”๊ฐ€์— ๋”ฐ๋ผ ์˜ˆ์ธก ๊ฐ€๋Šฅํ•œ ์ „๋žต์„ ๋„์ถœํ•œ๋‹ค๋Š” ์ „์ œ์— ๊ธฐ๋ฐ˜ํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ(Aumann 1987, Brandenburger & Dekel 1987 ๋“ฑ)๋Š” ์œ ํ•œ ํ˜น์€ ์—ฐ์†์ ์ธ ๋ณด์ˆ˜(payoff) ๊ตฌ์กฐ, ํ˜น์€ 2์ธ ๊ฒŒ์ž„์— ํ•œ์ •๋œ ๋ชจ๋ธ์„ ์ฃผ๋กœ ๋‹ค๋ฃจ์—ˆ๋‹ค. ์—„๊ฒฉ ์ง€๋ฐฐ(IESDS) ์— ๋Œ€ํ•œ ์ธ์‹๋ก ์  ์ ‘๊ทผ๋„ ๋Œ€๋ถ€๋ถ„ ์œ ํ•œ ๊ฒŒ์ž„ ์— ๊ตญํ•œ๋˜์—ˆ์œผ๋ฉฐ, ์•ฝํ•œ ์ง€๋ฐฐ(weak dominance) ๋Š” ์•„์ง ์ถฉ๋ถ„ํžˆ

Game Theory Computer Science
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Conservative evaluation of the uncertainty in the LAGEOS-LAGEOS II Lense-Thirring test

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ๋ผ์„ผโ€‘ํ„ฐ๋จธ ํšจ๊ณผ ๋Š” ํšŒ์ „ํ•˜๋Š” ์งˆ๋Ÿ‰์ด ๋งŒ๋“  ์ค‘๋ ฅโ€‘์ž๊ธฐ์žฅ(๊ทธ๋ผ๋น„ํ† โ€‘๋งˆ๊ทธ๋„คํ‹ฑ) ํ˜„์ƒ์œผ๋กœ, ์œ„์„ฑ ๊ถค๋„ ๋…ธ๋“œ์˜ ์„œ๊ตฌ์  ์ „์ง„(precession)์œผ๋กœ ๊ด€์ธก๋œ๋‹ค. LAGEOS / LAGEOS II๋Š” ๊ณ ๋„ยท๊ถค๋„ ์•ˆ์ •์„ฑ์ด ๋›ฐ์–ด๋‚˜ ๋ผ์„ผโ€‘ํ„ฐ๋จธ ๊ฒ€์ฆ์— ๊ฐ€์žฅ ๋งŽ์ด ์ด์šฉ๋œ ์ธ๊ณต์œ„์„ฑ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ง์ˆ˜ ๊ตฌ๋ฉด์กฐํ™”๊ณ„์ˆ˜ (J {ell}) (ํŠนํžˆ (ell 2,4,6,dots))๊ฐ€ ๊ถค๋„ ์ „์ง„์— ๋ฏธ์น˜๋Š” โ€œ๋ณ„๋„ ์ „์ง„โ€์ด ๋ผ์„ผโ€‘ํ„ฐ๋จธ ์‹ ํ˜ธ์™€ ๊ฑฐ์˜ ๋™์ผํ•œ ํฌ๊ธฐ๋กœ ๋‚˜ํƒ€๋‚˜, ๋ชจ๋ธ๋ง ์˜ค์ฐจ๊ฐ€ ์‹คํ—˜ ์ •ํ™•๋„๋ฅผ ์ œํ•œํ•œ๋‹ค. 2. ๊ธฐ์กด ์˜ค๋ฅ˜ ์ถ”์ • ๋ฐฉ์‹์˜ ํ•œ

Astrophysics Physics General Relativity
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Coupling of Surface and Volume Dipole Oscillations in C-60 Molecules

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ํ•ต ๋ฌผ๋ฆฌํ•™์—์„œ์˜ GDR (giant dipole resonance)๊ณผ ๊ธˆ์† ํด๋Ÿฌ์Šคํ„ฐ์—์„œ์˜ ํ”Œ๋ผ์Šค๋ชฌ์€ ๋ชจ๋‘ ์œ ํ•œ ํŽ˜๋ฅด๋ฏธ ์‹œ์Šคํ…œ ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์ง‘๋‹จ์  ์ „์ž(๋˜๋Š” ํ•ต) ์ง„๋™์ด๋‹ค. ๊ธฐ์กด ๋ชจ๋ธ(Goldhaberโ€‘Teller, Steinwedelโ€‘Jensen)์€ ๊ฐ๊ฐ ์ „์ด ๋ชจ๋“œ ์™€ ์••์ถ• ๋ชจ๋“œ ๋ฅผ ์„ค๋ช…ํ–ˆ์ง€๋งŒ, ์‹ค์ œ ์‹คํ—˜ ๋ฐ์ดํ„ฐ๋Š” ๋‘ ๋ชจ๋“œ๊ฐ€ ๋™์‹œ ๊ฒฐํ•ฉ ๋œ ํ˜•ํƒœ๋ฅผ ์š”๊ตฌํ•œ๋‹ค. ์ €์ž๋“ค์€ ์ด๋Ÿฌํ•œ ๊ฒฐํ•ฉ์„ ๋ฐ˜๊ณ ์ „์  ์ด๋ฉด์„œ๋„ ์–‘์ž์  ํ‰๊ท ์žฅ (HF/KS) ๊ธฐ๋ฐ˜์œผ๋กœ ๋‹ค๋ฃฐ ์ˆ˜ ์žˆ๋Š” LCA ๋ฅผ ์ œ์‹œํ•˜๊ณ , ๊ธˆ์† ํด๋Ÿฌ์Šคํ„ฐ์™€ Cโ‚†โ‚€์— ์ ์šฉํ•ด ๊ฒ€์ฆ

Physics NUCL-TH
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Crystallographic modelling of protein loops and their heterogeneity with Rappertk

1. ์—ฐ๊ตฌ ๋™๊ธฐ์™€ ํ•„์š”์„ฑ ์ž๋™ํ™” ์š”๊ตฌ : ๊ธฐ์กด CCP4, Phenix, CNS ๋“ฑ์€ ๊ฐ•๋ ฅํ•˜์ง€๋งŒ ์ •์ œ ๊ณผ์ •์ด ์—ฌ์ „ํžˆ ์ „๋ฌธ๊ฐ€ ์˜์กด์ ์ด๋ฉฐ, ๊ฒฐ๊ณผ๊ฐ€ ๋‹จ์ผโ€‘์ปจํฌ๋จธ ๋ชจ๋ธ์— ํŽธ์ค‘๋œ๋‹ค. ๊ตฌ์กฐ ์ด์งˆ์„ฑ : ๋‹จ๋ฐฑ์งˆ์€ ๊ธฐ๋Šฅ์„ ์œ„ํ•ด ์ผ์ • ์ˆ˜์ค€์˜ ๋™์ ์„ฑ์„ ๊ฐ€์ ธ์•ผ ํ•˜๋ฉฐ, ์ด๋Š” ๊ฒฐ์ • ๋‚ด์—์„œ๋„ ๋ณด์กด๋œ๋‹ค. ๋‹ค์ค‘โ€‘์ปจํฌ๋จธ ๋ชจ๋ธ๋ง์€ ๊ฒฐํ•ฉ ๋ถ€์œ„, ์‚ฌ์ด๋“œ์ฒด์ธ ๋ฐฉํ–ฅ, ๋น„๊ณต์œ  ์ƒํ˜ธ์ž‘์šฉ ๋“ฑ์„ ๋ณด๋‹ค ์ •ํ™•ํžˆ ํ•ด์„ํ•˜๊ฒŒ ํ•œ๋‹ค. ๋ฃจํ”„์˜ ํŠน์ˆ˜์„ฑ : ๋ฃจํ”„๋Š” Cฮฑ ์œ„์น˜ ๋ถˆํ™•์‹ค์„ฑ์ด ํฌ๊ณ , ์ „ํ†ต์ ์ธ ์ „์ž๋ฐ€๋„ ๊ธฐ๋ฐ˜ ์ž๋™ํ™”๊ฐ€ ์–ด๋ ค์šด ์˜์—ญ์ด๋‹ค. ๋”ฐ๋ผ์„œ ์ œํ•œ๋œ ์ œ์•ฝ์กฐ๊ฑด ํ•˜์—์„œ๋„ ํšจ์œจ์ ์œผ๋กœ ์ƒ˜ํ”Œ๋งํ•  ์ˆ˜

Quantitative Biology Model
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Damping of sound waves in superfluid nucleon-hyperon matter of neutron stars

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ฒœ๋ฌธํ•™์  ๊ด€์ธก : ์ตœ๊ทผ ๊ด€์ธก๋œ ๊ณ ์ฃผํŒŒ ์ „์ž๊ธฐ ํ”Œ๋ ˆ์–ด(ํŠนํžˆ ๊ฐ•๋ ฅ ํ”Œ๋ ˆ์–ด์˜ ํ€˜์ด์‚ฌ ์ง„๋™)์™€ ์ฐจ์„ธ๋Œ€ ์ค‘๋ ฅํŒŒ ํƒ์ง€๊ธฐ์˜ ๊ฐ๋„ ํ–ฅ์ƒ์€ ์ค‘์„ฑ์ž๋ณ„ ๋‚ด๋ถ€ ๊ตฌ์กฐ ์— ๋Œ€ํ•œ ์ •๋ฐ€ ๋ชจ๋ธ๋ง์„ ์š”๊ตฌํ•œ๋‹ค. ์ดˆ์œ ์ฒด ํšจ๊ณผ : ํ•ต์žโ€‘ํ•˜์ดํผ์˜จ ๋ฌผ์งˆ์€ ํ•ต์ž๋“ค(์ค‘์„ฑ์žยท์–‘์„ฑ์ž)๊ณผ ํ•˜์ดํผ์˜จ(ฮ›, ฮฃ)์ด ์ดˆ์œ ์ฒด ์ƒํƒœ ๊ฐ€ ๋  ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์œผ๋ฉฐ, ์ด๋Š” ์ „ํ†ต์ ์ธ ๋‹จ์ผ ์œ ์ฒด ์ˆ˜์†Œํ•™์œผ๋กœ๋Š” ์„ค๋ช…ํ•  ์ˆ˜ ์—†๋Š” ์ƒˆ๋กœ์šด ์ž์œ ๋„(์ดˆ์œ ์ฒด ํ๋ฆ„)์™€ ์—”ํŠธ๋ ˆ์ธ๋จผํŠธ(entrainment) ํšจ๊ณผ๋ฅผ ๋„์ž…ํ•œ๋‹ค. 2. ์ด๋ก ์  ํ”„๋ ˆ์ž„์›Œํฌ ์ƒ๋Œ€๋ก ์  ์ดˆ์œ ์ฒด ์ˆ˜์†Œํ•™ (Gusakov & Kanto

