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Combined dynamic-kinematic validation of droplet-wall impact modeling

Combined dynamic-kinematic validation of droplet-wall impact modeling

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์•ก์ โ€‘๋ฒฝ ์ถฉ๋Œ์€ ๊ณ ์˜จ ๋ƒ‰๊ฐ, ์Šคํ”„๋ ˆ์ด ์ฝ”ํŒ…, 3D ๋ฐ”์ด์˜คํ”„๋ฆฐํŒ…, ์ž‰ํฌ์ ฏ ์ธ์‡„ ๋“ฑ ์‚ฐ์—…ยทํ•™๋ฌธ ์ „๋ฐ˜์— ๊ฑธ์ณ ํ•ต์‹ฌ ํ˜„์ƒ์ด๋‹ค. ๊ธฐ์กด CFD ๊ฒ€์ฆ์€ ์ฃผ๋กœ ์ตœ๋Œ€ ์ „๊ฐœ ์ง๊ฒฝ ํ˜น์€ ๋ฐฉ์šธ ํ˜•ํƒœ ์— ๊ตญํ•œ๋ผ, ๋‚ด๋ถ€ ์œ ๋™(๋ฐฉ์‚ฌํ˜• ์†๋„, ์ „๊ฐœ ์†๋„)๊ณผ ๊ฐ™์€ ๋™์—ญํ•™์  ์ง€ํ‘œ๋ฅผ ๋ฌด์‹œํ•œ๋‹ค. ์ด๋Š” ๋ชจ๋ธ์ด ์‹ค์ œ ๋ฌผ๋ฆฌ์  ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์ œ๋Œ€๋กœ ํฌ์ฐฉํ–ˆ๋Š”์ง€ ํŒ๋‹จํ•˜๊ธฐ์— ๋ถ€์กฑํ•œ ๊ทผ๊ฑฐ๊ฐ€ ๋œ๋‹ค. 2. ๋ฐฉ๋ฒ•๋ก  | ๊ตฌ๋ถ„ | ๋‚ด์šฉ | | | | | ์‹คํ—˜ | ๋ฌผโ€‘๊ธ€๋ฆฌ์„ธ๋กค(60 % / 40 %) ํ˜ผํ•ฉ์•ก, ์ž…์ž 1 g/L, ์ดˆ๊ธฐ ์ง๊ฒฝ Dโ‚€ 2.9 mm. ๊ณ ์† ์ดฌ์˜ +

Model Physics
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Constraint-Informed Active Learning for End-to-End ACOPF Optimization Proxies

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ACOPF ๋ฌธ์ œ ๋Š” ์ „๋ ฅ ์‹œ์Šคํ…œ ์šด์˜์—์„œ ํ•ต์‹ฌ์ด์ง€๋งŒ, ๋น„์„ ํ˜•ยท๋น„๋ณผ๋ก์„ฑ ๋•Œ๋ฌธ์— ์‹ค์‹œ๊ฐ„ ํ•ด๊ฒฐ์ด ์–ด๋ ค์›€. ์ตœ์ ํ™” ํ”„๋ก์‹œ (ML ๋ชจ๋ธ)๋Š” ๊ทผ์‚ฌ ํ•ด๋ฅผ ๋น ๋ฅด๊ฒŒ ์ œ๊ณตํ•˜์ง€๋งŒ, ํ•™์Šต ๋ฐ์ดํ„ฐ๊ฐ€ ์‹ค์ œ ์šด์˜ ์ƒํ™ฉ์„ ์ถฉ๋ถ„ํžˆ ์ปค๋ฒ„ํ•˜์ง€ ๋ชปํ•˜๋ฉด ์„ฑ๋Šฅ์ด ๊ธ‰๊ฒฉํžˆ ์ €ํ•˜๋œ๋‹ค. ๊ธฐ์กด ๋ฐ์ดํ„ฐ ์ƒ์„ฑ ๋ฐฉ์‹์€ ๋ฌด์ž‘์œ„ ํ˜น์€ ์‚ฌ์ „ ์ •์˜๋œ ์‹œ๋‚˜๋ฆฌ์˜ค์— ์˜์กดํ•ด ๋‹ค์–‘์„ฑยทํ˜„์‹ค์„ฑ ์ด ๋ถ€์กฑํ–ˆ๋‹ค๋Š” ์ ์ด ํ•œ๊ณ„์ ์œผ๋กœ ์ง€์ ๋œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ๋Šฅ๋™ ํ•™์Šต(active learning) ๊ฐœ๋…์„ ACOPF์— ์ ์šฉํ•ด, ๋ชจ๋ธ์ด ํ˜„์žฌ ์•ฝ์ ์œผ๋กœ ํŒ๋‹จํ•˜๋Š” ์˜์—ญ์„ ๋ชฉํ‘œ๋กœ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜

Learning
Cost-Efficient Cross-Lingual Retrieval-Augmented Generation for Low-Resource Languages: A Case Study in Bengali Agricultural Advisory

Cost-Efficient Cross-Lingual Retrieval-Augmented Generation for Low-Resource Languages: A Case Study in Bengali Agricultural Advisory

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์–ธ์–ด ์žฅ๋ฒฝ : ๋ฐฉ๊ธ€๋ผ๋ฐ์‹œ์™€ ๊ฐ™์€ ๊ฐœ๋ฐœ๋„์ƒ๊ตญ์—์„œ๋Š” ๋†์—… ๋งค๋‰ด์–ผ์ด ์˜์–ด๋กœ๋งŒ ์ œ๊ณต๋ผ, ํ˜„์ง€ ๋†๋ฏผ์ด ์ง์ ‘ ํ™œ์šฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค. LLM ํ•œ๊ณ„ : ๊ธฐ์กด ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ์€ ์˜์–ด ์ค‘์‹ฌ ํ•™์Šต์œผ๋กœ ๋ฒต๊ณจ์–ด ์ถœ๋ ฅ ์‹œ ์œ ์ฐฝ์„ฑยท์‚ฌ์‹ค์„ฑ ์ €ํ•˜๊ฐ€ ๋นˆ๋ฒˆํ•˜๊ณ , ํด๋ผ์šฐ๋“œ ์„œ๋น„์Šค ๋น„์šฉ์ด ๋†’์•„ ํ˜„์ง€ ๋ฐฐํฌ๊ฐ€ ๋น„ํ˜„์‹ค์ ์ด๋‹ค. RAG์˜ ์ž ์žฌ๋ ฅ : ์™ธ๋ถ€ ๋ฌธ์„œ์— ๊ทผ๊ฑฐํ•œ ์ƒ์„ฑ์€ โ€˜ํ™˜๊ฐ(hallucination)โ€™์„ ์–ต์ œํ•˜์ง€๋งŒ, ๊ธฐ์กด RAG๋Š” ์ฃผ๋กœ ์˜์–ด ํ™˜๊ฒฝ์— ์ตœ์ ํ™”๋ผ ์žˆ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ๋ฐ ์„ค๊ณ„ | ๋‹จ๊ณ„ | ํ•ต์‹ฌ ๊ธฐ์ˆ  | ์—ญํ•  | | | | | | 1.

NLP Computer Science
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Coverage Guarantees for Pseudo-Calibrated Conformal Prediction under Distribution Shift

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์ปจํฌ๋ฉ€ ์˜ˆ์ธก ์€ ๊ต์ • ๋ฐ์ดํ„ฐ์™€ ํ…Œ์ŠคํŠธ ๋ฐ์ดํ„ฐ๊ฐ€ ๊ตํ™˜ ๊ฐ€๋Šฅํ•  ๋•Œ, ์œ ํ•œ ํ‘œ๋ณธ ์—์„œ๋„ ์ •ํ™•ํžˆ ์ง€์ •๋œ ์˜ค์ฐจ ์ˆ˜์ค€(1โ€‘ฮฑ) ์ดํ•˜์˜ ๋งˆ์ง„ ์ปค๋ฒ„๋ฆฌ์ง€๋ฅผ ๋ณด์žฅํ•œ๋‹ค. ๋ถ„ํฌ ์ด๋™ (distribution shift) ์ƒํ™ฉ์—์„œ๋Š” ๊ตํ™˜ ๊ฐ€๋Šฅ ๊ฐ€์ •์ด ๊นจ์ ธ ๊ธฐ์กด ๋ณด์žฅ์ด ๋ฌดํšจํ™”๋œ๋‹ค. ํŠนํžˆ ๋ผ๋ฒจโ€‘์กฐ๊ฑด๋ถ€ ๊ณต๋ณ€๋Ÿ‰ ์ด๋™ (labelโ€‘conditional covariate shift)์—์„œ๋Š” ๋ผ๋ฒจ ๋ถ„ํฌ๋Š” ๋™์ผํ•˜์ง€๋งŒ ์ž…๋ ฅ ๋ถ„ํฌ๊ฐ€ ๋ผ๋ฒจ๋ณ„๋กœ ๋‹ค๋ฅด๊ฒŒ ๋ณ€ํ•œ๋‹ค. ๋ผ๋ฒจ์ด ์—†๋Š” ํƒ€๊นƒ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด ์˜์‚ฌ ๋ผ๋ฒจ(pseudoโ€‘labels) ์„ ์‚ฌ์šฉํ•˜๋ฉด ๊ต์ •์ด

Machine Learning Computer Science
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Cross-Field Interface-Aware Neural Operators for Multiphase Flow Simulation

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ๋‹ค์ค‘์ƒ ํ๋ฆ„์˜ ๋‚œ์ด๋„ : ์ƒ๊ณ„๋ฉด์—์„œ ๋ฌผ๋ฆฌ๋Ÿ‰์ด ๊ธ‰๊ฒฉํžˆ ๋ณ€ํ•˜๊ณ , ์ธํ„ฐํŽ˜์ด์Šค๊ฐ€ ์›€์ง์ด๋ฉฐ ์œ„์ƒ ๋ณ€ํ™”๋ฅผ ๊ฒช๋Š”๋‹ค. ์ „ํ†ต์ ์ธ CFD๋Š” ๋ฏธ์„ธ ๊ฒฉ์ž๋ฅผ ํ•„์š”๋กœ ํ•˜์—ฌ ์—ฐ์‚ฐ ๋น„์šฉ์ด ๊ธ‰์ฆํ•œ๋‹ค. ์‹ ๊ฒฝ ์—ฐ์‚ฐ์ž์˜ ์žฅ์  : ํ•œ ๋ฒˆ ํ•™์Šตํ•˜๋ฉด ๋‹ค์–‘ํ•œ ์ดˆ๊ธฐยท๊ฒฝ๊ณ„ ์กฐ๊ฑด์— ๋Œ€ํ•ด ๋น ๋ฅด๊ฒŒ ํ•ด๋ฅผ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ธฐ์กด NO๋Š” ์ŠคํŽ™ํŠธ๋Ÿผ ๋ฐ”์ด์–ด์Šค (์ €์ฃผํŒŒ ์„ฑ๋ถ„์— ํŽธํ–ฅ)์™€ ๋ฐ์ดํ„ฐ ํฌ์†Œ์„ฑ ๋ฌธ์ œ์— ์ทจ์•ฝํ•˜๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ ์ธํ„ฐํŽ˜์ด์Šค ์ •๋ณด ํ™œ์šฉ ์ธํ„ฐํŽ˜์ด์Šค ๋ฐ์ดํ„ฐ์˜ ํŠน์„ฑ : ์ƒ๊ณ„๋ฉด ์œ„์น˜ยทํ˜•์ƒ์€ ๊ณ ์ฃผํŒŒ ์ •๋ณด๋ฅผ ๋‚ดํฌํ•˜๊ณ  ์žˆ์–ด, ๋ฌผ๋ฆฌ ํ•„๋“œ(์••๋ ฅ,

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Deep (Predictive) Discounted Counterfactual Regret Minimization

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ CFR์˜ ์ค‘์š”์„ฑ : ๋ถˆ์™„์ „ ์ •๋ณด ๊ฒŒ์ž„(์˜ˆ: ํฌ์ปค, ๋ธ”๋Ÿฌํ”„ ๊ฒŒ์ž„)์—์„œ ๋‚ด์‹œ ๊ท ํ˜•(Nash equilibrium)์„ ๊ทผ์‚ฌํ•˜๋Š” ํ‘œ์ค€ ๋ฐฉ๋ฒ•์œผ๋กœ ์ž๋ฆฌ ์žก์Œ. ์‹ ๊ฒฝ๋ง ๊ธฐ๋ฐ˜ CFR : ๊ฒŒ์ž„ ํŠธ๋ฆฌ์˜ ๊ทœ๋ชจ๊ฐ€ ํญ๋ฐœ์ ์œผ๋กœ ์ปค์ง€๋Š” ๊ฒฝ์šฐ, ์ „ํ†ต์ ์ธ ํ…Œ์ด๋ธ” ๊ธฐ๋ฐ˜ CFR์€ ๋ฉ”๋ชจ๋ฆฌยท์‹œ๊ฐ„ ์ œ์•ฝ์— ๋ถ€๋”ชํžŒ๋‹ค. ๋”ฐ๋ผ์„œ ํ•จ์ˆ˜ ๊ทผ์‚ฌ(ํŠนํžˆ ๋”ฅ๋Ÿฌ๋‹)๋ฅผ ์ด์šฉํ•ด ์ „๋žตยท๊ฐ€์น˜๋ฅผ ์••์ถ•ํ•˜๋Š” ์ ‘๊ทผ์ด ํ™œ๋ฐœํžˆ ์—ฐ๊ตฌ๋จ. ๊ธฐ์กด ํ•œ๊ณ„ : ๋Œ€๋ถ€๋ถ„์˜ ์‹ ๊ฒฝ๋ง CFR๋Š” vanilla CFR(๊ธฐ๋ณธ ํ˜•ํƒœ)์˜ ์—…๋ฐ์ดํŠธ ๊ทœ์น™์—๋งŒ ๋งž์ถฐ ์„ค๊ณ„๋ผ, Discounted CFR , Reg

