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    ๊ฐ€์šฐ์‹œ์•ˆ ์ƒํƒœ๋ฅผ ์ด์šฉํ•œ ์–‘์ž ๊ณ„์ธก๊ณผ ํšจ์œจ์ ์ธ ๋ฒ ์ด์ง€์•ˆ ์˜ค๋ฅ˜ ๊ฒ€์ •

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ๋ฌผ๋ฆฌยท์ฒœ๋ฌธํ•™๋ถ€(๋ฌผ๋ฆฌํ•™์ „๊ณต),2020. 2. ์ •ํ˜„์„.Precise measurement of physical quantities plays a crucial role in the development of science and technology. The main purpose of the dissertation is two-fold: to investigate the ultimate precision for estimation of physical quantities using Gaussian states and to propose an efficient method for certification of Bayesian error region in general quantum parameter estimation. In the first part, we begin with analyzing sensitivity for estimating a phase difference in an optical interferometer. Optical interferometry is widely used in science and industry for measuring small displacements. Recently, a large-scale optical interferometer so-called the Laser interferometer Gravitational-Wave Observatory (LIGO) has succeeded in detecting a gravitational wave, the signal of which is extremely small. On the other hand, it has been shown that a non-classical feature of quantum states can improve the sensitivity of estimation, such as in optical interferometer, including the LIGO. From a practical point of view, we inspect the practically achievable precision using non-classical Gaussian states in Mach-Zehnder interferometer with feasible measurements and realistic photon loss. We then investigate the precision of single-mode phase estimation using Gaussian measurement, which can be realized by using homodyne detection, and show that non-Gaussian measurement is necessary to utilize the power of Gaussian input probes optimally. Finally, we find the optimal measurement for general Gaussian quantum metrology and identify three distinct optimal measurements corresponding to different circumstances. In the second part, we study the Bayesian error region, which is a crucial concept for a general estimation process. When estimating a physical quantity, one has to supply the error interval (single-parameter) or error region (multi-parameter) as well as the estimate. However, it has been shown that as the dimension of quantum systems of interest grows, it becomes intractable to calculate the size and credibility of Bayesian error regions. As an alternative, we derive an analytical expression for the properties, the size and credibility, of Bayesian error regions, in an asymptotic regime. We then propose an efficient numerical method to calculate them for high-dimensional quantum systems even in a non-asymptotic regime.๋ฌผ๋ฆฌ์ ์ธ ์–‘์˜ ์ •ํ™•ํ•œ ์ธก์ •์€ ๊ณผํ•™ ๊ธฐ์ˆ ์—์„œ ํ•ต์‹ฌ์ ์ด๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์˜ ์ฃผ์ œ๋Š” ๊ฐ€์šฐ์‹œ์•ˆ ์ƒํƒœ๋ฅผ ์ด์šฉํ•œ ์–‘์ž ๊ณ„์ธก๊ณผ ์–‘์ž ๊ณ„์ธก์— ์žˆ์–ด์„œ ํšจ์œจ์ ์ธ ๋ฒ ์ด์ง€์•ˆ ์˜ค๋ฅ˜ ๊ฒ€์ • ๋ฐฉ์‹์„ ์ œ์•ˆํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋จผ์ € ์šฐ๋ฆฌ๋Š” ๊ด‘ํ•™์  ๊ฐ„์„ญ๊ณ„์—์„œ ์œ„์ƒ ์ฐจ์ด๋ฅผ ๊ณ„์ธกํ•˜๋Š” ๊ฒƒ์„ ๋ถ„์„ํ•œ๋‹ค. ๊ด‘ํ•™์  ๊ฐ„์„ญ๊ณ„๋Š” ๋งค์šฐ ์ž‘์€ ๋ณ€์œ„๋ฅผ ์ธก์ •ํ•˜๊ณ ์ž ํ•  ๋•Œ ๊ณผํ•™ ๋ฐ ๊ธฐ์ˆ ์— ์žˆ์–ด์„œ ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ์‚ฌ์šฉ๋˜๋Š” ๋„๊ตฌ์ด๋‹ค. ์ตœ๊ทผ์—๋Š” ๋ ˆ์ด์ € ๊ฐ„์„ญ๊ณ„ ์ค‘๋ ฅํŒŒ ๊ด€์ธก์†Œ๋ผ๊ณ  ๋ถˆ๋ฆฌ๋Š” ๊ณณ์— ์žˆ๋Š” ํฐ ๊ทœ๋ชจ์˜ ๊ด‘ํ•™์  ๊ฐ„์„ญ๊ณ„๋ฅผ ์ด์šฉํ•˜์—ฌ ๋งค์šฐ ์ž‘์€ ์‹ ํ˜ธ์˜ ์ค‘๋ ฅํŒŒ๋ฅผ ๊ด€์ธกํ•ด๋‚ด๋Š”๋ฐ ์„ฑ๊ณตํ•˜์˜€๋‹ค. ํ•œํŽธ, ๋น„๊ณ ์ „์ ์ธ ์–‘์ž ์ƒํƒœ๋ฅผ ์‚ฌ์šฉํ•  ๊ฒฝ์šฐ, ๊ด‘ํ•™์  ๊ฐ„์„ญ๊ณ„ ๋“ฑ์—์„œ ๋†’์€ ์ •ํ™•๋„๋ฅผ ๊ฐ™๊ฒŒ ๋œ๋‹ค๋Š” ๊ฒƒ์ด ์•Œ๋ ค์กŒ๋‹ค. ์šฐ๋ฆฌ๋Š” ๋จผ์ € ๋น„๊ณ ์ „์ ์ธ ๊ฐ€์šฐ์‹œ์•ˆ ์ƒํƒœ์™€ ์‹คํ—˜์ ์œผ๋กœ ๊ตฌํ˜„ ๊ฐ€๋Šฅํ•œ ์ธก์ •์„ ์‚ฌ์šฉํ•˜์˜€์„ ๋•Œ, ๋งˆํ-์  ๋” ๊ฐ„์„ญ๊ณ„์—์„œ ์–ป์„ ์ˆ˜ ์žˆ๋Š” ์ •ํ™•๋„์— ๋Œ€ํ•ด์„œ ๋ถ„์„ํ•œ๋‹ค. ๊ทธ๋ฆฌ ๊ณ  ๋‹จ์ผ ๋ชจ๋“œ ์œ„์ƒ ์ถ”์ •์—์„œ ํ˜ธ๋ชจ๋‹ค์ธ ์ธก์ •๋งŒ์„ ์ด์šฉํ•˜์—ฌ ๊ตฌํ˜„ ๊ฐ€๋Šฅํ•œ ๊ฐ€์šฐ์‹œ์•ˆ ์ธก์ •์„ ์‚ฌ์šฉํ•˜์˜€์„ ๋•Œ ์ตœ์ ์˜ ์ •ํ™•๋„๋ฅผ ๋„๋‹ฌํ•  ์ˆ˜ ์žˆ๋Š”์ง€ ์•Œ์•„๋ณด๊ณ , ๊ฒฐ๋ก ์ ์œผ๋กœ ๋น„๊ฐ€์šฐ์‹œ์•ˆ ์ธก์ •์ด ๋ฐ˜๋“œ์‹œ ํ•„์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๋ฐํ˜€๋‚ด์—ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ผ๋ฐ˜์ ์ธ ๊ฐ€์šฐ์‹œ์•ˆ ์–‘์ž ๊ณ„์ธก์— ์žˆ์–ด์„œ ์ตœ์ ์˜ ์ธก์ •์„ ์ฐพ๊ณ , ๋‹จ์ผ ๋ชจ๋“œ์˜ ๊ฒฝ์šฐ์—๋Š” ์ƒํ™ฉ์— ๋”ฐ๋ผ ์„ธ ๊ฐ€์ง€ ์„œ๋กœ ๋‹ค๋ฅธ ์ตœ์ ์˜ ์ธก์ • ์žฅ์น˜๊ฐ€ ์กด์žฌํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๊ทœ๋ช…ํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ ์šฐ๋ฆฌ๋Š” ์–‘์ž ๊ณ„์ธก์—์„œ ํ•ต์‹ฌ์ ์ธ ์—ญํ• ์„ ํ•˜๋Š” ๋ฒ ์ด์ง€์•ˆ ์˜ค๋ฅ˜ ์˜์—ญ์— ๋Œ€ ํ•ด์„œ ์•Œ์•„๋ณธ๋‹ค. ์–ด๋–ค ๋ฌผ๋ฆฌ์ ์ธ ๊ฐ’์„ ์ถ”์ •ํ•จ์— ์žˆ์–ด์„œ ์šฐ๋ฆฌ๋Š” ์ถ”์ •๊ฐ’ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊ทธ์— ๋Œ€์‘ํ•˜๋Š” ์˜ค๋ฅ˜ ์˜์—ญ์„ ๋ฐ˜๋“œ์‹œ ์ œ๊ณตํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ํ•˜์ง€๋งŒ ์–‘์ž ๊ณ„์ธก์— ์žˆ์–ด์„œ ๋‹ค๋ฃจ๊ณ ์ž ํ•˜๋Š” ๊ณ„์˜ ์ฐจ์›์ด ์ปค์ง์— ๋”ฐ๋ผ ์˜ค๋ฅ˜ ์˜์—ญ์˜ ํฌ๊ธฐ์™€ ์‹ ์šฉ๋„๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” ๊ฒƒ์ด ๊ธฐํ•˜๊ธ‰์ˆ˜์ ์œผ๋กœ ์˜ค๋ž˜๊ฑธ๋ฆฐ๋‹ค๋Š” ๊ฒƒ์ด ๋ฐํ˜€์กŒ๋‹ค. ์šฐ๋ฆฌ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์ ๊ทผ์ ์ธ ์˜์—ญ์—์„œ ์˜ค๋ฅ˜ ์˜์—ญ์˜ ํฌ๊ธฐ์™€ ์‹ ์šฉ๋„์˜ ๊ทผ์‚ฌ์  ํ‘œํ˜„์„ ์œ ๋„ ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๋น„์ ๊ทผ์ ์ธ ์˜์—ญ์—์„œ๋„ ์ ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ํšจ์œจ์ ์ธ ์ˆ˜์น˜์  ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๋‹ค.I. Introduction 1 II. Preliminary 5 2.1 Continuous variable system 5 2.2 Gaussian states 10 2.3 Quantum estimation theory 12 III. Quantum Metrology using Gaussian states 17 3.1 Introduction 17 3.2 Advanced Mach-Zehnder Interferometer 19 3.2.1 Comparison between Coherent & Squeezed vacuum state and two-mode squeezed vacuum state 24 3.2.2 Remarks 32 3.3 Gaussian measurements for single-mode phase estimation with Gaussian states 33 3.3.1 Optimal Sensitivity 34 3.3.2 Optimal Gaussian measurement 36 3.3.3 Optimal measurement 41 3.3.4 Remarks 42 3.4 Optimal measurements for Quantum fidelity and Quantum Fisher information of Gaussian states 44 3.4.1 Optimal measurement for Gaussian quantum fidelity 45 3.4.2 Optimal measurements for single-mode Gaussian states 49 3.5 Conclusion 58 3.6 Appendix 59 IV. Bayesian Error Certification 77 4.1 Introduction 77 4.2 Bayesian Error Region 78 4.3 Analytical approximation 82 4.3.1 Case 1: Interior-point theory for a full likelihood 83 4.3.2 Case 2: Interior-point theory for a truncated likelihood 85 4.3.3 Case 3: Boundary-point theory 88 4.3.4 Remarks on logarithmic divergence and V_{R_0} 91 4.3.5 Examples in quantum-state tomography 92 4.3.6 Remarks 99 4.4 Efficient Monte-Carlo Method 100 4.4.1 In-region sampling 101 4.4.2 Region capacity 103 4.4.3 Numerical Complexity estimation of hit-and-run algorithm 108 4.4.4 Remarks 112 4.5 Conclusion 113 4.6 Appendix 114 V. Conclusion 127 Bibliography 131 Abstract in Korean 143Docto

    ์ธ๊ณต์ง€๋Šฅ์„ ์ด์šฉํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ธฐ๋ฐ˜์˜ ์‹œ์Šคํ…œ๊ณผ ์‚ฌ์šฉ์ž์˜ ์ธํ„ฐ๋ž™์…˜์— ๋Œ€ํ•œ ์ดํ•ด

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(๋””์ง€ํ„ธ์ •๋ณด์œตํ•ฉ์ „๊ณต), 2019. 2. ์„œ๋ด‰์›.์ปดํ“จํŒ… ํŒŒ์›Œ์˜ ๊ฐœ์„ , ์ธํ„ฐ๋„ท๊ณผ ์†Œ์…œ๋ฏธ๋””์–ด, ๋ชจ๋ฐ”์ผ ๋””๋ฐ”์ด์Šค ๋“ฑ์˜ ๋ณด๊ธ‰์„ ํ†ตํ•œ ์ˆ˜๋งŽ์€ ๋ฐ์ดํ„ฐ์˜ ์ถ•์ , ๋”ฅ๋Ÿฌ๋‹์„ ๋น„๋กฏํ•œ ๊ธฐ๊ณ„ํ•™์Šต ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๋ฐœ์ „์œผ๋กœ ์ธ๊ณต์ง€๋Šฅ ๊ธฐ์ˆ ์ด ์–ด๋Š๋•Œ๋ณด๋‹ค ๋”์šฑ ํฐ ์„ฑ๊ณผ๋ฅผ ๋ณด์ด๊ณ  ์žˆ๋‹ค. ์Œ์„ฑ ์ธ์‹, ์ปดํ“จํ„ฐ ๋น„์ „, ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ ๋“ฑ์˜ ๋ถ„์•ผ์—์„œ ์ธ๊ณต์ง€๋Šฅ์€ ์ด๋ฏธ ์ธ๊ฐ„์— ํ•„์ ํ•˜๊ฑฐ๋‚˜ ํ˜น์€ ์ธ๊ฐ„์„ ๋›ฐ์–ด๋„˜๋Š” ์„ฑ๋Šฅ์„ ๋ณด์ด๊ณ  ์žˆ์œผ๋ฉฐ, ์ž์œจ์ฃผํ–‰, ๋กœ๋ด‡, ์˜๋ฃŒ์„œ๋น„์Šค ๋“ฑ์˜ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์— ์ ์šฉ๋˜์–ด ์šฐ๋ฆฌ์˜ ์‚ถ์— ๋งŽ์€ ๋ณ€ํ™”๋ฅผ ๊ฐ€์ ธ์˜ฌ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค. ํ•˜์ง€๋งŒ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์ธก๋ฉด์—์„œ์˜ ๊ธฐ์ˆ ์ ์ธ ๋ฐœ์ „์— ๋น„ํ•ด ์ธ๊ณต์ง€๋Šฅ์˜ ์ธ๊ฐ„๊ณตํ•™์  ์š”์†Œ์™€ ์‚ฌ์šฉ์ž ๊ฒฝํ—˜์— ๋Œ€ํ•œ ๊ด€์‹ฌ๊ณผ ๋…ผ์˜๊ฐ€ ์ƒ๋Œ€์ ์œผ๋กœ ๋ถ€์กฑํ•œ ํŽธ์ด๋‹ค. ์ด์— ์ด ์—ฐ๊ตฌ๋Š” ์ธ๊ฐ„์ปดํ“จํ„ฐ์ƒํ˜ธ์ž‘์šฉ์˜ ๊ด€์ ์—์„œ ์ธ๊ณต์ง€๋Šฅ๊ณผ ์‚ฌ์šฉ์ž๊ฐ€ ์ƒํ˜ธ์ž‘์šฉ ํ•˜๋Š” ๋ฐฉ์‹์— ๋Œ€ํ•ด ๋‹ค์ธต์ ์ด๊ณ  ํ†ตํ•ฉ์ ์œผ๋กœ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•˜๊ณ  ์ด๋ฅผ ํ†ตํ•ด ์ธ๊ณต์ง€๋Šฅ ๊ธฐ๋ฐ˜์˜ ์‚ฌ์šฉ์ž ์ธํ„ฐํŽ˜์ด์Šค ๋””์ž์ธ์„ ์œ„ํ•œ ํ•จ์˜์ ์„ ๋„์ถœํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ํŠนํžˆ ์ด ๋…ผ๋ฌธ์€ ์ธ๊ณต์ง€๋Šฅ ๊ธฐ์ˆ ์„ ์ด์šฉํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ธฐ๋ฐ˜์˜ ์‹œ์Šคํ…œ๊ณผ ์‚ฌ์šฉ์ž์˜ ์ƒํ˜ธ์ž‘์šฉ์— ์ฃผ๋ชฉํ•˜๊ณ , ์ด๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์ธ์ง€, ํ•ด์„ ๋ฐ ํ‰๊ฐ€, ์ง€์†์ ์ธ ์ธํ„ฐ๋ž™์…˜, ์‹ค์šฉ์ ์ธ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ์ฃผ์ œ๋กœ ํ•œ ๋„ค ๋‹จ๊ณ„์˜ ์—ฐ๊ตฌ๋ฅผ ๊ธฐํšํ•˜๊ณ  ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์ฒซ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ์ธ๊ณต์ง€๋Šฅ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๋Œ€ํ•œ ์‚ฌ๋žŒ๋“ค์˜ ์„ ํ—˜์  ์ธ์‹์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์—ฐ๋ น๊ณผ ์„ฑ๋ณ„, ์ง์—…์˜ ๋‹ค์–‘์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ๋Œ€ํ‘œ์„ฑ์„ ๊ฐ–๋Š” ์ฐธ๊ฐ€์ž๋ฅผ ๋ชจ์ง‘ํ•˜์˜€์œผ๋ฉฐ, ์ด๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ์ธ๊ณต์ง€๋Šฅ ์ธ์‹์— ๋Œ€ํ•œ ์ •์„ฑ์  ๋ฐฉ์‹์˜ ์กฐ์‚ฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์กฐ์‚ฌ ๊ฒฐ๊ณผ ์‚ฌ๋žŒ๋“ค์ด ์ธ๊ณต์ง€๋Šฅ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๋Œ€ํ•ด ๊ฐ–๋Š” ์„ ์ž…๊ฒฌ๊ณผ ๊ณ ์ •๊ด€๋…์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ์‚ฌ๋žŒ๋“ค์ด ์ธ๊ณต์ง€๋Šฅ์„ ์˜์ธํ™” ํ•  ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ํƒ€์žํ™” ํ•˜๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์Œ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ์ธ๊ณต์ง€๋Šฅ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ์‚ฌ์šฉ์ž์˜ ๊ด€๊ณ„์—์„œ ์ง€์†์ ์ด๊ณ  ์ „์ฒด์ ์ธ ๊ฒฝํ—˜์ด ์ค‘์š”ํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‘๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ์ธ๊ณต์ง€๋Šฅ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๋Œ€ํ•œ ์‚ฌ์šฉ์ž์˜ ํ•ด์„๊ณผ ํ‰๊ฐ€์— ๊ด€ํ•œ ๊ฒƒ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ด๋ฏธ์ง€์˜ ๋ฏธ์  ์ ์ˆ˜๋ฅผ ๊ณ„์‚ฐํ•ด์ฃผ๋Š” ์‹ ๊ฒฝ๋ง ๊ธฐ๋ฐ˜์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ๊ตฌํ˜„๋œ AI Mirror๋ผ๋Š” ์—ฐ๊ตฌ ํ”„๋กœํ† ํƒ€์ž…์„ ์ œ์ž‘ํ•˜์˜€์œผ๋ฉฐ, ์ธ๊ณต์ง€๋Šฅ/๊ธฐ๊ณ„ํ•™์Šต ๋ถ„์•ผ์˜ ์ „๋ฌธ๊ฐ€, ์‚ฌ์ง„์ „๋ฌธ๊ฐ€, ์ผ๋ฐ˜์ธ์œผ๋กœ ๊ตฌ๋ถ„๋œ ์„ธ ์ง‘๋‹จ์˜ ์‚ฌ์šฉ์ž๋ฅผ ๋ชจ์ง‘ํ•˜์—ฌ ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์‚ฌ์šฉ์ž๋Š” ์ €๋งˆ๋‹ค ๋‹ค๋ฅธ ๋ฐฐ๊ฒฝ ์ง€์‹์„ ๋ฐ˜์˜ํ•ด ์ธ๊ณต์ง€๋Šฅ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ•ด์„ํ•˜๊ณ  ํ‰๊ฐ€ํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์˜€๋‹ค. ์‚ฌ์ง„์ „๋ฌธ๊ฐ€ ์ง‘๋‹จ์ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐ€์žฅ ๋†’์€ ์ •๋„๋กœ ํ•ด์„ํ•˜์˜€์œผ๋ฉฐ ํ•ฉ๋ฆฌ์ ์ด๋ผ๊ณ  ์—ฌ๊ธด ๋ฐ˜๋ฉด, ์ธ๊ณต์ง€๋Šฅ/๊ธฐ๊ณ„ํ•™์Šต ์ „๋ฌธ๊ฐ€ ์ง‘๋‹จ์€ ๊ฐ€์žฅ ๋‚ฎ์€ ์ •๋„๋กœ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ•ด์„ํ•˜๊ณ  ํ‰๊ฐ€ํ–ˆ๋‹ค. ์‚ฌ์šฉ์ž๋Š” ๋‹ค์–‘ํ•œ ์ „๋žต์„ ํ†ตํ•ด ์ธ๊ณต์ง€๋Šฅ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์›๋ฆฌ๋ฅผ ์ถ”๋ก ํ•˜๊ณ ์ž ํ•˜์˜€์œผ๋ฉฐ ์ด๋ฅผ ํ†ตํ•ด ์ธ๊ณต์ง€๋Šฅ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ์˜ ์ฐจ์ด๋ฅผ ์ขํ˜€๊ฐˆ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ์‚ฌ์šฉ์ž๋Š” ์ธ๊ณต์ง€๋Šฅ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ์Œ๋ฐฉ ์†Œํ†ต์„ ํ†ตํ•ด ์˜๊ฒฌ์„ ๊ตํ™˜ํ•˜๊ณ ์ž ํ•˜๋Š” ๋‹ˆ์ฆˆ๋ฅผ ํ‘œ์ถœํ•˜์˜€๋‹ค. ์„ธ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ์ธ๊ณต์ง€๋Šฅ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ์‚ฌ์šฉ์ž๊ฐ€ ๊ณต๋™์˜ ๋ชฉํ‘œ๋ฅผ ๋‘๊ณ  ์ง€์†์ ์ธ ์ธํ„ฐ๋ž™์…˜์„ ์ด์–ด๊ฐ€๋Š” ๊ณผ์ •์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ๋ชฉํ‘œ๋กœ ํ•˜์˜€๋‹ค. ์‚ฌ์šฉ์ž๊ฐ€ ์ผ๋ถ€ ๊ทธ๋ฆฐ ๋ฌผ์ฒด๋ฅผ ์™„์„ฑํ•˜๊ณ  ์Šค์ผ€์น˜์— ์ƒ‰์น ์„ ์ž๋™์œผ๋กœ ์™„์„ฑํ•ด์ฃผ๋Š” ์‹ ๊ฒฝ๋ง ๊ธฐ๋ฐ˜์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜ API๋ฅผ ์ด์šฉํ•˜์—ฌ DuetDraw๋ผ๋Š” ๋ฆฌ์„œ์น˜ ํ”„๋กœํ† ํƒ€์ž…์„ ์ œ์ž‘ํ•˜์˜€๊ณ , ์ •๋Ÿ‰ ๋ฐ ์ •์„ฑ์  ๋ฐฉ๋ฒ•์œผ๋กœ ์ด์— ๋Œ€ํ•œ ์‚ฌ์šฉ์ž ํ‰๊ฐ€๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์‚ฌ์šฉ์ž