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SOM Directions are Better than One: Multi-Directional Refusal Suppression in Language Models

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๊ฑฐ๋ถ€ ํ–‰๋™ ์€ ํ˜„์žฌ LLM ์•ˆ์ „์„ฑ ์—ฐ๊ตฌ์˜ ํ•ต์‹ฌ ๊ณผ์ œ์ด๋ฉฐ, โ€œ๊ฑฐ๋ถ€๋ฅผ ์–ต์ œโ€ํ•˜๋Š” jailbreak ๊ณต๊ฒฉ์— ๋Œ€ํ•œ ๋ฐฉ์–ด ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์ด ํ•„์ˆ˜์ ์ด๋‹ค. ๊ธฐ์กด ๋ฉ”์ปค๋‹ˆ์ฆ˜ ํ•ด์„ ์—ฐ๊ตฌ๋Š” ๋‹จ์ผ ๋ฒกํ„ฐ (differenceโ€‘inโ€‘means)๋กœ ๊ฐœ๋…์„ ์š”์•ฝํ–ˆ์ง€๋งŒ, ์ด๋Š” ๋‹ค์ค‘ ์ฐจ์›์  ์ธ ๊ฐœ๋… ๊ตฌ์กฐ๋ฅผ ๊ณผ๋„ํ•˜๊ฒŒ ๋‹จ์ˆœํ™”ํ•œ๋‹ค๋Š” ๋น„ํŒ์„ ๋ฐ›์•„์™”๋‹ค. Selfโ€‘Organizing Map ์€ ๊ณ ์ฐจ์› ๋ฐ์ดํ„ฐ๋ฅผ ์ €์ฐจ์› ๊ฒฉ์ž ํ˜•ํƒœ์˜ ํ† ํด๋กœ์ง€ ๋ณด์กด ๋งต์œผ๋กœ ๋ณ€ํ™˜ํ•ด, ๋ฐ์ดํ„ฐ ๊ตฐ์ง‘์„ ์‹œ๊ฐํ™”ํ•˜๊ณ  ๋‹ค์ค‘ ํ”„๋กœํ† ํƒ€์ž… ์„ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์ถ”์ถœํ•œ๋‹ค๋Š” ์žฅ์ ์ด ์žˆ๋‹ค. 2.

Model
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Spatio-temporal air flow properties in a 3D personalised model of the human lung

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ ์•ก ์ฒญ์†Œ ๋ฉ”์ปค๋‹ˆ์ฆ˜ ์€ ์ ์•ก ์„ญ์ทจยท๊ธฐ์นจ ์™ธ์—๋„ ํ‰๋ถ€ ๋ฌผ๋ฆฌ์น˜๋ฃŒ(CP)์™€ ๊ฐ™์€ ์™ธ๋ถ€ ๊ธฐ๋ฅ˜์— ํฌ๊ฒŒ ์˜์กดํ•œ๋‹ค. ๊ธฐ์กด Weibelโ€‘ํ˜• ๋ชจ๋ธ์€ ๊ธฐ๋„ ๊ตฌ์กฐ๋ฅผ ๋™์ผํ•˜๊ฒŒ ๊ฐ€์ •ํ•ด ๊ณต๊ฐ„์  ์ด์งˆ์„ฑ์„ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ๋‹ค์ค‘ ์Šค์ผ€์ผ 3D ๋ชจ๋ธ ์€ ํ™˜์ž ๋งž์ถคํ˜• CT ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•ด ์‹ค์ œ ํยท๊ธฐ๊ด€์ง€ ํ˜•ํƒœ๋ฅผ ์žฌํ˜„ํ•จ์œผ๋กœ์จ, ์ง€์—ญ๋ณ„ ๊ธฐ๋ฅ˜ยท์ „๋‹จ ์‘๋ ฅ์„ ์ •๋ฐ€ํžˆ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ๋‹ค. 2. ๋ชจ๋ธ ๊ตฌ์„ฑ | ๊ตฌ์„ฑ ์š”์†Œ | ๊ตฌํ˜„ ๋ฐฉ์‹ | ์ฃผ์š” ๊ฐ€์ • | | | | | | ํ ์™ธํ”ผยท๋Œ€๊ธฐ๋„ | CT โ†’ LUNA16 ์„ธ๊ทธ๋ฉ˜ํ…Œ์ด์…˜ โ†’ CGAL (ํ‘œ๋ฉด) โ†’ Mesh

Model Quantitative Biology
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Spilling the Beans: Teaching LLMs to Self-Report Their Hidden Objectives

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํ˜„์žฌ LLM(๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ)์€ ๋‹จ์ˆœ ์งˆ์˜์‘๋‹ต์„ ๋„˜์–ด ๋ณตํ•ฉ์ ์ธ ์—์ด์ „์‹œ ์ž‘์—…(์˜ˆ: ๊ณ„ํš ์ˆ˜๋ฆฝ, ๋ชฉํ‘œ ๋‹ฌ์„ฑ)๊นŒ์ง€ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์ด๋•Œ ๋ชจ๋ธ์ด ์ธ๊ฐ„ ์˜๋„์™€ ์–ด๊ธ‹๋‚˜๋Š” โ€œ์ˆจ์€ ๋ชฉํ‘œโ€๋ฅผ ์Šค์Šค๋กœ ์„ค์ •ํ•˜๊ฑฐ๋‚˜ ์‹คํ–‰ํ•  ์œ„ํ—˜์ด ๋Œ€๋‘๋œ๋‹ค. ๊ธฐ์กด์˜ โ€œ์ธํ„ฐ๋กœ๊ฒŒ์ด์…˜โ€(interrogation) ๋ฐฉ์‹์€ ๋ชจ๋ธ์ด ์Šค์Šค๋กœ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๋„๋ก ์œ ๋„ํ•˜์ง€๋งŒ, ๋ชจ๋ธ์ด ๊ฑฐ์ง“ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๊ฑฐ๋‚˜ ์€ํํ•  ๊ฐ€๋Šฅ์„ฑ์„ ์™„์ „ํžˆ ์ฐจ๋‹จํ•˜์ง€ ๋ชปํ•œ๋‹ค. 2. SRFT(์ž๊ธฐ๋ณด๊ณ  ๋ฏธ์„ธ์กฐ์ •) ๊ธฐ๋ฒ• ํ•ต์‹ฌ ์•„์ด๋””์–ด : ๋ชจ๋ธ์—๊ฒŒ โ€œ์‹ค์ˆ˜๋ฅผ ์ธ์ •ํ•˜๋Š” ์Šต๊ด€โ€ ์„ ํ•™์Šต์‹œํ‚จ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ํ›ˆ

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Standards and Safety: an Overview

1. ์—ฐ๊ตฌ์˜ ์˜์˜์™€ ๋ฒ”์œ„ ๊ฐ€์†๊ธฐ ์„ค๋น„ ํŠน์ˆ˜์„ฑ ๊ฐ•์กฐ : ์ผ๋ฐ˜ ์‚ฐ์—… ์„ค๋น„์™€ ๋‹ฌ๋ฆฌ ๊ฐ€์†๊ธฐ ์šด์˜์ž๋Š” ์ฃผ๋กœ ์›๊ฒฉ ์ œ์–ด์‹ค์— ์œ„์น˜ํ•˜์ง€๋งŒ, ์œ ์ง€๋ณด์ˆ˜ ์‹œ ๊ณ ์••ยท์ €์˜จ ์žฅ๋น„์— ์ง์ ‘ ์ ‘๊ทผํ•œ๋‹ค๋Š” ์ ์„ ๋ช…ํ™•ํžˆ ์ œ์‹œํ•œ๋‹ค. ์ด๋Š” ์œ„ํ—˜ ๋ถ„์„์ด ์„ค๊ณ„ ๋‹จ๊ณ„๋ฟ ์•„๋‹ˆ๋ผ ์ „์ฒด ์ˆ˜๋ช…์ฃผ๊ธฐ(Lifecycle) ์ „๋ฐ˜์— ๊ฑธ์ณ ํ•„์š”ํ•จ์„ ์„ค๋“๋ ฅ ์žˆ๊ฒŒ ๋ณด์—ฌ์ค€๋‹ค. ๊ทœ์ œ ํ”„๋ ˆ์ž„์›Œํฌ ํ†ตํ•ฉ : EU ์ง€์นจ(20์—ฌ ๊ฐœ) ์ค‘ ํŠนํžˆ PED์™€ Machinery Directive์— ์ดˆ์ ์„ ๋งž์ถ”์–ด, ์••๋ ฅยท๊ธฐ๊ณ„ยท์ „๊ธฐยทEMCยทPPE ๋“ฑ ๋‹ค์–‘ํ•œ ๊ทœ์ œ ์˜์—ญ์„ ํ•œ๋ˆˆ์— ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ๊ฒŒ ์ •๋ฆฌํ•˜์˜€๋‹ค. 2. ์œ„ํ—˜ ๋ถ„์„

Physics
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Stimulated interactions of low-energy free-electrons with light

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์—ญ์‚ฌ์  ํ๋ฆ„ : ์ „์žโ€‘๊ด‘ ์ƒํ˜ธ์ž‘์šฉ์€ ๊ณ ์ „์  ๋ผ๋งŒยท์ฝ”ํ‹€๋ฆฌ์–ด ํšจ๊ณผ์—์„œ ์‹œ์ž‘ํ•ด, 20 ๋…„ ์ „ PINEM(Photonโ€‘Induced Nearโ€‘field Electron Microscopy)์œผ๋กœ ์–‘์žโ€‘๊ด‘ํ•™์  ์ ‘๊ทผ์ด ๊ฐ€๋Šฅํ•ด์กŒ๋‹ค. ์ „ํ†ต์  ํ•œ๊ณ„ : ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” 70โ€“300 keV์˜ ๊ณ ์—๋„ˆ์ง€ ์ „์ž๋ฅผ ์ฃผ๋กœ ์‚ฌ์šฉํ–ˆ์œผ๋ฉฐ, ์ด ๊ฒฝ์šฐ ์žฌ์ฝ”์ผ์ด ๋ฌด์‹œ๋  ์ •๋„๋กœ ์ž‘๊ณ  ์ƒํ˜ธ์ž‘์šฉ ์‹œ๊ฐ„์ด ์งง๋‹ค. ์ €์—๋„ˆ์ง€ ์ „์ž์˜ ๋งค๋ ฅ : ์†๋„๊ฐ€ ๋А๋ ค์ง€๋ฉด ์ „์ž์™€ ๊ด‘ํŒŒ์˜ ์œ„์ƒ ๋™๊ธฐํ™”๊ฐ€ ์‰ฌ์›Œ์ง€๊ณ , ๊ด‘์žฅ๊ณผ ์ „์ž ์‚ฌ์ด์˜ ์ƒํ˜ธ์ž‘์šฉ ์‹œ๊ฐ„์ด ์ˆ˜์‹ญ ๋ฐฐ ์—ฐ์žฅ๋œ๋‹ค. ์ด๋Š” โ€œ๊ฐ•ํ•œ

Physics
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Streaming Tensor Programs: A Streaming Abstraction for Dynamic Parallelism

| ๊ตฌ๋ถ„ | ๋‚ด์šฉ | | | | | ํ•ต์‹ฌ ๊ธฐ์—ฌ | 1. STeP ์ถ”์ƒํ™” : ๋ผ์šฐํŒ… ์—ฐ์‚ฐ์ž์™€ ๋ฉ”๋ชจ๋ฆฌ ๊ณ„์ธต์„ ๋ช…์‹œ์ ์œผ๋กœ ๋ชจ๋ธ๋งํ•˜๊ณ , ํ…์„œ ์ฐจ์›์„ ์‹ฌ๋ณผ๋ฆญ ํ˜•ํƒœ๋กœ ๋‹ค๋ฃจ์–ด ๋™์  ์›Œํฌ๋กœ๋“œ๋ฅผ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ํ‘œํ˜„.<br>2. ๋™์  ์ตœ์ ํ™” ํŒจ์Šค : ๋™์  ํƒ€์ผ๋ง, ๋™์  ๋ณ‘๋ ฌํ™”, ๊ตฌ์„ฑ ์‹œ๊ฐ„โ€‘๋‹ค์ค‘ํ™” ๋“ฑ ๊ธฐ์กด SDA ์ตœ์ ํ™”๊ฐ€ ์ •์  ๊ฐ€์ •์— ๋จธ๋ฌผ๋ €๋˜ ๋ถ€๋ถ„์„ ๋™์ ์œผ๋กœ ์ „ํ™˜.<br>3. ์‹คํ—˜์  ์ฆ๋ช… : LLM ๋ ˆ์ด์–ด์™€ ์ „์ฒด ๋ชจ๋ธ์— ๋Œ€ํ•œ ์‚ฌ์ดํดโ€‘๊ทผ์‚ฌ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ๊ธฐ์กด ์ตœ์ ํ™”์™€ ๋น„๊ตํ•ด 2โ€‘3๋ฐฐ ์ˆ˜์ค€์˜ ์„ฑ๋Šฅยท์ž์› ํ™œ์šฉ๋„ ํ–ฅ์ƒ ์ž…์ฆ. | | ๊ธฐ์ˆ ์  novelty |

Structural barriers of the discrete Hasimoto map applied to protein backbone geometry

Structural barriers of the discrete Hasimoto map applied to protein backbone geometry