Astrophysics NUCL-TH
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Design of Fault-Tolerant and Dynamically-Reconfigurable Microfluidic Biochips

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋””์ง€ํ„ธ ๋งˆ์ดํฌ๋กœํ”Œ๋ฃจ์ด๋”• ์€ ์ „๊ทน ๋ฐฐ์—ด์„ ์ „์••์œผ๋กœ ์ œ์–ดํ•ด ์•ก์ ์„ ์ž์œ ๋กญ๊ฒŒ ์ด๋™ยทํ˜ผํ•ฉํ•˜๋ฏ€๋กœ, ๋™์‹œ ๋‹ค์ค‘ ๋ฐ”์ด์˜ค์–ด์„ธ์ด ์ˆ˜ํ–‰์ด ๊ฐ€๋Šฅํ•˜๊ณ , ์„ค๊ณ„ ๋‹จ๊ณ„์—์„œ ํ•˜๋“œ์›จ์–ด ๊ตฌ์กฐ๊ฐ€ ๊ณ ์ •๋˜์ง€ ์•Š์•„ ์žฌ๊ตฌ์„ฑ์„ฑ ์ด ๋›ฐ์–ด๋‚˜๋‹ค. ์˜๋ฃŒยทํ™˜๊ฒฝ ๋ชจ๋‹ˆํ„ฐ๋ง ๋“ฑ ์•ˆ์ „โ€‘์ค‘์š” ์‘์šฉ ๋ถ„์•ผ์—์„œ๋Š” ๊ฒฐํ•จ ๋ฐœ์ƒ ์‹œ์—๋„ ์ง€์†์ ์ธ ๋™์ž‘์ด ์š”๊ตฌ๋˜๋ฏ€๋กœ ๊ณ ์žฅโ€‘๋‚ด์„ฑ ์„ค๊ณ„๊ฐ€ ํ•„์ˆ˜์ ์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์—ฐ์† ํ๋ฆ„ ๋ฐ”์ด์˜ค์นฉ์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์œผ๋ฉฐ, ๋””์ง€ํ„ธ ๋ฐ”์ด์˜ค์นฉ์— ๋Œ€ํ•œ ์‹œ์Šคํ…œโ€‘๋ ˆ๋ฒจ CAD ๋„๊ตฌ๋Š” ๋ถ€์กฑํ–ˆ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๊ธฐ์—ฌ ๋‚ด์šฉ | ์˜์˜ | | | | | | 1

Hardware Architecture Computer Science
Deterministic Secure Positioning in Wireless Sensor Networks

Deterministic Secure Positioning in Wireless Sensor Networks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์œ„์น˜ ๊ธฐ๋ฐ˜ ์„œ๋น„์Šค์™€ ๋ผ์šฐํŒ… ๋“ฑ WSN ์‘์šฉ์—์„œ ์ •ํ™•ํ•œ ๋…ธ๋“œ ์œ„์น˜๋Š” ํ•„์ˆ˜์ ์ด๋ฉฐ, ์œ„์น˜ ์œ„์กฐ๋Š” ์ „์ฒด ์‹œ์Šคํ…œ ์„ฑ๋Šฅ์„ ํฌ๊ฒŒ ์ €ํ•˜์‹œํ‚จ๋‹ค. ๊ธฐ์กด ์ ‘๊ทผ๋ฒ•์€ ์‹ ๋ขฐ ๊ฒ€์ฆ์ž(Trusted Verifiers) ๋ฅผ ์ „์ œ๋กœ ํ•˜์—ฌ, ๊ฒ€์ฆ์ž๊ฐ€ ์ œ๊ณตํ•˜๋Š” ๊ฑฐ๋ฆฌยท์œ„์น˜ ์ •๋ณด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์œ„์กฐ๋ฅผ ํƒ์ง€ํ•œ๋‹ค. ํ•˜์ง€๋งŒ ๊ฒ€์ฆ์ž ๋ฐฐ์น˜ยท์œ ์ง€ ๊ด€๋ฆฌ ๋น„์šฉ์ด ํฌ๊ณ , ์™„์ „ ๋ถ„์‚ฐ ์ž๊ฐ€ ์กฐ์ง(selfโ€‘organization)์ด ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. 2. NTS ๋ชจ๋ธ์˜ ์˜์˜ ์‹ ๋ขฐ ๊ฒ€์ฆ์ž๊ฐ€ ์ „ํ˜€ ์—†๋Š” ์ƒํ™ฉ ์„ ๊ฐ€์ •ํ•จ์œผ๋กœ์จ, ์™„์ „ ๋ถ„์‚ฐํ˜• ๋„คํŠธ์›Œํฌ์—์„œ๋„ ๋ณด์•ˆ ์œ„

Cryptography and Security Distributed Computing Data Structures Computer Science Networking Network
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Distributed spatial multiplexing with 1-bit feedback

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋ถ„์‚ฐ ๋น”ํฌ๋ฐ ์€ ๋‹ค์ˆ˜์˜ ์ €์ „๋ ฅ ๋‹จ๋ง์ด ํ˜‘์กฐ ์—†์ด๋„ ๊ณต๋™์œผ๋กœ ๋น”์„ ํ˜•์„ฑํ•ด ์ „์†ก ํšจ์œจ์„ ๋†’์ด๋Š” ๊ธฐ์ˆ ์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ(Mudumbai et al.)๋Š” ๋‹จ์ผ ๊ทธ๋ฃนโ€‘๋‹จ์ผ ์ˆ˜์‹ ๊ธฐ ์ƒํ™ฉ์—์„œ 1โ€‘๋น„ํŠธ ํ”ผ๋“œ๋ฐฑ๋งŒ์œผ๋กœ๋„ ์ฑ„๋„์„ ํ•™์Šตํ•˜๊ณ  ๋น”ํฌ๋ฐ์„ ๊ตฌํ˜„ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์˜€๋‹ค. ์‹ค์ œ ์‹œ์Šคํ…œ์—์„œ๋Š” ๋‹ค์ค‘ ์‚ฌ์šฉ์ž ๊ฐ€ ๋™์‹œ์— ์กด์žฌํ•˜๊ณ , ๊ฐ ์‚ฌ์šฉ์ž ๊ทธ๋ฃน์ด ์„œ๋กœ ๊ฐ„์„ญ์„ ์ผ์œผํ‚ค๋Š” ๋‹ค์ค‘ ์‚ฌ์šฉ์ž ๊ฐ„์„ญ ๋„คํŠธ์›Œํฌ ๊ฐ€ ์ผ๋ฐ˜์ ์ด๋‹ค. ์ด๋•Œ ์™„์ „ํ•œ CSI(์ฑ„๋„ ์ƒํƒœ ์ •๋ณด)๋ฅผ ํš๋“ํ•˜๊ธฐ๋Š” ํ˜„์‹ค์ ์œผ๋กœ ์–ด๋ ต๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ด๋Ÿฌํ•œ ๋‹ค์ค‘ ์‚ฌ์šฉ์ž ํ™˜๊ฒฝ์— 1โ€‘๋น„ํŠธ ํ”ผ๋“œ๋ฐฑ์„

Information Theory Mathematics Computer Science
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Does Sex Induce a Phase Transition?

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์งˆ๋ฌธ ์„ค์ • ์—ผ์ƒ‰์ฒด ๊ธธ์ด์™€ ๋Œ์—ฐ๋ณ€์ด ์Šค์ผ€์ผ๋ง : ์ „ํ†ต์ ์ธ ๋ฌด์„ฑ ๋ชจ๋ธ์—์„œ๋Š” ๋Œ์—ฐ๋ณ€์ด ์ˆ˜๋ฅผ ์—ผ์ƒ‰์ฒด ๊ธธ์ด L ์— ๋น„๋ก€ํ•ด ์กฐ์ •(m/L const)ํ•˜๋ฉด, ํ‰๊ท  ๋Œ์—ฐ๋ณ€์ด ์ˆ˜ N ๊ณผ ๋ถ„์‚ฐ ฮ”N ๋„ L ์— ๋น„๋ก€ํ•ด ์Šค์ผ€์ผ๋ง๋œ๋‹ค. ์ด๋Š” โ€œ์„ ํ˜• ์Šค์ผ€์ผ๋งโ€์ด๋ผ๊ณ  ๋ถ€๋ฅผ ์ˆ˜ ์žˆ๋‹ค. ์„ฑ์  ๋ฒˆ์‹์˜ ๋น„์„ ํ˜•์„ฑ : ์ดํ˜•(๋‘ ๊ฐœ์˜ ์ƒ๋™ ์—ผ์ƒ‰์ฒด)๊ณผ ๊ต์ฐจ(overโ€‘lap) ๊ณผ์ •์€ ์œ ์ „ ์ •๋ณด๊ฐ€ 1์ฐจ์› ๋น„ํŠธ์—ด์—๋งŒ ๊ตญํ•œ๋˜์ง€ ์•Š์œผ๋ฉฐ, ๋Œ์—ฐ๋ณ€์ด์™€ ์„ ํƒ ์‚ฌ์ด์˜ ๊ฒฝ์Ÿ์„ ๋ณต์žกํ•˜๊ฒŒ ๋งŒ๋“ ๋‹ค. ๋”ฐ๋ผ์„œ ๋™์ผํ•œ m/L ๋น„์œจ์ด ์œ ์ง€๋˜๋Š”์ง€๊ฐ€ ์˜๋ฌธ์ด ๋œ๋‹ค. 2. ๋ชจ๋ธ ๊ตฌ์„ฑ | ์š”์†Œ

