Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching

In sponsored search advertising (SSA), advertisers need to select keywords and determine matching types for selected keywords simultaneously, i.e., keyword targeting. An optimal keyword targeting strategy guarantees reaching the right population effectively. This paper aims to address the keyword targeting problem, which is a challenging task because of the incomplete information of historical advertising performance indices and the high uncertainty in SSA environments. First, we construct a data distribution estimation model and apply a Markov Chain Monte Carlo method to make inference about unobserved indices (i.e., impression and click-through rate) over three keyword matching types (i.e., broad, phrase and exact). Second, we formulate a stochastic keyword targeting model (BB-KSM) combining operations of keyword selection and keyword matching to maximize the expected profit under the chance constraint of the budget, and develop a branch-and-bound algorithm incorporating a stochastic simulation process for our keyword targeting model. Finally, based on a realworld dataset collected from field reports and logs of past SSA campaigns, computational experiments are conducted to evaluate the performance of our keyword targeting strategy. Experimental results show that, (a) BB-KSM outperforms seven baselines in terms of profit; (b) BB-KSM shows its superiority as the budget increases, especially in situations with more keywords and keyword combinations; (c) the proposed data distribution estimation approach can effectively address the problem of incomplete performance indices over the three matching types and in turn significantly promotes the performance of keyword targeting decisions. This research makes important contributions to the SSA literature and the results offer critical insights into keyword management for SSA advertisers.Comment: 38 pages, 4 figures, 5 table

MADS1 maintains barley spike morphology at high ambient temperatures

Temperature stresses affect plant phenotypic diversity. The developmental stability of the inflorescence, required for reproductive success, is tightly regulated by the interplay of genetic and environmental factors. However, the mechanisms underpinning how plant inflorescence architecture responds to temperature are largely unknown. We demonstrate that the barley SEPALLATA MADS-box protein HvMADS1 is responsible for maintaining an unbranched spike architecture at high temperatures, while the loss-of-function mutant forms a branched inflorescence-like structure. HvMADS1 exhibits increased binding to target promoters via A-tract CArG-box motifs, which change conformation with temperature. Target genes for high-temperature-dependent HvMADS1 activation are predominantly associated with inflorescence differentiation and phytohormone signalling. HvMADS1 directly regulates the cytokinin-degrading enzyme HvCKX3 to integrate temperature response and cytokinin homeostasis, which is required to repress meristem cell cycle/division. Our findings reveal a mechanism by which genetic factors direct plant thermomorphogenesis, extending the recognized role of plant MADS-box proteins in floral development.Gang Li, Hendrik N. J. Kuijer, Xiujuan Yang, Huiran Liu, Chaoqun Shen, Jin Shi ... et al

Multi-Scale Measurement of Regional Inequality in Mainland China during 2005–2010 Using DMSP/OLS Night Light Imagery and Population Density Grid Data

This study used the Night Light Development Index (NLDI) to measure the regional inequality of public services in Mainland China at multiple scales. The NLDI was extracted based on a Gini Coefficient approach to measure the spatial differences of population distribution and night light distribution. Population data were derived from the dataset of China’s population density grid, and night light data were acquired from satellite imagery. In the multi-scale analysis, we calculated the NLDI for China as a whole, eight economic regions, 31 provincial regions, and 354 prefectural cities for the two years of 2005 and 2010. The results indicate that Southwest China and Northwest China are the regions with the most unequal public services, with NLDI values of 0.7116 and 0.7251 for 2005, respectively, and 0.6678 and 0.6304 for 2010, respectively. In contrast, Northern Coastal China had the lowest NLDI values of 0.4775 and 0.4312 for 2005 and 2010, respectively, indicating that this region had the most equal public services. Also, the regional inequality of Mainland China in terms of NLDI has been reduced from 0.6161 to 0.5743 during 2005–2010. The same pattern was observed from the provincial and prefectural analysis, suggesting that public services in Mainland China became more equal within the five-year period. A regression analysis indicated that provincial and prefectural regions with more public services per capita and higher population density had more equal public services

Development and validation of SIRT3-related nomogram predictive of overall survival in patients with serous ovarian cancer

