L^p MIXED-NORM ESTIMATES FOR CONVOLUTION OPERATOR DEFINED BY SINGULAR MEASURES

Let F be a C^∞ curve in IR^n and μ be the measure induced by Lebesgue measure on F, multiplied by a smooth cut-off function. In this paper, we will prove some mixednorm estimates based on the average decay estimates of the Fourier transform of μ.

Pose-robust feature learning for facial expression recognition

Automatic facial expression recognition （FER） from non-frontal views is a challenging research topic which has recently started to attract the attention of the research community. Pose variations are difficult to tackle and many face analysis methods require the use of sophisticated nor- malization and initialization procedures. Thus head-pose in- variant facial expression recognition continues to be an is- sue to traditional methods. In this paper, we propose a novel approach for pose-invariant FER based on pose-robust fea- tures which are learned by deep learning methods -- prin- cipal component analysis network （PCANet） and convolu- tional neural networks （CNN） （PRP-CNN）. In the first stage, unlabeled frontal face images are used to learn features by PCANet. The features, in the second stage, are used as the tar- get of CNN to learn a feature mapping between frontal faces and non-frontal faces. We then describe the non-frontal face images using the novel descriptions generated by the maps, and get unified descriptors for arbitrary face images. Finally, the pose-robust features are used to train a single classifier for FER instead of training multiple models for each spe- cific pose. Our method, on the whole, does not require pose/ landmark annotation and can recognize facial expression in a wide range of orientations. Extensive experiments on two public databases show that our framework yields dramatic improvements in facial expression analysis.

Algorithm Contest of Calibration-free Motor Imagery BCI in the BCI Controlled Robot Contest in World Robot Contest 2021:A survey

Objective:From September 10 to 13,2021,the finals of the BCI Controlled Robot Contest in World Robot Contest 2021 were held in Beijing,China.Eleven teams participated in the Algorithm Contest of Calibration-free Motor Imagery BCI.The participants employed both traditional electroencephalograph(EEG)analysis methods and deep learning-based methods in the contest.In this paper,we reviewed the algorithms utilized by the participants,extracted the trends and highlighted interesting approaches from these methods to inform future contests and research recommendations.Method:First,we analyzed the algorithms in separate steps,including EEG channel and signal segment setup,prepossessing technology,and classification model.Then,we emphasized the highlights of each algorithm.Finally,we compared the competition algorithm with the SOTA algorithm.Results:The algorithm employed in the finals performed better than that of the SOTA algorithm.During the final stage of the contest,four of the top five teams used convolutional neural network models,suggesting that with the rapid development of deep learning,convolutional neural network-based models have been the most popular methods in the field of motor imagery BCI.