Qicong Wang

Briefing in family characteristics of microRNAs and their applications in cancer research

MicroRNAs (miRNAs) are endogenous, short, non-coding RNA molecules that are directly involved in the post-transcriptional regulation of gene expression. Dysregulation of miRNAs is usually associated with diseases. Since miRNAs in a family intend to have common functional characteristics, proper assignment of miRNA family becomes heuristic for better understanding of miRNA nature and their potentials in clinic. In this review, we will briefly discuss the recent progress in miRNA research, particularly its impact on protein and its clinical application in cancer research in a view of miRNA family. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

Sky Region Detection in a Single Image for Autonomous Ground Robot Navigation

The sky region in an image provides horizontal and background information for autonomous ground robots and is important for vision-based autonomous ground robot navigation. This paper proposes a sky region detection algorithm within a single image based on gradient information and energy function optimization. Unlike most existing methods, the proposed algorithm is applicable to both colour and greyscale images. Firstly, the gradient information of the image is obtained. Then, the optimal segmentation threshold in the gradient domain is calculated according to the energy function optimization and the preliminary sky region is estimated. Finally, a post-processing method is applied in order to refine the preliminary sky region detection result when no sky region appears in the image or when objects extrude from the ground. Experimental results have proven that the detection accuracy is greater than 95% in our test set with 1,000 images, while the processing time is about 150ms for an image with a resolution of 640×480 on a modern laptop using only a single core.

Supervised sparse manifold regression for head pose estimation in 3D space

In estimating the head pose angles in 3D space by manifold learning, the results currently are not very satisfactory. We need to preserve the local geometry structure effectively and need a learned projective function that can reveal the dominant features better. To address these problems, we propose a Supervised Sparse Manifold Regression (SSMR) method that incorporates both the supervised graph Laplacian regularization and the sparse regression into manifold learning. In SSMR, on the one hand, a low-dimensional projection is embedded to represent intrinsic features by using supervised information while the local structure can be preserved more effectively by using the Laplacian regularization term in the objective function. On the other hand, by casting the problem of learning projective function into a regression with L1 norm regularizer, a projection is mapped to carry out the sparse representation of high dimension features, rather than a compact linear combination, so as to describe the dominant features better. Experiments show that our proposed method SSMR is beneficial for head pose angle estimation in 3D space. HighlightsA supervised sparse manifold regression method is proposed for manifold learning.A low-dimensional projection is embedded to represent intrinsic features.A projection is used to fulfill the sparse representation of high dimension features.The local geometry is preserved and the dominant features are revealed better.The proposed method is beneficial for head pose angle estimation.

Supervised locality discriminant manifold learning for head pose estimation

In this paper, we propose a novel supervised manifold learning approach, supervised locality discriminant manifold learning (SLDML), for head pose estimation. Traditional manifold learning methods focus on preserving only the intra-class geometric properties of the manifold embedded in the high-dimensional ambient space, so they cannot fully utilize the underlying discriminative knowledge of the data. The proposed SLDML aims to explore both geometric structure and discriminant information of the data, and yields a smooth and discriminative low-dimensional embedding by adding the local discriminant terms in the optimization objectives of manifold learning. Moreover, for efficiently handling out-of-sample extension and learning with the local consistency, we decompose the manifold learning as a two-step approach. We incorporate the manifold learning and the regression with a learned discriminant manifold-based projection function obtained by discriminatively Laplacian regularized least squares. The SLDML provides both the low-dimensional embedding and projection function with better intra-class compactness and inter-class separability, therefore preserves the local geometric structures more effectively. Meanwhile, the SLDML is supervised by both biased distance and continuous head pose angle information when constructing the graph, embedding the graph and learning the projection function. Our experiments demonstrate the superiority of the proposed SLDML over several current state-of-art approaches for head pose estimation on the publicly available FacePix dataset.

Benchmark comparison of ab initio microRNA identification methods and software.

MicroRNAs (miRNAs) are short, non-coding RNA molecules that play an important role in the world of genes, especially in regulating the gene expression of target messenger RNAs through cleavage or translational repression of messenger RNA. Ab initio methods have become popular in computational miRNA detection. Most software tools are designed to distinguish miRNA precursors from pseudo-hairpins, but a few can mine miRNA from genome or expressed sequence tag sequences. We prepared novel testing datasets to measure and compare the performance of various software tools. Furthermore, we summarized the miRNA mining methods that study next-generation sequencing data for bioinformatics researchers who are analyzing these data. Because secondary structure is an important feature in the identification of miRNA, we analyzed the influence of various secondary structure prediction software tools on miRNA identification. MiPred was the most effective for classifying real-/pseudo-pre-miRNA sequences, and miRAbela performed relatively better for mining miRNA precursors from genome or expressed sequence tag sequences. RNA-fold performed better than m-fold for extracting secondary structure features of miRNA precursors.

