ReFu: Refine and Fuse the Unobserved View for Detail-Preserving Single-Image 3D Human Reconstruction

Single-image 3D human reconstruction aims to reconstruct the 3D textured surface of the human body given a single image. While implicit function-based methods recently achieved reasonable reconstruction performance, they still bear limitations showing degraded quality in both surface geometry and texture from an unobserved view. In response, to generate a realistic textured surface, we propose ReFu, a coarse-to-fine approach that refines the projected backside view image and fuses the refined image to predict the final human body. To suppress the diffused occupancy that causes noise in projection images and reconstructed meshes, we propose to train occupancy probability by simultaneously utilizing 2D and 3D supervisions with occupancy-based volume rendering. We also introduce a refinement architecture that generates detail-preserving backside-view images with front-to-back warping. Extensive experiments demonstrate that our method achieves state-of-the-art performance in 3D human reconstruction from a single image, showing enhanced geometry and texture quality from an unobserved view.Comment: Accepted at ACM MM 202

PixelHuman: Animatable Neural Radiance Fields from Few Images

In this paper, we propose PixelHuman, a novel human rendering model that generates animatable human scenes from a few images of a person with unseen identity, views, and poses. Previous work have demonstrated reasonable performance in novel view and pose synthesis, but they rely on a large number of images to train and are trained per scene from videos, which requires significant amount of time to produce animatable scenes from unseen human images. Our method differs from existing methods in that it can generalize to any input image for animatable human synthesis. Given a random pose sequence, our method synthesizes each target scene using a neural radiance field that is conditioned on a canonical representation and pose-aware pixel-aligned features, both of which can be obtained through deformation fields learned in a data-driven manner. Our experiments show that our method achieves state-of-the-art performance in multiview and novel pose synthesis from few-shot images.Comment: 8 page

Abnormality Diagnosis Model for Nuclear Power Plants Using Two-Stage Gated Recurrent Units

A nuclear power plant is a large complex system with tens of thousands of components. To ensure plant safety, the early and accurate diagnosis of abnormal situations is an important factor. To prevent misdiagnosis, operating procedures provide the anticipated symptoms of abnormal situations. While the more severe emergency situations total less than ten cases and can be diagnosed by dozens of key plant parameters, abnormal situations on the other hand include hundreds of cases and a multitude of parameters that should be considered for diagnosis. The tasks required of operators to select the appropriate operating procedure by monitoring large amounts of information within a limited amount of time can burden operators. This paper aims to develop a system that can, in a short time and with high accuracy, select the appropriate operating procedure and sub-procedure in an abnormal situation. Correspondingly, the proposed model has two levels of prediction to determine the procedure level and the detailed cause of an event. Simulations were conducted to evaluate the developed model, with results demonstrating high levels of performance. The model is expected to reduce the workload of operators in abnormal situations by providing the appropriate procedure to ultimately improve plant safety. (c) 2020 Korean Nuclear Society, Published by Elsevier Korea LLC

Time Series Forecasting with Hypernetworks Generating Parameters in Advance

Forecasting future outcomes from recent time series data is not easy, especially when the future data are different from the past (i.e. time series are under temporal drifts). Existing approaches show limited performances under data drifts, and we identify the main reason: It takes time for a model to collect sufficient training data and adjust its parameters for complicated temporal patterns whenever the underlying dynamics change. To address this issue, we study a new approach; instead of adjusting model parameters (by continuously re-training a model on new data), we build a hypernetwork that generates other target models' parameters expected to perform well on the future data. Therefore, we can adjust the model parameters beforehand (if the hypernetwork is correct). We conduct extensive experiments with 6 target models, 6 baselines, and 4 datasets, and show that our HyperGPA outperforms other baselines.Comment: 7 pages, preprint (we open our code after being accepted

Effects of Cognitive Load Reduction Strategies and Prior Knowledge Levels on Comprehension of Speed Simulation, Cognitive Load, and Learning Efficiency for Fifth Grade Elementary Students

