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A Conversation with Dorothy Gilford

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

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
Analysis of Proton Radiography Images of Shock Melted/Damaged Tin

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
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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

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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 Networking Computer Science Network Distributed Computing
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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 Mathematics Information Theory 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€

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

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
Comparaison entre calculs de vulnerabilite sismique et   proprietes dynamiques mesurees

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
Computational Intelligence Characterization Method of Semiconductor   Device

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
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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

General Relativity Physics Astrophysics
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Cooperative Relaying with State Available at the Relay

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ฑ„๋„ ์ƒํƒœ ์˜์กด์„ฑ ์€ ๋‹จ์ผยท๋‹ค์ค‘ ์‚ฌ์šฉ์ž ์‹œ์Šคํ…œ ๋ชจ๋‘์—์„œ ํ™œ๋ฐœํžˆ ์—ฐ๊ตฌ๋ผ ์™”์œผ๋ฉฐ, ๋Œ€๋ถ€๋ถ„์€ ๋ชจ๋“  ์†ก์‹ ๊ธฐ๊ฐ€ CSI๋ฅผ ๊ณต์œ  ํ•˜๋Š” ์ƒํ™ฉ์„ ์ „์ œ๋กœ ํ•œ๋‹ค. ์‹ค์ œ ๋ฌด์„  ๋„คํŠธ์›Œํฌ์—์„œ๋Š” ์„ผ์„œ, IoT ๋””๋ฐ”์ด์Šค ๋“ฑ ์ด ์ œํ•œ๋œ ํ•˜๋“œ์›จ์–ดยท์ „๋ ฅ์œผ๋กœ ์ธํ•ด CSI๋ฅผ ์™„์ „ํ•˜๊ฒŒ ๊ณต์œ ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋น„๋Œ€์นญ CSI (์ผ๋ถ€ ๋…ธ๋“œ๋งŒ ์ƒํƒœ๋ฅผ ์•Œ์Œ) ์ƒํ™ฉ์„ ๋‹ค๋ฃจ๋Š” ๊ฒƒ์ด ์‹ค์šฉ์ ์ด๋ฉฐ, ํŠนํžˆ ๋ฆด๋ ˆ์ด๊ฐ€ CSI๋ฅผ ๊ฐ–๋Š” ๊ฒฝ์šฐ ๋Š” โ€œ๋ฆด๋ ˆ์ด๊ฐ€ ์ค‘๊ณ„์™€ ๋™์‹œ์— ๊ฐ„์„ญ ์–ต์ œ ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ๋‹ค ๋Š” ์ƒˆ๋กœ์šด ๊ฐ€๋Šฅ์„ฑ์„ ์ œ๊ณตํ•œ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๊ตฌ๋ถ„ | ๋‚ด์šฉ | ์˜์˜

Mathematics Information Theory Computer Science
Coupling of Surface and Volume Dipole Oscillations in C-60 Molecules

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˜

Model Quantitative Biology
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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
Distributed spatial multiplexing with 1-bit feedback

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
No Image

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˜์‘์‹

Quantitative Biology Physics Condensed Matter
Exchange Reactions with Dick Dalitz

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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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
Fluctuation of the download network

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
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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
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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
Limitations on intermittent forecasting

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
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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…

Quantitative Biology Condensed Matter
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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
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Modelling the spatial organization of cell proliferation in the developing central nervous system