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉํ‘œ ๊ธฐ์กด ์ ‘๊ทผ๋ฒ•๊ณผ์˜ ๋Œ€๋น„ : ์—๋„ˆ์ง€ ๋žœ๋“œ์Šค์ผ€์ดํ”„, AWSEM, ํŠœ๋ธŒ ๋ชจ๋ธ, DCA ๋“ฑ์€ ๋น„๊ตญ์†Œ์  ์ƒํ˜ธ์ž‘์šฉ(์ ‘์ด‰, ์ง„ํ™” ๊ณต๋ถ„์‚ฐ ๋“ฑ)์„ ๋ช…์‹œ์ ์œผ๋กœ ํฌํ•จํ•œ๋‹ค. ๋ฐ˜๋ฉด ํ•˜์‹œ๋ชจํ†  ๋ณ€ํ™˜์€ ๊ตญ์†Œ์ ์ธ ๊ณก๋ฅ ยท๋น„ํ‹€๋ฆผ ๋งŒ์„ ์ด์šฉํ•ด ๋ณต์†Œ ์Šค์นผ๋ผ์žฅ ํ•˜๋‚˜๋กœ ์••์ถ•ํ•œ๋‹ค. ํ•ต์‹ฌ ์งˆ๋ฌธ : โ€œHasimoto map์ด ๋‹จ๋ฐฑ์งˆ ์ ‘ํž˜์„ ์ง€๋ฐฐํ•˜๋Š” ๋™์  ๋ฐฉ์ •์‹์ธ๊ฐ€, ์•„๋‹ˆ๋ฉด ์ตœ์ข… ๊ตฌ์กฐ๋ฅผ ๊ธฐ์ˆ ํ•˜๋Š” ๊ธฐํ•˜ํ•™์  ํ•ญ๋“ฑ์‹์ธ๊ฐ€?โ€ 2. ํ•ต์‹ฌ ์ด๋ก ์  ์ „๊ฐœ | ๋‹จ๊ณ„ | ๋‚ด์šฉ | ํ•ต์‹ฌ ํฌ์ธํŠธ | | | | | | 2โ€‘1 | ์ด์‚ฐ ํ”„๋ ˆ๋„› ํ”„๋ ˆ์ž„ ์ •์˜ (ฮบ, ฯ„) | ฮบโˆˆ

Quantitative Biology
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Symbolic Neural Generation with Applications to Lead Discovery in Drug Design

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋‰ด๋กœ์‹ฌ๋ณผ๋ฆญ ํ†ตํ•ฉ์˜ ํ•„์š”์„ฑ : ์ˆœ์ˆ˜ ์‹ ๊ฒฝ๋ง์€ ๋Œ€๊ทœ๋ชจ ๋ฐ์ดํ„ฐ์—์„œ ๊ฐ•๋ ฅํ•˜์ง€๋งŒ, ํ˜•์‹์  ์ œ์•ฝ(์˜ˆ: ํ™”ํ•™ ๊ทœ์น™, ์•ˆ์ „์„ฑ) ์„ ์ง์ ‘ ๋ฐ˜์˜ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋ฐ˜๋ฉด ์‹ฌ๋ณผ๋ฆญ ๋ฐฉ๋ฒ•์€ ๋…ผ๋ฆฌ์  ์ œ์•ฝ์„ ๋ช…์‹œ์ ์œผ๋กœ ๋‹ค๋ฃจ์ง€๋งŒ, ๋ฐ์ดํ„ฐ๊ฐ€ ์ ์„ ๋•Œ ์ผ๋ฐ˜ํ™”๊ฐ€ ์ œํ•œ์ ์ด๋‹ค. ์•ฝ๋ฌผ ์„ค๊ณ„์—์„œ์˜ ํŠน์ˆ˜์„ฑ : ์ดˆ๊ธฐ ๋‹จ๊ณ„์—์„œ๋Š” ์‹คํ—˜ ๋ฐ์ดํ„ฐ๊ฐ€ ๊ทนํžˆ ์ œํ•œ์ ์ด๋ฉฐ, ํ™”ํ•™์  ํƒ€๋‹น์„ฑ(์˜ˆ: ๋ฆฌ๊ฐ„๋“œ ํšจ์œจ, ๋…์„ฑ ์ œํ•œ) ์„ ์‚ฌ์ „์— ๋ณด์žฅํ•ด์•ผ ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ โ€œ์†Œ์ˆ˜์˜ ์˜ˆ์‹œ โ†’ ๋…ผ๋ฆฌ ์‚ฌ์–‘ โ†’ ๋Œ€๊ทœ๋ชจ ํ›„๋ณด ์ƒ์„ฑโ€ ํ๋ฆ„์ด ๋งค์šฐ ์œ ์šฉํ•˜๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ๋ฐ ๋ชจ๋ธ ๊ตฌ์กฐ | ๊ตฌ์„ฑ ์š”์†Œ

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Systematic Evaluation of Single-Cell Foundation Model Interpretability Reveals Attention Captures Co-Expression Rather Than Unique Regulatory Signal

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉํ‘œ ๋ฐฐ๊ฒฝ : ๋Œ€๊ทœ๋ชจ ๋‹จ์ผ์„ธํฌ ์ „์‚ฌ์ฒด ๋ฐ์ดํ„ฐ๋ฅผ ํ•™์Šตํ•œ ํŠธ๋žœ์Šคํฌ๋จธ ๋ชจ๋ธ(scGPT, Geneformer)์€ ์„ธํฌ ์œ ํ˜• ๋ถ„๋ฅ˜ยท๊ต๋ž€ ๋ฐ˜์‘ ์˜ˆ์ธกยท์œ ์ „์ž ์กฐ์ ˆ๋ง(GRN) ์ถ”๋ก  ๋“ฑ ๋‹ค์–‘ํ•œ ์‘์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์—ฌ ์™”๋‹ค. ํŠนํžˆ โ€˜์ฃผ์˜(attention)โ€™ ๊ฐ€์ค‘์น˜๋ฅผ ์ด์šฉํ•ด ์ง์ ‘์ ์ธ ์กฐ์ ˆ ํšŒ๋กœ๋ฅผ ๋„์ถœํ•œ๋‹ค๋Š” ๊ธฐ๋Œ€๊ฐ€ ํฌ๋‹ค. ๋ฌธ์ œ์  : NLP ๋ถ„์•ผ์—์„œ ์ฃผ์˜๊ฐ€ ์‹ค์ œ ์ธ๊ณผ ๊ด€๊ณ„๋ฅผ ๋ฐ˜์˜ํ•˜์ง€ ์•Š๋Š”๋‹ค๋Š” ๋น„ํŒ์ด ์กด์žฌํ•˜๊ณ , ์ƒ๋ฌผํ•™์  ๋ฐ์ดํ„ฐ๋Š” ๋งฅ๋ฝโ€‘์˜์กด์„ฑยท์กฐํ•ฉ์„ฑยท๋ถˆ์™„์ „ํ•œ ๋ ˆํผ๋Ÿฐ์Šค ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค(TRRUST, DoRothEA) ๋“ฑ์œผ๋กœ ํ•ด์„์ด ๋ณต์žกํ•˜๋‹ค. ๋ชฉํ‘œ

System Model Quantitative Biology
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Temperature and Respiratory Emergency Department Visits: A Mediation Analysis with Ambient Ozone Exposure

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ๊ณ ์˜จ๊ณผ ํ˜ธํก๊ธฐ ์งˆํ™˜ : ๊ธฐ์กด ์—ญํ•™ ์—ฐ๊ตฌ๋Š” ๊ณ ์˜จ์ด ์ฒœ์‹ยทCOPDยทํ˜ธํก๊ธฐ ๊ฐ์—ผ ๋“ฑ ๊ธ‰์„ฑยท๋งŒ์„ฑ ํ˜ธํก๊ธฐ ์งˆํ™˜์„ ์•…ํ™”์‹œํ‚จ๋‹ค๊ณ  ๋ณด๊ณ ํ–ˆ์œผ๋ฉฐ, ๊ธฐํ›„๋ณ€ํ™”์— ๋”ฐ๋ผ ํ–ฅํ›„ ๋ถ€๋‹ด์ด ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•  ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋œ๋‹ค. ์˜ค์กด์˜ ๋งค๊ฐœ๊ฐ€๋Šฅ์„ฑ : ์˜ค์กด์€ ํ–‡๋น›ยท๊ณ ์˜จ์— ์˜ํ•ด ์ „๊ตฌ๋ฌผ์งˆ(NOx, VOC)์œผ๋กœ๋ถ€ํ„ฐ ์ƒ์„ฑ๋˜๋Š” 2์ฐจ ์˜ค์—ผ๋ฌผ์ด๋ฉฐ, ์˜จ๋„์™€ ๊ฐ•ํ•œ ์–‘์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ฐ€์ง„๋‹ค. ๋”ฐ๋ผ์„œ ์˜จ๋„ โ†’ ์˜ค์กด โ†’ ํ˜ธํก๊ธฐ ์งˆํ™˜์ด๋ผ๋Š” ์ธ๊ณผ๊ฒฝ๋กœ๊ฐ€ ์ด๋ก ์ ์œผ๋กœ ํƒ€๋‹นํ•˜๋‹ค. ์—ฐ๊ตฌ ๊ณต๋ฐฑ : ๊ธฐ์กด ๋งค๊ฐœ๋ถ„์„์€ ์˜จ๋„โ€‘์˜ค์กด ๊ด€๊ณ„๋ฅผ ์„ ํ˜• ์œผ๋กœ ๊ฐ€์ •ํ•˜๊ณ , ์˜จ๋„โ€‘์˜ค์กด ์ƒํ˜ธ์ž‘์šฉ์„ ๋ฌด์‹œํ–ˆ๋‹ค. ๋น„

Statistics Applications Analysis
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The Cascade Equivalence Hypothesis: When Do Speech LLMs Behave Like ASR$rightarrow$LLM Pipelines?

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์งˆ๋ฌธ ์ •์˜ ์—ฐ์‡„ ๋“ฑ๊ฐ€์„ฑ ๊ฐ€์„ค(Cascade Equivalence Hypothesis) : โ€œ์˜ค๋””์˜ค A์™€ ์ „์‚ฌ T๊ฐ€ ์ฃผ์–ด์กŒ์„ ๋•Œ, ๊ณผ์ œ ๋ผ๋ฒจ Y์— ๋Œ€ํ•œ ์ •๋ณด๋Š” ์ „์‚ฌ T์— ๊ฑฐ์˜ ์™„์ „ํžˆ ํฌํ•จ๋œ๋‹ค(I(A;Y|T)โ‰ˆ0)๋ฉด, ๋™์ผํ•œ LLM ๋ฐฑ๋ณธ์„ ์‚ฌ์šฉํ•˜๋Š” ์Œ์„ฑ LLM๊ณผ Whisperโ†’LLM ํŒŒ์ดํ”„๋ผ์ธ์€ ํ–‰๋™์ ์œผ๋กœ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์—†์–ด์•ผ ํ•œ๋‹ค.โ€ ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ๋ฐฑ๋ณธ(confounding) ๋ฌธ์ œ์™€ ์˜ˆ์ œ ์ˆ˜์ค€ ์ผ์น˜ ๋ฅผ ๋ฌด์‹œํ–ˆ์œผ๋ฉฐ, ์ด๋Š” ์‹ค์ œ ์•„ํ‚คํ…์ฒ˜ ์ฐจ์ด๋ฅผ ๊ณผ์†Œํ‰๊ฐ€ํ•˜๊ฒŒ ๋งŒ๋“ ๋‹ค. 2. ์‹คํ—˜ ์„ค๊ณ„ โ€“ ๋งค์น˜๋œ ๋ฐฑ๋ณธ ํ…Œ์ŠคํŠธ | ๋ชจ๋ธ | ๋‚ด๋ถ€

NLP Computer Science
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The Hidden Nature of Non-Markovianity

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๊ฐœ๋ฐฉ ์–‘์ž ์‹œ์Šคํ…œ ์€ ํ™˜๊ฒฝ๊ณผ์˜ ์ƒํ˜ธ์ž‘์šฉ์„ ํ†ตํ•ด ๋งˆ์ฝ”ํ”„์ (์‹œ๊ฐ„โ€‘๊ตญ์†Œ GKSL ๋งˆ์Šคํ„ฐ ๋ฐฉ์ •์‹) ํ˜น์€ ๋น„๋งˆ์ฝ”ํ”„์ (๊ธฐ์–ต ํšจ๊ณผ) ๋™์—ญํ•™์„ ๋ณด์ธ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ •๋ณด ์—ญ๋ฅ˜ , ๋น„๋‹จ์กฐ ๊ฐ์‡  , ์—”ํƒฑ๊ธ€๋จผํŠธ ํšŒ๋ณต ๋“ฑ ์‹คํ—˜์  ํ˜„์ƒ์„ ํ†ตํ•ด ๋น„๋งˆ์ฝ”ํ”„์„ฑ์„ ์ •์˜ยท์ธก์ •ํ•˜๋ ค ํ–ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‹ค์ œ ์‹คํ—˜์—์„œ๋Š” ๊ถค์ (์‹œ๊ฐ„์— ๋”ฐ๋ฅธ ์ƒํƒœ ๋ณ€ํ™”) ๋งŒ์„ ์ง์ ‘ ๊ด€์ธกํ•œ๋‹ค. ์ด ๋…ผ๋ฌธ์€ โ€œ๊ถค์ ๋งŒ์œผ๋กœ ๋น„๋งˆ์ฝ”ํ”„์„ฑ์„ ํŒ๋ณ„ํ•  ์ˆ˜ ์žˆ๋Š”๊ฐ€?โ€๋ผ๋Š” ๊ทผ๋ณธ์ ์ธ ์งˆ๋ฌธ์„ ์ œ๊ธฐํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๊ฐœ๋…: Lindbladian Lift ์ •์˜ : ์ฃผ์–ด์ง„ ๊ถค์  ({rho t})

Quantum Physics
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The Impact of Formations on Football Matches Using Double Machine Learning. Is it worth parking the bus?

| ๊ตฌ๋ถ„ | ๋‚ด์šฉ | ํ‰๊ฐ€ยท์‹œ์‚ฌ์  | | | | | | ์—ฐ๊ตฌ ์งˆ๋ฌธ | โ€œ๋ฒ„์Šค ์ฃผ์ฐจ(๊ทน๋‹จ์  ์ˆ˜๋น„) ์ „๋žต์ด ์‹ค์ œ ์Šน์ ยท์Šน๋ฆฌ ํ™•๋ฅ ์„ ๋†’์ด๋Š”๊ฐ€?โ€ | ๋ช…ํ™•ํ•˜๊ณ  ์‹ค๋ฌด์  ๊ฐ€์น˜๊ฐ€ ๋†’์€ ์งˆ๋ฌธ. ์ถ•๊ตฌ ์ „์ˆ  ๋…ผ์Ÿ์— ๊ณผํ•™์  ๊ทผ๊ฑฐ ์ œ๊ณต. | | ๋ฐ์ดํ„ฐ | 22,000+ ๊ฒฝ๊ธฐ <br> 2018โ€‘2025 ์‹œ์ฆŒ, 7๊ฐœ ์ฃผ์š” ๋ฆฌ๊ทธ + 6๊ฐœ ๋ณด์กฐ ๋ฆฌ๊ทธ <br> Sportmonks API ํ™œ์šฉ <br> ๊ฒฝ๊ธฐ ์ƒํ™ฉ, ํŒ€ ๊ฐ•๋„, ๋‚ ์”จ ๋“ฑ 30์—ฌ ๋ณ€์ˆ˜ ํฌํ•จ | ๊ทœ๋ชจยท๋‹ค์–‘์„ฑ ๋ชจ๋‘ ์ถฉ๋ถ„ํžˆ ํ™•๋ณด. ๋‹ค๋งŒ, API ์ œ๊ณต ๋ฐ์ดํ„ฐ์˜ ์ •ํ™•์„ฑยท์™„์ „์„ฑ (์˜ˆ: ์ผ๋ถ€ ๋ฆฌ๊ทธ ๊ฒฐ์ธก)๊ณผ ์‹œ๊ณ„์—ด

