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Discrete reliability for high-order Crouzeix--Raviart finite elements

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋น„์ •ํ•ฉ FEM์ธ Crouzeixโ€“Raviart ์š”์†Œ๋Š” ๊ฒฝ๊ณ„์กฐ๊ฑด์„ ์™„ํ™”ํ•˜๋ฉด์„œ๋„ ๋†’์€ ์ฐจ์ˆ˜ ๋‹คํ•ญ์‹์œผ๋กœ ํ™•์žฅ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์žฅ์ ์ด ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ฐจ์ˆ˜๊ฐ€ ๋†’์•„์งˆ์ˆ˜๋ก ๋น„์ •ํ•ฉ ๊ณต๊ฐ„์ด ๋ฉ”์‹œ ์ •์ œ ์‹œ์— ๋น„์ค‘์ฒฉ(nonโ€‘nested) ์ด ๋˜๋Š” ๋ฌธ์ œ๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค. ์ด๋Š” ์ ์‘ํ˜• ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์ด๋ก ์  ์ˆ˜๋ ด ๋ถ„์„, ํŠนํžˆ โ€œ์ ์‘์„ฑ ๊ณต๋ฆฌโ€ ์ค‘ ์ด์‚ฐ ์‹ ๋ขฐ์„ฑ(A3) ์„ ์ฆ๋ช…ํ•˜๋Š” ๋ฐ ํฐ ์žฅ์• ๋ฌผ์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ CR( 1) (์ตœ์ € ์ฐจ์ˆ˜) ์— ํ•œ์ •๋˜์–ด ์žˆ์—ˆ์œผ๋ฉฐ, ๊ณ ์ฐจ์ˆ˜์— ๋Œ€ํ•œ ์ผ๋ฐ˜์ ์ธ ์ด๋ก ์€ ๋ถ€์žฌํ–ˆ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ ์ƒˆ๋กœ์šด ๊ตญ์†Œ ์ค€๋ณด๊ฐ„ ์—ฐ์‚ฐ

Mathematics
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Disjoint Correspondence Colorings for $K_5$-Minor-free Graphs

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ Thomassen์˜ 5โ€‘choosable ์ •๋ฆฌ ๋Š” ํ‰๋ฉด ๊ทธ๋ž˜ํ”„๊ฐ€ ๋ชจ๋“  ์ •์ ์— 5๊ฐœ์˜ ์ƒ‰์„ ํ• ๋‹นํ•ด๋„ ์ ์ ˆํžˆ ์ƒ‰์น ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋Œ€์‘ ์ƒ‰์ฑ„ ๋Š” ๋ฆฌ์ŠคํŠธ ์ƒ‰์ฑ„๋ฅผ ์ผ๋ฐ˜ํ™”ํ•œ ๊ฐœ๋…์œผ๋กœ, ๊ฐ ๊ฐ„์„ ๋งˆ๋‹ค ์ƒ‰ ์‚ฌ์ด์˜ ๋งค์นญ์„ ์ง€์ •ํ•œ๋‹ค. ์ด๋Š” ๊ธฐ์กด ๋ฆฌ์ŠคํŠธ ์ƒ‰์ฑ„๋ณด๋‹ค ๊ฐ•๋ ฅํ•œ ์ œ์•ฝ์„ ๊ฐ€์ง„๋‹ค. Kโ‚… ๋งˆ์ด๋„ˆ ์ž์œ  ๊ทธ๋ž˜ํ”„ ๋Š” ํ‰๋ฉด ๊ทธ๋ž˜ํ”„๋ฅผ ํฌํ•จํ•˜๋Š” ๋„“์€ ํด๋ž˜์Šค์ด๋ฉฐ, ์ด๋“ค์— ๋Œ€ํ•œ ์ƒ‰์ฑ„ ์ด๋ก ์€ ์•„์ง ์ถฉ๋ถ„ํžˆ ์ •๋ฆฝ๋˜์ง€ ์•Š์•˜๋‹ค. ๊ฐ€์ค‘ ฮตโ€‘์œ ์—ฐ์„ฑ (weighted ฮตโ€‘flexibility) ๋ฌธ์ œ๋Š” ๊ฐ ์ •์ ยท์ƒ‰์— ์ผ์ • ํ™•๋ฅ ์„ ๋ณด์žฅํ•˜๋Š” ์ƒ‰์น  ๋ถ„ํฌ

Mathematics
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Distilling Diversity and Control in Diffusion Models

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋””ํ“จ์ „ ๋ชจ๋ธ ์ฆ๋ฅ˜ ๋Š” ์ด๋ฏธ์ง€ ์ƒ์„ฑ ์†๋„๋ฅผ ์ˆ˜์‹ญ ๋ฐฐ ๊ฐ€์†ํ™”ํ•˜์ง€๋งŒ, โ€œ๋‹ค์–‘์„ฑ ๊ฐ์†Œโ€๋ผ๋Š” ์‹ฌ๊ฐํ•œ ๋ถ€์ž‘์šฉ์„ ๋™๋ฐ˜ํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์†๋„โ€‘ํ’ˆ์งˆ ํŠธ๋ ˆ์ด๋“œ์˜คํ”„ ์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์œผ๋ฉฐ, ๋‹ค์–‘์„ฑ ์†์‹ค ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ๋Œ€ํ•œ ์ฒด๊ณ„์  ํƒ๊ตฌ๋Š” ๋ถ€์กฑํ–ˆ๋‹ค. 2. ํ•ต์‹ฌ ์งˆ๋ฌธ 1. ์ฆ๋ฅ˜๊ฐ€ ๊ฐœ๋… ํ‘œํ˜„์„ ์†์ƒ์‹œํ‚ค๋Š”๊ฐ€? 2. ๋‹ค์–‘์„ฑ ์†์‹ค์˜ ๊ทผ๋ณธ ์›์ธ์€ ์–ธ์ œ, ์–ด๋””์„œ ๋ฐœ์ƒํ•˜๋Š”๊ฐ€? 3. ๋ฐฉ๋ฒ•๋ก  | ๋‹จ๊ณ„ | ๋‚ด์šฉ | ๋ชฉ์  | | | | | | ํ‘œํ˜„ ๊ตฌ์กฐ ๊ฒ€์ฆ | Concept Slider, LoRA ์ ์šฉ ํ›„ ์„ฑ๋Šฅ ๋น„๊ต | ์ฆ๋ฅ˜ ๋ชจ๋ธ์ด ๊ธฐ๋ณธ ๋ชจ๋ธ์˜ ์ž 

Model
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Distributional Deep Learning for Super-Resolution of 4D Flow MRI under Domain Shift

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ž„์ƒ์  ๋ฌธ์ œ : ๋‡Œ๋™๋งฅ๋ฅ˜๋Š” ์ „์ฒด ์ธ๊ตฌ์˜ ์•ฝ 6 %์—์„œ ๋ฐœ๊ฒฌ๋˜๋ฉฐ, ํŒŒ์—ด ์‹œ ๋†’์€ ์‚ฌ๋ง๋ฅ ์„ ์ดˆ๋ž˜ํ•œ๋‹ค. ํŒŒ์—ด ์œ„ํ—˜์„ ์˜ˆ์ธกํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ํ˜ˆ๋ฅ˜์—ญํ•™(ํŠนํžˆ ์›”๋ฒฝ ์ „๋‹จ์‘๋ ฅยท์ „๋‹จ๋†๋„์ง€์ˆ˜)์ด ํ•ต์‹ฌ ๋ณ€์ˆ˜์ด๋‹ค. 4D Flow MRI์˜ ํ•œ๊ณ„ : ์‹ค์ œ ํ™˜์ž ์Šค์บ”์—์„œ ์–ป์–ด์ง€๋Š” 4D Flow MRI๋Š” ์žก์Œยท์•„ํ‹ฐํŒฉํŠธ์™€ ๋‚ฎ์€ ๊ณต๊ฐ„ ํ•ด์ƒ๋„๋กœ ์ธํ•ด ์ž‘์€ ํ˜ˆ๋ฅ˜ ๊ตฌ์กฐ๋ฅผ ๋†“์น˜๊ธฐ ์‰ฝ๋‹ค. CFD์™€์˜ ๊ฒฉ์ฐจ : CFD๋Š” ๊ณ ํ•ด์ƒ๋„ยท๋…ธ์ด์ฆˆ ์—†๋Š” ํ๋ฆ„์žฅ์„ ์ œ๊ณตํ•˜์ง€๋งŒ, ๋ชจ๋ธ๋ง ๊ฐ€์ •ยท์ดˆ๊ธฐ์กฐ๊ฑด์— ํฌ๊ฒŒ ์˜์กดํ•˜๊ณ  ์ž„์ƒ ์ ์šฉ์ด ์–ด๋ ค์›Œ ์‹ค์ œ MRI์™€ ์ง์ ‘ ๋งคํ•‘ํ•˜๊ธฐ์—” ํ•œ๊ณ„

Computer Science Learning Computer Vision
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Drivetrain simulation using variational autoencoders

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฐ์ดํ„ฐ ๋ถ€์กฑ ๋ฌธ์ œ : ์ „๋™์ฐจ ๊ตฌ๋™๊ณ„ ์‹คํ—˜์€ ๊ณ ๊ฐ€ ์žฅ๋น„์™€ ๊ธด ํ…Œ์ŠคํŠธ ์‹œ๊ฐ„์ด ํ•„์š”ํ•ด ๋ฐ์ดํ„ฐ ํ™•๋ณด๊ฐ€ ์–ด๋ ค์›€. ์ „ํ†ต ๋ฌผ๋ฆฌ ๋ชจ๋ธ์˜ ํ•œ๊ณ„ : ์ •ํ™•ํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ ์‹๋ณ„์ด ํ•„์š”ํ•˜๊ณ , ๋ณต์žกํ•œ ๋น„์„ ํ˜• ํ˜„์ƒ์„ ๋ชจ๋‘ ํฌ์ฐฉํ•˜๊ธฐ ํž˜๋“ฆ. 2. ํ•ต์‹ฌ ๋ฐฉ๋ฒ•๋ก  | ์š”์†Œ | ์„ค๋ช… | | | | | Variational Autoencoder (VAE) | ์ž…๋ ฅ(ํ† ํฌ) โ†’ ์ž ์žฌ ๋ณ€์ˆ˜(z) โ†’ ์ถœ๋ ฅ(jerk) ํ˜•ํƒœ์˜ ํ™•๋ฅ ์  ์ƒ์„ฑ ๋ชจ๋ธ. ํ•™์Šต ์‹œ ELBO(์ฆ๊ฑฐ ํ•˜ํ•œ) ์ตœ์ ํ™”. | | Unconditional VAE | ํ† ํฌ ์ •๋ณด๋ฅผ ์ž…๋ ฅ์— ํฌํ•จํ•˜์ง€ ์•Š

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Drug Release Modeling using Physics-Informed Neural Networks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ œ์–ด ๋ฐฉ์ถœ์˜ ์ž„์ƒ์  ์ค‘์š”์„ฑ : ์•” ์น˜๋ฃŒยท๋งŒ์„ฑ ์งˆํ™˜ ๊ด€๋ฆฌ ๋“ฑ์—์„œ ์•ฝ๋ฌผ ๋†๋„์™€ ํˆฌ์—ฌ ์‹œ์ ์„ ์ •๋ฐ€ํžˆ ์กฐ์ ˆํ•ด์•ผ ํ•จ. ๊ณ ์ „ ๋ชจ๋ธ์˜ ํ•œ๊ณ„ : Fick, Higuchi, Peppas๋Š” ํŠน์ • ๊ธฐํ•˜ํ•™ยท์กฐ๊ฑด์— ์ตœ์ ํ™”๋ผ ๋ณต์žกํ•œ 3D ๊ตฌ์กฐยท๋น„์„ ํ˜• ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๋‹ค๋ฃจ๊ธฐ ์–ด๋ ค์›€. ๋ฐ์ดํ„ฐยท๊ณ„์‚ฐ ๋น„์šฉ ๋ฌธ์ œ : ์ „ํ†ต์ ์ธ ์ˆ˜์น˜ ํ•ด์„(FEM, FDM)์€ ๊ณ ํ•ด์ƒ๋„ ์‹คํ—˜ ๋ฐ์ดํ„ฐ์™€ ๊ธด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์‹œ๊ฐ„์ด ์š”๊ตฌ๋จ. 2. ์ œ์•ˆ ๋ฐฉ๋ฒ•๋ก  | ์š”์†Œ | ๊ตฌํ˜„ ์ƒ์„ธ | ์—ญํ•  | | | | | | Physicsโ€‘Informed Neural Network (PINN

Computer Science Network Machine Learning Model
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Early stages of collective cell invasion: Biomechanics

