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A Computational View of Market Efficiency

A Computational View of Market Efficiency

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ „ํ†ต EMH ๋Š” โ€œ๊ฐ€๊ฒฉ์ด ๋ชจ๋“  ์ •๋ณด๋ฅผ ์™„์ „ํ•˜๊ฒŒ ๋ฐ˜์˜ํ•œ๋‹คโ€๋Š” ๊ฐ€์ • ํ•˜์— ๊ฐ€๊ฒฉ ๋ณ€๋™์ด ์˜ˆ์ธก ๋ถˆ๊ฐ€๋Šฅ ํ•˜๊ณ  ๋งˆ๋ฅดํ‹ฐ๊ฐˆ ํ˜•ํƒœ๋ผ๊ณ  ์ฃผ์žฅํ•œ๋‹ค. ์‹ค์ œ ๊ธˆ์œต ํ˜„์žฅ์—์„œ๋Š” ์ •๋ณดยท์—ฐ์‚ฐ ์ž์›์˜ ๋น„๋Œ€์นญ ์ด ์กด์žฌํ•˜๋ฉฐ, ์ผ๋ถ€ ํŠธ๋ ˆ์ด๋”๋Š” ๋ณต์žกํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„, ๋‹ค๋ฅธ ์ด๋“ค์€ ๋‹จ์ˆœ ๊ทœ์น™๋งŒ์„ ์‚ฌ์šฉํ•œ๋‹ค. ์ €์ž๋Š” ์ด๋Ÿฌํ•œ ๋น„๋Œ€์นญ์„ ์ปดํ“จํ„ฐ ๊ณผํ•™์˜ ๋ณต์žก๋„ ์ด๋ก  ์— ๋นŒ๋ ค์™€, โ€œํšจ์œจ์„ฑ ์ œํ•œ๋œ ์ž์› ๋‚ด์—์„œ ์ด์ต์„ ๋งŒ๋“ค ์ˆ˜ ์—†๋Š” ์ƒํƒœโ€๋กœ ์žฌ์ •์˜ํ•œ๋‹ค. 2. ๋ชจ๋ธ๋ง ์ ‘๊ทผ | ์š”์†Œ | ์„ค๋ช… | ์žฅ์  | | | | | | ์‹œ์žฅ ํŒจํ„ด | ๊ณ ์ •๋œ ๊ธธ์ด p ์˜ ์ˆ˜์ต๋ฅ  ์‹œํ€€์Šค๋ฅผ

Quantitative Finance Computational Engineering Computational Complexity Computer Science
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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
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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
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A new proof of Gromovs theorem on groups of polynomial growth

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๊ทธ๋กœ๋ชจํ”„ ์ •๋ฆฌ ๋Š” ๊ธฐํ•˜โ€‘๋Œ€์ˆ˜์  ๊ตฐ ์ด๋ก ์˜ ํ•ต์‹ฌ ๊ฒฐ๊ณผ๋กœ, ๋‹คํ•ญ ์„ฑ์žฅ ๊ตฐ์ด ๊ฐ€์ƒ ๋‹ํฌํ…ํŠธ์ž„์„ ๋ณด์—ฌ์ค€๋‹ค. ์›๋ž˜ ์ฆ๋ช…์€ ๋กœ์ปฌ ์ปดํŒฉํŠธ ๊ตฐ์˜ ๊ตฌ์กฐ ์ด๋ก (๋ชฌํŠธ๊ณ ๋ฉ”๋ฆฌโ€‘์ง€ํ•€โ€‘์•ผ๋งˆ๋ฒ )๊ณผ ๋ณต์žกํ•œ ๋ฆฌํ”„๋ ˆ์‹œ๋จผํŠธ ๊ธฐ๋ฒ•์— ํฌ๊ฒŒ ์˜์กดํ•œ๋‹ค. ์ตœ๊ทผ ์ฝœ๋”ฉโ€‘๋ฏธ๋‹ˆ์ฝ”์น˜ ์˜ ์กฐํ™”ํ•จ์ˆ˜ ์ฐจ์› ์ œํ•œ ๊ฒฐ๊ณผ(

Mathematics
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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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Analysis of Proton Radiography Images of Shock Melted/Damaged Tin

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ๋™์  ์ƒ๋ณ€ํ™”์™€ EOS ์—ฐ๊ตฌ : ๊ณ ์••ยท๊ณ ์˜จ ์กฐ๊ฑด์—์„œ ๊ธˆ์†์ด ๊ณ ์ฒดโ€‘์•ก์ฒด ์ „์ดํ•˜๋Š” ํ˜„์ƒ์€ ์ฒœ์ฒด ๋ฌผ๋ฆฌํ•™์  ์ƒํ™ฉ๊ณผ ์œ ์‚ฌํ•ด, ๋ฐฉ์ •์‹โ€‘์ƒํƒœ(EOS) ๋ชจ๋ธ ๊ฒ€์ฆ์— ํ•ต์‹ฌ์ ์ธ ์‹œํ—˜์ด๋‹ค. ํ”„๋กœํ†ค ๋ฐฉ์‚ฌ์„  ์‚ฌ์ง„(PRAD)์˜ ํ•„์š”์„ฑ : ์ฃผ์„๊ณผ ๊ฐ™์€ ๊ณ ๋ฐ€๋„ ๊ธˆ์†์€ Xโ€‘ray๋กœ๋Š” ํˆฌ๊ณผ๊ฐ€ ์–ด๋ ค์›Œ, 800 MeV ํ”„๋กœํ†ค ๋น”์„ ์ด์šฉํ•œ PRAD๊ฐ€ ๋‚ด๋ถ€ ๋ฐ€๋„ยท๊ตฌ์กฐ ๋ณ€ํ™”๋ฅผ ๋น„์นจํˆฌ์ ์œผ๋กœ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ๋Š” ์œ ์ผํ•œ ๋ฐฉ๋ฒ•์ด๋‹ค. 2. ์‹คํ—˜ ์„ค๊ณ„ | ๋ณ€์ˆ˜ | ๊ฐ’ | | | | | ์‹œํŽธ ์ง๊ฒฝ | 5.1 cm | | ์‹œํŽธ ๋‘๊ป˜ | 4.76 mm ~ 12.7 mm

