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A 3D virtual geographic environment for flood representation towards risk communication

A 3D virtual geographic environment for flood representation towards risk communication

๋ณธ ์—ฐ๊ตฌ๋Š” ์žฌ๋‚œ ์œ„ํ—˜์†Œํ†ต ๋ถ„์•ผ์—์„œ โ€˜์ „๋ฌธ๊ฐ€์™€ ๋น„์ „๋ฌธ๊ฐ€ ์‚ฌ์ด์˜ ์ •๋ณด ๊ฒฉ์ฐจโ€™๋ฅผ ํ•ด์†Œํ•˜๊ณ ์ž ํ•˜๋Š” ์‹œ๋„๋กœ์„œ, ๊ธฐ์ˆ ์  ๋ณต์žก์„ฑ์„ ์ตœ์†Œํ™”ํ•˜๋ฉด์„œ๋„ ๊ณผํ•™์  ์ •ํ™•์„ฑ์„ ์œ ์ง€ํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ํ•ต์‹ฌ์€ ํ™์ˆ˜ ๋ชจ๋ธ๋ง ๋‹จ๊ณ„์—์„œ ๊ธฐ์กด์˜ 1์ฐจ์› ํ˜น์€ 2์ฐจ์› ์ˆ˜์น˜ ๋ชจ๋ธ์„ ๋„˜์–ด 3์ฐจ์› ์ง€ํ˜• ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•œ๋‹ค๋Š” ์ ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ณ ํ•ด์ƒ๋„ ๋””์ง€ํ„ธ ๊ณ ๋„ ๋ชจ๋ธ(DEM)๊ณผ GIS ๊ธฐ๋ฐ˜ ํ† ์ง€ ์ด์šฉ ์ •๋ณด๋ฅผ ์ž…๋ ฅ์œผ๋กœ ์‚ฌ์šฉํ•˜๊ณ , ์œ ์—ญ ๋‚ด ๊ฐ•์šฐโ€‘์œ ์ถœโ€‘์ „ํŒŒ ๊ณผ์ •์„ ๋ฌผ๋ฆฌ ๊ธฐ๋ฐ˜ ๋ฐฉ์ •์‹์œผ๋กœ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ ํ•ต์‹ฌ์€ ๋ณ‘๋ ฌ ์ปดํ“จํŒ…์„ ๋„์ž…ํ•ด ๊ณ„์‚ฐ ์‹œ๊ฐ„์„ ํฌ๊ฒŒ ๋‹จ์ถ•ํ•œ ๊ฒƒ์ด๋‹ค

A construction of relatively pure submodules

A construction of relatively pure submodules

๋ณธ ๋…ผ๋ฌธ์€ ํ˜„๋Œ€ ๋ชจ๋“ˆ ์ด๋ก ์—์„œ ํ•ต์‹ฌ์ ์ธ ์œ„์น˜๋ฅผ ์ฐจ์ง€ํ•˜๋Š” ์ƒ๋Œ€์ ์œผ๋กœ ์ˆœ์ˆ˜ํ•œ ๋ถ€๋ถ„๋ชจ๋“ˆ ์˜ ์กด์žฌ ๋ฌธ์ œ๋ฅผ ์ƒˆ๋กœ์šด ๋ฒ”์ฃผ์  ํ™˜๊ฒฝ์œผ๋กœ ํ™•์žฅํ•œ๋‹ค๋Š” ์ ์—์„œ ํ•™์ˆ ์  ์˜์˜๊ฐ€ ํฌ๋‹ค. ๊ธฐ์กด์˜ Bicanโ€‘El Bashir ์ •๋ฆฌ๋Š” ๋‹จ์ผ ๊ฐ์ฒด๋ฅผ ๊ฐ–๋Š” ํ™˜ (R ) ์œ„์˜ ๋ชจ๋“ˆ ๋ฒ”์ฃผ ( operatorname{Mod}R ) ์—์„œ๋งŒ ์ ์šฉ ๊ฐ€๋Šฅํ–ˆ์œผ๋ฉฐ, ๊ทธ ์ฆ๋ช…์€ ๋ณต์žกํ•œ ์ง‘ํ•ฉ๋ก ์  ๊ตฌ์„ฑ๊ณผ ๊ธด ๋…ผ์ฆ ๋•Œ๋ฌธ์— ์‹ค๋ฌด์—์„œ ํ™œ์šฉ๋„๊ฐ€ ๋‚ฎ์•˜๋‹ค. ์ €์ž๋Š” ์ด๋ฅผ ๋‹ค์ค‘ ๊ฐ์ฒด ํ™˜ (๋‹ค์ค‘ ๊ฐ์ฒด๋ฅผ ๊ฐ–๋Š” ์ž‘์€ ์„ ํ˜• ๋ฒ”์ฃผ) ์œ„์˜ ๋ชจ๋“ˆ ๋ฒ”์ฃผ ( operatorname{Mod} mathcal{C} )

Mathematics
A Multidimensional Exponential Utility Indifference Pricing Model with   Applications to Counterparty Risk

A Multidimensional Exponential Utility Indifference Pricing Model with Applications to Counterparty Risk

์ด ์—ฐ๊ตฌ๋Š” ๋‘ ๊ฐ€์ง€ ์ค‘์š”ํ•œ ํ•™๋ฌธ์ ยท์‹ค๋ฌด์  ๋ฌธ์ œ๋ฅผ ๋™์‹œ์— ๋‹ค๋ฃฌ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ๋‹ค์ฐจ์› ๋น„๊ฑฐ๋ž˜ ์ž์‚ฐ ๋ชจ๋ธ์—์„œ ์ง€์ˆ˜ํ˜• ํšจ์šฉ ๋ฌด์ฐจ๋ณ„ ๊ฐ€๊ฒฉ์„ ์ •์˜ํ•˜๊ณ , ์ด๋ฅผ PDE(ํŽธ๋ฏธ๋ถ„๋ฐฉ์ •์‹) ํ˜•ํƒœ๋กœ ๊ธฐ์ˆ ํ•œ๋‹ค๋Š” ์ ์ด๋‹ค. ๊ธฐ์กด ๋ฌธํ—Œ์—์„œ๋Š” 1์ฐจ์› ๋งˆ์ฝ”ํ”„ ๋ชจ๋ธ์— ํ•œ์ •ํ•ด Coleโ€‘Hopf ๋ณ€ํ™˜์ด๋‚˜ ์™œ๊ณก ํŒŒ์›Œ ๋ณ€ํ™˜์„ ์ด์šฉํ•ด ๋น„์„ ํ˜• ๊ฐ€์น˜ ํ•จ์ˆ˜ PDE๋ฅผ ์„ ํ˜•ํ™”ํ•˜๊ณ , ๋ช…์‹œ์  ํ•ด๋ฅผ ๋„์ถœํ•œ ์‚ฌ๋ก€๊ฐ€ ์žˆ๋‹ค(์˜ˆ: HendersonยทHobson, MusielaยทZariphopoulou). ๊ทธ๋Ÿฌ๋‚˜ ๋‹ค์ฐจ์›์œผ๋กœ ํ™•์žฅํ•˜๊ณ  ๋™์‹œ์— ์‹œ์ ๋ณ„ ๋””ํดํŠธ ์œ„ํ—˜์„ ํฌํ•จํ•˜๋ฉด, ์ด๋Ÿฌํ•œ ๋ณ€ํ™˜๋งŒ์œผ๋กœ๋Š” ํ•ด๋ฅผ ๊ตฌ

Mathematics Quantitative Finance Model
A Multimodal Conversational Agent for Tabular Data Analysis

A Multimodal Conversational Agent for Tabular Data Analysis

Talk2Data ๋…ผ๋ฌธ์€ ํ˜„์žฌ ๋ฐ์ดํ„ฐ ๋ถ„์„ ๋„๊ตฌ๊ฐ€ ์ง๋ฉดํ•œ ๋‘ ๊ฐ€์ง€ ํ•ต์‹ฌ ๋ฌธ์ œ, ์ฆ‰ ์‚ฌ์šฉ์ž ์ธํ„ฐํŽ˜์ด์Šค์˜ ๋น„์ „๋ฌธ์„ฑ ๊ณผ ๋ถ„์„ ๊ฒฐ๊ณผ์˜ ๋ถˆํˆฌ๋ช…์„ฑ ์„ LLM ๊ธฐ๋ฐ˜ ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ ์—์ด์ „ํŠธ๋ฅผ ํ†ตํ•ด ํ•ด๊ฒฐํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋จผ์ € ์‹œ์Šคํ…œ ์•„ํ‚คํ…์ฒ˜๋ฅผ ์‚ดํŽด๋ณด๋ฉด, ์ž…๋ ฅ ๋‹จ๊ณ„์—์„œ Whisper ASR์ด ์Œ์„ฑ ๋ช…๋ น์„ ํ…์ŠคํŠธ๋กœ ๋ณ€ํ™˜ํ•˜๊ณ , ์ด ํ…์ŠคํŠธ๋Š” ๋Œ€ํ™” ๊ด€๋ฆฌ ๋ชจ๋“ˆ์— ์ „๋‹ฌ๋œ๋‹ค. ๋Œ€ํ™” ๊ด€๋ฆฌ ๋ชจ๋“ˆ์€ ํ˜„์žฌ ๋Œ€ํ™” ์ƒํƒœ์™€ ๋ฐ์ดํ„ฐ์…‹ ์Šคํ‚ค๋งˆ ์ •๋ณด๋ฅผ ํ”„๋กฌํ”„ํŠธ์— ํฌํ•จ์‹œ์ผœ Qwenโ€‘coder์—๊ฒŒ ์ฝ”๋“œ ์ƒ์„ฑ ์„ ์š”์ฒญํ•œ๋‹ค. ์ƒ์„ฑ๋œ ํŒŒ์ด์ฌ ์ฝ”๋“œ๋Š” ๊ฒฉ๋ฆฌ๋œ ์ƒŒ๋“œ๋ฐ•์Šค ํ™˜๊ฒฝ์—์„œ ์‹คํ–‰๋˜๋ฉฐ, ์‹คํ–‰ ๊ฒฐ๊ณผ(ํ”Œ

Analysis Data
A Systematic Characterization of LLM Inference on GPUs

A Systematic Characterization of LLM Inference on GPUs

์ด ๋…ผ๋ฌธ์€ ์ตœ๊ทผ LLM ์ถ”๋ก ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์‚ฐ์žฌํ•˜๊ณ  ์žˆ๋Š” ์ƒํ™ฉ์„ ์ง„๋‹จํ•˜๊ณ , ์ด๋ฅผ ํ•˜๋‚˜์˜ ์ผ๊ด€๋œ ๋ถ„์„ ์ฒด๊ณ„๋กœ ํ†ตํ•ฉํ•˜๋ ค๋Š” ์‹œ๋„๋‹ค. ์ฒซ ๋ฒˆ์งธ ์ถ•์ธ โ€œ๋‘ ๋‹จ๊ณ„ ์ด์งˆ์„ฑ ๊ด€์ฐฐโ€์€ ์ถ”๋ก  ๊ณผ์ •์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์„ฑ๋Šฅ ๋ณ€๋™์„ ๋‘ ๋‹จ๊ณ„, ์ฆ‰ ์ž…๋ ฅ ์ „์ฒ˜๋ฆฌ ๋‹จ๊ณ„์™€ ์‹ค์ œ ์—ฐ์‚ฐ ๋‹จ๊ณ„๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ๊ฐ๊ฐ์˜ ์ด์งˆ์„ฑ์„ ์ •๋Ÿ‰ํ™”ํ•œ๋‹ค๋Š” ์ ์—์„œ ์˜๋ฏธ๊ฐ€ ํฌ๋‹ค. ์ด๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ๊ฐ€ ์ฃผ๋กœ ์ „์ฒด ์ง€์—ฐ ์‹œ๊ฐ„๋งŒ์„ ๋ณด๊ณ ํ–ˆ์ง€๋งŒ, ์„ธ๋ถ€ ๋‹จ๊ณ„๋ณ„ ๋ณ‘๋ชฉ์„ ๋“œ๋Ÿฌ๋‚ด์–ด ๋ฏธ์„ธ ์กฐ์ •์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์—ด์–ด์ค€๋‹ค. ๋‘ ๋ฒˆ์งธ ์ถ•์ธ โ€œ๋งˆ์ดํฌ๋กœ์•„ํ‚คํ…์ฒ˜ ๊ทผ๋ณธ ์›์ธ ๋ถ„์„โ€์€ CPU, GPU, ๊ฐ€์†๊ธฐ ๋“ฑ ๋‹ค์–‘ํ•œ ํ•˜๋“œ์›จ์–ด ํ”Œ๋žซํผ์—์„œ

System
A Women's Health Benchmark for Large Language Models

A Women's Health Benchmark for Large Language Models

๋ณธ ๋…ผ๋ฌธ์€ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM)๋“ค์ด ์—ฌ์„ฑ ๊ฑด๊ฐ• ์ •๋ณด์˜ ์ฃผ์š” ์ถœ์ฒ˜๋กœ ํ™œ์šฉ๋˜๊ณ  ์žˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์ด๋“ค์˜ ์ •ํ™•์„ฑ์ด ์ œ๋Œ€๋กœ ๊ฒ€์ฆ๋˜์ง€ ์•Š์•˜์Œ์„ ์ง€์ ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด Women's Health Benchmark (WHB)์„ ๊ฐœ๋ฐœํ•˜์—ฌ LLM์˜ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ•˜์˜€์Šต๋‹ˆ๋‹ค. WHB์€ ๋‹ค์„ฏ ๊ฐ€์ง€ ์˜๋ฃŒ ์ „๋ฌธ ๋ถ„์•ผ์™€ ์„ธ ๊ฐ€์ง€ ์ฟผ๋ฆฌ ์œ ํ˜•, ๊ทธ๋ฆฌ๊ณ  ์—ฌ๋Ÿ ๊ฐ€์ง€ ์˜ค๋ฅ˜ ์œ ํ˜•์„ ํฌํ•จํ•˜๊ณ  ์žˆ์–ด, ์—ฌ์„ฑ ๊ฑด๊ฐ• ์ •๋ณด ์ œ๊ณต์—์„œ LLM์ด ์–ด๋–ค ๋ฌธ์ œ๋ฅผ ๊ฒช๊ณ  ์žˆ๋Š”์ง€ ์ž์„ธํžˆ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค. ํ‰๊ฐ€ ๊ฒฐ๊ณผ, ํ˜„์žฌ์˜ LLM๋“ค์€ WHB์—์„œ ์•ฝ 60%์˜ ์‹คํŒจ์œจ์„

Model
Adaptive Regime-Switching Forecasts with Distribution-Free Uncertainty: Deep Switching State-Space Models Meet Conformal Prediction

Adaptive Regime-Switching Forecasts with Distribution-Free Uncertainty: Deep Switching State-Space Models Meet Conformal Prediction

