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Generative Multi-Form Bayesian Optimization

๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ณต์žกํ•œ ๊ตฌ์กฐ์˜ ์ž…๋ ฅ ๊ณต๊ฐ„์„ ์ตœ์ ํ™”ํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์ƒ์„ฑ ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ์ ‘๊ทผ๋ฒ•์„ ํƒ๊ตฌํ•œ๋‹ค. GMO๋Š” ์ž ์žฌ ๊ณต๊ฐ„์œผ๋กœ์˜ ๋งคํ•‘์„ ํ†ตํ•ด ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ์ œ์‹œํ•˜์ง€๋งŒ, ์ž ์žฌ ์ฐจ์›์˜ ๊ฒฐ์ •์€ ์–ด๋ ค์šด ๊ณผ์ œ์ด๋‹ค. ์šฐ๋ฆฌ๋Š” ๋‹คํ˜• GMO์ธ GMFoO๋ฅผ ์ œ์•ˆํ•˜์—ฌ ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ณ ์ž ํ•œ๋‹ค. GMFoO๋Š” ์—ฌ๋Ÿฌ ๊ฐœ์˜ ์ž ์žฌ ๊ณต๊ฐ„์—์„œ ๋™์‹œ์— ์ตœ์ ํ™”๋ฅผ ์ˆ˜ํ–‰ํ•จ์œผ๋กœ์จ ์„œ๋กœ ๋ณด์™„์ ์œผ๋กœ ์ž‘๋™ํ•œ๋‹ค. ๋˜ํ•œ ์ƒ์„ฑ ๋ชจ๋ธ์„ ํ†ตํ•ด ์ž ์žฌ ๊ณต๊ฐ„ ๊ฐ„์˜ ์–‘์˜ ์ƒ๊ด€ ๊ด€๊ณ„๋ฅผ ์œ ๋„ํ•˜๊ณ , BO๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ •๋ณด๋ฅผ ๊ตํ™˜ํ•˜๋Š” ์ „๋žต์„ ์ œ์‹œํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ๋ฒ•์€ ์ œ

Generic Modal Cut Elimination Applied to Conditional Logics

Generic Modal Cut Elimination Applied to Conditional Logics

์ด ์—ฐ๊ตฌ๋Š” ์ฆ๋ช… ์ด๋ก ์—์„œ ๊ฐ€์žฅ ํ•ต์‹ฌ์ ์ธ ๊ฐœ๋… ์ค‘ ํ•˜๋‚˜์ธ ์ ˆ๋‹จ ์ œ๊ฑฐ(cut elimination)๋ฅผ ๋ชจ๋‹ฌ ๋ฐ ์กฐ๊ฑด๋ถ€ ๋…ผ๋ฆฌ ์ „๋ฐ˜์— ๊ฑธ์ณ ์ผ๋ฐ˜ํ™”ํ•˜๋ ค๋Š” ์‹œ๋„์ด๋‹ค. ๊ธฐ์กด ๋ฌธํ—Œ์—์„œ๋Š” ์ฃผ๋กœ ์ •์ƒ(normal) ๋ชจ๋‹ฌ ๋…ผ๋ฆฌ, ํŠนํžˆ K, K4, T์™€ ๊ฐ™์ด ๋ชจ๋‹ฌ ์—ฐ์‚ฐ์ž์˜ ์ค‘์ฒฉ ๊นŠ์ด๊ฐ€ 1์ธ โ€˜rankโ€‘1โ€™ ์ฒด๊ณ„์— ํ•œ์ •๋œ ์ ˆ๋‹จ ์ œ๊ฑฐ ๊ฒฐ๊ณผ๊ฐ€ ์ œ์‹œ๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‹ค์ œ ์‘์šฉ ๋ถ„์•ผโ€”๋™์‹œ์„ฑ ์ด๋ก , ์ง€์‹ ํ‘œํ˜„, ์ธ๊ณต์ง€๋Šฅ์˜ ๋น„ํ‘œ์ค€ ๋…ผ๋ฆฌ ๋“ฑโ€”์—์„œ๋Š” ๋น„์ •๊ทœ(nonโ€‘normal) ๋ชจ๋‹ฌ ๊ทœ์น™์ด๋‚˜ ๋ณตํ•ฉ์ ์ธ ์กฐ๊ฑด๋ถ€ ์—ฐ์‚ฐ์ž๋ฅผ ํฌํ•จํ•˜๋Š” ์‹œ์Šคํ…œ์ด ๋นˆ๋ฒˆํžˆ ๋“ฑ์žฅํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์‹œ์Šคํ…œ์— ๋Œ€

Computer Science Logic
Geographic constraints on social network groups

Geographic constraints on social network groups

๋ณธ ๋…ผ๋ฌธ์€ ๋ชจ๋ฐ”์ผ ํ†ต์‹  ๊ธฐ๋ก์ด๋ผ๋Š” ์‹ค์‹œ๊ฐ„ยท๋Œ€๊ทœ๋ชจ ์œ„์น˜ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•ด ์‚ฌํšŒ ๋„คํŠธ์›Œํฌ ๋‚ด ์ปค๋ฎค๋‹ˆํ‹ฐ์˜ ์ง€๋ฆฌ์  ํŠน์„ฑ์„ ์ตœ์ดˆ๋กœ ์ •๋Ÿ‰์ ์œผ๋กœ ํƒ๊ตฌํ•œ ์ ์—์„œ ํ•™๋ฌธ์  ์˜์˜๊ฐ€ ํฌ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ฃผ๋กœ ์„ค๋ฌธยท๋ธ”๋กœ๊ทธ ํ”„๋กœํ•„ ๋“ฑ ์ž๋ฐœ์  ๋ณด๊ณ ์— ์˜์กดํ–ˆ์œผ๋ฉฐ, ๊ฑฐ๋ฆฌ์™€ ์นœ๋ฐ€๋„ ์‚ฌ์ด์˜ ๊ฐ์†Œ ๊ด€๊ณ„๋ฅผ ์ œ์‹œํ–ˆ์ง€๋งŒ, ์‹ค์ œ ํ†ต์‹  ์œ„์น˜์™€ ๋„คํŠธ์›Œํฌ ์œ„์ƒ์ด ๋™์‹œ์— ๊ณ ๋ ค๋œ ๋ถ„์„์€ ๋“œ๋ฌผ์—ˆ๋‹ค. ์ €์ž๋“ค์€ โ€˜๊ฐ€์žฅ ๋งŽ์ด ์ด์šฉํ•œ ๊ธฐ์ง€๊ตญโ€™์„ ๊ฐœ์ธ์˜ ์ฃผ์š” ๊ฑฐ์ฃผยทํ™œ๋™์ง€์ ์œผ๋กœ ๊ฐ€์ •ํ•จ์œผ๋กœ์จ, ๊ฐœ์ธ๋ณ„ ์ง€๋ฆฌ์  ์ค‘์‹ฌ์„ ํ•ฉ๋ฆฌ์ ์œผ๋กœ ์ถ”์ •ํ•˜์˜€๋‹ค. ์ด๋Š” ํ†ต์‹  ๊ธฐ๋ก์ด ์ผ์ƒ ์ƒํ™œ ์ „๋ฐ˜์— ๊ฑธ์ณ ์ง€์†์ ์œผ๋กœ ๋ฐœ์ƒํ•œ๋‹ค

Physics Network Condensed Matter Social Networks Computer Science
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GHOST: Solving the Traveling Salesman Problem on Graphs of Convex Sets

์—ฌํ–‰ํ•˜๋Š” ์˜์—…์‚ฌ์› ๋ฌธ์ œ(TSP)๋Š” ์ตœ์ ํ™” ๋ถ„์•ผ์—์„œ ์ž˜ ์•Œ๋ ค์ง„ ๊ณ ์ „์ ์ธ ๋ฌธ์ œ๋กœ, ๋‹ค์–‘ํ•œ ์‘์šฉ ๋ถ„์•ผ์— ์ ์šฉ๋  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” TSP์˜ ์ƒˆ๋กœ์šด ๋ณ€ํ˜•์„ ์†Œ๊ฐœํ•˜๊ณ , ์ด๋ฅผ ๋ณผ๋ก ์ง‘ํ•ฉ ๊ทธ๋ž˜ํ”„(GCS) ์ƒ์—์„œ ์ •์˜ํ•ฉ๋‹ˆ๋‹ค. GCS๋Š” ๊ถค์  ๊ณ„ํš์— ๊ฐ•๋ ฅํ•œ ํ‘œํ˜„์„ ์ œ๊ณตํ•˜๋ฉฐ, ๊ตฌ์„ฑ ๊ณต๊ฐ„์„ ํšจ์œจ์ ์œผ๋กœ ๋ถ„ํ•ดํ•˜์—ฌ ์ตœ์ ํ™” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ๋ฐ ๋„์›€์„ ์ค๋‹ˆ๋‹ค. ์ „ํ†ต์ ์ธ TSP ๋ฐฉ๋ฒ•์€ ๊ณ ์ •๋œ ๋ณ€ ๋น„์šฉ์„ ๊ฐ€์ •ํ•˜์ง€๋งŒ, GCS TSP์—์„œ๋Š” ๊ฐ ๊ฒฝ๋กœ์— ๋”ฐ๋ผ ๋ณ€์˜ ๋น„์šฉ์ด ๋‹ฌ๋ผ์ง‘๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ €์ž๋“ค์€ GHOST๋ผ๋Š” ์ƒˆ๋กœ์šด ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ์ด๋Š” ์กฐํ•ฉ๋ก ์  ํˆฌ์–ด

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Global Gevrey Hypoellipticity of Involutive Systems on Non-Compact Manifolds

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ „์—ญ ํ•˜์ดํฌ์—˜๋ฆฝํ‹ฐ์‹œํ‹ฐ ๋Š” ๋น„์••์ถ• ๋‹ค์–‘์ฒด์—์„œ ๋ฏธ๋ถ„ ์—ฐ์‚ฐ์ž์˜ ์ •๊ทœ์„ฑ ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃจ๋Š” ํ•ต์‹ฌ ์ฃผ์ œ์ด๋ฉฐ, ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์ฝคํŒฉํŠธ ๊ฒฝ์šฐ์— ๊ตญํ•œ๋˜์—ˆ๋‹ค. ๊ฒŒ๋น„์— ํด๋ž˜์Šค ๋Š” ์‹คํ•ด์„๊ณผ ์ดˆ์‹คํ•ด์„ ์‚ฌ์ด์˜ ์ค‘๊ฐ„ ๋‹จ๊ณ„๋กœ, (s 1)์ด๋ฉด ์‹คํ•ด์„, (s>1)์ด๋ฉด ์ดˆ์‹คํ•ด์„์  ์„ฑ์งˆ์„ ์ œ๊ณตํ•œ๋‹ค. ๋น„์••์ถ• ์ƒํ™ฉ์—์„œ ๊ฒŒ๋น„์— ๊ณ„์ˆ˜๋ฅผ ๊ฐ€์ง„ ์—ฐ์‚ฐ์ž๋ฅผ ๋‹ค๋ฃจ๋Š” ๊ฒƒ์€ ๊ธฐ์ˆ ์ ์œผ๋กœ ๋งค์šฐ ๊นŒ๋‹ค๋กญ๋‹ค. ์ €์ž๋“ค์€ tubeโ€‘type involutive structures ๋ผ๋Š” ํŠน์ˆ˜ํ•œ ๊ธฐํ•˜ํ•™์  ๊ตฌ์กฐ๋ฅผ ํ†ตํ•ด ์ด ๋ฌธ์ œ๋ฅผ ์ผ๋ฐ˜ํ™”ํ•˜๊ณ , ๊ธฐ์กด์˜ โ€œ๋ถ„์„์  ๋ฉ”ํŠธ๋ฆญโ€ ๋Œ€์‹  ์Šค์บ

System Mathematics
GMRT observations of the Ophiuchus galaxy cluster

GMRT observations of the Ophiuchus galaxy cluster

: ์˜คํ”ผ์šฐ์ฟ ์Šค ์€ํ•˜๋‹จ์€ ์šฐ์ฃผ์—์„œ ์ค‘๋ ฅ์œผ๋กœ ๋ฌถ์ธ ๊ฐ€์žฅ ํฐ ๊ตฌ์กฐ ์ค‘ ํ•˜๋‚˜๋กœ, ๋น„์—ด์  ์„ฑ๋ถ„๊ณผ ๊ทธ ์ƒํ˜ธ์ž‘์šฉ์„ ์—ฐ๊ตฌํ•˜๊ธฐ ์œ„ํ•œ ์ค‘์š”ํ•œ ๋Œ€์ƒ์ž…๋‹ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์€ ์€ํ•˜๋‹จ ๋‚ด ์ƒ๋Œ€๋ก ์  ์ „์ž์˜ ๋ผ๋””์˜ค ๋ฐ ํ•˜๋“œ X์„  ๋ฐฉ์ถœ์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ €์ž๋“ค์€ VLA์™€ GMRT๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์ฃผํŒŒ์ˆ˜์—์„œ ๊ด€์ธก์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ๋ฏธ๋‹ˆ ํ• ๋กœ์˜ ์กด์žฌ๋ฅผ ํ™•์ธํ–ˆ์Šต๋‹ˆ๋‹ค. ๋ฏธ๋‹ˆ ํ• ๋กœ๋Š” ์€ํ•˜๋‹จ์˜ ์ค‘์‹ฌ ์˜์—ญ, ํŠนํžˆ ์™„๋งŒํ•œ ์ŠคํŽ™ํŠธ๋Ÿผ์„ ๊ฐ€์ง„ ํ™•์‚ฐ๋œ ์†Œ์Šค๋กœ ๋‚˜ํƒ€๋‚ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฏธ๋‹ˆ ํ• ๋กœ๋Š” ํ•ฉ๋ณ‘ ์€ํ•˜๋‹จ์ด๋‚˜ ์ด์™„๋˜๊ณ  ๋ƒ‰๊ฐ ์ฝ”์–ด๋ฅผ ๊ฐ€์ง„ ์€ํ•˜๋‹จ์—์„œ ํ”ํžˆ ๋ฐœ๊ฒฌ๋˜๋ฉฐ, ์ƒ๋Œ€์ ์œผ๋กœ ์ž‘์€ ๊ฐ ํฌ๊ธฐ๋‚˜

Astrophysics
Graph-theoretical Constructions for Graph Entropy and Network Coding   Based Communications

Graph-theoretical Constructions for Graph Entropy and Network Coding Based Communications

