KOINEU Logo
No Image

WireBend-kit: A Computational Design and Fabrication Toolkit for Wirebending Custom 3D Wireframe Structures

์™€์ด์–ด๋ฒค๋“œ ํ‚ท์€ ์•Œ๋ฃจ๋ฏธ๋Š„ ์™€์ด์–ด๋กœ๋ถ€ํ„ฐ ๋งž์ถคํ˜• 3D ๊ตฌ์กฐ๋ฅผ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ํ˜์‹ ์ ์ธ ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ์ด ์‹œ์Šคํ…œ์€ ์ €๋ ดํ•œ ํ•˜๋“œ์›จ์–ด์™€ ์†Œํ”„ํŠธ์›จ์–ด ๋„๊ตฌ๋ฅผ ๊ฒฐํ•ฉํ•˜์—ฌ ์‚ฌ์šฉ์ž๋“ค์ด ๋ณต์žกํ•œ ์™€์ด์–ดํ”„๋ ˆ์ž„ ๋””์ž์ธ์„ ์ œ์ž‘ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•ฉ๋‹ˆ๋‹ค. ๊ฒฝ๋กœ ๊ณ„ํš ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์žฌ๋ฃŒ์˜ ๋ฌผ๋ฆฌ์  ํŠน์„ฑ๊ณผ ๊ธฐ๊ณ„์  ์˜ค๋ฅ˜๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์ •ํ™•ํ•œ ์ œ์ž‘ ์ง€์นจ์„ ์ƒ์„ฑํ•˜๋ฉฐ, ์ด๋Š” ์ •๋ฐ€ํ•œ ๊ตฌ์กฐ๋ฌผ์„ ์ƒ์‚ฐํ•˜๋Š” ๋ฐ ํ•„์ˆ˜์ ์ž…๋‹ˆ๋‹ค. ๋˜ํ•œ, ์ด ์‹œ์Šคํ…œ์€ ๊ฐ€์ƒ ๋””์ž์ธ๊ณผ ์‹ค์ œ ์ œ์ž‘ ์‚ฌ์ด์˜ ๊ฐ„๊ฒฉ์„ ์ขํ˜€ ์‚ฌ์šฉ์ž๋“ค์ด ์™€์ด์–ดํ”„๋ ˆ์ž„ ๊ตฌ์กฐ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ํƒ์ƒ‰ํ•˜๊ณ  ์‹คํ—˜ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•ฉ๋‹ˆ๋‹ค.

XTE J1701-462 and its Implications for the Nature of Subclasses in   Low-Magnetic-Field Neutron Star Low-Mass X-Ray Binaries

XTE J1701-462 and its Implications for the Nature of Subclasses in Low-Magnetic-Field Neutron Star Low-Mass X-Ray Binaries

: ์ด ๋…ผ๋ฌธ์€ ์ค‘์„ฑ์ž๋ณ„ ์ €์งˆ๋Ÿ‰ X ์„  ์Œ์„ฑ๊ณ„(NS LMXB)์˜ ํ•˜์œ„ ๋ถ„๋ฅ˜์— ๋Œ€ํ•œ ์‹ฌ๋„ ์žˆ๋Š” ๋ถ„์„์„ ํ†ตํ•ด ํฅ๋ฏธ๋กœ์šด ํ†ต์ฐฐ๋ ฅ์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. NS LMXB๋Š” ์ž๊ธฐ์žฅ์ด ์•ฝํ•œ ์ค‘์„ฑ์ž๋ณ„๊ณผ ๋‚ฎ์€ ์งˆ๋Ÿ‰์˜ ๋™๋ฐ˜์„ฑ์œผ๋กœ ๊ตฌ์„ฑ๋œ ์‹œ์Šคํ…œ์œผ๋กœ, ๋‹ค์–‘ํ•œ X ์„  ์ŠคํŽ™ํŠธ๋Ÿผ๊ณผ ๋ณ€๋™์„ฑ์„ ๋ณด์—ฌ์ค๋‹ˆ๋‹ค. ์—ฐ๊ตฌ์ง„์€ ์ด๋Ÿฌํ•œ ํ•˜์œ„ ๋ถ„๋ฅ˜ ๊ฐ„์˜ ์ „์ด์™€ ๋™์ž‘์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ์‹ฌํ™”ํ•˜๊ธฐ ์œ„ํ•ด XTE J1701 462๋ฅผ ์ง‘์ค‘์ ์œผ๋กœ ๋ถ„์„ํ–ˆ์Šต๋‹ˆ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” Z ์†Œ์Šค์™€ Atoll ์†Œ์Šค ๊ฐ„์˜ ์ „ํ™˜์ด ์ฃผ๋กœ ์ƒ‰์ƒ ์ƒ‰์ƒ ๋ฐ ๊ฐ•๋„ ๊ฐ•๋„ ๋‹ค์ด์–ด๊ทธ๋žจ์—์„œ ํŠธ๋ž™์˜ ๋ชจ์–‘ ๋ณ€ํ™”๋กœ ๋‚˜ํƒ€๋‚œ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ฃผ์—ˆ์Šต๋‹ˆ๋‹ค.

Astrophysics
No Image

Z[1/p]-motivic resolution of singularities

: ์ด ๋…ผ๋ฌธ์€ ํŠน์„ฑ 0 ์ฒด ์œ„์˜ ํŠน์ด์  ํ•ด์†Œ๊ฐ€ Voevodsky์˜ ๋™๊ธฐ ์ด๋ก ์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ์— ๊ธฐ๋ฐ˜์„ ๋‘๊ณ  ์žˆ๋‹ค. ๊ฐ€๋ฒ„์˜ ์ตœ๊ทผ ๊ฒฐ๊ณผ๋Š” ์™„๋ฒฝํ•œ ํŠน์„ฑ $p$ ์ฒด ์œ„์˜ '$Z(l)$ ํŠน์ด์  ํ•ด์†Œ'๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋ฉฐ, ์ด๋Š” $Z

Mathematics
Zipfs Law Leads to Heaps Law: Analyzing Their Relation in Finite-Size   Systems

Zipfs Law Leads to Heaps Law: Analyzing Their Relation in Finite-Size Systems

: ๋ณต์žก์„ฑ ๊ณผํ•™ ๋ถ„์•ผ์—์„œ ์ง‘ํ”„์˜ ๋ฒ•์น™๊ณผ ํž™์Šค์˜ ๋ฒ•์น™์€ ๋‹ค์–‘ํ•œ ์‹œ์Šคํ…œ์—์„œ ๊ด€์ฐฐ๋˜๋Š” ๋ณดํŽธ์ ์ธ ๋ฒ•์น™์ž…๋‹ˆ๋‹ค. ํŠนํžˆ, ์ด ๋‘ ๋ฒ•์น™์€ ํ•จ๊ป˜ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์œผ๋ฉฐ, ์ด๋Š” ํฅ๋ฏธ๋กœ์šด ํ˜„์ƒ์œผ๋กœ ์—ฌ๊ฒจ์ง‘๋‹ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๊ณต์กด ํ˜„์ƒ์„ ๋ถ„์„ํ•˜๊ณ , ๋‘ ๋ฒ•์น™ ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ๋ช…ํ™•ํžˆ ํ•˜๊ณ ์ž ํ•ฉ๋‹ˆ๋‹ค. ๋จผ์ €, ์ง‘ํ”„์˜ ๋ฒ•์น™์— ๋Œ€ํ•ด ์‚ดํŽด๋ณด๊ฒ ์Šต๋‹ˆ๋‹ค. ์ง‘ํ”„์˜ ๋ฒ•์น™์€ ๋‹จ์–ด ๋นˆ๋„ ๋ถ„ํฌ์—์„œ ๋ฐœ๊ฒฌ๋˜๋Š” ์Šค์ผ€์ผ๋ง ๋ฒ•์น™์œผ๋กœ, ๋ชจ๋“  ๋‹จ์–ด๋ฅผ ๋ฐœ์ƒ ๋นˆ๋„์— ๋”ฐ๋ผ ์ˆœ์œ„๋ฅผ ๋งค๊ฒผ์„ ๋•Œ ํŠน์ • ์ „๋ ฅ๋ฒ• ๊ทœ์น™์„ ๋”ฐ๋ฆ…๋‹ˆ๋‹ค. ์ด ๋ฒ•์น™์€ ๋‹ค์–‘ํ•œ ๋ณต์žกํ•œ ์‹œ์Šคํ…œ์—์„œ ๊ด€์ฐฐ๋˜๋ฉฐ, ๊ธฐ์—… ๊ทœ๋ชจ, ๋ถ€์™€ ์†Œ๋“,

Physics System
ํ•˜์ดํผ๋ณผ๋ฆญRAG ๊ทธ๋ž˜ํ”„ ๊ธฐ๋ฐ˜ ๊ฒ€์ƒ‰ ์ฆ๊ฐ• ์ƒ์„ฑ์˜ ๊นŠ์ด ์ธ์‹ ํ”„๋ ˆ์ž„์›Œํฌ

ํ•˜์ดํผ๋ณผ๋ฆญRAG ๊ทธ๋ž˜ํ”„ ๊ธฐ๋ฐ˜ ๊ฒ€์ƒ‰ ์ฆ๊ฐ• ์ƒ์„ฑ์˜ ๊นŠ์ด ์ธ์‹ ํ”„๋ ˆ์ž„์›Œํฌ

: HyperbolicRAG๋Š” ๋ณต์žกํ•œ ์ง€์‹ ๊ทธ๋ž˜ํ”„์˜ ๊ณ„์ธต์  ์ข…์†์„ฑ์„ ํšจ๊ณผ์ ์œผ๋กœ ํ‘œํ˜„ํ•˜๊ณ  ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•œ ํ˜์‹ ์ ์ธ ์ ‘๊ทผ ๋ฐฉ์‹์ด๋‹ค. ๊ธฐ์กด ์œ ํด๋ฆฌ๋“œ ์ž„๋ฒ ๋”ฉ์€ ํ‰ํ‰ํ•œ ๊ณต๊ฐ„์—์„œ ๋…ธ๋“œ๋ฅผ ๋ฐฐ์น˜ํ•˜์—ฌ ์ผ๋ฐ˜ ๊ฐœ๋…๊ณผ ํŠน์ • ๊ฐœ๋…์„ ๊ตฌ๋ถ„ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์ด๋Š” ์˜๋ฏธ์  ํ—ˆ๋ธŒ๋ฅผ ์ƒ์„ฑํ•˜๊ณ , ์ƒ์œ„ ์ˆ˜์ค€์˜ ๊ฐœ๋…์ด ํ•˜์œ„ ์ปจํ…์ŠคํŠธ์˜ ํ‰๊ท ์„ ๊ทผ์‚ฌํ•˜๊ฒŒ ๋งŒ๋“ ๋‹ค. ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋กœ ์ธํ•ด ๊ทธ๋ž˜ํ”„ ํƒ์ƒ‰์€ ๊ณ„์ธต ๊ตฌ์กฐ์— ๋Œ€ํ•œ ์ธ์‹์ด ๋ถ€์กฑํ•ด์ง„๋‹ค. ๋ฐ˜๋ฉด, ์ดˆ์›” ๊ธฐํ•˜ํ•™์„ ๋„์ž…ํ•œ HyperbolicRAG๋Š” ๋ฐฉ์‚ฌํ˜• ๊ฑฐ๋ฆฌ์™€ ์ง€์ˆ˜ ํ™•์žฅ์„ ํ†ตํ•ด ์˜๋ฏธ์  ๊นŠ์ด์™€ ํฌํ•จ ๊ด€๊ณ„๋ฅผ ๋ชจ๋ธ๋งํ•œ๋‹ค. ์ผ๋ฐ˜ ๊ฐœ๋…์€ ์ค‘์‹ฌ์— ์œ„

No Image

$k$-means considered harmful: On arbitrary topological changes in Mapper complexes

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ Mapper์™€ TDA : Mapper๋Š” ๋ฐ์ดํ„ฐ ์ง‘ํ•ฉ์„ ์ปค๋ฒ„๋งํ•˜๊ณ , ๊ฐ ์ปค๋ฒ„์— ๋Œ€ํ•œ ํด๋Ÿฌ์Šคํ„ฐ๋ง์„ ์ˆ˜ํ–‰ํ•œ ๋’ค, ๊ฒน์น˜๋Š” ํด๋Ÿฌ์Šคํ„ฐ๋“ค์„ ์—ฐ๊ฒฐํ•ด ์œ„์ƒ์  ์š”์•ฝ์„ ๋งŒ๋“ ๋‹ค. ์ด ๊ณผ์ •์—์„œ ํด๋Ÿฌ์Šคํ„ฐ๋ง ๋‹จ๊ณ„๋Š” Mapper ๊ฒฐ๊ณผ์˜ ์œ„์ƒ ๊ตฌ์กฐ๋ฅผ ๊ฒฐ์ •์ง“๋Š” ํ•ต์‹ฌ ์š”์†Œ์ด๋‹ค. kโ€‘means์˜ ์ธ๊ธฐ : kโ€‘means๋Š” ๊ตฌํ˜„์ด ๊ฐ„๋‹จํ•˜๊ณ  ๋น ๋ฅด๋ฉฐ, ํŒŒ๋ผ๋ฏธํ„ฐ๊ฐ€ ์ ์–ด ์‹ค๋ฌด์—์„œ ์ž์ฃผ ์‚ฌ์šฉ๋œ๋‹ค. ํ•˜์ง€๋งŒ kโ€‘means๋Š” ๊ตฌํ˜• ๊ตฐ์ง‘ ๊ฐ€์ •, ์ดˆ๊ธฐ ์ค‘์‹ฌ์  ์˜์กด์„ฑ, ๊ฑฐ๋ฆฌ ๊ธฐ๋ฐ˜ ๊ตฐ์ง‘ํ™”๋ผ๋Š” ์ œํ•œ์„ ๊ฐ€์ง„๋‹ค. 2. ์ฃผ์š” ์ฃผ์žฅ ์ž„์˜์ ์ธ ์œ„์ƒ ๋ณ€ํ™” : ์ €์ž๋“ค์€ kโ€‘mean

