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Large-Scale Simulations of Turbulent Flows using Lattice Boltzmann Methods on Heterogeneous High Performance Computers

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋‚œ๋ฅ˜ ํ๋ฆ„์€ ๋‹ค์ค‘ ์Šค์ผ€์ผ ํ˜„์ƒ๊ณผ ๋ณต์žกํ•œ ๊ฒฝ๊ณ„ ์กฐ๊ฑด ๋•Œ๋ฌธ์— ์ „ํ†ต์ ์ธ CFD ๋ฐฉ๋ฒ•์œผ๋กœ๋Š” ๊ณ„์‚ฐ ๋น„์šฉ์ด ๊ธ‰์ฆํ•œ๋‹ค. GPU ๊ธฐ๋ฐ˜ ์Šˆํผ์ปดํ“จํ„ฐ๋Š” ๋ฉ”๋ชจ๋ฆฌ ๋Œ€์—ญํญ๊ณผ ์—ฐ์‚ฐ๋Ÿ‰์—์„œ CPUโ€‘์ค‘์‹ฌ ์‹œ์Šคํ…œ์„ ํฌ๊ฒŒ ์•ž์„œ๋ฉฐ, LBM์€ ๊ตฌ์กฐ๊ฐ€ ๋‹จ์ˆœํ•˜๊ณ  ๋กœ์ปฌ ์—ฐ์‚ฐ์ด ๋งŽ์•„ GPU์— ์ตœ์ ํ™”ํ•˜๊ธฐ ์šฉ์ดํ•˜๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ ์ƒˆ๋กœ์šด LBM ์Šคํ‚ค๋งˆ : ๋ฒฝโ€‘๋ชจ๋ธ๋ง LES( Largeโ€‘Eddy Simulation)๋ฅผ ๋ณตํ•ฉ ํ˜•์ƒ์— ์ ์šฉํ•˜๊ธฐ ์œ„ํ•œ ์Šคํ‚ค๋งˆ๋ฅผ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ด๋Š” ๊ธฐ์กด LBMโ€‘LES์™€ ๋‹ฌ๋ฆฌ ๊ฒฝ๊ณ„์ธต์—์„œ์˜ ์Šค์ผ€์ผ ๋ถ„๋ฆฌ๋ฅผ ๋ช…์‹œ์ ์œผ๋กœ ์ฒ˜๋ฆฌํ•˜๊ณ , wallโ€‘

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Latency-aware Human-in-the-Loop Reinforcement Learning for Semantic Communications

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ SemCom์˜ ํ•œ๊ณ„ : ๊ธฐ์กด SemCom ์‹œ์Šคํ…œ์€ ํ•™์Šต ํ›„ ๊ณ ์ •๋œ ์˜๋ฏธ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•œ๋‹ค. ๋ฌด์„  ํ™˜๊ฒฝยท์‚ฌ์šฉ์ž ์„ ํ˜ธยท์ž‘์—… ๋ชฉํ‘œ๊ฐ€ ๋ณ€ํ•  ๋•Œ ์˜๋ฏธ ์ •๋ ฌ์ด ๊นจ์ง€๋Š” ๋ฌธ์ œ๊ฐ€ ์žˆ๋‹ค. ์ธ๊ฐ„โ€‘ํ”ผ๋“œ๋ฐฑ์˜ ํ•„์š”์„ฑ : RLHF(๊ฐ•ํ™”ํ•™์Šตโ€‘์ธ๊ฐ„โ€‘ํ”ผ๋“œ๋ฐฑ) ์—ฐ๊ตฌ๊ฐ€ ๋กœ๋ด‡ยทํ…์ŠคํŠธ ์ƒ์„ฑ ๋“ฑ์—์„œ ์„ฑ๊ณตํ–ˆ์œผ๋ฉฐ, ์˜๋ฏธํ†ต์‹ ์—์„œ๋„ ์‚ฌ์šฉ์ž ์ฒด๊ฐ ํšจ์šฉ์„ ๋ฐ˜์˜ํ•  ํ•„์š”๊ฐ€ ๋Œ€๋‘๋œ๋‹ค. ์‹œ๊ฐ„ ์ œ์•ฝ : ์ธ๊ฐ„ ํ”ผ๋“œ๋ฐฑ ์ž์ฒด๊ฐ€ ๋Œ€์—ญํญยท์ง€์—ฐ ์ œํ•œ์„ ๋ฐ›์œผ๋ฉฐ, ์˜๋ฏธ ๋ชจ๋ธ ์—…๋ฐ์ดํŠธ๊ฐ€ ์‹ค์‹œ๊ฐ„ ์„œ๋น„์Šค์— ํˆฌ์ž…๋  ๊ฒฝ์šฐ ์ง€์—ฐ ์ดˆ๊ณผ๊ฐ€ QoE ์ €ํ•˜๋กœ ์ด์–ด์ง„๋‹ค. ๋”ฐ๋ผ์„œ ์‹œ๊ฐ„โ€‘์ธ์‹ ์˜์‚ฌ๊ฒฐ์ • ์ด ํ•„

Learning Electrical Engineering and Systems Science
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Lattice XBAR Filters in Thin-Film Lithium Niobate

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๊ณ ์ฃผํŒŒ ๋ฌด์„ ยท์„ผ์„œ ์‹œ์žฅ : 5G/6G, mmWave, ๋ ˆ์ด๋”ยทLiDAR ๋“ฑ์—์„œ 10 GHz ์ด์ƒ ๋Œ€์—ญ์ด ๊ธ‰์ฆํ•˜๋ฉด์„œ ๊ธฐ์กด SAW/FBAR ํ•„ํ„ฐ๋Š” ์Šค์ผ€์ผ๋ง ํ•œ๊ณ„์— ๋ด‰์ฐฉํ•œ๋‹ค. XBAR์˜ ์žฅ์  : ์–‡์€ TFLN ์œ„์— ์ธก๋ฉด ์ „๊ทน์„ ๋ฐฐ์น˜ํ•ด ํšก๋ฐฉํ–ฅ ์Œํ–ฅ ๋ชจ๋“œ๋ฅผ ์ด์šฉํ•˜๋ฉด, 10 GHz๋ฅผ ๋„˜์–ด 100 GHz๊นŒ์ง€๋„ ๋†’์€ ์ „๊ธฐโ€‘๊ธฐ๊ณ„ ๊ฒฐํ•ฉ๊ณ„์ˆ˜(kยฒ)์™€ Q๋ฅผ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ๋‹ค. P3F ๊ตฌ์กฐ : ์ฃผ๊ธฐ์  ํด๋ง์„ ํ†ตํ•ด ์ „๊ธฐโ€‘๊ธฐ๊ณ„ ๊ฒฐํ•ฉ์„ ๊ฐ•ํ™”ํ•˜๊ณ , ๋‘๊ป˜ ๋น„๋Œ€์นญ์œผ๋กœ ์ธํ•ด ๋Œ€์นญยท๋น„๋Œ€์นญ Lamb ๋ชจ๋“œ ๋ชจ๋‘๋ฅผ ํ™œ์šฉ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜์—ฌ ์„ค๊ณ„ ์ž์œ ๋„๋ฅผ

Electrical Engineering and Systems Science
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Law in Silico: Simulating Legal Society with LLM-Based Agents

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฒ•์‚ฌํšŒ ์‹คํ—˜์˜ ํ•œ๊ณ„ : ์‹ค์ œ ์‚ฌํšŒ์—์„œ ๋ฒ•์ œ๋„ ๋ณ€ํ™”๋ฅผ ์‹คํ—˜ํ•˜๋ ค๋ฉด ๋Œ€๊ทœ๋ชจ ์ •์ฑ… ์‹œํ–‰ยท์‹œํ–‰ ํ›„ ํ‰๊ฐ€๊ฐ€ ํ•„์š”ํ•˜๊ณ , ์ด๋Š” ๋น„์šฉยท์‹œ๊ฐ„ยท์œค๋ฆฌ์  ๋ฌธ์ œ๋ฅผ ๋™๋ฐ˜ํ•œ๋‹ค. AI ์‹œ๋ฎฌ๋ ˆ์ด์…˜์˜ ๊ฐ€๋Šฅ์„ฑ : LLM์€ ๋ฐฉ๋Œ€ํ•œ ํ…์ŠคํŠธ ์ฝ”ํผ์Šค๋ฅผ ํ•™์Šตํ•ด ๋ฒ•๋ฅ  ์šฉ์–ด, ํŒ๋ก€, ๊ทœ๋ฒ” ๋“ฑ์„ ๋‚ด์žฌํ•˜๊ณ  ์žˆ์–ด, ์ธ๊ฐ„๊ณผ ์œ ์‚ฌํ•œ โ€œ๋ฒ•๋ฅ ๊ฐ€โ€ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๊ตฌ๋ถ„ | ๋‚ด์šฉ | ์˜์˜ | | | | | | ํ”„๋ ˆ์ž„์›Œํฌ ์„ค๊ณ„ | LLM ๊ธฐ๋ฐ˜ ์—์ด์ „ํŠธ๋ฅผ โ€œ์‹œ๋ฏผโ€, โ€œ์ž…๋ฒ•์žโ€, โ€œํŒ์‚ฌโ€, โ€œ์ง‘ํ–‰๊ด€โ€ ๋“ฑ ์—ญํ• ์— ๋งคํ•‘ํ•˜๊ณ , ์ด๋“ค ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ์„ ํ”„๋กœ

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Learning Data-Efficient and Generalizable Neural Operators via Fundamental Physics Knowledge

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ SciML๊ณผ ๋‰ด๋Ÿด ์˜คํผ๋ ˆ์ดํ„ฐ์˜ ํ˜„์ฃผ์†Œ : ๊ธฐ์กด NO๋Š” ๋น ๋ฅธ ์ถ”๋ก  ์†๋„์™€ ๋†’์€ ์ •ํ™•๋„๋ฅผ ์ œ๊ณตํ•˜์ง€๋งŒ, ๋ฌผ๋ฆฌ์  ์ธ๋•ํ‹ฐ๋ธŒ ๋ฐ”์ด์–ด์Šค๊ฐ€ ๋ถ€์กฑํ•ด ๋ฐ์ดํ„ฐ ์˜์กด๋„๊ฐ€ ๋†’๊ณ  ๋ถ„ํฌ ์ด๋™์— ์ทจ์•ฝ ํ•˜๋‹ค. ์ˆ˜์น˜ ํ•ด์„๊ธฐ์™€์˜ ์ฐจ์ด : ์ „ํ†ต์ ์ธ ์ˆ˜์น˜ ํ•ด์„๊ธฐ๋Š” ๋ฌผ๋ฆฌ ๋ฒ•์น™(๋ณด์กด, ๋Œ€์นญ ๋“ฑ)์„ ๋‚ด์žฌํ•˜๊ณ  ์žˆ์–ด ํŒŒ๋ผ๋ฏธํ„ฐยท๊ฒฝ๊ณ„์กฐ๊ฑดยท๊ฒฉ์ž ๋ณ€ํ™”์— ๊ฐ•์ธํ•˜์ง€๋งŒ, ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์€ ์ด๋Ÿฌํ•œ ๊ฐ•๊ฑด์„ฑ์„ ์ž๋™์œผ๋กœ ํš๋“ํ•˜์ง€ ๋ชปํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ ๊ธฐ๋ณธ ๋ฌผ๋ฆฌ ํ˜•ํƒœ์˜ ๋„์ž… ๊ธฐ๋ณธ ํ˜•ํƒœ ์ •์˜ : ์›๋ณธ PDE์—์„œ ํ•ต์‹ฌ ๋™์—ญํ•™์„ ๋‹ด๋‹นํ•˜๋Š” ํ•ญ (์˜ˆ: ๋Œ€๋ฅ˜ยทํ™•์‚ฐยท๋น„

Data Computer Science Learning Machine Learning
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Learning Glioblastoma Tumor Heterogeneity Using Brain Inspired Topological Neural Networks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ GBM ์ด์งˆ์„ฑ : ๊ดด์‚ฌ ์ฝ”์–ด, ์นจ์œค ๊ฒฝ๊ณ„, ๋น„์—ฐ์†์  ๊ฐ•ํ™” ์˜์—ญ ๋“ฑ ๋‹ค์ค‘ ์Šค์ผ€์ผ ํ˜•ํƒœํ•™์  ๋ณ€์ด๊ฐ€ ์˜ˆํ›„์— ํ•ต์‹ฌ์ ์ธ ์—ญํ• ์„ ํ•จ. ์Šค์บ๋„ˆ ํŽธํ–ฅ : MRI ํ”„๋กœํ† ์ฝœยท์Šค์บ๋„ˆ ์ฐจ์ด์— ๋”ฐ๋ผ ์ด๋ฏธ์ง€ ํ†ต๊ณ„๊ฐ€ ํฌ๊ฒŒ ๋ณ€ํ•ด, ํ•™์Šต๋œ ํŠน์ง•์ด ๋ณ‘์› ๊ฐ„ ์ผ๋ฐ˜ํ™”์— ์‹คํŒจ. ๊ธฐ์กด ๋ชจ๋ธ ํ•œ๊ณ„ : CNNยทViT ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์€ ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์ด์ง€๋งŒ, ๋ธ”๋ž™๋ฐ•์Šค ํŠน์„ฑยท์Šค์บ๋„ˆ ๋ฏผ๊ฐ๋„๋กœ ์ž„์ƒ ์ ์šฉ์ด ์–ด๋ ค์›€. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ ํ† ํด๋กœ์ง€ ์ •๊ทœํ™” TopoLoss : ์ง€์†์„ฑ ํ˜ธ๋ชฐ๋กœ์ง€(๋ฒ ํ‹ฐโ€‘์ˆ˜ ๋ฐ”์ฝ”๋“œ)๋ฅผ ์†์‹ค์— ์ง์ ‘ ํฌํ•จ, ์ž ์žฌ ๋ฒกํ„ฐ๋ฅผ 16ร—16 ๊ฒฉ์ž