ํ‰๊ฐ€ ๊ฒฐ๊ณผ ์‚ฌ์šฉ์ž๋Š” ์ธ๊ณต์ง€๋Šฅ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ์˜ ํ˜‘์—… ๊ณผ์ •์—์„œ ์ธ๊ณต์ง€๋Šฅ์œผ๋กœ๋ถ€ํ„ฐ ๋‹จ์ˆœํ•œ ํ”ผ๋“œ๋ฐฑ ๋ณด๋‹ค๋Š” ์ž์„ธํ•œ ์„ค๋ช…์„ ์ œ๊ณต๋ฐ›๊ธฐ๋ฅผ ์›ํ–ˆ์œผ๋ฉฐ, ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ์˜ ๊ด€๊ณ„์—์„œ ํ•ญ์ƒ ์ฃผ๋„์ ์ธ ์œ„์น˜์— ์žˆ๊ณ ์ž ํ•˜์˜€๋‹ค. ์ธ๊ณต์ง€๋Šฅ๊ณผ์˜ ์ธํ„ฐ๋ž™์…˜์€ ๊ณผ์—… ์ˆ˜ํ–‰์— ๋Œ€ํ•œ ์‚ฌ์šฉ์ž์˜ ์˜ˆ์ธก๊ฐ€๋Šฅ์„ฑ, ์ดํ•ด๋„, ํ†ต์ œ๋ ฅ์„ ๋‚ฎ์ถ”๋Š” ๊ฒฝํ•ญ์ด ์žˆ์—ˆ์ง€๋งŒ, ์‚ฌ์šฉ์ž์—๊ฒŒ ์ƒ๋Œ€์ ์œผ๋กœ ๋†’์€ ์‚ฌ์šฉ์„ฑ์„ ์ œ๊ณตํ•˜์˜€์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์‚ฌ์šฉ์ž๊ฐ€ ์ „๋ฐ˜์ ์œผ๋กœ ๋งŒ์กฑ์Šค๋Ÿฌ์šด ๊ฒฝํ—˜์„ ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ๋์œผ๋กœ, ๋„ค๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ๋ณด๋‹ค ์‹ค์šฉ์ ์ธ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ์ œ์ž‘ํ•˜์—ฌ ์ด์— ๋Œ€ํ•œ ์‚ฌ์šฉ์ž ์ธํ„ฐ๋ž™์…˜์„ ์ดํ•ดํ•˜๊ณ ์ž ํ•˜์˜€์œผ๋ฉฐ, ์ด์— ์ตœ๊ทผ ํฐ ๊ฐ๊ด‘์„ ๋ฐ›๊ณ  ์žˆ๋Š” ๋กœ๋ด‡์ €๋„๋ฆฌ์ฆ˜ ๊ธฐ์ˆ ์„ ๊ตฌํ˜„ํ•œ NewsRobot์„ ์ œ์ž‘ํ•˜์˜€๋‹ค. NewsRobot์€ 2018 ํ‰์ฐฝ๋™๊ณ„์˜ฌ๋ฆผํ”ฝ์˜ ์ฃผ์š” ๊ฒฝ๊ธฐ ๊ฒฐ๊ณผ๋ฅผ ์ž๋™์œผ๋กœ ์ˆ˜์ง‘ํ•˜๊ณ  ์š”์•ฝํ•˜๋ฉฐ, ๋‚ด์šฉ๊ณผ ํ˜•์‹์„ ๊ฐ๊ฐ ์ข…ํ•ฉ๋‰ด์Šค-์„ ํƒ๋‰ด์Šค, ํ…์ŠคํŠธ-์นด๋“œ-๋™์˜์ƒ์œผ๋กœ ๋‹ฌ๋ฆฌํ•˜์—ฌ ๋‰ด์Šค๋ฅผ ์ƒ์„ฑํ•œ๋‹ค. ์ •๋Ÿ‰ ๋ฐ ์ •์„ฑ์  ๋ฐฉ๋ฒ•์˜ ์‚ฌ์šฉ์ž ํ‰๊ฐ€ ๊ฒฐ๊ณผ, ์„ ํƒ๋‰ด์Šค๊ฐ€ ์ข…ํ•ฉ๋‰ด์Šค์— ๋น„ํ•ด ๋‚ฎ์€ ์‹ ๋ขฐ๋„๋ฅผ ๋ณด์˜€์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์„ ํƒ๋‰ด์Šค์— ๋Œ€ํ•œ ์‚ฌ์šฉ์ž์˜ ๋†’์€ ์„ ํ˜ธ๋„๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ๋ฉ€ํ‹ฐ๋ฏธ๋””์–ด ๋ชจ๋‹ฌ๋ฆฌํ‹ฐ๊ฐ€ ๋†’์•„์งˆ์ˆ˜๋ก ์‚ฌ์šฉ์ž์˜ ๋‰ด์Šค์— ๋Œ€ํ•œ ๋งŒ์กฑ๋„๊ฐ€ ๋†’์•„์ง€์ง€๋งŒ ์‚ฌ์šฉ์ž์˜ ๊ธฐ๋Œ€์ˆ˜์ค€์— ์–ด๊ธ‹๋‚œ ๊ฒฝ์šฐ ์˜คํžˆ๋ ค ๋‚ฎ์€ ํ‰๊ฐ€๋ฅผ ๋ฐ›๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์‚ฌ์šฉ์ž๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์ž๋™์œผ๋กœ ์ƒ์„ฑํ•œ ๋‰ด์Šค์— ๋Œ€ํ•ด ์ •ํ™•ํ•˜๊ณ  ๊ฐ๊ด€์ ์ด๋ผ๊ณ  ํ‰๊ฐ€ํ•˜์˜€์œผ๋ฉฐ, ๋น ๋ฅธ ๋‰ด์Šค ์ƒ์„ฑ ์†๋„์™€ ๋‹ค์–‘ํ•œ ์ •๋ณด ์‹œ๊ฐํ™” ์š”์†Œ์— ๋Œ€ํ•ด์„œ๋„ ๋งŒ์กฑ๊ฐ์„ ๋“œ๋Ÿฌ๋ƒˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด ๋„ค ๊ฐ€์ง€ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋“ค์„ ๋ฐ”ํƒ•์œผ๋กœ ์ธ๊ฐ„-์ธ๊ณต์ง€๋Šฅ ์ƒํ˜ธ์ž‘์šฉ์— ๋Œ€ํ•œ ๋‹ค์–‘ํ•œ ์‹œ์‚ฌ์ ๋“ค์„ ๋„์ถœํ•˜์˜€์œผ๋ฉฐ, ์ธ๊ณต์ง€๋Šฅ์„ ์ด์šฉํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ธฐ๋ฐ˜์˜ ์‹œ์Šคํ…œ์˜ ์‚ฌ์šฉ์ž ์ธํ„ฐํŽ˜์ด์Šค ๋””์ž์ธ์„ ์œ„ํ•œ ํ•จ์˜์ ๋“ค์„ ์ œ์•ˆํ•œ๋‹ค.The recent development of artificial intelligence (AI) algorithms is affecting our daily lives in numerous areas. Moreover, AI is expected to evolve rapidly, bringing tremendous economic value. However, compared to the attention these technological improvements receive, there is relatively little discussion on human factors and user experience related to AI algorithms. Thus, this thesis aims to better understand how users interact with AI algorithms. Specifically, this work examined algorithm-based humanโ€“AI interaction in four stages, through various modes of human-computer interaction: The first study investigated how people perceive algorithm-based systems using AI, finding that people tend to anthropomorphize as well as alienate them, which is distinct from their perceptions of computers. The second study investigated how people interpret and evaluate the output from AI algorithms through a prototype, AI Mirror, which assigned aesthetic scores to images based on a neural network algorithm. The results revealed that people interpret AI algorithms differently based on their backgrounds, and that they want to understand and communicate with AI systems. The third study investigated how people build a sequence of actions with AI algorithms through a mixed method study using a research prototype called DuetDraw, a drawing tool in which users and AI can draw pictures together. The results showed that people want to lead collaborations while hoping to get appropriate instructions from the AI algorithm. Lastly, a case study on a practical application of AI was conducted with a research prototype called