Condensed Matter Quantitative Biology Physics
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Dynamic parameters of structures extracted from ambient vibration measurements: an aid for the seismic vulnerability assessment of existing buildings in moderate seismic hazard regions

| ๊ตฌ๋ถ„ | ๋‚ด์šฉ | ์˜์˜ / ๋น„ํŒ | | | | | | ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ | ๊ธฐ์กด ๊ฑด๋ฌผ์˜ ์ง€์ง„ ์ทจ์•ฝ์„ฑ ํ‰๊ฐ€๋Š” ๊ตฌ์กฐ๋ฌผ์˜ ๊ฐ•์„ฑ ๋ถ„ํฌ ์™€ ๋ชจ๋‹ฌ ํŒŒ๋ผ๋ฏธํ„ฐ ์— ํฌ๊ฒŒ ์˜์กดํ•˜์ง€๋งŒ, ํ˜„์žฅ ์กฐ์‚ฌ ์‹œ ์ƒ์„ธ ์„ค๊ณ„ ์ •๋ณด๊ฐ€ ๋ถ€์กฑํ•จ. | ์‹ค์ œ ํ˜„์žฅ ์ ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ๋†’์ด๊ธฐ ์œ„ํ•ด ์ €๋น„์šฉยท๋น„์นจ์Šต์ ์ธ ๋ฐฉ๋ฒ•์ด ํ•„์š”ํ•จ์„ ์ž˜ ์งš๊ณ  ์žˆ์Œ. | | ํ•ต์‹ฌ ๋ฐฉ๋ฒ•๋ก  | 1. FDD ๋ฅผ ์ด์šฉํ•ด ํ™˜๊ฒฝ์ง„๋™์œผ๋กœ๋ถ€ํ„ฐ ๊ณ ์œ ์ฃผํŒŒ์ˆ˜ยท๋ชจ๋“œ ํ˜•์ƒยท๊ฐ์‡ ๋น„ ์ถ”์ • <br>2. ์ „๋‹จ ๋น”(lumpedโ€‘mass shear beam) ๋ชจ๋ธ ์„ ๊ฐ€์ •ํ•˜๊ณ , ๋ชจ๋“œ ํ˜•์ƒ์œผ๋กœ๋ถ€ํ„ฐ ์ธต๋ณ„ ๊ฐ•์„ฑ ์„ ์—ญ์‚ฐ <br>3. ์ถ”์ •๋œ ๊ฐ•์„ฑ

Physics
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Entropy production of cyclic population dynamics

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋น„ํ‰ํ˜• ํ†ต๊ณ„์—ญํ•™์˜ ๋‚œ์ œ : ํ‰ํ˜•๊ณ„์—์„œ๋Š” ์—”ํŠธ๋กœํ”ผ ์ฆ๊ฐ€ ๋ฒ•์น™์ด ๋ณดํŽธ์ ์ด์ง€๋งŒ, ๋น„ํ‰ํ˜•๊ณ„์—์„œ๋Š” ์ „์—ญ์ ์ธ โ€œ์—”ํŠธ๋กœํ”ผโ€ ๊ฐœ๋…์ด ์•„์ง ํ™•๋ฆฝ๋˜์ง€ ์•Š์•˜๋‹ค. ์ƒํƒœ๊ณ„์˜ ์ˆœํ™˜ ๊ฒฝ์Ÿ : A โ†’ B โ†’ C โ†’ A ํ˜•ํƒœ์˜ ์ƒํ˜ธ ์–ต์ œยท์ด‰์ง„ ๊ด€๊ณ„๋Š” ์ž์—ฐ๊ณ„(์˜ˆ: ์บ˜๋ฆฌํฌ๋‹ˆ์•„ ๋„๋งˆ๋ฑ€, ๋ฏธ์ƒ๋ฌผ ๊ตฐ์ง‘)์—์„œ ๊ด€์ฐฐ๋˜๋ฉฐ, ๋‚ด์žฌ์  ์žก์Œ๊ณผ ์œ ํ•œ ๊ฐœ์ฒด์ˆ˜ ๋•Œ๋ฌธ์— ๋น„ํ‰ํ˜• ์ง„๋™์„ ๋งŒ๋“ ๋‹ค. 2. ๋ชจ๋ธ ์ •์˜ ์„ธ ์ข… A, B, C ์™€ ์ด ๊ฐœ์ฒด์ˆ˜ (N) ๊ฐ€ ๋ณด์กด๋˜๋Š” ๋งˆ์Šคํ„ฐ ๋ฐฉ์ •์‹ ๊ธฐ๋ฐ˜ ๋ชจ๋ธ. ๋ฐ˜์‘์‹

Condensed Matter Physics Quantitative Biology
Epigenetic Chromatin Silencing: Bistability and Front Propagation

Epigenetic Chromatin Silencing: Bistability and Front Propagation

1. ๋ชจ๋ธ์˜ ํ•ต์‹ฌ ๊ฐ€์ •๊ณผ ์ˆ˜ํ•™์  ๊ตฌ์กฐ | ์š”์†Œ | ์ƒ๋ฌผํ•™์  ์˜๋ฏธ | ์ˆ˜ํ•™์  ํ‘œํ˜„ | | | | | | ํžˆ์Šคํ†ค ์•„์„ธํ‹ธํ™” (A i) | ์ „์‚ฌ ํ™œ์„ฑํ™”์™€ ์ง์ ‘ ์—ฐ๊ด€ | (dot A i alpha(1 A i) lambda A i sum j gamma {ij} S j A i) | | Sir ๋ณตํ•ฉ์ฒด ์ ์œ  (S i) | ์นจ๋ฌต ๋ณตํ•ฉ์ฒด์˜ ๊ฒฐํ•ฉยทํƒˆ์ฐฉ | (dot S i rho i f(A i) eta S i) | | ํ˜‘๋™ ์ฐจ์ˆ˜ (n) | ํƒˆ์•„์„ธํ‹ธํ™”๋œ ํžˆ์Šคํ†ค์ด Sir ๋ณตํ•ฉ์ฒด๋ฅผ ๋” ๋งŽ์ด ๋Œ์–ด๋“ค์ž„ | (f(A) A^n) |

Quantitative Biology
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Equivariant Lefschetz maps for simplicial complexes and smooth manifolds

| ํ•ญ๋ชฉ | ๋‚ด์šฉ ๋ฐ ํ‰๊ฐ€ | | | | | ํ•ต์‹ฌ ์•„์ด๋””์–ด | ์ถ”์ƒ์  ์ด์ค‘์„ฑ ((P,Theta)) ๋ฅผ ์ด์šฉํ•ด (RKK^{G}(X;C {0}(X),mathbb{C})) ๋ฅผ (KK^{G}(C {0}(X),mathbb{C})) ๋กœ ์‚ฌ์ƒํ•˜๋Š” ๋ ˆํ”„์‹œ์ธ  ๋งต ์„ ์ •์˜ํ•œ๋‹ค.<br> ์ด ๋งต์€ (psi)์˜ (G) equivariant ๋™ํ˜• ์‚ฌ์ƒ ํด๋ž˜์Šค์—๋งŒ ์˜์กดํ•˜๋ฏ€๋กœ, ๋™ํ˜• ๋™๋ฅ˜์— ๋Œ€ํ•œ ๋ถˆ๋ณ€๋Ÿ‰์„ ์ œ๊ณตํ•œ๋‹ค. | | ๊ธฐ์ˆ ์  ํ† ๋Œ€ | Kasparov์˜ bivariant Kโ€‘theory ์™€ RKK ์ด๋ก ์„ ํ•ต์‹ฌ ๋„๊ตฌ๋กœ ์‚ฌ์šฉ.<br> K

Mathematics
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Extension of Wirtinger Calculus in RKH Spaces and the Complex Kernel LMS

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ปค๋„ ๋ฐฉ๋ฒ•์˜ ์˜จ๋ผ์ธ ์ ์šฉ : ๊ธฐ์กด KLMSยทSVM ๋“ฑ์€ ์‹ค์ˆ˜ ์ปค๋„์„ ์ „์ œ๋กœ ๋ฐฐ์น˜ ๋ฐฉ์‹์— ์ตœ์ ํ™”๋ผ ์žˆ์—ˆ์œผ๋ฉฐ, ๋ณต์†Œ์ˆ˜ ์ž…๋ ฅ์„ ์ง์ ‘ ๋‹ค๋ฃจ๋Š” ๋ฐฉ๋ฒ•๋ก ์€ ๋ถ€์žฌํ–ˆ์Šต๋‹ˆ๋‹ค. ๋ณต์†Œ ์‹ ํ˜ธ ์ฒ˜๋ฆฌ์˜ ํ•„์š”์„ฑ : ํ†ต์‹ , ๋ ˆ์ด๋”, ์˜๋ฃŒ ์˜์ƒ ๋“ฑ์—์„œ ๋ณต์†Œ์ˆ˜ ํ˜•ํƒœ์˜ ๋ฐ์ดํ„ฐ๊ฐ€ ์ผ๋ฐ˜์ ์ด๋ฉฐ, ๋น„์„ ํ˜• ์™œ๊ณก์„ ๋™์‹œ์— ๋ณด์ •ํ•  ์ˆ˜ ์žˆ๋Š” ์ ์‘ ํ•„ํ„ฐ๊ฐ€ ์š”๊ตฌ๋ฉ๋‹ˆ๋‹ค. Wirtinger ๋ฏธ๋ถ„์˜ ๋ถ€์ƒ : ๋ณต์†Œ๊ฐ’ ๋น„์šฉํ•จ์ˆ˜์˜ ๊ธฐ์šธ๊ธฐ ๊ณ„์‚ฐ์„ ๊ฐ„์†Œํ™”ํ•˜๋Š” Wirtinger ๋ฏธ๋ถ„์€ Widelyโ€‘Linear ํ•„ํ„ฐ ์„ค๊ณ„์— ๋„๋ฆฌ ์“ฐ์ด์ง€๋งŒ, ๋ฌดํ•œ ์ฐจ์›์˜ RKHS์— ์ง์ ‘ ์ ์šฉํ•˜๊ธฐ์—”

Machine Learning Computer Science
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Fluctuation of the download network