Computer Science Network Learning Machine Learning
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Learning Preference from Observed Rankings

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๊ฒฝ์ œยท๋งˆ์ผ€ํŒ…์—์„œ ์„ ํ˜ธ ์ถ”์ • ์€ ๊ตฌ์กฐ์  ์ˆ˜์š” ๋ชจ๋ธ, ๊ฐ€๊ฒฉยทํ’ˆ๋ชฉ ์ตœ์ ํ™”, ๊ทธ๋ฆฌ๊ณ  ์„ค๋ฌธ ๊ธฐ๋ฐ˜ ์ปจ์กฐ์ธํŠธ ๋ถ„์„ ๋“ฑ ๋‹ค์–‘ํ•œ ์˜์‚ฌ๊ฒฐ์ •์— ํ•ต์‹ฌ์ด๋‹ค. ๋””์ง€ํ„ธ ํ”Œ๋žซํผ์—์„œ๋Š” ๊ฐœ์ธํ™” ๊ฐ€ ํ•„์ˆ˜์ด๋ฉฐ, ๊ธฐ์กด ํ˜‘์—… ํ•„ํ„ฐ๋งยท์ž ์žฌ ์š”์ธ ๋ชจ๋ธ์€ ์ฃผ๋กœ ์•”๋ฌต ํ”ผ๋“œ๋ฐฑ(implicit feedback) ์„ ์Œ๋ณ„ ์ˆœ์œ„ ํ˜•ํƒœ๋กœ ํ•™์Šตํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋Œ€๋ถ€๋ถ„์˜ ์‹ค๋ฌด ๋ฐ์ดํ„ฐ๋Š” ์™„์ „ ์ˆœ์œ„ ๊ฐ€ ์•„๋‹ˆ๋ผ ๋ถ€๋ถ„ ์ˆœ์œ„ (์˜ˆ: ์„ค๋ฌธ์—์„œ ์ผ๋ถ€ ์•„์ดํ…œ๋งŒ ๋น„๊ต, ๊ตฌ๋งคยท๋ฏธ๊ตฌ๋งค๋ฅผ ํ†ตํ•œ ์•”๋ฌต ์ˆœ์œ„)์ด๋ฉฐ, ์ด๋•Œ ๊ธฐ์กด ๋ชจ๋ธ์€ ์ •๋ณด ์†์‹ค ํ˜น์€ ํŽธํ–ฅ์„ ์ดˆ๋ž˜ํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๋ชจ๋ธ ๊ตฌ์กฐ | ๊ตฌ

Statistics Learning Machine Learning
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Learning Reward Machines from Partially Observed Policies

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์—ญ๊ฐ•ํ™”ํ•™์Šต(IRL) ์€ ๋ณด์ƒ ํ•จ์ˆ˜๋ฅผ ์ง์ ‘ ์„ค๊ณ„ํ•˜๊ธฐ ์–ด๋ ค์šด ๋ณต์žกํ•œ ํ™˜๊ฒฝ์—์„œ ๋„๋ฆฌ ์‚ฌ์šฉ๋œ๋‹ค. ๊ธฐ์กด IRL ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์Šค์นผ๋ผ ๋ณด์ƒ ํ˜น์€ ๋ณด์ƒ ํ•จ์ˆ˜์˜ ํŒŒ๋ผ๋ฏธํ„ฐํ™” ์— ์ดˆ์ ์„ ๋งž์ท„์ง€๋งŒ, ๋ณด์ƒ ๋จธ์‹  ์ฒ˜๋Ÿผ ๊ตฌ์กฐํ™”๋œ ๋ณด์ƒ ํ‘œํ˜„์„ ๋‹ค๋ฃจ๋Š” ๊ฒฝ์šฐ๋Š” ๋“œ๋ฌผ๋‹ค. ๋ณด์ƒ ๋จธ์‹ ์€ ์ •๊ทœ ์–ธ์–ด ์™€ ์œ ์‚ฌํ•˜๊ฒŒ, ์ƒํƒœ ์ „์ด์™€ ์›์ž ๋ช…์ œ์— ๊ธฐ๋ฐ˜ํ•ด ๋ณด์ƒ์„ ์ •์˜ํ•œ๋‹ค. ์ด๋Š” ์‹œ๊ณ„์—ด ๋ชฉํ‘œ (์˜ˆ: โ€œ๋จผ์ € A๋ฅผ ๋‹ฌ์„ฑํ•˜๊ณ  ๊ทธ ๋‹ค์Œ B๋ฅผ ๋‹ฌ์„ฑโ€)๋‚˜ ์กฐ๊ฑด๋ถ€ ๋ณด์ƒ (์˜ˆ: โ€œ๋ฌธ์„ ์—ด๋ฉด ๋ณด์ƒ์ด ์ฃผ์–ด์ง„๋‹คโ€)๋ฅผ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ๋ชจ๋ธ๋งํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ณด์ƒ ๋จธ์‹ ์„ ์—ญ์œผ๋กœ ์ถ”์ • ํ•˜๋ ค๋ฉด

Learning
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Learning with Locally Private Examples by Inverse Weierstrass Private Stochastic Gradient Descent

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ํ”„๋ผ์ด๋ฒ„์‹œ ์œ„ํ˜‘ : ์˜๋ฃŒยท์Œ์„ฑยท์˜ˆ์ธก ๋“ฑ ๋ฏผ๊ฐํ•œ ๋ถ„์•ผ์—์„œ ๋ชจ๋ธ์ด ํ›ˆ๋ จ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ์ถ”๋ก  ๊ณต๊ฒฉ์— ๋…ธ์ถœ๋  ์œ„ํ—˜์ด ์ปค์ง€๊ณ  ์žˆ๋‹ค. DP vs. LDP : ์ „ํ†ต์ ์ธ ์ฐจ๋“ฑ ํ”„๋ผ์ด๋ฒ„์‹œ(DP)๋Š” ์ค‘์•™ ์ง‘์ค‘ํ˜• ์‹ ๋ขฐ ๊ธฐ๊ด€์ด ํ•„์š”ํ•˜์ง€๋งŒ, LDP๋Š” ๊ฐ ์‚ฌ์šฉ์ž๊ฐ€ ๋ฐ์ดํ„ฐ๋ฅผ ๋กœ์ปฌ์—์„œ ์ง์ ‘ ๋ณ€ํ˜•ํ•ด ์ „์†กํ•˜๋ฏ€๋กœ ์‹ ๋ขฐ ๊ธฐ๊ด€์— ๋Œ€ํ•œ ์˜์กด์„ฑ์„ ์—†์•ค๋‹ค. ๋น„๋Œ€ํ™”ํ˜•ยทํƒœ์Šคํฌโ€‘์• ๊ทธ๋…ธ์Šคํ‹ฑ LDP : ํ•œ ๋ฒˆ์— ๊ณต๊ฐœ๋œ ๋ฐ์ดํ„ฐ๊ฐ€ ํ–ฅํ›„ ๋‹ค์–‘ํ•œ ๋ถ„์„์— ์žฌ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค๋Š” ์š”๊ตฌ๊ฐ€ ์‹ค๋ฌด์—์„œ ์ ์  ๊ฐ•์กฐ๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋น„๋Œ€ํ™”ํ˜• LDP๋Š” ๋…ธ์ด์ฆˆ๊ฐ€ ๊ณ ์ •๋˜์–ด ์žˆ์–ด

Computer Science Learning Machine Learning
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Left-right asymmetry in predicting brain activity from LLMs' representations emerges with their formal linguistic competence

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์งˆ๋ฌธ ๋‡Œโ€‘LLM ์ •ํ•ฉ์„ฑ : fMRIยทMEGยทECoG ๋“ฑ์—์„œ ํ…์ŠคํŠธ์— ๋Œ€ํ•œ ๋‡Œ ๋ฐ˜์‘์„ LLM ๋‚ด๋ถ€ ํ‘œํ˜„์œผ๋กœ ์˜ˆ์ธกํ•˜๋Š” ์—ฐ๊ตฌ๊ฐ€ ๊ธ‰์ฆํ•˜๊ณ  ์žˆ๋‹ค. ์ขŒโ€‘์šฐ ๋น„๋Œ€์นญ : ์ดˆ๊ธฐ ์—ฐ๊ตฌ๋Š” ์ขŒยท์šฐ ๋ฐ˜๊ตฌ๊ฐ€ ๊ฑฐ์˜ ๋Œ€์นญ์ ์ธ โ€˜brain scoreโ€™๋ฅผ ๋ณด์˜€์œผ๋‚˜, ํŒŒ๋ผ๋ฏธํ„ฐยท์„ฑ๋Šฅ์ด ํฐ ์ตœ์‹  LLM์—์„œ๋Š” ์ขŒ๋ฐ˜๊ตฌ๊ฐ€ ์šฐ๋ฐ˜๊ตฌ๋ณด๋‹ค ๋” ๋†’์€ ์˜ˆ์ธก๋ ฅ์„ ๋ณด์ธ๋‹ค. ํ•ต์‹ฌ ์งˆ๋ฌธ : โ€œ์–ด๋–ค ํ•™์Šต๋œ ๋Šฅ๋ ฅ์ด ์ขŒโ€‘์šฐ ๋น„๋Œ€์นญ์„ ์ผ์œผํ‚ค๋Š”๊ฐ€?โ€ 2. ์‹คํ—˜ ์„ค๊ณ„ | ๋‹จ๊ณ„ | ๋‚ด์šฉ | ๋ชฉ์  | | | | | | โ‘  ๋ชจ๋ธยท๋ฐ์ดํ„ฐ | OLMoโ€‘2 7B (10 ์ฒดํฌํฌ์ธํŠธ) + Pyth

Computer Science NLP
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Leicester's Tale: Another Perspective on the EPL 2015/16 Through Expected Goals (xG) Modelling

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ๋ถˆํ™•์‹ค์„ฑ ๊ด€๋ฆฌ : ์ถ•๊ตฌ๋Š” โ€˜๋ถˆํ™•์‹ค์„ฑโ€™์ด ํŒฌ ํฅ๋ฏธ๋ฅผ ์œ ์ง€์‹œํ‚ค๋Š” ํ•ต์‹ฌ ์š”์†Œ์ด๋ฉฐ, ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๋ถ„์„์„ ํ†ตํ•ด ์ด๋ฅผ ์ •๋Ÿ‰ํ™”ํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ ๋Š˜์–ด๋‚˜๊ณ  ์žˆ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ : ๋Œ€๋ถ€๋ถ„์˜ x G ์—ฐ๊ตฌ๋Š” ์ƒท ํ’ˆ์งˆ ํ‰๊ฐ€ ํ˜น์€ ๊ฒฝ๊ธฐโ€‘๋‹จ์œ„ ์ŠนํŒจ ์˜ˆ์ธก์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์œผ๋ฉฐ, ์‹œ์ฆŒโ€‘์ „์ฒด ์ˆœ์œ„์™€ ์‹ค์ œ ์„ฑ๊ณผ ๊ฐ„์˜ ๋ถˆ์ผ์น˜๋ฅผ ์ฒด๊ณ„์ ์œผ๋กœ ํƒ๊ตฌํ•˜์ง€ ์•Š์•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๋ชฉํ‘œ : (1) x G ๊ธฐ๋ฐ˜ ํ™•๋ฅ  ๋ชจ๋ธ์„ ํ†ตํ•ด ๋ฆฌ๊ทธ ์ˆœ์œ„์˜ โ€˜ranking uncertaintyโ€™ (์ˆœ์œ„ ๋ถˆํ™•์‹ค์„ฑ)๋ฅผ ์ •๋Ÿ‰ํ™”ํ•˜๊ณ , (2) ํฌ๊ท€ํ•œ ์‹œ์ฆŒ ๊ฒฐ๊ณผ(์˜ˆ: ๋ ˆ์Šคํ„ฐ ์šฐ์Šน)์˜ ๋ฐœ์ƒ

Statistics Model
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LLM-based Fusion of Multi-modal Features for Commercial Memorability Prediction

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ๊ด‘๊ณ ยท๋ธŒ๋žœ๋“œ ๊ธฐ์–ต๋ ฅ์€ ๋งˆ์ผ€ํŒ… ROI๋ฅผ ์ง์ ‘์ ์œผ๋กœ ์ขŒ์šฐํ•˜๋Š” ํ•ต์‹ฌ ์ง€ํ‘œ์ด๋ฉฐ, ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์‹œ๊ฐ์  ํŠน์ง•์— ์˜์กดํ•˜๊ฑฐ๋‚˜ ๋‹จ์ผ ๋ชจ๋‹ฌ(ํ…์ŠคํŠธยท์ด๋ฏธ์ง€)๋งŒ์„ ํ™œ์šฉํ–ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ ์ •๋ณด๋ฅผ ๋™์‹œ์— ํ™œ์šฉํ•˜๋ฉด์„œ, LLM์„ ์œตํ•ฉ ๋ฉ”์ปค๋‹ˆ์ฆ˜์˜ โ€œ์ธ์ง€์  ๊ฐ€์ด๋“œโ€๋กœ ํ™œ์šฉํ•œ๋‹ค๋Š” ์ ์—์„œ ์ฐจ๋ณ„์„ฑ์„ ๊ฐ€์ง„๋‹ค. 2. ๋ชจ๋ธ ์•„ํ‚คํ…์ฒ˜ ๋ฐฑ๋ณธ : Gemmaโ€‘3 LLM์„ ํ•ต์‹ฌ์œผ๋กœ ์‚ฌ์šฉํ•ด ํ…์ŠคํŠธ์™€ ์‹œ๊ฐ ํŠน์ง•์„ ๋™์ผํ•œ ์ž„๋ฒ ๋”ฉ ๊ณต๊ฐ„์— ๋งคํ•‘ํ•œ๋‹ค. ํŠน์ง• ์ถ”์ถœ : ์‚ฌ์ „ ํ•™์Šต๋œ Vision Transformer(ViT)์™€ ํ…์ŠคํŠธ ์ž„๋ฒ ๋”ฉ ๋ชจ๋ธ(E5)์—์„œ ๊ฐ๊ฐ

