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A Formulation of the Channel Capacity of Multiple-Access Channel

A Formulation of the Channel Capacity of Multiple-Access Channel

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ๋‹ค์ค‘์ ‘์† ์ฑ„๋„(MAC) ์€ ์—ฌ๋Ÿฌ ์†ก์‹ ๊ธฐ๊ฐ€ ํ•˜๋‚˜์˜ ์ˆ˜์‹ ๊ธฐ๋กœ ์ •๋ณด๋ฅผ ์ „์†กํ•˜๋Š” ๊ธฐ๋ณธ ๋ชจ๋ธ์ด๋ฉฐ, ์šฉ๋Ÿ‰ ์˜์—ญ์„ ๊ตฌํ•˜๋Š” ๊ฒƒ์ด ์ •๋ณด์ด๋ก ์˜ ํ•ต์‹ฌ ๊ณผ์ œ์ด๋‹ค. ๊ธฐ์กด์—๋Š” ์ฟ ํ”โ€‘ํ„ฐํ‚ค(KKT) ์กฐ๊ฑด ์ด ํ•„์š”์กฐ๊ฑด์œผ๋กœ๋งŒ ์•Œ๋ ค์กŒ์œผ๋ฉฐ, ์ถฉ๋ถ„์กฐ๊ฑด์œผ๋กœ์„œ์˜ ์ผ๋ฐ˜์  ์ฆ๋ช…์€ ์—†์—ˆ๋‹ค(๋‹จ์ผ ์‚ฌ์šฉ์ž DMC์—์„œ๋Š” ์ถฉ๋ถ„์กฐ๊ฑด์ด ์„ฑ๋ฆฝ). ๋”ฐ๋ผ์„œ โ€œ ์–ธ์ œ KKT ์กฐ๊ฑด์ด ์ถฉ๋ถ„์กฐ๊ฑด์ด ๋˜๋Š”๊ฐ€? โ€๋ผ๋Š” ์งˆ๋ฌธ์ด ๋‚จ์•„ ์žˆ์—ˆ์œผ๋ฉฐ, ์ด๋Š” ๋น„์„ ํ˜• ์ตœ์ ํ™”์™€ ํ™•๋ฅ ๋‹จ์ˆœ์ฒด ๊ตฌ์กฐ๊ฐ€ ๋ณตํ•ฉ์ ์œผ๋กœ ์–ฝํžŒ ์–ด๋ ค์šด ๋ฌธ์ œ์ด๋‹ค. 2. ์ฃผ์š” ๊ฐœ๋…: ์ดˆ๋“ฑ MAC (Elementary MAC) ์ •์˜

Mathematics Information Theory Computer Science
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A Generalized Sampling Theorem for Frequency Localized Signals

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์‹œ๊ฐ„โ€‘์ฃผํŒŒ์ˆ˜ ๋กœ์ปฌ๋ผ์ด์ œ์ด์…˜ ์€ ์‹ ํ˜ธ๊ฐ€ ์‹œ๊ฐ„ยท์ฃผํŒŒ์ˆ˜ ์–‘์ชฝ์—์„œ ๊ธ‰๊ฒฉํžˆ ๊ฐ์†Œํ•˜๋Š” ํŠน์„ฑ์„ ์˜๋ฏธํ•œ๋‹ค(๋ฌธํ—Œ

Mathematics Information Theory Computer Science
A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts   and their Usage

A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and their Usage

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 1990โ€‘2005๋…„ ์‚ฌ์ด Thomson Scientific ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์— ๋“ฑ์žฌ๋œ ๋…ผ๋ฌธ ์ˆ˜๊ฐ€ 8.7 ร— 10โต โ†’ 1.5 ร— 10โถ ๋กœ ๊ธ‰์ฆํ–ˆ์œผ๋ฉฐ, ์‹ค์ œ ํ•™์ˆ  ๊ธฐ๋ก์€ ์ด๋ณด๋‹ค ํ›จ์”ฌ ๋ฐฉ๋Œ€ํ•˜๋‹ค(ํ”„๋ฆฌํ”„๋ฆฐํŠธ, ๋ฐ์ดํ„ฐ์…‹, ์†Œํ”„ํŠธ์›จ์–ด ๋“ฑ). ์ด์šฉ ํ–‰์œ„(์ „์ฒด ํ…์ŠคํŠธ ๋‹ค์šด๋กœ๋“œ, ์ดˆ๋ก ์ด๋ฉ”์ผ, ๋งํฌ ์„œ๋ฒ„ ์š”์ฒญ ๋“ฑ) ์—ญ์‹œ ์ˆ˜์‹ญ์–ต ๊ฑด์— ๋‹ฌํ•˜์ง€๋งŒ, ๊ธฐ์กด ๋ฉ”ํƒ€๋ฐ์ดํ„ฐ์™€ ๋‹ฌ๋ฆฌ ํ”„๋ผ์ด๋ฒ„์‹œยทํ‘œ์ค€ํ™” ๋ฌธ์ œ๋กœ ์ฒด๊ณ„์  ์ˆ˜์ง‘ยท๋ถ„์„์ด ์–ด๋ ค์› ๋‹ค. MESUR ํ”„๋กœ์ ํŠธ๋Š” ์ด๋Ÿฌํ•œ ๋Œ€๊ทœ๋ชจ ์ด์šฉ ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ƒˆ๋กœ์šด ๊ฐ€์น˜ ์ง€ํ‘œ(์ „ํ†ต์ ์ธ Impact Fact

Digital Libraries Model Computer Science Artificial Intelligence
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A Probability Model for Lifetime of Wireless Sensor Networks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์„ผ์„œ ์ „์› ์ œํ•œ : ๋ฐฐํ„ฐ๋ฆฌ ๊ธฐ๋ฐ˜ ์„ผ์„œ๋Š” ๊ต์ฒด๊ฐ€ ์–ด๋ ค์›Œ ์ดˆ๊ธฐ ์—๋„ˆ์ง€์— ๊ธฐ๋ฐ˜ํ•œ ์ˆ˜๋ช… ์˜ˆ์ธก์ด ์„ค๊ณ„ ๋‹จ๊ณ„์—์„œ ํ•ต์‹ฌ์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ : ๊ธฐ์กด ๋ฌธํ—Œ์€ ์ฃผ๋กœ ์ •์  ๊ฐ€์ •(๋ฌดํ•œ ์˜์—ญ, ๋ฌดํ•œ ๋…ธ๋“œ ์ˆ˜)์ด๋‚˜ ํŠน์ • ํŠธ๋ž˜ํ”ฝ ๋ชจ๋ธ ์— ์˜์กดํ•ด ๋„คํŠธ์›Œํฌ ์ˆ˜๋ช…์„ ์ถ”์ •ํ•˜์˜€๋‹ค. ํŠนํžˆ, ์„ผ์„œ ๊ฐœ๋ณ„ ์ˆ˜๋ช…์„ ๋ฏธ๋ฆฌ ์ฃผ์–ด์ง€๋Š” ๊ฐ’์œผ๋กœ ๊ฐ€์ •ํ•˜๊ฑฐ๋‚˜, ํŠน์ • ๋ฐฐ์น˜(์˜ˆ: ๊ฒฉ์žํ˜• ์…€)๋งŒ์„ ๊ณ ๋ คํ–ˆ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๊ธฐ์—ฌ ๋‚ด์šฉ | ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„์  | | | | | | 1 | ์„ผ์„œ ์ˆ˜๋ช…์„ ํ™•๋ฅ  ๋ณ€์ˆ˜ ๋กœ ๋ชจ๋ธ๋งํ•˜๊ณ , ์ „๋ ฅ ์†Œ๋ชจยทํŒจํ‚ท ์ƒ์„ฑ ๋ชจ๋ธ์„

Model Network Networking Computer Science
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Adjusted Viterbi training for hidden Markov models

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ EM vs. VT : EM์€ ์ตœ๋Œ€์šฐ๋„ ์ถ”์ •์— ์ตœ์ ์ด์ง€๋งŒ, ๊ณ ์ฐจ์›ยท๋Œ€์šฉ๋Ÿ‰ ๋ฐ์ดํ„ฐ์—์„œ Eโ€‘step์˜ ๋ณต์žก๋„๊ฐ€ (O(nK)) (K: ์ƒํƒœ ์ˆ˜) ์ด์ƒ์œผ๋กœ ๊ธ‰์ฆํ•œ๋‹ค. ๋ฐ˜๋ฉด VT๋Š” Viterbi ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•ด ๊ฐ€์žฅ ๊ฐ€๋Šฅ์„ฑ ๋†’์€ ์ƒํƒœ ๊ฒฝ๋กœ๋งŒ์„ ์„ ํƒํ•˜๊ณ , ๊ทธ ๊ฒฝ๋กœ์— ์†ํ•œ ๊ด€์ธก์„ ๊ฐ ์ƒํƒœ๋ณ„๋กœ ๋ฌถ์–ด MLE๋ฅผ ๋ฐ”๋กœ ๊ณ„์‚ฐํ•œ๋‹ค. VT์˜ ๋ฌธ์ œ์  : ํŽธํ–ฅ(bias)๊ณผ ์ผ๊ด€์„ฑ(consistency) ๊ฒฐ์—ฌ. ํŠนํžˆ โ€œ์ง„์งœ ํŒŒ๋ผ๋ฏธํ„ฐ๊ฐ€ ๊ณ ์ •์ ์ด ์•„๋‹ˆ๋‹คโ€๋Š” ์‚ฌ์‹ค์€ ์ด๋ก ์ ยท์‹ค์šฉ์  ํ•œ๊ณ„๋กœ ์ž‘์šฉํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ ์žฅ๋ฒฝ๊ณผ ์žฌ์ƒ์„ฑ์„ฑ B

Model Mathematics Statistics
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Bayesian segmentation of hyperspectral images

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ดˆ๋ถ„๊ด‘ ์ด๋ฏธ์ง€ ๋Š” ์ˆ˜๋ฐฑ ๊ฐœ์— ๋‹ฌํ•˜๋Š” ์ŠคํŽ™ํŠธ๋Ÿผ ๋ฐด๋“œ๋ฅผ ์ œ๊ณตํ•ด ๋ฌผ์งˆ ๊ตฌ๋ถ„์— ๋›ฐ์–ด๋‚œ ์ •๋ณด๋ฅผ ๋‹ด๊ณ  ์žˆ์ง€๋งŒ, ๋ฐ์ดํ„ฐ ์–‘์ด ๋ฐฉ๋Œ€ํ•ด ์ „ํ†ต์ ์ธ ๋ถ„๋ฅ˜ยท์„ธ๋ถ„ํ™” ๊ธฐ๋ฒ•์œผ๋กœ๋Š” ์ถฉ๋ถ„ํ•œ ์„ฑ๋Šฅ์„ ๋‚ด๊ธฐ ์–ด๋ ต๋‹ค. ๊ธฐ์กด์˜ ๋‹ค์ค‘ยท๋‹ค์ŠคํŽ™ํŠธ๋Ÿผ ์ด๋ฏธ์ง€ ์„ธ๋ถ„ํ™” ๋ฐฉ๋ฒ•์€ ๋ฐด๋“œ ๊ฐ„ ๋…๋ฆฝ์„ฑ ์„ ์ „์ œ๋กœ ํ•˜์—ฌ, ์ดˆ๋ถ„๊ด‘ ๋ฐ์ดํ„ฐ์˜ ์ŠคํŽ™ํŠธ๋Ÿผ ์—ฐ์†์„ฑ ์„ ํ™œ์šฉํ•˜์ง€ ๋ชปํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด | ์š”์†Œ | ๊ธฐ์กด ์ ‘๊ทผ | ๋ณธ ๋…ผ๋ฌธ์˜ ์ ‘๊ทผ | | | | | | ๋ ˆ์ด๋ธ” ๋ชจ๋ธ๋ง | ๋…๋ฆฝ์ ์ธ ๋ ˆ์ด๋ธ” ํ˜น์€ ๋‹จ์ˆœ ๊ณต๊ฐ„ ๋งˆ์ฝ”ํ”„ | ๊ณตํ†ต ๋ ˆ์ด๋ธ” z ๋ฅผ ๋ชจ๋“  ๋ฐด๋“œ์— ์ ์šฉ, ํฌ์ธ  MRF

Physics
Cheeger constants of surfaces and isoperimetric inequalities

Cheeger constants of surfaces and isoperimetric inequalities

1. ์ฃผ์š” ๊ฒฐ๊ณผ ์š”์•ฝ | ๋ฒˆํ˜ธ | ์ •๋ฆฌ/๋ช…์ œ | ํ•ต์‹ฌ ๋‚ด์šฉ | | | | | | Proposition 2.3 | ๊ตฌ๋ฉด (S) (๋ฆฌ๋งŒ ํ˜น์€ ๋‹จ์ˆœ ๋ณตํ•ฉ์ฒด) ์— ๋Œ€ํ•ด (h(S) le dfrac{4}{A(S)}) (๊ตฌ์ฒด์  ์ƒ์ˆ˜๋Š” ๋…ผ๋ฌธ์— ๋ช…์‹œ) | | Theorem 2.6 | ํ๊ณก๋ฉด(์ข… (gge1))์— ๋Œ€ํ•ด (h(S) le C(g)/A(S)) โ€“ ์ข…์— ๋”ฐ๋ผ ์ƒ์ˆ˜๊ฐ€ ๋‹ฌ๋ผ์ง | | Corollary (From Gromov) | ํ‰๋ฉด (S) ์—์„œ ๋“ฑ์  ํ”„๋กœํŒŒ์ผ์ด (sqrt{t})๋ณด๋‹ค ๋น ๋ฅด๋ฉด ์„ ํ˜• ํ•˜ํ•œ ์กด์žฌ | | T