2007The purpose of this study was to investigate how cognitive load reduction strategies and learners' prior knowledge affect on comprehension of speed simulation, cognitive load, and learning efficiency. It was randomly sampled 77 participants among fifth grade students of an elementary school in Seoul city, Korea. They were divided into two groups of prior knowledge (higher and lower) by two different treatment groups (visual worked-example simulation group, visual-auditory worked-example simulation group). Dependent variables were comprehension of speed simulation, cognitive load, and learning efficiency. Results showed that visual-auditory worked-example simulation group was more efficient on comprehension of speed simulation than visual worked-example simulation group, regardless of learners prior knowledge level, so that less cognitive load led to higher level of comprehension

Carbon Dioxide-Catalyzed Stereoselective Cyanation Reaction

© 2019 American Chemical Society.We report a Michael-type cyanation reaction of coumarins by using CO2 as a catalyst. The delivery of the nucleophilic cyanide was realized by catalytic amounts of CO2, which forms cyanoformate and bicarbonate in the presence of water. Under ambient conditions, CO2-catalyzed reactions afforded high chemo- A nd diastereoselectivity of β-nitrile carbonyls, whereas only low reactivities were observed under argon or N2. Computational and experimental data suggest the catalytic role of CO2, which functions as a Lewis acid, and a protecting group to mask the reactivity of the product, suppressing byproducts and polymerization. The utility of this convenient method was demonstrated by preparing biologically relevant heterocyclic compounds with ease11sciescopu

Enzymatic production of indigestible maltooligosaccharides using glucansucrases from Leuconostoc mesenteroides B-512FMCM and B-1355CF10

OAIID:RECH_ACHV_DSTSH_NO:A201702459RECH_ACHV_FG:RR00200003ADJUST_YN:EMP_ID:A079459CITE_RATE:FILENAME:동구.pdfDEPT_NM:국제농업기술학과EMAIL:[email protected]_YN:FILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/fe71fb5d-1d6e-4cb2-8b2b-28f93b1bca3c/linkCONFIRM:

Genomic diversity of Mycobacterium avium subsp. paratuberculosis: pangenomic approach for highlighting unique genomic features with newly constructed complete genomes

Mycobacterium avium subsp. paratuberculosis (MAP) is a causative agent of Johne's disease, which is a chronic granulomatous enteropathy in ruminants. Determining the genetic diversity of MAP is necessary to understand the epidemiology and biology of MAP, as well as establishing disease control strategies. In the present study, whole genome-based alignment and comparative analysis were performed using 40 publicly available MAP genomes, including newly sequenced Korean isolates. First, whole genome-based alignment was employed to identify new genomic structures in MAP genomes. Second, the genomic diversity of the MAP population was described by pangenome analysis. A phylogenetic tree based on the core genome and pangenome showed that the MAP was differentiated into two major types (C- and S-type), which was in keeping with the findings of previous studies. However, B-type strains were discriminated from C-type strains. Finally, functional analysis of the pangenome was performed using three virulence factor databases (i.e., PATRIC, VFDB, and Victors) to predict the phenotypic diversity of MAP in terms of pathogenicity. Based on the results of the pangenome analysis, we developed a real-time PCR technique to distinguish among S-, B- and C-type strains. In conclusion, the results of our study suggest that the phenotypic differences between MAP strains can be explained by their genetic polymorphisms. These results may help to elucidate the diversity of MAP, extending from genomic features to phenotypic traits