No Image

3DiFACE: Synthesizing and Editing Holistic 3D Facial Animation

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ํ˜„์‹ค์  ์ œ์•ฝ : ๊ธฐ์กด ์Œ์„ฑโ€‘๊ตฌ๋™ 3D ์–ผ๊ตด ์• ๋‹ˆ๋ฉ”์ด์…˜ ์‹œ์Šคํ…œ์€ ์ž…์ˆ  ์›€์ง์ž„(๋ฆฌํ•‘)๊ณผ ๋จธ๋ฆฌ ์›€์ง์ž„์„ ๋ณ„๋„๋กœ ๋‹ค๋ฃจ๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์•„, ์ „์ฒด์ ์ธ โ€˜ํ™€๋ฆฌ์Šคํ‹ฑโ€™ํ•œ ์›€์ง์ž„์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ํŽธ์ง‘ ์–ด๋ ค์›€ : ์ƒ์„ฑ๋œ ์• ๋‹ˆ๋ฉ”์ด์…˜์„ ๋ถ€๋ถ„์ ์œผ๋กœ ์ˆ˜์ •ํ•˜๋ ค๋ฉด ์ „๋ฌธ ์• ๋‹ˆ๋ฉ”์ดํ„ฐ๊ฐ€ ํ‚คํ”„๋ ˆ์ž„์„ ์ผ์ผ์ด ์กฐ์ •ํ•ด์•ผ ํ•˜๋Š”๋ฐ, ์ด๋Š” ๋น„์šฉยท์‹œ๊ฐ„์ด ๋งŽ์ด ๋“ ๋‹ค. ๋‹ค์–‘์„ฑ ๊ฒฐ์—ฌ : ๋™์ผํ•œ ์Œ์„ฑ์— ๋Œ€ํ•ด ์—ฌ๋Ÿฌ ๊ฐ€๋Šฅํ•œ ์ž…์ˆ ยท๋จธ๋ฆฌ ์กฐํ•ฉ์ด ์กด์žฌํ•จ์—๋„, ๋Œ€๋ถ€๋ถ„์˜ ๋ชจ๋ธ์€ ํ•˜๋‚˜์˜ โ€˜์ •๋‹ตโ€™๋งŒ์„ ์ถœ๋ ฅํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๊ธฐ์—ฌ ๋‚ด์šฉ | ์˜์˜ | | | |

No Image

A Comparison of Bayesian Prediction Techniques for Mobile Robot Trajectory Tracking

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฒ ์ด์ฆˆ ์ถ”์ •์˜ ํ•ต์‹ฌ์„ฑ : ๋กœ๋ด‡ยท์ž๋™์ฐจยทํ•ญ๊ณต ๋“ฑ ์‹ค์‹œ๊ฐ„ ์ œ์–ด ์‹œ์Šคํ…œ์—์„œ ์ƒํƒœ ์ถ”์ • ์ •ํ™•๋„๋Š” ์ „์ฒด ์„ฑ๋Šฅ์„ ์ขŒ์šฐํ•œ๋‹ค(๋ฌธํ—Œ

Robotics Computer Science
No Image

A consequence of failed sequential learning: A computational account of developmental amnesia

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๊ธฐ์กด ์ด๋ก ๊ณผ์˜ ์ฐจ๋ณ„์  | ๊ธฐ์กด ์ด๋ก  | ํ•ต์‹ฌ ์ฃผ์žฅ | ํ•œ๊ณ„ | | | | | | ์—ํ”ผ์†Œ๋“œโ€‘์˜๋ฏธ ์—ฐ๊ณ„ ๊ฐ€์„ค (Eichenbaum ๋“ฑ) | ์˜๋ฏธ ๊ธฐ์–ต์€ ์—ํ”ผ์†Œ๋“œ ๊ธฐ์–ต์˜ ์žฅ๊ธฐ ํ†ตํ•ฉ ๊ฒฐ๊ณผ | ํ•ด๋งˆ ์†์ƒ ์‹œ ์˜๋ฏธ ๊ธฐ์–ต๋„ ํฌ๊ฒŒ ์ €ํ•˜๋  ๊ฒƒ์ด๋ผ ์˜ˆ์ธก | | ์ ์‘โ€‘๋ณด์ƒ ๊ฐ€์„ค (Squire & Zola ๋“ฑ) | ์กฐ๊ธฐ ์†์ƒ์ด ์‹ ๊ฒฝ ๊ฐ€์†Œ์„ฑ์„ ์ด‰์ง„ํ•ด ์˜๋ฏธ ํ•™์Šต์„ ๋ณด์ƒ | ๊ตฌ์ฒด์  ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด ์ œ์‹œ๋˜์ง€ ์•Š์Œ | | ์ž”์กด ์—ํ”ผ์†Œ๋“œ ๊ธฐ์–ต ๊ฐ€์„ค | ๋‚จ์•„์žˆ๋Š” ๋ฏธ์„ธ ์†์ƒ๋œ ์—ํ”ผ์†Œ๋“œ ๊ธฐ์–ต์ด ์˜๋ฏธ ํ•™์Šต์„ ์ง€์› | ์ž”์กด ๊ธฐ์–ต์ด ์ถฉ๋ถ„ํžˆ ๊ฐ•๋ ฅํ•˜๋‹ค๋Š” ๊ฐ€์ •์ด ์•ฝ

Learning Quantitative Biology
No Image

A dialog between cell adhesion and topology at the core of morphogenesis

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•ต์‹ฌ ์งˆ๋ฌธ ํ˜•ํƒœ ๋ฐœ์ƒ์˜ ๊ธฐ๋ณธ ์ œ์•ฝ : ์™ธ๋ถ€ ํž˜ ์—†์ด ์ž์ฒด์ ์ธ ์„ธํฌ ์ง‘๋‹จ ํ–‰๋™๋งŒ์œผ๋กœ ๋ณต์žกํ•œ ํ˜•ํƒœ๋ฅผ ๋งŒ๋“ ๋‹ค. ์œ„์ƒํ•™์  ๊ด€์  : ์กฐ์ง์„ โ€œ๊ตฌ๋ฉยท๊ด€ํ˜•ยท๊ตฌํ˜•โ€ ๋“ฑ ํ˜•ํƒœ๊ฐ€ ์•„๋‹Œ ์—ฐ๊ฒฐ๋ง (cellโ€‘cell contact network)์œผ๋กœ ์ •์˜ํ•œ๋‹ค. ํ•ต์‹ฌ ์งˆ๋ฌธ 1. ์œ„์ƒ ํŒจํ„ด์€ ์–ด๋–ป๊ฒŒ ์กฐ์ ˆ๋˜๋Š”๊ฐ€? 2. ์œ„์ƒ ๊ตฌ์กฐ์™€ ๋ฌผ์งˆ์ (๊ธฐ๊ณ„์ ) ํŠน์„ฑ ์‚ฌ์ด์— ํ”ผ๋“œ๋ฐฑ์ด ์กด์žฌํ•˜๋Š”๊ฐ€? 3. ๋‹ค์–‘ํ•œ ์œ„์ƒ ํ‘œํ˜„ํ˜•์€ ์–ด๋–ค ๋ฉ”์ปค๋‹ˆ์ฆ˜์œผ๋กœ ์„ ํƒ๋˜๋Š”๊ฐ€? 2. ์ด๋ก ์  ํ”„๋ ˆ์ž„์›Œํฌ | ์š”์†Œ | ์„ค๋ช… | ์ฃผ์š” ์‹/๊ฐœ๋… | | | | | | ์ ‘์ฐฉ ๊ฐ•๋„ ฮฑ | ์„ธํฌโ€‘์•ก

Quantitative Biology
No Image

A Dynamical Microscope for Multivariate Oscillatory Signals: Validating Regime Recovery on Shared Manifolds

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฉ”ํƒ€์•ˆ์ •์„ฑ(Metastability) : ๋ณต์žก๊ณ„๋Š” ๊ณ ์ •๋œ ํ‰ํ˜• ์ƒํƒœ๊ฐ€ ์•„๋‹ˆ๋ผ ์—ฌ๋Ÿฌ ๋ฐ˜์•ˆ์ •์  ๊ตฌ์„ฑ์„ ๋น ๋ฅด๊ฒŒ ์ „์ดํ•œ๋‹ค. ๊ธฐ์กด์˜ ์ŠคํŽ™ํŠธ๋Ÿผยท์—ฐ๊ฒฐ์„ฑ ๋งคํŠธ๋ฆญ์Šค์™€ ๊ฐ™์€ ์ •์  ์š”์•ฝ์€ ์ด๋Ÿฌํ•œ ์ „์ด๋ฅผ ๊ฐ€๋ ค๋ฒ„๋ฆฐ๋‹ค. ์—ฐ์† ๊ถค์  ๊ด€์  : ์‹ ํ˜ธ๋ฅผ ๊ณ ์ฐจ์› ์ƒํƒœ๊ณต๊ฐ„์˜ ์—ฐ์†์ ์ธ ๊ถค์ ์œผ๋กœ ๋ฐ”๋ผ๋ณด๋ฉด, ๋ผ๊ทธ๋ž‘์ง€์•ˆ(Lagrangian) (๊ถค์ )๊ณผ ์˜ค์ผ๋Ÿฌ๋ฆฌ์•ˆ(Eulerian) (๋ฐ€๋„ยทํ๋ฆ„) ๋‘ ์‹œ๊ฐ์„ ๋™์‹œ์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ โ€œ๋™์  ํ˜„๋ฏธ๊ฒฝโ€ | ๋‹จ๊ณ„ | ๋‚ด์šฉ | ๋ชฉ์  | | | | | | ์œ„์ƒโ€‘์ง„ํญ ์ธ์ฝ”๋”ฉ | ๊ฐ ์ฑ„๋„์—

Quantitative Biology
No Image

A general framework for modeling Gaussian process with qualitative and quantitative factors

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ QQ ์ž…๋ ฅ์˜ ์‹ค๋ฌด์  ์ค‘์š”์„ฑ : ์ œ์‹œ๋œ ๋‘ ์‚ฌ๋ก€(์ œ๋ฐฉ ์„ค๊ณ„, ๋ฐ์ดํ„ฐ์„ผํ„ฐ ์—ด์—ญํ•™)์ฒ˜๋Ÿผ ์ •์„ฑ ์š”์ธ์€ ์„ค๊ณ„ยท์šด์˜ ์˜ต์…˜์„, ์ •๋Ÿ‰ ์š”์ธ์€ ๋ฌผ๋ฆฌ๋Ÿ‰์„ ๋‚˜ํƒ€๋‚ด๋ฉฐ, ๋‘˜์„ ๋™์‹œ์— ๊ณ ๋ คํ•ด์•ผ๋งŒ ํ˜„์‹ค์ ์ธ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ชจ๋ธ๋ง์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ๊ธฐ์กด ์ ‘๊ทผ๋ฒ•์˜ ํ•œ๊ณ„ : ์ „์šฉ ๊ณต๋ถ„์‚ฐ ๊ตฌ์กฐ (์˜ˆ: Qian et al., 2008; Deng et al., 2017) โ€“ ์ง๊ด€์ ์ด์ง€๋งŒ ๋ฐฉ๋ฒ•๋งˆ๋‹ค ๋…๋ฆฝ์ ์ธ ๊ฐ€์ •์ด ์กด์žฌ. ์ž ์žฌ ๋ณ€์ˆ˜ ๊ธฐ๋ฐ˜ (Zhang et al., 2020) โ€“ ์ •์„ฑ ์š”์ธ์„ ์—ฐ์†ํ™”ํ–ˆ์ง€๋งŒ ์ปค๋„ ์„ ํƒ์ด ์ œํ•œ์ ์ด๋ฉฐ, ์ผ๋ฐ˜ํ™”๊ฐ€ ์–ด๋ ค์›€. ์ธ์ฝ”๋”ฉ ๊ธฐ๋ฐ˜

Statistics Framework Model
No Image

A Generative-First Neural Audio Autoencoder

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ์  ์žฌ๊ตฌ์„ฑโ€‘์šฐ์„  ์„ค๊ณ„์˜ ํ•œ๊ณ„ : ๊ธฐ์กด SoundStream, EnCodec, DAC ๋“ฑ์€ ์ฃผ๋กœ ๊ณ ํ’ˆ์งˆ ์žฌ๊ตฌ์„ฑ์„ ๋ชฉํ‘œ๋กœ ์„ค๊ณ„๋ผ, ์ƒ์„ฑ ๋ชจ๋ธ์— ํ•„์š”ํ•œ ๋‚ฎ์€ ํ† ํฐ ๋น„์œจยท๋น ๋ฅธ ์ธ์ฝ”๋”ฉ์„ ์ œ๊ณตํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์—ฐ์† vs. ์ด์‚ฐ ์ž ์žฌ์˜ ๋ถ„๋ฆฌ : ํ™•์‚ฐ ๋ชจ๋ธ์€ ์—ฐ์† ์ž ์žฌ๋ฅผ, ์–ธ์–ด ๋ชจ๋ธ์€ ์ด์‚ฐ ํ† ํฐ์„ ํ•„์š”๋กœ ํ•˜๋Š”๋ฐ, ํ˜„์žฌ๋Š” ๊ฐ๊ฐ ๋ณ„๋„ ๋ชจ๋ธ์„ ํ›ˆ๋ จํ•ด์•ผ ํ•˜๋Š” ๋น„ํšจ์œจ์„ฑ์ด ์กด์žฌํ•œ๋‹ค. ์ฑ„๋„ ํฌ๋งท ๋‹ค์–‘์„ฑ ๋ฏธ์ง€์› : ์Šคํ…Œ๋ ˆ์˜ค, ๋ชจ๋…ธ, Mid/Side ๋“ฑ ๋‹ค์–‘ํ•œ ํฌ๋งท์„ ํ•˜๋‚˜์˜ ๋ชจ๋ธ์ด ์ฒ˜๋ฆฌํ•˜์ง€ ๋ชปํ•ด ์ „์ฒ˜๋ฆฌยทํ›„์ฒ˜๋ฆฌ ํŒŒ์ดํ”„๋ผ์ธ์ด ๋ณต์žกํ•ด์ง„๋‹ค. 2. ํ•ต