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ๋ณตํ•ฉ ๋„คํŠธ์›Œํฌ ๋™์—ญํ•™ : ๊ธฐ์กด ๋ณตํ•ฉ ๋„คํŠธ์›Œํฌ ์—ฐ๊ตฌ๋Š” ์ •์  ํ† ํด๋กœ์ง€์— ์ดˆ์ ์„ ๋งž์ถ”๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์•˜์œผ๋‚˜, ๋ณธ ๋…ผ๋ฌธ์€ ์‹œ๊ฐ„์— ๋”ฐ๋ฅธ ํŠธ๋ž˜ํ”ฝ ๋ณ€๋™ ์„ ์ค‘์‹ฌ์œผ๋กœ ๋„คํŠธ์›Œํฌ๋ฅผ ๋ถ„์„ํ•œ๋‹ค๋Š” ์ ์—์„œ ์ฐจ๋ณ„์„ฑ์„ ๊ฐ€์ง„๋‹ค. ๋ณดํŽธ์  ๋ณ€๋™ ํด๋ž˜์Šค ๋…ผ์Ÿ : Menezes & Barabรกsi(2004)์˜ โ€œฮฑ 0.5 vs 1โ€ ๋‘ ํด๋ž˜์Šค ๊ฐ€์„ค์— ๋Œ€ํ•œ ๊ฒ€์ฆยทํ™•์žฅ์„ ์‹œ๋„ํ•œ๋‹ค. ๋‹ค์šด๋กœ๋“œ ๋„คํŠธ์›Œํฌ๋ผ๋Š” ์†Œ๊ทœ๋ชจยท์ „๋ฌธ ๋ถ„์•ผ ์›น์‚ฌ์ดํŠธ ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•จ์œผ๋กœ์จ, ๊ธฐ์กด ์—ฐ๊ตฌ๊ฐ€ ์ฃผ๋กœ ๋‹ค๋ฃฌ ๋Œ€๊ทœ๋ชจ ์ธํ”„๋ผ(์ธํ„ฐ๋„ท, ๋งˆ์ดํฌ๋กœ์นฉ ๋“ฑ)์™€๋Š” ๋‹ค๋ฅธ ํ™˜๊ฒฝ์„ ์ œ๊ณตํ•œ๋‹ค. 2. ๋ฐ์ดํ„ฐ

Condensed Matter Physics NUCL-TH Network
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Horn versus full first-order: complexity dichotomies in algebraic constraint satisfaction

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ œ์•ฝ ๋งŒ์กฑ ๋ฌธ์ œ(CSP) ๋Š” ์ธ๊ณต์ง€๋Šฅ, ๊ทธ๋ž˜ํ”„ ์ด๋ก , ์Šค์ผ€์ค„๋ง ๋“ฑ ๊ฑฐ์˜ ๋ชจ๋“  ์ปดํ“จํ„ฐ ๊ณผํ•™ ๋ถ„์•ผ์— ๋“ฑ์žฅํ•œ๋‹ค. ์œ ํ•œ ๋„๋ฉ”์ธ์— ๋Œ€ํ•œ ๋‹ค์ด์ฝ”ํŠธ๋ฏธ ์ •๋ฆฌ (Federโ€‘Vardi conjecture)๋Š” ์ด๋ฏธ ํ•ด๊ฒฐ๋์ง€๋งŒ, ๋ฌดํ•œ ๋„๋ฉ”์ธ ํŠนํžˆ ฯ‰โ€‘categorical ์ด ์•„๋‹Œ ๊ตฌ์กฐ์— ๋Œ€ํ•œ ์ฒด๊ณ„์ ์ธ ๋ณต์žก๋„ ๋ถ„๋ฅ˜๋Š” ์•„์ง ๋ฏธ๋น„ํ–ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ด๋Ÿฌํ•œ ๊ณต๋ฐฑ์„ ๋ฉ”์šฐ๋ฉฐ, ฯ‰โ€‘categorical์ด ์•„๋‹Œ ๋‘ ์ž์—ฐ์Šค๋Ÿฌ์šด ๋ฌดํ•œ ๋„๋ฉ”์ธ ๊ตฌ์กฐ์— ๋Œ€ํ•ด ์™„์ „ํ•œ ๋ณต์žก๋„ ์ด๋ถ„๋ฒ•์„ ์ œ๊ณตํ•œ๋‹ค. 2. ์ฃผ์š” ๊ฒฐ๊ณผ ์š”์•ฝ | ๊ตฌ์กฐ | ์ •์˜ | ๋ณต์žก๋„ ์ด๋ถ„๋ฒ• |

Mathematics Logic Computational Complexity Computer Science
How AI Agents Follow the Herd of AI? Network Effects, History, and Machine Optimism

How AI Agents Follow the Herd of AI? Network Effects, History, and Machine Optimism

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ๋„คํŠธ์›Œํฌ ํšจ๊ณผ ๋Š” ๊ธฐ์ˆ  ์ฑ„ํƒ, ์†Œ์…œ ํ”Œ๋žซํผ, ๊ธˆ์œต ์‹œ์žฅ ๋“ฑ ํ˜„์‹ค ์„ธ๊ณ„์—์„œ ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ํ˜„์ƒ์ด๋ฉฐ, ๊ธฐ์กด ๊ฒŒ์ž„ ์ด๋ก ์—์„œ๋Š” โ€˜์™„์ „ํ•œ ๊ธฐ๋Œ€ ๊ท ํ˜•(FEE)โ€™์„ ํ†ตํ•ด ๋ถ„์„ํ•œ๋‹ค. ํ•˜์ง€๋งŒ ์ธ๊ฐ„์€ ์„ ํ˜•์ ์ด๊ณ  ๋ถˆ๋ณ€ํ•˜๋Š” ๊ณผ๊ฑฐ ๊ธฐ๋ก์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์ถ”๋ก ํ•˜๋Š” ๋ฐ˜๋ฉด, LLM ๊ธฐ๋ฐ˜ ์—์ด์ „ํŠธ๋Š” ํ”„๋กฌํ”„ํŠธ์™€ ๋ฐ์ดํ„ฐ ์ „์ฒ˜๋ฆฌ ์— ๋”ฐ๋ผ ๊ณผ๊ฑฐ๋ฅผ ์žฌ๊ตฌ์„ฑํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด ์ฐจ์ด๋ฅผ ์‹คํ—˜์ ์œผ๋กœ ๊ฒ€์ฆํ•œ ์ ์ด ๋ณธ ๋…ผ๋ฌธ์˜ ๊ฐ€์žฅ ํฐ ํ˜์‹ ์ด๋‹ค. 2. ์‹คํ—˜ ์„ค๊ณ„ ๋ฐ ๋ฐฉ๋ฒ•๋ก  | ์š”์†Œ | ์„ค๊ณ„ ๋‚ด์šฉ | | | | | ์—์ด์ „ํŠธ | Qwenโ€‘max, Qwenโ€‘turbo,

Network
Intraday pattern in bid-ask spreads and its power-law relaxation for   Chinese A-share stocks

Intraday pattern in bid-ask spreads and its power-law relaxation for Chinese A-share stocks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์‹œ์žฅ ๋ฏธ์‹œ๊ตฌ์กฐ ์—์„œ ํ˜ธ๊ฐ€ ์Šคํ”„๋ ˆ๋“œ๋Š” ์œ ๋™์„ฑ, ๊ฑฐ๋ž˜๋น„์šฉ, ์ •๋ณด ๋น„๋Œ€์นญ์„ ํ‰๊ฐ€ํ•˜๋Š” ํ•ต์‹ฌ ์ง€ํ‘œ์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ NYSEยทNASDAQยทLSE ๋“ฑ ์„œ๊ตฌ ์‹œ์žฅ์˜ Uโ€‘ํ˜• ํ˜น์€ ์•ˆ์ •ํ˜• ์Šคํ”„๋ ˆ๋“œ ํŒจํ„ด์„ ๋ณด๊ณ ํ–ˆ์œผ๋ฉฐ, ์ค‘๊ตญ ์ฃผ๋ฌธโ€‘์ฃผ๋„(orderโ€‘driven) ์‹œ์žฅ ์˜ ์ผ์ค‘ ์Šคํ”„๋ ˆ๋“œ ํŠน์„ฑ์€ ๊ฑฐ์˜ ํƒ๊ตฌ๋˜์ง€ ์•Š์•˜๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์€ ์ค‘๊ตญ Aโ€‘์ฃผ์‹ ์ด๋ผ๋Š” ํŠน์ˆ˜ํ•œ ์‹œ์žฅ ๊ตฌ์กฐ(๊ฒฝ๋งคโ€‘ํ›„ ๋ƒ‰๊ฐโ€‘์—ฐ์† ์ด์ค‘๊ฒฝ๋งค)์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์Šคํ”„๋ ˆ๋“œ ๋™์—ญํ•™์„ ์ตœ์ดˆ๋กœ ์ฒด๊ณ„์ ์œผ๋กœ ๊ทœ๋ช…ํ•œ๋‹ค๋Š” ์ ์—์„œ ํ•™์ˆ ์ ยท์‹ค๋ฌด์  ๊ฐ€์น˜๋ฅผ ๊ฐ€์ง„๋‹ค. 2. ๋ฐ์ดํ„ฐ ๋ฐ ์ „์ฒ˜๋ฆฌ |

Quantitative Finance Physics
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Language recognition by generalized quantum finite automata with unbounded error (abstract & poster)

| ๊ตฌ๋ถ„ | ๋‚ด์šฉ | ํ‰๊ฐ€ | | | | | | ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ | ๊ธฐ์กด ์—ฐ๊ตฌ(Kondacsโ€‘Watrous QFA, Nayak QFA ๋“ฑ)๋Š” boundedโ€‘error ์ƒํ™ฉ์—์„œ ์–‘์ž ์ž๋™๊ธฐ๊ฐ€ ํด๋ž˜์‹ PFA๋ณด๋‹ค ๋” ๊ฐ•๋ ฅํ•จ์„ ๋ณด์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ unboundedโ€‘error (์˜ค๋ฅ˜ ํ—ˆ์šฉ๋ฅ ์ด ยฝ ์ดํ•˜๊ฐ€ ์•„๋‹Œ ๊ฒฝ์šฐ)์—์„œ๋Š” ์–‘์ž์™€ ํด๋ž˜์‹์˜ ์ฐจ์ด๊ฐ€ ๋ถˆ๋ถ„๋ช…ํ–ˆ๋‹ค. | ์ค‘์š”ํ•œ ์งˆ๋ฌธ ์ œ๊ธฐ: โ€œ์–‘์ž ๋ชจ๋ธ์ด ๋ฌดํ•œ ์˜ค๋ฅ˜์—์„œ๋„ ํด๋ž˜์‹๋ณด๋‹ค ์šฐ์›”ํ•œ๊ฐ€?โ€ | | ํ•ต์‹ฌ ๊ธฐ์—ฌ | 1๏ธโƒฃ ๋ชจ๋“  ์ผ๋ฐ˜ํ™”๋œ ์ผ๋ฐฉํ–ฅ QFA๋ฅผ GPFA(์ผ๋ฐ˜ํ™” ํ™•๋ฅ ์  ์ž๋™๊ธฐ)์™€ ๋‹คํ•ญ์‹ ํฌ๊ธฐ ๋ณ€ํ™˜ ์„ ํ†ตํ•ด

