Ensemble learning with dynamic weighting for response modeling in direct marketing

Response modeling, a key to successful direct marketing, has become increasingly prevalent in recent years. However, it practically suffers from the difficulty of class imbalance, i.e., the number of responding (target) customers is often much smaller than that of the non-responding customers. This issue would result in a response model that is biased to the majority class, leading to the low prediction accuracy on the responding customers. In this study, we develop an Ensemble Learning with Dynamic Weighting (ELDW) approach to address the above problem. The proposed ELDW includes two stages. In the first stage, all the minority class instances are combined with different majority class instances to form a number of training subsets, and a base classifiers is trained in each subset. In the second stage, the results of the base classifiers are dynamically integrated, in which two factors are considered. The first factor is the cross entropy of neighbors in each subset, and the second factor is the feature similarity to the minority class instances. In order to evaluate the performance of ELDW, we conduct experimental studies on 10 imbalanced benchmark datasets. The results show that compared with other state-of-the-art imbalance classification algorithms, ELDW achieves higher accuracy on the minority class. Last, we apply the ELDW to a direct marketing activity of an insurance company to identify the target customers under a limited budget

Regression-based surface water fraction mapping using a synthetic spectral library for monitoring small water bodies

Small water bodies (SWBs), such as ponds and on-farm reservoirs, are a key part of the hydrological system and play important roles in diverse domains from agriculture to conservation. The monitoring of SWBs has been greatly facilitated by medium-spatial-resolution satellite images, but the monitoring accuracy is considerably affected by the mixed-pixel problem. Although various spectral unmixing methods have been applied to map sub-pixel surface water fractions for large water bodies, such as lakes and reservoirs, it is challenging to map SWBs that are small in size relative to the image pixel and have dissimilar spectral properties. In this study, a novel regression-based surface water fraction mapping method (RSWFM) using a random forest and a synthetic spectral library is proposed for mapping 10 m spatial resolution surface water fractions from Sentinel-2 imagery. The RSWFM inputs a few endmembers of water, vegetation, impervious surfaces, and soil to simulate a spectral library, and considers spectral variations in endmembers for different SWBs. Additionally, RSWFM applies noise-based data augmentation on pure endmembers to overcome the limitation often arising from the use of a small set of pure spectra in training the regression model. RSWFM was assessed in ten study sites and compared with the fully constrained least squares (FCLS) linear spectral mixture analysis, multiple endmember spectral mixture analysis (MESMA), and the nonlinear random forest (RF) regression without data-augmentation. The results showed that RSWFM decreases the water fraction mapping errors by ~ 30%, ~15%, and ~ 11% in root mean square error compared with the linear FCLS, MESMA unmixings, and the nonlinear RF regression without data-augmentation respectively. RSWFM has an accuracy of approximately 0.85 in R2 in estimating the area of SWBs smaller than 1 ha

Unmixing-based Spatiotemporal Image Fusion Based on the Self-trained Random Forest Regression and Residual Compensation

Spatiotemporal satellite image fusion (STIF) has been widely applied in land surface monitoring to generate high spatial and high temporal reflectance images from satellite sensors. This paper proposed a new unmixing-based spatiotemporal fusion method that is composed of a self-trained random forest machine learning regression (R), low resolution (LR) endmember estimation (E), high resolution (HR) surface reflectance image reconstruction (R), and residual compensation (C), that is, RERC. RERC uses a self-trained random forest to train and predict the relationship between spectra and the corresponding class fractions. This process is flexible without any ancillary training dataset, and does not possess the limitations of linear spectral unmixing, which requires the number of endmembers to be no more than the number of spectral bands. The running time of the random forest regression is about ~1% of the running time of the linear mixture model. In addition, RERC adopts a spectral reflectance residual compensation approach to refine the fused image to make full use of the information from the LR image. RERC was assessed in the fusion of a prediction time MODIS with a Landsat image using two benchmark datasets, and was assessed in fusing images with different numbers of spectral bands by fusing a known time Landsat image (seven bands used) with a known time very-high-resolution PlanetScope image (four spectral bands). RERC was assessed in the fusion of MODIS-Landsat imagery in large areas at the national scale for the Republic of Ireland and France. The code is available at https://www.researchgate.net/proiile/Xiao_Li52

Deep Feature and Domain Knowledge Fusion Network for Mapping Surface Water Bodies by Fusing Google Earth RGB and Sentinel-2 images