Computer Science Sound
No Image

A Geometric Approach to Feedback Stabilization of Nonlinear Systems with Drift

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ๋“œ๋ฆฌํ”„ํŠธ๊ฐ€ ์žˆ๋Š” ์‹œ์Šคํ…œ ์€ ์ œ์–ด ์ž…๋ ฅ์ด ์—†๋Š” ๊ฒฝ์šฐ์—๋„ ์ƒํƒœ๊ฐ€ ์ž์ฒด์ ์œผ๋กœ ๋ณ€ํ•˜๋Š” ๋น„์„ ํ˜• ๋™์—ญํ•™์„ ์˜๋ฏธํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์‹œ์Šคํ…œ์€ Brockett์˜ ํ•„์š”์กฐ๊ฑด์„ ๋งŒ์กฑํ•˜์ง€ ์•Š์•„ ์—ฐ์†์ ์ธ ์ƒํƒœ ํ”ผ๋“œ๋ฐฑ์œผ๋กœ๋Š” ์ „์—ญ ์•ˆ์ •ํ™”๊ฐ€ ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” (i) ํŠน์ • ๊ตฌ์กฐ์— ์ œํ•œ๋œ ํ•ด๋ฒ•, (ii) ์‹œ๊ฐ„โ€‘๊ฐ€๋ณ€ ๊ฐœ๋ฐฉโ€‘๋ฃจํ”„ ์ œ์–ด, (iii) ๋ถˆ์—ฐ์† ํ”ผ๋“œ๋ฐฑ ๋“ฑ์œผ๋กœ ์ ‘๊ทผํ–ˆ์œผ๋ฉฐ, ๋Œ€๋ถ€๋ถ„ ์ โ€‘๋Œ€โ€‘์  ์กฐํ–ฅ ์„ ์ „์ œํ•œ๋‹ค. ์ด๋Š” ๊ณ ์ฐจ์› ์‹œ์Šคํ…œ์—์„œ ๊ณ„์‚ฐ ๋ณต์žก๋„๋ฅผ ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€์‹œํ‚จ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„์  | |

System Mathematics
No Image

A golden-ratio partition of information and the balance between prediction and surprise: a neuro-cognitive route to antifragility

| ๊ตฌ๋ถ„ | ํ•ต์‹ฌ ๋‚ด์šฉ | ๊ฐ•์  | ์•ฝ์  / ์˜๋ฌธ์  | | | | | | | 1. ์ด๋ก ์  ๊ธฐ์ดˆ | (f(p)) ๋ฅผ ์ •๋ณดโ€‘์ด๋ก ์  ์ˆœ ๊ธฐ๋Œ€ ๋†€๋ผ์›€ ์œผ๋กœ ์ •์˜<br> ํ•จ์ˆ˜์˜ ๋ณผ๋ก์„ฑ ์ฆ๋ช… ๊ณผ ๊ทน๊ฐ’ ์กด์žฌยท์œ ์ผ์„ฑ ์„ ์—„๊ฒฉํžˆ ๋ณด์ด๋ฉฐ, ์ˆ˜์น˜์ ์œผ๋กœ (p^{ } 0.882) ๋„์ถœ | โ€ข ์ˆ˜ํ•™์  ์—„๋ฐ€์„ฑ (์ •๋ฆฌยท์ฆ๋ช…) <br>โ€ข ๊ธฐ์กด ๋ฒ ๋ฅด๋ˆ„์ด ์—”ํŠธ๋กœํ”ผ์™€์˜ ์—ฐ๊ฒฐ ๊ณ ๋ฆฌ ๋ช…ํ™• | โ€ข (f(p)) ๊ฐ€ ์‹ค์ œ ์‹ ๊ฒฝโ€‘์ธ์ง€ ๊ณผ์ •์— ์–ด๋–ป๊ฒŒ ๋งคํ•‘๋˜๋Š”์ง€ ๊ตฌ์ฒด์  ์‹คํ—˜ ๋ฐ์ดํ„ฐ ๋ถ€์žฌ | | 2. ํ™ฉ๊ธˆ๋น„ ์ž๊ธฐโ€‘์œ ์‚ฌ์„ฑ | ๋น„์œจ (p:(1 p) 1:p) ๋กœ๋ถ€ํ„ฐ (p

Mathematics
No Image

A hybrid solution approach for the Integrated Healthcare Timetabling Competition 2024

1. ๋ฌธ์ œ ์ •์˜์™€ ๋Œ€ํšŒ ๋ฐฐ๊ฒฝ ํ†ตํ•ฉ ํ—ฌ์Šค์ผ€์–ด ์ผ์ • ๋ฌธ์ œ๋Š” ์˜๋ฃŒ ์ธ๋ ฅ, ์žฅ๋น„, ํ™˜์ž ํ๋ฆ„ ๋“ฑ ๋‹ค์ค‘ ์ œ์•ฝ์„ ๋™์‹œ์— ๋งŒ์กฑ์‹œ์ผœ์•ผ ํ•˜๋Š” ๋ณตํ•ฉ ์Šค์ผ€์ค„๋ง ๋ฌธ์ œ์ด๋‹ค. 2024๋…„ ๋Œ€ํšŒ๋Š” ์‹ค์ œ ๋ณ‘์› ์šด์˜ ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ๋Œ€๊ทœ๋ชจ ์ธ์Šคํ„ด์Šค๋ฅผ ์ œ๊ณตํ–ˆ์œผ๋ฉฐ, ์ตœ์ ํ•ด์— ๊ทผ์ ‘ํ•œ ํ•ด๋ฅผ ์ฐพ๋Š” ๊ฒƒ์ด ํ•ต์‹ฌ ๋ชฉํ‘œ์˜€๋‹ค. 2. ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ตฌ์กฐ โ€“ 3๋‹จ๊ณ„ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ ‘๊ทผ 1๋‹จ๊ณ„: ๋ฌธ์ œ ๋ถ„ํ•ด ๋ฐ MIP ๋ชจ๋ธ๋ง ์ „์ฒด ์ผ์ • ๋ฌธ์ œ๋ฅผ โ€œ์ธ๋ ฅ ๋ฐฐ์ •โ€๊ณผ โ€œ์ž์› ํ• ๋‹นโ€ ๋‘ ๊ฐœ์˜ ์ฃผ์š” ์„œ๋ธŒํ”„๋กœ๋ธ”๋Ÿผ์œผ๋กœ ๋ถ„ํ•ดํ•˜์˜€๋‹ค. ๊ฐ ์„œ๋ธŒ๋ฌธ์ œ๋Š” ํ˜ผํ•ฉ ์ •์ˆ˜ ๊ณ„ํš๋ฒ•์œผ๋กœ ๋ชจ๋ธ๋ง๋˜์–ด, CPLEX/Gurobi์™€ ๊ฐ™์€

No Image

A Lorentzian Equivariant Index Theorem

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์•„ํ‹€๋ฆฌ์•ผโ€‘์‹ฑ์–ด ์ •๋ฆฌ ๋Š” ๋ฆฌ๋งŒ ๊ธฐํ•˜์—์„œ ๋””๋ž™ ์—ฐ์‚ฐ์ž์˜ ์ง€์ˆ˜๋ฅผ ์œ„์ƒยท๊ธฐํ•˜ํ•™์  ๋ฐ์ดํ„ฐ(ร‚โ€‘ํ˜•, ์ฒด๋ฅด๋‹ˆโ€‘ํด๋ผ์Šค)์™€ ์—ฐ๊ฒฐํ•œ๋‹ค. ๋“ฑ๊ฐ€(Equivariant) ๋ฒ„์ „ ์€ ๊ตฐ (Gamma)๊ฐ€ ์ž‘์šฉํ•  ๋•Œ ์ง€์ˆ˜๋ฅผ (Gamma)โ€‘ํ•จ์ˆ˜๋กœ ๊ฐ•ํ™”ํ•œ๋‹ค(์•„ํ‹€๋ฆฌ์•ผโ€‘์„ธ๊ฐˆโ€‘์‹ฑ์–ด, ๋„๋„ฌ๋ฆฌ). ๋กœ๋ Œ์ธ  ์‹œ๊ณต๊ฐ„์—์„œ๋Š” ๋””๋ž™ ์—ฐ์‚ฐ์ž๊ฐ€ ์Œ๊ณกํ˜•(Hyperbolic) ์ด๋ผ์„œ ์ „ํ†ต์ ์ธ ํƒ€์›ํ˜•(elliptic) ๊ธฐ๋ฒ•์ด ๋ฐ”๋กœ ์ ์šฉ๋˜์ง€ ์•Š๋Š”๋‹ค. ๋ฐ”๋ฅดโ€‘์ŠคํŠธ๋กœ๋งˆ์ด์–ด(2019)๋Š” APS ๊ฒฝ๊ณ„์กฐ๊ฑด ํ•˜์— ๋กœ๋ Œ์ธ โ€‘์ง€์ˆ˜ ์ •๋ฆฌ ๋ฅผ ์ฆ๋ช…ํ–ˆ์œผ๋ฉฐ, ์ด๋Š” ๋ฆฌ๋งŒโ€‘์ง€์ˆ˜ ์ •๋ฆฌ์™€ ๋™

Mathematics
No Image

A Mathematical Theory of Redox Biology

1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํ˜„ํ™ฉ : ๋ ˆ๋…์Šค ์ƒ๋ฌผํ•™์€ ์‹คํ—˜์ ์œผ๋กœ ๊ณ ํ•ด์ƒ๋„(์‹œ๊ฐ„ยท๊ณต๊ฐ„ยท๋ถ„์ž) ๋ฐ์ดํ„ฐ๋ฅผ ์ƒ์‚ฐํ•˜์ง€๋งŒ, ์ด๋ฅผ ํ†ตํ•ฉยทํ•ด์„ํ•  ์ด๋ก ์  ํ‹€์ด ๋ถ€์กฑํ•˜๋‹ค. ๊ธฐ์กด์˜ โ€œ๋ฐ˜์‘ ์นดํƒˆ๋กœ๊ทธโ€๋Š” ๋ถ„์ž๋ฅผ ํ™˜๊ฒฝ์—์„œ ๋ถ„๋ฆฌ์‹œ์ผœ ๊ตฌ์กฐ์ ยท๋™์—ญํ•™์  ๊ด€๊ณ„๋ฅผ ๊ฐ€์‹œํ™”ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ๋ฌธ์ œ์  : ๋ ˆ๋…์Šค ํ˜„์ƒ์ด ๋งฅ๋ฝ ์˜์กด์  ์ด๋ฉฐ, ๊ฐ™์€ ๋ถ„์ž๊ฐ€ ์‚ฐํ™”ยทํ™˜์› ์—ญํ• ์„ ๋™์‹œ์— ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์„ ์„ค๋ช…ํ•˜๊ธฐ ์œ„ํ•œ ์ฒด๊ณ„์  ๋ชจ๋ธ์ด ๋ถ€์žฌํ•จ. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด์™€ ์ˆ˜ํ•™์  ํ”„๋ ˆ์ž„์›Œํฌ | ์š”์†Œ | ์„ค๋ช… | ์ˆ˜ํ•™์  ๋Œ€์‘ | | | | | | Core Redox Module (CRM) | ๋ ˆ๋…

Quantitative Biology
No Image

A Memory-Efficient Retrieval Architecture for RAG-Enabled Wearable Medical LLMs-Agents

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์˜๋ฃŒ ๋ถ„์•ผ LLM ํ™œ์šฉ : ๊ฐœ์ธ ์˜๋ฃŒ ๊ธฐ๋ก, ์ตœ์‹  ๋…ผ๋ฌธ, ์•ฝ๋ฌผ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๋“ฑ ๋ฐฉ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์‹ค์‹œ๊ฐ„์œผ๋กœ ํ†ตํ•ฉยทํ•ด์„ํ•ด์•ผ ํ•จ. RAG์˜ ์žฅ์  : LLM ์ž์ฒด๋ฅผ ์žฌํ•™์Šตํ•˜์ง€ ์•Š๊ณ ๋„ ์™ธ๋ถ€ ์ง€์‹๋ฒ ์ด์Šค๋ฅผ ๋™์ ์œผ๋กœ ์—ฐ๊ฒฐํ•ด ์ตœ์‹  ์ •๋ณด๋ฅผ ๋ฐ˜์˜ ๊ฐ€๋Šฅ. ์—์ง€ ๋””๋ฐ”์ด์Šค์˜ ์ œ์•ฝ : ๋ฉ”๋ชจ๋ฆฌ ์šฉ๋Ÿ‰ยท์ „๋ ฅ ์ œํ•œ์ด ์‹ฌํ•ด, ๊ธฐ์กด RAG(ํŠนํžˆ ์ „์ฒด ์ž„๋ฒ ๋”ฉ์„ INT8 ํ˜•ํƒœ๋กœ ์ €์žฅยท๊ฒ€์ƒ‰) ๋ฐฉ์‹์€ ์‹ค์šฉ์„ฑ์ด ๋‚ฎ์Œ. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด | ๋‹จ๊ณ„ | ๋ชฉํ‘œ | ๊ตฌํ˜„ ๋ฐฉ์‹ | | | | | | 1๋‹จ๊ณ„ (๊ทผ์‚ฌ ๊ฒ€์ƒ‰) | ์ „์ฒด ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์—์„œ ์ž ์žฌ ํ›„๋ณด ๋ฅผ ๋น 

No Image

A model agnostic eXplainable AI based fuzzy framework for sensor constrained Aerospace maintenance applications