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

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

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ Thomassen์˜ 5โ€‘๋ฆฌ์ŠคํŠธ ์ƒ‰์น  ์ •๋ฆฌ ๋Š” ํ”Œ๋ž˜๋„ˆ ๊ทธ๋ž˜ํ”„๊ฐ€ 5โ€‘๋ฆฌ์ŠคํŠธ ์ƒ‰์น  ๊ฐ€๋Šฅํ•จ์„ ๋ณด์—ฌ์ฃผ๋ฉฐ, ๊ทธ๋ž˜ํ”„ ์ƒ‰์ฑ„ ์ด๋ก ์˜ ํ•ต์‹ฌ ๊ฒฐ๊ณผ ์ค‘ ํ•˜๋‚˜๋‹ค. ๋Œ€์‘ ์ƒ‰์ฑ„(correspondence coloring) ์€ ๋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„ ๋ถ€์—ฌํ•œ๋‹ค. Kโ‚…โ€‘๋งˆ์ด๋„ˆ ์ž์œ  ๊ทธ๋ž˜ํ”„๋Š” ํ”Œ๋ž˜๋„ˆ ๊ทธ๋ž˜ํ”„๋ฅผ ํฌํ•จํ•˜๋Š” ๋„“์€ ํด๋ž˜์Šค์ด๋ฉฐ, ์ด๋“ค์— ๋Œ€ํ•œ ์ƒ‰์ฑ„ ์ด๋ก ์€ ์•„์ง ์ถฉ๋ถ„ํžˆ ์ •๋ฆฝ๋˜์ง€ ์•Š์•˜๋‹ค. ํŠนํžˆ, ๋Œ€์‘ ์ƒ‰์ฑ„ ํฌ์žฅ ์ˆ˜ (chi^{star} c

Mathematics
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Dosimetric Study of Lung Modulation and Motion Effects in Carbon ion Therapy for Lung Cancer

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํƒ„์†Œ์ด์˜จ(Cโ€‘ion) ์น˜๋ฃŒ๋Š” Bragg Peak ์˜ ๊ธ‰๊ฒฉํ•œ ์—๋„ˆ์ง€ ๊ฐ์†Œ์™€ ๋†’์€ RBE (์ƒ๋Œ€์ƒ๋ฌผํ•™์ ํšจ๊ณผ) ๋•๋ถ„์— ๋ฐฉ์‚ฌ์„  ์ €ํ•ญ์„ฑ ๋น„์†Œ์„ธํฌํ์•”(NSCLC) ์น˜๋ฃŒ์— ์œ ๋งํ•˜์ง€๋งŒ, ์ •๋ฐ€ํ•œ ์„ ๋Ÿ‰ ์ „๋‹ฌ ์ด ํ•„์ˆ˜์ ์ด๋‹ค. ํ‰๋ถ€์—์„œ๋Š” ํ˜ธํก์— ์˜ํ•œ ์›€์ง์ž„ ๊ณผ ํ ์กฐ์ง์˜ ๋ฏธ์„ธ ์ด์งˆ์„ฑ (๊ณต๊ธฐโ€‘์กฐ์ง ํ˜ผํ•ฉ ๊ตฌ์กฐ) ๋•Œ๋ฌธ์— ๋น”์˜ ๋ฒ”์œ„์™€ ์—๋„ˆ์ง€ ๋ถˆํ™•์‹ค์„ฑ์ด ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ๊ฐ๊ฐ์˜ ํšจ๊ณผ(interplay, lung modulation)๋ฅผ ๋ณ„๋„๋กœ ๋‹ค๋ฃจ์—ˆ์œผ๋‚˜, ์‹ค์ œ ์ž„์ƒ ์ƒํ™ฉ ์„ ๋ฐ˜์˜ํ•˜๋ ค๋ฉด ๋‘ ํšจ๊ณผ๋ฅผ ๋™์‹œ์— ๊ณ ๋ คํ•ด์•ผ ํ•œ๋‹ค๋Š” ์ ์ด ๋ณธ

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

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

| ๊ตฌ๋ถ„ | ๋‚ด์šฉ | | | | | ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ | ํ”Œ๋Ÿฌํ„ฐ๋Š” ํ•ญ๊ณตยทํ•ด์–‘ ๊ตฌ์กฐ๋ฌผ ์„ค๊ณ„์—์„œ ์ œํ•œ ์š”์†Œ๋กœ ์ธ์‹๋ผ ์™”์ง€๋งŒ, ์ตœ๊ทผ์—๋Š” ํ”Œ๋Ÿฌํ„ฐ๋ฅผ ์ด์šฉํ•œ ์—๋„ˆ์ง€ ์ˆ˜ํ™•(ํ”Œ๋Ÿฌํ„ฐโ€‘๊ธฐ๋ฐ˜ ํ„ฐ๋นˆ) ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํžˆ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. 2โ€‘D ํฌ์ผ์€ ๋ฌผ๋ฆฌยท์ˆ˜ํ•™์ ์œผ๋กœ ๋‹ค๋ฃจ๊ธฐ ์‰ฌ์›Œ ๊ธฐ๋ณธ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ํƒ๊ตฌํ•˜๋Š” ๋ชจ๋ธ ์‹œ์Šคํ…œ์œผ๋กœ ๋„๋ฆฌ ์‚ฌ์šฉ๋œ๋‹ค. ๊ธฐ์กด ํ•ด์„์€ ์ฃผ๋กœ ๊ฐ•์ฒด ํฌ์ผ(Theodorsen)์ด๋‚˜ ์ฒซ ๋ฒˆ์งธ ๊ตฝํž˜ ๋ชจ๋“œ๋งŒ ๊ณ ๋ คํ–ˆ์œผ๋ฉฐ, ๊ฐ•์„ฑ ๊ฐ์†Œ์— ๋”ฐ๋ฅธ ์ƒˆ๋กœ์šด ๋ถˆ์•ˆ์ • ๋ชจ๋“œ ํƒ์ง€๋Š” ์–ด๋ ค์› ๋‹ค. | | ํ•ต์‹ฌ ๊ธฐ์—ฌ | 1. ์ค‘๋ ฅ ํฌํ•จ โ€“ ๊ธฐ์กด ๋ชจ๋ธ์— ์ค‘๋ ฅ(ํ”„๋ฃจ๋“œ ์ˆ˜ (Fr)

Physics
Effective local differential topology of algebraic varieties over local fields of positive characteristics

Effective local differential topology of algebraic varieties over local fields of positive characteristics

1. ์—ฐ๊ตฌ ๋™๊ธฐ์™€ ๋ฐฐ๊ฒฝ ์–‘์˜ ํŠน์„ฑ์—์„œ์˜ ์–ด๋ ค์›€ : ํŠน์„ฑ 0์—์„œ๋Š” ๋ฏธ๋ถ„๊ธฐํ•˜ํ•™์  ๋„๊ตฌ(์˜ˆ: ํ•ด์„์  ์ „๊ฐœ, ํŒŒ๋ผ๋ฉ”ํŠธ๋ฆฌํ™”)๊ฐ€ ํ’๋ถ€ํ•˜์ง€๋งŒ, ์–‘์˜ ํŠน์„ฑ, ํŠนํžˆ ์ž‘์€ ํŠน์„ฑ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋„๊ตฌ๊ฐ€ ์ œํ•œ๋œ๋‹ค. ์ €์ž๋“ค์€ ์ด๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ๋ณผ๊ณผ ์ธก๋„ ๋ผ๋Š” ๋น„์•„ํ‚ค๋ฉ”๋ฐ์Šค์  ๊ฐœ๋…์„ ๋„์ž…ํ•œ๋‹ค. ์‘์šฉ ๋ชฉํ‘œ : ํ›„์† ์—ฐ๊ตฌ(

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

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‰๋ถ€์ƒํ•˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ Scoreโ€‘Entropy Discrete Diffusion (SEDD) ์€ ์ž๋™ ํšŒ๊ท€ ๋ชจ๋ธ์„ ๋„˜์–ด์„œ๋Š” ์„ฑ๋Šฅ์„ ๋ณด์ด๋ฉฐ, ์ƒ์„ฑ ์ˆœ์„œ์— ์–ฝ๋งค์ด์ง€ ์•Š๋Š” ์œ ์—ฐ์„ฑ์„ ์ œ๊ณตํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด ์ด๋ก ์€ ์–ดํœ˜ ํฌ๊ธฐ (S) ์™€ ์ฐจ์› (d) ์— ๋Œ€ํ•ด ์„ ํ˜• ํ˜น์€ ๊ทธ๋ณด๋‹ค ๋” ํฐ ๋ณต์žก๋„๋ฅผ ๋ณด์ด๋ฉฐ, ์‹ค์ œ ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ(์˜ˆ: GPTโ€‘2, (Sapprox5times10^4), (dapprox10^3))์—

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

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

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์‹ค์šฉ์„ฑ ์žฅ๋ฒฝ : ๊ธฐ์กด EEGโ€‘toโ€‘Image ๋ชจ๋ธ์€ (โ‘  ํ”ผํ—˜์ž๋ณ„ ์žฅ์‹œ๊ฐ„ ํ•™์Šต, โ‘ก ์ €๊ฐ€ํ˜• ํ•˜๋“œ์›จ์–ด์— ๋Œ€ํ•œ ์ทจ์•ฝ์„ฑ, โ‘ข ๊ฑฐ๋Œ€ํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ ๊ทœ๋ชจ) ์„ธ ๊ฐ€์ง€ ํ•ต์‹ฌ ์ œ์•ฝ์œผ๋กœ ์‹ค์ œ BCI ์ ์šฉ์— ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ๋‹ค. EEG vs fMRI : EEG๋Š” ๋†’์€ ์‹œ๊ฐ„ ํ•ด์ƒ๋„์™€ ์ €๋น„์šฉยทํœด๋Œ€์„ฑ์„ ์ œ๊ณตํ•˜์ง€๋งŒ, ๋‚ฎ์€ ์‹ ํ˜ธโ€‘๋Œ€โ€‘๋…ธ์ด์ฆˆ(SNR)์™€ ๊ณต๊ฐ„ ํ•ด์ƒ๋„๋กœ ์ธํ•ด ์ด๋ฏธ์ง€ ๋ณต์›์— ์–ด๋ ค์›€์ด ์žˆ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | | | | | (1) | 15๋ถ„ ๋ฐ์ดํ„ฐ ๋งŒ์œผ๋กœ ์ƒˆ๋กœ์šด ํ”ผํ—˜์ž์— ๋น ๋ฅธ ํŒŒ์ธํŠœ๋‹ ๊ฐ€๋Šฅ | ํ˜„์žฅ

Quantitative Biology
Equilibrium statistical mechanics of waves in inhomogeneous moving media

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. ํŠนํžˆ, ์งง์€ ํŒŒ๋™์ด ํฐ ๊ทœ๋ชจ์˜ ํ๋ฆ„ยท์ง€ํ˜•์— ์˜ํ•ด ๊ตด์ ˆยท์‚ฐ๋ž€๋˜๋Š” ๊ฒฝ์šฐ ๋ ˆ์ด ํŠธ๋ ˆ์ด์‹ฑ์ด ์ผ๋ฐ˜์ ์ธ ์˜ˆ์ธก ๋„๊ตฌ์ด์ง€๋งŒ, ๋‹ค์ˆ˜์˜ ๋ ˆ์ด๋ฅผ ์ถ”์ ํ•ด์•ผ ํ•˜๋Š” ํ†ต๊ณ„์  ๋ฌธ์ œ์—์„œ๋Š” ๊ณ„์‚ฐ๋Ÿ‰์ด ๊ธ‰์ฆํ•œ๋‹ค. ์ €์ž๋Š” ์ด์ „ ์—ฐ๊ตฌ์—์„œ ๊ทผ์ธ์˜คํ„ด ํŒŒ๋™์„ ์ „ํ•˜ ์ž…์ž์™€ ์œ ์‚ฌํ•˜๊ฒŒ ๋‹ค๋ฃจ์–ด ํ†ต๊ณ„์—ญํ•™์  ์ ‘๊ทผ์„ ์„ฑ๊ณต์‹œ์ผฐ์ง€๋งŒ, ์ผ๋ฐ˜์ ์ธ ํŒŒ๋™(์ค‘๋ ฅยท๋ชจ์„ธ๊ด€ ํŒŒ๋™ ๋“ฑ)์—๋Š” ์ง์ ‘์ ์ธ ์ž…์ž ์œ ์‚ฌ ๋ชจ๋ธ์ด ์กด์žฌํ•˜์ง€ ์•Š์•„ โ€œ๋งˆ์ดํฌ๋กœ์บ๋…ธ๋‹ˆ์ปฌโ€ ๋ฐฉ๋ฒ•์ด ๊ฐ€๋Šฅํ•œ์ง€ ์˜๋ฌธ์„ ์ œ๊ธฐํ•œ๋‹ค. 2.

