편측 선천성 근위 요척골 골결합 환자에서 완관절의 보상 회전 운동에 대한 3차원 운동 분석 연구

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의학과, 2021. 2. 백구현.Objectives: The aim of the present study was to investigate compensatory rotational movements of the wrist using a valid and reliable three-dimensional (3D) motion analysis technique in patients with unilateral proximal congenital radioulnar synostosis (CRUS). Background: Although forearm rotation is usually limited in patients with proximal CRUS, compensatory motions of the adjacent joints enable activities of daily living (ADL) in most patients. There have been a few reports on study of the compensatory rotational movement around the wrist in patients with proximal CRUS. Increased rotational movements of the wrist to compensate congenital fusion was not measured objectively. Patients and methods: Twenty patients (six females, 14 males; mean age = 6-32 years) in whom unilateral proximal CRUS was diagnosed but not operated on were enrolled this study. Patients were then categorized into two groups: Group I included five patients younger than 10 years and Group II included 15 patients older than 10 years. Eighteen light-reflective skin markers were placed on both upper limbs, and both distal forearms were fixed using a U-shaped device to minimize forearm rotation. Each patient grasped the handle of an instrument that used a goniometer to measure wrist rotation; maximal passive pronation and supination angles of the wrist were both measured in this manner and via 3D motion analysis. Results: There was a significant correlation between the measurements using the goniometer and 3D motion analysis (r = 0.985, p < 0.001). The test-retest reliability of the 3D motion analysis was acceptable on both affected side (ICC = 0.992) and the contralateral normal side (ICC = 0.997) with low measurement error (1.3° and 0.8°, respectively). Although there was no significant difference in the range of wrist rotation between the affected and the contralateral sides in Group I (p = 0.686), a significant difference was observed in Group II (p = 0.001). Conclusions: The 3D motion analysis technique seems to be a valid and reliable method to measure the rotation of the wrist joint. Patients with unilateral proximal CRUS older than 10 years of age may develop rotational hypermobility of the wrist joint compared to the contralateral normal side as a compensatory phenomenon.목적: 본 연구의 목적은 편측 선천성 요척골 골결합 (CRUS) 환자에서 유효하고 신뢰할 수 있는 3차원 (3D) 운동 분석 기법을 사용하여 손목 회전 운동을 정량적으로 측정하고자 연구를 진행하였다. 배경: 근위 CRUS은 요골과 척골 사이의 어느 부분에서든지 발생하며 주로 근위 요척 관절에 발생하는 비교적 드문 상지의 선천성 질환이다. 임상적으로는 전완부의 회전 장애를 일으키며 주로는 회내전 상태로 고정되어 있으므로 특히 양측 선천성 요척골 골결합 환자들의 일상생활 활동에 상당한 불편을 초래한다. 하지만 편측 CRUS 환자들은 일상생활 활동에서 환측 어깨와 손목의 보상 운동으로 생활에 큰 지장이 없어 임상에서 늘 늦게 발견되고 있다. 그리고 이 질환의 치료방법도 현재 다양한 수술방법들이 시도되고 있지만 만족한 결과를 얻은 치료법이 제시되지 못하고 있는 실정이다. 방법 및 결과: 편측 근위 CRUS 진단을 받고 수술을 시행하지 않은 20명의 환자를 모집하였고 그중 10세 미만의 환자 5명 (그룹 I) 과 10세 이상 환자 15명 (그룹 II)의 두 그룹으로 나뉘었다. 18개의 피부 마커를 양쪽 상지 해부학적 위치에 부착하고 원위 팔뚝을 고정하여 팔뚝의 회전을 최소화 하였다. 각 환자는 완관절의 최대 회내전과 회외전 범위를 각도계와 3D 운동 분석을 통해 각각 측정하였다. 각도계를 사용한 측정과 3D 운동 분석 사이에는 유의한 상관 관계가 있다 (r = 0.985, p < 0.001). 3D 운동 분석의 테스트-재테스트 신뢰도는 매우 우수 하며 (0.992, 0.997) 측정 오류가 낮았다 (1.3°, 0.8°). 그룹 I 환자들에서는 환측과 정상측의 완관절의 회전 범위에 유의한 차이가 없었지만 (p = 0.686), 그룹 II 환자들에서는 유의한 차이가 관찰되었다 (p = 0.001). 결론: 3D 운동 분석 방법은 완관절의 회전을 정확하게 측정하는 유효하고 신뢰할 수 있는 방법이며 10세 이상의 편측 근위 CRUS 환자에서 환측의 완관절 회전 범위가 정상측 보다 더 많다는 소견이 관찰 되었다.Table of Contents Chapter 1. Introduction················································1 1.1 Study Background·················································1 1.2 Purpose of Research··············································2 Chapter 2. Patients and methods··································3 2.1 patients···································································3 2.2 Experimental setup······································4 2.3 The upper limb model··································5 2.4 Motion capture and data analysis················7 2.5 Statistical analysis·········································7 Chapter 3. Results·························································9 Chapter 4. Discussion···················································11 Chapter 5. Conclusion··················································19 References····································································20 Tables ＆ Figures···························································25 Abstract in Korean························································36Docto