NewsRobot, which automatically generated news articles with different content and styles. Findings showed that users prefer selective news and multimedia news that have more functionality and modality, but at the same time they do not want AI to boast about its ability. With these distinct but intertwined studies, this thesis argues the importance of understanding human factors in the user interfaces of AI-based systems and suggests design principles to this end.1 INTRODUCTION 1 1.1 Background 1 1.2 Research Goal 10 1.3 Research Questions 11 1.4 How People Perceive Algorithm-based Systems Using Artificial Intelligence 12 1.5 How People Interpret and Evaluate Algorithm-based Systems Using Artificial Intelligence 13 1.6 How People Build Sequential Actions with Algorithm-Based Systems Using Artificial Intelligence 15 1.7 How People Use a Practical Application of an Algorithm-based Systems Using Artificial Intelligence 17 1.8 Thesis Statement 18 1.9 Contributions 18 1.10 Thesis Overview 20 2 RELATED WORK 22 2.1 Human Perception of AI Algorithms 22 2.1.1 Technophobia 22 2.1.2 Anthropomorphism 23 2.2 Users Interpretation and Evaluation of AI Algorithms 24 2.2.1 Interpretability of Algorithms and Users Concerns 24 2.2.2 Sense-making and Gap between Users and AI algorithms 25 2.2.3 User Control in Intelligent Systems 26 2.3 How People Build Sequential Actions with AI Algorithms 26 2.3.1 AI, Deep Learning, and New UX in Creative Works 27 2.3.2 Communication and Leadership among Users and AI 28 2.4 Practical Design of Algorithm-based Systems Using AI 29 2.4.1 Automated Journalism 30 2.4.2 Personalization of News Content 31 2.4.3 Effect of Multimedia Modality on User Experience 32 3 HOW PEOPLE PERCEIVE ALGORITHM-BASED SYSTEMS USING ARTIFICIAL INTELLIGENCE 33 3.1 Motivation 34 3.2 Google DeepMind Challenge Match 36 3.3 Methodology 38 3.3.1 Participant Recruitment 38 3.3.2 Interview Process 39 3.3.3 Interview Analysis 40 3.4 Findings 41 3.4.1 Preconceptions about Artificial Intelligence 41 3.4.2 Confrontation: Us vs. Artificial Intelligence 43 3.4.3 Anthropomorphizing AlphaGo 47 3.4.4 Alienating AlphaGo 49 3.4.5 Concerns about the Future of AI 52 3.5 Limitations 55 3.6 Summary 56 4 HOW PEOPLE INTERPRET AND EVALUATE ALGORITHM-BASED SYSTEMS USING ARTIFICIAL INTELLIGENCE 57 4.1 Motivation 58 4.2 AI Mirror 60 4.2.1 Design Goal 60 4.2.2 Image Assessment Algorithm 61 4.2.3 Design of User Interface 61 4.3 Study Design 62 4.3.1 Participant Recruitment 63 4.3.2 Experimental Settings 64 4.3.3 Procedure 65 4.3.4 Analysis Methods 66 4.4 Result 1: Quantitative Analysis 67 4.4.1 Difference 68 4.4.2 Interpretability 69 4.4.3 Reasonability 70 4.5 Result 2: Qualitative Analysis 71 4.5.1 People Understand AI Based on What They Know 71 4.5.2 People Reduce Difference Using Various Strategies 73 4.5.3 People Want to Actively Communicate with AI 76 4.6 Limitations 78 4.7 Conclusion 78 5 HOW PEOPLE BUILD SEQUENTIAL ACTIONS WITH ALGORITHM-BASED SYSTEMS USING ARTIFICIAL INTELLIGENCE 80 5.1 Motivation 81 5.2 Duet Draw 84 5.2.1 Five AI Functions of DuetDraw 84 5.2.2 Initiative and Communication Styles of DuetDraw 85 5.3 Study Design 86 5.3.1 Participants 87 5.3.2 Tasks and Procedures 87 5.3.3 Drawing Scenarios 88 5.3.4 Survey 89 5.3.5 Think-aloud and Interview 89 5.3.6 Analysis Methods 90 5.4 Result 1: Quantitative Analysis 92 5.4.1 Detailed Instruction is Preferred over Basic Instruction 93 5.4.2 UX Could Be Worse with Lead-Basic than Assist-Detailed 94 5.4.3 AI is Fun, Useful, Effective, and Efficient 94 5.4.4 No-AI is more Predictable, Comprehensible, and Controllable 95 5.4.5 Even if Predictability is Low, Fun and Interest Can Increase 96 5.5 Result 2: Qualitative Analysis 96 5.5.1 Just Enough Instruction 97 5.5.2 Users Always Want to Lead 99 5.5.3 AI is Similar to Humans But Unpredictable 101 5.5.4 Co-Creation with AI 102 5.6 Limitations 105 5.7 Conclusion 105 6 HOW PEOPLE USE A PRACTICAL APPLICATION OF AN ALGORITHM-BASED