Mathematics
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Coexisting stochastic and coherence resonance in a mean-field dynamo model for Earths magnetic field reversals

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ์ง€๊ตฌ ์ž๊ธฐ์žฅ ์—ญ์ „ ์€ ์ˆ˜๋ฐฑ๋งŒ ๋…„์— ๊ฑธ์ณ ์ˆ˜์ฒœ ์ฐจ๋ก€ ๋ฐœ์ƒํ–ˆ์œผ๋ฉฐ, ํ‰๊ท  ์—ญ์ „์œจ์€ ์•ฝ 4โ€“5 /Myr์ด๋‹ค. ์—ญ์ „ ์‹œ ๋น„๋Œ€์นญ์„ฑ (๊ฐ์†Œ๋Š” ๋А๋ฆฌ๊ฒŒ, ์žฌ์ƒ์€ ๋น ๋ฅด๊ฒŒ)๊ณผ ํด๋Ÿฌ์Šคํ„ฐ๋ง , ๊ทธ๋ฆฌ๊ณ  100 kyr ์ฃผ๊ธฐ์˜ ์ฒด๋ฅ˜์‹œ๊ฐ„ ๋ถ„ํฌ ๊ฐ€ ๊ด€์ธก๋œ๋‹ค. ๊ธฐ์กด 3D ์ง€์˜ค๋‹ค์ด๋„ˆ๋ชจ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์€ ํŒŒ๋ผ๋ฏธํ„ฐ ๋ฒ”์œ„๊ฐ€ ์‹ค์ œ ํ•ต ์กฐ๊ฑด๊ณผ ํฌ๊ฒŒ ์ฐจ์ด ๋‚˜๋ฏ€๋กœ, ๋‹จ์ˆœํ™”๋œ ํ‰๊ท ์žฅ ๋ชจ๋ธ ์ด ํ•ต์‹ฌ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ํŒŒ์•…ํ•˜๋Š” ๋ฐ ์œ ์šฉํ•˜๋‹ค. 2. ๋ชจ๋ธ ๊ตฌ์„ฑ | ์š”์†Œ | ์„ค๋ช… | | | | | ๊ธฐ๋ณธ ๋ฐฉ์ •์‹ | ์œ ๋„ ๋ฐฉ์ •์‹์— ฮฑโ€‘ํšจ๊ณผ๋ฅผ ํฌํ•จํ•œ ฮฑยฒโ€‘๋‹ค์ด๋„ˆ๋ชจ (spherical

Model Astrophysics Physics Nonlinear Sciences
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Cohomology theories for homotopy algebras and noncommutative geometry

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ โˆžโ€‘๋Œ€์ˆ˜์˜ ์ค‘์š”์„ฑ : $A infty$, $C infty$, $L infty$โ€‘๋Œ€์ˆ˜๋Š” ์ „ํ†ต์ ์ธ ์—ฐ์‚ฐ์ž ๋Œ€์ˆ˜๋ฅผ ๊ณ ์ฐจ ์—ฐ์‚ฐ์œผ๋กœ ํ™•์žฅํ•œ ๊ตฌ์กฐ๋กœ, Hโ€‘๊ณต๊ฐ„, ๋ฌธ์ž์—ด์žฅ ์ด๋ก , ์œ„์ƒ ฮฃโ€‘๋ชจ๋ธ ๋“ฑ ๋ฌผ๋ฆฌยท์ˆ˜ํ•™ ์ „๋ฐ˜์— ๊ฑธ์ณ ํ•ต์‹ฌ ์—ญํ• ์„ ํ•œ๋‹ค. ๊ธฐ์กด ์ ‘๊ทผ์˜ ํ•œ๊ณ„ : ์ „ํ†ต์ ์ธ ์ •์˜๋Š” ๋ณต์žกํ•œ ๊ณ ์ฐจ ๊ณฑ์…ˆ ์—ฐ์‚ฐ $m n$๋“ค์˜ ๊ด€๊ณ„์‹์— ์˜์กดํ•ด ๊ณ„์‚ฐ์ด ๋‚œํ•ดํ•˜๊ณ , ์ฝ”ํ˜ธ๋ชฐ๋กœ์ง€ ์ด๋ก ์„ ๋‹ค๋ฃฐ ๋•Œ๋Š” ๋ณต์žกํ•œ ์กฐํ•ฉ๋ก ์ด ํ•„์—ฐ์ ์œผ๋กœ ๋“ฑ์žฅํ•œ๋‹ค. ๋น„๊ฐ€ํ™˜ ๊ธฐํ•˜ํ•™๊ณผ์˜ ์—ฐ๊ฒฐ : Connesโ€‘Kontsevich์˜ ๋น„๊ฐ€ํ™˜ ๊ธฐํ•˜ํ•™์€ โ€œ๋น„๊ฐ€ํ™˜ ํ•จ์ˆ˜๋Œ€โ€๋ฅผ โ€˜๋น„๊ฐ€ํ™˜

Mathematics
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Consistency of support vector machines for forecasting the evolution of an unknown ergodic dynamical system from observations with unknown noise

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ Ergodic ๋™์—ญํ•™ ์‹œ์Šคํ…œ ((F^n) {nge0}) ์€ ์•Œ๋ ค์ง€์ง€ ์•Š์€ ๋งคํ•‘ (F:Mto M) (compact (Msubsetmathbb{R}^d)) ๋กœ ์ •์˜๋˜๊ณ , ๊ณ ์œ ํ•œ ergodic ์ธก๋„ (mu) ๋ฅผ ๊ฐ€์ง„๋‹ค. ๊ด€์ธก๊ฐ’์€ additive ์žก์Œ (varepsilon n) ๊ฐ€ ์„ž์ธ ํ˜•ํƒœ (X n F^n(x 0)+varepsilon n) ๋กœ ์ฃผ์–ด์ง€๋ฉฐ, ์žก์Œ ๊ณผ์ •์€ ์ •์ƒ(stationary) ์ด์ง€๋งŒ ๋ถ„ํฌ (nu) ์—ญ์‹œ ๋ฏธ์ง€์ด๋‹ค. ๋ชฉํ‘œ๋Š” ์œ ํ•œํ•œ ๊ด€์ธก ์‹œํ€€์Šค (T {X

Mathematics System Statistics
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Cooperative Multiplexing in a Half Duplex Relay Network: Performance and Constraints

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ MIMO์˜ ๋‘ ํ•ต์‹ฌ ์ด๋“ (๋‹ค์ค‘ํ™”ยท๋‹ค์–‘์„ฑ)์€ ์ „ํ†ต์ ์œผ๋กœ ๋‹ค์ค‘ ์•ˆํ…Œ๋‚˜๋ฅผ ์ง์ ‘ ์žฅ์ฐฉํ•œ ๋‹จ๋ง์—์„œ๋งŒ ์‹คํ˜„ ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์—ฌ๊ฒจ์กŒ๋‹ค. ๋ฐ˜์ด์ค‘ ๋ฆด๋ ˆ์ด ๋„คํŠธ์›Œํฌ๋Š” ์‹œ๊ฐ„ ์Šฌ๋กฏ์„ ์ถ”๊ฐ€ ํ•ด์•ผ ํ•˜๋ฏ€๋กœ ๋‹ค์ค‘ํ™” ์ด๋“ ํ™•๋ณด๊ฐ€ ์–ด๋ ค์šด ๊ตฌ์กฐ์ด๋ฉฐ, ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ๋‹ค์–‘์„ฑ ํ–ฅ์ƒ ์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ โ€œ ๋ฐ˜์ด์ค‘ + ๋‹ค์ค‘ ์•ˆํ…Œ๋‚˜ ๋ฆด๋ ˆ์ด โ€๋ผ๋Š” ์ƒˆ๋กœ์šด ์กฐํ•ฉ์„ ํ†ตํ•ด ๋‹ค์ค‘ํ™” ์ด๋“ ์„ ์–ป์„ ์ˆ˜ ์žˆ์Œ์„ ์ตœ์ดˆ๋กœ ์ œ์‹œํ•œ๋‹ค. 2. ์‹œ์Šคํ…œ ๋ชจ๋ธ ๋ฐ ํ•ต์‹ฌ ๊ฐ€์ • | ์š”์†Œ | ๊ตฌ์„ฑ | ์ฃผ์š” ๊ฐ€์ • | | | | | | ์†Œ์Šค (S) | 1 ์•ˆํ…Œ๋‚˜ | CS

Network Mathematics Information Theory Computer Science
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Credit risk - A structural model with jumps and correlations

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋ฌผ๋ฆฌํ•™โ€‘๊ธˆ์œต ๊ต์ฐจ์  : ์ตœ๊ทผ ๋ฌผ๋ฆฌํ•™์ž๋“ค์ด ๋ณต์žก๊ณ„ยทํ†ต๊ณ„๋ฌผ๋ฆฌํ•™ ๋„๊ตฌ๋ฅผ ๊ธˆ์œต์‹œ์žฅ์— ์ ์šฉํ•˜๋ฉด์„œ, ํŠนํžˆ โ€œfatโ€‘tailโ€ ํ˜„์ƒ์„ ์„ค๋ช…ํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ ํ™œ๋ฐœํžˆ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ์‹ ์šฉ์œ„ํ—˜์˜ ํŠน์ˆ˜์„ฑ : ์‹œ์žฅ์œ„ํ—˜๊ณผ ๋‹ฌ๋ฆฌ ์†์‹ค๋ถ„ํฌ๊ฐ€ ๊ฐ•ํ•˜๊ฒŒ ๋น„๋Œ€์นญ์ด๋ฉฐ, ๊ทน๋‹จ ์†์‹ค(ํ…Œ์ผ ์œ„ํ—˜)์ด ์‹ค์งˆ์ ์ธ ์ž๋ณธ ์†์‹ค์„ ์ดˆ๋ž˜ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ํ‰๊ท ยท๋ถ„์‚ฐ๋งŒ์œผ๋กœ๋Š” ์œ„ํ—˜์„ ์ถฉ๋ถ„ํžˆ ํ‰๊ฐ€ํ•  ์ˆ˜ ์—†์œผ๋ฉฐ, ๊ผฌ๋ฆฌ ์œ„ํ—˜์„ ์ •๋Ÿ‰ํ™”ํ•˜๋Š” ๋ชจ๋ธ์ด ํ•„์š”ํ•˜๋‹ค. 2. ๋ชจ๋ธ ์„ค๊ณ„ | ์š”์†Œ | ๋‚ด์šฉ | ๋ฌผ๋ฆฌํ•™์  ํ•ด์„ | | | | | | ๊ธฐ์ดˆ ํ”„๋กœ์„ธ์Šค | ๊ธฐ์—… ์ž์‚ฐ (A i(t)) ๋ฅผ

Quantitative Finance Model Physics Condensed Matter
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Critical Line in Random Threshold Networks with Inhomogeneous Thresholds

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋ณต์žก๊ณ„ ๋„คํŠธ์›Œํฌ์™€ ๋™์—ญํ•™ : ์‹ ๊ฒฝ๋ง, ์œ ์ „์ž ์กฐ์ ˆ๋ง, ์ „๋ ฅ๋ง ๋“ฑ ๋‹ค์–‘ํ•œ ์‹ค์„ธ๊ณ„ ์‹œ์Šคํ…œ์€ ๋Œ€๊ทœ๋ชจ ์ƒํ˜ธ์ž‘์šฉ์„ ๊ฐ–๋Š”๋‹ค. Kauffman์˜ ๋žœ๋ค Boolean ๋„คํŠธ์›Œํฌ(RBN)๋Š” ์ด๋Ÿฌํ•œ ์‹œ์Šคํ…œ์„ ๋ชจ๋ธ๋งํ•˜๋Š” ๋Œ€ํ‘œ์  ํ”„๋ ˆ์ž„์›Œํฌ์˜€์ง€๋งŒ, ๊ณ„์‚ฐ ๋ณต์žก๋„์™€ ๋น„์„ ํ˜•์„ฑ ๋•Œ๋ฌธ์— ๋‹จ์ˆœํ™”๋œ ๋ชจ๋ธ์ด ํ•„์š”ํ–ˆ๋‹ค. RTN์˜ ์žฅ์  : RTN์€ ์ž…๋ ฅ ๊ฐ€์ค‘์น˜์™€ ์ž„๊ณ„๊ฐ’์„ ์ด์šฉํ•ด ์ด์ง„ ์ƒํƒœ๋ฅผ ๊ฒฐ์ •ํ•˜๋ฏ€๋กœ, RBN๋ณด๋‹ค ๊ณ„์‚ฐ๋Ÿ‰์ด ์ ๊ณ  ๋ถ„์„์ด ์šฉ์ดํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ๋™์งˆ ์ž„๊ณ„๊ฐ’ (๋ชจ๋“  ๋…ธ๋“œ๊ฐ€ ๊ฐ™์€ (h))์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์œผ๋ฉฐ, ์ด์งˆ ์ž„๊ณ„๊ฐ’ ์ด

Network Quantitative Biology Nonlinear Sciences Condensed Matter
Diagonal fibrations are pointwise fibrations

