KOINEU Logo
Enhancing Cross Domain SAR Oil Spill Segmentation via Morphological Region Perturbation and Synthetic Label-to-SAR Generation

Enhancing Cross Domain SAR Oil Spill Segmentation via Morphological Region Perturbation and Synthetic Label-to-SAR Generation

๋งค๋ ฅ์ ์ธ ํ•œ๊ธ€ ์ œ๋ชฉ: ํ•ด์–‘ ์œ ์ถœ ์‚ฌ๊ณ  ๊ฐ์ง€: ๋„๋ฉ”์ธ ๊ฐ„ ์ผ๋ฐ˜ํ™”๋ฅผ ์œ„ํ•œ ํ•ฉ์„ฑ ๋ฐ์ดํ„ฐ ์ฆ๊ฐ• ๊ธฐ๋ฒ• ์ดˆ๋ก ์ „์ฒด ๋ฒˆ์—ญ ๋ฐ ์ •๋ฆฌ: ํ•ด์–‘ ์œ ์ถœ ์‚ฌ๊ณ ๋Š” ํ•ด์–‘ ์ƒํƒœ๊ณ„์™€ ๊ฒฝ์ œ์— ์‹ฌ๊ฐํ•œ ํ”ผํ•ด๋ฅผ ์ž…ํžˆ๋ฉฐ, ์ด๋ฅผ ์‹ ์†ํ•˜๊ณ  ์ •ํ™•ํ•˜๊ฒŒ ๊ฐ์ง€ํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ํ•ฉ์„ฑ ๊ฐœ๊ตฌ ๋ ˆ์ด๋”(SAR)๋Š” ์ด๋Ÿฌํ•œ ๋ชจ๋‹ˆํ„ฐ๋ง์—์„œ ํšจ๊ณผ์ ์ด๋‚˜, SAR ์ด๋ฏธ์ง€๋ฅผ ํ•ด์„ํ•˜๋Š” ๊ฒƒ์€ ์—ฌ์ „ํžˆ ๋„์ „ ๊ณผ์ œ๋‹ค. ๋”ฅ ๋Ÿฌ๋‹์€ ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ๋˜์—ˆ์œผ๋‚˜, ์ง€๋ฆฌ์  ๋„๋ฉ”์ธ ๊ฐ„์˜ ์ผ๋ฐ˜ํ™” ๋ฌธ์ œ๊ฐ€ ๋‚จ์•„ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋„์ „์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์‹œํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ํŽ˜๋ฃจ ํ•ด์•ˆ์—์„œ

Evolving CNN Architectures: From Custom Designs to Deep Residual Models for Diverse Image Classification and Detection Tasks

Evolving CNN Architectures: From Custom Designs to Deep Residual Models for Diverse Image Classification and Detection Tasks

: ๋ณธ ์—ฐ๊ตฌ๋Š” ๋‹ค์–‘ํ•œ ์ด๋ฏธ์ง€ ๋ฐ์ดํ„ฐ์…‹์— ๋Œ€ํ•œ ์ปจ๋ณผ๋ฃจ์…˜ ์‹ ๊ฒฝ๋ง(CNN)์˜ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ•˜๋ฉฐ, ํŠนํžˆ ๋งž์ถคํ˜• CNN ์•„ํ‚คํ…์ฒ˜์™€ ํ‘œ์ค€ ๋”ฅ ํ•™์Šต ๋ชจ๋ธ ๊ฐ„์˜ ์ฐจ์ด์ ์„ ๋ถ„์„ํ•œ๋‹ค. ๋‹ค์„ฏ ๊ฐ€์ง€ ๋ฐ์ดํ„ฐ์…‹(๋„๋กœ ์†์ƒ ๊ฐ์ง€, ๋ณดํ–‰๋กœ ์นจ๋ฒ” ๊ฐ์ง€, ๋ง๊ณ  ํ’ˆ์ข… ์ธ์‹, ์Œ€ ์ข…๋ฅ˜ ๋ถ„๋ฅ˜, ์˜คํ† ๋ฆญ์ƒค ๊ฐ์ง€)์„ ์‚ฌ์šฉํ•˜์—ฌ CNN์˜ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ•˜๊ณ , ํ‘œ์ค€ ๋”ฅ ํ•™์Šต ์•„ํ‚คํ…์ฒ˜์™€ ๋งž์ถคํ˜• CNN ์•„ํ‚คํ…์ฒ˜ ๊ฐ„์˜ ์ฐจ์ด์ ์„ ๋ถ„์„ํ•œ๋‹ค. 1. ๋ฐ์ดํ„ฐ์…‹ ๋ฐ ๋ฌธ์ œ ์ •์˜ ๋„๋กœ ์†์ƒ ๊ฐ์ง€ : ์ด์ง„ ๋ถ„๋ฅ˜ ์ž‘์—…์œผ๋กœ ๋„๋กœ์˜ ์†์ƒ ์—ฌ๋ถ€๋ฅผ ํŒ๋‹จํ•œ๋‹ค. ๋ณดํ–‰๋กœ ์นจ๋ฒ” ๊ฐ์ง€ : ๋ณดํ–‰๋กœ๊ฐ€ ์นจ๋ฒ”๋˜์—ˆ๋Š”์ง€๋ฅผ ํŒ๋ณ„ํ•˜๋Š” ์ด

Computer Science Computer Vision Detection Model
From Compound Figures to Composite Understanding: Developing a Multi-Modal LLM from Biomedical Literature with Medical Multiple-Image Benchmarking and Validation

From Compound Figures to Composite Understanding: Developing a Multi-Modal LLM from Biomedical Literature with Medical Multiple-Image Benchmarking and Validation

: ๋ณธ ๋…ผ๋ฌธ์€ ์˜๋ฃŒ ๋ถ„์•ผ์—์„œ ๋‹ค์ค‘ ๋ชจ๋‹ฌ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(MMLMs)์˜ ํ™œ์šฉ์„ ํ†ตํ•ด ๋ณตํ•ฉ ์ด๋ฏธ์ง€ ์ดํ•ด๋ฅผ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ๋‹ค. ํŠนํžˆ, ์ƒ๋ฌผ์˜ํ•™ ๋ฌธํ—Œ์—์„œ ๊ณต๊ฐœ์ ์œผ๋กœ ์ ‘๊ทผ ๊ฐ€๋Šฅํ•œ ๋ณตํ•ฉ ์ด๋ฏธ์ง€๋ฅผ ํ™œ์šฉํ•ด ์˜๋ฃŒ MLLM์ธ M3LLM์„ ๊ฐœ๋ฐœํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ๋‹ค์–‘ํ•œ ์˜๋ฃŒ ๋ถ„์•ผ์—์„œ ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ค€๋‹ค. 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋ณตํ•ฉ ์ด๋ฏธ์ง€ ์ดํ•ด์˜ ์ค‘์š”์„ฑ: ์ž„์ƒ ์›Œํฌํ”Œ๋กœ์šฐ์—์„œ๋Š” ์งˆ๋ณ‘ ์ง„ํ–‰ ์ถ”์ ๊ณผ ํฌ๊ด„์ ์ธ ์˜๋ฃŒ ์‚ฌ๋ก€ ์ดํ•ด๋ฅผ ์œ„ํ•ด ๋‹ค์ค‘ ์ด๋ฏธ์ง€ ๋ถ„์„์ด ํ•„์ˆ˜์ ์ด๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ์žฅ๊ธฐ ๋ชจ๋‹ˆํ„ฐ๋ง์€ ๋‹ค์–‘ํ•œ ์‹œ๊ฐ„์ ์—์„œ ์ˆ˜์ง‘๋œ ์—ฌ๋Ÿฌ ์ด๋ฏธ์ง€๋ฅผ ๋น„๊ตํ•˜์—ฌ ์งˆ

From Isolation to Entanglement: When Do Interpretability Methods Identify and Disentangle Known Concepts?

From Isolation to Entanglement: When Do Interpretability Methods Identify and Disentangle Known Concepts?

: ๋ณธ ๋…ผ๋ฌธ์€ ์‹ ๊ฒฝ๋ง ๋‚ด์—์„œ ์›์ธ ๋ณ€์ˆ˜์™€ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์ดํ•ดํ•˜๊ณ  ์ œ์–ดํ•˜๊ธฐ ์œ„ํ•œ ํ•ด์„ ๊ฐ€๋Šฅ์„ฑ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„๋ฅผ ํƒ์ƒ‰ํ•œ๋‹ค. ํŠนํžˆ, ํฌ์†Œ ์ž๊ฐ€ ์ธ์ฝ”๋”(SAE)์™€ ๊ฐ™์€ ํŠน์ง•ํ™” ๋ฐฉ๋ฒ•์ด ํ™œ์„ฑํ™” ๋ฒกํ„ฐ๋ฅผ ๋” ํฌ์†Œํ•œ ๊ณต๊ฐ„์œผ๋กœ ๋งคํ•‘ํ•˜์—ฌ ์ฐจ์›๊ณผ ๊ฐœ๋… ๊ฐ„์˜ ์ผ๋Œ€์ผ ๊ด€๊ณ„๋ฅผ ๊ฐ•ํ™”ํ•˜๋Š” ๋ฐฉ์‹์„ ๋ถ„์„ํ•˜๊ณ  ์žˆ๋‹ค. 1. ํ•ด์„ ๊ฐ€๋Šฅ์„ฑ๊ณผ ์›์ธ ๋ณ€์ˆ˜ ์ œ์–ด ํ•ด์„ ๊ฐ€๋Šฅ์„ฑ์€ ์‹ ๊ฒฝ๋ง์˜ ํ–‰๋™ ์ดํ•ด ๋ฐ ์ œ์–ด๋ฅผ ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ๋ถ„์•ผ์ด๋‹ค. ์ด๋Š” ๊ธฐ๋ณธ์ ์ธ ์›์ธ ๋ณ€์ˆ˜์™€ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ํŒŒ์•…ํ•˜๋ ค๋Š” ์‹œ๋„๋ฅผ ํฌํ•จํ•œ๋‹ค. ํฌ์†Œ ์ž๊ฐ€ ์ธ์ฝ”๋”(SAE)์™€ ๊ฐ™์€ ํŠน์ง•ํ™” ๋ฐฉ๋ฒ•์ด ํ™œ์„ฑํ™” ๋ฒกํ„ฐ๋ฅผ ๋” ํฌ์†Œํ•œ ๊ณต๊ฐ„์œผ๋กœ ๋งคํ•‘ํ•˜์—ฌ

GRC-Net: Gram Residual Co-attention Net for epilepsy prediction

GRC-Net: Gram Residual Co-attention Net for epilepsy prediction

: 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ๊ณผ ํ•„์š”์„ฑ ๋‡Œ์ „๋„(EEG) ์‹ ํ˜ธ๋Š” ๋‡Œ ํ™œ๋™ ๋ถ„์„, ๋‡Œ ๊ธฐ๋Šฅ ์ดํ•ด ๋ฐ ์ธ์ง€ ๋ฉ”์ปค๋‹ˆ์ฆ˜ ํƒ๊ตฌ์— ํ•„์ˆ˜์ ์ž…๋‹ˆ๋‹ค. ํŠนํžˆ ๋ฐœ์ž‘ ์˜ˆ์ธก์€ ์ด๋Ÿฌํ•œ ์‹ ํ˜ธ๋ฅผ ํ™œ์šฉํ•œ ์ค‘์š”ํ•œ ์‘์šฉ ๋ถ„์•ผ ์ค‘ ํ•˜๋‚˜๋กœ, ์ •ํ™•ํ•œ ์˜ˆ์ธก์„ ํ†ตํ•ด ํ™˜์ž์˜ ์‚ถ์˜ ์งˆ ํ–ฅ์ƒ๊ณผ ์น˜๋ฃŒ ํšจ๊ณผ ๊ฐœ์„ ์ด ๊ฐ€๋Šฅํ•ฉ๋‹ˆ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ 1์ฐจ์› ์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ EEG ์‹ ํ˜ธ์— ์ ์šฉํ•ด์™”์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด ๋ฐฉ๋ฒ•์€ ๋ฐ์ดํ„ฐ ๋‚ด ์ž ์žฌ์  ๊ด€๊ณ„์„ฑ์„ ์™„์ „ํžˆ ํฌ์ฐฉํ•˜์ง€ ๋ชปํ•˜๊ณ , ์œ ์˜๋ฏธํ•œ ์‹ ํ˜ธ ์ •๋ณด์™€ ๊ฐ€์šฐ์‹œ์•ˆ ๋…ธ์ด์ฆˆ๋ฅผ ๊ตฌ๋ณ„ํ•˜๋Š” ๋ฐ ์‹คํŒจํ•˜๋ฉฐ, ์‹œ๊ฐ„ ์˜์กด์„ฑ์ด ์†์‹ค๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ์Šต๋‹ˆ๋‹ค. 2. GRC

Gutenberg-Richter-like relations in physical systems

Gutenberg-Richter-like relations in physical systems

: ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‚จ๋ถ€ ์บ˜๋ฆฌํฌ๋‹ˆ์•„์™€ ์ผ๋ณธ์˜ ์ง€์ง„ ์นดํƒˆ๋กœ๊ทธ๋ฅผ ํ†ตํ•ด ์‹ค์ œ ์ง€์ง„๊ณผ ์œ ์‚ฌํ•œ ๋™์—ญํ•™์„ ๊ฐ€์ง„ ๋‹ค์–‘ํ•œ ์—๋„ˆ์ง€ ๋ถ„ํฌ๋ฅผ ๋ถ„์„ํ•˜์˜€์Šต๋‹ˆ๋‹ค. ๊ตฌํ…๋ฒ ๋ฅดํฌ ๋ฆฌ์ฒ˜ํ„ฐ(Gutenberg Richter) ๋ฒ•์น™์€ ์ง€์ง„ ๊ทœ๋ชจ์™€ ๋ฐœ์ƒ ๋นˆ๋„ ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ์„ค๋ช…ํ•˜๋Š” ์ค‘์š”ํ•œ ์ด๋ก ์œผ๋กœ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋ฒ•์น™๊ณผ ์œ ์‚ฌํ•œ ์—๋„ˆ์ง€ ๋ถ„ํฌ ํŒจํ„ด์„ ํƒ๊ตฌํ•˜์˜€์Šต๋‹ˆ๋‹ค. 1. ๊ตฌํ…๋ฒ ๋ฅดํฌ ๋ฆฌ์ฒ˜ํ„ฐ ๋ฒ•์น™๊ณผ ์—๋„ˆ์ง€ ๋ถ„ํฌ ๋ณธ ๋…ผ๋ฌธ์€ ์ง€์ง„์˜ ์—๋„ˆ์ง€ ๋ฐฉ์ถœ ํŒจํ„ด์ด ๊ตฌํ…๋ฒ ๋ฅดํฌ ๋ฆฌ์ฒ˜ํ„ฐ(Gutenberg Richter) ๋ฒ•์น™๊ณผ ์œ ์‚ฌํ•œ ํ˜•ํƒœ๋ฅผ ๋ ๋Š” ๊ฒƒ์„ ๋ฐœ๊ฒฌํ•˜์˜€์Šต๋‹ˆ๋‹ค. ๊ตฌํ…๋ฒ ๋ฅดํฌ ๋ฆฌ์ฒ˜ํ„ฐ ๋ฒ•์น™์€

