Web Mining For Financial Market Prediction Based On Online Sentiments

Financial market prediction is a critically important research topic in financial data mining because of its potential commerce application and attractive profits. Previous studies in financial market prediction mainly focus on financial and economic indicators. Web information, as an information repository, has been used in customer relationship management and recommendation, but it is rarely considered to be useful in financial market prediction. In this paper, a combined web mining and sentiment analysis method is proposed to forecast financial markets using web information. In the proposed method, a spider is firstly employed to crawl tweets from Twitter. Secondly, Opinion Finder is offered to mining the online sentiments hidden in tweets. Thirdly, some new sentiment indicators are suggested and a stochastic time effective function (STEF) is introduced to integrate everyday sentiments. Fourthly, support vector regressions (SVRs) are used to model the relationship between online sentiments and financial market prices. Finally, the selective model can be serviced for financial market prediction. To validate the proposed method, Standard and Poor’s 500 Index (S&P 500) is used for evaluation. The empirical results show that our proposed forecasting method outperforms the traditional forecasting methods, and meanwhile, the proposed method can also capture individual behavior in financial market quickly and easily. These findings imply that the proposed method is a promising approach for financial market prediction

Degenerated primer design to amplify the heavy chain variable region from immunoglobulin cDNA

BACKGROUND: The amplification of variable regions of immunoglobulins has become a major challenge in the cloning of antibody genes, whether from hybridoma cell lines or splenic B cells. Using conventional protocols, the heavy-chain variable region genes often are not amplified successfully from the hybridoma cell lines. RESULTS: A novel method was developed to design the degenerated primer of immunoglobulin cDNA and to amplify cDNA ends rapidly. Polymerase chain reaction protocols were performed to recognize the VH gene from the hybridoma cell line. The most highly conserved region in the middle of the VH regions of the Ig cDNA was identified, and a degenerated 5'primer was designed, using our algorithms. The VH gene was amplified by both the 3'RACE and 5'RACE. The VH sequence of CSA cells was 399 bp. CONCLUSION: The new protocol rescued the amplifications of the VH gene that had failed under conventional protocols. In addition, there was a notable increase in amplification specificity. Moreover, the algorithm improved the primer design efficiency and was shown to be useful both for building VH and VL gene libraries and for the cloning of unknown genes in gene families

Local-to-Global Information Communication for Real-Time Semantic Segmentation Network Search

Neural Architecture Search (NAS) has shown great potentials in automatically designing neural network architectures for real-time semantic segmentation. Unlike previous works that utilize a simplified search space with cell-sharing way, we introduce a new search space where a lightweight model can be more effectively searched by replacing the cell-sharing manner with cell-independent one. Based on this, the communication of local to global information is achieved through two well-designed modules. For local information exchange, a graph convolutional network (GCN) guided module is seamlessly integrated as a communication deliver between cells. For global information aggregation, we propose a novel dense-connected fusion module (cell) which aggregates long-range multi-level features in the network automatically. In addition, a latency-oriented constraint is endowed into the search process to balance the accuracy and latency. We name the proposed framework as Local-to-Global Information Communication Network Search (LGCNet). Extensive experiments on Cityscapes and CamVid datasets demonstrate that LGCNet achieves the new state-of-the-art trade-off between accuracy and speed. In particular, on Cityscapes dataset, LGCNet achieves the new best performance of 74.0\% mIoU with the speed of 115.2 FPS on Titan Xp.Comment: arXiv admin note: text overlap with arXiv:1909.0679