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Locally Adaptive Multi-Objective Learning

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์˜จ๋ผ์ธ ํ•™์Šต ์€ ๋ฐ์ดํ„ฐ ๋ถ„ํฌ๊ฐ€ ๋น„์ •์ƒ์ ์ด๊ฑฐ๋‚˜ ์ ๋Œ€์ ์œผ๋กœ ๋ณ€ํ•  ๋•Œ๋„ ๊ฐ•๊ฑดํ•œ ์˜ˆ์ธก์„ ์š”๊ตฌํ•œ๋‹ค. ๋‹ค๋ชฉํ‘œ ํ•™์Šต ์€ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜, ๋‹ค์ค‘ ์ •ํ™•๋„, ๋‹ค์ค‘ ๊ทธ๋ฃน ํ•™์Šต ๋“ฑ ์—ฌ๋Ÿฌ ์‹ค์šฉ์  ๋ชฉํ‘œ๋ฅผ ๋™์‹œ์— ๋งŒ์กฑ์‹œ์ผœ์•ผ ํ•˜๋Š” ์ƒํ™ฉ์„ ํฌ๊ด„ํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ(Lee et al., 2022 ๋“ฑ)๋Š” ์ „์—ญ ์ ์‘์„ฑ ์— ์ดˆ์ ์„ ๋งž์ถ”์–ด ์ „์ฒด ๊ตฌ๊ฐ„์— ๋Œ€ํ•œ ์ตœ์•…โ€‘์‚ฌ๋ก€ ์†์‹ค์„ ์ตœ์†Œํ™”ํ–ˆ์œผ๋ฉฐ, ์ง€์—ญ ์ ์‘์„ฑ ์„ ๋ณด์žฅํ•˜๊ธฐ ์œ„ํ•ด ๋ชจ๋“  ์—ฐ์† ๊ตฌ๊ฐ„์— ๋Œ€ํ•œ ์ถ”๊ฐ€ ๋ชฉํ‘œ๋ฅผ ๋„์ž…ํ–ˆ์ง€๋งŒ, ์‹คํ—˜์  ๊ฒ€์ฆ์ด ๋ถ€์กฑํ–ˆ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | | | | |

Computer Science Learning Machine Learning
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Logarithmic Hurwitz Spaces in Mixed and Positive Characteristic with Wild Ramification

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ฐจ์ˆ˜ (p) ์ปค๋ฒ„์˜ ํŠน์ˆ˜์„ฑ : ์ฐจ์ˆ˜ (p)์ธ ์œ ํ•œ ์ปค๋ฒ„๋Š” ํŠน์„ฑ 0์—์„œ๋Š” ๋‹จ์ˆœํžˆ ๊ตฐ๋ก ์  ๋ชจ๋…ธ๋“œ๋ฆฌ(๋‹จ์ผ ์ˆœํ™˜๊ตฐ)์œผ๋กœ ๊ธฐ์ˆ ๋˜์ง€๋งŒ, ํŠน์„ฑ (p)์—์„œ๋Š” ๋ถˆ๊ฐ€๋ถ„(inseparable) ์ปค๋ฒ„๊ฐ€ ๋‚˜ํƒ€๋‚˜๋ฉฐ, ์ด๋Š” ์ƒ๋Œ€ Frobenius์— ์˜ํ•ด ์„ค๋ช…๋œ๋‹ค. ๊ธฐ์กด Hurwitz ๊ณต๊ฐ„ ์—ฐ๊ตฌ๋Š” ์ด๋Ÿฌํ•œ โ€œ์•ผ์ƒโ€ ํ˜„์ƒ์„ ํšŒํ”ผํ•˜๊ณ , ์ฐจ์ˆ˜ (p) ์ปค๋ฒ„๋ฅผ ํŠน์„ฑ (p)์—์„œ ๋ฐฐ์ œํ•˜๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์—ˆ๋‹ค. BT20์˜ ๋ฆฌํ”„ํŒ… ๊ธฐ์ค€ : Bhattโ€“Tian(2020)์˜ ๊ฒฐ๊ณผ๋Š” ํŠน์„ฑ (p) ์ปค๋ฒ„๊ฐ€ ์–ธ์ œ ํŠน์„ฑ 0์œผ๋กœ ๋ฆฌํ”„ํŒ…๋  ์ˆ˜ ์žˆ๋Š”์ง€์—

Mathematics
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Logit Distance Bounds Representational Similarity

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ํ‘œํ˜„ ํ•™์Šต์˜ ์ค‘์š”์„ฑ : ๋”ฅ๋Ÿฌ๋‹ ์„ฑ๊ณต ์š”์ธ์„ โ€œ์ข‹์€ ํ‘œํ˜„โ€์ด๋ผ๊ณ  ๋ณด๋Š” ๊ด€์ ์€ ์˜ค๋ž˜์ „๋ถ€ํ„ฐ ์ œ์‹œ๋ผ ์™”์œผ๋ฉฐ, ์ตœ๊ทผ์—๋Š” ์„ ํ˜• ํ”„๋กœ๋ธŒ(linear probe) ๋กœ ์ธ๊ฐ„์ด ํ•ด์„ ๊ฐ€๋Šฅํ•œ ๊ฐœ๋…์„ ์ถ”์ถœํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์‹ค์ฆ์  ์ฆ๊ฑฐ๊ฐ€ ๋งŽ๋‹ค. ์‹๋ณ„์„ฑ ์ด๋ก  : Khemakhem et al., Roeder et al., Lachapelle et al. ๋“ฑ์€ ๋‹ค์–‘์„ฑ(diversity) ๊ฐ€์ • ํ•˜์— ๋™์ผํ•œ ์กฐ๊ฑด๋ถ€ ๋ถ„ํฌ๋ฅผ ์ƒ์„ฑํ•˜๋Š” ๋‘ ๋ชจ๋ธ์˜ ์ž„๋ฒ ๋”ฉ์ด ๊ฐ€์—ญ ์„ ํ˜• ๋ณ€ํ™˜์œผ๋กœ ์—ฐ๊ฒฐ๋œ๋‹ค๋Š” ๊ฐ•๋ ฅํ•œ ๊ฒฐ๊ณผ๋ฅผ ์ œ์‹œํ–ˆ๋‹ค. ๊ทผ์‚ฌ ์‹๋ณ„์„ฑ ๋ฌธ์ œ : ์‹ค์ œ ํ•™์Šต์—์„œ๋Š”

Computer Science Machine Learning
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Machine Learning in Epidemiology

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฐ์ดํ„ฐ ๋ณต์žก์„ฑ ์ฆ๊ฐ€ : ์ „์ž๊ฑด๊ฐ•๊ธฐ๋ก(EHR), ์›จ์–ด๋Ÿฌ๋ธ”, SNS, ์œ ์ „์ฒด ๋“ฑ ๋‹ค์–‘ํ•œ ์ถœ์ฒ˜์—์„œ ์ˆ˜์ง‘๋˜๋Š” ๋ฐ์ดํ„ฐ๋Š” ์ฐจ์›๊ณผ ๊ทœ๋ชจ๊ฐ€ ๊ธ‰์ฆํ•˜๊ณ  ์žˆ์–ด ์ „ํ†ต์ ์ธ ํšŒ๊ท€ยท์ƒ์กด๋ถ„์„๋งŒ์œผ๋กœ๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ์Œ. ML์˜ ์žฅ์  : ๋ชจ๋ธ ์‚ฌ์–‘์„ ๋ฏธ๋ฆฌ ์ •ํ™•ํžˆ ์ •์˜ํ•  ํ•„์š” ์—†์ด ํŠน์ง•(Feature) ๋งŒ ์ œ๊ณตํ•˜๋ฉด ์ž๋™์œผ๋กœ ๋น„์„ ํ˜• ์ƒํ˜ธ์ž‘์šฉยท๋ณตํ•ฉ ํŒจํ„ด์„ ํƒ์ƒ‰ ๊ฐ€๋Šฅ. ์ด๋Š” ์—ญํ•™ ์—ฐ๊ตฌ์—์„œ ๋‹ค์ค‘ ์œ„ํ—˜์ธ์ž์™€ ๋ณตํ•ฉ ํšจ๊ณผ๋ฅผ ํŒŒ์•…ํ•˜๋Š” ๋ฐ ์œ ๋ฆฌํ•จ. 2. ์ฃผ์š” ๋‚ด์šฉ ์š”์•ฝ | ์„น์…˜ | ํ•ต์‹ฌ ๋‚ด์šฉ | ํ•™์Šต ๋ชฉํ‘œ | | | | | | 2. ๊ฐ๋…ํ•™์Šต | ํ•จ์ˆ˜ (f:

Statistics Learning Machine Learning
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Markov processes forced on a subspace by a large drift, with applications to population genetics

| ๊ตฌ๋ถ„ | ๋‚ด์šฉ | | | | | ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ | ๋А๋ฆฐโ€‘๋น ๋ฅธ ์‹œ์Šคํ…œ(slowโ€‘fast systems)์€ ํ™•๋ฅ  ๋ชจ๋ธ, ํŠนํžˆ ์ƒ๋ฌผํ•™ยท๋ฌผ๋ฆฌํ•™ยทํ™”ํ•™์—์„œ ๋นˆ๋ฒˆํžˆ ๋“ฑ์žฅํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ(Katzenberger 1991, Ball et al. 2006 ๋“ฑ)๋Š” ๋ฐ˜๊ฐ•์ œ(Lyapunov) ๋ฐฉ๋ฒ•์ด๋‚˜ ๋ฐ˜๋งˆํŒ…๊ฒŒ์ผ ๊ธฐ๋ฒ•์„ ์ฃผ๋กœ ์‚ฌ์šฉํ–ˆ์œผ๋ฉฐ, ๊ฒฝ๋กœ ๊ณต๊ฐ„์—์„œ์˜ ๊ฐ•ํ•œ ์ˆ˜๋ ด์„ ๋ชฉํ‘œ๋กœ ํ–ˆ๋‹ค. | | ํ•ต์‹ฌ ์•„์ด๋””์–ด | ์ €์ž๋Š” ๋งˆํŒ…๊ฒŒ์ผ ๋ฌธ์ œ ์™€ ์ ์œ  ์ธก๋„(occupation measure) ๊ฐœ๋…์„ ๊ฒฐํ•ฉํ•ด โ€œ์ธก๋„ ์˜๋ฏธ์˜ ์ˆ˜๋ ดโ€์„ ๋„์ž…ํ•œ๋‹ค. ์ด๋Š” (X^{N}) ๊ฐ€ ๋น ๋ฅด๊ฒŒ

Mathematics
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MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Markets

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์ „๋ ฅ ์‹œ์Šคํ…œ์˜ ํƒˆ์ค‘์•™ํ™” : DER(๋ถ„์‚ฐํ˜• ์—๋„ˆ์ง€ ์ž์›)์˜ ๊ธ‰์ฆ์œผ๋กœ ๊ธฐ์กด ์ค‘์•™์ง‘์ค‘์‹ ์šด์˜์ด ํ•œ๊ณ„์— ๋ด‰์ฐฉ. LEM ํŠธ๋ฆด๋ ˆ๋งˆ (ํšจ์œจยท๋ฌผ๋ฆฌยทํ”„๋ผ์ด๋ฒ„์‹œ) โ†’ ๋ถ„์‚ฐ ํ•™์Šต ๊ณผ ๋ฌผ๋ฆฌโ€‘๊ฒฝ์ œ ์—ฐ๊ณ„ ๊ฐ€ ๋™์‹œ์— ํ•„์š”. ๊ธฐ์กด ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ๋Š” ์ „๋ ฅ๋ง ์ „์šฉ (GridLABโ€‘D, MATPOWER) ํ˜น์€ ์‹œ์žฅยท์—์ด์ „ํŠธ ์ „์šฉ (Lemlab, CityLearn)์œผ๋กœ, ๋‘ ์˜์—ญ์„ ํ†ตํ•ฉ ํ•˜์ง€ ๋ชปํ•จ. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ ๋ฐ ํ˜์‹ ์„ฑ | ๋ฒˆํ˜ธ | ๊ธฐ์—ฌ ๋‚ด์šฉ | ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„์  | | | | | |โ‘ | ํ†ตํ•ฉ MARLโ€‘LEM ํ™˜๊ฒฝ (์‹œ์žฅ + ๊ทธ๋ฆฌ๋“œ +

Learning Electrical Engineering and Systems Science Framework
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Mathematical modeling of 1,2-propanediol utilization bacterial microcompartments in vivo activity

