A Survey of Chinese Public Service Advertising From Systemic Functional Perspective

This paper studies the lexicogrammatical resources that realize interpersonal meanings in the discourse of public service advertising (PSA for short) in an attempt to explore the relationship established between the advertiser and the audience and how it contributes to the communicative effect of the target discourse.The qualitative analysis is complemented with quantitative study to produce more sensible generalizations about the features of this discourse type on the basis of systemic functional linguistics

Revisiting adverse effects of cross-hybridization in Affymetrix gene expression data: do they matter for correlation analysis?

<p>Abstract</p> <p>Background.</p> <p>This work was undertaken in response to a recently published paper by Okoniewski and Miller (BMC Bioinformatics 2006, <b>7</b>: Article 276). The authors of that paper came to the conclusion that the process of multiple targeting in short oligonucleotide microarrays induces spurious correlations and this effect may deteriorate the inference on correlation coefficients. The design of their study and supporting simulations cast serious doubt upon the validity of this conclusion. The work by Okoniewski and Miller drove us to revisit the issue by means of experimentation with biological data and probabilistic modeling of cross-hybridization effects.</p> <p>Results.</p> <p>We have identified two serious flaws in the study by Okoniewski and Miller: (1) The data used in their paper are not amenable to correlation analysis; (2) The proposed simulation model is inadequate for studying the effects of cross-hybridization. Using two other data sets, we have shown that removing multiply targeted probe sets does not lead to a shift in the histogram of sample correlation coefficients towards smaller values. A more realistic approach to mathematical modeling of cross-hybridization demonstrates that this process is by far more complex than the simplistic model considered by the authors. A diversity of correlation effects (such as the induction of positive or negative correlations) caused by cross-hybridization can be expected in theory but there are natural limitations on the ability to provide quantitative insights into such effects due to the fact that they are not directly observable.</p> <p>Conclusion.</p> <p>The proposed stochastic model is instrumental in studying general regularities in hybridization interaction between probe sets in microarray data. As the problem stands now, there is no compelling reason to believe that multiple targeting causes a large-scale effect on the correlation structure of Affymetrix gene expression data. Our analysis suggests that the observed long-range correlations in microarray data are of a biological nature rather than a technological flaw.</p> <p>Reviewers:</p> <p>The paper was reviewed by I. K. Jordan, D. P. Gaile (nominated by E. Koonin), and W. Huber (nominated by S. Dudoit).</p

Business Process Analysis and Implementation Strategies of Greening Logistics in Appliances Retail Industry

The whole world ecological environment worsens, and low-carbon economical and sustainable development becomes the general trend. The retail trade as the middle link of the whole economic cycle plays an important role on the relations of each essential factor in the entire economic environment. The concepts of low-carbon economy, and green logistics, and electrical appliances retail trade are discussed and the problems existing in the green logistics of the electrical appliances retail trade are explored. Based on the analysis of the business process and functions of green logistics in the electrical appliances retail trade, the system architecture of green logistics in the electrical appliances retail trade is established. Finally, the strategies and measures of greening logistics in the electrical appliances retail industry are proposed

Metabolic Pathway Genes Associated with Susceptibility Genes to Coronary Artery Disease

Coronary artery disease (CAD) is one of the leading threats to global health. Previous research has proven that metabolic pathway disorders, such as high blood lipids and diabetes, are one of the risk factors that mostly cause CAD. However, the crosstalk between metabolic pathways and CAD was mostly studied on physiology processes by analyzing a single gene function. A canonical correlation analysis was used to identify the metabolic pathways, which were integrated as a unit to coexpress with CAD susceptibility genes, and to resolve additional metabolic factors that are related to CAD. Seven pathways, including citrate cycle, ubiquinone, terpenoid quinone biosynthesis, and N-glycan biosynthesis, were identified as an integrated unit coexpressed with CAD genes. These pathways could not be revealed as a coexpressed pathway through traditional methods as each single gene has weak correlation. Furthermore, sets of genes in these pathways were candidate markers for diagnosis and detection from patients’ serum

An Attention-Based Multi-Domain Bi-Hemisphere Discrepancy Feature Fusion Model for EEG Emotion Recognition

Electroencephalogram (EEG)-based emotion recognition has become a research hotspot in the field of brain-computer interface. Previous emotion recognition methods have overlooked the fusion of multi-domain emotion-specific information to improve performance, and faced the challenge of insufficient interpretability. In this paper, we proposed a novel EEG emotion recognition model that combined the asymmetry of the brain hemisphere, and the spatial, spectral, and temporal multi-domain properties of EEG signals, aiming to improve emotion recognition performance. Based on the 10-20 standard system, a global spatial projection matrix (GSPM) and a bi-hemisphere discrepancy projection matrix (BDPM) are constructed. A dual-stream spatial-spectral-temporal convolution neural network is designed to extract depth features from the two matrix paradigms. Finally, the transformer-based fusion module is used to learn the dependence of fused features, and to retain the discriminative information. We conducted extensive experiments on the SEED, SEED-IV, and DEAP public datasets, achieving excellent average results of 98.33/2.46&lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\%$&lt;/tex-math&gt;&lt;/inline-formula&gt;, 92.15/5.13&lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\%$&lt;/tex-math&gt;&lt;/inline-formula&gt;, 97.60/1.68&lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\%$&lt;/tex-math&gt;&lt;/inline-formula&gt;(valence), and 97.48/1.42&lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\%$&lt;/tex-math&gt;&lt;/inline-formula&gt;(arousal) respectively. Visualization analysis supports the interpretability of the model, and ablation experiments validate the effectiveness of multi-domain and bi-hemisphere discrepancy information fusion.</p

An Attention-Based Multi-Domain Bi-Hemisphere Discrepancy Feature Fusion Model for EEG Emotion Recognition

Electroencephalogram (EEG)-based emotion recognition has become a research hotspot in the field of brain-computer interface. Previous emotion recognition methods have overlooked the fusion of multi-domain emotion-specific information to improve performance, and faced the challenge of insufficient interpretability. In this paper, we proposed a novel EEG emotion recognition model that combined the asymmetry of the brain hemisphere, and the spatial, spectral, and temporal multi-domain properties of EEG signals, aiming to improve emotion recognition performance. Based on the 10-20 standard system, a global spatial projection matrix (GSPM) and a bi-hemisphere discrepancy projection matrix (BDPM) are constructed. A dual-stream spatial-spectral-temporal convolution neural network is designed to extract depth features from the two matrix paradigms. Finally, the transformer-based fusion module is used to learn the dependence of fused features, and to retain the discriminative information. We conducted extensive experiments on the SEED, SEED-IV, and DEAP public datasets, achieving excellent average results of 98.33/2.46&lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\%$&lt;/tex-math&gt;&lt;/inline-formula&gt;, 92.15/5.13&lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\%$&lt;/tex-math&gt;&lt;/inline-formula&gt;, 97.60/1.68&lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\%$&lt;/tex-math&gt;&lt;/inline-formula&gt;(valence), and 97.48/1.42&lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\%$&lt;/tex-math&gt;&lt;/inline-formula&gt;(arousal) respectively. Visualization analysis supports the interpretability of the model, and ablation experiments validate the effectiveness of multi-domain and bi-hemisphere discrepancy information fusion.</p