Physics Analysis
Approximating max-min linear programs with local algorithms

Approximating max-min linear programs with local algorithms

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ง€์—ญ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์€ ๋…ธ๋“œ๋‹น ์ž…๋ ฅ ํฌ๊ธฐ๊ฐ€ ์ƒ์ˆ˜์ด๊ณ , ํ†ต์‹ ยท์‹œ๊ฐ„ยท๊ณต๊ฐ„ ๋ณต์žก๋„๊ฐ€ ๋ชจ๋‘ ์ƒ์ˆ˜์ธ ๊ฒฝ์šฐ์— ๋งค์šฐ ๊ฐ•๋ ฅํ•œ ํ™•์žฅ์„ฑ์„ ์ œ๊ณตํ•œ๋‹ค. ์ด๋Š” NCโฐ ํšŒ๋กœ์™€ ๋™์ผ์‹œ๋  ์ˆ˜ ์žˆ์–ด ์ด๋ก ์ ยท์‹ค์šฉ์  ์˜๋ฏธ๊ฐ€ ํฌ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” packing/covering LP (์ฆ‰, (|K| 1))์— ์ง‘์ค‘ํ–ˆ์œผ๋ฉฐ, ๊ทธ ๊ฒฝ์šฐ์—๋Š” ์ผ์ • ์กฐ๊ฑด ํ•˜์— ์ง€์—ญ ๊ทผ์‚ฌ ์Šคํ‚ด ์ด ์กด์žฌํ•œ๋‹ค๋Š” ๊ธ์ •์  ๊ฒฐ๊ณผ๊ฐ€ ์žˆ์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‹ค์ œ ๋„คํŠธ์›Œํฌ(์„ผ์„œยท๋ฆด๋ ˆ์ด, ISPยท๊ณ ๊ฐ ๋“ฑ)์—์„œ๋Š” ์—ฌ๋Ÿฌ ์ดํ•ด๊ด€๊ณ„์ž((K))๊ฐ€ ๋™์‹œ์— ๋งŒ์กฑ๋˜์–ด์•ผ ํ•˜๋Š” maxโ€‘min ํ˜•ํƒœ๊ฐ€ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ๋“ฑ์žฅ

Distributed Computing Computer Science
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Classification of complete Finsler manifolds through a second order differential equation

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ 2์ฐจ ๋ฏธ๋ถ„๋ฐฉ์ •์‹ (nablanablarho varphi g) ์€ ๋ฆฌ๋งŒ ๊ธฐํ•˜ํ•™์—์„œ ์ปจํฌ๋ฉ€ ๋ณ€ํ™˜ ๊ณผ ์ง€์˜ค๋ฐ์‹ ์„œํด ์„ ์—ฐ๊ฒฐํ•˜๋Š” ํ•ต์‹ฌ ์‹์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ(

Mathematics
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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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Comparaison entre calculs de vulnerabilite sismique et proprietes dynamiques mesurees

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ๋ฐฐ๊ฒฝ : ๋Œ€๊ทœ๋ชจ ์žฌํ•ด ์œ„ํ—˜ ํ‰๊ฐ€(Hazardโ€‘UE, HAZUS ๋“ฑ)์—์„œ๋Š” ๊ฑด๋ฌผ ์œ ํ˜•๋ณ„๋กœ ๋‹จ์ˆœํ™”๋œ ์ฃผ๊ธฐยท์šฉ๋Ÿ‰๊ณก์„ ์„ ์‚ฌ์šฉํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ๊ณต์‹์€ ์†Œ์ˆ˜์˜ ์‹ค์ธก ๋ฐ์ดํ„ฐ์— ๊ธฐ๋ฐ˜ํ•ด ๊ฐœ๋ฐœ๋์œผ๋ฉฐ, ๋ถˆํ™•์‹ค์„ฑ ์ •๋ณด๊ฐ€ ์ œ๊ณต๋˜์ง€ ์•Š๋Š”๋‹ค. ๋ชฉ์  : (1) ์‹ค์ œ ๊ฑด๋ฌผ์˜ 1์ฐจ ๊ณ ์œ ์ฃผ๊ธฐ๋ฅผ ์ฃผ๋ณ€ ์ง„๋™(ambient vibration)์œผ๋กœ ์ธก์ •ํ•˜๊ณ , (2) ์„ค๊ณ„์ฝ”๋“œยทRiskโ€‘UE์—์„œ ์ œ์‹œํ•˜๋Š” ์ฃผ๊ธฐ ๊ณต์‹ ๋ฐ ์„ ํ˜• ์šฉ๋Ÿ‰๊ณก์„ ๊ณผ ๋น„๊ตํ•ด ๊ทธ ์ ํ•ฉ์„ฑ์„ ํ‰๊ฐ€ํ•œ๋‹ค. 2. ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ๋ฐ ์‹คํ—˜ ๋ฐฉ๋ฒ• | ์—ฐ๋„ | ์ธก์ • ๋Œ€์ƒ | ๊ฑด๋ฌผ ์ˆ˜ | ๊ตฌ์กฐ ์œ ํ˜• |

Physics
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Computational Intelligence Characterization Method of Semiconductor Device

| ๊ตฌ๋ถ„ | ๋‚ด์šฉ | ํ‰๊ฐ€ยท์ฝ”๋ฉ˜ํŠธ | | | | | | ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ | ๋ฐ˜๋„์ฒด ์ƒ์‚ฐ ํ…Œ์ŠคํŠธ์™€ ์—”์ง€๋‹ˆ์–ด๋ง ํŠน์„ฑํ™” ํ…Œ์ŠคํŠธ์˜ ์ฐจ์ด์  ๊ฐ•์กฐ<br> ๊ธฐ์กด ํŠน์„ฑํ™”๋Š” ๋‹จ์ผ ํŠธ๋ฆฝ ํฌ์ธํŠธ์™€ ์ œํ•œ๋œ ํ…Œ์ŠคํŠธ ํŒจํ„ด์— ์˜์กด, ์ตœ์•… ์ƒํ™ฉ์„ ๋†“์น˜๊ธฐ ์‰ฌ์›€ | ํ˜„์žฅ ์‹ค๋ฌด์—์„œ ํ”ํžˆ ๊ฒช๋Š” ๋ฌธ์ œ๋ฅผ ์ •ํ™•ํžˆ ์งš์–ด๋‚ด๋ฉฐ ์—ฐ๊ตฌ ๋™๊ธฐ๋ฅผ ์„ค๋“๋ ฅ ์žˆ๊ฒŒ ์ œ์‹œํ•จ | | ํ•ต์‹ฌ ์•„์ด๋””์–ด | 1. ๋‹ค์ค‘ ํŠธ๋ฆฝ ํฌ์ธํŠธ ๊ฐœ๋… ๋„์ž… โ†’ ๋‹ค์–‘ํ•œ ๋žœ๋ค ํ…Œ์ŠคํŠธ์— ๋Œ€ํ•œ ํŠธ๋ฆฝ ํฌ์ธํŠธ๋ฅผ ๋™์‹œ์— ์ธก์ •<br>2. Searchโ€‘untilโ€‘Tripโ€‘Point ์•Œ๊ณ ๋ฆฌ์ฆ˜ โ†’ ์ „์ฒด ๋ฒ”์œ„ ํƒ์ƒ‰ ๋Œ€์‹  ๊ธฐ์ค€ ํŠธ๋ฆฝ ํฌ

Neural Computing Computer Science Artificial Intelligence
Computational study of the vibrating disturbances to the lung function