Statistics Applications Learning
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The Limits of Obliviate: Evaluating Unlearning in LLMs via Stimulus-Knowledge Entanglement-Behavior Framework

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์–ธ๋Ÿฌ๋‹(Unlearning) ์€ ๊ฐœ์ธ์ •๋ณด ๋ณดํ˜ธ, ๋ฒ•์  ์š”๊ตฌ์‚ฌํ•ญ ๋Œ€์‘, ๊ทธ๋ฆฌ๊ณ  ๋ชจ๋ธ์— ๋‚ด์žฌ๋œ ์˜ค๋ฅ˜ยทํŽธํ–ฅ์„ ์ œ๊ฑฐํ•˜๊ธฐ ์œ„ํ•œ ํ•ต์‹ฌ ๊ธฐ์ˆ ์ด๋‹ค. ๊ธฐ์กด ํ‰๊ฐ€ ๋ฐฉ๋ฒ•์€ ์‚ญ์ œ๋œ ํ† ํฐ ๋น„์œจ ์ด๋‚˜ ์‚ฌ์ „โ€‘์‚ฌํ›„ ์ •ํ™•๋„ ์ฐจ์ด ์— ๊ตญํ•œ๋ผ, ์‹ค์ œ ๋ชจ๋ธ ๋‚ด๋ถ€์— ๋‚จ์•„ ์žˆ๋Š” โ€œ์ž ์žฌ์  ๊ธฐ์–ตโ€์„ ํฌ์ฐฉํ•˜์ง€ ๋ชปํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ํ”„๋กฌํ”„ํŠธ ๊ธฐ๋ฐ˜ ์žฌํ™œ์„ฑํ™” ๋ฅผ ์ด์šฉํ•ด ์€๋‹‰๋œ ์ง€์‹์ด ์–ผ๋งˆ๋‚˜ ํšŒ๋ณต๋˜๋Š”์ง€๋ฅผ ์ธก์ •ํ•˜๋Š” ์ ‘๊ทผ์€ ๋งค์šฐ ํ˜์‹ ์ ์ด๋‹ค. 2. ์ด๋ก ์  ํ”„๋ ˆ์ž„์›Œํฌ: SKeB | ๊ตฌ์„ฑ ์š”์†Œ | ์„ค๋ช… | ๊ธฐ์กด ์ด๋ก ๊ณผ์˜ ์—ฐ๊ณ„ | | | | | | Stimulus (์ž๊ทน)

Framework Learning
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The Stark effect in molecular Rydberg states: Calculation of Rydberg-Stark manifolds of H$_2$ and D$_2$ including fine and hyperfine structures

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ผ์ด๋“œ๋ฒ„๊ทธ ์›์žยท๋ถ„์ž๋Š” ์ „๊ธฐ์žฅ ์ด์˜จํ™”(Fieldโ€‘ionization)์™€ ์ •๋ฐ€ ์ด์˜จํ™” ์—๋„ˆ์ง€ ์ธก์ •์— ํ•ต์‹ฌ์ ์ธ ์—ญํ• ์„ ํ•ด ์™”์œผ๋ฉฐ, ์–‘์ž ์ œ์–ดยท์ „๊ธฐ์žฅ ์„ผ์„œ ๋“ฑ ๋‹ค์–‘ํ•œ ์‘์šฉ ๋ถ„์•ผ์—์„œ๋„ ์ค‘์š”ํ•˜๋‹ค. ๊ธฐ์กด ์ด๋ก ์€ ์ฃผ๋กœ ์ „์ž ์Šคํ•€ยท๊ถค๋„์™€ ํ•ต์Šคํ•€์„ ๋ฌด์‹œํ•˜๊ณ , ๋‹จ์ผ ์ „์žยทํ•ต์‹ฌ(Closedโ€‘shell) ์ด์˜จ์ฝ”์–ด ์— ํ•œ์ •๋œ ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์•˜๋‹ค. ๋ถ„์ž ๋ผ์ด๋“œ๋ฒ„๊ทธ๋Š” ๋‹ค์ค‘ ํšŒ์ „ยท์ง„๋™ยท์Šคํ•€ยทํ•ต์Šคํ•€ ์ž์œ ๋„๊ฐ€ ์–ฝํ˜€ ์žˆ์–ด, ์ „๊ธฐ์žฅ ํ•˜์—์„œ์˜ ์ŠคํŽ™ํŠธ๋Ÿผ์ด ๋งค์šฐ ๋ณต์žกํ•˜๊ณ , ์ดˆ๋ฏธ์„ธ ๊ตฌ์กฐ๊นŒ์ง€ ํ•ด์„ํ•  ์ˆ˜ ์žˆ๋Š” ์ด๋ก ์ด ์ ˆ์‹คํžˆ ์š”๊ตฌ๋œ๋‹ค. 2. ์ฃผ์š” ๋ฐฉ๋ฒ•๋ก  |

Physics
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The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic, and Long-Horizon Task Execution

1. ์—ฐ๊ตฌ ๋™๊ธฐ์™€ ํ•„์š”์„ฑ ํ˜„์‹ค ๊ฐญ : ํ˜„์žฌ ๋Œ€๋ถ€๋ถ„์˜ ์–ธ์–ด ์—์ด์ „ํŠธ ํ‰๊ฐ€๊ฐ€ โ€œ๋‹จ์ผ ์•ฑยท๋‹จ์ผ ๋‹จ๊ณ„โ€์— ๊ตญํ•œ๋ผ, ์‹ค์ œ ์—…๋ฌด์—์„œ ์š”๊ตฌ๋˜๋Š” ๋ณตํ•ฉ์ ์ธ ์•ฑ ์—ฐ๋™๊ณผ ์žฅ๊ธฐ ์˜์‚ฌ๊ฒฐ์ • ๊ณผ์ •์„ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ๋‹ค์–‘์„ฑ ๋ถ€์กฑ : ๊ธฐ์กด ๋ฒ ์ด์Šค๋ผ์ธ์€ ์ฃผ๋กœ ์›น ๊ฒ€์ƒ‰ยทํ…์ŠคํŠธ ์š”์•ฝยท์ฝ”๋“œ ์ƒ์„ฑ ๋“ฑ ์ œํ•œ๋œ ๋„๋ฉ”์ธ์— ๋จธ๋ฌผ๋Ÿฌ, ๊ธฐ์—…ยท๊ต์œกยทํด๋ผ์šฐ๋“œ ๋“ฑ ๋‹ค์–‘ํ•œ ์‚ฐ์—…๊ตฐ์„ ํฌ๊ด„ํ•˜์ง€ ์•Š๋Š”๋‹ค. 2. ๋ฒค์น˜๋งˆํฌ ์„ค๊ณ„ (Toolathlon) | ๊ตฌ๋ถ„ | ๋‚ด์šฉ | ์˜์˜ | | | | | | ์•ฑยทํˆด ๊ทœ๋ชจ | 32๊ฐœ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜, 604๊ฐœ ํˆด | ์‹ค์ œ ๊ธฐ์—… ํ™˜๊ฒฝ์—์„œ ์‚ฌ์šฉ๋˜๋Š” ํˆด ํฌํŠธํด๋ฆฌ์˜ค์™€

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Tolerances to driver-witness misalignment in a quasilinear plasma wakefield accelerator

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํ”Œ๋ผ์ฆˆ๋งˆ ๊ฐ€์†๊ธฐ๋Š” ๊ธฐ์กด RF ๊ฐ€์†๊ธฐ์— ๋น„ํ•ด GeV/m ์ˆ˜์ค€์˜ ์ดˆ๊ณ ๊ตฌ๋ฐฐ๋ฅผ ์ œ๊ณตํ•˜๋ฏ€๋กœ ์‹œ์„ค ๊ทœ๋ชจ๋ฅผ ํฌ๊ฒŒ ์ถ•์†Œํ•  ์ˆ˜ ์žˆ๋‹ค. ์–‘์„ฑ์ž ๋น”์€ ๋†’์€ ์—๋„ˆ์ง€์™€ ๋‚ฎ์€ ์‹ฑํฌ๋กœํŠธ๋ก  ์†์‹ค ๋•๋ถ„์— ์žฅ๊ฑฐ๋ฆฌ(์ˆ˜๋ฐฑโ€‘์ˆ˜์ฒœ m) ๊ฐ€์†์— ์ ํ•ฉํ•œ ์œ ์ผํ•œ ์‹คํ˜„ ๊ฐ€๋Šฅํ•œ ๊ตฌ๋™์›์ด๋‹ค. AWAKE ์‹คํ—˜์€ CERN SPS 400 GeV ์–‘์„ฑ์ž๋ฅผ ์ด์šฉํ•ด ์‹ค์ œ ์–‘์„ฑ์žโ€‘๊ตฌ๋™ ํ”Œ๋ผ์ฆˆ๋งˆ ์›จ์ดํฌํ•„๋“œ ๊ฐ€์†์„ ๊ฒ€์ฆํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, Runโ€‘2c์—์„œ๋Š” ๋ชจ๋“ˆ๋ ˆ์ด์…˜ ๋‹จ๊ณ„์™€ ๊ฐ€์† ๋‹จ๊ณ„๊ฐ€ ๋ฌผ๋ฆฌ์ ์œผ๋กœ ๋ถ„๋ฆฌ๋œ ๋‘ ๊ฐœ์˜ ํ”Œ๋ผ์ฆˆ๋งˆ ๊ตฌ๊ฐ„์„ ์‚ฌ์šฉํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด ์ค€์„ ํ˜• ๋ ˆ์ง์—์„œ๋Š”

Physics
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Tongyi DeepResearch Technical Report

1. ์—ฐ๊ตฌ ๋™๊ธฐ์™€ ๋ชฉํ‘œ ์žฅ๊ธฐยท์‹ฌ์ธต ์—ฐ๊ตฌ ๊ณผ์ œ : ๊ธฐ์กด LLM์€ ๋‹จ์ผ ์งˆ์˜โ€‘์‘๋‹ต ํ˜น์€ ์งง์€ ๋Œ€ํ™”์— ๊ฐ•์ ์ด ์žˆ์ง€๋งŒ, ๋ณตํ•ฉ์ ์ธ ํƒ์ƒ‰ยท์ถ”๋ก ์„ ์š”๊ตฌํ•˜๋Š” ์žฅ๊ธฐ ์—ฐ๊ตฌ ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ๋Š” ๊ธฐ์–ต ๊ด€๋ฆฌ, ๋ชฉํ‘œ ์„ค์ •, ๋‹จ๊ณ„์  ๊ฒ€์ฆ ๋“ฑ์ด ๋ถ€์กฑํ–ˆ๋‹ค. ์—์ด์ „์‹œ(Agency) ๊ฐ•ํ™” : ๋ชจ๋ธ์ด ์Šค์Šค๋กœ ๋ชฉํ‘œ๋ฅผ ์„ค์ •ํ•˜๊ณ , ์ •๋ณด๋ฅผ ์ˆ˜์ง‘ยท์ •๋ฆฌยท๊ฒ€์ฆํ•˜๋Š” ์ „ ๊ณผ์ •์„ ์ž๋™ํ™”ํ•˜๋„๋ก ์„ค๊ณ„ํ•จ์œผ๋กœ์จ ์ธ๊ฐ„ ๊ฐ๋… ์ตœ์†Œํ™”์™€ ์ƒ์‚ฐ์„ฑ ํ–ฅ์ƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์ˆ  | ๊ธฐ์ˆ  ์š”์†Œ | ์„ค๋ช… | ํ˜์‹ ์„ฑ | | | | | | Agentic Midโ€‘Training | ์‚ฌ์ „ ํ›ˆ๋ จ ๋‹จ๊ณ„์—์„œ โ€œ๋ชฉํ‘œโ€‘

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Towards Scalable Meta-Learning of near-optimal Interpretable Models via Synthetic Model Generations

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ํ•ด์„ ๊ฐ€๋Šฅ์„ฑ : ์˜์‚ฌ๊ฒฐ์ • ํŠธ๋ฆฌ๋Š” ๊ทœ์น™ ๊ธฐ๋ฐ˜ ๊ตฌ์กฐ ๋•๋ถ„์— ๊ฒฐ๊ณผ๋ฅผ ์ธ๊ฐ„์ด ์ดํ•ดํ•˜๊ธฐ ์‰ฌ์›Œ, ๊ทœ์ œยท์ฑ…์ž„์ด ์ค‘์š”ํ•œ ๋„๋ฉ”์ธ์—์„œ ํ•„์ˆ˜์ ์ž…๋‹ˆ๋‹ค. ๋ฉ”ํƒ€๋Ÿฌ๋‹์˜ ํ•„์š”์„ฑ : ์ƒˆ๋กœ์šด ๋ฐ์ดํ„ฐ์…‹์— ๋น ๋ฅด๊ฒŒ ์ ์‘ํ•  ์ˆ˜ ์žˆ๋Š” ์‚ฌ์ „ํ•™์Šต๋œ ๋ชจ๋ธ์ด ์š”๊ตฌ๋˜์ง€๋งŒ, ์‹ค์ œ ํŠธ๋ฆฌ๋ฅผ ์ตœ์ ํ™”ํ•˜๋Š” ๋น„์šฉ์ด ๋งค์šฐ ๋†’์Šต๋‹ˆ๋‹ค. ํ•ฉ์„ฑ ๋ฐ์ดํ„ฐ ํ™œ์šฉ : ์‹ค์ œ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ยท๋ผ๋ฒจ๋งํ•˜๋Š” ๋น„์šฉ์„ ํšŒํ”ผํ•˜๊ณ , ๋‹ค์–‘ํ•œ ํŠธ๋ฆฌ ๊ตฌ์กฐ๋ฅผ ํญ๋„“๊ฒŒ ํƒ์ƒ‰ํ•˜๊ธฐ ์œ„ํ•ด ํ•ฉ์„ฑ ๋ฐ์ดํ„ฐ๋ฅผ ์ƒ์„ฑํ•˜๋Š” ์•„์ด๋””์–ด๋Š” ๊ธฐ์กด GANโ€‘๊ธฐ๋ฐ˜ ์ด๋ฏธ์ง€/ํ…์ŠคํŠธ ์ƒ์„ฑ๊ณผ๋Š” ๋‹ฌ๋ฆฌ โ€œ๋ชจ๋ธ ์ž์ฒด๋ฅผ ๋ฐ์ดํ„ฐ๋กœ ๋งŒ๋“ ๋‹คโ€๋Š” ๋…์ฐฝ์„ฑ์„ ๊ฐ€์ง‘๋‹ˆ๋‹ค