๋ ˆ์ง ์ „ํ™˜(regime transition)์€ ์‹œ๊ณ„์—ด ๋ฐ์ดํ„ฐ์—์„œ ํ‰๊ท , ๋ถ„์‚ฐ, ์ž๊ธฐ์ƒ๊ด€ ๊ตฌ์กฐ๊ฐ€ ๊ธ‰๊ฒฉํžˆ ๋ฐ”๋€Œ๋Š” ํ˜„์ƒ์„ ์˜๋ฏธํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋น„์ •์ƒ์„ฑ์€ ์ „ํ†ต์ ์ธ ์‹œ๊ณ„์—ด ๋ชจ๋ธ์ด ๊ฐ€์ •ํ•˜๋Š” ์ •์ (stationary) ํŠน์„ฑ์„ ์œ„๋ฐฐํ•˜๋ฏ€๋กœ, ์˜ˆ์ธก๊ฐ’ ์ž์ฒด์˜ ์ •ํ™•๋„๋ฟ ์•„๋‹ˆ๋ผ ์˜ˆ์ธก ๋ถˆํ™•์‹ค์„ฑ์˜ ์ •ํ™•ํ•œ ์ถ”์ •์ด ํ•„์ˆ˜์ ์ด๋‹ค. ํŠนํžˆ, ์‹ค์‹œ๊ฐ„ ์˜์‚ฌ๊ฒฐ์ •์ด๋‚˜ ์œ„ํ—˜ ๊ด€๋ฆฌ์™€ ๊ฐ™์ด ๋ถˆํ™•์‹ค์„ฑ์— ๋Œ€ํ•œ ์‹ ๋ขฐ ๊ตฌ๊ฐ„์ด ์ง์ ‘์ ์ธ ๋น„์šฉยท์ด์ต์— ์—ฐ๊ฒฐ๋˜๋Š” ๋ถ„์•ผ์—์„œ๋Š” โ€œ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜๋œโ€ ๋ถˆํ™•์‹ค์„ฑ ์ถ”์ •์ด ํ•ต์‹ฌ ์š”๊ตฌ์‚ฌํ•ญ์ด ๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋‘ ๊ฐ€์ง€ ํ•ต์‹ฌ ๊ธฐ์ˆ ์„ ๊ฒฐํ•ฉํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” Deep Swi

Model
Adversarial Attack-Defense Co-Evolution for LLM Safety Alignment via Tree-Group Dual-Aware Search and Optimization

Adversarial Attack-Defense Co-Evolution for LLM Safety Alignment via Tree-Group Dual-Aware Search and Optimization

๋ณธ ๋…ผ๋ฌธ์€ LLM ์•ˆ์ „์„ฑ ์—ฐ๊ตฌ์—์„œ โ€œ๊ณต๋™์ง„ํ™”(coโ€‘evolution)โ€๋ผ๋Š” ์ƒˆ๋กœ์šด ํŒจ๋Ÿฌ๋‹ค์ž„์„ ์ œ์‹œํ•œ๋‹ค๋Š” ์ ์—์„œ ํ•™์ˆ ์ ยท์‹ค์šฉ์  ์˜์˜๊ฐ€ ํฌ๋‹ค. ๊ธฐ์กด์˜ ํƒˆ์˜ฅ ๊ณต๊ฒฉ ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์ •์ ์ธ ํ”„๋กฌํ”„ํŠธ ๋ณ€ํ˜•์ด๋‚˜ ์‚ฌ์ „ ์ •์˜๋œ ๊ณต๊ฒฉ ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋จธ๋ฌผ๋ €์œผ๋ฉฐ, ๋ฐฉ์–ด ์ธก๋ฉด์—์„œ๋„ ์‚ฌํ›„ ์ฐจ๋‹จ ํ•„ํ„ฐ๋‚˜ ์ •๊ทœํ™” ๊ธฐ๋ฒ•์— ์˜์กดํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์•˜๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ๋ฒ•์€ ์‹ค์ œ ์„œ๋น„์Šค ํ™˜๊ฒฝ์—์„œ ๊ณต๊ฒฉ์ž๊ฐ€ ์ง€์†์ ์œผ๋กœ ์ƒˆ๋กœ์šด ํ”„๋กฌํ”„ํŠธ๋ฅผ ์„ค๊ณ„ํ•˜๊ณ , ๋ฐฉ์–ด ์‹œ์Šคํ…œ์ด ์ด๋ฅผ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๋”ฐ๋ผ์žก๊ธฐ ์–ด๋ ค์šด โ€˜๋™์  ๊ฒฝ์Ÿ ๊ตฌ๋„โ€™๋ฅผ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ACEโ€‘Safety๋Š” ์ด ๋ฌธ์ œ๋ฅผ ๋‘ ๋‹จ๊ณ„์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ

AI-Trader: Benchmarking Autonomous Agents in Real-Time Financial Markets

AI-Trader: Benchmarking Autonomous Agents in Real-Time Financial Markets

์ด ๋…ผ๋ฌธ์€ ๊ธˆ์œต ์˜์‚ฌ๊ฒฐ์ •์—์„œ LLM์˜ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ•˜๋Š” ์ฒซ ๋ฒˆ์งธ ์™„์ „ ์ž๋™ํ™”๋œ ๋ฒค์น˜๋งˆํฌ์ธ AI Trader๋ฅผ ์†Œ๊ฐœํ•ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์‹ค์‹œ๊ฐ„ ํ™˜๊ฒฝ์—์„œ์˜ ์˜์‚ฌ๊ฒฐ์ • ๋Šฅ๋ ฅ์— ์ค‘์ ์„ ๋‘๊ณ  ์žˆ์œผ๋ฉฐ, ํŠนํžˆ ๊ธˆ์œต ๋ถ„์•ผ์—์„œ๋Š” ์—„๊ฒฉํ•œ ์š”๊ตฌ ์‚ฌํ•ญ์ด ์žˆ์–ด ์‹ค์ œ ์„ธ๊ณ„ ์ ์šฉ์„ ์œ„ํ•œ ์ฒด๊ณ„์ ์ธ ํ‰๊ฐ€๊ฐ€ ์ค‘์š”ํ•˜๋‹ค๋Š” ์ ์„ ๊ฐ•์กฐํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. AI Trader๋Š” ๋ฏธ๊ตญ ์ฃผ์‹, A์ฃผ, ์•”ํ˜ธํ™”ํ ์‹œ์žฅ์—์„œ ๋‹ค์–‘ํ•œ ๊ฑฐ๋ž˜ ๋‹จ์œ„๋ฅผ ํ†ตํ•ด ์‹ค์‹œ๊ฐ„ ํ™˜๊ฒฝ์„ ๋ชจ๋ฐฉํ•˜๋ฉฐ, LLM ์—์ด์ „ํŠธ๋“ค์ด ์ธ๊ฐ„์˜ ๊ฐœ์ž… ์—†์ด ๋…๋ฆฝ์ ์œผ๋กœ ์ •๋ณด๋ฅผ ๊ฒ€์ƒ‰ํ•˜๊ณ  ํ†ตํ•ฉํ•  ์ˆ˜ ์žˆ๋Š” ์ตœ์†Œ ์ •๋ณด ํŒจ๋Ÿฌ๋‹ค์ž„์„ ๊ตฌํ˜„ํ•ฉ๋‹ˆ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ

Anatomy-Guided Representation Learning Using a Transformer-Based Network for Thyroid Nodule Segmentation in Ultrasound Images

Anatomy-Guided Representation Learning Using a Transformer-Based Network for Thyroid Nodule Segmentation in Ultrasound Images

SSMT Net์€ ์ดˆ์ŒํŒŒ ์ด๋ฏธ์ง€์—์„œ ๊ฐ‘์ƒ์„  ์ข…์–‘์˜ ๋ถ„ํ• ์„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•œ ํ˜์‹ ์ ์ธ ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ด ๋ชจ๋ธ์€ ํŠนํžˆ ์–ด๋…ธํ…Œ์ด์…˜์ด ๋ถ€์กฑํ•œ ๋ฐ์ดํ„ฐ์…‹์—์„œ๋„ ํšจ๊ณผ์ ์œผ๋กœ ์ž‘๋™ํ•˜๋„๋ก ์„ค๊ณ„๋˜์—ˆ์œผ๋ฉฐ, ์ด๋ฅผ ์œ„ํ•ด ์ดˆ๊ธฐ ๋‹จ๊ณ„์—์„œ๋Š” ๋ฏธํ‘œ์‹œ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ณ€ํ™˜๊ธฐ ๊ธฐ๋ฐ˜ ์ธ์ฝ”๋”์˜ ํŠน์ง• ์ถ”์ถœ ๋Šฅ๋ ฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋ฐ˜๊ฐ๋… ํ•™์Šต์„ ์ ์šฉํ•œ๋‹ค. ์ด๋กœ์จ ๋ชจ๋ธ์€ ๋‹ค์–‘ํ•œ ์ข…์–‘ ํฌ๊ธฐ์™€ ํ˜•ํƒœ์— ๋Œ€ํ•ด ๋” ์ •ํ™•ํ•˜๊ฒŒ ๋ถ„ํ• ์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋œ๋‹ค. ๋˜ํ•œ, SSMT Net์€ ๊ฐ‘์ƒ์„  ์„ ์ฒด์˜ ์ „์ฒด์ ์ธ ๊ตฌ์กฐ๋ฅผ ์ดํ•ดํ•˜๋Š” ๋ฐ ํ•„์š”ํ•œ ์ „์—ญ์  ๋ฌธ๋งฅ ์ •๋ณด์™€ ์ข…์–‘์˜ ์„ธ๋ถ€ ํŠน์ง•์„ ํŒŒ์•…ํ•˜๋Š” ์ง€์—ญ์  ๋ฌธ๋งฅ

Network Learning
AQUA-Net: Adaptive Frequency Fusion and Illumination Aware Network for Underwater Image Enhancement

AQUA-Net: Adaptive Frequency Fusion and Illumination Aware Network for Underwater Image Enhancement

๋ณธ ๋…ผ๋ฌธ์€ ์ˆ˜์ค‘ ์ด๋ฏธ์ง€ ๋ณต์› ๋ถ„์•ผ์—์„œ ๋‘ ๊ฐ€์ง€ ํ•ต์‹ฌ ๋ฌธ์ œโ€”๊ด‘ํ•™์  ์™œ๊ณก๊ณผ ์‹ค์‹œ๊ฐ„ ์ฒ˜๋ฆฌ ๊ฐ€๋Šฅ์„ฑโ€”๋ฅผ ๋™์‹œ์— ํ•ด๊ฒฐํ•˜๋ ค๋Š” ์‹œ๋„๋กœ ์ฃผ๋ชฉํ•  ๋งŒํ•˜๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ, ๊ธฐ์กด์˜ ๋Œ€๋ถ€๋ถ„ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ์ˆ˜์ค‘ ์ด๋ฏธ์ง€ ํ–ฅ์ƒ ๋ชจ๋ธ์€ RGB ๊ณต๊ฐ„์—์„œ๋งŒ ํŠน์ง•์„ ์ถ”์ถœํ•˜๊ณ , ์ƒ‰ ์™œ๊ณก์„ ๋ณด์ •ํ•˜๊ธฐ ์œ„ํ•ด ๋ณต์žกํ•œ ๋ฌผ๋ฆฌ ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์ด๋‚˜ ๋Œ€๊ทœ๋ชจ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์‚ฌ์šฉํ•œ๋‹ค. ์ด์— ๋น„ํ•ด AQUAโ€‘Net์€ ํ‘ธ๋ฆฌ์— ๋ณ€ํ™˜์„ ์ด์šฉํ•œ ์ฃผํŒŒ์ˆ˜ ๋„๋ฉ”์ธ ์ •๋ณด๋ฅผ ์ธ์ฝ”๋”์— ํ†ตํ•ฉํ•จ์œผ๋กœ์จ, ์ €์ฃผํŒŒ์—์„œ๋Š” ์ „๋ฐ˜์ ์ธ ์ƒ‰์กฐ์™€ ์กฐ๋ช…์„, ๊ณ ์ฃผํŒŒ์—์„œ๋Š” ํ…์Šค์ฒ˜์™€ ๊ฐ€์žฅ์ž๋ฆฌ ์ •๋ณด๋ฅผ ๋ณ„๋„๋กœ ๊ฐ•ํ™”ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ฃผํŒŒ์ˆ˜ ์œตํ•ฉ์€ ํŠนํžˆ ๊นŠ์€

Network
ARIAL: An Agentic Framework for Document VQA with Precise Answer Localization

ARIAL: An Agentic Framework for Document VQA with Precise Answer Localization

ARIAL์€ ๋ฌธ์„œ VQA ๋ฌธ์ œ๋ฅผ โ€œํ…์ŠคํŠธ ์ถ”์ถœ โ†’ ์ปจํ…์ŠคํŠธ ๊ฒ€์ƒ‰ โ†’ ๋‹ต๋ณ€ ์ƒ์„ฑ โ†’ ์œ„์น˜ ์ง€์ •โ€์ด๋ผ๋Š” ๋„ค ๋‹จ๊ณ„๋กœ ๋ช…ํ™•ํžˆ ๋ถ„ํ•ดํ•œ๋‹ค๋Š” ์ ์—์„œ ๊ธฐ์กด ์—”๋“œโ€‘ํˆฌโ€‘์—”๋“œ ๋ชจ๋ธ๊ณผ ์ฐจ๋ณ„ํ™”๋œ๋‹ค. ์ฒซ ๋‹จ๊ณ„์—์„œ TrOCR๋ฅผ ์ด์šฉํ•ด ๊ณ ํ•ด์ƒ๋„ ๋ฌธ์„œ ์ด๋ฏธ์ง€์—์„œ ๋ฌธ์ž ์ˆ˜์ค€์˜ ํ…์ŠคํŠธ๋ฅผ ์ถ”์ถœํ•˜๋Š”๋ฐ, ์ด๋Š” ์ตœ์‹  OCR ๋ชจ๋ธ์ด ์ œ๊ณตํ•˜๋Š” ๋†’์€ ์ธ์‹๋ฅ ์„ ๊ทธ๋Œ€๋กœ ํ™œ์šฉํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ ๋‹จ๊ณ„๋Š” ์˜๋ฏธ ๊ธฐ๋ฐ˜ ๊ฒ€์ƒ‰ ์—”์ง„์„ ํ†ตํ•ด ์งˆ๋ฌธ๊ณผ ๊ฐ€์žฅ ์—ฐ๊ด€์„ฑ์ด ๋†’์€ ๋ฌธ์„œ ๊ตฌ๊ฐ„์„ ์„ ๋ณ„ํ•จ์œผ๋กœ์จ, ๋Œ€๊ทœ๋ชจ ์‚ฌ์ „ ํ•™์Šต ์–ธ์–ด ๋ชจ๋ธ์ด ๋ถˆํ•„์š”ํ•œ ์ •๋ณด์— ๋…ธ์ถœ๋˜๋Š” ์œ„ํ—˜์„ ์ตœ์†Œํ™”ํ•œ๋‹ค. ์„ธ ๋ฒˆ์งธ ๋‹จ๊ณ„์—์„œ๋Š” Gemma