Diagonal fibrations are pointwise fibrations

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ด์ค‘ ๋‹จ์ˆœ์ง‘ํ•ฉ ์€ ๋‘ ์ฐจ์›์˜ simplicial ๊ตฌ์กฐ๋ฅผ ๋™์‹œ์— ๊ฐ–๋Š” ๊ฐ์ฒด๋กœ, ๊ณ ์ฐจ์› ํ˜ธ๋ชฐ๋กœ์ง€ ์ด๋ก , ๊ณ ์ฐจ์› ๋ฒ”์ฃผ๋ก , ๊ทธ๋ฆฌ๊ณ  ๋ชจ๋ธ ๋ฒ”์ฃผ ์ด๋ก ์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. Quillen ๋ชจ๋ธ ๊ตฌ์กฐ ๋Š” ํ˜ธ๋ชฐ๋กœ์ง€ ์ด๋ก ์„ ๋ฒ”์ฃผ๋ก ์ ์œผ๋กœ ์ •ํ˜•ํ™”ํ•˜๋Š” ๋„๊ตฌ์ด๋ฉฐ, ๊ฐ™์€ ๋Œ€์ƒ์— ๋Œ€ํ•ด ์—ฌ๋Ÿฌ ๋ชจ๋ธ ๊ตฌ์กฐ๋ฅผ ์ •์˜ํ•  ์ˆ˜ ์žˆ๋‹ค. Bousfieldโ€‘Kan ๊ตฌ์กฐ์™€ Moerdijk ๊ตฌ์กฐ๋Š” ๊ฐ๊ฐ ์ ๋ณ„ ๊ณผ ๋Œ€๊ฐ์„  ๊ด€์ ์„ ๊ฐ•์กฐํ•œ๋‹ค. ๋‘ ๊ตฌ์กฐ ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ๋ช…ํ™•ํžˆ ์ดํ•ดํ•˜๋ฉด, ์ด์ค‘ ๋‹จ์ˆœ์ง‘ํ•ฉ์„ ์ด์šฉํ•œ ๊ณ„์‚ฐ์ด๋‚˜ ์ด๋ก  ์ „๊ฐœ ์‹œ ์–ด๋А ๊ตฌ์กฐ๋ฅผ ์„ ํƒํ•ด๋„ ์†์‹ค์ด ์—†์Œ

Mathematics
Discrete Denoising with Shifts

Discrete Denoising with Shifts

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ DUDE (Discrete Universal DEnoiser, Weissman et al., 2005)๋Š” โ€œ๊ณ ์ •๋œ ์Šฌ๋ผ์ด๋”ฉ ์œˆ๋„์šฐโ€ ์ „๋ฌธ๊ฐ€์™€ ๊ฒฝ์Ÿํ•˜๋Š” ๋ณดํŽธ์  ๋””๋…ธ์ด์ง• ๊ธฐ๋ฒ•์œผ๋กœ, ๊ฐœ๋ณ„ ์‹œํ€€์Šค์— ๋Œ€ํ•ด ์ตœ์ ์˜ ๊ณ ์ •โ€‘์œˆ๋„์šฐ ๋””๋…ธ์ด์ €์™€ ๊ฑฐ์˜ ๋™์ผํ•œ ์„ฑ๋Šฅ์„ ๋ณด์žฅํ•œ๋‹ค. ์‹ค์ œ ์‹ ํ˜ธยท์ด๋ฏธ์ง€ยทํ†ต์‹  ๋ฐ์ดํ„ฐ๋Š” ์‹œ๊ฐ„ยท๊ณต๊ฐ„์— ๋”ฐ๋ผ ํ†ต๊ณ„๊ฐ€ ๊ธ‰๋ณ€ ํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ๋‹ค(์˜ˆ: ์˜์ƒ์˜ ๊ฒฝ๊ณ„, ์ฑ„๋„ ์ƒํƒœ ๋ณ€ํ™”). ๊ณ ์ • ์œˆ๋„์šฐ๋งŒ์œผ๋กœ๋Š” ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋ฅผ ์ถ”์ ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋”ฐ๋ผ์„œ ์ „๋ฌธ๊ฐ€ ์ „ํ™˜(shift) ์„ ํ—ˆ์šฉํ•˜๋Š” ๋ณตํ•ฉ ํ–‰๋™ ๋ชจ๋ธ์„ ๋„์ž…ํ•˜๋ฉด, ๊ตฌ๊ฐ„๋งˆ๋‹ค

Mathematics Information Theory Computer Science
Discussion of ``2004 IMS Medallion Lecture: Local Rademacher   complexities and oracle inequalities in risk minimization by V.   Koltchinskii

Discussion of ``2004 IMS Medallion Lecture: Local Rademacher complexities and oracle inequalities in risk minimization by V. Koltchinskii

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•ต์‹ฌ ์งˆ๋ฌธ ์ดˆ๊ณผ ์œ„ํ—˜ (R(f n) R {text{OR}}) ๋ฅผ ์ตœ์†Œํ™”ํ•˜๋Š” ์ถ”์ •๋Ÿ‰์„ ์ฐพ๋Š” ๊ฒƒ์ด ํ†ต๊ณ„ ํ•™์Šต์˜ ํ•ต์‹ฌ ๋ชฉํ‘œ์ด๋‹ค. ๋ชจ๋“  ๋ถ„ํฌ (P) ์— ๋Œ€ํ•ด ๋™์‹œ์— ์ตœ์ ์„ฑ์„ ๋‹ฌ์„ฑํ•  ์ˆ˜ ์—†์œผ๋ฏ€๋กœ, ๋ฏธ๋‹ˆ๋งฅ์Šค ๊ด€์  (๋ถ„ํฌ ํด๋ž˜์Šค (mathcal{P}) ์ •์˜)์—์„œ ์ตœ์  ์†๋„ (varepsilon {n,M}) ๋ฅผ ์ •์˜ํ•œ๋‹ค. ๋‘ ๊ฐ€์ง€ ์ง‘๊ณ„ ๋ชฉํ‘œ๊ฐ€ ์กด์žฌํ•œ๋‹ค. 1. MSโ€‘aggregation : (displaystyle R {text{MS}} inf {jin{1,dots,M}}R(f j)) (ํ•˜๋‚˜์˜

Quantitative Finance Mathematics Statistics
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Dispersionful analogue of the Whitham hierarchy

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ | ํ•ต์‹ฌ ํฌ์ธํŠธ | ๋‚ด์šฉ | | | | | Whitham ๊ณ„์ธต | Krichever๊ฐ€ ์ œ์‹œํ•œ ๋ณดํŽธ์  Whitham ๊ณ„์ธต์€ ๋ฆฌ๋งŒ ๊ณก๋ฉด์˜ ๋ชจ๋“ˆ๋ฆฌ ๊ณต๊ฐ„์„ ์ด์šฉํ•ด ๋ฌด๋ถ„์‚ฐ(dispersionless) ๋น„์„ ํ˜• ์ ๋ถ„๊ณ„(system)๋ฅผ ์ฒด๊ณ„ํ™”ํ•œ๋‹ค. ์˜โ€‘์ข… ๊ฒฝ์šฐ๊ฐ€ โ€œWhitham hierarchyโ€๋ผ ๋ถˆ๋ฆฌ๋ฉฐ KdV, Toda, AKNS ๋“ฑ ๋‹ค์–‘ํ•œ ๋ฌด๋ถ„์‚ฐ ์†”๋ฆฌํ†ค ๋ฐฉ์ •์‹์˜ ๊ทผ๋ณธ ๊ตฌ์กฐ๋ฅผ ์ œ๊ณตํ•œ๋‹ค. | | ๋ถ„์‚ฐ์„ฑ ์ด๋ก ์˜ ๋ถ€์žฌ | ๊ธฐ์กด Whitham ๊ณ„์ธต์„ โ€œ์–‘์žํ™”โ€ํ•ด ๋ถ„์‚ฐ์„ฑ์„ ๋„์ž…ํ•˜๋ ค๋ฉด ๊ณ ์ฐจ ์œ ํ•œ ๊ทน์ (finite pole) ๊ฐœ๋…

Mathematics Nonlinear Sciences MATH-PH
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Diversity-Multiplexing Tradeoff of Asynchronous Cooperative Diversity in Wireless Networks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋ฆด๋ ˆ์ด ๋™๊ธฐํ™”์˜ ์–ด๋ ค์›€ : ๊ธฐ์กด ํ˜‘๋™ ๋‹ค์ด๋ฒ„์‹œํ‹ฐ ์—ฐ๊ตฌ๋Š” ๋ฆด๋ ˆ์ด ๊ฐ„ ์™„๋ฒฝํ•œ ์‹ฌ๋ณผ ์ˆ˜์ค€ ๋™๊ธฐํ™”๋ฅผ ์ „์ œํ•œ๋‹ค. ์‹ค์ œ ์ธํ”„๋ผ๊ฐ€ ์—†๋Š” adโ€‘hocยท์„ผ์„œ ๋„คํŠธ์›Œํฌ์—์„œ๋Š” ์ด ๊ฐ€์ •์ด ํ˜„์‹ค์ ์œผ๋กœ ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค. DM ํŠธ๋ ˆ์ด๋“œ์˜คํ”„ : Zhengโ€‘Tse ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ๋ฆด๋ ˆ์ด ์ฑ„๋„์— ์ ์šฉํ•ด ๋‹ค์ด๋ฒ„์‹œํ‹ฐ์™€ ์ŠคํŽ™ํŠธ๋Ÿผ ํšจ์œจ ์‚ฌ์ด์˜ ๊ทผ๋ณธ์ ์ธ ๊ด€๊ณ„๋ฅผ ์ •๋Ÿ‰ํ™”ํ•œ๋‹ค. ์ด๋Š” ์‹œ์Šคํ…œ ์„ค๊ณ„ ์‹œ โ€œ์–ผ๋งˆ๋‚˜ ๋งŽ์€ ๋‹ค์ด๋ฒ„์‹œํ‹ฐ๋ฅผ ํฌ๊ธฐํ•˜๊ณ  ๋ฉ€ํ‹ฐํ”Œ๋ ‰์‹ฑ์„ ์–ป์„ ๊ฒƒ์ธ๊ฐ€โ€๋ฅผ ๋ช…ํ™•ํžˆ ํŒ๋‹จํ•˜๊ฒŒ ํ•ด์ค€๋‹ค. 2. ์ œ์•ˆ๋œ ๋น„๋™๊ธฐ ์Šคํ‚ด | ์Šคํ‚ด | ํ•ต์‹ฌ ์•„์ด๋””์–ด | ์ฃผ์š” ๊ฐ€์ • | ๊ธฐ๋Œ€

Network Mathematics Information Theory Computer Science
Expert Elicitation for Reliable System Design

Expert Elicitation for Reliable System Design

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํ†ต๊ณ„์™€ ์‹ ๋ขฐ์„ฑ ๊ณตํ•™์˜ ์ ‘์  : ๊ธฐ์กด ๋ฌธํ—Œ์€ ํ†ต๊ณ„๊ฐ€ ์‹ ๋ขฐ์„ฑ ๊ณตํ•™์˜ ํ•ต์‹ฌ ๋„๊ตฌ์ž„์„ ๊ฐ•์กฐํ•˜์ง€๋งŒ, ๋ณต์žกํ•œ ์‹œ์Šคํ…œยท์šด์šฉ ํ™˜๊ฒฝ์„ ๋ฐ˜์˜ํ•˜๊ธฐ์—” ๋ถ€์กฑํ•˜๋‹ค. ์‹œ์Šคํ…œ ์—”์ง€๋‹ˆ์–ด๋ง๊ณผ ์‹ ๋ขฐ์„ฑ : ์„ค๊ณ„ยท์ œ์กฐยท์šด์šฉยทํ๊ธฐ๊นŒ์ง€ ์ „ ๋‹จ๊ณ„์— ๊ฑธ์ณ ์‹ ๋ขฐ์„ฑ(RAM) ์š”๊ตฌ๊ฐ€ ์กด์žฌํ•จ์—๋„, ์ „ํ†ต์ ์ธ ํ™•๋ฅ  ๋ชจ๋ธ์€ ์„ค๊ณ„ ์ดˆ๊ธฐ์—๋งŒ ์ ์šฉ๋˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ๋‹ค. ์ „๋ฌธ๊ฐ€ ํŒ๋‹จ์˜ ํ™•๋Œ€ : ๊ฐ๊ด€ ๋ฐ์ดํ„ฐ๊ฐ€ ๋ถ€์กฑํ•œ ์ดˆ๊ธฐ ๋‹จ๊ณ„์—์„œ โ€˜์ฃผ๊ด€์ โ€™ ์ „๋ฌธ๊ฐ€ ์˜๊ฒฌ์„ ๊ตฌ์กฐํ™”ยท์ •๋Ÿ‰ํ™”ํ•˜๋Š” ํ•„์š”์„ฑ์ด ๋Œ€๋‘๋œ๋‹ค. 2. ์ฃผ์š” ๋‚ด์šฉ ์š”์•ฝ | ๊ตฌ๋ถ„ | ํ•ต์‹ฌ ์ฃผ์žฅยท๋‚ด์šฉ | | | | | ์„ค๊ณ„ ํ”„๋กœ์„ธ์Šค

System Statistics
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Extreme Synergy in a Retinal Code: Spatiotemporal Correlations Enable Rapid Image Reconstruction