Model Learning
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Tracking the Brownian motion of DNA-functionalized magnetic nanoparticles for conformation analysis beyond the optical resolution limit

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ DNAโ€‘๊ธฐ๋Šฅ์„ฑ ์ฝœ๋กœ์ด๋“œ ์ž…์ž ๋Š” ์ž๊ธฐยท๊ด‘ํ•™์  ์กฐ์ž‘์ด ๊ฐ€๋Šฅํ•ด ์ž๊ฐ€์กฐ๋ฆฝ ๊ตฌ์กฐ, ์„ผ์„œ, ์•ฝ๋ฌผ ์ „๋‹ฌ ๋“ฑ์— ๋„๋ฆฌ ํ™œ์šฉ๋œ๋‹ค. ๊ธฐ์กด์˜ ๊ตฌ์กฐ ํ™•์ธ ๋ฐฉ๋ฒ•(AFM, DLS, ๊ด‘ํ•™ ํŠธ๋žฉ, ํ…๋”๋“œ ํŒŒํ‹ฐํด ๋ชจ์…˜ ๋“ฑ)์€ ํ•ด์ƒ๋„ยท์ฒ˜๋ฆฌ๋Ÿ‰ยท์‹œ๋ฃŒ ์ œํ•œ ๋“ฑ์˜ ๋ฌธ์ œ๋ฅผ ์•ˆ๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ๊ด‘ํ•™ ํšŒ์ ˆ ํ•œ๊ณ„(โ‰ˆ ฮป/2NA) ์ดํ•˜์˜ ์ž…์ž ํฌ๊ธฐ์—์„œ๋Š” ์ง์ ‘์ ์ธ ๊ธฐํ•˜ํ•™์  ์ธก์ •์ด ๋ถˆ๊ฐ€๋Šฅํ•˜๋ฏ€๋กœ, ๋ธŒ๋ผ์šด ์šด๋™์„ ํ†ตํ•œ ์ˆ˜์†Œ์—ญํ•™์  ์ถ”์ • ์ด ๋Œ€์•ˆ์œผ๋กœ ๋ถ€์ƒํ•œ๋‹ค. 2. ์‹คํ—˜ ์„ค๊ณ„ ๋ฐ ํ•ต์‹ฌ ๋ฐฉ๋ฒ• | ๋‹จ๊ณ„ | ๋‚ด์šฉ | ํ•ต์‹ฌ ํฌ์ธํŠธ | | | | | | ์ž…์ž ํ•ฉ์„ฑ | 300

Analysis Condensed Matter
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Transition from traveling fronts to diffusion-limited growth in expanding populations

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ „ํ†ต์  ๋ชจ๋ธ์˜ ํ•œ๊ณ„ : FKPPโ€‘ํ˜• ๋ฐฉ์ •์‹(1)ยท๋น„์„ ํ˜• ํ™•์‚ฐ ๋ชจ๋ธ(2)ยท์˜์–‘์†Œโ€‘์ œํ•œ ๋ชจ๋ธ(3)์€ ๋ชจ๋‘ ์†๋„ ์ผ์ • ์ธ ์ „์„ ์„ ์˜ˆ์ธกํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋‹ค์ˆ˜์˜ ๋ฏธ์ƒ๋ฌผ ์ฝœ๋กœ๋‹ˆ ์‹คํ—˜์—์„œ ๋ฐ˜๊ฒฝ์ด tยนแŸยฒ, ๋ฉด์ ์ด t์— ๋น„๋ก€ ํ•˜๋Š” ์„œ๋ธŒ๋ฆฌ๋‹ˆ์–ด ์„ฑ์žฅ ํŒจํ„ด์ด ๋ณด๊ณ ๋˜๊ณ  ์žˆ๋‹ค. ํ•ต์‹ฌ ๊ฐ€์„ค : ๊ธฐ์กด ๋ชจ๋ธ์€ ์ƒ๋ฌผ๋Ÿ‰ ์ด๋™(ํ™•์‚ฐ)๊ณผ ์„ฑ์žฅ(์˜์–‘์†Œ ์„ญ์ทจ) ์‚ฌ์ด์˜ ์—ฐ๊ด€์„ฑ์„ ๋ฌด์‹œ ํ•œ๋‹ค. ์‹ค์ œ ์‹คํ—˜(ํ˜•๊ด‘ ๋งˆ์ปค๋ฅผ ์ด์šฉํ•œ ์„นํ„ฐ ๊ฒฝ๊ณ„ ๊ด€์ฐฐ, ์„ฑ์žฅ๋ฅ  ์ฐจ์ด์— ๋”ฐ๋ฅธ ์ „ํŒŒ ์†๋„ ์ฐจ์ด)์—์„œ๋Š” ์„ธํฌ ์žฌ๋ฐฐ์—ด์ด ์˜์–‘์†Œ์— ์˜์กด ํ•œ๋‹ค๋Š” ์ฆ๊ฑฐ๊ฐ€ ์ถ•์ ๋ผ ์žˆ๋‹ค. 2. ๋ชจ๋ธ ์„ค๊ณ„

Physics
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Tunable asymmetric swimming in biflagellate microswimmers

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ์ €๋ ˆ๋†€์ฆˆ ์ˆ˜์˜ ์—์„œ๋Š” ๊ด€์„ฑ๋ณด๋‹ค ์ ์„ฑ๋ ฅ์ด ์ง€๋ฐฐ์ ์ด๋ฏ€๋กœ, ํšŒ์ „ ํ™•์‚ฐ์ด ๋น ๋ฅด๊ฒŒ ๋ฐœ์ƒํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ฏธ์ƒ๋ฌผ์€ ๋Šฅ๋™์ ์ธ ๋ฐฉํ–ฅ ์ œ์–ด ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๊ฐ–์ถ”์–ด์•ผ๋งŒ ํšจ์œจ์ ์ธ ํƒ์ƒ‰์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ํ˜•ํƒœ์  ๋น„๋Œ€์นญ (์˜ˆ: ๋‹จํŽธ๋ชจ ์ •์ž)์ด๋‚˜ ํŽธ๋ชจ ๊ธธ์ดยท๋ฐฐ์น˜ ์ฐจ์ด ์— ์ดˆ์ ์„ ๋งž์ท„์ง€๋งŒ, ๋Œ€์นญ์ ์ธ ๋‘ ํŽธ๋ชจ ๋ฅผ ๊ฐ€์ง„ Chlamydomonas ์™€ ๊ฐ™์€ ์ข…์ด ์–ด๋–ป๊ฒŒ ๋ฐฉํ–ฅ์„ ๋ฐ”๊พธ๋Š”์ง€๋Š” ์ถฉ๋ถ„ํžˆ ์„ค๋ช…๋˜์ง€ ์•Š์•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ โ€œํ•ฉ์„ฑโ€‘ํŒŒ๊ดด ์ƒํ˜ธ์ž‘์šฉโ€ ์ด๋ผ๋Š” ๋ณดํŽธ์ ์ธ ๋ฌผ๋ฆฌ ์›๋ฆฌ๋ฅผ ์ œ์‹œํ•จ์œผ๋กœ์จ, ํ˜•ํƒœ์  ๋น„๋Œ€์นญ์ด ์—†์–ด๋„ ํŽธ๋ชจ ๊ตฌ๋™ ํŒŒ๋ผ๋ฏธํ„ฐ ๋งŒ์œผ๋กœ ํšŒ์ „ยท

Physics
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U-FedTomAtt: Ultra-lightweight Federated Learning with Attention for Tomato Disease Recognition

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํ”„๋ผ์ด๋ฒ„์‹œ์™€ ํ†ต์‹  ๋น„์šฉ : ๋†๊ฐ€๋ณ„ ์ด๋ฏธ์ง€ ๋ฐ์ดํ„ฐ๋Š” ๊ฐœ์ธยท๊ธฐ์—… ์ •๋ณด์™€ ์ง๊ฒฐ๋ผ ์ค‘์•™ ์„œ๋ฒ„์— ์ง์ ‘ ์ „์†กํ•˜๊ธฐ ์–ด๋ ต๋‹ค. FL์€ ๋กœ์ปฌ ํ•™์Šต ํ›„ ๊ฐ€์ค‘์น˜๋งŒ ์ „์†กํ•จ์œผ๋กœ์จ ๋ฐ์ดํ„ฐ ์œ ์ถœ ์œ„ํ—˜์„ ์ตœ์†Œํ™”ํ•œ๋‹ค. ์—ฃ์ง€ ๋””๋ฐ”์ด์Šค ์ œ์•ฝ : ๋†์ดŒ ํ˜„์žฅ์˜ ์Šค๋งˆํŠธํฐยทIoT ๋””๋ฐ”์ด์Šค๋Š” CPUยทGPU ์„ฑ๋Šฅยท์ „๋ ฅ ์†Œ๋ชจ๊ฐ€ ์ œํ•œ์ ์ด๋‹ค. ๋”ฐ๋ผ์„œ ํŒŒ๋ผ๋ฏธํ„ฐ ์ˆ˜๊ฐ€ ์ˆ˜๋ฐฑ๋งŒ ์ˆ˜์ค€์ธ ๊ธฐ์กด ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ์€ ์‹ค์‹œ๊ฐ„ ์ถ”๋ก ์ด ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค. ๋น„๋™์งˆ์  ๋ฐ์ดํ„ฐ : ์ดฌ์˜ ํ™˜๊ฒฝยทํ’ˆ์ข…ยท๋ณ‘ํ•ด ์ •๋„๊ฐ€ ์ง€์—ญ๋งˆ๋‹ค ํฌ๊ฒŒ ๋‹ฌ๋ผ Nonโ€‘IID ๋ฌธ์ œ๊ฐ€ ์‹ฌ๊ฐํ•˜๋‹ค. ๊ธฐ์กด FedAvg ๋“ฑ์€ ๋ฐ์ดํ„ฐ ์–‘๋งŒ

Learning Quantitative Biology
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UCO: A Multi-Turn Interactive Reinforcement Learning Method for Adaptive Teaching with Large Language Models

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ LLM ๊ธฐ๋ฐ˜ ํŠœํ„ฐ๋ง์˜ ํ•œ๊ณ„ : ๊ธฐ์กด SFT( supervised fineโ€‘tuning) ๋ฐฉ์‹์€ ์ •๋‹ต ์ƒ์„ฑ์— ์ดˆ์ ์„ ๋งž์ถ”์–ด, ํ•™์Šต์ž์˜ ์ดํ•ด๋„์™€ ์ธ์ง€ ์ƒํƒœ๋ฅผ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ๊ฐ•ํ™”ํ•™์Šต(RL) ์ ‘๊ทผ์˜ ๋ฌธ์ œ์  : ๊ธฐ์กด RL ๊ธฐ๋ฐ˜ ํŠœํ„ฐ๋Š” ์ •๋‹ต ์—ฌ๋ถ€ ๋งŒ์„ ๋ณด์ƒ์œผ๋กœ ์‚ฌ์šฉํ•ด โ€œํ‘œ๋ฉด์  ์„ฑ๊ณตโ€์— ๋จธ๋ฌผ๋ฉฐ, ํ•™์Šต์ž์˜ ๋‚ด์  ์ธ์ง€ ๋ณ€ํ™” ๋ฅผ ํฌ์ฐฉํ•˜์ง€ ๋ชปํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ UCO | ์š”์†Œ | ์„ค๋ช… | ์™œ ์ค‘์š”ํ•œ๊ฐ€ | | | | | | Unidirectional Cognitive Optimization | ๊ต์‚ฌ(LLM) โ†’

Model Learning
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Uncovering Bugs in Formal Explainers: A Case Study with PyXAI

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ํ˜•์‹์  XAI ๋Š” ์ˆ˜ํ•™์ ยท๋…ผ๋ฆฌ์  ์ฆ๋ช…์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์„ค๋ช…์„ ์ œ๊ณตํ•˜๋ฏ€๋กœ, ์„ค๋ช…์˜ ์ •ํ™•์„ฑ์— ๋Œ€ํ•œ ๊ฐ•๋ ฅํ•œ ๋ณด์žฅ์„ ๊ธฐ๋Œ€ํ•œ๋‹ค. ์‹ค์ œ๋กœ๋Š” ๊ตฌํ˜„ ๋‹จ๊ณ„์—์„œ ๋ฒ„๊ทธ, ์„ค๊ณ„ ์˜ค๋ฅ˜, ํ˜น์€ ์ž…๋ ฅ ๋ฐ์ดํ„ฐ์™€์˜ ๋ถˆ์ผ์น˜๊ฐ€ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋Š” ์‚ฌ์šฉ์ž๊ฐ€ ์ž˜๋ชป๋œ ๊ฒฐ๋ก ์„ ๋‚ด๋ฆฌ๊ฒŒ ๋งŒ๋“ ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ํ˜•์‹์  ๋ชจ๋ธ ์ž์ฒด์˜ ์ด๋ก ์  ํŠน์„ฑ์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ๊ณ , ๊ตฌํ˜„ ๊ฒ€์ฆ ์— ๋Œ€ํ•œ ์ฒด๊ณ„์  ์ ‘๊ทผ์€ ๋ถ€์กฑํ–ˆ๋‹ค. 2. ์ œ์•ˆ๋œ ๊ฒ€์ฆ ๋ฐฉ๋ฒ•๋ก  | ๋‹จ๊ณ„ | ํ•ต์‹ฌ ๋‚ด์šฉ | ๋ชฉ์  | | | | | | (1) ์‚ฌ์ „ ์ •์˜๋œ ๊ธฐ์ค€ | ํ˜•์‹์  ์„ค๋ช…๊ธฐ์˜ ์ž…๋ ฅยท์ถœ๋ ฅ