System
HealthContradict: Evaluating Biomedical Knowledge Conflicts in Language Models

HealthContradict: Evaluating Biomedical Knowledge Conflicts in Language Models

) 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ฌธ์ œ ์ •์˜ ์–ธ์–ด ๋ชจ๋ธ์ด ํ•ฉ๋ฆฌ์ ์ด๋ฉด์„œ๋„ ๋น„์‚ฌ์‹ค์ ์ธ ์ฝ˜ํ…์ธ ๋ฅผ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ๋Š” ์ทจ์•ฝ์„ฑ์€ ํŠนํžˆ ์˜๋ฃŒ ์กฐ์–ธ ์ œ๊ณต ์‹œ ์‹ ๋ขฐ์„ฑ์„ ํฌ๊ฒŒ ์ €ํ•˜์‹œํ‚ค๋Š” ์š”์ธ์ด๋‹ค. ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ์ง€์‹ ๊ฐˆ๋“ฑ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ๋ฐ์ดํ„ฐ์…‹ HEALTHCONTRADICT ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. 2. HEALTHCONTRADICT ๋ฐ์ดํ„ฐ์…‹ ๊ตฌ์„ฑ ๋ฐ์ดํ„ฐ์…‹ ๊ตฌ์„ฑ : ๊ฑด๊ฐ• ๊ด€๋ จ ์งˆ๋ฌธ๊ณผ ๋‘ ๊ฐœ์˜ ์ƒ๋ฐ˜๋œ ์ž…์žฅ์„ ๊ฐ€์ง„ ๋ฌธ์„œ๋กœ ๊ตฌ์„ฑ๋˜๋ฉฐ, ๊ฐ ์ธ์Šคํ„ด์Šค๋Š” ๊ณผํ•™์  ์ฆ๊ฑฐ์— ๊ธฐ๋ฐ˜ํ•œ ์‚ฌ์‹ค์ ์ธ ๋‹ต๋ณ€์„ ํฌํ•จํ•œ๋‹ค. ์งˆ๋ฌธ ์ฃผ์ œ : ์งˆ๋ณ‘ ์น˜๋ฃŒ์™€ ๊ฑด๊ฐ• ๋ฌธ์ œ๋ฅผ ํฌํ•จํ•˜๋ฉฐ, 50๊ฐ€์ง€ ์ด์ƒ์˜ ์งˆ๋ณ‘

Model
Hybrid Stackelberg Game and Diffusion-based Auction for Two-tier Agentic AI Task Offloading in Internet of Agents

Hybrid Stackelberg Game and Diffusion-based Auction for Two-tier Agentic AI Task Offloading in Internet of Agents

: ๋ณธ ๋…ผ๋ฌธ์€ ์ธํ„ฐ๋„ท ์—์ด์ „ํŠธ(IoA) ํ™˜๊ฒฝ์—์„œ ๋ฌด์„  ์—์ด์ „ํŠธ(WA)์˜ ์ปดํ“จํŒ… ์ง‘์•ฝ์ ์ธ AI ์ž‘์—… ๋ถ„์‚ฐ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์‹œํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ํŠนํžˆ ์ž์› ์ œ์•ฝ์ด ์žˆ๋Š” WA๊ฐ€ ๊ทผ์ฒ˜ ์„œ๋ฒ„์— ์ž‘์—…์„ ๋ถ„์‚ฐ์‹œํ‚ค๋Š” ๋ฐ ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ์ธํ„ฐ๋„ท ์—์ด์ „ํŠธ(IoA)๋Š” ๋‹ค์–‘ํ•œ ์ง€๋Šฅํ˜• ์—์ด์ „ํŠธ์˜ ์›ํ™œํ•œ ์—ฐ๊ฒฐ๊ณผ ํ˜‘์—… ์ถ”๋ก ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋Š” ์ƒˆ๋กœ์šด ๋„คํŠธ์›Œํ‚น ํŒจ๋Ÿฌ๋‹ค์ž„์ด๋‹ค. ํŠนํžˆ, ์ž์œจ์ฃผํ–‰์ฐจ(AV)์™€ ๊ฐ™์€ ๋ฌด์„  ์—์ด์ „ํŠธ(WA)๋Š” ์ฃผ๋ณ€ ํ™˜๊ฒฝ ์ธ์‹ ๋ฐ ๋ฌผ๋ฆฌ์  ํ–‰๋™ ์‹คํ–‰์— ํ•„์š”ํ•œ ๊ณ„์‚ฐ ์ง‘์•ฝ์ ์ธ ์ž‘์—…์„ ์ˆ˜ํ–‰ํ•ด์•ผ ํ•˜์ง€

Interpretable Plant Leaf Disease Detection Using Attention-Enhanced CNN

Interpretable Plant Leaf Disease Detection Using Attention-Enhanced CNN

: ๋ณธ ๋…ผ๋ฌธ์€ ์‹๋ฌผ ์žŽ ์งˆ๋ณ‘ ๊ฐ์ง€ ๋ถ„์•ผ์—์„œ ์„ค๋ช… ๊ฐ€๋Šฅํ•œ ๋”ฅ๋Ÿฌ๋‹ ์ ‘๊ทผ๋ฒ•์˜ ์ค‘์š”์„ฑ์„ ๊ฐ•์กฐํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๋†์—… ์‚ฐ์—…์˜ ํšจ์œจ์„ฑ๊ณผ ์ •ํ™•์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ฃผ์š” ๋‚ด์šฉ์€ ๋‹ค์Œ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค: 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ : ๋†์—… ๋ถ„์•ผ์—์„œ ์‹๋ฌผ ์งˆ๋ณ‘์€ ๊ทน๋‹จ์ ์ธ ๊ธฐํ›„ ๋ณ€ํ™”, ๋ฏธ์ƒ๋ฌผ ๊ฐ์—ผ ๋“ฑ ๋‹ค์–‘ํ•œ ์š”์ธ์— ์˜ํ•ด ๋ฐœ์ƒํ•˜๋ฉฐ, ์ด๋กœ ์ธํ•ด ์ž‘๋ฌผ์˜ ์ƒ์‚ฐ์„ฑ์ด ํฌ๊ฒŒ ์ €ํ•˜๋ฉ๋‹ˆ๋‹ค. ์ „ํ†ต์ ์œผ๋กœ ์ธ๊ฐ„ ๊ฒ€์‚ฌ๋ฅผ ํ†ตํ•ด ์งˆ๋ณ‘์„ ํ™•์ธํ–ˆ์ง€๋งŒ, ์ด๋Š” ์‹œ๊ฐ„๊ณผ ๋…ธ๋™๋ ฅ์ด ๋งŽ์ด ์†Œ์š”๋˜๋ฉฐ ๋Œ€๊ทœ๋ชจ ๋†์žฅ์—์„œ๋Š” ํšจ๊ณผ์ ์ด์ง€ ์•Š์Šต๋‹ˆ๋‹ค. 2. ๊ธฐ์กด ์—ฐ๊ตฌ ๋ฐ ๋ฌธ์ œ์  : ์ตœ๊ทผ์—๋Š” CN

Detection
Lang3D-XL: Language Embedded 3D Gaussians for Large-scale Scenes

Lang3D-XL: Language Embedded 3D Gaussians for Large-scale Scenes

) Lang3D XL์€ ๋Œ€๊ทœ๋ชจ ์žฅ๋ฉด์˜ ํ…์ŠคํŠธ ๊ธฐ๋ฐ˜ ํƒ์ƒ‰์„ ์œ„ํ•œ ํ˜์‹ ์ ์ธ ์ ‘๊ทผ๋ฒ•์œผ๋กœ, 3D ๊ฐ€์šฐ์‹œ์•ˆ ํ‘œํ˜„์— ํ•™์Šต ๊ฐ€๋Šฅํ•œ ์˜๋ฏธ์  ๋ณ‘๋ชฉ ์ง€์ ์„ ๋„์ž…ํ•˜์—ฌ ๋Œ€๊ทœ๋ชจ ์ •์  ์ธํ„ฐ๋„ท ์žฅ๋ฉด์—์„œ์˜ ์ƒํ˜ธ์ž‘์šฉ์ ์ธ ๊ฐ€์ƒ ํƒ์ƒ‰์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๊ธฐ์กด ์ฆ๋ฅ˜ ๋ฐฉ๋ฒ•์ด ๋Œ€๊ทœ๋ชจ ์ธํ„ฐ๋„ท ๋ฐ์ดํ„ฐ๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ํ•™์Šตํ•˜๋Š” ๋ฐ ์–ด๋ ค์›€์„ ๊ฒช๊ณ  ์žˆ๋‹ค๋Š” ๋ฌธ์ œ์ ์„ ์ง€์ ํ•˜๊ณ , ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. 1. ์˜๋ฏธ์  ๋ณ‘๋ชฉ ํŠน์„ฑ์˜ ๋„์ž… Lang3D XL์€ ์ €์ฐจ์› ์˜๋ฏธ์  ๋ณ‘๋ชฉ ํŠน์„ฑ์„ 3D ๊ฐ€์šฐ์‹œ์•ˆ ํ‘œํ˜„์˜ ๊ธฐ๋ณธ ๊ตฌ์„ฑ ์š”์†Œ๋กœ ์ถ”๊ฐ€ํ•˜์—ฌ, ๋ Œ๋”๋ง ํ›„ ๋‹ค์ค‘ ํ•ด์ƒ๋„ ๋ฐ

Length-Aware Adversarial Training for Variable-Length Trajectories: Digital Twins for Mall Shopper Paths

Length-Aware Adversarial Training for Variable-Length Trajectories: Digital Twins for Mall Shopper Paths

๋ณธ ๋…ผ๋ฌธ์€ ๋ณ€๋™ ๊ธธ์ด์˜ ๊ถค์  ๋ฐ์ดํ„ฐ๋ฅผ ์ƒ์„ฑํ•˜๊ณ  ๋ถ„์„ํ•˜๋Š” ๋ฌธ์ œ์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ ์‡ผํ•‘๋ชฐ์—์„œ ์ˆ˜์ง‘๋œ ์ต๋ช… ๋ฐœ๊ฑธ์Œ ๊ถค์  ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋””์ง€ํ„ธ ํŠธ์œˆ์„ ๊ตฌ์ถ•ํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ๋‹ค์–‘ํ•œ ์‹œ๋‚˜๋ฆฌ์˜ค(์˜ˆ: ์•ต์ปค ๋งค์žฅ ํ์‡„, ํ…Œ๋„ŒํŠธ ํ˜ผํ•ฉ ๋ณ€๊ฒฝ ๋“ฑ)์— ๋Œ€ํ•œ ๋Œ€์•ˆ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๊ธฐ์กด์˜ ํ™•๋ฅ ์  ๋ฏธ๋‹ˆ ๋ฐฐ์น˜ ํ›ˆ๋ จ ๋ฐฉ๋ฒ•์ด ๊ธธ์ด ๋ถˆ๊ท ํ˜•์œผ๋กœ ์ธํ•ด ์ฐจ๋ณ„์ž/๋น„ํŒ์ž๊ฐ€ ๊ธธ์ด ๊ด€๋ จ ์‹ ํ˜ธ๋ฅผ ์ด์šฉํ•˜๊ฒŒ ๋˜์–ด ๊ถค์  ์œ ๋ž˜ ๋ณ€์ˆ˜(์˜ˆ: ์ „์ฒด ์ง€์† ์‹œ๊ฐ„, ๋‹จ๊ณ„๋‹น ํ‰๊ท  ์‹œ๊ฐ„) ๋ถ„ํฌ ์ผ์น˜์— ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค๋Š” ๋ฌธ์ œ์ ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์ œ์•ˆํ•˜๋Š” ๊ธธ์ด ์ธ์‹ ์ƒ˜

Computer Science Machine Learning
Log Anomaly Detection with Large Language Models via Knowledge-Enriched Fusion

Log Anomaly Detection with Large Language Models via Knowledge-Enriched Fusion

: 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ๊ณผ ๋ฌธ์ œ ์ธ์‹ ์‹œ์Šคํ…œ ๋กœ๊ทธ๋Š” ๋ถ„์‚ฐ ์‹œ์Šคํ…œ์˜ ์ง„๋‹จ, ์„ฑ๋Šฅ ์ €ํ•˜ ์‹๋ณ„ ๋ฐ ๋ณด์•ˆ ์‚ฌ๊ณ  ํƒ์ง€์— ์ค‘์š”ํ•œ ์ •๋ณด์›์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด ์ ‘๊ทผ ๋ฐฉ์‹์€ ๋กœ๊ทธ์˜ ๋ฐ˜๊ตฌ์กฐ์  ํŠน์„ฑ๊ณผ ๋‹ค์–‘ํ•œ ํ˜•์‹์œผ๋กœ ์ธํ•ด ์ •ํ™•ํ•œ ์ด์ƒ ํƒ์ง€๋ฅผ ์–ด๋ ต๊ฒŒ ํ•œ๋‹ค. ํŠนํžˆ, ํ…œํ”Œ๋ฆฟ ๊ธฐ๋ฐ˜ ๋˜๋Š” ์‹œํ€€์Šค ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•๋“ค์€ ์›์‹œ ๋กœ๊ทธ์—์„œ ์˜๋ฏธ ์ •๋ณด๋ฅผ ๋†“์น˜๊ฑฐ๋‚˜ ์• ๋งคํ•œ ํŒจํ„ด์„ ์ฒ˜๋ฆฌํ•˜๋Š” ๋ฐ ์–ด๋ ค์›€์ด ์žˆ๋‹ค. 2. EnrichLog ํ”„๋ ˆ์ž„์›Œํฌ์˜ ํ•ต์‹ฌ ๊ฐœ๋… EnrichLog๋Š” ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ(LLM)์„ ํ™œ์šฉํ•˜์—ฌ ์›์‹œ ๋กœ๊ทธ ํ•ญ๋ชฉ์— ์ฝ”ํผ์Šค ๋ฐ ์ƒ˜ํ”Œ ํŠน์ • ์ง€์‹์„ ์ถ”๊ฐ€ํ•จ์œผ๋กœ์จ ์ด์ƒ ํƒ์ง€๋ฅผ ์ˆ˜ํ–‰ํ•œ๋‹ค

Detection Model
MAR-FL: A Communication Efficient Peer-to-Peer Federated Learning System