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์‹œ๋„ˆ์ง€ ๋…ผ์Ÿ : ๋ง๋ง‰ ์‹ ๊ฒฝ์ ˆ ์„ธํฌ ๊ฐ„์˜ ์ •๋ณด ๊ณต์œ ๊ฐ€ ์ค‘๋ณต , ๋…๋ฆฝ , ํ˜น์€ ์‹œ๋„ˆ์ง€ ์ค‘ ์–ด๋А ์ชฝ์— ๋” ๊ฐ€๊นŒ์šด๊ฐ€์— ๋Œ€ํ•œ ๋…ผ๋ž€์ด ์˜ค๋ž˜ ์ง€์†๋ผ ์™”๋‹ค(์˜ˆ: Latham & Nirenberg, 2005). ์‹œ๊ฐ„ ์ œ์•ฝ : ํ–‰๋™ํ•™์  ์—ฐ๊ตฌ์— ๋”ฐ๋ฅด๋ฉด ์‹œ๊ฐ ์ •๋ณด๋Š” 50โ€“300 ms ์•ˆ์— ์ฒ˜๋ฆฌ๋˜์–ด์•ผ ํ•˜๋ฉฐ, ์ด ์งง์€ ์ฐฝ์—์„œ ์ „ํ†ต์ ์ธ ์ŠคํŒŒ์ดํฌ ๋ ˆ์ดํŠธ ์ฝ”๋“œ ๋งŒ์œผ๋กœ๋Š” ์ถฉ๋ถ„ํ•œ ์ •๋ณด๋ฅผ ์ „๋‹ฌํ•˜๊ธฐ ์–ด๋ ค์šธ ์ˆ˜ ์žˆ๋‹ค. ์ƒˆ๋กœ์šด ์ ‘๊ทผ : ๋ณธ ๋…ผ๋ฌธ์€ ์‹œ๊ณต๊ฐ„ ์ƒ๊ด€๊ด€๊ณ„ ๋ฅผ ๋น„์„ ํ˜•์ ์œผ๋กœ ํ™œ์šฉํ•ด, ์ˆ˜๋ฐฑ~์ˆ˜์ฒœ ๊ฐœ์˜ ๋‰ด๋Ÿฐ์ด ๋™์‹œ์— ์ฝ”๋”ฉํ•˜๋Š” ๋ฐฉ์‹์„ ์ œ์•ˆํ•œ๋‹ค. ์ด๋Š”

Quantitative Biology
Generating models for temporal representations

Generating models for temporal representations

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉํ‘œ ์‹œ๊ฐ„ยท์‚ฌ๊ฑด ๋ชจ๋ธ๋ง ์€ ์ž์—ฐ์–ด ์˜๋ฏธ๋ก ์—์„œ ํ•ต์‹ฌ ๊ณผ์ œ์ด๋ฉฐ, ๊ธฐ์กด ์ž‘์—…์€ ์ฃผ๋กœ ์ •๋ฆฌ ์ฆ๋ช…(theorem proving) ์— ์ดˆ์ ์„ ๋งž์ท„๋‹ค. Blackburn & Bos(2005)์˜ Curt ์•„ํ‚คํ…์ฒ˜ ๋Š” ์ •๋ฆฌ ์ฆ๋ช…๊ณผ ๋ชจ๋ธ ๊ตฌ์ถ•(model building) ์„ ๊ฒฐํ•ฉํ–ˆ์ง€๋งŒ, ์‹œ๊ฐ„ยท์ƒ ํ‘œํ˜„์„ ์ถฉ๋ถ„ํžˆ ๋‹ค๋ฃจ์ง€๋Š” ๋ชปํ–ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ (i) ํ’๋ถ€ํ•œ 1์ฐจ ๋…ผ๋ฆฌ ์ด๋ก  , (ii) ๋ชจ๋ธ ๊ต๋ž€ ์•Œ๊ณ ๋ฆฌ์ฆ˜ , (iii) ๊ตฌํ˜„ ํ”„๋ ˆ์ž„์›Œํฌ ๋ฅผ ํ†ตํ•ด โ€œ์‹œ๊ฐ„์  ์˜๋ฏธ๋ก ์  ๋ชจ๋ธ๋งโ€์„ ์‹ค์šฉํ™”ํ•˜๊ณ ์ž ํ•œ๋‹ค. 2. ์ด๋ก ์  ๊ธฐ์ดˆ 2.1 ์‹œ๊ฐ„ยท์‚ฌ๊ฑด ์˜จํ†จ๋กœ์ง€

Model NLP Computer Science
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Gradient Representations and the Perception of Luminosity

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ œ๊ธฐ ๋ฐœ๊ด‘ ์ง€๊ฐ vs. ๋ฌผ๋ฆฌ์  ๋ฐœ๊ด‘ : ํ…”๋ ˆ๋น„์ „ยท์ปดํ“จํ„ฐ ํ™”๋ฉด์ฒ˜๋Ÿผ ์‹ค์ œ๋กœ ๋น›์„ ๋‚ด๋Š” ๋ฌผ์ฒด๊ฐ€ ๋ฐ˜๋“œ์‹œ ๋ฐœ๊ด‘์œผ๋กœ ์ง€๊ฐ๋˜๋Š” ๊ฒƒ์€ ์•„๋‹ˆ๋‹ค. ๋ฐ˜๋Œ€๋กœ, ํšŒํ™”์—์„œ halo(ํ›„๊ด‘)์™€ ๊ฐ™์€ ๊ธฐ๋ฒ•์œผ๋กœ ๋ฌผ๋ฆฌ์  ๊ด‘์› ์—†์ด๋„ ๋ฐœ๊ด‘ ํšจ๊ณผ๋ฅผ ๋งŒ๋“ค ์ˆ˜ ์žˆ๋‹ค. ๊ธฐ์กด ๊ฐ€์„ค : ๋ฐœ๊ด‘์€ ๋ณ„๋„ ์‹œ๊ฐ ๊ฒฝ๋กœ์— ์˜ํ•ด ์ฒ˜๋ฆฌ๋˜๋Š” ๋…๋ฆฝ์ ์ธ ํŠน์„ฑ(feature)์ด๋ผ๋Š” ๊ฐ€์„ค์ด ์žˆ์—ˆ์œผ๋‚˜, ์ตœ๊ทผ ์‹ฌ๋ฆฌ๋ฌผ๋ฆฌํ•™ ์—ฐ๊ตฌ(

Quantitative Biology
Identification of candidate regulatory sequences in mammalian 3 UTRs by   statistical analysis of oligonucleotide distributions

Identification of candidate regulatory sequences in mammalian 3 UTRs by statistical analysis of oligonucleotide distributions

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉํ‘œ 3โ€ฒ UTR์˜ ๊ธฐ๋Šฅ : mRNA ์•ˆ์ •์„ฑ, ์„ธํฌ ๋‚ด ์œ„์น˜, ๋ฒˆ์—ญ ํšจ์œจ์„ ์กฐ์ ˆํ•˜๋Š” ์งง์€ ์„œ์—ด ์š”์†Œ๋“ค์˜ ์ €์žฅ์†Œ์ด๋ฉฐ, miRNA ๊ฒฐํ•ฉ ๋ถ€์œ„๊ฐ€ ๋Œ€ํ‘œ์ ์ด๋‹ค. miRNA์˜ ์ƒ๋ฌผํ•™์  ์˜์˜ : ๋ฐœ๋‹ฌ, ์„ธํฌ ์‚ฌ๋ฉธ, ์งˆ๋ณ‘ ๋“ฑ ๊ด‘๋ฒ”์œ„ํ•œ ๊ณผ์ •์— ๊ด€์—ฌํ•˜๋ฉฐ, ์ธ๊ฐ„ ์œ ์ „์ž์˜ ์•ฝ 1/3์ด miRNA์— ์˜ํ•ด ์กฐ์ ˆ๋œ๋‹ค๊ณ  ์ถ”์ •๋œ๋‹ค. ๊ธฐ์กด ์˜ˆ์ธก ๋ฐฉ๋ฒ•์˜ ํ•œ๊ณ„ : ๋ณดํ†ต โ€˜๋ณด์กด๋œ ์„œ์—ดโ€™, โ€˜seed์™€์˜ Watsonโ€‘Crick ๋งค์นญโ€™, โ€˜์„œ์—ด ํ’๋ถ€๋„โ€™, โ€˜2์ฐจ ๊ตฌ์กฐโ€™ ๋“ฑ์„ ๊ฒฐํ•ฉํ•˜์ง€๋งŒ, ๊ฐ๊ฐ์€ ๋ณ„๋„์˜ ๊ฐ€์ •์„ ํ•„์š”๋กœ ํ•˜๋ฉฐ, ์ƒˆ๋กœ์šด miRNAยทํ‘œ์ ์„ ๋†“์น˜๊ธฐ

Quantitative Biology Analysis
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L.V.Kantorovich and Linear Programming

1. ์นธํ† ๋กœ๋น„์น˜์˜ ์ดˆ๊ธฐ ์—ฐ๊ตฌ์™€ ์„ ํ˜•๊ณ„ํš๋ฒ•์˜ ํƒ„์ƒ 1939๋…„ ์ €์„œ์™€ โ€œํ•ด๊ฒฐ ์Šน์ˆ˜โ€ ๊ฐœ๋…์€ ์˜ค๋Š˜๋‚  ์ด์ค‘ ๋ฌธ์ œ(Dual Problem)์™€ ๋ผ๊ทธ๋ž‘์ฃผ ์Šน์ˆ˜๋ฒ•์˜ ๊ฒฝ์ œ์  ํ•ด์„์„ ์ตœ์ดˆ๋กœ ์ œ์‹œํ•˜์˜€๋‹ค. ์ด๋Š” โ€œ๊ฐ๊ด€์ ์œผ๋กœ ๊ฒฐ์ •๋œ ๊ฐ€์น˜(objectively determined valuations)โ€๋ผ๋Š” ์šฉ์–ด๋กœ ๊ฐ€๊ฒฉ ๊ฐœ๋…์„ ์ˆ˜ํ•™์ ์œผ๋กœ ์ •๋ฆฝํ•œ ์ ์—์„œ ํ˜์‹ ์ ์ด๋‹ค. ์ด๋ก ์  ๊ธฐ๋ฐ˜์€ ํ•จ์ˆ˜ํ•ด์„ํ•™(ํŠนํžˆ M.G. Krein์˜ ํ•™ํŒŒ)๊ณผ ์—ฐ๊ณ„๋˜์–ด, Lโ€‘๋ชจ๋ฉ˜ํŠธ ๋ฌธ์ œ์™€ ๊ฐ™์€ ๊ณ ์ „์  ๋ฌธ์ œ์— ์ตœ์ ํ™” ๊ธฐ๋ฒ•์„ ๋„์ž…ํ•˜์˜€๋‹ค. 2. ์†Œ๋ จ ์ฒด์ œ์™€ ์ด๋…์  ์ €ํ•ญ 1940โ€‘50๋…„๋Œ€๋Š” โ€˜๋ช…๋ นโ€‘ํ–‰์ •

Mathematics
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Leibniz seminorms for 'Matrix algebras converge to the sphere'

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋ฌผ๋ฆฌํ•™์  ๋™๊ธฐ : ๊ณ ์—๋„ˆ์ง€ ๋ฌผ๋ฆฌยท์ŠคํŠธ๋ง ์ด๋ก ์—์„œ๋Š” โ€œ๋งคํŠธ๋ฆญ์Šค ๋ชจ๋ธ์ด ๊ตฌ๋ฉด(๋˜๋Š” ๋‹ค๋ฅธ ๊ณต๊ฐ„)์œผ๋กœ ์ˆ˜๋ ดํ•œ๋‹คโ€๋Š” ์„œ์ˆ ์ด ์ž์ฃผ ๋“ฑ์žฅํ•œ๋‹ค. ํŠนํžˆ ๋‹จ๊ทน์ž(๋ชจ๋…ธํด) ๋ฒˆ๋“ค ์ด ๋งคํŠธ๋ฆญ์Šค ๋Œ€์ˆ˜ ์œ„์˜ ๋น„๊ฐ€ํ™˜ ๋ฒˆ๋“ค์— ๋Œ€์‘ํ•œ๋‹ค๋Š” ์ฃผ์žฅ์— ์ˆ˜ํ•™์  ๊ทผ๊ฑฐ๊ฐ€ ํ•„์š”ํ–ˆ๋‹ค. ์ˆ˜ํ•™์  ์ „์ œ : ์ €์ž๋Š” ์ด์ „ ๋…ผ๋ฌธ(

Mathematics HEP-TH
Mosaic Pruning: A Hierarchical Framework for Generalizable Pruning of Mixture-of-Experts Models

Mosaic Pruning: A Hierarchical Framework for Generalizable Pruning of Mixture-of-Experts Models

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ SMoE์˜ ์žฅ์  : ํ† ํฐ๋‹น ํ™œ์„ฑํ™”๋˜๋Š” ํŒŒ๋ผ๋ฏธํ„ฐ ์ˆ˜๋ฅผ ํฌ๊ฒŒ ์ค„์—ฌ ๋Œ€๊ทœ๋ชจ ๋ชจ๋ธ์˜ ์ถ”๋ก  ํšจ์œจ์„ฑ์„ ๋†’์ธ๋‹ค. ํ•ต์‹ฌ ๋ณ‘๋ชฉ : ๋ชจ๋“  ์ „๋ฌธ๊ฐ€๋ฅผ ๋ฉ”๋ชจ๋ฆฌ์— ์ƒ์ฃผ์‹œ์ผœ์•ผ ํ•˜๋ฏ€๋กœ GPU ๋ฉ”๋ชจ๋ฆฌ ์š”๊ตฌ๋Ÿ‰์ด ์ˆ˜์‹ญ GB์— ๋‹ฌํ•œ๋‹ค(์˜ˆ: Mixtralโ€‘8x7B > 80 GB). ์ „๋ฌธ๊ฐ€ ์ค‘๋ณต : ํ•™์Šต ๊ณผ์ •์—์„œ ์ผ๋ถ€ ์ „๋ฌธ๊ฐ€๊ฐ€ ์„œ๋กœ ์œ ์‚ฌํ•œ ๊ธฐ๋Šฅ์„ ์ˆ˜ํ–‰ํ•˜๊ฑฐ๋‚˜ ๊ฑฐ์˜ ์‚ฌ์šฉ๋˜์ง€ ์•Š์•„ ์••์ถ• ๊ฐ€๋Šฅ์„ฑ์ด ์กด์žฌํ•œ๋‹ค. ๊ธฐ์กด ํ”„๋ฃจ๋‹ ํ•œ๊ณ„ : Enumeration Pruning ๋“ฑ์€ ์ผ๋ฐ˜ ์ฝ”ํผ์Šค(C4, WikiText) ๊ธฐ๋ฐ˜ ์†์‹ค ์ตœ์†Œํ™”์— ์ดˆ์ ์„ ๋งž์ถ”์–ด, ์ „๋ฌธ