Physics
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ERGMs on block models

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ „ํ†ต ERGM์˜ ํ•œ๊ณ„ : ๊ธฐ์กด์˜ ERGM์€ ์ •์  ๊ฐ„ ๋™์งˆ์„ฑ์„ ๊ฐ€์ •ํ•˜๊ณ , ์ •์  ์žฌ๋ฐฐ์—ด์— ๋Œ€ํ•ด ์™„์ „ ๋ถˆ๋ณ€์ด๋‹ค. ์‹ค์ œ ์‚ฌํšŒยท์ƒ๋ฌผยท๊ธฐ์ˆ  ๋„คํŠธ์›Œํฌ๋Š” ์ •์ ๋งˆ๋‹ค ์†์„ฑ(์˜ˆ: ์ง€์—ญ, ๊ด€์‹ฌ์‚ฌ, ์ง์—… ๋“ฑ)์ด ์กด์žฌํ•ด ์—ฐ๊ฒฐ ํ™•๋ฅ ์— ํฐ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๋ธ”๋ก(์ƒ‰) ๋ชจ๋ธ์˜ ํ•„์š”์„ฑ : ์ •์ ์„ ์œ ํ•œ ๊ฐœ์˜ ๋ธ”๋ก์œผ๋กœ ๋‚˜๋ˆ„์–ด ๊ฐ ๋ธ”๋ก ๊ฐ„ยท๋‚ด๋ถ€ ์—ฐ๊ฒฐ ๊ฐ•๋„๋ฅผ ๋‹ค๋ฅด๊ฒŒ ์„ค์ •ํ•˜๋ฉด, ์ปค๋ฎค๋‹ˆํ‹ฐ ๊ตฌ์กฐ, ์ •์น˜์  ์„ฑํ–ฅ, ๋ถ„์•ผ๋ณ„ ํ˜‘์—… ๋“ฑ ๋ณต์žกํ•œ ์ด์งˆ์„ฑ์„ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ํฌ์ฐฉํ•  ์ˆ˜ ์žˆ๋‹ค. 2. ๋ชจ๋ธ ์ •์˜ ๋ธ”๋ก ๊ตฌ์กฐ : ์ •์  ์ง‘ํ•ฉ (

Model Mathematics
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Evolutionary Optimization Trumps Adam Optimization on Embedding Space Exploration

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ตœ๊ทผ ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ์—์„œ ๊ณ ์ฐจ์› ์ž„๋ฒ ๋”ฉ ๊ณต๊ฐ„์„ ํšจ์œจ์ ์œผ๋กœ ํƒ์ƒ‰ํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ๊ธ‰์ฆํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ „ํ†ต์ ์œผ๋กœ๋Š” ํ™•๋ฅ ์  ๊ฒฝ์‚ฌ ํ•˜๊ฐ•๋ฒ• ๊ธฐ๋ฐ˜ ์˜ตํ‹ฐ๋งˆ์ด์ €์ธ Adam์ด ๋น ๋ฅธ ์ˆ˜๋ ด ์†๋„์™€ ์•ˆ์ •์„ฑ ๋•Œ๋ฌธ์— ๋„๋ฆฌ ์‚ฌ์šฉ๋˜์–ด ์™”์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ž„๋ฒ ๋”ฉ ๊ณต๊ฐ„์€ ์ข…์ข… ๋น„์„ ํ˜•์ ์ด๊ณ  ๋‹ค์ค‘๊ทน๊ฐ’์„ ๊ฐ€์ง€๋ฉฐ, ์ „์—ญ ์ตœ์ ํ•ด๋ฅผ ์ฐพ๊ธฐ ์–ด๋ ค์šด ํŠน์„ฑ์„ ์ง€๋‹™๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ํŠน์„ฑ์€ ํƒ์ƒ‰ ๊ณผ์ •์—์„œ ์ง€์—ญ ์ตœ์ ์ ์— ๋น ์ง€๊ธฐ ์‰ฌ์šด ๊ตฌ์กฐ์  ๋ฌธ์ œ๋ฅผ ์•ผ๊ธฐํ•ฉ๋‹ˆ๋‹ค. 2. ์ง„ํ™” ์ตœ์ ํ™”(Evolutionary Optimization, EO)์˜ ํ•ต์‹ฌ ์•„์ด๋””์–ด EO๋Š” ์ง‘๋‹จ ๊ธฐ๋ฐ˜

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ExplicitLM: Decoupling Knowledge from Parameters via Explicit Memory Banks

1. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๊ธฐ์—ฌ ๋‚ด์šฉ | ์˜์˜ | | | | | | โ‘  | ์™ธ๋ถ€ ๋ช…์‹œ์  ๋ฉ”๋ชจ๋ฆฌ ๋ฑ…ํฌ (1M ๊ทœ๋ชจ) ๋„์ž… โ†’ ์ธ๊ฐ„์ด ์ง์ ‘ ์ฝ๊ณ  ์ˆ˜์ • ๊ฐ€๋Šฅ | ๋ชจ๋ธ ํˆฌ๋ช…์„ฑยท์ง€์‹ ์—…๋ฐ์ดํŠธ ๋น„์šฉ ์ ˆ๊ฐ | | โ‘ก | Product Key Decomposition ์„ ํ™œ์šฉํ•œ ๋ณตํ•ฉ ๊ฒ€์ƒ‰ ๋ณต์žก๋„ ๊ฐ์†Œ | ๋Œ€๊ทœ๋ชจ ๋ฉ”๋ชจ๋ฆฌ์—์„œ๋„ ์‹ค์‹œ๊ฐ„ ๊ฒ€์ƒ‰ ๊ฐ€๋Šฅ | | โ‘ข | Gumbelโ€‘Softmax ๊ธฐ๋ฐ˜ ๋ฏธ๋ถ„ ๊ฐ€๋Šฅํ•œ ์ •๋ฐ€ ๋งค์นญ | ์—”๋“œโ€‘ํˆฌโ€‘์—”๋“œ ํ•™์Šต์„ ์œ ์ง€ํ•˜๋ฉด์„œ ์ •ํ™•๋„ ํ–ฅ์ƒ | | โ‘ฃ | Dualโ€‘system ์ง€์‹ ๋ถ„ํ•  (๋ช…์‹œ์  20 % / ์•”๋ฌต์  80 %) ๋ฐ

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Extending numerical simulations in SIMPSON: Electron paramagnetic resonance, dynamic nuclear polarisation, propagator splitting, pulse transients, and quadrupolar cross terms

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฉ€ํ‹ฐ์Šคํ•€ยท๋ฉ€ํ‹ฐ์Šค์ผ€์ผ ์š”๊ตฌ : ์ตœ์‹  NMRยทEPRยทDNP ์‹คํ—˜์€ ์ „์ž์™€ ํ•ต ์Šคํ•€์ด ๋™์‹œ์— ์ž‘์šฉํ•˜๋Š” ๋ณตํ•ฉ ์‹œ์Šคํ…œ์„ ๋‹ค๋ฃจ๋ฉฐ, ์‹œ๊ฐ„ยท์ฃผํŒŒ์ˆ˜ ์Šค์ผ€์ผ์ด ํฌ๊ฒŒ ์ฐจ์ด ๋‚œ๋‹ค. ๊ธฐ์กด SIMPSON(๋ฒ„์ „ 4.0)์€ ์ฃผ๋กœ ๊ณ ์ฒด NMR์— ์ดˆ์ ์„ ๋งž์ท„๊ณ , EPRยทDNP ๊ธฐ๋Šฅ์€ ์ œํ•œ์ ์ด์—ˆ๋‹ค. ๊ณ„์‚ฐ ๋ณ‘๋ชฉ : ๋ฐ€๋„ ํ–‰๋ ฌ์˜ ์‹œ๊ฐ„ ์ง„ํ™”์™€ ์ตœ์  ์ œ์–ด(Optimal Control) ๋ฐ˜๋ณต ๊ณ„์‚ฐ์—์„œ ํ–‰๋ ฌ ์ง€์ˆ˜(exp) ์—ฐ์‚ฐ์ด ์ฃผ์š” ๋ณ‘๋ชฉ์œผ๋กœ ์ž‘์šฉํ•œ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์ˆ ์  ํ˜์‹  | ๊ธฐ๋Šฅ | ๊ธฐ์กด ํ•œ๊ณ„ | ์ƒˆ ๋ฒ„์ „(6.0)์—์„œ์˜ ๊ฐœ์„  | ๊ธฐ๋Œ€ ํšจ๊ณผ |

Physics
No Image

Finite integration time can shift optimal sensitivity away from criticality

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ž„๊ณ„์„ฑ(criticality) ์€ ๋‡Œ, ์ƒˆ ๋ฌด๋ฆฌ, ์ค„๊ธฐ์„ธํฌ ๋“ฑ ๋‹ค์–‘ํ•œ ์ƒ๋ฌผํ•™์  ์‹œ์Šคํ…œ์—์„œ ๊ด€์ธก๋˜๋Š” ๋ณดํŽธ์  ํ˜„์ƒ์ด๋ฉฐ, ๊ฐ๋„ยท๋™์  ๋ฒ”์œ„ยท์ •๋ณด ํ๋ฆ„ ๋“ฑ์„ ์ตœ์ ํ™”ํ•œ๋‹ค๋Š” ์ด๋ก ์  ๊ทผ๊ฑฐ๊ฐ€ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์ž„๊ณ„ ์Šฌ๋กœ์ž‰(critical slowing down) ๊ณผ ํ”Œ๋Ÿญํˆฌ์—์ด์…˜ ํ™•๋Œ€ ๋Š” ์‹ค์ œ ์‹œ์Šคํ…œ์ด ์ œํ•œ๋œ ์‹œ๊ฐ„ ์•ˆ์— ๊ฒฐ์ •์„ ๋‚ด๋ ค์•ผ ํ•  ๋•Œ ํฐ ๋น„์šฉ์„ ์ดˆ๋ž˜ํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ๋ฌดํ•œ ์ ๋ถ„ ์‹œ๊ฐ„ (T โ†’ โˆž) ๊ฐ€์ •ํ•˜์— ์ž„๊ณ„์  ๊ทผ์ฒ˜๊ฐ€ ์ตœ์ ์ด๋ผ๊ณ  ์ฃผ์žฅํ–ˆ์œผ๋ฉฐ, ์œ ํ•œ ์‹œ๊ฐ„ ํšจ๊ณผ๋Š” ๊ฑฐ์˜ ๋‹ค๋ฃจ์ง€ ์•Š์•˜๋‹ค. 2. ๋ชจ๋ธ ๋ฐ ํ•ต์‹ฌ ๋ณ€์ˆ˜ | ์š”์†Œ

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

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 ์€ ์ตœ๊ทผ ์–‘์ž ๋“œ๋กญ๋ฆฟ ๋ฐ BEC์—์„œ ๊ด€์ฐฐ๋œ ๋ฐ”์™€ ๊ฐ™์ด, ๊ฒฝ์Ÿ์  ๋น„์„ ํ˜•์„ฑ(ํ๋น…โ€‘ํ€ธํ‹ฑ)์œผ๋กœ ์ธํ•ด ํ‰ํƒ„ํ•œ ํŒŒํ˜•์„ ์œ ์ง€ํ•œ๋‹ค. ๊ณ ์ฐจ ์ง์ˆ˜ ์ฐจ์ˆ˜ ๋ถ„์‚ฐ(PHEOD) ์€ ์ „ํ†ต์ ์ธ 2์ฐจ(ฮฒโ‚‚) ๋ถ„์‚ฐ์„ ๋„˜์–ด 4์ฐจ, 6์ฐจ ๋“ฑ ๋†’์€ ์ฐจ์ˆ˜์˜ ๊ตฐ์ง‘๋œ ๋ถ„์‚ฐ์„ ์˜๋ฏธํ•œ๋‹ค. ์ˆœ์ˆ˜ 4์ฐจ ์†”๋ฆฌํ†ค(PQS)์˜ ์‹คํ—˜์  ๊ตฌํ˜„(ํฌํ†ค๊ฒฐ์ • ํŒŒ๋™๊ฐ€์ด๋“œ) ์ดํ›„, m 6, 8, 10์—์„œ๋„ ์œ ์‚ฌ ํ˜„์ƒ์ด ๊ธฐ๋Œ€๋˜์—ˆ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ๋‹จ์ผ ํŒŒ๋™ ํ˜น์€ ๋ฐ”์ธ๋”ฉ๋œ ์ƒํƒœ ์— ์ดˆ์ ์„ ๋งž์ท„์œผ๋ฉฐ, FT ํ˜•ํƒœ์˜ ์†”๋ฆฌํ†ค๊ณผ ๊ทธ ์ƒํ˜ธ์ž‘์šฉ์€ ์•„์ง ํƒ๊ตฌ๋˜์ง€ ์•Š์•˜๋‹ค. 2

Physics
FractalBench: Diagnosing Visual-Mathematical Reasoning Through Recursive Program Synthesis

FractalBench: Diagnosing Visual-Mathematical Reasoning Through Recursive Program Synthesis