Computational study of the vibrating disturbances to the lung function

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ธ๊ฐ„ ์ƒํ™œ ํ™˜๊ฒฝ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์‚ฐ์—…ยท๊ตํ†ต ์†Œ์Œ์€ 100 dB ์ด์ƒ์ผ ๊ฒฝ์šฐ ์ „์‹  ๊ธฐ๋Šฅ ์žฅ์• ๋ฅผ ์œ ๋ฐœํ•œ๋‹ค๋Š” ๊ธฐ์กด ๋ฌธํ—Œ์„ ์ž˜ ์ •๋ฆฌํ•˜๊ณ , ํŠนํžˆ ํ๊ฐ€ ์Œํ–ฅ ์ž๊ทน์— ๊ฐ€์žฅ ๋ฏผ๊ฐํ•˜๋‹ค๋Š” ์ ์„ ๊ฐ•์กฐํ•œ๋‹ค. โ€œ์ˆ˜์น˜ ์ƒ๋ฆฌํ•™โ€์ด๋ผ๋Š” ๊ด€์ ์—์„œ, ์‹คํ—˜์  ํ•œ๊ณ„(์ธ์ฒด ์ง์ ‘ ์ธก์ • ์–ด๋ ค์›€)๋ฅผ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•œ ๋ชจ๋ธ๋ง ์ ‘๊ทผ์„ ์ œ์‹œํ•œ ์ ์ด ์˜์˜๊ฐ€ ํฌ๋‹ค. 2. ๋ชจ๋ธ๋ง ์ „๋žต ์ „๋„๊ตฌ์—ญ : ๊ธฐ์กด 1โ€‘component ํ ๋ชจ๋ธ์„ ํ™•์žฅํ•ด, ๊ฐ ์„ธ๋Œ€๋ณ„ ๊ด€์„ ํƒ„์„ฑ ์›ํ†ต์œผ๋กœ ๊ฐ€์ •ํ•˜๊ณ  ๋น„์ •์ƒ(๋น„์„ ํ˜•) 1์ฐจ์› ํ๋ฆ„ ๋ฐฉ์ •์‹์„ ์ ์šฉํ•˜์˜€๋‹ค. ์ด๋Š” ํ˜ˆ๊ด€ ํ๋ฆ„ ๋ชจ๋ธ๋ง(๋Œ€ํ˜• ํ˜ˆ๊ด€)

Physics
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Concentric Characterization and Classification of Complex Network Nodes: Theory and Application to Institutional Collaboration

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋‹ค์ค‘ ์Šค์ผ€์ผ ํ˜„์ƒ : ์‹ค์ œ ๋„คํŠธ์›Œํฌ๋Š” ๋‹จ์ผ ์ง€ํ‘œ(์˜ˆ: ์ฐจ์ˆ˜)๋งŒ์œผ๋กœ๋Š” ์ปค๋ฎค๋‹ˆํ‹ฐ ๊ตฌ์กฐ๋ฅผ ์ถฉ๋ถ„ํžˆ ์„ค๋ช…ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋™์‹ฌ์› ์ธก์ •์˜ ์žฅ์  : ๋…ธ๋“œ ์ฃผ๋ณ€์˜ ์—ฌ๋Ÿฌ ๋ ˆ๋ฒจ(๊ฑฐ๋ฆฌ d)๊นŒ์ง€ ํ™•์žฅ๋œ ๊ตฌ์กฐ์  ์ •๋ณด๋ฅผ ์ œ๊ณตํ•จ์œผ๋กœ์จ, ์ „ํ†ต์ ์ธ ๋กœ์ปฌ ์ธก์ •์ด ๋†“์น  ์ˆ˜ ์žˆ๋Š” ์žฅ๊ฑฐ๋ฆฌ ์ƒํ˜ธ์ž‘์šฉ์„ ํฌ์ฐฉํ•œ๋‹ค. 2. ์ฃผ์š” ๋ฐฉ๋ฒ•๋ก  | ๋‹จ๊ณ„ | ๋‚ด์šฉ | ํ•ต์‹ฌ ํฌ์ธํŠธ | | | | | | ๋™์‹ฌ์› ์ธก์ • ์ •์˜ | ๋™์‹ฌ์› ๋ ˆ๋ฒจ d์— ์†ํ•˜๋Š” ๋…ธ๋“œ ์ง‘ํ•ฉ R<sub>d</sub>(i)์™€ ๊ทธ ์‚ฌ์ด์˜ ์—ฐ๊ฒฐ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์ฐจ์ˆ˜, ํด๋Ÿฌ์Šคํ„ฐ๋ง ๊ณ„์ˆ˜, ์ˆ˜๋ ด ๋น„์œจ ๋“ฑ 6๊ฐ€์ง€ ์ง€ํ‘œ

Network Physics
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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
Data Mining-based Materialized View and Index Selection in Data   Warehouses

Data Mining-based Materialized View and Index Selection in Data Warehouses

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ๋ฐ์ดํ„ฐ ์›จ์–ดํ•˜์šฐ์Šค ๊ด€๋ฆฌ : ๋…ผ๋ฆฌ์  ์„ค๊ณ„(์Šคํ‚ค๋งˆ)์™€ ๋ฌผ๋ฆฌ์  ์„ค๊ณ„(ํŒŒ์ผยท๋””์Šคํฌ) ๋ชจ๋‘๋ฅผ ์ตœ์ ํ™”ํ•ด์•ผ ํ•˜๋Š” ๋ณตํ•ฉ ๊ณผ์ œ. ๋ฌผ๋ฆฌํ™” ๋ทฐ์™€ ์ธ๋ฑ์Šค : ๊ฐ๊ฐ์€ ์ฟผ๋ฆฌ ์‘๋‹ต ์‹œ๊ฐ„ ๊ฐ์†Œ ์™€ ์ง์ ‘ ๋ฐ์ดํ„ฐ ์ ‘๊ทผ ์„ ์ œ๊ณตํ•˜์ง€๋งŒ, ์Šคํ† ๋ฆฌ์ง€ ๋น„์šฉ ๊ณผ ๊ฐฑ์‹ (maintenance) ์˜ค๋ฒ„ํ—ค๋“œ ๋ฅผ ๋™๋ฐ˜ํ•œ๋‹ค. ํ•ต์‹ฌ ๋ฌธ์ œ : ์ œํ•œ๋œ ์ €์žฅ ๊ณต๊ฐ„ ๋‚ด์—์„œ ์ „์ฒด ์›Œํฌ๋กœ๋“œ(Q) ์— ๋Œ€ํ•œ ์‹คํ–‰ ๋น„์šฉ์„ ์ตœ์†Œํ™”ํ•˜๋Š” ๋ฌผ๋ฆฌํ™” ๋ทฐโ€‘์ธ๋ฑ์Šค ์ง‘ํ•ฉ O โІ (VC โˆช IC) ๋ฅผ ์ฐพ๋Š” NPโ€‘hard ๋ฌธ์ œ(๋‹ค์ค‘ ๋ฐฐ๋‚ญ ๋ฌธ์ œ์™€ ๋™๋“ฑ). 2. ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ | ๊ตฌ๋ถ„ | ๊ธฐ์กด

Databases Computer Science Data
Distributed Source Coding in the Presence of Byzantine Sensors