Model Framework
Motivation, Design, and Ubiquity: A Discussion of Research Ethics and   Computer Science

Motivation, Design, and Ubiquity: A Discussion of Research Ethics and Computer Science

1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ „์ฒœํ›„ ์˜ํ–ฅ๋ ฅ : ์ปดํ“จํ„ฐยท์†Œํ”„ํŠธ์›จ์–ด๊ฐ€ ์ œ์กฐ, ๊ตํ†ต, ์˜๋ฃŒ, ๊ธˆ์œต, ์ •๋ถ€ ๋“ฑ ๊ฑฐ์˜ ๋ชจ๋“  ๋ถ„์•ผ์— ํ•„์ˆ˜์ ์ธ ์ธํ”„๋ผ๋กœ ์ž๋ฆฌ ์žก์Œ์— ๋”ฐ๋ผ, ์ปดํ“จํ„ฐ ๊ณผํ•™ ์—ฐ๊ตฌ๋Š” ์ง์ ‘ยท๊ฐ„์ ‘์ ์œผ๋กœ ์ „ ์„ธ๊ณ„ ์ธ๋ฅ˜์˜ ์ผ์ƒ์— ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ์œค๋ฆฌ์  ์ฑ…์ž„์˜ ํ™•๋Œ€ : ๊ธฐ์กด ๊ณตํ•™ ์œค๋ฆฌ(ํžˆํฌํฌ๋ผํ…Œ์Šค ์„ ์„œ์‹)๋ณด๋‹ค ๋” ๋†’์€ ์ˆ˜์ค€์˜ ์œค๋ฆฌ ๊ธฐ์ค€์ด ์š”๊ตฌ๋˜๋ฉฐ, ์ด๋Š” ์ด๋ก ยท์‘์šฉ์„ ๋™์‹œ์— ์ˆ˜ํ–‰ํ•˜๋Š” ์ปดํ“จํ„ฐ ๊ณผํ•™์˜ ํŠน์ˆ˜์„ฑ์—์„œ ๋น„๋กฏ๋œ๋‹ค. 2. ์ฃผ์š” ์œค๋ฆฌ์  ์Ÿ์  | ๊ตฌ๋ถ„ | ํ•ต์‹ฌ ๋‚ด์šฉ | ์—ฐ๊ตฌ ํ˜„์žฅ์—์„œ์˜ ํ•จ์˜ | | | | | | ์ธ๊ฐ„ ๋Œ€์ƒ ์—ฐ๊ตฌ | ์ธ๊ฐ„ ์ฐธ์—ฌ์ž ์‹๋ณ„์ด

General Literature Computer Science
Mutual information in random Boolean models of regulatory networks

Mutual information in random Boolean models of regulatory networks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ƒ๋ฌผํ•™์  ๋งฅ๋ฝ : ์„ธํฌ ๋‚ด ์œ ์ „์ž ์กฐ์ ˆ๋ง์€ ์ˆ˜์ฒœ ๊ฐœ์˜ ์œ ์ „์ž๊ฐ€ ๋ณต์žกํ•˜๊ฒŒ ์–ฝํžŒ ๋„คํŠธ์›Œํฌ์ด๋ฉฐ, ์ด๋“ค์˜ ๋™์‹œ์ ยทํ˜‘์กฐ์  ํ™œ๋™์„ ์ •๋Ÿ‰ํ™”ํ•˜๋Š” ๊ฒƒ์ด ํ•ต์‹ฌ ๊ณผ์ œ์ด๋‹ค. ์ •๋ณด ์ด๋ก ์  ์ ‘๊ทผ : ๋‘ ๋…ธ๋“œ์˜ ์‹œ๊ฐ„ ์‹œ๊ณ„์—ด ๊ฐ„ ์ƒํ˜ธ ์ •๋ณด๋Š” ํ•œ ๋…ธ๋“œ์˜ ์ƒํƒœ๊ฐ€ ๋‹ค๋ฅธ ๋…ธ๋“œ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ธก์ •ํ•œ๋‹ค. ๋„คํŠธ์›Œํฌ ์ „์ฒด์— ๋Œ€ํ•œ ํ‰๊ท  โŸจIโŸฉ๋Š” โ€œ์ „์—ญ ํ˜‘์กฐ์„ฑโ€์„ ๋‚˜ํƒ€๋‚ด๋Š” ์ž์—ฐ์Šค๋Ÿฌ์šด ์ฒ™๋„๋‹ค. ์ž„๊ณ„์„ฑ ๊ฐ€์„ค : ๊ธฐ์กด ์—ฐ๊ตฌ(์˜ˆ: Kauffman, Aldana ๋“ฑ)๋Š” ์œ ์ „์ž ๋„คํŠธ์›Œํฌ๊ฐ€ ์ž„๊ณ„(critical) ์ƒํƒœ์— ์žˆ์„ ๊ฒฝ์šฐ, ์•ˆ์ •์„ฑ๊ณผ ์œ ์—ฐ์„ฑ ์‚ฌ์ด์˜ ์ตœ์  ๊ท 

Model Network Quantitative Biology
Noise effects in extended chaotic system: study on the Lorenz96 model

Noise effects in extended chaotic system: study on the Lorenz96 model

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ํ˜ผ๋ˆ vs. ์žก์Œ : ํ˜ผ๋ˆ์€ ๊ฒฐ์ •๋ก ์ ์ด์ง€๋งŒ ์˜ˆ์ธก ๋ถˆ๊ฐ€๋Šฅํ•œ โ€˜์˜์‚ฌโ€‘๋ฌด์ž‘์œ„โ€™ ํ˜„์ƒ์ด๋ฉฐ, ์žก์Œ์€ ์™ธ๋ถ€์—์„œ ์ฃผ์ž…๋˜๋Š” ์ง„์ •ํ•œ ํ™•๋ฅ ์  ์š”์ธ์ด๋‹ค. ๋‘ ํ˜„์ƒ์ด ๋™์‹œ์— ์กด์žฌํ•  ๋•Œ, ํŠนํžˆ ๊ณต๊ฐ„์ ์œผ๋กœ ํ™•์žฅ๋œ ์‹œ์Šคํ…œ ์—์„œ์˜ ์ƒํ˜ธ์ž‘์šฉ์€ ์•„์ง ์ถฉ๋ถ„ํžˆ ๊ทœ๋ช…๋˜์ง€ ์•Š์•˜๋‹ค. Lorenzโ€‘96 ๋ชจ๋ธ : ๋Œ€๊ธฐ ์ „์—ญ ๋ชจ๋ธ์˜ ํ•ต์‹ฌ์ ์ธ ๋ฌผ๋ฆฌ ๊ณผ์ •์„ ๋‹จ์ˆœํ™”ํ•œ โ€˜toy modelโ€™๋กœ, F > 9/8 ์ผ ๋•Œ ํŒŒ๋™ ํ˜•ํƒœ์˜ ์ด๋™ ํŒจํ„ด๊ณผ ํ•จ๊ป˜ ํ™•์žฅ ํ˜ผ๋ˆ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. ๊ธฐํ›„ ์˜ˆ์ธก ์—ฐ๊ตฌ์—์„œ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๋ฏ€๋กœ, ์žก์Œ ํšจ๊ณผ๋ฅผ ํƒ๊ตฌํ•˜๋Š” ๋ฐ ์ ํ•ฉํ•œ ์‹œํ—˜๋Œ€๊ฐ€ ๋œ๋‹ค. 2. ๋ชจ

Physics System Nonlinear Sciences Condensed Matter Model
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Non-Regular Likelihood Inference for Seasonally Persistent Processes

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ๊ณ„์ ˆ์  ์žฅ๊ธฐ ์˜์กด์„ฑ ์€ ์ŠคํŽ™ํŠธ๋Ÿผ์ด 0์ด ์•„๋‹Œ ์ฃผํŒŒ์ˆ˜ ฮพ์— ๊ทน์ ์„ ๊ฐ–๋Š” ๊ฒฝ์šฐ๋ฅผ ์˜๋ฏธํ•œ๋‹ค. ๊ธฐ์กด์˜ Whittle likelihood๋Š” ์ด๋Ÿฌํ•œ ๊ทน์  ์œ„์น˜๋ฅผ ์ •ํ™•ํžˆ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•ด ฮพฬ‚(์ถ”์ •๊ฐ’)์˜ ๋ถ„ํฌ๋ฅผ ์ด๋ก ์ ์œผ๋กœ ๊ทœ๋ช…ํ•˜๊ธฐ ์–ด๋ ค์› ๋‹ค(Giraitis et al., 2001). ๋”ฐ๋ผ์„œ ๊ทน์  ์œ„์น˜์™€ ์žฅ๊ธฐ ์˜์กด์„ฑ ํŒŒ๋ผ๋ฏธํ„ฐ(ฮด) ๋ฅผ ๋™์‹œ์— ์ถ”์ •ํ•˜๋ฉด์„œ, ๊ทธ ์ถ”์ •๋Ÿ‰์˜ ๋Œ€์ˆ˜์  ํŠน์„ฑ์„ ์ œ๊ณตํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ์ ˆ์‹คํžˆ ์š”๊ตฌ๋œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ๋ฐ ๋ฐฉ๋ฒ•๋ก  | ๋‹จ๊ณ„ | ๋‚ด์šฉ | ์˜์˜ | | | | | | (a) ๋””๋ชจ๋“ˆ๋ ˆ์ด์…˜ + ์—ญDFT

Applications Statistics
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Nonlinear option pricing models for illiquid markets: scaling properties and explicit solutions

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ „ํ†ต์ ์ธ Blackโ€‘Scholes ๋ชจ๋ธ์€ ์™„์ „ ์œ ๋™์„ฑ ๊ณผ ๋ฌด๋งˆ์ฐฐ ์„ ์ „์ œ๋กœ ํ•˜์ง€๋งŒ, ์‹ค์ œ ์‹œ์žฅ์—์„œ๋Š” ๋Œ€๊ทœ๋ชจ ํ—ค์ง€ ๊ฑฐ๋ž˜๊ฐ€ ๊ฐ€๊ฒฉ์— ์˜ํ–ฅ์„ ๋ฏธ์ณ ์‹œ์žฅ ์ถฉ๊ฒฉ(market impact) ์ด ๋ฐœ์ƒํ•œ๋‹ค. ์ตœ๊ทผ ๊ธˆ์œต๊ณตํ•™์—์„œ๋Š” ๊ฑฐ๋ž˜๋น„์šฉ, SDEโ€‘๊ธฐ๋ฐ˜ ๋ชจ๋ธ, ๊ท ํ˜•(Reactionโ€‘function) ๋ชจ๋ธ ๋“ฑ ์„ธ ๊ฐ€์ง€ ์ ‘๊ทผ๋ฒ•์ด ์ œ์‹œ๋˜์—ˆ์œผ๋ฉฐ, ์ด๋“ค ๋ชจ๋‘ ํŒŒ์ƒ์ƒํ’ˆ ๊ฐ€๊ฒฉ์„ ๋น„์„ ํ˜• PDE ๋กœ ๊ธฐ์ˆ ํ•œ๋‹ค๋Š” ๊ณตํ†ต์ ์„ ๊ฐ€์ง„๋‹ค. ๋น„์„ ํ˜• PDE๋Š” ํ•ด์„์  ํ•ด๊ฐ€ ๋“œ๋ฌผ์–ด ์ˆ˜์น˜ํ•ด์„ ์— ์˜์กดํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ๋‹ค. ๋”ฐ๋ผ์„œ ๋ช…์‹œ์  ํ•ด ๋ฅผ ํ™•๋ณดํ•˜๋Š” ๊ฒƒ์€ ๋ชจ๋ธ ๊ฒ€

Quantitative Finance Mathematics Model
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On elliptic differential operators with shifts: II. The cohomological index formula

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์‹œํ”„ํŠธ๊ฐ€ ์žˆ๋Š” (pseudo)๋ฏธ๋ถ„ ์—ฐ์‚ฐ์ž๋Š” ์ „ํ†ต์ ์ธ ๊ตญ์†Œ ์—ฐ์‚ฐ์ž์™€ ๋‹ฌ๋ฆฌ ๋น„๊ตญ์†Œ(coefficient) ๊ตฌ์กฐ ๋ฅผ ๊ฐ–๋Š”๋‹ค. ์ด๋Š” ๊ตฐ ฮ“๊ฐ€ ๋งค๋‹ˆํด๋“œ M์— ์ž‘์šฉํ•˜๋ฉด์„œ ์ •์˜๋˜๋Š” โ€˜์‹œํ”„ํŠธ ์—ฐ์‚ฐ์žโ€™

Mathematics
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On the optimal contact potential of proteins