1. ์—ฐ๊ตฌ ๋™๊ธฐ์™€ ์˜์˜ ์‹œ๊ฐโ€‘์ˆ˜ํ•™์  ์ถ”๋ก ์˜ ํ•ต์‹ฌ : ์ธ๊ฐ„์€ ๋ณต์žกํ•œ ์‹œ๊ฐ ํŒจํ„ด์„ ๋ณด๊ณ  ๊ทธ ๋’ค์— ์ˆจ์€ ๊ทœ์น™(์˜ˆ: ํ”„๋ž™ํƒˆ์˜ ์žฌ๊ท€์  ๋ณ€ํ™˜)์„ ์ถ”๋ก ํ•œ๋‹ค. ์ด๋Š” โ€œ์œ ํ•œ โ†’ ๋ฌดํ•œโ€์˜ ์ผ๋ฐ˜ํ™” ๋Šฅ๋ ฅ์ด๋ฉฐ, ํ˜„์žฌ ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ AI๊ฐ€ ์ง„์ •์œผ๋กœ ๊ฐ–์ถ”์–ด์•ผ ํ•  ํ•ต์‹ฌ ์—ญ๋Ÿ‰์ด๋‹ค. ํ”„๋ž™ํƒˆ์ด๋ผ๋Š” ์ด์ƒ์  ์‹œํ—˜๋Œ€ : ํ”„๋ž™ํƒˆ์€ ์†Œ์ˆ˜์˜ ๋ณ€ํ™˜ ๊ทœ์น™ ๋งŒ์œผ๋กœ๋„ ๋ฌดํ•œํžˆ ๋ณต์žกํ•œ ๊ตฌ์กฐ๋ฅผ ์ƒ์„ฑํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ชจ๋ธ์ด โ€œ์ด๋ฏธ์ง€ โ†’ ๊ทœ์น™ โ†’ ์ฝ”๋“œโ€๋ผ๋Š” ์ „ ๊ณผ์ •์„ ์ˆ˜ํ–‰ํ–ˆ๋Š”์ง€ ๋ช…ํ™•ํžˆ ๊ฒ€์ฆํ•  ์ˆ˜ ์žˆ๋‹ค. 2. ๋ฒค์น˜๋งˆํฌ ์„ค๊ณ„ | ์š”์†Œ | ์„ค๋ช… | ํ‰๊ฐ€ ํฌ์ธํŠธ | | | | | | ์ž…๋ ฅ | ํ”„๋ž™ํƒˆ ์ด๋ฏธ์ง€

Genus two Goeritz equivalence in lens spaces $L(p,1)$

Genus two Goeritz equivalence in lens spaces $L(p,1)$

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ Berge ์ถ”์ธก ๊ณผ ๊ทธ ๋ณ€ํ˜•๋“ค์€ โ€œํŠน์ • ์ฐจ์ˆ˜ 2 Heegaard ๋ถ„ํ•  ์œ„์— ๋งค๋‹ฌ๋ฆฐ ๋งค๋“ญ์ด ์–ด๋–ค Dehn ์ˆ˜์ˆ ์„ ํ•  ๋•Œ ์–ด๋–ค 3โ€‘๋‹ค์–‘์ฒด๋ฅผ ๋งŒ๋“ ๋‹คโ€๋Š” ์งˆ๋ฌธ์„ ์ค‘์‹ฌ์œผ๋กœ ์ „๊ฐœ๋ผ ์™”๋‹ค. Goeritz ๊ตฐ์€ โ€œHeegaard ๋ถ„ํ• ์„ ๋ณด์กดํ•˜๋Š” ๋ฐฉํ–ฅ ๋ณด์กด ๋™ํ˜•์‚ฌ์ƒ์˜ ๋™ํ˜•๋ฅ˜โ€์ด๋ฉฐ, ๋งค๋“ญ์˜ ์œ„์น˜๊ฐ€ ์„œ๋กœ ๋‹ค๋ฅธ์ง€ ํŒ๋‹จํ•˜๋Š” ์ž์—ฐ์Šค๋Ÿฌ์šด ๋„๊ตฌ๋‹ค. ์ €์ž๋Š” ์ด์ „ ๋…ผ๋ฌธ(

Mathematics
Gradient Networks for Universal Magnetic Modeling of Synchronous Machines

Gradient Networks for Universal Magnetic Modeling of Synchronous Machines

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ „๋™๊ธฐ ์ œ์–ดยท์ถ”์ • ์— ํ•„์ˆ˜์ ์ธ ๊ณ ์ •๋ฐ€ ์ž๊ธฐ ๋ชจ๋ธ์€ ํฌํ™”์™€ ๊ณ ์กฐํŒŒ๋ฅผ ์ •ํ™•ํžˆ ๋ฐ˜์˜ํ•ด์•ผ ํ•œ๋‹ค. ๊ธฐ์กด ์ ‘๊ทผ๋ฒ•(๋ถ„์„์‹, LUT, ๋ธ”๋ž™๋ฐ•์Šค NN)์€ ๊ฐ๊ฐ ํ‘œํ˜„๋ ฅ ์ œํ•œ, ์ฐจ์› ์ €์ฃผ, ๋ฐ์ดํ„ฐยท๋ฉ”๋ชจ๋ฆฌ ์š”๊ตฌ๋Ÿ‰ ๋“ฑ์˜ ๋ฌธ์ œ๋ฅผ ์•ˆ๊ณ  ์žˆ๋‹ค. PINN ๊ณผ Hamiltonian NN ์€ ๋ฌผ๋ฆฌ์  ์ œ์•ฝ์„ ๋„์ž…ํ•ด ๋ฐ์ดํ„ฐ ํšจ์œจ์„ฑ์„ ๋†’์˜€์ง€๋งŒ, ์ˆ˜์น˜ ๋ฏธ๋ถ„ ์— ์˜์กดํ•˜๋Š” ๊ตฌ์กฐ๋Š” ํ•™์Šต ์•ˆ์ •์„ฑ์„ ์ €ํ•ดํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๊ตฌ๋ถ„ | ๋‚ด์šฉ | ์˜์˜ | | | | | | ๊ทธ๋ผ๋””์–ธํŠธ ๋„คํŠธ์›Œํฌ ์„ค๊ณ„ | ์—๋„ˆ์ง€ ํ•จ์ˆ˜ (W(psi s,theta)) ๋ฅผ

Network Model Electrical Engineering and Systems Science
No Image

Hard vs. Noise: Resolving Hard-Noisy Sample Confusion in Recommender Systems via Large Language Models

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์•”์‹œ์  ํ”ผ๋“œ๋ฐฑ์˜ ๋ณธ์งˆ์  ๋…ธ์ด์ฆˆ : ํด๋ฆญยท๋…ธ์ถœ ๋ฐ์ดํ„ฐ๋Š” ์‚ฌ์šฉ์ž์˜ ์‹ค์ œ ์„ ํ˜ธ์™€ ๋ฌด๊ด€ํ•œ ํ–‰๋™(์˜คํด๋ฆญ, ์œ„์น˜ ํŽธํ–ฅ ๋“ฑ)๊นŒ์ง€ ํฌํ•จํ•œ๋‹ค. Hardโ€‘Noisy ํ˜ผ๋™ : ๊ธฐ์กด ๋ฐฉ๋ฒ•์€ โ€œ์†์‹ค์ด ํฐ ์ƒ˜ํ”Œ ๋…ธ์ด์ฆˆโ€๋ผ๋Š” ๊ฐ€์ •์— ์˜์กดํ•˜์ง€๋งŒ, ์†์‹ค์ด ํฐ hard ์ƒ˜ํ”Œ ๋„ ์กด์žฌํ•œ๋‹ค. ์ด๋“ค์„ ๋™์ผํ•˜๊ฒŒ ์ฒ˜๋ฆฌํ•˜๋ฉด ๋ชจ๋ธ์ด ์–ด๋ ค์šด ๊ฒฝ๊ณ„ ์‚ฌ๋ก€๋ฅผ ํ•™์Šตํ•  ๊ธฐํšŒ๋ฅผ ์žƒ๊ฒŒ ๋œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด LLM ํ™œ์šฉ : ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ์€ ํ’๋ถ€ํ•œ ์„ธ๊ณ„ ์ง€์‹๊ณผ ๋ฌธ๋งฅ ์ดํ•ด ๋Šฅ๋ ฅ์„ ๊ฐ–์ถ”๊ณ  ์žˆ์–ด, ์‚ฌ์šฉ์ž์™€ ์•„์ดํ…œ ๊ฐ„์˜ ์‹œ๋งจํ‹ฑยท๋…ผ๋ฆฌ์  ์—ฐ๊ด€์„ฑ ์„ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ๋‹ค.

System Model
Hensel minimality, $p$-adic exponentiation and Tate uniformization

Hensel minimality, $p$-adic exponentiation and Tate uniformization

| ๊ตฌ๋ถ„ | ํ•ต์‹ฌ ๋‚ด์šฉ | ์˜์˜ ๋ฐ ๋น„ํ‰ | | | | | | ํ—จ์…€ ์ตœ์†Œ์„ฑ์˜ ๋„์ž… | 1โ€‘hโ€‘minimality๋Š” ๊ธฐ์กด์˜ Pโ€‘minimal, Cโ€‘minimal๋ณด๋‹ค ์ผ๋ฐ˜์ ์ธ ํ‹€์„ ์ œ๊ณตํ•œ๋‹ค. ์ €์ž๋Š” ์ด๋ก ์  ๋ฐฐ๊ฒฝ์„ ์ •๋ฆฌํ•˜๊ณ , Jacobian ์„ฑ์งˆยท์•Œ์ œ๋ธŒ๋ผ์  Skolem ํ•จ์ˆ˜ยท์ „์น˜(pregeometry) ๋“ฑ์„ ํ™œ์šฉํ•ด ๋ชจ๋ธ ์ด๋ก ์  ๊ธฐ๋ฐ˜์„ ํ™•๋ฆฝํ•œ๋‹ค. | ํ—จ์…€ ์ตœ์†Œ์„ฑ์˜ ํ•ต์‹ฌ ํŠน์„ฑ(ํŠนํžˆ ์ •์˜ ๊ฐ€๋Šฅํ•œ ํ•จ์ˆ˜๋“ค์˜ โ€œ๋‹จ์กฐ์„ฑโ€๊ณผ ์ฐจ์› ์ด๋ก )์„ ๋ช…ํ™•ํžˆ ์ œ์‹œํ•จ์œผ๋กœ์จ, ๋น„์•„ํ‚ค๋ฉ”๋ฐ์•„ ์ฒด๊ณ„์—์„œ๋„ oโ€‘minimality์™€ ์œ ์‚ฌํ•œ โ€œtamnessโ€๋ฅผ ํ™•๋ณดํ•œ๋‹ค๋Š” ์ ์ด

Mathematics
Hierarchical paraproducts

Hierarchical paraproducts

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๊ทธ๋ž˜ํ”„ยท๋งค๋‹ˆํด๋“œ ์‹ ํ˜ธ ์ฒ˜๋ฆฌ ๋ถ„์•ผ์—์„œ๋Š” ์œ ํด๋ฆฌ๋“œ ๊ณต๊ฐ„์—์„œ์˜ ์กฐํ™”ํ•ด์„ ๋„๊ตฌ(์›จ์ด๋ธŒ๋ ›, ํŒŒ๋ผํ”„๋กœ๋•ํŠธ ๋“ฑ)๋ฅผ ๊ทธ๋ž˜ํ”„์— ์˜ฎ๊ธฐ๋Š” ์‹œ๋„๊ฐ€ ํ™œ๋ฐœํžˆ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค(

Mathematics
Hypercontractivity for a family of quantum Ornstein-Uhlenbeck semigroups

Hypercontractivity for a family of quantum Ornstein-Uhlenbeck semigroups

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๊ณ ์ „์  ๊ฒฐ๊ณผ : Nelson(1973, 1974)์™€ Gross(1975)๋Š” ๊ณ ์ „์ ์ธ Ornsteinโ€‘Uhlenbeck ์„ธ๋ฏธ๊ทธ๋ฃน์ด ์ดˆ์ˆ˜์ถ•์„ฑ์„ ๊ฐ–๋Š” ๊ฒƒ์„ ๋ณด์˜€๊ณ , ์ด๋ฅผ ๋กœ๊ทธโ€‘Sobolev ๋ถ€๋“ฑ์‹๊ณผ ๋™๋“ฑ์‹œ์ผฐ๋‹ค. ์ด๋Ÿฌํ•œ ์ด๋ก ์€ ์–‘์ž์žฅ๋ก , ํ†ต๊ณ„์—ญํ•™, ์ •๋ณด์ด๋ก  ๋“ฑ์—์„œ ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ํ™œ์šฉ๋œ๋‹ค. ์–‘์ž ๋งˆ๋ฅด์ฝ”ํ”„ ์„ธ๋ฏธ๊ทธ๋ฃน : Cipriani et al.(2012)์™€ Carlenโ€‘Maas ๋“ฑ์€ ๋น„๊ฐ€์—ญ์ ์ธ(๋น„ํŠธ๋ ˆ์ด์…œ) ์ƒํƒœ์— ๋Œ€ํ•ด ๋Œ€์ˆ˜์  ๊ตฌ์กฐ๋ฅผ ๋ณด์กดํ•˜๋Š” ์–‘์ž ๋งˆ๋ฅด์ฝ”ํ”„ ์„ธ๋ฏธ๊ทธ๋ฃน์„ ๊ตฌ์ถ•ํ–ˆ์œผ๋ฉฐ, ํŠนํžˆ ์–‘์ž Ornsteinโ€‘Uhlenbec

Mathematics
Importance inversion transfer identifies shared principles for cross-domain learning

Importance inversion transfer identifies shared principles for cross-domain learning