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ๊ณต๊ฐ„โ€‘์‹œ๊ฐ„ ์กฐ์ง์˜ ์ค‘์š”์„ฑ : ๋ฐœ๋‹ฌ์ƒ๋ฌผํ•™์—์„œ ์„ธํฌ ์ฆ์‹ยท์ด๋™ยท๋ถ„ํ™”๋Š” ์„œ๋กœ ์–ฝํžŒ ๋„คํŠธ์›Œํฌ๋ฅผ ์ด๋ฃจ๋ฉฐ, ํŠนํžˆ CNS์™€ ๊ฐ™์€ ๋‹ค์ธต ๊ตฌ์กฐ์—์„œ๋Š” ์ถ• ๋ฐฉํ–ฅ(๋จธ๋ฆฌโ€‘๊ผฌ๋ฆฌ, ๋“ฑโ€‘๋ณต๋ถ€)์œผ๋กœ์˜ ์‹ ํ˜ธ ๊ตฌ๋ฐฐ๊ฐ€ ํ•ต์‹ฌ์ ์ธ ํŒจํ„ด ํ˜•์„ฑ ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด๋‹ค. ์ •๋Ÿ‰์  ๋ชจ๋ธ๋ง ํ•„์š”์„ฑ : ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ํ†ต๊ณ„์ ยทํ™•๋ฅ ์  ์ ‘๊ทผ(์  ๊ณผ์ •, ์ƒ๊ด€ ๋ถ„์„)์œผ๋กœ NE ์„ธํฌ์˜ ์ƒํ˜ธ์ž‘์šฉ์„ ์„ค๋ช…ํ–ˆ์ง€๋งŒ, ๊ฒฐ์ •๋ก ์  PDE ๋ชจ๋ธ์€ ์—ฐ์†์ ์ธ ์„ธํฌ ๋ฐ€๋„์™€ ํ™•์‚ฐ์„ ๋™์‹œ์— ๋‹ค๋ฃฐ ์ˆ˜ ์žˆ์–ด ์ „๋ฐ˜์ ์ธ ๋ฐœ๋‹ฌ ํ๋ฆ„์„ ์˜ˆ์ธกํ•˜๋Š” ๋ฐ ์œ ๋ฆฌํ•˜๋‹ค. 2. ๋ชจ๋ธ๋ง ์ ‘๊ทผ๋ฒ• | ์š”์†Œ | ์ˆ˜ํ•™์  ํ‘œํ˜„ | ์ƒ๋ฌผ

Model Quantitative Biology Mathematics System
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Models for dependent extremes using stable mixtures

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ œ๊ธฐ ๋‹ค๋ณ€๋Ÿ‰ ๊ทน๊ฐ’ ์ด๋ก ์€ ํ™˜๊ฒฝยท๊ฒฝ์ œยท๊ณตํ•™ ๋ถ„์•ผ์—์„œ ๋™์‹œ ๋ฐœ์ƒํ•˜๋Š” ๊ทน๋‹จ ํ˜„์ƒ ์„ ์„ค๋ช…ํ•˜๋Š” ๋ฐ ํ•„์ˆ˜์ ์ด์ง€๋งŒ, 2~3 ์ฐจ์› ์„ ๋„˜์–ด์„  ์‹ค์ œ ์ ์šฉ ์‚ฌ๋ก€๋Š” ๋“œ๋ฌผ๋‹ค. ๊ธฐ์กด ๋ชจ๋ธ์€ ์กฐ๊ฑด๋ถ€(๋žœ๋ค ํšจ๊ณผ๊ฐ€ ์ฃผ์–ด์กŒ์„ ๋•Œ) ํ˜น์€ ๋ฌด์กฐ๊ฑด๋ถ€ ์ค‘ ํ•˜๋‚˜๋งŒ EV ํŠน์„ฑ์„ ๋งŒ์กฑ์‹œ์ผœ, ๋ชจ๋ธ ์„ ํƒ์— ํ˜ผ๋ž€ ์„ ์•ผ๊ธฐํ•œ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๊ตฌ๋ถ„ | ๋‚ด์šฉ | ์˜์˜ | | | | | | ํ†ตํ•ฉ ํ”„๋ ˆ์ž„์›Œํฌ | ์–‘์กฐ๊ฑดยท๋ฌด์กฐ๊ฑด ๋ชจ๋‘ EV์ธ ๋ชจ๋ธ์„ ์–‘์˜ ฮฑโ€‘stable ํ˜ผํ•ฉ ์œผ๋กœ ์ •์˜ | ๋ชจ๋ธ ์„ ํƒ์˜ ์ด์ค‘์„ฑ ํ•ด์†Œ, ์ด๋ก ์  ์ผ๊ด€์„ฑ ํ™•๋ณด | | ์„ธ ๊ฐ€์ง€ ํ•ด์„ |

Model Mathematics Statistics
Multi-User Diversity vs. Accurate Channel Feedback for MIMO Broadcast   Channels

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
MultiNoC: A Multiprocessing System Enabled by a Network on Chip

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
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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 Computer Science Data
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Noise threshold for universality of 2-input gates