Framework
Benchmarking LLM Agents for Wealth-Management Workflows

Benchmarking LLM Agents for Wealth-Management Workflows

๋ณธ ๋…ผ๋ฌธ์€ ๊ธฐ์กด์˜ ํ˜‘์—… ๋„๊ตฌ๊ฐ€ ์ œ๊ณตํ•˜๋Š” ๊ธฐ๋Šฅ์  ํ•œ๊ณ„๋ฅผ ์ธ์‹ํ•˜๊ณ , ์žฌ๋ฌดยท์ž์‚ฐ ๊ด€๋ฆฌ๋ผ๋Š” ๊ณ ๋ถ€๊ฐ€๊ฐ€์น˜ ์˜์—ญ์— LLM ๊ธฐ๋ฐ˜ ์—์ด์ „ํŠธ๋ฅผ ์ ์šฉํ•จ์œผ๋กœ์จ ์ž๋™ํ™” ๊ฐ€๋Šฅ์„ฑ์„ ํƒ์ƒ‰ํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ํ•ต์‹ฌ ๊ธฐ์—ฌ๋Š” TheAgentCompany ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์žฌ๋ฌด ์ „์šฉ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํ™˜๊ฒฝ์œผ๋กœ ํ™•์žฅํ•œ ์ ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ €์ž๋Š” ์‹ค์ œ ๊ธˆ์œต ์—…๋ฌด์—์„œ ํ”ํžˆ ๋ฐœ์ƒํ•˜๋Š” ํฌํŠธํด๋ฆฌ์˜ค ์กฐํšŒ, ํˆฌ์ž ์„ฑ๊ณผ ๋ถ„์„, ๊ณ ๊ฐ ๋ณด๊ณ ์„œ ์ž‘์„ฑ ๋“ฑ์„ ๋ชจ๋ธ๋งํ•˜๊ณ , ๊ฐ€์ƒ์˜ ๋™๋ฃŒ(์˜ˆ: ์žฌ๋ฌด ๋ถ„์„๊ฐ€, ๊ณ ๊ฐ ์„œ๋น„์Šค ๋‹ด๋‹น์ž)์™€์˜ ์ธํ„ฐ๋ž™์…˜์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ ๊ธฐ์—ฌ๋Š” ํ•ฉ์„ฑ ๋„๋ฉ”์ธ ๋ฐ์ดํ„ฐ๋ฅผ ์ฒด๊ณ„์ ์œผ๋กœ ์ƒ์„ฑํ•œ

BlendedNet++: A Large-Scale Blended Wing Body Aerodynamics Dataset and Benchmark

BlendedNet++: A Large-Scale Blended Wing Body Aerodynamics Dataset and Benchmark

BlendedNet++๋Š” ํ˜„์žฌ ํ•ญ๊ณต๊ธฐ ์„ค๊ณ„ ๋ถ„์•ผ์—์„œ ๊ฐ€์žฅ ์‹œ๊ธ‰ํ•œ ๋‘ ๊ฐ€์ง€ ๋ฌธ์ œ, ์ฆ‰ ๊ณ ํ•ด์ƒ๋„ ์ ๋ณ„ ๊ณต๊ธฐ์—ญํ•™ ๋ฐ์ดํ„ฐ์˜ ๋ถ€์žฌ์™€ ์—ญ์„ค๊ณ„ ๊ณผ์ •์˜ ์žฌํ˜„์„ฑ ๋ถ€์กฑ์„ ๋™์‹œ์— ํ•ด๊ฒฐํ•˜๋ ค๋Š” ์‹œ๋„์ด๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ, 12,000์—ฌ ๊ฐœ์˜ ์„œ๋กœ ๋‹ค๋ฅธ BWB ํ˜•์ƒ์„ ํฌํ•จํ•˜๊ณ  ๊ฐ๊ฐ์— ๋Œ€ํ•ด ์ •๋ฐ€ RANS CFD ํ•ด์„์„ ์ˆ˜ํ–‰ํ•œ 12,490๊ฐœ์˜ ์ƒ˜ํ”Œ์€ ๊ธฐ์กด ๊ณต๊ฐœ ๋ฐ์ดํ„ฐ์…‹์ด ๋ช‡ ๋ฐฑ ๊ฐœ ์ˆ˜์ค€์— ๋จธ๋ฌผ๋ €๋˜ ๊ฒƒ์— ๋น„ํ•ด ํš๊ธฐ์ ์œผ๋กœ ๊ทœ๋ชจ๊ฐ€ ํ™•๋Œ€๋œ ์ ์ด ํฐ ์žฅ์ ์ด๋‹ค. ํŠนํžˆ ์••๋ ฅยทํ”ผ๋ถ€๋งˆ์ฐฐ ๊ณ„์ˆ˜์™€ ๊ฐ™์€ ํ‘œ๋ฉด ํ•„๋“œ๋ฅผ ๋ฐ€์ง‘ํ•˜๊ฒŒ ์ œ๊ณตํ•จ์œผ๋กœ์จ, ์ „ํ†ต์ ์ธ ํ†ตํ•ฉ ๊ณ„์ˆ˜(C L, C D, C M)๋ฟ

Data
CAPTURE: A Benchmark and Evaluation for LVLMs in CAPTCHA Resolving

CAPTURE: A Benchmark and Evaluation for LVLMs in CAPTCHA Resolving

์ด ๋…ผ๋ฌธ์€ ๋Œ€ํ˜• ์‹œ๊ฐ ์–ธ์–ด ๋ชจ๋ธ(LVLM)์„ ํ‰๊ฐ€ํ•˜๊ณ  ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด CAPTCHA ๋ฒค์น˜๋งˆํฌ์ธ CAPTURE๋ฅผ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ๊ธฐ์กด์˜ CAPTCHA ๋ฒค์น˜๋งˆํฌ๋Š” ํŠน์ • ์—ฐ๊ตฌ ๋ชฉํ‘œ์— ๋งž์ถฐ ์„ค๊ณ„๋˜์–ด ์žˆ์–ด, ๋ชจ๋“  CAPTCHA ์œ ํ˜•์„ ํฌ๊ด„ํ•˜์ง€ ๋ชปํ•˜๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ์Šต๋‹ˆ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด CAPTURE๋Š” ๋‹ค์–‘ํ•œ CAPTCHA ์œ ํ˜•๊ณผ ํ•˜์œ„ ์œ ํ˜•์„ ํฌํ•จํ•˜์—ฌ LVLM ์„ฑ๋Šฅ์„ ๋‹ค๊ฐ๋„๋กœ ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ๋„๋ก ์„ค๊ณ„๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ, ๊ธฐ์กด์˜ LVLM์€ CAPTCHA๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ๋ฐ์—์„œ ์ œ์•ฝ์ด ์žˆ์—ˆ์ง€๋งŒ, CRRD ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ํ†ตํ•ด ์„ฑ๋Šฅ์ด ํฌ๊ฒŒ ํ–ฅ์ƒ๋˜์—ˆ์Šต๋‹ˆ๋‹ค.

Circuits, Features, and Heuristics in Molecular Transformers

Circuits, Features, and Heuristics in Molecular Transformers

๋ณธ ๋…ผ๋ฌธ์€ ํŠธ๋žœ์Šคํฌ๋จธ ๋ชจ๋ธ์ด ์–ด๋–ป๊ฒŒ ์•ฝ๋ฌผ๊ณผ ์œ ์‚ฌํ•œ ์†Œ๋ถ„์ž๋ฅผ ์ƒ์„ฑํ•˜๋Š”์ง€์— ๋Œ€ํ•ด ๊นŠ๊ฒŒ ๋ถ„์„ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ, ํŠธ๋žœ์Šคํฌ๋จธ๊ฐ€ ์–ด๋–ป๊ฒŒ ํ™”ํ•™ ๊ตฌ์กฐ์˜ ์œ ํšจ์„ฑ๊ณผ ๋‹ค์–‘์„ฑ์„ ๋ณด์žฅํ•˜๋ฉด์„œ ์ƒˆ๋กœ์šด ๋ถ„์ž๋ฅผ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ๋Š”์ง€๋ฅผ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ๊ณ„์‚ฐ ํŒจํ„ด์„ ์‹๋ณ„ํ•ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๋‚ฎ์€ ์ˆ˜์ค€์˜ ๋ฌธ๋ฒ• ํ•ด์„๋ถ€ํ„ฐ ๋” ์ถ”์ƒ์ ์ธ ํ™”ํ•™์  ์œ ํšจ์„ฑ ์ œ์•ฝ ์กฐ๊ฑด๊นŒ์ง€, ํŠธ๋žœ์Šคํฌ๋จธ๊ฐ€ ์–ด๋–ป๊ฒŒ ์ด๋Ÿฌํ•œ ๋ณต์žกํ•œ ๊ทœ์น™๋“ค์„ ํ•™์Šตํ•˜๊ณ  ์ ์šฉํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ํ†ต์ฐฐ๋ ฅ์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ํฌ์†Œ ์˜คํ† ์ธ์ฝ”๋”(SAEs)๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํŠน์ง• ์‚ฌ์ „์„ ์ถ”์ถœํ•จ์œผ๋กœ์จ, ๋ชจ๋ธ์ด ์–ด๋–ค ํ™”ํ•™์  ํŠน์„ฑ์„ ์ดํ•ดํ•˜๊ณ  ์žˆ๋Š”์ง€๋ฅผ ํŒŒ์•…ํ• 

Composite Classifier-Free Guidance for Multi-Modal Conditioning in Wind Dynamics Super-Resolution

Composite Classifier-Free Guidance for Multi-Modal Conditioning in Wind Dynamics Super-Resolution

๋ณธ ๋…ผ๋ฌธ์€ ํ’์† ๋ฐ์ดํ„ฐ์™€ ๊ฐ™์€ ๋‹ค์ฑ„๋„ ์‹œ๊ณ„์—ดยท๊ณต๊ฐ„ ๋ฐ์ดํ„ฐ๋ฅผ ๊ณ ํ•ด์ƒ๋„๋กœ ๋ณต์›ํ•˜๋Š” ๋ฌธ์ œ์— ํ™•์‚ฐ ๋ชจ๋ธ์„ ์ ์šฉํ•˜๋ฉด์„œ, ๊ธฐ์กด ํด๋ž˜์Šคํ”„๋ฆฌ ๊ฐ€์ด๋˜์Šค(CFG)์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ๋‹ค. ์ „ํ†ต์ ์ธ CFG๋Š” ํ•˜๋‚˜์˜ ์กฐ๊ฑด(์˜ˆ: ํ…์ŠคํŠธ ํ”„๋กฌํ”„ํŠธ)๋งŒ์„ ๊ณ ๋ คํ•˜๋„๋ก ์„ค๊ณ„๋ผ, ๋‹ค์ค‘ ์กฐ๊ฑด์ด ๋™์‹œ์— ์กด์žฌํ•˜๋Š” ์ƒํ™ฉ์—์„œ๋Š” ๊ฐ€์ด๋“œ ํšจ๊ณผ๊ฐ€ ํฌ์„๋œ๋‹ค. ํ’์† ์ดˆํ•ด์ƒ๋„์—์„œ๋Š” ์ง€ํ˜• ๊ณ ๋„, ์˜จ๋„, ์Šต๋„, ๋Œ€๊ธฐ์•• ๋“ฑ 10๊ฐœ ์ด์ƒ์˜ ๋ณด์กฐ ๋ณ€์ˆ˜๋“ค์ด ๋ชจ๋ธ ์ž…๋ ฅ์œผ๋กœ ์‚ฌ์šฉ๋˜๋ฉฐ, ์ด๋“ค ๊ฐ๊ฐ์ด ์žฌ๊ตฌ์„ฑ ํ’ˆ์งˆ์— ์ค‘์š”ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ์ €์ž๋“ค์€ ์ด๋Ÿฌํ•œ ๋‹ค์ค‘ ์กฐ๊ฑด์„ ํ•˜๋‚˜์˜ ๋ณตํ•ฉ

Conditional sampling for barrier option pricing under the LT method

Conditional sampling for barrier option pricing under the LT method

์ด ์—ฐ๊ตฌ๋Š” ๋ฐฐ๋ฆฌ์–ด ์˜ต์…˜, ํŠนํžˆ ์ด์‚ฐ์ ์œผ๋กœ ๋ชจ๋‹ˆํ„ฐ๋ง๋˜๋Š” ๋…ธํฌโ€‘์•„์›ƒ/๋…ธํฌโ€‘์ธ ์˜ต์…˜์˜ ๊ฐ€๊ฒฉ ํ‰๊ฐ€์— ์žˆ์–ด ๋‘ ๊ฐ€์ง€ ์ฃผ์š” ๋‚œ๊ด€์„ ๋™์‹œ์— ํ•ด๊ฒฐํ•œ๋‹ค๋Š” ์ ์—์„œ ์˜๋ฏธ๊ฐ€ ํฌ๋‹ค. ์ฒซ ๋ฒˆ์งธ ๋‚œ๊ด€์€ โ€œํฌ์†Œํ•œ ์„ฑ๊ณต ๊ฒฝ๋กœโ€ ๋ฌธ์ œ์ด๋‹ค. ๋ฐฐ๋ฆฌ์–ด์— ๋„๋‹ฌํ•˜์ง€ ์•Š๋Š” ๊ฒฝ๋กœ๊ฐ€ ๊ทนํžˆ ์ ์€ ๊ฒฝ์šฐ, ์ „ํ†ต์ ์ธ Monteโ€‘Carlo ์‹œ๋ฎฌ๋ ˆ์ด์…˜์€ ๋Œ€๋ถ€๋ถ„ 0์˜ ์ง€๊ธ‰์•ก์„ ์‚ฐ์ถœํ•ด ์ถ”์ •๋Ÿ‰์˜ ๋ถ„์‚ฐ์ด ๊ธ‰๊ฒฉํžˆ ์ปค์ง„๋‹ค. GlassermanยทStaโ€‹um(2001)์€ ๋งค ๋ชจ๋‹ˆํ„ฐ๋ง ์‹œ์ ๋งˆ๋‹ค โ€˜์ƒ์กด(survival)โ€™ ์กฐ๊ฑด์„ ๊ฐ•์ œํ•˜๋Š” ์กฐ๊ฑด๋ถ€ ์ƒ˜ํ”Œ๋ง์„ ์ œ์•ˆํ–ˆ์ง€๋งŒ, ์ด ๋ฐฉ๋ฒ•์€ ๊ฒฝ๋กœ ์ƒ์„ฑ์— ์‚ฌ์šฉ๋˜๋Š” ๊ธฐ๋ณธ ๋‚œ

Mathematics Quantitative Finance
Constant-Time Motion Planning with Manipulation Behaviors

Constant-Time Motion Planning with Manipulation Behaviors

๋ณธ ๋…ผ๋ฌธ์ด ์ œ์‹œํ•˜๋Š” Bโ€‘CTMP๋Š” ๊ธฐ์กด CTMP ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„๋ฅผ ๋›ฐ์–ด๋„˜๋Š” ์ค‘์š”ํ•œ ์ง„์ „์ด๋‹ค. CTMP๋Š” ์‚ฌ์ „ ๊ณ„์‚ฐ์„ ํ†ตํ•ด ์ฟผ๋ฆฌ ๋‹จ๊ณ„์—์„œ ์ผ์ •ํ•œ ์‹คํ–‰ ์‹œ๊ฐ„์„ ๋ณด์žฅํ•œ๋‹ค๋Š” ์ ์—์„œ ์‹ค์‹œ๊ฐ„ ๋กœ๋ด‡ ์‹œ์Šคํ…œ์— ๋งค๋ ฅ์ ์ด์ง€๋งŒ, ์‹ค์ œ ์กฐ์ž‘ ์ž‘์—…์€ ๋‹จ์ˆœํžˆ ์ถฉ๋Œ์„ ํ”ผํ•˜๋Š” ๊ฒƒ๋งŒ์œผ๋กœ๋Š” ์ถฉ๋ถ„ํ•˜์ง€ ์•Š๋‹ค. ๋ฌผ์ฒด๋ฅผ ์žก๊ฑฐ๋‚˜ ์‚ฝ์ž…ํ•˜๋Š” ํ–‰๋™์€ ๋กœ๋ด‡์˜ ์—”๋“œ ์ดํŽ™ํ„ฐ์™€ ๋Œ€์ƒ ๋ฌผ์ฒด ์‚ฌ์ด์˜ ๋ณต์žกํ•œ ์ ‘์ด‰ ์—ญํ•™์„ ํฌํ•จํ•˜๋ฉฐ, ์ด๋Š” ์ผ๋ฐ˜์ ์ธ ๊ฒฝ๋กœ ๊ณ„ํš ๊ณต๊ฐ„๊ณผ๋Š” ๋ณ„๊ฐœ์˜ ์ฐจ์›์œผ๋กœ ์ทจ๊ธ‰๋  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. Bโ€‘CTMP๋Š” ์ด๋Ÿฌํ•œ ๋‘ ๋‹จ๊ณ„ ๊ณผ์ •์„ ํ•˜๋‚˜์˜ ํ†ตํ•ฉ ํ”„๋ ˆ์ž„์›Œํฌ๋กœ ๋ฌถ์Œ์œผ๋กœ์จ, โ€œ์ด๋™โ€‘ํ–‰๋™โ€ ์—ฐ์‡„