MAR-FL: A Communication Efficient Peer-to-Peer Federated Learning System

: ๋ณธ ๋…ผ๋ฌธ์€ AI์™€ ์ฐจ์„ธ๋Œ€ ๋ฌด์„  ๋„คํŠธ์›Œํฌ์˜ ์œตํ•ฉ์ด ๋ถ„์‚ฐ ์ปดํ“จํŒ…๊ณผ ํ˜‘์—… ํ•™์Šต์— ๊ฐ€์ ธ์˜ค๋Š” ๋ณ€ํ™”๋ฅผ ํƒ๊ตฌํ•˜๋ฉฐ, ํŠนํžˆ ์ค‘์•™ํ™”๋œ ๋จธ์‹  ๋Ÿฌ๋‹(ML)์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ์—ฐ๋ฐฉ ํ•™์Šต(Federated Learning, FL)์„ ํ™œ์šฉํ•˜๋Š” ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•œ๋‹ค. ์ด๋Š” ๋ฐ์ดํ„ฐ ํ”„๋ผ์ด๋ฒ„์‹œ ๋ณดํ˜ธ์™€ ๋ฌด์„  ํ™˜๊ฒฝ์˜ ๋Œ€์—ญํญ ์ œ์•ฝ์„ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋‹ค. P2P(Peer to Peer) FL ์‹œ์Šคํ…œ์€ ์ค‘์•™ ์„œ๋ฒ„ ์—†์ด๋„ ํšจ์œจ์ ์ธ ํ•™์Šต์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋ฉฐ, ํŠนํžˆ MAR FL(Moshpit All Reduce Federated Learning)์ด๋ผ๋Š” ์ƒˆ๋กœ์šด P2P FL ์‹œ์Šคํ…œ์„

System Learning
Matching Ranks Over Probability Yields Truly Deep Safety Alignment

Matching Ranks Over Probability Yields Truly Deep Safety Alignment

: ๋ณธ ๋…ผ๋ฌธ์€ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM)์˜ ์•ˆ์ „์„ฑ ๊ฐ•ํ™”๋ฅผ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์•ˆํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, ์‚ฌ์ „ ์ฑ„์šฐ๊ธฐ ๊ณต๊ฒฉ๊ณผ ์ด๋ฅผ ์šฐํšŒํ•˜๋Š” ๋ฐฉ์‹์— ๋Œ€ํ•œ ์‹ฌ๋„ ์žˆ๋Š” ๋ถ„์„์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. 1. ์‚ฌ์ „ ์ฑ„์šฐ๊ธฐ ๊ณต๊ฒฉ ๋ฐ RAP ๊ณต๊ฒฉ ์‚ฌ์ „ ์ฑ„์šฐ๊ธฐ ๊ณต๊ฒฉ์€ ์‚ฌ์šฉ์ž๊ฐ€ LLM์— ์œ ํ•ดํ•œ ์š”์ฒญ์„ ํ•  ๋•Œ, ํ™•์ธ์„ ์œ„ํ•œ ๊ธ์ •์ ์ธ ํ…์ŠคํŠธ๋ฅผ ๋ฏธ๋ฆฌ ์ž…๋ ฅํ•˜์—ฌ ๋””์ฝ”๋”ฉ ๊ณผ์ •์„ ์‹œ์ž‘ํ•˜๋Š” ๋ฐฉ๋ฒ•์ž…๋‹ˆ๋‹ค. ์ด๋Š” LLM์ด ์•ˆ์ „ ์ •๋ ฌ๋˜์–ด ์žˆ์–ด๋„ ์œ ํ•ดํ•œ ๋‚ด์šฉ์„ ์ถ”์ถœํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค. RAP (Rank Assisted Prefilling) ๊ณต๊ฒฉ์€ ์‚ฌ์ „ ์ฑ„์šฐ๊ธฐ์™€ ๊ฐ ๋””์ฝ”๋”ฉ ๋‹จ๊ณ„์—์„œ ์ƒ์œ„

Med-CMR: A Fine-Grained Benchmark Integrating Visual Evidence and Clinical Logic for Medical Complex Multimodal Reasoning

Med-CMR: A Fine-Grained Benchmark Integrating Visual Evidence and Clinical Logic for Medical Complex Multimodal Reasoning

: 1. ๋ฉ”๋“œ CMR์˜ ํ•ต์‹ฌ ํŠน์ง• ๋ฐ ์ค‘์š”์„ฑ ๋ฉ”๋“œ CMR์€ ์‹œ๊ฐ์  ์ฆ๊ฑฐ์™€ ์ž„์ƒ ๋…ผ๋ฆฌ๋ฅผ ํ†ตํ•ฉํ•˜์—ฌ ๋ณต์žกํ•œ ์˜๋ฃŒ ์ถ”๋ก  ๋Šฅ๋ ฅ์„ ํ‰๊ฐ€ํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ฒค์น˜๋งˆํฌ์ž…๋‹ˆ๋‹ค. ์ด๋Š” ๊ธฐ์กด ๋ฒค์น˜๋งˆํฌ์—์„œ ๋‹ค๋ฃจ์ง€ ๋ชปํ–ˆ๋˜ ๋‹ค์–‘ํ•œ ๋ณต์žก์„ฑ์„ ์ฒด๊ณ„์ ์œผ๋กœ ๋ถ„ํ•ดํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ๋ชจ๋ธ์˜ ์„ฑ๋Šฅ์„ ์„ธ๋ฐ€ํ•˜๊ฒŒ ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ๋Š” ํ‹€์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ์ฒด๊ณ„์ ์ธ ๊ธฐ๋Šฅ ๋ถ„ํ•ด: ๋ฉ”๋“œ CMR์€ ์˜๋ฃŒ ์ถ”๋ก ์„ ์‹œ๊ฐ์  ์ดํ•ด์™€ ์ž„์ƒ ๋…ผ๋ฆฌ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ๊ฐ๊ฐ์„ ๋” ์„ธ๋ถ„ํ™”ํ•œ ํ•˜์œ„ ์ฐจ์›์œผ๋กœ ํ‰๊ฐ€ํ•ฉ๋‹ˆ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋ชจ๋ธ์˜ ํŠน์ • ๋Šฅ๋ ฅ์„ ์ •ํ™•ํ•˜๊ฒŒ ์ธก์ •ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๋„์ „์ ์ธ ์ž‘์—… ์„ค๊ณ„: ๋ฉ”๋“œ CMR์€ ์ž‘์€ ๋ฌผ์ฒด ๊ฐ์ง€

Mirror Mode in Fire Emblem: Beating Players at their own Game with Imitation and Reinforcement Learning

Mirror Mode in Fire Emblem: Beating Players at their own Game with Imitation and Reinforcement Learning

๋ณธ ์—ฐ๊ตฌ๋Š” ํ„ด์ œ ์ „๋žต ๊ฒŒ์ž„์—์„œ ์  AI๊ฐ€ ํ”Œ๋ ˆ์ด์–ด์˜ ์ „๋žต์„ ๋ชจ๋ฐฉํ•˜๋Š” '๊ฑฐ์šธ ๋ชจ๋“œ'๋ฅผ ๊ฐœ๋ฐœํ•˜๊ณ  ํ‰๊ฐ€ํ•œ ๋‚ด์šฉ์„ ๋‹ค๋ฃน๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๊ฒŒ์ž„ ๊ฒฝํ—˜์„ ํ˜์‹ ์ ์œผ๋กœ ๋ณ€ํ™”์‹œํ‚ค๊ณ , ํ”Œ๋ ˆ์ด์–ด ๋งŒ์กฑ๋„๋ฅผ ๋†’์ด๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. 1. ๊ฑฐ์šธ ๋ชจ๋“œ์˜ ๊ฐœ๋… ๋ฐ ๊ตฌํ˜„ ๊ฑฐ์šธ ๋ชจ๋“œ๋Š” ์  AI๊ฐ€ ํ”Œ๋ ˆ์ด์–ด์˜ ์ „๋žต์„ ํ•™์Šตํ•˜๊ณ  ์ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ–‰๋™ํ•˜๋Š” ๋ฐฉ์‹์ž…๋‹ˆ๋‹ค. ์ด๋Š” ๊ฒŒ์ž„ ๊ฒฝํ—˜์„ ๋”์šฑ ํฅ๋ฏธ๋กญ๊ณ  ๋„์ „์ ์œผ๋กœ ๋งŒ๋“ญ๋‹ˆ๋‹ค. ์—ฐ๊ตฌํŒ€์€ Unity ์—”์ง„์„ ์‚ฌ์šฉํ•˜์—ฌ Fire Emblem Heroes ์˜ ๊ฐ„์†Œํ™”๋œ ๋ฒ„์ „์—์„œ ํ‘œ์ค€ ๋ชจ๋“œ์™€ ๊ฑฐ์šธ ๋ชจ๋“œ๋ฅผ ๊ตฌํ˜„ํ–ˆ์Šต๋‹ˆ๋‹ค. 2. ํ•™์Šต ์•Œ๊ณ ๋ฆฌ

Learning
Modeling and Optimizing Performance Bottlenecks for Neuromorphic Accelerators

Modeling and Optimizing Performance Bottlenecks for Neuromorphic Accelerators

: ๋ณธ ๋…ผ๋ฌธ์€ ๋‰ด๋กœ๋ชจ๋ฅดํ”„ ๊ฐ€์†๊ธฐ์˜ ์„ฑ๋Šฅ ๋ณ‘๋ชฉ ํ˜„์ƒ์„ ๋ถ„์„ํ•˜๊ณ  ์ด๋ฅผ ํ†ตํ•ด ์„ฑ๋Šฅ์„ ๊ฐœ์„ ํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ๋‹ค. ๋‰ด๋กœ๋ชจ๋ฅดํ”„ ๊ฐ€์†๊ธฐ๋Š” ํฌ์†Œ ์‹ ๊ฒฝ๋ง ์ถ”๋ก ์— ํŠนํ™”๋œ ๋…ํŠนํ•œ ์„ค๊ณ„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฉฐ, ๊ฐ ๋…ผ๋ฆฌ ๋‰ด๋Ÿฐ์ด ์˜จ์นฉ์˜ ์ „์šฉ ๋ฌผ๋ฆฌ ๊ณ„์‚ฐ ๋‹จ์œ„์— ๋งคํ•‘๋˜๋Š” ๊ฒƒ์ด ํŠน์ง•์ด๋‹ค. ์ด๋Š” ๊ธฐ์กด์˜ ์‹œ๊ฐ„ ๋‹ค์ค‘ ์ ‘์† ๋ฐฉ์‹๊ณผ ๋Œ€์กฐ๋œ๋‹ค. ์ตœ๊ทผ ๊ตฌํ˜„์€ ์—ฃ์ง€ GPU์™€ ๋น„๊ตํ•ด ์„ฑ๋Šฅ ๋ฐ ์—๋„ˆ์ง€ ํšจ์œจ์„ฑ์ด ์šฐ์ˆ˜ํ•˜์ง€๋งŒ, ๋‰ด๋กœ๋ชจ๋ฅดํ”„ ๊ฐ€์†๊ธฐ์˜ ๋…ํŠนํ•œ ์•„ํ‚คํ…์ฒ˜ ํŠน์„ฑ์œผ๋กœ ์ธํ•ด ์„ฑ๋Šฅ ์—ญํ•™์— ๋Œ€ํ•œ ์ดํ•ด๊ฐ€ ๋ถ€์กฑํ•˜๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๊ฐ„๊ทน์„ ํ•ด์†Œํ•˜๊ธฐ ์œ„ํ•ด '๋ณ‘๋ชฉ ํ˜„์ƒ ๋ถ„์„' ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ

Model
More Consistent Accuracy PINN via Alternating Easy-Hard Training

More Consistent Accuracy PINN via Alternating Easy-Hard Training

๋ณธ ๋…ผ๋ฌธ์€ PINNs์˜ ์•ˆ์ •์„ฑ๊ณผ ์ •ํ™•์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ํ•™์Šต ์ „๋žต์„ ์ œ์•ˆํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, ํ•˜๋“œ ์šฐ์„ ์ˆœ์œ„์™€ ์‰ฌ์šด ์šฐ์„ ์ˆœ์œ„ ์ง€์ •์ด๋ผ๋Š” ๋‘ ๊ฐ€์ง€ ๊ธฐ์กด ์ ‘๊ทผ ๋ฐฉ์‹์„ ๊ฒฐํ•ฉํ•œ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ํ†ตํ•ด PINNs์˜ ์„ฑ๋Šฅ์„ ๊ฐœ์„ ํ•˜๋ ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. ํ•™์Šต ์ „๋žต ๋น„๊ต ํ•˜๋“œ ์šฐ์„ ์ˆœ์œ„: ์ด ๋ฐฉ๋ฒ•์€ ๋†’์€ ์†์‹ค ์ƒ˜ํ”Œ ํฌ์ธํŠธ์— ์ดˆ์ ์„ ๋งž์ถ”์–ด ๋ชจ๋ธ์ด PDE ๋„๋ฉ”์ธ์˜ ๋” ๋„์ „์ ์ธ ์˜์—ญ์— ์ง‘์ค‘ํ•˜๋„๋ก ์œ ๋„ํ•ฉ๋‹ˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ์ž”์—ฌ ๊ธฐ๋ฐ˜ Smote(RSmote)๋Š” ๋ถˆ๊ท ํ˜• ํ•™์Šต ์ „๋žต์„ ์œ„ํ•œ ์ง€์—ญ์  ์ ์‘ํ˜• ์ƒ˜ํ”Œ๋ง ๋ฐฉ๋ฒ•์œผ๋กœ, ๊ณ„์‚ฐ์ ์œผ๋กœ ํฐ ์ž”์—ฌ ์†์‹ค์ด ์žˆ๋Š” ์˜

Multi-granularity Interactive Attention Framework for Residual Hierarchical Pronunciation Assessment

Multi-granularity Interactive Attention Framework for Residual Hierarchical Pronunciation Assessment