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉํ‘œ ์ž„์ƒ์  ์ค‘์š”์„ฑ : ์•” ์ „์ด์˜ ์ฒซ ๋‹จ๊ณ„์ธ ์กฐ์ง ์นจํˆฌ๋Š” ์น˜๋ฃŒ ํ‘œ์ ์œผ๋กœ์„œ ๋งค์šฐ ์ค‘์š”ํ•˜์ง€๋งŒ, ๊ธฐ์กด ๋ชจ๋ธ์€ ์ฃผ๋กœ ์„ธํฌ ์ฆ์‹ยท์‹ ํ˜ธ ์ „๋‹ฌ์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ๋‹ค. ๊ธฐ๊ณ„ํ•™์  ๊ณต๋ฐฑ : EMTยทNotch ์‹ ํ˜ธ์™€ ๊ฐ™์€ ์ƒํ™”ํ•™์  ๊ฒฝ๋กœ๋Š” ์ž˜ ์•Œ๋ ค์กŒ์œผ๋‚˜, ์„ธํฌโ€‘๊ธฐ์งˆ ๊ธฐ๊ณ„ ์ƒํ˜ธ์ž‘์šฉ (๋‘๋ฃจํƒ์‹œ, ํ™œ์„ฑ ํž˜)๊ณผ ๊ทธ์— ๋”ฐ๋ฅธ ์ง‘๋‹จ ํ–‰๋™์„ ๋™์‹œ์— ๋‹ค๋ฃจ๋Š” ๋ชจ๋ธ์€ ๋ถ€์กฑํ–ˆ๋‹ค. ๋ชฉํ‘œ : ์„ธํฌ ์œ ํ˜•๋ณ„( E, M, E/M ) ๊ธฐ๊ณ„์  ํŠน์„ฑ์„ ๋ช…์‹œํ•˜๊ณ , ๋ถ„์ˆ˜ ๋‹จ๊ณ„ ๋ฅผ ์ด์šฉํ•ด ๋‘๋ฃจํƒ์‹œ์™€ ํ™œ์„ฑ ํž˜์„ ๋…๋ฆฝ์ ์œผ๋กœ ์ ์šฉํ•จ์œผ๋กœ์จ ์ดˆ๊ธฐ ์นจํˆฌ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์ •๋Ÿ‰ํ™”ํ•œ๋‹ค. 2. ๋ชจ๋ธ๋ง

Condensed Matter
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Eastward Transients in the Dayside Ionosphere II: A Parallel-plate Capacitor-Like Effect

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ „๋ฆฌ์ธต ์ปคํ”„ ์ง€์—ญ์€ ์ „์žยท์–‘์„ฑ์ž ์ž…์ž ์นจํˆฌ์™€ ๊ฐ•ํ•œ ์ „๊ธฐ์žฅ ๋ณ€๋™์ด ๋™์‹œ์— ์ผ์–ด๋‚˜๋Š” ๋ณตํ•ฉ ํ™˜๊ฒฝ์œผ๋กœ, ๊ธฐ์กด์—๋Š” ์ฃผ๋กœ ๊ทน์ชฝ(๋ถ์ชฝ)์œผ๋กœ ์ด๋™ํ•˜๋Š” ์˜ค๋กœ๋ผ ํ˜•ํƒœ ๊ฐ€ ์ „๊ธฐ์žฅ ๋ณ€๋™์„ ์„ค๋ช…ํ•˜๋Š” ์ฃผ์š” ๋ฉ”์ปค๋‹ˆ์ฆ˜์œผ๋กœ ๋ฐ›์•„๋“ค์—ฌ์กŒ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ตœ๊ทผ ๊ด€์ธก(2023 4 23, 2024 5)์—์„œ๋Š” ๋™์ชฝ์œผ๋กœ ๊ธ‰์† ์ด๋™ํ•˜๋Š” ์ „๊ธฐ์žฅ ๊ตฌ์กฐ ๊ฐ€ ๋‚˜ํƒ€๋‚˜๋ฉฐ, ์ด๋Š” ๊ธฐ์กด ์ด๋ก ์œผ๋กœ๋Š” ์ถฉ๋ถ„ํžˆ ์„ค๋ช…๋˜์ง€ ์•Š๋Š”๋‹ค. 2. ํ•ต์‹ฌ ๊ด€์ธก ๊ฒฐ๊ณผ ์ฝ”๋Ÿฌ์ŠคํŒŒโ€‘๊ตฌ๋™ ์ž…์ž ์นจํˆฌ : ๊ณ ์—๋„ˆ์ง€ ์ „์žยท์–‘์„ฑ์ž๊ฐ€ ํ์‡„๋œ ์ž๊ธฐ์žฅ์„ ์— ๋”ฐ๋ผ ๋‚ฎ์€ ์œ„๋„(๋™์ชฝ)๊นŒ์ง€ ๋„๋‹ฌ, ์ž…์žโ€‘ํ”Œ๋ผ์ฆˆ๋งˆ ์ƒํ˜ธ์ž‘์šฉ์„

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Ecosystems in the Anthropocene: transformative drivers

1. ์—ฐ๊ตฌ์˜ ํ•ต์‹ฌ ์ฃผ์žฅ 1. ์ธ๋ฅ˜์„ธ๋Š” ๋‹จ์ผ ์ข…(์ธ๋ฅ˜)์˜ ํ™œ๋™์— ์˜ํ•ด ์ง€๊ตฌ ์‹œ์Šคํ…œ์ด ์ „๋ฉด์ ์œผ๋กœ ์žฌ๊ตฌ์„ฑ๋˜๋Š” ์‹œ๊ธฐ ์ด๋ฉฐ, ์ด๋Š” ๊ธฐ์กด ์ƒํƒœ๊ณ„์˜ ํ‡ด๋ณด์™€ ์‹ ์ƒํƒœ๊ณ„์˜ ์ถœํ˜„์„ ๋™์‹œ์— ์•ผ๊ธฐํ•œ๋‹ค. 2. ๋ณ€ํ˜์  ๋“œ๋ผ์ด๋ฒ„์™€ ์™„ํ™”ยท๋ณต์›์  ๋“œ๋ผ์ด๋ฒ„ ๋ฅผ ๊ตฌ๋ถ„ํ•จ์œผ๋กœ์จ, ์ธ๊ฐ„ ํ™œ๋™์ด ์ƒํƒœ๊ณ„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ โ€˜๊ฐ€์†โ€™๊ณผ โ€˜๊ฐ์†โ€™ ๋‘ ์ถ•์œผ๋กœ ํ•ด์„ํ•œ๋‹ค. 3. ์‹ ์ƒํƒœ๊ณ„๋Š” โ€˜์ž์—ฐ์ โ€™์ด๋ฉด์„œ๋„ โ€˜์ธ์œ„์ โ€™์ธ ์ค‘๊ฐ„ ์˜์—ญ ์œผ๋กœ, ์ง€์†์ ์ธ ์ธ๊ฐ„ ๊ฐœ์ž… ์—†์ด๋„ ์ž์ฒด ์œ ์ง€ยท๋ฐœ์ „์ด ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์ ์—์„œ ๊ธฐ์กด ๋ณด์ „ ๊ฐœ๋…์„ ์žฌ์ •์˜ํ•œ๋‹ค. 2. ๋ณ€ํ˜์  ๋“œ๋ผ์ด๋ฒ„(Highโ€‘Impact) | ๋“œ๋ผ์ด๋ฒ„ |

System Quantitative Biology
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Effect of flexibility on the pitch-heave flutter instability of a flexible foil elastically supported on its leading edge

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํ”Œ๋Ÿฌํ„ฐ๋Š” ํ•ญ๊ณต๊ธฐ ๋‚ ๊ฐœ ์„ค๊ณ„๋ฟ ์•„๋‹ˆ๋ผ ์ตœ๊ทผ์—๋Š” ์ˆ˜๋™ ํ”Œ๋žฉํ„ฐ ํ„ฐ๋นˆยท์—๋„ˆ์ง€ ์ˆ˜ํ™• ์‹œ์Šคํ…œ์—์„œ๋„ ํ•ต์‹ฌ ํ˜„์ƒ์œผ๋กœ ๋ถ€๊ฐ๋˜๊ณ  ์žˆ๋‹ค. ๊ธฐ์กด์˜ Theodorsen ์ด๋ก ์€ ๊ฐ•์ฒด ํฌ์ผ์— ํ•œ์ •๋˜๋ฉฐ, ์œ ์—ฐ์„ฑยท์ค‘๋ ฅยท๋‹ค์ค‘ ๊ตฝํž˜ ๋ชจ๋“œ๊ฐ€ ํฌํ•จ๋œ ํ•ด์„์€ ๊ฑฐ์˜ ์—†์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์„ค๊ณ„ ๋‹จ๊ณ„์—์„œ ๋น ๋ฅด๊ฒŒ ํŒŒ๋ผ๋ฏธํ„ฐ ์Šค์œ•์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ„๋‹จํ•˜๋ฉด์„œ๋„ ์ •ํ™•ํ•œ ๋ชจ๋ธ์ด ์š”๊ตฌ๋œ๋‹ค. 2. ํ•ต์‹ฌ ์ด๋ก ์  ๋ฐœ์ „ ์ƒˆ๋กœ์šด FSI ํ•ด์„์‹ : ๋ฌด์ ์„ฑ ์œ ๋™๊ณผ Eulerโ€‘Bernoulli ๋น” ๋ฐฉ์ •์‹์„ ๊ฒฐํ•ฉํ•ด 5์ฐจ ๋‹คํ•ญ์‹ ํ˜•ํƒœ(์ฒซ ๋‘ ๊ตฝํž˜ ๋ชจ๋“œ ํฌํ•จ)๋กœ ํฌ์ผ ๋ณ€ํ˜•์„ ์ „๊ฐœํ•˜์˜€๋‹ค

Physics
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Efficient Sampling with Discrete Diffusion Models: Sharp and Adaptive Guarantees

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ด์‚ฐ ํ™•์‚ฐ ๋ชจ๋ธ ์€ ํ…์ŠคํŠธ, ๊ทธ๋ž˜ํ”„, ์นดํ…Œ๊ณ ๋ฆฌ ๋ผ๋ฒจ ๋“ฑ ์—ฐ์†ํ˜• ๋ฐ์ดํ„ฐ๊ฐ€ ์•„๋‹Œ ์˜์—ญ์—์„œ ์ตœ๊ทผ ๊ธ‰๊ฒฉํžˆ ์„ฑ์žฅํ•˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ SEDD (Scoreโ€‘Entropy Discrete Diffusion)์™€ ๊ฐ™์€ ๋ฐฉ๋ฒ•์ด ์ž๋™ ํšŒ๊ท€ ๋ชจ๋ธ์„ ๋Šฅ๊ฐ€ํ•˜๋Š” ์„ฑ๋Šฅ์„ ๋ณด์ด๋ฉฐ, ์ƒ์„ฑ ์ˆœ์„œ์— ์–ฝ๋งค์ด์ง€ ์•Š๋Š” ์œ ์—ฐ์„ฑ์„ ์ œ๊ณตํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด ์ด๋ก ์€ ์ƒ˜ํ”Œ๋ง ๋ณต์žก๋„๊ฐ€ ์–ดํœ˜ ํฌ๊ธฐ S์™€ ์ฐจ์› d์— ๋ชจ๋‘ ์„ ํ˜• ์œผ๋กœ ์˜์กดํ•œ๋‹ค๋Š” ์ ์—์„œ ์‹ค์šฉ์„ฑ์— ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ๋‹ค. ์ด๋Š” GPTโ€‘2 ์ˆ˜์ค€( (Sapprox5times10^4, dapprox10^3) )์˜

Computer Science Machine Learning Model
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Either a Confidence Interval Covers, or It Doesn't (Or Does It?): A Model-Based View of Ex-Post Coverage Probability

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ œ๊ธฐ ๋„ค์ด๋งŒ์˜ ์›์น™ : ฮธ๋Š” ๊ณ ์ •๋œ ๋ฏธ์ง€์ˆ˜์ด๋ฉฐ, ๋ฐ์ดํ„ฐ X๊ฐ€ ๋žœ๋คํ•˜๊ฒŒ ์ƒ์„ฑ๋  ๋•Œ๋งŒ ํ™•๋ฅ ์ด ์ •์˜๋œ๋‹ค. ๊ด€์ธก๋œ X xแตข ํ›„์—๋Š” P(L(X)โ‰คฮธโ‰คU(X))๊ฐ€ 0 ๋˜๋Š” 1์ด ๋˜๋ฏ€๋กœ โ€œ์‚ฌํ›„ ํ™•๋ฅ โ€์€ ์˜๋ฏธ๊ฐ€ ์—†๋‹ค๊ณ  ๋ณธ๋‹ค. ์‹ค์ œ ํ†ต๊ณ„ ์‹ค๋ฌด์™€์˜ ๊ดด๋ฆฌ : ์˜๋ฃŒ ์ง„๋‹จ, ํ’ˆ์งˆ ๊ด€๋ฆฌ, ๊ธฐ๊ณ„ ๊ณต์ • ๋“ฑ์—์„œ ์šฐ๋ฆฌ๋Š” ๊ด€์ธก๋œ ๊ฒฐ๊ณผ์— ๋Œ€ํ•ด ์‚ฌํ›„ ํ™•๋ฅ  (์˜ˆ: ์–‘์„ฑ์˜ˆ์ธก๊ฐ’, ๊ฒฐํ•จ๋ฅ )์„ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์‚ฌ์šฉํ•œ๋‹ค. โ€œeitherโ€‘orโ€ ํ•ด์„์„ ๊ณ ์ˆ˜ํ•˜๋ฉด ์ด๋Ÿฌํ•œ ์‹ค์šฉ์  ํŒ๋‹จ์ด ๋ถˆ๊ฐ€๋Šฅํ•ด์ง„๋‹ค. 2. ์‚ฌ๊ณ ์‹คํ—˜์„ ํ†ตํ•œ ์ง๊ด€์  ๋ฐ˜์ฆ | ์‚ฌ๊ณ ์‹คํ—˜ | ํ•ต์‹ฌ ๋‚ด์šฉ | ๋“œ