Parallelizing Convolutional Neural Networks for Action Event Recognition in Surveillance Videos

In order to deal with action recognition for large scale video data, this paper presents a MapReduce based parallel algorithm for SASTCNN, a sparse auto-combination spatio-temporal convolutional neural network. We design and implement a parallel matrix multiplication algorithm based on MapReduce. We use the MapReduce programming model to parallelize SASTCNN on a Hadoop platform. In order to take advantage of the computing power of multi-core CPU, the Map and Reduce processes of MapReduce are implemented using a multi-thread technique. A series of experiments on both WEIZMAN and KTH data sets are carried out. Compared with traditional serial algorithms, the feasibility, stability and correctness of the parallel SASTCNN are validated and a speedup in computation is obtained. Experimental results also show that the proposed method could provide more competitive results on the two data sets than other benchmark methods.

Transfer learning-based online multiperson tracking with Gaussian process regression: Transfer learning-based online multiperson tracking with Gaussian process regression

Most existing tracking‐by‐detection approaches are affected by abrupt pedestrian pose changes, lighting conditions, scale changes, and real‐time processing, which leads to issues such as detection errors and drifts. To deal with these issues, we present a novel multi‐person tracking framework by introducing a new Gaussian Process Regression based observation model, which learns in a semi‐supervised manner. The background information is taken into consideration to build the discriminative tracker, training samples are re‐weighted appropriately to ease the impact of the potential sample misalignment and noisy during model updating. Unlabeled samples from the current frame provide rich information, which is used for enhancing the tracking inference. Experimental results show that the proposed approach outperforms a number of state‐of‐the‐art methods on some benchmark datasets.

Temporal sparse feature auto-combination deep network for video action recognition: Temporal sparse feature auto-combination deep network for video action recognition

In order to deal with action recognition for large‐scale video data, we present a spatio‐temporal auto‐combination deep network, which is able to extract deep features from short video segments by making full use of temporal contextual correlation of corresponding pixels among successive video frames. Based on conventional sparse encoding, we further consider the representative features in adjacent nodes of the hidden layers according to activation states similarities. A sparse auto‐combination strategy is applied to multiple input maps in each convolution stage. An information constraint of the representative features of hidden layer nodes is imposed to handle the adaptive sparse encoding of the topology. As a result, the learned features can represent the spatio‐temporal transition relationships better and the number of hidden nodes can be restricted to a certain range. We conduct a series of experiments on two public data sets. The experimental results show that our approach is more effective and robust in video action recognition compared with traditional methods.

Preserving discriminant manifold subspace learning for plant leaf recognition

To achieve the effective plant leaf classification using manifold learning, the local geometry structure of plant leaves is able to be preserved effectively and a discriminant manifold-based projection should be learned to capture the dominant structure features better. We firstly use Gabor filter to model the texture of plant leaf images as the samples. Then for the high-dimensional features, we construct the adjacency information graph based on two constraints, i.e., low rank and sparsity. Thereby, we propose a novel Preserving Discriminant Manifold Subspace Learning (PDMSL) to embed the information graph and learn a common subspace by introducing both graph Lapla-cian and sparse regularizers. The low-dimensional embedding and projection corresponding to the learned manifold subspace have better intra-class similarity and inter-class discriminant ability of Gabor features of the leaf, and can also deal with out-of-sample extension efficiently. Our experiments on Swedish leaf datasets demonstrate that the proposed method is much more effective than other baseline methods.

Face recognition by decision fusion of two-dimensional linear discriminant analysis and local binary pattern

To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting global and local features are studied through the experiments respectively on the Olivetti Research Laboratory database and the other three databases (the three subsets of illumination, expression and posture that are constructed by selecting images from several existing face databases). By taking the above experimental results into consideration, two schemes of face recognition which are based on the decision fusion of the two-dimensional linear discriminant analysis (2DLDA) and local binary pattern (LBP) are proposed in this paper to heighten the recognition rates. In addition, partitioning a face non-uniformly for its LBP histograms is conducted to improve the performance. Our experimental results have shown the complementarities of the two kinds of features, the 2DLDA and LBP, and have verified the effectiveness of the proposed fusion algorithms.