Contemporary Shrinking of Colombia's Highest Mountains: Pico Simon Bolivar and Pico Cristobal Colon

Contemporary Shrinking of Colombia's Highest Mountains: Pico Simon Bolivar and Pico Cristobal Colon

์ด ๋…ผ๋ฌธ์€ ์ฝœ๋Ÿผ๋น„์•„์˜ ์ตœ๊ณ ๋ด‰์ธ ํฌ๋ฆฌ์Šคํ†  ์ฝœ๋ก ๊ณผ ์‹œ๋ฉ”์˜จ ๋ณผ๋ฆฌ๋ฐ”๋ฅด์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ๊ณ ๋„ ์ธก์ • ๊ฒฐ๊ณผ๋ฅผ ์ œ์‹œํ•˜๋ฉฐ, ๊ธฐํ›„ ๋ณ€ํ™”๋กœ ์ธํ•ด ๋‘ ์‚ฐ ์ •์ƒ์˜ ๋น™ํ•˜๊ฐ€ ํฌ๊ฒŒ ์ค„์–ด๋“ค์—ˆ๋‹ค๋Š” ๊ฒƒ์„ ๊ฐ•์กฐํ•ฉ๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” 1939๋…„ ์ดํ›„ ์ฒ˜์Œ์œผ๋กœ ์ •ํ™•ํ•œ ์ธก์ •์„ ์ˆ˜ํ–‰ํ–ˆ์œผ๋ฉฐ, ์ด๋ฅผ ์œ„ํ•ด ์ฐจ๋“ฑ GPS์™€ ์•„๋ธŒ๋‹ˆ ๋ ˆ๋ฒจ์ด๋ผ๋Š” ๊ณ ๋„ ์ธก์ • ์žฅ๋น„๋ฅผ ์‚ฌ์šฉํ–ˆ์Šต๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ธฐ์ˆ ์˜ ํ™œ์šฉ์€ ๊ณผ๊ฑฐ์— ๋น„ํ•ด ํ›จ์”ฌ ๋” ์ •๋ฐ€ํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•˜์˜€์Šต๋‹ˆ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด, ์‹œ๋ฉ”์˜จ ๋ณผ๋ฆฌ๋ฐ”๋ฅด์™€ ํฌ๋ฆฌ์Šคํ†  ์ฝœ๋ก ์˜ ๋น™ํ•˜๊ฐ€ ๊ฐ๊ฐ 53m์™€ 62m ์ค„์–ด๋“ค์—ˆ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€์Šต๋‹ˆ๋‹ค. ์ด๋Š” ๊ธฐํ›„

Context-Aware Agentic Power Resources Optimisation in EV using Smart2ChargeApp

Context-Aware Agentic Power Resources Optimisation in EV using Smart2ChargeApp

๋ณธ ๋…ผ๋ฌธ์€ ์Šค๋งˆํŠธ ์ „๊ธฐ์ฐจ(EV) ์ถฉ์ „ ์ƒํƒœ๊ณ„๋ฅผ ์ตœ์ ํ™”ํ•˜๊ธฐ ์œ„ํ•œ CAMAC DRA ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์‹œํ•˜๋ฉฐ, ์ด๋Š” ์ƒํ™ฉ ์ธ์‹ ๋‹ค์ค‘ ์—์ด์ „ํŠธ ์กฐ์ •์„ ํ†ตํ•ด ๋™์ ์ธ ํ™˜๊ฒฝ ์กฐ๊ฑด์— ์ ์‘ํ•˜๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์ด๋‹ค. ํŠนํžˆ, ๋ณธ ๋…ผ๋ฌธ์€ ๊ธฐ์กด์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜๋“ค๊ณผ ๋น„๊ตํ•˜์—ฌ ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ๋ฉฐ, ๋‹ค์–‘ํ•œ ์ดํ•ด๊ด€๊ณ„์ž๋“ค์˜ ์š”๊ตฌ๋ฅผ ๊ท ํ˜• ์žˆ๊ฒŒ ์ถฉ์กฑ์‹œํ‚ค๋Š” ๋ฐ ์ค‘์ ์„ ๋‘๊ณ  ์žˆ๋‹ค. CAMAC DRA ํ”„๋ ˆ์ž„์›Œํฌ๋Š” 250๋Œ€์˜ EV์™€ 45๊ฐœ์˜ ์ถฉ์ „์†Œ ๋„คํŠธ์›Œํฌ์— ๊ฑธ์ณ ์ž์œจ์ ์ธ ์ถฉ์ „ ์—์ด์ „ํŠธ๋ฅผ ์กฐ์ •ํ•˜๊ณ , ๋‚ ์”จ ํŒจํ„ด, ๊ตํ†ต ์ƒํ™ฉ, ์ „๋ ฅ๋ง ๋ถ€ํ•˜ ๋ณ€๋™, ์ „๊ธฐ ์š”๊ธˆ ๋“ฑ ๋‹ค์–‘ํ•œ ์ปจํ…์ŠคํŠธ ํŠน

Critical Analysis of the Binomial-Tree approach to Convertible Bonds in   the framework of Tsiveriotis-Fernandes model

Critical Analysis of the Binomial-Tree approach to Convertible Bonds in the framework of Tsiveriotis-Fernandes model

๋ณธ ๋…ผ๋ฌธ์€ ์ „ํ™˜์‚ฌ์ฑ„(โ€‹Convertible Bond, ์ดํ•˜ CB) ๊ฐ€๊ฒฉ ๊ฒฐ์ • ๋ถ„์•ผ์—์„œ ๊ฐ€์žฅ ์˜ค๋ž˜๋œ ์ˆ˜์น˜ ๊ธฐ๋ฒ• ์ค‘ ํ•˜๋‚˜์ธ ์ดํ•ญโ€‘ํŠธ๋ฆฌ ์ ‘๊ทผ๋ฒ•์„ ๊ทผ๋ณธ์ ์œผ๋กœ ์žฌ๊ฒ€ํ† ํ•œ๋‹ค๋Š” ์ ์—์„œ ํ•™์ˆ ์ ยท์‹ค๋ฌด์  ์˜์˜๊ฐ€ ํฌ๋‹ค. ๋จผ์ € ์ €์ž๋Š” TF ๋ชจ๋ธ์ด โ€œํ˜„๊ธˆ ํ๋ฆ„ ๋ถ€๋ถ„(์‹ ์šฉ ์œ„ํ—˜์— ๋…ธ์ถœ)โ€๊ณผ โ€œ์ฃผ์‹ ๋ถ€๋ถ„(์‹ ์šฉ ์œ„ํ—˜์— ๋ฌด๊ด€)โ€์„ ๋ช…ํ™•ํžˆ ๊ตฌ๋ถ„ํ•จ์œผ๋กœ์จ ๋‘ ๊ฐœ์˜ ์—ฐ๊ณ„๋œ ํŽธ๋ฏธ๋ถ„ ๋ฐฉ์ •์‹(PDE) ์‹œ์Šคํ…œ์„ ๋„์ถœํ•œ๋‹ค๋Š” ์ ์„ ๊ฐ•์กฐํ•œ๋‹ค. ์ด ๊ตฌ์กฐ๋Š” CB๊ฐ€ ์ฑ„๊ถŒ๊ณผ ์ฃผ์‹ ๋‘ ์„ฑ๊ฒฉ์„ ๋™์‹œ์— ๊ฐ–๋Š” ๋ณตํ•ฉ ํŒŒ์ƒ์ƒํ’ˆ์ด๋ผ๋Š” ๋ณธ์งˆ์„ ์ •ํ™•ํžˆ ๋ฐ˜์˜ํ•œ๋‹ค. ํ•˜์ง€๋งŒ ๊ธฐ์กด์˜ Hull์ด ์ œ์•ˆํ•œ CRR ๊ธฐ๋ฐ˜

Model Quantitative Finance Analysis Framework
Critical point correlations in random gaussian fields

Critical point correlations in random gaussian fields

๋ณธ ๋…ผ๋ฌธ์€ ๋ฌด์ž‘์œ„ ๊ฐ€์šฐ์‹œ์•ˆ ์žฅ์˜ ์ž„๊ณ„์  ํ†ต๊ณ„์— ๋Œ€ํ•œ ๊ทผ๋ณธ์ ์ธ ์งˆ๋ฌธ์„ ๋‹ค๋ฃฌ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์ž„๊ณ„์ ์˜ ํ‰๊ท  ๋ฐ€๋„, ์ฆ‰ Kacโ€‘Rice ๊ณต์‹์— ์˜ํ•ด ์–ป์–ด์ง€๋Š” ( rho ) ์ž์ฒด์— ์ดˆ์ ์„ ๋งž์ถ”์–ด ์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‹ค์ œ ๋ฌผ๋ฆฌ ์‹œ์Šคํ…œโ€”์˜ˆ๋ฅผ ๋“ค์–ด ์šฐ์ฃผ ๋Œ€๊ทœ๋ชจ ๊ตฌ์กฐ, ๊ด‘ํ•™ ํฌํ…์…œ์— ๊ฐ‡ํžŒ ์ดˆ์ €์˜จ ์›์ž, ํ˜น์€ ๋ณต์žกํ•œ ์—๋„ˆ์ง€ ์ง€ํ˜•โ€”์—์„œ๋Š” ์ผ์ • ๋ถ€ํ”ผ ์•ˆ์— ์กด์žฌํ•˜๋Š” ์ž„๊ณ„์  ์ˆ˜์˜ ๋ณ€๋™์ด ์ค‘์š”ํ•œ ๋ฌผ๋ฆฌ๋Ÿ‰์ด ๋œ๋‹ค. ์ด ๋ณ€๋™์„ ์ •๋Ÿ‰ํ™”ํ•˜๋ ค๋ฉด ๋‘ ์  ์ƒ๊ด€ํ•จ์ˆ˜ (C { rho}( mathbf r) ) ๊ฐ€ ํ•„์š”ํ•˜๋ฉฐ, ์ด๋Š” โ€œ์ž„๊ณ„์  ์Œ์˜ ์ƒ๊ด€โ€์ด๋ผ๊ณ ๋„ ํ•  ์ˆ˜ ์žˆ๋‹ค.

Physics Nonlinear Sciences Condensed Matter
Data-Free Pruning of Self-Attention Layers in LLMs

Data-Free Pruning of Self-Attention Layers in LLMs

๋ณธ ๋…ผ๋ฌธ์€ ๋Œ€๊ทœ๋ชจ ์–ธ์–ด ๋ชจ๋ธ(Large Language Model, LLM)์˜ ๊ตฌ์กฐ์  ํšจ์œจ์„ฑ์„ ์žฌ์กฐ๋ช…ํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ๋Š” ๋ชจ๋ธ ๊ฒฝ๋Ÿ‰ํ™”๋ฅผ ์œ„ํ•ด ํŒŒ๋ผ๋ฏธํ„ฐ ์–‘์„ ์ค„์ด๊ฑฐ๋‚˜, ์ง€์‹ ์ฆ๋ฅ˜, ์–‘์žํ™”์™€ ๊ฐ™์€ ๊ธฐ์ˆ ์„ ํ™œ์šฉํ–ˆ์ง€๋งŒ, ๋Œ€๋ถ€๋ถ„์€ ์‚ฌ์ „ ํ•™์Šต๋œ ๊ฐ€์ค‘์น˜๋ฅผ ์žฌ์กฐ์ •ํ•˜๊ฑฐ๋‚˜ ๋Œ€๋Ÿ‰์˜ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ ๋ฐ์ดํ„ฐ๋ฅผ ํ•„์š”๋กœ ํ–ˆ๋‹ค. ์—ฌ๊ธฐ์„œ ์ €์ž๋“ค์€ โ€œAttention Suppression Hypothesisโ€(์ฃผ์˜ ์–ต์ œ ๊ฐ€์„ค)๋ฅผ ์ œ์‹œํ•œ๋‹ค. ์ด ๊ฐ€์„ค์— ๋”ฐ๋ฅด๋ฉด, ์‚ฌ์ „ ํ•™์Šต ๋‹จ๊ณ„์—์„œ ์ผ๋ถ€ ๊นŠ์€ selfโ€‘attention ๋ ˆ์ด์–ด๋Š” ์ž์‹ ์ด ์ƒ์„ฑํ•˜๋Š” ์–ดํ…์…˜ ๊ฐ€์ค‘์น˜๋ฅผ ์˜๋„์ ์œผ๋กœ

Data
DC-Biased Homogenized Harmonic Balance Finite Element Method

DC-Biased Homogenized Harmonic Balance Finite Element Method

๋ณธ ๋…ผ๋ฌธ์€ ์ „ํ†ต์ ์ธ ์ „์ž๊ธฐ ํ•ด์„์—์„œ ๊ฐ€์žฅ ํฐ ๋ณ‘๋ชฉ ํ˜„์ƒ ์ค‘ ํ•˜๋‚˜์ธ ์™€์ „๋ฅ˜ ๋ฌธ์ œ๋ฅผ ํš๊ธฐ์ ์œผ๋กœ ๊ฐ€์†ํ™”ํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ๋‹ค. ๊ธฐ์กด์˜ ์‹œ๊ฐ„ ์˜์—ญ ์ „์ด ํ•ด์„์€ ๋ฏธ์„ธํ•œ ๋ž˜๋ฏธ๋„ค์ดํŠธ ๊ตฌ์กฐ๋ฅผ ์ง์ ‘ ๋ฉ”์‰ฌํ•ด์•ผ ํ•˜๋ฏ€๋กœ ์ž์œ ๋„๊ฐ€ ๊ธ‰์ฆํ•˜๊ณ , ๋น„์„ ํ˜• ์žฌ๋ฃŒ ํŠน์„ฑ(ํŠนํžˆ ๊ฐ•์ž์„ฑ ํฌํ™”) ๋•Œ๋ฌธ์— ์ˆ˜๋ ด์ด ์–ด๋ ค์›Œ์ง„๋‹ค. ์ €์ž๋“ค์€ ์กฐํ™”๊ท ํ˜•(Harmonic Balance) ๊ธฐ๋ฒ•์„ ์ด์šฉํ•ด ๋น„์„ ํ˜• ์‹œ์Šคํ…œ์„ ์ฃผํŒŒ์ˆ˜ ์˜์—ญ์—์„œ ๋‹ค๋ฃจ๋ฉด์„œ, ๋™์‹œ์— ๋™์งˆํ™”(Homogenization) ๊ธฐ๋ฒ•์œผ๋กœ ๋ž˜๋ฏธ๋„ค์ดํŠธ ์ธต์„ ํ‰๊ท ํ™”ํ•œ๋‹ค. ํ•ต์‹ฌ ์•„์ด๋””์–ด๋Š” โ€˜์ „๋ฅ˜๊ฐ€ ํ๋ฅด๋Š” ๊ฐ ์ฃผํŒŒ์ˆ˜ ์„ฑ๋ถ„์— ๋Œ€ํ•ด ๋“ฑ๊ฐ€ ํˆฌ์ž์œจ

