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Introduction: It is increasingly becoming integral to analyze the collected information effectively.Methods: We propose a representation and classification method for collective investor attention in the financial market, taking the Chinese stock market as an example. The method includes three key steps: 1) converting the hourly search volume of each stock per week to an image representation for describing the changes of collective investor attention; 2) extracting features of each image by utilizing a self-encoding algorithm in deep learning; and 3) clustering generated images by K-means to arrange stocks into different groups.Results: The empirical results show that the portfolio considering the clustering information outperforms the HS300 index.Discussion: The method may not only use deep learning features for stock similarity measurement, but also shed some light on profoundly understanding the mechanisms of the collective investor attention for the financial market.

Additional file 1: Supplementary Methods 1. MRI data Acquisition and Processing in UKB. SupplementaryMethods 2. MRI data Acquisition and Processing in ABCD. SupplementaryMethods 3. Included measures. Fig. S1. Polygenic risk scores. Fig.S2. Associations between AD PRS (constructed using GWAS data reported by Kunkle et al.) and cortical macrostructural MRI metrics in ABCD. Fig.S3. Associations between AD PRS (constructed using GWAS data reported by Kunkle et al.) and volume of subcortical structures in ABCD. Fig.S4. Associations between AD PRS (constructed using GWAS data reported by Kunkle et al.) and white matter microstructural MRI metrics inABCD. Fig. S5. The correlation between PRSs constructed by different GWAS data (Schwartzentruber et al. 2021 and Kunkle et al. 2019) in ABCD sample. Fig. S6. Associations between AD PRS (excluding APOE along with adjust APOE genotype)and cortical macrostructural MRI metrics. Fig. S7. Associations betweenAD PRS (excluding APOE along with adjust APOE genotype) and volume ofsubcortical structures. Fig. S8. Associations between AD PRS (excluding APOE along with adjust APOE genotype)and white matter microstructural MRI metrics. Fig. S9. Trends in thecorrelation between association results (adjust 40PCs or 10PCs) of PRSs withbrain structures in UKB samples. Fig. S10. Trends in the correlationbetween association results (adjust birth weight and gestational age or not) ofPRSs with brain structures in ABCD samples. Fig. S11. Trends in thecorrelation between association results (outliers simply removed or winsorized)of PRSs with brain structures in ABCD samples.

Influence overlap is a universal phenomenon in influence spreading for social networks. In this paper, we argue that the redundant influence generated by influence overlap cause negative effect for maximizing spreading influence. Firstly, we present a theoretical method to calculate the influence overlap and record the redundant influence. Then in term of eliminating redundant influence, we present two algorithms, namely, Degree-Redundant-Influence (DRS) and Degree-Second-Neighborhood (DSN) for multiple spreaders identification. The experiments for four empirical social networks successfully verify the methods, and the spreaders selected by the DSN algorithm show smaller degree and k-core values., Comment: 20 pages, 6 figures

The optical properties of nanoparticles and their array are closely related to their surface plasmon resonance of the particle and periodic structure parameters. In this paper, optical response features of single Ag nanosphere and periodical two-dimensional structure arrays are theoretically studied. The Mie theories and the multipole resonance theory are employed in the simulation. For Ag spheres each with a radius of less than 40 nm, one extinction peak can be observed and attributed to electric dipole resonance. When the radius of Ag sphere is more than 40 nm, apart from the peak contributed by the electric dipole, there is a peak of extinction at short wavelength, caused by resonance of the electric quadrupole. Generally, the frequency of multipole resonance decreases with increasing particle radius. The simulated results are in accord with the experimental data. For an infinite two-dimensional Ag-nanosphere arrays, two resonance peaks come from the dipole resonance of single particle and the Wood-Rayleigh anomalous diffraction. The frequency of multipole resonance can be controlled by tuning the size and the periodicity distribution of arrays. This paper provides a significant method to design advanced nanostructures with particular optical properties.