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ํ•˜๋“œ์›จ์–ด ๋ฏธ๋‹ˆ์–ด์ฒ˜ํ™” ๊ฐ€ ์ง„ํ–‰๋จ์— ๋”ฐ๋ผ ํŠธ๋žœ์ง€์Šคํ„ฐ ์ˆ˜์ค€์—์„œ ์†Œํ”„ํŠธ ์˜ค๋ฅ˜(soft error) ๊ฐ€ ๊ธ‰์ฆํ•˜๊ณ  ์žˆ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ(von Neumann, Pippenger, Feder, EvansยทSchulman ๋“ฑ)๋Š” ๋‹ค์ž…๋ ฅ(kโ€‘fanโ€‘in) ๊ฒŒ์ดํŠธ ์— ๋Œ€ํ•œ ์žก์Œ ํ•œ๊ณ„๋งŒ์„ ๋‹ค๋ฃจ์—ˆ์œผ๋ฉฐ, ์ง์ˆ˜ fanโ€‘in(ํŠนํžˆ k 2) ์— ๋Œ€ํ•ด์„œ๋Š” ์ •ํ™•ํ•œ ์ž„๊ณ„๊ฐ’์ด ์•Œ๋ ค์ง€์ง€ ์•Š์•˜๋‹ค. 2์ž…๋ ฅ NAND ๊ฒŒ์ดํŠธ๋Š” ์‹ค์ œ ๋””์ง€ํ„ธ ์„ค๊ณ„์—์„œ ๊ฐ€์žฅ ํ”ํžˆ ์‚ฌ์šฉ๋˜๋ฏ€๋กœ, ์ด ๊ฒฝ์šฐ์˜ ์ •๋ฐ€ํ•œ ์žก์Œ ํ•œ๊ณ„ ๋ฅผ ๊ทœ๋ช…ํ•˜๋Š” ๊ฒƒ์€ ์ด๋ก ์ ยท์‹ค์šฉ์  ์˜๋ฏธ๊ฐ€ ํฌ๋‹ค. 2. ์ฃผ์š”

Mathematics Information Theory Computational Complexity Computer Science
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Nuclear kinetic energy spectra of D_2^+ in intense laser field: Beyond Born Oppenheimer approximation

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ๋ณด๋ฅธโ€‘์˜คํŽœํ•˜์ด๋จธ(BO) ๊ทผ์‚ฌ์˜ ํ•œ๊ณ„ ๊ฐ•๋ ฌ ๋ ˆ์ด์ €์™€ ๊ฐ™์€ ์ดˆ๊ณ ์† ์ „์žยทํ•ต ์ƒํ˜ธ์ž‘์šฉ์—์„œ๋Š” ์ „์ž์™€ ํ•ต์ด ๋™์‹œ์— ์›€์ง์ด๋ฉฐ, BO ๊ทผ์‚ฌ๋Š” ์ „์ž์™€ ํ•ต์„ ๋ถ„๋ฆฌํ•ด ๋‹ค๋ฃจ๋Š” ์ „ํ†ต์  ๋ฐฉ๋ฒ•์ด๋‹ค. ์ด ๊ทผ์‚ฌ๋Š” ์ „์ž ์šด๋™์ด ํ•ต ์šด๋™๋ณด๋‹ค ํ›จ์”ฌ ๋น ๋ฅด๋‹ค๋Š” ๊ฐ€์ •์— ๊ธฐ๋ฐ˜ํ•˜์ง€๋งŒ, ๋ ˆ์ด์ €๊ฐ€ ํ•ต ๊ฐ„ ๊ฑฐ๋ฆฌ๋ฅผ ํฌ๊ฒŒ ๋ณ€ํ˜•์‹œํ‚ค๋Š” ๊ฒฝ์šฐ(ํŠนํžˆ โ€˜๊ฐ•ํ™” ์ด์˜จํ™”โ€™ ํ˜„์ƒ)์—๋Š” ๋ถ€์ •ํ™•ํ•ด์ง„๋‹ค. ์†Œํ”„ํŠธโ€‘์ฝ”์–ด(SC) ์ „์œ„์˜ ๋…ผ๋ž€ ๋‹ค์ „์ž ์‹œ์Šคํ…œ์„ 1D ํ˜น์€ 2D๋กœ ์ถ•์†Œํ•ด ๊ณ„์‚ฐ๋Ÿ‰์„ ์ค„์ด๊ธฐ ์œ„ํ•ด SC ์ „์œ„๊ฐ€ ๋„๋ฆฌ ์‚ฌ์šฉ๋ผ ์™”์ง€๋งŒ, ์‹ค์ œ ์ฟจ๋กฑ ์ „์œ„์™€์˜ ์ฐจ์ด๋กœ ์ธํ•ด ์ •๋Ÿ‰์  ์˜ˆ์ธก์— ํ•œ