SYSTEM USIGN ARTIFICIAL INTELLIGENCE 107 6.1 Motivation 108 6.2 News Robot 110 6.2.1 Selecting Main Event and Data Source 111 6.2.2 Designing News Article Structure 113 6.2.3 Content and Style 113 6.2.4 Generating News Articles 115 6.2.5 Designing NewsRobot User Interface 116 6.3 Study Design 117 6.3.1 Participants 117 6.3.2 Procedures 118 6.3.3 Analysis Methods 119 6.4 Results 1: Quantitative Analysis 120 6.4.1 Selective News Is Less Credible 120 6.4.2 Users Like Both Multimedia and Personalization 121 6.4.3 Quality of Video Is Not Rated Highest 122 6.4.4 NewsRobot Is Accurate but Not Sensational 123 6.5 Results 2: Qualitative Analysis 124 6.5.1 Users Evaluate NewsRobot Features Highly 124 6.5.2 NewsRobot Is Unbiased but Predictable 127 6.5.3 Benefits and Drawbacks of Using Multimedia 128 6.6 Limitations 130 6.7 Conclusion 130 7 DISCUSSION 131 7.1 Human Perception of AI Algorithms 131 7.1.1 Cognitive Dissonance 131 7.1.2 Beyond Technophobia 132 7.1.3 Toward a New Chapter in Human-Computer Interaction 134 7.1.4 Coping with the Potential Danger 135 7.2 Users Interpretation and Evaluation of AI Algorithms 135 7.2.1 Integrate Diverse Expertise and User Perspectives 136 7.2.2 Take Advantage of Peoples Curiosity about AI Principles 137 7.2.3 Provide AI and Users with Mutual Communication 138 7.3 How People Build Sequential Actions with AI Algorithms 139 7.3.1 Let the User Take the Initiative 140 7.3.2 Provide Just Enough Instruction 140 7.3.3 Embed Interesting Elements in the Interaction 141 7.3.4 Ensure Balance 142 7.4 Practical Design of Algorithm-based Systems Using AI 142 7.4.1 Provide Selective news with Adaptable Interface 142 7.4.2 Present Various Multimedia Elements but Not Too Many 144 7.4.3 Importance of Quality Data and Algorithm Refinement 145 7.5 Principles 146 8 CONCLUSION 148 8.1 Summary of Contributions 149 8.2 Future Directions 150 Bibliography 153 ๋…ผ๋ฌธ์ดˆ๋ก 173 ๊ฐ์‚ฌ์˜ ๊ธ€ 176Docto

    SAMHD1 ๊ฒฐํ•์— ์˜ํ•œ ์ž๊ฐ€๋ฉด์—ญ ์งˆํ™˜ ์ƒ์„ฑ ๊ธฐ์ž‘์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ์œ ์ „๊ณตํ•™์ „๊ณต, 2018. 2. ์•ˆ๊ด‘์„.SAMHD1 is an enzyme which has dual enzymatic activities: deoxynucleoside triphosphohydrolase (dNTPase) and phosphorolytic 3โ€ฒ-5โ€ฒ exoribonuclease. Even though SAMHD1 was identified initially as the human ortholog of the mouse IFN-induced gene Mg11, studies about SAMHD1 have focused overwhelmingly on the inhibitory mechanism of SAMHD1 against HIV-1 replication. SAMHD1 was demonstrated to restrict HIV-1 replication by reducing cellular dNTP concentrations below the levels required for retroviral reverse transcription in dNTPase dependent manner. In addition, it is also suggested that SAMHD1 can bind to and degrade HIV-1 RNA to restrict the replication of HIV-1 through the RNase activity. A disturbance of the type I interferon homeostasis is central to the pathogenesis of the autoimmune diseases. The autoimmune disorder Aicardi-Goutiรจres syndrome (AGS) is characterized by a constitutive type I interferon response clinically overlapping with congenital infection and systemic lupus erythematosus (SLE). All the genes that are mutated in patients with AGS encode enzymes (TREX1, RNASEH2, ADAR, SAMHD and IFIH1) that are associated with nucleic acid metabolism, leading to the hypothesis that the inappropriate accumulation of endogenous nucleic acid species resulting from the dysfunction of AGS-related enzymes triggers the chronic type I interferon response. The mechanisms by which malfunctions of TREX1, RNASEH2, ADAR1 and IFIH1 occur AGS are considerably investigated and suggested. However, how SAMHD1-deficiency causes the type I interferon response in patients with AGS remains unknown, even though mutations in SAMHD1 cause AGS. In addition, Samhd1-deficient mice did not exhibit any distinct clinical phenotypes. Therefore, it is very important to identify the mechanism by which SAMHD1-deficiency results in AGS in human patients. Here, I generated