Distributed Source Coding in the Presence of Byzantine Sensors

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ๋ฒ ์ด์ฆˆ ๊ณต๊ฒฉ ์€ ๊ณ ์ „์ ์ธ โ€œ๋ฒ ์ด์ฆˆ ์žฅ๊ตฐ ๋ฌธ์ œโ€๋ฅผ ํ†ต์‹ ยท์ •๋ณด ์ด๋ก ์— ์ ์šฉํ•œ ํ˜•ํƒœ๋กœ, ์ผ๋ถ€ ์„ผ์„œ๊ฐ€ ์•…์˜์ ์œผ๋กœ ํ˜‘๋ ฅํ•ด ์ „์ฒด ๋„คํŠธ์›Œํฌ์˜ ๋ชฉํ‘œ(์ •ํ™•ํ•œ ๋ฐ์ดํ„ฐ ๋ณต์›)๋ฅผ ๋ฐฉํ•ดํ•œ๋‹ค. ๊ธฐ์กด Slepianโ€‘Wolf ์ฝ”๋”ฉ ์€ ์„ผ์„œ ๊ฐ„ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ํ™œ์šฉํ•ด ํ•ฉ๊ณ„ ์ „์†ก๋ฅ ์„ H(Xโ‚,โ€ฆ,X m) ๊นŒ์ง€ ๋‚ฎ์ถœ ์ˆ˜ ์žˆ์ง€๋งŒ, ์ธ์ฝ”๋”ฉ ๊ทœ์น™ ์œ„๋ฐ˜ ์— ๋Œ€ํ•œ ๋ฐฉ์–ด ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด ์ „ํ˜€ ์—†๋‹ค. ๋”ฐ๋ผ์„œ โ€œ์ •ํ™•๋„ ๋ณด์žฅโ€ ์„ โ€œ์ •์งํ•œ ์„ผ์„œ๋งŒโ€์— ํ•œ์ •ํ•˜๊ณ , ๋ฐฐ์‹ ์ž ์ง‘ํ•ฉ์„ ์‚ฌ์ „์— ์•Œ ์ˆ˜ ์—†๋Š” ์ƒํ™ฉ์—์„œ ์–ด๋–ค ๋ ˆ์ดํŠธ ์ „๋žต์ด ์ตœ์ ์ธ๊ฐ€ ๋ฅผ ํƒ๊ตฌํ•œ๋‹ค. 2. ๊ฐ€๋ณ€ ๋ ˆ์ดํŠธ

Mathematics Information Theory Computer Science
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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„

Mathematics Information Theory 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

Quantitative Biology Physics Condensed Matter
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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
Efficient Skyline Querying with Variable User Preferences on Nominal   Attributes

Efficient Skyline Querying with Variable User Preferences on Nominal Attributes

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์Šค์นด์ด๋ผ์ธ ์€ ๋‹ค์ฐจ์› ๋ฐ์ดํ„ฐ์—์„œ โ€œ์ง€๋ฐฐ๋˜์ง€ ์•Š๋Š”โ€ ์ ๋“ค์˜ ์ง‘ํ•ฉ์œผ๋กœ, ๋‹ค์ค‘ ๊ธฐ์ค€ ์˜์‚ฌ๊ฒฐ์ •์— ๋„๋ฆฌ ํ™œ์šฉ๋œ๋‹ค. ๋Œ€๋ถ€๋ถ„์˜ ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ˆ˜์น˜ํ˜• ์†์„ฑ์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์œผ๋ฉฐ, ๋ช…๋ชฉํ˜•(์นดํ…Œ๊ณ ๋ฆฌ) ์†์„ฑ์€ ๊ณ ์ •๋œ(์ „์ฒด/๋ถ€๋ถ„) ์ˆœ์„œ ๋งŒ์„ ๊ฐ€์ •ํ•œ๋‹ค. ์‹ค์ œ ๋น„์ฆˆ๋‹ˆ์Šค(๋ถ€๋™์‚ฐ, ํ•ญ๊ณต๊ถŒ ๋“ฑ)์—์„œ๋Š” ๋™์  ์„ ํ˜ธ ๊ฐ€ ์ผ๋ฐ˜์ ์ด๋‹ค. ์˜ˆ: ๊ณ ๊ฐ๋งˆ๋‹ค ์„ ํ˜ธํ•˜๋Š” ํ˜ธํ…” ๊ทธ๋ฃน, ํ•ญ๊ณต์‚ฌ, ์ง€์—ญ ๋“ฑ์ด ๋‹ค๋ฅด๋ฉฐ, ์‚ฌ์šฉ์ž๋Š” ์ „์ฒด ์ˆœ์„œ๋ฅผ ์ œ์‹œํ•˜์ง€ ์•Š๊ณ  ๋ช‡ ๊ฐœ์˜ ์ตœ์šฐ์„  ์„ ํƒ ๋งŒ ๋ช…์‹œํ•œ๋‹ค(โ€œv โ‰บ v' โ‰บ โ€). 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | |

Databases Computer Science
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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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Exchange Reactions with Dick Dalitz

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์—ญ์‚ฌ์  ์˜์˜ ๋”• ๋‹ฌ๋ฆฌ์ธ ์™€์˜ ํ˜‘์—… : 1950โ€‘~1980๋…„๋Œ€ ์ดˆ์ €์— ๊ฑธ์นœ Kโปโ€‘์—๋ฉ€์ „ ์‹คํ—˜์€ ๋‹น์‹œ ์ดˆํ•ต ๋ฌผ๋ฆฌํ•™์˜ ํ•ต์‹ฌ์ด์—ˆ์œผ๋ฉฐ, ๋‹ฌ๋ฆฌ์ธ  ๊ต์ˆ˜๋Š” ์ด ๋ถ„์•ผ์˜ ์ด๋ก ์  ๊ฑฐ๋ชฉ์œผ๋กœ์„œ ์‹คํ—˜ํŒ€์— ์ง€์†์ ์ธ ํ”ผ๋“œ๋ฐฑ์„ ์ œ๊ณตํ–ˆ๋‹ค. ์ €์ž๋Š” ๋‹ฌ๋ฆฌ์ธ ์™€์˜ ์ฒซ ๋งŒ๋‚จ์„ โ€œlowโ€‘energy KN systemโ€ ์„ธ๋ฏธ๋‚˜์—์„œ ์‹œ์ž‘๋œ ๊ฒƒ์œผ๋กœ ์„œ์ˆ ํ•˜๋ฉฐ, ์ด๋Š” Dalitzโ€‘Tuan ๋…ผ๋ฌธ์˜ ์ „์‹ ์ด ๋œ๋‹ค. ์ดˆํ•ต ์—ฐ๊ตฌ์˜ ์ „ํ™˜์  : 1960๋…„๋Œ€ ์ดˆ๋ฐ˜, ฮ› ํ•ต ํผํ…์…œ ๊นŠ์ด(D ฮ› โ‰ˆ 30 MeV)์™€ B ฮ› ๊ฐ’์— ๋Œ€ํ•œ ์ตœ์ดˆ์˜ ์ง์ ‘ ์‹คํ—˜์  ์ถ”์ •์ด ์ด๋ฃจ์–ด์กŒ์œผ๋ฉฐ, ์ด๋Š” ๋‹ฌ๋ฆฌ

Physics
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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

Network Physics NUCL-TH Condensed Matter
Fractional Power Control for Decentralized Wireless Networks