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ๊ณผ ๋ฌธ์ œ์  ๋ณธ ๋…ผ๋ฌธ์€ ์ž๋™ ๋ฐœ์Œ ํ‰๊ฐ€(APA)์˜ ์ค‘์š”์„ฑ์„ ๊ฐ•์กฐํ•˜๋ฉฐ, ํŠนํžˆ ์ปดํ“จํ„ฐ ์ง€์› ๋ฐœ์Œ ํ›ˆ๋ จ ์‹œ์Šคํ…œ(CAPT)์—์„œ ํ•ต์‹ฌ์ ์ธ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•˜๊ณ  ์žˆ์Œ์„ ์„ค๋ช…ํ•ฉ๋‹ˆ๋‹ค. APA๋Š” ํ™”์ž์˜ ๋ฐœ์Œ ํ’ˆ์งˆ์„ ํ‰๊ฐ€ํ•˜๊ณ  ์„ธ๋ถ€์ ์ธ ํ”ผ๋“œ๋ฐฑ์„ ์ œ๊ณตํ•˜์—ฌ ์™ธ๊ตญ์–ด ํ•™์Šต์„ ์ง€์›ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•ฉ๋‹ˆ๋‹ค. ๊ธฐ์กด์˜ APA ์—ฐ๊ตฌ๋“ค์€ ์ฃผ๋กœ ๋‹จ์ผ ์ˆ˜์ค€์—์„œ ๋ฐœ์Œ์„ ํ‰๊ฐ€ํ•˜๊ฑฐ๋‚˜, ์—ฌ๋Ÿฌ ์ˆ˜์ค€ ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ์„ ๊ณ ๋ คํ•˜์ง€ ๋ชปํ•œ ์ฑ„ ์ผ๋ฐฉํ–ฅ ์˜์กด์„ฑ์„ ๋ชจ๋ธ๋งํ•˜์˜€์Šต๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์€ ์ž์—ฐ์Šค๋Ÿฌ์šด ๋ณต์žก์„ฑ๊ณผ ๋‹ค์ˆ˜์ค€ ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜์ง€ ๋ชปํ•˜๋Š” ํ•œ๊ณ„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. 2. HIA ํ”„๋ ˆ

Computer Science Framework NLP
Multi-view diffusion geometry using intertwined diffusion trajectories

Multi-view diffusion geometry using intertwined diffusion trajectories

: ์ด ๋…ผ๋ฌธ์€ ๋‹ค์ค‘ ๋ทฐ ๋ฐ์ดํ„ฐ๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ์ฒ˜๋ฆฌํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ํ”„๋ ˆ์ž„์›Œํฌ์ธ Multi View Diffusion Trajectories (MDTs)๋ฅผ ์ œ์‹œํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. MDTs๋Š” ์‹œ๊ฐ„ ๋ถˆ์ผ์น˜ ํ™•์‚ฐ ๊ณผ์ •์„ ํ†ตํ•ด ์—ฌ๋Ÿฌ ๋ทฐ์˜ ๋ฐ์ดํ„ฐ๋ฅผ ํƒ์ƒ‰ํ•˜๋Š” ์ผ๋ จ์˜ ์–ฝํžŒ ์—ฐ์‚ฐ์ž๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋ณต์žกํ•œ ์ƒํ˜ธ ์ž‘์šฉ์„ ํฌ์ฐฉํ•ฉ๋‹ˆ๋‹ค. 1. ๊ธฐ์กด ์—ฐ๊ตฌ์™€์˜ ์ฐจ๋ณ„ํ™” ๊ธฐ์กด ๋‹ค์ค‘ ๊ด€์  ํ™•์‚ฐ ๋ฐฉ๋ฒ•๋“ค์€ ์ฃผ๋กœ ๋‹จ์ผ ํ•ฉ์„ฑ ์—ฐ์‚ฐ์ž๋กœ ์—ฌ๋Ÿฌ ๋ทฐ๋ฅผ ์œตํ•ฉํ•˜๋Š” ๋ฐฉ์‹์„ ์ทจํ–ˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด ์ ‘๊ทผ๋ฒ•์€ ์œ ์—ฐ์„ฑ์ด ๋ถ€์กฑํ•˜๊ณ , ๊ฐ ๋ทฐ ๊ฐ„์˜ ์ƒํ˜ธ ์ž‘์šฉ ํŒจํ„ด์„ ์ •ํ™•ํ•˜๊ฒŒ ํฌ์ฐฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ์Šต๋‹ˆ

Network of Theseus (like the ship)

Network of Theseus (like the ship)

: 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์ด ๋…ผ๋ฌธ์€ ๊ธฐ๊ณ„ ํ•™์Šต์—์„œ ํ›ˆ๋ จ๊ณผ ์ถ”๋ก  ์‚ฌ์ด์˜ ์ „ํ†ต์ ์ธ ์—ฐ๊ฒฐ์„ฑ์— ๋„์ „ํ•œ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ, ํŠน์ • ์•„ํ‚คํ…์ฒ˜๊ฐ€ ํŠน์ • ๊ณผ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ํ›ˆ๋ จ๋˜๋ฉด, ๊ทธ์™€ ๊ฐ™์€ ์•„ํ‚คํ…์ฒ˜๋Š” ์ถ”๋ก  ์‹œ์—๋„ ์‚ฌ์šฉ๋˜์–ด์•ผ ํ•œ๋‹ค๋Š” ๊ฐ€์ •์ด ๋„๋ฆฌ ๋ฐ›์•„๋“ค์—ฌ์ ธ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด ๋…ผ๋ฌธ์€ ์ด๋Ÿฌํ•œ ์ „ํ†ต์ ์ธ ์—ฐ๊ฒฐ์„ฑ์„ ๋„์ „ํ•˜๋ฉฐ, ๋Œ€์‹  ํ›ˆ๋ จ๊ณผ ์ถ”๋ก ์„ ๋ถ„๋ฆฌํ•˜๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. 2. Network of Theseus (NoT)์˜ ๊ฐœ๋… Network of Theseus (NoT)๋Š” ํŠน์ • ๊ฐ€์ด๋“œ ์•„ํ‚คํ…์ฒ˜์—์„œ ์‹œ์ž‘ํ•˜์—ฌ ์ ์ง„์ ์œผ๋กœ ๋‹ค๋ฅธ ํƒ€๊ฒŸ ๋ชจ๋“ˆ๋กœ ๋ณ€ํ™˜ํ•˜๋Š” ๊ณผ

Network
Not All Transparency Is Equal: Source Presentation Effects on Attention, Interaction, and Persuasion in Conversational Search

Not All Transparency Is Equal: Source Presentation Effects on Attention, Interaction, and Persuasion in Conversational Search

๋ณธ ๋…ผ๋ฌธ์€ ๋Œ€ํ™”ํ˜• ๊ฒ€์ƒ‰ ์‹œ์Šคํ…œ์—์„œ ๋‹ค์–‘ํ•œ ์ถœ์ฒ˜ ํ‘œ์‹œ ๋ฐฉ์‹์ด ์‚ฌ์šฉ์ž์˜ ํ–‰๋™๊ณผ ํƒœ๋„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ฒด๊ณ„์ ์œผ๋กœ ๋ถ„์„ํ•œ ์—ฐ๊ตฌ์ด๋‹ค. ํŠนํžˆ, ๋„ค ๊ฐ€์ง€ ๋‹ค๋ฅธ ์ถœ์ฒ˜ ํ‘œ์‹œ ๋””์ž์ธ(์ ‘๊ธฐ ๊ฐ€๋Šฅํ•œ ๋ชฉ๋ก, ํ˜ธ๋ฒ„ ์นด๋“œ, ํ‘ธํ„ฐ ๋ชฉ๋ก, ์ •๋ ฌ๋œ ์‚ฌ์ด๋“œ๋ฐ”)์ด ์‚ฌ์šฉ์ž์˜ ์ฃผ์˜๋ ฅ, ์ƒํ˜ธ์ž‘์šฉ ๋ฐ ์„ค๋“์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์‹คํ—˜์ ์œผ๋กœ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์ถœ์ฒ˜ ํ‘œ์‹œ ๋ฐฉ์‹์ด ๋‹จ์ˆœํžˆ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๋Š” ๊ฒƒ ์ด์ƒ์œผ๋กœ ์‚ฌ์šฉ์ž ํ–‰๋™๊ณผ ํƒœ๋„ ๋ณ€ํ™”์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ์ž…์ฆํ•œ๋‹ค. 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  ๋Œ€ํ™”ํ˜• ๊ฒ€์ƒ‰ ์‹œ์Šคํ…œ์€ ์ ์ฐจ ์ถœ์ฒ˜ ์ธ์šฉ์„ ์ œ๊ณตํ•˜๊ณ  ์žˆ์ง€๋งŒ, ์ด๋Ÿฌํ•œ ์ถœ์ฒ˜ ํ‘œ์‹œ ๋ฐฉ์‹

ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems

ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems

: ๋ณธ ๋…ผ๋ฌธ์€ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM) ๊ธฐ๋ฐ˜ ํ”„๋กœ์•กํ‹ฐ๋ธŒ ์—์ด์ „ํŠธ ์‹œ์Šคํ…œ์ธ ProAgent ๋ฅผ ์†Œ๊ฐœํ•˜๋ฉฐ, ์ด๋Š” ์‚ฌ์šฉ์ž์˜ ๋ฌผ๋ฆฌ์  ๋ฐ ์ธ์ง€์  ๋ถ€๋‹ด์„ ์ค„์ด๋Š” ์„ ์ œ์ ์ธ ์ง€์› ์„œ๋น„์Šค๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ProAgent์˜ ํ•ต์‹ฌ์€ ๋‹ค์–‘ํ•œ ์„ผ์„œ ๋ฐ์ดํ„ฐ์™€ LLM ์ถ”๋ก ์„ ํ†ตํ•ฉํ•˜์—ฌ ์‚ฌ์šฉ์ž ์ฃผ๋ณ€ ๋งฅ๋ฝ์„ ์ง€์†์ ์œผ๋กœ ํŒŒ์•…ํ•˜๊ณ  ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ ์‹œ์— ๋„์›€์„ ์ œ๊ณตํ•˜๋Š” ๊ฒƒ์ด๋‹ค. 1. ํ”„๋กœ์•กํ‹ฐ๋ธŒ ์—์ด์ „ํŠธ์˜ ํ•„์š”์„ฑ ๊ธฐ์กด์˜ ๋ฐ˜์‘ํ˜• ์—์ด์ „ํŠธ๋Š” ๋ช…์‹œ์ ์ธ ์‚ฌ์šฉ์ž ์ง€์‹œ๊ฐ€ ์žˆ์–ด์•ผ ์„œ๋น„์Šค๋ฅผ ์‹œ์ž‘ํ•˜๋ฏ€๋กœ, ์‚ฌ์šฉ์ž์˜ ๋ฌผ๋ฆฌ์  ๋ฐ ์ธ์ง€์  ๋ถ€๋‹ด์ด ํฌ๋‹ค. ์ด์— ๋ฐ˜ํ•ด ํ”„๋กœ์•กํ‹ฐ๋ธŒ ์—์ด์ „ํŠธ๋Š” ํ™˜๊ฒฝ์„ ์ž๋™์œผ

System
The Discovery Gap: How Product Hunt Startups Vanish in LLM Organic Discovery Queries

The Discovery Gap: How Product Hunt Startups Vanish in LLM Organic Discovery Queries

: ์ด ์—ฐ๊ตฌ๋Š” ChatGPT์™€ Perplexity ๊ฐ™์€ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM)์—์„œ Product Hunt ์Šคํƒ€ํŠธ์—…์˜ ๊ฐ€์‹œ์„ฑ์„ ๋ถ„์„ํ•˜๋Š” ๋ฐ ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, LLM ๊ฒ€์ƒ‰ ๊ฒฐ๊ณผ์— ์Šคํƒ€ํŠธ์—…์ด ์–ผ๋งˆ๋‚˜ ์ž˜ ๋…ธ์ถœ๋˜๋Š”์ง€ ํƒ๊ตฌํ•˜๊ณ ์ž ํ•ฉ๋‹ˆ๋‹ค. 1. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๋ก  ์ƒ˜ํ”Œ ์„ ํƒ : 112๊ฐœ์˜ Product Hunt ์Šคํƒ€ํŠธ์—…์„ ๋ฌด์ž‘์œ„๋กœ ์„ ์ •ํ•˜์—ฌ ์ด 2,240๊ฐœ์˜ ์ฟผ๋ฆฌ๋ฅผ ์‹คํ–‰ํ–ˆ์Šต๋‹ˆ๋‹ค. LLM ํ…Œ์ŠคํŠธ : ChatGPT์™€ Perplexity ๋‘ ๊ฐ€์ง€ LLM์— ๋Œ€ํ•ด ์ง์ ‘์ ์ธ ์ฟผ๋ฆฌ์™€ ๋ฐœ๊ฒฌ์„ฑ ์Šคํƒ€์ผ์˜ ์งˆ๋ฌธ์„ ์‚ฌ์šฉํ•˜์—ฌ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ–ˆ์Šต๋‹ˆ๋‹ค. 2. ์—ฐ๊ตฌ ๊ฒฐ

The Effect of Document Summarization on LLM-Based Relevance Judgments

The Effect of Document Summarization on LLM-Based Relevance Judgments

๋งค๋ ฅ์ ์ธ ํ•œ๊ธ€ ์ œ๋ชฉ: ์š”์•ฝ ๊ธฐ๋ฐ˜ ํŒ๋‹จ์ด LLM ๊ธฐ๋ฐ˜ ๊ด€๋ จ์„ฑ ํ‰๊ฐ€์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ: TREC ๋ฐ์ดํ„ฐ์…‹์„ ์ค‘์‹ฌ์œผ๋กœ ์ดˆ๋ก ์ „์ฒด ๋ฒˆ์—ญ ๋ฐ ์ •๋ฆฌ: ๋ณธ ๋…ผ๋ฌธ์€ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM)์˜ ๋ฐœ์ „๊ณผ ํ•จ๊ป˜ ์ž๋™ํ™”๋œ ๊ด€๋ จ์„ฑ ํŒ๋‹จ์ด ์ •๋ณด ๊ฒ€์ƒ‰(IR)์—์„œ ์ธ๊ฐ„ ์ฃผ์„๊ฐ€๋ฅผ ๋Œ€์ฒดํ•  ์ˆ˜ ์žˆ๋Š” ์ž ์žฌ๋ ฅ์„ ์กฐ์‚ฌํ•œ๋‹ค. ํŠนํžˆ, LLM ๊ธฐ๋ฐ˜ ํŒ๋‹จ์ด ์ „์ฒด ๋ฌธ์„œ์™€ ์š”์•ฝ์„ ์‚ฌ์šฉํ•œ ๊ฒฝ์šฐ์— ์–ด๋–ป๊ฒŒ ์ž‘์šฉํ•˜๋Š”์ง€ ๋น„๊ตํ•˜๊ณ  ๋ถ„์„ํ•œ๋‹ค. ์„ธ ๊ฐ€์ง€ TREC ๋ฐ์ดํ„ฐ์…‹ (DL 19, DL 20, RAG 24)์„ ์ด์šฉํ•˜์—ฌ, ์ธ๊ฐ„ ์ฃผ์„๊ฐ€์˜ ๋ ˆ์ด๋ธ”๊ณผ LLM ๊ธฐ๋ฐ˜ ํŒ๋‹จ ๊ฐ„์˜ ์ผ์น˜๋„๋ฅผ ํ‰๊ฐ€ํ•˜๋ฉฐ, ์‹œ์Šคํ…œ ํšจ๊ณผ