SAMHD1-deficient THP-1 cell lines and showed that those cell lines recapitulate AGS phenotypes which include the activation of type I interferon response and delayed cell cycle progression. I further showed that SAMHD1 proteins purified from undifferentiated THP-1 cells possessed RNase activity. Then, RNA derived from SAMHD1-deficient cells, but not that from wild-type cells neither DNA derived from wild-type and SAMHD1-deficient cells, significantly activated IFN-ฮฑ expression. In addition, the reconstitution of wild-type SAMHD1 and SAMHD1D137N, which possess RNase activity, only repressed the IFN- induction in SAMHD1-deficient cells. These results suggest that cytosolic RNA species accumulated in the absence of SAMHD1 act as a major immunogenic source for the type I interferon response. I also proposed that innate sensing of the endogenous retroelements-derived transcripts accumulated in SAMHD1-deficient cells results in significant type I interferon response and this accounts for the cause of SAMHD1-related AGS. This was supported with my data showing significant portion of the retroelement RNAs identified in SAMHD1 CLIP-seq and upregulated in SAMHD1-deficient cells. Even though many nucleic acid sensing pathways were already identified, this IFN signature occurred independently of all previously known nucleic acid sensing pathways that ultimately converge on activation of TBK1. Only IRF3 was indispensable for the spontaneous IFN signature in SAMHD1-deficient cells. Therefore, I sought to identify the RNA sensing pathway associated with ISG induction in SAMHD1-deficient cells and showed that the PI3K/AKT/IRF3 signaling pathway is essential for the type I interferon response in SAMHD1-deficient THP-1 cells. AKT and IRF3 were highly activated in SAMHD1-deficient cells, as assessed by the phosphorylation levels of these molecules. Then, treatment of PI3K or AKT inhibitors dramatically reduced the type I interferon signatures in SAMHD1-deficient cells. Moreover, SAMHD1/AKT1 double knockout relieved the type I interferon signatures to the levels observed in wild-type cells. The reconstitution of wild-type SAMHD1 and SAMHD1D137N inhibited the activation of AKT in SAMHD1-deficient cells, showing that RNase activity of SAMHD1 is critical for AKT activation as well as spontaneous IFN response. In human PBMCs, siRNA-mediated SAMHD1 silencing recapitulated the phenotypes seen in SAMHD1-deficient THP-1 cells. By comparison, knockout of SAMHD1 in not only HEK293T and HeLa cells but also PMA-differentiated THP-1 cells did not result in the activation of STAT1 or the induction of ISGs, suggesting that the type I interferonopathy associated with SAMHD1-deficiency is cell type-specific. My data provide an insight not only into the pathogenesis of the type I interferonopathies but also will encourage the development and use of immunosuppressive therapies in AGS and related autoimmune diseases.INTRODUCTION 1 1 Characteristics of SAMHD1 protein 2 2. SAMHD1-mediated retroviral restriction 5 3. SAMHD1 and Aicardi-Goutires syndrome 9 MATERIALS AND METHODS 14 1. Generation of knockout cell lines 14 2. Cells and human blood cell isolation 16 3. Reagents and antibodies 16 4. RNA interference and transfection 17 5. Genomic DNA and RNA preparation 18 6. Quantitative real-time reverse transcription PCR 18 7. Cell cycle analysis 21 8. In vitro nuclease assay by immunoprecipitation 21 9. CLIP-seq and RNA-seq 22 10. Bioinformatics 24 11. Ethical statement 26 12. Statistical analysis 26 RESULTS 28 1. SAMHD1-deficient human monocytic cells display a heightened IFN signature 28 2. RNA enriched in the absence of SAMHD1 is a major source of the IFN- response 43 3. ISG activation in SAMHD1-deficient cells is dependent on IRF3 & Type I IFN receptor 68 4. The PI3K/AKT signaling pathway is involved in linking SAMHD1-deficiency to the IFN response 76 DISCUSSION 110 REFERENCES 117 ABSTRACT IN KOREAN 124 APPENDIX 129Docto