Computational Complexity Computer Science
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Market completion using options

| ํ•ญ๋ชฉ | ๋‚ด์šฉ ๋ฐ ํ‰๊ฐ€ | | | | | ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ | Blackโ€‘Scholes ๋ชจ๋ธ์€ ์™„์ „ํ•˜์ง€๋งŒ, ์‹ค์ฆ์  ๊ฒฐํ•จ์„ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•ด stochastic volatility, jumps ๋“ฑ์„ ๋„์ž…ํ•˜๋ฉด ์™„์ „์„ฑ์ด ์‚ฌ๋ผ์ง„๋‹ค.<br> ์‹ค์ œ ์‹œ์žฅ์—์„œ๋Š” ๋‹ค์–‘ํ•œ ์˜ต์…˜์ด ๊ฑฐ๋ž˜๋˜๋ฏ€๋กœ, ์˜ต์…˜์„ ํฌํ•จํ–ˆ์„ ๋•Œ ์‹œ์žฅ์ด ๋‹ค์‹œ ์™„์ „ํ•ด์ง€๋Š”๊ฐ€๊ฐ€ ํ•ต์‹ฌ ์งˆ๋ฌธ์ด๋‹ค. | | ํ•ต์‹ฌ ๊ธฐ์—ฌ | 1. ๊ธฐํ•˜ํ•™์  ์กฐ๊ฑด์˜ ์•ฝํ™” : ๊ธฐ์กด์— ์ œ์‹œ๋œ โ€œ๊ธฐํ•˜ํ•™์  ์กฐ๊ฑดโ€(ํŠน์ • ํ–‰๋ ฌ์ด ์ „ ๊ตฌ๊ฐ„์—์„œ ๋น„ํ‡ดํ™”)์„ ํ•„์š”์ถฉ๋ถ„์กฐ๊ฑด ์œผ๋กœ ์žฌ์ •์˜ํ•˜๊ณ , ์ด๋Š” โ€œํ–‰๋ ฌ G(t,ฮพ t)์˜ ์˜์  ์ง‘ํ•ฉ S๊ฐ€

Quantitative Finance Mathematics
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Metadynamic sampling of the free energy landscapes of proteins coupled with a Monte Carlo algorithm

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฉ”ํƒ€๋‹ค์ด๋‚ด๋ฏน์Šค ๋Š” ์ œํ•œ๋œ ์ˆ˜์˜ ๋А๋ฆฐ ์ง‘ํ•ฉ ๋ณ€์ˆ˜(s)๋งŒ์„ ์ด์šฉํ•ด ๊ณ ์ฐจ์› ์‹œ์Šคํ…œ์˜ ์ž์œ ์—๋„ˆ์ง€ ์žฅ๋ฒฝ์„ โ€œ์ฑ„์šฐ๋Š”โ€ ๋ฐฉ๋ฒ•์œผ๋กœ, ๊ธฐ์กด MD ๊ธฐ๋ฐ˜ ๊ตฌํ˜„์—์„œ๋Š” ๋†’์€ ๊ณ„์‚ฐ ๋น„์šฉ์ด ๋ฌธ์ œ์˜€๋‹ค. ๋ชฌํ…Œ์นด๋ฅผ๋กœ ๋Š” ์›์ž ์ˆ˜์ค€์ด ์•„๋‹Œ ์ฝ”์Šค๊ทธ๋ ˆ์ด๋“œ(beadโ€‘onโ€‘Cฮฑ) ๋ชจ๋ธ์— ์ ํ•ฉํ•˜๊ณ , ๊ตฌํ˜„์ด ๊ฐ„๋‹จํ•˜๋ฉฐ ๋Œ€๊ทœ๋ชจ ์ƒ˜ํ”Œ๋ง์— ์œ ๋ฆฌํ•˜์ง€๋งŒ, ์—๋„ˆ์ง€ ์žฅ๋ฒฝ์— ๊ฑธ๋ฆฌ๊ธฐ ์‰ฌ์šด ๋‹จ์ ์ด ์žˆ๋‹ค. ๋‘ ๊ธฐ๋ฒ•์„ ํ†ตํ•ฉ ํ•˜๋ฉด ๋ฉ”ํƒ€๋‹ค์ด๋‚ด๋ฏน์Šค๊ฐ€ ์ œ๊ณตํ•˜๋Š” โ€œ์žฅ๋ฒฝ ํšŒํ”ผโ€์™€ MC์˜ โ€œ์ €๋น„์šฉ ๊ตฌํ˜„โ€์„ ๋™์‹œ์— ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. 2. ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์„ค๊ณ„ ๋ฐ ์ด๋ก ์  ์ •๋‹น์„ฑ | ๋‹จ๊ณ„ | ์„ค๋ช…

Condensed Matter Quantitative Biology
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Minimum Cost Homomorphisms to Locally Semicomplete and Quasi-Transitive Digraphs

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋™ํ˜• ์‚ฌ์ƒ ๋ฌธ์ œ(HOM) ์™€ ๋ฆฌ์ŠคํŠธ ๋™ํ˜• ์‚ฌ์ƒ(ListHOM) ์€ ๊ทธ๋ž˜ํ”„ ์ด๋ก ยท์ปดํ“จํ„ฐ ๊ณผํ•™์—์„œ ์˜ค๋ž˜์ „๋ถ€ํ„ฐ ์ค‘์š”ํ•œ ๊ฒฐ์ • ๋ฌธ์ œ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ์ตœ์†Œ ๋น„์šฉ ๋™ํ˜• ์‚ฌ์ƒ(MinHOM) ์€ ์œ„ ๋‘ ๋ฌธ์ œ์— ๋น„์šฉ ๊ตฌ์กฐ๋ฅผ ์ถ”๊ฐ€ํ•œ ์ตœ์ ํ™” ๋ฒ„์ „์œผ๋กœ, ๋ฐฉ์œ„ ๊ทธ๋ž˜ํ”„(ํŠนํžˆ ํ† ๋„ˆ๋จผํŠธ์™€ ๊ทธ ์ผ๋ฐ˜ํ™”)์—์„œ ๋‹ค์–‘ํ•œ ์‹ค์ œ ์‘์šฉ(๋ฐฉ์œ„ ๋ฌผ๋ฅ˜, ์ƒ‰์น  ํŒŒํ‹ฐ์…˜, ๋ณต๊ตฌ ๋ถ„์„ ๋“ฑ)๊ณผ ์—ฐ๊ฒฐ๋œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ๋ฌด๋ฐฉํ–ฅ ๊ทธ๋ž˜ํ”„ ์™€ ์„ธ๋ฏธ์™„์ „ยท์„ธ๋ฏธ์™„์ „ ๋‹ค์ค‘ ํŒŒํ‹ฐํŠธ ๊ทธ๋ž˜ํ”„ ์— ๋Œ€ํ•ด ์ด๋ถ„๋ฒ•์„ ์ œ๊ณตํ–ˆ์ง€๋งŒ, ์ง€์—ญ ์™„์ „ ยท ์ค€์ „์ด ์™€ ๊ฐ™์ด ํ† ๋„ˆ๋จผํŠธ๋ฅผ ์ผ๋ฐ˜ํ™”ํ•œ ๋‘ ํด๋ž˜์Šค๋ฅผ ๋Œ€

Discrete Mathematics Computer Science
Multi-Placement Structures for Fast and Optimized Placement in Analog   Circuit Synthesis

Multi-Placement Structures for Fast and Optimized Placement in Analog Circuit Synthesis

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์•„๋‚ ๋กœ๊ทธ ํšŒ๋กœ ์„ค๊ณ„ ์—์„œ ๋ฐฐ์น˜๋Š” ๋ ˆ์ด์•„์›ƒ ํ’ˆ์งˆ๊ณผ ํšŒ๋กœ ์„ฑ๋Šฅ์— ์ง์ ‘์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ํ•ต์‹ฌ ๋‹จ๊ณ„์ด๋‹ค. ๊ธฐ์กด ๋ฐฉ๋ฒ•์€ ์ ˆ์ฐจ์  ํ…œํ”Œ๋ฆฟ ๊ธฐ๋ฐ˜ (์˜ˆ: BALLISTIC, MOGLAN, MSL) ํ˜น์€ ์ตœ์ ํ™” ๊ธฐ๋ฐ˜ ์ ‘๊ทผ๋ฒ•์œผ๋กœ ๋‚˜๋‰˜๋ฉฐ, ๊ฐ๊ฐ ์†๋„ ์™€ ํƒ์ƒ‰ ๋ฒ”์œ„ ์‚ฌ์ด์— ํŠธ๋ ˆ์ด๋“œ์˜คํ”„๊ฐ€ ์กด์žฌํ•œ๋‹ค. ํ…œํ”Œ๋ฆฟ ๊ธฐ๋ฐ˜์€ ๋น ๋ฅธ ์ธ์Šคํ„ด์Šคํ™”๋ฅผ ์ œ๊ณตํ•˜์ง€๋งŒ, ๊ณ ์ •๋œ ๋ฐฐ์น˜ ํ˜•ํƒœ ๋•Œ๋ฌธ์— ์„ค๊ณ„ ํŒŒ๋ผ๋ฏธํ„ฐ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์ตœ์  ๋ฐฐ์น˜๋ฅผ ํƒ์ƒ‰ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์ตœ์ ํ™” ๊ธฐ๋ฐ˜์€ ๋„“์€ ํƒ์ƒ‰ ๊ณต๊ฐ„์„ ์ œ๊ณตํ•˜์ง€๋งŒ, ๋ฐฐ์น˜ ํƒ์ƒ‰ ์ž์ฒด๊ฐ€ ๊ณ„์‚ฐ์ ์œผ๋กœ ๋น„์‹ธ๋‹ค. 2. ์ œ์•ˆ ๊ธฐ๋ฒ•์˜ ํ•ต์‹ฌ

Hardware Architecture Computer Science
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Multi-User Diversity vs. Accurate Channel Feedback for MIMO Broadcast Channels