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉํ‘œ ๋‹จ๋ฐฑ์งˆ ๊ตฌ์กฐ์™€ ์„œ์—ด : ๋‹จ๋ฐฑ์งˆ 3์ฐจ์› ๊ตฌ์กฐ๋Š” ์•„๋ฏธ๋…ธ์‚ฐ ์„œ์—ด์— ์˜ํ•ด ๊ฒฐ์ •๋˜๋ฉฐ, ์ด๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๋ฌธ์ œ๋Š” โ€œ์ž ์žฌ ์—๋„ˆ์ง€ ์ตœ์†Œํ™”โ€๋ผ๋Š” ์ตœ์ ํ™” ๋ฌธ์ œ๋กœ ๊ท€๊ฒฐ๋œ๋‹ค. ์ ‘์ด‰ ํฌํ…์…œ์˜ ์ค‘์š”์„ฑ : ํ†ต๊ณ„์  ์ ‘์ด‰ ํฌํ…์…œ์€ ๊ณ„์‚ฐ ํšจ์œจ์„ฑ ๋•Œ๋ฌธ์— ๋„๋ฆฌ ์‚ฌ์šฉ๋˜์ง€๋งŒ, ๊ธฐ์กด ํฌํ…์…œ์€ ์„œ์—ด ์˜์กด์„ฑ์„ ์ถฉ๋ถ„ํžˆ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ ํ•ต์‹ฌ ์งˆ๋ฌธ : โ€œ์ ‘์ด‰ ํ–‰๋ ฌ๋งŒ์œผ๋กœ ํ‘œํ˜„๋œ ์—๋„ˆ์ง€ ํ•จ์ˆ˜๊ฐ€ ๋„ค์ดํ‹ฐ๋ธŒ ๊ตฌ์กฐ์— ๋Œ€ํ•ด ์ด๋ก ์  ํ•˜ํ•œ์„ ๋‹ฌ์„ฑํ•˜๋ ค๋ฉด ์–ด๋–ค ํ˜•ํƒœ์—ฌ์•ผ ํ•˜๋Š”๊ฐ€?โ€ 2. ์ฃผ์š” ๊ฐ€์ • ๋ฐ ์ˆ˜์‹ ์ „๊ฐœ | ๊ฐ€์ • | ๋‚ด์šฉ | | | | | A1 |

Quantitative Biology Physics
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PathFMTools ๋ณ‘๋ฆฌํ•™ ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์˜ ํšจ์œจ์  ์ ์šฉ๊ณผ ํ‰๊ฐ€๋ฅผ ์œ„ํ•œ ํŒŒ์ด์ฌ ํˆดํ‚ท

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํŒŒ์šด๋ฐ์ด์…˜ ๋ชจ๋ธ์˜ ์•ฝ์† : ์ตœ๊ทผ ๋Œ€๊ทœ๋ชจ ์…€ํ”„โ€‘์Šˆํผ๋ฐ”์ด์ฆˆ๋“œ ํ•™์Šต์„ ํ†ตํ•ด ๋ณ‘๋ฆฌํ•™ ์ด๋ฏธ์ง€์—์„œ ์ผ๋ฐ˜ํ™” ๊ฐ€๋Šฅํ•œ ํ˜•ํƒœํ•™์  ํŒจํ„ด์„ ํ•™์Šตํ•œ๋‹ค๋Š” ์ ์—์„œ, ๋ฐ์ดํ„ฐ๊ฐ€ ๋ถ€์กฑํ•œ ์ž„์ƒ ๊ณผ์ œ์— ํŠนํžˆ ์œ ๋ฆฌํ•˜๋‹ค( Li et al., 2025; Bilal et al., 2025). ์‹ค์ œ ์ ์šฉ ์žฅ๋ฒฝ : 1. WSI ๊ทœ๋ชจ (์ˆ˜์ฒœ~์ˆ˜๋งŒ ร— ์ˆ˜์ฒœ ํ”ฝ์…€)์™€ ๋ณต์žกํ•œ ์ „์ฒ˜๋ฆฌยทํŒจ์น˜ ์ถ”์ถœ ํŒŒ์ดํ”„๋ผ์ธ. 2. ํŠน์ง• ๊ณต๊ฐ„์˜ ๋ถˆํˆฌ๋ช…์„ฑ โ†’ ์ƒ๋ฌผํ•™์  ํ•ด์„ยท๊ฒ€์ฆ ๋„๊ตฌ ๋ถ€์กฑ. 3. ๋‹ค์–‘ํ•œ ์ ์‘ ์ „๋žต (์ œ๋กœโ€‘์ƒท, ํŒŒ์ธโ€‘ํŠœ๋‹, MIL ๋“ฑ) ๊ฐ„ ์„ฑ๋Šฅยท์ž์› ํŠธ๋ ˆ์ด๋“œโ€‘์˜คํ”„๊ฐ€

Point estimation with exponentially tilted empirical likelihood

Point estimation with exponentially tilted empirical likelihood

** ์ผ๋ฐ˜ ์ถ”์ • ๋ฐฉ์ •์‹(GEE)์œผ๋กœ ์ •์˜๋œ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ๊ฒฝํ—˜์  ๊ฐ€๋Šฅ๋„(EL)๋กœ ์ถ”์ •ํ•˜๋ฉด, Newey์™€ Smith(2004)๊ฐ€ ์ œ์‹œํ•œ ๋ฐ”์™€ ๊ฐ™์ด $O(n^{-1})$ ์ˆ˜์ค€์˜ ์ž‘์€ ํŽธํ–ฅ๊ณผ ํŽธํ–ฅ ๋ณด์ • EL์˜ ๊ณ ์ฐจ ํšจ์œจ์„ฑ์„ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ชจํ˜•์ด ์ž˜๋ชป ์ง€์ •(misspecified)๋œ ๊ฒฝ์šฐ, ํŠนํžˆ ๋ชจ๋ฉ˜ํŠธ ํ•จ์ˆ˜๊ฐ€ ๋ฌดํ•œ๋Œ€ ๊ฐ’์„ ๊ฐ€์งˆ ์ˆ˜ ์žˆ์œผ๋ฉด EL์€ $n^{1/2}$ ์ˆ˜๋ ด์„ฑ์„ ์ƒ์‹คํ•œ๋‹ค. ๋ฐ˜๋ฉด, ์ง€์ˆ˜ ๊ธฐ์šธ๊ธฐ(ET) ์ถ”์ •๋ฒ•์€ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํšŒํ”ผํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ EL๊ณผ ET๋ฅผ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ๊ฒฐํ•ฉํ•œ **์ง€์ˆ˜ ๊ธฐ์šธ๊ธฐ ๊ฒฝํ—˜์  ๊ฐ€๋Šฅ๋„(ETEL)** ๋ฅผ

Quantitative Finance Mathematics Statistics
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Progress with Particle Flow Calorimetry

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉํ‘œ ILC ๋ฌผ๋ฆฌ ๋ชฉํ‘œ : W โ†’ qqโ€ฒ, Z โ†’ qq์™€ ๊ฐ™์€ ๋‹ค์ค‘ ์ œํŠธ ์ตœ์ข… ์ƒํƒœ๋ฅผ ์ •๋ฐ€ํ•˜๊ฒŒ ๊ตฌ๋ถ„ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ œํŠธโ€‘์ œํŠธ ๋ถˆ๋ณ€์งˆ๋Ÿ‰์„ ์ž์—ฐํญ(ฮ“) ์ˆ˜์ค€์œผ๋กœ ์žฌ๊ตฌ์„ฑํ•ด์•ผ ํ•œ๋‹ค. ์ด๋Š” ฯƒโ‚˜/m โ‰ˆ 2.7 % ์ˆ˜์ค€์˜ ์ œํŠธ ์—๋„ˆ์ง€ ํ•ด์ƒ๋„(ฯƒ E/E โ‰ˆ 0.3/โˆšE

Physics
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Protein domains as units of genetic transfer

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ์ˆ˜์ง์  ์œ ์ „ vs. ํšก์  ์œ ์ „(LGT) : ์ „ํ†ต์ ์œผ๋กœ ์œ ์ „์ฒด ์ง„ํ™”๋Š” ๋ถ€๋ชจโ€‘์ž์‹ ๊ฐ„ ์ˆ˜์ง ์ „์ด๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ์ดํ•ด๋ผ ์™”์ง€๋งŒ, ์›ํ•ต์ƒ๋ฌผ์—์„œ๋Š” ํšก์  ์œ ์ „์ด ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•จ์ด ๋ฐํ˜€์กŒ๋‹ค. ์ „์ด ๋‹จ์œ„์˜ ๋ฏธ์ • : ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ „์ฒด ์œ ์ „์ž, ๋‹ค์œ ์ „์ž ํด๋Ÿฌ์Šคํ„ฐ, ํ˜น์€ ์œ ์ „์ž ์กฐ๊ฐ์ด ์ „์ด๋  ์ˆ˜ ์žˆ๋‹ค๊ณ  ์ œ์‹œํ–ˆ์ง€๋งŒ, ์ „์ด ๋‹จ์œ„๊ฐ€ ์‹ค์ œ๋กœ ์–ด๋А ์ •๋„ ๊ตฌ์กฐ์ ยท๊ธฐ๋Šฅ์  ๊ฒฝ๊ณ„๋ฅผ ๋”ฐ๋ฅด๋Š”์ง€๋Š” ๋ช…ํ™•ํžˆ ๊ทœ๋ช…๋˜์ง€ ์•Š์•˜๋‹ค. ๊ฐ€์„ค : ๋‹จ๋ฐฑ์งˆ ๋„๋ฉ”์ธ(์ž์œจ์ ์œผ๋กœ ์ ‘ํžˆ๋Š” ๊ตฌ์กฐ ๋‹จ์œ„)์ด ์ „์ด ๋‹จ์œ„๊ฐ€ ๋  ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ๋‹ค. 2. ๋ฐ์ดํ„ฐ ๋ฐ ์ „์ฒ˜๋ฆฌ ๋Œ€์ƒ ๊ฒŒ๋†ˆ : 14

Quantitative Biology
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Quantum Signatures of Solar System Dynamics

1. ์—ฐ๊ตฌ ๋™๊ธฐ์™€ ๋ฐฐ๊ฒฝ ์—ญ์‚ฌ์  ์—ฐ๊ฒฐ ๊ณ ๋ฆฌ : ์ €์ž๋Š” 1920โ€‘๋Œ€ โ€œ๊ตฌ ์–‘์ž์—ญํ•™โ€์ด ์›์ž ์ŠคํŽ™ํŠธ๋Ÿผ์„ ์„ค๋ช…ํ•˜๋ฉด์„œ ๋งˆ์ฃผํ•œ ๊ณต๋ช…(Resonance)ยทํ‡ดํ™”(Accidental Degeneracy) ๋ฌธ์ œ๋ฅผ ์ฒœ์ฒด์—ญํ•™์— ๊ทธ๋Œ€๋กœ ์ ์šฉํ•œ๋‹ค๋Š” ๋…ํŠนํ•œ ๊ด€์ ์„ ์ œ์‹œํ•œ๋‹ค. ์ด๋Š” Bohrโ€‘Sommerfeld ์–‘์žํ™”๊ฐ€ ํ–‰์„ฑ ๊ถค๋„์—๋„ ์ ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฐ€์„ค์„ ์ „์ œ๋กœ ํ•œ๋‹ค. Heisenberg์˜ ํ˜์‹  : Heisenberg๊ฐ€ ๋งคํŠธ๋ฆญ์Šค ์—ญํ•™์„ ์ฐฝ์‹œํ•œ ๋ฐฐ๊ฒฝ์„ โ€œ๊ณต๋ช… ์กฐ๊ฑด์„ ๋งŒ์กฑ์‹œํ‚ค๋Š” ๊ณ ์ „์  ์ฃผ๊ธฐ ๊ถค๋„โ€์˜ ์กด์žฌ์™€ ์—ฐ๊ฒฐ์‹œ์ผœ, ์–‘์žํ™” ๊ทœ์น™์„ ํ–‰์„ฑ๊ณ„์— ๊ทธ๋Œ€๋กœ ์˜ฎ๊ธธ ์ˆ˜ ์žˆ์Œ์„

Physics System
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Schwingers Magnetic Model of Matter: Can It Help Us With Grand Unification?