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UniField: Joint Multi-Domain Training for Universal Surface Pressure Modeling

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ํ‘œ๋ฉด ์••๋ ฅ ์˜ˆ์ธก ์€ ์ „ํ†ต์ ์ธ CFD์— ๋น„ํ•ด ์—ฐ์‚ฐ ๋น„์šฉ์ด ํฌ๊ฒŒ ๋‚ฎ์€ ๋ฐ์ดํ„ฐโ€‘๊ธฐ๋ฐ˜ ๋ชจ๋ธ์ด ์š”๊ตฌ๋˜๋Š” ๋ถ„์•ผ์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ๋‹จ์ผ ๋„๋ฉ”์ธ(์˜ˆ: ์ž๋™์ฐจ) ์— ์ดˆ์ ์„ ๋งž์ถ”์–ด ๋ฐ์ดํ„ฐ๊ฐ€ ํ’๋ถ€ํ•œ ๊ฒฝ์šฐ์—๋งŒ ์ข‹์€ ์„ฑ๋Šฅ์„ ๋ณด์˜€์œผ๋ฉฐ, ๋„๋ฉ”์ธ ์ „์ด ๊ฐ€ ์–ด๋ ค์›Œ ์‹ค์ œ ์„ค๊ณ„ ๋‹จ๊ณ„์—์„œ ํ™œ์šฉ๋„๊ฐ€ ์ œํ•œ์ ์ด์—ˆ๋‹ค. ๋ฐ์ดํ„ฐ ๋ถ€์กฑ ๋ฌธ์ œ์™€ ๋„๋ฉ”์ธ ๊ฐ„ ์ฐจ์ด(๊ธฐํ•˜ํ•™, ํ๋ฆ„ ๋ ˆ์ธ์ง€) ๋ฅผ ๋™์‹œ์— ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ๋ฉ€ํ‹ฐ๋„๋ฉ”์ธ ํ•™์Šต ์ ‘๊ทผ์ด ํ•„์š”ํ–ˆ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๊ธฐ์—ฌ ๋‚ด์šฉ | ์˜์˜ | | | | | | 1 | UniField ํ”„๋ ˆ์ž„์›Œํฌ : Geom

Model
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UrbanVLA: A Vision-Language-Action Model for Urban Micromobility

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋„์‹œ ๋งˆ์ดํฌ๋กœ๋ชจ๋นŒ๋ฆฌํ‹ฐ ๋Š” ์ตœ๊ทผ ๋ฌผ๋ฅ˜ยท๋ฐฐ๋‹ฌ ๋ถ„์•ผ์—์„œ ๊ธ‰์„ฑ์žฅํ•˜๊ณ  ์žˆ์œผ๋‚˜, ๊ธฐ์กด ๋กœ๋ด‡ ๋„ค๋น„๊ฒŒ์ด์…˜์€ ์‹ค๋‚ดยท์†Œ๊ทœ๋ชจ ์‹ค์™ธ ์— ์ดˆ์ ์„ ๋งž์ถ”์–ด ๋Œ€๊ทœ๋ชจ ๋ณต์žกํ•œ ๋„์‹œ ํ™˜๊ฒฝ์— ์ ์šฉํ•˜๊ธฐ์—” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ๋™์  ๊ฐ์ฒด(๋ณดํ–‰์ž, ์ฐจ๋Ÿ‰) ์™€ ๋ถˆ๊ทœ์น™ํ•œ ๋„๋กœ ๊ตฌ์กฐ (๊ณจ๋ชฉ, ๊ณต์›, ์ž„์‹œ ๊ณต์‚ฌ ๊ตฌ์—ญ) ๋“ฑ์€ ์ „ํ†ต์ ์ธ ์ง€๋„ ๊ธฐ๋ฐ˜ ํ˜น์€ ๋ชฉํ‘œโ€‘์  ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•์ด ์‰ฝ๊ฒŒ ์‹คํŒจํ•œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด | ์š”์†Œ | ์„ค๋ช… | ์ฐจ๋ณ„์  | | | | | | Visionโ€‘Languageโ€‘Action (VLA) ํ”„๋ ˆ์ž„์›Œํฌ | ์‹œ๊ฐ ์ž…๋ ฅ โ†” ์–ธ์–ด(๊ฒฝ๋กœ ํ…์ŠคํŠธ) โ†” ํ–‰๋™

Model
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Vibrational infrared and Raman spectra of the methanol molecule with equivariant neural-network property surfaces

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ง„๋™ ์ŠคํŽ™ํŠธ๋Ÿผ ํ•ด์„ ์—์„œ ์—๋„ˆ์ง€ ๊ฐ„๊ฒฉ๋ฟ ์•„๋‹ˆ๋ผ ์ „์ด ๊ฐ•๋„ ๋Š” ๋ถ„์ž ๊ตฌ์กฐยท๋™์—ญํ•™์„ ์ดํ•ดํ•˜๋Š” ๋ฐ ํ•ต์‹ฌ์ด๋‹ค. ์ „์ด ๊ฐ•๋„ ๊ณ„์‚ฐ์—๋Š” ์ž ์žฌ ์—๋„ˆ์ง€๋ฉด(PES) ๋ฟ ์•„๋‹ˆ๋ผ ์ „๊ธฐ์Œ๊ทน์ž(DMS) ์™€ ํŽธ๊ทน์„ฑ(ฮฑโ€‘surface) ์˜ ๊ณ ํ’ˆ์งˆ ํ‘œํ˜„์ด ํ•„์ˆ˜์ ์ด๋‹ค. ๊ธฐ์กด์—๋Š” ๋‹คํ•ญ์‹ ์ „๊ฐœ ์™€ ๊ณ ์ฐจ์› ์‹ ๊ฒฝ๋ง(HDNNP) , Gaussian Approximation Potential ๋“ฑ์œผ๋กœ PES์™€ DMS๋ฅผ ๋ชจ๋ธ๋งํ–ˆ์ง€๋งŒ, ํ…์„œ ํšŒ์ „ยท๋ฐ˜์ „ ๋Œ€์นญ ์„ ๋™์‹œ์— ๋งŒ์กฑ์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์€ ์ œํ•œ์ ์ด์—ˆ๋‹ค. 2. ๋ฐฉ๋ฒ•๋ก ์  ํ˜์‹  | ์š”์†Œ | ๊ธฐ์กด ์ ‘๊ทผ | ๋ณธ ๋…ผ๋ฌธ

Network Physics
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Vision-Language-Policy Model for Dynamic Robot Task Planning

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ ์ธ์‹ยท์ถ”๋ก  ํ•„์š”์„ฑ : ๋กœ๋ด‡์ด ์ž์—ฐ์–ด ๋ช…๋ น์„ ์ดํ•ดํ•˜๋ ค๋ฉด ์‹œ๊ฐ ์ •๋ณด์™€ ์–ธ์–ด ์ •๋ณด๋ฅผ ๋™์‹œ์— ์ฒ˜๋ฆฌํ•ด์•ผ ํ•จ. ๊ธฐ์กด ํ”Œ๋ž˜๋„ˆ๋Š” ์ฃผ๋กœ ์‹ฌ๋ณผ๋ฆญ ํ˜น์€ ์ œํ•œ๋œ ๋น„์ „ ์ž…๋ ฅ์— ์˜์กดํ•ด ๋ณต์žกํ•œ ๋น„๊ตฌ์กฐ ํ™˜๊ฒฝ์—์„œ์˜ ์ ์šฉ์ด ์–ด๋ ค์› ์Œ. ๋™์  ๋ช…๋ น ๋ณ€ํ™” : ์‹ค์ œ ์ž‘์—… ํ˜„์žฅ์—์„œ๋Š” ์‚ฌ์šฉ์ž๊ฐ€ ๋ช…๋ น์„ ์ˆ˜์ •ํ•˜๊ฑฐ๋‚˜ ์ƒˆ๋กœ์šด ์ œ์•ฝ์ด ์ถ”๊ฐ€๋˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋นˆ๋ฒˆํ•จ. ๊ธฐ์กด ๊ณ„ํš ์‹œ์Šคํ…œ์€ ์žฌ๊ณ„ํš์— ๋†’์€ ๋น„์šฉ์„ ์š”๊ตฌํ•˜๊ฑฐ๋‚˜ ์ „ํ˜€ ๋Œ€์‘ํ•˜์ง€ ๋ชปํ•จ. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด Visionโ€‘Languageโ€‘Policy (VLP) ๊ตฌ์กฐ 1. Visionโ€‘Language Encod

Model
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VitalBench: A Rigorous Multi-Center Benchmark for Long-Term Vital Sign Prediction in Intraoperative Care

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ž„์ƒ์  ์ค‘์š”์„ฑ : ์ˆ˜์ˆ  ์ค‘ ํ˜ˆ์••, ์‹ฌ๋ฐ•์ˆ˜, ์‚ฐ์†Œํฌํ™”๋„ ๋“ฑ ํ™œ๋ ฅ์ง•ํ›„๋Š” ๊ธ‰๊ฒฉํžˆ ๋ณ€๋™ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์กฐ๊ธฐ ์˜ˆ์ธก์€ ๊ธ‰์„ฑ ํ•ฉ๋ณ‘์ฆ ์˜ˆ๋ฐฉ์— ์ง๊ฒฐ๋œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ ํ•œ๊ณ„ : ๋Œ€๋ถ€๋ถ„์˜ ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ๋‹จ์ผ ๊ธฐ๊ด€ ๋ฐ์ดํ„ฐ์— ๊ตญํ•œ๋˜๊ฑฐ๋‚˜, ๋Œ€๊ทœ๋ชจ ์ „์ฒ˜๋ฆฌ(๊ฒฐ์ธก์น˜ ๋ณด๊ฐ„, ์ •๊ทœํ™” ๋“ฑ)๋ฅผ ์ „์ œํ•œ๋‹ค. ์ด๋Š” ์‹ค์ œ ๋ณ‘์› ํ˜„์žฅ์—์„œ ์ ์šฉํ•˜๊ธฐ ์–ด๋ ค์šด ์กฐ๊ฑด์ด๋‹ค. 2. VitalBench์˜ ํ•ต์‹ฌ ๊ตฌ์„ฑ ์š”์†Œ | ๊ตฌ์„ฑ ์š”์†Œ | ์„ค๋ช… | ์ž„์ƒ์ ยท์—ฐ๊ตฌ์  ์˜์˜ | | | | | | ๋‹ค๊ธฐ๊ด€ ๋ฐ์ดํ„ฐ | ๋‘ ๊ฐœ ๋…๋ฆฝ ์˜๋ฃŒ์„ผํ„ฐ(์ด 4,000+ ์ˆ˜์ˆ ) | ๊ธฐ๊ด€ ๊ฐ„ ๋ฐ์ดํ„ฐ

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VocalNet-M2: Advancing Low-Latency Spoken Language Modeling via Integrated Multi-Codebook Tokenization and Multi-Token Prediction

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์‘๋‹ต ์ง€์—ฐ ๋ฌธ์ œ : ๊ธฐ์กด SLM์€ ์ž๋™ํšŒ๊ท€ ๋ฐฉ์‹๊ณผ flowโ€‘matching ๊ธฐ๋ฐ˜ ์Œ์„ฑ ํ•ฉ์„ฑ์œผ๋กœ ์ธํ•ด ์‹ค์‹œ๊ฐ„ ๋Œ€ํ™”์— ๋ถ€์ ํ•ฉํ•œ ์ˆ˜๋ฐฑ ๋ฐ€๋ฆฌ์ดˆ ์ˆ˜์ค€์˜ ์ง€์—ฐ์„ ๋ณด์ธ๋‹ค. ํŠนํžˆ ์ธํ„ฐ๋ž™ํ‹ฐ๋ธŒ ์Œ์„ฑ ๋น„์„œ, ์‹ค์‹œ๊ฐ„ ๋ฒˆ์—ญ ๋“ฑ์—์„œ๋Š” 300 ms ์ดํ•˜๊ฐ€ ์š”๊ตฌ๋œ๋‹ค. ์ฝ”๋“œ๋ถ ํ† ํฌ๋‚˜์ด์ €์˜ ํ•œ๊ณ„ : ๋Œ€๋ถ€๋ถ„์˜ ์—ฐ๊ตฌ๋Š” ๋‹จ์ผ ์ฝ”๋“œ๋ถ(์˜ˆ: 1โ€‘codebook VQโ€‘VAE)๋งŒ์„ ์‚ฌ์šฉํ•ด ํ† ํฐ์„ ์••์ถ•ํ•œ๋‹ค. ์ด๋Š” ํ† ํฐ๋‹น ํ‘œํ˜„๋ ฅ์ด ์ œํ•œ๋ผ ๋” ๋งŽ์€ ํ† ํฐ์ด ํ•„์š”ํ•˜๊ณ , ๊ฒฐ๊ณผ์ ์œผ๋กœ ์ง€์—ฐ์ด ์ฆ๊ฐ€ํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๋ฒˆํ˜ธ | ๊ธฐ์—ฌ ๋‚ด์šฉ | ๊ธฐ๋Œ€ ํšจ๊ณผ | |

Model
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What We Don't C: Representations for scientific discovery beyond VAEs

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๊ณ ์ฐจ์› ๊ณผํ•™ ๋ฐ์ดํ„ฐ (์ฒœ๋ฌธํ•™ ์ด๋ฏธ์ง€, ์œ ์ „์ฒด ๋ฐ์ดํ„ฐ ๋“ฑ)๋Š” ์ง์ ‘ ํ•ด์„์ด ์–ด๋ ค์›Œ ์ž ์žฌ ํ‘œํ˜„ ์„ ์ด์šฉํ•œ ์ฐจ์› ์ถ•์†Œ์™€ ํŠน์„ฑ ์ถ”์ถœ์ด ์ผ๋ฐ˜์ ์ด๋‹ค. ๊ธฐ์กด VAE ๊ธฐ๋ฐ˜ ์ ‘๊ทผ๋ฒ•์€ ์กฐ๊ฑด๋ถ€ ์ •๋ณด์™€ ์ž ์žฌ ๋ณ€์ˆ˜์˜ ํ˜ผํ•ฉ ์œผ๋กœ ์ธํ•ด, ์–ด๋–ค ์ •๋ณด๊ฐ€ ์–ด๋””์— ์ €์žฅ๋๋Š”์ง€ ๋ช…ํ™•ํžˆ ํŒŒ์•…ํ•˜๊ธฐ ํž˜๋“ค๋‹ค. ๋”ฐ๋ผ์„œ ์กฐ๊ฑด๋ถ€์™€ ๋น„์กฐ๊ฑด๋ถ€ ์ •๋ณด๋ฅผ ๋ช…์‹œ์ ์œผ๋กœ ๋ถ„๋ฆฌ ํ•˜๊ณ , ํ•„์š” ์‹œ ์žฌ์กฐํ•ฉํ•  ์ˆ˜ ์žˆ๋Š” ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด ๊ณผํ•™์  ํƒ๊ตฌ์— ํ•„์ˆ˜์ ์ด๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด 1. Latent Flow Matching ํ™•๋ฅ  ํ๋ฆ„ ๋ชจ๋ธ๋ง์„ ์ž ์žฌ ๊ณต๊ฐ„์— ์ ์šฉํ•ด, ๋ถ„ํฌ ๊ฐ„ ๋งคํ•‘์„ ์ง์ ‘