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ MIMO ๋ฐฉ์†ก ์ฑ„๋„ ์€ ๋‹ค์ค‘ ์‚ฌ์šฉ์ž ๋‹ค์ค‘ ์•ˆํ…Œ๋‚˜(MUโ€‘MIMO) ๊ธฐ์ˆ ์˜ ํ•ต์‹ฌ ๋ชจ๋ธ์ด๋ฉฐ, ์ „์†ก ์•ˆํ…Œ๋‚˜ ์ˆ˜ M๋งŒํผ์˜ ๋‹ค์ค‘ํ™” ์ด๋“์„ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. ์ œํ•œ๋œ ํ”ผ๋“œ๋ฐฑ ์ƒํ™ฉ์€ ์‹ค์ œ FDDยทTDD ์‹œ์Šคํ…œ์—์„œ CSI๋ฅผ ์–ป๋Š” ์œ ์ผํ•œ ๋ฐฉ๋ฒ•์ด๋ฏ€๋กœ, ํ”ผ๋“œ๋ฐฑ ์„ค๊ณ„๋Š” ์‹œ์Šคํ…œ ์„ค๊ณ„์˜ ํ•ต์‹ฌ ๊ณผ์ œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ๋‘ ๊ทน๋‹จ์„ ๋‹ค๋ฃจ์—ˆ๋‹ค. 1. ์†Œ๊ทœ๋ชจ ์‚ฌ์šฉ์ž ์ง‘ํ•ฉ (Kโ‰ˆM) โ†’ ๊ณ ์ •๋ฐ€ CSI ํ•„์š” โ†’ ๋†’์€ ํ”ผ๋“œ๋ฐฑ ๋น„ํŠธ ์š”๊ตฌ. 2. ๋ฌดํ•œ๋Œ€ ์‚ฌ์šฉ์ž ์ง‘ํ•ฉ โ†’ ๋‹ค์ค‘ ์‚ฌ์šฉ์ž ๋‹ค์–‘์„ฑ ํ™œ์šฉ โ†’ ๋งค์šฐ ๋‚ฎ์€ ํ”ผ๋“œ๋ฐฑ ๋น„ํŠธ๋งŒ์œผ๋กœ๋„ ๊ฑฐ์˜ ์ตœ์  ์„ฑ๋Šฅ ๋‹ฌ์„ฑ(์˜ˆ: Ra

Information Theory Mathematics Computer Science
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MultiNoC: A Multiprocessing System Enabled by a Network on Chip

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ SoC ํŠธ๋ Œ๋“œ : ITRS ์˜ˆ์ธก์— ๋”ฐ๋ฅด๋ฉด 2012๋…„ ๊ธฐ์ค€ ์ˆ˜๋ฐฑ ๊ฐœ IP ์ฝ”์–ด์™€ 10 GHz ์ˆ˜์ค€์˜ ํด๋Ÿญ์ด ์ผ๋ฐ˜ํ™”๋  ์ „๋ง์ด๋ฉฐ, ์ „ํ†ต์ ์ธ ๋ฒ„์Šค ๊ตฌ์กฐ๋Š” ๋Œ€์—ญํญยทํ™•์žฅ์„ฑ ํ•œ๊ณ„์— ์ง๋ฉดํ•œ๋‹ค. NOC ๋„์ž… ์ด์œ  : ์—๋„ˆ์ง€ ํšจ์œจยท์‹ ๋ขฐ์„ฑ, ๋Œ€์—ญํญ ํ™•์žฅ์„ฑ, ์žฌ์‚ฌ์šฉ์„ฑ, ๋ถ„์‚ฐ ๋ผ์šฐํŒ… ๊ฒฐ์ • ๋“ฑ ๋„ค ๊ฐ€์ง€ ํ•ต์‹ฌ ์žฅ์ ์ด NOC์„ ์ฐจ์„ธ๋Œ€ ์˜จโ€‘์นฉ ์ธํ„ฐ์ปค๋„ฅ์…˜์˜ ํ•ต์‹ฌ ํ›„๋ณด๋กœ ๋งŒ๋“ ๋‹ค. 2. ์‹œ์Šคํ…œ ๊ตฌ์กฐ | ๊ตฌ์„ฑ ์š”์†Œ | ์„ค๋ช… | ํŠน์ง• | | | | | | R8 ํ”„๋กœ์„ธ์„œ (2๊ฐœ) | 16โ€‘bit ๋กœ๋“œโ€‘์Šคํ† ์–ด, 36๋ช…๋ น์–ด, 1 K ์›Œ๋“œ ๋กœ์ปฌ ๋ฉ”๋ชจ๋ฆฌ

System Hardware Architecture Network Computer Science
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Mutation Sampling Technique for the Generation of Structural Test Data

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๊ฒ€์ฆ ๋ฐ์ดํ„ฐ์™€ ๊ตฌ์กฐ ํ…Œ์ŠคํŠธ์˜ ๊ฒฉ์ฐจ : ์ „ํ†ต์ ์œผ๋กœ ๊ฒ€์ฆ(Validation) ๋‹จ๊ณ„์™€ ๊ตฌ์กฐ ํ…Œ์ŠคํŠธ(Structural Test) ๋‹จ๊ณ„๋Š” ์„œ๋กœ ๋‹ค๋ฅธ ๋ชฉํ‘œ์™€ ๋ฐฉ๋ฒ•๋ก ์„ ์‚ฌ์šฉํ•œ๋‹ค. ๊ฒ€์ฆ์€ ์„ค๊ณ„ ์˜๋„์™€ ๊ธฐ๋Šฅ์„ ํ™•์ธํ•˜๋Š” ๋ฐ ์ดˆ์ ์„ ๋งž์ถ”๊ณ , ๊ตฌ์กฐ ํ…Œ์ŠคํŠธ๋Š” ์ œ์กฐ ๊ฒฐํ•จ(stuckโ€‘at ๋“ฑ)์„ ์ฐพ์•„๋‚ด๋Š” ๋ฐ ์ง‘์ค‘ํ•œ๋‹ค. ์žฌ์‚ฌ์šฉ ๊ฐ€๋Šฅ์„ฑ : ๊ฒ€์ฆ ๋‹จ๊ณ„์—์„œ ์ด๋ฏธ ์ƒ์„ฑ๋œ ํ…Œ์ŠคํŠธ ๋ฒกํ„ฐ๋ฅผ ๊ตฌ์กฐ ํ…Œ์ŠคํŠธ์— โ€œ๋ฌด๋ฃŒโ€๋กœ ํ™œ์šฉํ•œ๋‹ค๋ฉด, ATPG์— ์†Œ์š”๋˜๋Š” ์‹œ๊ฐ„ยท์ž์›์„ ํฌ๊ฒŒ ์ ˆ๊ฐํ•  ์ˆ˜ ์žˆ๋‹ค. 2. ๋ณ€์ด ํ…Œ์ŠคํŠธ(Mutation Testing)์™€ ๋ณ€์ด ์ƒ˜ํ”Œ๋ง

Other CS Data Computer Science
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On The Linear Behaviour of the Throughput of IEEE 802.11 DCF in Non-Saturated Conditions

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋น„ํฌํ™” ํŠธ๋ž˜ํ”ฝ ์€ ์‹ค์ œ WLAN์—์„œ ๋Œ€๋ถ€๋ถ„์„ ์ฐจ์ง€ํ•œ๋‹ค(๋ฒ„์ŠคํŠธ์„ฑ, ๋‚ฎ์€ ํ‰๊ท  ๋„์ฐฉ๋ฅ ). ๊ธฐ์กด์˜ Bianchi ๋ชจ๋ธ ์€ ํฌํ™” ์ƒํƒœ(๋ฌดํ•œ ๋ฒ„ํผ, ์ง€์†์ ์ธ ์ „์†ก)๋งŒ์„ ๊ฐ€์ •ํ•˜๋ฏ€๋กœ, ๋น„ํฌํ™” ์ƒํ™ฉ์—์„œ์˜ ์Šค๋ฃจํ’‹ ์˜ˆ์ธก ์ •ํ™•๋„๊ฐ€ ๋–จ์–ด์ง„๋‹ค. Liaw et al.์ด ์ œ์•ˆํ•œ idle state ๋ฅผ ํฌํ•จํ•œ 2์ฐจ์› ๋งˆ์ฝ”ํ”„ ์ฒด์ธ์€ ๋น„ํฌํ™” ์ƒํ™ฉ์„ ๋‹ค๋ฃจ์ง€๋งŒ, ์—ฌ์ „ํžˆ ์ˆ˜์น˜ํ•ด์„ ์— ์˜์กดํ•œ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | | | | | 1 | ์Šค๋ฃจํ’‹์„ ์„ ํ˜•์‹ S(ฮป) NยทE

Networking Computer Science
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Online Learning of Noisy Data with Kernels

1. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | | | | | โ‘  | ๋ฌดํŽธํ–ฅ ๋น„์„ ํ˜• ํ•จ์ˆ˜ ์ถ”์ • ๊ธฐ๋ฒ• (๋ฌด์ž‘์œ„ ์ƒ˜ํ”Œ ์ˆ˜ ๊ธฐ๋ฐ˜) ์ œ์•ˆ | ๊ธฐ์กด ํ‰๊ท โ€‘ํ‰๊ท  ์ถ”์ • ๋ฐฉ์‹์€ ๋…ธ์ด์ฆˆ ๊ทœ๋ชจ์— ๋”ฐ๋ผ ์ƒ˜ํ”Œ ์ˆ˜๊ฐ€ ์ปค์ ธ ๋น„ํšจ์œจ์ ์ด์—ˆ์Œ. ์ด ๋ฐฉ๋ฒ•์€ ๊ณ ์ •๋œ(ํ™•๋ฅ ์ ) ์ƒ˜ํ”Œ ์ˆ˜๋กœ๋„ ๋ฌดํŽธํ–ฅ์„ฑ์„ ํ™•๋ณด. | | โ‘ก | ๋…ธ์ด์ฆˆ๊ฐ€ ์žˆ๋Š” ์˜จ๋ผ์ธ ์ปค๋„ ํ•™์Šต ์„ ์œ„ํ•œ OGD ๋ณ€ํ˜• ์„ค๊ณ„ | ์ ๊ณฑยท๊ฐ€์šฐ์‹œ์•ˆ ์ปค๋„ ์ „๋ฐ˜์— ์ ์šฉ ๊ฐ€๋Šฅํ•˜๋ฉฐ, ์ž„์˜์˜ ๋ถ„์„์  convex ์†์‹ค ํ•จ์ˆ˜(์ œ๊ณฑ, ์ง€์ˆ˜, ๋ถ€๋“œ๋Ÿฌ์šด ํžŒ์ง€ ๋“ฑ)์™€ ํ˜ธํ™˜. | | โ‘ข | ๋‹ค์ค‘ ์งˆ์˜ ํ•„์š”์„ฑ ์— ๋Œ€ํ•œ ๋ถˆ๊ฐ€๋Šฅ์„ฑ ์ฆ๋ช… | ๋‹จ์ผ