๋ณธ ๋…ผ๋ฌธ์€ ๋„คํŠธ์›Œํฌ ์ฝ”๋”ฉ ์ด๋ก ๊ณผ ๊ทธ๋ž˜ํ”„ ์—”ํŠธ๋กœํ”ผ(์ถ”์ธก ์ˆ˜) ์‚ฌ์ด์˜ ๊นŠ์€ ์—ฐ๊ฒฐ ๊ณ ๋ฆฌ๋ฅผ ์ƒˆ๋กญ๊ฒŒ ์ •๋ฆฝํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ๋Š” ๋„คํŠธ์›Œํฌ ์ฝ”๋”ฉ ๊ฐ€๋Šฅ์„ฑ์„ ํŒ๋‹จํ•˜๊ธฐ ์œ„ํ•ด ์ „์ฒด ๋„คํŠธ์›Œํฌ์˜ ๋ผ์šฐํŒ…ยท์ฝ”๋”ฉ ์Šคํ‚ด์„ ์ „๋ฉด ํƒ์ƒ‰ํ•ด์•ผ ํ–ˆ์œผ๋ฉฐ, ์ด๋Š” ์กฐํ•ฉ ํญ๋ฐœ๋กœ ์ธํ•ด ์‹ค์šฉ์„ฑ์ด ๋–จ์–ด์กŒ๋‹ค. ์ €์ž๋“ค์€ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ์ถ”์ธก ๊ทธ๋ž˜ํ”„ ๋ผ๋Š” ์ƒˆ๋กœ์šด ๊ทธ๋ž˜ํ”„ ๊ตฌ์กฐ๋กœ ๋ณ€ํ™˜ํ•จ์œผ๋กœ์จ, ๋ณต์žกํ•œ ์—ฐ์‚ฐ ํƒ์ƒ‰์„ ์ตœ๋Œ€ ๋…๋ฆฝ ์ง‘ํ•ฉ ์ฐพ๊ธฐ ๋ผ๋Š” ์ „ํ˜•์ ์ธ ๊ทธ๋ž˜ํ”„ ์ด๋ก  ๋ฌธ์ œ๋กœ ํ™˜์›ํ•œ๋‹ค. 1. ์ถ”์ธก ๊ทธ๋ž˜ํ”„์™€ ์ถ”์ธก ์ˆ˜์˜ ๋™๋“ฑ์„ฑ ๋””๊ทธ๋ž˜ํ”„ D์˜ ๋ชจ๋“  ๊ฐ€๋Šฅํ•œ ์•ŒํŒŒ๋ฒณ ์œ„ ๊ตฌ์„ฑ xโˆˆA^{|V(D)|}์„ ์ •์ ์œผ๋กœ ํ•˜

Network Mathematics Networking Computer Science Information Theory
Gravitational Wave Emission from the Single-Degenerate Channel of Type   Ia Supernovae

Gravitational Wave Emission from the Single-Degenerate Channel of Type Ia Supernovae

๋ณธ ๋…ผ๋ฌธ์€ Iaํ˜• ์ดˆ์‹ ์„ฑ(SN Ia)์˜ ๋‹จ์ผ์„ฑ๋ถ„(Singleโ€‘Degenerate, SD) ์ฑ„๋„์„ ๋Œ€์ƒ์œผ๋กœ ์ค‘๋ ฅํŒŒ(GW) ๋ฐฉ์ถœ ํŠน์„ฑ์„ ์ตœ์ดˆ๋กœ ์ •๋Ÿ‰์ ์œผ๋กœ ํ‰๊ฐ€ํ•œ ์—ฐ๊ตฌ์ด๋‹ค. ๊ธฐ์กด์— SN II๊ฐ€ 10ยฒโ€“10ยณ Hz ๋Œ€์—ญ์—์„œ GW ํ›„๋ณด์›์œผ๋กœ ๋…ผ์˜๋œ ๋ฐ˜๋ฉด, SN Ia๋Š” ํญ๋ฐœ ๊ทœ๋ชจ๊ฐ€ ๋น„์Šทํ•˜๋ฉด์„œ๋„ ๋น„๋Œ€์นญ์„ฑ์ด ๊ฐ•ํ•˜๋‹ค๋Š” ์ ์—์„œ ์ €์ฃผํŒŒ GW ๋ฐฉ์ถœ ๊ฐ€๋Šฅ์„ฑ์ด ์ œ๊ธฐ๋˜์—ˆ๋‹ค. ์ €์ž๋“ค์€ ๋‘ ๊ฐ€์ง€ ์ฃผ์š” ํญ๋ฐœ ๋ฉ”์ปค๋‹ˆ์ฆ˜์ธ ๋””ํ”Œ๋ž˜๊ทธ๋ ˆ์ด์…˜โ€‘ํญ๋ฐœ ์ „์ด(DDT)์™€ ์ค‘๋ ฅ๊ตฌ์† ํญ๋ฐœ(GCD)์„ ๋น„๊ตํ•˜๊ณ , ํŠนํžˆ GCD๊ฐ€ ๋น„๋Œ€์นญ์„ฑ์„ ๊ทน๋Œ€ํ™”ํ•œ๋‹ค๋Š” ์ ์— ์ฃผ๋ชฉํ•œ๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜์€ ๋‰ดํ„ด ์‚ฌ์ค‘

Astrophysics General Relativity
Half-Duplex Active Eavesdropping in Fast Fading Channels: A Block-Markov   Wyner Secrecy Encoding Scheme

Half-Duplex Active Eavesdropping in Fast Fading Channels: A Block-Markov Wyner Secrecy Encoding Scheme

: ๋ณธ ๋…ผ๋ฌธ์€ ๋ฌผ๋ฆฌ ๊ณ„์ธต ๋ณด์•ˆ์—์„œ ๋Šฅ๋™ ๋„์ฒญ์ž์˜ ์กด์žฌ๋ฅผ ๊ณ ๋ คํ•œ ํ˜์‹ ์ ์ธ ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ฃผ๋กœ ๊ณ ์ „์ ์ธ ์™€์ด์–ดํƒญํผ ์‹œ๋‚˜๋ฆฌ์˜ค์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์ง€๋งŒ, ์ €์ž๋“ค์€ ๋” ๊ฐ•๋ ฅํ•œ ์ ๋Œ€์ž์ธ ๋Šฅ๋™ ๋„์ฒญ์ž๋ฅผ ๋‹ค๋ฃจ๋ฉฐ ํ˜„์‹ค์ ์ธ ๋ณด์•ˆ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ณ ์ž ํ•ฉ๋‹ˆ๋‹ค. ๋น ๋ฅธ ํŽ˜์ด๋”ฉ ์ฑ„๋„ ํ™˜๊ฒฝ์—์„œ ๋Šฅ๋™ ๋„์ฒญ์ž๋Š” ํ•ฉ๋ฒ•์ ์ธ ์†ก์‹ ๊ธฐ์™€ ์ˆ˜์‹ ๊ธฐ ๊ฐ„์˜ ํ†ต์‹ ์„ ๋ฐฉํ•ดํ•˜๊ฑฐ๋‚˜ ๋„์ฒญํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Š” ์ „ํ†ต์ ์ธ ๋ณด์•ˆ ๋ฐฉ์‹์˜ ํ•œ๊ณ„๋ฅผ ๋“œ๋Ÿฌ๋‚ด๋Š”๋ฐ, ํŠนํžˆ ๋„์ฒญ์ž์˜ ์ฑ„๋„์ด ๋” ๊นจ๋—ํ•œ ๊ฒฝ์šฐ ๋น„๋ฐ€ ๋ฉ”์‹œ์ง€ ์ „์†ก์— ์–ด๋ ค์›€์„ ๊ฒช์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ €์ž๋“ค์€ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด

Computer Science Cryptography and Security Information Theory Mathematics
Hard X-ray and Gamma-Ray Detectors

Hard X-ray and Gamma-Ray Detectors

๋ณธ ๋ฆฌ๋ทฐ๋Š” 10 keV ~ > 1 GeV ๋ฒ”์œ„์˜ ์šฐ์ฃผ ๊ณ ์—๋„ˆ์ง€ ๊ด‘์ž ๊ฒ€์ถœ๊ธฐ์— ์‚ฌ์šฉ๋˜๋Š” ์žฌ๋ฃŒ์™€ ๊ตฌ์กฐ๋ฅผ ์ฒด๊ณ„์ ์œผ๋กœ ์ •๋ฆฌํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ํ•ต์‹ฌ์€ ๊ฒ€์ถœ๊ธฐ ๊ธฐ์ˆ  ์„ ํƒ์ด โ€œ์—๋„ˆ์ง€ ๊ตฌ๊ฐ„โ€์— ๋”ฐ๋ผ ํฌ๊ฒŒ ๋‹ฌ๋ผ์ง„๋‹ค๋Š” ์ ์ด๋‹ค. ์ €์—๋„ˆ์ง€(10 ~ 300 keV)์—์„œ๋Š” ๊ด‘์ „ ํก์ˆ˜๊ฐ€ ๋‹จ๋ฉด์ด ํฌ๊ฒŒ ๋‚˜ํƒ€๋‚˜๊ณ  ์›์ž ๋ฒˆํ˜ธ(Z)์— ๊ฐ•ํ•˜๊ฒŒ ์˜์กดํ•œ๋‹ค. ์‹ค๋ฆฌ์ฝ˜(Si) 1 mm ๋‘๊ป˜๋Š” 23 keV ์ดํ•˜ Xโ€‘ray์˜ ์ ˆ๋ฐ˜ ์ด์ƒ์„ ํก์ˆ˜ํ•˜์ง€๋งŒ, CdTe์™€ ๊ฐ™์€ ๊ณ Z ๋ฌผ์งˆ์€ 110 keV๊นŒ์ง€ ๋™์ผ ํก์ˆ˜์œจ์„ ์œ ์ง€ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์ €์—๋„ˆ์ง€ ์˜์—ญ์—์„œ๋Š” ์–‡๊ณ  ๊ฐ€๋ฒผ์šด ๊ฒ€์ถœ๊ธฐ๊ฐ€ ๊ฐ€๋Šฅํ•˜๋ฉฐ, ๊ณ ํ•ด์ƒ

Astrophysics
No Image

Hardware Implementation of Ring Oscillator Networks Coupled by BEOL Integrated ReRAM for Associative Memory Tasks

๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ReRAM ๊ธฐ์ˆ ์„ ์ด์šฉํ•˜์—ฌ ์ง„๋™ ์‹ ๊ฒฝ๋ง์„ ํ•˜๋“œ์›จ์–ด์— ๊ตฌํ˜„ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ReRAM์€ ๋ฉ”๋ชจ๋ฆฌ ์…€ ๊ฐ„์˜ ๊ฒฐํ•ฉ ๊ฐ•๋„๋ฅผ ์กฐ์ ˆํ•˜์—ฌ, ๋‰ด๋Ÿฐ๋“ค ์‚ฌ์ด์˜ ์ƒํ˜ธ์ž‘์šฉ์„ ์ œ์–ดํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์ง„๋™๊ธฐ ๋„คํŠธ์›Œํฌ์˜ ๋™์  ํŠน์„ฑ์„ ์กฐ์ ˆํ•˜๊ณ , ์•„๋‚ ๋กœ๊ทธ ์ •๋ณด๋ฅผ ์ฒ˜๋ฆฌํ•  ์ˆ˜ ์žˆ๋Š” ๋Šฅ๋ ฅ์„ ์ œ๊ณตํ•œ๋‹ค. ๋˜ํ•œ BEOL ํ†ตํ•ฉ์„ ํ†ตํ•ด ๋†’์€ ์ง‘์ ๋„์™€ ์ €์ „๋ ฅ ์†Œ๋ชจ๋ฅผ ๋‹ฌ์„ฑํ•˜์˜€๋‹ค. ์‹คํ—˜์—์„œ๋Š” 2x2 ๋ง ์ง„๋™๊ธฐ ๋„คํŠธ์›Œํฌ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ํŒจํ„ด ๊ฒ€์ƒ‰ ๊ณผ์ œ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๊ณ , ์„ฑ๊ณต์ ์ธ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์˜€๋‹ค. ์ด๋Š” ReRAM ๊ธฐ๋ฐ˜์˜ ONN์ด ๋ฉ”๋ชจ๋ฆฌ ๋‚ด ์—ฐ์‚ฐ(in memory compu

Network
Helium Clusters Capture of Heliophobes, Strong Depletion and Spin   dependent Pick-up Statistics

Helium Clusters Capture of Heliophobes, Strong Depletion and Spin dependent Pick-up Statistics

: ํ—ฌ๋ฅจ ํด๋Ÿฌ์Šคํ„ฐ ๋น”์€ 1980๋…„๋Œ€๋ถ€ํ„ฐ ์—ฐ๊ตฌ๋˜์–ด ์™”์ง€๋งŒ, ์—ฌ์ „ํžˆ ๋งŽ์€ ๋ฏธ๊ฐœ์ฒ™ ๋ถ„์•ผ๊ฐ€ ์กด์žฌํ•ฉ๋‹ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋ถ„์•ผ์— ๋Œ€ํ•œ ํ†ต์ฐฐ๋ ฅ์„ ์ œ๊ณตํ•˜๋ฉฐ, ํŠนํžˆ ์‹คํŒจ ์‚ฌ๋ก€๋ฅผ ํ†ตํ•ด ์ƒˆ๋กœ์šด ํƒ๊ตฌ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ๋จผ์ €, ์ž๊ธฐ ํŒŒ๊ดด ๋น”๊ณผ ์‚ฐ๋ž€ ์…€์˜ ๊ฐœ๋…์„ ์†Œ๊ฐœํ•˜๋Š”๋ฐ, ์ด๋Š” ๊ธฐ์กด์— ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๋˜ ๋ฐฉ๋ฒ•์ด์ง€๋งŒ ์ž˜๋ชป๋œ ์œ ๋„๋‚˜ ์˜ค๋ฅ˜๋กœ ์ธํ•ด ์ œ๋Œ€๋กœ ํ™œ์šฉ๋˜์ง€ ๋ชปํ–ˆ๋˜ ๋ถ€๋ถ„์ž…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์•„์ด๋””์–ด๋ฅผ ํ†ตํ•ด ํ—ฌ๋ฅจ ํด๋Ÿฌ์Šคํ„ฐ ๋น”์˜ ์•ˆ์ •์„ฑ๊ณผ ํšจ์œจ์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๋˜ํ•œ, ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด์˜จํ™” ์ˆ˜์œจ ๊ณก์„ ์˜ ์ค‘์š”์„ฑ์„ ๊ฐ•์กฐํ•ฉ๋‹ˆ๋‹ค. ์ด์˜จํ™” ์ˆ˜์œจ ๊ณก์„ ์€ ํ‘œ๋ฉด ์œ„