The Initialization Determines Whether In-Context Learning Is Gradient Descent

The Initialization Determines Whether In-Context Learning Is Gradient Descent

: ์ด ๋…ผ๋ฌธ์€ ์ธ์ปจํ…์ŠคํŠธ ํ•™์Šต(ICL)์—์„œ ์ดˆ๊ธฐ ์ถ”์ธก์˜ ์ค‘์š”์„ฑ์„ ๊ฐ•์กฐํ•˜๋ฉฐ, ํŠนํžˆ ๊ทธ๋ž˜๋””์–ธํŠธ ํ•˜๊ฐ•(GD)๊ณผ ICL ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ(LLM)์˜ ICL ๋Šฅ๋ ฅ์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ , ์ด๋ฅผ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด ์„ ํ˜• ์ž๊ธฐ ์ฃผ์˜(LSA) ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ์ด๋ก ์  ๋ฐ ์‹คํ—˜์ ์ธ ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ทจํ•ฉ๋‹ˆ๋‹ค. 1. ์ด๋ก ์  ๋ฐฐ๊ฒฝ ๋…ผ๋ฌธ์€ ๋จผ์ € LSA๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ICL ๋ฌธ์ œ๋ฅผ ์ •์˜ํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ์ดˆ๊ธฐ ์ถ”์ธก(yq LSA)์ด ์–ด๋–ป๊ฒŒ GD์™€ ๊ด€๋ จ๋˜์–ด ์žˆ๋Š”์ง€๋ฅผ ๋ถ„์„ํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, ๋…ผ๋ฌธ์€ ์‚ฌ์ „ ํ‰๊ท (w )๊ฐ€ ์˜(0)์ธ์ง€ ์•„๋‹Œ์ง€์— ๋”ฐ

Learning
The Loss Landscape of Powder X-Ray Diffraction-Based Structure Optimization Is Too Rough for Gradient Descent

The Loss Landscape of Powder X-Ray Diffraction-Based Structure Optimization Is Too Rough for Gradient Descent

: ์ด ๋…ผ๋ฌธ์€ ๋ถ„๋ง X ์„  ํšŒ์ ˆ(XRD) ํŒจํ„ด์„ ํ†ตํ•ด ๊ฒฐ์ •์ฒด์˜ ์›์ž ๊ตฌ์กฐ๋ฅผ ๋ณต์›ํ•˜๋Š” ๋ฌธ์ œ์— ๋Œ€ํ•ด ์‹ฌ๋„ ์žˆ๊ฒŒ ๋‹ค๋ฃน๋‹ˆ๋‹ค. ํŠนํžˆ, ๊ฒฝ์‚ฌ ํ•˜๊ฐ•๋ฒ•(Gradient Descent, GD)์„ ํ™œ์šฉํ•œ ์ตœ์ ํ™” ์ ‘๊ทผ ๋ฐฉ์‹์ด ๊ฒฉ์ž ์™œ๊ณก ๋ฐ ์›์ž ์œ„์น˜์˜ ์ž‘์€ ๋ณ€ํ™”์— ๋ฏผ๊ฐํ•˜์—ฌ ๊ตญ๋ถ€ ์ตœ์ €์ ์— ๊ฐ‡ํžˆ๋Š” ๋ฌธ์ œ๋ฅผ ๊ฒฝํ—˜ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค๋‹ˆ๋‹ค. 1. XRD ํŒจํ„ด๊ณผ ๊ตฌ์กฐ ๋ณต์›์˜ ์–ด๋ ค์›€ XRD๋Š” ๊ฒฐ์ • ๊ณ ์ฒด์˜ ์‹๋ณ„ ๋ฐ ํŠน์„ฑํ™”์— ๋„๋ฆฌ ํ™œ์šฉ๋˜์ง€๋งŒ, ์œ„์ƒ ์ •๋ณด ์†์‹ค๋กœ ์ธํ•ด ์™„์ „ํ•œ 3์ฐจ์› ๊ตฌ์กฐ ๋ณต์›์ด ์–ด๋ ต์Šต๋‹ˆ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด, ๊ด€์ฐฐ๋œ XRD ์ŠคํŽ™ํŠธ๋Ÿผ์„ ์ฐธ์กฐ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์™€

The Machine Learning Canvas: Empirical Findings on Why Strategy Matters More Than AI Code Generation

The Machine Learning Canvas: Empirical Findings on Why Strategy Matters More Than AI Code Generation

๋ณธ ๋…ผ๋ฌธ์€ AI ๋ฐ ๋จธ์‹ ๋Ÿฌ๋‹(ML) ํ”„๋กœ์ ํŠธ์˜ ์„ฑ๊ณต์— ๋Œ€ํ•œ ํฌ๊ด„์ ์ธ ๋ถ„์„์„ ์ œ๊ณตํ•œ๋‹ค. ํŠนํžˆ, ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ(LLM)์ด ๊ฐœ๋ฐœ์ž์˜ ์ƒ์‚ฐ์„ฑ์„ ๋†’์ผ ์ˆ˜ ์žˆ์ง€๋งŒ, ์ด๋กœ ์ธํ•œ ๋ฏธ์‹œ์  ํšจ์œจ์„ฑ ํ–ฅ์ƒ์ด ๊ฑฐ์‹œ์  ํ”„๋กœ์ ํŠธ ์„ฑ๊ณต์œผ๋กœ ์ง์ ‘ ์—ฐ๊ฒฐ๋˜์ง€ ์•Š๋Š”๋‹ค๋Š” ์ ์„ ๊ฐ•์กฐํ•œ๋‹ค. LLM์˜ ์˜ํ–ฅ LLM์€ ์ฝ”๋“œ ์ƒ์„ฑ๊ณผ ์ผ์ƒ์ ์ธ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ์ž‘์—…์˜ ์ธ์ง€ ๋ถ€๋‹ด ๊ฐ์†Œ์— ํšจ๊ณผ์ ์ด์ง€๋งŒ, ๋ณต์žกํ•œ ์•„ํ‚คํ…์ฒ˜ ๊ฒฐ์ •์—๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, Copilot์„ ์‚ฌ์šฉํ•˜๋Š” ๊ฐœ๋ฐœ์ž๋Š” ์ž‘์—… ์™„๋ฃŒ ์†๋„๊ฐ€ ์ตœ๋Œ€ 55% ๋น ๋ฅด๊ณ  ์ธ์ง€ ๋ถ€๋‹ด์ด ์ค„์–ด๋“ ๋‹ค

Learning
Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm

Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm

: ๋ณธ ๋…ผ๋ฌธ์€ 6์„ธ๋Œ€(6G) ๋ฌด์„  ํ†ต์‹ ๋ง์˜ ํ•ต์‹ฌ ๊ธฐ์ˆ ์ธ ์„ธ๋งคํ‹ฑ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜(SemCom)๊ณผ ์—์ด์ „ํŠธ ์ง€๋Šฅ์„ ํ†ตํ•ฉํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด ๊นŠ๊ฒŒ ํƒ๊ตฌํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” SemCom๊ณผ ์—์ด์ „ํŠธ ์ง€๋Šฅ์„ ์–ด๋–ป๊ฒŒ ๊ณต๋™ ์„ค๊ณ„ํ•˜๊ณ  ๋ฐฐํฌํ•  ์ˆ˜ ์žˆ๋Š”์ง€ ์ œ์‹œํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ํ์‡„ ๋ฃจํ”„ ๋ฐ ์ž๊ธฐ ์ง„ํ™”ํ˜• ๋„ค์ดํ‹ฐ๋ธŒ AI RAN์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•ฉ๋‹ˆ๋‹ค. 1. 6G์˜ ๋ชฉํ‘œ์™€ SemCom์˜ ์ค‘์š”์„ฑ 6G๋Š” ๋‹จ์ˆœํžˆ ๋น ๋ฅธ ๋ฐ์ดํ„ฐ ์†๋„์™€ ๋‚ฎ์€ ์ง€์—ฐ ์‹œ๊ฐ„์„ ๋„˜์–ด, ํ™€๋กœ๊ทธ๋ž˜ํ”ฝ ํ™•์žฅ ํ˜„์‹ค(XR), ์‚ฐ์—… ๋””์ง€ํ„ธ ํŠธ์œˆ, ๋Œ€๊ทœ๋ชจ ์‚ฌ๋ฌผ ์ธํ„ฐ๋„ท(IoT) ๋ฐ ์ž์œจ ์ฃผํ–‰ ์ฐจ

Network
Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings

Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings

: ์ด ๋…ผ๋ฌธ์€ ์ธก์ •๋˜์ง€ ์•Š์€ ์œ ์—ญ์—์„œ์˜ ์˜ˆ์ธก ์ •ํ™•๋„๋ฅผ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋ฐ ์žˆ์–ด ์ง€๊ตฌ ๊ธฐ์ดˆ ๋ชจ๋ธ(FMs)์˜ ์ž ์žฌ๋ ฅ์„ ํƒ๊ตฌํ•ฉ๋‹ˆ๋‹ค. FMs๋Š” ๋Œ€๊ทœ๋ชจ ๋น„์ง€๋„ ์ง€๊ตฌ ์‹œ์Šคํ…œ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์ „ ํ›ˆ๋ จํ•˜์—ฌ ๋ณต์žกํ•œ ํ™˜๊ฒฝ ์‹ ํ˜ธ๋ฅผ ๋ฒ”์šฉ ์ž„๋ฒ ๋”ฉ์œผ๋กœ ์ถ”์ถœํ•˜๋Š” ์ž๊ธฐ ์ง€๋„ ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ž„๋ฒ ๋”ฉ์€ ๊ฐ ์œ„์น˜๋ฅผ ํ’๋ถ€ํ•˜๊ณ  ์ „์ด ๊ฐ€๋Šฅํ•œ ํ‘œํ˜„์œผ๋กœ ์„ค๋ช…ํ•˜๋ฉฐ, ์ด๋Š” ์ „ํ†ต์ ์ธ ์ˆ˜์ž‘์—… ์†์„ฑ๋ณด๋‹ค ๋” ๋‚˜์€ ์œ ์‚ฌ์„ฑ ์ •์˜๋ฅผ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ „ํ†ต์ ์ธ ์œ ์‚ฌ์„ฑ ๋ถ„์„์€ ์œ ์—ญ ์„ค๋ช…์ž์˜ ๊ฑฐ๋ฆฌ์™€ ์œ ์‚ฌ์„ฑ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜์ง€๋งŒ, ๊ณต๊ฐ„์  ๊ทผ์ ‘์„ฑ๊ณผ ํฌ๋ฐ•ํ•œ ์†์„ฑ์€ ํ•œ๊ณ„๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, ์ˆ˜๋ฌธํ•™์  ๊ณผ์ •์€ ๊ณต

Model
Waveform-Based Probabilistic Seismic Hazard Analysis Using Ground-Motion Generative Models

Waveform-Based Probabilistic Seismic Hazard Analysis Using Ground-Motion Generative Models

: ๋ณธ ๋…ผ๋ฌธ์€ ํ™•๋ฅ ์  ์ง€์ง„์œ„ํ—˜ ๋ถ„์„(PSHA)์˜ ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•˜๋ฉฐ, ํŠนํžˆ ํŒŒํ˜• ๊ธฐ๋ฐ˜์˜ ๋ฐฉ๋ฒ•๋ก ์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ์ „ํ†ต์ ์ธ PSHA๋Š” ์ฃผ๋กœ ํŠน์ • ๊ฐ•๋„ ์ˆ˜์ค€์„ ์ดˆ๊ณผํ•  ํ™•๋ฅ ๋กœ ์ง€์ง„ ์œ„ํ—˜์„ ํ‰๊ฐ€ํ•˜์ง€๋งŒ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ง€๋ฉด ์šด๋™ ํŒŒํ˜•์„ ์ง์ ‘ ๋ชจ๋ธ๋งํ•˜์—ฌ ์ด ์œ„ํ—˜์„ ํ‘œํ˜„ํ•˜๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. 1. ํŒŒํ˜• ๊ธฐ๋ฐ˜ PSHA์˜ ๊ฐœ๋… ๋ฐ ๋ฐฉ๋ฒ•๋ก  ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์‹œ๋œ ํŒŒํ˜• ๊ธฐ๋ฐ˜ PSHA๋Š” ์ง€์ง„ ์›์ฒœ, ๊ฒฝ๋กœ, ํ˜„์žฅ ์ •๋ณด๋ฅผ ์กฐ๊ฑด์œผ๋กœ ํ•˜๋Š” ์ง€๋ฉด ์šด๋™ ํŒŒํ˜•์˜ ํ™•๋ฅ  ๋ถ„ํฌ๋ฅผ ๋ชจ๋ธ๋งํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์„ธ ๊ฐ€์ง€ ์ง€๋ฉด ์šด๋™ ์ƒ์„ฑ ๋ชจ๋ธ(S GMGM, CW G

Analysis Model
ํ™•๋ฅ ์  ํŠธ๋ฆฌ ํƒ์ƒ‰์œผ๋กœ ๊ฐ•ํ™”๋œ ํ™•์‚ฐ ์–ธ์–ด ๋ชจ๋ธ ์ถ”๋ก 

ํ™•๋ฅ ์  ํŠธ๋ฆฌ ํƒ์ƒ‰์œผ๋กœ ๊ฐ•ํ™”๋œ ํ™•์‚ฐ ์–ธ์–ด ๋ชจ๋ธ ์ถ”๋ก 

: ๋ณธ ๋…ผ๋ฌธ์€ ํ™•์‚ฐ ์–ธ์–ด ๋ชจ๋ธ(DLMs)์˜ ์ถ”๋ก  ๊ณผ์ •์„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์‹œํ•œ๋‹ค. DLMs๋Š” ์ด์‚ฐ ์‹œํ€€์Šค ์ƒ์„ฑ์—์„œ ์ž์œจ์ (Autoregressive, AR) ๋ชจ๋ธ์— ๋Œ€ํ•ญํ•˜๋Š” ๊ฐ•๋ ฅํ•œ ๋„์ „์ž๋กœ ๋ถ€์ƒํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ชจ๋ธ์€ ํ† ํฐ์„ ๋…๋ฆฝ์ ์œผ๋กœ ๋งˆ์Šคํ‚นํ•˜์—ฌ ์˜ค์—ผ ๊ณผ์ •์„ ์—ญ์ „์‹œํ‚ค๋Š” ๋ฐฉ์‹์œผ๋กœ ์ž‘๋™ํ•˜๋ฉฐ, ์ด๋Š” ๋ณ‘๋ ฌ ์ •์ œ์™€ ๊ธ€๋กœ๋ฒŒ ์ผ๊ด€์„ฑ ํ–ฅ์ƒ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•œ๋‹ค. DLMs์˜ ์ถ”๋ก  ๊ณผ์ • DLMs์˜ ์ถ”๋ก  ๊ณผ์ •์€ ๋ณธ์งˆ์ ์œผ๋กœ ๊ฒ€์ƒ‰ ๋ฌธ์ œ๋กœ ํ‘œํ˜„๋  ์ˆ˜ ์žˆ๋‹ค. ์˜ค์—ผ๋œ ์‹œํ€€์Šค๋กœ๋ถ€ํ„ฐ ์‹œ์ž‘ํ•˜์—ฌ, ๋ชจ๋ธ์€ ์–ด๋–ค ์œ„์น˜๋ฅผ ํ•ด์ œํ•˜๊ณ  ์–ด๋–ค ํ† ํฐ์„ ํ• ๋‹นํ• ์ง€ ๋ฐ˜๋ณต์ 