Physics
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On the Distribution of Penalized Maximum Likelihood Estimators: The LASSO, SCAD, and Thresholding

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๊ธฐ์กด ๋ฌธํ—Œ๊ณผ์˜ ์ฐจ๋ณ„์  Knight & Fu (2000) , Fan & Li (2001) ๋“ฑ์€ ๊ฐ๊ฐ LASSO์™€ SCAD์˜ ์ ๋ณ„ asymptotic ๋ถ„ํฌ๋ฅผ ์ œ์‹œํ–ˆ์œผ๋ฉฐ, ํŠนํžˆ SCAD์— ๋Œ€ํ•ด โ€˜oracle propertyโ€™๋ฅผ ์ฃผ์žฅํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ ๋ณ„ ์ ‘๊ทผ์€ ๋ชจ๋ธ ์„ ํƒ ์ด ํฌํ•จ๋œ ์ถ”์ •๊ธฐ์˜ ์‹ค์ œ ๋™์ž‘์„ ๊ณผ๋Œ€ํ‰๊ฐ€ํ•œ๋‹ค๋Š” ์ ์ด Leeb & Pรถtscher (2005, 2008) ๋“ฑ์—์„œ ์ง€์ ๋˜์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ โ€˜์ด๋™ ํŒŒ๋ผ๋ฏธํ„ฐโ€™ (parameterโ€‘moving) ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ๋„์ž…ํ•ด, ํŒŒ๋ผ๋ฏธํ„ฐ๊ฐ€ (n)์— ๋”ฐ๋ผ ๋ณ€ํ•  ๋•Œ ๋ฐœ์ƒํ•˜๋Š”

Mathematics Machine Learning Statistics
On The Linear Behaviour of the Throughput of IEEE 802.11 DCF in   Non-Saturated Conditions

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

Machine Learning Learning Computer Science Data
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)์—

Quantitative Biology Physics Condensed Matter
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Similarity search for local protein structures at atomic resolution by exploiting a database management system

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๊ตฌ์กฐโ€‘๊ธฐ๋Šฅ ๊ด€๊ณ„์˜ ํ•œ๊ณ„ : ์ „ํ†ต์ ์ธ ์„œ์—ดยทํด๋“œ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•์€ ์ „์ฒด์ ์ธ ๊ตฌ์กฐ์™€ ๊ธฐ๋Šฅ ์‚ฌ์ด์˜ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ์•ฝํ•จ์„ ๋ณด์—ฌ์ค€๋‹ค. ํŠนํžˆ ํšจ์†Œ ํ™œ์„ฑ ๋ถ€์œ„๋‚˜ ๋ฆฌ๊ฐ„๋“œ ๊ฒฐํ•ฉ ๋ถ€์œ„์™€ ๊ฐ™์ด ์†Œ์ˆ˜์˜ ์›์ž๋งŒ์ด ๊ธฐ๋Šฅ์„ ๋‹ด๋‹นํ•˜๋Š” ๊ฒฝ์šฐ, ๋กœ์ปฌ ๊ตฌ์กฐ ๋น„๊ต ๊ฐ€ ํ•„์ˆ˜์ ์ด๋‹ค. ๋ฐ์ดํ„ฐ ๊ทœ๋ชจ ๊ธ‰์ฆ : 2007๋…„ ๊ธฐ์ค€ PDB์— 43 755๊ฐœ์˜ ์—”ํŠธ๋ฆฌ๊ฐ€ ์กด์žฌํ–ˆ์œผ๋ฉฐ, ํ˜„์žฌ๋Š” 200 000๊ฐœ๊ฐ€ ๋„˜๋Š”๋‹ค. ๊ธฐ์กด์˜ GH(Geometric Hashing) ๋“ฑ ๋ฉ”๋ชจ๋ฆฌ ์ค‘์‹ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ GB ์ˆ˜์ค€์˜ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋ฅผ RAM์— ์ ์žฌํ•˜๊ธฐ ์–ด๋ ค์›Œ ํ™•์žฅ์„ฑ์ด ๋–จ์–ด์ง„๋‹ค. 2. ํ•ต์‹ฌ