Abstract Objective Our aim is to analyzed the expression pattern of sirtuin(SIRT) superfamily and evaluated their prognostic values in serous ovarian cancer patients. Methods We first analyzed the differential expression of SIRT members among fallopian tube epithelium (FTE), primary serous ovarian cancers/tubal cancers (PSOCs/PSTCs), and omental metastases using GSE10971 and GSE30587 datasets. The prognostic values of SIRT members were evaluated using TCGA and GSE9891 dataset. Results SIRT3 and SIRT5 expression were significantly decreased and increased in PSOCs/PSTCs compared with that in normal counterparts, respectively. SIRT6 and SIRT7 were overexpressed in ometal metastases compared with corresponding primary counterparts. With respect to recurrence free survival, however, SIRT7 overexpression was correlated with better prognosis. A similar trend was observed by multivariable analysis. Regarding overall survival (OS), increased expression of SIRT3, SIRT5, and SIRT7 were associated with better survival by univariable analysis. Subsequent multivariable analysis showed that SIRT3 remained an independent favorable prognostic factor for OS. The SIRT3-related nomogram illustrated age at initial diagnosis as sharing the largest contribution to OS, followed by SIRT3 expression and FIGO stage. The C-index for OS prediction was 0.65 (95%CI, 0.61–0.69) in training cohort (TCGA dataset) and 0.65 (95%CI, 0.59–0.71) in validation cohort (GSE9891 dataset), respectively. The calibration plots showed optimal agreement between the prediction by SIRT3-related nomogram and actual observation for 1-, 3-, and 5-year OS probability. Conclusion In conclusion, SIRT3 was an independent favorable prognostic factor for OS in serous ovarian cancer, and added prognostic value to the traditional clinicopathological factors used to evaluate patients’ prognosis

The epigenetic factor CHD4 contributes to metastasis by regulating the EZH2/β-catenin axis and acts as a therapeutic target in ovarian cancer

Abstract Background The overall survival rate of patients with advanced ovarian cancer (OC) has remained static for several decades. Advanced ovarian cancer is known for its poor prognosis due to extensive metastasis. Epigenetic alterations contribute to tumour progression and therefore are of interest for potential therapeutic strategies. Methods Following our previous study, we identified that CHD4, a chromatin remodelling factor, plays a strong role in ovarian cancer cell metastasis. We investigated the clinical significance of CHD4 through TCGA and GEO database analyses and explored the effect of CHD4 expression modulation and romidepsin treatment on the biological behaviour of ovarian cancer through CCK-8 and transwell assays. Bioluminescence imaging of tumours in xenografted mice was applied to determine the therapeutic effect of romidepsin. GSEA and western blotting were used to screen the regulatory mechanism of CHD4. Results In ovarian cancer patient specimens, high CHD4 expression was associated with a poor prognosis. Loss of function of CHD4 in ovarian cancer cells induced suppression of migration and invasion. Mechanistically, CHD4 knockdown suppressed the expression of EZH2 and the nuclear accumulation of β-catenin. CHD4 also suppressed the metastasis of ovarian cancer cells and prevented disease progression in a mouse model. To inhibit the functions of CHD4 that are mediated by histone deacetylase, we evaluated the effect of the HDAC1/2 selective inhibitor romidepsin. Our findings indicated that treatment with romidepsin suppressed the progression of metastases in vitro and in vivo. Conclusions Collectively, our results uncovered an oncogenic function of CHD4 in ovarian cancer and provide a rationale for clinical trials of romidepsin in ovarian cancer patients

Spatiotemporal Heterogeneity and the Key Influencing Factors of PM2.5 and PM10 in Heilongjiang, China from 2014 to 2018

Particulate matter (PM) degrades air quality and negatively impacts human health. The spatial???temporal heterogeneity of PM (PM2.5 and PM10) concentration in Heilongjiang Province during 2014???2018 and the key impacting factors were investigated based on principal component analysis-based ordinary least square regression (PCA-OLS), PCA-based geographically weighted regression (PCA-GWR), PCA-based temporally weighted regression (PCA-TWR), and PCA-based geographically and temporally weighted regression (PCA-GTWR). Results showed that six principal components represented the temperature, wind speed, air pressure, atmospheric pollution, humidity, and vegetation cover factor, respectively, contributing 87% of original variables. All the local models (PCA-GWR, PCA-TWR, and PCA-GTWR) were superior to the global model (PCA-OLS), and PCA-GTWR has the best performance. PM had greater temporal than spatial heterogeneity due to seasonal periodicity. Air pollutants (i.e., SO2, NO2, and CO) and pressure were promoted whereas temperature, wind speed, and vegetation cover inhibited the PM concentration. The downward trend of annual PM concentration is obvious, especially after 2017, and the hot spot gradually changed from southwestern to southeastern cities. This study laid the foundation for precise local government prevention and control by addressing both excessive effect factors (i.e., meteorological factors, air pollutants, vegetation cover) and spatial-temporal heterogeneity of PM. ?? 2022 by the authors

Improving Super-Resolution Mapping by Combining Multiple Realizations Obtained Using the Indicator-Geostatistics Based Method