A Comprehensive Framework for Automated Quality Control in the Automotive Industry

A Comprehensive Framework for Automated Quality Control in the Automotive Industry

: ์ž๋™์ฐจ ์‚ฐ์—…์—์„œ ํ’ˆ์งˆ ๊ด€๋ฆฌ๋Š” ์ œํ’ˆ์˜ ์„ฑ๋Šฅ๊ณผ ์•ˆ์ „์„ฑ์„ ๋ณด์žฅํ•˜๊ธฐ ์œ„ํ•ด ํ•„์ˆ˜์ ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ž๋™์ฐจ ์ œ์กฐ์—…์˜ ํ’ˆ์งˆ ๊ด€๋ฆฌ ๊ณผ์ •์„ ์ž๋™ํ™”ํ•˜๊ณ  ํ˜์‹ ํ•˜๋Š” ๋ฐ ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ์ „ํ†ต์ ์œผ๋กœ ์‹œ๊ฐ์  ๊ฒ€์‚ฌ์— ์˜์กดํ•ด ์™”๋˜ ์ด ๋ถ„์•ผ์— ๋กœ๋ด‡ ์†”๋ฃจ์…˜์„ ๋„์ž…ํ•จ์œผ๋กœ์จ, ์ƒ์‚ฐ ํšจ์œจ์„ฑ๊ณผ ์ •ํ™•๋„๋ฅผ ํ–ฅ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐํšŒ๊ฐ€ ์—ด๋ฆฐ๋‹ค. ์—ฐ๊ตฌ์ž๋“ค์€ ์•Œ๋ฃจ๋ฏธ๋Š„ HPDC ๋ถ€ํ’ˆ์˜ ํ‘œ๋ฉด๊ณผ ๋‚˜์‚ฌ์‚ฐ ๊ฒฐํ•จ์„ ์ž๋™์œผ๋กœ ๊ฒ€์‚ฌํ•˜๋Š” ์‹œ์Šคํ…œ์„ ๊ฐœ๋ฐœํ–ˆ๋‹ค. ์ด ์‹œ์Šคํ…œ์€ ์„ธ ๊ฐ€์ง€ ์ฃผ์š” ํ•˜๋“œ์›จ์–ด ์š”์†Œ, ์ฆ‰ ๊ณ ํ•ด์ƒ๋„ ์นด๋ฉ”๋ผ ๊ธฐ๋ฐ˜ ๋น„์ „ ์‹œ์Šคํ…œ, ์ „๋ฌธ ์กฐ๋ช… ์žฅ์น˜, ํ˜‘๋™ ๋กœ๋ด‡์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ํ†ตํ•ฉ์€

Framework
Do Large Language Models Walk Their Talk? Measuring the Gap Between Implicit Associations, Self-Report, and Behavioral Altruism

Do Large Language Models Walk Their Talk? Measuring the Gap Between Implicit Associations, Self-Report, and Behavioral Altruism

LLM์ด ์‚ฌํšŒ์— ํ†ตํ•ฉ๋จ์— ๋”ฐ๋ผ, ๊ทธ๋“ค์˜ ๊ฐ€์น˜์™€ ํ–‰๋™ ์„ฑํ–ฅ์„ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์€ ๋งค์šฐ ์ค‘์š”ํ•ฉ๋‹ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ LLM์˜ ์ดํƒ€์‹ฌ์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ , ์„ธ ๊ฐ€์ง€ ์ธก๋ฉด(์•”๋ฌต์ , ํ–‰๋™์ , ๋ช…์‹œ์ )์—์„œ ์ด๋ฅผ ์ธก์ •ํ•ฉ๋‹ˆ๋‹ค. ์—ฐ๊ตฌ์ง„์€ 24๊ฐœ์˜ ์ตœ์ฒจ๋‹จ LLM์„ ๋Œ€์ƒ์œผ๋กœ ์•”๋ฌต์  ์—ฐ๊ด€์„ฑ ๊ฒ€์‚ฌ(IAT), ๊ฐ•์ œ ์ด์ง„ ์„ ํƒ ์ž‘์—…, ์ž๊ธฐ ํ‰๊ฐ€ ์ฒ™๋„๋ฅผ ํ†ตํ•ด ํ…Œ์ŠคํŠธ๋ฅผ ์ง„ํ–‰ํ–ˆ์Šต๋‹ˆ๋‹ค. ์•”๋ฌต์  ์—ฐ๊ด€์„ฑ๊ณผ IAT ์•”๋ฌต์  ์—ฐ๊ด€์„ฑ์€ ๋ชจ๋ธ์ด ๊ธ์ •์ ์ธ ๊ฐœ๋…์„ ํƒ€์ธ์˜ ์ด์ต๊ณผ ์ž์‹ ์˜ ์ด์ต ์ค‘ ์–ด๋А ๊ฒƒ๊ณผ ์—ฐ๊ด€ ์ง“๋Š”์ง€๋ฅผ ์ธก์ •ํ•ฉ๋‹ˆ๋‹ค. ์—ฐ๊ตฌ์ง„์€ Self Other IAT(SOI IAT)๋ฅผ LLM์— ๋งž

Model
Engineering Attack Vectors and Detecting Anomalies in Additive Manufacturing

Engineering Attack Vectors and Detecting Anomalies in Additive Manufacturing

: ๋ณธ ์—ฐ๊ตฌ๋Š” 3D ํ”„๋ฆฐํŒ… ์‹œ์Šคํ…œ์˜ ๋ณด์•ˆ ์ทจ์•ฝ์ ์„ ์‹ฌ์ธต์ ์œผ๋กœ ๋ถ„์„ํ•˜๊ณ , ํŠนํžˆ G ์ฝ”๋“œ ์กฐ์ž‘๊ณผ ๊ด€๋ จ๋œ ๊ณต๊ฒฉ ๋ฒกํ„ฐ๋ฅผ ํƒ๊ตฌํ•œ๋‹ค. Creality K1 Max์™€ Ender 3 ํ”„๋ฆฐํ„ฐ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๋‹ค์–‘ํ•œ ๊ณต๊ฒฉ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๊ตฌํ˜„ํ•˜๊ณ , ์ด๋ฅผ ๊ฐ์ง€ํ•˜๊ธฐ ์œ„ํ•œ ๋น„๊ฐ๋…ํ˜• ์นจ์ž… ํƒ์ง€ ์‹œ์Šคํ…œ(IDS)์„ ์ œ์•ˆํ•œ๋‹ค. 1. ๊ณต๊ฒฉ ๋ฒกํ„ฐ ๋ถ„์„ Man in the Middle (MitM) ์นจ์ž… : ์‚ฌ์šฉ์ž ์ธํ„ฐํŽ˜์ด์Šค์—์„œ ํ”„๋ฆฐํ„ฐ ํŽŒ์›จ์–ด๋กœ G ์ฝ”๋“œ ํŒŒ์ผ์ด ์—…๋กœ๋“œ๋˜๋Š” ๋™์•ˆ ์ด๋ฅผ ๊ฐ€๋กœ์ฑ„๊ณ  ์กฐ์ž‘ํ•œ๋‹ค. ์ง€์—ฐ๋œ ์ธ์‡„ ์ฐฉ์ทจ(Deferred Print Exploit) : G ์ฝ”๋“œ ์‹คํ–‰์„

Computer Science Cryptography and Security
No Image

Differentially Private Rankings via Outranking Methods and Performance Data Aggregation

MCDM์€ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ์˜์‚ฌ ๊ฒฐ์ •์— ํ™œ์šฉ๋˜๋Š” ์ค‘์š”ํ•œ ๋„๊ตฌ์ž…๋‹ˆ๋‹ค. ํŠนํžˆ ์ถ”์ฒœ ์‹œ์Šคํ…œ๊ณผ ๊ฐ™์€ ๋ฐ์ดํ„ฐ ์ค‘์‹ฌ์˜ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์—์„œ๋Š” ๋ฏผ๊ฐํ•œ ๊ฐœ์ธ์ •๋ณด๋ฅผ ๋‹ค๋ฃจ๋Š” ๊ฒƒ์ด ํ•„์ˆ˜์ ์ž…๋‹ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•˜๋Š” ํ†ตํ•ฉ ์ ‘๊ทผ๋ฒ•์€ ํ”„๋ผ์ด๋ฒ„์‹œ ๋ณดํ˜ธ์™€ ๋žญํ‚น ๋ฌธ์ œ ํ•ด๊ฒฐ์„ ๋™์‹œ์— ๋‹ฌ์„ฑํ•ฉ๋‹ˆ๋‹ค. ์•„์›ƒ๋žญํ‚น ๋ฐฉ๋ฒ•๊ณผ ์„ฑ๋Šฅ ๋ฐ์ดํ„ฐ ์ง‘๊ณ„๋ฅผ ํ†ตํ•ด ๊ฐœ์ธ์˜ ๊ธฐ์—ฌ๋„๋ฅผ ๋ณดํ˜ธํ•˜๋ฉด์„œ๋„ ์ •ํ™•ํ•œ ๋žญํ‚น ๊ฒฐ๊ณผ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๋˜ํ•œ, ํ‰๊ฐ€ ๊ฒฐ๊ณผ๋Š” ์ด ์ ‘๊ทผ๋ฒ•์˜ ํšจ๊ณผ์„ฑ๊ณผ ๊ฐœ์ธ์ •๋ณด ๋ณดํ˜ธ์— ๋Œ€ํ•œ ๊ฐ•๋ ฅํ•œ ๋ณด์žฅ์„ ๋ณด์—ฌ์ค๋‹ˆ๋‹ค.

Data
HEAR ๊ธฐ๋ฐ˜ ์Œ์•… ๋ฏธํ•™ ํ‰๊ฐ€ ํ”„๋ ˆ์ž„์›Œํฌ

HEAR ๊ธฐ๋ฐ˜ ์Œ์•… ๋ฏธํ•™ ํ‰๊ฐ€ ํ”„๋ ˆ์ž„์›Œํฌ

: ์Œ์•…์˜ ๋ฏธํ•™์  ํ‰๊ฐ€๋ฅผ ์ž๋™ํ™”ํ•˜๋Š” ๊ฒƒ์€ ์Œ์•… ์ƒ์„ฑ ๋ชจ๋ธ์˜ ๋ฐœ์ „๊ณผ ํ•จ๊ป˜ ์ค‘์š”ํ•œ ๊ณผ์ œ๋กœ ๋– ์˜ค๋ฅด๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๊ธฐ์กด ์ ‘๊ทผ ๋ฐฉ์‹์€ ํ’๋ถ€ํ•œ ์Œ์•…์  ํŠน์„ฑ์„ ํฌ์ฐฉํ•˜๋Š” ๋ฐ ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ์Šต๋‹ˆ๋‹ค. HEAR ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์ฐจ์›์ ์ธ ์Œ์•… ๋ฏธํ•™ ํ‰๊ฐ€๋ฅผ ์œ„ํ•œ ๊ฒฌ๊ณ ํ•œ ์†”๋ฃจ์…˜์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. HEAR์˜ ํ•ต์‹ฌ์€ ๋ณด์™„์ ์ธ ์ง€์—ญ ๋ฐ ์ „์—ญ ์ˆ˜์ค€์˜ ์Œ์•… ํ‘œํ˜„์„ ์ถ”์ถœํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. Songformer์™€ MuQ ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์Šค์ผ€์ผ์˜ ์Œ์•…์  ๋‹จ์„œ๋ฅผ ํฌ์ฐฉํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ์‹œ๊ฐ„์ , ์ŠคํŽ™ํŠธ๋Ÿผ, ์กฐํ™”, ์ฝ˜ํ…์ธ  ์ธก๋ฉด์—์„œ ๋ฏธํ•™ ํ‰๊ฐ€๋ฅผ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ์Šต

No Image

Introducing Visual Scenes and Reasoning: A More Realistic Benchmark for Spoken Language Understanding

: ๊ตฌ์–ด ์–ธ์–ด ์ดํ•ด(SLU)๋Š” ๋‹ค์–‘ํ•œ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋ฉฐ, ํŠนํžˆ ์Šค๋งˆํŠธ ์Šคํ”ผ์ปค๋‚˜ ๊ฐ€์ƒ ๋น„์„œ์™€ ๊ฐ™์€ ์ž‘์—… ์ง€ํ–ฅ ๋Œ€ํ™” ์‹œ์Šคํ…œ์˜ ํ•ต์‹ฌ์ž…๋‹ˆ๋‹ค. SLU ์—ฐ๊ตฌ๋Š” ์ „ํ†ต์ ์œผ๋กœ ๋ช…ํ™•ํ•˜๊ณ  ์• ๋งคํ•˜์ง€ ์•Š์€ ๋ฐœํ™”์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์ง€๋งŒ, ์‹ค์ œ ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ๋Š” ์• ๋งคํ•œ ๋ฐœํ™”๊ฐ€ ๋นˆ๋ฒˆํ•˜๊ฒŒ ๋ฐœ์ƒํ•ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋„์ „์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ํ”„๋กœํ•„ ๊ธฐ๋ฐ˜ SLU(ProSLU)๊ฐ€ ์ œ์•ˆ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ProSLU๋Š” CA, UP, KG๋ฅผ ํ†ตํ•ฉํ•˜์—ฌ ๋ชจ๋ธ์ด ๋” ๋‚˜์€ ๋งฅ๋ฝ ์ดํ•ด์™€ ์ถ”๋ก ์„ ํ•  ์ˆ˜ ์žˆ๋„๋ก ๋•์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ProSLU์—๋„ ํ•œ๊ณ„๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค. ์ฒซ์งธ๋กœ, CA ํ‘œํ˜„์ด ์ง€๋‚˜์น˜

Learning Solution Operators for Partial Differential Equations via Monte Carlo-Type Approximation

Learning Solution Operators for Partial Differential Equations via Monte Carlo-Type Approximation