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋งˆ์ดํฌ๋กœ์ปดํŒŒํŠธ๋จผํŠธ(MCP) ๋Š” ๋…์„ฑ ์ค‘๊ฐ„์ฒด(ํ”„๋กœํ”ผ์˜ค๋‚ ๋ฐํ•˜์ด๋“œ)๋ฅผ ๊ฒฉ๋ฆฌํ•จ์œผ๋กœ์จ ์„ธ๊ท ์— ์„ฑ์žฅ ์ด์ ์„ ์ œ๊ณตํ•œ๋‹ค๋Š” ๊ฐ€์„ค์ด ์˜ค๋ž˜์ „๋ถ€ํ„ฐ ์ œ์‹œ๋ผ ์™”๋‹ค. ๊ธฐ์กด ์ธโ€‘๋น„ํŠธ๋กœ ์‹คํ—˜(์ •์ œ๋œ MCP)์—์„œ๋Š” 1,2โ€‘PD๊ฐ€ 1์‹œ๊ฐ„ ์ด๋‚ด์— ๊ฑฐ์˜ ์™„์ „ํžˆ ์†Œ๋ชจ๋˜๋Š” ๋ฐ˜๋ฉด, ์ธโ€‘๋น„๋ณด (WT LT2)์—์„œ๋Š” 18์‹œ๊ฐ„์— ๊ฑธ์ณ ์„œ์„œํžˆ ์†Œ๋ชจ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ์ฐจ์ด๋Š” ๋Œ€์‚ฌ ๊ฒฝ๋กœ์˜ ์‹ค์ œ ์กฐ์ง(์„ธํฌ์งˆ vs. MCP ๋‚ด๋ถ€) ๊ณผ ์„ธํฌ ์„ฑ์žฅ์— ๋”ฐ๋ฅธ ๋ฌผ์งˆ ์ˆ˜์š” ๋ฅผ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•œ ๋ชจ๋ธ๋ง ํ•œ๊ณ„์—์„œ ๋น„๋กฏ๋œ๋‹ค๊ณ  ํŒ๋‹จ๋œ๋‹ค. 2. ์‹คํ—˜ ์„ค๊ณ„ ๋ฐ ํ•ต์‹ฌ ๊ฒฐ๊ณผ | ์‹คํ—˜ | ์ฃผ์š” ์กฐ๊ฑด

Quantitative Biology Model
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Measurement-Based Validation of Geometry-Driven RIS Beam Steering in Industrial Environments

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ์‚ฐ์—…์šฉ ๋ฌด์„  ๋„คํŠธ์›Œํฌ ๋Š” ์ดˆ๊ณ ์‹ ๋ขฐยท์ดˆ์ €์ง€์—ฐ์„ ์š”๊ตฌํ•˜์ง€๋งŒ, ๊ธˆ์† ๊ตฌ์กฐ๋ฌผ๊ณผ ๋‹ค์ค‘ ๊ฒฝ๋กœ๊ฐ€ ์ง€๋ฐฐ์ ์ธ ํ™˜๊ฒฝ์œผ๋กœ ์ •์  ํ”Œ๋ž˜๋‹์— ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. RIS ๋Š” ๋ฐ˜์‚ฌ๋ฉด์„ ํ”„๋กœ๊ทธ๋ž˜๋ฐํ•ด ์ „ํŒŒ ๊ฒฝ๋กœ๋ฅผ ์žฌ๊ตฌ์„ฑํ•จ์œผ๋กœ์จ ์ปค๋ฒ„๋ฆฌ์ง€ยท์—๋„ˆ์ง€ ํšจ์œจ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ์‹ค์‹œ๊ฐ„ ์ตœ์ ํ™”๋ฅผ ์œ„ํ•ด ๊ธฐํ•˜ํ•™ ๊ธฐ๋ฐ˜ ๋ชจ๋ธ ์ด ๋งค๋ ฅ์ ์ด์ง€๋งŒ, ์‹ค์ œ ์‚ฐ์—… ํ˜„์žฅ์˜ ๋ณต์žกํ•œ ์ „ํŒŒ ํŠน์„ฑ์—์„œ ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ์„ค์ •์ด ์œ ํšจํ•œ์ง€ ๊ฒ€์ฆ์ด ํ•„์š”ํ–ˆ๋‹ค. 2. ์‹คํ—˜ ์„ค๊ณ„ | ์š”์†Œ | ๋‚ด์šฉ | | | | | ์ฃผํŒŒ์ˆ˜ | 5 GHz (5.375 GHz) | | RIS ํ”„๋กœํ† ํƒ€์ž… | 6 ๋ชจ๋“ˆ(3 ร—

Electrical Engineering and Systems Science
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Measuring the locations and properties of VHF sources emitted from an aircraft flying through high clouds

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ๊ธฐ์กด VHF ๊ด€์ธก์€ ์ฃผ๋กœ ๋Œ€๊ธฐ ์ „๊ธฐํ˜„์ƒ(๋ฒˆ๊ฐœ, ์Šคํ”„๋ผ์ดํŠธ ๋“ฑ)์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์œผ๋ฉฐ, ํ•ญ๊ณต๊ธฐ ์ž์ฒด์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๊ณ ์ฃผํŒŒ ๋ฐฉ์ถœ์€ ๊ฑฐ์˜ ๋‹ค๋ฃจ์–ด์ง€์ง€ ์•Š์•˜๋‹ค. LOFAR์™€ ๊ฐ™์€ ๊ณ ๊ฐ๋„ ์ €์ฃผํŒŒ ๋ฐฐ์—ด์„ ์ด์šฉํ•ด ํ•ญ๊ณต๊ธฐ ์ฃผ๋ณ€ ์ „์ž๊ธฐ ํ™˜๊ฒฝ์„ ์ •๋ฐ€ํ•˜๊ฒŒ ํƒ์ง€ํ•จ์œผ๋กœ์จ, ํ•ญ๊ณต๊ธฐ์˜ ์ „ํ•˜ ๋ฐฉ์ „ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์ง์ ‘ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ์ฐฝ์„ ์—ด์—ˆ๋‹ค. 2. ๋ฐฉ๋ฒ•๋ก  ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ : LOFAR๋Š” ์ˆ˜์ฒœ ๊ฐœ์˜ ์•ˆํ…Œ๋‚˜๋ฅผ ํ™œ์šฉํ•ด VHF(30โ€“300 MHz) ๋Œ€์—ญ์˜ ์ „ํŒŒ๋ฅผ ๊ณ ์‹œ๊ฐ„ยท๊ณ ๊ณต๊ฐ„ ํ•ด์ƒ๋„๋กœ ๊ธฐ๋กํ•œ๋‹ค. ํ•ญ๊ณต๊ธฐ๊ฐ€ ๊ตฌ๋ฆ„์„ ํ†ต๊ณผํ•˜๋Š” ๋™์•ˆ ๋ฐœ์ƒํ•œ ํŽ„์Šคํ˜• ์‹ ํ˜ธ

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Melting Coulomb clusters through nonreciprocity-enhanced parametric pumping

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ๋น„ํ‰ํ˜• ํ†ต๊ณ„๋ฌผ๋ฆฌํ•™ ์—์„œ โ€˜์ƒ์„ธ ๊ท ํ˜•(detailed balance)โ€™ ํŒŒ๊ดด์™€ ์ง€์†์ ์ธ ์—”ํŠธ๋กœํ”ผ ์ƒ์‚ฐ์€ ์ƒˆ๋กœ์šด ๋™์—ญํ•™๊ณผ ๊ธฐ๋Šฅ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•œ๋‹ค. ํ™œ์„ฑ ๋ฌผ์งˆ(active matter) ์€ ๊ฐœ๋ณ„ ์ž…์ž์˜ ๋‚ด๋ถ€ ์—๋„ˆ์ง€ ์†Œ๋ชจ์— ์ดˆ์ ์„ ๋งž์ถ”์ง€๋งŒ, ๋น„์ƒํ˜ธ์ž‘์šฉ ์€ ์ž…์ž ๊ฐ„ ํž˜์ด ๋‰ดํ„ด ์ œ3๋ฒ•์น™์„ ์œ„๋ฐ˜ํ•˜๋Š” ๊ฒฝ์šฐ๋กœ, ์™ธ๋ถ€ ๊ตฌ๋™ ๋งค์ฒด(ํ”Œ๋ผ์ฆˆ๋งˆ, ํŒŒ๋™, ์ „์ž๊ธฐ์žฅ ๋“ฑ)๋ฅผ ํ†ตํ•ด ์—๋„ˆ์ง€๋ฅผ ์ „๋‹ฌํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ๋ฌดํ•œ๊ณ„(thermodynamic limit)์—์„œ ๋น„์ƒํ˜ธ์ž‘์šฉ์ด ๋งŒ๋“  ํŒŒ๋™ยทํŒจํ„ด์„ ์ฃผ๋กœ ๋‹ค๋ฃจ์—ˆ์œผ๋ฉฐ, ์œ ํ•œํ•œ ์†Œ์ˆ˜ ์ž…์ž ์‹œ์Šคํ…œ ์—์„œ์˜

Condensed Matter
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MEmilio -- A high performance Modular EpideMIcs simuLatIOn software for multi-scale and comparative simulations of infectious disease dynamics

| ๊ตฌ๋ถ„ | ๋‚ด์šฉ | ํ‰๊ฐ€ยท์‹œ์‚ฌ์  | | | | | | 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ | ์ „์—ผ๋ณ‘ ๋ชจ๋ธ๋ง์€ PBM, MPM, ABM ๋“ฑ ๋‹ค์–‘ํ•œ ํ•ด์ƒ๋„์™€ ์ ‘๊ทผ๋ฒ•์ด ์กด์žฌํ•˜์ง€๋งŒ, ๊ฐ๊ฐ ์ „์šฉ ์†Œํ”„ํŠธ์›จ์–ด๊ฐ€ ํŒŒํŽธํ™”๋ผ ์žˆ์–ด ๋ชจ๋ธ ์ „ํ™˜ยท๋น„๊ต๊ฐ€ ์–ด๋ ค์›€. ํŠนํžˆ ๋Œ€๊ทœ๋ชจ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ •ยท๋ถˆํ™•์‹ค์„ฑ ๋ถ„์„์„ ์œ„ํ•œ HPC ์ง€์›์ด ๋ถ€์กฑํ•จ. | ํ˜„์žฌ ์ƒํƒœ๊ณ„์˜ ๊ตฌ์กฐ์  ํ•œ๊ณ„๋ฅผ ์ •ํ™•ํžˆ ์งš์–ด๋‚ด๋ฉฐ, ํ†ตํ•ฉ ํ”„๋ ˆ์ž„์›Œํฌ์˜ ํ•„์š”์„ฑ์„ ์„ค๋“๋ ฅ ์žˆ๊ฒŒ ์ œ์‹œํ•จ. | | 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ํ†ตํ•ฉ ์•„ํ‚คํ…์ฒ˜ : ๋™์ผํ•œ ๋ชจ๋ธ ์„œ์ˆ  ์–ธ์–ด๋กœ PBM, MPM, ABM๋ฅผ ๊ตฌํ˜„.<br> ๊ณ ์„ฑ๋Šฅ ๋ฐฑ์—”๋“œ : C++

Quantitative Biology
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Metasurface-encoded optical neural network wavefront sensing for high-speed adaptive optics

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ž์œ ๊ณต๊ฐ„ ๊ด‘ํ†ต์‹ (FSO) ์€ ์œ„์„ฑโ€‘์ง€์ƒ ๋งํฌ์—์„œ ๋Œ€์—ญํญยท๋ณด์•ˆยท์ „๋ ฅ ํšจ์œจ์„ฑ์„ ํฌ๊ฒŒ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ์ง€๋งŒ, ๋Œ€๊ธฐ ๋‚œ๋ฅ˜์— ์˜ํ•ด ํŒŒ๋ฉด์ด ์™œ๊ณก๋˜์–ด ๊ฒฐํ•ฉ ํšจ์œจ(CE)๊ณผ ๋น„ํŠธ ์˜ค๋ฅ˜์œจ(BER)์ด ๊ธ‰๊ฒฉํžˆ ์•…ํ™”๋œ๋‹ค. ๊ธฐ์กด Shackโ€‘Hartmann ํŒŒ๋ฉด ์„ผ์„œ(SHWS) ๋Š” ๊ณ ์† ์นด๋ฉ”๋ผ์™€ ๋ Œ์ฆˆ๋ › ์–ด๋ ˆ์ด๋ฅผ ํ•„์š”๋กœ ํ•˜๋ฉฐ, ๋ฌด๊ฒŒยท์ „๋ ฅยท๋น„์šฉ์ด ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•œ๋‹ค. ๋˜ํ•œ, ํŒŒ๋ฉด ์žฌ๊ตฌ์„ฑ์— ๋ณต์žกํ•œ ์—ฐ์‚ฐ์ด ์š”๊ตฌ๋ผ ์‹ค์‹œ๊ฐ„ AO(Adaptive Optics) ์‹œ์Šคํ…œ์— ๋ณ‘๋ชฉ์ด ๋œ๋‹ค. ๊ด‘์‹ ๊ฒฝ๋ง(ONN) ์€ ํŒŒ๋ฉด์„ ์ง์ ‘ ๊ด‘ํ•™์ ์œผ๋กœ ์ธ์ฝ”๋”ฉํ•ด ์ „์ž์  ์žฌ๊ตฌ์„ฑ

Physics Network
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Mixture-of-Experts under Finite-Rate Gating: Communication--Generalization Trade-offs

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ MoE์˜ ์„ฑ๊ณต : Switch Transformer ๋“ฑ ๋Œ€๊ทœ๋ชจ ๋ชจ๋ธ์—์„œ ์ „๋ฌธ๊ฐ€๋ฅผ ์„ ํƒ์ ์œผ๋กœ ํ™œ์„ฑํ™”ํ•จ์œผ๋กœ์จ ํŒŒ๋ผ๋ฏธํ„ฐ ํšจ์œจ์„ฑ์„ ํฌ๊ฒŒ ํ–ฅ์ƒ์‹œ์ผฐ๋‹ค. ์ด๋ก ์  ๊ณต๋ฐฑ : ๊ธฐ์กด ์ผ๋ฐ˜ํ™” ๋ถ„์„(

Statistics Machine Learning
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Modeling Protein Evolution via Generative Inference From Monte Carlo Chains to Population Genetics

