Robust Subcluster Search and Mergence Clustering.

In recent years, graph-based clustering presents outstanding performance and has been widely investigated. It segments the data similarity graph into multiple subgraphs as final clusters. Many methods integrate graph learning and segmentation into a unified optimization problem to explore the graph structure. However, existing research 1) attempts to derive the final clusters from the learned graph directly, which relies on a highly tight internal distribution within each cluster, and is too strict for the real-world data; 2) generally constructs a holistic full sample graph, which means the outliers are involved in graph learning explicitly, and may corrupt the graph quality. To overcome the above limitations, a new clustering model called robust subcluster search and mergence (RSSM) is established in this article. Inspired by the positive-incentive noise (Pi-Noise), RSSM assumes that the outliers are useful for learning the data structure. Considering a few samples with large errors as outliers, RSSM finds the subcentroids by searching an imbalanced residue distribution. In this way, the subcentroids pull the normal samples together and push the outliers far away. Compared with the traditional clusters, the subclusters indicated by the subcentroids are more explicit, where the normal samples are tightly connected. After that, a subcluster similarity graph is constructed to guide the mergence of subclusters. To sum up, RSSM performs the search and mergence of subclusters simultaneously with the help of outliers, and generates a graph that is more suitable for clustering. Experiments on several datasets demonstrate the rationality and superiority of RSSM.

Evaluating the surface water pollution risk of mineral resource exploitation via an improved approach: a case study in Liaoning Province, Northeastern China.

Pollution from mineral exploitation is an important risk factor affecting surface water environment in mineral regions. It is urgent to construct a simple and accurate model to assess the surface water pollution risk from mineral exploitation in the regional scale. Thus, taking a mining province namely Liaoning in northeastern China as the study area, we proposed a framework to simulate the transport process of pollutants from mineral exploitation points to the surrounding surface water based on the "source-sink" theory. In our framework, we adopted the regional growth method (RGM) to extract the potential polluted water area as the certain "sink" considering the influence of the topography, and then applied Minimum Cumulative Resistance (MCR) model to assess the surface water pollution risk from mineral exploitation. The results revealed that: (1) 9.5% of the water areas were located at the potential impact area of MEPs. (2) The total value of resistance surface in Liaoning is relatively low, and gradually decreased from west to east. (3) MEPs in Liaoning had a high risk and seriously threatened the surface water environment, among 2125 MEPs, 733 MEPs (32.99%) were assessed as extremely high risk level, and about 35% of the MEPs were distributed within 10KM buffer zone of surface water. (4) Water pollution risk of MEPs in Dalian, Tieling, Fuxin and Dandong need to be emphasized. (5) Compared to previous studies, we considered the topographical influence before applying MCR model directly, so the results of water pollution risk were more reliable. This study provides a methodological support and scientific reference for the water environment protection and regional sustainable development.

ARRDC3 regulates the targeted therapy sensitivity of clear cell renal cell carcinoma by promoting AXL degradation.

AXL plays crucial roles in the tumorigenesis, progression, and drug resistance of neoplasms; however, the mechanisms associated with AXL overexpression in tumors remain largely unknown. In this study, to investigate these molecular mechanisms, wildtype and mutant proteins of arrestin domain-containing protein 3 (ARRDC3) and AXL were expressed, and co-immunoprecipitation analyses were performed. ARRDC3-deficient cells generated using the CRISPR-Cas9 system were treated with different concentrations of the tyrosine kinase inhibitor sunitinib and subjected to cell biological, molecular, and pharmacological experiments. Furthermore, immunohistochemistry was used to analyze the correlation between ARRDC3 and AXL protein expressions in renal cancer tissue specimens. The experimental results demonstrated that ARRDC3 interacts with AXL to promote AXL ubiquitination and degradation, followed by the negative regulation of downstream signaling mechanisms, including the phosphorylation of protein kinase B and extracellular signal-regulated kinase. Notably, ARRDC3 deficiency decreased the sunitinib sensitivity of clear cell renal cell carcinoma (ccRCC) cells in a manner dependent on the regulation of AXL stability. Overall, our results suggest that ARRDC3 is a negative regulator of AXL and can serve as a novel predictor of sunitinib therapeutic response in patients with ccRCC.

Zoom Text Detector.