1. ์—ฐ๊ตฌ ๋™๊ธฐ์™€ ๋ชฉํ‘œ ๋Œ€ํ†ต์ผ์˜ ์‹คํ—˜์  ๊ฒ€์ฆ ์š”๊ตฌ : ์ €์ž๋Š” โ€œ์ƒˆ๋กœ์šด ์ด๋ก ์ด ์‹คํ—˜์ ์œผ๋กœ ๊ฒ€์ฆ๋  ๋•Œ๊นŒ์ง€๋Š” ์˜๋ฏธ๊ฐ€ ์—†๋‹คโ€๋Š” ์ž…์žฅ์„ ๊ฐ•์กฐํ•˜๋ฉฐ, ๊ธฐ์กด ์ดˆ๋ˆ ์ด๋ก  ๋“ฑ ๊ณ ์ฐจ์›ยท๊ณ ์—๋„ˆ์ง€ ๋ชจ๋ธ์ด ํ˜„์žฌ ์‹คํ—˜๊ณผ ๊ดด๋ฆฌ๋ผ ์žˆ์Œ์„ ๋น„ํŒํ•œ๋‹ค. ์†”๋ฆฌํ†ค ๊ธฐ๋ฐ˜ ์œ ํ•œ ์ด๋ก  : ์ „์ž์™€ ๊ฐ™์€ ์ ์ „ํ•˜ ๋ชจ๋ธ์ด ์ดˆ๊ธฐ์— ๋ฌดํ•œ ์ž๊ธฐ์—๋„ˆ์ง€๋ฅผ ์ดˆ๋ž˜ํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ํ”ผํ•˜๊ธฐ ์œ„ํ•ด, ์ž…์ž๋ฅผ ์œ ํ•œํ•œ ๋ฐ˜๊ฒฝ์„ ๊ฐ€์ง„ ์†”๋ฆฌํ†ค ์œผ๋กœ ๋ชจ๋ธ๋งํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ด๋Š” ์ „ํ†ต์ ์ธ ์ •๊ทœํ™”ยท์žฌ๊ทœ๊ฒฉํ™” ์ ˆ์ฐจ๋ฅผ ํšŒํ”ผํ•˜๋Š” ๋ฐฉํ–ฅ์ด๋‹ค. 2. ํ•ต์‹ฌ ์ œ์•ˆ โ€“ ์Šˆ์œ™๊ฑฐ์˜ MMM ์žฌ๊ฒ€ํ†  | ์š”์†Œ | ๊ธฐ์กด QCD์™€์˜ ์ฐจ์ด์  | ๋…ผ๋ฌธ์—์„œ ์ œ

Model Physics
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Selection simultanee dindex et de vues materialisees

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฐ์ดํ„ฐ ์›จ์–ดํ•˜์šฐ์Šค ํŠน์„ฑ : ์Šคํƒ€ ์Šคํ‚ค๋งˆยท์Šค๋…ธ์šฐํ”Œ๋ ˆ์ดํฌ ์Šคํ‚ค๋งˆ์—์„œ ๋‹ค์ˆ˜์˜ ์กฐ์ธ๊ณผ ์ง‘๊ณ„๊ฐ€ ๋นˆ๋ฒˆํžˆ ๋ฐœ์ƒ, ์ด๋Š” I/O ๋น„์šฉ์„ ๊ธ‰์ฆ์‹œํ‚จ๋‹ค. ์ธ๋ฑ์Šค vs ๋ฌผ๋ฆฌํ™”๋œ ๋ทฐ : ์ธ๋ฑ์Šค๋Š” ์„ ํƒ๋„(Selectivity)๊ฐ€ ๋†’์€ ์ปฌ๋Ÿผ์— ์œ ๋ฆฌํ•˜๊ณ , ์กฐ์ธ ๋น„์šฉ์„ ๊ฐ์†Œ์‹œํ‚จ๋‹ค. ๋ฌผ๋ฆฌํ™”๋œ ๋ทฐ๋Š” ์ง‘๊ณ„ยท์กฐ์ธ ๊ฒฐ๊ณผ๋ฅผ ๋ฏธ๋ฆฌ ์ €์žฅํ•ด, ๋Œ€๊ทœ๋ชจ ์Šค์บ”์„ ํšŒํ”ผํ•œ๋‹ค. ๊ณตํ†ต ์ œ์•ฝ : ๋‘ ๊ตฌ์กฐ ๋ชจ๋‘ ์ €์žฅ ๊ณต๊ฐ„ ๊ณผ ๊ฐฑ์‹  ๋น„์šฉ ์„ ๊ณต์œ ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋™์‹œ์— ๊ณ ๋ คํ•˜์ง€ ์•Š์œผ๋ฉด ๋น„ํšจ์œจ์ ์ธ ์ž์› ์‚ฌ์šฉ์ด ๋ฐœ์ƒํ•œ๋‹ค. 2. ์ฃผ์š” ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๋‚ด์šฉ | ์˜์˜ | | | |

Databases Computer Science
Splay Trees, Davenport-Schinzel Sequences, and the Deque Conjecture

Splay Trees, Davenport-Schinzel Sequences, and the Deque Conjecture

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์Šคํ”Œ๋ ˆ์ด ํŠธ๋ฆฌ๋Š” ์ž๊ธฐ์กฐ์ •(selfโ€‘adjusting) ์ด์ง„ ํƒ์ƒ‰ ํŠธ๋ฆฌ๋กœ, ์‚ฝ์ž…ยท์‚ญ์ œยท๊ฒ€์ƒ‰ ์—ฐ์‚ฐ ์‹œ๋งˆ๋‹ค ์ ‘๊ทผ๋œ ๋…ธ๋“œ๋ฅผ ๋ฃจํŠธ๋กœ ๋Œ์–ด์˜ฌ๋ฆฌ๋Š” splay ์—ฐ์‚ฐ์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ๋™์  ์ตœ์ ์„ฑ(Dynamic Optimality) ์ถ”์ธก : ์–ด๋–ค ์˜จ๋ผ์ธ ์ด์ง„ ํƒ์ƒ‰ ํŠธ๋ฆฌ๋ผ๋„ ์‚ฌ์ „์— ์ „์ฒด ์ ‘๊ทผ ์ˆœ์„œ๋ฅผ ์•Œ๋ฉด ์ตœ์ ์˜ ๋น„์šฉ์— ์ƒ์ˆ˜๋ฐฐ ์ด๋‚ด๋กœ ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค๋Š” ๊ฐ•๋ ฅํ•œ ๊ฐ€์„ค. ํ˜„์žฌ๊นŒ์ง€๋Š” subโ€‘logarithmic ๊ฒฝ์Ÿ๋น„์œจ์„ ๋ณด์žฅํ•˜๋Š” ๊ฒฐ๊ณผ์กฐ์ฐจ ์กด์žฌํ•˜์ง€ ์•Š์œผ๋ฉฐ, ์ฃผ์š” ๋ฏธํ•ด๊ฒฐ ๋ฌธ์ œ๋Š” Deque , Traversal , Split ์ถ”์ธก์ด๋‹ค.

Data Structures Computer Science
Stable stochastic dynamics in yeast cell cycle

Stable stochastic dynamics in yeast cell cycle

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ๋…ธ์ด์ฆˆ์™€ ์ƒ๋ช… ํ˜„์ƒ : ์„ธํฌ ๋‚ด ํ™”ํ•™ ๋ฐ˜์‘์€ ๋ถ„์ž ์ˆ˜๊ฐ€ ์ ์–ด ๋‚ด์žฌ์ ( intrinsic) ๋…ธ์ด์ฆˆ๊ฐ€ ํฌ๊ฒŒ ์ž‘์šฉํ•œ๋‹ค. ํšจ๋ชจ์™€ ๊ฐ™์€ ๋‹จ์„ธํฌ ์ƒ๋ฌผ์—์„œ๋Š” ๊ฐ™์€ ์œ ์ „์ž๋ฅผ ๊ฐ€์ง„ ๊ฐœ์ฒด ๊ฐ„์—๋„ ๋ฐœํ˜„ ์ˆ˜์ค€์ด ํฌ๊ฒŒ ๋‹ฌ๋ผ์ง„๋‹ค(1โ€‘3). ๊ธฐ์กด ๋ชจ๋ธ์˜ ํ•œ๊ณ„ : Tyson ๋“ฑ์€ ๋ฏธ๋ถ„๋ฐฉ์ •์‹ ๊ธฐ๋ฐ˜์˜ ๊ฒฐ์ •๋ก ์  ๋ชจ๋ธ์„, Li ๋“ฑ์€ Boolean ๋„คํŠธ์›Œํฌ๋ฅผ ์ œ์‹œํ–ˆ์ง€๋งŒ, ๋‘ ์ ‘๊ทผ ๋ชจ๋‘ ๋…ธ์ด์ฆˆ ๋ฐœ์ƒ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์™ธ๋ถ€ ์ž…๋ ฅ์œผ๋กœ๋งŒ ์ทจ๊ธ‰ํ–ˆ๋‹ค. ๋”ฐ๋ผ์„œ โ€œ๋…ธ์ด์ฆˆ๊ฐ€ ์‹ค์ œ๋กœ ์–ด๋–ป๊ฒŒ ์ƒ์„ฑ๋˜๊ณ , ์–ด๋–ป๊ฒŒ ์–ต์ œ๋˜๋Š”๊ฐ€?โ€์— ๋Œ€ํ•œ ๋‹ต์ด ๋ถ€์กฑํ–ˆ๋‹ค. 2. ๋ชจ๋ธ ์„ค๊ณ„ | ์š”์†Œ

Quantitative Biology
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The climate version of the Eta regional forecast model. 1. Evaluation of consistency between the Eta model and HadAM3P global model

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋Œ€๊ทœ๋ชจ ๊ธฐํ›„์™€ ์ง€์—ญ ๊ธฐํ›„์˜ ๊ฒฉ์ฐจ : ์ „ํ†ต์ ์ธ ๊ด€์ธก๋ง์€ 500 km ์ด์ƒ ๊ทœ๋ชจ์˜ ํ‰๊ท ์žฅ๋งŒ์„ ํฌ์ฐฉํ•œ๋‹ค. ๋†์—…ยท์—๋„ˆ์ง€ยท์‚ฐ์—… ๊ณ„ํš์— ํ•„์š”ํ•œ 10โ€‘100 km ์ˆ˜์ค€์˜ ์ง€์—ญ ๊ธฐํ›„ ์ •๋ณด๋Š” ๋™์  ๋‹ค์šด์Šค์ผ€์ผ๋ง์„ ํ†ตํ•ด์„œ๋งŒ ํ™•๋ณด ๊ฐ€๋Šฅํ•˜๋‹ค. ๋‚จ๋ฏธ ์—ฐ๊ตฌ ํ˜„ํ™ฉ : ๊ธฐ์กด์—๋Š” ์œ ๋Ÿฝยท๋ถ๋ฏธยท์•„ํ”„๋ฆฌ์นด ์ค‘์‹ฌ์˜ RCM ๋‹ค์šด์Šค์ผ€์ผ๋ง ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํ–ˆ์œผ๋ฉฐ, ๋‚จ๋ฏธ์— ๋Œ€ํ•œ ์‹œ๋„๋Š” PRECISยทRegCM3 ๋“ฑ ์ œํ•œ์ ์ด์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋‚จ๋ฏธ ์ „์—ญ์„ ํฌ๊ด„ํ•˜๋Š” ๊ณ ํ•ด์ƒ๋„ RCM์ด ์ ˆ์‹คํžˆ ์š”๊ตฌ๋œ๋‹ค. 2. ๋ชจ๋ธ ๊ฐœ๋ฐœ ๋ฐ ๊ตฌํ˜„ | ํ•ญ๋ชฉ | ๋‚ด์šฉ | ์˜์˜ | | | | |

Model Physics
The Cyborg Astrobiologist: Porting from a wearable computer to the   Astrobiology Phone-cam

The Cyborg Astrobiologist: Porting from a wearable computer to the Astrobiology Phone-cam

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์šฐ์ฃผยท์ง€๊ตฌ ํƒ์‚ฌ์˜ ์ž๋™ํ™” ๋Š” ๊ณ ์„ฑ๋Šฅ ์ปดํ“จํ„ฐ ๋น„์ „ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํ˜„์žฅ ๊ฒ€์ฆ์ด ์ „์ œ๋˜์–ด์•ผ ํ•จ. ๊ธฐ์กด ์›จ์–ด๋Ÿฌ๋ธ” ์‹œ์Šคํ…œ์€ ์žฅ๋น„ ๋ฌด๊ฒŒยท๋ณต์žก์„ฑยทํ›ˆ๋ จ ๋น„์šฉ์ด ๋†’์•„ ํ˜„์žฅ ์ ์šฉ์— ์ œ์•ฝ์ด ์žˆ์—ˆ๋‹ค. ์Šค๋งˆํŠธํฐ ๋ณด๊ธ‰๊ณผ ์นด๋ฉ”๋ผยทํ†ต์‹  ๊ธฐ์ˆ ์˜ ๊ธ‰๊ฒฉํ•œ ๋ฐœ์ „์€ ์ €๋น„์šฉยท๊ณ ์ด๋™์„ฑ ํ”Œ๋žซํผ์„ ์ œ๊ณตํ•œ๋‹ค๋Š” ์ ์—์„œ ๋ณธ ์—ฐ๊ตฌ์˜ ์ถœ๋ฐœ์ ์ด ๋œ๋‹ค. 2. ์‹œ์Šคํ…œ ์•„ํ‚คํ…์ฒ˜ | ๊ตฌ์„ฑ ์š”์†Œ | ์—ญํ•  | ์ฃผ์š” ๊ตฌํ˜„ ๊ธฐ์ˆ  | | | | | | ์นด๋ฉ”๋ผํฐ | ํ˜„์žฅ ์ด๋ฏธ์ง€ ์ดฌ์˜ยทMMS ์ „์†ก | ๊ธฐ๋ณธ ๋””์ง€ํ„ธ ์นด๋ฉ”๋ผ(640ร—480) | | ํ†ต์‹  | ์ด๋ฏธ์ง€ ์ „์†ก (MMS)ยท๊ฒฐ๊ณผ ์ „

Computer Vision Computational Engineering Astrophysics Software Engineering Networking Artificial Intelligence Computer Science Robotics
The Description of Information in 4-Dimensional Pseudo-Euclidean   Information Space

The Description of Information in 4-Dimensional Pseudo-Euclidean Information Space