Quantitative Biology System Data
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Statistically Optimal Strategy Analysis of a Competing Portfolio Market with a Polyvariant Profit Function

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ Zeitnot ๊ฐ€์ • : ์‹ค์ œ ํˆฌ์ž์ž๋“ค์€ ์ œํ•œ๋œ ์‹œ๊ฐ„ ์•ˆ์— ๋‹ค์ˆ˜์˜ ํ›„๋ณด ์ฃผ์‹์„ ๋น„๊ตยทํ‰๊ฐ€ํ•ด์•ผ ํ•œ๋‹ค. ์ด๋•Œ โ€œ๋น„๊ต ์ •๋ณด๋งŒ ์ œ๊ณตโ€๋˜๋Š” ์ƒํ™ฉ์„ ๋ชจ๋ธ๋งํ•จ์œผ๋กœ์จ ๊ธฐ์กด์˜ ์ „ํ†ต์  ํฌํŠธํด๋ฆฌ์˜ค ์„ ํƒ ๋ฌธ์ œ์™€ ์ฐจ๋ณ„ํ™”ํ•œ๋‹ค. ๋‹ค๋ณ€๋Ÿ‰ ์ด์ต ํ•จ์ˆ˜ : ๋‹จ์ผ ์ˆ˜์ต ์™ธ์— ์œ„ํ—˜ยท์œ ๋™์„ฑยท์‹œ์žฅ ์ถฉ๊ฒฉ ๋“ฑ ์—ฌ๋Ÿฌ ์ฐจ์›์„ ๋™์‹œ์— ๊ณ ๋ คํ•˜๋Š” ๋‹ค๋ณ€๋Ÿ‰(๋‹ค์ค‘) ์ด์ต ํ•จ์ˆ˜๋ฅผ ๋„์ž…ํ•ด ํ˜„์‹ค์„ฑ์„ ๋†’์˜€๋‹ค. ๊ฒฝ์Ÿ ์ƒํ™ฉ : ์—ฌ๋Ÿฌ ๊ณ ๊ฐ์ด ๋™์‹œ์— ๋™์ผ ํฌํŠธํด๋ฆฌ์˜ค๋ฅผ ํƒ์ƒ‰ํ•˜๊ณ , ๊ฐ€์žฅ ๋น ๋ฅธ ๊ณ ๊ฐ์ด ์‹ค์ œ ๋งค์ˆ˜๋ฅผ ์ฐจ์ง€ํ•œ๋‹ค๋Š” โ€œ๊ฒฝ์Ÿโ€ ์š”์†Œ๋ฅผ ํฌํ•จ์‹œ์ผœ, ๊ฒŒ์ž„ ์ด๋ก ์  ์ธก๋ฉด์„ ์•”์‹œํ•œ๋‹ค. 2.

Quantitative Finance Mathematics Analysis Statistics
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
์ˆ˜์••์ด ์•„๋‹Œ ๋ฌผ ํ๋ฆ„์ด ์ด‰๋ฐœํ•˜๋Š” ๋…ธ๋ฅด์›จ์ด ๋Œ€๊ทœ๋ชจ ์‚ฌ๋ฉด ๋ฏธ๋„๋Ÿผ์˜ ๋ฏธ์„ธ์—ญํ•™

์ˆ˜์••์ด ์•„๋‹Œ ๋ฌผ ํ๋ฆ„์ด ์ด‰๋ฐœํ•˜๋Š” ๋…ธ๋ฅด์›จ์ด ๋Œ€๊ทœ๋ชจ ์‚ฌ๋ฉด ๋ฏธ๋„๋Ÿผ์˜ ๋ฏธ์„ธ์—ญํ•™

