Robust Independent Component Analysis via Minimum Divergence Estimation

Independent component analysis (ICA) has been shown to be useful in many applications. However, most ICA methods are sensitive to data contamination and outliers. In this article we introduce a general minimum U-divergence framework for ICA, which covers some standard ICA methods as special cases. Within the U-family we further focus on the gamma-divergence due to its desirable property of super robustness, which gives the proposed method gamma-ICA. Statistical properties and technical conditions for the consistency of gamma-ICA are rigorously studied. In the limiting case, it leads to a necessary and sufficient condition for the consistency of MLE-ICA. This necessary and sufficient condition is weaker than the condition known in the literature. Since the parameter of interest in ICA is an orthogonal matrix, a geometrical algorithm based on gradient flows on special orthogonal group is introduced to implement gamma-ICA. Furthermore, a data-driven selection for the gamma value, which is critical to the achievement of gamma-ICA, is developed. The performance, especially the robustness, of gamma-ICA in comparison with standard ICA methods is demonstrated through experimental studies using simulated data and image data.Comment: 7 figure

The safety and efficacy of hypothermia combining mechanical thrombectomy or thrombolysis in the treatment of ischemic stroke: A systematic meta-analysis

Background: Stroke is a major global public health problem, affecting 13.7 million people worldwide. Previous studies have found a neuroprotective effect of hypothermia therapy and the efficacy and safety of combined hypothermia and mechanical thrombectomy or thrombolysis in the treatment of ischemic stroke have also attracted attention. Objective: In the present research, the authors conducted a meta-analysis to comprehensively assess the safety and efficacy of hypothermia combining mechanical thrombectomy or thrombolysis in the treatment of ischemic stroke. Methods: Articles published from January 2001 to May 2022 were searched from Google Scholar, Baidu Scholar and PubMed to evaluate the clinical significance of hypothermia treatment in ischemic stroke. Complications, short-term mortality, and the modified Rankin Scale (mRS) in the full text was extracted. Results: 89 publications were selected and 9 among them were included in this study with sample size of 643. All selected studies are in accordance with the inclusion criteria. Forest plot of clinical characteristics was as follows: complications (RR = 1.132, 95% CI 0.942‒1.361, p = 0.186, I2 = 37.2%), mortality within 3 months (RR = 1.076, 95% CI 0.694‒1.669, p = 0.744, I2 = 0.00%), mRS ≤ 1 at 3 months (RR = 1.138, 95% CI 0.829‒1.563, p = 0.423, I2 = 26.0%), mRS ≤ 2 at 3 months (RR = 1.672, 95% CI 1.236‒2.263, p = 0.001, I2=49.6%) and mRS ≤ 3 at 3 months (RR = 1.518, 95% CI 1.128‒2.043, p = 0.006, I2 = 0.00%). The funnel plot suggested that there was no significant publication bias in the meta-analysis on complications, mortality within 3 months, mRS ≤ 1 at 3 months and mRS ≤ 2 at 3 months. Conclusion: In summary, the results showed that hypothermia treatment was correlated with mRS ≤ 2 at 3 months, but not linked with complications and mortality within 3 months

Voice Conversion Based on Cross-Domain Features Using Variational Auto Encoders

An effective approach to non-parallel voice conversion (VC) is to utilize deep neural networks (DNNs), specifically variational auto encoders (VAEs), to model the latent structure of speech in an unsupervised manner. A previous study has confirmed the ef- fectiveness of VAE using the STRAIGHT spectra for VC. How- ever, VAE using other types of spectral features such as mel- cepstral coefficients (MCCs), which are related to human per- ception and have been widely used in VC, have not been prop- erly investigated. Instead of using one specific type of spectral feature, it is expected that VAE may benefit from using multi- ple types of spectral features simultaneously, thereby improving the capability of VAE for VC. To this end, we propose a novel VAE framework (called cross-domain VAE, CDVAE) for VC. Specifically, the proposed framework utilizes both STRAIGHT spectra and MCCs by explicitly regularizing multiple objectives in order to constrain the behavior of the learned encoder and de- coder. Experimental results demonstrate that the proposed CD- VAE framework outperforms the conventional VAE framework in terms of subjective tests.Comment: Accepted to ISCSLP 201

Macrophage migration inhibitory factor (MIF) family in arthropods : Cloning and expression analysis of two MIF and one D-dopachrome tautomerase (DDT) homologues in Mud crabs, Scylla paramamosain

Acknowledgements This research was supported by grants from the National Natural Science Foundation of China (Nos. 31172438 and U1205123), the Natural Science Foundation of Fujian Province (No. 2012J06008 and 201311180002) and the projects-sponsored by SRF. TW received funding from the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland) funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions.Peer reviewedPostprin

Statistical delay distribution analysis on high-speed railway trains

Published by SpringerOpen, Heidelber

New solutions for the color-flavor locked strangelets

Recent publications rule out the negatively charged beta equilibrium strangelets in ordinary phase, and the color-flavor locked (CFL) strangelets are reported to be also positively charged. This letter presents new solutions to the system equations where CFL strangelets are slightly negatively charged. If the ratio of the square-root bag constant to the gap parameter is smaller than 170 MeV, the CFL strangelets are more stable than iron and the normal unpaired strangelets. For the same parameters, however, the positively charged CFL strangelets are more stable.Comment: 5 pages, 4 figures, Revtex4 styl