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Cell division cycle-associated (CDCA) gene family members are essential cell proliferation regulators and play critical roles in various cancers. However, the function of the CDCA family genes in gliomas remains unclear. This study aims to elucidate the role of CDCA family members in gliomas using in vitro and in vivo experiments and bioinformatic analyses. We included eight glioma cohorts in this study. An unsupervised clustering algorithm was used to identify novel CDCA gene family clusters. Then, we utilized multi-omics data to elucidate the prognostic disparities, biological functionalities, genomic alterations, and immune microenvironment among glioma patients. Subsequently, the scRNA-seq analysis and spatial transcriptomic sequencing analysis were carried out to explore the expression distribution of CDCA2 in glioma samples. In vivo and in vitro experiments were used to investigate the effects of CDCA2 on the viability, migration, and invasion of glioma cells. Finally, based on ten machine-learning algorithms, we constructed an artificial intelligence-driven CDCA gene family signature called the machine learning-based CDCA gene family score (MLCS). Our results suggested that patients with the higher expression levels of CDCA family genes had a worse prognosis, more activated RAS signaling pathways, and more activated immunosuppressive microenvironments. CDCA2 knockdown inhibited the proliferation, migration, and invasion of glioma cells. In addition, the MLCS had robust and favorable prognostic predictive ability and could predict the response to immunotherapy and chemotherapy drug sensitivity. • An integration analysis of CDCA gene family in gliomas by multiple omics data. • Novel CDCA family clusters with distinct signaling, genomic features, TIME, and prognosis. • Experiments showed CDCA2 acted as an oncogene in gliomas by RAS signaling. • An artificial intelligence-driven CDCA family signature was developed. • The MLCS was a stable and robust biomarker for prognosis and immunotherapy. [ABSTRACT FROM AUTHOR]

Porosity produced in laser metal deposition hampers its application due to the absence of an effective prediction method. Measured thermal images of the melt pool provide a unique opportunity for porosity analytics. Furthermore, a physical model may provide complementary rich data that cannot be measured otherwise. How to leverage both types of data to predict porosity is very challenging. This paper presents a physics-driven deep learning model to predict porosity by integrating both measured and predicted data of the melt pool. The model fidelity is validated with the predicted pore occurrence and size with enhanced interpretability of Ti–6Al–4V thin-wall structures. [ABSTRACT FROM AUTHOR]

Abstract: Recently, image representation based on bag-of-visual-words (BoW) model has been popularly applied in image and vision domains. In BoW, a visual codebook of visual words is defined, usually by clustering local features, to represent any novel image with the occurrence of its contained visual words. Given a set of images, we argue that the significance of each image is determined by the significance of its contained visual words. Traditionally, the significances of visual words are defined by term frequency-inverse document frequency (tf-idf), which cannot necessarily capture the intrinsic visual context. In this paper, we propose a new scheme of latent visual context learning (LVCL). The visual context among images and visual words is formulated from latent semantic context and visual link graph analysis. With LVCL, the importance of visual words and images will be distinguished from each other, which will facilitate image level applications, such as image re-ranking and canonical image selection. We validate our approach on text-query based search results returned by Google Image. Experimental results demonstrate the effectiveness and potentials of our LVCL in applications of image re-ranking and canonical image selection, over the state-of-the-art approaches. [Copyright &y& Elsevier]

Abstract: For learning-based tasks such as image classification, the feature dimension is usually very high. The learning is afflicted by the curse of dimensionality as the search space grows exponentially with the dimension. Discriminant-EM (DEM) proposed a framework by applying self-supervised learning in a discriminating subspace. This paper extends the linear DEM to a nonlinear kernel algorithm, Kernel DEM (KDEM), and evaluates KDEM extensively on benchmark image databases and synthetic data. Various comparisons with other state-of-the-art learning techniques are investigated for several tasks of image classification. Extensive results show the effectiveness of our approach. [Copyright &y& Elsevier]

• We enlarge search space by introducing the Cartesian product of operators. • We propose a factorized search to search separately in two subspaces. • We optimize architectural parameters sequentially, avoiding undesirable competition. • We perform diagnostic experiments and explanations, demonstrating our superiority. • Our approach achieves SOTA performance on CIFAR10, ImageNet, and COCO. Neural Architecture Search (NAS) replaces manually designed networks with automatically searched networks and has become a hot topic in machine learning and computer vision. One key factor of NAS, scaling up the search space by adding operators, could help bring about more possibilities for effective architectures. However, existing works require high search costs and are prone to make confused selections caused by competition among similar operators. This paper presents a scalable NAS that utilizes a factorized method, which improves existing art in the following aspects. (1) Our work explores a broader search space. We construct an ample search space through the cartesian product of activation operators and regular operators. (2) Our work experiences a limited computation burden even though searching in a more extensive search space. We factorize a search space into two subspaces and adopt different architectural parameters to control corresponding subspaces. (3) Our work avoids competition among similarly combined operators (e.g. , ReLU & sep-conv-3x3 and ReLU6 & sep-conv-3x3 and PReLU & sep-conv-3x3). That is because our architectural parameters are optimized sequentially. Experimental results show that our approach achieves SOTA on CIFAR10 (2. 38 % test error) and ImageNet (23. 8 % test error). Furthermore, the excellent performance of 35. 8 % (AP) and 55. 7 % ( AP 50 ) on COCO further proves the superiority of our factorized method. [ABSTRACT FROM AUTHOR]