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ œ๊ธฐ ๊ธฐํ›„ ๋ณ€ํ™”์™€ ์‚ฌ๋ฉด ์•ˆ์ •์„ฑ : ๊ฐ•์šฐยท๋ˆˆ๋…น์ด ์ฆ๊ฐ€ํ•˜๋ฉด์„œ ์ „ ์„ธ๊ณ„์ ์œผ๋กœ ์‚ฌ๋ฉด ๋ถ•๊ดด ์œ„ํ—˜์ด ํ™•๋Œ€๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ, ๋ฏธ์„ธ์ž…์žยท์ ํ† ๊ฐ€ ํ’๋ถ€ํ•œ ์ „๋‹จ๋Œ€๋Š” โ€˜์ฒœ์ฒœํžˆ(mmโ€‘cm/yr) ์›€์ง์ด๋Š” ํฌ๋ฆฌํ”„โ€™๋ฅผ ๋ณด์ด๋ฉฐ, ๊ฐ•์šฐ์— ์˜ํ•ด ๊ธ‰๊ฒฉํžˆ ๊ฐ€์†๋˜๋Š” โ€˜ํฌ๋ฆฌํ”„ ๋ฒ„์ŠคํŠธโ€™๋ฅผ ๋‚˜ํƒ€๋‚ธ๋‹ค. ์ „ํ†ต์  ์ด๋ก ์˜ ํ•œ๊ณ„ : Terzaghi์˜ ์œ ํšจ์‘๋ ฅ ์ด๋ก ์€ ๊ณต๊ทน์••๋ ฅ(p) ์ด ์ •์ƒ์‘๋ ฅ(ฯƒโ‚™)์„ ๊ฐ์†Œ์‹œ์ผœ ๋งˆ์ฐฐ ์ €ํ•ญ์„ ์•ฝํ™”์‹œํ‚จ๋‹ค๊ณ  ๊ฐ€์ •ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ร…knes ํ˜„์žฅ์—์„œ ์ธก์ •๋œ ์ „๋‹จ๋Œ€ ๋‚ด ์••๋ ฅ์€ ๊ฑฐ์˜ ๋ณ€ํ•˜์ง€ ์•Š์œผ๋ฉฐ, ๊ฐ•์šฐ์™€ ํฌ๋ฆฌํ”„ ๊ฐ€์† ์‚ฌ์ด์˜ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ์กด์žฌ

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์˜๋ฃŒ AI ์‹ ๋ขฐ์„ฑ ํ–ฅ์ƒ: MedBayes Lite

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๊ณผ์‹ (overโ€‘confidence) ์€ ์ž„์ƒ AI์˜ ๊ฐ€์žฅ ํฐ ์œ„ํ—˜ ์š”์ธ์ด๋ฉฐ, ํŠนํžˆ ํฌ๊ท€ ์งˆํ™˜ยท๋ชจ์ˆœ ์ฆ์ƒ ์ƒํ™ฉ์—์„œ ์น˜๋ช…์  ์˜ค๋ฅ˜๋ฅผ ์ดˆ๋ž˜ํ•œ๋‹ค. ๊ธฐ์กด UQ(๋ถˆํ™•์‹ค์„ฑ ์ •๋Ÿ‰ํ™”) ๋ฐฉ๋ฒ•์€ ํฌ๊ฒŒ (i) ์‚ฌํ›„ ๋ณด์ • , (ii) ๊ณ ๋น„์šฉ ์•™์ƒ๋ธ” , (iii) ๋ถ€๋ถ„์  ๋ฒ ์ด์ง€์•ˆ ์œผ๋กœ ๊ตฌ๋ถ„๋˜๋ฉฐ, ๊ฐ๊ฐ ์‹ ๋ขฐ์„ฑยทํšจ์œจ์„ฑ ์‚ฌ์ด์— ํŠธ๋ ˆ์ด๋“œ์˜คํ”„๊ฐ€ ์กด์žฌํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๊ฒฝ๋Ÿ‰์ด๋ฉด์„œ ์ „์ธต(์ž„๋ฒ ๋”ฉโ†’์–ดํ…์…˜โ†’๊ฒฐ์ •)์œผ๋กœ ๋ถˆํ™•์‹ค์„ฑ์„ ์ „ํŒŒ ํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ์ ˆ์‹คํžˆ ์š”๊ตฌ๋œ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ๋ฒ• | ๋ชจ๋“ˆ | ์ฃผ์š” ์•„์ด๋””์–ด | ๊ตฌํ˜„ ๋ฐฉ์‹ | ์ถ”๊ฐ€ ๋น„์šฉ | | | | | |