: ๋ชฌํ…Œ์นด๋ฅผ๋กœ ์œ ํ˜• ๋‰ด๋Ÿด ์˜คํผ๋ ˆ์ดํ„ฐ(MCNO)๋Š” ํŽธ๋ฏธ๋ถ„ ๋ฐฉ์ •์‹์˜ ํ•ด ์˜คํผ๋ ˆ์ดํ„ฐ๋ฅผ ํšจ์œจ์ ์œผ๋กœ ๊ทผ์‚ฌํ•˜๊ธฐ ์œ„ํ•œ ํ˜์‹ ์ ์ธ ์ ‘๊ทผ๋ฒ•์ž…๋‹ˆ๋‹ค. ์ด ์•„ํ‚คํ…์ฒ˜๋Š” ๊ธฐ์กด ๋ฐฉ๋ฒ•๊ณผ ๋‹ฌ๋ฆฌ ์ŠคํŽ™ํŠธ๋Ÿผ ๋ณ€ํ™˜์ด๋‚˜ ๊นŠ์€ ๊ณ„์ธต์  ๊ตฌ์กฐ๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค. ๋Œ€์‹ , MCNO๋Š” ๋ชฌํ…Œ์นด๋ฅผ๋กœ ์ƒ˜ํ”Œ๋ง์„ ํ†ตํ•ด ์ ๋ถ„์„ ๊ทผ์‚ฌํ•จ์œผ๋กœ์จ ๋‹จ์ˆœํ•˜๋ฉด์„œ๋„ ํšจ๊ณผ์ ์ธ ๋ฐฉ๋ฒ•์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. MCNO์˜ ํ•ต์‹ฌ ์•„์ด๋””์–ด๋Š” ์ปค๋„ ์ ๋ถ„์„ ๊ณ ์ •๋œ ๋ฌด์ž‘์œ„ ์ƒ˜ํ”Œ๋ง ํฌ์ธํŠธ ์„ธํŠธ์— ๋Œ€ํ•ด ์ง์ ‘ ๊ณ„์‚ฐํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ๋ฒ•์€ ํ›ˆ๋ จ ์ค‘ ๋ฐ˜๋ณต์ ์ธ ์ƒ˜ํ”Œ๋ง์ด ํ•„์š”ํ•˜์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์— ๊ณ„์‚ฐ ๋น„์šฉ์„ ํฌ๊ฒŒ ์ค„์ผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๋˜ํ•œ,

Learning
LLM ๊ธฐ๋ฐ˜ Git bisect๋กœ ์‹œ๋งจํ‹ฑ ๊ฒฐํ•จ ํƒ์ง€ ํ˜์‹ 

LLM ๊ธฐ๋ฐ˜ Git bisect๋กœ ์‹œ๋งจํ‹ฑ ๊ฒฐํ•จ ํƒ์ง€ ํ˜์‹ 

๋งค๋ ฅ์ ์ธ ํ•œ๊ธ€ ์ œ๋ชฉ: [LLM Git Bisect]: ์˜๋ฏธ๋ก ์  ๊ฒฐํ•จ ํƒ์ง€๋ฅผ ์œ„ํ•œ ํ˜์‹ ์ ์ธ AI ๋„์šฐ๋ฏธ ์ดˆ๋ก ์ „์ฒด ๋ฒˆ์—ญ ๋ฐ ์ •๋ฆฌ: ์ด ๋…ผ๋ฌธ์—์„œ๋Š” Git bisect ๊ณผ์ •์— ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM)์„ ํ†ตํ•ฉํ•˜์—ฌ ์˜๋ฏธ๋ก ์  ๊ฒฐํ•จ์„ ๋กœ์ปฌ๋ผ์ด์ฆˆํ•˜๋Š” ์ƒˆ๋กœ์šด ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์†Œ๊ฐœํ•ฉ๋‹ˆ๋‹ค. ์ „ํ†ต์ ์ธ bisect๋Š” ๊ฒฐ์ •๋ก ์ ์ด๊ณ  ์ด์ง„ ์‹คํŒจ ์ƒํƒœ๋ฅผ ๊ฐ€์ •ํ•˜์ง€๋งŒ, ์ด๋Ÿฌํ•œ ๊ฐ€์ •์€ ํ˜„๋Œ€ ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ์˜ ๋ณต์žกํ•œ ํ™˜๊ฒฝ์—์„œ ์ž์ฃผ ๊นจ์ง‘๋‹ˆ๋‹ค. ์ €ํฌ ์‹œ์Šคํ…œ์€ LLM์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ตฌ์กฐํ™”๋œ ์‚ฌ๊ณ  ์—ฐ์‡„๋ฅผ ํ†ตํ•ด bisect ํƒ์ƒ‰์„ ๋ณด์™„ํ•˜๊ณ , ์ปค๋ฐ‹ ๋‹จ์œ„์˜ ๋ถ„์„์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค. ์—ฌ๋Ÿฌ

No Image

MultiBanAbs: A Comprehensive Multi-Domain Bangla Abstractive Text Summarization Dataset

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๋‹ค์–‘์„ฑ ๊ฒฐํ• : ๊ธฐ์กด ๋ฐฉ๊ธ€๋ผ์–ด ์š”์•ฝ ๋ฐ์ดํ„ฐ์…‹(BWSD, MASBA ๋“ฑ)์€ ๋Œ€๋ถ€๋ถ„ ๋‰ด์Šค ๊ธฐ์‚ฌ์— ๊ตญํ•œ๋ผ ์žˆ์–ด ๋ฌธ์ฒดยท์–ดํœ˜๊ฐ€ ํš์ผ์ ์ด๋‹ค. ์‹ค์ œ ๋””์ง€ํ„ธ ํ™˜๊ฒฝ์—์„œ๋Š” ๋ธ”๋กœ๊ทธ, SNS, ๋น„์ฆˆ๋‹ˆ์Šค ๊ธฐ์‚ฌ ๋“ฑ ๋‹ค์–‘ํ•œ ์žฅ๋ฅด๊ฐ€ ๊ณต์กดํ•œ๋‹ค. ์ •๋ณด ๊ณผ๋ถ€ํ•˜ : ๋ฐฉ๊ธ€๋ผ์–ด ์ฝ˜ํ…์ธ ๊ฐ€ ๊ธ‰์ฆํ•จ์— ๋”ฐ๋ผ ์ž๋™ ์š”์•ฝ ๊ธฐ์ˆ ์ด ๋…์ž์˜ ์ •๋ณด ์†Œํ™”์— ํ•„์ˆ˜์ ์ด๋‹ค. 2. ๋ฐ์ดํ„ฐ์…‹ ๊ตฌ์ถ• | ์ถœ์ฒ˜ | ๊ธฐ์‚ฌ ์ˆ˜ | ์ฃผ์š” ํŠน์„ฑ | | | | | | Samakal | 41,675 | ์ •ํ˜• ๋‰ด์Šค, ๊ณต์‹์ ์ธ ๋ฌธ์ฒด | | The Business Standard | 12,25

Data
No Image

Using Span Queries to Optimize for Cache and Attention Locality

: Span ์ฟผ๋ฆฌ๋Š” ๋‹ค์–‘ํ•œ ์‚ฌ์šฉ ์‚ฌ๋ก€๋ฅผ ํฌ๊ด„ํ•˜๋Š” ์ผ๋ฐ˜ํ™”๋œ ์ธํ„ฐํŽ˜์ด์Šค๋กœ์„œ, ์ฑ„ํŒ…๋ถ€ํ„ฐ RAG, ์ถ”๋ก  ์‹œ ํ™•์žฅ ๋ฐ ์—์ด์ „ํŠธ ์›Œํฌ๋กœ๋“œ๊นŒ์ง€ ๋ชจ๋‘ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ ์†Œ๊ฐœ๋œ span ์ฟผ๋ฆฌ๋Š” ๊ตํ™˜๋ฒ•์น™ ์ œ์•ฝ๊ณผ ํ•จ๊ป˜ ์—ฐ๊ฒฐ๋œ ์ถ”๋ก  ํ˜ธ์ถœ์˜ ํ‘œํ˜„ ํŠธ๋ฆฌ์ž…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์€ ์บ์‹œ ์ง€์—ญ์„ฑ๊ณผ ์ฃผ์˜ ์ง‘์ค‘ ์ง€์—ญ์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋ฐ ๋„์›€์ด ๋ฉ๋‹ˆ๋‹ค. ์ €์ž๋“ค์€ vLLM์— ๋Œ€ํ•œ ์ž‘์€ ๋ณ€๊ฒฝ๋งŒ์œผ๋กœ๋„ span ์ฟผ๋ฆฌ์˜ ๊ณ ์„ฑ๋Šฅ ์‹คํ–‰์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ค๋‹ˆ๋‹ค. ์ด๋Š” ๋‘ ๊ฐ€์ง€ ์„œ๋กœ ๋‹ค๋ฅธ ๋น„์ฑ„ํŒ… ์‚ฌ์šฉ ์‚ฌ๋ก€์—์„œ 10~20๋ฐฐ์˜ TTFT ๊ฐ์†Œ๋ฅผ ๋‹ฌ์„ฑํ•  ์ˆ˜ ์žˆ์Šต

No Image

๊ธฐ๊ด€ ๋ฌด๊ด€ ์ข…์–‘ ๋ถ„ํ• ์„ ์œ„ํ•œ ๊ฐœ์ธํ™” ์—ฐํ•ฉ ํ•™์Šต

: ํŽ˜๋”๋ ˆ์ดํ‹ฐ๋“œ ๋Ÿฌ๋‹์€ ์—ฌ๋Ÿฌ ํด๋ผ์ด์–ธํŠธ๋กœ๋ถ€ํ„ฐ ๋ถ„์‚ฐ๋œ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ชจ๋ธ์„ ํ›ˆ๋ จํ•˜๋Š” ๊ฐ•๋ ฅํ•œ ๊ธฐ์ˆ ์ž…๋‹ˆ๋‹ค. ํŠนํžˆ ์˜๋ฃŒ ๋ถ„์•ผ์™€ ๊ฐ™์€ ๋ฏผ๊ฐํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ๋‹ค๋ฃจ๋Š” ๊ฒฝ์šฐ, ๊ฐœ์ธ์ •๋ณด ๋ณดํ˜ธ ๊ทœ์ •์„ ์ค€์ˆ˜ํ•˜๋ฉด์„œ ํšจ๊ณผ์ ์ธ ํ•™์Šต์ด ๊ฐ€๋Šฅํ•˜๋„๋ก ํ•ฉ๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋น„๋…๋ฆฝ์ ์ด๊ณ  ๋™์ผํ•˜๊ฒŒ ๋ถ„ํฌ๋˜์ง€ ์•Š์€ ๋ฐ์ดํ„ฐ(Non IID)๋Š” FL์˜ ์ฃผ์š” ๋„์ „ ๊ณผ์ œ ์ค‘ ํ•˜๋‚˜์ž…๋‹ˆ๋‹ค. ๊ธฐ์กด PFL ๋ฐฉ๋ฒ•๋“ค์€ ๊ฐ ํด๋ผ์ด์–ธํŠธ์˜ ๊ณ ์œ ํ•œ ๋ฐ์ดํ„ฐ ๋ถ„ํฌ์— ๋งž๊ฒŒ ๋กœ์ปฌ ๋ชจ๋ธ์„ ๊ฐœ์ธํ™”ํ•˜๋Š” ๋ฐ ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์Šต๋‹ˆ๋‹ค. ์ด๋Š” ๋ฐ์ดํ„ฐ ํ”„๋ผ์ด๋ฒ„์‹œ๋ฅผ ์œ ์ง€ํ•˜๋ฉด์„œ๋„ ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ์œ„ํ•œ ํšจ๊ณผ์ ์ธ ์ „๋žต์œผ๋กœ ์—ฌ๊ฒจ์กŒ์Šต๋‹ˆ๋‹ค. ํ•˜

๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ PํŒŒ ์ฒซ์šด๋™๊ทน์„ฑ ์ž๋™ ํŒ๋ณ„๋กœ ๋ณด๋Š” 2022 ๋ฃจ๋”ฉ ์ง€์ง„์—ด์˜ ์ดˆ์†Œํ˜• ์ง€์ง„ ๋ฉ”์ปค๋‹ˆ์ฆ˜

๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ PํŒŒ ์ฒซ์šด๋™๊ทน์„ฑ ์ž๋™ ํŒ๋ณ„๋กœ ๋ณด๋Š” 2022 ๋ฃจ๋”ฉ ์ง€์ง„์—ด์˜ ์ดˆ์†Œํ˜• ์ง€์ง„ ๋ฉ”์ปค๋‹ˆ์ฆ˜

๋ณธ ๋…ผ๋ฌธ์€ ๊ทœ๋ชจ๊ฐ€ ์ž‘๊ฑฐ๋‚˜ ์ค‘๊ฐ„์ธ ์ง€์ง„(M โ‰ค 4.5)์˜ ์ดˆ์  ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ํšจ์œจ์ ์œผ๋กœ ๋„์ถœํ•˜๊ธฐ ์œ„ํ•ด, ์ „ํ†ต์ ์ธ ํŒŒํ˜•โ€‘์—ญ์ „ ๋ฐฉ์‹์ด ๊ฐ–๋Š” ํ•œ๊ณ„๋ฅผ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ์ž๋™ ํŒ๋ณ„๊ธฐ๋กœ ๋ณด์™„ํ•œ ์ ์ด ๊ฐ€์žฅ ํฐ ํŠน์ง•์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ ๊ฐ•์กฐ๋œ ๋ฐ”์™€ ๊ฐ™์ด, PํŒŒ ์ฒซโ€‘์šด๋™ ๊ทน์„ฑ์€ ์†๋„ ๋ชจ๋ธ์— ๋Œ€ํ•œ ์˜์กด๋„๊ฐ€ ๋‚ฎ๊ณ  ๋ฌผ๋ฆฌ์  ์˜๋ฏธ๊ฐ€ ๋ช…ํ™•ํ•˜์—ฌ ์ดˆ์  ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์ถ”์ •ํ•˜๋Š” ๋ฐ ๊ฐ•์ธํ•œ ๊ด€์ธก๊ฐ’์œผ๋กœ ํ™œ์šฉ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ˆ˜๋™์œผ๋กœ ๊ทน์„ฑ์„ ์ฃผ์„ํ•˜๋Š” ์ž‘์—…์€ ์ธ๋ ฅ ์†Œ๋ชจ๊ฐ€ ํฌ๊ณ , ๋Œ€๊ทœ๋ชจ ๋ฐ์ดํ„ฐ์…‹์—์„œ๋Š” ์‹ค์šฉ์„ฑ์ด ๋–จ์–ด์ง„๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ณ ์ž ์ €์ž๋“ค์€ ๋‘ ๋‹จ๊ณ„์˜ ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š”

No Image

ํ”„๋ก์‹œ ์—ฐ์‚ฐ์ž๋ฅผ ํ™œ์šฉํ•œ ํšจ์œจ์ ์ธ ํ…์ŠคํŠธโ€‘ํˆฌโ€‘์ด๋ฏธ์ง€ ํ™•์‚ฐ ๋ชจ๋ธ ProxT2I