Physics
High-energy astroparticle physics

High-energy astroparticle physics

๋ณธ ๊ฐ•์˜๋Š” ๊ณ ์—๋„ˆ์ง€ ์ฒœ์ฒด๋ฌผ๋ฆฌํ•™์„ ํฌ๊ฒŒ ์„ธ ์ถ•์œผ๋กœ ๋‚˜๋ˆ„์–ด ์ฒด๊ณ„์ ์œผ๋กœ ์ ‘๊ทผํ•œ๋‹ค๋Š” ์ ์—์„œ ๊ต์œกยท์—ฐ๊ตฌ ์–‘์ชฝ ๋ชจ๋‘์— ํฐ ๊ฐ€์น˜๋ฅผ ์ง€๋‹Œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ํŒŒํŠธ์ธ ์ดˆ๊ณ ์—๋„ˆ์ง€ ์šฐ์ฃผ์„ (UHECR)์—์„œ๋Š” ์ŠคํŽ™ํŠธ๋Ÿผ์˜ โ€˜๋ฌด๋ฆŽ(knee)โ€™๊ณผ โ€˜๋ฐœ๋ชฉ(ankle)โ€™์ด๋ผ๋Š” ์ „ํ˜•์ ์ธ ์ „์ด์ ๋“ค์„ ์ƒ์„ธํžˆ ์„ค๋ช…ํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ์€ํ•˜๊ณ„์™€ ์€ํ•˜์™ธ ๊ฐ€์†์› ์‚ฌ์ด์˜ ์—๋„ˆ์ง€ ๊ตฌ๋ถ„์„ ๋ช…ํ™•ํžˆ ํ•œ๋‹ค. ํŠนํžˆ ํž๋ผ์Šค ์กฐ๊ฑด์„ ๋„์ž…ํ•ด ๊ฐ€๋Šฅํ•œ ๊ฐ€์†๊ธฐ(ํ™œ๋™ ์€ํ•˜ํ•ต, ์ดˆ์‹ ์„ฑ ์ž”ํ•ด, ์ถฉ๊ฒฉํŒŒ ๋“ฑ)์˜ ๊ทœ๋ชจ์™€ ์ž๊ธฐ์žฅ ๊ฐ•๋„๋ฅผ ์ •๋Ÿ‰์ ์œผ๋กœ ์—ฐ๊ฒฐํ•จ์œผ๋กœ์จ, ์ด๋ก ์  ํ•œ๊ณ„์™€ ๊ด€์ธก์  ์ œ์•ฝ์„ ๋™์‹œ์— ์ œ์‹œํ•œ๋‹ค. UHECR๊ฐ€ ์€

Astrophysics HEP-PH
History-sensitive versus future-sensitive approaches to security in   distributed systems

History-sensitive versus future-sensitive approaches to security in distributed systems

(800์ž ์ด์ƒ) ๋ณธ ๋…ผ๋ฌธ์€ ๋ถ„์‚ฐ ์‹œ์Šคํ…œ ๋ณด์•ˆ์— ์žˆ์–ด ์‹œ๊ฐ„์  ๊ด€์  ์„ ๋‘ ์ถ•์œผ๋กœ ๊ตฌ๋ถ„ํ•œ๋‹ค๋Š” ์ ์—์„œ ์ด๋ก ์ ยท์‹ค์šฉ์  ์˜์˜๋ฅผ ๋™์‹œ์— ์ง€๋‹Œ๋‹ค. ๊ธฐ์กด์˜ ๋ฏธ๋ž˜โ€‘๊ฐ์ง€ ์ ‘๊ทผ๋ฒ•์€ ์ •์  ๋ถ„์„์„ ํ†ตํ•ด ํ”„๋กœ๊ทธ๋žจ์ด ์•ž์œผ๋กœ ์ˆ˜ํ–‰ํ•  ํ–‰๋™์„ ์˜ˆ์ธกํ•˜๊ณ , ๊ทธ ์˜ˆ์ธก ๊ฒฐ๊ณผ์— ๋”ฐ๋ผ ์ ‘๊ทผ์„ ํ—ˆ์šฉํ•˜๊ฑฐ๋‚˜ ์ฐจ๋‹จํ•œ๋‹ค. ์ด ๋ฐฉ์‹์€ ์ •๋ณด ํ๋ฆ„ ์ œ์–ด ์— ๊ฐ•์ ์„ ๋ณด์ด๋ฉฐ, ํŠนํžˆ ์ˆœ์ฐจ์  ํ”„๋กœ๊ทธ๋žจ์ด๋‚˜ ์ œํ•œ๋œ ๋™์‹œ์„ฑ์„ ๊ฐ–๋Š” ์‹œ์Šคํ…œ์—์„œ ํšจ๊ณผ์ ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ถ„์‚ฐ ํ™˜๊ฒฝ์€ ๋‹ค์ค‘ ์œ„์น˜ ๊ฐ„ ๋™์‹œ ์ƒํ˜ธ์ž‘์šฉ ๊ณผ ๋น„๋™๊ธฐ์  ๋ฉ”์‹œ์ง• ์ด ๋นˆ๋ฒˆํžˆ ์ผ์–ด๋‚˜๋ฏ€๋กœ, ๋‹จ์ผ ํ”„๋กœ์„ธ์Šค์˜ ๋ฏธ๋ž˜ ํ–‰๋™๋งŒ์„ ๊ณ ๋ คํ•˜๋Š” ๊ฒƒ์œผ๋กœ๋Š” ์ „์ฒด ์‹œ

Cryptography and Security Programming Languages System Distributed Computing Computer Science
Homogenization of the Vlasov Equation and of the Vlasov - Poisson System   with a Strong External Magnetic Field

Homogenization of the Vlasov Equation and of the Vlasov - Poisson System with a Strong External Magnetic Field

๋ณธ ์—ฐ๊ตฌ๋Š” ํ”Œ๋ผ์ฆˆ๋งˆ ๋ฌผ๋ฆฌํ•™๊ณผ ์ˆ˜์น˜ ํ•ด์„ ๋ถ„์•ผ์—์„œ ์˜ค๋ž˜๋œ ๋‚œ์ œ์ธ โ€œ๊ฐ•ํ•œ ์™ธ๋ถ€ ์ž๊ธฐ์žฅ์— ์˜ํ•œ ๋‹ค์ค‘ ์‹œ๊ฐ„ ์ฒ™๋„ ๋ฌธ์ œโ€๋ฅผ ์ˆ˜ํ•™์ ์œผ๋กœ ์ •๋ฐ€ํ•˜๊ฒŒ ๋‹ค๋ฃฌ๋‹ค. ์ „ํ†ต์ ์œผ๋กœ ์ž…์ž ๊ถค์ ์ด ์ž๊ธฐ์žฅ ์„ ์„ ๋”ฐ๋ผ ๋‚˜์„ ํ˜•์œผ๋กœ ํšŒ์ „ํ•˜๋ฉด์„œ, ํšŒ์ „ ๋ฐ˜๊ฒฝ์ด (|B|^{ 1}) ์— ๋น„๋ก€ํ•˜๊ธฐ ๋•Œ๋ฌธ์— (|B|) ๊ฐ€ ์ปค์งˆ์ˆ˜๋ก ์ž…์ž๋Š” ๊ฑฐ์˜ ๊ณ ์ •๋œ ๊ฒฝ๋กœ๋ฅผ ๋”ฐ๋ผ ์›€์ง์ธ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‹ค์ œ ์ž…์ž ์†๋„๋Š” ๋น ๋ฅธ ํšŒ์ „ ์„ฑ๋ถ„๊ณผ ๋А๋ฆฐ ํ‰๊ท  ์„ฑ๋ถ„์ด ํ˜ผํ•ฉ๋œ ํ˜•ํƒœ์ด๋ฉฐ, ๊ด€์ธก ๊ฐ€๋Šฅํ•œ โ€˜ํ‘œ๋ฉด ์†๋„โ€™๋Š” ํ‰๊ท  ์„ฑ๋ถ„๋งŒ์„ ๋ฐ˜์˜ํ•œ๋‹ค. ์ด ๋ฌผ๋ฆฌ์  ์ง๊ด€์€ ๊ฐ€์ด๋“œโ€‘์„ผํ„ฐ(guidingโ€‘center) ๊ทผ์‚ฌ๋กœ ์•Œ๋ ค์ ธ

Computer Science Numerical Analysis System Mathematics
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Hypercontractivity for a family of quantum Ornstein-Uhlenbeck semigroups

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๊ณ ์ „ Ornsteinโ€‘Uhlenbeck ์„ธ๋ฏธ๊ทธ๋ฃน : Nelson(1973, 1974)์™€ Gross(1975)์˜ ์ž‘์—…์„ ํ†ตํ•ด ์ดˆ์ˆ˜์ถ•์„ฑ๊ณผ ๋กœ๊ทธโ€‘Sobolev ๋ถ€๋“ฑ์‹ ์‚ฌ์ด์˜ ๋“ฑ๊ฐ€์„ฑ์ด ๋ฐํ˜€์กŒ์œผ๋ฉฐ, ์ด๋Š” ํ™•๋ฅ ๋ก ยทํ†ต๊ณ„์—ญํ•™ยท์–‘์ž ์ •๋ณด ์ด๋ก  ์ „๋ฐ˜์— ๊ฑธ์ณ ์ค‘์š”ํ•œ ๋„๊ตฌ๊ฐ€ ๋˜์—ˆ๋‹ค. ์–‘์ž ๋งˆ์ฝ”ํ”„ ์„ธ๋ฏธ๊ทธ๋ฃน : ๋ฌดํ•œ ์ฐจ์› ํž๋ฒ ๋ฅดํŠธ ๊ณต๊ฐ„ ์œ„์˜ ๋น„๊ฐ€์—ญ์ ์ธ ๊ฐœ๋ฐฉ ์–‘์ž ์‹œ์Šคํ…œ์„ ๊ธฐ์ˆ ํ•˜๋Š” ํ•ต์‹ฌ ํ”„๋ ˆ์ž„์›Œํฌ์ด๋ฉฐ, ํŠนํžˆ ์–‘์ž Ornsteinโ€‘Uhlenbeck ์„ธ๋ฏธ๊ทธ๋ฃน ์€ ๊ด‘ํ•™ยท๋ ˆ์ด์ € ๋ชจ๋ธ์—์„œ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ๋“ฑ์žฅํ•œ๋‹ค. Ko ๋“ฑ(2019) ์˜ ์ผ๋ฐ˜ํ™” :

Mathematics
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ICP-4D: Bridging Iterative Closest Point and LiDAR Panoptic Segmentation

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ์  ๊ธฐ์กด 4D ๋ผ์ด๋‹ค ํŒŒ๋…ธํ”„ํ‹ฑ ์„ธ๊ทธ๋ฉ˜ํ…Œ์ด์…˜์€ ๋Œ€๊ทœ๋ชจ ํฌ์ธํŠธ ํด๋ผ์šฐ๋“œ์— ๋Œ€ํ•œ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ํ•™์Šต์ด ํ•„์ˆ˜์ด๋ฉฐ, ์ธ์Šคํ„ด์Šค ์—ฐ๊ด€์„ ์œ„ํ•ด ๋ณ„๋„ ๋ชจ๋“ˆ์„ ์„ค๊ณ„ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐฉ์‹์€ (โ‘ ) ํฌ์ธํŠธ ์ค‘๋ณต ์ฒ˜๋ฆฌ โ†’ ๋†’์€ ์—ฐ์‚ฐยท๋ฉ”๋ชจ๋ฆฌ ๋น„์šฉ, (โ‘ก) ์›์‹œ ํฌ์ธํŠธ ํด๋ผ์šฐ๋“œ๊ฐ€ ์ œ๊ณตํ•˜๋Š” ๊ธฐํ•˜ํ•™์  ์‚ฌ์ „์ง€์‹(์˜ˆ: ๋ฌผ์ฒด ํ˜•ํƒœ, ์›€์ง์ž„) ํ™œ์šฉ ๋ถ€์กฑ์ด๋ผ๋Š” ๋‘ ๊ฐ€์ง€ ๊ทผ๋ณธ์ ์ธ ํ•œ๊ณ„๋ฅผ ๊ฐ€์ง„๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ ICP์™€ Sinkhorn ๋งค์นญ์˜ ๊ฒฐํ•ฉ ICP : ์ „ํ†ต์ ์ธ ํฌ์ธํŠธ ํด๋ผ์šฐ๋“œ ์ •ํ•ฉ ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ, ๋‘ ํฌ์ธํŠธ ์ง‘ํ•ฉ ์‚ฌ์ด์˜ rigid ๋ณ€ํ™˜(ํšŒ์ „ยท

Identification of a Population of X-ray Emitting Massive Stars in the   Galactic Plane

Identification of a Population of X-ray Emitting Massive Stars in the Galactic Plane

๋ณธ ์—ฐ๊ตฌ๋Š” ์€ํ•˜๋ฉด(๊ฐˆ๋ผ์ง„ ์€ํ•˜๋ฉด) ์˜์—ญ์—์„œ ๊ธฐ์กด Xโ€‘์„  ์กฐ์‚ฌ(ASCA Galactic Plane Survey)๋กœ๋งŒ ์•Œ๋ ค์กŒ๋˜ ๋ฏธํ™•์ธ Xโ€‘์„ ์›์„ ์ฐจ๋ผ(Chandra) ๊ณ ํ•ด์ƒ๋„ ๊ด€์ธก์„ ํ†ตํ•ด ์ •ํ™•ํžˆ ์œ„์น˜์‹œํ‚ค๊ณ , ๋‹ค์ค‘ ํŒŒ์žฅ(์ ์™ธ์„ ยท๊ด‘ํ•™ยท์ „ํŒŒ) ๋ฐ์ดํ„ฐ๋ฅผ ๊ฒฐํ•ฉํ•ด ๋ฌผ๋ฆฌ์  ์„ฑ์งˆ์„ ๊ทœ๋ช…ํ•œ ์ ์—์„œ ํฐ ์˜๋ฏธ๋ฅผ ๊ฐ€์ง„๋‹ค. ์ฒซ ๋ฒˆ์งธ ํ•ต์‹ฌ ๊ฒฐ๊ณผ๋Š” ๋„ค ๊ฐœ์˜ Xโ€‘์„ ์›์ด ๋ชจ๋‘ ์งˆ๋Ÿ‰์ด ํฐ ๋ณ„(WR ํ˜น์€ Of ๊ณ„์—ด)์ด๋ผ๋Š” ์ ์ด๋‹ค. ์ŠคํŽ™ํŠธ๋Ÿผ ๋ถ„๋ฅ˜๊ฐ€ Ofpe/WN9, WN7, WN7โ€‘8h, OIfโบ ๋กœ ๋‹ค์–‘ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š”๋ฐ, ์ด๋Š” ๊ฐ๊ฐ ์ง„ํ™” ๋‹จ๊ณ„๊ฐ€ ๋‹ค์†Œ ์ฐจ์ด๋‚˜๋Š” ๊ฑฐ๋Œ€๋ณ„์ž„์„