PathFMTools ๋ณ‘๋ฆฌํ•™ ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์˜ ํšจ์œจ์  ์ ์šฉ๊ณผ ํ‰๊ฐ€๋ฅผ ์œ„ํ•œ ํŒŒ์ด์ฌ ํˆดํ‚ท

PathFMTools ๋ณ‘๋ฆฌํ•™ ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์˜ ํšจ์œจ์  ์ ์šฉ๊ณผ ํ‰๊ฐ€๋ฅผ ์œ„ํ•œ ํŒŒ์ด์ฌ ํˆดํ‚ท

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํŒŒ์šด๋ฐ์ด์…˜ ๋ชจ๋ธ์˜ ์•ฝ์† : ์ตœ๊ทผ ๋Œ€๊ทœ๋ชจ ์…€ํ”„โ€‘์Šˆํผ๋ฐ”์ด์ฆˆ๋“œ ํ•™์Šต์„ ํ†ตํ•ด ๋ณ‘๋ฆฌํ•™ ์ด๋ฏธ์ง€์—์„œ ์ผ๋ฐ˜ํ™” ๊ฐ€๋Šฅํ•œ ํ˜•ํƒœํ•™์  ํŒจํ„ด์„ ํ•™์Šตํ•œ๋‹ค๋Š” ์ ์—์„œ, ๋ฐ์ดํ„ฐ๊ฐ€ ๋ถ€์กฑํ•œ ์ž„์ƒ ๊ณผ์ œ์— ํŠนํžˆ ์œ ๋ฆฌํ•˜๋‹ค( Li et al., 2025; Bilal et al., 2025). ์‹ค์ œ ์ ์šฉ ์žฅ๋ฒฝ : 1. WSI ๊ทœ๋ชจ (์ˆ˜์ฒœ~์ˆ˜๋งŒ ร— ์ˆ˜์ฒœ ํ”ฝ์…€)์™€ ๋ณต์žกํ•œ ์ „์ฒ˜๋ฆฌยทํŒจ์น˜ ์ถ”์ถœ ํŒŒ์ดํ”„๋ผ์ธ. 2. ํŠน์ง• ๊ณต๊ฐ„์˜ ๋ถˆํˆฌ๋ช…์„ฑ โ†’ ์ƒ๋ฌผํ•™์  ํ•ด์„ยท๊ฒ€์ฆ ๋„๊ตฌ ๋ถ€์กฑ. 3. ๋‹ค์–‘ํ•œ ์ ์‘ ์ „๋žต (์ œ๋กœโ€‘์ƒท, ํŒŒ์ธโ€‘ํŠœ๋‹, MIL ๋“ฑ) ๊ฐ„ ์„ฑ๋Šฅยท์ž์› ํŠธ๋ ˆ์ด๋“œโ€‘์˜คํ”„๊ฐ€