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์„ผ์„œ ์ œ์•ฝ ๋ฌธ์ œ : ํ•ญ๊ณต์ •๋น„ ํ˜„์žฅ์€ ๋น„์šฉยท๋ฌด๊ฒŒยท๊ณต๊ฐ„ ์ œ์•ฝ์œผ๋กœ ์ธํ•ด ๋ชจ๋“  ๋ถ€ํ’ˆ์— ์„ผ์„œ๋ฅผ ๋ถ€์ฐฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๊ธฐ์กด์˜ ๋ฐ์ดํ„ฐโ€‘์ค‘์‹ฌ ์œ ์ง€๋ณด์ˆ˜ ๋ชจ๋ธ์€ ํ’๋ถ€ํ•œ ๋ผ๋ฒจ๋ง ๋ฐ์ดํ„ฐ๋ฅผ ์ „์ œ๋กœ ํ•˜๋ฏ€๋กœ, ์„ผ์„œ๊ฐ€ ๋ถ€์กฑํ•œ ์ƒํ™ฉ์— ์ ์šฉํ•˜๊ธฐ ํž˜๋“ค๋‹ค. ์„ค๋ช…๊ฐ€๋Šฅ AI (XAI)์˜ ์—ญํ•  : ์œ ์ง€๋ณด์ˆ˜ ๋‹ด๋‹น์ž๋Š” ๋ชจ๋ธ์˜ ํŒ๋‹จ ๊ทผ๊ฑฐ๋ฅผ ์ดํ•ดํ•ด์•ผ ์˜์‚ฌ๊ฒฐ์ •์— ์‹ ๋ขฐ๋ฅผ ๊ฐ€์งˆ ์ˆ˜ ์žˆ๋‹ค. SHAP(Shapley Additive exPlanations)์€ ๊ฐœ๋ณ„ ์˜ˆ์ธก์— ๋Œ€ํ•œ ๊ธฐ์—ฌ๋„๋ฅผ ์ •๋Ÿ‰ํ™”ํ•ด ํˆฌ๋ช…์„ฑ์„ ์ œ๊ณตํ•œ๋‹ค. 2. ์ œ์•ˆ ํ”„๋ ˆ์ž„์›Œํฌ ๊ตฌ์„ฑ | ๋‹จ๊ณ„ | ์‚ฌ์šฉ ๊ธฐ์ˆ  |

Framework Model
No Image

A novel fast sweeping method for computing the attenuation operator $t^*$ in absorbing media

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๊ฐ์‡  ์—ฐ์‚ฐ์ž $t^ $์˜ ์ค‘์š”์„ฑ : ์ง€์ง„ํŒŒ์˜ ์ง„ํญ ๊ฐ์†Œ๋ฅผ ์ •๋Ÿ‰ํ™”ํ•ด ์ง€๊ตฌ ๋‚ด๋ถ€์˜ ๋ฌผ์„ฑ(์˜ˆ: Qโ€‘๊ฐ’) ์ถ”์ •์— ์ง์ ‘ ์—ฐ๊ฒฐ๋œ๋‹ค. ์ „ํ†ต์  ๋ ˆ์ด ํŠธ๋ ˆ์ด์‹ฑ์˜ ํ•œ๊ณ„ : ๊ณ„์‚ฐ ๋ณต์žก๋„ : ๋ณต์žกํ•œ ๋งค์งˆ์—์„œ๋Š” ๋‹ค์ˆ˜์˜ ๋ ˆ์ด์™€ ๋‹ค์ค‘ ๊ฒฝ๋กœ๊ฐ€ ๋ฐœ์ƒํ•ด ๋น„์šฉ์ด ๊ธฐํ•˜๊ธ‰์ˆ˜์ ์œผ๋กœ ์ฆ๊ฐ€. ์ˆ˜์น˜ ๋ถˆ์•ˆ์ •์„ฑ : ์ด์งˆ์„ฑ์ด ์•ฝ๊ฐ„๋งŒ ์žˆ์–ด๋„ ๋ ˆ์ด ๋ฐฉ์ •์‹์ด ๋ฐœ์‚ฐํ•˜๊ฑฐ๋‚˜ ๊ฒฝ๋กœ๊ฐ€ ๋Š์–ด์ง€๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋นˆ๋ฒˆ. 2. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•๋ก  (MFSM) | ๋‹จ๊ณ„ | ํ•ต์‹ฌ ์•„์ด๋””์–ด | ์ˆ˜์น˜ ๊ธฐ๋ฒ• | ๊ธฐ๋Œ€ ํšจ๊ณผ | | | | | | | โ‘  ์—ฌํ–‰์‹œ๊ฐ„ ๊ณ„์‚ฐ | ์ด์ฝ”๋‚  ๋ฐฉ์ •์‹ โ†’ $T(ma

No Image

A refinement of the Lorentz local field expression with impact on the Clausius-Mossotti and Lorentz-Lorenz models

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๊ณ ์ „ ๋ชจ๋ธ์˜ ํ•œ๊ณ„ : ํด๋ผ์šฐ์ง€์šฐ์Šคโ€‘๋ชจ์†Œํ‹ฐ(CM)์™€ ๋กœ๋ Œ์ธ โ€‘๋กœ๋ Œ์ฆˆ(LL) ๋ฐฉ์ •์‹์€ 19์„ธ๊ธฐ๋ถ€ํ„ฐ ์ „์ž๊ธฐํ•™ ๊ต๊ณผ์„œ์— ํ•„์ˆ˜์ ์ธ ๊ณต์‹์œผ๋กœ ์ž๋ฆฌ ์žก์•˜๋‹ค. ํ•˜์ง€๋งŒ ์ด๋“ค์€ โ€˜๊ตฌํ˜• ๊ณต๋™ ๋‚ด๋ถ€์˜ ํ‰๊ท  ์ „๊ณ„โ€™๋ฅผ ๊ทผ์‚ฌ์ ์œผ๋กœ ๊ณ„์‚ฐํ•˜๋ฉด์„œ, ํ•ด๋‹น ๋ถ„์ž ์ž์ฒด๊ฐ€ ๋งŒ๋“  ์ „๊ณ„(์ž๊ธฐ์žฅ)๋ฅผ ๋ฌด์‹œํ•œ๋‹ค. ๊ณ ๋ฐ€๋„ ํ™˜๊ฒฝ : ๊ณ ์ฒด, ๊ณ ์•• ์œ ์ „์ฒด, ํ˜น์€ ๋‚˜๋…ธ๊ตฌ์กฐ ๋ฌผ์งˆ ๋“ฑ์—์„œ ์ž…์ž ๊ฐ„ ๊ฑฐ๋ฆฌ๊ฐ€ ์งง์•„์ง€๋ฉด, ๋ถ„์ž ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ์ด ๊ฐ•ํ•ด์ง€๊ณ  ๊ตญ์†Œ ์ „๊ณ„์˜ ์ •ํ™•ํ•œ ๊ณ„์‚ฐ์ด ํ•„์š”ํ•ด์ง„๋‹ค. ๊ธฐ์กด ๊ทผ์‚ฌ๋Š” ์˜ค์ฐจ๋ฅผ ํฌ๊ฒŒ ๋งŒ๋“ค ์ˆ˜ ์žˆ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ 1. ๊ตญ์†Œ ์ „๊ณ„์˜ ์ •ํ™•ํ•œ ํ•ด

Model
No Image

A Rough Functional Breuer-Major Theorem

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ Breuerโ€‘Major ์ •๋ฆฌ ๋Š” Gaussian ์‹œ๊ณ„์—ด์— ๋น„์„ ํ˜• ๋ณ€ํ™˜์„ ์ ์šฉํ–ˆ์„ ๋•Œ CLT๊ฐ€ ์„ฑ๋ฆฝํ•˜๋Š” ์ถฉ๋ถ„์กฐ๊ฑด์„ ์ œ๊ณตํ•œ๋‹ค. ๊ธฐ์กด์—๋Š” 1์ฐจ ๊ณผ์ • (ํ•ฉ๊ณ„) ์ˆ˜์ค€์—์„œ๋งŒ ๊ธฐ๋Šฅ์  ์ˆ˜๋ ด์ด ๋‹ค๋ฃจ์–ด์กŒ์œผ๋ฉฐ, ํ•จ์ˆ˜์˜ Hermite ๊ณ„์ˆ˜ ๊ฐ์†Œ ์™€ ๊ฐ™์€ ๊ฐ•ํ•œ ๊ฐ€์ •์ด ํ•„์š”ํ–ˆ๋‹ค. Nourdinโ€‘Nualart(2020) ์€ ์ด๋Ÿฌํ•œ ๊ฐ€์ •์„ ์™„ํ™”ํ•ด (fin L^{p} {1}(gamma),,p {1}>2) ๋งŒ์œผ๋กœ๋„ Skorokhod ๊ณต๊ฐ„์—์„œ์˜ ํ•จ์ˆ˜ํ˜• ์ˆ˜๋ ด์„ ์–ป์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ฑฐ์นœ ๊ฒฝ๋กœ (rough path) ์ˆ˜์ค€์—์„œ๋Š” 2์ฐจ ์ด์ƒ ๋ฐ˜

Mathematics
A Unified Architecture for N-Dimensional Visualization and Simulation: 4D Implementation and Evaluation including Boolean Operations

A Unified Architecture for N-Dimensional Visualization and Simulation: 4D Implementation and Evaluation including Boolean Operations

์ด ๋…ผ๋ฌธ์€ ๊ณ ์ฐจ์›(ํŠนํžˆ 4์ฐจ์›) ๋ฐ์ดํ„ฐ์˜ ์‹œ๊ฐํ™”์™€ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ•˜๋‚˜์˜ ํ†ตํ•ฉ ํ”„๋ ˆ์ž„์›Œํฌ๋กœ ๊ตฌํ˜„ํ•˜๋ ค๋Š” ์‹œ๋„๋ฅผ ๋‹ด๊ณ  ์žˆ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ๋ณดํ†ต ์ฐจ์›๋ณ„๋กœ ํŠนํ™”๋œ ๋„๊ตฌ๋ฅผ ๊ฐœ๋ฐœํ•˜๊ฑฐ๋‚˜, ์ฐจ์› ์ถ•์†Œ ๊ธฐ๋ฒ•์„ ์ด์šฉํ•ด 3D ํ˜น์€ 2D ํ™”๋ฉด์— ํˆฌ์˜ํ•˜๋Š” ๋ฐฉ์‹์„ ์ฑ„ํƒํ•œ๋‹ค. ๋ฐ˜๋ฉด ๋ณธ ์—ฐ๊ตฌ๋Š” โ€œNโ€‘dimensionalโ€์ด๋ผ๋Š” ์ถ”์ƒ์  ๊ฐœ๋…์„ ์„ค๊ณ„ ๋‹จ๊ณ„๋ถ€ํ„ฐ ๋ฐ˜์˜ํ•จ์œผ๋กœ์จ, ์ฐจ์›์— ๊ตฌ์• ๋ฐ›์ง€ ์•Š๋Š” ๋ชจ๋“ˆ์‹ ๊ตฌ์กฐ๋ฅผ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค๋Š” ์ ์—์„œ ์ฐจ๋ณ„์„ฑ์„ ๊ฐ€์ง„๋‹ค. ํ•ต์‹ฌ ๊ธฐ์ˆ  ์š”์†Œ๋Š” ๋„ค ๊ฐ€์ง€๋กœ ์š”์•ฝ๋œ๋‹ค. ์ฒซ์งธ, ํ€ตํ— ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•œ ๋ณผ๋ก ๊ป์งˆ ๋ฉ”์‰ฌ ์ƒ์„ฑ์€ ๊ณ ์ฐจ์› ๋ฐ์ดํ„ฐ์˜ ๊ธฐํ•˜ํ•™์  ๊ฒฝ

No Image

A Universal Neural Receiver that Learns at the Speed of Wireless

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ | ๋ฌธ์ œ์  | ๊ธฐ์กด ์ ‘๊ทผ๋ฒ• | ํ•œ๊ณ„ | | | | | | ๋ชจ๋ธยท์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ฒฉ์ฐจ | ํŒŒ๋ผ๋ฏธํ„ฐํ™”๋œ ์ „ํŒŒ ๋ชจ๋ธ + 3GPP ์ตœ์ ํ™” | ๋ณต์žกํ•œ ํ† ํด๋กœ์ง€ยท๋‹ค์ค‘ numerologyยท๋น„์„ ํ˜• ์†Œ์ž์— ์ ์šฉ ์–ด๋ ค์›€ | | ์˜คํ”„๋ผ์ธ ํ•™์Šต ์˜์กด | ์ด๋ฏธ์ง€ยทNLP์—์„œ ์„ฑ๊ณตํ•œ ๋Œ€๊ทœ๋ชจ ์‚ฌ์ „ํ•™์Šต | ๋ฌด์„  ํ™˜๊ฒฝ์€ ์„œ๋ธŒ๋ฐ€๋ฆฌ์ดˆ๋งˆ๋‹ค ๊ธ‰๋ณ€ โ†’ ํ•™์Šตโ€‘์ „ํŒŒ ์ง€์—ฐ ๋ฐœ์ƒ | | ์ „์—ญ ์ผ๋ฐ˜ํ™” ๋ถˆ๊ฐ€๋Šฅ | ๋‰ด์š•ยท์‹œ์นด๊ณ  ๋“ฑ ํŠน์ • ์ง€์—ญ ๋ฐ์ดํ„ฐ | ์ธ๋„ยท๋ด๋ฒ„ ๋“ฑ ๋‹ค๋ฅธ ์ง€์—ญยท์ฃผํŒŒ์ˆ˜ ๋Œ€์—ญ์—์„œ ์„ฑ๋Šฅ ๊ธ‰๋ฝ | ๋…ผ๋ฌธ์€ โ€œ๋ฌด์„  ์†๋„์— ๋งž์ถฐ ํ•™์Šต(Learning at the