Statistics Model
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Embedding Economic Input-Output Models in Systems of Systems: An MBSE and Hetero-functional Graph Theory Approach

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ธ๋ฅ˜์„ธ์™€ SoS : ์ธ๊ฐ„ ํ™œ๋™์ด ์ง€๊ตฌ ์‹œ์Šคํ…œ์„ ์ง€๋ฐฐํ•˜๊ฒŒ ๋œ ์ธ๋ฅ˜์„ธ๋Š” ๊ฒฝ์ œยท์‚ฌํšŒยทํ™˜๊ฒฝยท๊ธฐ์ˆ ์ด ๋ณตํ•ฉ์ ์œผ๋กœ ์–ฝํžŒ SoS๋ฅผ ํ˜•์„ฑํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ๋Œ€๋ถ€๋ถ„ ๊ฐœ๋ณ„ ๋ถ„์•ผ(์˜ˆ: ๊ธฐํ›„, ์ˆ˜์ž์›)๋งŒ์„ ๋‹ค๋ฃจ์–ด ์ƒํ˜ธ์ž‘์šฉ์„ ์ถฉ๋ถ„ํžˆ ํฌ์ฐฉํ•˜์ง€ ๋ชปํ•œ๋‹ค. ๊ฒฝ์ œ ์‹œ์Šคํ…œ์˜ ์ค‘์‹ฌ์„ฑ : ๊ฒฝ์ œ๋Š” ๋‹ค๋ฅธ ์ž์—ฐยท์‚ฌํšŒ ์‹œ์Šคํ…œ์— ์ง์ ‘ยท๊ฐ„์ ‘์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฉฐ, ๋™์‹œ์— ์™ธ๋ถ€ ์ถฉ๊ฒฉ(๊ธฐํ›„๋ณ€ํ™”, ์ž์› ๊ณ ๊ฐˆ ๋“ฑ)์— ์˜ํ•ด ํฌ๊ฒŒ ๋ณ€๋™ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๊ฒฝ์ œ ๋ชจ๋ธ์„ SoS์— ๋‚ด์žฌํ™”ํ•˜๋Š” ๊ฒƒ์ด ํ•„์ˆ˜์ ์ด๋‹ค. ํ˜„์กด ๋ชจ๋ธ์˜ ํ•œ๊ณ„ : ์ „ํ†ต์ ์ธ EIO ๋ชจ๋ธ์€ ์„ ํ˜•ยท์ •์ ์ด๋ฉฐ, ๋‹ค๋ฅธ ๋ชจ๋ธ๊ณผ

System Electrical Engineering and Systems Science Model
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EMERALD-UI: An interactive web application to unveil novel protein biology hidden in the suboptimal-alignment space

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ตœ์  ์ •๋ ฌ ํŽธํ–ฅ : ์ „ํ†ต์ ์ธ Needlemanโ€‘WunschยทBLAST ๊ณ„์—ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ํ•˜๋‚˜์˜ ์ตœ์  ๊ฒฝ๋กœ๋งŒ์„ ์ œ๊ณตํ•œ๋‹ค. ๋Œ€๊ทœ๋ชจ ํŽ˜์–ด์™€์ด์ฆˆ ๋น„๊ต์—์„œ ์ด ์ตœ์  ๊ฒฝ๋กœ๊ฐ€ ์‹ค์ œ ์ง„ํ™”์ ยท๊ตฌ์กฐ์  ๋ณด์กด์„ ์™„์ „ํžˆ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ์ ์ด ์ตœ๊ทผ ์—ฐ๊ตฌ์—์„œ ์ง€์ ๋˜์—ˆ๋‹ค. ์„œ๋ธŒ์˜ตํ‹ฐ๋ฉ€ ์ •๋ ฌ์˜ ๊ฐ€์น˜ : ์ €์ž๋“ค์€ ์ด์ „ ๋…ผ๋ฌธ(6)์—์„œ โ€œ์ •๋ ฌ ์•ˆ์ „ ๊ตฌ๊ฐ„โ€ ๊ฐœ๋…์„ ๋„์ž…ํ•ด, ๋‹ค์ˆ˜์˜ ์„œ๋ธŒ์˜ตํ‹ฐ๋ฉ€ ์ •๋ ฌ์—์„œ ์ผ๊ด€๋˜๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ๊ตฌ๊ฐ„์ด ๊ตฌ์กฐ์ ์œผ๋กœ ์•ˆ์ •๋œ ์ž”๊ธฐ๋ฅผ ํฌํ•จํ•œ๋‹ค๋Š” ์ฆ๊ฑฐ๋ฅผ ์ œ์‹œํ–ˆ๋‹ค. ์ด๋Š” ํŠนํžˆ ์ง„ํ™”์ ์œผ๋กœ ๋ฉ€๋ฆฌ ๋–จ์–ด์ง„ ๋‹จ๋ฐฑ์งˆ ์Œ์—์„œ ์ค‘์š”ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•œ

Quantitative Biology
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Emergent Crowds Dynamics from Language-Driven Multi-Agent Interactions

1. ์—ฐ๊ตฌ ๋™๊ธฐ์™€ ๋ฐฐ๊ฒฝ ์–ธ์–ดยท๋Œ€ํ™”์˜ ์ค‘์š”์„ฑ : ์ธ๊ฐ„ ๊ตฐ์ค‘์—์„œ ๋Œ€ํ™”๋Š” ๋ชฉํ‘œ ํ˜‘์˜, ์œ„ํ—˜ ๊ฒฝ๊ณ , ์‚ฌํšŒ์  ๊ฒฐ์† ๋“ฑ ๋‹ค์–‘ํ•œ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ๊ธฐ์กด ๋ฌผ๋ฆฌโ€‘๊ธฐ๋ฐ˜ ํ˜น์€ ๊ทœ์น™โ€‘๊ธฐ๋ฐ˜ ๊ตฐ์ค‘ ๋ชจ๋ธ์€ ์ด๋Ÿฌํ•œ ๋น„๋ฌผ๋ฆฌ์  ์š”์ธ์„ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค. LLM์˜ ํ™œ์šฉ ๊ฐ€๋Šฅ์„ฑ : ์ตœ๊ทผ LLM์€ ๋ณต์žกํ•œ ์ƒํ™ฉ ์ถ”๋ก , ์ธ๋ฌผ์„ฑ ์œ ์ง€, ๊ฐ์ • ํ‘œํ˜„ ๋“ฑ์— ๊ฐ•์ ์„ ๋ณด์—ฌ, ์—์ด์ „ํŠธ์˜ โ€œ๋‚ด์  ์ƒํƒœโ€๋ฅผ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ๋ชจ๋ธ๋งํ•  ์ˆ˜ ์žˆ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๊ธฐ์—ฌ ๋‚ด์šฉ | ์˜์˜ | | | | | | 1 | LLM ๊ธฐ๋ฐ˜ ๋Œ€ํ™” ์ƒ์„ฑ : ์—์ด์ „ํŠธ ์„ฑ๊ฒฉยท๊ด€๊ณ„ยท์š•๊ตฌ๋ฅผ ํ”„๋กฌํ”„ํŠธ์— ํฌํ•จํ•ด ์ƒํ™ฉ์—

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Empirical Cumulative Distribution Function Clustering for LLM-based Agent System Analysis

| ๊ตฌ๋ถ„ | ํ•ต์‹ฌ ๋‚ด์šฉ | ๊ฐ•์  | ํ•œ๊ณ„ยท๋ณด์™„์  | | | | | | | ๋ฌธ์ œ ์ •์˜ | ๋‹ค์ˆ˜์˜ LLM ์‘๋‹ต์„ ํ•˜๋‚˜์˜ ์ •๋‹ต์œผ๋กœ ์••์ถ•ํ•˜๋ฉด ๊ฐœ๋ณ„ ์‘๋‹ต ํ’ˆ์งˆยท๋‹ค์–‘์„ฑ์„ ๋†“์นœ๋‹ค. | ์‹ค์ œ QAยทํ† ๋ก  ์ƒํ™ฉ์—์„œ โ€œ๋ถ€๋ถ„์ ์œผ๋กœ ์˜ณ์€โ€ ๋‹ต๋ณ€์„ ๋ฌด์‹œํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ์ •ํ™•ํžˆ ์ง€์ . | ๊ธฐ์กด ํ‰๊ฐ€ ์ง€ํ‘œ(Exactโ€‘Match, BLEU ๋“ฑ)์™€ ๋น„๊ต ์‹คํ—˜์ด ๋ถ€์กฑํ•ด ์ƒ๋Œ€์  ์šฐ์œ„๊ฐ€ ๋ช…ํ™•ํžˆ ๋“œ๋Ÿฌ๋‚˜์ง€ ์•Š์Œ. | | ECDF ํ™œ์šฉ | ๊ฐ ์‘๋‹ตโ€‘์ •๋‹ต ์Œ์˜ ์ฝ”์‚ฌ์ธ ์œ ์‚ฌ๋„๋ฅผ ๊ตฌํ•ด ECDF๋ฅผ ๋งŒ๋“ ๋‹ค. <br>ยท ํžˆ์Šคํ† ๊ทธ๋žจ๊ณผ ๋‹ฌ๋ฆฌ bin ์„ ํƒ ํ•„์š” ์—†์Œ.<br>ยท ๊ธธ์ด ์ฐจ์ด ์žˆ๋Š” ์‘

Statistics System Machine Learning Analysis
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Enabling Fast, Accurate, and Efficient Real-Time Genome Analysis via New Algorithms and Techniques

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋…ธ์ด์ฆˆ ๋ฌธ์ œ์˜ ํ•ต์‹ฌ์„ฑ : ๋‚˜๋…ธํฌ์–ด ์‹œํ€€์‹ฑ์€ ์ „๊ธฐ ์‹ ํ˜ธ๋ฅผ ์ง์ ‘ ์ธก์ •ํ•˜๋ฏ€๋กœ, ์ „์•• ๋ณ€๋™ยท์‹œํ€€์Šค ์˜ค๋ฅ˜ยทํ™˜๊ฒฝ ์š”์ธ ๋“ฑ ๋‹ค์–‘ํ•œ ์žก์Œ์ด ์„ž์ธ๋‹ค. ๊ธฐ์กด ํŒŒ์ดํ”„๋ผ์ธ์€ ๋ฒ ์ด์Šค์ฝœ๋ง ํ›„ ์ •์ œ๋œ ์„œ์—ด์— ์˜์กดํ•ด ๋…ธ์ด์ฆˆ๋ฅผ ๊ฐ„์ ‘์ ์œผ๋กœ๋งŒ ์ฒ˜๋ฆฌํ•œ๋‹ค. ์‹ค์‹œ๊ฐ„ ๋ถ„์„ ์š”๊ตฌ : ์ž„์ƒ ํ˜„์žฅยทํ˜„์žฅ ๊ฐ์‹œ ๋“ฑ์—์„œ๋Š” ๋ฐ์ดํ„ฐ๊ฐ€ ์ƒ์„ฑ๋˜๋Š” ์ฆ‰์‹œ ๋ถ„์„์ด ํ•„์š”ํ•˜์ง€๋งŒ, ์ „ํ†ต์ ์ธ ์–ด์…ˆ๋ธ”๋ฆฌยท์ •๋ ฌ ํŒŒ์ดํ”„๋ผ์ธ์€ ์ˆ˜์‹œ๊ฐ„~์ˆ˜์ผ์ด ์†Œ์š”๋œ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ์™€ ํ˜์‹ ์„ฑ | ๊ธฐ๋ฒ• | ํ•ต์‹ฌ ์•„์ด๋””์–ด | ์ฐจ๋ณ„์  | | | | | | BLEND | ์„œ์—ด์„ ๋‹ค์ค‘ ํ•ด์‹œ(๋‹ค์ค‘โ€‘kโ€‘mer)๋กœ

Analysis
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Endowing GPT-4 with a Humanoid Body: Building the Bridge Between Off-the-Shelf VLMs and the Physical World

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋ฐ์ดํ„ฐ ๋น„์šฉ ๋ฌธ์ œ : ์ธ๊ฐ„ํ˜• ๋กœ๋ด‡์„ ์œ„ํ•œ ๋Œ€๊ทœ๋ชจ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ยท์‹ค์ œ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘์€ ์‹œ๊ฐ„ยท์žฌ์ •์  ๋ถ€๋‹ด์ด ํฌ๋‹ค. VLM์˜ ์ผ๋ฐ˜ํ™” ๋Šฅ๋ ฅ : GPTโ€‘4์™€ ๊ฐ™์€ ์ตœ์‹  VLM์€ ๋ฐฉ๋Œ€ํ•œ ์›น ํ…์ŠคํŠธยท์ด๋ฏธ์ง€ ๋ฐ์ดํ„ฐ๋ฅผ ํ•™์Šตํ•ด ๊ด‘๋ฒ”์œ„ํ•œ ์ƒํ™ฉ ์ธ์‹ยท์ถ”๋ก ์ด ๊ฐ€๋Šฅํ•˜๋ฏ€๋กœ, ๋กœ๋ด‡ ์ œ์–ด์— ์ง์ ‘ ํ™œ์šฉํ•˜๋ฉด ๋ฐ์ดํ„ฐ ์˜์กด๋„๋ฅผ ํฌ๊ฒŒ ๋‚ฎ์ถœ ์ˆ˜ ์žˆ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๊ธฐ์—ฌ ๋‚ด์šฉ | ์˜์˜ | | | | | | 1 | Embodied Instruction Compiler : VLM์ด ์‹œ๊ฐยท์–ธ์–ด ์ž…๋ ฅ์„ ๋ฐ›์•„ ํ™˜๊ฒฝ ์ƒํƒœ๋ฅผ ํŒŒ์•…ํ•˜๊ณ , ๊ณ ์ˆ˜์ค€ ๋ช…๋ น์„