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉํ‘œ ์ƒ์„ฑ ๋ชจ๋ธ(DCA) : ์ž์—ฐ ์„œ์—ด ์ •๋ ฌ๋กœ๋ถ€ํ„ฐ Potts ๋ชจ๋ธ์„ ํ•™์Šตํ•ด ์‚ฌ์ดํŠธโ€‘ํŠน์ด์  ์ œ์•ฝ๊ณผ ์žฅ๊ฑฐ๋ฆฌ ์ƒํ˜ธ์ž‘์šฉ(์—ํ”ผ์Šคํƒ€์‹œ์Šค)์„ ํฌ์ฐฉํ•œ๋‹ค. ๊ธฐ์กด์—๋Š” ํ‰ํ˜• ์ƒ˜ํ”Œ๋ง์— ์ฃผ๋กœ ์‚ฌ์šฉ๋์ง€๋งŒ, ์ตœ๊ทผ์—๋Š” ํ”ผํŠธ๋‹ˆ์Šค ํ•จ์ˆ˜ ๋กœ ํ•ด์„ํ•ด ๋น„ํ‰ํ˜• ์ง„ํ™” ์‹œ๋ฎฌ๋ ˆ์ด์…˜์— ์ ์šฉํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ ๋Š˜๊ณ  ์žˆ๋‹ค. ๋ฌธ์ œ์  : ๊ธฐ์กด DCAโ€‘๊ธฐ๋ฐ˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์€ ๋…๋ฆฝ์ ์ธ ๊ถค์ ์„ ๊ฐ€์ •ํ•ด ๊ณ„ํ†ต์  ์ƒ๊ด€๊ณผ ์ง‘๋‹จ ๊ทœ๋ชจ ํšจ๊ณผ๋ฅผ ๋ฌด์‹œํ•œ๋‹ค. ์ด๋Š” ์‹ค์ œ inโ€‘vitro ์ง„ํ™” ์‹คํ—˜์—์„œ ๊ด€์ฐฐ๋˜๋Š” ๋ณตํ•ฉ์ ์ธ ์„ ํƒ ์Šค์œ•๊ณผ ๊ณ„ํ†ต ๊ตฌ์กฐ๋ฅผ ์žฌํ˜„ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ๋ชฉํ‘œ : (i) ๋…๋ฆฝ MCMC, (ii

Quantitative Biology Model
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Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ํ™”ํ•™ ๊ณต๊ฐ„์˜ ๊ทœ๋ชจ : ํ•ฉ์„ฑ ๊ฐ€๋Šฅํ•œ ์•ฝ๋ฌผ ํ›„๋ณด๋Š” ์•ฝ 10ยนโฐ์ข…์ด์ง€๋งŒ, ์ „์ฒด ๊ฐ€๋Šฅํ•œ ๋ถ„์ž ์กฐํ•ฉ์€ 10โถโฐ ์ด์ƒ์œผ๋กœ, ๋ฐ์ดํ„ฐ ๋ถ€์กฑ์ด ๋ชจ๋ธ ์ผ๋ฐ˜ํ™”์— ํฐ ์ œ์•ฝ์„ ๋งŒ๋“ ๋‹ค. ๊ธฐ์กด ์ œ๋„ค๋ ˆ์ดํ‹ฐ๋ธŒ ๋ชจ๋ธ์˜ ํ•œ๊ณ„ 1. Drugโ€‘likeness ๋น„์œจ ๋‚ฎ์Œ 2. ๋‹ค์–‘์„ฑ vs. ํŠน์„ฑ ํŠธ๋ ˆ์ด๋“œโ€‘์˜คํ”„ ์–‘์ž ๋จธ์‹ ๋Ÿฌ๋‹(QML) ์€ ์ดˆ์ „๋„ ์–‘์ž ๋น„ํŠธ์™€ ์–‘์ž ์–ด๋‹๋ง์„ ํ™œ์šฉํ•ด ๊ณ ์ฐจ์› ์—๋„ˆ์ง€ ์ง€ํ˜•์„ ํšจ์œจ์ ์œผ๋กœ ํƒ์ƒ‰ํ•  ๊ฐ€๋Šฅ์„ฑ์„ ์ œ๊ณตํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์ˆ  โ€“ Neural Hash Function (NHF) | ํŠน์ง• | ๊ธฐ์กด ๋ฐฉ๋ฒ• (Gumbelโ€‘S

Data Quantitative Biology Model
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Morphological instability of an invasive active-passive interface

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์•” ์กฐ์ง์˜ ์นจ์Šตยท์ „์ด ๊ณผ์ •์—์„œ ์ง‘๋‹จ ์„ธํฌ ์ด๋™๊ณผ ์ฃผ๋ณ€ ๋งคํŠธ๋ฆญ์Šค์™€์˜ ๊ธฐ๊ณ„์  ์ƒํ˜ธ์ž‘์šฉ์€ ํ•ต์‹ฌ ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์„ธํฌ ์ˆ˜์ค€(agentโ€‘based) ๋ชจ๋ธ์ด๋‚˜ ๋‹จ์ˆœํ•œ ์—ฐ์†์ฒด ๋ชจ๋ธ์— ๋จธ๋ฌผ๋ €์ง€๋งŒ, ๋งคํŠธ๋ฆญ์Šค์˜ ์ ํƒ„์„ฑ ํŠน์„ฑ์ด ์นจ์Šต ํ˜•ํƒœ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ฒด๊ณ„์ ์œผ๋กœ ์—ฐ๊ฒฐํ•œ ์ด๋ก ์€ ๋ถ€์กฑํ–ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ํ™œ์„ฑ ์•ก์ฒด ์™€ ์ ํƒ„์„ฑ ๋งคํŠธ๋ฆญ์Šค ๋ฅผ ํ•˜๋‚˜์˜ ์—ฐ์†์ฒด ์œ„์ƒ์žฅ(phaseโ€‘field) ํ”„๋ ˆ์ž„์›Œํฌ์— ํ†ตํ•ฉํ•จ์œผ๋กœ์จ, ์‹คํ—˜์—์„œ ๊ด€์ฐฐ๋œ โ€œํƒ„์„ฑ ๋งคํŠธ๋ฆญ์Šค โ†’ ํ‰ํƒ„, ์•ก์ฒดโ€‘์„ฑ ๋งคํŠธ๋ฆญ์Šค โ†’ ์š”๋™โ€ ํ˜„์ƒ์„ ์ •๋Ÿ‰์ ์œผ๋กœ ์„ค๋ช…ํ•œ๋‹ค. ์ด๋Š” ์•” ๋ฏธ์„ธํ™˜

Condensed Matter
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Multi-Agent Combinatorial-Multi-Armed-Bandit framework for the Submodular Welfare Problem under Bandit Feedback

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ SWP์™€ ์„œ๋ธŒ๋ชจ๋“ˆ๋ผ ์ตœ์ ํ™” : ์•„์ดํ…œ์„ ์—ฌ๋Ÿฌ ์—์ด์ „ํŠธ์—๊ฒŒ ํ• ๋‹นํ•ด ์ „์ฒด ๋ณต์ง€๋ฅผ ์ตœ๋Œ€ํ™”ํ•˜๋Š” ๋ฌธ์ œ๋กœ, ์˜คํ”„๋ผ์ธ์—์„œ๋Š” ์—ฐ์†โ€‘๊ทธ๋ฆฌ๋””์™€ ํŒŒ์ดํ”„๋ผ์ธ ๋ผ์šด๋”ฉ์„ ํ†ตํ•ด ((1 1/e)) ์ตœ์  ๊ทผ์‚ฌ์œจ์„ ์–ป๋Š”๋‹ค( Vondrรกk, 2008). ๋ฐด๋”ง ํ”ผ๋“œ๋ฐฑ์˜ ํ˜„์‹ค์„ฑ : ๋งŽ์€ ์‹ค์ œ ์„œ๋น„์Šค(์ถ”์ฒœ, ๋ฐ์ดํ„ฐ ์š”์•ฝ, ๊ณต์ • ํ• ๋‹น ๋“ฑ)๋Š” ๊ฐœ๋ณ„ ์•„์ดํ…œ ๋ณด์ƒ์„ ๊ด€์ธกํ•  ์ˆ˜ ์—†์œผ๋ฉฐ, ์˜ค์ง ์ „์ฒด ๊ฒฐ๊ณผ๋งŒ์ด ๋…ธ์ถœ๋œ๋‹ค. ์ด๋Š” ์ „โ€‘๋ฐด๋”ง ์ƒํ™ฉ์ด๋ฉฐ, ๊ธฐ์กด ๋ฐ˜๋ฐด๋”ง(semiโ€‘bandit) ๊ธฐ๋ฐ˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ ์šฉ ๋ถˆ๊ฐ€ํ•˜๋‹ค. ๋‹ค์ค‘โ€‘์—์ด์ „ํŠธยท๋น„ํ†ต์‹  ์„ค์ • : ๊ธฐ์กด ๋‹ค์ค‘โ€‘์—

Computer Science Framework Game Theory
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Multi-Channel Replay Speech Detection using Acoustic Maps

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋ฆฌํ”Œ๋ ˆ์ด ๊ณต๊ฒฉ ์€ ๋ฌผ๋ฆฌ์  ์ ‘๊ทผ(PA) ๊ณต๊ฒฉ ์ค‘ ๊ฐ€์žฅ ๊ตฌํ˜„์ด ์‰ฌ์›Œ ์‹ค์ œ ์„œ๋น„์Šค(์Œ์„ฑ ๋น„์„œ, IoT)์—์„œ ํฐ ์œ„ํ—˜ ์š”์†Œ์ด๋‹ค. ๊ธฐ์กด ๋‹จ์ผ์ฑ„๋„ ๋ฐฉ๋ฒ•์€ ์ŠคํŽ™ํŠธ๋Ÿผยท์‹œ๊ฐ„ ํŠน์„ฑ์— ์˜์กดํ•˜์ง€๋งŒ, ๊ณต๊ฒฉ์ž๋Š” ์ด๋Ÿฌํ•œ ํŠน์„ฑ์„ ์‰ฝ๊ฒŒ ๋ณ€์กฐํ•  ์ˆ˜ ์žˆ๋‹ค. ๋‹ค์ฑ„๋„ ๋ฐฐ์—ด ์€ ๊ณต๊ฐ„ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๋ฏ€๋กœ, ์ธ๊ฐ„์˜ ๋ณตํ•ฉ์ ์ธ ์Œํ–ฅ ๋ฐฉ์‚ฌ์™€ ์Šคํ”ผ์ปค์˜ ๋‹จ์ผ์  ๋ฐฉ์‚ฌ ์‚ฌ์ด์˜ ์ฐจ์ด๋ฅผ ํ™œ์šฉํ•  ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | | | | | 1 | ์Œํ–ฅ ์ง€๋„ ๋ฅผ ๋”œ๋ ˆ์ดโ€‘์•คโ€‘์„ฌ ๋น”ํฌ๋ฐ ๊ธฐ๋ฐ˜์œผ๋กœ ์ •์˜, ๋ฐฉํ–ฅ์„ฑ ์—๋„ˆ์ง€ ๋ถ„ํฌ๋ฅผ 3โ€‘D ํ…์„œ๋กœ

Audio Processing Electrical Engineering and Systems Science Detection
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Multifluid Hydrodynamic Simulation of Metallic-Plate Collision Using the VOF Method

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํญ๋ฐœ์šฉ์ ‘์€ ๊ณ ์† ์ถฉ๊ฒฉ์— ์˜ํ•ด ์ด์ข… ๊ธˆ์†์ด ๊ฒฐํ•ฉ๋˜๋Š” ๊ธฐ์ˆ ๋กœ, ์ถฉ๋Œ ์งํ›„ ๊ธˆ์†์ด โ€˜์˜์‚ฌโ€‘์œ ์ฒด(pseudoโ€‘fluid)โ€™ ์ƒํƒœ๋ฅผ ๋ณด์ธ๋‹ค. ์ด ํ˜„์ƒ์„ ์ •ํ™•ํžˆ ์˜ˆ์ธกํ•˜๋ ค๋ฉด ์••์ถ•์„ฑ, ๋น„ํ˜ผํ•ฉ์„ฑ, ๊ทธ๋ฆฌ๊ณ  ๊ธ‰๊ฒฉํ•œ ํŒŒ๋™ ์ „ํŒŒ๋ฅผ ๋™์‹œ์— ๋‹ค๋ฃฐ ์ˆ˜ ์žˆ๋Š” ๋‹ค์ค‘์œ ์ฒด ์ˆ˜์น˜ ๋ชจ๋ธ์ด ํ•„์š”ํ•˜๋‹ค. 2. ์ˆ˜ํ•™ยท๋ฌผ๋ฆฌ ๋ชจ๋ธ ๋‹ค์ค‘์œ ์ฒด Godunovโ€‘type ์•Œ๊ณ ๋ฆฌ์ฆ˜ : ๊ธฐ๊ณ„์  ํ‰ํ˜•(Euler ๋ฐฉ์ •์‹) ๊ธฐ๋ฐ˜ 5โ€‘๋ฐฉ์ •์‹ Vโ€‘p ๋ชจ๋ธ์„ ์ฑ„ํƒํ•˜๊ณ , ์••๋ ฅ ์™„ํ™”๋ฅผ ํ†ตํ•ด ๊ฐ ์ƒ์˜ ์••๋ ฅ์„ ์ฆ‰์‹œ ๋™์ผํ•˜๊ฒŒ ๋งŒ๋“ ๋‹ค(์••๋ ฅ ํ‰ํ˜• ๊ฐ€์ •). ์ด๋Š” ๋น„ํ‰ํ˜• Baerโ€‘Nunziat

Physics
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Multiplierless DFT Approximation Based on the Prime Factor Algorithm