To pursue comprehensive performance, recent text detectors improve detection speed at the expense of accuracy. They adopt shrink-mask-based text representation strategies, which leads to a high dependence of detection accuracy on shrink-masks. Unfortunately, three disadvantages cause unreliable shrink-masks. Specifically, these methods try to strengthen the discrimination of shrink-masks from the background by semantic information. However, the feature defocusing phenomenon that coarse layers are optimized by fine-grained objectives limits the extraction of semantic features. Meanwhile, since both shrink-masks and the margins belong to texts, the detail loss phenomenon that the margins are ignored hinders the distinguishment of shrink-masks from the margins, which causes ambiguous shrink-mask edges. Moreover, false-positive samples enjoy similar visual features with shrink-masks. They aggravate the decline of shrink-masks recognition. To avoid the above problems, we propose a zoom text detector (ZTD) inspired by the zoom process of the camera. Specifically, zoomed-out view module (ZOM) is introduced to provide coarse-grained optimization objectives for coarse layers to avoid feature defocusing. Meanwhile, zoomed-in view module (ZIM) is presented to enhance the margins recognition to prevent detail loss. Furthermore, sequential-visual discriminator (SVD) is designed to suppress false-positive samples by sequential and visual features. Experiments verify the superior comprehensive performance of ZTD.

Feature Weighted Non-Negative Matrix Factorization.

Non-negative matrix factorization (NMF) is one of the most popular techniques for data representation and clustering and has been widely used in machine learning and data analysis. NMF concentrates the features of each sample into a vector and approximates it by the linear combination of basis vectors, such that the low-dimensional representations are achieved. However, in real-world applications, the features usually have different importance. To exploit the discriminative features, some methods project the samples into the subspace with a transformation matrix, which disturbs the original feature attributes and neglects the diversity of samples. To alleviate the above problems, we propose the feature weighted NMF (FNMF) in this article. The salient properties of FNMF can be summarized as three-fold: 1) it learns the weights of features adaptively according to their importance; 2) it utilizes multiple feature weighting components to preserve the diversity; and 3) it can be solved efficiently with the suggested optimization algorithm. The performance on synthetic and real-world datasets demonstrates that the proposed method obtains the state-of-the-art performance.

Entropy Minimizing Matrix Factorization.

Nonnegative matrix factorization (NMF) is a widely used data analysis technique and has yielded impressive results in many real-world tasks. Generally, existing NMF methods represent each sample with several centroids and find the optimal centroids by minimizing the sum of the residual errors. However, outliers deviating from the normal data distribution may have large residues and then dominate the objective value. In this study, an entropy minimizing matrix factorization (EMMF) framework is developed to tackle the above problem. Considering that outliers are usually much less than the normal samples, a new entropy loss function is established for matrix factorization, which minimizes the entropy of the residue distribution and allows a few samples to have large errors. In this way, the outliers do not affect the approximation of normal samples. Multiplicative updating rules for EMMF are derived, and the convergence is proven theoretically. In addition, a Graph regularized version of EMMF (G-EMMF) is also presented, which uses a data graph to capture the data relationship. Clustering results on various synthetic and real-world datasets demonstrate the advantages of the proposed models, and the effectiveness is also verified through the comparison with state-of-the-art methods.

Compact and low-loss TM-pass polarizer based on a hybrid plasmonic waveguide with a semiround arch Si core.

A compact and low loss TM-pass polarizer based on a hybrid plasmonic waveguide (HPW) has been demonstrated. By introducing the hollow HPW with a semiround arch (SRA) Si core, the unwanted TE mode can be effectively cut off and the TM mode can pass through by hybrid plasmonic mode with excellent transmission characteristics. The hollow structure realizes lower index with n=1 due to the air region, and the SRA construction effectively suppresses the energy loss of the TM mode caused by the corner effect. Thus, TM modes pass through with negligible loss and exhibit the characteristic of strong mode limitation. By optimizing the width of metal, the width of the HPW, and the length of the tapered mode converter, an optimum performance with a high polarization extinction ratio of 67.87 dB and a low insert loss of 0.029 dB at the work wavelength=1550nm is achieved. Detailed analysis also proves that the proposed polarizer has a compact size of only 7 µm and a great fabrication tolerance. This work offers a simple and effective scheme of polarization control on-chip.

CM-Net: Concentric Mask Based Arbitrary-Shaped Text Detection.