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Energy budgets govern synaptic precision and its regulation during plasticity

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์งˆ๋ฌธ ์‹œ๋ƒ…์Šค ๋ณ€๋™์„ฑ ์€ ์‹ ๊ฒฝ ํšŒ๋กœ์˜ ์ •๋ณด ์ „๋‹ฌ์— ํ•ต์‹ฌ์ ์ธ ์ œํ•œ ์š”์†Œ์ด๋ฉฐ, ๋ณ€๋™์„ฑ์„ ์ค„์ด๋ฉด ์‹ ํ˜ธ ์‹ ๋ขฐ๋„๊ฐ€ ๋†’์•„์ง€์ง€๋งŒ ATP ์†Œ๋ชจ๊ฐ€ ๊ธ‰์ฆํ•œ๋‹ค๋Š” ๊ธฐ์กด ์ด๋ก ์ด ์žˆ๋‹ค. ๊ธฐ์กด ์ด๋ก ์€ ์ฃผ๋กœ ์‹œ๋ƒ…์Šค ํ‰๊ท  ๊ฐ•๋„(์‹œ๋ƒ…์Šค ๊ฐ€์ค‘์น˜)์˜ ๋ณ€ํ™” ์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์œผ๋ฉฐ, ๋ถ„์‚ฐ(์ •๋ฐ€๋„)์˜ ์กฐ์ ˆ ๋ฉ”์ปค๋‹ˆ์ฆ˜ ์€ ์ถฉ๋ถ„ํžˆ ์„ค๋ช…ํ•˜์ง€ ๋ชปํ–ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ โ€œ ์‹œ๋ƒ…์Šค ์ •๋ฐ€๋„๋Š” ์—๋„ˆ์ง€ ์˜ˆ์‚ฐ์— ์˜ํ•ด ์ œํ•œ๋œ๋‹ค โ€๋Š” ๊ฐ€์„ค์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ์—๋„ˆ์ง€โ€‘์ œ์•ฝ ์ตœ์ ํ™” ๋ชจ๋ธ ์„ ๋„์ž…ํ•œ๋‹ค. 2. ๋ชจ๋ธ ์„ค๊ณ„ | ์š”์†Œ | ์„ค๋ช… | ์ˆ˜ํ•™์  ํ‘œํ˜„ | | | | | | ์–‘์ž ํŒŒ๋ผ๋ฏธํ„ฐ | ๐‘›

Quantitative Biology
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Enhanced Connectivity in Ambient Backscatter Communications via Fluid Antenna Readers

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ AmBC์˜ ๊ทผ๋ณธ์  ํ•œ๊ณ„ : ๋‘ ๋‹จ๊ณ„ ๊ฒฝ๋กœ ์†์‹ค๊ณผ ๊ณฑ์…ˆ ํŽ˜์ด๋”ฉ์œผ๋กœ ์ธํ•œ ์‹ฌ๊ฐํ•œ SNR ์ €ํ•˜, ๊ทธ๋ฆฌ๊ณ  BD๊ฐ€ ๋งค์šฐ ์•ฝํ•œ ๋ฐ˜์‚ฌ ์‹ ํ˜ธ๋งŒ์„ ์ œ๊ณตํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ „ํ†ต์ ์ธ CSI ์ถ”์ •์ด ๋ถˆ๊ฐ€๋Šฅํ•จ. ์œ ๋™ ์•ˆํ…Œ๋‚˜ ์‹œ์Šคํ…œ(FAS)์˜ ๊ฐ€๋Šฅ์„ฑ : ๊ธฐ์กด ๊ณ ์ • ์•ˆํ…Œ๋‚˜ ๋Œ€๋น„ ๊ณต๊ฐ„์  ๋‹ค์–‘์„ฑ์„ ์ œ๊ณตํ•˜๋ฉด์„œ๋„ ํ•˜๋‚˜์˜ RF ์ฒด์ธ๋งŒ์œผ๋กœ ๊ตฌํ˜„ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์ ์—์„œ ์ €์ „๋ ฅ IoT ๋ฆฌ๋”๊ธฐ์— ์ ํ•ฉ. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๊ธฐ์—ฌ ๋‚ด์šฉ | ์ฐจ๋ณ„์  | | | | | | 1 | ํ”ฝ์…€ ๊ธฐ๋ฐ˜ FAS ๋ฅผ ์ด์šฉํ•œ ๊ด€์ธกโ€‘๊ธฐ๋ฐ˜ ํฌํŠธ ์„ ํƒ ๋ฉ”์ปค๋‹ˆ์ฆ˜ ์ œ์‹œ | CSI ์—†์ด๋„

Computer Science Information Theory
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Enhancing Interpretability for Vision Models via Shapley Value Optimization

๋”ฅ ๋‰ด๋Ÿด ๋„คํŠธ์›Œํฌ(DNN)๋Š” ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ๊ฐ•๋ ฅํ•œ ์„ฑ๋Šฅ์„ ๋ฐœํœ˜ํ•˜์ง€๋งŒ, ๊ทธ ๋‚ด๋ถ€ ์ž‘๋™ ์›๋ฆฌ์™€ ์˜์‚ฌ๊ฒฐ์ • ๊ณผ์ •์€ ์—ฌ์ „ํžˆ ๋ถˆํˆฌ๋ช…ํ•˜๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์—ฐ๊ตฌ์ž๋“ค์€ ์‚ฌํ›„ ์„ค๋ช… ๋ฐฉ๋ฒ•๊ณผ ์ž์ฒด ์„ค๋ช… ๊ฐ€๋Šฅํ•œ ์‹ ๊ฒฝ๋ง์ด๋ผ๋Š” ๋‘ ๊ฐ€์ง€ ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์‹œํ•ด ์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•๋“ค์—๋Š” ๊ฐ๊ฐ์˜ ์ œ์•ฝ์ด ์žˆ๋‹ค: ์‚ฌํ›„ ์„ค๋ช… ๋ฐฉ๋ฒ•๋“ค์€ ์ข…์ข… ๋ชจ๋ธ์˜ ์‹ค์ œ ์˜์‚ฌ๊ฒฐ์ • ๊ณผ์ •์„ ์ •ํ™•ํ•˜๊ฒŒ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•˜๊ณ , ์ž์ฒด ์„ค๋ช… ๊ฐ€๋Šฅํ•œ ์‹ ๊ฒฝ๋ง์€ ์„ฑ๋Šฅ ์ €ํ•˜์™€ ํ˜ธํ™˜์„ฑ ๋ฌธ์ œ๋ฅผ ์•ผ๊ธฐํ•œ๋‹ค. ์ œ์•ˆ๋œ ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด Shapley ๊ฐ’ ์ถ”์ •์„ ํ†ตํ•ฉํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹

Model
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ENIGMA: EEG-to-Image in 15 Minutes Using Less Than 1% of the Parameters

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ EEGโ€‘toโ€‘Image ๋Š” fMRIโ€‘toโ€‘Image์— ๋น„ํ•ด ์‹ ํ˜ธโ€‘๋Œ€โ€‘๋…ธ์ด์ฆˆ ๋น„๊ฐ€ ๋‚ฎ๊ณ  ๊ณต๊ฐ„ ํ•ด์ƒ๋„๊ฐ€ ์ œํ•œ์ ์ด์–ด์„œ ์žฌ๊ตฌ์„ฑ ๋‚œ์ด๋„๊ฐ€ ๋†’๋‹ค. ๊ธฐ์กด ๋ชจ๋ธ(์˜ˆ: Perceptogram, ATMโ€‘S)์€ โ‘  ํ”ผํ—˜์ž๋ณ„ ์ „์šฉ ํ•™์Šต , โ‘ก ๊ณ ๊ฐ€ ์žฅ๋น„ ์˜์กด , โ‘ข ๊ฑฐ๋Œ€ํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ ๊ทœ๋ชจ ๋ผ๋Š” ์„ธ ๊ฐ€์ง€ ์‹ค์šฉ์  ์žฅ์• ๋ฌผ์„ ๊ฐ€์ง€๊ณ  ์žˆ์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ œ์•ฝ์€ BCI๋ฅผ ์‹ค์‹œ๊ฐ„ยท์ €๋น„์šฉยท๋‹ค์ธ์› ํ™˜๊ฒฝ์— ์ ์šฉํ•˜๋Š” ๊ฒƒ์„ ๋ฐฉํ•ดํ•œ๋‹ค. 2. ENIGMA์˜ ํ•ต์‹ฌ ์•„์ด๋””์–ด | ๊ตฌ์„ฑ ์š”์†Œ | ์—ญํ•  | ๊ธฐ์กด ๋ฐฉ๋ฒ• ๋Œ€๋น„ ์ฐจ๋ณ„์  | | | | | | ์‹œ๊ณต๊ฐ„ ์ปจ๋ณผ

Quantitative Biology
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Enroll-on-Wakeup: A First Comparative Study of Target Speech Extraction for Seamless Interaction in Real Noisy Human-Machine Dialogue Scenarios

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ „ํ†ต์  TSE ๋Š” โ€œ์‚ฌ์ „ ์ˆ˜์ง‘๋œ ๊ณ ํ’ˆ์งˆ ๋“ฑ๋ก ์Œ์„ฑ โ†’ ์Šคํ”ผ์ปค ์ž„๋ฒ ๋”ฉ โ†’ ์ถ”์ถœโ€ ํ๋ฆ„์„ ์ „์ œ๋กœ ํ•œ๋‹ค. ์ด๋Š” ์ฒซ ์‚ฌ์šฉ์ž ํ˜น์€ ์ŠคํŒŸ ์ธํ„ฐ๋ž™์…˜ ์—์„œ ํฐ ์žฅ์• ๋ฌผ์ด ๋œ๋‹ค. ์ธ๊ฐ„โ€‘๊ธฐ๊ณ„ ๋Œ€ํ™”์—์„œ ๊ฐ€์žฅ ์ž์—ฐ์Šค๋Ÿฌ์šด ์›จ์ดํฌ์›Œ๋“œ (์˜ˆ: โ€œHi, Pandoraโ€)๋Š” ์ด๋ฏธ ์‹œ์Šคํ…œ์ด ๋“ฃ๊ณ  ์žˆ๋Š” ์ˆœ๊ฐ„์ด๋ฏ€๋กœ, ์ด๋ฅผ ์ž๋™ ๋“ฑ๋ก ์œผ๋กœ ํ™œ์šฉํ•˜๋ฉด Zeroโ€‘Effort Interaction ์„ ์‹คํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ Enrollโ€‘onโ€‘Wakeup (EoW) | ๋‹จ๊ณ„ | ์„ค๋ช… | | | | | KWSโ€‘Segmentation |

Audio Processing Electrical Engineering and Systems Science
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Entrance laws for coalescing and annihilating Brownian motions

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๊ฒฐํ•ฉยท์†Œ๋ฉธ ๋ธŒ๋ผ์šด ์šด๋™ ์€ Arratia ํ๋ฆ„(๋ชจ๋“  ์ ์— ์ž…์ž๋ฅผ ๋‘๋Š” ๊ฒฝ์šฐ) ๋“ฑ์—์„œ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ํ™•๋ฅ ์  ์ƒํ˜ธ์ž‘์šฉ ๋ชจ๋ธ์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ(

Mathematics
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Equilibrium statistical mechanics of waves in inhomogeneous moving media

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ํŒŒ๋™โ€‘ํ‰๊ท  ํ๋ฆ„ ์ƒํ˜ธ์ž‘์šฉ์€ ์‹คํ—˜ ์œ ์ฒด์—ญํ•™, ํ•ด์–‘ยท๋Œ€๊ธฐ ๋™์—ญํ•™ ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ํ•ต์‹ฌ ํ˜„์ƒ์ด๋‹ค. ๊ธฐ์กด์—๋Š” ๋ ˆ์ด ํŠธ๋ ˆ์ด์‹ฑ(๊ธฐํ•˜๊ด‘ํ•™์  ์ ‘๊ทผ)์œผ๋กœ ํŒŒ๋™ ์ „ํŒŒ๋ฅผ ๊ธฐ์ˆ ํ–ˆ์ง€๋งŒ, ๋‹ค์ˆ˜์˜ ๋ ˆ์ด๋ฅผ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•ด์•ผ ํ•˜๋Š” ํ†ต๊ณ„์  ๋ฌธ์ œ๋Š” ๊ณ„์‚ฐ ๋น„์šฉ์ด ํฌ๊ฒŒ ๋Š˜์–ด๋‚˜๊ณ , ํฐ ์Šค์ผ€์ผ์—์„œ์˜ ์กฐ์ง ์›๋ฆฌ๋ฅผ ์ œ๊ณตํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์ €์ž๋“ค์€ ์ตœ๊ทผ ๊ทผ์ธ์„ฑ ํŒŒ๋™์ด ์ „ํ•˜ ์ž…์ž์™€ ์œ ์‚ฌํ•˜๊ฒŒ ํ–‰๋™ํ•œ๋‹ค๋Š” ์‚ฌ์‹ค์„ ์ด์šฉํ•ด ํ†ต๊ณ„์—ญํ•™์  ์ ‘๊ทผ์„ ์‹œ๋„ํ–ˆ์ง€๋งŒ, ์ผ๋ฐ˜์ ์ธ ํŒŒ๋™โ€‘์ด๋™ ๋งค์งˆ์— ๋Œ€ํ•œ ์ •ํ™•ํ•œ ์ž…์ž ํŒŒ๋™ ๋Œ€์‘์€ ์กด์žฌํ•˜์ง€ ์•Š์•˜๋‹ค. ๋”ฐ๋ผ์„œ โ€œ๋งˆ์ดํฌ๋กœ์บ๋…ธ๋‹ˆ์ปฌ ํ†ต๊ณ„์—ญํ•™์„ ํŒŒ๋™์—

