Retrieval Based Cartoon Synthesis via Heterogeneous Features Learning

Author(s):  
Zhang Liang ◽  
Jun Xiao ◽  
Hong Pan
2015 ◽  
Vol 10 (4) ◽  
pp. 414-424 ◽  
Author(s):  
Abbasali Emamjomeh ◽  
Bahram Goliaei ◽  
Javad Zahiri ◽  
Reza Ebrahimpour

2015 ◽  
Vol 8 (1) ◽  
pp. 309-317 ◽  
Author(s):  
Xing Liting ◽  
Zhou Juan ◽  
Zhang Fengjuan ◽  
Wang Song ◽  
Dou Tongwen ◽  
...  

In karst regions, due to the heterogeneous features of karst medium, the characteristics of the groundwater flow turn to be of high complexity. Researchers have been seeking proper forecasting methods for karst water dynamic for many years. This paper, taking the spring in Jinan as an example, using regression analysis, analyzed the factors influencing spring water dynamic, and quantitatively evaluated the influencing coefficients of spring water level concerning rainfall, exploitation and recharge as well as the natural decay coefficient of spring water in dry seasons. The prediction model coupling multiple factors was built by investigating natural and anthropogenic factors influencing groundwater level, which could be used for forecasting dynamic of spring water in Jinan. The calculated value of model was highly coincided with the observed value. In consideration of the characteristics of uneven precipitation in Jinan, the suitable zones and volume of artificial recharge were investigated finally, which could help to sustain the spewing of Jinan springs significantly.


2021 ◽  
pp. 1-13
Author(s):  
Kai Zhuang ◽  
Sen Wu ◽  
Xiaonan Gao

To deal with the systematic risk of financial institutions and the rapid increasing of loan applications, it is becoming extremely important to automatically predict the default probability of a loan. However, this task is non-trivial due to the insufficient default samples, hard decision boundaries and numerous heterogeneous features. To the best of our knowledge, existing related researches fail in handling these three difficulties simultaneously. In this paper, we propose a weakly supervised loan default prediction model WEAKLOAN that systematically solves all these challenges based on deep metric learning. WEAKLOAN is composed of three key modules which are used for encoding loan features, learning evaluation metrics and calculating default risk scores. By doing so, WEAKLOAN can not only extract the features of a loan itself, but also model the hidden relationships in loan pairs. Extensive experiments on real-life datasets show that WEAKLOAN significantly outperforms all compared baselines even though the default loans for training are limited.


Molecules ◽  
2019 ◽  
Vol 24 (3) ◽  
pp. 380 ◽  
Author(s):  
Pengmian Feng ◽  
Zhaochun Xu ◽  
Hui Yang ◽  
Hao Lv ◽  
Hui Ding ◽  
...  

As an abundant post-transcriptional modification, dihydrouridine (D) has been found in transfer RNA (tRNA) from bacteria, eukaryotes, and archaea. Nonetheless, knowledge of the exact biochemical roles of dihydrouridine in mediating tRNA function is still limited. Accurate identification of the position of D sites is essential for understanding their functions. Therefore, it is desirable to develop novel methods to identify D sites. In this study, an ensemble classifier was proposed for the detection of D modification sites in the Saccharomyces cerevisiae transcriptome by using heterogeneous features. The jackknife test results demonstrate that the proposed predictor is promising for the identification of D modification sites. It is anticipated that the proposed method can be widely used for identifying D modification sites in tRNA.


2011 ◽  
Vol 300 (5) ◽  
pp. G723-G728 ◽  
Author(s):  
Luke Barron ◽  
Thomas A. Wynn

Dysregulated wound healing leads to fibrosis, whereby fibroblasts synthesize excess extracellular matrix and scarring impairs proper organ function. Although fibrotic diseases arise from diverse causes and display heterogeneous features, fibrosis commonly associates with chronic inflammation. Recent discoveries reinforce the idea that communication between fibroblasts, macrophages, and CD4 T cells integrates the processes of wound healing and host defense. Signals between macrophages and fibroblasts can exacerbate, suppress, or reverse fibrosis. Fibroblasts and macrophages are activated by T cells, but their activation also engages negative feedback loops that reduce fibrosis by restraining the immune response, particularly when the Th2 cytokine IL-13 contributes to pathology. Thus the interactions among fibroblasts, macrophages, and CD4 T cells likely play general and critical roles in initiating, perpetuating, and resolving fibrosis in both experimental and clinical conditions.


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