Decoding Human and AI Persuasion in National College Debate: Analyzing Prepared Arguments Through Aristotle's Rhetorical Principles

Decoding Human and AI Persuasion in National College Debate: Analyzing Prepared Arguments Through Aristotle's Rhetorical Principles

๋ณธ ์—ฐ๊ตฌ๋Š” ์ธ๊ณต ์ง€๋Šฅ, ํŠนํžˆ GPT 4๊ฐ€ ์˜์–ด ์–ธ์–ด ์˜ˆ์ˆ (ELA)์—์„œ ํ† ๋ก ์˜ ์ค‘์š”ํ•œ ๊ตฌ์„ฑ ์š”์†Œ์ธ ๋…ผ์ฆ ๊ธฐ์ˆ ์— ์–ด๋–ป๊ฒŒ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋Š”์ง€๋ฅผ ํƒ๊ตฌํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ํ•™์ƒ๋“ค์ด ํšจ๊ณผ์ ์ธ ์ฆ๊ฑฐ ์นด๋“œ๋ฅผ ์ž‘์„ฑํ•˜๋Š” ๋ฐ ํ•„์š”ํ•œ ์ง€์›์„ ์ œ๊ณตํ•จ์œผ๋กœ์จ, ๋น„ํŒ์  ์‚ฌ๊ณ ์™€ ๋…ผ์ฆ ๋Šฅ๋ ฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ์—ฐ๊ตฌ์˜ ํ•ต์‹ฌ์€ GPT 4๊ฐ€ ์ƒ์„ฑํ•œ ์ฆ๊ฑฐ ์นด๋“œ์™€ ์ธ๊ฐ„ ์ฐธ๊ฐ€์ž๊ฐ€ ๋งŒ๋“  ๊ฒƒ ์‚ฌ์ด์—์„œ ์งˆ์ ์ธ ์ฐจ์ด๋ฅผ ๋ถ„์„ํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์•„๋ฆฌ์Šคํ† ํ…”๋ ˆ์Šค์˜ ์ˆ˜์‚ฌํ•™ ์›์น™์ธ ์—ํ† ์Šค, ํŒŒํ† ์Šค, ๋กœ๊ณ ์Šค๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ฒด๊ณ„์ ์ธ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ–ˆ์Šต๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ

Determining a rotation of a tetrahedron from a projection

Determining a rotation of a tetrahedron from a projection

๋ณธ ์—ฐ๊ตฌ๋Š” ์ปดํ“จํ„ฐ ๋น„์ „ ๋ถ„์•ผ์˜ ๊ณ ์ „์ ์ธ PnP(์ โ€‘๋Œ€โ€‘์ ) ๋ฌธ์ œ๋ฅผ 4์ (์‚ฌ๋ฉด์ฒด) ์ƒํ™ฉ์— ํŠนํ™”์‹œ์ผœ, ๋ผ๋ฒจ๋ง(์ •์ โ€‘ํˆฌ์˜ ๋Œ€์‘) ์—†์ด ํšŒ์ „ ๋ณต์›์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋Š” ์กฐ๊ฑด์„ ํƒ๊ตฌํ•œ๋‹ค๋Š” ์ ์—์„œ ์˜๋ฏธ๊ฐ€ ํฌ๋‹ค. ๋จผ์ € ์ €์ž๋Š” ( phi )๋ฅผ ์›์  ์ค‘์‹ฌ ํšŒ์ „๊ณผ ์ˆ˜ํ‰ยท์ˆ˜์ง ํ‰ํ–‰ ์ด๋™์˜ ํ•ฉ์œผ๋กœ ๋ถ„ํ•ดํ•˜๊ณ , ํˆฌ์˜ ๊ฒฐ๊ณผ (U )๊ฐ€ ์ˆ˜์ง ์ด๋™์— ๋ฌด๊ฐ๊ฐํ•จ์„ ์ด์šฉํ•ด ์ˆ˜ํ‰ ์ด๋™์€ ์–ธ์ œ๋‚˜ ๊ณ ์œ ํ•˜๊ฒŒ ๊ฒฐ์ •๋œ๋‹ค๊ณ  ์ฆ๋ช…ํ•œ๋‹ค. ์ด๋Š” ( phi T )์˜ ๋ฌด๊ฒŒ์ค‘์‹ฌ์ด (U )์˜ ๋ฌด๊ฒŒ์ค‘์‹ฌ์„ ์ง€๋‚˜๋Š” ์ˆ˜์ง์„  ์œ„์— ๋†“์—ฌ์•ผ ํ•œ๋‹ค๋Š” ๊ฐ„๋‹จํ•œ ๊ธฐํ•˜ํ•™์  ์‚ฌ์‹ค์—์„œ ๋น„๋กฏ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ฌธ์ œ๋Š” ์ˆœ์ˆ˜

Mathematics Computational Geometry Computer Vision Computer Science
DGGAN: Degradation Guided Generative Adversarial Network for Real-time Endoscopic Video Enhancement

DGGAN: Degradation Guided Generative Adversarial Network for Real-time Endoscopic Video Enhancement

์ด ๋…ผ๋ฌธ์€ ๋‚ด์‹œ๊ฒฝ ์ˆ˜์ˆ  ํ˜„์žฅ์—์„œ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๊ณ ํ’ˆ์งˆ ์˜์ƒ์„ ์ œ๊ณตํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค๋Š” ์ ์—์„œ ์ž„์ƒ ์ ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ํฌ๊ฒŒ ๋†’์˜€๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ฃผ๋กœ ๋‹จ์ผ ์ด๋ฏธ์ง€ ํ˜น์€ ์งง์€ ํด๋ฆฝ์— ์ดˆ์ ์„ ๋งž์ถ”์–ด ๋ณต์žกํ•œ ๋„คํŠธ์›Œํฌ ๊ตฌ์กฐ๋‚˜ ๋Œ€๊ทœ๋ชจ ์—ฐ์‚ฐ์„ ์š”๊ตฌํ–ˆ์œผ๋ฉฐ, ์ด๋Š” ์‹ค์ œ ์ˆ˜์ˆ ์‹ค์˜ ํ•˜๋“œ์›จ์–ด ์ œ์•ฝ๊ณผ ์ถฉ๋Œํ•œ๋‹ค. ์ €์ž๋“ค์€ ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด โ€˜์ €ํ•˜ ์ธ์‹(degradationโ€‘aware)โ€™์ด๋ผ๋Š” ๊ฐœ๋…์„ ๋„์ž…ํ•˜์˜€๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ๋Œ€๋น„ ํ•™์Šต(contrastive learning)์„ ์ด์šฉํ•ด ๊ฐ ํ”„๋ ˆ์ž„์ด ๊ฒช๋Š” ์กฐ๋ช… ๋ถˆ๊ท ํ˜•, ์Šค์บํ„ฐ๋ง, ๋ธ”๋Ÿฌ ๋“ฑ ๋‹ค

Network
Directional Optimization Asymmetry in Transformers: A Synthetic Stress Test

Directional Optimization Asymmetry in Transformers: A Synthetic Stress Test

์ด ๋…ผ๋ฌธ์€ ํŠธ๋žœ์Šคํฌ๋จธ ๋ชจ๋ธ์ด ์ด๋ก ์ ์œผ๋กœ๋Š” ์ž…๋ ฅ ์ˆœ์„œ๋ฅผ ๋’ค์ง‘๋Š” ์ž‘์—…๊ณผ ์›๋ž˜ ์ˆœ์„œ๋ฅผ ์œ ์ง€ํ•˜๋Š” ์ž‘์—…์„ ๋™๋“ฑํ•˜๊ฒŒ ํ•™์Šตํ•  ์ˆ˜ ์žˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์‹ค์ œ ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ ์‹คํ—˜์—์„œ๋Š” โ€œ์—ญ์ „(curse of reversal)โ€์ด๋ผ ๋ถˆ๋ฆฌ๋Š” ํ˜„์ƒ์ด ์ง€์†์ ์œผ๋กœ ๋‚˜ํƒ€๋‚œ๋‹ค๋Š” ์ ์— ์ฃผ๋ชฉํ•œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ด๋Ÿฌํ•œ ํ˜„์ƒ์ด ๋ฐ์ดํ„ฐ ์ž์ฒด์— ๋‚ด์žฌ๋œ ์‹œ๊ฐ„์  ๋น„๋Œ€์นญ์„ฑโ€”์˜ˆ๋ฅผ ๋“ค์–ด, ์„œ์ˆ ์ด ์ง„ํ–‰๋ ์ˆ˜๋ก ์ •๋ณด๊ฐ€ ๋ˆ„์ ๋˜๋Š” ๊ตฌ์กฐโ€”์— ๊ธฐ์ธํ•œ๋‹ค๊ณ  ์ฃผ์žฅํ–ˆ์ง€๋งŒ, ๋ฐ์ดํ„ฐ์˜ ํ†ต๊ณ„์  ํŠน์„ฑ๊ณผ ๋ชจ๋ธ ๊ตฌ์กฐ ์‚ฌ์ด์˜ ์›์ธ ๊ทœ๋ช…์„ ์œ„ํ•ด์„œ๋Š” ์™„์ „ํžˆ ํ†ต์ œ๋œ ์‹คํ—˜ ํ™˜๊ฒฝ์ด ํ•„์š”ํ•˜๋‹ค. ์ €์ž๋“ค์€ ์ด๋ฅผ ์œ„ํ•ด โ€˜์—”ํŠธ๋กœํ”ผ

Dora: QoE-Aware Hybrid Parallelism for Distributed Edge AI

Dora: QoE-Aware Hybrid Parallelism for Distributed Edge AI

Dora ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ์—์ง€ AI ํ™˜๊ฒฝ์—์„œ ์‚ฌ์šฉ์ž ํ’ˆ์งˆ ๊ฒฝํ—˜(QoE)์„ ์ตœ์ ํ™”ํ•˜๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์€ ๊ธฐ์กด์˜ ๋ณ‘๋ ฌ ์ฒ˜๋ฆฌ ๊ณ„ํš์ž๊ฐ€ ์ฃผ๋กœ ์žฅ์น˜ ํ™œ์šฉ๋„๋‚˜ ์ฒ˜๋ฆฌ๋Ÿ‰์— ์ง‘์ค‘ํ•˜์—ฌ QoE๋ฅผ ๋ฌด์‹œํ•˜๋Š” ๋ฌธ์ œ์ ์„ ์ง€์ ํ•˜๊ณ , ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด Dora ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์†Œ๊ฐœํ•ฉ๋‹ˆ๋‹ค. Dora๋Š” ์„ธ ๊ฐ€์ง€ ํ•ต์‹ฌ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ํ†ตํ•ด ์ด์งˆ์ ์ธ ํ™˜๊ฒฝ์—์„œ์˜ ๊ณ„์‚ฐ๊ณผ ๋„คํŠธ์›Œํฌ ๊ฒฝ์Ÿ ์œ„ํ—˜์„ ๊ด€๋ฆฌํ•˜๋ฉฐ, ๋‹ค์ฐจ์› QoE ๋ชฉํ‘œ๋ฅผ ์ถฉ์กฑ์‹œํ‚ค๋„๋ก ์„ค๊ณ„๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ ๋ฉ”์ปค๋‹ˆ์ฆ˜์€ ๋ชจ๋ธ ๋ถ„ํ• ๊ธฐ๋กœ, ์ด๋Š” ์žฅ์น˜ ๊ฐ„์— ๋ชจ๋ธ ํŒŒํ‹ฐ์…˜์„ ๊ฒฐ์ •ํ•˜๊ณ  ํ• ๋‹นํ•˜์—ฌ QoE ์ค€์ˆ˜ ๊ณ„ํš ์„ธํŠธ๋ฅผ ํ˜•

EfficientFlow: Efficient Equivariant Flow Policy Learning for Embodied AI

EfficientFlow: Efficient Equivariant Flow Policy Learning for Embodied AI

EfficientFlow ๋…ผ๋ฌธ์€ ํ˜„์žฌ ์ƒ์„ฑ ๊ธฐ๋ฐ˜ ๋กœ๋ด‡ ์ •์ฑ…์ด ์ง๋ฉดํ•œ ๋‘ ๊ฐ€์ง€ ํ•ต์‹ฌ ๋ณ‘๋ชฉ, ์ฆ‰ ๋ฐ์ดํ„ฐ ๋น„ํšจ์œจ์„ฑ๊ณผ ์ƒ˜ํ”Œ๋ง ์ง€์—ฐ์„ ๋™์‹œ์— ํ•ด๊ฒฐํ•˜๋ ค๋Š” ์‹œ๋„๋กœ ๋ˆˆ๊ธธ์„ ๋ˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ ๊ธฐ์—ฌ๋Š” ํ๋ฆ„ ๋งค์นญ(flow matching) ํ”„๋ ˆ์ž„์›Œํฌ์— ๋“ฑ๋ณ€์„ฑ(equivariance)์„ ๋„์ž…ํ•œ ์ ์ด๋‹ค. ๋“ฑ๋ณ€์„ฑ์€ ์ž…๋ ฅ ๊ณต๊ฐ„(์˜ˆ: ๋กœ๋ด‡์˜ ๊ด€์ธก์ด๋‚˜ ๋ชฉํ‘œ ์œ„์น˜)์ด ํšŒ์ „ยท์ด๋™ ๋“ฑ ๋ณ€ํ™˜์„ ๋ฐ›์„ ๋•Œ, ์ •์ฑ…์ด ๋™์ผํ•œ ๋ณ€ํ™˜์„ ํ–‰๋™์— ๋ฐ˜์˜ํ•˜๋„๋ก ๋ณด์žฅํ•œ๋‹ค. ์ €์ž๋“ค์€ ๋“ฑ๋ฐฉ์„ฑ ๊ฐ€์šฐ์‹œ์•ˆ ์‚ฌ์ „(p(z) ๐’ฉ(0,I))๊ณผ ๋“ฑ๋ณ€ ์†๋„ ์˜ˆ์ธก ๋„คํŠธ์›Œํฌ vฮธ(x,z)๋ฅผ ๊ฒฐํ•ฉํ•˜๋ฉด, ์ตœ์ข… ํ–‰๋™