Fractional Power Control for Decentralized Wireless Networks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ „๋ ฅ ์ œ์–ด ๋Š” ๋ชจ๋“  ๋ฌด์„  ์‹œ์Šคํ…œ์—์„œ ๊ธฐ๋ณธ์ ์ธ ์ ์‘ ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด๋ฉฐ, ๊ธฐ์กด์—๋Š” ์ˆ˜์‹ ๊ธฐ ์ค‘์‹ฌ์˜ ์ฑ„๋„ ์ธ๋ฒ„์ „ ํ˜น์€ ์›Œํ„ฐํ•„๋ง ์ด ์ฃผ๋กœ ๋…ผ์˜๋˜์—ˆ๋‹ค. ํƒˆ์ค‘์•™ ๋„คํŠธ์›Œํฌ(๋ฌด์„  adโ€‘hoc, Wiโ€‘Fi, ์ŠคํŽ™ํŠธ๋Ÿผ ๊ณต์œ )์—์„œ๋Š” ๊ฐ ์†ก์‹ ๊ธฐ๊ฐ€ ์„œ๋กœ ๋‹ค๋ฅธ ์ˆ˜์‹ ๊ธฐ์™€ ๋…๋ฆฝ์ ์œผ๋กœ ํ†ต์‹  ํ•˜๊ณ , ๊ฐ„์„ญ์ด ์ฃผ์š” ์ œ์•ฝ ์ด ๋œ๋‹ค. ๊ธฐ์กด์˜ ์ฑ„๋„ ์ธ๋ฒ„์ „์€ ๊ฐ„์„ญ์„ ํฌ๊ฒŒ ์ฆ๊ฐ€์‹œ์ผœ ์„ฑ๋Šฅ์ด ์ €ํ•˜๋˜๋Š” ๊ฒƒ์ด ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์ ์„ ํ•ด๊ฒฐํ•˜๊ณ ์ž ์ค‘๊ฐ„ ์ •์ฑ… ์„ ํƒ์ƒ‰ํ•˜๊ณ , ์ „๋ ฅ ์ œ์–ด ํŒŒ๋ผ๋ฏธํ„ฐ (s) ๋ฅผ ๋„์ž…ํ•ด โ€œ์–ผ๋งˆ๋‚˜ ์ฑ„๋„์„ ๋ณด์ •ํ•  ๊ฒƒ์ธ๊ฐ€?โ€ ๋ผ๋Š” ์งˆ๋ฌธ์— ๋‹ตํ•˜

Network Mathematics Information Theory Computer Science
Generalizations of Sch'{o}bis Tetrahedral Dissection

Generalizations of Sch'{o}bis Tetrahedral Dissection

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ Hilbert ์ œ3๋ฌธ์ œ์™€ ๋‹ค๋ฉด์ฒด ๋“ฑ๋ถ„ํ•ด : ๋‘ ๋‹ค๋ฉด์ฒด๊ฐ€ ๊ฐ™์€ ๋ถ€ํ”ผ๋ฅผ ๊ฐ€์งˆ ๋•Œ, ๋“ฑ๋ถ„ํ•ด ๊ฐ€๋Šฅํ•œ๊ฐ€?๋Š” ๊ณ ์ „์ ์ธ ์งˆ๋ฌธ์ด๋ฉฐ, Hadwiger๋Š” (Q n(w))์™€ ์ •์œก๋ฉด์ฒด ์‚ฌ์ด์— ๋“ฑ๋ถ„ํ•ด๊ฐ€ ์กด์žฌํ•จ์„ ๋ณด์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ทธ์˜ ์ฆ๋ช…์€ ๊ตฌ์„ฑ์ ์ด์ง€ ์•Š์•„ ์‹ค์ œ ์ ˆ๋‹จ ๋ฐฉ๋ฒ•์„ ์ œ๊ณตํ•˜์ง€ ๋ชป ํ•œ๋‹ค. ์‹ค์šฉ์  ํ•„์š”์„ฑ : ํ†ต์‹ ยท์•”ํ˜ธ ๋ถ„์•ผ์—์„œ ์ƒ์ˆ˜ ๊ฐ€์ค‘์น˜ ์ฝ”๋“œ ๋ฅผ ๋‹ค๋ฃจ๋Š” ๊ฒฝ์šฐ, ๋‹ค๋ฉด์ฒด (O n Q n(0))๋ฅผ ์ง์œก๋ฉด์ฒด(๋˜๋Š” โ€œbrickโ€)์— ๋งคํ•‘ํ•˜๋Š” ํšจ์œจ์ ์ธ ํŒŒ๋ผ๋ฏธํ„ฐํ™” ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ๋ฒ• โ€“ โ€œTwo Tile Theoremโ€

Mathematics
Generic Trace Semantics via Coinduction

Generic Trace Semantics via Coinduction

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ํŠธ๋ ˆ์ด์Šค ์˜๋ฏธ๋ก  ์€ ์„ ํ˜• ์‹œ๊ฐ„โ€‘๋ถ„๊ธฐ ์‹œ๊ฐ„ ์ŠคํŽ™ํŠธ๋Ÿผ์—์„œ ๊ฐ€์žฅ ๊ฑฐ์นœ(์ฝ”์–ด์ŠคํŠธ) ์˜๋ฏธ๋ก ์œผ๋กœ, ๋น„๊ฒฐ์ •๋ก ยทํ™•๋ฅ ๋ก  ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„๊ธฐ ํ˜•ํƒœ์— ์ ์šฉ๋œ๋‹ค. ๊ธฐ์กด ์ฝ”์•Œ์ œ๋ธŒ๋ผ ์ด๋ก ์€ Sets ์—์„œ ๋ฐ”์ด์‹œ๋ชฐ๋Ÿฌ์‹œ(bisimilarity) ๋ฅผ ์™„์ „ํ•˜๊ฒŒ ํฌ์ฐฉํ–ˆ์ง€๋งŒ, ํŠธ๋ ˆ์ด์Šค์™€ ๊ฐ™์€ ๋” ๊ฑฐ์นœ ๋™๋“ฑ์„ฑ ์„ ์ฝ”์ธ๋•์…˜์œผ๋กœ ๋‹ค๋ฃจ๋Š” ์—ฐ๊ตฌ๋Š” ๋ถ€์กฑํ–ˆ๋‹ค. ์ €์ž๋“ค์€ โ€œKleisli ๋ฒ”์ฃผ์—์„œ ์ฝ”์ธ๋•์…˜์„ ์ ์šฉํ•˜๋ฉด ํŠธ๋ ˆ์ด์Šค ์˜๋ฏธ๋ก ์„ ํ†ต์ผ์ ์œผ๋กœ ๊ธฐ์ˆ ํ•  ์ˆ˜ ์žˆ๋‹คโ€๋Š” ๊ฐ€์„ค์„ ์„ธ์šฐ๊ณ , ์ด๋ฅผ ์ผ๋ฐ˜์ ์ธ ๋ชจ๋‚˜๋“œโ€‘ํŽ‘ํ„ฐ ์Œ ((T,F)) ๋กœ ํ™•์žฅํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์ˆ ์  ๊ฒฐ

Logic 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
Inflatable Dome for Moon, Mars, Asteroids and Satellites