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋‹คํ•™์ œ ์ง€์‹ ์ „์ด์˜ ํ•ต์‹ฌ ๊ฐ€์ • : ์„œ๋กœ ๋‹ค๋ฅธ ๋ถ„์•ผ๊ฐ€ ๋™์ผํ•œ ์กฐ์ง ์›๋ฆฌ๋ฅผ ๊ณต์œ ํ•œ๋‹ค๋Š” ์ „์ œ๋Š” ๊ธฐ์กด ์ „์ด ํ•™์Šต ์ด๋ก (๋ถ„ํฌ ์ฐจ์ด, ์˜ค๋ฅ˜ ๊ฒฝ๊ณ„)๊ณผ๋Š” ๋ณ„๊ฐœ๋กœ, ์‹ค์ œ ๊ณผํ•™ยท๊ณตํ•™ ๋ฌธ์ œ์—์„œ โ€œ๊ณตํ†ต ๊ตฌ์กฐโ€๋ฅผ ์ฐพ์•„์•ผ ํ•จ์„ ๊ฐ•์กฐํ•œ๋‹ค. ๊ธฐ์กด ๋ฐฉ๋ฒ•์˜ ํ•œ๊ณ„ : ์ž ์žฌ ๊ณต๊ฐ„ ์ •๋ ฌ(Latent Alignment) ์€ ๊ณ ์ฐจ์› ์ž„๋ฒ ๋”ฉ์„ ํ†ตํ•ด ๋„๋ฉ”์ธ ์ฐจ์ด๋ฅผ ์ตœ์†Œํ™”ํ•˜์ง€๋งŒ, ์ด ๊ณผ์ •์ด ๋ถˆํˆฌ๋ช…ํ•˜๊ณ  ๋„๋ฉ”์ธ ํŠน์ด์  ์žก์Œ์— ์ทจ์•ฝํ•˜๋‹ค. ๋ฐ์ดํ„ฐ ํฌ์†Œยท๋…ธ์ด์ฆˆ ์ƒํ™ฉ์—์„œ ์ •๊ทœํ™”๋œ ์ž„๋ฒ ๋”ฉ์ด ๋ถ•๊ดด๋˜๋Š” ํ˜„์ƒ์ด ๋นˆ๋ฒˆํžˆ ๋ณด๊ณ ๋œ๋‹ค. 2. Xโ€‘CDTL ํ”„๋ ˆ์ž„์›Œํฌ ์„ค๊ณ„ ๋„คํŠธ์›Œ

Machine Learning Computer Science Learning
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Improving Industrial Injection Molding Processes with Explainable AI for Quality Classification

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋จธ์‹ ๋Ÿฌ๋‹ ์ ์šฉ ์žฅ๋ฒฝ : ๊ณ ์„ฑ๋Šฅ ๋ชจ๋ธ์€ ์ผ๋ฐ˜์ ์œผ๋กœ ๋ธ”๋ž™๋ฐ•์Šค ํ˜•ํƒœ์ด๋ฉฐ, ํ˜„์žฅ ์—”์ง€๋‹ˆ์–ด๊ฐ€ ๊ฒฐ๊ณผ๋ฅผ ์‹ ๋ขฐํ•˜๊ณ  ์กฐ์ •ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ์„ผ์„œ ์ œํ•œ : ์‚ฌ์ถœ ์„ฑํ˜• ์„ค๋น„๋Š” ๋น„์šฉยท๊ณต๊ฐ„ยท๋‚ด๊ตฌ์„ฑ ๋ฌธ์ œ๋กœ ๋ชจ๋“  ๊ณต์ • ๋ณ€์ˆ˜๋ฅผ ์‹ค์‹œ๊ฐ„ ์ธก์ •ํ•˜๊ธฐ ํž˜๋“ค๋‹ค. ๋ฐ์ดํ„ฐ ๊ฒฐํ•์€ ๋ชจ๋ธ ์„ฑ๋Šฅ ์ €ํ•˜์™€ ๊ณผ์ ํ•ฉ ์œ„ํ—˜์„ ๋†’์ธ๋‹ค. XAI์˜ ์—ญํ•  : SHAP, Gradโ€‘CAM, LIME ๋“ฑ์€ ๋ชจ๋ธ์ด ์–ด๋–ค ์ž…๋ ฅ์— ์˜์กดํ•˜๋Š”์ง€ ์‹œ๊ฐยท์ˆ˜์น˜์ ์œผ๋กœ ์ œ๊ณตํ•ด, ๋ถˆํ•„์š”ํ•œ ํŠน์ง•์„ ์‹๋ณ„ํ•˜๊ณ  ํ˜„์žฅ ์ ์šฉ์„ฑ์„ ๋†’์ธ๋‹ค. 2. ์—ฐ๊ตฌ ๋ชฉํ‘œ 1. LSTM ๊ธฐ๋ฐ˜ ํ’ˆ์งˆ ๋ถ„๋ฅ˜ ๋ชจ๋ธ์— XAI ๊ธฐ๋ฒ•์„ ์ ์šฉ

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InteractComp: Evaluating Search Agents With Ambiguous Queries

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํ˜„์‹ค๊ณผ์˜ ๊ดด๋ฆฌ : ๋Œ€๋ถ€๋ถ„์˜ ๊ธฐ์กด ๊ฒ€์ƒ‰ ์—์ด์ „ํŠธ๋Š” โ€œ์™„์ „ํ•˜๊ณ  ๋ช…ํ™•ํ•œ ์งˆ์˜โ€๋ฅผ ์ „์ œ๋กœ ์„ค๊ณ„๋ผ, ์‹ค์ œ ์‚ฌ์šฉ์ž์™€์˜ ์ดˆ๊ธฐ ๋Œ€ํ™”์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋ชจํ˜ธ์„ฑ์„ ๋‹ค๋ฃจ์ง€ ๋ชปํ•œ๋‹ค. ๋ฒค์น˜๋งˆํฌ ๋ถ€์žฌ : ํ˜„์žฌ ๊ณต๊ฐœ๋œ IRยท๊ฒ€์ƒ‰ ๋ฒค์น˜๋งˆํฌ(์˜ˆ: MS MARCO, TREC ๋“ฑ)๋Š” ์งˆ์˜๊ฐ€ ์ด๋ฏธ ๋ช…ํ™•ํžˆ ์ •์˜๋œ ์ƒํƒœ์—์„œ ๋ชจ๋ธ์˜ ์žฌํ˜„์„ฑยท์ •ํ™•์„ฑ์„ ์ธก์ •ํ•œ๋‹ค. ์ƒํ˜ธ์ž‘์šฉ์„ ์š”๊ตฌํ•˜๋Š” ์‹œ๋‚˜๋ฆฌ์˜ค๋Š” ์ „ํ˜€ ํฌํ•จ๋˜์ง€ ์•Š๋Š”๋‹ค. ์—ฐ๊ตฌ ๋ชฉํ‘œ : ๋ชจํ˜ธํ•œ ์งˆ์˜๋ฅผ ์ธ์‹ํ•˜๊ณ , ๋Œ€ํ™”ํ˜•์œผ๋กœ ๋ช…ํ™•ํžˆ ํ•˜๋Š” ๋Šฅ๋ ฅ์„ ์ •๋Ÿ‰ํ™”ยท๋น„๊ตํ•  ์ˆ˜ ์žˆ๋Š” ํ‰๊ฐ€ ์ฒด๊ณ„๋ฅผ ์ œ๊ณตํ•œ๋‹ค๋Š” ์ ์—์„œ ํฐ ์˜์˜๊ฐ€ ์žˆ๋‹ค.

Kalman Filtering Based Flight Management System Modeling for AAM Aircraft

Kalman Filtering Based Flight Management System Modeling for AAM Aircraft

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ AAM ํŠน์„ฑ : ๋„์‹ฌ ๊ทผ์ ‘ยท๋‹จ๊ฑฐ๋ฆฌ ๋น„ํ–‰, ๊ณ ์ž์œจ์„ฑ, ATC ๋ถ€ํ•˜ ์ตœ์†Œํ™”๊ฐ€ ํ•ต์‹ฌ. ํ˜„ํ–‰ ํ•œ๊ณ„ : ๊ธฐ์กด ์ „๋žต์  ํ”Œ๋ž˜๋‹ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์™„์ „ํ•œ ์ด์ƒ ํ™˜๊ฒฝ์„ ๊ฐ€์ •ํ•˜๊ฑฐ๋‚˜, ์™ธ๋ถ€ ๊ต๋ž€์„ ๋ณด์ˆ˜์ ์ธ ์„ ํ˜• ๋ถˆํ™•์‹ค์„ฑ ๋ชจ๋ธ๋กœ๋งŒ ์ฒ˜๋ฆฌํ•œ๋‹ค. ์ด๋Š” ์‹ค์ œ ์šด์šฉ ์‹œ ๊ณผ๋„ํ•œ ์•ˆ์ „ ๋งˆ์ง„์„ ์ดˆ๋ž˜ํ•˜๊ฑฐ๋‚˜, ํ’๋™ยทํ’ํ–ฅ ๋ณ€ํ™”์™€ ๊ฐ™์€ ์ œ์–ด ์ž…๋ ฅ์— ๋Œ€ํ•œ ๋ฏผ๊ฐ๋„๋ฅผ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | | | | | 1 | ์‹œ๊ทธ๋ชจ์ด๋“œ ๋ธ”๋ Œ๋”ฉ ์ธก์ • ์žก์Œ ๊ณต๋ถ„์‚ฐ ์„ ๋„์ž…ํ•ด ์ง„ํ–‰๋ฅ (p) ๊ธฐ๋ฐ˜์œผ๋กœ Qโ‚˜โ‚โ‚“โ†”Qโ‚˜แตขโ‚™์„ ์—ฐ์† ์ „ํ™˜ | ๊ณ ์ •

Computer Science System Robotics Model
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Languages are Modalities: Cross-Lingual Alignment via Encoder Injection

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์–ธ์–ด์™€ ๋ชจ๋‹ฌ๋ฆฌํ‹ฐ์˜ ์œ ์‚ฌ์„ฑ : ์ธ๊ฐ„์ด ์‹œ๊ฐยท์ฒญ๊ฐ ๋“ฑ ๋‹ค์–‘ํ•œ ๊ฐ๊ฐ์„ ํ†ตํ•ด ์ •๋ณด๋ฅผ ํ†ตํ•ฉํ•˜๋“ฏ, ์–ธ์–ด๋„ ์„œ๋กœ ๋‹ค๋ฅธ โ€œํ‘œํ˜„ ๋ชจ๋‹ฌ๋ฆฌํ‹ฐโ€๋ผ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๊ธฐ์กด ๋‹ค๊ตญ์–ด ๋ชจ๋ธ์€ ์ด ์ฐจ์ด๋ฅผ ์™„์ „ํžˆ ๋ฌด์‹œํ•˜๊ณ  ํ•˜๋‚˜์˜ ๊ณต์œ  ํŒŒ๋ผ๋ฏธํ„ฐ ์ง‘ํ•ฉ์— ๊ฐ•์ œ ๋งคํ•‘ํ•œ๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ๊ธฐ์กด ๊ต์ฐจ์–ธ์–ด ์ •๋ ฌ์˜ ํ•œ๊ณ„ : ๋Œ€๊ทœ๋ชจ ๋ณ‘๋ ฌ ์ฝ”ํผ์Šค์— ์˜์กดํ•˜๊ฑฐ๋‚˜, ์–ธ์–ด๋ณ„ ์–ดํœ˜ยท๋ฌธ๋ฒ• ์ฐจ์ด๋ฅผ ์ถฉ๋ถ„ํžˆ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ๋‹ค. ํŠนํžˆ ์ €์ž์› ์–ธ์–ด์—์„œ๋Š” ์„ฑ๋Šฅ ์ €ํ•˜๊ฐ€ ์‹ฌ๊ฐํ•˜๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ Encoder Injection ๊ตฌ์กฐ : ๊ธฐ๋ณธ ๋‹ค๊ตญ์–ด ์ธ์ฝ”๋”(์˜ˆ: Tr

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

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ SemCom์˜ ํ•ต์‹ฌ ๊ฐ€์น˜ : ์˜๋ฏธ ์ˆ˜์ค€์—์„œ ์ •๋ณด๋ฅผ ์••์ถ•ยท์ „์†กํ•จ์œผ๋กœ์จ ์ „์†ก๋Ÿ‰์„ ์ตœ์†Œํ™”ํ•˜๊ณ , ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ JSCC๊ฐ€ ์ฑ„๋„ ๋ณ€๋™์— ๊ฐ•์ธํ•จ์„ ๋ณด์ธ๋‹ค. ์ •์  ๋ชจ๋ธ์˜ ํ•œ๊ณ„ : ๋ฌด์„  ์ฑ„๋„, ์‚ฌ์šฉ์ž ์š”๊ตฌ, ์„œ๋น„์Šค ๋ชฉํ‘œ๊ฐ€ ์‹œ์‹œ๊ฐ๊ฐ ๋ณ€ํ•˜๋Š” 5G/6G ํ™˜๊ฒฝ์—์„œ๋Š” ๊ณ ์ •๋œ ์˜๋ฏธ ๋ชจ๋ธ์ด ์˜๋ฏธ ์ผ๊ด€์„ฑ์„ ์œ ์ง€ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์ธ๊ฐ„โ€‘ํ”ผ๋“œ๋ฐฑ์˜ ํ•„์š”์„ฑ : RLHF(์ธ๊ฐ„ ํ”ผ๋“œ๋ฐฑ ๊ฐ•ํ™”ํ•™์Šต)์™€ ๊ฐ™์€ ์ตœ์‹  AI ํŠธ๋ Œ๋“œ๊ฐ€ ๋ณด์—ฌ์ฃผ๋“ฏ, ์ธ๊ฐ„ ์ฃผ๊ด€์  ์„ ํ˜ธ๋ฅผ ์ง์ ‘ ๋ณด์ƒ์— ๋ฐ˜์˜ํ•˜๋ฉด ์˜๋ฏธ ๋ชจ๋ธ์„ ์‹ค์ œ QoE์— ๋งž๊ฒŒ ์กฐ์ •ํ•  ์ˆ˜ ์žˆ๋‹ค. ์‹œ๊ฐ„ ์ œ์•ฝ์˜ ์ค‘์š”์„ฑ : ์ธ๊ฐ„ ํ”ผ

Electrical Engineering and Systems Science Learning
Learning Distributed Equilibria in Linear-Quadratic Stochastic Differential Games: An $ฮฑ$-Potential Approach