Computer Science Information Theory
No Image

Activation-Space Uncertainty Quantification for Pretrained Networks

1. ํ•ต์‹ฌ ์•„์ด๋””์–ด์™€ ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | | | | |โ‘ | ํ™œ์„ฑํ™”โ€‘๊ณต๊ฐ„ ๋ถˆํ™•์‹ค์„ฑ : ๊ฐ€์ค‘์น˜๋ฅผ ๊ณ ์ •ํ•˜๊ณ , ๊ฐ ๋ ˆ์ด์–ด์˜ ๋น„์„ ํ˜•์„ GP๋กœ ๋Œ€์ฒดํ•ด ๋ถˆํ™•์‹ค์„ฑ์„ ๋ชจ๋ธ๋ง | ์‚ฌ์ „ํ•™์Šต๋œ ๊ฑฐ๋Œ€ ๋ชจ๋ธ์„ ๊ทธ๋Œ€๋กœ ์‚ฌ์šฉํ•˜๋ฉด์„œ๋„ ๋ฒ ์ด์ง€์•ˆ ๋ถˆํ™•์‹ค์„ฑ์„ ๋ถ€์—ฌ | |โ‘ก| Meanโ€‘Preserving GP : GP posterior mean์„ ์›๋ž˜ ํ™œ์„ฑํ™”์™€ ๋™์ผํ•˜๊ฒŒ ๊ฐ•์ œ โ†’ ๋ฐฑ๋ณธ ์˜ˆ์ธก ๋ถˆ๋ณ€ | ์‹ค๋ฌด์—์„œ โ€œ์˜ˆ์ธก์ด ๋ฐ”๋€Œ๋ฉด ์•ˆ ๋œ๋‹คโ€๋Š” ์š”๊ตฌ๋ฅผ ๋งŒ์กฑ | |โ‘ข| ํฌ์†Œ ์ธ๋•ํŒ… + ๋กœ์ปฌ kโ€‘NN : ์ „์ฒด ์บ์‹œ ๋Œ€์‹  ์••์ถ•๋œ ์ธ๋•ํŒ… ํฌ์ธํŠธ์™€ ํ…Œ์ŠคํŠธ ์‹œ ๊ทผ์ ‘ ์ด์›ƒ

Statistics Network Machine Learning
No Image

Active RIS-Assisted MIMO System for Vital Signs Extraction: ISAC Modeling, Deep Learning, and Prototype Measurements

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ 6G ์‹œ๋Œ€์˜ ํ•ต์‹ฌ ๊ธฐ์ˆ ๋กœ ๋ถ€์ƒํ•˜๊ณ  ์žˆ๋Š” ํ†ตํ•ฉ ๊ฐ์ง€ยทํ†ต์‹ (ISAC) ์€ ์ŠคํŽ™ํŠธ๋Ÿผยทํ•˜๋“œ์›จ์–ด ํšจ์œจ์„ฑ์„ ๋™์‹œ์— ๋‹ฌ์„ฑํ•ด์•ผ ํ•œ๋‹ค. ๊ธฐ์กด MIMOโ€‘OFDM ๊ธฐ๋ฐ˜ ๋น„์ ‘์ด‰ ๋ฐ”์ดํƒˆ ์‚ฌ์ธ์Šค(ํ˜ธํกยท์‹ฌ๋ฐ•) ์ธก์ •์€ ๋ฏธ์„ธ ์›€์ง์ž„ ๊ณผ ๋™๊ธฐํ™” ์˜ค์ฐจ(SFO, CFO, PDD) ๋กœ ์ธํ•ด ์œ„์ƒ ๋“œ๋ฆฌํ”„ํŠธ๊ฐ€ ๋ฐœ์ƒ, ์‹ ํ˜ธ ํ’ˆ์งˆ์ด ํฌ๊ฒŒ ์ €ํ•˜๋œ๋‹ค. RIS๋Š” ์ „ํŒŒ ํ™˜๊ฒฝ์„ ํ”„๋กœ๊ทธ๋ž˜๋ฐ์ ์œผ๋กœ ์ œ์–ดํ•ด ์‹ ํ˜ธ ํ’ˆ์งˆ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ์ง€๋งŒ, ์ œ์–ด ์˜ค๋ฒ„ํ—ค๋“œ ์™€ ์œ„์ƒ ๋“œ๋ฆฌํ”„ํŠธ์— ๋ฏผ๊ฐํ•œ ์ตœ์ ํ™” ๊ฐ€ ์‹ค์šฉํ™”์˜ ์žฅ์• ๋ฌผ์ด๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | |

System Learning Electrical Engineering and Systems Science Model
No Image

Adaptive Selection of Codebook Using Assistance Information and Artificial Intelligence for 6G Systems

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ Mโ€‘MIMOยท5G NR โ†’ 6G : ํ˜„์žฌ 5G NR์—์„œ ์‚ฌ์šฉ๋˜๋Š” Typeโ€‘1/2/โ€‘eTypeโ€‘2 ์ฝ”๋“œ๋ถ์€ ๊ณต๊ฐ„ยท์ฃผํŒŒ์ˆ˜ยท์‹œ๊ฐ„(๋„ํ”Œ๋Ÿฌ) ์ฐจ์›์—์„œ CSI ์••์ถ•์„ ์ง€์›ํ•œ๋‹ค. 6G์—์„œ๋Š” ๋” ๋†’์€ ์ŠคํŽ™ํŠธ๋Ÿผ ํšจ์œจ๊ณผ ์ดˆ์ €์ง€์—ฐ์„ ์œ„ํ•ด AI ๊ธฐ๋ฐ˜ ์ž๋™์ธ์ฝ”๋”์™€ ๊ฐ™์€ ๊ณ ์••์ถ• ๊ธฐ๋ฒ•์ด ๋„์ž…๋  ์ „๋ง์ด๋‹ค. CB ์„ ํƒ์˜ ๋‚œ์  : ๊ธฐ์กด BS๋Š” ๋ชจ๋“  UE์— ๋™์ผํ•œ CB ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์ ์šฉํ•˜๊ฑฐ๋‚˜, LoS/NLoS์™€ ๊ฐ™์€ ๋‹จ์ˆœ ์ง€ํ‘œ์— ๊ธฐ๋ฐ˜ํ•œ ์Šค์œ„์นญ๋งŒ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์ด๋Š” ์ฑ„๋„ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ UE๋งˆ๋‹ค ํฌ๊ฒŒ ๋‹ค๋ฅธ ์‹ค์ œ ํ™˜๊ฒฝ์—์„œ ๋น„ํšจ์œจ์ ์ด๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””

System Electrical Engineering and Systems Science
No Image

Adaptive Semi-Supervised Training of P300 ERP-BCI Speller System with Minimum Calibration Effort

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ ๋น„์šฉ : P300 ์ŠคํŽ ๋Ÿฌ๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ 10~20ํšŒ ์ด์ƒ์˜ ๋ฐ˜๋ณต์„ ํ†ตํ•ด ์ถฉ๋ถ„ํ•œ ๋ผ๋ฒจ ๋ฐ์ดํ„ฐ๋ฅผ ํ™•๋ณดํ•ด์•ผ ํ•œ๋‹ค. ์ด๋Š” ์‚ฌ์šฉ์ž์˜ ํ”ผ๋กœ์™€ ๋ฐ์ดํ„ฐ ํ’ˆ์งˆ ์ €ํ•˜๋ฅผ ์•ผ๊ธฐํ•œ๋‹ค. ๋ฐ˜์ง€๋„ํ•™์Šต์˜ ๊ฐ€๋Šฅ์„ฑ : ๋ผ๋ฒจ์ด ์—†๋Š” ๋Œ€๋Ÿ‰์˜ EEG ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•ด ๋ชจ๋ธ์„ ์ง€์†์ ์œผ๋กœ ๊ฐœ์„ ํ•  ์ˆ˜ ์žˆ๋‹ค๋ฉด, ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ ์‹œ๊ฐ„์„ ํฌ๊ฒŒ ๋‹จ์ถ•ํ•  ์ˆ˜ ์žˆ๋‹ค. 2. ์ œ์•ˆ ๋ฐฉ๋ฒ•์˜ ํ•ต์‹ฌ ์•„์ด๋””์–ด | ์š”์†Œ | ๊ธฐ์กด ๋ฐฉ๋ฒ• | ์ œ์•ˆ ๋ฐฉ๋ฒ• | | | | | | ์ดˆ๊ธฐ ๋ชจ๋ธ | ๋Œ€๊ทœ๋ชจ ๋ผ๋ฒจ ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ SVM, LDA ๋“ฑ | ์†Œ์ˆ˜ ๋ผ๋ฒจ(๋ช‡ ์‹œํ€€์Šค)๋กœ ํ‰๊ท ยท๊ณต๋ถ„์‚ฐ

Computer Science System Machine Learning
No Image

Advancing Equitable AI: Evaluating Cultural Expressiveness in LLMs for Latin American Contexts

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํŽธํ–ฅ์˜ ๊ตฌ์กฐ์  ์›์ธ : ํ˜„์žฌ ๋Œ€๋ถ€๋ถ„์˜ ๋Œ€ํ˜• LLM์€ ์˜์–ด ์œ„์ฃผ ๋ฐ์ดํ„ฐ์™€ ์„œ๊ตฌ ๋ฌธํ™”์— ์ตœ์ ํ™”๋ผ ์žˆ์–ด, ๋ผํ‹ด์•„๋ฉ”๋ฆฌ์นด์™€ ๊ฐ™์€ ๋น„์˜์–ด๊ถŒ ์ง€์—ญ์˜ ์–ธ์–ดยท๋ฌธํ™”๊ฐ€ ์ถฉ๋ถ„ํžˆ ๋ฐ˜์˜๋˜์ง€ ์•Š๋Š”๋‹ค. ์ด๋Š” ๊ธฐ์ˆ  ๊ฒฉ์ฐจ๋ฅผ ์‹ฌํ™”์‹œํ‚ค๊ณ , ํ˜„์ง€ ์‚ฌ์šฉ์ž์—๊ฒŒ ๋ถ€์ •ํ™•ํ•˜๊ฑฐ๋‚˜ ๋ฌธํ™”์ ์œผ๋กœ ๋ถ€์ ์ ˆํ•œ ๊ฒฐ๊ณผ๋ฅผ ์ดˆ๋ž˜ํ•œ๋‹ค. ์–ธ์–ด ๋‹ค์–‘์„ฑ : ์ŠคํŽ˜์ธ์–ดยทํฌ๋ฅดํˆฌ๊ฐˆ์–ด ์™ธ์—๋„ ์ผ€์ถ”์•„์–ดยท๋‚˜์™€ํ‹€ ๋“ฑ ํ† ์ฐฉ ์–ธ์–ด๊ฐ€ ์กด์žฌํ•จ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์ด๋“ค ์–ธ์–ด๋Š” ํ•™์Šต ์ฝ”ํผ์Šค์—์„œ ๊ฑฐ์˜ ๋ฐฐ์ œ๋ผ ์žˆ๋‹ค. ์ด๋Š” ๋ฌธํ™”์  ์ •์ฒด์„ฑ ์ƒ์‹ค๊ณผ ์ง€์‹ ์ „์Šน์— ๋ถ€์ •์  ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. 2. ๋ฐ์ดํ„ฐ์…‹ ์„ค๊ณ„ ๋ฌธํ™”ยท์—ญ์‚ฌ ๊ธฐ

No Image

Advancing Industry 4.0: Multimodal Sensor Fusion for AI-Based Fault Detection in 3D Printing

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ FDM์˜ ํ•ต์‹ฌ ๋ฌธ์ œ : ์—ดยท๊ธฐ๊ณ„์  ๋ณ€๋™์ด ๋ˆ„์ ๋ผ ๋ ˆ์ด์–ด ๊ฒฐํ•จ, ๊ธฐ๊ณ„์  ์•ฝํ™”, ํ‘œ๋ฉด ๊ฒฐํ•จ์„ ์ดˆ๋ž˜ํ•œ๋‹ค(์˜ˆ: ๋…ธ์ฆ ๊ณผ์—ด, ๋ฒ elt ์Šฌ๋ฆฝ). ๊ธฐ์กด ๋ชจ๋‹ˆํ„ฐ๋ง ํ•œ๊ณ„ : CT, ์ธ์žฅ ์‹œํ—˜ ๋“ฑ์€ ์‚ฌํ›„ ๊ฒ€์ฆ์— ๋จธ๋ฌผ๋ฉฐ, ์‹ค์‹œ๊ฐ„ ๊ณต์ • ์‹ ํ˜ธ(์—ด ๋“œ๋ฆฌํ”„ํŠธ, ์ง„๋™, ์Œํ–ฅ)๋ฅผ ํฌ์ฐฉํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์‚ฐ์—… 4.0 ์š”๊ตฌ : ์‹ค์‹œ๊ฐ„ ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ์˜์‚ฌ๊ฒฐ์ •, ํ๊ธฐ๋ฌผ ์ตœ์†Œํ™”, ์ž๋™ํ™”๋œ ํ’ˆ์งˆ ๊ด€๋ฆฌ๊ฐ€ ํ•„์ˆ˜๋‹ค. 2. ์ œ์•ˆ ์‹œ์Šคํ…œ ๊ตฌ์กฐ | ๊ตฌ์„ฑ ์š”์†Œ | ์—ญํ•  | ์นจํˆฌ์„ฑ | ๋น„์šฉยทํœด๋Œ€์„ฑ | | | | | | | ์Œํ–ฅ ์„ผ์„œ(์ฝ˜๋ด์„œ ๋งˆ์ดํฌ 2๊ฐœ) | ๊ณต์ • ์ค‘

Electrical Engineering and Systems Science Detection
No Image

An Energy-Aware RIoT System: Analysis, Modeling and Prediction in the SUPERIOT Framework