Physics
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Estimating Human Muscular Fatigue in Dynamic Collaborative Robotic Tasks with Learning-Based Models

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ pHRI์—์„œ ์ธ๊ฐ„ ์š”์†Œ์˜ ์ค‘์š”์„ฑ : ์ธ๊ฐ„์˜ ํ”ผ๋กœ๋Š” ์ž‘์—… ํšจ์œจ ์ €ํ•˜์™€ ๊ทผ๊ณจ๊ฒฉ๊ณ„ ๋ถ€์ƒ์˜ ์œ„ํ—˜์„ ๋™์‹œ์— ์ฆ๊ฐ€์‹œํ‚จ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ํž˜ ์„ผ์„œ๋งŒ์„ ์ด์šฉํ•ด ์ธ๊ฐ„์˜ ๋…ธ๋ ฅ ์ •๋„๋ฅผ ์ถ”์ •ํ–ˆ์œผ๋‚˜, ํž˜๋งŒ์œผ๋กœ๋Š” ๊ทผ์œก ํ”ผ๋กœ๋ฅผ ์ถฉ๋ถ„ํžˆ ํŒŒ์•…ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. EMG ๊ธฐ๋ฐ˜ ํ”ผ๋กœ ์ธก์ • : ํ‘œ๋ฉด ๊ทผ์ „๋„๋Š” ๊ทผ์œก ํ™œ์„ฑ๋„๋ฅผ ์ง์ ‘ ๋ฐ˜์˜ํ•˜๋ฏ€๋กœ ํ”ผ๋กœ ์ง„ํ–‰์„ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๊ฐ์ง€ํ•˜๋Š” ๋ฐ ์œ ๋ฆฌํ•˜๋‹ค. ํŠนํžˆ ๋™์ (๋“ฑ์ฒ™์„ฑยท๋“ฑ์†์„ฑ) ์ž‘์—…์—์„œ๋Š” ์ฃผํŒŒ์ˆ˜ ๊ธฐ๋ฐ˜ ํŠน์ง•(MNF, MDF ๋“ฑ)์˜ ๋ณ€ํ™”๊ฐ€ ๋ฏธ๋ฏธํ•ด ๊ธฐ์กด ์ •์  ์ž‘์—…์šฉ ์ง€ํ‘œ๋งŒ์œผ๋กœ๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด

Robotics Computer Science Learning Model
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Estimation of Conformal Metrics

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ปจํฌ๋ฉ€ ๊ฑฐ๋ฆฌ ๋Š” ๊ธฐ์กด Riemannian ๋ฉ”ํŠธ๋ฆญ์— ์Šค์นผ๋ผ ํŒฉํ„ฐ fยฒ ๋ฅผ ๊ณฑํ•œ ํ˜•ํƒœ๋กœ, ํŠนํžˆ Fermat ๊ฑฐ๋ฆฌ (๋ฐ€๋„ ฯ ์˜ ์—ญ์ˆ˜์— ๋น„๋ก€)์™€ ์ง์ ‘ ์—ฐ๊ฒฐ๋œ๋‹ค. ์ด๋Š” ๋น„๊ท ์งˆ ๋ฐ์ดํ„ฐ(์˜ˆ: ๋ฐ€๋„ ๋ณ€๋™์ด ํฐ ์ƒ˜ํ”Œ)์—์„œ ๊ฑฐ๋ฆฌ ๊ธฐ๋ฐ˜ ํ•™์Šตยท์‹œ๊ฐํ™”์— ์œ ์šฉํ•˜๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ(

Mathematics
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EU-Agent-Bench: Measuring Illegal Behavior of LLM Agents Under EU Law

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ LLM ์—์ด์ „ํŠธ์˜ ๊ธ‰์ฆ : ์ตœ๊ทผ LLM์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ž๋™ํ™” ์—์ด์ „ํŠธ๊ฐ€ ๋„๊ตฌ ํ˜ธ์ถœ, ์›น ๊ฒ€์ƒ‰, ๋ฐ์ดํ„ฐ ์กฐ์ž‘ ๋“ฑ ์‹ค์ œ ์—…๋ฌด์— ํˆฌ์ž…๋˜๊ณ  ์žˆ๋‹ค. ๋ฒ•์  ์œ„ํ—˜์„ฑ : EU GDPR, ์ฐจ๋ณ„ ๊ธˆ์ง€๋ฒ•, ๊ณผํ•™ ์—ฐ๊ตฌ ์œค๋ฆฌ ๊ทœ์ • ๋“ฑ ๊ฐ•๋ ฅํ•œ ๊ทœ์ œ๊ฐ€ ์กด์žฌํ•จ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์—์ด์ „ํŠธ๊ฐ€ โ€œ๋ฌดํ•ดํ•ด ๋ณด์ด๋Š”โ€ ์‚ฌ์šฉ์ž ์ž…๋ ฅ์„ ํ†ตํ•ด ๋ฒ•์„ ์œ„๋ฐ˜ํ•  ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ๋‹ค. ๋ฒค์น˜๋งˆํฌ ๋ถ€์žฌ : ๊ธฐ์กด LLM ์•ˆ์ „ ๋ฒค์น˜๋งˆํฌ๋Š” ์ฃผ๋กœ ํ—ˆ์œ„ ์ •๋ณด, ์œ ํ•ด ์ฝ˜ํ…์ธ  ๋“ฑ์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์œผ๋ฉฐ, ๋ฒ•์  ์ค€์ˆ˜ ๋ฅผ ์ฒด๊ณ„์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜๋Š” ๋„๊ตฌ๋Š” ๊ฑฐ์˜ ์—†์—ˆ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ |

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EVA: Towards a universal model of the immune system

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ ํ†ตํ•ฉ์˜ ๋ถ€์žฌ : ํ˜„์žฌ ๋Œ€๋ถ€๋ถ„์˜ ์ƒ๋ฌผํ•™์  ๊ธฐ์ดˆ ๋ชจ๋ธ์€ ๋‹จ์ผ ๋ชจ๋‹ฌ๋ฆฌํ‹ฐ(์ „์‚ฌ์ฒด, ์กฐ์งํ•™, ๋‹จ๋ฐฑ์งˆ ๋“ฑ)๋งŒ์„ ๋‹ค๋ฃจ๋ฉฐ, ์„œ๋กœ ๋‹ค๋ฅธ ๋ฐ์ดํ„ฐ ์œ ํ˜•์„ ๊ฒฐํ•ฉํ•œ ์ฒด๊ณ„์ ์ธ ์ ‘๊ทผ์ด ๋ถ€์กฑํ•˜๋‹ค. ๋‹จ์ผ์„ธํฌ ์ค‘์‹ฌ ํŽธํ–ฅ : ์ „์‚ฌ์ฒด ๋ถ„์•ผ์—์„œ๋Š” ๊ณ ํ•ด์ƒ๋„ singleโ€‘cell ๋ชจ๋ธ์ด ์ฃผ๋ฅ˜๋ฅผ ์ด๋ฃจ์ง€๋งŒ, ์‹ค์ œ ์•ฝ๋ฌผ ๊ฐœ๋ฐœ์—์„œ๋Š” bulk ํ˜น์€ pseudoโ€‘bulk ๋ฐ์ดํ„ฐ์™€์˜ ์—ฐ๊ณ„๊ฐ€ ํ•„์ˆ˜์ ์ด๋‹ค. ๊ต์ฐจ ์ข… ์ „์ด ํ•™์Šต์˜ ๊ธฐํšŒ : ๋ฉด์—ญยท์—ผ์ฆ ์งˆํ™˜์€ ์ธ๊ฐ„ยท๋งˆ์šฐ์Šค ๊ฐ„์— ์‹ ํ˜ธ์ „๋‹ฌ ๊ฒฝ๋กœ(TNF, JAKโ€‘STAT)์™€ ์œ ์ „์  ์œ„ํ—˜์š”์†Œ๊ฐ€ ๊ณ ๋„๋กœ ๋ณด์กด๋ผ, ์ข… ๊ฐ„

System Quantitative Biology Model
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Evolutionarily Primitive Social Entities

1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ œ๊ธฐ ์‚ฌํšŒ ์กด์žฌ๋ก (social ontology) : Searle(1995, 2010)๋Š” ์‚ฌํšŒ ์‹ค์ฒด๊ฐ€ โ€˜์ง‘๋‹จ ์ธ์ •(collective acceptance)โ€™์— ์˜ํ•ด ์กด์žฌํ•œ๋‹ค๊ณ  ์ฃผ์žฅํ•œ๋‹ค. ์ง‘๋‹จ ์˜๋„์˜ ๋‘ ์ฐจ์› : 1. ์ง„ํ™”์  ์›์‹œ์„ฑ โ€“ ์ธ๊ฐ„ ์™ธ ์ข…๋„ ์ตœ์†Œ ์ˆ˜์ค€์˜ ์ง‘๋‹จ ์˜๋„๋ฅผ ๊ฐ€์งˆ ์ˆ˜ ์žˆ๋‹ค. 2. ํ˜•์ด์ƒํ•™์  ์›์‹œ์„ฑ โ€“ ์ง‘๋‹จ ์˜๋„๊ฐ€ ๊ฐœ๋ณ„ ์˜๋„์™€ ํ™˜์› ๋ถˆ๊ฐ€๋Šฅํ•œ ๋…๋ฆฝ์  ์‹ค์žฌ๋ผ๋Š” ์ฃผ์žฅ. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” Rakoczy & Tomasello(2007) ์™€ Tomasello(2014) ๊ฐ€ ์ธ๊ฐ„๋งŒ์ด โ€˜์ง‘๋‹จ ์˜๋„โ€™๋ฅผ ๊ฐ€์งˆ ์ˆ˜ ์žˆ๋‹ค

Quantitative Biology
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Examining Fast Radiative Feedbacks Using Machine-Learning Weather Emulators

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉํ‘œ ๋น ๋ฅธ vs. ๋А๋ฆฐ ํ”ผ๋“œ๋ฐฑ : ๋น ๋ฅธ ํ”ผ๋“œ๋ฐฑ์€ ๋Œ€๊ธฐ ๋ฐฉ์‚ฌยท๋Œ€๋ฅ˜๊ฐ€ ์ฃผ๋„ํ•˜๋Š” ์ฃผ๊ฐ„ยท์ผ๊ฐ„ ์Šค์ผ€์ผ์ด๋ฉฐ, ํ˜„์žฌ ๊ธฐํ›„์— ์ด๋ฏธ ๋‚ด์žฌ๋ผ ์žˆ๋‹ค. ๋ฐ˜๋ฉด ๋А๋ฆฐ ํ”ผ๋“œ๋ฐฑ์€ ํ•ด์–‘ ์—ด์šฉ๋Ÿ‰์— ์˜์กดํ•ด ์ˆ˜์‹ญ ๋…„ ์ด์ƒ ์ง€์†๋œ๋‹ค. RCE์˜ ํ•ต์‹ฌ์„ฑ : ๋ฐฉ์‚ฌโ€‘๋Œ€๋ฅ˜ ํ‰ํ˜•์€ ๋Œ€๊ธฐ ๋ณต์‚ฌ ๋ƒ‰๊ฐ๊ณผ ์ˆ˜์ฆ๊ธฐ ์ƒ์Šนยท์‘๊ฒฐ์— ์˜ํ•œ ์ž ์—ด ๋ฐฉ์ถœ์„ ๊ท ํ˜• ๋งž์ถ”๋Š” ๊ณผ์ •์œผ๋กœ, ์ „ ์ง€๊ตฌ ๊ฐ•์ˆ˜๋Ÿ‰์„ ๊ฒฐ์ •์ง“๋Š” โ€˜์‹œ๊ฐ„ ์ƒ์ˆ˜ โ‰ˆ 1์ฃผโ€™์˜ ํ•ต์‹ฌ ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด๋‹ค. ML ์—๋ฎฌ๋ ˆ์ดํ„ฐ ํ™œ์šฉ ๊ฐ€๋Šฅ์„ฑ : ์žฌ๋ถ„์„ ๋ฐ์ดํ„ฐ์— ํฌํ•จ๋œ ๋น ๋ฅธ ํ”ผ๋“œ๋ฐฑ์„ ํ•™์Šตํ•œ ML ๋ชจ๋ธ์ด, ๋ณ„๋„ ์žฌํ›ˆ๋ จ ์—†์ด๋„ COโ‚‚ ๊ธ‰๋ณ€์— ๋Œ€ํ•œ ์ฆ‰

Physics Learning
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Experimental Assortments for Choice Estimation and Nest Identification