Learning Distributed Equilibria in Linear-Quadratic Stochastic Differential Games: An $ฮฑ$-Potential Approach

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ๋ฉ€ํ‹ฐ์—์ด์ „ํŠธ ๊ฐ•ํ™”ํ•™์Šต(MARL)๊ณผ ํ™•๋ฅ  ๋ฏธ๋ถ„ ๊ฒŒ์ž„ ์—ฐ์†์‹œ๊ฐ„ยท์—ฐ์†์ƒํƒœยท์—ฐ์†์•ก์…˜์„ ๊ฐ–๋Š” ๋™์  ๊ฒŒ์ž„์€ ์ž์œจ์ฃผํ–‰, ์‹ ๊ฒฝ๊ณผํ•™, ์ƒํƒœ๊ณ„, ์ตœ์  ๊ฑฐ๋ž˜ ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ํ•ต์‹ฌ ๋ชจ๋ธ๋กœ ํ™œ์šฉ๋œ๋‹ค. ๊ธฐ์กด MARL ์ด๋ก ์€ ์ฃผ๋กœ ์ด์‚ฐยท์œ ํ•œ ์ƒํƒœยท์•ก์…˜ ๊ณต๊ฐ„์— ๊ตญํ•œ๋ผ ์žˆ์–ด, ์—ฐ์†์‹œ๊ฐ„ ํ™•๋ฅ  ๋ฏธ๋ถ„ ๊ฒŒ์ž„์— ๋Œ€ํ•œ ์ˆ˜๋ ด ๋ณด์žฅ์€ ๊ฑฐ์˜ ์—†๋‹ค. ์Šค์ผ€์ผ๋Ÿฌ๋นŒ๋ฆฌํ‹ฐ์™€ ๋ถ„์‚ฐ ์ •์ฑ… N์ด ์ปค์งˆ์ˆ˜๋ก ๊ณต๋™ ์ „๋žต๊ณต๊ฐ„์€ ์ง€์ˆ˜์ ์œผ๋กœ ํŒฝ์ฐฝํ•œ๋‹ค. ๊ฐ ํ”Œ๋ ˆ์ด์–ด๊ฐ€ ์ž์‹ ์˜ ์ƒํƒœ๋งŒ ์„ ์ด์šฉํ•˜๋Š” ๋ถ„์‚ฐ ์ •์ฑ…์„ ์ฑ„ํƒํ•˜๋ฉด ํ†ต์‹ ยท๊ณ„์‚ฐ ๋ณต์žก๋„๊ฐ€ O(N)์œผ๋กœ ๊ฐ์†Œํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ถ„์‚ฐ ์ •์ฑ… ํ•˜

Mathematics Learning
Learning Glioblastoma Tumor Heterogeneity Using Brain Inspired Topological Neural Networks

Learning Glioblastoma Tumor Heterogeneity Using Brain Inspired Topological Neural Networks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ GBM ์ด์งˆ์„ฑ : ๊ดด์‚ฌํ•ต, ๋ถ€์ข…, ๋น„์ •ํ˜• ๊ฒฝ๊ณ„ ๋“ฑ ๋‹ค์ค‘ ์Šค์ผ€์ผ ๊ตฌ์กฐ๊ฐ€ ํ™˜์ž๋งˆ๋‹ค ํฌ๊ฒŒ ๋‹ค๋ฅด๋ฉฐ, ์ด๋Š” ์ƒ์กด ์˜ˆ์ธก์— ํ•ต์‹ฌ์ ์ธ ๋ฐ”์ด์˜ค๋งˆ์ปค์ด๋‹ค. ์Šค์บ๋„ˆ ๊ฐ„ ๋ณ€์ด : MRI ํ”„๋กœํ† ์ฝœยท๊ธฐ๊ธฐ ์ฐจ์ด๋กœ ์ธํ•ด ํ•™์Šต๋œ ๋ชจ๋ธ์ด ์ƒˆ๋กœ์šด ๊ธฐ๊ด€์—์„œ ๊ธ‰๊ฒฉํžˆ ์„ฑ๋Šฅ์ด ๋–จ์–ด์ง€๋Š” โ€˜๋„๋ฉ”์ธ ์‰ฌํ”„ํŠธโ€™ ๋ฌธ์ œ๊ฐ€ ์‹ฌ๊ฐํ•˜๋‹ค. ๊ธฐ์กด ๋”ฅ๋Ÿฌ๋‹ ํ•œ๊ณ„ : CNNยทViT ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์€ ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์ด์ง€๋งŒ, ๋ธ”๋ž™๋ฐ•์Šค ํŠน์„ฑยทํ•ด์„ ๋ถˆ๊ฐ€๋Šฅยท์Šค์บ๋„ˆ ์˜์กด์„ฑ์ด ๋†’์•„ ์ž„์ƒ ์ ์šฉ์ด ์ œํ•œ์ ์ด๋‹ค. 2. ์ œ์•ˆ ๋ฐฉ๋ฒ• โ€“ TopoGBM | ๊ตฌ์„ฑ ์š”์†Œ | ํ•ต์‹ฌ ์•„์ด๋””์–ด | ๊ตฌํ˜„ ์ƒ์„ธ | |

Machine Learning Computer Science Network Learning
Level structures on cyclic covers of $mathbb{P}^n$ and the homology of Fermat hypersurfaces

Level structures on cyclic covers of $mathbb{P}^n$ and the homology of Fermat hypersurfaces

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ดˆ๊ณก๋ฉด (Z') ์™€ ๊ทธ ์œ„์— ์ •์˜๋œ ์ˆœํ™˜ ์ปค๋ฒ„ (Z) ์‚ฌ์ด์˜ ํ˜ธ๋ชฐ๋กœ์ง€ ๊ด€๊ณ„๋Š” ๊ณ ์ „์ ์ธ ๋Œ€์ˆ˜์œ„์ƒํ•™(ํŠนํžˆ Lefschetz ์ด๋ก )์—์„œ ์˜ค๋žซ๋™์•ˆ ์—ฐ๊ตฌ๋˜์–ด ์™”๋‹ค. ํ•˜์ง€๋งŒ ๋ ˆ๋ฒจ ๊ตฌ์กฐ โ€”์ฆ‰, ์›์‹œ ์ฝ”ํ˜ธ๋ชฐ๋กœ์ง€์˜ (mathbb Z/d)โ€‘๋ชจ๋“ˆ์— ๋Œ€ํ•œ ์™„์ „ํ•œ ๊ธฐ์ € ์„ ํƒโ€”๋ฅผ ์ปค๋ฒ„์™€ ๋ถ„๊ธฐ์  ์‚ฌ์ด์— ์ง์ ‘ ์—ฐ๊ฒฐํ•˜๋Š” ์ฒด๊ณ„์ ์ธ ๋ฐฉ๋ฒ•์€ ๋ถ€์กฑํ–ˆ๋‹ค. Beauville๊ฐ€ ์ œ๊ธฐํ•œ โ€œ์ž…๋ฐฉ์ฒด ๋งˆํ‚น์ด ์ž…๋ฐฉ 3์ฐจ์›์ฒด์˜ ๋ ˆ๋ฒจ 3 ๊ตฌ์กฐ๋ฅผ ๊ฒฐ์ •ํ•˜๋Š”๊ฐ€?โ€๋ผ๋Š” ์งˆ๋ฌธ์€ ๋ฐ”๋กœ ์ด๋Ÿฌํ•œ ๊ณต๋ฐฑ์„ ๊ฒจ๋ƒฅํ•œ๋‹ค. 2. ์ฃผ์š” ์•„์ด๋””์–ด์™€ ๊ธฐ์ˆ ์  ํ•ต์‹ฌ | ๋‹จ๊ณ„ | ๋‚ด์šฉ

Mathematics
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Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ณต์žก๋„ ์ฆ๊ฐ€ : 5Gยท6G ์ „ํ™˜, ์—ฃ์ง€ ์ปดํ“จํŒ…, ๊ฐ€์ƒํ™” ๋“ฑ์œผ๋กœ ๋„คํŠธ์›Œํฌ ํ† ํด๋กœ์ง€๊ฐ€ ๋‹ค์ธตํ™”ยท๋™์ ํ™”๋จ. AI ์ ์šฉ ํ•œ๊ณ„ : ๊ธฐ์กด LLM/ML ๋ชจ๋ธ์€ ํŠน์ • ์„œ๋ธŒ์‹œ์Šคํ…œ์— ๊ตญํ•œ๋˜๊ณ , ๋ผ๋ฒจ๋ง ๋น„์šฉ์ด ๋†’์œผ๋ฉฐ, ํ˜„์žฅ ์ ์šฉ ์‹œ โ€œ๋„๋ฉ”์ธ ๊ฐญโ€์ด ๋ฐœ์ƒ. ์ „๋ฌธ๊ฐ€ ์˜์กด : ํ˜„์žฌ ํŠธ๋Ÿฌ๋ธ”์ŠˆํŒ…์€ SME๊ฐ€ ๋กœ๊ทธยท์‹œ๊ทธ๋„ยท๊ตฌ์„ฑ ์ •๋ณด๋ฅผ ์ˆ˜์ž‘์—…์œผ๋กœ ์—ฐ๊ณ„ํ•ด ์›์ธ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜๋Š”๋ฐ, ์ด๋Š” ์‹œ๊ฐ„ยท๋น„์šฉยท์ธ์  ์˜ค๋ฅ˜ ์œ„ํ—˜์„ ์ดˆ๋ž˜ํ•œ๋‹ค. 2. ์ œ์•ˆ ์‹œ์Šคํ…œ ๊ตฌ์กฐ | ์—์ด์ „ํŠธ | ์—ญํ•  | ์ฃผ์š” ๊ธฐ์ˆ /๋„๊ตฌ | | | | | | Orchestrator | ์ „์ฒด ์›Œ

Network System Model
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Long cycles in vertex transitive digraphs

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์ •์  ์ „์ด์™€ ๋Œ€์นญ์„ฑ : ์ •์  ์ „์ด ๊ทธ๋ž˜ํ”„๋Š” ๋ชจ๋“  ์ •์ ์ด ๋™์ผํ•œ ๊ตฌ์กฐ์  ์ž…์žฅ์„ ๊ฐ–๋Š”๋‹ค๋Š” ๊ฐ•ํ•œ ๋Œ€์นญ์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ์–ด, ํ•ด๋ฐ€ํ† ๋‹ˆ์•ˆ ์กด์žฌ ์—ฌ๋ถ€๋ฅผ ํŒ๋‹จํ•˜๋Š” ๋ฐ ์ž์—ฐ์Šค๋Ÿฌ์šด ํ›„๋ณด๊ฐ€ ๋œ๋‹ค. ๋ฌด๋ฐฉํ–ฅ ๊ฒฝ์šฐ์—๋Š” ์•„์ง ํ•ด๊ฒฐ๋˜์ง€ ์•Š์€ Thomassenโ€‘Lovรกsz ์ถ”์ธก์ด ์กด์žฌํ•œ๋‹ค. ๋ฐฉํ–ฅ ๊ทธ๋ž˜ํ”„์˜ ์ถ”๊ฐ€ ๋‚œ์ด๋„ : ๋ฐฉํ–ฅ์„ฑ์„ ๋ถ€์—ฌํ•˜๋ฉด ๊ฐ•ํ•œ ์—ฐ๊ฒฐ์„ฑ(strong connectivity)๊ณผ 2โ€‘์—ฐ๊ฒฐ์„ฑ ๊ฐ™์€ ๊ฐœ๋…์ด ๋ฌด๋ฐฉํ–ฅ๊ณผ ๋‹ค๋ฅด๊ฒŒ ๋™์ž‘ํ•œ๋‹ค. ํŠนํžˆ โ€œ๋‘ ์ตœ์žฅ ์‚ฌ์ดํด์ด ๋ฐ˜๋“œ์‹œ ๊ต์ฐจํ•œ๋‹คโ€๋Š” ์„ฑ์งˆ์ด ์„ฑ๋ฆฝํ•˜์ง€ ์•Š์•„ ๊ธฐ์กด ๋ฌด๋ฐฉํ–ฅ ๊ธฐ๋ฒ•์„ ๊ทธ๋Œ€๋กœ ์˜ฎ๊ธธ ์ˆ˜

Mathematics
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M-Guarding in K-Visibility

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ kโ€‘๊ฐ€์‹œ์„ฑ ์€ ๋กœ๋ด‡ ๊ฐ์‹œ, ๋ฌด์ธ ํ•ญ๊ณต๊ธฐ ๊ฒฝ๋กœ ๊ณ„ํš ๋“ฑ์—์„œ โ€œ๋ฒฝ์„ ๋ช‡ ๋ฒˆ ํ†ต๊ณผํ•ด๋„ ์ธ์‹ ๊ฐ€๋Šฅโ€ํ•œ ์ƒํ™ฉ์„ ๋ชจ๋ธ๋งํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ 0โ€‘๊ฐ€์‹œ์„ฑ(์ง์„  ๊ฐ€์‹œ์„ฑ) ์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์œผ๋ฉฐ, ๊ตฌ๋ฉ์ด ์žˆ๋Š” ๋‹ค๊ฐํ˜•์— ๋Œ€ํ•œ ๋‹ค์ค‘ ๊ฐ€์‹œ์„ฑ(Mโ€‘guarding) ๊ฒฐ๊ณผ๋Š” ์ œํ•œ์ ์ด์—ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์€ k โ‰ฅ 2 ๋ผ๋Š” ๋ณด๋‹ค ์ผ๋ฐ˜์ ์ธ ๊ฐ€์‹œ์„ฑ ๋ชจ๋ธ์„ ๋„์ž…ํ•จ์œผ๋กœ์จ, ์‹ค์ œ ํ™˜๊ฒฝ์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” โ€œ๋ถ€๋ถ„ ๊ฐ€๋ฆผโ€ ์ƒํ™ฉ์„ ์ด๋ก ์ ์œผ๋กœ ํฌ๊ด„ํ•œ๋‹ค. 2. ์ฃผ์š” ์ •๋ฆฌ ๋ฐ ์ฆ๋ช… ์•„์ด๋””์–ด | ์ •๋ฆฌ | ๋‚ด์šฉ | ํ•ต์‹ฌ ์•„์ด๋””์–ด | | | | | | Theorem