Recently fast arbitrary-shaped text detection has become an attractive research topic. However, most existing methods are non-real-time, which may fall short in intelligent systems. Although a few real-time text methods are proposed, the detection accuracy is far behind non-real-time methods. To improve the detection accuracy and speed simultaneously, we propose a novel fast and accurate text detection framework, namely CM-Net, which is constructed based on a new text representation method and a multi-perspective feature (MPF) module. The former can fit arbitrary-shaped text contours by concentric mask (CM) in an efficient and robust way. The latter encourages the network to learn more CM-related discriminative features from multiple perspectives and brings no extra computational cost. Benefiting the advantages of CM and MPF, the proposed CM-Net only needs to predict one CM of the text instance to rebuild the text contour and achieves the best balance between detection accuracy and speed compared with previous works. Moreover, to ensure that multi-perspective features are effectively learned, the multi-factor constraints loss is proposed. Extensive experiments demonstrate the proposed CM is efficient and robust to fit arbitrary-shaped text instances, and also validate the effectiveness of MPF and constraints loss for discriminative text features recognition. Furthermore, experimental results show that the proposed CM-Net is superior to existing state-of-the-art (SOTA) real-time text detection methods in both detection speed and accuracy on MSRA-TD500, CTW1500, Total-Text, and ICDAR2015 datasets.

Focusing characteristics of cylindrical vector beams through a multi-focal all-dielectric grating lens.

A novel, to the best of our knowledge, type of multi-focal all-dielectric grating lens is proposed in this work, and focusing characteristics of cylindrical vector beams through the lens are investigated in detail. Based on the negative refraction mechanism of negative-first-order diffraction and Fermat's principle, a multi-focal lens is designed. By analyzing the diffraction effect of the grating, the essential factor that affects the focus quality is found. Through a two-step optimization process, secondary foci and the focal displacement of primary foci caused by high-order diffractions are overcome, and the quality of the focal field is significantly improved. This work provides a reference for micro-lens design for focus modulation, and the research results also have potential applications in the fields of light-field manipulation and optical tweezers.

Robust Rank-Constrained Sparse Learning: A Graph-Based Framework for Single View and Multiview Clustering.

Graph-based clustering aims to partition the data according to a similarity graph, which has shown impressive performance on various kinds of tasks. The quality of similarity graph largely determines the clustering results, but it is difficult to produce a high-quality one, especially when data contain noises and outliers. To solve this problem, we propose a robust rank constrained sparse learning (RRCSL) method in this article. The L2,1 -norm is adopted into the objective function of sparse representation to learn the optimal graph with robustness. To preserve the data structure, we construct an initial graph and search the graph within its neighborhood. By incorporating a rank constraint, the learned graph can be directly used as the cluster indicator, and the final results are obtained without additional postprocessing. In addition, the proposed method cannot only be applied to single-view clustering but also extended to multiview clustering. Plenty of experiments on synthetic and real-world datasets have demonstrated the superiority and robustness of the proposed framework.

Locality Adaptive Discriminant Analysis Framework.

Linear discriminant analysis (LDA) is a well-known technique for supervised dimensionality reduction and has been extensively applied in many real-world applications. LDA assumes that the samples are Gaussian distributed, and the local data distribution is consistent with the global distribution. However, real-world data seldom satisfy this assumption. To handle the data with complex distributions, some methods emphasize the local geometrical structure and perform discriminant analysis between neighbors. But the neighboring relationship tends to be affected by the noise in the input space. In this research, we propose a new supervised dimensionality reduction method, namely, locality adaptive discriminant analysis (LADA). In order to directly process the data with matrix representation, such as images, the 2-D LADA (2DLADA) is also developed. The proposed methods have the following salient properties: 1) they find the principle projection directions without imposing any assumption on the data distribution; 2) they explore the data relationship in the desired subspace, which contains less noise; and 3) they find the local data relationship automatically without the efforts for tuning parameters. The performance of dimensionality reduction shows the superiorities of the proposed methods over the state of the art.

Autoweighted Multiview Feature Selection With Graph Optimization.

In this article, we focus on the unsupervised multiview feature selection, which tries to handle high-dimensional data in the field of multiview learning. Although some graph-based methods have achieved satisfactory performance, they ignore the underlying data structure across different views. Besides, their predefined Laplacian graphs are sensitive to the noises in the original data space and fail to obtain the optimal neighbor assignment. To address the above problems, we propose a novel unsupervised multiview feature selection model based on graph learning, and the contributions are three-fold: 1) during the feature selection procedure, the consensus similarity graph shared by different views is learned. Therefore, the proposed model can reveal the data relationship from the feature subset; 2) a reasonable rank constraint is added to optimize the similarity matrix to obtain more accurate information; and 3) an autoweighted framework is presented to assign view weights adaptively, and an effective alternative iterative algorithm is proposed to optimize the problem. Experiments on various datasets demonstrate the superiority of the proposed method compared to the state-of-the-art methods.