Mapping surface water bodies from fine spatial resolution optical remote sensing imagery is essential for the understanding of the global hydrologic cycle. Although satellite data are useful for mapping, the limited spectral information captured by some satellite systems can be sub-optimal for the task. For example, the very high resolution images of Google Earth (GE) only contain RGB bands, which often means many water bodies and land objects are confused. Sentinel-2 (S2) imagery have a spectral resolution more suitable for mapping water bodies, but its medium spatial resolution limits the ability for detailed mapping of water-land boundaries. This letter proposes a deep feature and domain knowledge fusion network (DFDKFNet) for mapping surface water bodies by fusing GE and S2 images while incorporating domain knowledge. DFDKFNet uses the remote sensing indices of normalized difference water index (NDWI) and normalized difference vegetation index (NDVI) derived from the S2 image as the representative domain knowledge to better extract water bodies from terrestrial features. A similar pixel-based approach is used to downscaling the NDWI and NDVI maps to match the spatial resolution between the GE and S2 images. The DFDKFNet uses the GE and downscaled NDWI and NDVI images to extract the deep semantic features of water bodies, which are fused with the domain knowledge extracted from the NDWI and NDVI images. DFDKFNet was compared with several state-of-the-art algorithms, and the results show that DFDKFNet can enhance water body mapping accuracy

Dynamic Transcriptome Analysis Reveals Potential Long Non-coding RNAs Governing Postnatal Pineal Development in Pig

Postnatal development and maturation of pineal gland is a highly dynamic period of tissue remodeling and phenotype maintenance, which is genetically controlled by programmed gene expression regulations. However, limited molecular characterization, particularly regarding long noncoding RNAs (lncRNA), is available for postnatal pineal at a whole transcriptome level. The present study first characterized the comprehensive pineal transcriptome profiles using strand-specific RNA-seq to illustrate the dynamic mRNA/lncRNA expression at three developmental stages (infancy, puberty, and adulthood). The results showed that 21,448 mRNAs and 8,166 novel lncRNAs were expressed in pig postnatal pineal gland. Among these genes, 3,573 mRNAs and 851 lncRNAs, including the 5-hydroxytryptamine receptors, exhibited significant dynamic regulation along maturation process, while the expression of homeobox genes didn’t show significant differences. Gene Ontology analysis revealed that the differentially expressed genes (DEGs) were significantly enriched in ion transport and synaptic transmission, highlighting the critical role of calcium signaling in postnatal pineal development. Additionally, co-expression analysis revealed the DEGs could be grouped into 12 clusters with distinct expression patterns. Many differential lncRNAs were functionally enriched in co-expressed clusters of genes related to ion transport, transcription regulation, DNA binding, and visual perception. Our study first provided an overview of postnatal pineal transcriptome dynamics in pig and demonstrated that dynamic lncRNA regulation of developmental transitions impact pineal physiology

Intragrain impurity annihilation for highly efficient and stable perovskite solar cells

Intragrain impurities can impart detrimental effects on the efficiency and stability of perovskite solar cells, but they are indiscernible to conventional characterizations and thus remain unexplored. Using in situ scanning transmission electron microscopy, we reveal that intragrain impurity nano-clusters inherited from either the solution synthesis or post-synthesis storage can revert to perovskites upon irradiation stimuli, leading to the counterintuitive amendment of crystalline grains. In conjunction with computational modelling, we atomically resolve crystallographic transformation modes for the annihilation of intragrain impurity nano-clusters and probe their impacts on optoelectronic properties. Such critical fundamental findings are translated for the device advancement. Adopting a scanning laser stimulus proven to heal intragrain impurity nano-clusters, we simultaneously boost the efficiency and stability of formamidinium-cesium perovskite solar cells, by virtual of improved optoelectronic properties and relaxed intra-crystal strain, respectively. This device engineering, inspired and guided by atomic-scale in situ microscopic imaging, presents a new prototype for solar cell advancement

Monitoring high spatiotemporal water dynamics by fusing MODIS, Landsat, water occurrence data and DEM