Inflatable Dome for Moon, Mars, Asteroids and Satellites

1. ์—ฐ๊ตฌ์˜ ์˜์˜์™€ ํ˜์‹ ์„ฑ | ์š”์†Œ | ๊ธฐ์กด ๊ธฐ์ˆ  ๋Œ€๋น„ ์ฐจ๋ณ„์  | ๊ธฐ๋Œ€ ํšจ๊ณผ | | | | | | ์ธํ”Œ๋ ˆ์ดํ„ฐ๋ธ” ๊ตฌ์กฐ | ์ดˆ๋ฐ•๋ง‰ ์ด์ค‘ ํ•„๋ฆ„ + ๋‚ด๋ถ€ ๊ณต๊ธฐ ์ ˆ์—ฐ์ธต | ๊ฒฝ๋Ÿ‰ยท์ €๋น„์šฉ ๊ฑด์„ค, ๋น ๋ฅธ ์ „๊ฐœ | | ๊ฐ€๋ณ€ ํˆฌ๋ช…๋„ยท์•Œ๋ฃจ๋ฏธ๋Š„ ์ฝ”ํŒ… | ์‹ค์‹œ๊ฐ„ ์ผ์‚ฌ๋Ÿ‰ ์กฐ์ ˆ + ์—ด ๋ฐ˜์‚ฌ | ์˜จ๋„ยท์กฐ๋„ ์ž๋™ ์ œ์–ด, ์—๋„ˆ์ง€ ํšŒ์ˆ˜ | | ๊ณ ๊ณ ๋„ ์ž๊ธฐ ๋ถ€์–‘ ๊ฑฐ์šธ | ์ „์ž๊ธฐ ๋ถ€์–‘์„ ์ด์šฉํ•œ ๊ฑฐ์šธ ๊ณ ์ •ยท์ง‘๊ด‘ | ๋‚ฎยท๋ฐค ๋ชจ๋‘ ์ผ์ •ํ•œ ์ผ์กฐ ํ™•๋ณด, ๋ฐฉ์‚ฌ์„  ์ฐจํ | | ํ์‡„ํ˜• ์ƒํƒœ๊ณ„ | ํ† ์–‘ยท๊ด€๊ฐœยท์‚ฐ์†Œยท์‹๋Ÿ‰ ์ˆœํ™˜ ์„ค๊ณ„ | ์™ธ๋ถ€ ์ž์› ์ตœ์†Œ ์˜์กด, ์žฅ๊ธฐ ์ž๊ธ‰์ž์กฑ ๊ฐ€๋Šฅ | ์ด๋Ÿฌํ•œ

Physics
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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Limitations on intermittent forecasting

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ | ๊ตฌ๋ถ„ | ๊ธฐ์กด ๊ฒฐ๊ณผ | ํ•œ๊ณ„ | | | | | | Bailey (1995) | ๋ชจ๋“  ์ •์ƒยท์—๋ฅด๊ณ ๋”• ์ด์ง„ ์‹œ๊ณ„์—ด์— ๋Œ€ํ•ด ์ ๋ณ„ ์˜ˆ์ธก ๋ถˆ๊ฐ€๋Šฅ | ๋งˆ์ฝ”ํ”„ ์ฒด์ธ์—์„œ๋Š” ๊ฐ€๋Šฅ | | Morvai (2000) | ์ •์ง€์‹œ๊ฐ„ ({lambda n})์™€ ์ถ”์ •๊ธฐ ({h n})๋ฅผ ์ด์šฉํ•ด ์ผ๊ด€์„ฑ ํ™•๋ณด | ์ •์ง€์‹œ๊ฐ„์ด ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€ํ•ด ์‹ค์šฉ์„ฑ ์ €ํ•˜ | | Morvai & Weiss (2005) | ์ •์ง€์‹œ๊ฐ„ ์„ฑ์žฅ๋ฅ ์„ ์™„ํ™”, ์—ฐ์† ์กฐ๊ฑด๋ถ€ ํ™•๋ฅ  ์„ ๊ฐ–๋Š” ๊ณผ์ •์—๋งŒ ์ผ๊ด€์„ฑ ๋ณด์žฅ | ๋งˆ์ฝ”ํ”„ ์ฒด์ธ(ํŠนํžˆ ๋น„์—ฐ์† ๊ฒฝ์šฐ) ํฌํ•จ ์—ฌ๋ถ€

Mathematics Information Theory Computer Science
Linguistic Information Energy

Linguistic Information Energy

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ๋ฐฐ๊ฒฝ : ์ธ๊ฐ„์ด ํ…์ŠคํŠธ๋ฅผ ์ฝ์„ ๋•Œ ๊ฐ ๋‹จ์–ด๋ฅผ ์ธ์‹ํ•˜๊ณ  ์˜๋ฏธ๋ฅผ ํ†ตํ•ฉํ•˜๋Š” ๊ณผ์ •์€ ์ธ์ง€ ๋ถ€ํ•˜(cognitive load)์™€ ๋ฐ€์ ‘ํ•˜๊ฒŒ ์—ฐ๊ด€๋œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์‹ฌ๋ฆฌํ•™์  ์‹คํ—˜์ด๋‚˜ ํ†ต๊ณ„์  ์–ธ์–ด ๋ชจ๋ธ์— ์˜์กดํ–ˆ์ง€๋งŒ, ๋ฌผ๋ฆฌยท๊ณตํ•™์  ์‹œ์Šคํ…œ ์ด๋ก ์„ ์ ์šฉํ•œ ์‹œ๋„๋Š” ๋“œ๋ฌผ๋‹ค. ๋ชฉ์  : ํ…์ŠคํŠธ๋ฅผ โ€œ๋‹จ์–ด ์ถฉ๋™โ€์ด๋ผ๋Š” ๋ฌผ๋ฆฌ๋Ÿ‰์œผ๋กœ ๋ณ€ํ™˜ํ•˜๊ณ , ์ด๋ฅผ ์‹œ๊ฐ„ ์—ฐ์†์ ์ธ ์‹œ์Šคํ…œ ์‘๋‹ต์œผ๋กœ ๋ชจ๋ธ๋งํ•จ์œผ๋กœ์จ ์ฝ๊ธฐ ๋น„์šฉ ์„ ์ •๋Ÿ‰ํ™”ํ•œ๋‹ค. ์ •์˜๋œ โ€˜์ •๋ณด ์—๋„ˆ์ง€โ€™๋ฅผ ํ†ตํ•ด ํ…์ŠคํŠธ์˜ ๋ณต์žก๋„, ์ถœํŒ ์‹œ๊ธฐ, ๊ทธ๋ฆฌ๊ณ  ํ…์ŠคํŠธ ๊ฐ„ ์œ ์‚ฌ์„ฑ์„ ์ž๋™์œผ๋กœ ์ถ”์ •ํ•˜๊ณ ์ž ํ•œ๋‹ค. 2.