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ DFT๋Š” ์ŠคํŽ™ํŠธ๋Ÿผ ๋ถ„์„ยทํ•„ํ„ฐ๋งยท์••์ถ•ยท์ปจ๋ณผ๋ฃจ์…˜ ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ํ•ต์‹ฌ์ ์ธ ๋ณ€ํ™˜์ด๋ฉฐ, FFT๋ฅผ ํ†ตํ•ด O(N log N) ๋ณต์žก๋„๋กœ ๊ตฌํ˜„๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ €์ „๋ ฅยท์ €๋ฉด์ ์ด ์š”๊ตฌ๋˜๋Š” ์ž„๋ฒ ๋””๋“œยทIoTยท๋ฌด์„ ํ†ต์‹  ํ™˜๊ฒฝ์—์„œ๋Š” ์—ฌ์ „ํžˆ ์—ฐ์‚ฐ๋Ÿ‰(ํŠนํžˆ ์‹ค์ˆ˜ ๊ณฑ์…ˆ)์ด ๋ณ‘๋ชฉ์ด ๋œ๋‹ค. ๊ธฐ์กด์˜ ๋ฉ€ํ‹ฐํ”Œ๋ผ์ด์–ดโ€‘ํ”„๋ฆฌ DFT ๊ทผ์‚ฌ๋Š” 8ยท16ยท32โ€‘์  ์ •๋„์˜ ์ž‘์€ ๋ธ”๋ก์— ๊ตญํ•œ๋˜์—ˆ์œผ๋ฉฐ, ํฐ ๋ธ”๋ก์„ ๋งŒ๋“ค๊ธฐ ์œ„ํ•ด Cooleyโ€‘Tukey(Cโ€‘T) ์žฌ๊ท€๋ฅผ ์‚ฌ์šฉํ•˜๋ฉด ํŠธ์œ„๋“ค ํŒฉํ„ฐ๊ฐ€ ๊ทธ๋Œ€๋กœ ๋‚จ์•„ ๊ณฑ์…ˆ์ด ๋ฐœ์ƒํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ Prime Factor A

Electrical Engineering and Systems Science
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Natural direct effects of vaccines and post-vaccination behaviour

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์œ„ํ—˜ ๋ณด์ƒ(Risk Compensation) : ๋ฐฑ์‹  ๋ณดํ˜ธ์— ๋Œ€ํ•œ ์ธ์‹์ด ํ–‰๋™ ๋ณ€ํ™”๋ฅผ ์ด‰์ง„ํ•ด ์‹ค์ œ ๊ฐ์—ผ ์œ„ํ—˜์„ ์ฆ๊ฐ€์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. COVIDโ€‘19 ํŒฌ๋ฐ๋ฏน ๋™์•ˆ ์—ฌ๋Ÿฌ ๊ด€์ฐฐ ์—ฐ๊ตฌ๊ฐ€ ๋ฐฑ์‹  ์ ‘์ข… ํ›„ ์‚ฌํšŒ์  ์ ‘์ด‰ ์ฆ๊ฐ€ยท๋งˆ์Šคํฌ ์ฐฉ์šฉ ๊ฐ์†Œ ๋“ฑ์„ ๋ณด๊ณ ํ•˜์˜€๋‹ค. ๊ธฐ์กด ๋ฐฑ์‹  ํšจ๊ณผ ์ถ”์ •์˜ ํ•œ๊ณ„ : ๋Œ€๋ถ€๋ถ„์˜ ๋ฐฑ์‹  ํšจ๊ณผ ์—ฐ๊ตฌ๋Š” ์ด ํšจ๊ณผ(์ด ์ง์ ‘+๊ฐ„์ ‘ ํšจ๊ณผ)๋งŒ์„ ์ถ”์ •ํ•˜๊ณ , ๋ฐฑ์‹ ์ด ํ–‰๋™์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํ†ต์ œํ•˜์ง€ ์•Š๋Š”๋‹ค. ๋”ฐ๋ผ์„œ โ€œ์‹ค์ œ ์„ธ๊ณ„(realโ€‘world) ํšจ๊ณผโ€์™€ โ€œ๋ธ”๋ผ์ธ๋“œ ์ž„์ƒ์‹œํ—˜์—์„œ ๊ด€์ฐฐ๋œ ํšจ๊ณผโ€ ์‚ฌ์ด์— ์ฐจ์ด๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค. 2

Statistics
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Network geometry of the Drosophila brain

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์Šค์ผ€์ผ : ๊ธฐ์กด C. elegans(โ‰ˆ300 ๋‰ด๋Ÿฐ)ยท์ดˆํŒŒ๋ฆฌ ์œ ์ถฉ(โ‰ˆ3 000 ๋‰ด๋Ÿฐ) ๋Œ€๋น„ 100๋ฐฐ ์ด์ƒ ํฐ ๋„คํŠธ์›Œํฌ. ๊ตฌ์กฐ์  ํŠน์ง• : ์ด์ „ ์—ฐ๊ตฌ์—์„œ ํ™•์ธ๋œ ์Šค์ผ€์ผโ€‘ํ”„๋ฆฌ ์ฐจ์ˆ˜ ๋ถ„ํฌ์™€ ๋ฆฌ์น˜โ€‘ํด๋Ÿฝ ํ˜„์ƒ์€ ํ•˜์ดํผ๋ณผ๋ฆญ ๊ณต๊ฐ„์ด ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ๋Š” ํ† ํด๋กœ์ง€์™€ ์ผ์น˜ํ•œ๋‹ค. ์ž„๋ฒ ๋”ฉ ํ•„์š”์„ฑ : ๊ณ ์ฐจ์› ๊ทธ๋ž˜ํ”„๋ฅผ ์ขŒํ‘œํ™” ํ•จ์œผ๋กœ์จ ์ปค๋ฎค๋‹ˆํ‹ฐ ํƒ์ง€, ๋งํฌ ์˜ˆ์ธก, ๋„ค๋น„๊ฒŒ์ด์…˜ ๋“ฑ ๋‹ค์–‘ํ•œ ๋จธ์‹ ๋Ÿฌ๋‹ ํŒŒ์ดํ”„๋ผ์ธ์— ์ง์ ‘ ํ™œ์šฉ ๊ฐ€๋Šฅ. 2. ๋ฐฉ๋ฒ•๋ก  ํ‰๊ฐ€ | ๊ตฌ๋ถ„ | ์•Œ๊ณ ๋ฆฌ์ฆ˜ | ์ฐจ์› | ์ฃผ์š” ์žฅ์  | ์ฃผ์š” ํ•œ๊ณ„ | | | | | | | | ํ•˜

Physics Network
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Neural Diversity Regularizes Hallucinations in Language Models

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ํ™˜๊ฐ ํ˜„์ƒ ์€ ์ƒ์„ฑํ˜• ์–ธ์–ด ๋ชจ๋ธ์ด ์‚ฌ์‹ค๊ณผ ๋ฌด๊ด€ํ•œ ์ •๋ณด๋ฅผ ์ถœ๋ ฅํ•˜๋Š” ํ˜„์ƒ์œผ๋กœ, ์‹ค์ œ ์„œ๋น„์Šค ์ ์šฉ ์‹œ ํฐ ์œ„ํ—˜ ์š”์†Œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ๋ชจ๋ธ ๊ทœ๋ชจ ํ™•๋Œ€ , ๋ฐ์ดํ„ฐ ์–‘ ์ฆ๊ฐ€ , ํ”„๋กฌํ”„ํŠธ ์—”์ง€๋‹ˆ์–ด๋ง ๋“ฑ์„ ํ†ตํ•ด ์ •ํ™•๋„๋ฅผ ๋†’์ด๋ ค ํ–ˆ์ง€๋งŒ, ํ™˜๊ฐ ๊ฐ์†Œ์— ๋Œ€ํ•œ ์ฒด๊ณ„์  ์ด๋ก ์€ ๋ถ€์กฑํ–ˆ๋‹ค. 2. ์‹ ๊ฒฝ ๋‹ค์–‘์„ฑ(Neural Diversity)์˜ ๊ฐœ๋…์  ์ •์˜ ์‹ ๊ฒฝ ๋‹ค์–‘์„ฑ ์€ ๋™์ผ ์ž…๋ ฅ์— ๋Œ€ํ•ด ์„œ๋กœ ๋‹ค๋ฅธ ํŒŒ๋ผ๋ฏธํ„ฐ ์„œ๋ธŒ์…‹(์—ฌ๊ธฐ์„œ๋Š” LoRA ์–ด๋Œ‘ํ„ฐ)๋“ค์ด ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ๋‚ฎ์€ ์ถœ๋ ฅ์„ ์ƒ์„ฑํ•˜๋„๋ก ๊ฐ•์ œํ•˜๋Š” ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด๋‹ค. ์ด๋Š” ์•™์ƒ๋ธ” ํ•™์Šต ์—์„œ โ€œ๋‹ค์–‘

Model
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Neural Scaling Laws for Boosted Jet Tagging

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ LLM ์Šค์ผ€์ผ๋ง ๋ฒ•์น™ (Kaplan et al., 2020; Hoffmann et al., 2022)์€ ๋ชจ๋ธยท๋ฐ์ดํ„ฐยท์ปดํ“จํŒ…์„ ๋™์‹œ์— ํ™•๋Œ€ํ•˜๋ฉด ์†์‹ค์ด ์ „๋ ฅ๋ฒ•์น™ ์œผ๋กœ ๊ฐ์†Œํ•œ๋‹ค๋Š” ๊ฒฝํ—˜์  ๊ทœ์น™์„ ์ œ์‹œํ•œ๋‹ค. HEP์—์„œ๋Š” ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋น„์šฉ ์ด ๋งค์šฐ ๋†’์•„ ํ˜„์žฌ๊นŒ์ง€๋Š” ์ˆ˜๋ฐฑ๋งŒ~์ˆ˜์ฒœ๋งŒ ์ œํŠธ ์ˆ˜์ค€์˜ ๋ฐ์ดํ„ฐ๋งŒ ํ™œ์šฉํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์ปดํ“จํŒ…ยท๋ฐ์ดํ„ฐยท๋ชจ๋ธ ๊ทœ๋ชจ ์‚ฌ์ด์˜ ์ตœ์  ๋ฐฐ๋ถ„์„ ์ •๋Ÿ‰ํ™”ํ•˜๋Š” ๊ฒƒ์ด ํ•„์ˆ˜์ ์ด๋‹ค. 2. ๋ฐ์ดํ„ฐยท๋ชจ๋ธ ์„ค๊ณ„ | ์š”์†Œ | ์ƒ์„ธ ๋‚ด์šฉ | | | | | ๋ฐ์ดํ„ฐ | JetClass (100 M ํ›ˆ๋ จ, 5 M ๊ฒ€์ฆ, 20 M

HEP-EX
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Neural-POD: A Plug-and-Play Neural Operator Framework for Infinite-Dimensional Functional Nonlinear Proper Orthogonal Decomposition

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ „ํ†ต POD์˜ ํ•œ๊ณ„ : (i) ๊ฒฉ์ž ์˜์กด์„ฑ โ†’ ๋‹ค๋ฅธ ํ•ด์ƒ๋„์—์„œ ์„ฑ๋Šฅ ๊ธ‰๋ฝ, (ii) Lยฒ ์ตœ์ ํ™”์— ๊ตญํ•œ โ†’ ๋น„์„ ํ˜•ยท๋ถˆ์—ฐ์† ํ˜„์ƒ ํฌ์ฐฉ ์–ด๋ ค์›€, (iii) ํŒŒ๋ผ๋ฏธํ„ฐ ๋ณ€ํ™”์— ๋Œ€ํ•œ ์ผ๋ฐ˜ํ™” ๋ถ€์กฑ. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋Š” ๋ฉ€ํ‹ฐโ€‘ํ•ด์ƒ๋„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ , ์‹ค์‹œ๊ฐ„/๋‹ค์ค‘โ€‘์ฟผ๋ฆฌ ์ƒํ™ฉ, ๊ทธ๋ฆฌ๊ณ  ์—ฐ์‚ฐ์ž ํ•™์Šต ์—์„œ ์น˜๋ช…์ ์ด๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ๋ฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜ | ๋‹จ๊ณ„ | ๋‚ด์šฉ | ์ฃผ์š” ํฌ์ธํŠธ | | | | | | ์ดˆ๊ธฐ ๋ชจ๋“œ ํ•™์Šต | ์ „์ฒด ์Šค๋ƒ…์ƒท์„ ๋Œ€์ƒ์œผ๋กœ ์‹ ๊ฒฝ๋ง ฮฆโ‚€(x;ฮธโ‚€)์™€ ์‹œ๊ฐ„ ๊ณ„์ˆ˜ aโ‚€(t)๋ฅผ ํ•™์Šต | ๊ฐ€์žฅ ํฐ ์—๋„ˆ์ง€(๊ตฌ์กฐ) ํฌ์ฐฉ |

Physics Framework
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Node Preservation and its Effect on Crossover in Cartesian Genetic Programming