Statistical-learning-based object detection is an important topic in computer vision. It learns visual representation from annotated exemplars to identify semantic defined objects in images. High-performance object detection is usually carried out in feature space and effective feature representation can improve the performance significantly. Feature representation is the encoding process which maps raw image pixels inside local regions into discriminant feature space. The motivation of this paper is to present a review on feature representation in recent object detection methods. Visual features applied in object detection are categorized according to the differences in computation and visual properties. The most valued features are introduced and discussed in detail. Representative extensions are introduced briefly for comparison. Descriptive power, robustness, compactness as well as computational efficiency are viewed as important properties. According to these properties, discussions are presented on the advantages and drawbacks of features. Besides, generic techniques such as dimension reduction and combination are introduced. Through this review, we would like to draw the feature sketch and provide new insights for feature utilization, in order to tackle future challenges of object detection. [ABSTRACT FROM AUTHOR]

Feature detection is a fundamental and important problem in computer vision and image processing. It is a low-level processing step which serves as the essential part for computer vision based applications. The goal of this paper is to present a survey of recent progress and advances in visual feature detection. Firstly we describe the relations among edges, corners and blobs from the psychological view. Secondly we classify the algorithms in detecting edges, corners and blobs into different categories and provide detailed descriptions for representative recent algorithms in each category. Considering that machine learning becomes more involved in visual feature detection, we put more emphasis on machine learning based feature detection methods. Thirdly, evaluation standards and databases are also introduced. Through this survey we would like to present the recent progress in visual feature detection and identify future trends as well as challenges. [ABSTRACT FROM AUTHOR]

Deep model-based semantic segmentation has received ever increasing research focus in recent years. However, due to the complex model architectures, existing works are still unable to achieve high accuracy in real-time applications. In this paper, we propose a novel Sequential Prediction Network (termed SPNet) to seek a better trade-off between accuracy and efficiency. SPNet is also an end-to-end encoder-decoder architecture, which introduces a sequential prediction method to spread the contextual information from the low-level layers to the high-level layers. Besides, the proposed method is equipped with a stream Spatial Semantic and Edge Loss (termed SEL) and an adversarial network at multiple resolutions, which greatly improves the segmentation accuracy with a negligible increase in computation cost. To further utilize the extra unlabeled data, we present a knowledge distillation scheme to distill the structured knowledge from cumbersome to compact networks. Without using any pre-trained model, our method achieves state-of-the-art performance among exiting real-time segmentation models on several challenging datasets. Impressively, on the Cityscapes test dataset, it obtains 75.8 % mIoU at a speed of 61.2 FPS. [ABSTRACT FROM AUTHOR]

Traditional Bag-of-Words (BoWs) model is commonly generated with many steps, including local feature extraction, codebook generation and feature quantization, etc. Those steps are relatively independent with each other and are hard to be jointly optimized. Moreover, the dependency on hand-crafted local feature makes BoWs model not effective in conveying high-level semantics. These issues largely hinder the performance of BoWs model in large-scale image applications. To conquer these issues, we propose an End-to-End BoWs (E2BoWs) model based on Deep Convolutional Neural Network (DCNN). Our model takes an image as input, then identifies and separates semantic objects in it, and finally outputs visual words with high semantic discriminative power. Specifically, our model firstly generates Semantic Feature Maps (SFMs) corresponding to different object categories through convolutional layers, then introduces Bag-of-Words Layers (BoWL) to generate visual words from each individual feature map. We also introduce a novel learning algorithm to reinforce the sparsity of the generated E2BoWs model, which further ensures the time and memory efficiency. We evaluate the proposed E2BoWs model on several image search datasets including MNIST, SVHN, CIFAR-10, CIFAR-100, MIRFLICKR-25K and NUS-WIDE. Experimental results show that our method achieves promising accuracy and efficiency compared with recent deep learning based retrieval works. [ABSTRACT FROM AUTHOR]

Due to the ability of capturing the geometry structure of data manifold, context-sensitive similarity has demonstrated impressive performances in the retrieval task. The key idea of context-sensitive similarity is that the similarity between two data points can be more reliably estimated with the local context of other points in the affinity graph. Therefore, neighborhood selection is a crucial factor for those algorithms, which affects the performance dramatically. In this paper, we propose a new algorithm called Smooth Neighborhood (SN) that mines the neighborhood structure to satisfy the manifold assumption. By doing so, nearby points on the underlying manifold are guaranteed to yield similar neighbors as much as possible. Moreover, SN is adjusted to tackle multiple affinity graphs by imposing a weight learning paradigm, and this is the primary difference compared with related works which are only applicable with one affinity graph. Finally, we integrate SN with Sparse Contextual Activation (SCA), a representative context-sensitive similarity proposed recently. Extensive experimental results and comparisons manifest that with the neighborhood structure generated by SN, the proposed framework can yield state-of-the-art performances on shape retrieval, image retrieval and 3D model retrieval. [ABSTRACT FROM AUTHOR]