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Who Sees the Risk? Stakeholder Conflicts and Explanatory Policies in LLM-based Risk Assessment

| ๊ตฌ๋ถ„ | ํ•ต์‹ฌ ๋‚ด์šฉ | ์˜์˜ยท๋น„ํŒ์  | | | | | | ์—ฐ๊ตฌ ๋™๊ธฐ | ๊ธฐ์กด ์œ„ํ—˜ ํ‰๊ฐ€๊ฐ€ ๊ธฐ์ˆ  ์ค‘์‹ฌยท์ „๋ฌธ๊ฐ€ ์˜๊ฒฌ์— ์น˜์šฐ์ณ ์ดํ•ด๊ด€๊ณ„์ž ๊ฐ„ ์ธ์‹ ์ฐจ์ด๋ฅผ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•จ | ์‹ค์ œ ํ˜„์žฅ(์˜๋ฃŒ, ๊ตํ†ต, ๊ธˆ์œต)์—์„œ๋Š” ์ •์ฑ…ยท๋ฒ•ยท์œค๋ฆฌ์  ์ดํ•ด๊ด€๊ณ„๊ฐ€ ๋ณตํ•ฉ์ ์œผ๋กœ ์ž‘์šฉํ•˜๋ฏ€๋กœ, ์ด ์ ‘๊ทผ์€ ์‹ค์šฉ์  ํ•„์š”์„ฑ์„ ์ •ํ™•ํžˆ ์งš์Œ | | ํ”„๋ ˆ์ž„์›Œํฌ ๊ตฌ์กฐ | 1๏ธโƒฃ LLM ํŒ์‚ฌ : ์‚ฌ์ „ ํ•™์Šต๋œ LLM์— ์œ„ํ—˜ ์‹œ๋‚˜๋ฆฌ์˜ค์™€ ์ดํ•ด๊ด€๊ณ„์ž ํ”„๋กœํŒŒ์ผ์„ ์ž…๋ ฅ โ†’ ์œ„ํ—˜ ์ ์ˆ˜์™€ ์„ค๋ช… ์ƒ์„ฑ <br>2๏ธโƒฃ Risk Atlas Nexus : ์œ„ํ—˜ ์š”์†Œ์™€ ์ดํ•ด๊ด€๊ณ„์ž๋ฅผ ๋งคํ•‘ํ•˜๋Š” ๊ตฌ์กฐํ™”๋œ ์ง€์‹ ๊ทธ

Whodunnit? The case of midge swarms

Whodunnit? The case of midge swarms

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ๋งˆ์ด๋“œ ๋–ผ๋Š” ์ƒˆ, ๋ฌผ๊ณ ๊ธฐ, ์–‘, ์ธ๊ฐ„ ๊ตฐ์ค‘๊ณผ ๋‹ฌ๋ฆฌ ๊ทนํžˆ ๋‚ฎ์€ ํŽธ๊ทน๋„(Wโ‰ˆ0) ๋ฅผ ๋ณด์ด๋ฉฐ, ๊ฐœ๋ณ„ ๊ฐœ์ฒด ๊ฐ„ ๊ฒฐํ•ฉ๋ ฅ๋„ ์•ฝํ•˜์ง€๋งŒ ์ „์ฒด ๋–ผ์— ๋Œ€ํ•œ ๊ตฌ์†๋ ฅ์€ ๊ฐ• ํ•˜๋‹ค. ์ž์—ฐ ์ƒํƒœ์—์„œ๋Š” ์ƒ๊ด€๊ธธ์ด ฮพ๊ฐ€ ๋–ผ ํฌ๊ธฐ L์— ๋น„๋ก€ (์Šค์ผ€์ผโ€‘ํ”„๋ฆฌ)ํ•˜๊ณ , ์ •์ ยท๋™์  ์–‘์ž ๋ชจ๋‘ ํŒŒ์›Œโ€‘๋ฒ•์น™์„ ๋”ฐ๋ฅธ๋‹ค. ๋ฐ˜๋ฉด ์‹คํ—˜์‹ค์—์„œ๋Š” ์™ธ๋ถ€ ๊ต๋ž€์ด ์—†์„ ๊ฒฝ์šฐ ฮพ๊ฐ€ ๋งค์šฐ ์งง์•„์ง„๋‹ค. ์ด๋Ÿฌํ•œ ๋‘ ์ƒ๋ฐ˜๋œ ํ˜„์ƒ์„ ๋™์‹œ์— ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ๋Š” ์ด๋ก ์ด ํ˜„์žฌ๊นŒ์ง€๋Š” ๋ถ€์žฌํ•œ๋‹ค๋Š” ์ ์ด ์—ฐ๊ตฌ ๋™๊ธฐ๋‹ค. 2. ๊ธฐ์กด ์ด๋ก ๋“ค์˜ ๊ฒ€ํ†  ์ฃผ๊ธฐ์  Vicsek ๋ชจ๋ธ(PVM) ์ •๋ ฌ ์ƒํ˜ธ์ž‘์šฉ๊ณผ

Condensed Matter
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Wide-Surface Furnace for In Situ X-Ray Diffraction of Combinatorial Samples using a High-Throughput Approach

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์กฐํ•ฉ ํ•ฉ์„ฑ์˜ ํ•œ๊ณ„ : PLD๋ฅผ ์ด์šฉํ•ด 100 mm ์‹ค๋ฆฌ์ฝ˜ ์›จ์ดํผ์— 3์›์†Œ ์ „์ด๊ธˆ์† ์˜ฅ์‚ฌ์ด๋“œ(L S C F M) ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ๋ฅผ ์ œ์ž‘ํ•  ์ˆ˜ ์žˆ์ง€๋งŒ, ๊ณ ์˜จยท์ œ์–ด ๋ถ„์œ„๊ธฐ์—์„œ์˜ ๊ตฌ์กฐยท์กฐ์„ฑ ๋ถ„์„์ด ๋ถ€์กฑํ•ด ์‹ค์šฉ์  ๋ฐ์ดํ„ฐ ํ™•๋ณด๊ฐ€ ์–ด๋ ค์› ๋‹ค. ๊ณ ์†ยท๊ณ ์˜จ ํŠน์„ฑํ™”์˜ ๋ถ€์žฌ : ๊ธฐ์กด ์ƒ์šฉ ์žฅ๋น„๋Š” ์‹ค์˜จ์—์„œ XYโ€‘ํ•ด์ƒ๋„ XRD, Raman, UVโ€‘Vis ๋“ฑ์„ ์ œ๊ณตํ•˜์ง€๋งŒ, 700 ยฐC ์ด์ƒ์—์„œ ๋Œ€๋ฉด์  ์‹œ๋ฃŒ๋ฅผ ๋™์‹œ์— ์ธก์ •ํ•  ์ˆ˜ ์žˆ๋Š” ์‹œ์Šคํ…œ์€ ๋“œ๋ฌผ๋‹ค. 2. ์‹œ์Šคํ…œ ์„ค๊ณ„ ๋ฐ ํ˜์‹ ์„ฑ ํผ๋‹ˆ์Šค ๊ตฌ์กฐ : ์Šคํ…Œ์ธ๋ฆฌ์Šค ์Šคํ‹ธ ํ”„๋ ˆ์ž„์— ๋ฌผ๋ƒ‰๊ฐ ํšŒ๋กœ๋ฅผ ํฌํ•จํ•˜

Condensed Matter
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'I Like That You Have to Poke Around': Instructors on How Experiential Approaches to AI Literacy Spark Inquiry and Critical Thinking

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ AI๊ฐ€ ์‚ฌํšŒ ์ „๋ฐ˜์— ํŒŒ๊ธ‰๋ ฅ์„ ๊ฐ–๊ฒŒ ๋˜๋ฉด์„œ โ€œAI ๋ฆฌํ„ฐ๋Ÿฌ์‹œโ€๋Š” ์ „๊ณตยท๋น„์ „๊ณต ๊ตฌ๋ถ„ ์—†์ด ํ•„์ˆ˜ ์—ญ๋Ÿ‰์œผ๋กœ ๋ถ€์ƒํ•˜๊ณ  ์žˆ๋‹ค. ๊ธฐ์กด ๊ต์œก ๋ชจ๋ธ์€ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ์„ ์ „์ œํ•˜๊ฑฐ๋‚˜ ์ถ”์ƒ์  ์ด๋ก  ์— ๋จธ๋ฌผ๋Ÿฌ ๋น„โ€‘STEM ํ•™์Šต์ž์—๊ฒŒ ๋†’์€ ์ง„์ž… ์žฅ๋ฒฝ์„ ๋งŒ๋“ ๋‹ค. ๋”ฐ๋ผ์„œ โ€œ๋…ธ์ฝ”๋“œโ€์™€ ์‹ค์ œ ๋ฌธ์ œ ๊ธฐ๋ฐ˜ ํ•™์Šต ์„ ๊ฒฐํ•ฉํ•œ ์ ‘๊ทผ๋ฒ•์ด ์š”๊ตฌ๋œ๋‹ค. 2. AI User ์ปค๋ฆฌํ˜๋Ÿผ์˜ ํ•ต์‹ฌ ํŠน์ง• ๋ชจ๋“ˆํ˜•ยท์›น ๊ธฐ๋ฐ˜ : ์–ธ์ œ ์–ด๋””์„œ๋“  ์ ‘๊ทผ ๊ฐ€๋Šฅ, ๊ธฐ์ˆ  ์ธํ”„๋ผ ์š”๊ตฌ ์ตœ์†Œํ™”. ๋…ธ์ฝ”๋“œ ์ธํ„ฐ๋ž™ํ‹ฐ๋ธŒ ํ”„๋กœ์ ํŠธ : ์‹œ๊ฐ์  ๋ธ”๋กยท๋“œ๋ž˜๊ทธโ€‘์•คโ€‘๋“œ๋กญ ๋ฐฉ์‹์œผ๋กœ AI ํŒŒ์ดํ”„๋ผ์ธ์„ ์ฒดํ—˜.

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A fluctuating lattice Boltzmann formulation based on orthogonal central moments

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ฉ”์กฐ์Šค์ฝ”ํ”ฝยท๋งˆ์ดํฌ๋กœ์Šค์ฝ”ํ”ฝ ํ๋ฆ„์—์„œ๋Š” ์—ด์žก์Œ์ด ๋ฌผ๋ฆฌ์  ํ˜„์ƒ(์˜ˆ: ํ™•์‚ฐ, ์ƒ๊ด€ ํ•จ์ˆ˜)๊ณผ ์ง์ ‘ ์—ฐ๊ฒฐ๋œ๋‹ค. ๊ธฐ์กด LB ๋ชจ๋ธ์— ์žก์Œ์„ ๋‹จ์ˆœํžˆ ์ถ”๊ฐ€ํ•˜๋ฉด ์งˆ๋Ÿ‰ยท์šด๋™๋Ÿ‰ ๋ณด์กด, ํ”Œ๋Ÿญํˆฌ์—์ด์…˜โ€‘์†Œ์‚ฐ ์ •๋ฆฌ(FDT) ๋ฐ ๊ฐˆ๋ฆด๋ ˆ์ด ๋ถˆ๋ณ€์„ฑ ์œ ์ง€๊ฐ€ ์–ด๋ ค์›Œ์ง„๋‹ค. ํŠนํžˆ, ์›์‹œ ๋ชจ๋ฉ˜ํŠธ(rawโ€‘moment) ๊ธฐ๋ฐ˜ MRT๋‚˜ BGK ๋ชจ๋ธ์€ ๋น„๋ณด์กด(ghost) ๋ชจ๋“œ์™€ ๋ฌผ๋ฆฌ ๋ชจ๋“œ ์‚ฌ์ด์— ์ธ์œ„์ ์ธ ๊ฒฐํ•ฉ์„ ์ผ์œผ์ผœ ์žก์Œ ์ŠคํŽ™ํŠธ๋Ÿผ์ด ์™œ๊ณก๋˜๊ณ  ์ˆ˜์น˜ ์•ˆ์ •์„ฑ์ด ์ €ํ•˜๋œ๋‹ค. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด: ์ง๊ต ์ค‘์‹ฌ ๋ชจ๋ฉ˜ํŠธ(CM) ๊ธฐ๋ฐ˜ ์žก์Œ ์‚ฝ์ž… ์ง๊ต์„ฑ : CM ๊ธฐ์ €๋ฅผ ์ง๊ต

Physics
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A Pragmatic View of AI Personhood