Monitoring the spatiotemporal dynamics of surface water from remote sensing imagery is essential for understanding water's impact on the global ecosystem and climate change. There is often a tradeoff between the spatial and temporal resolutions of imagery acquired from current satellite sensors and as such various spatiotemporal image fusion methods have been explored to circumvent the challenges this situation presents (e.g., STARFM). However, some challenges persist in mapping surface water at the desired fine spatial and temporal resolution. Principally, the spatiotemporal changes of water bodies are often abrupt and controlled by topographic conditions, which are usually unaddressed in current spatiotemporal image fusion methods. This paper proposes the SpatioTemporal Surface Water Mapping (STSWM) method, which aims to predict Landsat-like, 30 m, surface water maps at an 8-day time step (same as the MODIS 8-day composite product) by integrating topographic information into the analysis. In addition to MODIS imagery acquired on the date of map prediction and a pair of MODIS and Landsat images acquired temporally close to the date of prediction, STSWM also uses the surface water occurrence (SWO, which represents the frequency with which water is present in a pixel) and DEM data to provide, respectively, topographic information below and above the water surface. These data are used to translate the coarse spatial resolution water distribution representation observed by MODIS into a 30 m spatial resolution water distribution map. The STSWM was used to generate an 8-day time series surface water maps of 30 m resolution in six inundation regions globally, and was compared with several other state-of-the-art spatiotemporal methods. The stratified random sampling design was used, and unbiased estimators of the accuracies were provided. The results show that STSWM generated the most accurate surface water map in which the spatial details of surface water were well-represented

Strategies for indica rice adapted to high-temperature stress in the middle and lower reaches of the Yangtze River

High temperatures caused by climate warming severely affect the grain yield and quality of rice. In this study, the rice cultivars Longliangyou Huazhan (LLYHZ) and Quanliangyou 2118 (QLY2118) were selected as the experimental materials for investigation of an optimal cultivation system under high-temperature treatment. In addition, the heat-resistant cultivar Huanghuazhan (HHZ) and heat-sensitive cultivar Huiliangyou 858 (HLY858) were chosen as the experimental materials to study the effects of exogenous plant growth regulators on heat stress responses under high-temperature treatment. The results showed that mechanical transplanting of carpet seedlings and delayed sowing effectively increased the leaf area index and reduced the canopy temperature of LLYHZ and QLY2118. Furthermore, carpet seedling mechanical transplantation and delayed sowing improved grain yield and quality. Spray application of five plant growth regulators revealed that brassinolide and salicylic acid had the strongest effects on significantly improving antioxidant enzyme activities in the panicle, which would reduce the damage caused by the accumulation of reactive oxygen species and enhance plant tolerance of high-temperature stress. In addition, brassinolide and salicylic acid enhanced the percentage of anther dehiscence and percentage seed set. In this study, a set of simplified eco-friendly cultivation techniques for single-season indica rice adaptation to high-temperature stress was established. These results will be of great importance in alleviating the effects of high-temperature stress on rice production

RING finger 138 deregulation distorts NF-кB signaling and facilities colitis switch to aggressive malignancy

Prolonged activation of nuclear factor (NF)-кB signaling significantly contributes to the development of colorectal cancer (CRC). New therapeutic opportunities are emerging from targeting this distorted cell signaling transduction. Here, we discovered the critical role of RING finger 138 (RNF138) in CRC tumorigenesis through regulating the NF-кB signaling, which is independent of its Ubiquitin-E3 ligase activity involved in DNA damage response. RNF138(−/−) mice were hyper-susceptible to the switch from colitis to aggressive malignancy, which coincided with sustained aberrant NF-кB signaling in the colonic cells. Furthermore, RNF138 suppresses the activation of NF-кB signaling pathway through preventing the translocation of NIK and IKK-Beta Binding Protein (NIBP) to the cytoplasm, which requires the ubiquitin interaction motif (UIM) domain. More importantly, we uncovered a significant correlation between poor prognosis and the downregulation of RNF138 associated with reinforced NF-кB signaling in clinical settings, raising the possibility of RNF138 dysregulation as an indicator for the therapeutic intervention targeting NF-кB signaling. Using the xenograft models built upon either RNF138-dificient CRC cells or the cells derived from the RNF138-dysregulated CRC patients, we demonstrated that the inhibition of NF-кB signaling effectively hampered tumor growth. Overall, our work defined the pathogenic role of aberrant NF-кB signaling due to RNF138 downregulation in the cascade events from the colitis switch to colonic neoplastic transformation and progression, and also highlights the possibility of targeting the NF-кB signaling in treating specific subtypes of CRC indicated by RNF138-ablation