    Improvement of GTTI Using non-Parametric Approach and Water Breakthrough Time Prediction

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์—๋„ˆ์ง€์‹œ์Šคํ…œ๊ณตํ•™๋ถ€, 2014. 8. ์ตœ์ข…๊ทผ.์ €๋ฅ˜์ธตํŠน์„ฑํ™”์™€ ๋ถˆํ™•์‹ค์„ฑ ์ •๋Ÿ‰ํ™”๋Š”, ์œ ์ „๊ฐœ๋ฐœ๊ณผ์ • ์ƒ์˜ ์˜์‚ฌ๊ฒฐ์ •์„ ์œ„ํ•œ ํ•„์ˆ˜ ๊ณผ์ •์ด๋‹ค. ์ดˆ๊ธฐ์— ์ œํ•œ๋œ ์ •๋ณด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ตฌ์ถ•๋œ ์ €๋ฅ˜์ธต ๋ชจ๋ธ์€ ํฐ ๋ถˆํ™•์‹ค์„ฑ์„ ๊ฐ€์ง„๋‹ค. ๋”ฐ๋ผ์„œ ์ถ”๊ฐ€์ ์œผ๋กœ ํš๋“ํ•œ ๋™์ ์ž๋ฃŒ๋ฅผ ๊ฒฐํ•ฉํ•˜์—ฌ ๋ถˆํ™•์‹ค ์„ฑ์„ ์ค„์ด๋Š” ์ €๋ฅ˜์ธตํŠน์„ฑํ™”๋ฅผ ์‹ค์‹œํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋‹ค์–‘ํ•œ ๋“ฑ๊ฐ€์˜ ๋ชจ๋ธ์„ ๊ตฌ์ถ•ํ•˜๊ณ  ๋ถˆํ™•์‹ค์„ฑ์„ ๋ถ„์„ํ•˜๋Š” ๊ฒƒ์„ ๋ถˆํ™•์‹ค์„ฑ ์ •๋Ÿ‰ํ™”๋ผ๊ณ  ํ•œ๋‹ค. ๋ถˆํ™•์‹ค์„ฑ ์ •๋Ÿ‰ํ™”๋Š” ๋‹ค์ˆ˜์˜ ๋ชจ๋ธ์„ ๊ณ„์‚ฐํ•˜๋ฉฐ ๊ฐ ๋ชจ๋ธ์— ๋Œ€ํ•œ ์ตœ์ ํ™” ๊ณผ์ •์— ๋งŽ์€ ๊ณ„์‚ฐ๋Ÿ‰์ด ํ•„์š”ํ•˜๋ฏ€๋กœ GTTI(generalized travel time inversion)์™€ ๊ฐ™์€ ์œ ์„  ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ธฐ๋ฐ˜ ์—ญ์‚ฐ์„ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ถˆํ™•์‹ค์„ฑ ์ •๋Ÿ‰ํ™”๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์—ญ์‚ฐ์—์„œ ๋„์ถœ๋˜๋Š” ๋‹ค์ˆ˜์˜ ๋ชจ๋ธ๋“ค์ด ์‹ค์ œ ํ•„๋“œ๋ฅผ ์ž˜ ๋Œ€ํ‘œํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ํ•˜์ง€๋งŒ GTTI๋Š” ์‹ค์ œ ํ•„๋“œ์˜ ๋ฌผ์„ฑ์น˜๊ฐ€ ๊ฐ•ํ•œ ๋น„์ •๊ทœ์„ฑ์„ ๊ฐ€์ง€๋ฉด ํ•„๋“œ์˜ ๋ฌผ์„ฑ์น˜ ๋ถ„ํฌ๋ฅผ ๋ฐ”๋ฅด๊ฒŒ ์˜ˆ์ธกํ•˜์ง€ ๋ชปํ•œ๋‹ค. ๋˜ํ•œ GTTI๋Š” ๊ธฐ๋ฒ• ์ƒ์˜ ํ•œ๊ณ„๋กœ ์ธํ•ด ๋ฌผ ๋ŒํŒŒ๊ฐ€ ๋ฐœ์ƒํ•˜์ง€ ์•Š์€ ์œ ์ •๋“ค์— ๋Œ€ํ•ด์„œ๋Š” ์—ญ์‚ฐ์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์—†๋‹ค. ๋”ฐ๋ผ์„œ ์ด๋Ÿฌํ•œ ๊ฒฝ์šฐ๋“ค์— GTTI๋กœ ๊ตฌ์ถ•๋œ ๋ชจ๋ธ๋“ค์€ ์‹ค์ œ ํ•„๋“œ๋ฅผ ๋Œ€ํ‘œํ•˜์ง€ ๋ชปํ•˜๋ฏ€๋กœ, ๋ถˆํ™•์‹ค์„ฑ ์ •๋Ÿ‰ํ™” ๊ฒฐ๊ณผ๋„ ์˜ฌ๋ฐ”๋ฅด์ง€ ์•Š๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” GTTI ๊ธฐ๋ฒ•์˜ ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋ฅผ ๋น„๋ชจ์ˆ˜์ ‘๊ทผ๋ฒ•๊ณผ ๋ฌผ ๋ŒํŒŒ์‹œ๊ฐ„ ์˜ˆ์ธก์„ ํ†ตํ•ด ๊ทน๋ณตํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋น„๋ชจ์ˆ˜์ ‘๊ทผ๋ฒ•๊ณผ ์ •๊ทœ์Šค์ฝ”์–ด๋ณ€ํ™˜์„ ํ™œ์šฉํ•จ์œผ ๋กœ์จ ๋ฌผ์„ฑ์น˜๊ฐ€ ๊ฐ•ํ•œ ๋น„์ •๊ทœ์„ฑ์„ ๊ฐ–๋Š” ๊ฒฝ์šฐ์— ๋Œ€ํ•œ ๋ฌผ์„ฑ์น˜๋ถ„ํฌ ์˜ˆ์ธก์„ฑ๋Šฅ์„ ๋†’์˜€๋‹ค. ๋˜ํ•œ ์•„์ง ๋ฌผ ๋ŒํŒŒ๊ฐ€ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์€ ์œ ์ •๋“ค์˜ ๋ฌผ ๋ŒํŒŒ์‹œ๊ฐ„์„ ์˜ˆ์ธก ํ•˜๊ณ , ์—ญ์‚ฐ๊ณผ์ •์—์„œ ๊ฐ ์œ ์ •๋ณ„๋กœ ์˜ˆ์ธก๋œ ๋ฌผ ๋ŒํŒŒ์‹œ๊ฐ„์„ ๋ฐ˜์˜ํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ํ•ด๋‹น ์œ ์ •๋“ค์—์„œ ์•„์ง ๋ฌผ ๋ŒํŒŒ๊ฐ€ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜๋‹ค๋Š” ์ •๋ณด๋ฅผ ์˜จ์ „ํžˆ ํ™œ์šฉํ•˜๊ณ  ๋ถˆํ™•์‹ค์„ฑ์„ ์ตœ์†Œํ™”ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ฐœ์„ ๋œ GTTI๋ฅผ ํ™œ์šฉํ•œ ๊ฒฐ๊ณผ, ์•ž์„œ ์–ธ๊ธ‰ํ•œ ์ƒํ™ฉ๋“ค์—์„œ ์‹ค์ œ ํ•„๋“œ๋ฅผ ๋ณด๋‹ค ์ž˜ ๋Œ€ํ‘œํ•˜๋Š” ๋ชจ๋ธ๋“ค์„ ๊ตฌ์ถ•ํ•˜๊ณ  ๋ถˆํ™•์‹ค์„ฑ ์ •๋Ÿ‰ํ™”๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.์ดˆ ๋ก i List of Tables iii List of Figures iv 1. ์„œ ๋ก  1 2. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 6 2.1 ์œ ์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 6 2.2 GTTI(generalized travel time inversion) ๊ธฐ๋ฒ• 14 2.3 GTTI ๊ธฐ๋ฒ•์ด ๊ฐ–๋Š” ํ•œ๊ณ„ 27 2.4 ๋น„๋ชจ์ˆ˜์ ‘๊ทผ๋ฒ• 29 2.5 ์ž„์˜์ตœ๋Œ€๊ฐ€๋Šฅ๋„์ถ”์ •๋ฒ• 32 3. ๋น„๋ชจ์ˆ˜์ ‘๊ทผ๋ฒ•๊ณผ ๋ฌผ ๋ŒํŒŒ์‹œ๊ฐ„ ์˜ˆ์ธก์„ ์ด์šฉํ•œ GTTI ๊ธฐ๋ฒ•์˜ ๊ฐœ์„  34 3.1 ๋น„๋ชจ์ˆ˜์ ‘๊ทผ๋ฒ•์„ ์ ์šฉํ•œ GTTI 34 3.2 ์˜ˆ์ธก ๋ฌผ ๋ŒํŒŒ์‹œ๊ฐ„์„ ์ ์šฉํ•œ GTTI 38 4. ์—ฐ๊ตฌ๊ฒฐ๊ณผ 42 4.1 ์ฐธ์กฐํ•„๋“œ์˜ ์„ค์ • 42 4.2 ์ด์ค‘์ตœ๋นˆ๊ฐ’ ๋ถ„ํฌ ์ดˆ๊ธฐ ์•™์ƒ๋ธ” ์ƒ์„ฑ 52 4.3 ๋น„๋ชจ์ˆ˜์ ‘๊ทผ๋ฒ• ์ ์šฉ์ด ํŠน์„ฑํ™” ๊ฒฐ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 57 4.4 ์˜ˆ์ธก ๋ฌผ ๋ŒํŒŒ์‹œ๊ฐ„ ์ ์šฉ์ด ํŠน์„ฑํ™” ๊ฒฐ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 78 5. ๊ฒฐ ๋ก  96 ์ฐธ๊ณ  ๋ฌธํ—Œ 100 ABSTRACT 105Maste

    Epi poly๋ฅผ ์ด์šฉํ•œ MEMS ์†Œ์ž์šฉ ์›จ์ดํผ ๋‹จ์œ„์˜ ์ง„๊ณต ํŒจํ‚ค์ง•์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ „๊ธฐ๊ณตํ•™๋ถ€,2000.Maste

    ๋ฌด์„  ๋„คํŠธ์›Œํฌ๋ฅผ ์ด์šฉํ•œ ์—”์ง„ ํ”ผ์Šคํ†ค ์˜จ๋„ ์ธก์ • ํ…”๋ ˆ๋ฉ”ํŠธ๋ฆฌ ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€,2005.Maste
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