393 research outputs found
sj-rar-3-tej-10.1177_20417314221132093 – Supplemental material for A new cell-free therapeutic strategy for liver regeneration: Human placental mesenchymal stem cell-derived extracellular vesicles
No full textSupplemental material, sj-rar-3-tej-10.1177_20417314221132093 for A new cell-free therapeutic strategy for liver regeneration: Human placental mesenchymal stem cell-derived extracellular vesicles by Ting Li, Yu Fu, Zeyi Guo, Honglei Zhu, Hangyu Liao, Xiaoge Niu, Lin Zhou, Shunjun Fu, Yang Li, Shao Li, Lujia Wang, Yizhou Zheng, Lei Feng, Yi Gao and Guolin He in Journal of Tissue Engineering</p
sj-jpg-2-tej-10.1177_20417314221132093 – Supplemental material for A new cell-free therapeutic strategy for liver regeneration: Human placental mesenchymal stem cell-derived extracellular vesicles
No full textSupplemental material, sj-jpg-2-tej-10.1177_20417314221132093 for A new cell-free therapeutic strategy for liver regeneration: Human placental mesenchymal stem cell-derived extracellular vesicles by Ting Li, Yu Fu, Zeyi Guo, Honglei Zhu, Hangyu Liao, Xiaoge Niu, Lin Zhou, Shunjun Fu, Yang Li, Shao Li, Lujia Wang, Yizhou Zheng, Lei Feng, Yi Gao and Guolin He in Journal of Tissue Engineering</p
sj-docx-1-tej-10.1177_20417314221132093 – Supplemental material for A new cell-free therapeutic strategy for liver regeneration: Human placental mesenchymal stem cell-derived extracellular vesicles
No full textSupplemental material, sj-docx-1-tej-10.1177_20417314221132093 for A new cell-free therapeutic strategy for liver regeneration: Human placental mesenchymal stem cell-derived extracellular vesicles by Ting Li, Yu Fu, Zeyi Guo, Honglei Zhu, Hangyu Liao, Xiaoge Niu, Lin Zhou, Shunjun Fu, Yang Li, Shao Li, Lujia Wang, Yizhou Zheng, Lei Feng, Yi Gao and Guolin He in Journal of Tissue Engineering</p
sj-rar-4-tej-10.1177_20417314221132093 – Supplemental material for A new cell-free therapeutic strategy for liver regeneration: Human placental mesenchymal stem cell-derived extracellular vesicles
No full textSupplemental material, sj-rar-4-tej-10.1177_20417314221132093 for A new cell-free therapeutic strategy for liver regeneration: Human placental mesenchymal stem cell-derived extracellular vesicles by Ting Li, Yu Fu, Zeyi Guo, Honglei Zhu, Hangyu Liao, Xiaoge Niu, Lin Zhou, Shunjun Fu, Yang Li, Shao Li, Lujia Wang, Yizhou Zheng, Lei Feng, Yi Gao and Guolin He in Journal of Tissue Engineering</p
sj-tif-5-tej-10.1177_20417314221132093 – Supplemental material for A new cell-free therapeutic strategy for liver regeneration: Human placental mesenchymal stem cell-derived extracellular vesicles
No full textSupplemental material, sj-tif-5-tej-10.1177_20417314221132093 for A new cell-free therapeutic strategy for liver regeneration: Human placental mesenchymal stem cell-derived extracellular vesicles by Ting Li, Yu Fu, Zeyi Guo, Honglei Zhu, Hangyu Liao, Xiaoge Niu, Lin Zhou, Shunjun Fu, Yang Li, Shao Li, Lujia Wang, Yizhou Zheng, Lei Feng, Yi Gao and Guolin He in Journal of Tissue Engineering</p
Replication Data for: Orbital Identification of Hydrated Silica in Jezero Crater, Mars
No full textThese are the ROIs of pixels in images from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) used to generate the spectra shown in "Orbital Identification of Hydrated Silica in Jezero Crater, Mars" by J.D. Tarnas; J.F. Mustard; Honglei Lin; T.A. Goudge; E.S. Amador; M.S. Bramble; C.H. Kremer; X. Zhang; Y. Itoh; M. Parente and published in Geophysical Research Letters (https://doi.org/10.1029/2019GL085584
Replication Data for: Orbital Identification of Hydrated Silica in Jezero Crater, Mars
No full textThese are the ROIs of pixels in images from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) used to generate the spectra shown in "Orbital Identification of Hydrated Silica in Jezero Crater, Mars" by J.D. Tarnas; J.F. Mustard; Honglei Lin; T.A. Goudge; E.S. Amador; M.S. Bramble; C.H. Kremer; X. Zhang; Y. Itoh; M. Parente and published in Geophysical Research Letters (https://doi.org/10.1029/2019GL085584
Text mining with word embedding for outlier and sentiment analysis
No full textThe technology today makes it unprecedentedly easy to collect and store massive text data in various domains such as online social networks, medical records and news reports. In contrast to the gigantic volume of text data, human capabilities to read and process text data is limited. Hence, there is an emerging demand for automatic text mining tools to analyze massive text data.