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ CGP์™€ ๊ต์ฐจ์˜ ๋”œ๋ ˆ๋งˆ : CGP๋Š” ์ฃผ๋กœ ๋ณ€์ด ๊ธฐ๋ฐ˜ $(1+ฮป)$ ์ „๋žต์— ์˜์กดํ•ด ์™”์œผ๋ฉฐ, ๊ต์ฐจ๊ฐ€ ์„ฑ๋Šฅ์„ ์ €ํ•˜์‹œํ‚จ๋‹ค๋Š” ๊ฒฝํ—˜์  ๋ณด๊ณ ๊ฐ€ ๋งŽ๋‹ค. ์ด๋Š” CGP์˜ ํ‘œํ˜„ ๋ฐฉ์‹(๋…ธ๋“œ ๊ธฐ๋ฐ˜, ๋น„์—ฐ์†์ ์ธ ์—ฐ๊ฒฐ ๊ตฌ์กฐ) ๋•Œ๋ฌธ์— ๊ต์ฐจ๊ฐ€ ์œ ํšจํ•œ ์„œ๋ธŒ๊ตฌ์กฐ๋ฅผ ํŒŒ๊ดดํ•˜๊ธฐ ์‰ฝ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋…ธ๋“œ ๋ณด์กด ๊ฐœ๋… : ๊ธฐ์กด ๊ต์ฐจ ์—ฐ์‚ฐ์€ ๋…ธ๋“œ(์—ฐ์‚ฐ์ž+์ž…๋ ฅ) ๋‹จ์œ„๊ฐ€ ์•„๋‹Œ, ์œ ์ „์ž์˜ ์—ฐ์†์ ์ธ ๋น„ํŠธ ํ˜น์€ ์ธ๋ฑ์Šค ๊ตฌ๊ฐ„์„ ์„ž๋Š”๋‹ค. โ€œ๋…ธ๋“œ ๋ณด์กดโ€์€ ๊ต์ฐจ ์‹œ ๊ฐ ๋…ธ๋“œ๊ฐ€ ์™„์ „ํ•œ ์ƒํƒœ(์—ฐ์‚ฐ์ž์™€ ๊ทธ ์ž…๋ ฅ ์—ฐ๊ฒฐ)๋กœ ์œ ์ง€๋˜๋„๋ก ์ œํ•œํ•œ๋‹ค. ์ด๋Š” ๊ต์ฐจ๊ฐ€ ์˜๋ฏธ ์žˆ๋Š” ๊ธฐ๋Šฅ ๋ธ”๋ก์„

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Noncooperative Coordination for Decentralized Air Traffic Management

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ค‘์•™์ง‘์ค‘์‹ ATM์˜ ํ•œ๊ณ„ : ํŠธ๋ž˜ํ”ฝ ๋ฐ€๋„ ์ฆ๊ฐ€, ๋„์‹ฌํ•ญ๊ณต๋ชจ๋นŒ๋ฆฌํ‹ฐ(UAM) ๋“ฑ์žฅ ๋“ฑ์œผ๋กœ ํ™•์žฅ์„ฑยทํƒ„๋ ฅ์„ฑ ๋ฌธ์ œ๊ฐ€ ๋Œ€๋‘๋จ. ๋ถ„์‚ฐํ˜• ATM์˜ ํ˜„์‹ค : ํ•ญ๊ณต์‚ฌยท๊ตฌ์—ญยท์ง€์—ญ ๋‹น๊ตญ ๋“ฑ ๋‹ค์ˆ˜ ์ดํ•ด๊ด€๊ณ„์ž๊ฐ€ ๋…๋ฆฝ์ ์ธ ๋ชฉํ‘œ์™€ ๋น„๊ณต๊ฐœ ๋น„์šฉ ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์–ด, ์ „ํ†ต์ ์ธ ํ˜‘๋ ฅ ๊ฐ€์ •์ด ๋ถ€์ ์ ˆํ•จ. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๊ตฌ๋ถ„ | ๋‚ด์šฉ | ์˜์˜ | | | | | | (1) Reducedโ€‘Rank Correlated Equilibrium (RRCE) | ๋‹ค์ˆ˜ Nash ๊ท ํ˜•์˜ convex hull์— ์ œํ•œํ•ด ์ฐจ์› ์ถ•์†Œ, ๊ณ„์‚ฐ ๋ณต์žก๋„ ํฌ๊ฒŒ ๊ฐ์†Œ |

Electrical Engineering and Systems Science
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Nonlinear Schrรถdinger equations with a critical, inverse-square potential

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋ฌผ๋ฆฌ์  ์˜๋ฏธ : ์—ญ์ œ๊ณฑ ํผํ…์…œ (mu|x|^{ 2}) ์€ Hardyโ€‘type ์ž ์žฌ๋กœ ๋ถˆ๋ฆฌ๋ฉฐ, ๊ด‘ํ•™ ๊ฒฐ์ •(ํฌํ†ค๊ฒฐ์ •)์ด๋‚˜ ์–‘์ž์—ญํ•™ยทํ•ตยท๋ถ„์ž ๋ฌผ๋ฆฌ, ์‹ฌ์ง€์–ด ์–‘์ž์šฐ์ฃผ๋ก ์—์„œ๋„ ๊ฒฐํ•จ์ด๋‚˜ ๊ฐ•ํ•œ ์ค‘์‹ฌ์žฅ์— ์˜ํ•ด ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ๋“ฑ์žฅํ•œ๋‹ค. ์ˆ˜ํ•™์  ๋‚œ์  : ์ž„๊ณ„ ์ƒ์ˆ˜ (mu frac{(N 2)^2}{4}) ๋ฅผ ์‚ฌ์šฉํ•˜๋ฉด Hardy ๋ถ€๋“ฑ์‹์˜ ์ตœ์  ์ƒ์ˆ˜๊ฐ€ ๊ทธ๋Œ€๋กœ ๋ฐฉ์ •์‹์— ๋“ค์–ด๊ฐ€๊ฒŒ ๋˜๋ฉฐ, ์ด๋•Œ ์ „ํ†ต์ ์ธ Sobolev ๊ณต๊ฐ„ (H^{1}(mathbb R^{N})) ์˜ ๋…ธ๋ฆ„๊ณผ ๋™๋“ฑํ•˜์ง€ ์•Š๋‹ค. ๋”ฐ๋ผ์„œ ๋ณ€๋ถ„ ๋ฐฉ๋ฒ•์„ ์ ์šฉํ•˜๋ ค๋ฉด ์ƒˆ๋กœ

Mathematics
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Nonparametric Identification and Inference for Counterfactual Distributions with Confounding

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ๋ถ„ํฌโ€‘์ˆ˜์ค€ ์ธ๊ณผ ์ถ”๋ก  : ํ‰๊ท  ์น˜๋ฃŒ ํšจ๊ณผ(ATE)๋งŒ์œผ๋กœ๋Š” ์ถฉ๋ถ„ํžˆ ์„ค๋ช…๋˜์ง€ ์•Š๋Š” โ€œ์ด๋“ ํ™•๋ฅ โ€, โ€œ๋ถ„์œ„์ˆ˜ ํšจ๊ณผโ€, โ€œ์ „์ฒด ๋ถ„ํฌ ์ด๋™โ€ ๋“ฑ ๊ณผํ•™์  ์งˆ๋ฌธ์ด ๋Š˜์–ด๋‚˜๊ณ  ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„œ๋Š” ์ž ์žฌ ๊ฒฐ๊ณผ๋“ค์˜ ์ „์ฒด ๋ถ„ํฌ , ํŠนํžˆ ๊ณต๋™ ๋ถ„ํฌ ๊ฐ€ ํ•„์š”ํ•˜์ง€๋งŒ, ๊ต๋ž€์ด ์กด์žฌํ•˜๋ฉด ์ด๋Š” ์ง์ ‘ ๊ด€์ธกํ•  ์ˆ˜ ์—†๋‹ค. ๋‘ ๊ฐ€์ง€ ๋‚œ๊ด€ 1. ๊ด€์ธก ๊ฐ€๋Šฅํ•œ ๊ต๋ž€ ์ด ์žˆ์„ ๋•Œ๋„ ๊ณต๋™ ๋ถ„ํฌ ๋Š” ๋น„์‹๋ณ„์ ์ด๋ฉฐ, ๊ธฐ์กด ๋ฐฉ๋ฒ•์€ ๋‹จ์ˆœํžˆ Frรฉchetโ€‘Hoeffding ๊ฒฝ๊ณ„์— ๋จธ๋ฌผ๋Ÿฌ ์žˆ๋‹ค. 2. ๋น„๊ด€์ธก ๊ต๋ž€ ์ด ์กด์žฌํ•˜๋ฉด ์ž ์žฌ ๊ฒฐ๊ณผ์˜ ์ฃผ๋ณ€ ๋ถ„ํฌ์กฐ์ฐจ ๋น„์‹๋ณ„์ ์ด

Statistics
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Nonplanar Model Predictive Control for Autonomous Vehicles with Recursive Sparse Gaussian Process Dynamics

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๊ธฐ์กด ์ž์œจ์ฃผํ–‰ ์—ฐ๊ตฌ๋Š” ๋Œ€๋ถ€๋ถ„ ํ‰ํƒ„ํ•œ ๋„๋กœ๋ฅผ ์ „์ œ๋กœ ํ•˜๋ฉฐ, ๋น„ํ‰๋ฉดยท๋น„๊ตฌ์กฐํ™”๋œ ์˜คํ”„๋กœ๋“œ ํ™˜๊ฒฝ์— ๋Œ€ํ•œ ๋ชจ๋ธ๋งยท์ œ์–ด๋Š” ์•„์ง ๋ฏธ๋น„ํ•˜๋‹ค. ๋น„ํ‰๋ฉด ์ง€ํ˜•์—์„œ๋Š” ์ฐจ๋Ÿ‰์˜ ๋กคยทํ”ผ์น˜ ๊ฐ๋„๊ฐ€ ๊ธ‰๊ฒฉํžˆ ๋ณ€ํ•˜๊ณ , ์ง€๋ฉด๊ณผ์˜ ์ ‘์ด‰๋ ฅยท๋งˆ์ฐฐ ํŠน์„ฑ์ด ๋ณตํ•ฉ์ ์œผ๋กœ ์ž‘์šฉํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ํšจ๊ณผ๋ฅผ ์ „ํ†ต์ ์ธ ํ‰๋ฉด ๊ธฐ๋ฐ˜ ๋‹จ์ผ ํŠธ๋ž™ ๋ชจ๋ธ์— ๊ทธ๋Œ€๋กœ ์ ์šฉํ•˜๋ฉด ํฐ ๋ชจ๋ธ๋ง ์˜ค์ฐจ๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๊ธฐ์—ฌ ๋‚ด์šฉ | ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„์  | | | | | | 1 | ๊ธฐํ•˜ํ•™โ€‘์ธ์‹ ์ž”์ฐจ GP ๋ชจ๋ธ : ํ‰๋ฉด ๊ธฐ๋ฐ˜ ๋‹จ์ผ ํŠธ๋ž™ ๋ชจ๋ธ์— ์ง€ํ˜• ๊ธฐํ•˜ํ•™(๋กคยทํ”ผ์น˜)๊ณผ ์ƒ

Robotics Computer Science Model
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Normative Reasoning in Large Language Models: A Comparative Benchmark from Logical and Modal Perspectives

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๊ทœ๋ฒ”์  ์ถ”๋ก ์˜ ์ค‘์š”์„ฑ : ๋ฒ•๋ฅ ยท์œค๋ฆฌยท์ธ๊ณต์ง€๋Šฅ ์•ˆ์ „์„ฑ ๋“ฑ ์‹ค์„ธ๊ณ„ ์‘์šฉ์—์„œ โ€˜๋ฌด์—‡์„ ํ•ด์•ผ ํ•˜๋Š”๊ฐ€โ€™๋ฅผ ํŒ๋‹จํ•˜๋Š” ๋Šฅ๋ ฅ์€ ํ•„์ˆ˜์ ์ด๋‹ค. LLM์˜ ํ˜„์žฌ ํ•œ๊ณ„ : ๊ธฐ์กด ๋ฒค์น˜๋งˆํฌ๋Š” ์ฃผ๋กœ ์‚ฌ์‹คยท๋…ผ๋ฆฌยท์ˆ˜ํ•™์  ์ถ”๋ก ์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์œผ๋ฉฐ, ๊ทœ๋ฒ”ยท๋ช…๋ น์  ๋ชจ๋‹ฌ๋ฆฌํ‹ฐ๋Š” ์ถฉ๋ถ„ํžˆ ํ…Œ์ŠคํŠธ๋˜์ง€ ์•Š์•˜๋‹ค. 2. ๋ฐ์ดํ„ฐ์…‹ ์„ค๊ณ„ (NeuBAROCO) | ๊ตฌ๋ถ„ | ๋‚ด์šฉ | ํŠน์ง• | | | | | | ํ˜•์‹์  ํŒจํ„ด | ๊ทœ๋ฒ”ยท์ธ์‹ ๋ชจ๋‹ฌ๋ฆฌํ‹ฐ์˜ ์ „ํ˜•์ ์ธ ๋…ผ๋ฆฌ ๊ตฌ์กฐ (์˜ˆ: โ–กp โ†’ p, โ—‡p โ†’ ยฌโ–กยฌp ๋“ฑ) | ๋…ผ๋ฆฌ์  ํƒ€๋‹น์„ฑ ๊ฒ€์ฆ ๊ฐ€๋Šฅ | | ๋น„ํ˜•์‹์  ์ธ์ง€ ์š”์ธ

Model
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Numerical study of non-relativistic quantum systems and small oscillations induced in a helically twisted geometry

