Graph learning models have received increasing attention in the computational analysis of single-cell RNA sequencing (scRNA-seq) data. Compared with conventional deep neural networks, graph neural networks and language models have exhibited superior performance by extracting graph-structured data from raw gene count matrices. Established deep neural network-based clustering approaches generally focus on temporal expression patterns while ignoring inherent interactions at gene-level as well as cell-level, which could be regarded as spatial dynamics in single-cell data. Both gene-gene and cell-cell interactions are able to boost the performance of cell type detection, under the framework of multi-view modeling. In this study, spatiotemporal embedding and cell graphs are extracted to capture spatial dynamics at the molecular level. In order to enhance the accuracy of cell type detection, this study proposes the scHybridBERT architecture to conduct multi-view modeling of scRNA-seq data using extracted spatiotemporal patterns. In this scHybridBERT method, graph learning models are employed to deal with cell graphs and the Performer model employs spatiotemporal embeddings. Experimental outcomes about benchmark scRNA-seq datasets indicate that the proposed scHybridBERT method is able to enhance the accuracy of single-cell clustering tasks by integrating spatiotemporal embeddings and cell graphs.

A novel bitrate model with low complexity is proposed for perceived compression distortion assessment of mobile video with low resolution, which is extremely useful in intermediate network nodes for quality monitoring. Without fully decoding, parameters are extracted by bitstream analysing, such as bitrate, frame type, quantisation parameter, DCT coefficient, motion vector. Bitrate is regarded as an essential parameter meanwhile the bitrate–MOS curve is determined by video content. Respectively, spatial factor is estimated using quantisation parameter and DCT coefficient and temporal factor is estimated using motion vector. Apart from bitrate, the spatial and temporal factors, which reflect the characteristic of video content, are considered in the proposed model to obtain a more accurate evaluation. Experimental results show that the overall performance of proposed model significantly outperforms that of the other five bitrate models in terms of widely used performance criteria, including the Pearson correlation coefficient (PCC), the Spearman rank‐order correlation coefficient (SROCC), the root‐mean‐squared error (RMSE) and the outlier ratio (OR).

The Four Great Classical Literatures(FGCL) including Treatise on Febrile Disease, Synopsis Golden Chamber, The Medical Classic of the Yellow Emperor, and Febrile disease defense are the basis of Chinese knowledge but were being used less and less because of the weak basis and education of the ancient Chinese medical language. For the purpose of letting more TCM doctors can comprehend FGCL better, a system was set up for the seamless connection between the arcane language in FGCL and the modern language. Each sentence in the FGCL mat contains formulas will be translated by clinic doctors using literatures reading books on three levels, (primary, middle, senior), by the standands of Terminology of traditional Chinese medicine clinical diagnosis and the systematized nomenclature of human medicine(SNOMED). Finally a Vertical professional search engine was bulit mat contains such parameters as symptoms, syndromes, famous discourses, typical case. The primary results was present on the web(see for http://www.jinhuatuo.com/biaozhun.php). After a period of clinical trials of the database, the system partly meets the need of searching the formulas in FGCL, but still need to be more associated and human-intelligent.

This paper proposes a fast joint frame synchronization and frequency offset acquisition algorithm for OFDM systems. A training sequence composed by linear frequency modulation signals is superimposed on the data signal at the transmitter side. Then, by fractional Fourier transform the frame synchronization and frequency offset acquisition can be obtained from the received signal simultaneously. Even under the low signal to noise ratio conditions, this algorithm can keep the estimation accuracy with lower computational complexity. Theoretical analysis and simulation results over AWGN and multi-path channel prove this point.

This correspondence presents the switch strategies for switch-type hybrid hard decision decoding algorithms for regular low-density parity-check (LDPC) codes. After the piecewise analysis of extrinsic information transfer functions for Gallager decoding algorithm B (GB), the normalized switch scheme (NSS), for which the majority-based algorithms and GB are two examples, is proposed. Then, several other examples of NSSs are presented and their convergence properties are analyzed based on the extrinsic information transfer (EXIT) functions. In simulations, the proposed NSSs show meaningful performance improvements and less sensitivity to channel parameter underestimations compared with GB for small and moderate block length codes.