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ML-EcoLyzer: Quantifying the Environmental Cost of Machine Learning Inference Across Frameworks and Hardware

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ถ”๋ก  ๋‹จ๊ณ„์˜ ๊ทœ๋ชจ : ํด๋ผ์šฐ๋“œยท์—ฃ์ง€ ๋ชจ๋‘์—์„œ ๋งค์ผ ์ˆ˜์‹ญ์–ต ๊ฑด์˜ ์ถ”๋ก ์ด ์‹คํ–‰๋˜๋ฉฐ, ์ด๋Š” ํ•™์Šต ๋‹จ๊ณ„๋ณด๋‹ค ์ „์ฒด ์—๋„ˆ์ง€ยทํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์— ํฐ ๋น„์ค‘์„ ์ฐจ์ง€ํ•œ๋‹ค. ํ™˜๊ฒฝ ๋น„์šฉ ์ธก์ •์˜ ๊ณต๋ฐฑ : ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ํ•™์Šต ๋น„์šฉ์— ์ง‘์ค‘ํ–ˆ์œผ๋ฉฐ, ํ•˜๋“œ์›จ์–ด๋ณ„ยทํ”„๋ ˆ์ž„์›Œํฌ๋ณ„ ์ฐจ์ด๋ฅผ ์ •๋Ÿ‰ํ™”ํ•œ ์ฒด๊ณ„์ ์ธ ๋„๊ตฌ๊ฐ€ ๋ถ€์กฑํ–ˆ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | | | | | 1 | MLโ€‘EcoLyzer : TensorFlow, PyTorch, ONNX ๋“ฑ ๋‹ค์ค‘ ํ”„๋ ˆ์ž„์›Œํฌ์™€ CPU, consumerโ€‘GPU, ๋ฐ์ดํ„ฐ์„ผํ„ฐ ๊ฐ€์†๊ธฐ(A100,

Framework Learning
Multiplierless DFT Approximation Based on the Prime Factor Algorithm

Multiplierless DFT Approximation Based on the Prime Factor Algorithm

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ DFT์˜ ์‹ค์‹œ๊ฐ„ยท์ €์ „๋ ฅ ์š”๊ตฌ : ๋ฌด์„ ํ†ต์‹ , ์ž„๋ฒ ๋””๋“œ ์‹œ์Šคํ…œ, IoT ๋“ฑ ์ „๋ ฅยท์—ฐ์‚ฐ ์ œํ•œ์ด ์‹ฌํ•œ ํ™˜๊ฒฝ์—์„œ FFT์˜ ๋‚จ์€ ์—ฐ์‚ฐ๋Ÿ‰(ํŠนํžˆ ์‹ค์ˆ˜ ๊ณฑ์…ˆ)์€ ์—ฌ์ „ํžˆ ๋ณ‘๋ชฉ์ด ๋œ๋‹ค. ๊ธฐ์กด ๋ฌด๊ณฑ์…ˆ ์ ‘๊ทผ๋ฒ•์˜ ํ•œ๊ณ„ : Cooleyโ€‘Tukey ๊ธฐ๋ฐ˜ ๊ทผ์‚ฌ๋Š” ์ž‘์€ ๋ธ”๋ก์˜ ๋ฌด๊ณฑ์…ˆ ๊ทผ์‚ฌ๋ฅผ ์žฌ์‚ฌ์šฉํ•˜์ง€๋งŒ, ํŠธ์œ„๋“ค ํŒฉํ„ฐ๋Š” ๊ทธ๋Œ€๋กœ ๋‚จ์•„ ์ „์ฒด ๋ณ€ํ™˜์ด ์™„์ „ ๋ฌด๊ณฑ์…ˆ์ด ๋˜์ง€ ์•Š๋Š”๋‹ค. ํŠธ์œ„๋“ค ํŒฉํ„ฐ๋ฅผ ๊ทผ์‚ฌํ•˜๋ฉด ์˜ค์ฐจ ์ „ํŒŒ๊ฐ€ ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€ํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ์†Œ์ˆ˜์ธ์ž ์•Œ๊ณ ๋ฆฌ์ฆ˜(PFA) ํ™œ์šฉ : PFA๋Š” N Nโ‚ยทNโ‚‚ (gcd(Nโ‚,Nโ‚‚) 1)์ธ ๊ฒฝ์šฐ,

Electrical Engineering and Systems Science
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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… ๊ฐ„ ์ตœ์  tradeโ€‘off๋ฅผ ์ˆ˜ํ•™์ ์œผ๋กœ ์ œ์‹œํ•œ๋‹ค. HEP์—์„œ๋Š” ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋น„์šฉ ์ด ๋งค์šฐ ๋†’์•„ ๋ฐ์ดํ„ฐ ๊ทœ๋ชจ๊ฐ€ ์ œํ•œ์ ์ด๋ฉฐ, ๊ธฐ์กด ์ œํŠธ ํƒœ๊น… ๋ชจ๋ธ์€ ์ˆ˜์‹ญ์–ต ํŒŒ๋ผ๋ฏธํ„ฐ ์ˆ˜์ค€์— ๋จธ๋ฌผ๋Ÿฌ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ โ€œ ์ปดํ“จํŒ…์„ ํ™•๋Œ€ํ•˜๋ฉด ์„ฑ๋Šฅ์ด ์–ด๋–ป๊ฒŒ ๋ณ€ํ•˜๋Š”๊ฐ€? โ€์™€ โ€œ ๋ฐ์ดํ„ฐ ๋ฐ˜๋ณต์ด ์‹ค์ œ๋กœ ๋ฐ์ดํ„ฐ ์–‘์„ ๋Š˜๋ฆฐ ๊ฒƒ๊ณผ ๋™๋“ฑํ•œ ํšจ๊ณผ๋ฅผ ๊ฐ–๋Š”๊ฐ€? โ€๋ฅผ ์ •๋Ÿ‰ํ™”ํ•˜๋Š” ๊ฒƒ์ด ํ•ต์‹ฌ ๊ณผ์ œ๋‹ค. 2. ๋ฐ์ดํ„ฐยท๋ชจ๋ธ ์„ค๊ณ„ | ์š”์†Œ | ์„ค๋ช…

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

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

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ | ๊ธฐ์กด POD์˜ ํ•œ๊ณ„ | Neuralโ€‘POD์ด ํ•ด๊ฒฐํ•˜๊ณ ์ž ํ•˜๋Š” ํ•ต์‹ฌ ๊ณผ์ œ | | | | | ํ•ด์ƒ๋„ ์˜์กด์„ฑ โ€“ ํ•œ ๊ฒฉ์ž์—์„œ ํ•™์Šต๋œ ๋ชจ๋“œ๊ฐ€ ๋‹ค๋ฅธ ๊ฒฉ์ž์—์„œ ๋น„ํšจ์œจ | ํ•ด์ƒ๋„ ๋…๋ฆฝ์ ์ธ ํ•จ์ˆ˜ ๋ฅผ ํ•™์Šตํ•ด ์–ด๋А ๊ฒฉ์ž์—์„œ๋„ ๋™์ผํ•˜๊ฒŒ ์‚ฌ์šฉ | | ์„ ํ˜• ์„œ๋ธŒ์ŠคํŽ˜์ด์Šค โ€“ ๋น„์„ ํ˜•ยท๋ถˆ์—ฐ์† ํ˜„์ƒ ํฌ์ฐฉ ์–ด๋ ค์›€ | ๋น„์„ ํ˜• ๊ธฐ์ € ๋ฅผ ์‹ ๊ฒฝ๋ง์œผ๋กœ ํ‘œํ˜„, ๊ธ‰๊ฒฉํ•œ ๋ณ€ํ™”ยท์ถฉ๊ฒฉํŒŒ ๋“ฑ ํฌ์ฐฉ | | Lยฒ ์ตœ์ ํ™”์— ๊ตญํ•œ โ€“ ํŠน์ • ์†์‹ค์—๋งŒ ์ตœ์ ํ™” | ๋‹ค์–‘ํ•œ ๋…ธ๋ฆ„ (Lยน, Hยน ๋“ฑ)์œผ๋กœ ์†์‹ค ์ •์˜ ๊ฐ€๋Šฅ, ๋ฌธ์ œ ํŠน์„ฑ์— ๋งž์ถค | | ํŒŒ๋ผ๋ฏธํ„ฐ ์™ธ์‚ฝ ํ•œ๊ณ„

Framework Physics
On the uniqueness and structural stability of Couette-Poiseuille flow in a channel for arbitrary values of the flux

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„ 2์ฐจ์› ๋„๋ฉ”์ธ์—์„œ ์ž„์˜์˜ ํ”Œ๋Ÿญ์Šค (Phi) ๋ฅผ ๊ฐ–๋Š” ํฌ์•„์ฃ„๋ฅ˜ ํ๋ฆ„์œผ๋กœ ์ˆ˜๋ ดํ•˜๋Š” ์ •์ƒํ•ด ์กด์žฌ ์—ฌ๋ถ€๋Š” 60๋…„๋Œ€๋ถ€ํ„ฐ ๋ฏธํ•ด๊ฒฐ ๋ฌธ์ œ์˜€๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ(Amick, Ladyzhenskayaโ€‘Solonnikov ๋“ฑ)๋Š” ํ”Œ๋Ÿญ์Šค๊ฐ€ โ€œ์ž‘๋‹คโ€๋Š” ๊ฐ€์ • ํ•˜์— ์กด์žฌ์™€ ์ˆ˜๋ ด์„ ๋ณด์˜€์œผ๋ฉฐ, ํ”Œ๋Ÿญ์Šค๊ฐ€ ํฐ ๊ฒฝ์šฐ์—๋Š” ๋‹ค๋ฅธ ํ•ด๊ฐ€ ์กด์žฌํ•  ๊ฐ€๋Šฅ์„ฑ์„ ๋ฐฐ์ œํ•˜์ง€ ๋ชปํ–ˆ๋‹ค. ์œ ์ผ์„ฑ ์—ฐ๊ตฌ : ์ด์ „ ๋ฌธํ—Œ(

Mathematics
Optimal training-conditional regret for online conformal prediction

Optimal training-conditional regret for online conformal prediction

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ปจํฌ๋ฉ€ ์˜ˆ์ธก ์€ ๊ตํ™˜๊ฐ€๋Šฅ(exchangeable) ๊ฐ€์ • ํ•˜์— ์œ ํ•œํ‘œ๋ณธ ์ปค๋ฒ„๋ฆฌ์ง€๋ฅผ ๋ณด์žฅํ•˜์ง€๋งŒ, ์‹ค์ œ ์ŠคํŠธ๋ฆฌ๋ฐ ํ™˜๊ฒฝ์—์„œ๋Š” ๋ถ„ํฌ ๋“œ๋ฆฌํ”„ํŠธ ๊ฐ€ ํ”ํžˆ ๋ฐœ์ƒํ•œ๋‹ค. ๊ธฐ์กด ์˜จ๋ผ์ธ ์ปจํฌ๋ฉ€ ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์‹œ๊ฐ„ ํ‰๊ท  ์ปค๋ฒ„๋ฆฌ์ง€ ํ˜น์€ ์ ๋Œ€์  ๋ ˆ๊ทธ๋ ˆ ๋ฅผ ์‚ฌ์šฉํ–ˆ์œผ๋ฉฐ, ์ด๋Š” ๊ฐœ๋ณ„ ์‹œ์  ์˜ ์ปค๋ฒ„๋ฆฌ์ง€ ํ’ˆ์งˆ์„ ๋ณด์žฅํ•˜์ง€ ๋ชปํ•˜๊ณ , ๊ฒฝ์šฐ์— ๋”ฐ๋ผ ๋ฌด์˜๋ฏธํ•œ(๋นˆ) ์˜ˆ์ธก ์ง‘ํ•ฉ ์„ ์ดˆ๋ž˜ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋…๋ฆฝ ๋ฐ์ดํ„ฐ ๋ผ๋Š” ์•ฝํ•œ ๊ฐ€์ •์„ ๋„์ž…ํ•ด ํ›ˆ๋ จโ€‘์กฐ๊ฑด๋ถ€ ์ปค๋ฒ„๋ฆฌ์ง€๋ฅผ ์ง์ ‘ ์ธก์ •ํ•˜๋Š” ๋ˆ„์  ๋ ˆ๊ทธ๋ ˆ ๋ฅผ ์ •์˜ํ•จ์œผ๋กœ์จ, ํ†ต๊ณ„์  ํƒ€๋‹น์„ฑ๊ณผ ์˜จ๋ผ์ธ ์ ์‘์„ฑ์„ ๋™์‹œ์— ๋‹ฌ์„ฑํ•œ๋‹ค๋Š”

Mathematics
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Optimizing Reasoning Efficiency through Prompt Difficulty Prediction