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์„ ํƒ ๋ชจ๋ธ ์ถ”์ • ์€ ์†Œ๋งคยท์ž๋™์ฐจยท๊ตํ†ต ์ •์ฑ… ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ํ•ต์‹ฌ์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ๊ด€์ฐฐ ๋ฐ์ดํ„ฐ ์— ์˜์กดํ•˜๊ฑฐ๋‚˜, ๋ฌด์ž‘์œ„ assortments ๋ฅผ ์‚ฌ์šฉํ•ด ๋ฐ์ดํ„ฐ๋ฅผ ์ƒ์„ฑํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ฌด์ž‘์œ„ ์„ค๊ณ„๋Š” ์ •๋ณด ํšจ์œจ์„ฑ์ด ๋‚ฎ์•„ ๋ณต์žกํ•œ ๋Œ€์ฒด ๊ด€๊ณ„(์˜ˆ: Nested Logit) ํŒŒ์•…์— ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ํŠนํžˆ nest ์‹๋ณ„ ์€ ๊ธฐ์กด ํ†ต๊ณ„ ํŒจํ‚ค์ง€์™€ ๋ฌธํ—Œ์—์„œ ๊ฑฐ์˜ ๋‹ค๋ฃจ์–ด์ง€์ง€ ์•Š์•„, ์‹ค๋ฌด์—์„œ ์ฃผ๊ด€์  ๋ถ„๋ฅ˜ ์— ์˜์กดํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๊ตฌ๋ถ„ | ๋‚ด์šฉ | ์˜์˜ | | | | | | ์‹คํ—˜ ์„ค๊ณ„ | ๊ฐ ์•„์ดํ…œ์— ์ด์ง„ ์ธ

Statistics
Explainable Adversarial-Robust Vision-Language-Action Model for Robotic Manipulation

Explainable Adversarial-Robust Vision-Language-Action Model for Robotic Manipulation

๋ณธ ๋…ผ๋ฌธ์€ ์Šค๋งˆํŠธ ๋†์—… ์‹œ์Šคํ…œ์ด ๊ด‘ํ•™์  ๋ณ€๋™์— ์ทจ์•ฝํ•˜๋‹ค๋Š” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด, ์ ๋Œ€ ๊ณต๊ฒฉ ์ƒํ™ฉ์—์„œ๋„ ๊ฒฌ๊ณ ํ•œ ๋™์ž‘ ์˜ˆ์ธก์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ๋ชจ๋ธ์˜ ํ•ต์‹ฌ์€ OpenVLA OFT ํ”„๋ ˆ์ž„์›Œํฌ์™€ Evidence 3 ๋ชจ๋“ˆ์„ ํ†ตํ•ฉํ•˜๋Š” ๊ฒƒ์ด๋‹ค. Evidence 3 ๋ชจ๋“ˆ์€ ๊ด‘ํ•™์  ๋ณ€๋™์„ ๊ฐ์ง€ํ•˜๊ณ , ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๊ฐ€ ์‹œ์Šคํ…œ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ž์—ฐ์–ด๋กœ ์„ค๋ช…ํ•จ์œผ๋กœ์จ, ์‹œ์Šคํ…œ์˜ ์ž‘๋™ ์›๋ฆฌ๋ฅผ ์ดํ•ดํ•˜๊ธฐ ์‰ฝ๊ฒŒ ๋งŒ๋“ ๋‹ค. ์ด ๋ชจ๋ธ์ด ๊ธฐ์กด ๋ชจ๋ธ๋ณด๋‹ค ํ˜„์žฌ ํ–‰๋™๊ณผ ๋‹ค์Œ ํ–‰๋™ ์˜ˆ์ธก์—์„œ ๊ฐ๊ฐ 21.7%์™€ 18.4%์˜ L1 ์†์‹ค ๊ฐ์†Œ๋ฅผ ๋ณด์ธ ๊ฒƒ์€, ์ ๋Œ€

Model
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Explainable e-sports win prediction through Machine Learning classification in streaming

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ eโ€‘์Šคํฌ์ธ  ์‹œ์žฅ ๊ทœ๋ชจ๊ฐ€ ๊ธ‰์„ฑ์žฅํ•˜๋ฉด์„œ ์‹ค์‹œ๊ฐ„ ๋ฐ์ดํ„ฐ ๋ถ„์„์˜ ์ค‘์š”์„ฑ์ด ์ปค์กŒ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์ •์ (๋ฐฐ์น˜) ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•ด ์ŠนํŒจ๋ฅผ ์˜ˆ์ธกํ–ˆ์œผ๋ฉฐ, ์‹ค์‹œ๊ฐ„ ๋ณ€ํ™”์— ๋Œ€ํ•œ ๋Œ€์‘์ด ๋ถ€์กฑํ–ˆ๋‹ค. ๋˜ํ•œ, AI ๋ชจ๋ธ์˜ โ€˜๋ธ”๋ž™๋ฐ•์Šคโ€™ ํŠน์„ฑ์œผ๋กœ ์ธํ•ด ์ฝ”์น˜ยท์„ ์ˆ˜ยท๊ด€์ค‘์ด ๊ฒฐ๊ณผ๋ฅผ ์‹ ๋ขฐํ•˜๊ธฐ ์–ด๋ ค์šด ์ ์ด ์žˆ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์„ค๋ช…๊ฐ€๋Šฅ์„ฑ(Explainability) ์„ ๊ฒฐํ•ฉํ•œ ์‹ค์‹œ๊ฐ„ ์˜ˆ์ธก ๋ชจ๋ธ์ด ์š”๊ตฌ๋œ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ ์ŠคํŠธ๋ฆฌ๋ฐ ๋ถ„๋ฅ˜ ํ”„๋ ˆ์ž„์›Œํฌ : ๋ฐ์ดํ„ฐ ์ŠคํŠธ๋ฆผ์„ ์—ฌ๋Ÿฌ ์Šฌ๋ผ์ด๋”ฉ ์œˆ๋„์šฐ(์˜ˆ: 30์ดˆ, 1๋ถ„ ๋“ฑ)๋กœ ๋‚˜๋ˆ„์–ด, ๊ฐ ์œˆ๋„์šฐ๋งˆ๋‹ค ์ตœ์‹  ๊ฒŒ์ž„

Learning
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Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋‹จ๋ฐฑ์งˆ ์„ค๊ณ„์—์„œ ํ‘œํ˜„ ์„ ํƒ์˜ ์ค‘์š”์„ฑ : ๊ธฐ์กด OHE, ๋ฌผ๋ฆฌโ€‘ํ™”ํ•™ ํŠน์„ฑ ๋“ฑ์€ ์ธ๊ฐ„์ด ์ •์˜ํ•œ ๊ทœ์น™์— ์˜์กดํ•˜์ง€๋งŒ, ์ตœ์‹  ํŠธ๋žœ์Šคํฌ๋จธ ๊ธฐ๋ฐ˜ pLM์€ ๋Œ€๊ทœ๋ชจ ๋น„์ง€๋„ ํ•™์Šต์„ ํ†ตํ•ด ๋ณตํ•ฉ์ ์ธ ๊ตฌ์กฐยท๊ธฐ๋Šฅ ์ •๋ณด๋ฅผ ๋‚ด์žฌํ•œ๋‹ค. ๋ฐ”์ด์˜ค์—”์ง€๋‹ˆ์–ด๋ง ๋ฐ์ดํ„ฐ์˜ ํŠน์ˆ˜์„ฑ : ๊นŠ์ด ๋ณ€์ด ์Šค์บ”(์ „์—ญ ๋ณ€์ด)๊ณผ ๊ณ ๋ฐ€๋„ ๊ตญ์†Œ ๋ณ€์ด(ํŠน์ • ๋ถ€์œ„๋งŒ) ๋‘ ๊ฐ€์ง€ ํ˜•ํƒœ๊ฐ€ ์กด์žฌํ•˜์ง€๋งŒ, ์‹ค์ œ ์œ ์ „์ž ์น˜๋ฃŒ์šฉ AAV ์บก์‹œ๋“œ์™€ ๊ฐ™์€ ๊ฒฝ์šฐ๋Š” ๊ทนํžˆ ์ œํ•œ๋œ ๊ตฌ์—ญ(20โ€‘50 aa) ์— ๋ณ€์ด๊ฐ€ ์ง‘์ค‘๋œ๋‹ค. ์ด๋Š” โ€œ์ „์ฒด ์„œ์—ด์„ ๋ณด๋Š” ์ „์—ญ ์ž„๋ฒ ๋”ฉโ€์ด ์‹ ํ˜ธ๋ฅผ ํฌ์„์‹œํ‚ฌ ์œ„ํ—˜์„ ๋‚ดํฌํ•œ๋‹ค.

Quantitative Biology
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Factor-Adjusted Multiple Testing for High-Dimensional Individual Mediation Effects

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๊ณ ์ฐจ์› ๋งค๊ฐœ๋ถ„์„ : ํ˜„๋Œ€ ์œ ์ „์ฒดยท๋‹ค์ค‘์˜ค๋ฏน์Šค, ๊ธˆ์œต ๋“ฑ์—์„œ ์ˆ˜์ฒœ~์ˆ˜๋งŒ ๊ฐœ์˜ ํ›„๋ณด ๋งค๊ฐœ๋ณ€์ˆ˜๊ฐ€ ๋™์‹œ์— ์ธก์ •๋œ๋‹ค. ๊ฐœ๋ณ„ ๋งค๊ฐœํšจ๊ณผ๋ฅผ ์‹๋ณ„ํ•˜๋ฉด ์ƒ๋ฌผํ•™์ ยท๊ฒฝ์ œ์  ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๊ตฌ์ฒด์ ์œผ๋กœ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ธฐ์กด ์ ‘๊ทผ๋ฒ•์˜ ํ•œ๊ณ„ ๋งˆ์ง„์–ผ( marginal) ๋ฐฉ๋ฒ• ์€ ๋งค๊ฐœ๋ณ€์ˆ˜ ๊ฐ„ ๋…๋ฆฝ์„ ๊ฐ€์ •ํ•ด ๋‹ค์ค‘๊ฒ€์ • ์‹œ FDR๊ฐ€ ํฌ๊ฒŒ ๋ถ€ํ’€์–ด ์˜ค๋ฅธ๋‹ค. ๊ณต๋™ ๋ชจ๋ธ(joint) ๋ฐฉ๋ฒ• ์€ ๊ณ ์ฐจ์› LassoยทDebiased Lasso ๋“ฑ์„ ์‚ฌ์šฉํ•˜์ง€๋งŒ, ๊ฐ•ํ•œ ์ƒ๊ด€๊ตฌ์กฐ (๊ณตํ†ต ๊ฒฝ๋กœ, ๋ฐฐ๊ฒฝ ์š”์ธ ๋“ฑ) ๋•Œ๋ฌธ์— RE/irrepresentable ์กฐ๊ฑด์ด ๊นจ์ ธ ์ถ”์ •

Statistics
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Failure-Aware Access Point Selection for Resilient Cell-Free Massive MIMO Networks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ CFโ€‘mMIMO ๋Š” ๋‹ค์ˆ˜์˜ ๋ถ„์‚ฐ AP๊ฐ€ ๊ณต๋™์œผ๋กœ ์‚ฌ์šฉ์ž์—๊ฒŒ ์„œ๋น„์Šค๋ฅผ ์ œ๊ณตํ•จ์œผ๋กœ์จ ๋งคํฌ๋กœ๋‹ค์ด๋ฒ„์‹œํ‹ฐ์™€ ์…€โ€‘์—ฃ์ง€ ๋ฌธ์ œ ์™„ํ™” ๋“ฑ ๋†’์€ ์‹ ๋ขฐ์„ฑ์„ ๊ธฐ๋Œ€ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‹ค์ œ ๋ฐฐ์น˜์—์„œ๋Š” ์ „์› ์žฅ์• , ์žฅ๋น„ ๊ณ ์žฅ, ํ”„๋ก ํŠธํ™€ ์žฅ์•  ๋“ฑ์œผ๋กœ AP๊ฐ€ ๋…๋ฆฝ์ ์œผ๋กœ ๊ณ ์žฅํ•  ๊ฐ€๋Šฅ์„ฑ์ด ์กด์žฌํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์ •์„ฑ์  ๋…ผ์˜์— ๋จธ๋ฌผ๋ €์œผ๋ฉฐ, ํ™•๋ฅ ์  ๊ณ ์žฅ ๋ชจ๋ธ ์„ ์‹œ์Šคํ…œ ์„ค๊ณ„์— ๋ฐ˜์˜ํ•œ ์—ฐ๊ตฌ๋Š” ๋ถ€์กฑํ–ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๊ณ ์žฅ ์ธ์‹(APโ€‘aware) ์‚ฌ์šฉ์žโ€‘AP ๋งคํ•‘ ์ด ํ•„์š”ํ•˜๋ฉฐ, ์ด๋Š” 6G ์‹œ๋Œ€ โ€œResilienceโ€‘byโ€‘Designโ€ ํŒจ๋Ÿฌ๋‹ค์ž„๊ณผ๋„ ์ง๊ฒฐ๋œ