Astrophysics
Improved Tactile Resonance Sensor for Robotic Assisted Surgery

Improved Tactile Resonance Sensor for Robotic Assisted Surgery

: ๋ณธ ๋…ผ๋ฌธ์€ ์™ธ๊ณผ ์˜์‚ฌ์˜ ์ด‰๊ฐ ๊ฐ๊ฐ์„ ํ–ฅ์ƒํ•˜์—ฌ ๋‡Œ์ข…์–‘ ์ˆ˜์ˆ ์˜ ์ •ํ™•์„ฑ๊ณผ ์•ˆ์ „์„ฑ์„ ๋†’์ด๋Š” ๋ฐ ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ตœ๊ทผ ์ตœ์†Œ ์นจ์Šต์  ์ˆ˜์ˆ  ๊ธฐ๋ฒ•์˜ ๋ฐœ์ „์œผ๋กœ ์ธํ•ด, ์™ธ๊ณผ์˜์‚ฌ๋Š” ์‹œ๊ฐ์ ์ธ ์ •๋ณด์™€ ํ•จ๊ป˜ ์ด‰๊ฐ ์ •๋ณด๋ฅผ ํ†ตํ•ด ์กฐ์ง์˜ ์ผ๊ด€์„ฑ, ๊ฒฐ์ ˆ, ๊ดด์‚ฌ ๋“ฑ์„ ๊ตฌ๋ณ„ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ์ˆ˜์ˆ  ๊ธฐ๋ฒ•์—์„œ๋Š” ์ด‰๊ฐ ๊ฐ๊ฐ์ด ์ œํ•œ๋˜๊ฑฐ๋‚˜ ์•„์˜ˆ ์ œ๊ณต๋˜์ง€ ์•Š๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์Šต๋‹ˆ๋‹ค. ์ด์— ๋”ฐ๋ผ ๋ณธ ๋…ผ๋ฌธ์€ ์••์ „ ๋ฐ”์ด๋ชจํ”„๋ฅผ ์ด์šฉํ•œ ์ด‰๊ฐ ์„ผ์„œ๋ฅผ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ์ด ์„ผ์„œ๋Š” ๊ธฐ๊ณ„์  ์ง„๋™์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ถ€๋“œ๋Ÿฌ์šด ์ƒ๋ฌผํ•™์  ์กฐ์ง์„ ์ž๊ทนํ•˜๊ณ , ๊ทธ ํŠน์„ฑ์„ ์ธก์ •ํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, ์ ํƒ„์„ฑ ํŠน์„ฑ

Physics Electrical Engineering and Systems Science
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Improving Topic Modeling of Social Media Short Texts with Rephrasing: A Case Study of COVID-19 Related Tweets

๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” TM Rephrase ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•˜์—ฌ ์†Œ์…œ ๋ฏธ๋””์–ด ๋‹จ๋ฌธ ํ…์ŠคํŠธ์˜ ์ฃผ์ œ ๋ชจ๋ธ๋ง์„ ๊ฐœ์„ ํ•ฉ๋‹ˆ๋‹ค. ์ด ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ์–ธ์–ด๋ชจ๋ธ(LLMs)์„ ํ™œ์šฉํ•˜์—ฌ ์›๋ณธ ํŠธ์œ—์„ ์žฌํ‘œํ˜„ํ•จ์œผ๋กœ์จ, ๋น„๊ฒฉ์‹์ ์ด๊ณ  ๋…ธ์ด์ฆˆ๊ฐ€ ์žˆ๋Š” ํ…์ŠคํŠธ๋ฅผ ํ‘œ์ค€ํ™”๋˜๊ณ  ๊ฒฉ์‹์ ์ธ ์–ธ์–ด๋กœ ๋ณ€ํ™˜ํ•ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ๋ฒ•์€ ์ฃผ์ œ ๋ชจ๋ธ๋ง์˜ ์ •ํ™•์„ฑ๊ณผ ํ•ด์„ ๊ฐ€๋Šฅ์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•ฉ๋‹ˆ๋‹ค. ์—ฐ๊ตฌ์ง„์€ COVID 19 ๊ด€๋ จ ํŠธ์œ„ํ„ฐ ๋ฐ์ดํ„ฐ์…‹์„ ์‚ฌ์šฉํ•˜์—ฌ TM Rephrase ํ”„๋ ˆ์ž„์›Œํฌ์˜ ํšจ๊ณผ๋ฅผ ํ‰๊ฐ€ํ–ˆ์Šต๋‹ˆ๋‹ค. ๋‘ ๊ฐ€์ง€ ์žฌํ‘œํ˜„ ์ „๋žต, ์ฆ‰ ์ผ๋ฐ˜์  ์žฌํ‘œํ˜„๊ณผ ๋น„๊ฒฉ์‹์  ํ‘œํ˜„์—์„œ ๊ฒฉ์‹์  ํ‘œํ˜„์œผ๋กœ์˜

Model
In-materio neuromimetic devices: Dynamics, information processing and   pattern recognition

In-materio neuromimetic devices: Dynamics, information processing and pattern recognition

1. ๋ฐฐ๊ฒฝ๊ณผ ํ•„์š”์„ฑ ํ˜„๋Œ€ ์ „์ž๊ณตํ•™์€ ํŠธ๋žœ์ง€์Šคํ„ฐ ์ˆ˜๊ฐ€ 10โน ๋‹จ์œ„์— ์ด๋ฅด๋Š” ๋งˆ์ดํฌ๋กœํ”„๋กœ์„ธ์„œ๋ฅผ ๊ตฌํ˜„ํ–ˆ์ง€๋งŒ, ๋ฌผ๋ฆฌ์  ์Šค์ผ€์ผ์ด 10 nm ์ดํ•˜๋กœ ๋‚ด๋ ค๊ฐ€๋ฉด์„œ ์ „๋ฅ˜ ๋ˆ„์„ค, ์—ด ๊ด€๋ฆฌ, ์–‘์ž ํšจ๊ณผ ๋“ฑ ๊ธฐ์ƒ ํ˜„์ƒ์ด ๊ธ‰์ฆํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋Š” ๊ธฐ์กด ์‹ค๋ฆฌ์ฝ˜ ๊ธฐ๋ฐ˜ ์•„ํ‚คํ…์ฒ˜๊ฐ€ ์ธ๊ฐ„ ๋‡Œ์™€ ๊ฐ™์€ ๊ณ ๋„ ๋ณต์žก์„ฑ์„ ๋ชจ๋ฐฉํ•˜๊ธฐ ์–ด๋ ต๊ฒŒ ๋งŒ๋“ ๋‹ค. ์ธ๊ฐ„ ๋Œ€๋‡Œ ํ”ผ์งˆ์€ ์•ฝ 10โน ~ 10ยนโฐ๊ฐœ์˜ ๋‰ด๋Ÿฐ๊ณผ 10ยนโด ~ 10ยนโต๊ฐœ์˜ ์‹œ๋ƒ…์Šค๋ฅผ ๋ณด์œ ํ•˜๋ฉฐ, ์‹œ๋ƒ…์Šค ๊ฐ€์†Œ์„ฑ, ๋‹ค์ค‘ ์‹œ๊ฐ„ยท๊ณต๊ฐ„ ์Šค์ผ€์ผ์˜ ๋™์  ์ƒํ˜ธ์ž‘์šฉ์„ ํ†ตํ•ด ํ•™์Šต๊ณผ ์ ์‘์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ฌผ๋ฆฌ์ ยทํ™”ํ•™์  ๋งค์ฒด ์ž์ฒด๊ฐ€ ๋‰ด๋Ÿฐยท์‹œ๋ƒ…์Šค

Physics Computer Science Emerging Technologies
Indigenous Astronomies and Progress in Modern Astronomy

Indigenous Astronomies and Progress in Modern Astronomy

์ด ๋…ผ๋ฌธ์€ ์ฒœ๋ฌธํ•™์ด๋ผ๋Š” ๊ณผํ•™ ๋ถ„์•ผ๊ฐ€ ๋ฌธํ™”์ ยท์‚ฌํšŒ์  ๋งฅ๋ฝ ์†์—์„œ ์–ด๋–ป๊ฒŒ ์ธ์‹๋˜๊ณ  ์ˆ˜์šฉ๋˜๋Š”์ง€๋ฅผ ์ธ๋ฅ˜ํ•™์  ํ”„๋ ˆ์ž„์›Œํฌ๋กœ ์žฌ์กฐ๋ช…ํ•œ๋‹ค๋Š” ์ ์—์„œ ํ•™์ œ๊ฐ„ ์—ฐ๊ตฌ์˜ ๋ชจ๋ฒ”์„ ์ œ์‹œํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ํ•ต์‹ฌ ์ฃผ์žฅ์€ โ€œ์ง„๋ณดโ€๋ผ๋Š” ์šฉ์–ด๊ฐ€ ๋‚ดํฌํ•˜๋Š” ์„œ๊ตฌ ์ค‘์‹ฌ์  ์„œ์ˆ ์ด ๋‹ค๋ฅธ ๋ฌธํ™”๊ถŒ์˜ ์šฐ์ฃผ๊ด€์„ ๋ฌด์‹œํ•˜๊ฑฐ๋‚˜ ์–ต์••ํ•œ๋‹ค๋Š” ๋น„ํŒ์ด๋‹ค. ์ด๋Š” ๊ณผํ•™์‚ฌ์—์„œ ํ”ํžˆ ๊ฐ„๊ณผ๋˜๋Š” โ€˜๋ฌธํ™”์  ํŽธํ–ฅโ€™ ๋ฌธ์ œ๋ฅผ ๋“œ๋Ÿฌ๋‚ด๋ฉฐ, ํŠนํžˆ ํ•˜์™€์ด์™€ ๊ฐ™์€ ๋‹ค๋ฌธํ™” ์‚ฌํšŒ์—์„œ ๊ณผํ•™ ์ •์ฑ…์ด ํ˜„์ง€ ๊ณต๋™์ฒด์™€ ์ถฉ๋Œํ•  ์œ„ํ—˜์„ ๊ฒฝ๊ณ ํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ, ์ €์ž๋Š” ๊ณผํ•™๊ณผ ๋น„๊ณผํ•™ ์‚ฌ์ด์˜ ๊ฒฝ๊ณ„๊ฐ€ ํ˜„๋Œ€ ๋Œ€์ค‘ ๋งค์ฒด์™€ ๊ต์œก ํ˜„์žฅ์—์„œ ํ๋ ค์ง€๊ณ  ์žˆ์Œ์„

Physics Astrophysics
Intermittent Activity of Jets in AGN

Intermittent Activity of Jets in AGN

์ด ๋…ผ๋ฌธ์€ AGN ์ œํŠธ๊ฐ€ ์‹œ๊ฐ„์ ์œผ๋กœ ๋ถˆ์—ฐ์†์ ์ธ ํ™œ๋™์„ ๋ณด์ธ๋‹ค๋Š” ์˜ค๋ž˜๋œ ๊ฐ€์„ค์„ ์ตœ์‹  ๊ด€์ธก๊ณผ ์ด๋ก  ๋ชจ๋ธ์„ ํ†ตํ•ด ์žฌ์กฐ๋ช…ํ•œ๋‹ค. ์ €์ž๋“ค์€ ํฌ๊ฒŒ ๋„ค ๊ฐ€์ง€ ํ๋ฆ„์œผ๋กœ ๋…ผ์ง€๋ฅผ ์ „๊ฐœํ•œ๋‹ค. 1. ๊ด€์ธก์  ๋ฐฐ๊ฒฝ 100 kpc ๊ทœ๋ชจ์˜ Xโ€‘์„  ์ œํŠธ์™€ ๊ตฐ์ง‘ ๋‚ด cD ์€ํ•˜์˜ ๋ผ๋””์˜ค ์ œํŠธ๊ฐ€ ์„œ๋กœ ๋‹ค๋ฅธ ์‹œ๊ธฐ์— ์ผœ์กŒ๋‹ค ๊บผ์กŒ๋‹ค ํ•˜๋Š” ํ”์ ์„ ๋ณด์—ฌ์ค€๋‹ค. ์ด๋Š” ์ œํŠธ๊ฐ€ ๋‹จ์ผ, ์—ฐ์†์ ์ธ ํ๋ฆ„์ด ์•„๋‹ˆ๋ผ โ€˜์Šค์œ„์น˜โ€‘์˜จ/์˜คํ”„โ€™ ์‚ฌ์ดํด์„ ๊ฐ–๋Š”๋‹ค๋Š” ์ง์ ‘์ ์ธ ์ฆ๊ฑฐ๋‹ค. CSO์™€ GPS/CSS ์†Œ์Šค๋Š” ์„ ํ˜• ํฌ๊ธฐ๊ฐ€ 1 kpc ์ดํ•˜์ด๋ฉฐ, VLBI๋ฅผ ํ†ตํ•œ ํ•ซ์ŠคํŒŸ ํŒฝ์ฐฝ ์†๋„ ์ธก์ •์œผ๋กœ ์—ฐ๋ น์ด 10ยณ ~

Astrophysics
Interpreting the bounds on Solar Dark Matter induced muons at   Super-Kamiokande in the light of CDMS results

Interpreting the bounds on Solar Dark Matter induced muons at Super-Kamiokande in the light of CDMS results

๋ณธ ๋…ผ๋ฌธ์€ ์ง์ ‘ ๊ฒ€์ถœ ์‹คํ—˜์ธ CDMS II๊ฐ€ ์ œ๊ณตํ•œ ์œ„ํ”„โ€‘ํ•ต ํƒ„์„ฑ ์‚ฐ๋ž€ ๋‹จ๋ฉด (sigma {chi N})์— ๋Œ€ํ•œ ์ตœ์‹  90 % C.L. ์ƒํ•œ์„ ์ถœ๋ฐœ์ ์œผ๋กœ ์‚ผ์•„, ํƒœ์–‘ ๋‚ด๋ถ€์— ํฌํš๋œ ์œ„ํ”„๊ฐ€ ์†Œ๋ฉธํ•˜๋ฉด์„œ ์ƒ์„ฑํ•˜๋Š” ์ค‘์„ฑ๋ฏธ์ž ํ”Œ๋Ÿญ์Šค๋ฅผ ์ •๋ฐ€ํžˆ ์ถ”์ •ํ•œ๋‹ค. ํ•ต์‹ฌ์€ ๋‘ ๋‹จ๊ณ„์˜ ๋ฌผ๋ฆฌ์  ์—ฐ๊ฒฐ ๊ณ ๋ฆฌ์ด๋‹ค. ์ฒซ์งธ, ์œ„ํ”„ ํฌํš๋ฅ  (C)๋Š” (sigma {chi N}/m {chi})์— ๋น„๋ก€ํ•œ๋‹ค๋Š” ์ฒœ์ฒด๋ฌผ๋ฆฌํ•™์  ๊ทผ์‚ฌ์‹์„ ์‚ฌ์šฉํ•ด CDMS ์ œํ•œ์„ ์ง์ ‘์ ์ธ ํฌํš์œจ ์ œํ•œ์œผ๋กœ ๋ณ€ํ™˜ํ•œ๋‹ค. ๋‘˜์งธ, ํฌํš๋œ ์œ„ํ”„๊ฐ€ ์Œ์†Œ๋ฉธํ•˜์—ฌ (b,,c,,t) ์ฟผํฌ, (

Astrophysics HEP-PH
No Image

Introducing the b-value: combining unbiased and biased estimators from a sensitivity analysis perspective