Word embedding is an emerging text analysis technique that leverages the fine-grained statistics of context information to map each word to a vector in the embedding space which reflects the semantic proximity between words. Embedding techniques not only enrich the statistical signals to utilize in downstream text mining applications, but also provide the possibility to characterize and represent higher-level objects in the embedding space, such as sentences, documents or topics.
This study integrates word embedding techniques into a series of text mining approaches and models. The general idea is to take a text object such as a document or a sentence as a bag of embedding vectors and characterize their distributions in the embedding space. Specifically, this study focuses on two tasks: outlier analysis and weakly-supervised sentiment analysis.
Outlier analysis aims to identify documents that topically deviate from the majority of a given corpus. We develop an unsupervised generative model to identify frequent and representative semantic regions in the word embedding space to represent the given corpus. Then we propose a novel outlierness measure to identify outlier documents. We also study the cost-sensitive scenario of outlier analysis.
Sentiment analysis typically identifies the subjective opinion (e.g., positive vs. negative) in a piece of text. Despite being extensively studied as a supervised learning task, we tackle the problem in a weakly-supervised fashion, where users only provide a small set of seed words as guidance. We study to identify aspects and corresponding sentiments at both document and sentence level.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01The student, Honglei Zhuang, accepted the attached license on 2019-04-18 at 15:34.The student, Honglei Zhuang, submitted this Dissertation for approval on 2019-04-18 at 15:41.This Dissertation was approved for publication on 2019-04-19 at 07:59.DSpace SAF Submission Ingestion Package generated from Vireo submission #13755 on 2019-08-22 at 15:07:24Made available in DSpace on 2019-08-23T20:36:02Z (GMT). No. of bitstreams: 2
ZHUANG-DISSERTATION-2019.pdf: 1936201 bytes, checksum: 582988d6c3866bb5b18dc28edf5f174d (MD5)
LICENSE.txt: 4211 bytes, checksum: bf3c0cbad5f8744a7b46c76a5bdbfb01 (MD5)
Previous issue date: 2019-04-19Embargo set by: Seth Robbins for item 112177
Lift date: 2021-08-23T20:36:18Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 112177 on 2021-08-24T09:15:34Z
Optimal state feedback for constrained nonlinear systems
No full textIn this paper, we consider a general nonlinear control system that is subject to both terminal state and continuous inequality constraints. The continuous inequality constraints must be satisfied at every point in the time horizon—an infinite number of points. Our aim is to design an optimal feedback controller that yields efficient system performance and satisfaction of all constraints. We first formulate this problem as a semi-infinite optimization problem. We then show that, by using a novel exact penalty approach, this semi-infinite optimization problem can be converted into a sequence of nonlinear programming problems, each of which can be solved using standard numerical techniques. We conclude the paper with some convergence results
Robust Optimal Control of Continuous Linear Quadratic System Subject to Disturbances
No full textIn this chapter, the robust optimal control of linear quadratic system is considered. This problem is first formulated as a minimax optimal control problem. We prove that it admits a solution. Based on this result, we show that this infinite-dimensional minimax optimal control problem can be approximated by a sequence of finite-dimensional minimax optimal parameter selection problems. Furthermore, these finite-dimensional minimax optimal parameter selection problems can be transformed into semi-definite programming problems or standard minimization problems. A numerical example is presented to illustrate the developed method
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