Network Electrical Engineering and Systems Science
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Fast Online Learning with Gaussian Prior-Driven Hierarchical Unimodal Thompson Sampling

| ํ•ญ๋ชฉ | ๋‚ด์šฉ ๋ฐ ํ‰๊ฐ€ | | | | | ๋ฌธ์ œ ์ •์˜ ๋ฐ ๋™๊ธฐ | ๋ณด์ƒ์ด ๊ฐ€์šฐ์‹œ์•ˆ์ด๋ผ๋Š” ๊ฐ€์ •์€ ๋ฌด์„  ํ†ต์‹ , ๊ธˆ์œต ๋“ฑ ์‹ค์„ธ๊ณ„ ์‹œ์Šคํ…œ์—์„œ ๋„๋ฆฌ ํƒ€๋‹นํ•จ.<br> ํด๋Ÿฌ์Šคํ„ฐ(์˜ˆ: ์ฃผํŒŒ์ˆ˜ยท๋น”, ์‹œ์žฅยทํฌํŠธํด๋ฆฌ์˜ค)์™€ ๋‹จ์ผ๊ทน์  ๊ตฌ์กฐ๋ฅผ ๋™์‹œ์— ํ™œ์šฉํ•˜๋ฉด ํƒ์ƒ‰ ๊ณต๊ฐ„์„ ํฌ๊ฒŒ ์ถ•์†Œํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์„ ๋ช…ํ™•ํžˆ ์ œ์‹œ. | | ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„์  | ๊ธฐ์กด ๊ณ„์ธตํ˜• MAB(์˜ˆ: TLP, HTS, DTโ€‘TMP)์™€ ๋‹จ์ผ๊ทน์  ๋ฐด๋”ง(UTS, OSUB) ์—ฐ๊ตฌ๋ฅผ ํฌ๊ด„์ ์œผ๋กœ ๋ฆฌ๋ทฐํ•˜๊ณ , ๊ฐ๊ฐ์ด ํด๋Ÿฌ์Šคํ„ฐ ์™€ ๋‹จ์ผ๊ทน์  ์„ ๋™์‹œ์— ํ™œ์šฉํ•˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ํ•œ๊ณ„๋ฅผ ์ง€์ .<br> ํŠนํžˆ ๊ฐ€์šฐ์‹œ์•ˆ

Computer Science Learning Machine Learning
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Fault Detection in Electrical Distribution System using Autoencoders

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ „๋ ฅ๋ง์€ ํ˜„๋Œ€ ์‚ฌํšŒ์˜ ํ•ต์‹ฌ ์ธํ”„๋ผ์ด๋ฉฐ, ๊ณ ์žฅ ๋ฐœ์ƒ ์‹œ ์‹ ์†ํ•œ ํƒ์ง€ยท์ฐจ๋‹จ์ด ์ „๋ ฅ ์•ˆ์ •์„ฑ ํ™•๋ณด์— ํ•„์ˆ˜์ ์ด๋‹ค. ๊ธฐ์กด ๋ณดํ˜ธ ๋ฐฉ์‹์€ ๋ชจ๋ธ ๊ธฐ๋ฐ˜(์ „๋ ฅ๊ณ„ํ†ต ๋ฐฉ์ •์‹) ํ˜น์€ ์ „ํ†ต์ ์ธ ๋จธ์‹ ๋Ÿฌ๋‹(ANN, SVM) ๋“ฑ์— ์˜์กดํ•˜์ง€๋งŒ, ๋ผ๋ฒจ๋ง๋œ ๋ฐ์ดํ„ฐ ๋ถ€์กฑ ๊ณผ ๋‹ค์–‘ํ•œ ๊ณ ์žฅ ์œ ํ˜•ยท์œ„์น˜ ์— ๋Œ€ํ•œ ์ผ๋ฐ˜ํ™” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ์ตœ๊ทผ ๋น„์ง€๋„ ํ•™์Šต ๊ธฐ๋ฐ˜ ์ด์ƒ ํƒ์ง€ ๊ฐ€ ๋ผ๋ฒจ๋ง ๋น„์šฉ์„ ์ ˆ๊ฐํ•˜๊ณ  ์‹ค์‹œ๊ฐ„ ์ ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์—ฌ์ฃผ๋ฉด์„œ ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋‹ค. 2. ์ œ์•ˆ ๋ฐฉ๋ฒ•์˜ ํ•ต์‹ฌ ์•„์ด๋””์–ด | ๋‹จ๊ณ„ | ์„ค๋ช… | | | | | ๋ฐ์ดํ„ฐ ์ „์ฒ˜๋ฆฌ | ์ „๋ฅ˜ยท์ „์•• ์‹œ๊ณ„์—ด์„ ๊ณ ์ •

System Electrical Engineering and Systems Science Detection
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Feature-based morphological analysis of shape graph data

| ๊ตฌ๋ถ„ | ํ•ต์‹ฌ ๋‚ด์šฉ | ์žฅ์  | ํ•œ๊ณ„ยท์ถ”๊ฐ€ ์—ฐ๊ตฌ ๊ณผ์ œ | | | | | | | ๋ฌธ์ œ ์ •์˜ | ํ˜•ํƒœ ๊ทธ๋ž˜ํ”„๋Š” ์œ„์ƒ(์—ฐ๊ฒฐ ๊ตฌ์กฐ) + ๊ธฐํ•˜(๋ธŒ๋žœ์น˜ ํ˜•ํƒœ) ๋ฅผ ๋™์‹œ์— ๊ณ ๋ คํ•ด์•ผ ํ•˜๋Š” ๋ณตํ•ฉ ๊ฐ์ฒด. ๊ธฐ์กด ๋ฐฉ๋ฒ•์€ (1) ๊ฑฐ๋ฆฌยท๋ฆฌ๋งŒ ๋ฉ”ํŠธ๋ฆญ ๊ธฐ๋ฐ˜, (2) ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ํŠน์ง• ํ•™์Šต์œผ๋กœ ๋‚˜๋‰จ. | ๋‘ ์ถ•์„ ๋ชจ๋‘ ๋‹ค๋ฃจ๋Š” ํ•„์š”์„ฑ ๋ช…ํ™•ํžˆ ์ œ์‹œ.<br> ์‹ค์ œ ๋ฐ์ดํ„ฐ(๋„์‹œยท์ƒ๋ฌผํ•™)์—์„œ ๋ณ€ํ˜•ยท์ƒ˜ํ”Œ๋ง ์ฐจ์ด๊ฐ€ ํฐ ์  ๊ฐ•์กฐ. | โ€œํ˜•ํƒœ ๊ทธ๋ž˜ํ”„โ€ ์ •์˜๊ฐ€ ์—ฐ์† ๊ณก์„  vs. ์ด์‚ฐ ๊ณก์„  ์‚ฌ์ด์— ๋ชจํ˜ธํ•จ์ด ๋‚จ์Œ. | | ์ œ์•ˆ ๋ฐฉ๋ฒ• | 1. ๋ฐ์ดํ„ฐ ๋ชจ๋ธ๋ง : G (V, E, B)

Data Computer Science Machine Learning Analysis
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FinAudio: A Benchmark for Audio Large Language Models in Financial Applications

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์˜ค๋””์˜คLLM์˜ ๊ธ‰๋ถ€์ƒ : Whisper, AudioGPT ๋“ฑ ์ตœ์‹  ๋ชจ๋ธ์ด ์Œ์„ฑ ์ธ์‹ยท๋Œ€ํ™”ยท์Œ์•… ์ดํ•ด ๋“ฑ์—์„œ ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์ด๋ฉฐ ์—ฐ๊ตฌยท์‚ฐ์—… ํ˜„์žฅ์—์„œ ํ™œ๋ฐœํžˆ ํ™œ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ๊ธˆ์œต ์˜ค๋””์˜ค ๋ฐ์ดํ„ฐ์˜ ๊ฐ€์น˜ : ๊ธฐ์—… ์‹ค์  ๋ฐœํ‘œ, ํˆฌ์ž์ž ์ปจํผ๋Ÿฐ์Šค ์ฝœ, CEO ์ธํ„ฐ๋ทฐ ๋“ฑ์€ ํ…์ŠคํŠธ๋ณด๋‹ค ํ’๋ถ€ํ•œ ๋‰˜์•™์Šค์™€ ์‹ค์‹œ๊ฐ„ ๊ฐ์ •์„ ํฌํ•จํ•˜๊ณ  ์žˆ์–ด ํˆฌ์ž ํŒ๋‹จ์— ์ค‘์š”ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ๋ฒค์น˜๋งˆํฌ ๋ถ€์žฌ : ๊ธฐ์กด ASRยท์š”์•ฝ ๋ฒค์น˜๋งˆํฌ(์˜ˆ: LibriSpeech, How2, Podcast Summarization)๋Š” ์ผ๋ฐ˜ ๋„๋ฉ”์ธ์— ์ดˆ์ ์ด ๋งž์ถฐ์ ธ

Model
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Fine-Tuning LLMs to Generate Economical and Reliable Actions for the Power Grid

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ PSPS ๋Š” ํ™”์žฌ ์œ„ํ—˜์„ ์ค„์ด๊ธฐ ์œ„ํ•ด ๋Œ€๊ทœ๋ชจ ๋ผ์ธ์„ ์ฐจ๋‹จํ•˜๋Š” ๋น„์ƒ ์กฐ์น˜์ด๋ฉฐ, ์ฐจ๋‹จ๋œ ๋ผ์ธ ์™ธ์— ์ถ”๊ฐ€์ ์ธ ๊ฐœ๋ฐฉํ˜•(openโ€‘only) ์Šค์œ„์นญ ์„ ํ†ตํ•ด ๊ณผ๋ถ€ํ•˜ ์™„ํ™”์™€ ๋ถ€ํ•˜ ์ฐจ๋‹จ ์ตœ์†Œํ™”๋ฅผ ๋„๋ชจํ•œ๋‹ค. ์ „ํ†ต์ ์ธ MILP ๊ธฐ๋ฐ˜ ์ตœ์ ํ™”๋Š” ์‹œ๊ฐ„ ์ œ์•ฝ ์ด ํฐ ํ˜„์žฅ ์ƒํ™ฉ์—์„œ ์‹ค์‹œ๊ฐ„ ์ ์šฉ์ด ์–ด๋ ค์›Œ, ํ•™์Šต ๊ธฐ๋ฐ˜ ํ”„๋ก์‹œ ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. LLM์€ ์ž์—ฐ์–ด ์ž…๋ ฅ โ†’ ๊ตฌ์กฐํ™”๋œ ์ถœ๋ ฅ ๋ณ€ํ™˜์— ๊ฐ•์ ์ด ์žˆ์–ด, ์šด์˜์ž๊ฐ€ ์‹œ๋‚˜๋ฆฌ์˜ค ์š”์•ฝ ์„ ํ…์ŠคํŠธ๋กœ ์ œ๊ณตํ•˜๊ณ , ๋ชจ๋ธ์ด ๊ฒ€์ฆ ๊ฐ€๋Šฅํ•œ ์•ก์…˜ ๋ฆฌ์ŠคํŠธ ๋ฅผ ๋ฐ˜ํ™˜ํ•˜๋„๋ก ์„ค๊ณ„ํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๋ฐฉ๋ฒ•๋ก  | ๋‹จ๊ณ„ | ๋ชฉ์ 

Electrical Engineering and Systems Science
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Finite elements for the space approximation of a differential model for salts crystallization

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์„์žฌยท๋ฒฝ๋Œ ๋“ฑ ๋ฌธํ™”์žฌ ์žฌ๋ฃŒ๋Š” ๋‹ค๊ณต์„ฑ ๋งค์ฒด์ด๋ฉฐ, ์Šต๊ธฐ์™€ ์—ผ๋ฅ˜๊ฐ€ ์นจํˆฌํ•˜๋ฉด์„œ ์—ผ๋ฅ˜ ๊ฒฐ์ •ํ™” (efflorescenceยทsubโ€‘efflorescence) ํ˜„์ƒ์ด ๋ฐœ์ƒํ•ด ๊ธฐ๊ณต์ด ๋ง‰ํžˆ๊ณ  ๊ตฌ์กฐ์  ์†์ƒ์ด ๊ฐ€์†ํ™”๋œ๋‹ค. ๊ธฐ์กด ์ˆ˜ํ•™ ๋ชจ๋ธ(

Model Mathematics
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Finite integration time can shift optimal sensitivity away from criticality

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์งˆ๋ฌธ ์ž„๊ณ„์  ๊ฐ€์„ค : ๋งŽ์€ ์ƒ๋ฌผํ•™์ ยท์ธ๊ณต ์‹œ์Šคํ…œ์ด ๋†’์€ ๋ฏผ๊ฐ๋„ยท๋‹ค์ด๋‚˜๋ฏน ๋ ˆ์ธ์ง€ ๋“ฑ์„ ์–ป๊ธฐ ์œ„ํ•ด ์ž„๊ณ„์  ๊ทผ์ฒ˜์—์„œ ์ž‘๋™ํ•œ๋‹ค๋Š” ๊ฐ€์„ค์ด ๋„๋ฆฌ ๋ฐ›์•„๋“ค์—ฌ์ ธ ์žˆ๋‹ค. ๋ฌธ์ œ์  : ์ž„๊ณ„์  ๊ทผ์ฒ˜์—์„œ๋Š” ํฌ๋ฆฌํ‹ฐ์ปฌ ์Šฌ๋กœ์ž‰(critical slowing down) ๊ณผ ํฐ ๋ณ€๋™์ด ๋ฐœ์ƒํ•ด, ์‹ค์ œ ์ž‘์—…์— ํ•„์š”ํ•œ ์œ ํ•œํ•œ ๊ด€์ธก/ํ†ตํ•ฉ ์‹œ๊ฐ„ ์•ˆ์—์„œ๋Š” ์ด๋“์ด ๊ฐ์†Œํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•ต์‹ฌ ์งˆ๋ฌธ : โ€œ์ฃผ์–ด์ง„ ์œ ํ•œ ํ†ตํ•ฉ ์‹œ๊ฐ„ T ์—์„œ ์žฌ๊ท€ ์‹ ๊ฒฝ๋ง์ด ์ตœ์ ์˜ ๋ฏผ๊ฐ๋„๋ฅผ ๋ฐœํœ˜ํ•˜๋ ค๋ฉด ์–ด๋А ์ •๋„ ์ž„๊ณ„์ ์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ•˜๋Š”๊ฐ€?โ€ 2. ๋ชจ๋ธ ๋ฐ ๋ฐฉ๋ฒ• | ์š”์†Œ | ์„ค๋ช… | | |

