Motor intent recognition of multi-feature fusion EEG signals by UMAP algorithm.

The key to the analysis of electroencephalogram (EEG) signals lies in the extraction of effective features from the raw EEG signals, which can then be utilized to augment the classification accuracy of motor imagery (MI) applications in brain-computer interface (BCI). It can be argued that the utilization of features from multiple domains can be a more effective approach to feature extraction for MI pattern classification, as it can provide a more comprehensive set of information that the traditional single feature extraction method may not be able to capture. In this paper, a multi-feature fusion algorithm based on uniform manifold approximate and projection (UMAP) is proposed for motor imagery EEG signals. The brain functional network and common spatial pattern (CSP) are initially extracted as features. Subsequently, UMAP is utilized to fuse the extracted multi-domain features to generate low-dimensional features with improved discriminative capability. Finally, the k-nearest neighbor (KNN) classifier is applied in a lower dimensional space. The proposed method is evaluated using left-right hand EEG signals, and achieved the average accuracy of over 92%. The results indicate that, compared with single-domain-based feature extraction methods, multi-feature fusion EEG signal classification based on the UMAP algorithm yields superior classification and visualization performance. Feature extraction and fusion based on UMAP algorithm of left-right hand motor imagery.

A Novel Human Papillomavirus 16 L1 Pentamer-Loaded Hybrid Particles Vaccine System: Influence of Size on Immune Responses.

Cervical cancer remains the second-most prevalent female malignancy around the world, leading to a great majority of cancer-related mortality that occurs mainly in developing countries. Developing an effective and low-cost vaccine against human papillomavirus (HPV) infection, especially in medically underfunded areas, is urgent. Compared with vaccines based on HPV L1 viruslike particles (VLPs) in the market, recombinant HPV L1 pentamer expressed in Escherichia coli represents a promising and potentially cost-effective vaccine for preventing HPV infection. Hybrid particles comprising a polymer core and lipid shell have shown great potential compared to conventional aluminum salts adjuvant and is urgently needed for HPV L1 pentamer vaccines. It is well-reported that particle sizes are crucial in regulating immune responses. Nevertheless, reports on the relationship between the particulate size and the resultant immune response have been in conflict, and there is no answer to how the size of particles regulates specific immune response for HPV L1 pentamer-based candidate vaccines. Here, we fabricated HPV 16 L1 pentamer-loaded poly(d,l-lactide- co-glycolide) (PLGA)/lecithin hybrid particles with uniform sizes (0.3, 1, and 3 µm) and investigated the particle size effects on antigen release, activation of lymphocytes, dendritic cells (DCs) activation and maturation, follicular helper CD4+ T (TFH) cells differentiation, and release of pro-inflammatory cytokines and chemokines. Compared with the other particle sizes, 1 µm particles induced more powerful antibody protection and yielded more persistent antibody responses, as well as more heightened anamnestic responses upon repeat vaccination. The superior immune responses might be attributed to sustainable antigen release and robust antigen uptake and transport and then further promoted a series of cascade reactions, including enhanced DCs maturation, increased lymphocytes activation, and augmented TFH cells differentiation in draining lymph nodes (DLNs). Here, a powerful and economical platform for HPV vaccine and a comprehensive understanding of particle size effect on immune responses for HPV L1 pentamer-based candidate vaccines are provided.

Learning Consensus Representation for Weak Style Classification.

Style classification (e.g., Baroque and Gothic architecture style) is grabbing increasing attention in many fields such as fashion, architecture, and manga. Most existing methods focus on extracting discriminative features from local patches or patterns. However, the spread out phenomenon in style classification has not been recognized yet. It means that visually less representative images in a style class are usually very diverse and easily getting misclassified. We name them weak style images. Another issue when employing multiple visual features towards effective weak style classification is lack of consensus among different features. That is, weights for different visual features in the local patch should have been allocated similar values. To address these issues, we propose a Consensus Style Centralizing Auto-Encoder (CSCAE) for learning robust style features representation, especially for weak style classification. First, we propose a Style Centralizing Auto-Encoder (SCAE) which centralizes weak style features in a progressive way. Then, based on SCAE, we propose both the non-linear and linear version CSCAE which adaptively allocate weights for different features during the progressive centralization process. Consensus constraints are added based on the assumption that the weights of different features of the same patch should be similar. Specifically, the proposed linear counterpart of CSCAE motivated by the "shared weights" idea as well as group sparsity improves both efficacy and efficiency. For evaluations, we experiment extensively on fashion, manga and architecture style classification problems. In addition, we collect a new dataset-Online Shopping, for fashion style classification, which will be publicly available for vision based fashion style research. Experiments demonstrate the effectiveness of the SCAE and CSCAE on both public and newly collected datasets when compared with the most recent state-of-the-art works.

Stacked Denoising Tensor Auto-Encoder for Action Recognition With Spatiotemporal Corruptions.

Spatially or temporally corrupted action videos are impractical for recognition via vision or learning models. It usually happens when streaming data are captured from unintended moving cameras, which bring occlusion or camera vibration and accordingly result in arbitrary loss of spatiotemporal information. In reality, it is intractable to deal with both spatial and temporal corruptions at the same time. In this paper, we propose a coupled stacked denoising tensor auto-encoder (CSDTAE) model, which approaches this corruption problem in a divide-and-conquer fashion by jointing both the spatial and temporal schemes together. In particular, each scheme is a SDTAE designed to handle either spatial or temporal corruption, respectively. SDTAE is composed of several blocks, each of which is a denoising tensor auto-encoder (DTAE). Therefore, CSDTAE is designed based on several DTAE building blocks to solve the spatiotemporal corruption problem simultaneously. In one DTAE, the video features are represented as a high-order tensor to preserve the spatiotemporal structure of data, where the temporal and spatial information are processed separately in different hidden layers via tensor unfolding. In summary, DTAE explores the spatial and temporal structure of the tensor representation, and SDTAE handles different corrupted ratios progressively to extract more discriminative features. CSDTAE couples the temporal and spatial corruptions of the same data through a thorough step-by-step procedure based on canonical correlation analysis, which integrates the two sub-problems into one problem. The key point is solving the spatiotemporal corruption in one model by considering them as noises in either spatial or temporal direction. Extensive experiments on three action data sets demonstrate the effectiveness of our model, especially when large volumes of corruption in the video.

Sparse Canonical Temporal Alignment with Deep Tensor Decomposition for Action Recognition.

In this paper we solve three problems in action recognition: sub-action, multi-subject, and multi-modality, by reducing the diversity of intra-class samples. The main stage contains canonical temporal alignment and key frames selection. As we know, temporal alignment aims to reduce the diversity of intra-class samples, however, dense frames may yield misalignment or overlapped alignment and decrease recognition performance. To overcome this problem, we propose a Sparse Canonical Temporal Alignment (SCTA) method which selects and aligns key frames from two sequences to reduce diversity. To extract better features from the key frames, we propose a Deep Non-negative Tensor Factorization (DNTF) method to find a tensor subspace integrated with SCTA scheme. First we model an action sequence as a third-order tensor with spatiotemporal structure. Then we design a DNTF scheme to find a tensor subspace in both spatial and temporal directions. Particularly, in the first layer the original tensor is decomposed into two lowrank tensors by Non-negative Tensor Factorization (NTF), and in the second layer each low-rank tensor is further decomposed by Tensor-Train (TT) for time efficiency. Finally, our framework composed of SCTA and DNTF could solve the three problems and extract effective features for action recognition. Experiments on synthetic data, MSRDailyActivity3D and MSRActionPairs datasets show that our method works better than competitive methods in terms of accuracy.