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ถ”๋ก  ๋ชจ๋ธ์˜ ๋น„์šฉ ๋ฌธ์ œ : ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(Large Language Model, LLM)์€ ๋ณต์žกํ•œ ๋…ผ๋ฆฌยท์ˆ˜ํ•™ ๋ฌธ์ œ ํ•ด๊ฒฐ์— ๊ฐ•์ ์ด ์žˆ์ง€๋งŒ, ํŒŒ๋ผ๋ฏธํ„ฐ ์ˆ˜์™€ ์ถ”๋ก  ๋‹จ๊ณ„๊ฐ€ ๋งŽ์•„ ์‹ค์‹œ๊ฐ„ ์„œ๋น„์Šค์— ์ ์šฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋‹ค์–‘ํ•œ ๋‚œ์ด๋„์˜ ๋ฌธ์ œ : ์‹ค์ œ ์„œ๋น„์Šค์—์„œ๋Š” ์‰ฌ์šด ๋ฌธ์ œ๋ถ€ํ„ฐ ์–ด๋ ค์šด ๋ฌธ์ œ๊นŒ์ง€ ํญ๋„“๊ฒŒ ๋“ฑ์žฅํ•œ๋‹ค. ๋ชจ๋“  ๋ฌธ์ œ์— ๋™์ผํ•œ ๋Œ€ํ˜• ๋ชจ๋ธ์„ ์ ์šฉํ•˜๋ฉด ๋ถˆํ•„์š”ํ•œ ์—ฐ์‚ฐ์ด ๋ฐœ์ƒํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ๋‚œ์ด๋„โ€‘์˜ˆ์ธก ๋ผ์šฐํŒ… : ๋ฌธ์ œ๋ฅผ ์‚ฌ์ „์— โ€œ๋‚œ์ด๋„โ€ ํ˜น์€ โ€œํŠน์ • ๋ชจ๋ธ์ด ์ •๋‹ต์„ ๋‚ผ ํ™•๋ฅ โ€์„ ์˜ˆ์ธกํ•˜๊ณ , ๊ทธ ์˜ˆ์ธก๊ฐ’์— ๋”ฐ๋ผ ๊ฐ€์žฅ ์ž‘์€(

No Image

Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ MNAR ์ƒํ™ฉ์—์„œ๋Š” ๊ด€์ธก๋œ ๋ฐ์ดํ„ฐ๋งŒ์œผ๋กœ ํ‰๊ท ยท๋ถ„ํฌ๋ฅผ ์ง์ ‘ ์ถ”์ •ํ•  ์ˆ˜ ์—†์œผ๋ฉฐ, ๊ธฐ์กด์˜ Heckman ์„ ํƒ ๋ชจ๋ธ , ํŒจํ„ดโ€‘๋ฏน์Šค์ฒ˜ ๋ชจ๋ธ , ๊ทธ๋ž˜ํ”„ ๊ธฐ๋ฐ˜ ์ ‘๊ทผ ๋“ฑ์€ ๊ฐ•ํ•œ ๊ตฌ์กฐ์  ๊ฐ€์ •์— ์˜์กดํ•œ๋‹ค. ์„€๋„์šฐ ๋ณ€์ˆ˜ (shadow variable) ์ ‘๊ทผ๋ฒ•์€ ๋ˆ„๋ฝ ๋ฉ”์ปค๋‹ˆ์ฆ˜๊ณผ ๋…๋ฆฝ์ ์ธ ๋ณด์กฐ๋ณ€์ˆ˜๋ฅผ ํ™œ์šฉํ•ด ์ ์‹๋ณ„์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜์ง€๋งŒ, ์™„์ „์„ฑ(completeness) ์กฐ๊ฑด์„ ๋งŒ์กฑํ•ด์•ผ ํ•œ๋‹ค๋Š” ์‹ค๋ฌด์  ์ œ์•ฝ์ด ์žˆ๋‹ค. ์ตœ๊ทผ ํ”„๋ฆฌํŠธ๋ ˆ์ธ๋“œ ๋ชจ๋ธยทLLM ์ด ๋‹ค์–‘ํ•œ ๋„๋ฉ”์ธ์—์„œ ๋†’์€ ์˜ˆ์ธก๋ ฅ์„ ๋ณด์ด์ง€๋งŒ, ์˜ˆ์ธก์˜ค์ฐจ์™€ ํŽธํ–ฅ์œผ๋กœ ์ธํ•ด ์™„์ „ํ•œ ๋ณด์กฐ๋ณ€์ˆ˜๋กœ

Machine Learning Statistics Model Data
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Pattern-Aware Diffusion Synthesis of fMRI/dMRI with Tissue and Microstructural Refinement

1. ๋ฌธ์ œ ์ •์˜์™€ ๊ธฐ์กด ์ ‘๊ทผ๋ฒ•์˜ ํ•œ๊ณ„ ์‹ ํ˜ธ ์ฐจ์ด : fMRI๋Š” BOLD ์‹ ํ˜ธ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์‹œ๊ฐ„์  ๋ณ€๋™์„ ํฌ์ฐฉํ•˜๊ณ , dMRI๋Š” ํ™•์‚ฐ ๊ฐ€์ค‘ ์‹ ํ˜ธ๋ฅผ ํ†ตํ•ด ๋ฏธ์„ธ๊ตฌ์กฐ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ๋‘ ๋ชจ๋‹ฌ๋ฆฌํ‹ฐ๋Š” ๋ฌผ๋ฆฌ์  ์ธก์ • ์›๋ฆฌ์™€ ์Šค์บ” ํŒŒ๋ผ๋ฏธํ„ฐ๊ฐ€ ํฌ๊ฒŒ ๋‹ฌ๋ผ, ๋‹จ์ˆœํ•œ ํ”ฝ์…€โ€‘๋ ˆ๋ฒจ ๋งคํ•‘์œผ๋กœ๋Š” ์ถฉ๋ถ„ํžˆ ํ‘œํ˜„๋˜์ง€ ์•Š๋Š”๋‹ค. ์งˆ๋ณ‘ ํŒจํ„ด ๋ฐ˜์˜ ๋ถ€์กฑ : ๊ธฐ์กด GANยท๋””ํ“จ์ „ ๋ชจ๋ธ์€ ์ „์—ญ์ ์ธ ์ด๋ฏธ์ง€ ํ’ˆ์งˆ ํ–ฅ์ƒ์— ์ดˆ์ ์„ ๋งž์ถ”๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์•„, ์•Œ์ธ ํ•˜์ด๋จธยทMCI์™€ ๊ฐ™์€ ์งˆํ™˜์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ํŠน์ • ํ•ด๋ถ€ํ•™์ ยท๋ฏธ์„ธ๊ตฌ์กฐ ๋ณ€ํ™”๋ฅผ ์ถฉ๋ถ„ํžˆ ํ•™์Šตํ•˜์ง€ ๋ชปํ•œ๋‹ค. 2. PDS์˜ ํ•ต์‹ฌ ์„ค๊ณ„ ํŒจํ„ดโ€‘์ธ์‹ ์ด์ค‘

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Physics-Informed Anomaly Detection of Terrain Material Change in Radar Imagery

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ ˆ์ด๋” ์˜์ƒ์—์„œ ๋ฏธ์„ธํ•œ ๋ฌผ์งˆ ๋ณ€ํ™”๋ฅผ ํƒ์ง€ํ•˜๋Š” ๊ฒƒ์€ ์ธํ”„๋ผ ๊ฐ์‹œยทํ™˜๊ฒฝ ๋ชจ๋‹ˆํ„ฐ๋งยท๋ณด์•ˆ ๋“ฑ ์‹ค์šฉ์  ์‘์šฉ์— ํ•ต์‹ฌ์ด๋‹ค. ๊ธฐ์กด ๋ฐฉ๋ฒ•์€ ๊ฐ•๋„ ๊ธฐ๋ฐ˜ ๋ณ€ํ™” ํƒ์ง€์™€ ๊ฐ„์„ญ ์ฝ”ํžˆ๋Ÿฐ์Šค(Interferometric Coherence)๋ฅผ ์ด์šฉํ•œ CCD์— ํฌ๊ฒŒ ์˜์กดํ•˜์ง€๋งŒ, ๋ฌผ๋ฆฌ์  ๋งค๊ฐœ๋ณ€์ˆ˜(์œ ์ „์œจ, ๊ฑฐ์น ๊ธฐ)์™€ ๋ ˆ์ด๋” ๋ฐฑ์Šค์บํ„ฐ ์‚ฌ์ด์˜ ์ •๋Ÿ‰์  ์—ฐ๊ฒฐ ๊ณ ๋ฆฌ๊ฐ€ ๋ถ€์กฑํ–ˆ๋‹ค. ๋˜ํ•œ, ์ „ํ†ต์ ์ธ RX ํƒ์ง€๋Š” ๊ฐ€์šฐ์‹œ์•ˆ ๋ฐฐ๊ฒฝ ๊ฐ€์ •์„ ์ „์ œ๋กœ ํ•˜์—ฌ ๋ฌด๊ฑฐ์šด ๊ผฌ๋ฆฌ ์žก์Œ(์˜ˆ: Kโ€‘๋ถ„ํฌ)์—์„œ๋Š” CFAR(Constant False Alarm Rate) ์œ ์ง€๊ฐ€ ์–ด๋ ค์šด ๊ฒƒ

Electrical Engineering and Systems Science Detection
Piezoelectric MEMS Phase Modulator for Silicon Nitride Platform in the Visible Spectrum

Piezoelectric MEMS Phase Modulator for Silicon Nitride Platform in the Visible Spectrum

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์‹œ๊ฐ ํŒŒ์žฅ๋Œ€ PIC : Siโ‚ƒNโ‚„๋Š” ๋‚ฎ์€ ์†์‹คยท๋„“์€ ํˆฌ๋ช…์„ฑยทCMOS ํ˜ธํ™˜์„ฑ ๋•๋ถ„์— ์‹œ๊ฐ ํŒŒ์žฅ๋Œ€์—์„œ ๊ฐ€์žฅ ๋„๋ฆฌ ์“ฐ์ด๋Š” ํ”Œ๋žซํผ์ด์ง€๋งŒ, ์ž์ฒด์ ์ธ ์ „๊ธฐยท์—ดยท์••์ „ ์‘๋‹ต์ด ์•ฝํ•ด ํ™œ์„ฑํ™”๊ฐ€ ์–ด๋ ค์›€. ๊ธฐ์กด ์œ„์ƒ ๋ณ€์กฐ ๊ธฐ์ˆ  : ์—ด๊ด‘ํ•™(Thermoโ€‘optic) : ์ „๋ ฅ ์†Œ๋ชจ๊ฐ€ mW ์ˆ˜์ค€์ด๋ฉฐ, ์—ด crosstalkยท๋Œ€์—ญํญ ์ œํ•œ์ด ์‹ฌํ•จ. ํ”ผ์—์กฐ ์ „๊ธฐ(Pockels) EO : ๋น ๋ฅด๊ณ  ํšจ์œจ์ ์ด์ง€๋งŒ PZTยทLiNbOโ‚ƒ ๋“ฑ ์™ธ๋ถ€ ๋ฐ•๋ง‰์„ ์‚ฝ์ž…ํ•˜๋ฉด ๊ด‘์†์‹ค์ด ํฌ๊ฒŒ ์ฆ๊ฐ€. ์ŠคํŠธ๋ ˆ์Šคโ€‘์˜ตํ‹ฑ : PZT ๋ฐ•๋ง‰์„ ์ด์šฉํ•ด ์‘๋ ฅ์œผ๋กœ ๊ตด์ ˆ๋ฅ ์„ ์กฐ์ ˆํ•˜์ง€๋งŒ V

Physics
Pitts and Intuitionistic Multi-Succedent: Uniform Interpolation for KM

Pitts and Intuitionistic Multi-Succedent: Uniform Interpolation for KM

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๊ท ์ผ ๋ณด๊ฐ„ ์€ ์ „ํ†ต์ ์ธ ๋ณด๊ฐ„๋ณด๋‹ค ๊ฐ•๋ ฅํ•œ ์„ฑ์งˆ๋กœ, ๋ณ€์ˆ˜ p์— ๋Œ€ํ•ด ์–ธ์ œ๋‚˜ pโ€‘์ž์œ ํ•œ ์ตœ๊ฐ•ยท์ตœ์•ฝ ๊ณต์‹ โˆ€p ฯ†, โˆƒp ฯ†๊ฐ€ ์กด์žฌํ•จ์„ ๋ณด์ธ๋‹ค. ๊ธฐ์กด์—๋Š” ๋ชจ๋ธ ์ด๋ก  , ๋ณดํŽธ ๋Œ€์ˆ˜ํ•™ ๊ทธ๋ฆฌ๊ณ  ์ฆ๋ช… ์ด๋ก  (Pitts 1992) ์ ‘๊ทผ๋ฒ•์ด ์•Œ๋ ค์ ธ ์žˆ์œผ๋‚˜, ์ฆ๋ช… ์ด๋ก ์  ๋ฐฉ๋ฒ•์€ ์ข…๋ฃŒ(sequent calculus) ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. Pitts๋Š” ๋‹จ์ผโ€‘ํ›„์† (singleโ€‘succedent) ๊ณ„์‚ฐ G4iP๋ฅผ ์ด์šฉํ•ด ์ง๊ด€์ฃผ์˜ ๋…ผ๋ฆฌ IPC์— ์ ์šฉํ–ˆ์œผ๋ฉฐ, ์ดํ›„ iSL , iK ๋“ฑ ์—ฌ๋Ÿฌ ์ง๊ด€์ฃผ์˜ ๋ชจ๋‹ฌ ๋…ผ๋ฆฌ์— ํ™•์žฅ๋˜์—ˆ๋‹ค. ๋‹ค์ค‘โ€‘ํ›„์† ๊ณ„์‚ฐ์€ ํด

Computer Science Logic

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