508 research outputs found
CUHK electronic theses & dissertations collection
No full textLyu, Kai.Thesis Ph.D. Chinese University of Hong Kong 2015.Includes bibliographical references (leaves 498-539).Abstracts also in Chinese.Title from PDF title page (viewed on 06, December, 2016)
CUHK electronic theses & dissertations collection
No full textLyu, Chen.Thesis Ph.D. Chinese University of Hong Kong 2014.Includes bibliographical references (leaves 148-159).Abstracts also in Chinese.Title from PDF title page (viewed on 04, October, 2016)
CUHK electronic theses & dissertations collection
No full textLyu, Peng.Thesis M.Phil. Chinese University of Hong Kong 2015.Includes bibliographical references (leaves 42-44).Abstracts also in Chinese.Title from PDF title page (viewed on 14, November, 2016)
The analysis of reservoir operation rules in the southwestern United States using a hidden Markov-decision tree model
No full textRealistic reservoir operation is an important component in basin hydrological models. However, the operation rules can be complex under various situations and are not well understood yet. The objective of the study is to find out patterns of actual reservoir operation rules in a specific region under different conditions. The results are helpful to understand realistic reservoir operation behaviors. Moreover, the work will contribute to building a reservoir component for hydrological models and provide references for improving reservoir operation decisions in the future.
A hidden Markov-decision tree (HM-DT) model is applied in this study. Hidden Markov Model is used to describe the transition between operation states while each state corresponds to a decision tree model to determine daily reservoir releases. This study collected the historical records of 66 reservoirs in the southwestern United States and 8 reservoirs in the northern United States and identified their regional representative operation modules.
A comparison between the representative operation modules was conducted to show the spatial variability of reservoir operations. Further, by analyzing the historical operation patterns of several reservoirs, the transitions between operation rules in different time periods were explored, including periodicity and special responses to floods. The operation rules adopted by reservoirs with different operation functions are also analyzed and compared. A robustness analysis was performed to confirm the robustness of the HM-DT model outputs. Finally, several attempts have been made to link the HM-DT model with Soil and Water Assessment Tool (SWAT) and future work is proposed.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01The student, Lingqi Lyu, accepted the attached license on 2021-04-24 at 09:10.The student, Lingqi Lyu, submitted this Thesis for approval on 2021-04-24 at 09:21.This Thesis was approved for publication on 2021-04-27 at 16:26.DSpace SAF Submission Ingestion Package generated from Vireo submission #16543 on 2021-09-16 at 17:05:11Made available in DSpace on 2021-09-17T02:34:47Z (GMT). No. of bitstreams: 2
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Risk Prediction with Longitudinal Gene Expression Data Using Statistical and Machine Learning Method
No full textHigh-Throughput and Computational Study of Leaf Senescence through a Phenomic Approach
No full textLeaf senescence is influenced by its life history, comprising a series of developmental and physiological experiences. Exploration of the biological principles underlying leaf lifespan and senescence requires a schema to trace leaf phenotypes, based on the interaction of genetic and environmental factors. We developed a new approach and concept that will facilitate systemic biological understanding of leaf lifespan and senescence, utilizing the phenome high-throughput investigator (PHI) with a single-leaf-basis phenotyping platform. Our pilot tests showed empirical evidence for the feasibility of PHI for quantitative measurement of leaf senescence responses and improved performance in order to dissect the progression of senescence triggered by different senescence-inducing factors as well as genetic mutations. Such an establishment enables new perspectives to be proposed, which will be challenged for enhancing our fundamental understanding on the complex process of leaf senescence. We further envision that integration of phenomic data with other multi-omics data obtained from transcriptomic, proteomic, and metabolic studies will enable us to address the underlying principles of senescence, passing through different layers of information from molecule to organism.
© 2017 Lyu, Baek, Jung, Chu, Nam, Kim and Lim. This is an open-access
article distributed under the terms of the Creative Commons Attribution License
(CC BY). The use, distribution or reproduction in other forums is permitted, provided
the original author(s) or licensor are credited and that the original publication in this
journal is cited, in accordance with accepted academic practice. No use, distribution
or reproduction is permitted which does not comply with these terms. © 2017 Lyu, Baek, Jung, Chu, Nam, Kim and Lim. This is an open-access
article distributed under the terms of the Creative Commons Attribution License
(CC BY). The use, distribution or reproduction in other forums is permitted, provided
the original author(s) or licensor are credited and that the original publication in this
journal is cited, in accordance with accepted academic practice. No use, distribution
or reproduction is permitted which does not comply with these terms.
© 2017 Lyu, Baek, Jung, Chu, Nam, Kim and Lim. This is an open-access
article distributed under the terms of the Creative Commons Attribution License
(CC BY). The use, distribution or reproduction in other forums is permitted, provided
the original author(s) or licensor are credited and that the original publication in this
journal is cited, in accordance with accepted academic practice. No use, distribution
or reproduction is permitted which does not comply with these terms.2
2006 Author Recognition Bibliography
No full texthttps://scholarworks.gvsu.edu/authorrecognition/1008/thumbnail.jp
The Digital Economy and Agricultural Modernization in China: Measurement, Mechanisms, and Implications
No full textPromoting the progression of agricultural modernization and digital economy integration has introduced new opportunities and challenges for the advancement of China’s agriculture towards high-quality development. Based on panel data from 31 provinces in China from 2011 to 2020, we use the entropy method, coupling coordination model, spatial auto-correlation model, and obstacle model to explore the degree of coupling coordination and influencing factors of China’s digital economy and agricultural modernization. The results show the following. (1) China’s digital economy and agricultural modernization are underdeveloped on the whole, but they show a positive upward trajectory. The digital economy is outpacing the development of agricultural modernization, but the gap between the two is gradually narrowing. (2) The degree of coupling coordination between China’s digital economy and agricultural modernization shows an upward trend. Additionally, there is regional heterogeneity and uneven development of the coupling coordination, i.e., it is high in the eastern region and low in the western region, accompanied by strong spatial agglomeration and correlation. (3) The main obstacle to the progression of the digital economy is digital infrastructure, while the main obstacle to the progression of agricultural modernization is agricultural production capacity. Based on China’s national conditions, we should fully promote the role of the digital economy, explore plans and strategies for promoting the digital economy in differentiated areas, mitigate any imbalanced development, promote the coordinated development of the digital economy and agricultural modernization, and provide decision-making references for the implementation of a digitized China, as well as rural revitalization strategies
Listwise Explanations for Ranking Models Using Multiple Explainers
No full textThis paper proposes a novel approach towards better interpretability of a trained text-based ranking model in a post-hoc manner. A popular approach for post-hoc interpretability text ranking models are based on locally approximating the model behavior using a simple ranker. Since rankings have multiple relevance factors and are aggregations of predictions, existing approaches that use a single ranker might not be sufficient to approximate a complex model, resulting in low fidelity. In this paper, we overcome this problem by considering multiple simple rankers to better approximate the entire ranking list from a black-box ranking model. We pose the problem of local approximation as a Generalized Preference Coverage (GPC) problem that incorporates multiple simple rankers towards the listwise explanation of ranking models. Our method Multiplex uses a linear programming approach to judiciously extract the explanation terms, so that to explain the entire ranking list. We conduct extensive experiments on a variety of ranking models and report fidelity improvements of 37%–54% over existing competitors. We finally compare explanations in terms of multiple relevance factors and topic aspects to better understand the logic of ranking decisions, showcasing our explainers’ practical utility.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information System
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