Mathematics NLP Computer Science Information Theory
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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

Mathematics Information Theory 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

Network Hardware Architecture System Computer Science
Mutual information in random Boolean models of regulatory networks

Mutual information in random Boolean models of regulatory networks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ƒ๋ฌผํ•™์  ๋งฅ๋ฝ : ์„ธํฌ ๋‚ด ์œ ์ „์ž ์กฐ์ ˆ๋ง์€ ์ˆ˜์ฒœ ๊ฐœ์˜ ์œ ์ „์ž๊ฐ€ ๋ณต์žกํ•˜๊ฒŒ ์–ฝํžŒ ๋„คํŠธ์›Œํฌ์ด๋ฉฐ, ์ด๋“ค์˜ ๋™์‹œ์ ยทํ˜‘์กฐ์  ํ™œ๋™์„ ์ •๋Ÿ‰ํ™”ํ•˜๋Š” ๊ฒƒ์ด ํ•ต์‹ฌ ๊ณผ์ œ์ด๋‹ค. ์ •๋ณด ์ด๋ก ์  ์ ‘๊ทผ : ๋‘ ๋…ธ๋“œ์˜ ์‹œ๊ฐ„ ์‹œ๊ณ„์—ด ๊ฐ„ ์ƒํ˜ธ ์ •๋ณด๋Š” ํ•œ ๋…ธ๋“œ์˜ ์ƒํƒœ๊ฐ€ ๋‹ค๋ฅธ ๋…ธ๋“œ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ธก์ •ํ•œ๋‹ค. ๋„คํŠธ์›Œํฌ ์ „์ฒด์— ๋Œ€ํ•œ ํ‰๊ท  โŸจIโŸฉ๋Š” โ€œ์ „์—ญ ํ˜‘์กฐ์„ฑโ€์„ ๋‚˜ํƒ€๋‚ด๋Š” ์ž์—ฐ์Šค๋Ÿฌ์šด ์ฒ™๋„๋‹ค. ์ž„๊ณ„์„ฑ ๊ฐ€์„ค : ๊ธฐ์กด ์—ฐ๊ตฌ(์˜ˆ: Kauffman, Aldana ๋“ฑ)๋Š” ์œ ์ „์ž ๋„คํŠธ์›Œํฌ๊ฐ€ ์ž„๊ณ„(critical) ์ƒํƒœ์— ์žˆ์„ ๊ฒฝ์šฐ, ์•ˆ์ •์„ฑ๊ณผ ์œ ์—ฐ์„ฑ ์‚ฌ์ด์˜ ์ตœ์  ๊ท 

Model Network Quantitative Biology
Non-rational configurations, polytopes, and surfaces

Non-rational configurations, polytopes, and surfaces

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋น„์œ ๋ฆฌ ๋‹ค๋ฉด์ฒด ๋Š” โ€œ์กฐํ•ฉ์  ์œ ํ˜•์€ ์กด์žฌํ•˜์ง€๋งŒ, ์–ด๋–ค ์œ ๋ฆฌ ์ขŒํ‘œ๊ณ„์—์„œ๋„ ์‹คํ˜„ ๋ถˆ๊ฐ€โ€๋ผ๋Š” ์—ญ์„ค์ ์ธ ํ˜„์ƒ์ด๋‹ค. ๊ธฐ์กด์— ์•Œ๋ ค์ง„ ์˜ˆ(์˜ˆ: 8์ฐจ์› 12์ •์  ๋‹ค๋ฉด์ฒด)๋Š” Perles๊ฐ€ Gale ๋„ํ‘œ ๋ฅผ ์ด์šฉํ•ด ๋งŒ๋“ค์—ˆ์œผ๋ฉฐ, ์‹œ๊ฐํ™”๊ฐ€ ์–ด๋ ค์›Œ ์ง๊ด€์  ์ดํ•ด๊ฐ€ ์ œํ•œ์ ์ด์—ˆ๋‹ค. ์ €์ž๋Š” โ€œ๊ณ ์ฐจ์› ๊ธฐํ•˜ํ•™์ด ์ด์ƒํ•˜๋‹คโ€๋Š” ์ „ํ†ต์  ์„ค๋ช…์„ ๋„˜์–ด, ํ‰๋ฉด์˜ ๋น„์œ ๋ฆฌ ์  ๊ตฌ์„ฑ ์ด ๊ณ ์ฐจ์› ๊ตฌ์กฐ๋ฅผ ๊ฐ•์ œํ•œ๋‹ค๋Š” ์‚ฌ์‹ค์„ ๊ฐ•์กฐํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด: Lawrence ํ™•์žฅ ์ž…๋ ฅ : ํ‰๋ฉด์— ์กด์žฌํ•˜๋Š” ๋น„์œ ๋ฆฌ ์  ๊ตฌ์„ฑ (V {v 1,dots,v n}subs

Mathematics
On Outage Behavior of Wideband Slow-Fading Channels

On Outage Behavior of Wideband Slow-Fading Channels

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์™€์ด๋“œ๋ฐด๋“œ ์ „์†ก์˜ ์‹ค์šฉ์„ฑ : ์ฐจ์„ธ๋Œ€ ๋ฌด์„  ์‹œ์Šคํ…œ์—์„œ ์ŠคํŽ™ํŠธ๋Ÿผ ํšจ์œจ๋ณด๋‹ค ์ „๋ ฅ ํšจ์œจ์„ ์šฐ์„ ์‹œํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋Š˜์–ด๋‚˜๊ณ  ์žˆ๋‹ค. OFDM ๋“ฑ ๋‹ค์ค‘ ์บ๋ฆฌ์–ด ๋ฐฉ์‹์„ ํ†ตํ•ด ๋„“์€ ๋Œ€์—ญํญ์— ์ „๋ ฅ์„ ๋ถ„์‚ฐ์‹œํ‚ค๋ฉด, ๋‚ฎ์€ ์ „๋ ฅ์œผ๋กœ๋„ ์ถฉ๋ถ„ํ•œ ์ „์†ก๋ฅ ์„ ํ™•๋ณดํ•  ์ˆ˜ ์žˆ๋‹ค. ์Šฌ๋กœ์šฐ ํŽ˜์ด๋”ฉ(Quasiโ€‘static) ํ™˜๊ฒฝ : ์‹ค๋‚ดยท์‹ค์ œ ์ธก์ • ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด, ์ฑ„๋„ ์ƒํƒœ๊ฐ€ ์ฝ”๋”ฉ ๋ธ”๋ก ์ „์ฒด์— ๊ฑธ์ณ ๊ฑฐ์˜ ๋ณ€ํ•˜์ง€ ์•Š๋Š”๋‹ค. ๋”ฐ๋ผ์„œ ์ „ํ†ต์ ์ธ ํ‰๊ท (ergodic) ์šฉ๋Ÿ‰ ๋Œ€์‹  ์•„์›ƒ์ง€ ํ™•๋ฅ  ์ด ์ฃผ์š” ์„ฑ๋Šฅ ์ง€ํ‘œ๊ฐ€ ๋œ๋‹ค. 2. ํ•ต์‹ฌ ๊ฐœ๋… โ€“ ์™€์ด๋“œ๋ฐด๋“œ ์•„์›ƒ์ง€ ์ง€์ˆ˜ ์ •์˜

Mathematics Information Theory Computer Science
On the distribution of career longevity and the evolution of home run   prowess in professional baseball

On the distribution of career longevity and the evolution of home run prowess in professional baseball