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์‹ค์ œ ์—ฐ๊ตฌ ์ƒํ™ฉ : RCT์™€ ๊ด€์ฐฐ์—ฐ๊ตฌ, OLS์™€ IV ๋“ฑ ์„œ๋กœ ๋‹ค๋ฅธ ๋ฐ์ดํ„ฐยท๋ชจ๋ธ ๊ฐ€์ •์—์„œ ์–ป์€ ์—ฌ๋Ÿฌ ์ถ”์ •๋Ÿ‰์ด ๋™์‹œ์— ์กด์žฌํ•œ๋‹ค. ํ•ต์‹ฌ ์งˆ๋ฌธ : ํŽธํ–ฅ์ด ์•Œ๋ ค์ง€์ง€ ์•Š์€ ์ƒํ™ฉ์—์„œ, ํŽธํ–ฅ์ด ์žˆ๋Š” ๊ณ ์ •๋ฐ€ ์ถ”์ •๋Ÿ‰๊ณผ ํŽธํ–ฅ์ด ์—†๋Š” ์ €์ •๋ฐ€ ์ถ”์ •๋Ÿ‰์„ ์–ด๋–ป๊ฒŒ ๊ฒฐํ•ฉํ•ด ์ถ”๋ก ์˜ ์ •ํ™•๋„์™€ ์‹ ๋ขฐ์„ฑ์„ ๋™์‹œ์— ํ™•๋ณดํ•  ์ˆ˜ ์žˆ๋Š”๊ฐ€? ๊ธฐ์กด ๋ฌธํ—Œ์€ ์ ์ถ”์ • ์— ์ง‘์ค‘ํ–ˆ์œผ๋ฉฐ, ์‹ ๋ขฐ๊ตฌ๊ฐ„ยท๊ฐ€์„ค๊ฒ€์ • ์— ๋Œ€ํ•œ ์ด๋ก ์€ ๊ฑฐ์˜ ๋ถ€์žฌํ–ˆ๋‹ค๋Š” ์ ์„ ๋ช…ํ™•ํžˆ ์งš๊ณ  ์žˆ๋‹ค. 2. ๋ฐฉ๋ฒ•๋ก ์  ํ•ต์‹ฌ ์•„์ด๋””์–ด 1. Gaussian ๊ทผ์‚ฌ Le Cam์˜ ๋น„๋Œ€์นญ ์ •๊ทœ ๊ทผ์‚ฌ๋ฅผ ์ด์šฉํ•ด,

Statistics Analysis
Introduction to Quantum Integrability

Introduction to Quantum Integrability

๋ณธ ๋…ผ๋ฌธ์€ ์–‘์ž ์ ๋ถ„๊ฐ€๋Šฅ์„ฑ(Quantum Integrability)์˜ ์ „๋ฐ˜์ ์ธ ํ‹€์„ โ€˜๋Œ€์ˆ˜์  ์ ‘๊ทผโ€™์ด๋ผ๋Š” ๊ด€์ ์—์„œ ์žฌ๊ตฌ์„ฑํ•˜๊ณ  ์žˆ๋‹ค. ์ „ํ†ต์ ์œผ๋กœ ์ ๋ถ„๊ฐ€๋Šฅ ๋ชจ๋ธ์€ ํ•ด๋ฐ€ํ† ๋‹ˆ์•ˆ์ด ๋ฌด์ˆ˜ํžˆ ๋งŽ์€ ๋ณด์กด๋Ÿ‰์„ ๊ฐ–๋Š”๋‹ค๋Š” ๋ฌผ๋ฆฌ์  ์ •์˜์— ๋จธ๋ฌผ๋ €์ง€๋งŒ, ์ €์ž๋Š” ์ด๋ฅผ โ€˜์–‘โ€‘๋ฐฅํ„ฐ ๋ฐฉ์ •์‹(Yangโ€‘Baxter equation)โ€™๊ณผ โ€˜์–‘์ž๊ตฐ(Quantum groups)โ€™์ด๋ผ๋Š” ์ˆ˜ํ•™์  ๊ตฌ์กฐ์™€ ์—ฐ๊ฒฐํ•จ์œผ๋กœ์จ ๋ณด๋‹ค ์ฒด๊ณ„์ ์ธ ์ดํ•ด๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ๊ฐ•์ ์€ ๊ตฌ์กฐ์  ํ๋ฆ„ ์ด๋‹ค. ์ œ2์ ˆ์—์„œ ํ…์„œ๊ณฑ ํ‘œ๊ธฐ๋ฒ•๊ณผ su(2) ๋Œ€์ˆ˜์˜ ๊ธฐ๋ณธ์„ ์ •๋ฆฌํ•œ ๋’ค, Heisenberg ๋ชจ๋ธ์„ ๋„์ž…

MATH-PH Mathematics Condensed Matter HEP-TH Nonlinear Sciences
Investigating modularity in the analysis of process algebra models of   biochemical systems

Investigating modularity in the analysis of process algebra models of biochemical systems

: ์ƒํ™”ํ•™ ์‹œ์Šคํ…œ์˜ ๋ณต์žก์„ฑ์€ ๋ชจ๋ธ๋ง๊ณผ ๋ถ„์„์— ํฐ ๋„์ „ ๊ณผ์ œ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์‹œ์Šคํ…œ์€ ์ผ๋ฐ˜์ ์œผ๋กœ ํฌ๊ณ  ์ƒํ˜ธ ์—ฐ๊ฒฐ๋˜์–ด ์žˆ์œผ๋ฉฐ, ๋™์  ํ–‰๋™์ด ๊ฐ•ํ•˜๊ฒŒ ์ƒํ˜ธ ์˜์กด์ ์ด๋‹ค. ๋”ฐ๋ผ์„œ ์ผ์ฒด์ ์ธ ์ ‘๊ทผ๋ฒ•์€ ์–ด๋ ค์›€์„ ๊ฒช๊ณ , ๊ตฌ์„ฑ์  ๊ตฌ์กฐ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ์ž ์žฌ์ ์ธ ํ•ด๋ฒ•์œผ๋กœ ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋‹ค. ํ”„๋กœ์„ธ์Šค ๋Œ€์ˆ˜๋Š” ์ƒํ™”ํ•™ ์‹œ์Šคํ…œ์˜ ๋ชจ๋ธ๋ง์— ์ ํ•ฉํ•œ ํ˜•์‹์ฃผ์˜๋กœ ์ œ์•ˆ๋˜์—ˆ์œผ๋ฉฐ, ๊ทธ์ค‘์—์„œ๋„ ๊ตฌ์„ฑ์„ฑ์€ ํ•ต์‹ฌ ํŠน์ง• ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ”„๋กœ์„ธ์Šค ๋Œ€์ˆ˜ ๋ชจ๋ธ์˜ ๋‚ด์žฌ๋œ ๊ตฌ์„ฑ์„ฑ์„ ํ™œ์šฉํ•˜์—ฌ ๋ถ„ํ•ด๋œ ์†”๋ฃจ์…˜์„ ์œ ๋„ํ•˜๊ณ  ๋ถ„์„ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ํƒ๊ตฌํ•œ๋‹ค. ์ด๋Š” ๋ชจ๋“ˆ ๊ฐœ๋…๊ณผ ํ•จ๊ป˜

Analysis Computational Engineering Quantitative Biology System Model Computer Science
Irresponsible AI: big tech's influence on AI research and associated impacts

Irresponsible AI: big tech's influence on AI research and associated impacts

๋น…ํ…Œํฌ๋Š” ์–ด๋–ป๊ฒŒ AI์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”๊ฐ€? ๊ธฐ์ˆ  ์‚ฐ์—…์€ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์ปดํ“จํ„ฐ ๊ณผํ•™ ์—ฐ๊ตฌ์— ๊ด€์‹ฌ์„ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฉฐ, ์—ญ์‚ฌ์ ์œผ๋กœ ๊ทธ ๋ฐœ์ „์„ ์˜ํ–ฅ๋ ฅ ์žˆ๊ฒŒ ์ด๋Œ์–ด ์™”๋‹ค(Mahoney, 1998). ๊ทธ๋Ÿฌ๋‚˜ 2010๋…„๋Œ€ ์ดˆ๋ถ€ํ„ฐ Sevilla et al. (2022)๊ฐ€ ์‹ฌ์ธต ํ•™์Šต ๋ฐ ๋Œ€๊ทœ๋ชจ ์‹œ๋Œ€๋ผ๊ณ  ๋ช…๋ช…ํ•œ ์ด๋ž˜๋กœ ๋น…ํ…Œํฌ์˜ AI ์—ฐ๊ตฌ์— ๋Œ€ํ•œ ์˜ํ–ฅ๋ ฅ์€ ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€ํ•˜์—ฌ ํ˜„์žฌ ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ํผ์ ธ ์žˆ๋‹ค(Ahmed et al., 2023). ๋น…ํ…Œํฌ๊ฐ€ ์ด๋Ÿฌํ•œ ์˜ํ–ฅ์„ ํ–‰์‚ฌํ•˜๋Š” ์ „์ˆ ๊ณผ ํ–‰๋™์€ ๋‹ค์–‘ํ•˜๋ฉฐ, ์ด๋Š” ์—ฐ๊ตฌ ์ปค๋ฎค๋‹ˆํ‹ฐ์—์„œ ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ๋ฐฉ์‹์œผ๋กœ ๋ฐ˜์˜๋œ๋‹ค. Birhane

ISIS at its apogee: the Arabic discourse on Twitter and what we can   learn from that about ISIS support and Foreign Fighters

ISIS at its apogee: the Arabic discourse on Twitter and what we can learn from that about ISIS support and Foreign Fighters

๋ณธ ๋…ผ๋ฌธ์€ ISIS๊ฐ€ ์ „์„ฑ๊ธฐ์— ์ ‘์–ด๋“ค๋ฉด์„œ ์ „ํ†ต ๋งค์ฒด๋ฅผ ๋„˜์–ด ์†Œ์…œ ๋„คํŠธ์›Œํฌ ์„œ๋น„์Šค(SNS)๋ฅผ ์ „๋žต์ ์œผ๋กœ ํ™œ์šฉํ•œ ์‚ฌ๋ก€๋ฅผ ์ •๋Ÿ‰์ ์œผ๋กœ ๊ฒ€์ฆํ•œ๋‹ค๋Š” ์ ์—์„œ ํ•™์ˆ ์ ยท์‹ค๋ฌด์  ์˜์˜๊ฐ€ ํฌ๋‹ค. ์ฒซ์งธ, 2,620๋งŒ ๊ฑด์ด๋ผ๋Š” ๋ฐฉ๋Œ€ํ•œ ๋ฐ์ดํ„ฐ์…‹์„ ๊ฐ๋… ํ•™์Šต ๊ธฐ๋ฐ˜ ๊ฐ์„ฑ ๋ถ„์„์— ์ ์šฉํ•จ์œผ๋กœ์จ โ€˜์•„๋ž์–ด ํŠธ์œ„ํ„ฐ ์—ฌ๋ก โ€™์ด๋ผ๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„๋ฅผ ๋›ฐ์–ด๋„˜์—ˆ๋‹ค. ๊ฐ์„ฑ ์‚ฌ์ „ ๊ตฌ์ถ•๊ณผ ๋ผ๋ฒจ๋ง ๊ณผ์ •์—์„œ ๋†’์€ ์ •ํ™•๋„๋ฅผ ํ™•๋ณดํ–ˆ์„ ๊ฒƒ์œผ๋กœ ์ถ”์ •๋˜๋ฉฐ, ์ด๋Š” ๋น„๋ผํ‹ด ๋ฌธ์ž ๊ธฐ๋ฐ˜ ์–ธ์–ด์— ๋Œ€ํ•œ ์ž๋™ ๋ถ„์„ ๊ธฐ์ˆ ์ด ์•„์ง ๋ฏธ๋น„ํ•œ ์ƒํ™ฉ์—์„œ ์ค‘์š”ํ•œ ์„ ๋ก€๊ฐ€ ๋œ๋‹ค. ๋‘˜์งธ, ์‹œ๊ฐ„์  ์„ธ๋ถ„์„ฑ์„ ํ™œ์šฉํ•œ ์‚ฌ๊ฑดโ€‘์—ฌ๋ก  ๋งค

Computer Science Social Networks NLP
Knowledge Discovery from Social Media using Big Data provided Sentiment   Analysis (SoMABiT)

Knowledge Discovery from Social Media using Big Data provided Sentiment Analysis (SoMABiT)

๋ณธ ๋…ผ๋ฌธ์€ โ€œ์†Œ์…œ ๋ฏธ๋””์–ดยท๋น…๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๊ฐ์„ฑ ๋ถ„์„ ํ”Œ๋žซํผ SoMABiTโ€๋ผ๋Š” ๋ช…์นญ ์•„๋ž˜, ๋Œ€๊ทœ๋ชจ ๋น„์ •ํ˜• ๋ฐ์ดํ„ฐ์˜ ์ˆ˜์ง‘ยท์ €์žฅยท๋ถ„์„ยทํ™œ์šฉ ์ „ ๊ณผ์ •์„ ํ•˜๋‚˜์˜ ํ†ตํ•ฉ ํ”„๋ ˆ์ž„์›Œํฌ๋กœ ๊ตฌํ˜„ํ•œ ์ ์ด ๊ฐ€์žฅ ํฐ ํŠน์ง•์ด๋‹ค. 1. ๋ฌธ์ œ ์ •์˜์™€ ์—ฐ๊ตฌ ๋™๊ธฐ ์†Œ์…œ ๋ฏธ๋””์–ด๊ฐ€ ์ œ๊ณตํ•˜๋Š” ๋ฐฉ๋Œ€ํ•œ UGC(์‚ฌ์šฉ์ž ์ƒ์„ฑ ์ฝ˜ํ…์ธ )๋Š” ์ œํ’ˆยท์„œ๋น„์Šค์— ๋Œ€ํ•œ ์‹ค์‹œ๊ฐ„ ์˜๊ฒฌ์„ ๋ฐ˜์˜ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํŠธ์œ„ํ„ฐยทํŽ˜์ด์Šค๋ถยท์œ ํŠœ๋ธŒ ๋“ฑ์—์„œ ๋งค์ผ ์ƒ์„ฑ๋˜๋Š” ๋ฐ์ดํ„ฐ ์–‘(์ˆ˜๋ฐฑ์–ต ๊ฑด, ์ˆ˜๋ฐฑ ํ…Œ๋ผ๋ฐ”์ดํŠธ ์ˆ˜์ค€)์€ ์ „ํ†ต์ ์ธ RDBMS ๊ธฐ๋ฐ˜ ๋ถ„์„์— ํ•œ๊ณ„๋ฅผ ๋งŒ๋“ ๋‹ค. ๋…ผ๋ฌธ์€ ์ด๋Ÿฌํ•œ ๋ฐ์ดํ„ฐ๋ฅผ โ€˜์‹œ๊ฐ„ยท๋น„์šฉยทํ™•์žฅ์„ฑโ€™์ด๋ผ๋Š” ์„ธ

Analysis Social Networks Machine Learning Databases Data Computer Science
No Image

Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules

์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์•Œ๋ ค์ง„ ๋ถ„ํฌ๋ฅผ ๋ฒ—์–ด๋‚œ ํŠน์„ฑ ๊ฐ’์„ ์˜ˆ์ธกํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ด๋Š” ์žฌ๋ฃŒ ๊ณผํ•™ ๋ฐ ๋ถ„์ž ์„ค๊ณ„ ๋ถ„์•ผ์—์„œ ๊ทน๋‹จ์ ์ธ ํŠน์„ฑ์„ ์ฐพ๋Š” ๋ฐ ํ•„์ˆ˜์ ์ด๋‹ค. ์šฐ๋ฆฌ๋Š” ์œ ๋„์  ์ ‘๊ทผ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ํ›ˆ๋ จ ๋ฐ์ดํ„ฐ์˜ ๋ฒ”์œ„๋ฅผ ๋„˜์–ด์„œ๋Š” ์˜ˆ์ธก์„ ์ˆ˜ํ–‰ํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด OOD ๋ถ„๋ฅ˜์˜ ์ •ํ™•๋„๋ฅผ ํฌ๊ฒŒ ํ–ฅ์ƒ์‹œ์ผฐ๋‹ค. ํŠนํžˆ, TPR์ด ํฌ๊ฒŒ ๊ฐœ์„ ๋˜์—ˆ์œผ๋ฉฐ, ์ด๋Š” ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์ด ํ›ˆ๋ จ ์„ธํŠธ์— ์—†๋Š” ์ƒˆ๋กœ์šด ์‚ฌ๋ก€๋ฅผ ์‹๋ณ„ํ•˜๋Š” ๋Šฅ๋ ฅ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋˜ํ•œ, ์šฐ๋ฆฌ ๋ฐฉ๋ฒ•์€ ๋‹ค์–‘ํ•œ ์žฌ๋ฃŒ ๋ฐ ๋ถ„์ž ์ž‘์—…์— ์ ์šฉ ๊ฐ€๋Šฅํ•˜๋ฉฐ, ์ž…๋ ฅ๊ณผ ๋ชฉํ‘œ ๊ฐ„์˜ ์œ ์‚ฌ์„ฑ์„ ํ™œ์šฉํ•˜์—ฌ ์ผ๋ฐ˜ํ™” ๋Šฅ๋ ฅ์„ ํ–ฅ์ƒ์‹œํ‚จ๋‹ค.

