Robust adaptive estimator based on a novel objective function—Using the L1-norm and L0-norm

To fully take advantage of LMS,LMAT,and SELMS,a novel adaptive estimator using the L1-norm and L0-norm of the estimated error is proposed in this paper.Then based on minimizing the mean-square deviation at the current time,the optimal step-size,parameters𝛿and𝜃of the proposed adaptive estimator are obtained.Besides,the stability and computational complexity of the mean estimation error is analyzed theoretically.Experimental results(both simulation and real mechanical system datasets)show that the proposed adaptive estimator is more robust to input signals and a variety of measurement noises(Gaussian and non-Gaussian noises).In addition,it is superior to LMS,LMAT,SELMS,the convex combination of LMS and LMAT algorithm,the convex combination of LMS and SELMS algorithm,and the convex combination of SELMS and LMAT algorithm.The theoretical analysis is consistent with the Monte-Carlo results.Both of them show that the adaptive estimator has an excellent performance in the estimation of unknown linear systems under various measurement noises.

Spline adaptive filtering algorithm based on different iterative gradients:Performance analysis and comparison

Two novel spline adaptive filtering(SAF)algorithms are proposed by combining different iterative gradient methods,i.e.,Adagrad and RMSProp,named SAF-Adagrad and SAF-RMSProp,in this paper.Detailed convergence performance and computational complexity analyses are carried out also.Furthermore,compared with existing SAF algorithms,the influence of step-size and noise types on SAF algorithms are explored for nonlinear system identification under artificial datasets.Numerical results show that the SAF-Adagrad and SAFRMSProp algorithms have better convergence performance than some existing SAF algorithms(i.e.,SAF-SGD,SAF-ARC-MMSGD,and SAF-LHC-MNAG).The analysis results of various measured real datasets also verify this conclusion.Overall,the effectiveness of SAF-Adagrad and SAF-RMSProp are confirmed for the accurate identification of nonlinear systems.

ILipo-PseAAC: Identification of Lipoylation Sites Using Statistical Moments and General PseAAC

Lysine Lipoylation is a protective and conserved Post Translational Modification(PTM)in proteomics research like prokaryotes and eukaryotes.It is connected with many biological processes and closely linked with many metabolic diseases.To develop a perfect and accurate classification model for identifying lipoylation sites at the protein level,the computational methods and several other factors play a key role in this purpose.Usually,most of the techniques and different traditional experimental models have a very high cost.They are time-consuming;so,it is required to construct a predictor model to extract lysine lipoylation sites.This study proposes a model that could predict lysine lipoylation sites with the help of a classification method known as Artificial Neural Network(ANN).The ANN algorithm deals with the noise problem and imbalance classification in lipoylation sites dataset samples.As the result shows in ten-fold cross-validation,a brilliant performance is achieved through the predictor model with an accuracy of 99.88%,and also achieved 0.9976 as the highest value of MCC.So,the predictor model is a very useful and helpful tool for lipoylation sites prediction.Some of the residues around lysine lipoylation sites play a vital part in prediction,as demonstrated during feature analysis.The wonderful results reported through the evaluation and prediction of this model can provide an informative and relative explanation for lipoylation and its molecular mechanisms.

Interaction Between Memory Load and Experimental Design on Brain Connectivity and Network Topology

The conventional approach to investigating functional connectivity in the block-designed study usually concatenates task blocks or employs residuals of task activation.While providing many insights into brain functions,the block design adds more manipulation in functional network analysis that may reduce the purity of the blood oxygenation level-dependent signal.Recent studies utilized one single long run for task trials of the same condition,the so-called continuous design,to investigate functional connectivity based on task functional magnetic resonance imaging.Continuous brain activities associated with the single-task condition can be directly utilized for task-related functional connectivity assessment,which has been examined for working memory,sensory,motor,and semantic task experiments in previous research.But it remains unclear how the block and continuous design influence the assessment of task-related functional connectivity networks.This study aimed to disentangle the separable effects of block/continuous design and working memory load on task-related functional connectivity networks,by using repeated-measures analysis of variance.Across 50 young healthy adults,behavioral results of accuracy and reaction time showed a significant main effect of design as well as interaction between design and load.Imaging results revealed that the cingulo-opercular,fronto-parietal,and default model networks were associated with not only task activation,but significant main effects of design and load as well as their interaction on intra-and inter-network functional connectivity and global network topology.Moreover,a significant behavior-brain association was identified for the continuous design.This work has extended the evidence that continuous design can be used to study task-related functional connectivity and subtle brain-behavioral relationships.

迈向脑连接组学:静息态和观看电影时的脑连接研究

神经电影学通过开辟、拓展电影学与脑科学之间的学术交叉地带,不仅为电影理论的确立和实践评估提供脑科学基础,而且能够促进脑科学相关领域的新实验范式的涌现和新理论新方法的发展。研究者采用电影材料和静息态作为脑科学研究的实验范式,基于自然刺激和静息态脑功能成像以及脑连接组前沿技术,探索了在不同自然刺激和静息态下人脑功能活动的不同特征和个体差异,为促进电影艺术对脑科学研究的贡献开辟了新范式、新思潮与热点话题。

A review of resting-state fMRI and its use to examine psychiatric disorders

Resting-state fMRI(rs-fMRI)has emerged as an alternative method to study brain function in human and animal models.In humans,it has been widely used to study psychiatric disorders including schizophrenia,bipolar disorder,autism spectrum disorders,and attention deficit hyperactivity disorders.In this review,rs-fMRI and its advantages over task based fMRI,its currently used analysis methods,and its application in psychiatric disorders using different analysis methods are discussed.Finally,several limitations and challenges of rs-fMRI applications are also discussed.