Learning Machine Learning Data Computer Science
Optimal encoding on discrete lattice with translational invariant   constrains using statistical algorithms

Optimal encoding on discrete lattice with translational invariant constrains using statistical algorithms

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์ œ์•ฝ์ด ์žˆ๋Š” ์ด์‚ฐ ์‹œ์Šคํ…œ (Hardโ€‘Square, kโ€‘spacing ๋“ฑ)์€ ํ†ต์‹ ยท์ €์žฅ ๋งค์ฒด, ์Šคํ•€ ๋ชจ๋ธ, ์ด๋ฏธ์ง€ ์••์ถ• ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ๋‚˜ํƒ€๋‚œ๋‹ค. ์ œ์•ฝ์ด ์กด์žฌํ•˜๋ฉด ๊ฐ€๋Šฅํ•œ ๋ฐฐ์น˜ ์ˆ˜๊ฐ€ ๊ธ‰๊ฒฉํžˆ ๊ฐ์†Œํ•ด ์—”ํŠธ๋กœํ”ผ๊ฐ€ ๊ฐ์†Œ ํ•˜๊ณ , ์ด๋Š” ๊ณง ์ •๋ณด ์šฉ๋Ÿ‰ ๊ฐ์†Œ๋ฅผ ์˜๋ฏธํ•œ๋‹ค. ๊ธฐ์กด ์ ‘๊ทผ๋ฒ•์€ ์ฃผ๋กœ Monteโ€‘Carlo ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์ด๋‚˜ ์ „์—ญ ์ตœ์ ํ™” ์— ์˜์กดํ–ˆ์œผ๋ฉฐ, ์‹ค์ œ ์ธ์ฝ”๋”ฉ ๋‹จ๊ณ„์—์„œ ๋น„ํšจ์œจ์ ์ด์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ โ€œํ†ต๊ณ„โ€‘์•Œ๊ณ ๋ฆฌ์ฆ˜โ€ ์ด๋ผ๋Š” ์ƒˆ๋กœ์šด ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ๋„์ž…ํ•ด, ์ œ์•ฝ์„ ๋งŒ์กฑํ•˜๋ฉด์„œ๋„ ๊ท ๋“ฑ ๋ถ„ํฌ์— ๊ทผ์ ‘ ํ•˜๋„๋ก ๋ฐฐ์น˜๋ฅผ ์ƒ์„ฑํ•จ์œผ๋กœ์จ ์ด

Information Theory Mathematics Computer Science
Peptide Folding Kinetics from Replica Exchange Molecular Dynamics

Peptide Folding Kinetics from Replica Exchange Molecular Dynamics

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ์  REMD์˜ ์žฅ์  : ๊ณ ์˜จ ๋ณต์ œ์—์„œ ์–ป์€ ์žฅ๋ฒฝ ํ†ต๊ณผ ํšจ์œจ์„ ์ €์˜จ ๋ณต์ œ๋กœ ์ „๋‹ฌํ•จ์œผ๋กœ์จ ๊ตฌ์ƒ ๊ณต๊ฐ„ ํƒ์ƒ‰์„ ํฌ๊ฒŒ ํ–ฅ์ƒ์‹œํ‚จ๋‹ค. ์ „ํ†ต์  ํ•œ๊ณ„ : ๋ณต์ œ ๊ตํ™˜์œผ๋กœ ์ธํ•ด ๊ถค์ ์ด ๋ถˆ์—ฐ์†์ ์ด์–ด์„œ ๊ตํ™˜ ์‹œ๊ฐ„๋ณด๋‹ค ๊ธด ์‹œ๊ฐ„ ์ƒ๊ด€ํ•จ์ˆ˜๋ฅผ ๊ณ„์‚ฐํ•˜๊ธฐ ์–ด๋ ต๊ณ , ๋”ฐ๋ผ์„œ ๋™์—ญํ•™(์ „์ด์œจ) ์ •๋ณด๋ฅผ ์ง์ ‘ ์–ป๊ธฐ ํž˜๋“ค๋‹ค. ๊ธฐ์กด์—๋Š” ๋‘ ์ƒํƒœ ์‹œ์Šคํ…œ์— ๋Œ€ํ•ด ์˜จ๋„ ์˜์กด์„ฑ์„ ๊ฐ€์ •ํ•˜๋Š” ๊ฐ„์ ‘ ๋ฐฉ๋ฒ•๋งŒ์ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ์งง์€ ์‹œ๊ฐ„ ์ „ํŒŒ์ž ํ™œ์šฉ : ๊ตํ™˜ ์งํ›„์˜ ์ดˆ๊ธฐ ๊ตฌ์„ฑ์ด ์ด๋ฏธ ์ •๊ทœํ™”๋œ ํ‰ํ˜• ๋ถ„ํฌ์ด๋ฏ€๋กœ, ๋‹ค์Œ ๊ตํ™˜๊นŒ์ง€์˜ ์งง์€ ๊ตฌ๊ฐ„(โ‰ค ฮดt REMD)์—

Condensed Matter Physics Quantitative Biology
Prediction and verification of indirect interactions in densely   interconnected regulatory networks

Prediction and verification of indirect interactions in densely interconnected regulatory networks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๊ณ ์ฒ˜๋ฆฌ๋Ÿ‰ ์‹คํ—˜๊ธฐ์ˆ ์˜ ๋ฐœ๋‹ฌ๋กœ ๋ชจ๋ธ ์ƒ๋ฌผ์˜ ์กฐ์ ˆ ๊ด€๊ณ„ ๋ฐ์ดํ„ฐ๊ฐ€ ํญ์ฆํ–ˆ์ง€๋งŒ, ์ง์ ‘ยท๊ฐ„์ ‘ ๊ตฌ๋ถ„ ๊ณผ ๋ถ€ํ˜ธ(ํ™œ์„ฑ/์–ต์ œ) ์ •๋ณด๋Š” ์•„์ง ๋ถˆ์™„์ „ํ•˜๋‹ค. ๊ธฐ์กด ๋ฐฉ๋ฒ•์€ ํฌ์†Œ ๋„คํŠธ์›Œํฌ ์— ์ดˆ์ ์„ ๋งž์ถ”์–ด ๋ชจ๋“  ๋งํฌ๋ฅผ ๋™์ผ ์‹ ๋ขฐ๋„๋กœ ๊ฐ€์ •ํ•˜๊ฑฐ๋‚˜, ๊ฐ„์ ‘ ๊ด€๊ณ„๋ฅผ ๋‹จ์ˆœํžˆ ์ œ๊ฑฐํ•˜๋Š” ์ˆ˜์ค€์— ๋จธ๋ฌผ๋ €๋‹ค. ๋”ฐ๋ผ์„œ ๊ณ ๋ฐ€๋„ยท๋Œ€๊ทœ๋ชจ ๋„คํŠธ์›Œํฌ ์—์„œ ๊ฐ„์ ‘ ๊ด€๊ณ„๋ฅผ ์ •๋Ÿ‰์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜๊ณ  ์ƒˆ๋กœ์šด ๊ด€๊ณ„๋ฅผ ์ œ์‹œํ•  ์ˆ˜ ์žˆ๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์š”๊ตฌ๋œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด์™€ ์ˆ˜ํ•™์  ๊ตฌํ˜„ | ์š”์†Œ | ์„ค๋ช… | ๊ตฌํ˜„ ๋ฐฉ์‹ | | | | | | ์ „์ด์„ฑ ๊ฐ€์ • | i โ†’ k, k โ†’ j

Quantitative Biology Network
Solar System motions and the cosmological constant: a new approach

Solar System motions and the cosmological constant: a new approach

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์šฐ์ฃผ ์ƒ์ˆ˜ ฮ› ๋Š” 1917๋…„ ์ •์  ์šฐ์ฃผ ๋ชจ๋ธ์„ ์œ„ํ•ด ๋„์ž…๋์œผ๋‚˜, 1998โ€‘1999๋…„ ์ดˆ์‹ ์„ฑ ๊ด€์ธก์„ ํ†ตํ•ด ๊ฐ€์† ํŒฝ์ฐฝ์„ ์„ค๋ช…ํ•˜๋Š” ๊ฐ€์žฅ ๊ฐ„๋‹จํ•œ ํ˜•ํƒœ๋กœ ์žฌ์กฐ๋ช…๋˜์—ˆ๋‹ค. ฮ›๊ฐ€ ๊ตญ์†Œ(์ฒœ์ฒด๊ณ„) ํ˜„์ƒ ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์€ ๋งค์šฐ ๋ฏธ๋ฏธํ•˜๋‹ค๋Š” ๊ฒƒ์ด ์ „ํ†ต์ ์ธ ๊ฒฌํ•ด์˜€์œผ๋ฉฐ, ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ๊ฐ ํ–‰์„ฑยท์ด์ง„ ํŽ„์„œ ๋ฅผ ๊ฐœ๋ณ„์ ์œผ๋กœ ๋ถ„์„ํ•ด ฮ›์— ๋Œ€ํ•œ ์ƒํ•œ์„ ์ œ์‹œํ–ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ โ€œ๋น„์œจโ€ ์ ‘๊ทผ๋ฒ• ์„ ๋„์ž…ํ•ด, ฮ›โ€‘์œ ๋„ ์„ธ์ฐจ๊ฐ€ ํ–‰์„ฑ ๊ฐ„์— ํŠน์ • ๋น„๋ก€ ๊ด€๊ณ„ ((dotvarpipropto a^{3}(1 e^{2})))๋ฅผ ๋งŒ์กฑํ•ด์•ผ ํ•œ๋‹ค๋Š” ์ ์„ ์ด์šฉํ•œ๋‹ค