No Image

KP-PINNs: Kernel Packet Accelerated Physics Informed Neural Networks

๋ฏธ๋ถ„ ๋ฐฉ์ •์‹์€ ๊ณตํ•™ ๋ฌธ์ œ ๋ชจ๋ธ๋ง์— ํ•„์ˆ˜์ ์ธ ์š”์†Œ์ด๋‹ค. ์ „ํ†ต์ ์ธ ๋ฐฉ๋ฒ•์œผ๋กœ๋Š” ๋ณต์žกํ•œ ๋ฏธ๋ถ„ ๋ฐฉ์ •์‹์„ ํ•ด๊ฒฐํ•˜๋Š” ๋ฐ ํ•œ๊ณ„๊ฐ€ ์žˆ๊ธฐ์—, ์ตœ๊ทผ์—๋Š” ์‹ ๊ฒฝ๋ง์„ ํ™œ์šฉํ•˜์—ฌ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. PINNs๋Š” ๋ฌผ๋ฆฌ ๋ฒ•์น™์„ ๊ณ ๋ คํ•˜์—ฌ ๋ฏธ๋ถ„ ๋ฐฉ์ •์‹์„ ํ‘ธ๋Š” ํ”„๋ ˆ์ž„์›Œํฌ๋กœ, L2 ์†์‹ค ํ•จ์ˆ˜๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํ›ˆ๋ จ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ผ๋ถ€ ๋ณต์žกํ•œ ๋ฐฉ์ •์‹์—์„œ๋Š” L2 ์†์‹ค ํ•จ์ˆ˜๊ฐ€ ๋ถ€์ •ํ™•ํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๋ฌธ์ œ๊ฐ€ ์ œ๊ธฐ๋˜์—ˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” RKHS ๋…ธ๋ฆ„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์ƒˆ๋กœ์šด ์†์‹ค ํ•จ์ˆ˜ ํ‘œํ˜„์„ ์ œ์•ˆํ•˜๊ณ , ๊ณ„์‚ฐ ๊ฐ€์†์„ ์œ„ํ•œ KP ๋ฐฉ๋ฒ•์„ ๋„์ž…ํ•˜์—ฌ KP PI

Network
No Image

LEAD: An EEG Foundation Model for Alzheimer's Disease Detection

์ด ๋…ผ๋ฌธ์€ ์•Œ์ธ ํ•˜์ด๋จธ๋ณ‘ ์ง„๋‹จ์„ ์œ„ํ•œ ๋‡ŒํŒŒ(EEG)์˜ ํ™œ์šฉ์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ๊ธฐ์กด ๋ฐฉ๋ฒ•๋“ค์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด, ์—ฐ๊ตฌ์ง„์€ ๋Œ€๊ทœ๋ชจ EEG AD ์ฝ”ํผ์Šค๋ฅผ ๊ตฌ์ถ•ํ•˜๊ณ , ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ LEAD๋ผ๋Š” ์ƒˆ๋กœ์šด ๊ธฐ์ดˆ ๋ชจ๋ธ์„ ์ œ์•ˆํ–ˆ๋‹ค. LEAD๋Š” ๊ฒŒ์ดํŠธํ™”๋œ ์‹œ๊ฐ„ ๊ณต๊ฐ„ ํŠธ๋žœ์Šคํฌ๋จธ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ EEG ๊ธฐ๋ก์— ์ ์‘ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋Œ€์ƒ ์ •๊ทœํ™” ํ›ˆ๋ จ ์ „๋žต์„ ํ†ตํ•ด ๋Œ€์ƒ๋ณ„ ํŠน์ง• ํ•™์Šต์„ ํ–ฅ์ƒ์‹œํ‚จ๋‹ค. ๋˜ํ•œ ์˜๋ฃŒ์šฉ ๋Œ€์กฐ ํ•™์Šต์„ ํ†ตํ•ด ์‚ฌ์ „ ํ•™์Šต์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ์„ฑ๋Šฅ์„ ๋”์šฑ ๊ฐœ์„ ์‹œ์ผฐ๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์€ ๊ธฐ์กด ๋ฐฉ๋ฒ•๋“ค์„ ๋Šฅ๊ฐ€ํ•˜๋Š” ๋›ฐ์–ด๋‚œ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์—ฌ์ฃผ๋ฉฐ, ์‹ค์ œ ์ž„์ƒ

Detection Model
Learned-Rule-Augmented Large Language Model Evaluators

Learned-Rule-Augmented Large Language Model Evaluators

์ตœ๊ทผ ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ์˜ ๋ฐœ์ „์€ ์ž์—ฐ์–ด ์ƒ์„ฑ(NLG) ์ž‘์—…์—์„œ ํ…์ŠคํŠธ ํ’ˆ์งˆ์„ ํ‰๊ฐ€ํ•˜๋Š” ๋ฐ ์žˆ์–ด LLM์ด ํšจ๊ณผ์ ์ด๊ณ  ํ™•์žฅ ๊ฐ€๋Šฅํ•œ ํ‰๊ฐ€์ž๋กœ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ฃผ์—ˆ์Šต๋‹ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ LLM ํ‰๊ฐ€์ž์˜ ํŒจ๋Ÿฌ๋‹ค์ž„์„ ๋‹ค์–‘ํ•œ ์ž‘์—…์œผ๋กœ ํ™•์žฅํ•  ์ˆ˜ ์žˆ๋Š”์ง€ ํƒ๊ตฌํ•ฉ๋‹ˆ๋‹ค. ์—ฐ๊ตฌ์ž๋“ค์€ LLM์˜ ๋Šฅ๋ ฅ์„ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์— ๊ฑธ์ณ ํ™•์ธํ–ˆ์Šต๋‹ˆ๋‹ค: ์—์„ธ์ด ์ฑ„์ , ์ธ์šฉ ์ค‘์š”๋„ ํ‰๊ฐ€ ๋“ฑ. ๊ทธ๋Ÿฌ๋‚˜ LLM์„ ์‹ ๋ขฐํ•  ์ˆ˜ ์žˆ๋Š” ์ผ๋ฐ˜ ํ‰๊ฐ€์ž๋กœ ํ™œ์šฉํ•˜๋Š” ๋ฐ๋Š” ์—ฌ์ „ํžˆ ๋„์ „ ๊ณผ์ œ๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์ ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ทœ์น™ ๊ฐ•ํ™” ํ•™์Šต์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ๋จผ์ €,

Model
Learning spatial hearing via innate mechanisms

Learning spatial hearing via innate mechanisms

๋ณธ ๋…ผ๋ฌธ์€ ์ฒญ๊ฐ ๊ณต๊ฐ„ ์ง€๋„ ํ˜•์„ฑยท์œ ์ง€์— ์žˆ์–ด โ€˜๊ต์‚ฌโ€™ ์—ญํ• ์„ ํ•˜๋Š” ์™ธ๋ถ€ ํ”ผ๋“œ๋ฐฑ์ด ๋ฐ˜๋“œ์‹œ ํ•„์š”ํ•˜์ง€ ์•Š๋‹ค๋Š” ์ ์„ ์ด๋ก ยท์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์–‘์ธก๋ฉด์—์„œ ์„ค๋“๋ ฅ ์žˆ๊ฒŒ ์ž…์ฆํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์‹œ๊ฐ ํ”ผ๋“œ๋ฐฑ์ด ์ฒญ๊ฐ ์œ„์น˜ ์ถ”์ •์˜ ๊ต์ • ์‹ ํ˜ธ๋กœ ์ž‘์šฉํ•œ๋‹ค๋Š” ๊ฐ€์„ค์„ ๋’ท๋ฐ›์นจํ–ˆ์ง€๋งŒ, ์‹œ๊ฐ ์žฅ์• ์ธ์ด๋‚˜ ์˜์•„์—์„œ๋„ ์ •์ƒ ์ˆ˜์ค€ ํ˜น์€ ๊ทธ ์ด์ƒ์˜ ์œ„์น˜ ํŒ๋‹จ ๋Šฅ๋ ฅ์ด ๋‚˜ํƒ€๋‚˜๋Š” ํ˜„์ƒ์€ ์ด๋Ÿฌํ•œ ๊ฐ€์„ค์— ํ•œ๊ณ„๋ฅผ ๋“œ๋Ÿฌ๋‚ธ๋‹ค. ์ €์ž๋Š” ์ด๋ฅผ ์„ค๋ช…ํ•˜๊ธฐ ์œ„ํ•ด โ€˜๋ถ€ํŠธ์ŠคํŠธ๋žฉ ํ•™์Šตโ€™์ด๋ผ๋Š” ์ƒˆ๋กœ์šด ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ๋„์ž…ํ•œ๋‹ค. ํ•ต์‹ฌ ์•„์ด๋””์–ด๋Š” ๋‘ ๊ฐ€์ง€ ์‹ ๊ฒฝ ํšŒ๋กœโ€”โ€˜๊ต์‚ฌ(Teacher)โ€™์™€ โ€˜ํ•™์ƒ(Student)

Neural Computing Audio Processing Quantitative Biology Learning Computer Science Electrical Engineering and Systems Science
Learning to Code with Context: A Study-Based Approach

Learning to Code with Context: A Study-Based Approach

์ด ๋…ผ๋ฌธ์€ ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ ๊ณผ์ •์˜ ๋‹ค์–‘ํ•œ ๋‹จ๊ณ„์—์„œ ํ•™์ƒ๋“ค์ด ์ƒ์„ฑ์  AI ๋„๊ตฌ๋ฅผ ์–ด๋–ป๊ฒŒ ์‚ฌ์šฉํ•˜๋Š”์ง€ ์กฐ์‚ฌํ•ฉ๋‹ˆ๋‹ค. ๋˜ํ•œ ์ด๋Ÿฌํ•œ ๋„๊ตฌ๊ฐ€ ๊ฐ€์žฅ ํšจ๊ณผ์ ์ธ ์ž‘์—… ์œ ํ˜•์„ ์‹๋ณ„ํ•˜๊ณ , ํ•™์ƒ๋“ค์ด ์ง๋ฉดํ•œ ๋„์ „ ๊ณผ์ œ๋ฅผ ๋ถ„์„ํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, ์ €์žฅ์†Œ ์ธ์‹ LLM ๋ณด์กฐ ์žฅ์น˜๋ฅผ ์„ค๊ณ„ํ•˜์—ฌ ํ”„๋กœ์ ํŠธ์— ๋Œ€ํ•œ ๋งฅ๋ฝํ™”๋œ ์ง€์›์„ ์ œ๊ณตํ–ˆ์Šต๋‹ˆ๋‹ค. ์ด ์‹œ์Šคํ…œ์€ RAG๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์‘๋‹ต์„ ๊ด€๋ จ ๋ฌธ์„œ ๋ฐ ์†Œ์Šค ์ฝ”๋“œ๋กœ ์—ฐ๊ฒฐํ•จ์œผ๋กœ์จ ๋ชจ๋ธ ํ–‰๋™, ๋งค๊ฐœ ๋ณ€์ˆ˜ ๋ฏผ๊ฐ๋„ ๋ฐ ์ผ๋ฐ˜์ ์ธ ์‹คํŒจ ๋ชจ๋“œ๋ฅผ ์ž์„ธํžˆ ๋ถ„์„ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ๋ฒ•์€ ๊ต์œก์šฉ ์†Œํ”„ํŠธ์›จ์–ด ํ”„๋กœ์ ํŠธ์—์„œ ์ปจํ…์ŠคํŠธ ์ธ์‹ AI ์ง€์›์—

Learning
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LeMiCa: Lexicographic Minimax Path Caching for Efficient Diffusion-Based Video Generation

ํ™•์‚ฐ ๊ธฐ๋ฐ˜ ๋น„๋””์˜ค ์ƒ์„ฑ์€ ์ตœ๊ทผ ์ฃผ๋ชฉ์„ ๋ฐ›๊ณ  ์žˆ์ง€๋งŒ, ์ถ”๋ก  ์†๋„๋ฅผ ๋†’์ด๋Š” ๊ฒƒ์€ ์—ฌ์ „ํžˆ ๋„์ „ ๊ณผ์ œ์ž…๋‹ˆ๋‹ค. LeMiCa๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์„ค๊ณ„๋œ ํ˜์‹ ์ ์ธ ํ”„๋ ˆ์ž„์›Œํฌ์ž…๋‹ˆ๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ์บ์‹œ ์Šค์ผ€์ค„๋ง์„ ๊ทธ๋ž˜ํ”„๋กœ ํ‘œํ˜„ํ•˜๊ณ , ์ตœ์•…์˜ ๊ฒฝ๋กœ ์˜ค๋ฅ˜๋ฅผ ์ œํ•œํ•˜๋Š” ์ „๋žต์„ ํ†ตํ•ด ๊ธ€๋กœ๋ฒŒ ์ฝ˜ํ…์ธ  ์ผ๊ด€์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ต๋‹ˆ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ, LeMiCa๋Š” ๊ธฐ์กด ๊ธฐ๋ฒ•๋ณด๋‹ค ๋น ๋ฅธ ์†๋„์™€ ๋” ๋‚˜์€ ํ’ˆ์งˆ์„ ๋ณด์—ฌ์ค๋‹ˆ๋‹ค. ํŠนํžˆ, LPIPS ์ ์ˆ˜๊ฐ€ ๋‚ฎ๋‹ค๋Š” ๊ฒƒ์€ ์ƒ์„ฑ๋œ ๋น„๋””์˜ค๊ฐ€ ์›๋ณธ์— ๋งค์šฐ ๊ฐ€๊น๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•ฉ๋‹ˆ๋‹ค. ๋˜ํ•œ, ์ด ํ”„๋ ˆ์ž„์›Œํฌ์˜ ์ผ๋ฐ˜ํ™” ๋Šฅ๋ ฅ์€ ๋‹ค์–‘ํ•œ ๋ฒค์น˜๋งˆํฌ์—์„œ