์•„๋‚ ๋กœ๊ทธ CAM ๊ธฐ๋ฐ˜ ํŠธ๋žœ์Šคํฌ๋จธ ๊ฐ€์†๊ธฐ CAMformer

์•„๋‚ ๋กœ๊ทธ CAM ๊ธฐ๋ฐ˜ ํŠธ๋žœ์Šคํฌ๋จธ ๊ฐ€์†๊ธฐ CAMformer

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ Transformer์˜ ๋ณ‘๋ชฉ : Selfโ€‘attention์€ (QK^{top}) ๋ฐ (AV) ์—ฐ์‚ฐ์—์„œ (O(N^2)) ๋ณต์žก๋„๋ฅผ ๊ฐ–๊ณ , ๋ฉ”๋ชจ๋ฆฌ ๋Œ€์—ญํญ๊ณผ ์ „๋ ฅ ์†Œ๋ชจ๋ฅผ ํฌ๊ฒŒ ์ฆ๊ฐ€์‹œํ‚จ๋‹ค. ๊ธฐ์กด ๊ฐ€์†๊ธฐ๋“ค์€ ๋งคํŠธ๋ฆญ์Šค ๊ณฑ ์ตœ์ ํ™”(์ €์ •๋ฐ€ ์—ฐ์‚ฐ, ํฌ์†Œ์„ฑ ํ™œ์šฉ, ๋ฉ”๋ชจ๋ฆฌ ํƒ€์ผ๋ง ๋“ฑ)๋กœ ์ด๋ฅผ ์™„ํ™”ํ•˜๋ ค ํ–ˆ์ง€๋งŒ, ๊ทผ๋ณธ์ ์ธ โ€œ๋ฐ€์ง‘ ์—ฐ์‚ฐโ€ ๊ตฌ์กฐ๋Š” ์—ฌ์ „ํžˆ ๋‚จ์•„ ์žˆ๋‹ค. ์—ฐ๊ด€ ๋ฉ”๋ชจ๋ฆฌ(CAM)์™€์˜ ์—ฐ๊ณ„ : CAM์€ ์ž…๋ ฅ ๋ฐ์ดํ„ฐ๋ฅผ ์ „์ฒด ๋ฉ”๋ชจ๋ฆฌ ์…€์— ๋™์‹œ์— ๋น„๊ตํ•ด ์ผ์น˜ ์ฃผ์†Œ๋ฅผ ๋ฐ˜ํ™˜ํ•˜๋Š” โ€œ๋‚ด์šฉ ๊ธฐ๋ฐ˜ ๊ฒ€์ƒ‰โ€ ํŠน์„ฑ์„ ๊ฐ–๋Š”๋‹ค. ์ด๋Š” โ€œ์ฟผ๋ฆฌโ€‘

์ด๋ฏธ์ง€์™€ ์‚ฌ๊ณ ๋ฅผ ์—ฐ๊ฒฐํ•˜๋Š” VISTA Gym ๋„๊ตฌ ํ†ตํ•ฉ ์‹œ๊ฐ ์ถ”๋ก ์„ ์œ„ํ•œ ๋Œ€๊ทœ๋ชจ ํ•™์Šต ํ™˜๊ฒฝ

์ด๋ฏธ์ง€์™€ ์‚ฌ๊ณ ๋ฅผ ์—ฐ๊ฒฐํ•˜๋Š” VISTA Gym ๋„๊ตฌ ํ†ตํ•ฉ ์‹œ๊ฐ ์ถ”๋ก ์„ ์œ„ํ•œ ๋Œ€๊ทœ๋ชจ ํ•™์Šต ํ™˜๊ฒฝ

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๊ธฐ์กด VLM์˜ ํ•œ๊ณ„ : ๋Œ€๋ถ€๋ถ„์˜ ์ตœ์‹  VLM์€ ์ •์ ์ธ ์ด๋ฏธ์ง€ ์ž„๋ฒ ๋”ฉ๊ณผ ์–•์€ ๊ต์ฐจโ€‘๋ชจ๋‹ฌ ์ •๋ ฌ์— ์˜์กดํ•ด, ์„ธ๋ฐ€ํ•œ ๊ณต๊ฐ„ ๊ด€๊ณ„ยท์ˆ˜๋Ÿ‰์  ์˜์กด์„ฑ์„ ํฌ์ฐฉํ•˜์ง€ ๋ชปํ•จ. Toolโ€‘Integrated Reasoning (TIR) : ์™ธ๋ถ€ ๋„๊ตฌ(grounding, zoomโ€‘in, ์ฐจํŠธ ํ•ด์„ ๋“ฑ)๋ฅผ ํ™œ์šฉํ•ด ์‹œ๊ฐ ์ •๋ณด๋ฅผ ์„ธ๋ถ„ํ™”ํ•˜๊ณ , ๋‹จ๊ณ„๋ณ„ ์ถ”๋ก ์„ ๊ฐ•ํ™”ํ•˜๋ ค๋Š” ์‹œ๋„๋Š” ์กด์žฌํ•˜์ง€๋งŒ, ๋„๊ตฌ ์„ ํƒยทํ˜ธ์ถœยท์กฐ์ • ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด ๋ฏธ๋น„ํ•˜๊ณ , ๋Œ€๋ถ€๋ถ„ ํŠน์ • ํƒœ์Šคํฌ์— ๊ตญํ•œ๋จ. ํ›ˆ๋ จ ํ™˜๊ฒฝ ๋ถ€์žฌ : ํ…์ŠคํŠธโ€‘์ „์šฉ RL ํ™˜๊ฒฝ์€ ๋‹ค์ˆ˜ ์กด์žฌํ•˜์ง€๋งŒ, ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌยท๋„๊ตฌ ์—ฐ๋™์„

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