1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์ •๋ณด๋ฅผ ๋ฌผ๋ฆฌ์  ํ˜„์ƒ๊ณผ ๋™๋“ฑ์‹œ : ๊ธฐ์กด ์ •๋ณด์ด๋ก (์ƒค๋…ผ ์—”ํŠธ๋กœํ”ผ)๊ณผ ๋‹ฌ๋ฆฌ, ์ •๋ณด ์ž์ฒด๋ฅผ โ€˜๊ฐ๊ด€์  ์‹ค์žฌโ€™๋ผ ๋ณด๊ณ  ๋ฌผ๋ฆฌ์  ์‹œ๊ณต๊ฐ„์— ๋งคํ•‘ํ•œ๋‹ค๋Š” ์‹œ๋„๋Š” ๋…์ฐฝ์ ์ด๋‹ค. ํŠนํžˆ โ€˜์ •๋ณด ์ „์†ก ์ „์ž๊ธฐ ํŒŒ๋™์˜ ๋ฐ˜ํŒŒ ์ด๋™โ€™์ด๋ผ๋Š” ๊ฐ€์ •์€ ๋ฌผ๋ฆฌโ€‘์ •๋ณด ์œตํ•ฉ ์—ฐ๊ตฌ์— ์ƒˆ๋กœ์šด ๊ด€์ ์„ ์ œ๊ณตํ•œ๋‹ค. ์ƒ๋Œ€์„ฑ ์ด๋ก  ์ ์šฉ : Poincarรฉ ๋ณ€ํ™˜๊ตฐ์„ ์ •๋ณด๊ณต๊ฐ„์— ๊ทธ๋Œ€๋กœ ์ ์šฉํ•จ์œผ๋กœ์จ โ€˜๋ถˆ๋ณ€ ์ •๋ณด ์†๋„(LIV)โ€™๋ผ๋Š” ๊ฐœ๋…์„ ๋„์ž…ํ•œ ์ ์€ ๋ฌผ๋ฆฌํ•™์—์„œ์˜ ๋ถˆ๋ณ€๊ด‘์†(c)๊ณผ ์ง์ ‘์ ์ธ ์œ ์‚ฌ์„ฑ์„ ๊ฐ–๋Š”๋‹ค. ์ด๋Š” ์ •๋ณด ์ „์†ก์˜ ํ•œ๊ณ„์™€ ๋ณด์กด ๋ฒ•์น™์„ ๋ฌผ๋ฆฌํ•™์  ํ‹€ ์•ˆ์—์„œ ๋…ผ์˜ํ•  ์ˆ˜

Physics Nonlinear Sciences Computer Science Information Theory Mathematics
The few-body problem in terms of correlated gaussians

The few-body problem in terms of correlated gaussians

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋‹ค์ฒด ๋ฌธ์ œ ๋Š” ์–‘์ž์—ญํ•™์—์„œ ํ•ต, ์›์žยท๋ถ„์ž, ๊ทธ๋ฆฌ๊ณ  ์ตœ๊ทผ์˜ ์ดˆ์ €์˜จ ๋ฌผ๋ฆฌยทํ•ต๋ฌผ๋ฆฌ ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ํ•ต์‹ฌ์ ์ธ ๋‚œ์ œ์ด๋‹ค. ํ™•๋ฅ ์  ๋ณ€๋ถ„๋ฒ•(SVM) ์€ ๋น„์ง๊ต ๊ธฐ์ €(ํŠนํžˆ ์—ฐ๊ด€ ๊ฐ€์šฐ์‹œ์•ˆ)๋ฅผ ์ด์šฉํ•ด ํฐ ์ฐจ์›์˜ ๋ณ€๋ถ„ ๊ณต๊ฐ„์„ ํšจ์œจ์ ์œผ๋กœ ํƒ์ƒ‰ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์žฅ์ ์ด ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๋น„์ง๊ต์„ฑ์œผ๋กœ ์ธํ•œ ๊ณผ์ž‰์™„๋น„(overcompleteness)์™€ ์Šคํ‘ธ๋ฆฌ์–ด์Šค ์ƒํƒœ(spurious states) ๋ฌธ์ œ๊ฐ€ ์กด์žฌํ•œ๋‹ค. SuzukiยทVarga ๊ต๊ณผ์„œ๋Š” SVM์˜ ์ด๋ก ์  ํ† ๋Œ€๋ฅผ ์ œ๊ณตํ•˜์ง€๋งŒ, ์ƒ๋Œ€๋ก ์  ์šด๋™ ์—๋„ˆ์ง€ ์™€ ํ‘ธ๋ฆฌ์— ๋ณ€ํ™˜ ์— ๋Œ€ํ•œ ๊ตฌ์ฒด์ ์ธ

HEP-PH Physics
The Isoconditioning Loci of Planar Three-DOF Parallel Manipulators

The Isoconditioning Loci of Planar Three-DOF Parallel Manipulators

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ๋ณ‘๋ ฌ ๋งค๋‹ˆํ“ฐ๋ ˆ์ดํ„ฐ์˜ ์„ฑ๋Šฅ ์ง€ํ‘œ ๋Š” ํฌ๊ฒŒ ์ •์ ยท๋™์  ์ •ํ™•๋„, ์ž‘์—…๊ณต๊ฐ„, ๊ฐ•์„ฑ ๋“ฑ์„ ํฌํ•จํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ๋Š” ์กฐ๊ฑด์ˆ˜ ๋ฅผ ํ™œ์šฉํ•œ ์ •๋ฐ€๋„ ์ง€ํ‘œ๊ฐ€ ๋„๋ฆฌ ์“ฐ์˜€์ง€๋งŒ, ์•ผ์ฝ”๋น„์•ˆ ํ–‰๋ ฌ์˜ ์ฐจ์› ๋น„๋™์งˆ์„ฑ ๋•Œ๋ฌธ์— ์ง์ ‘ ์ ์šฉ์ด ์–ด๋ ค์› ๋‹ค. ์ €์ž๋“ค์€ ํŠน์„ฑ ๊ธธ์ด(L) ๊ฐœ๋…์„ ๋„์ž…ํ•ด ํ–‰๋ ฌ์„ ๋ฌด์ฐจ์›ํ™”ํ•˜๊ณ , ์ •๊ทœํ™”๋œ ์กฐ๊ฑด์ˆ˜ ๋ฅผ ํ†ตํ•ด ์ „์—ญ์ ์ธ ์„ฑ๋Šฅ ์ง€ํ‘œ (๋“ฑ์กฐ๊ฑด๊ณก์„ )๋ฅผ ์ œ์‹œํ•จ์œผ๋กœ์จ ์„ค๊ณ„ยท์ œ์–ด ๋‹จ๊ณ„์—์„œ ์ง๊ด€์ ์ธ ํŒ๋‹จ ๊ธฐ์ค€์„ ์ œ๊ณตํ•œ๋‹ค. 2. ์ฃผ์š” ์ด๋ก  ๋ฐ ์ˆ˜์‹ ์ •๋ฆฌ | ํ•ญ๋ชฉ | ๋‚ด์šฉ | ๋น„๊ณ  | | | | | | ์•ผ์ฝ”๋น„์•ˆ ํ–‰๋ ฌ A | ์ง์ ‘๊ธฐ๊ตฌํ•™

Robotics Computer Science
The logistic equation and a critique of the theory of natural selection

The logistic equation and a critique of the theory of natural selection

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ๊ฒฝ์Ÿ ๋ฐฐ์ œ ์›๋ฆฌ(CEP) ์™€ ์ œํ•œ๋œ ์œ ์‚ฌ์„ฑ ์ด๋ก (Limiting Similarity) ์€ ์˜ค๋žซ๋™์•ˆ โ€œ๋น„์Šทํ•œ ์ข…์€ ๋” ๊ฐ•ํ•˜๊ฒŒ ๊ฒฝ์Ÿํ•œ๋‹คโ€๋Š” ๋‹ค์œˆ์˜ ๊ฒฝ์Ÿ ์›๋ฆฌ(DCP) ๋ฅผ ์ˆ˜ํ•™์ ์œผ๋กœ ๋’ท๋ฐ›์นจํ•ด ์™”๋‹ค. ์ €์ž๋Š” ์ž์—ฐ์„ ํƒ์ด ๊ฒฝ์Ÿ ๋ชจ๋ธ๊ณผ ์ถฉ๋Œ ํ•œ๋‹ค๋Š” ์ ์„ ๊ฐ•์กฐํ•œ๋‹ค. ์ž์—ฐ์„ ํƒ์ด ์ž‘๋™ํ•˜๋ ค๋ฉด ๋™์ข… ๊ฒฝ์Ÿ์ด ์ด์ข… ๊ฒฝ์Ÿ๋ณด๋‹ค ์•ฝํ•ด์•ผ ํ•˜๋Š”๋ฐ, LV ๋ชจ๋ธ์—์„œ๋Š” ์ด๋ฅผ ๋งŒ์กฑ์‹œํ‚ค๋Š” ๊ฒฝ์šฐ๊ฐ€ ๊ฑฐ์˜ ์—†์œผ๋ฉฐ, ์˜คํžˆ๋ ค ๋™์ผํ•œ ์ข…(ฮฑ ik 1, K i K k) ์ผ ๋•Œ๋งŒ ์˜๊ตฌ์  ๊ณต์กด์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์ฃผ์žฅํ•œ๋‹ค. 2. ์ฃผ์š” ์ด๋ก  ์ „๊ฐœ | ๋‹จ๊ณ„ | ๋‚ด์šฉ | ํ•ต์‹ฌ

Quantitative Biology
No Image

The Parallel-Sequential Duality : Matrices and Graphs

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋ณ‘๋ ฌ vs. ์ˆœ์ฐจ : ์ „ํ†ต์ ์ธ ์„ ํ˜•๋Œ€์ˆ˜์—์„œ๋Š” ํ–‰๋ ฌ ์—ฐ์‚ฐ์„ โ€œ๋™์‹œโ€์— ์ˆ˜ํ–‰ํ•œ๋‹ค๋Š” ๊ฐ€์ •์ด ๊ธฐ๋ณธ์ด๋‹ค. ์‹ค์ œ ๊ตฌํ˜„์—์„œ๋Š” ์ž…๋ ฅ ๋ณต์‚ฌ์™€ ์ž„์‹œ ๋ฒ„ํผ๊ฐ€ ํ•„์š”ํ•ด ๋ฉ”๋ชจ๋ฆฌยท์‹œ๊ฐ„ ๋ณต์žก๋„๊ฐ€ ๋Š˜์–ด๋‚œ๋‹ค. ์ธโ€‘์‹œํˆฌ ์—ฐ์‚ฐ : ์ž…๋ ฅ ๋ฐ์ดํ„ฐ๋ฅผ ๊ทธ๋Œ€๋กœ ์žฌ์‚ฌ์šฉํ•˜๋ฉด์„œ ์—ฐ์‚ฐ์„ ์ง„ํ–‰ํ•˜๋Š” โ€œinโ€‘situโ€ ๋ฐฉ์‹์€ ๋ฉ”๋ชจ๋ฆฌ ์ œ์•ฝ์ด ํฐ ์ž„๋ฒ ๋””๋“œยท์ŠคํŠธ๋ฆฌ๋ฐ ํ™˜๊ฒฝ์—์„œ ํฐ ์žฅ์ ์ด ๋œ๋‹ค. ์ €์ž๋Š” ์ด๋ฅผ ํ–‰๋ ฌยท๊ทธ๋ž˜ํ”„ ์ด๋ก ์— ์ •ํ˜•ํ™”ํ•œ๋‹ค. 2. ์ฃผ์š” ๊ฐœ๋… ์ •๋ฆฌ | ๊ฐœ๋… | ์ •์˜ | ํ•ต์‹ฌ ํฌ์ธํŠธ | | | | | | ๋ณ‘๋ ฌ ๋งคํ•‘ (M^ ) | ์ „ํ†ต์ ์ธ ํ–‰๋ ฌ ๋ฒกํ„ฐ ๊ณฑ |

Mathematics Information Theory Computer Science
No Image

The Poisson bracket compatible with the classical reflection equation algebra

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ๋ฐ˜์‚ฌ ๋ฐฉ์ •์‹ ๋Œ€์ˆ˜(REA) ๋Š” ์–‘์ž ์—ญํ•™์—์„œ ์—ญ์‚ฐ์ˆ ์ (Quantum Inverse Scattering) ๋ฐฉ๋ฒ•์˜ ํ•ต์‹ฌ ๊ตฌ์กฐ์ด๋ฉฐ, ๊ณ ์ „์  ์ ๋ถ„๊ฐ€๋Šฅ๊ณ„์˜ ๋ถ„๋ฅ˜์™€ ํ•ด์„์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ๊ธฐ์กด์—๋Š” REA์— ๋Œ€์‘ํ•˜๋Š” ํ•˜๋‚˜์˜ ํฌ์•„์†ก ๊ด„ํ˜ธ๋งŒ์ด ์•Œ๋ ค์ ธ ์žˆ์—ˆ๋‹ค. ์ €์ž๋Š” ๋‘ ๊ฐœ์˜ ํ˜ธํ™˜ ๊ฐ€๋Šฅํ•œ ํฌ์•„์†ก ๊ด„ํ˜ธ ๋ฅผ ๋™์‹œ์— ๊ตฌ์ถ•ํ•จ์œผ๋กœ์จ ๋ฐ”์ด ํ•ด๋ฐ€ํ† ๋‹ˆ์•ˆ ๊ตฌ์กฐ ๋ฅผ ์ œ๊ณตํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ๋‹ค์–‘ํ•œ ๋ฌผ๋ฆฌยท์ˆ˜ํ•™์  ๋ชจ๋ธ์„ ํ†ตํ•ฉ์ ์œผ๋กœ ๋‹ค๋ฃจ๊ณ ์ž ํ•œ๋‹ค. 2. ์ฃผ์š” ๊ฒฐ๊ณผ ์š”์•ฝ | ํ•ญ๋ชฉ | ๋‚ด์šฉ | | | | | ํฌ์•„์†ก ๊ด„ํ˜ธ ๊ณ„์—ด | ${, , ,

Nonlinear Sciences

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โ†ต
ESC
โŒ˜K Shortcut