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ RIoT ๋…ธ๋“œ์˜ ์—๋„ˆ์ง€ ๋ฌธ์ œ : ์žฌ๊ตฌ์„ฑํ˜• IoT๋Š” ๋‹ค์ค‘ ๋ฌด์„ ยท๊ด‘ํ†ต์‹  ์ธํ„ฐํŽ˜์ด์Šค์™€ ์„ผ์„œ๋ฅผ ํ†ตํ•ฉํ•ด ๋†’์€ ๊ธฐ๋Šฅ์„ฑ์„ ์ œ๊ณตํ•˜์ง€๋งŒ, ๋ฐฐํ„ฐ๋ฆฌ ์ˆ˜๋ช… ์ œํ•œ์ด ์„œ๋น„์Šค ์—ฐ์†์„ฑ์— ํฐ ์ œ์•ฝ์ด ๋œ๋‹ค. SUPERIOT ํ”„๋กœ์ ํŠธ : ์ดˆ์ €์ „๋ ฅ, ๋‹ค์ค‘ ๋ชจ๋“œ ํ†ต์‹ ์„ ๋ชฉํ‘œ๋กœ ํ•˜๋Š” EUโ€‘H2020 ๊ธฐ๋ฐ˜ ์—ฐ๊ตฌ๋กœ, ๋ณธ ๋…ผ๋ฌธ์€ ์ฒซ ๋ฒˆ์งธ ํ”„๋กœํ† ํƒ€์ž…์˜ ์—๋„ˆ์ง€ ํŠน์„ฑ์„ ์ •๋Ÿ‰ํ™”ํ•จ์œผ๋กœ์จ ํ”„๋กœ์ ํŠธ ์ „์ฒด ๋กœ๋“œ๋งต์— ํ•ต์‹ฌ ๋ฒค์น˜๋งˆํฌ๋ฅผ ์ œ๊ณตํ•œ๋‹ค. 2. ์‹คํ—˜ ์„ค๊ณ„ ๋ฐ ์ธก์ • ๋ฐฉ๋ฒ• | ํ•ญ๋ชฉ | ์ธก์ • ๋Œ€์ƒ | ์ฃผ์š” ํŒŒ๋ผ๋ฏธํ„ฐ | ์ธก์ • ์žฅ๋น„ | | | | | | | BLE | ๊ด‘๊ณ 

System Framework Analysis Model
No Image

Analytical Nuclear Gradients of State-Averaged Configuration Interaction Singles Variants: Application to Conical Intersections

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์›๋ฟ” ๊ต์ฐจ์ (CX) ์€ ๋น„๋ณต์‚ฌ ์ „์ด์™€ ์ดˆ๊ณ ์† ๋‚ด๋ถ€ ์ „ํ™˜์„ ๋‹ด๋‹นํ•˜๋Š” ํ•ต์‹ฌ ๊ตฌ์กฐ์ด๋ฉฐ, ๋‹ค์ค‘ ์ „์ž ์ƒํƒœ๋ฅผ ๊ท ํ˜• ์žˆ๊ฒŒ ๋‹ค๋ฃจ๋Š” ๊ฒƒ์ด ํ•„์ˆ˜์ ์ด๋‹ค. ์ „ํ†ต์ ์ธ SAโ€‘CASSCF ๋“ฑ ๋‹ค์ค‘์ฐธ์กฐ ๋ฐฉ๋ฒ•์€ ์ •ํ™•ํ•˜์ง€๋งŒ ๊ณ„์‚ฐ ๋น„์šฉ์ด ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€ํ•˜๊ณ  ํ™œ์„ฑ๊ณต๊ฐ„ ์„ ํƒ์— ๋ฏผ๊ฐํ•ด ํฐ ์‹œ์Šคํ…œ์— ์ ์šฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค. CIS ์™€ TDDFT/TDA ๋Š” ์ €๋น„์šฉ์ด ์žฅ์ ์ด์ง€๋งŒ, ์ •์  ์ƒ๊ด€์ด ๋ถ€์กฑํ•ด CX ๊ทผ์ฒ˜์—์„œ ์‹คํŒจํ•œ๋‹ค. 2. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•๋ก  | ๋ฐฉ๋ฒ• | ํ•ต์‹ฌ ์•„์ด๋””์–ด | ์ฐจ๋ณ„์  | | | | | | SACIS (Stateโ€‘averaged CIS) | ์—ฌ๋Ÿฌ

Physics
No Image

AnnoGram: An Annotative Grammar of Graphics Extension

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ์  ์ฃผ์„์˜ ์ค‘์š”์„ฑ : ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”์—์„œ ํ•ด์„์„ ๋•๋Š” ๋ฉ”ํƒ€ ์ •๋ณด(๋ผ๋ฒจ, ํ™”์‚ดํ‘œ, ๊ฐ•์กฐ ๋“ฑ)๋Š” ๋…์ž์˜ ์ดํ•ด๋ฅผ ํฌ๊ฒŒ ์ขŒ์šฐํ•œ๋‹ค. ๊ธฐ์กด ๋„๊ตฌ์˜ ํ•œ๊ณ„ : D3, Tableau, PowerBI ๋“ฑ์€ ์ฃผ์„์„ ์ž„์‹œ์  UI ์š”์†Œ ํ˜น์€ ์ฝ”๋“œ ์ฃผ์„ ์ˆ˜์ค€ ์— ๋จธ๋ฌด๋ฅด๊ฒŒ ํ•˜๋ฉฐ, ์‹œ๊ฐํ™” ์ŠคํŽ™๊ณผ ๋ถ„๋ฆฌ๋ผ ์žฌ์‚ฌ์šฉยท์ž๋™ํ™”๊ฐ€ ์–ด๋ ต๋‹ค. 2. ์ œ์•ˆ๋œ ๋ฌธ๋ฒ•์  ํ™•์žฅ (AnnoGram) ์ฃผ์„์„ 1๊ธ‰ ๊ฐ์ฒด ๋กœ ์„ ์–ธ : `annotation { target: ..., type: ..., position: ... }` ํ˜•ํƒœ์˜ ์„ ์–ธ๋ฌธ์„ ํ†ตํ•ด ์ฃผ์„์„ ์‹œ๊ฐํ™” ์‚ฌ์–‘์—

No Image

Approximation Theory for Lipschitz Continuous Transformers

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์•ˆ์ •์„ฑยท๊ฐ•์ธ์„ฑ ๋ฌธ์ œ : ๊ธฐ์กด ํŠธ๋žœ์Šคํฌ๋จธ๋Š” ์ ์ง„์  ๊ณต๊ฒฉ(Adversarial)๊ณผ ํ•™์Šต ๋ถˆ์•ˆ์ •์„ฑ์— ์ทจ์•ฝํ•จ์ด ์—ฌ๋Ÿฌ ์—ฐ๊ตฌ(Gupta & Verma 2023, Liu et al. 2020 ๋“ฑ)์—์„œ ๋ณด๊ณ ๋˜์—ˆ๋‹ค. ๋ฆฌํ”„์‹œ์ธ  ์ œ์–ด์˜ ํ•„์š”์„ฑ : ์ž…๋ ฅโ€‘์ถœ๋ ฅ ๋งต์˜ ๋ฆฌํ”„์‹œ์ธ  ์ƒ์ˆ˜๋ฅผ ์ œํ•œํ•˜๋ฉด ๋น„ํŒฝ์ฐฝ์„ฑ(nonโ€‘expansiveness)๊ณผ ์ˆ˜๋ ด ๋ณด์žฅ์„ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ํ‘œ์ค€ ์…€ํ”„โ€‘์–ดํ…์…˜์€ ๋‚ด์ (dotโ€‘product) ์—ฐ์‚ฐ์ด ๋ฌดํ•œํžˆ ์ปค์งˆ ์ˆ˜ ์žˆ์–ด ์ „์—ญ์ ์ธ ๋ฆฌํ”„์‹œ์ธ  ์—ฐ์†์„ฑ์„ ํ™•๋ณดํ•˜๊ธฐ ์–ด๋ ต๋‹ค. 1โ€‘๋ฆฌํ”„์‹œ์ธ  ๋ชจ๋ธ์˜ ์žฅ์  : 1โ€‘๋ฆฌํ”„์‹œ์ธ ๋Š”

Computer Science Machine Learning
No Image

ASPEN: Spectral-Temporal Fusion for Cross-Subject Brain Decoding

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๊ต์ฐจ ํ”ผํ—˜์ž ์ผ๋ฐ˜ํ™” ๋Š” BCI ์‹ค์šฉํ™”์˜ ํ•ต์‹ฌ ์žฅ์• ๋ฌผ์ด๋ฉฐ, ๊ธฐ์กด ๋ฐฉ๋ฒ•๋“ค์€ ์ฃผ๋กœ ์‹œ๊ฐ„์  ๋ชจ๋ธ๋ง (CNN, Transformer ๋“ฑ)์— ์˜์กดํ•ด ์™”๋‹ค. ์‹œ๊ฐ„ ํŒŒํ˜•์€ ์œ„์ƒยท์ง€์—ฐยท์ง„ํญ ๋ณ€๋™์— ๋งค์šฐ ๋ฏผ๊ฐํ•ด ํ”ผํ—˜์ž ๊ฐ„ ์ฐจ์ด๋ฅผ ํฌ๊ฒŒ ๋ฐ˜์˜ํ•œ๋‹ค. ๋ฐ˜๋ฉด ์ฃผํŒŒ์ˆ˜ ์˜์—ญ ์€ ์‹œ๊ฐ„์  ์„ธ๋ถ€ ์ •๋ณด๋ฅผ ์ถ”์ƒํ™”ํ•˜๋ฉด์„œ๋„ ยตโ€‘๋ฆฌ๋“ฌ(8โ€‘12 Hz), ฮฒโ€‘๋ฆฌ๋“ฌ(13โ€‘30 Hz) ๋“ฑ BCI ํ•ต์‹ฌ ๋ฐ”์ด์˜ค๋งˆ์ปค๋ฅผ ๋ณด์กดํ•œ๋‹ค๋Š” ์ ์—์„œ ๊ต์ฐจ ํ”ผํ—˜์ž ์ „์ด ๊ฐ€๋Šฅ์„ฑ์ด ๋†’๋‹ค. 2. ํ•ต์‹ฌ ๊ฐ€์„ค ๊ฒ€์ฆ โ€“ ์ƒ๊ด€ ๋ถ„์„ ๋ฐ์ดํ„ฐ : SSVEP, P300, Motorโ€‘Imagery

Computer Science Machine Learning
No Image

Asymptotic Freedom in Parton Language: the Birth of Perturbative QCD

1. ์—ญ์‚ฌ์  ๋ฐฐ๊ฒฝ๊ณผ ํŒŒ๋ฅด์‹œ์˜ ์œ„์น˜ 1970๋…„๋Œ€ ์ดˆ๋ฐ˜, ๊ฐ•ํ•œ ์ƒํ˜ธ์ž‘์šฉ์— ๋Œ€ํ•œ ์ดํ•ด๋Š” โ€˜์“ธ๋ชจ์—†๋Š” ๋ชจ๋ธโ€™๊ณผ โ€˜๋น„์ •ํ˜•์ ์ธ ํ˜„์ƒโ€™ ์‚ฌ์ด์—์„œ ํ”๋“ค๋ฆฌ๊ณ  ์žˆ์—ˆ๋‹ค. ํŒŒ๋ฅด์‹œ๋Š” ์ด ์‹œ๊ธฐ์— ๋น„์ •์ƒ ์ž์œ (asymptotic freedom) ๊ฐœ๋…์„ ํŒŒํŠธ๋ก (parton) ์–ธ์–ด์™€ ์—ฐ๊ฒฐ์‹œ์ผœ, ๊ณ ์—๋„ˆ์ง€ ์‹คํ—˜์—์„œ ๊ด€์ธก๋˜๋Š” ์ ์  ๋” ์ž์œ ๋กœ์šด ์ฟผํฌยท๊ธ€๋ฃจ์˜จ ํ–‰๋™์„ ์ด๋ก ์ ์œผ๋กœ ์„ค๋ช…ํ–ˆ๋‹ค. ๊ทธ์˜ ์ž‘์—…์€ โ€˜ํŒŒ๋ผ๋‹ค์ž„ ์ „ํ™˜(paradigm shift)โ€™ ์„ ์ด‰์ง„ํ–ˆ์œผ๋ฉฐ, ์ด๋Š” โ€œ๊ฐ•ํ•œ ์ƒํ˜ธ์ž‘์šฉ์ด ๊ณ ์—๋„ˆ์ง€์—์„œ๋Š” ์•ฝํ•˜๊ฒŒ, ์ €์—๋„ˆ์ง€์—์„œ๋Š” ๊ฐ•ํ•˜๊ฒŒ ์ž‘์šฉํ•œ๋‹คโ€๋Š” ํ˜„๋Œ€ QCD์˜ ํ•ต์‹ฌ ์›๋ฆฌ๋ฅผ ์ •๋ฆฝํ•˜