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์œ„์ƒ ๊ฒฐํ•จ(์ฝ”์Šค๋ฏน ์ŠคํŠธ๋ง, ๊ฒฐ์ ˆ ๋“ฑ)๊ณผ ๊ด€๋ จ๋œ ์–‘์ž์—ญํ•™ ๋ฌธ์ œ๋Š” ์ตœ๊ทผ ๊ณ ์ฒด๋ฌผ๋ฆฌยท์šฐ์ฃผ๋ก ยท์–‘์ž์ค‘๋ ฅ ๋“ฑ ๋‹คํ•™์ œ ๋ถ„์•ผ์—์„œ ํ™œ๋ฐœํžˆ ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ํ† ์…˜(์ „๋‹จ ๋ณ€ํ˜•)์€ Einsteinโ€‘Cartan ์ด๋ก ๋ฟ ์•„๋‹ˆ๋ผ ๊ฒฐ์ • ๊ตฌ์กฐ์˜ ๊ฒฐํ•จ ๋ชจ๋ธ์—์„œ๋„ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ฃผ๋กœ ํ•ด์„์  ์ ‘๊ทผ(ํŠน์ˆ˜ ํ•จ์ˆ˜, ๊ธ‰์ˆ˜ ์ „๊ฐœ)์ด๋‚˜ ๊ทผ์‚ฌ ํ•ด๋ฒ•์— ์˜์กดํ–ˆ์œผ๋ฉฐ, ํŒŒ๋ผ๋ฏธํ„ฐ ๊ณต๊ฐ„ ์ „์ฒด๋ฅผ ์Šค์บ”ํ•˜๊ธฐ์—” ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ณ ์ž ์ˆ˜์น˜์  Sturmโ€‘Liouville ํ•ด๋ฒ• ์„ ์ฒด๊ณ„ํ™”ํ–ˆ๋‹ค. 2. ์ˆ˜ํ•™์ ยท๋ฌผ๋ฆฌ์  ๋ชจ๋ธ๋ง ํ—ฌ๋ฆฌ

System Quantum Physics
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NYUSIM: A Roadmap to AI-Enabled Statistical Channel Modeling and Simulation

| ๊ตฌ๋ถ„ | ํ•ต์‹ฌ ๋‚ด์šฉ | ์˜์˜ยท์‹œ์‚ฌ์  | | | | | | 1. ์ธก์ • ๊ธฐ๋ฐ˜ ๋ฐ์ดํ„ฐ์˜ ์ค‘์š”์„ฑ | 10๋…„ ์ด์ƒ ๋ˆ„์ ๋œ 28โ€‘142 GHz ์‹ค์™ธยท์‹ค๋‚ด ์ธก์ • ๋ฐ์ดํ„ฐ์™€ ์ตœ์‹  6.75/16.95 GHz ์ธก์ • ๊ฒฐ๊ณผ๋ฅผ ํ™œ์šฉ. | AI ๋ชจ๋ธ์ด ์‹ค์ œ ์ „ํŒŒ ํ˜„์ƒ์„ ํ•™์Šตํ•˜๋„๋ก ๋ณด์žฅ, ๊ธฐ์กด mmWaveยทsubโ€‘THz ๋ชจ๋ธ์˜ ๋ฌผ๋ฆฌ์  ํƒ€๋‹น์„ฑ์„ ์œ ์ง€. | | 2. FR3(7โ€‘24 GHz) ํ™•์žฅ | 6G ์ƒ์œ„ ์ค‘๋Œ€์—ญ์€ ์ „ ์„ธ๊ณ„ ๊ทœ์ œ๊ธฐ๊ด€(ITU, NTIA, FCC, WRCโ€‘23)์—์„œ ํ•ต์‹ฌ ๋ฐฐ์ • ํ›„๋ณด๋กœ ์ง€์ •. | 6Gยท๋””์ง€ํ„ธ ํŠธ์œˆ, ISAC ๋“ฑ ์ƒˆ๋กœ์šด ์„œ๋น„์Šค ์‹œ๋‚˜

Electrical Engineering and Systems Science Model
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On Convergence Analysis of Network-GIANT: An approximate Hessian-based fully distributed optimization algorithm

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์™„์ „ ๋ถ„์‚ฐ ์ตœ์ ํ™” ๋Š” ์ค‘์•™ ์„œ๋ฒ„ ์—†์ด ๋„คํŠธ์›Œํฌ ๋‚ด ๋…ธ๋“œ๋“ค์ด ์„œ๋กœ ์ด์›ƒ๊ณผ๋งŒ ํ†ต์‹ ํ•˜๋ฉด์„œ ์ „์—ญ ๋ชฉ์  ํ•จ์ˆ˜๋ฅผ ์ตœ์†Œํ™”ํ•œ๋‹ค. 1์ฐจ ๋ฐฉ๋ฒ•(๊ทธ๋ผ๋””์–ธํŠธ ๊ธฐ๋ฐ˜)์€ ํ†ต์‹  ๋ณต์žก๋„ ๊ฐ€ ๋‚ฎ์ง€๋งŒ, ๊ฐ•ํ•œ ๋ณผ๋ก์„ฑ ํ•˜์—์„œ๋งŒ ์„ ํ˜• ์ˆ˜๋ ด์„ ๋ณด์ธ๋‹ค. ๋‰ดํ„ดํ˜•(2์ฐจ) ๋ฐฉ๋ฒ• ์€ ์ค‘์•™ ์ง‘์ค‘ํ˜• ์ƒํ™ฉ์—์„œ ๊ตญ์†Œ ์ด์ฐจ ์ˆ˜๋ ด ์„ ์ œ๊ณตํ•˜์ง€๋งŒ, ํ—ค์‹œ์•ˆ ์ „์†กยท์—ญ์‚ฐ์ด ํ†ต์‹ ยท๊ณ„์‚ฐ ๋น„์šฉ์„ ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€์‹œ์ผœ ์™„์ „ ๋ถ„์‚ฐ ํ™˜๊ฒฝ์— ์ ์šฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ(

Network Analysis Mathematics
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On propagation of chaos for the Fisher-Rao gradient flow in entropic mean-field optimization

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ํ‰๊ท ์žฅ ์ตœ์ ํ™”์™€ ์‹ ๊ฒฝ๋ง : ์ตœ๊ทผ Mei et al. (2018), Hu et al. (2021) ๋“ฑ์€ ์‹ ๊ฒฝ๋ง ํ•™์Šต์„ ๋ฌดํ•œ ๋„ˆ๋น„/๋ฌดํ•œ ํญ ํ‰๊ท ์žฅ ๋ชจ๋ธ๋กœ ํ•ด์„ํ•˜์˜€๋‹ค. ์ด ์ ‘๊ทผ๋ฒ•์€ ํ™•๋ฅ ๋ถ„ํฌ ์œ„ ํ•จ์ˆ˜(alignment, loss)๋ฅผ ์ตœ์ ํ™”ํ•˜๋Š” ๋ฌธ์ œ๋กœ ๊ท€๊ฒฐ๋œ๋‹ค. ๊ทธ๋ž˜๋””์–ธํŠธ ํ๋ฆ„ : ์ „ํ†ต์ ์œผ๋กœ๋Š” ์›Œํ„ฐ์Šคํ†ค(Wasserstein) ๊ทธ๋ž˜๋””์–ธํŠธ ํ๋ฆ„ ์ด ์‚ฌ์šฉ๋ผ ์™”์œผ๋ฉฐ, ์ด๋Š” Fokkerโ€‘Planck ๋ฐฉ์ •์‹๊ณผ ์—ฐ๊ฒฐ๋ผ ์ž…์ž ๊ทผ์‚ฌ์™€ JKO ์Šคํ‚ค๋งˆ๊ฐ€ ํ’๋ถ€ํ•˜๊ฒŒ ์—ฐ๊ตฌ๋˜์—ˆ๋‹ค. ํ”ผ์…”โ€‘๋ผ์˜ค ํ๋ฆ„์˜ ํ•„์š”์„ฑ : ํ”ผ์…”โ€‘๋ผ์˜ค ๋ฉ”ํŠธ๋ฆญ์€ ์งˆ๋Ÿ‰(ํ™•๋ฅ ๋ฐ€๋„

Mathematics
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On sparsity, extremal structure, and monotonicity properties of Wasserstein and Gromov-Wasserstein optimal transport plans

| ํ•ญ๋ชฉ | ๋‚ด์šฉ ๋ฐ ํ‰๊ฐ€ | | | | | ์—ฐ๊ตฌ ๋™๊ธฐ | GW ๊ฑฐ๋ฆฌ์™€ ๊ด€๋ จ๋œ ๊ธฐ๋ณธ์ ์ธ ๊ตฌ์กฐ์  ์„ฑ์งˆ(ํฌ์†Œ์„ฑ, ๊ทน์ , ๋‹จ์กฐ์„ฑ)์ด ์•„์ง ํ†ตํ•ฉ์ ์œผ๋กœ ์ •๋ฆฌ๋˜์ง€ ์•Š์•˜๋‹ค๋Š” ์ ์„ ์ง€์ ํ•˜๊ณ , ์ด๋ฅผ ๋ณด์™„ํ•˜๋ ค๋Š” ์‹œ๋„๋Š” ๋งค์šฐ ์˜๋ฏธ ์žˆ๋‹ค. ํŠนํžˆ โ€œGW๋Š” ์•„์ง ๋งŽ์ด ์•Œ๋ ค์ง€์ง€ ์•Š์€ ๊ฐ์ฒดโ€๋ผ๋Š” ์ธ์‹์„ ๋ฐ”ํƒ•์œผ๋กœ, ์ดˆ๋ณด ์—ฐ๊ตฌ์ž์™€ ์‹ค๋ฌด์ž ๋ชจ๋‘์—๊ฒŒ ์œ ์šฉํ•œ ๊ต์žฌ์  ์—ญํ• ์„ ๋ชฉํ‘œ๋กœ ํ•œ ์ ์ด ์žฅ์ ์ด๋‹ค. | | ์ฃผ์š” ๊ธฐ์—ฌ | 1. ํฌ์†Œ์„ฑ โ€“ GW ์ตœ์  ๊ณ„ํš์ด ์กฐ๊ฑด๋ถ€ ์Œโ€‘๋ฐ˜์ • ์†์„ฑ์„ ๋งŒ์กฑํ•˜๋ฉด, ์„ ํ˜• OT์—์„œ์™€ ์œ ์‚ฌํ•˜๊ฒŒ โ€œ๊ทน์  โ†’ ์‚ฌ์ดํด์ด ์—†๋Š” ๊ทธ๋ž˜ํ”„ โ†’ ํฌ์†Œ์„ฑ(โ‰ค n + mโ€‘

Statistics Machine Learning
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On the Learning Dynamics of RLVR at the Edge of Competence

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ตœ๊ทผ OpenAIโ€‘o3 , DeepSeek ๋“ฑ ๋Œ€ํ˜• ์–ธ์–ด๋ชจ๋ธ์ด ๋ณต์žกํ•œ ์ถ”๋ก  ๊ณผ์ œ์—์„œ ๋›ฐ์–ด๋‚œ ์„ฑ๊ณผ๋ฅผ ๋ณด์ด๋ฉฐ, RLVR (Outcomeโ€‘based Reinforcement Learning with Verifiable Rewards)์ด ํ•ต์‹ฌ ๊ธฐ์ˆ ๋กœ ๋ถ€๊ฐ๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ณด์ƒ์ด ์ตœ์ข… ๊ฒฐ๊ณผ์—๋งŒ ์ฃผ์–ด์ง€๋Š” ์ƒํ™ฉ์—์„œ ๊ธด ์ˆ˜ํ‰(longโ€‘horizon) ๋ฌธ์ œ๋ฅผ ํ•™์Šตํ•˜๊ธฐ ์œ„ํ•œ ์‹ ํ˜ธ๊ฐ€ ์ถฉ๋ถ„ํžˆ ์ „๋‹ฌ๋  ์ˆ˜ ์žˆ๋Š”๊ฐ€? ๋ผ๋Š” ๊ทผ๋ณธ์ ์ธ ์˜๋ฌธ์ด ๋‚จ์•„ ์žˆ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ RLVR์ด โ€œ๋Šฅ๋ ฅ ํ•œ๊ณ„(edge of competence)โ€ ๊ทผ์ฒ˜์—์„œ

Computer Science Learning Machine Learning
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On the uniqueness and structural stability of Couette-Poiseuille flow in a channel for arbitrary values of the flux

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์„ค์ • Leray ๋ฌธ์ œ : ๋‹ค์ค‘ ์ถœ๊ตฌ๋ฅผ ๊ฐ€์ง„ ์™œ๊ณก ์ฑ„๋„์—์„œ ์ž„์˜์˜ ํ”Œ๋Ÿญ์Šค ฮฆ์— ๋Œ€ํ•ด ํฌ์•„์ฃ„๋ฅ˜ ํ๋ฆ„์œผ๋กœ ์ˆ˜๋ ดํ•˜๋Š” ์ •์ƒ ํ•ด๊ฐ€ ์กด์žฌํ•˜๋Š”๊ฐ€? ๊ธฐ์กด ์—ฐ๊ตฌ(Amick, Ladyzhenskayaโ€‘Solonnikov)๋Š” ํ”Œ๋Ÿญ์Šค ํฌ๊ธฐ์— ์ œํ•œ ์„ ๋‘๊ณ  ์กด์žฌ์„ฑ์„ ์ฆ๋ช…ํ–ˆ์œผ๋ฉฐ, ์ˆ˜๋ ด ์กฐ๊ฑด ์—ญ์‹œ ํ”Œ๋Ÿญ์Šค๊ฐ€ ์ž‘์„ ๋•Œ๋งŒ ๋ณด์žฅ๋˜์—ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์€ โ€œํ”Œ๋Ÿญ์Šค ํฌ๊ธฐ์— ๋ฌด๊ด€ํ•˜๊ฒŒโ€ ์ฟ ์—ํ…Œโ€‘ํฌ์•„์ฃ„๋ฅ˜ ํ๋ฆ„์ด ์ง€์—ญ์ ์œผ๋กœ ์œ ์ผ ํ•˜๊ณ  ๊ตฌ์กฐ์ ์œผ๋กœ ์•ˆ์ • ํ•จ์„ ๋ณด์ด๊ณ ์ž ํ•œ๋‹ค. 2. ์ฃผ์š” ๊ฒฐ๊ณผ ์š”์•ฝ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ํ•ต์‹ฌ ๊ฐ€์ • | | | | | | (1) | ์—ฐ์‚ฐ

Mathematics

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