Condensed Matter
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Fixed-Horizon Self-Normalized Inference for Adaptive Experiments via Martingale AIPW/DML with Logged Propensities

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์ ์‘ํ˜• ์‹คํ—˜ (responseโ€‘adaptive trials, contextual bandits ๋“ฑ)์€ ์‹ค์‹œ๊ฐ„ ํ•™์Šตยท๋ฐฐํฌ ๊ท ํ˜•์„ ์œ„ํ•ด ํ• ๋‹น ํ™•๋ฅ  ฯ€โ‚œ ๋ฅผ ์ง€์†์ ์œผ๋กœ ์—…๋ฐ์ดํŠธํ•œ๋‹ค. ๊ณ ์ • ์‹œ์ ( fixedโ€‘horizon ) ๋ณด๊ณ  ๋Š” ๊ธฐ์—…ยท์—ฐ๊ตฌ๊ธฐ๊ด€์—์„œ ์—ฌ์ „ํžˆ ์š”๊ตฌ๋˜๋Š” ์ „ํ†ต์ ์ธ ๋ถ„์„ ํ˜•ํƒœ์ด๋ฉฐ, ์—ฌ๊ธฐ์„œ๋Š” ์ „์ฒด ์ƒ˜ํ”Œ์ด ๋ชจ์ธ ์‹œ์ ์— ATE ๋ฅผ ํ•œ ๋ฒˆ์— ์ถ”์ •ํ•œ๋‹ค. ๊ธฐ์กด Wald ๊ฒ€์ •์€ ์˜ˆ์ธก ๊ฐ€๋Šฅํ•œ ์ด์ฐจ ๋ณ€๋™๋Ÿ‰ ์ด ํ™•์ •์ ์ธ ๋ถ„์‚ฐ ํ•œ๊ณ„๊ฐ’ ฯƒยฒ ๋กœ ์ˆ˜๋ ดํ•œ๋‹ค๋Š” ์ „์ œ์— ์˜์กดํ•œ๋‹ค. ์ ์‘ํ˜• ์ •์ฑ…์—์„œ๋Š” ฯ€โ‚œ ๊ฐ€ ๋ฐ์ดํ„ฐ์— ๋”ฐ๋ผ ๊ธ‰๊ฒฉํžˆ

Statistics
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Flat-top solitons and anomalous interactions in media with even-order dispersions and competing nonlinearities

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ํ‰ํƒ„ ์ƒ๋‹จ ์†”๋ฆฌํ†ค(FT) ์€ ์–‘์ž ๋“œ๋กญ๋ฆฟ, BEC ํ˜ผํ•ฉ๋ฌผ ๋“ฑ์—์„œ ๊ด€์ธก๋œ ๋ฐ”์™€ ๊ฐ™์ด, ๊ฒฝ์Ÿ ๋น„์„ ํ˜•์„ฑ(ํ๋น…โ€‘ํ€ธํ‹ฑ) ์— ์˜ํ•ด ํ˜•์„ฑ๋˜๋Š” ๋น„์„ ํ˜• ํŒŒ๋™ ๊ตฌ์กฐ์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ 2์ฐจ ์ฐจ๋ถ„($m 2$) ์—์„œ๋งŒ ๋‹ค๋ฃจ์–ด์กŒ์œผ๋ฉฐ, ๊ณ ์ฐจ ์ฐจ๋ถ„ ์— ๋Œ€ํ•œ ์ฒด๊ณ„์ ์ธ ํƒ๊ตฌ๋Š” ๋ถ€์กฑํ–ˆ๋‹ค. ์ˆœ์ˆ˜ 4์ฐจ ์ฐจ๋ถ„ ์†”๋ฆฌํ†ค(PQS) ์˜ ์‹คํ—˜์  ์„ฑ๊ณต(๊ด‘์ž๊ฒฐ์ •ํŒŒ๋™๊ฐ€์ด๋“œ)๊ณผ ๊ทธ ์™ธ $m 6,8,10$ ์ฐจ๋ถ„์— ๋Œ€ํ•œ ์ด๋ก ์  ์ œ์•ˆ์€ ์ƒˆ๋กœ์šด ์†”๋ฆฌํ†ค ๊ณ„์—ด์„ ํƒ์ƒ‰ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐํšŒ๋ฅผ ์ œ๊ณตํ•œ๋‹ค. 2. ๋ชจ๋ธ ๋ฐ ์ˆ˜์น˜ ๋ฐฉ๋ฒ• ์ˆ˜์‹ (1) : ๊ณ ์ฐจ ์ง์ˆ˜ ์ฐจ๋ถ„($m 4,6,8

Physics
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Fluctuation-induced acceleration of inter-ligand exciton transfer in bis(dipyrrinato)Zn(II) complex

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์—‘์‹œํ†ค ์ „์ด ์†๋„๋Š” ์ „์ž ๊ฒฐํ•ฉ V ์˜ ์ œ๊ณฑ์— ๋น„๋ก€ํ•œ๋‹ค๋Š” ํŒŒ๋™์˜ ๊ณจ๋“œ๋ฃฐ(Fermiโ€™s golden rule)์—์„œ ์ถœ๋ฐœํ•œ๋‹ค. ์ „ํ†ต์ ์œผ๋กœ V ๋Š” ๊ณ ์ •๋œ ๊ตฌ์กฐ์  ํŒŒ๋ผ๋ฏธํ„ฐ๋กœ ๊ฐ„์ฃผ๋ผ ์„ค๊ณ„ ๋‹จ๊ณ„์—์„œ๋งŒ ์กฐ์ ˆ๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‹ค์ œ ๋ฌผ์งˆ์€ ์—ด์šด๋™์— ์˜ํ•ด ๊ตฌ์กฐ๊ฐ€ ์ง€์†์ ์œผ๋กœ ๋ณ€๋™ํ•˜๋ฏ€๋กœ V ๋„ ์‹œ๊ฐ„โ€‘์˜์กด์ ์ผ ์ˆ˜ ์žˆ๋‹ค. ์ด ์ ์„ ์ •๋Ÿ‰์ ์œผ๋กœ ๋‹ค๋ฃฌ ์—ฐ๊ตฌ๋Š” ์•„์ง ๋“œ๋ฌผ๋‹ค. Zn(dp)โ‚‚ ๋ณตํ•ฉ์ฒด๋Š” ๋‘ dipyrrin ๋ฆฌ๊ฐ„๋“œ๊ฐ€ ์ •์ž์„ธ(90ยฐ)์—์„œ ์™„์ „ ์ง๊ตํ•ด V 0 ์ธ โ€˜์ •์ โ€™ ๊ตฌ์กฐ๋ฅผ ๊ฐ–์ง€๋งŒ, ์‹คํ—˜์ ์œผ๋กœ๋Š” ์ดˆ๊ณ ์†(โ‰ฅ 5 ร— 10ยนโฐ sโปยน) ์ „

Physics
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Focused Relative Risk Information Criterion for Variable Selection in Linear Regression

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ „ํ†ต์ ์ธ ๋ณ€์ˆ˜ ์„ ํƒ ๊ธฐ์ค€(AIC, BIC, Mallows (C p))์€ ์ „์ฒด ๋ชจ๋ธ ์ ํ•ฉ๋„ ํ˜น์€ ์˜ˆ์ธก ์ •ํ™•๋„ ์— ์ดˆ์ ์„ ๋งž์ถ”์ง€๋งŒ, ์‹ค์ œ ์‘์šฉ์—์„œ๋Š” ํŠน์ • ๊ด€์ธก์น˜(๋˜๋Š” ํŠน์ • ์ง‘๋‹จ)์˜ ํ‰๊ท ๊ฐ’ ์„ ์ •ํ™•ํžˆ ์ถ”์ •ํ•˜๋Š” ๊ฒƒ์ด ๋” ์ค‘์š”ํ•  ์ˆ˜ ์žˆ๋‹ค. Focused Information Criterion (FIC) ๊ณ„์—ด์€ ์ด๋Ÿฐ โ€œ๋งž์ถคํ˜•โ€ ๋ชฉ์ ์„ ์œ„ํ•ด ๊ฐœ๋ฐœ๋์ง€๋งŒ, ๋Œ€๋ถ€๋ถ„์ด ๋Œ€๊ทœ๋ชจ ๊ทผ์‚ฌ(largeโ€‘sample approximation) ์— ์˜์กดํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ FRIC | ์š”์†Œ | ์„ค๋ช… | | | | | ๊ด€์‹ฌ ํŒŒ๋ผ

Statistics
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Foundation Models for Medical Imaging: Status, Challenges, and Directions

1. ๋…ผ๋ฌธ์˜ ์ „์ฒด์ ์ธ ๊ตฌ์กฐ์™€ ๊ธฐ์—ฌ | ๊ตฌ๋ถ„ | ํ•ต์‹ฌ ๋‚ด์šฉ | ์˜์˜ | | | | | | ์„ค๊ณ„ ์›๋ฆฌ | โ€ข Transformer, ViT, Swin, Decoderโ€‘only, MoE ๋“ฑ ์ตœ์‹  ์•„ํ‚คํ…์ฒ˜ ์†Œ๊ฐœ <br>โ€ข Selfโ€‘supervised, contrastive, diffusion ๋“ฑ ์‚ฌ์ „ํ•™์Šต ์ „๋žต ์ •๋ฆฌ <br>โ€ข ํšจ์œจ์„ฑ ๊ธฐ๋ฒ•(ํฌ์†Œ/์„ ํ˜• ์–ดํ…์…˜, Multiโ€‘Query, Lowโ€‘rank ์••์ถ•) | ์˜๋ฃŒ ์˜์ƒ ํŠน์„ฑ(๊ณ ํ•ด์ƒ๋„, 3D, ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ)๊ณผ ๋งž๋ฌผ๋ฆฌ๋Š” ์Šค์ผ€์ผ๋Ÿฌ๋ธ” ์„ค๊ณ„ ๊ฐ€์ด๋“œ๋ผ์ธ ์ œ๊ณต | | ์‘์šฉ ๋ถ„์•ผ | โ€ข 2D/3D ์„ธ๊ทธ๋ฉ˜ํ…Œ์ด์…˜

Image Processing Electrical Engineering and Systems Science Model
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Fourier Neural Operators for Structural Dynamics Models: Challenges, Limitations and Advantages of Using a Spectrogram Loss

| ๊ตฌ๋ถ„ | ์ฃผ์š” ๋‚ด์šฉ | ์˜๋ฏธยท์‹œ์‚ฌ์  | | | | | | ์—ฐ๊ตฌ ๋ชฉ์  | FNO๋ฅผ ๋‹ค์–‘ํ•œ ๋น„์„ ํ˜•ยท๋น„์ •์ƒ ๊ตฌ์กฐ๋™์—ญํ•™ ์‹œ์Šคํ…œ์— ์ ์šฉํ•ด ํ•œ๊ณ„์™€ ์žฅ์ ์„ ๊ทœ๋ช… | FNO๊ฐ€ โ€œ๋ฒ”์šฉโ€ ๋Œ€์ฒด ๋ชจ๋ธ์ธ์ง€ ๊ฒ€์ฆํ•˜๊ณ , ์‹ค์ œ ์—”์ง€๋‹ˆ์–ด๋ง ์ ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ํƒ์ƒ‰ | | ์‹คํ—˜ ์„ค๊ณ„ | ๋‹ค์ˆ˜์˜ ๊ฐ•์ œ ํ•จ์ˆ˜(์ž„ํŽ„์Šค, ์‚ฌ์ธ, ๋žœ๋ค ๋“ฑ) <br> ์„ ํ˜• vs ๋น„์„ ํ˜• ์‹œ์Šคํ…œ (์˜ˆ: ๋‹จ์ผ ์ž์œ ๋„ ์ง„๋™๊ธฐ, ๋‹ค์ž์œ ๋„ ํ’๋ ฅํ„ฐ๋นˆ) <br> ํ•™์Šต ๋ฐ์ดํ„ฐ ๊ทœ๋ชจ ๋‹ค์–‘ํ™” | ์‹œ์Šคํ…œ ํŠน์„ฑ(์„ ํ˜•์„ฑ, ๋น„์„ ํ˜•์„ฑ)๊ณผ ๋ฐ์ดํ„ฐ ์–‘์ด ์„ฑ๋Šฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋…๋ฆฝ์ ์œผ๋กœ ํ‰๊ฐ€ | | ํ•ต์‹ฌ ๋ฐœ๊ฒฌ | 1. ์„ ํ˜•

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