Astrophysics General Relativity System Physics HEP-PH
No Image

Spontaneous Emergence of Modularity in a Model of Evolving Individuals

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์งˆ๋ฌธ ๋ชจ๋“ˆ๋Ÿฌ๋ฆฌํ‹ฐ์˜ ๋ณดํŽธ์„ฑ : ๋ถ„์žยท์„ธํฌยท๊ธฐ๊ด€ยท์‚ฌํšŒ๊นŒ์ง€ ๋‹ค์–‘ํ•œ ์ˆ˜์ค€์—์„œ ๊ด€์ฐฐ๋จ. ํ•ต์‹ฌ ๊ฐ€์„ค : โ€œ๋ณ€ํ™”ํ•˜๋Š” ํ™˜๊ฒฝ์—์„œ ๊ฐœ์ฒด๊ตฐ์ด ์ง„ํ™” ๊ฐ€๋Šฅ์„ฑ(evolvability) ์„ ์œ ์ง€ํ•˜๋ ค๋ฉด, ๊ตฌ์กฐ์ ์œผ๋กœ ๋ชจ๋“ˆํ™” ๋œ ํ˜•ํƒœ๊ฐ€ ์„ ํƒ๋  ๊ฒƒ์ด๋‹ค.โ€ ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„์  : ์ด์ „์—๋Š” ์•ˆ์ •์„ฑ, ๋น„์šฉ ์ตœ์†Œํ™” ๋“ฑ ํŠน์ • ์„ ํƒ์••์— ์˜ํ•ด ๋ชจ๋“ˆ๋Ÿฌ๋ฆฌํ‹ฐ๊ฐ€ ์„ค๋ช…๋˜์—ˆ์œผ๋‚˜, ํ™˜๊ฒฝ ๋ณ€๋™์„ฑ + HGT ๋ผ๋Š” ๋‘ ๊ฐ€์ง€ ์š”์†Œ๊ฐ€ ๋™์‹œ์— ํ•„์š”ํ•˜๋‹ค๋Š” ์ ์„ ์‹ค์ฆ์ ์œผ๋กœ ์ œ์‹œํ•œ๋‹ค. 2. ๋ชจ๋ธ๋ง ์ ‘๊ทผ | ์š”์†Œ | ๊ตฌํ˜„ ๋ฐฉ์‹ | ์˜๋ฏธ | | | | | | ๊ฐœ์ฒด(๋‹จ๋ฐฑ์งˆ ์„œ์—ด) | ๊ธธ์ด N

Quantitative Biology Model
Statistical Modeling of Pipeline Delay and Design of Pipeline under   Process Variation to Enhance Yield in sub-100nm Technologies

Statistical Modeling of Pipeline Delay and Design of Pipeline under Process Variation to Enhance Yield in sub-100nm Technologies

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๊ณต์ • ๋ณ€๋™ ํ™•๋Œ€ : 100 nm ์ดํ•˜ ๊ธฐ์ˆ ์—์„œ๋Š” ์ฑ„๋„ ๊ธธ์ดยทํญยท๋ฌธํ„ฑ ์ „์•• ๋“ฑ์˜ ํŒŒ๋ผ๋ฏธํ„ฐ ๋ณ€๋™์ด ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•ด, ํšŒ๋กœ ์ง€์—ฐ์ด ํ™•๋ฅ ์  ํŠน์„ฑ์„ ๋ค๋‹ค. ์ „ํ†ต์  ์„ค๊ณ„ ํ•œ๊ณ„ : ๊ธฐ์กด ํŒŒ์ดํ”„๋ผ์ธ ์„ค๊ณ„๋Š” โ€œ๋‹จ๊ณ„ ์ˆ˜ ์ฆ๊ฐ€ โ†’ ๋…ผ๋ฆฌ ๊นŠ์ด ๊ฐ์†Œโ€์™€ โ€œ๋‹จ๊ณ„ ์ง€์—ฐ ๊ท ํ˜•โ€์„ ํ†ตํ•ด ์ตœ๋Œ€ ํด๋Ÿญ ์ฃผํŒŒ์ˆ˜๋ฅผ ๋ชฉํ‘œํ–ˆ์ง€๋งŒ, ๋ณ€๋™์„ ๊ณ ๋ คํ•˜๋ฉด ์ตœ์•… ๋‹จ๊ณ„๊ฐ€ ์‚ฌ์ „์— ์‹๋ณ„๋˜์ง€ ์•Š์•„ ์ˆ˜์œจ ์˜ˆ์ธก์ด ์–ด๋ ค์›Œ์ง„๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | | | | |โ‘ | ๋‹จ๊ณ„๋ณ„ ์ง€์—ฐ์„ ๊ฐ€์šฐ์‹œ์•ˆ ๋ณ€์ˆ˜ ๋กœ ๋ชจ๋ธ๋งํ•˜๊ณ , ์ „์ฒด ํŒŒ์ดํ”„๋ผ์ธ ์ง€์—ฐ์„ max ์—ฐ์‚ฐ์˜

Hardware Architecture Computer Science Model
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Statistical Timing Based Optimization using Gate Sizing

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๊ณต์ • ๋ณ€๋™์˜ ๋ณ€ํ™” : ๋‚˜๋…ธ๋ฏธํ„ฐ ๊ณต์ •์—์„œ๋Š” intraโ€‘die ๋ณ€๋™ ์ด ์ „์ฒด ๋ณ€๋™์˜ ํฐ ๋น„์ค‘์„ ์ฐจ์ง€ํ•œ๋‹ค. ๊ธฐ์กด ์ฝ”๋„ˆโ€‘๋ฒ ์ด์Šค STA๋Š” ์ด๋Ÿฌํ•œ ๋ณ€๋™์„ ๋ฌด์‹œํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์‹ค์ œ ์‹ค๋ฆฌ์ฝ˜ ์ˆ˜์œจ์„ ๊ณผ๋Œ€/๊ณผ์†Œ ํ‰๊ฐ€ํ•œ๋‹ค. SSTA์˜ ๋“ฑ์žฅ : ํšŒ๋กœ ์ง€์—ฐ์„ ํ™•๋ฅ  ๋ณ€์ˆ˜ ๋กœ ๋ชจ๋ธ๋งํ•˜๊ณ , ๋„์ฐฉ ์‹œ๊ฐ„์˜ ํ•ฉ์„ฑ(์ปจ๋ณผ๋ฃจ์…˜)ยท์ตœ๋Œ€ ์—ฐ์‚ฐ์„ ํ†ต๊ณ„์ ์œผ๋กœ ์ˆ˜ํ–‰ํ•œ๋‹ค. ํ•˜์ง€๋งŒ SSTA ์ž์ฒด๊ฐ€ ์žฌ์ˆ˜๋ ด ํšŒ๋กœ ์—์„œ์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ์ •ํ™•ํžˆ ๊ณ„์‚ฐํ•˜๋ฉด ์ง€์ˆ˜์  ๋ณต์žก๋„๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค. 2. ๊ธฐ์กด ์—ฐ๊ตฌ์™€์˜ ์ฐจ๋ณ„์  | ์—ฐ๊ตฌ | ์ ‘๊ทผ๋ฒ• | ํ•œ๊ณ„ | | | | | |

Hardware Architecture Computer Science
The forgetful map in rational K-theory

The forgetful map in rational K-theory

| ํ•ญ๋ชฉ | ๋‚ด์šฉ | | | | | ์ฃผ์š” ์ •๋ฆฌ | Theorem 1.1 : ไปปๆ„์˜ ์—ฐ๊ฒฐ๋œ ํ™˜์›๊ตฐ (G)์™€ (G) ์Šคํ‚ค๋งˆ (X)์— ๋Œ€ํ•ด, (displaystyle operatorname{For}colon G(G,X) {mathbb{Q}}/I,G(G,X) {mathbb{Q}};xrightarrow{;cong;}; G(X) {mathbb{Q}}) ๊ฐ€ ๋™ํ˜•์ด๋‹ค. ์ฆ‰, augmentation ideal ๋กœ ๋‚˜๋ˆˆ ๋’ค (mathbb{Q})โ€‘๊ณ„์ˆ˜๋ฅผ ์ทจํ•˜๋ฉด Forgetful map ์€ ์™„์ „ํ•˜๊ฒŒ ์‚ฌ๋ผ์ง„๋‹ค.

Mathematics
The t copula with Multiple Parameters of Degrees of Freedom: Bivariate   Characteristics and Application to Risk Management

The t copula with Multiple Parameters of Degrees of Freedom: Bivariate Characteristics and Application to Risk Management

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ | ํฌ์ธํŠธ | ๊ธฐ์กด ์ ‘๊ทผ๋ฒ• | ํ•œ๊ณ„ | | | | | | ํ‘œ์ค€ t Copula | ํ•˜๋‚˜์˜ ์ž์œ ๋„(dof)์™€ ์ƒ๊ด€ํ–‰๋ ฌ๋งŒ ์‚ฌ์šฉ | ๋‹ค๋ณ€๋Ÿ‰ ๊ผฌ๋ฆฌ ์˜์กด์„ฑ์„ ์ถฉ๋ถ„ํžˆ ํ‘œํ˜„ํ•˜์ง€ ๋ชปํ•จ | | ๊ทธ๋ฃน๋“œ t Copula (Daul et al., 2003) | ์œ„ํ—˜ ์š”์ธ์„ ์‚ฌ์ „ ๊ทธ๋ฃนํ™”, ๊ฐ ๊ทธ๋ฃน๋งˆ๋‹ค ๋…๋ฆฝ์ ์ธ dof | ๊ทธ๋ฃน ์ •์˜๊ฐ€ ์ฃผ๊ด€์ ์ด๋ฉฐ, ์ž์—ฐ์Šค๋Ÿฌ์šด ๊ทธ๋ฃน์ด ์—†์„ ๊ฒฝ์šฐ ๊ณผ๋„ํ•œ ํƒ์ƒ‰ ๋น„์šฉ ๋ฐœ์ƒ | | ๋ณธ ๋…ผ๋ฌธ์˜ ฮฝ t Copula | ๊ฐ ์œ„ํ—˜ ์š”์ธ์„ ๊ฐœ๋ณ„ ๊ทธ๋ฃน์œผ๋กœ ์„ค์ • โ†’ ์ž์œ ๋„ ๋ฒกํ„ฐ ฮฝ (ฮฝโ‚,โ€ฆ,ฮฝโ‚™) | ๊ทธ๋ฃน ์„ ํƒ ํ•„์š”

Quantitative Finance Mathematics

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