: ์ตœ๊ทผ ์ƒ์„ฑ ์ธ๊ณต์ง€๋Šฅ์€ ๋‹ค์–‘ํ•œ ์ฝ˜ํ…์ธ  ์ œ์ž‘ ๋ถ„์•ผ์—์„œ ํ˜์‹ ์ ์ธ ๋ณ€ํ™”๋ฅผ ๊ฐ€์ ธ์™”๋‹ค. ํŠนํžˆ ํ™•์‚ฐ ๋ชจ๋ธ์€ ์ด๋ฏธ์ง€ ๋ฐ ํ…์ŠคํŠธ ์ด๋ฏธ์ง€(T2I) ์ƒ์„ฑ์— ์žˆ์–ด ์ฃผ๋ชฉํ•  ๋งŒํ•œ ์„ฑ๊ณผ๋ฅผ ๋ณด์ด๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ณ ํ’ˆ์งˆ ์ƒ˜ํ”Œ์„ ํšจ์œจ์ ์œผ๋กœ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์€ ์—ฌ์ „ํžˆ ํ•ด๊ฒฐํ•ด์•ผ ํ•  ๊ณผ์ œ์ด๋‹ค. ๊ธฐ์กด์˜ ์ ์ˆ˜ ๊ธฐ๋ฐ˜ ์ƒ˜ํ”Œ๋Ÿฌ๋“ค์€ ์ „๋ฐฉ ์ด์‚ฐํ™” ์†”๋ฒ„๋ฅผ ์‚ฌ์šฉํ•˜๋ฉฐ, ์ด๋Š” ์ƒ˜ํ”Œ๋ง ๋‹จ๊ณ„๋ฅผ ์ค„์ด๊ฑฐ๋‚˜ ์†๋„๋ฅผ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด ๋ณต์žกํ•œ ์กฐ์ •์ด ํ•„์š”ํ•˜๋‹ค. ์ด๋Ÿฌํ•œ ๋น„ํšจ์œจ์„ฑ์€ ์‹ค์ œ ๋ฐฐํฌ์— ์žˆ์–ด ํฐ ๊ฑธ๋ฆผ๋Œ์ด ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ›„๋ฐฉ ์ด์‚ฐํ™” ๊ธฐ๋ฐ˜์˜ ํ…์ŠคํŠธ ์ด๋ฏธ์ง€ ํ™•์‚ฐ ๋ชจ๋ธ์ธ ProxT2I๋ฅผ ๊ฐœ๋ฐœํ–ˆ๋‹ค. ์ด

No Image

A Convexity-dependent Two-Phase Training Algorithm for Deep Neural Networks

1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ ์†์‹ค ํ•จ์ˆ˜์˜ ๊ตฌ์กฐ์  ํŠน์„ฑ ์„ ํ™œ์šฉํ•œ ์ตœ์ ํ™”๋Š” ์ตœ๊ทผ ๋ช‡ ๋…„๊ฐ„ ํ™œ๋ฐœํžˆ ์—ฐ๊ตฌ๋˜์—ˆ์ง€๋งŒ, ๋Œ€๋ถ€๋ถ„์€ ์ „์—ญ ๋น„๋ณผ๋ก์„ฑ ์„ ์ „์ œ๋กœ Adam, RMSProp ๋“ฑ 1์ฐจ ๋ฐฉ๋ฒ•์— ์˜์กดํ•œ๋‹ค. ์‹ค์ œ ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ(ํŠนํžˆ ๊ณผ๋Œ€ ํŒŒ๋ผ๋ฏธํ„ฐํ™”๋œ ๋„คํŠธ์›Œํฌ)์—์„œ๋Š” ํ•™์Šต ์ดˆ๊ธฐ์— ๊ธ‰๊ฒฉํžˆ ๋ณ€๋™ํ•˜๋Š” ๋น„๋ณผ๋ก ์ง€ํ˜• ์ด ์กด์žฌํ•˜์ง€๋งŒ, ํ•™์Šต์ด ์ง„ํ–‰๋ ์ˆ˜๋ก ํŒŒ๋ผ๋ฏธํ„ฐ๊ฐ€ ์ตœ์ ์ ์— ์ ‘๊ทผํ•˜๋ฉด์„œ ์ง€์—ญ ๋ณผ๋ก์„ฑ์ด ๊ฐ•ํ™” ๋œ๋‹ค๋Š” ๊ด€์ฐฐ์ด ์—ฌ๋Ÿฌ ์‹คํ—˜์—์„œ ๋ณด๊ณ ๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ํ˜„์ƒ์„ ์ •๋Ÿ‰์ ์œผ๋กœ ํฌ์ฐฉํ•˜๊ณ , ๋ณผ๋ก์„ฑ ์ „์ด๋ฅผ ์ž๋™์œผ๋กœ ๊ฐ์ง€ ํ•ด ์ตœ์ ํ™” ์ „๋žต์„ ์ „ํ™˜ํ•˜๋Š” ์•„์ด๋””์–ด๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„ํ™”๋œ

Network
No Image

Align to Misalign: Automatic LLM Jailbreak with Meta-Optimized LLM Judges

์ด ๋…ผ๋ฌธ์€ LLM์˜ ์•ˆ์ „์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ๊ฐ์˜ฅ ํƒˆ์ถœ ๊ณต๊ฒฉ์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. AMIS ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ๊ฐ์˜ฅ ํƒˆ์ถœ ํ”„๋กฌํ”„ํŠธ์™€ ์ฑ„์  ํ…œํ”Œ๋ฆฟ์„ ํ•จ๊ป˜ ์ตœ์ ํ™”ํ•˜์—ฌ, ๋ณด๋‹ค ํšจ๊ณผ์ ์ด๊ณ  ์‹ ๋ขฐํ•  ์ˆ˜ ์žˆ๋Š” ๊ณต๊ฒฉ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค. ๋‚ด๋ถ€ ๋ฃจํ”„์˜ ์„ธ๋ฐ€ํ•œ ํ”ผ๋“œ๋ฐฑ๊ณผ ์™ธ๋ถ€ ๋ฃจํ”„์˜ ASR ์ •๋ ฌ ์ ์ˆ˜ ์ตœ์ ํ™”๋ฅผ ํ†ตํ•ด ๊ณต๊ฒฉ ์„ฑ๊ณต๋ฅ ์ด ํ–ฅ์ƒ๋˜๊ณ , ์ฑ„์  ๊ฒฐ๊ณผ์˜ ์ •ํ™•์„ฑ์ด ๋†’์•„์ง‘๋‹ˆ๋‹ค. ์ด๋Š” LLM์˜ ์ทจ์•ฝ์ ์„ ์‹๋ณ„ํ•˜๊ณ , ์ž ์žฌ์ ์ธ ์œ„ํ—˜์„ ์™„ํ™”ํ•˜๋Š” ๋ฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

No Image

Angular Steering: Behavior Control via Rotation in Activation Space

ํšŒ์ „ ๊ฐ๋„ ์กฐ์ ˆ์€ ํ–‰๋™ ์ œ์–ด๋ฅผ ์œ„ํ•œ ํ˜์‹ ์ ์ธ ์ ‘๊ทผ ๋ฐฉ์‹์œผ๋กœ, ํ™œ์„ฑํ™” ๊ณต๊ฐ„ ๋‚ด์—์„œ ํšŒ์ „์„ ํ†ตํ•ด ํŠน์ • ํ–‰๋™์„ ๋ฏธ์„ธํ•˜๊ฒŒ ์ œ์–ดํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Š” ๊ธฐ์กด ๋ฐฉ๋ฒ•์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ณ , ์„ ํƒ๋œ ๋งค๊ฐœ๋ณ€์ˆ˜์— ๋Œ€ํ•œ ๋ฏผ๊ฐ์„ฑ์„ ์ค„์ด๋ฉฐ, ์˜๋„ํ•˜์ง€ ์•Š์€ ์ƒํ˜ธ์ž‘์šฉ์„ ์ตœ์†Œํ™”ํ•ฉ๋‹ˆ๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ํ–‰๋™ ๋ฐฉํ–ฅ์— ๋Œ€ํ•œ ๊ธฐํ•˜ํ•™์  ํšŒ์ „ ๊ฐœ๋…์„ ๋„์ž…ํ•˜์—ฌ ์—ฐ์†์ ์ธ ์ œ์–ด๋ฅผ ์ œ๊ณตํ•˜๋ฉฐ, ํŠนํžˆ ๊ฑฐ๋ถ€์™€ ์ˆœ์‘๊ณผ ๊ฐ™์€ ๋ณต์žกํ•œ ํ–‰๋™์„ ์กฐ์ ˆํ•˜๋Š” ๋ฐ ํšจ๊ณผ์ ์ž…๋‹ˆ๋‹ค. ๋˜ํ•œ ์ ์‘ํ˜• ๋ณ€์ข…์„ ํ†ตํ•ด ํ‘œ์  ๊ธฐ๋Šฅ๊ณผ ์ •๋ ฌ๋œ ํ™œ์„ฑํ™”๋งŒ ์„ ํƒ์ ์œผ๋กœ ํšŒ์ „์‹œํ‚ด์œผ๋กœ์จ ์•ˆ์ •์„ฑ๊ณผ ์ผ๊ด€์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ต๋‹ˆ๋‹ค. ์ด ๊ธฐ์ˆ ์€ ๊ธฐ์กด ๋ง์…ˆ

Arxiv 2512.23731

Arxiv 2512.23731

๋ณธ ๋…ผ๋ฌธ์€ ์ง€๊ตฌ ๋‚ด๋ถ€์˜ ํƒ„์„ฑ ์ง„๋™์ด ๋Œ€๊ธฐ์™€ ์ง์ ‘์ ์ธ ํ”ผ๋“œ๋ฐฑ ๋ฃจํ”„๋ฅผ ํ˜•์„ฑํ•œ๋‹ค๋Š” ํš๊ธฐ์ ์ธ ๊ฐ€์„ค์„ ์ œ์‹œํ•œ๋‹ค. ๊ธฐ์กด ๊ธฐํ›„ ๋ชจ๋ธ์€ ํƒœ์–‘ ๋ณต์‚ฌ, ํ•ด์–‘โ€‘๋Œ€๊ธฐ ์ƒํ˜ธ์ž‘์šฉ, ์˜จ์‹ค๊ฐ€์Šค ๋†๋„ ๋“ฑ์„ ์ค‘์‹ฌ์œผ๋กœ ์ „๊ฐœ๋ผ ์™”์œผ๋ฉฐ, ์ง€๊ตฌ ๋‚ด๋ถ€ ์—ญํ•™์€ ๋ณดํ†ต ์žฅ๊ธฐ ์ง€์งˆํ•™์  ์‹œ๊ฐ„์ฒ™๋„(์ˆ˜๋ฐฑ๋งŒ ๋…„)์—์„œ๋งŒ ๊ณ ๋ ค๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ €์ž๋“ค์€ 2024โ€‘2025๋…„ ์‚ฌ์ด์— ๊ด€์ธก๋œ ๊ทน๋‹จ์ ์ธ ํ‰๊ท  ํ•ด์ˆ˜๋ฉด ๊ธฐ์••(์ตœ์ € 921 hPa, ์ตœ๊ณ  1 060 hPa)๊ณผ ์—ฐ์†์ ์ธ ๋Œ€๊ทœ๋ชจ ์ง€์ง„์„ ๋™์‹œ์— ๋ถ„์„ํ•จ์œผ๋กœ์จ, ๋‹จ๊ธฐ(์ˆ˜์ผโ€‘์ˆ˜์ฃผ) ๊ทœ๋ชจ์—์„œ๋„ ๋‚ด๋ถ€ ์ง„๋™์ด ๋Œ€๊ธฐ์•• ๋ณ€๋™์„ ์œ ๋ฐœํ•  ์ˆ˜ ์žˆ์Œ์„ ์‹ค์ฆํ•œ๋‹ค. ํ•ต์‹ฌ

No Image

Assessing the Human-Likeness of LLM-Driven Digital Twins in Simulating Health Care System Trust

: ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด ๋””์ง€ํ„ธ ํŠธ์œˆ์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์‘๋‹ต์€ ๋” ์ง‘์ค‘์ ์ด๊ณ  ๋ถ„์‚ฐ์ด ๋‚ฎ์•˜์œผ๋ฉฐ ๊ทน๋‹จ์ ์ธ ์˜ต์…˜ ์„ ํƒ์ด ์ ์—ˆ์Šต๋‹ˆ๋‹ค(๋ชจ๋‘ p<0.001). ๋””์ง€ํ„ธ ํŠธ์œˆ์€ ์ „๋ฐ˜์ ์œผ๋กœ ์ฃผ์š” ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ํŒจํ„ด, ์ฆ‰ ์—ฐ๋ น๊ณผ ์„ฑ๋ณ„์„ ์žฌํ˜„ํ•˜์ง€๋งŒ ๊ต์œก ์ˆ˜์ค€๊ณผ ๊ฐ™์€ ์„ธ๋ถ€ ์‚ฌํ•ญ์—์„œ ์ธ๊ฐ„์„ ํฌ์ฐฉํ•˜๋Š” ๋ฏผ๊ฐ๋„๊ฐ€ ์ƒ๋Œ€์ ์œผ๋กœ ๋‚ฎ์•˜์Šต๋‹ˆ๋‹ค. LLM ๊ธฐ๋ฐ˜ ๋””์ง€ํ„ธ ํŠธ์œˆ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์€ ์ธ๊ตฌ ์ถ”์„ธ๋ฅผ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•  ์ˆ˜ ์žˆ๋Š” ์ž ์žฌ๋ ฅ์„ ๊ฐ€์ง€๊ณ  ์žˆ์ง€๋งŒ, ์ธ๊ฐ„ ์ง‘๋‹จ์˜ ์„ธ๋ถ€์ ์ธ ์ฐจ์ด๋ฅผ ๊ตฌ๋ณ„ํ•˜๋Š” ๋ฐ๋Š” ์–ด๋ ค์›€์ด ์žˆ์Šต๋‹ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํ˜„์žฌ LLM์œผ๋กœ ๊ตฌ๋™๋˜๋Š” ๋””์ง€ํ„ธ ํŠธ์œˆ์ด ๋ณต์žกํ•œ ์ธ๊ฐ„์˜ ํƒœ๋„๋ฅผ ๋ชจ๋ธ๋ง

System

< Category Statistics (Total: 5012) >

Electrical Engineering and Systems Science
102
General
4156
General Relativity
2
HEP-EX
3
HEP-PH
1
HEP-TH
3
MATH-PH
6
Nonlinear Sciences
1
Quantum Physics
18

Start searching

Enter keywords to search articles

โ†‘โ†“
โ†ต
ESC
โŒ˜K Shortcut