Learning
Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges

Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges

๋ณธ ์„ค๋ฌธ์€ โ€˜๋‡Œโ€‘๋ชธ ๊ณต๋™ ์ง„ํ™”โ€™๋ผ๋Š” ์ƒ๋ฌผํ•™์  ํ˜„์ƒ์„ ์ธ๊ณต์ง€๋Šฅ ๋ฐ ๋กœ๋ณดํ‹ฑ์Šค ์„ค๊ณ„์— ์ ์šฉํ•œ ๊ตฌํ˜„ํ˜• ๊ณต๋™ ์„ค๊ณ„(Embodied Coโ€‘Design, ์ดํ•˜ ECD) ๋ถ„์•ผ์˜ ์ตœ์‹  ํ๋ฆ„์„ ํฌ๊ด„์ ์œผ๋กœ ์ •๋ฆฌํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ ์ €์ž๋Š” ECD๋ฅผ ๊ธฐ์กด์˜ ํ˜•ํƒœโ€‘์ œ์–ด ๊ณต๋™ ์ตœ์ ํ™”, ์ง„ํ™” ๋กœ๋ณดํ‹ฑ์Šค, ์‹ ๊ฒฝโ€‘ํ˜•ํƒœ ๊ณตํ•™ ๋“ฑ๊ณผ ๊ตฌ๋ณ„ํ•˜๊ธฐ ์œ„ํ•ด ์ˆ˜ํ•™์  ์ •์˜์™€ ๊ฐœ๋…์  ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ์—ฌ๊ธฐ์„œ ํ•ต์‹ฌ์€ โ€˜๋‡Œ(์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜)โ€™, โ€˜๋ชธ(ํ˜•ํƒœยท๋ฌผ๋ฆฌ์  ์†์„ฑ)โ€™, โ€˜ํ™˜๊ฒฝ(๊ณผ์ œ)โ€™์ด๋ผ๋Š” ์„ธ ์ถ•์„ ๋…๋ฆฝ์ ์œผ๋กœ ๋ชจ๋ธ๋งํ•˜๋ฉด์„œ๋„ ์ƒํ˜ธ ์˜์กด์„ฑ์„ ์œ ์ง€ํ•˜๋Š” ์ ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์‚ผ์› ๊ตฌ์กฐ๋Š” ์„ค๊ณ„ ๊ณต๊ฐ„

EmeraldMind: A Knowledge Graph-Augmented Framework for Greenwashing Detection

EmeraldMind: A Knowledge Graph-Augmented Framework for Greenwashing Detection

์ด ๋…ผ๋ฌธ์€ ๊ธฐ์—…์˜ ํ™˜๊ฒฝ ์ง€์† ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•œ ๋ถ€์ •ํ™•ํ•œ ์ฃผ์žฅ, ์ฆ‰ ๋…น์ƒ‰์„ธํƒ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ AI ํ”„๋ ˆ์ž„์›Œํฌ์ธ Emerald Mind๋ฅผ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ์ด ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ํŠน์ • ๋ถ„์•ผ์˜ ์ง€์‹ ๊ทธ๋ž˜ํ”„์™€ ๊ฒ€์ƒ‰ ๊ฐ•ํ™” ์ƒ์„ฑ์„ ํ†ตํ•ฉํ•˜์—ฌ ๋…น์ƒ‰์„ธํƒ ์ž๋™ ํƒ์ง€๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, ๊ธฐ์—…๋“ค์˜ ESG ๋ณด๊ณ ์„œ์—์„œ ์–ป์€ ์ •๋ณด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ตฌ์„ฑ๋œ EmeraldGraph๊ฐ€ ํ•ต์‹ฌ ์—ญํ• ์„ ํ•˜๋ฉฐ, ์ด๋Š” ์ผ๋ฐ˜์ ์ธ ์ง€์‹ ๊ทธ๋ž˜ํ”„์— ๋ถ€์กฑํ•œ ๊ฒ€์ฆ ๊ฐ€๋Šฅํ•œ ์ฆ๊ฑฐ๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ์ด ์ฃผ์žฅ์˜ ์ •ํ™•์„ฑ์„ ํ‰๊ฐ€ํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋ฉ๋‹ˆ๋‹ค. Emerald Mind ํ”„๋ ˆ์ž„์›Œ

Framework Detection
Enabling Conversational Behavior Reasoning Capabilities in Full-Duplex Speech

Enabling Conversational Behavior Reasoning Capabilities in Full-Duplex Speech

์ด ๋…ผ๋ฌธ์€ ์ธ๊ฐ„ ๋Œ€ํ™”์˜ ๊ทผ๋ณธ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ โ€œ์ƒ๊ฐ์˜ ์‚ฌ์Šฌโ€์ด๋ผ๋Š” ๋ฉ”ํƒ€ํฌ๋กœ ์ ‘๊ทผํ•œ๋‹ค๋Š” ์ ์—์„œ ์ด๋ก ์ ยท์‹ค์šฉ์  ์˜๋ฏธ๊ฐ€ ํฌ๋‹ค. ๊ธฐ์กด ๋Œ€ํ™” ์‹œ์Šคํ…œ์€ ์ฃผ๋กœ ๋ฐœํ™”โ€‘์‘๋‹ต ๋งคํ•‘์ด๋‚˜ ์‹œํ€€์Šคโ€‘ํˆฌโ€‘์‹œํ€€์Šค ๋ชจ๋ธ์— ์˜์กดํ–ˆ์œผ๋ฉฐ, ๋ฐœํ™” ๊ฐ„ ์ธ๊ณผ ๊ด€๊ณ„๋ฅผ ๋ช…์‹œ์ ์œผ๋กœ ๋‹ค๋ฃจ์ง€๋Š” ๋ชปํ–ˆ๋‹ค. ์—ฌ๊ธฐ์„œ๋Š” ๋Œ€ํ™”๋ฅผ โ€˜์˜๋„ โ†’ ํ–‰๋™โ€™์ด๋ผ๋Š” ๋‘ ๋‹จ๊ณ„๋กœ ๋ถ„ํ•ดํ•˜๊ณ , ๊ฐ๊ฐ์„ ๊ณ ์ˆ˜์ค€ ๋ผ๋ฒจ(์˜ˆ: ์งˆ๋ฌธ, ์ œ์•ˆ, ํ™•์ธ)๊ณผ ์ €์ˆ˜์ค€ ๋ผ๋ฒจ(์˜ˆ: ๋ฐœํ™” ์‹œ์ž‘, ์ค‘๋‹จ, ์ฒญ์ทจ)๋กœ ๊ณ„์ธตํ™”ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ผ๋ฒจ๋ง์€ ์ธ๊ณผ ๊ทธ๋ž˜ํ”„๋ฅผ ๊ตฌ์„ฑํ•˜๋Š” ๋…ธ๋“œ์™€ ์—ฃ์ง€๋ฅผ ์ •์˜ํ•˜๋Š”๋ฐ, ๋…ธ๋“œ๋Š” ํŠน์ • ์‹œ์ ์˜ ์‚ฌ๊ณ ยท์˜๋„ ์ƒํƒœ, ์—ฃ์ง€๋Š” ์‹œ๊ฐ„์ ยท์ธ

Enhancing Decision-Making in Windows PE Malware Classification During Dataset Shifts with Uncertainty Estimation

Enhancing Decision-Making in Windows PE Malware Classification During Dataset Shifts with Uncertainty Estimation

๋ณธ ์—ฐ๊ตฌ๋Š” PE ์•…์„ฑ์ฝ”๋“œ ํƒ์ง€ ๋ชจ๋ธ์ด ์‹ค์ œ ์šด์˜ ํ™˜๊ฒฝ์—์„œ ๋งˆ์ฃผํ•˜๋Š” ๋ฐ์ดํ„ฐ์…‹ ๋ณ€๋™(๋ฐ์ดํ„ฐ ์…‹ ์‹œํ”„ํŠธ) ๋ฌธ์ œ๋ฅผ ์ฒด๊ณ„์ ์œผ๋กœ ๋‹ค๋ฃฌ๋‹ค. ๊ธฐ์กด LightGBM ๊ธฐ๋ฐ˜ ํƒ์ง€๊ธฐ๋Š” ๋†’์€ ์ •ํ™•๋„๋ฅผ ๋ณด์ด์ง€๋งŒ, ํ›ˆ๋ จ ๋ฐ์ดํ„ฐ์™€ ํ…Œ์ŠคํŠธ ๋ฐ์ดํ„ฐ ๊ฐ„ ๋ถ„ํฌ ์ฐจ์ด๊ฐ€ ํด ๊ฒฝ์šฐ ํ™•๋ฅ  ์ถœ๋ ฅ์ด ๊ณผ์‹ (overโ€‘confidence)๋˜๋Š” ๊ฒฝํ–ฅ์ด ์žˆ๋‹ค. ์ด๋ฅผ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•ด ์ €์ž๋Š” ์„ธ ๊ฐ€์ง€ ์ฃผ์š” ๊ธฐ์ˆ ์„ ๊ฒฐํ•ฉํ•˜์˜€๋‹ค. ์ฒซ์งธ, ์‹ ๊ฒฝ๋ง(NN)๊ณผ PriorNet์„ LightGBM์— ๋ณ‘ํ•ฉํ•จ์œผ๋กœ์จ ๋น„์„ ํ˜• ํŠน์„ฑ์„ ํฌ์ฐฉํ•˜๊ณ  ์‚ฌ์ „ ํ™•๋ฅ  ๋ถ„ํฌ๋ฅผ ๋ช…์‹œ์ ์œผ๋กœ ๋ชจ๋ธ๋งํ•œ๋‹ค. ๋‘˜์งธ, ์—ฌ๋Ÿฌ ๋…๋ฆฝ์ ์ธ ์‹ ๊ฒฝ๋ง์„ ์•™

Data
Evidence-Driven Decision Support for AI Model Selection in Research Software Engineering

Evidence-Driven Decision Support for AI Model Selection in Research Software Engineering

์ด ๋…ผ๋ฌธ์€ ์ธ๊ณต์ง€๋Šฅ(AI) ๋ชจ๋ธ ์„ ํƒ์˜ ๋ณต์žก์„ฑ์„ ๋‹ค๋ฃจ๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์‹œํ•˜๋ฉฐ, ํŠนํžˆ ์—ฐ๊ตฌ ์†Œํ”„ํŠธ์›จ์–ด ์—”์ง€๋‹ˆ์–ด(RSEs)์™€ ๊ด€๋ จ๋œ ๋ฌธ์ œ์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. AI ๋ชจ๋ธ์˜ ๊ธ‰์†ํ•œ ๋ฐœ์ „๊ณผ ๋‹ค์–‘ํ™”๋กœ ์ธํ•ด RSEs๋Š” ์ ์ ˆํ•œ ๋ชจ๋ธ์„ ์„ ํƒํ•˜๊ณ  ํ†ตํ•ฉํ•˜๋Š” ๋ฐ ์–ด๋ ค์›€์„ ๊ฒช๊ฒŒ ๋˜๋Š”๋ฐ, ์ด ๋…ผ๋ฌธ์€ ์ด๋Ÿฌํ•œ ๊ณผ์ •์„ ๊ตฌ์กฐํ™”ํ•˜๊ณ  ํˆฌ๋ช…ํ•˜๊ฒŒ ๋งŒ๋“œ๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ์—ฐ๊ตฌํŒ€์€ AI ๋ชจ๋ธ ์„ ํƒ์„ ๋‹ค์ค‘ ๊ธฐ์ค€ ์˜์‚ฌ๊ฒฐ์ •(MCDM) ๋ฌธ์ œ๋กœ ๊ฐœ๋…ํ™”ํ•˜์˜€๊ณ , ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ModelSelect๋ผ๋Š” ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€์Šต๋‹ˆ๋‹ค. ์ด ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ์ž๋™ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ํŒŒ์ดํ”„

Model
Extracting Disaster Impacts and Impact Related Locations in Social Media Posts Using Large Language Models

Extracting Disaster Impacts and Impact Related Locations in Social Media Posts Using Large Language Models

๋ณธ ๋…ผ๋ฌธ์€ ์žฌ๋‚œ ์ƒํ™ฉ์—์„œ ์†Œ์…œ๋ฏธ๋””์–ด๊ฐ€ ์ œ๊ณตํ•˜๋Š” ๋น„์ •ํ˜• ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ๋ฅผ โ€˜์ง€์˜คโ€‘์„ผ์„œโ€™๋กœ ํ™œ์šฉํ•˜๋Š” ์ƒˆ๋กœ์šด ํŒจ๋Ÿฌ๋‹ค์ž„์„ ์ œ์‹œํ•œ๋‹ค. ๊ธฐ์กด์˜ ์žฌ๋‚œ ๊ฐ์‹œ ์ฒด๊ณ„๋Š” ์œ„์„ฑ ์˜์ƒยทํ˜„์žฅ ์„ผ์„œยท๊ณต๊ณต GIS ๋ฐ์ดํ„ฐ์— ํฌ๊ฒŒ ์˜์กดํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์œ„์„ฑ์€ ๊ตฌ๋ฆ„ยท์—ฐ๋ฌด ๋“ฑ ๋Œ€๊ธฐ ํ˜„์ƒ์— ์ทจ์•ฝํ•˜๊ณ , ์žฌ๋ฐฉ๋ฌธ ์ฃผ๊ธฐ๊ฐ€ ๊ธธ์–ด ์‹ค์‹œ๊ฐ„ ๊ฐ์‹œ๊ฐ€ ์–ด๋ ต๋‹ค. ํ˜„์žฅ ์„ผ์„œ๋Š” ์„ค์น˜ ๋น„์šฉยท์ „๋ ฅยทํ†ต์‹  ์ธํ”„๋ผ ์ œ์•ฝ์ด ์žˆ์œผ๋ฉฐ, ์žฌ๋‚œ ํ˜„์žฅ์—์„œ ์†์ƒ๋  ์œ„ํ—˜๋„ ์žˆ๋‹ค. ๋ฐ˜๋ฉด ์ผ๋ฐ˜ ๋Œ€์ค‘์ด ์Šค๋งˆํŠธํฐ์œผ๋กœ ์˜ฌ๋ฆฌ๋Š” ํŠธ์œ—ยทํŽ˜์ด์Šค๋ถยท์ธ์Šคํƒ€๊ทธ๋žจ ๋“ฑ์€ ์‹ค์‹œ๊ฐ„์„ฑ, ๊ณต๊ฐ„์  ์„ธ๋ถ„์„ฑ, ๊ทธ๋ฆฌ๊ณ  ์ธ๊ฐ„์˜ ์ฃผ๊ด€์  ์ธ์‹์„ ๋™์‹œ์— ๋‹ด๊ณ  ์žˆ๋‹ค.