자기주도 이러닝 학습에서 학습자의 과제 선택 지원 전략 개발 연구

학위논문 (박사)-- 서울대학교 대학원 : 교육학과 교육공학 전공, 2012. 8. 박성익.The purpose of this study is to develop learners task selection-supporting strategies in self-directed e-learning environment and examine their effects and optimality. To achieve the purpose, this study established the following two research questions: (1) developing learners task selection-supporting strategies and (2) verifying the effects and optimality of learners task selection-supporting strategies for self-directed learning. Further, the second research question comprised three subordinate research objectives: (1) investigating the effects of self-directed learning on learners task-selection accuracy, self-directed learning abilities, and learning achievements(2) verifying the effect of learners task selection-supporting strategies on learners task selection decision makingand (3) investigating the optimality of learners task selection-supporting strategies in self-directed learning. In Research 1, this study drew relevant strategies based on literature review to develop strategies to support learners task selection in self-directed learning and then validate the strategies through experts primary and secondary reviews. Then, this study developed an e-learning program with the final strategies reflected and subsequently validated the program through prototype evaluation on the storyboards and through usability evaluation. In Research 2.1, this study selected 236 sixth-grade elementary school students and applied the e-learning program with the final strategies reflected to investigate the effects of learners task selection-supporting strategies on learners task selection accuracy, self-directed learning abilities, and learning achievements for self-directed e-learning. Path analysis revealed that task selection-supporting strategies had a significant effect on learners task selection accuracy in self-directed e-learning, while learners task selection accuracy had a significant effect on their learning achievements. In Research 2.2, this study collected data from 27 sixth-grade elementary school students to examine the effect of learners task selection-supporting strategies on learners task selection decision making in self-directed e-learning. One-way ANOVA revealed that task selection-supporting strategies did not have a significant effect on learners task selection decision making in self-directed e-learning. In Research 2.3, this study collected qualitative data from 27 sixth-grade elementary school students and confirmed the optimality of task selection-supporting strategies in self-directed e-learning, Overall, this study has great significance in that it developed strategies to support learners task selection in self-directed learning and confirmed their positive effects on learners task selection accuracy and learning achievements and even confirmatively analyzed the future direction of learners task selection strategies by comprehending the optimality of the strategies.TABLE OF CONTENTS CHAPTER I. INTRODUCTION 1 1. Backgrounds of the Study 1 2. Statement of Problems 7 3. Research Questions 8 CHAPTER II. THEORETICAL BACKGROUND 10 1. Process and Outcome of Self-Directed Learning in Formal Education 10 1.1. Self-Directed Learning in Formal Education 11 1.2. Self-Directed Learning as an Outcome 12 1.3. Self-Directed Learning as a Process 13 1.4. Formal Education and Self-Directed Learning 18 2. Importance of Task Selection in Self-Directed Learning Process 20 3. Advantages and Disadvantages of Task Selection 22 4. Cognitive, Meta-Cognitive and Motivation Supports for Learners Task Selection 25 4.1. Cognitive Strategies for Supporting Learners Task-Selection 26 4.2. Meta-Cognitive Strategies for Supporting Learners Task-Selection 30 4.3. Motivation Strategies for Supporting Learners Task-Selection 32 CHAPTER III. RESEARCH MODEL AND HYPOTHESES 36 CHAPTER IV. METHODS 53 Research 1. Development of LTSS Strategies 56 1. Development of LTSS Strategies Through Literature Review 56 2. LTSS Strategies Validation by Expert Review 61 3. E-Learning Program Development and Evaluation 67 Research 2. Effects and Optimality of the Learners Task Selection Supporting Strategies 80 1. Effects of the Learners Task Selection Supporting Strategies on Task Selection Accuracy, Self-Directed Learning Ability, and Achievement 80 2. Effects of LTSS Strategies and Guidelines on Task Selection Decision Making and the Optimality of LTSS Strategies and Guidelines 90 3. Optimality of LTSS Strategies and Guidelines 91 CHAPTER V. RESULTS 92 Research 1. Learners Task Selection Supporting Strategies and Guidelines Development 93 Research 2. Effects and Optimality of LTSS Strategies and Guidelines 135 CHAPTER VI. DISCUSSION 150 REFERENCES 165 APPENDIXES 180Docto

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School of Business Administration (Management Engineering)clos