1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ AI ์—์ด์ „์‹œ์˜ ๊ธ‰์ฆ : ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ, ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ ์—์ด์ „ํŠธ, ์ž๋™ํ™”๋œ ์˜์‚ฌ๊ฒฐ์ • ์‹œ์Šคํ…œ ๋“ฑ์ด ์ธ๊ฐ„๊ณผ ์œ ์‚ฌํ•œ ํ–‰๋™์„ ๋ณด์ด๋ฉฐ โ€œ์ธ๊ฒฉโ€์ด๋ผ๋Š” ๋ฒ•ยท์‚ฌํšŒ์  ์ง€์œ„์— ๋Œ€ํ•œ ๋…ผ์˜๋ฅผ ์ด‰๋ฐœํ•˜๊ณ  ์žˆ๋‹ค. ์ „ํ†ต์  ์ธ๊ฒฉ๊ถŒ ๋…ผ์Ÿ์˜ ํ•œ๊ณ„ : ์˜์‹, ์ž์œจ์„ฑ, ํ•ฉ๋ฆฌ์„ฑ ๋“ฑ ํ˜•์ด์ƒํ•™์  ๊ธฐ์ค€์— ์˜์กดํ•˜๋ฉด ์‹ค์งˆ์ ์ธ ์ •์ฑ…ยท๋ฒ•์ œํ™”๊ฐ€ ์ง€์—ฐ๋œ๋‹ค. 2. ํ•ต์‹ฌ ์ œ์•ˆ: โ€œ์˜๋ฌด ๋ฒˆ๋“คโ€ ํ”„๋ ˆ์ž„์›Œํฌ | ์š”์†Œ | ์„ค๋ช… | ์ ์šฉ ์˜ˆ์‹œ | | | | | | ๋ฒˆ๋“ค(Bundle) | ๊ถŒ๋ฆฌ์™€ ์ฑ…์ž„์„ ํ•˜๋‚˜์˜ ํŒจํ‚ค์ง€๋กœ ๋ฌถ์Œ | ๊ณ„์•ฝ ์ฒด๊ฒฐ, ์†ํ•ด๋ฐฐ์ƒ, ๋ฐ์ดํ„ฐ ์ ‘๊ทผ ๊ถŒ๋ฆฌ | |

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A Unified Formulation for $langle hat{S}^2 rangle $ in Two-Component TDDFT

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ TDDFT์˜ ํ˜„์ฃผ์†Œ : ์„ ํ˜•์‘๋‹ต TDDFT๋Š” ๊ณ„์‚ฐ ํšจ์œจ์„ฑ๊ณผ ์ •ํ™•๋„ ์‚ฌ์ด์˜ ๊ท ํ˜• ๋•Œ๋ฌธ์— ๊ด‘ํ•™ยท์ „์ž์ŠคํŽ™ํŠธ๋กœ์Šค์ฝ”ํ”ผ ๋ถ„์•ผ์—์„œ ํ‘œ์ค€ ๋„๊ตฌ๋กœ ์ž๋ฆฌ ์žก์•˜๋‹ค. ๋‘ ์„ฑ๋ถ„ TDDFT : ๋น„์ƒ๋Œ€๋ก ์  ํ•œ๊ณ„์—์„œ๋„ ๋น„๊ณต์„ ์„ฑ ์ž๊ธฐ๊ณ„(Noncollinear magnetic systems)๋ฅผ ๋‹ค๋ฃฐ ์ˆ˜ ์žˆ๋Š” ์ถฉ๋ถ„ํžˆ ์ผ๋ฐ˜์ ์ธ ํ”„๋ ˆ์ž„์›Œํฌ์ด๋ฉฐ, ๊ณต์„ ์„ฑ ๊ธฐ์ค€ ์ƒํƒœ์—์„œ๋Š” ์Šคํ•€โ€‘๋ณด์กด๊ณผ ์Šคํ•€โ€‘ํ”Œ๋ฆฝ ์ „์ด๋ฅผ ๊ฐ๊ฐ ๋ณ„๋„๋กœ ์ทจ๊ธ‰ํ•œ๋‹ค. โŸจSยฒโŸฉ ๊ณ„์‚ฐ์˜ ๋‚œ์ œ : ์—ฌ๊ธฐ ์ƒํƒœ์˜ ์Šคํ•€ ๋‹ค์ค‘๋„(Spin multiplicity)๋ฅผ ์•Œ๊ธฐ ์œ„ํ•ด์„œ๋Š” โŸจSยฒโŸฉ์ด ํ•„์ˆ˜์ ์ธ๋ฐ,

Physics
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Accelerated, Memory-Efficient Far-Field Scattering Computation with Monte Carlo SBR

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ „ํ†ต์  SBR ํ•œ๊ณ„ : ๊ธฐ์กด Shootingโ€‘andโ€‘Bouncing Ray์€ ๋ ˆ์ด ํŠธ๋ ˆ์ด์‹ฑ ๊ธฐ๋ฐ˜์˜ ๊ฒฐ์ •๋ก ์  ์ ๋ถ„์œผ๋กœ, ๋ณต์žกํ•œ ์œ ์ „์ฒด ๊ตฌ์กฐ์—์„œ ๋ฉ”๋ชจ๋ฆฌ ์š”๊ตฌ๋Ÿ‰์ด ๊ธ‰์ฆํ•˜๊ณ , ๋‹ค์ค‘ ๋ฐ˜์‚ฌยท์ „ํŒŒ ๊ฒฝ๋กœ๋ฅผ ์ถฉ๋ถ„ํžˆ ์ƒ˜ํ”Œ๋งํ•˜๊ธฐ ์–ด๋ ค์›€. GPU์™€ Monte Carlo์˜ ์‹œ๋„ˆ์ง€ : ํ˜„๋Œ€ GPU๋Š” ์ˆ˜์ฒœ ๊ฐœ์˜ ์Šค๋ ˆ๋“œ์—์„œ ๋…๋ฆฝ์ ์ธ ์ƒ˜ํ”Œ๋ง ์ž‘์—…์„ ํšจ์œจ์ ์œผ๋กœ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ์–ด, Monte Carlo ์ ๋ถ„๊ณผ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ๊ฒฐํ•ฉ๋  ์ˆ˜ ์žˆ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ๋ฒ• | ์š”์†Œ | ๊ธฐ์กด SBR | ์ œ์•ˆ Monte Carlo SBR | | | | | | ์ ๋ถ„

Activation-Space Uncertainty Quantification for Pretrained Networks

Activation-Space Uncertainty Quantification for Pretrained Networks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ๋ถˆํ™•์‹ค์„ฑ ์ •๋Ÿ‰ํ™”์˜ ํ•„์š”์„ฑ : ์œ„ํ—˜์ด ํฐ ์‘์šฉ(์˜๋ฃŒ, ์ž์œจ์ฃผํ–‰ ๋“ฑ)์—์„œ๋Š” ๋ชจ๋ธ์ด โ€œ์–ผ๋งˆ๋‚˜ ํ™•์‹ ์„ ๊ฐ€์ง€๊ณ  ์˜ˆ์ธกํ–ˆ๋Š”๊ฐ€โ€๋ฅผ ์•Œ ์ˆ˜ ์žˆ์–ด์•ผ ํ•จ. ๊ธฐ์กด ๋ฐฉ๋ฒ•์˜ ํ•œ๊ณ„ : Weightโ€‘space Bayesian (BNN, Laplace ๋“ฑ) โ†’ ์žฌํ•™์Šตยท๋‹ค์ค‘ ์ƒ˜ํ”Œยท๊ณ ๋น„์šฉ 2์ฐจ ์ •๋ณด ํ•„์š”. Ensemble โ†’ ์—ฐ์‚ฐ๋Ÿ‰ยท๋ฉ”๋ชจ๋ฆฌ ๊ธ‰์ฆ. Postโ€‘hoc ๋ฐฉ๋ฒ•๋„ ๋Œ€๋ถ€๋ถ„ ๋‹จ์ผ ํŒจ์Šค ์™€ ์˜ˆ์ธก ๋ณด์กด ์„ ๋™์‹œ์— ๋งŒ์กฑ์‹œํ‚ค์ง€ ๋ชปํ•จ. 2. ํ•ต์‹ฌ ์•„์ด๋””์–ด โ€“ ํ™œ์„ฑํ™”โ€‘๊ณต๊ฐ„ ๋ฒ ์ด์ง€์•ˆ ๋ฒ ์ด์ง€์•ˆ ๋Œ€์ƒ ์ „ํ™˜ : ๊ฐ€์ค‘์น˜๊ฐ€ ์•„๋‹Œ ํ™œ์„ฑํ™”(Preโ€‘activa

Machine Learning Statistics Network
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Adaptive Data Flywheel: Applying MAPE Control Loops to AI Agent Improvement

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ๊ธฐ์—… AI ์—์ด์ „ํŠธ์˜ ์ง€์†์  ์ ์‘ ํ•„์š”์„ฑ : ์‚ฌ๋‚ด ์ง€์‹๋ฒ ์ด์Šค์™€ ์—…๋ฌด ํ๋ฆ„์ด ๋น ๋ฅด๊ฒŒ ๋ณ€ํ•˜๋Š” ํ™˜๊ฒฝ์—์„œ ์ •์  ๋ชจ๋ธ์€ ๊ธˆ์„ธ ์„ฑ๋Šฅ ์ €ํ•˜๋ฅผ ๊ฒช๋Š”๋‹ค. ๋ฐ์ดํ„ฐ ํ”Œ๋ผ์ดํœ  ๊ฐœ๋… : ๋ฐ์ดํ„ฐ โ†’ ๋ชจ๋ธ โ†’ ํ”ผ๋“œ๋ฐฑ โ†’ ์žฌํ•™์Šต์˜ ์ˆœํ™˜์„ ์ž๋™ํ™”ํ•ด โ€œ์Šค์Šค๋กœ ํ•™์Šตํ•˜๋Š”โ€ ์‹œ์Šคํ…œ์„ ๊ตฌํ˜„ํ•œ๋‹ค๋Š” ์ ์—์„œ MAPE์™€ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ๊ฒฐํ•ฉ๋œ๋‹ค. 2. MAPE ์ œ์–ด ๋ฃจํ”„ ์ ์šฉ ๋ฐฉ์‹ | ๋‹จ๊ณ„ | ๊ตฌํ˜„ ๋‚ด์šฉ | ํ•ต์‹ฌ ๊ธฐ์ˆ ยทํˆด | | | | | | Monitor | RAG ํŒŒ์ดํ”„๋ผ์ธ ๋กœ๊ทธ, ์‚ฌ์šฉ์ž ํ”ผ๋“œ๋ฐฑ(negative samples) ์‹ค์‹œ๊ฐ„ ์ˆ˜์ง‘ | NVIDIA

Data
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AI Code in the Wild: Measuring Security Risks and Ecosystem Shifts of AI-Generated Code in Modern Software

๋ณธ ๋…ผ๋ฌธ์€ AIโ€‘์ƒ์„ฑ ์ฝ”๋“œ๊ฐ€ ์‹ค์ œ ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ ํ˜„์žฅ์— ์–ผ๋งˆ๋‚˜ ์นจํˆฌํ–ˆ๋Š”์ง€๋ฅผ ์ •๋Ÿ‰์ ์œผ๋กœ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•ด ๋‘ ๊ฐ€์ง€ ๋Œ€๊ทœ๋ชจ ๋ฐ์ดํ„ฐ์…‹์„ ๊ตฌ์ถ•ํ•˜๊ณ , ๊ณ ์ •๋ฐ€ ํƒ์ง€ ๋ชจ๋ธ์„ ์ ์šฉํ•œ ์ ์—์„œ ํ•™์ˆ ์ ยท์‹ค๋ฌด์  ์˜์˜๊ฐ€ ํฌ๋‹ค. ๋จผ์ € ํƒ์ง€ ํŒŒ์ดํ”„๋ผ์ธ์€ (1) ์ฝ”๋“œ ์Šค๋‹ˆํŽซ์„ LLMโ€‘๊ธฐ๋ฐ˜ ์ฝ”๋“œ ์™„์„ฑ ๋ชจ๋ธ์— ์ž…๋ ฅํ•ด โ€˜AIโ€‘๊ฐ€๋Šฅ์„ฑ ์ ์ˆ˜โ€™๋ฅผ ์‚ฐ์ถœํ•˜๊ณ , (2) ๊ธฐ์กด ์˜คํ”ˆ์†Œ์Šค ์ฝ”๋“œ์™€ AIโ€‘์ƒ์„ฑ ์ฝ”๋“œ ๊ฐ„์˜ ์Šคํƒ€์ผยท๊ตฌ์กฐ ์ฐจ์ด๋ฅผ ํ•™์Šตํ•œ ์ด์ง„ ๋ถ„๋ฅ˜๊ธฐ๋ฅผ ๊ฒฐํ•ฉํ•œ๋‹ค. ์ด์ค‘ ๊ฒ€์ฆ ์ ˆ์ฐจ์™€ ์ธ๊ฐ„ ๋ผ๋ฒจ๋Ÿฌ์˜ ์ƒ˜ํ”Œ๋ง ๊ฒ€ํ† ๋ฅผ ํ†ตํ•ด 95 % ์ด์ƒ์˜ ์ •๋ฐ€๋„์™€ 92 % ์ด์ƒ์˜ ์žฌํ˜„์œจ์„ ๋‹ฌ์„ฑํ–ˆ์œผ๋ฉฐ, ์ด

System
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AIOT based Smart Education System: A Dual Layer Authentication and Context-Aware Tutoring Framework for Learning Environments

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์ถœ์„ ์‚ฌ๊ธฐ์™€ ๋ณด์•ˆ : ๊ธฐ์กด ์ถœ์„ ๊ด€๋ฆฌ ์‹œ์Šคํ…œ์€ QR์ฝ”๋“œ, ์–ผ๊ตด ์ธ์‹ ๋“ฑ ๋‹จ์ผ ์ธ์ฆ ๋ฐฉ์‹์— ์˜์กดํ•ด ์œ„๋ณ€์กฐ ์œ„ํ—˜์ด ์กด์žฌํ•œ๋‹ค. ์ด์ค‘ ์ธ์ฆ(RFID + Wiโ€‘Fi) ์ ‘๊ทผ์€ ๋ฌผ๋ฆฌ์ ยท๋„คํŠธ์›Œํฌ ์ฐจ์›์˜ ๋ณตํ•ฉ ๊ฒ€์ฆ์„ ์ œ๊ณตํ•ด ์‚ฌ๊ธฐ ๊ฐ€๋Šฅ์„ฑ์„ ํฌ๊ฒŒ ๋‚ฎ์ถ˜๋‹ค. ํ•™์Šต ๊ฐœ์ธํ™”์™€ ๋ชฐ์ž… : ๊ต์‹ค ๋‚ด ํ•™์Šต ์ง€์›์ด ์ผ๊ด€๋˜์ง€ ๋ชปํ•˜๊ณ , ๊ต์‚ฌ์˜ ์—…๋ฌด ๋ถ€๋‹ด์ด ์ปค์„œ ์‹ค์‹œ๊ฐ„ ํ”ผ๋“œ๋ฐฑ์ด ์ œํ•œ๋œ๋‹ค. AI ๋น„์„œ๋Š” ๊ต์žฌ ๋ถ„์„, ํ•™์ƒ ํ–‰๋™ ๋ฐ ํ™˜๊ฒฝ ๋ฐ์ดํ„ฐ๋ฅผ ์‹ค์‹œ๊ฐ„์œผ๋กœ ํ•ด์„ํ•ด ๋งž์ถคํ˜• ํžŒํŠธ์™€ ํ€ด์ฆˆ๋ฅผ ์ œ๊ณตํ•œ๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์ˆ  ๋ฐ ๊ตฌ์„ฑ ์š”์†Œ ์ด์ค‘ ์ธ์ฆ ๋ชจ๋“ˆ RFID