Model
Feasibility of Radio Frequency Based Wireless Sensing of Lead Contamination in Soil

Feasibility of Radio Frequency Based Wireless Sensing of Lead Contamination in Soil

๋ณธ ๋…ผ๋ฌธ์€ ๋„์‹œ ํ™˜๊ฒฝ์—์„œ์˜ ๋‚ฉ(Pb) ์˜ค์—ผ ๋ฌธ์ œ์— ๋Œ€ํ•ด ์ƒˆ๋กœ์šด ํ•ด๊ฒฐ์ฑ…์„ ์ œ์•ˆํ•˜๋Š” ๋ฐ ์ค‘์ ์„ ๋‘๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๋‚ฉ ์˜ค์—ผ์€ ์‹ํ’ˆ ์•ˆ์ „๊ณผ ๊ณต์ค‘ ๋ณด๊ฑด์— ์ง์ ‘์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฉฐ, ๋„์‹œ ๊ณต๊ฐ„์˜ ๋…นํ™”๋ฅผ ์ €ํ•ดํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ๋งค์šฐ ์ค‘์š”ํ•œ ์ด์Šˆ์ž…๋‹ˆ๋‹ค. ๊ธฐ์กด์˜ ๋‚ฉ ์ธก์ • ๋ฐฉ๋ฒ•๋“ค์€ ์ฃผ๋กœ ์‹คํ—˜์‹ค ํ™˜๊ฒฝ์—์„œ ์ด๋ฃจ์–ด์ง€๋ฉฐ, ๊ทธ ๊ณผ์ •์ด ์‹œ๊ฐ„๊ณผ ๋น„์šฉ ๋ฉด์—์„œ ๋ถ€๋‹ด์Šค๋Ÿฝ๋‹ค๋Š” ๋ฌธ์ œ์ ์ด ์žˆ์—ˆ์Šต๋‹ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋ฌด์„  ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์‹œํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ํŠนํžˆ ํ† ์–‘ ๋‚ด์˜ ์—ผ ์„ฑ๋ถ„๋“ค์ด ๋‹ค์–‘ํ•œ ์ฃผํŒŒ์ˆ˜ ๋Œ€์—ญ์˜ ๋ฌด์„  ์‹ ํ˜ธ ์ „ํŒŒ์— ๋ฏธ์น˜๋Š”

FIBER: A Multilingual Evaluation Resource for Factual Inference Bias

FIBER: A Multilingual Evaluation Resource for Factual Inference Bias

์ด ๋…ผ๋ฌธ์€ ์‚ฌ์‹ค ์ง€์‹์˜ ์‹ ๋ขฐ์„ฑ ๋ฐ ํŽธํ–ฅ์— ๋Œ€ํ•œ ํ‰๊ฐ€๋ฅผ ์œ„ํ•ด FIBER๋ผ๋Š” ์ƒˆ๋กœ์šด ๋‹ค์–ธ์–ด ๋ฒค์น˜๋งˆํฌ๋ฅผ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์ด ์ฃผ๋กœ ๋‹จ์ผ ์—”ํ‹ฐํ‹ฐ์™€ ๋‹จ์ผ ์–ธ์–ด ๋ฐ์ดํ„ฐ์— ์ง‘์ค‘ํ•œ ๋ฐ˜๋ฉด, ์ด ๋…ผ๋ฌธ์€ ๋‹ค์–‘ํ•œ ์–ธ์–ด์™€ ์—”ํ‹ฐํ‹ฐ ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ์„ ๊ณ ๋ คํ•˜์—ฌ ๋” ํฌ๊ด„์ ์ธ ํ‰๊ฐ€ ๋ฐฉ๋ฒ•๋ก ์„ ์ œ๊ณตํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. FIBER๋Š” ์˜์–ด, ์ดํƒˆ๋ฆฌ์•„์–ด, ํ„ฐํ‚ค์–ด๋กœ ๊ตฌ์„ฑ๋œ ๋ฌธ์žฅ ์™„์„ฑ, ์งˆ๋ฌธ ๋‹ต๋ณ€, ๊ฐ์ฒด ์ˆ˜ ์˜ˆ์ธก ์ž‘์—…์„ ํฌํ•จํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ์˜ ์„ฑ๋Šฅ๊ณผ ํ”„๋กฌํ”„ํŠธ ์–ธ์–ด์— ๋”ฐ๋ฅธ ํŽธํ–ฅ์„ ๋ถ„์„ํ•ฉ๋‹ˆ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ํ”„๋กฌํ”„ํŠธ ์–ธ์–ด๊ฐ€ ๋ชจ๋ธ์˜ ์ถœ๋ ฅ์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ์œผ๋ฉฐ ํŠนํžˆ

Four Degrees of Separation

Four Degrees of Separation

๋ณธ ๋…ผ๋ฌธ์€ ์‚ฌํšŒ ๋„คํŠธ์›Œํฌ ๊ณผํ•™ ๋ถ„์•ผ์—์„œ โ€œ์ž‘์€ ์„ธ๊ณ„(smallโ€‘world)โ€ ํ˜„์ƒ์„ ์‹ค์ฆ์ ์œผ๋กœ ๊ฒ€์ฆํ•œ ๊ฐ€์žฅ ๊ทœ๋ชจ๊ฐ€ ํฐ ์‚ฌ๋ก€๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ˆ˜์ฒœ์—์„œ ์ˆ˜๋ฐฑ๋งŒ ์ˆ˜์ค€์˜ ๊ทธ๋ž˜ํ”„๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ‰๊ท  ๊ฑฐ๋ฆฌ์™€ ํด๋Ÿฌ์Šคํ„ฐ๋ง ๊ณ„์ˆ˜๋ฅผ ์ธก์ •ํ–ˆ์ง€๋งŒ, ํŽ˜์ด์Šค๋ถ์ด๋ผ๋Š” ์‹ค์ œ ์˜จ๋ผ์ธ ์‚ฌํšŒ๋ง์„ ์ „์ฒด์ ์œผ๋กœ ๋ถ„์„ํ•จ์œผ๋กœ์จ ๋‘ ๊ฐ€์ง€ ์ค‘์š”ํ•œ ํ•™์ˆ ์ ยท์‹ค์šฉ์  ์‹œ์‚ฌ์ ์„ ์ œ๊ณตํ•œ๋‹ค. ์ฒซ์งธ, ํ‰๊ท  ๊ฑฐ๋ฆฌ 4.74๋ผ๋Š” ์ˆ˜์น˜๋Š” โ€œfour degrees of separationโ€์ด๋ผ๋Š” ์ƒˆ๋กœ์šด ๋ช…์ œ๋ฅผ ์ •๋‹นํ™”ํ•œ๋‹ค. ์ด๋Š” ์ „ํ†ต์ ์ธ 6๋‹จ๊ณ„ ๊ฐ€์„ค์ด ๊ณผ๋Œ€ํ‰๊ฐ€๋œ ๊ฒƒ์ด๋ฉฐ, ํ˜„๋Œ€ ๋””์ง€ํ„ธ ์‚ฌํšŒ์—์„œ

Physics Computer Science Social Networks
From In Silico to In Vitro: Evaluating Molecule Generative Models for Hit Generation

From In Silico to In Vitro: Evaluating Molecule Generative Models for Hit Generation

๋ณธ ๋…ผ๋ฌธ์€ ์‹ ์•ฝ ๊ฐœ๋ฐœ ๊ณผ์ •์—์„œ ์ธ๊ณต์ง€๋Šฅ, ํŠนํžˆ ์ƒ์„ฑ ๋ชจ๋ธ์ด ์–ด๋–ค ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ๋Š”์ง€๋ฅผ ํƒ๊ตฌํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์‹ ์•ฝ ๊ฐœ๋ฐœ์˜ ํ•ต์‹ฌ ๋‹จ๊ณ„ ์ค‘ ํ•˜๋‚˜์ธ 'ํƒ€๊ฒŸ ๋ถ„์ž ๋ฐœ๊ตด'์€ ์ „ํ†ต์ ์œผ๋กœ ๋Œ€๊ทœ๋ชจ ํ™”ํ•ฉ๋ฌผ ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์— ๋Œ€ํ•œ ๊ณ ์† ๊ฒ€์ƒ‰์„ ํ•„์š”๋กœ ํ•˜๋ฉฐ, ์ด๋Š” ์‹œ๊ฐ„๊ณผ ์ž์› ์†Œ๋น„๊ฐ€ ํฐ ๊ณผ์ •์ž…๋‹ˆ๋‹ค. ๊ฐ€์ƒ ์Šคํฌ๋ฆฌ๋‹ ๊ธฐ์ˆ ์˜ ๋„์ž…์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์—ฌ์ „ํžˆ ๋งŽ์€ ๋น„์šฉ๊ณผ ์‹œ๊ฐ„์ด ๋“ค๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ตœ๊ทผ ๋”ฅ๋Ÿฌ๋‹์˜ ๋ฐœ์ „์œผ๋กœ ๋ณต์žกํ•œ ๋ถ„์ž ๊ตฌ์กฐ๋ฅผ ํ•™์Šตํ•˜๊ณ  ์ƒˆ๋กœ์šด ํ™”ํ•ฉ๋ฌผ์„ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ๋Š” ์ƒ์„ฑ ๋ชจ๋ธ๋“ค์ด ๊ฐœ๋ฐœ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ๋ชจ๋ธ๋“ค์ด ์‹ ์•ฝ ๊ฐœ๋ฐœ ์ „์ฒด ๊ณผ์ •์„ ๋Œ€์ฒดํ•˜๋Š” ๊ฒƒ์€ ์•„์ง

Model
From Wearables to Warnings: Predicting Pain Spikes in Patients with Opioid Use Disorder

From Wearables to Warnings: Predicting Pain Spikes in Patients with Opioid Use Disorder

๋ณธ ์—ฐ๊ตฌ๋Š” ๋งŒ์„ฑํ†ต์ฆ๊ณผ ์˜คํ”ผ์˜ค์ด๋“œ ์‚ฌ์šฉ์žฅ์• ๋ผ๋Š” ๋‘ ๊ฐ€์ง€ ๋ณตํ•ฉ์ ์ธ ๋งŒ์„ฑ ์งˆํ™˜์„ ๋™์‹œ์— ๊ฒช๋Š” ํ™˜์ž๊ตฐ์„ ๋Œ€์ƒ์œผ๋กœ, ์›จ์–ด๋Ÿฌ๋ธ” ๋””๋ฐ”์ด์Šค์—์„œ ์ˆ˜์ง‘๋œ ์ƒ๋ฆฌํ•™์ ยทํ–‰๋™์  ๋ฐ์ดํ„ฐ์™€ ์ตœ์‹  ์ธ๊ณต์ง€๋Šฅ ๊ธฐ๋ฒ•์„ ๊ฒฐํ•ฉํ•ด ํ†ต์ฆ ๊ธ‰์ฆ(pain spikes)์„ ์˜ˆ์ธกํ•˜๊ณ  ๊ทธ ์ž„์ƒ์  ์—ฐ๊ด€์„ฑ์„ ํƒ์ƒ‰ํ•œ ์ตœ์ดˆ์˜ ํŒŒ์ผ๋Ÿฟ ์—ฐ๊ตฌ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๋จผ์ € ์—ฐ๊ตฌ ์„ค๊ณ„ ์ธก๋ฉด์—์„œ, 25๋ช…์˜ ํ™˜์ž๋ฅผ ๋ชจ์ง‘ํ•˜์—ฌ ์‹ค์‹œ๊ฐ„ ์‹ฌ๋ฐ•๋ณ€์ด๋„(HRV), ํ™œ๋™๋Ÿ‰, ์ˆ˜๋ฉด ํŒจํ„ด, ๊ทธ๋ฆฌ๊ณ  ์ž๊ฐ€ ๋ณด๊ณ ๋œ ์ŠคํŠธ๋ ˆ์Šค ์ˆ˜์ค€ ๋“ฑ์„ ๋‹ค๋ณ€๋Ÿ‰ ์‹œ๊ณ„์—ด ๋ฐ์ดํ„ฐ๋กœ ์ˆ˜์ง‘ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐ์ดํ„ฐ๋Š” ๊ธฐ์กด์˜ ์ •ํ˜•ํ™”๋œ ์„ค๋ฌธ์ง€ ๊ธฐ๋ฐ˜ ์—ฐ๊ตฌ์™€ ๋‹ฌ๋ฆฌ, ํ†ต์ฆ์ด

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Generative Adversarial Reasoner: Enhancing LLM Reasoning with Adversarial Reinforcement Learning

๋ณธ ๋…ผ๋ฌธ์€ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM)์˜ ์ˆ˜ํ•™์  ์ถ”๋ก  ๋Šฅ๋ ฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. ๊ธฐ์กด LLM๋“ค์€ ์ˆ˜ํ•™ ๋ฌธ์ œ ํ•ด๊ฒฐ์— ๋›ฐ์–ด๋‚˜์ง€๋งŒ, ํ”„๋กœ์„ธ์Šค ์˜ค๋ฅ˜๋ฅผ ๋ฒ”ํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์Šต๋‹ˆ๋‹ค. ์ด๋ฅผ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด Generative Adversarial Reasoner๋ผ๋Š” ์˜จ ํด๋ฆฌ์‹œ ํ•ฉ๋™ ํ›ˆ๋ จ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ๋„์ž…ํ•˜์˜€์Šต๋‹ˆ๋‹ค. ์ด ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ์ ๋Œ€ ๊ฐ•ํ™”ํ•™์Šต์„ ํ™œ์šฉํ•˜์—ฌ LLM ์ถ”๋ก ๊ธฐ์™€ ํŒ๋ณ„๊ธฐ๋ฅผ ๊ณต๋™์œผ๋กœ ์ง„ํ™”์‹œํ‚ค๋ฉฐ, ๊ฐ ์ถ”๋ก  ์ฒด์ธ์„ ๋…ผ๋ฆฌ์ ์œผ๋กœ ์™„์ „ํ•œ ์Šฌ๋ผ์ด์Šค๋กœ ๋ถ„ํ• ํ•˜๊ณ  ์ด๋ฅผ ํ‰๊ฐ€ํ•ฉ๋‹ˆ๋‹ค. ํ•ต์‹ฌ ์•„์ด๋””์–ด๋Š” LLM ์ถ”๋ก ๊ธฐ๊ฐ€ ์˜ฌ๋ฐ”๋ฅธ ๋‹ต๋ณ€์„ ์–ป๋Š” ๋ฐ ๋ณด

Learning
Graph-Based Bayesian Optimization for Quantum Circuit Architecture Search with Uncertainty Calibrated Surrogates

Graph-Based Bayesian Optimization for Quantum Circuit Architecture Search with Uncertainty Calibrated Surrogates

์ด ๋…ผ๋ฌธ์€ ์–‘์ž ๋จธ์‹ ๋Ÿฌ๋‹์—์„œ ๊ฐ€์žฅ ์‹ค์งˆ์ ์ธ ์žฅ์• ๋ฌผ ์ค‘ ํ•˜๋‚˜์ธ ๋ณ€๋ถ„ ์–‘์ž ํšŒ๋กœ(VQC)์˜ ์„ค๊ณ„ ๊ณผ์ •์„ ์ž๋™ํ™”ํ•œ๋‹ค๋Š” ์ ์—์„œ ํฐ ์˜์˜๋ฅผ ๊ฐ€์ง„๋‹ค. ํšŒ๋กœ๋ฅผ ๊ทธ๋ž˜ํ”„๋กœ ๋ชจ๋ธ๋งํ•˜๊ณ , ๊ทธ๋ž˜ํ”„ ์‹ ๊ฒฝ๋ง(GNN)์„ ์„œํ”„๋ผ์ด์ฆˆ ๋ชจ๋ธ๋กœ ํ™œ์šฉํ•œ ๊ฒƒ์€ ๊ธฐ์กด์˜ ๋ฒกํ„ฐ ๊ธฐ๋ฐ˜ ๋ฉ”ํƒ€๋ชจ๋ธ๋ณด๋‹ค ๊ตฌ์กฐ์  ์ •๋ณด๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ํฌ์ฐฉํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์žฅ์ ์„ ์ œ๊ณตํ•œ๋‹ค. ํŠนํžˆ, ๋ฒ ์ด์ง€์•ˆ ์ตœ์ ํ™” ํ”„๋ ˆ์ž„์›Œํฌ์— ๊ธฐ๋Œ€ ํ–ฅ์ƒ(Expected Improvement) ํš๋“ ํ•จ์ˆ˜๋ฅผ ์ ์šฉํ•˜๊ณ , ์„œํ”„๋ผ์ด์ฆˆ์˜ ๋ถˆํ™•์‹ค์„ฑ์„ ๋ชฌํ…Œ์นด๋ฅผ๋กœ ๋“œ๋กญ์•„์›ƒ์œผ๋กœ ์ถ”์ •ํ•จ์œผ๋กœ์จ ํƒ์ƒ‰ ํšจ์œจ์„ฑ์„ ํฌ๊ฒŒ ๋†’์˜€๋‹ค. ์ด๋Š” ํƒ์ƒ‰ ๊ณต๊ฐ„์ด ๊ธฐํ•˜๊ธ‰์ˆ˜