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Lightweight Security for Ambient-Powered Programmable Reflections with Reconfigurable Intelligent Surfaces

AIoT์˜ ๋ณด์•ˆ์€ ๋ฐ์ดํ„ฐ ๋ฌด๊ฒฐ์„ฑ๊ณผ ํ”„๋ผ์ด๋ฒ„์‹œ ๋ณดํ˜ธ๋ฅผ ๋ณด์žฅํ•˜๊ธฐ ์œ„ํ•ด ํ•„์ˆ˜์ ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ๊ธฐ๊ธฐ๋“ค์˜ ์—๋„ˆ์ง€ ๋ฐ ์—ฐ์‚ฐ ์ œํ•œ์œผ๋กœ ์ธํ•ด ์ „ํ†ต์ ์ธ ๋ณด์•ˆ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๊ตฌํ˜„ํ•˜๋Š” ๊ฒƒ์€ ๋งค์šฐ ์–ด๋ ต๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆ๋œ ๊ฒฝ๋Ÿ‰ ๋ณด์•ˆ ์†”๋ฃจ์…˜์€ ์ด๋Ÿฌํ•œ ๋„์ „์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ์ž ์žฌ์ ์ธ ์ ‘๊ทผ๋ฒ•์ด๋‹ค. ์ด ์†”๋ฃจ์…˜์€ ์ œ์–ด ์ฑ„๋„์˜ ๋ณด์•ˆ์„ ๋ณด์žฅํ•˜์—ฌ BS์™€ RIS ์ปจํŠธ๋กค๋Ÿฌ ๊ฐ„์˜ ํ†ต์‹ ์ด ์•ˆ์ „ํ•˜๊ฒŒ ์ด๋ฃจ์–ด์ง€๋„๋ก ํ•œ๋‹ค.

Limits of the seismogenic zone in the epicentral region of the 26   December 2004 great Sumatra-Andaman earthquake: Results from seismic   refraction and wide-angle reflection surveys and thermal modeling

Limits of the seismogenic zone in the epicentral region of the 26 December 2004 great Sumatra-Andaman earthquake: Results from seismic refraction and wide-angle reflection surveys and thermal modeling

: 2004๋…„ ์ˆ˜๋งˆํŠธ๋ผ ์•ˆ๋‹ค๋งŒ ๋Œ€์ง€์ง„์€ ์ง€๊ตฌ์ƒ์—์„œ ๊ฐ€์žฅ ๊ฐ•๋ ฅํ•œ ์ง€์ง„ ์ค‘ ํ•˜๋‚˜์˜€์Šต๋‹ˆ๋‹ค. ์ด ์ง€์ง„์€ ์ธ๋„ ์˜ค์ŠคํŠธ๋ ˆ์ผ๋ฆฌ์•„ ํŒ ๊ฒฝ๊ณ„๋ฉด์—์„œ ๋ฐœ์ƒํ•˜์—ฌ ์—„์ฒญ๋‚œ ์—๋„ˆ์ง€๋ฅผ ๋ฐฉ์ถœํ–ˆ์Šต๋‹ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ง€์ง„์˜ ์›์ฒœ ์ง€์—ญ์ธ ์ˆ˜๋งˆํŠธ๋ผ ์„ญ์ž…๋Œ€์˜ ์‹ฌ์ธต ๊ตฌ์กฐ๋ฅผ ๋ถ„์„ํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ์ง€์ง„ ๋ฐœ์ƒ ๋‹จ์ธต์˜ ํ•œ๊ณ„๋ฅผ ์˜ˆ์ธกํ•˜๊ณ ์ž ํ•ฉ๋‹ˆ๋‹ค. ์ˆ˜๋งˆํŠธ๋ผ OBS ์กฐ์‚ฌ๋ฅผ ํ†ตํ•ด ํš๋“ํ•œ ๋ฐ์ดํ„ฐ๋Š” ์ง„์•™ ์ง€์—ญ์„ ๊ฐ€๋กœ์ง€๋ฅด๋Š” ์ƒ์„ธํ•œ ์ง€์ง„ํ•™ ํ”„๋กœํŒŒ์ผ์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ์ด ๋ฐ์ดํ„ฐ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์—ฌํ–‰ ์‹œ๊ฐ„ ๋‹จ์ธต ์ดฌ์˜๊ณผ ์ „ํ–ฅ์„ฑ ๋ชจ๋ธ๋ง์„ ๊ฒฐํ•ฉํ•˜์—ฌ ์ง€์ง„ ์†๋„ ๋ชจ๋ธ์„ ๊ตฌ์ถ•ํ–ˆ์Šต๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ชจ๋ธ์€ ์„ญ์ž…ํ•˜๋Š” ์Šฌ๋ž˜๋ธŒ

Physics Model
Linear theory and violent relaxation in long-range systems: a test case

Linear theory and violent relaxation in long-range systems: a test case

: ๋ณธ ๋…ผ๋ฌธ์˜ ์ฃผ์š” ๊ธฐ์—ฌ๋Š” Hamiltonian Mean Field (HMF) ๋ชจ๋ธ์˜ ์„ ํ˜• ๋ถˆ์•ˆ์ •์„ฑ๊ณผ ์žํ™”๋˜์ง€ ์•Š์€ ๋ƒ‰๊ฐ ํ‰ํ˜• ์ƒํƒœ์— ๋Œ€ํ•œ ์‹ฌ์ธต์ ์ธ ๋ถ„์„์ด๋‹ค. HMF ๋ชจ๋ธ์€ ์žฅ๊ฑฐ๋ฆฌ ์ƒํ˜ธ์ž‘์šฉ์„ ๊ฐ€์ง„ ์ž…์ž๊ณ„์˜ ์—ญํ•™์„ ์ดํ•ดํ•˜๋Š” ๋ฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋ฉฐ, ํŠนํžˆ 1์ฐจ์› ์ค‘๋ ฅ๊ณ„์™€ ์ฟจ๋กฑ๊ณ„๋ฅผ ๋‹จ์ˆœํ™”ํ•œ ํ† ์ด ๋ชจ๋ธ๋กœ ์‚ฌ์šฉ๋œ๋‹ค. ์ €์ž๋“ค์€ ๋จผ์ € ์œ ํ•œํ•œ ์ˆ˜์˜ ์ž…์ž์— ๋Œ€ํ•œ ์ •ํ™•ํ•œ ์„ ํ˜• ์„ฑ์žฅ๋ฅ ์„ ์ œ์‹œํ•œ๋‹ค. ์ด๋Š” ๋ฌดํ•œํ•œ ์ˆ˜์˜ ์ž…์ž๋ฅผ ๊ฐ€์ •ํ•˜๋Š” ๊ธฐ์กด์˜ Vlasov ํ‹€๊ณผ๋Š” ๋‹ค๋ฅธ ์ ‘๊ทผ๋ฒ•์ด๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” HMF ์‹œ์Šคํ…œ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์„ ํ˜• ๋ถˆ์•ˆ์ •์„ฑ์„ ์ •ํ™•ํ•˜๊ฒŒ ์˜ˆ์ธกํ•˜๊ณ , ์ดˆ๊ธฐ

Physics System MATH-PH Mathematics
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LiveTradeBench: Seeking Real-World Alpha with Large Language Models

: LiveTradeBench๋Š” ํ˜„์‹ค์ ์ธ ๊ฑฐ๋ž˜ ํ™˜๊ฒฝ์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•˜์—ฌ LLM ์—์ด์ „ํŠธ์˜ ์‹ค์ œ ์„ธ๊ณ„์—์„œ์˜ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ•˜๋Š” ํ˜์‹ ์ ์ธ ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ์‹ค์‹œ๊ฐ„ ๋ฐ์ดํ„ฐ ์ŠคํŠธ๋ฆฌ๋ฐ๊ณผ ๋‹ค์ค‘ ์‹œ์žฅ ํ‰๊ฐ€๋ฅผ ํ†ตํ•ด, ์ด ๋ฒค์น˜๋งˆํฌ๋Š” ๋ชจ๋ธ์ด ๋ถˆํ™•์‹คํ•œ ์ƒํ™ฉ ํ•˜์—์„œ ์˜์‚ฌ ๊ฒฐ์ •์„ ๋‚ด๋ฆฌ๋Š” ๋Šฅ๋ ฅ์„ ํ…Œ์ŠคํŠธํ•ฉ๋‹ˆ๋‹ค. ๊ฒฐ๊ณผ๋Š” LLMs์˜ ์„ฑ๋Šฅ์ด ์ •์  ๋ฒค์น˜๋งˆํฌ ์ ์ˆ˜์™€ ์ง์ ‘์ ์œผ๋กœ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ์—†์Œ์„ ์‹œ์‚ฌํ•˜๋ฉฐ, ์ด๋Š” ์‹ค์ œ ์„ธ๊ณ„์—์„œ์˜ ์ ์šฉ ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•œ ์ค‘์š”ํ•œ ํ†ต์ฐฐ๋ ฅ์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ๋˜ํ•œ, ๊ฐ ๋ชจ๋ธ์€ ๊ณ ์œ ํ•œ ํฌํŠธํด๋ฆฌ์˜ค ๊ด€๋ฆฌ ์Šคํƒ€์ผ์„ ๋ณด์—ฌ์ฃผ๋ฉฐ, ์ด๋Š” ์œ„ํ—˜๊ณผ ์ˆ˜์ต ์‚ฌ์ด์˜ ๊ท ํ˜•์„ ์ฐพ

Model
Local Computation: Lower and Upper Bounds

Local Computation: Lower and Upper Bounds

๋ณธ ๋…ผ๋ฌธ์€ ๋ถ„์‚ฐ ์ปดํ“จํŒ…์—์„œ ๊ฐ€์žฅ ๊ธฐ๋ณธ์ ์ธ ์ œํ•œ์ธ โ€œ์ง€์—ญ์„ฑ ์ œํ•œ(locality constraint)โ€์„ ์ •๋Ÿ‰์ ์œผ๋กœ ๋ถ„์„ํ•œ ์ ์—์„œ ํ•™์ˆ ์  ์˜์˜๊ฐ€ ํฌ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ฃผ๋กœ ์‹œ๊ฐ„ ๋ณต์žก๋„ (๋ผ์šด๋“œ ์ˆ˜)์™€ ํ†ต์‹ ๋Ÿ‰ (๋ฉ”์‹œ์ง€ ํฌ๊ธฐ) ์‚ฌ์ด์˜ ํŠธ๋ ˆ์ด๋“œ์˜คํ”„์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์ง€๋งŒ, ์ด ๋…ผ๋ฌธ์€ ๋…ธ๋“œ๊ฐ€ ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ๋Š” ๊ทธ๋ž˜ํ”„ ๊ตฌ์กฐ์  ์ •๋ณด ์ž์ฒด๊ฐ€ ๊ณ„์‚ฐ ๊ฐ€๋Šฅ์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ง์ ‘์ ์œผ๋กœ ๋‹ค๋ฃฌ๋‹ค. ๋จผ์ € ์ €์ž๋“ค์€ LOCAL ๋ชจ๋ธ์„ ์ฑ„ํƒํ•œ๋‹ค. ์ด ๋ชจ๋ธ์€ ๋ฉ”์‹œ์ง€ ํฌ๊ธฐ์™€ ์˜ค๋ฅ˜, ๋น„๋™๊ธฐ์„ฑ ๋“ฑ์„ ๋ชจ๋‘ ๋ฌด์‹œํ•˜๊ณ  ์˜ค์ง ๋ผ์šด๋“œ ์ˆ˜ ์™€ ๋…ธ๋“œ๊ฐ€ ๋ณผ ์ˆ˜ ์žˆ๋Š” ๊ฑฐ๋ฆฌโ€‘(k) ์ด์›ƒ

Computer Science Distributed Computing
Logical Evaluation of Consciousness: For Incorporating Consciousness   into Machine Architecture

Logical Evaluation of Consciousness: For Incorporating Consciousness into Machine Architecture

: ์ด ๋…ผ๋ฌธ์€ ๊ธฐ๊ณ„ ์˜์‹์˜ ๊ฐœ๋…์„ ์‹ฌ๋„ ์žˆ๊ฒŒ ๋ถ„์„ํ•˜๋ฉฐ, ๊ทธ ์‹คํ˜„ ๊ฐ€๋Šฅ์„ฑ๊ณผ ์ž ์žฌ๋ ฅ์„ ํƒ๊ตฌํ•ฉ๋‹ˆ๋‹ค. ์ €์ž๋Š” ๋จผ์ € ์ƒ๋ฌผํ•™์  ์กด์žฌ์˜ ์˜์‹์ด ์–ด๋–ป๊ฒŒ ์กฐ์ง๋˜๊ณ  ์ž‘๋™ํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ์งˆ๋ฌธ์„ ๋˜์ง‘๋‹ˆ๋‹ค. ์ด๋Š” ๋‹ค์–‘ํ•œ ํ•™๋ฌธ ๋ถ„์•ผ์—์„œ ์˜ค๋žœ ๊ธฐ๊ฐ„ ๋…ผ์˜๋˜์–ด ์˜จ ์ฃผ์ œ์ด๋ฉฐ, ํŠนํžˆ ์ฒ ํ•™์—์„œ๋Š” ์˜์‹์— ๋Œ€ํ•œ ์—ฌ๋Ÿฌ ๊ฐœ๋…ํ™”๊ฐ€ ์กด์žฌํ•ฉ๋‹ˆ๋‹ค. ์ €์ž๋Š” ๊ณผํ•™์  ์—ฐ๊ตฌ๋งŒ์œผ๋กœ๋Š” ์„ค๋ช…๋˜์ง€ ์•Š๋Š” ํ˜„์ƒ๋“ค์„ ์–ธ๊ธ‰ํ•˜๋ฉฐ, ์ฒ ํ•™์ด ์ด๋Ÿฌํ•œ ํ˜„์ƒ์„ ์ดํ•ดํ•˜๊ณ  ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜๋Š” ๋ฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค๊ณ  ์ฃผ์žฅํ•ฉ๋‹ˆ๋‹ค. ๊ธฐ๊ณ„ ์•„ํ‚คํ…์ฒ˜์— ์˜์‹์„ ๋„์ž…ํ•˜๋ ค๋Š” ์‹œ๋„๋Š” ์ง€๋Šฅ์ ์ธ ์ธก๋ฉด์„ ์ž์œจ์ ์œผ๋กœ ์ž‘๋™ํ•˜๊ฒŒ ํ•˜์—ฌ ์ƒ๋ช…์ฒด์™€ ์œ 