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ๊ฒฝ์Ÿ ํ™˜๊ฒฝ์˜ ๋น„์ •์ƒ์„ฑ : ๊ธฐ์กด ์•ผ๊ตฌ ํ†ต๊ณ„๋Š” โ€˜๊ฒฝ์Ÿ์ด ์ •์ ์ด๋‹คโ€™๋ผ๋Š” ์ „์ œ ํ•˜์— ๊ธฐ๋ก์„ ๋น„๊ตํ•œ๋‹ค. ์ €์ž๋Š” ์ด ๊ฐ€์ •์ด ํ˜„์‹ค๊ณผ ๋งž์ง€ ์•Š์œผ๋ฉฐ, ๊ฒฝ์Ÿ์ด ์‹œ๊ฐ„์— ๋”ฐ๋ผ ๋ณ€๋™ํ•œ๋‹ค๋Š” ์ ์„ ๊ฐ•์กฐํ•œ๋‹ค. ๋ณต์žก๊ณ„ ์ด๋ก  ์ ์šฉ : ํŒŒ์›Œโ€‘๋กœ์šฐ(Powerโ€‘law) ๋ถ„ํฌ๋Š” ๊ฒฝ์Ÿโ€‘๊ตฌ๋™ ์‹œ์Šคํ…œ์—์„œ ํ”ํžˆ ๋‚˜ํƒ€๋‚˜๋Š” ํ˜„์ƒ์ด๋ฉฐ, ์ด๋ฅผ ์•ผ๊ตฌ ๊ฒฝ๋ ฅยท์„ฑ๊ณผ์— ์ ์šฉํ•จ์œผ๋กœ์จ ์ƒˆ๋กœ์šด ํ†ต๊ณ„์  ํ†ต์ฐฐ์„ ์–ป๊ณ ์ž ํ•œ๋‹ค. 2. ๋ฐ์ดํ„ฐ์™€ ๋ฐฉ๋ฒ•๋ก  ๋ฐ์ดํ„ฐ ์†Œ์Šค : Sean Lahmanโ€™s Baseball Archive (1871โ€‘2000) โ†’ ์•ฝ 10,000๋ช…์˜ ์„ ์ˆ˜ ๋ฐ์ดํ„ฐ ์‚ฌ์šฉ

Physics
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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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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

Mathematics Information Theory Computer Science
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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)์—

Quantitative Biology Physics Condensed Matter
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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

Network Quantitative Biology
Secrecy Capacity of the Wiretap Channel with Noisy Feedback

Secrecy Capacity of the Wiretap Channel with Noisy Feedback

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์™€์ด์–ดํƒญ ์ฑ„๋„ (Wyner, 1975)์€ ์žก์Œ์œผ๋กœ ์ธํ•ด ์ˆ˜์‹ ์ž๋ณด๋‹ค ์—ด๋“ฑํ•œ ์™€์ด์–ดํƒญ์—๊ฒŒ๋„ ๋น„๋ฐ€ ์ „์†ก์ด ๊ฐ€๋Šฅํ•จ์„ ๋ณด์˜€์ง€๋งŒ, ์ฑ„๋„์ด ์—ด๋“ฑ ํ•  ๊ฒฝ์šฐ(์ฆ‰, ์™€์ด์–ดํƒญ ์ฑ„๋„์ด ๋” ๊นจ๋—ํ•จ) ๋น„๋ฐ€ ์šฉ๋Ÿ‰์ด 0์ด ๋œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ(

Cryptography and Security Information Theory Computer Science Mathematics
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

Model Quantitative Biology
Spreadsheets in Clinical Medicine

Spreadsheets in Clinical Medicine

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์˜๋ฃŒ ์˜ค๋ฅ˜์™€ ์‚ฌ๋ง : ๋ฏธ๊ตญยท์˜๊ตญ์—์„œ ๊ฐ๊ฐ ์—ฐ๊ฐ„ 98,000๋ช…ยท30,000๋ช…์˜ ์‚ฌ๋ง์ด ์˜๋ฃŒ ์˜ค๋ฅ˜์™€ ์—ฐ๊ด€๋ผ ์žˆ๋‹ค๋Š” ํ†ต๊ณ„๋Š” ์ด๋ฏธ ๋„๋ฆฌ ์•Œ๋ ค์ง„ ์‚ฌ์‹ค์ด๋ฉฐ, ์ด๋Š” ์ „์ฒด ์‚ฌ๋ง ์›์ธ ์ค‘ 7๋ฒˆ์งธ์— ํ•ด๋‹นํ•œ๋‹ค. ์Šคํ”„๋ ˆ๋“œ์‹œํŠธ์˜ ๋ณดํŽธ์„ฑ : PubMed ๊ฒ€์ƒ‰ ๊ฒฐ๊ณผ์™€ ๊ตฌ๊ธ€ ์กฐ์‚ฌ์— ๋”ฐ๋ฅด๋ฉด, 800๊ฑด ์ด์ƒ์˜ ๋…ผ๋ฌธยท๋ณด๊ณ ์„œ๊ฐ€ ์ž„์ƒ ์Šคํ”„๋ ˆ๋“œ์‹œํŠธ ํ™œ์šฉ์„ ์–ธ๊ธ‰ํ•˜๊ณ  ์žˆ๋‹ค. ์‹ค์ œ ์‚ฌ์šฉ๋Ÿ‰์€ ์ด๋ณด๋‹ค ํ›จ์”ฌ ํด ๊ฒƒ์œผ๋กœ ์ถ”์ •๋œ๋‹ค(์˜ˆ: medal.org ์ผ์ผ 1,800๋ช… ๋ฐฉ๋ฌธ). ์ „๋ฌธ๊ฐ€์šฉ ์†Œํ”„ํŠธ์›จ์–ด์™€ ๋Œ€๋น„ : ๊ธฐ์กด ์˜๋ฃŒ ์†Œํ”„ํŠธ์›จ์–ด๋Š” ๊ทœ์ œยท๊ฒ€์ฆ ์ ˆ์ฐจ๊ฐ€ ์กด์žฌํ•˜์ง€

Computers and Society Computer Science
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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 $ell^2$-homology of even Coxeter groups

The $ell^2$-homology of even Coxeter groups

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ Singerโ€™s Conjecture ์€ ํ์‡„ ๋น„๊ตฌ๋ฉด(์•„์ŠคํŽ˜๋ฆฌ์ปฌ) ๋‹ค์–‘์ฒด (M^{n})์— ๋Œ€ํ•ด (ell^{2}) ๋™์งˆ๊ตฐ์ด ์ค‘๊ฐ„ ์ฐจ์›์—์„œ๋งŒ ๋น„์ž๋ช…ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฐ•๋ ฅํ•œ ์˜ˆ์ธก์ด๋‹ค. Coxeter ๊ตฐ์ด ์ž‘์šฉํ•˜๋Š” Davis ๋ณตํ•ฉ์ฒด (Sigma)๋Š” ๋น„๊ตฌ๋ฉด ์ด๋ฉฐ, ์‹ ๊ฒฝ (L)์ด ((n 1)) ๊ตฌ๋ฉด์„ ์‚ผ๊ฐ๋ถ„ํ• ํ•˜๋ฉด (Sigma)๋Š” (n) ์ฐจ์› ๋งค๋‹ˆํด๋“œ ๊ฐ€ ๋œ๋‹ค. ๋”ฐ๋ผ์„œ (Sigma)๋Š” Singerโ€‘conjecture ์„ ๊ฒ€์ฆํ•  ์ˆ˜ ์žˆ๋Š” ํ’๋ถ€ํ•œ ์‹œํ—˜๋Œ€๊ฐ€ ๋œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ(

Mathematics
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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

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