Abstract: Visual codebook serves as a fundamental component in many state-of-the-art visual search and object recognition systems. While most existing codebooks are built based solely on unsupervised patch quantization, there are few works exploited image labels to supervise its construction. The key challenge lies in the following: image labels are global, but patch supervision should be local. Such imbalanced supervision is beyond the scope of most existing supervised codebooks [9,10,12–15,29]. In this paper, we propose a weakly supervised codebook learning framework, which integrates image labels to supervise codebook building with two steps: the Label Propagation step propagates image labels into local patches by multiple instance learning and instance selection [20,21]. The Graph Quantization step integrates patch labels to build codebook using Mean Shift. Both steps are co-optimized in an Expectation Maximization framework: the E-phase selects the best patches that minimize the semantic distortions in quantization to propagate image labels; while the M-phase groups similar patches with related labels (modeled by WordNet [18]), which minimizes the visual distortions in quantization. In quantitative experiments, our codebook outperforms state-of-the-art unsupervised and supervised codebooks [1,10,11,25,29] using benchmark datasets. [Copyright &y& Elsevier]

Abstract: Recently, the sparse coding based codebook learning and local feature encoding have been widely used for image classification. The sparse coding model actually assumes the reconstruction error follows Gaussian or Laplacian distribution, which may not be accurate enough. Besides, the ignorance of spatial information during local feature encoding process also hinders the final image classification performance. To address these obstacles, we propose a new image classification method by spatial pyramid robust sparse coding (SP-RSC). The robust sparse coding tries to find the maximum likelihood estimation solution by alternatively optimizing over the codebook and local feature coding parameters, hence is more robust to outliers than traditional sparse coding based methods. Additionally, we adopt the robust sparse coding technique to encode visual features with the spatial constraint. Local features from the same spatial sub-region of images are collected to generate the visual codebook and encode local features. In this way, we are able to generate more discriminative codebooks and encoding parameters which eventually help to improve the image classification performance. Experiments on the Scene 15 dataset and the Caltech 256 dataset demonstrate the effectiveness of the proposed spatial pyramid robust sparse coding method. [Copyright &y& Elsevier]

Abstract: Currently, Nearest-Neighbor approaches (NN) have been applied to large scale real world image data mining. However, the following three disadvantages prevent them from wider application compared to other machine learning methods: (i) the performance is inferior on small datasets; (ii) the performance will degrade for data with high dimensions; (iii) they are heavily dependent on the chosen feature and distance measure. In this paper, we try to overcome the three mentioned intrinsic weaknesses by taking the abundant and diversified content of social media images into account. Firstly, we propose a novel neighborhood similarity measure which encodes both the local density information and semantic information, thus it has better generalization power than the original image-to-image similarity. Secondly, to enhance the scalability, we adopt kernelized Locality Sensitive Hashing (KLSH) to conduct approximated nearest neighbor search by utilizing a set of kernels calculated on several complementary image features. Finally, to enhance the robustness on diversified genres of images, we propose to fuse the discrimination power of different features by combining multiple neighborhood similarities calculated on different features/kernels with the entire retrieved nearest labeled and unlabeled image via the hashing systems. Experimental results on visual categorization on the Caltech-256 and two social media databases show the advantage of our method over traditional NN methods using the labeled data only. [Copyright &y& Elsevier]

• Multi-view Adaptive Semi-supervised Feature Selection framework is proposed. • The self-paced learning is applied to semi-supervised feature selection. • Multi-view learning is utilized to enhance the feature selection performance. • An iterative algorithm is proposed with convergence and complexity analysis. • Experiments show MASFS can obtain good semi-supervised feature selection performance. In recent decades, semi-supervised feature selection (SSFS) has gained extensive research in the field of machine learning and computer vision. Most SSFS algorithms are based on graph-based semi-supervised learning (GSSL), and their performance depend heavily on the quality of the Laplacian weight graph. However, the Laplacian weight graph can't be changed once it is constructed, which greatly restricts the performance of SSFS. To address this defect, in this paper we propose a novel Multi-view Adaptive Semi-supervised Feature Selection (MASFS) algorithm, which introduces the self-paced learning (SPL) into SSFS to make the Laplacian weight graph adaptively change according to the current predicted information. Meanwhile, the MASFS algorithm utilizes the multi-view learning to effectively explore the complementary and related information contained in different views to enhance SSFS performance. We propose a valid iterative algorithm for optimizing the objective function, followed by the convergence analysis and the complexity analysis. To illustrate the effectiveness of the MASFS algorithm, some experiments are carried out on NUS-WIDE dataset and MSRA-MM2.0 dataset and the experimental results indicate that MASFS has better performance than other SSFS algorithms based on GSSL. [ABSTRACT FROM AUTHOR]