No Image

Augmenting Von Neumann's Architecture for an Intelligent Future

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํฐ ๋…ธ์ด๋งŒ ๋ณ‘๋ชฉ : ํ˜„์žฌ ๋Œ€๋ถ€๋ถ„์˜ AI ์‹œ์Šคํ…œ์€ CPU/GPU ์œ„์— ์†Œํ”„ํŠธ์›จ์–ด ๋ ˆ์ด์–ด๋ฅผ ์–น์–ด ๊ตฌํ˜„ํ•œ๋‹ค. ์ด ๊ตฌ์กฐ๋Š” ๋ฐ์ดํ„ฐ ์ด๋™ ๋น„์šฉ๊ณผ ๋ฉ”๋ชจ๋ฆฌ ์ผ๊ด€์„ฑ ๋ฌธ์ œ๋กœ ์ธํ•ด ์‹ค์‹œ๊ฐ„ ๊ณ ์ฐจ์› ์ถ”๋ก  ์— ํ•œ๊ณ„๋ฅผ ๋ณด์ธ๋‹ค. AGI ์š”๊ตฌ : ์ธ๊ฐ„ ์ˆ˜์ค€์˜ ์ผ๋ฐ˜ ์ง€๋Šฅ์€ ๊ธฐํ˜ธ์  ์‚ฌ๊ณ  ์™€ ์—ฐ์†์  ํ•™์Šต ์„ ๋™์‹œ์— ์ˆ˜ํ–‰ํ•ด์•ผ ํ•˜๋ฉฐ, ์ด๋Š” ํ˜„์žฌ์˜ ๋”ฅ๋Ÿฌ๋‹โ€‘์ „์šฉ ๊ฐ€์†๊ธฐ๋งŒ์œผ๋กœ๋Š” ์ถฉ์กฑ์‹œํ‚ค๊ธฐ ์–ด๋ ต๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ Reasoning Unit (RU) | ์š”์†Œ | ์„ค๋ช… | ๊ธฐ๋Œ€ ํšจ๊ณผ | | | | | | ์ „์šฉ ISA | ๊ธฐํ˜ธ ์—ฐ์‚ฐ(์˜ˆ: ์ „์น˜, ์น˜

No Image

Automated Assessment of Kidney Ureteroscopy Exploration for Training

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ž„์ƒ ๋ฌธ์ œ : ์‹ ์žฅ ๊ฒฐ์„ ์ œ๊ฑฐ ์‹œ 20% ์ด์ƒ์ด ์žฌ์ˆ˜์ˆ  ํ•„์š”(๊ฒฐ์„ ๋ฏธํƒ์ƒ‰). ์ด๋Š” ์นผ๋ฆฌ์‹œ์Šค ์ „์ฒด ํƒ์ƒ‰ ์‹คํŒจ์™€ ์ง๊ฒฐ๋œ๋‹ค. ๊ต์œก์  ํ•œ๊ณ„ : ํ˜„์žฌ๋Š” OR ๋‚ด์—์„œ ์ „๋ฌธ๊ฐ€์™€ 1:1 ์ง€๋„, ํ”ผ๋“œ๋ฐฑ์ด ๊ตฌ๋‘ยท์ฃผ๊ด€์ ์ด๋ฉฐ, ํ›ˆ๋ จ ๊ธฐํšŒ๊ฐ€ ์ œํ•œ์ ์ด๋‹ค. ๊ธฐ์กด ์†”๋ฃจ์…˜์˜ ํ•œ๊ณ„ : ์ „์ž๊ธฐ(EM) ํŠธ๋ž˜ํ‚น ๊ธฐ๋ฐ˜ ์ž๋™ ํ”ผ๋“œ๋ฐฑ์€ ํ•˜๋“œ์›จ์–ด ๋น„์šฉยท๋ณต์žก์„ฑ์„ ์ฆ๊ฐ€์‹œ์ผœ ๋ณด๊ธ‰์ด ์–ด๋ ค์›€. 2. ์ œ์•ˆ ๋ฐฉ๋ฒ•์˜ ํ•ต์‹ฌ ์•„์ด๋””์–ด | ๋‹จ๊ณ„ | ๋‚ด์šฉ | ํ•ต์‹ฌ ๊ธฐ์ˆ  | | | | | | Stage 1 โ€“ ๋ ˆํผ๋Ÿฐ์Šค ๋ชจ๋ธ ์ƒ์„ฑ | ์ „๋ฌธ๊ฐ€๊ฐ€ ๋А๋ฆฐ ์†๋„๋กœ ๋‘ ๋ฒˆ ํƒ์ƒ‰ โ†’ 1

Image Processing Electrical Engineering and Systems Science
No Image

Automated Histopathology Report Generation via Pyramidal Feature Extraction and the UNI Foundation Model

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์กฐ์ง๋ณ‘๋ฆฌํ•™์€ ์•” ์ง„๋‹จ์˜ ๊ธˆ๋ณธ์œ„์ด๋ฉฐ, ์ „ํ†ต์ ์œผ๋กœ ๋ณ‘๋ฆฌํ•™์ž์˜ ์ฃผ๊ด€์  ํ•ด์„์— ์˜์กดํ•œ๋‹ค. ๋””์ง€ํ„ธ ๋ณ‘๋ฆฌํ•™์˜ ํ™•์‚ฐ์œผ๋กœ WSIs๋ฅผ ํ™œ์šฉํ•œ ์ž๋™ํ™”๊ฐ€ ๊ฐ€๋Šฅํ•ด์กŒ์ง€๋งŒ, ๊ธฐ๊ฐ€ํ”ฝ์…€ ๊ทœ๋ชจ ์™€ ์ง„๋‹จ์šฉ ์ „๋ฌธ ์šฉ์–ด ์‚ฌ์ด์˜ ๊ฒฉ์ฐจ๊ฐ€ ํฐ ์žฅ๋ฒฝ์œผ๋กœ ๋‚จ์•„ ์žˆ๋‹ค. ๊ธฐ์กด MIL ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•์€ ์Šฌ๋ผ์ด๋“œโ€‘๋ ˆ๋ฒจ ์˜ˆ์ธก์—๋Š” ์œ ๋ฆฌํ•˜์ง€๋งŒ, ์„ธ๋ฐ€ํ•œ ๊ณต๊ฐ„์  ๊ทผ๊ฑฐ ๋ฅผ ์ œ๊ณตํ•˜์ง€ ๋ชปํ•ด ํ…์ŠคํŠธ ์ƒ์„ฑ์— ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ (Contributions) | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | | | | | 1 | ํ”ผ๋ผ๋ฏธ๋“œํ˜• ์Šค์บ” ์ „๋žต(2ยณ~2โถ ๋ฐฐ์œจ) ์„ ๋„์ž…ํ•ด ์ €ํ•ด์ƒ๋„์—

Image Processing Electrical Engineering and Systems Science Model
No Image

Autonomous and non-autonomous fixed-time leader-follower consensus for second-order multi-agent systems

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋‹ค์ค‘์—์ด์ „ํŠธ ์‹œ์Šคํ…œ(MAS) ์€ ๋ณต์žกํ•œ ์ž‘์—…์„ ๋ถ„์‚ฐ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด ํ™œ๋ฐœํžˆ ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ๋ฆฌ๋”โ€‘ํŒ”๋กœ์›Œ ํ•ฉ์˜ ๋Š” ์ผ๋ถ€ ์—์ด์ „ํŠธ๋งŒ์ด ๋ฆฌ๋” ์ •๋ณด๋ฅผ ์ง์ ‘ ์–ป์„ ์ˆ˜ ์žˆ๋Š” ํ˜„์‹ค์ ์ธ ์ƒํ™ฉ์„ ๋ชจ๋ธ๋งํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ ๊ทผ์ (asymptotic) ์ˆ˜๋ ด ์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์œผ๋ฉฐ, ์œ ํ•œ์‹œ๊ฐ„(finiteโ€‘time) ํ˜น์€ ๊ณ ์ •์‹œ๊ฐ„(fixedโ€‘time) ์ˆ˜๋ ด์„ ์ œ๊ณตํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ์ œํ•œ์ ์ด๋‹ค. ๊ณ ์ •์‹œ๊ฐ„ ์ˆ˜๋ ด์€ ์ดˆ๊ธฐ ์กฐ๊ฑด์— ๋ฌด๊ด€ํ•˜๊ฒŒ ์ •ํ•ด์ง„ ์‹œ๊ฐ„ ์•ˆ์— ๋ชฉํ‘œ ์ƒํƒœ์— ๋„๋‹ฌํ•œ๋‹ค๋Š” ๊ฐ•์ ์„ ๊ฐ€์ง€๋ฉฐ, ์‹ค์‹œ๊ฐ„ ์ œ์–ดยท์•ˆ์ „ critical ์‹œ์Šคํ…œ์— ํ•„์ˆ˜์ 

System Electrical Engineering and Systems Science
No Image

Axisymmetric cavities in hypersonic flow

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์ถ•๋Œ€์นญ ๊ณต๋™์˜ ์‹ค์šฉ์„ฑ : ์†Œํ˜• ์„ผ์„œ ํ•˜์šฐ์ง•ยท๊ณ ์˜จ ์ฐจํ, ๋Œ€ํ˜• ์—ฐ์†Œ์‹คยทํ˜ผํ•ฉ ์ด‰์ง„ ๋“ฑ ๋‹ค์–‘ํ•œ ํ•ญ๊ณตยท์šฐ์ฃผ ๋ถ„์•ผ์—์„œ ์‚ฌ์šฉ๋œ๋‹ค. ํŠนํžˆ ๊ณ ์† ์žฌ์ง„์ž…์ฒด๋‚˜ ํ•˜์ดํผ์†Œ๋‹‰ ์ถ”์ง„๊ณ„์—์„œ ์—ด๋ถ€ํ•˜ ์žฌ๋ถ„๋ฐฐ๊ฐ€ ํ•ต์‹ฌ ๊ณผ์ œ์ด๋ฏ€๋กœ ์ „๋‹จ์ธต ์ƒํƒœ(์ธต๋ฅ˜ยท๋‚œ๋ฅ˜)์˜ ์ •ํ™•ํ•œ ํŒŒ์•…์ด ํ•„์š”ํ•˜๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„์  : 2โ€‘D ํ‰๋ฉด ๊ณต๋™์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ํ’๋ถ€ํ•˜์ง€๋งŒ, ์ถ•๋Œ€์นญ(3โ€‘D) ๊ณต๋™์€ ์Œํ–ฅยท์™€๋ฅ˜๊ฐ€ ๋ฐฉ์‚ฌํ˜•์œผ๋กœ ์ „ํŒŒ๋  ๊ฐ€๋Šฅ์„ฑ ๋•Œ๋ฌธ์— ์•„์ง ์ฒด๊ณ„์ ์ธ ์‹คํ—˜ ๋ฐ์ดํ„ฐ๊ฐ€ ๋ถ€์กฑํ–ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋ ˆ์ด๋†€์ฆˆ ์ˆ˜์™€ ๊ธฐํ•˜ํ•™์  ๋น„๋Œ€์นญ์„ ๋™์‹œ์— ์ œ์–ดํ•œ ์ตœ์ดˆ์˜ ๊ณ ํ•ด์ƒ๋„ ์‹คํ—˜ ์„ธํŠธ๋ฅผ ์ œ๊ณต

Physics
No Image

Balanced Stochastic Block Model for Community Detection in Signed Networks

| ๊ตฌ๋ถ„ | ํ•ต์‹ฌ ๋‚ด์šฉ | ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„์  | ์˜์˜ ๋ฐ ํ•œ๊ณ„ | | | | | | | ๋ฌธ์ œ ์ •์˜ | ๋ถ€ํ˜ธ ๋„คํŠธ์›Œํฌ์—์„œ ์—ฐ๊ฒฐ ํŒจํ„ด + ๋ถ€ํ˜ธ ๋ฅผ ๋™์‹œ์— ํ™œ์šฉํ•œ ์ปค๋ฎค๋‹ˆํ‹ฐ ํƒ์ง€ | ๋Œ€๋ถ€๋ถ„ ๊ธฐ์กด ๋ฐฉ๋ฒ•์€ ์–‘ยท์Œ ๋ถ€ํ˜ธ๋ฅผ ๋กœ์ปฌ ์Œ(pairwise) ์ˆ˜์ค€ ์—์„œ๋งŒ ์ด์šฉํ•˜๊ฑฐ๋‚˜, ๋ถ€ํ˜ธ๋งŒ์„ ์ด์šฉํ•œ ํ–‰๋ ฌ ์™„์„ฑ/์ €์ฐจ์› ์ž„๋ฒ ๋”ฉ์— ์˜์กด | ๊ตฌ์กฐ์  ๊ท ํ˜•์ด๋ผ๋Š” ์ „์—ญ ๊ณ ์ฐจ์› ์›๋ฆฌ ๋ฅผ ๋ช…์‹œ์ ์œผ๋กœ ๋ชจ๋ธ์— ํ†ตํ•ฉํ•จ์œผ๋กœ์จ, ๋กœ์ปฌ ์ •๋ณด๋งŒ์œผ๋กœ๋Š” ๊ตฌ๋ถ„์ด ์–ด๋ ค์šด ๊ฒฝ์šฐ์—๋„ ๊ตฌ๋ถ„ ๊ฐ€๋Šฅ | | ๋ชจ๋ธ ์„ค๊ณ„ | 1) SBM ๊ธฐ๋ฐ˜ โ†’ ์ปค๋ฎค๋‹ˆํ‹ฐ โ†’ ๋ฉ”ํƒ€โ€‘๊ทธ๋ฃน(2๊ฐœ) ๋งคํ•‘ ฮฝ(ยท) <br>2)

Statistics Network Detection Model
No Image

Bayesian Inference for Joint Tail Risk in Paired Biomarkers via Archimedean Copulas with Restricted Jeffreys Priors