Hamiltonian Connectivity of Twisted Hypercube-Like Networks under the   Large Fault Model

Hamiltonian Connectivity of Twisted Hypercube-Like Networks under the Large Fault Model

๋ณธ ๋…ผ๋ฌธ์€ ํ˜„๋Œ€ ๋ณ‘๋ ฌ ์ปดํ“จํŒ…์—์„œ ํ•ต์‹ฌ์ ์ธ ์—ญํ• ์„ ํ•˜๋Š” ์ƒํ˜ธ์—ฐ๊ฒฐ ๋„คํŠธ์›Œํฌ์˜ ๊ฒฐํ•จโ€‘๋‚ด์„ฑ ์„ ์ •๋Ÿ‰์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜๋Š” ์ค‘์š”ํ•œ ์—ฐ๊ตฌ์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ฃผ๋กœ ์†Œ๊ทœ๋ชจ ๊ฒฐํ•จ ๋ชจ๋ธ (๊ทธ๋ž˜ํ”„ ์ฐจ์ˆ˜ ์ดํ•˜์˜ ๊ฒฐํ•จ)์—์„œ ์‚ฌ์ดํดยท๊ฒฝ๋กœ ์ž„๋ฒ ๋”ฉ ๊ฐ€๋Šฅ์„ฑ์„ ์ž…์ฆํ•ด ์™”์œผ๋ฉฐ, ์ด๋Š” ์‹ค์ œ ๋Œ€๊ทœ๋ชจ ์‹œ์Šคํ…œ์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๋‹ค์ˆ˜์˜ ํ”„๋กœ์„ธ์„œยท๋งํฌ ๊ณ ์žฅ์„ ์ถฉ๋ถ„ํžˆ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ๋‹ค. ์ €์ž๋“ค์€ ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ณ ์ž ๋Œ€๊ทœ๋ชจ ๊ฒฐํ•จ ๋ชจ๋ธ ์„ ๋„์ž…ํ•˜์—ฌ, ์ฐจ์› (n )์ธ THLN์—์„œ ํ—ˆ์šฉ ๊ฐ€๋Šฅํ•œ ๊ฒฐํ•จ ์ˆ˜๋ฅผ (|F| le 2n 10 )์œผ๋กœ ํ™•๋Œ€ํ•˜์˜€๋‹ค. ์ด๋Š” ์ฐจ์›๋‹น ํ‰๊ท 

Network Computer Science Distributed Computing Model
Informing Acquisition Functions via Foundation Models for Molecular Discovery

Informing Acquisition Functions via Foundation Models for Molecular Discovery

๋ณธ ๋…ผ๋ฌธ์€ ๋ถ„์ž์˜ ์†์„ฑ์„ ์˜ˆ์ธกํ•˜๊ณ  ์ตœ์  ํ›„๋ณด๋ฅผ ์ฐพ๋Š” ๋ฐ ์‚ฌ์šฉ๋˜๋Š” ๋ฒ ์ด์ง€์•ˆ ์ตœ์ ํ™”(BO) ๋ฐฉ๋ฒ•๋ก ์„ ๊ฐœ์„ ํ•˜๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•ฉ๋‹ˆ๋‹ค. BO์˜ ์ฃผ์š” ๋ฌธ์ œ์  ์ค‘ ํ•˜๋‚˜๋Š” ๋‚ฎ์€ ๋ฐ์ดํ„ฐ ํ™˜๊ฒฝ์—์„œ ์‚ฌ์ „ ์ง€์‹์ด ๋ถ€์กฑํ•œ ๊ฒฝ์šฐ ์„ฑ๋Šฅ์ด ์ €ํ•˜๋œ๋‹ค๋Š” ์ ์ž…๋‹ˆ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด, ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM)๊ณผ ํ™”ํ•™ ๋ถ„์•ผ์— ํŠนํ™”๋œ ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜์—ฌ BO์˜ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ ์ด ๋…ผ๋ฌธ์€ ๊ณ ์ฐจ์› ํŠน์ง•๊ณผ in context ํ•™์Šต์˜ ๋น„์šฉ, ๊นŠ์€ ๋ฒ ์ด์ง€์•ˆ ๋Œ€๋ฆฌ๋ชจ๋ธ์˜ ๊ณ„์‚ฐ ๋ถ€๋‹ด ๋“ฑ LLM๊ณผ ํ™”ํ•™ ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์˜ ํ™œ์šฉ์— ๋Œ€ํ•œ ์ฃผ์š” ์žฅ์• ๋ฌผ์„ ๊ทน๋ณต

Model
Interpretable Link Prediction in AI-Driven Cancer Research: Uncovering Co-Authorship Patterns

Interpretable Link Prediction in AI-Driven Cancer Research: Uncovering Co-Authorship Patterns

๋ณธ ๋…ผ๋ฌธ์€ ์ธ๊ณต์ง€๋Šฅ(AI) ๊ธฐ์ˆ ์ด ์•” ์—ฐ๊ตฌ ๋ถ„์•ผ์—์„œ์˜ ํ˜‘์—… ํŒจํ„ด์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํƒ๊ตฌํ•œ๋‹ค. ํŠนํžˆ, ๊ณต๋™์ €์ž ๋„คํŠธ์›Œํฌ๋ฅผ ํ†ตํ•ด ์ƒˆ๋กœ์šด, ์ง€์†์ ์ธ, ๊ทธ๋ฆฌ๊ณ  ์ค‘๋‹จ๋œ ํ˜‘์—… ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•จ์œผ๋กœ์จ AI๊ฐ€ ์–ด๋–ป๊ฒŒ ์•” ์—ฐ๊ตฌ์˜ ๋‹คํ•™์ œ์  ์ ‘๊ทผ ๋ฐฉ์‹์„ ๋ณ€ํ™”์‹œํ‚ค๊ณ  ์žˆ๋Š”์ง€๋ฅผ ์‚ดํŽด๋ณธ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” 2000๋…„๋ถ€ํ„ฐ 2017๋…„๊นŒ์ง€์˜ 7,738๊ฐœ ์ถœํŒ๋ฌผ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ํ•™๋ฌธ์  ์œ ์‚ฌ๋„ ์ ์ˆ˜์™€ ์ƒ์‚ฐ์„ฑ ๋ฐ ๊ฒฝ๋ ฅ์ด ํ˜‘์—… ํŒจํ„ด์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•œ๋‹ค. ๋žœ๋ค ํฌ๋ ˆ์ŠคํŠธ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•œ ๊ฒฐ๊ณผ, ํ•™๋ฌธ์  ์œ ์‚ฌ๋„๊ฐ€ ์ƒˆ๋กœ์šด ๋ฐ ์ง€์†์ ์ธ ํ˜‘์—…์—๋Š” ๊ธ์ •์ ์œผ๋กœ ์ž‘์šฉํ•˜์ง€๋งŒ ์ค‘๋‹จ๋œ ํ˜‘

Kardia-R1: Unleashing LLMs to Reason toward Understanding and Empathy for Emotional Support via Rubric-as-Judge Reinforcement Learning

Kardia-R1: Unleashing LLMs to Reason toward Understanding and Empathy for Emotional Support via Rubric-as-Judge Reinforcement Learning

๋ณธ ๋…ผ๋ฌธ์€ ํ˜„์žฌ ๋Œ€ํ™”ํ˜• ์ธ๊ณต์ง€๋Šฅ์ด ์ง๋ฉดํ•œ ๋‘ ๊ฐ€์ง€ ๊ทผ๋ณธ์ ์ธ ๋ฌธ์ œ๋ฅผ ๋ช…ํ™•ํžˆ ์งš์–ด๋‚ธ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ๋ฐ์ดํ„ฐ ์ธก๋ฉด์—์„œ โ€œ์ƒํ™ฉ ์ค‘์‹ฌโ€์ด๋ผ๋Š” ๊ธฐ์กด ํŒจ๋Ÿฌ๋‹ค์ž„์ด ์‚ฌ์šฉ์ž์˜ ์ง€์†์ ์ธ ์ •์ฒด์„ฑ ์ •๋ณด๋ฅผ ๊ฒฐ์—ฌํ•˜๊ณ  ์žˆ๋‹ค๋Š” ์ ์ด๋‹ค. ์‹ค์ œ ์ธ๊ฐ„ ๋Œ€ํ™”์—์„œ๋Š” ๋Œ€ํ™” ์ƒ๋Œ€์˜ ๊ณผ๊ฑฐ ๊ฒฝํ—˜, ๊ฐ€์น˜๊ด€, ์„ฑ๊ฒฉ ํŠน์„ฑ ๋“ฑ์ด ๊ฐ์ • ํ‘œํ˜„๊ณผ ํ•ด์„์— ๊นŠ์ด ๊ด€์—ฌํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋Œ€๋ถ€๋ถ„์˜ ๊ณต๊ฐœ ๋ฐ์ดํ„ฐ์…‹์€ ๋‹จ์ผ ๋ฐœํ™” ํ˜น์€ ์งง์€ ๋Œ€ํ™” ํ๋ฆ„์— ๊ตญํ•œ๋ผ ์žˆ์–ด, ๋ชจ๋ธ์ด ๊ฐœ์ธํ™”๋œ ์ •์„œ ๋ฏธ๋ฌ˜ํ•จ์„ ํ•™์Šตํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋‘ ๋ฒˆ์งธ๋Š” ํ•™์Šต ๋ชฉํ‘œ ์„ค์ •์—์„œ โ€œ๋ถˆํˆฌ๋ช…ํ•˜๊ณ  ๊ฑฐ์นœโ€ ๋ณด์ƒ ์‹ ํ˜ธ๋ฅผ ์‚ฌ์šฉํ•œ๋‹ค๋Š” ์ ์ด๋‹ค. ๊ธฐ์กด ๊ฐ์ •ยท๊ณต๊ฐ

Learning
Large Language Models as Discounted Bayesian Filters

Large Language Models as Discounted Bayesian Filters

๋ณธ ์—ฐ๊ตฌ๋Š” ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(LLM)์ด ์ •์ ์ธ ํ…์ŠคํŠธ ๊ธฐ๋ฐ˜ ์ž‘์—…์—์„œ ๋›ฐ์–ด๋‚œ ์†Œ์ˆ˜ ์ƒท ์ผ๋ฐ˜ํ™” ๋Šฅ๋ ฅ์„ ๋ณด์ด๋Š” ๋ฐ˜๋ฉด, ์‹ค์ œ ์„ธ๊ณ„์™€ ๊ฐ™์ด ์ง€์†์ ์œผ๋กœ ๋ณ€ํ•˜๋Š” ํ™•๋ฅ ์  ์ƒํ™ฉ์—์„œ ์–ด๋–ป๊ฒŒ ๋ฏฟ์Œ์„ ์—…๋ฐ์ดํŠธํ•˜๋Š”์ง€๋ฅผ ๊ทœ๋ช…ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ €์ž๋“ค์€ ๋ฒ ์ด์ง€์•ˆ ํ•„ํ„ฐ๋ง์ด๋ผ๋Š” ํ†ต๊ณ„์  ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ฐจ์šฉํ–ˆ์œผ๋ฉฐ, ์ด๋Š” ๊ด€์ธก๋œ ๋ฐ์ดํ„ฐ์™€ ์‚ฌ์ „ ์ง€์‹์„ ๊ฒฐํ•ฉํ•ด ์‚ฌํ›„ ๋ถ„ํฌ๋ฅผ ์ˆœ์ฐจ์ ์œผ๋กœ ๊ฐฑ์‹ ํ•˜๋Š” ์ „ํ†ต์ ์ธ ๋ฐฉ๋ฒ•์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ LLM์ด ๋‚ด๋ถ€์ ์œผ๋กœ ๋ฒ ์ด์ง€์•ˆ ์—…๋ฐ์ดํŠธ๋ฅผ ์ •ํ™•ํžˆ ๊ตฌํ˜„ํ•˜๊ณ  ์žˆ๋Š”์ง€๋Š” ์•„์ง ๋ช…ํ™•ํ•˜์ง€ ์•Š์•˜๋‹ค. ์‹คํ—˜ ์„ค๊ณ„๋Š” ๋‘ ๊ฐ€์ง€ ์ถ•์„ ์ค‘์‹ฌ์œผ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ๋‹ค๋ณ€๋Ÿ‰

Model
Learned-Rule-Augmented Large Language Model Evaluators

Learned-Rule-Augmented Large Language Model Evaluators

์ด ๋…ผ๋ฌธ์€ ํ˜„์žฌ LLM์„ ํ‰๊ฐ€์ž๋กœ ํ™œ์šฉํ•  ๋•Œ ๋งˆ์ฃผ์น˜๋Š” ๋‘ ๊ฐ€์ง€ ๊ทผ๋ณธ์ ์ธ ๋ฌธ์ œโ€”์ธ๊ฐ„ ์„ค๊ณ„ ํ‰๊ฐ€ ๊ธฐ์ค€์˜ ๋น„์šฉ๊ณผ ๋ฐ์ดํ„ฐยท๋ชจ๋ธ ์ดํ•ด ์‚ฌ์ด์˜ ๋ถˆ์ผ์น˜โ€”๋ฅผ ์ •ํ™•ํžˆ ์งš์–ด๋‚ธ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ฃผ๋กœ ์‚ฌ์ „ ์ •์˜๋œ ๋ฉ”ํŠธ๋ฆญ(์˜ˆ: BLEU, ROUGE)์ด๋‚˜ ์ธ๊ฐ„์ด ๋งŒ๋“  ํ‰๊ฐ€ ์ง€์นจ์— LLM์„ ๋งคํ•‘ํ•˜๋Š” ๋ฐฉ์‹์„ ์‚ฌ์šฉํ–ˆ์œผ๋ฉฐ, ์ด๋Š” ํ‰๊ฐ€ ๋น„์šฉ์ด ๋†’๊ณ  ์ƒˆ๋กœ์šด ๋„๋ฉ”์ธ์— ์ ์šฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ๋‹ค. ์ €์ž๋“ค์€ ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด โ€˜๊ทœ์น™ ์ฆ๊ฐ• ํ‰๊ฐ€ ํŒจ๋Ÿฌ๋‹ค์ž„โ€™์ด๋ผ๋Š” ์ƒˆ๋กœ์šด ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ํ•ต์‹ฌ ์•„์ด๋””์–ด๋Š” ํ‰๊ฐ€ ๊ทœ์น™ ์ž์ฒด๋ฅผ ๋ฐ์ดํ„ฐ์—์„œ ์ž๋™์œผ๋กœ ์ถ”์ถœํ•˜๊ณ , ์ด

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