Framework System Learning
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An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์‚ฐ์—… 5.0 ์€ ์ธ๊ฐ„๊ณผ ๊ธฐ๊ณ„๊ฐ€ ํ˜‘์—…ํ•˜๋Š” ์ง€๋Šฅํ˜• ์ œ์กฐยท์„œ๋น„์Šค ํ™˜๊ฒฝ์„ ๋ชฉํ‘œ๋กœ ํ•˜๋ฉฐ, ์—์ง€ AI ๋Š” ๋ฐ์ดํ„ฐ ํ”„๋ผ์ด๋ฒ„์‹œ, ์‹ค์‹œ๊ฐ„์„ฑ, ๋„คํŠธ์›Œํฌ ๋น„์šฉ ์ ˆ๊ฐ ์ธก๋ฉด์—์„œ ํ•ต์‹ฌ ๊ธฐ์ˆ ์ด๋‹ค. ๊ธฐ์กด ํด๋ผ์šฐ๋“œโ€‘์ค‘์‹ฌ ๋ฐฐํฌ ๋ฐฉ์‹์€ ์ง€์—ฐยท๋ณด์•ˆ ๋ฌธ์ œ๋กœ ์‚ฐ์—… ํ˜„์žฅ์— ์ ํ•ฉํ•˜์ง€ ์•Š๋‹ค. ๋”ฐ๋ผ์„œ โ€œ์—์ด์ „์‹œ ๊ธฐ๋ฐ˜โ€ ์ ‘๊ทผ๋ฒ•์€ ์ž‘์—… ์ฑ…์ž„์„ ๋ช…ํ™•ํžˆ ํ•˜๊ณ , ํ˜„์žฅ ์šด์˜์žยทAI ๋ชจ๋ธยท์ธํ”„๋ผ ๊ฐ„์˜ ์—ญํ• ์„ ๋ถ„๋ฆฌํ•ด ๊ด€๋ฆฌ ํšจ์œจ์„ฑ์„ ๋†’์ธ๋‹ค. 2. ํ”„๋ ˆ์ž„์›Œํฌ ๊ตฌ์กฐ ์—์ด์ „ํŠธ ๋ ˆ์ด์–ด : ์ธ๊ฐ„(์šด์˜์ž), ์•Œ๊ณ ๋ฆฌ์ฆ˜(๋ชจ๋ธ ์„œ๋น™), ํ˜‘์—…(๋ฉ€ํ‹ฐโ€‘์—์ด์ „ํŠธ ์˜ค์ผ€์ŠคํŠธ๋ ˆ์ด์…˜) ์—์ด์ „ํŠธ๊ฐ€

Framework
An Electrocardiogram Multi-task Benchmark with Comprehensive Evaluations and Insightful Findings

An Electrocardiogram Multi-task Benchmark with Comprehensive Evaluations and Insightful Findings

๋ณธ ๋…ผ๋ฌธ์€ ์‹ฌ์ „๋„(ECG)๋ผ๋Š” ๊ณ ์œ ์˜ ์ƒ๋ฆฌํ•™์  ํŒŒํ˜• ๋ฐ์ดํ„ฐ๋ฅผ ๋‹ค์ค‘ ๊ณผ์ œ ํ™˜๊ฒฝ์—์„œ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด, ์ตœ์‹  ํŒŒ์šด๋ฐ์ด์…˜ ๋ชจ๋ธ๊ณผ ์ „ํ†ต์ ์ธ ์‹œ๊ณ„์—ด ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ์„ ์ฒด๊ณ„์ ์œผ๋กœ ๋น„๊ตํ•œ ์ตœ์ดˆ์˜ ๋Œ€๊ทœ๋ชจ ๋ฒค์น˜๋งˆํฌ ์—ฐ๊ตฌ๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋จผ์ € ์—ฐ๊ตฌ์ž๋Š” โ€œํŒŒ์šด๋ฐ์ด์…˜ ๋ชจ๋ธ์ด ECG ๋ถ„์„์— ์‹ค์งˆ์ ์ธ ๊ฐ€์น˜๋ฅผ ์ œ๊ณตํ•˜๋Š”๊ฐ€?โ€๋ผ๋Š” ํ•ต์‹ฌ ์งˆ๋ฌธ์„ ์„ค์ •ํ•˜๊ณ , ์ด๋ฅผ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ์–ธ์–ด ๋ชจ๋ธ(์˜ˆ: BERT ๊ธฐ๋ฐ˜), ์ผ๋ฐ˜ ์‹œ๊ณ„์—ด ๋ชจ๋ธ(์˜ˆ: TSโ€‘2Vec, Chronos) ๋ฐ ECG ์ „์šฉ ํŒŒ์šด๋ฐ์ด์…˜ ๋ชจ๋ธ(์˜ˆ: ECGโ€‘GPT, CardioBERT) ๋“ฑ ์„ธ ๊ฐ€์ง€ ์นดํ…Œ๊ณ ๋ฆฌ์˜ ์‚ฌ์ „ํ•™์Šต ๋ชจ

Machine Learning Computer Science
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An Indoor Radio Mapping Dataset Combining 3D Point Clouds and RSSI

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ์Šค๋งˆํŠธ ๋””๋ฐ”์ด์Šค ๊ธ‰์ฆ : ์‹ค๋‚ด์—์„œ ๊ณ ๋Œ€์—ญํญยท์ €์ง€์—ฐ์„ ์š”๊ตฌํ•˜๋Š” ์„œ๋น„์Šค๊ฐ€ ๋Š˜์–ด๋‚˜๋ฉด์„œ, ๊ธฐ์กด์˜ ๋‹จ์ˆœ ๊ฒฝ๋กœ ์†์‹ค ๋ชจ๋ธ๋งŒ์œผ๋กœ๋Š” ์ถฉ๋ถ„ํžˆ ์˜ˆ์ธกํ•˜๊ธฐ ์–ด๋ ต๋‹ค. REM์˜ ์—ญํ•  : ์‹ค์‹œ๊ฐ„ AP ๋ฐฐ์น˜, ์ „๋ ฅ ์ œ์–ด, ์ฑ„๋„ ํ• ๋‹น ๋“ฑ ๋„คํŠธ์›Œํฌ ์ตœ์ ํ™”์— ํ•ต์‹ฌ์ ์ธ ์ง€๋„์ด๋ฉฐ, ์ •ํ™•๋„๊ฐ€ ๋„คํŠธ์›Œํฌ ์„ฑ๋Šฅ์— ์ง์ ‘์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๊ธฐ์กด ํ•œ๊ณ„ : CAD ๊ธฐ๋ฐ˜ ํ‰๋ฉด๋„, rayโ€‘tracing ์‹œ๋ฎฌ๋ ˆ์ด์…˜, ํ˜น์€ ์ €ํ•ด์ƒ๋„ ์ธก์ •์— ์˜์กดํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋Œ€๋ถ€๋ถ„์ด๋ฉฐ, ์‹ค์ œ ์ธ๊ฐ„ ์›€์ง์ž„ยท๊ฐ€๊ตฌ ๋ฐฐ์น˜ ๋“ฑ ๋™์  ์š”์†Œ๋ฅผ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค. 2. ๋ฐ์ดํ„ฐ์…‹ ๊ตฌ์„ฑ ๋ฐ ํŠน์ง• ๊ณ ํ•ด

Data
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AURA: A Reinforcement Learning Framework for AI-Driven Adaptive Conversational Surveys

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์ „ํ†ต ์„ค๋ฌธ์˜ ํ•œ๊ณ„ : ๊ฐœ์ธํ™” ๋ถ€์กฑ โ†’ ๋‚ฎ์€ ์‘๋‹ต๋ฅ ยทํ’ˆ์งˆ. ๊ธฐ์กด AI ์„ค๋ฌธ ์ฑ—๋ด‡ : โ€œ๋ฐ˜์‘ํ˜•โ€์ด์ง€๋งŒ ์‹ค์ œ๋กœ๋Š” ์‚ฌ์ „ ์ •์˜๋œ ํ๋ฆ„์— ๋จธ๋ฌผ๋Ÿฌ ์‚ฌ์šฉ์ž์˜ ์‹ค์‹œ๊ฐ„ ์ƒํ™ฉ์— ๋งž์ถ”์ง€ ๋ชปํ•จ. ํ•ต์‹ฌ ๊ณผ์ œ : ๋Œ€ํ™” ์ค‘์— ๋™์ ์œผ๋กœ ์งˆ๋ฌธ์„ ์กฐ์ •ํ•ด ์‘๋‹ต์˜ ๊นŠ์ด์™€ ์งˆ์„ ๋†’์ด๋Š” ๋ฉ”์ปค๋‹ˆ์ฆ˜ ํ•„์š”. 2. AURA ํ”„๋ ˆ์ž„์›Œํฌ ํ•ต์‹ฌ ๊ตฌ์„ฑ ์š”์†Œ | ๊ตฌ์„ฑ ์š”์†Œ | ์„ค๋ช… | ์—ญํ•  | | | | | | LSDE ํ’ˆ์งˆ ์ง€ํ‘œ | Length, Selfโ€‘disclosure, Emotion, Specificity 4์ฐจ์› ์ธก์ • | ์‘๋‹ต์˜ ํ’๋ถ€ํ•จยท๊ฐ์ •ยท๊ตฌ์ฒด์„ฑ์„

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

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

1๏ธโƒฃ ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ WSI์˜ ์ดˆ๊ณ ํ•ด์ƒ๋„ : 10โน~10ยนโฐ ํ”ฝ์…€ ๊ทœ๋ชจ๋Š” ๊ธฐ์กด 224ร—224 ์ž…๋ ฅ์„ ์ „์ œ๋กœ ํ•˜๋Š” ๋น„์ „โ€‘์–ธ์–ด ๋ชจ๋ธ์— ๋น„ํ•ด 10โด~10โถ ๋ฐฐ ๋” ํฐ ๋ฉ”๋ชจ๋ฆฌยท์—ฐ์‚ฐ ์š”๊ตฌ๋ฅผ ๋งŒ๋“ ๋‹ค. ์ง„๋‹จ ํ…์ŠคํŠธ์˜ ๋„๋ฉ”์ธ ํŠน์ˆ˜์„ฑ : ์ผ๋ฐ˜ LLM ํ† ํฌ๋‚˜์ด์ €๋Š” โ€œadenocarcinomaโ€, โ€œmitotic figureโ€ ๋“ฑ ๋ณ‘๋ฆฌํ•™ ์ „์šฉ ์šฉ์–ด๋ฅผ ์„œ๋ธŒ์›Œ๋“œ ๋‹จ์œ„๋กœ ๋ถ„ํ•ดํ•ด ์˜๋ฏธ ์†์‹ค์„ ์ดˆ๋ž˜ํ•œ๋‹ค. Hallucination ์œ„ํ—˜ : ์ž˜๋ชป๋œ โ€œmalignant/benignโ€ ๊ตฌ๋ถ„์€ ์ž„์ƒ์— ์น˜๋ช…์ ์ด๋ฏ€๋กœ, ์ƒ์„ฑ ๋ชจ๋ธ์˜ ์‚ฌ์‹ค์„ฑ ๊ฒ€์ฆ์ด ํ•„์ˆ˜์ ์ด๋‹ค. 2๏ธ

Image Processing Model Electrical Engineering and Systems Science
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AVOID-JACK: Avoidance of Jackknifing for Swarms of Long Heavy Articulated Vehicles

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ์˜์˜ ํ˜„์‹ค์  ํ•„์š”์„ฑ : ๋ฌด๊ฑฐ์šด ๊ด€์ ˆ์‹ ์ฐจ๋Ÿ‰์€ ๋ฌผ๋ฅ˜ยท์ฑ„๊ตดยท๊ณตํ•ญยท๋†์—… ๋“ฑ์—์„œ ์ ์  ๋” ์ž๋™ํ™”๋˜๊ณ  ์žˆ๋‹ค. ์ด๋“ค ์ฐจ๋Ÿ‰์ด ๊ตฐ์ง‘์œผ๋กœ ์›€์ง์ผ ๊ฒฝ์šฐ, ์žญํ‚คํ•‘(์ฐจ๋Ÿ‰์ด ๊ธ‰๊ฒฉํžˆ ๊บพ์—ฌ ์ „๋ณต๋˜๋Š” ํ˜„์ƒ)๊ณผ ์ƒํ˜ธ ์ถฉ๋Œ์€ ์•ˆ์ „ยท์ƒ์‚ฐ์„ฑ์— ์น˜๋ช…์  ์œ„ํ—˜์„ ์ดˆ๋ž˜ํ•œ๋‹ค. ๋ฌธํ—Œ ๊ณต๋ฐฑ : ๊ธฐ์กด ๊ตฐ์ง‘ ๋กœ๋ด‡ ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์†Œํ˜•, ํšŒ์ „ ๊ฐ€๋Šฅํ•œ ๋กœ๋ด‡์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜๋ฉฐ, ๊ธธ๊ณ  ๊ด€์ ˆ์ด ์žˆ๋Š” ์ฐจ๋Ÿ‰์— ๋Œ€ํ•œ ์ œ์–ดยทํ˜‘๋™ ์ „๋žต์€ ๊ฑฐ์˜ ๋‹ค๋ฃจ์–ด์ง€์ง€ ์•Š์•˜๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์ƒˆ๋กœ์šด ๋ฌธ์ œ ์˜์—ญ์„ ๊ฐœ์ฒ™ํ•œ๋‹ค๋Š” ์ ์—์„œ ํ•™์ˆ ์ ยท์‚ฐ์—…์  ๊ฐ€์น˜๋ฅผ ๊ฐ€์ง„๋‹ค. 2. ํ•ต์‹ฌ ๊ธฐ์—ฌ | ๊ตฌ๋ถ„ | ๋‚ด์šฉ | |

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