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ŠคํŠธ๋ ˆ์Šคโ€‘๊ฐ•๋„ ๋ชจ๋ธ (R Pr(X > Y))์€ ์ „ํ†ต์ ์œผ๋กœ ์‹ ๋ขฐ์„ฑ ๋ถ„์„์—์„œ ์‚ฌ์šฉ๋ผ ์™”์œผ๋ฉฐ, ๋Œ€๋ถ€๋ถ„ ๋…๋ฆฝยท์ •๊ทœ ๊ฐ€์ •์— ์˜์กดํ•œ๋‹ค. ๊ณต๋™ ๊ทน๋‹จ ์œ„ํ—˜ (๋‘ ๋ฐ”์ด์˜ค๋งˆ์ปค๊ฐ€ ๋™์‹œ์— ๋น„์ •์ƒ ๊ตฌ๊ฐ„์— ์œ„์น˜ํ•  ํ™•๋ฅ )์€ ์ž„์ƒ์ ์œผ๋กœ ๋” ์ง์ ‘์ ์ธ ์˜๋ฏธ๋ฅผ ๊ฐ–์ง€๋งŒ, ๊ธฐ์กด ๋ฌธํ—Œ์—์„œ๋Š” ๊ฑฐ์˜ ๋‹ค๋ฃจ์–ด์ง€์ง€ ์•Š์•˜๋‹ค. Copula ๋Š” ์ฃผ๋ณ€๋ถ„ํฌ์™€ ์˜์กด๊ตฌ์กฐ๋ฅผ ๋ถ„๋ฆฌํ•  ์ˆ˜ ์žˆ์–ด, ํŠนํžˆ Archimedean ๊ณ„์—ด์€ ์ˆ˜์‹์ ์œผ๋กœ ๊ฐ„๋‹จํ•˜๊ณ  ํ•˜์œ„ยท์ƒ์œ„ ๊ผฌ๋ฆฌ ์˜์กด์„ ๊ฐ๊ฐ ๋ชจ๋ธ๋งํ•  ์ˆ˜ ์žˆ๋‹ค. 2. ์ฃผ์š” ๋ฐฉ๋ฒ•๋ก  | ๋‹จ๊ณ„ | ๋‚ด์šฉ | ํ•ต์‹ฌ ํฌ์ธํŠธ | | | | | | 2

Statistics
No Image

Bayesian Nonparametrics for Gene-Gene and Gene-Environment Interactions in Case-Control Studies: A Synthesis and Extension

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ Missing heritability ๋ฌธ์ œ์™€ ๋ณตํ•ฉ ์งˆํ™˜์˜ ๋‹ค์ค‘ ๊ฒฝ๋กœ์„ฑ์€ ๊ธฐ์กด ์„ ํ˜•ยท๊ฐ€๋ฒ• ๋ชจ๋ธ์ด ํฌ์ฐฉํ•˜์ง€ ๋ชปํ•˜๋Š” ๋น„์„ ํ˜•ยท๊ณ ์ฐจ ์ƒํ˜ธ์ž‘์šฉ์„ ์š”๊ตฌํ•œ๋‹ค. ์ธ๊ตฌ ๊ตฌ์กฐ(stratification) ๊ฐ€ ๋ฌด์‹œ๋  ๊ฒฝ์šฐ, ํŠนํžˆ ๋‹ค๋ฏผ์กฑยทํ˜ผํ•ฉ ์ธ๊ตฌ์—์„œ ๊ฑฐ์ง“ ์–‘์„ฑ๋ฅ ์ด ๊ธ‰์ฆํ•œ๋‹ค. ๊ณ„์‚ฐ ๋ณต์žก๋„ : ์ „ genomeโ€‘wide ์ˆ˜์ค€์—์„œ ๋ชจ๋“  SNPโ€‘SNPยทSNPโ€‘ํ™˜๊ฒฝ ์กฐํ•ฉ์„ ๊ฒ€์ •ํ•˜๋Š” ๊ฒƒ์€ NPโ€‘hard ๋ฌธ์ œ์ด๋ฉฐ, ํ˜„์žฌ๋Š” ์ฐจ์› ์ถ•์†Œยทํžˆ์–ด์Šคํ‹ฑ ์Šคํฌ๋ฆฌ๋‹์— ์˜์กดํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๋ฐฉ๋ฒ•๋ก  | ๊ตฌ์„ฑ ์š”์†Œ | ์„ค๋ช… | ๋ฒ ์ด์ง€์•ˆ ๋น„๋ชจ์ˆ˜์  ์žฅ์  | | |

Statistics
No Image

Bayesian Quadrature: Gaussian Processes for Integration

1. ์—ฐ๊ตฌ ๋™๊ธฐ์™€ ๋ฐฐ๊ฒฝ ์ ๋ถ„ ๋ฌธ์ œ์˜ ๋ณดํŽธ์„ฑ : ๋จธ์‹ ๋Ÿฌ๋‹ยท๋ฒ ์ด์ง€์•ˆ ํ†ต๊ณ„ยท๊ณตํ•™ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋“ฑ์—์„œ ๊ธฐ๋Œ€๊ฐ’ยท์ •๊ทœํ™” ์ƒ์ˆ˜ ๊ณ„์‚ฐ์€ ํ•„์ˆ˜์ ์ด๋ฉฐ, ๊ณ ์ฐจ์›ยท๋น„์„ ํ˜• ์ ๋ถ„์ด ํ”ํžˆ ๋ฐœ์ƒํ•œ๋‹ค. ์ „ํ†ต์  ๋ฐฉ๋ฒ•๊ณผ์˜ ๋Œ€๋น„ : ๊ฐ€์šฐ์‹œ์•ˆ(๋‹คํ•ญ) ์ ๋ถ„ : ๋‹คํ•ญ ๊ทผ์‚ฌ ๊ฐ€์ • โ†’ ๊ณ ์ฐจ์›์—์„œ ๋น„ํšจ์œจ. ๋ชฌํ…Œ์นด๋ฅผ๋กœ(MC) : ๊ฐ€์ • ๊ฑฐ์˜ ์—†์Œ โ†’ ์ˆ˜๋ ด ์†๋„ ๋А๋ฆผ. ์ฟผ์‹œ MC : ์ €๋ถˆ์ผ์น˜ ์‹œํ€€์Šค ์‚ฌ์šฉ โ†’ ๊ณ ์ฐจ์›์—์„œ ์ข‹์€ ์„ฑ๋Šฅ. ๋ฒ ์ด์ง€์•ˆ ์ ๋ถ„ ์€ ์‚ฌ์ „ ์ง€์‹์„ ๋ช…์‹œ์ ์œผ๋กœ ๋ชจ๋ธ๋งํ•˜๊ณ , ์‚ฌํ›„ ๋ถ„ํฌ๋ฅผ ํ†ตํ•ด ๋ถˆํ™•์‹ค์„ฑ์„ ์ •๋Ÿ‰ํ™”ํ•œ๋‹ค๋Š” ์ ์—์„œ ์ฐจ๋ณ„ํ™”๋œ๋‹ค. 2. ํ•ต์‹ฌ ๊ฐœ๋… ๋ฐ ์ •์˜ ๋ฒ ์ด์ง€์•ˆ ์ ๋ถ„ ์ •

Computer Science Machine Learning
No Image

bayesics: Core Statistical Methods via Bayesian Inference in R

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฒ ์ด์ง€์•ˆ vs. ๋นˆ๋„์ฃผ์˜ : ์ €์ž๋Š” ๋ฒ ์ด์ง€์•ˆ ์ถ”๋ก ์ด ๊ณผํ•™์  ์งˆ๋ฌธ์— ์ง์ ‘์ ์ธ ๋‹ต์„ ์ œ๊ณตํ•œ๋‹ค๋Š” ์ ์„ ๊ฐ•์กฐํ•˜๊ณ , ๋นˆ๋„์ฃผ์˜์˜ pโ€‘๊ฐ’ยท์‹ ๋ขฐ๊ตฌ๊ฐ„์ด ํ•ด์„์ƒ์˜ ํ˜ผ๋ž€์„ ์•ผ๊ธฐํ•œ๋‹ค๋Š” ๊ธฐ์กด ๋ฌธํ—Œ(์˜ˆ: Wasserstein & Lazar, 2016)์„ ์ธ์šฉํ•œ๋‹ค. ๋„๊ตฌ์˜ ๋ถ€์žฌ : ํ˜„์žฌ R์—๋Š” ๋ฒ ์ด์ง€์•ˆ ๋ถ„์„์„ ์œ„ํ•œ ์ „์šฉ ํŒจํ‚ค์ง€๊ฐ€ ๋‹ค์ˆ˜ ์กด์žฌํ•˜์ง€๋งŒ, ๋Œ€๋ถ€๋ถ„ ํŠน์ • ๋ชฉ์ ์— ๊ตญํ•œ ๋˜๊ฑฐ๋‚˜ ๋ณต์žกํ•œ MCMC ์„ค์ • ์„ ์š”๊ตฌํ•œ๋‹ค. ํŠนํžˆ, ์ถ”๋ก ์— ํ•„์š”ํ•œ ํ•ต์‹ฌ ์ง€ํ‘œ(ROPE, PDir ๋“ฑ)๋ฅผ ์ž๋™์œผ๋กœ ์ œ๊ณตํ•˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ์ ์ด ์ง€์ ๋œ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ |

Statistics
No Image

Benchmarking Self-Supervised Models for Cardiac Ultrasound View Classification

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ž„์ƒ์  ์ค‘์š”์„ฑ : ์‹ฌ์žฅ ์ดˆ์ŒํŒŒ๋Š” ์‹ค์‹œ๊ฐ„์œผ๋กœ ํ•ด๋ถ€ยท๊ธฐ๋Šฅ์„ ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ์–ด ์„ฑ์ธยทํƒœ์•„ ๋ชจ๋‘์—์„œ ํ•ต์‹ฌ ์˜์ƒ modality์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ทฐ ์‹๋ณ„์€ ๊ณ ๋„์˜ ์ „๋ฌธ ์ง€์‹๊ณผ ์‹œ๊ฐ„์ด ์š”๊ตฌ๋œ๋‹ค. ๋ฐ์ดํ„ฐ ๋ณ‘๋ชฉ : ๋ผ๋ฒจ๋ง ๋น„์šฉ์ด ๋†’๊ณ , ํŠนํžˆ ํƒœ์•„ ์ดˆ์ŒํŒŒ๋Š” ๊ตฌ์กฐ๊ฐ€ ์ž‘๊ณ  ์ž์„ธ๊ฐ€ ๋‹ค์–‘ํ•ด ๋ผ๋ฒจ๋ง์ด ๋”์šฑ ์–ด๋ ค์›Œ์ง„๋‹ค. SSL์˜ ๊ธฐ๋Œ€ : ๋ผ๋ฒจ์ด ์—†๋Š” ๋Œ€๊ทœ๋ชจ ์ดˆ์ŒํŒŒ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•ด ์˜๋ฏธ ์žˆ๋Š” ํ‘œํ˜„์„ ํ•™์Šตํ•˜๊ณ , ์†Œ๋Ÿ‰์˜ ๋ผ๋ฒจ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด ๋†’์€ ์„ฑ๋Šฅ์„ ๋‹ฌ์„ฑํ•  ์ˆ˜ ์žˆ๋‹ค. 2. ์‹คํ—˜ ์„ค๊ณ„ ๋ฐ ๋ฐฉ๋ฒ•๋ก  | ํ•ญ๋ชฉ | USFโ€‘MAE | MoCo v3 |

Image Processing Electrical Engineering and Systems Science Model
No Image

Beyond SMILES: Evaluating Agentic Systems for Drug Discovery

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์—์ด์ „ํŠธํ˜• AI์˜ ํ˜„์ฃผ์†Œ : Coscientist, ChemCrow, ChatInvent ๋“ฑ์€ LLM์ด ํˆด ํ˜ธ์ถœ์„ ์กฐ์œจํ•˜๋Š” โ€˜LLMโ€‘centricโ€™ ๊ตฌ์กฐ๋ฅผ ๊ณต์œ ํ•œ๋‹ค. ์ด๋“ค์€ ๋ฌธํ—Œ ์ •๋ฆฌยทํ•ฉ์„ฑ ๊ฒฝ๋กœ ํƒ์ƒ‰ยท๊ธฐ์ดˆ์ ์ธ ์•ˆ์ „์„ฑ ๋ถ„์„ ๋“ฑ ํ…์ŠคํŠธ ๊ธฐ๋ฐ˜ ์ž‘์—…์— ๊ฐ•์ ์„ ๋ณด์ด์ง€๋งŒ, โ€˜SMILESโ€‘์ค‘์‹ฌโ€™ ์ด๋ผ๋Š” ๊ทผ๋ณธ์ ์ธ ๊ฐ€์ •์— ๋ฌถ์—ฌ ์žˆ๋‹ค. ํŽฉํƒ€์ด๋“œยท๋ฐ”์ด์˜ค ์˜์•ฝํ’ˆ์˜ ๊ธ‰๋ถ€์ƒ : 5~50 ์•„๋ฏธ๋…ธ์‚ฐ ๊ธธ์ด์˜ ํŽฉํƒ€์ด๋“œ๋Š” ๊ตฌ์กฐ์  ์œ ์—ฐ์„ฑ, ํ”„๋กœํ…Œ์•„์ œ ์ €ํ•ญ์„ฑ, ๋ณตํ•ฉ์ ์ธ ADMET ํŠน์„ฑ ๋“ฑ ์†Œ๋ถ„์ž์™€๋Š” ์ „ํ˜€ ๋‹ค๋ฅธ ๊ณผํ•™์  ์š”๊ตฌ๋ฅผ ๊ฐ€์ง„๋‹ค. ๋”ฐ๋ผ์„œ ๊ธฐ์กด

System Quantitative Biology
No Image

Bias analysis of a linear order-statistic inequality index estimator: Unbiasedness under gamma populations

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์ˆœ์œ„โ€‘๊ธฐ๋ฐ˜ ๋ถˆํ‰๋“ฑ ์ง€ํ‘œ์˜ ํ•œ๊ณ„ : ๊ณ ์ „ ์ง€๋‹ˆ๊ณ„์ˆ˜๋Š” ์ž‘์€ ํ‘œ๋ณธยท๊ทน๋‹จ ๋น„๋Œ€์นญ(์Šคํ‚ค) ์ƒํ™ฉ์—์„œ ํŽธํ–ฅ์ด ํฌ๊ฒŒ ๋‚˜ํƒ€๋‚œ๋‹ค(๋ฌธํ—Œ

Analysis Mathematics

< Category Statistics (Total: 2829) >

Electrical Engineering and Systems Science
100
General
731
General Relativity
22
HEP-EX
17
HEP-LAT
3
HEP-PH
39
HEP-TH
19
MATH-PH
36
NUCL-EX
2
NUCL-TH
5
Quantum Physics
41

Start searching

Enter keywords to search articles

โ†‘โ†“
โ†ต
ESC
โŒ˜K Shortcut