MANAGING INFORMATION DIFFUSION IN ONLINE SOCIAL NETWORKS VIA STRUCTURAL ANALYSIS

Ph.DDOCTOR OF PHILOSOPH

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

Sensitivity analysis (SA) aims to identify the key parameters that affect model performance and it plays important roles in model parameterization, calibration, optimization, and uncertainty quantification. However, the increasing complexity of hydrological models means that a large number of parameters need to be estimated. To better understand how these complex models work, efficient SA methods should be applied before the application of hydrological modeling. This study provides a comprehensive review of global SA methods in the field of hydrological modeling. The common definitions of SA and the typical categories of SA methods are described. A wide variety of global SA methods have been introduced to provide a more efficient evaluation framework for hydrological modeling. We review, analyze, and categorize research into global SA methods and their applications, with an emphasis on the research accomplished in the hydrological modeling field. The advantages and disadvantages are also discussed and summarized. An application framework and the typical practical steps involved in SA for hydrological modeling are outlined. Further discussions cover several important and often overlooked topics, including the relationship between parameter identification, uncertainty analysis, and optimization in hydrological modeling, how to deal with correlated parameters, and time-varying SA. Finally, some conclusions and guidance recommendations on SA in hydrological modeling are provided, as well as a list of important future research directions that may facilitate more robust analyses when assessing hydrological modeling performance

Addressing Confounding Feature Issue for Causal Recommendation

In recommender system, some feature directly affects whether an interaction would happen, making the happened interactions not necessarily indicate user preference. For instance, short videos are objectively easier to be finished even though the user does not like the video. We term such feature as confounding feature, and video length is a confounding feature in video recommendation. If we fit a model on such interaction data, just as done by most data-driven recommender systems, the model will be biased to recommend short videos more, and deviate from user actual requirement. This work formulates and addresses the problem from the causal perspective. Assuming there are some factors affecting both the confounding feature and other item features, e.g., the video creator, we find the confounding feature opens a backdoor path behind user item matching and introduces spurious correlation. To remove the effect of backdoor path, we propose a framework named Deconfounding Causal Recommendation (DCR), which performs intervened inference with do-calculus. Nevertheless, evaluating do calculus requires to sum over the prediction on all possible values of confounding feature, significantly increasing the time cost. To address the efficiency challenge, we further propose a mixture-of experts (MoE) model architecture, modeling each value of confounding feature with a separate expert module. Through this way, we retain the model expressiveness with few additional costs. We demonstrate DCR on the backbone model of neural factorization machine (NFM), showing that DCR leads to more accurate prediction of user preference with small inference time cost

Exogenous leptin promotes the recovery of regressed ovary in fasted ducks

The present study was undertaken to examine the effect of administered recombinant mouse leptin on the recovery of regressed ovary in fasted ducks. Twenty-eight ducks were divided into live groups: fed ad libitum (control: n =5). fasted control (FC; n=5), fasted + low dose of leptin (F+ L; n=5). fasted +medium close of leptin (F+ M; n =5) and fasted + high dose of leptin (F+ H; n=3). All four fasted groups were fasted for 2 clays and then ad libitum and the clucks were treated with leptin at doses of 0 (control and FC). 50 (F+L), 250 (F+ M) and 10()0 (F+H) mu g/kg body weight/day on day 3-5. Results showed that a moderate close of leptin (250 mu g/kg, body weight/day) injected during the re-feeding period: (i) promoted the recovery of the regressed ovary as evidenced by an increase in ovary weight and recovery of yellow hierarchical follicles; (ii) elevated the plasma 17 beta-estradiol (E-2) level; (iii) increased the mRNA levels of ovary follicle-stimulating hormone receptor (FSHR). luteinizing hormone receptor (LHR) and estrogen receptor-beta (ER-beta). Furthermore, the results also showed that a high close of leptin (1000 mu g/kg body weight/day) may have a negative effect on the recovery of the regressed ovary. In conclusion, this Study indicates that. in clucks. leptin may be involved in the recovery of the regressed ovary caused by 2 days of fasting. This effect may be related to increased plasma E-2 levels and stimulation of the mRNA levels of ovarian FSHR. LHR and especially ER-beta. (C) 2008 Elsevier B.V. All rights reserved.National Natural Science Foundation [30400314