Norm Tweaking: High-performance Low-bit Quantization of Large Language Models

As the size of large language models (LLMs) continues to grow, model compression without sacrificing accuracy has become a crucial challenge for deployment. While some quantization methods, such as GPTQ, have made progress in achieving acceptable 4-bit weight-only quantization, attempts at lower bit quantization often result in severe performance degradation. In this paper, we introduce a technique called norm tweaking, which can be used as a plugin in current PTQ methods to achieve high precision while being cost-efficient. Our approach is inspired by the observation that rectifying the quantized activation distribution to match its float counterpart can readily restore accuracy for LLMs. To achieve this, we carefully design a tweaking strategy that includes calibration data generation and channel-wise distance constraint to update the weights of normalization layers for better generalization. We conduct extensive experiments on various datasets using several open-sourced LLMs. Our method demonstrates significant improvements in both weight-only quantization and joint quantization of weights and activations, surpassing existing PTQ methods. On GLM-130B and OPT-66B, our method even achieves the same level of accuracy at 2-bit quantization as their float ones. Our simple and effective approach makes it more practical for real-world applications

How Population Size and Density Affect the Spread of COVID-19 A Quantitative Study of the United States at the County Level

The scholarly debate about the influence of population density on COVID-19 spread points to a question: whether it is a larger population density, or a larger size of the population, that actually accelerate the spread of the virus . To figure out an answer in the US context, proper considerations should be taken to deal with three highly-influential determinants of the shape of a COVID-19 curve: the timeline of policy interventions, the metro and non-metro division, and the phase of pandemic. To safely unmask the effect of population size and density at county level, I introduce a group of “seasonal surges” and “COVID-19 policy reaction” variables, which measure to what extent a pandemic surge happened in a season, and whether the surge was followed by effective policy intervention within the season. Besides, a group of interaction variables based on the division of metro and non-metro counties are added to address some socio-cultural differences. To generally interpret the results, population density positively correlates with COVID-19 spread, while population negatively correlates with COVID-19 spread. However, in the early phase of pandemic, density had negative impact only in metro counties, although in later phases the effect of density no longer differed between metro and non-metro counties. The negative impact of population on COVID-19 cases are most observable in non-metro counties, while its coefficient for metro counties was evidently smaller

UMIE: Unified Multimodal Information Extraction with Instruction Tuning

Multimodal information extraction (MIE) gains significant attention as the popularity of multimedia content increases. However, current MIE methods often resort to using task-specific model structures, which results in limited generalizability across tasks and underutilizes shared knowledge across MIE tasks. To address these issues, we propose UMIE, a unified multimodal information extractor to unify three MIE tasks as a generation problem using instruction tuning, being able to effectively extract both textual and visual mentions. Extensive experiments show that our single UMIE outperforms various state-of-the-art (SoTA) methods across six MIE datasets on three tasks. Furthermore, in-depth analysis demonstrates UMIE's strong generalization in the zero-shot setting, robustness to instruction variants, and interpretability. Our research serves as an initial step towards a unified MIE model and initiates the exploration into both instruction tuning and large language models within the MIE domain. Our code, data, and model are available at https://github.com/ZUCC-AI/UMI