Indicator-geostatistics based super-resolution mapping (IGSRM) is a popular super-resolution mapping (SRM) method. Unlike most existing SRM methods that produce only one SRM result each, IGSRM generates multiple equally plausible super-resolution realizations (i.e., SRM results). However, multiple super-resolution realizations are not desirable in many applications, where only one SRM result is usually required. These super-resolution realizations may have different strengths and weaknesses. This paper proposes a novel two-step combination method of generating a single SRM result from multiple super-resolution realizations obtained by IGSRM. In the first step of the method, a constrained majority rule is proposed to combine multiple super-resolution realizations generated by IGSRM into a single SRM result under the class proportion constraint. In the second step, partial pixel swapping is proposed to further improve the SRM result obtained in the previous step. The proposed combination method was evaluated for two study areas. The proposed method was quantitatively compared with IGSRM and Multiple SRM (M-SRM), an existing multiple SRM result combination method, in terms of thematic accuracy and geometric accuracy. Experimental results show that the proposed method produces SRM results that are better than those of IGSRM and M-SRM. For example, in the first example, the overall accuracy of the proposed method is 7.43–10.96% higher than that of the IGSRM method for different scale factors, and 1.09–3.44% higher than that of the M-SRM, while, in the second example, the improvement in overall accuracy is 2.42–4.92%, and 0.08–0.90%, respectively. The proposed method provides a general framework for combining multiple results from different SRM methods

Functional connectivity-based signatures of schizophrenia revealed by multiclass pattern analysis of resting-state fMRI from schizophrenic patients and their healthy siblings

<p>Abstract</p> <p>Background</p> <p>Recently, a growing number of neuroimaging studies have begun to investigate the brains of schizophrenic patients and their healthy siblings to identify heritable biomarkers of this complex disorder. The objective of this study was to use multiclass pattern analysis to investigate the inheritable characters of schizophrenia at the individual level, by comparing whole-brain resting-state functional connectivity of patients with schizophrenia to their healthy siblings.</p> <p>Methods</p> <p>Twenty-four schizophrenic patients, twenty-five healthy siblings and twenty-two matched healthy controls underwent the resting-state functional Magnetic Resonance Imaging (rs-fMRI) scanning. A linear support vector machine along with principal component analysis was used to solve the multi-classification problem. By reconstructing the functional connectivities with high discriminative power, three types of functional connectivity-based signatures were identified: (i) state connectivity patterns, which characterize the nature of disruption in the brain network of patients with schizophrenia; (ii) trait connectivity patterns, reflecting shared connectivities of dysfunction in patients with schizophrenia and their healthy siblings, thereby providing a possible neuroendophenotype and revealing the genetic vulnerability to develop schizophrenia; and (iii) compensatory connectivity patterns, which underlie special brain connectivities by which healthy siblings might compensate for an increased genetic risk for developing schizophrenia.</p> <p>Results</p> <p>Our multiclass pattern analysis achieved 62.0% accuracy via leave-one-out cross-validation (p < 0.001). The identified state patterns related to the default mode network, the executive control network and the cerebellum. For the trait patterns, functional connectivities between the cerebellum and the prefrontal lobe, the middle temporal gyrus, the thalamus and the middle temporal poles were identified. Connectivities among the right precuneus, the left middle temporal gyrus, the left angular and the left rectus, as well as connectivities between the cingulate cortex and the left rectus showed higher discriminative power in the compensatory patterns.</p> <p>Conclusions</p> <p>Based on our experimental results, we saw some indication of differences in functional connectivity patterns in the healthy siblings of schizophrenic patients compared to other healthy individuals who have no relations with the patients. Our preliminary investigation suggested that the use of resting-state functional connectivities as classification features to discriminate among schizophrenic patients, their healthy siblings and healthy controls is meaningful.</p

Effective transfer of micron-size graphene to microfibers for photonic applications

We demonstrate an effective approach to transferring micron-size CVD graphene layers onto freestanding microfibers. With micro-manipulation, the coating position and length of the graphene films can be precisely controlled. By coating micrometer-scale (e.g., 20 mu M) graphene films onto microfibers with diameters down to 1 mu m, we can achieve significantly enhanced light-graphene interaction (e.g., a low saturable-absorption threshold of 40 MW/cm(2)) and simultaneously maintain a high transmission (73% in maximum) as well. In addition, we use these microscale CVD graphene-coated microfibers (GCMs) as saturable absorbers for all-optical modulation at 1550-nm wavelength with a modulation depth of 12% and passively mode-locked fiber lasing with pulse duration down to 970 fs. (C) 2015 Elsevier Ltd. All rights reserved