Computer Science Artificial Intelligence
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Long cycles in vertex transitive digraphs

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ Hamiltonicity ์— ๊ด€ํ•œ ๊ณ ์ „์  ์ถ”์ธก(Thomassen 1978, Lovรกsz 1969)์€ โ€œ์ถฉ๋ถ„ํžˆ ํฐ ์—ฐ๊ฒฐ ์ •์  ์ „์ด์„ฑ ๊ทธ๋ž˜ํ”„๋Š” Hamilton ์‚ฌ์ดํด์„ ๊ฐ–๋Š”๋‹คโ€๋Š” ๋‚ด์šฉ์ด๋‹ค. ๋ฐฉํ–ฅ ๊ทธ๋ž˜ํ”„ ๋ฒ„์ „์€ Rankin(1946)ยทRapaportโ€‘Strasser(1959) ๋“ฑ์—์„œ ์ด๋ฏธ ๋‹ค๋ฃจ์–ด์กŒ์œผ๋ฉฐ, Trotterโ€‘Erdล‘s(1978) ๊ฐ€ ๋ฌดํ•œํžˆ ๋งŽ์€ ๋น„ Hamiltonian ์ •์  ์ „์ด์„ฑ digraph๋ฅผ ์ œ์‹œํ•˜๋ฉด์„œ โ€œ์–ผ๋งˆ๋‚˜ ๊ธด ์‚ฌ์ดํด์„ ์ตœ์†Œ ๋ณด์žฅํ•  ์ˆ˜ ์žˆ๋‚˜?โ€๋ผ๋Š” ์งˆ๋ฌธ์ด ๋‚จ์•˜๋‹ค. Alspach(1981) ์€ p

Mathematics
Long-term X-ray Variability Study of IC342 from XMM-Newton Observations

Long-term X-ray Variability Study of IC342 from XMM-Newton Observations

๋ณธ ๋…ผ๋ฌธ์€ IC 342๋ผ๋Š” ๊ทผ๊ฑฐ๋ฆฌ ๋‚˜์„ ์€ํ•˜๋ฅผ ๋Œ€์ƒ์œผ๋กœ, XMMโ€‘Newton์˜ EPICโ€‘PN ๋ฐ MOS ์นด๋ฉ”๋ผ๋ฅผ ์ด์šฉํ•ด 4๋…„(2001โ€“2005) ๋™์•ˆ ๋„ค ์ฐจ๋ก€์— ๊ฑธ์นœ ๊ด€์ธก ๋ฐ์ดํ„ฐ๋ฅผ ์ •๋ฐ€ํ•˜๊ฒŒ ๋ถ„์„ํ•จ์œผ๋กœ์จ, ์€ํ•˜ ๋‚ด Xโ€‘์„  ์ ์›์ฒœ๋“ค์˜ ์žฅ๊ธฐ ๋ณ€๋™ ํŠน์„ฑ์„ ์ตœ์ดˆ๋กœ ์ „๋ฉด์ ์œผ๋กœ ์ œ์‹œํ•œ ์ ์—์„œ ํฐ ์˜๋ฏธ๋ฅผ ๊ฐ€์ง„๋‹ค. ์ฒซ์งธ, ๊ฒ€์ถœ๋œ 61๊ฐœ์˜ Xโ€‘์„  ์›์ฒœ ์ค‘ 39๊ฐœ๊ฐ€ ์žฅ๊ธฐ ๋ณ€๋™์„ ๋ณด์˜€๋‹ค๋Š” ๊ฒฐ๊ณผ๋Š” IC 342๊ฐ€ ๋‹จ์ˆœํžˆ ๋ช‡ ๊ฐœ์˜ ์ดˆ๊ด‘๋„ Xโ€‘์„  ์›์ฒœ(ULX)์œผ๋กœ๋งŒ ์ง€๋ฐฐ๋˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ, ๋‹ค์ˆ˜์˜ ๋ณ€๋™์„ฑ ๋†’์€ ์ €๊ด‘๋„ ์›์ฒœ๋“ค( (L {mathrm{X}} sim

Astrophysics
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Longitudinal EAS-Development Studies in the Air-Shower Experiment KASCADE-Grande

๋ณธ ์—ฐ๊ตฌ๋Š” KASCADEโ€‘Grande ์‹คํ—˜์— ๋ถ€์ฐฉ๋œ 128 mยฒ ๊ทœ๋ชจ์˜ ๋ฎค์˜จ ํŠธ๋ž˜ํ‚น ๊ฒ€์ถœ๊ธฐ(MTD)๋ฅผ ํ™œ์šฉํ•ด, ๋Œ€๊ธฐ ์ค‘ ๊ด‘๋ฒ”์œ„ํ•œ ์—์–ด ์ƒค์›Œ(EAS)์˜ ์ข…์ถ•(longitudinal) ๋ฐœ๋‹ฌ์„ ์ง์ ‘ ์žฌ๊ตฌ์„ฑํ•œ ์ตœ์ดˆ ์‚ฌ๋ก€ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๊ธฐ์กด์—๋Š” ๋ฎค์˜จ์ด ์ง€์ƒ์—์„œ ์ถฉ๋ถ„ํžˆ ์ •ํ™•ํ•œ ์ข…์ถ• ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ์œผ๋ฉฐ, ์ด๋Š” ๋Œ€๋ฉด์  ๋ฎค์˜จ ๋ง์›๊ฒฝ ๊ตฌ์ถ•์ด ๊ธฐ์ˆ ์ ์œผ๋กœ ์–ด๋ ค์› ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค

Astrophysics
Low Rank Matrix-Valued Chernoff Bounds and Approximate Matrix   Multiplication

Low Rank Matrix-Valued Chernoff Bounds and Approximate Matrix Multiplication

: ์ด ๋…ผ๋ฌธ์€ ํ–‰๋ ฌ ๊ณฑ์…ˆ๊ณผ ๊ฐ™์€ ํ‘œ์ค€ ํ–‰๋ ฌ ์—ฐ์‚ฐ์„ ๊ทผ์‚ฌํ™”ํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜๊ณ  ๋ถ„์„ํ•ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ทผ์‚ฌํ™”๋Š” ๋ฐ์ดํ„ฐ์˜ ํšจ์œจ์ ์ธ ์ฒ˜๋ฆฌ ๋ฐ ๊ณ„์‚ฐ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋„๋ฆฌ ์‚ฌ์šฉ๋ฉ๋‹ˆ๋‹ค. ์ €์ž๋“ค์€ ๊ธฐ์กด ๋ฐฉ๋ฒ•์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ณ  ๋” ๊ฐ•๋ ฅํ•œ ๊ฒฝ๊ณ„๊ฐ’์„ ์–ป๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ๋จผ์ €, ์ €์ž๋“ค์€ ํ–‰๋ ฌ ์ƒ˜ํ”Œ๋ง์— ๋Œ€ํ•œ ๋‘ ๊ฐ€์ง€ ๋‹ค๋ฅธ ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ํ•˜๋‚˜๋Š” ์ž…๋ ฅ ํ–‰๋ ฌ์˜ ํ–‰์„ ๋น„๊ท ์ผํ•˜๊ฒŒ ์ƒ˜ํ”Œ๋งํ•˜๋Š” ๊ฒƒ์ด๊ณ , ๋‹ค๋ฅธ ํ•˜๋‚˜๋Š” ๋ฌด์ž‘์œ„ ์„ ํ˜• ์กฐํ•ฉ์„ ์ทจํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•์€ ์ž…๋ ฅ ํ–‰๋ ฌ์˜ ์ˆœ์œ„๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ๊ฒฝ๊ณ„๊ฐ’์„ ์ œ๊ณตํ•˜๋ฉฐ, ํŠนํžˆ ์•ˆ์ •๋œ ์ˆœ

Computer Science Discrete Mathematics Data Structures Mathematics
Lower bounds for the maximum number of runners that cause loneliness,   and its application to Isolation

Lower bounds for the maximum number of runners that cause loneliness, and its application to Isolation

๋ณธ ๋…ผ๋ฌธ์€ ์ „ํ†ต์ ์ธ Lonely Runner Conjecture(LRC)์˜ โ€œ๋ชจ๋“  ์ฃผ์ž๊ฐ€ ์–ด๋А ์ˆœ๊ฐ„ ์™ธ๋กญ๊ฒŒ ๋œ๋‹คโ€๋Š” ์กด์žฌ๋ก ์  ๋ช…์ œ ๋Œ€์‹ , ์ •๋Ÿ‰์  ํ•œ๊ณ„๊ฐ’์„ ํƒ๊ตฌํ•œ๋‹ค๋Š” ์ ์—์„œ ๋…์ฐฝ์ ์ด๋‹ค. ์ €์ž๋“ค์€ ๋จผ์ € PMAX ๋ผ๋Š” ์ƒˆ๋กœ์šด ์ตœ์ ํ™” ๋ฌธ์ œ๋ฅผ ์ •์˜ํ•œ๋‹ค. ์—ฌ๊ธฐ์„œ โ€œ๋™์‹œ์— ๊ฑฐ๋ฆฌ (>d) ๋งŒํผ ๋–จ์–ด์ง„ ์ฃผ์žโ€๋ผ๋Š” ๊ฐœ๋…์€ ๊ธฐ์กด LRC์—์„œ ์š”๊ตฌํ•˜๋Š” โ€œ๊ฑฐ๋ฆฌ (ge frac{1}{n+1})โ€์™€ ์ง์ ‘์ ์ธ ๋Œ€์‘ ๊ด€๊ณ„์— ์žˆ๋‹ค. ํŠนํžˆ (d frac{1}{n+1})์ด๋ฉด PMAX (n)์ด๋ผ๋Š” ์‹์€ LRC๊ฐ€ ์ฐธ์ผ ๊ฒฝ์šฐ์™€ ๋™์น˜๊ฐ€ ๋˜๋ฏ€๋กœ, PMAX๋Š”

Computer Science Computational Geometry Computational Complexity
Lowest Degree Decomposition of Complex Networks

Lowest Degree Decomposition of Complex Networks

: ๋ณต์žกํ•œ ๋„คํŠธ์›Œํฌ์˜ ๊ตฌ์กฐ์™€ ๊ธฐ๋Šฅ์€ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ํŠนํžˆ, ํ•ต์‹ฌ ๋…ธ๋“œ๋ฅผ ์‹๋ณ„ํ•˜๋Š” ๊ฒƒ์€ ์‚ฌํšŒ ๊ด‘๊ณ , ์ „์—ผ๋ณ‘ ํ™•์‚ฐ ๋ฐฉ์ง€, ์•ฝ๋ฌผ ํ‘œ์  ํ›„๋ณด ๋ฐœ๊ฒฌ ๋“ฑ ์—ฌ๋Ÿฌ ์‘์šฉ ๋ถ„์•ผ์— ์ ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ํ•ต์‹ฌ ๋…ธ๋“œ ์‹๋ณ„์„ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์ธ ์ตœ์ € ์ฐจ์ˆ˜ ๋ถ„ํ•ด(LDD)๋ฅผ ์†Œ๊ฐœํ•œ๋‹ค. LDD๋Š” ๋„คํŠธ์›Œํฌ์˜ ์ค‘์ฒฉ๋œ ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ์„ค๊ณ„๋˜์—ˆ๋‹ค. ์ด๋Š” k core ๋ถ„ํ•ด์˜ ๋ณ€ํ˜•์œผ๋กœ, ์ตœ์†Œ ์ฐจ์ˆ˜๋ฅผ ๊ฐ€์ง„ ๋…ธ๋“œ๋ฅผ ์ œ๊ฑฐํ•˜๋Š” ๊ณผ์ •์„ ํ†ตํ•ด ์ง„ํ–‰๋œ๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ๊ฐ ์‰˜์— ์ˆ˜๋งŽ์€ ๋…ธ๋“œ๊ฐ€ ํฌํ•จ๋˜๋Š” k core ๋ถ„ํ•ด์˜ ๋‹จ์ ์„ ๊ทน๋ณตํ•˜๊ณ , ๋” ๋ช…ํ™•ํ•œ

Physics Network Mathematics Social Networks Computer Science
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LUME-DBN: Full Bayesian Learning of DBNs from Incomplete data in Intensive Care

๋™์  ๋ฒ ์ด์ฆˆ ๋„คํŠธ์›Œํฌ(DBN)๋Š” ์˜๋ฃŒ ๋ถ„์•ผ์—์„œ ๋ณต์žกํ•œ ์‹œ๊ฐ„์  ๊ด€๊ณ„๋ฅผ ๋ชจ๋ธ๋งํ•˜๋Š” ๋ฐ ์žˆ์–ด ์ค‘์š”ํ•œ ๋„๊ตฌ๋กœ ๋– ์˜ค๋ฅด๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, ์ง‘์ค‘ ์น˜๋ฃŒ์™€ ๊ฐ™์€ ํ™˜๊ฒฝ์—์„œ๋Š” ํ™˜์ž์˜ ์ƒํƒœ๊ฐ€ ๋น ๋ฅด๊ฒŒ ๋ณ€ํ™”ํ•˜๊ณ , ๋‹ค์–‘ํ•œ ์š”์ธ๋“ค์ด ์ƒํ˜ธ ์ž‘์šฉํ•˜๊ธฐ ๋•Œ๋ฌธ์— DBN์˜ ๋Šฅ๋ ฅ์€ ๋”์šฑ ์ค‘์š”ํ•ฉ๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด ๋ฐฉ๋ฒ•๋“ค์€ ์ •์ ์ธ ๋ฒ ์ด์ฆˆ ๋„คํŠธ์›Œํฌ์— ๊ธฐ๋ฐ˜์„ ๋‘๊ณ  ์žˆ์–ด, ์‹œ๊ฐ„์  ํŠน์„ฑ์„ ์ œ๋Œ€๋กœ ๊ณ ๋ คํ•˜์ง€ ๋ชปํ–ˆ์Šต๋‹ˆ๋‹ค. ์ด๋Š” ๋ชจ๋ธ์ด ์‹œ๊ฐ„์— ๋”ฐ๋ผ ์–ด๋–ป๊ฒŒ ๋ณ€ํ™”ํ•˜๋Š”์ง€, ๊ทธ๋ฆฌ๊ณ  ๋ถˆํ™•์‹ค์„ฑ์ด ์–ด๋–ป๊ฒŒ ์ฆ๊ฐ€ํ•˜๊ฑฐ๋‚˜ ๊ฐ์†Œํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ์ œํ•œํ•ฉ๋‹ˆ๋‹ค. ์ €ํฌ๊ฐ€ ์ œ์•ˆํ•œ LUME DBN ๋ฐฉ๋ฒ•์€ ์ด๋Ÿฌํ•œ ํ•œ๊ณ„

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