Concept Factorization With Local Centroids.

Data clustering is a fundamental problem in the field of machine learning. Among the numerous clustering techniques, matrix factorization-based methods have achieved impressive performances because they are able to provide a compact and interpretable representation of the input data. However, most of the existing works assume that each class has a global centroid, which does not hold for data with complicated structures. Besides, they cannot guarantee that the sample is associated with the nearest centroid. In this work, we present a concept factorization with the local centroids (CFLCs) approach for data clustering. The proposed model has the following advantages: 1) the samples from the same class are allowed to connect with multiple local centroids such that the manifold structure is captured; 2) the pairwise relationship between the samples and centroids is modeled to produce a reasonable label assignment; and 3) the clustering problem is formulated as a bipartite graph partitioning task, and an efficient algorithm is designed for optimization. Experiments on several data sets validate the effectiveness of the CFLC model and demonstrate its superior performance over the state of the arts.

Robust Matrix Factorization With Spectral Embedding.

Nonnegative matrix factorization (NMF) and spectral clustering are two of the most widely used clustering techniques. However, NMF cannot deal with the nonlinear data, and spectral clustering relies on the postprocessing. In this article, we propose a Robust Matrix factorization with Spectral embedding (RMS) approach for data clustering, which inherits the advantages of NMF and spectral clustering, while avoiding their shortcomings. In addition, to cluster the data represented by multiple views, we present the multiview version of RMS (M-RMS), and the weights of different views are self-tuned. The main contributions of this research are threefold: 1) by integrating spectral clustering and matrix factorization, the proposed methods are able to capture the nonlinear data structure and obtain the cluster indicator directly; 2) instead of using the squared Frobenius-norm, the objectives are developed with the l2,1 -norm, such that the effects of the outliers are alleviated; and 3) the proposed methods are totally parameter-free, which increases the applicability for various real-world problems. Extensive experiments on several single-view/multiview data sets demonstrate the effectiveness of our methods and verify their superior clustering performance over the state of the arts.

A novel classification regression method for gridded electric power consumption estimation in China.

Spatially explicit information on electric power consumption (EPC) is crucial for effective electricity allocation and utilization. Many studies have estimated fine-scale spatial EPC based on remotely sensed nighttime light (NTL). However, the spatial non-stationary relationship between EPC and NTL at prefectural level tends to be overlooked in existing literature. In this study, a classification regression method to estimate the gridded EPC in China based on imaging NTL via a Visible Infrared Imaging Radiometer Suite (VIIRS) was described. In addition, owing to some inherent omissions in the VIIRS NTL data, the study has employed the cubic Hermite interpolation to produce a more appropriate NTL dataset for estimation. The proposed method was compared with ordinary least squares (OLS) and geographically weighted regression (GWR) approaches. The results showed that our proposed method outperformed OLS and GWR in relative error (RE) and mean absolute percentage error (MAPE). The desirable results benefited mainly from a reasonable classification scheme that fully considered the spatial non-stationary relationship between EPC and NTL. Thus, the analysis suggested that the proposed classification regression method would enhance the accuracy of the gridded EPC estimation and provide a valuable reference predictive model for electricity consumption.

Quantifying and Detecting Collective Motion in Crowd Scenes.

People in crowd scenes always exhibit consistent behaviors and form collective motions. The analysis of collective motion has motivated a surge of interest in computer vision. Nevertheless, the effort is hampered by the complex nature of collective motions. Considering the fact that collective motions are formed by individuals, this paper proposes a new framework for both quantifying and detecting collective motion by investigating the spatio-temporal behavior of individuals. The main contributions of this work are threefold: 1) an intention-aware model is built to fully capture the intrinsic dynamics of individuals; 2) a structure-based collectiveness measurement is developed to accurately quantify the collective properties of crowds; 3) a multistage clustering strategy is formulated to detect both the local and global behavior consistency in crowd scenes. Experiments on real world data sets show that our method is able to handle crowds with various structures and time-varying dynamics. Especially, the proposed method shows nearly 10% improvement over the competitors in terms of NMI, Purity and RI. Its applicability is illustrated in the context of anomaly detection and semantic scene segmentation.

Detecting Coherent Groups in Crowd Scenes by Multiview Clustering.

Detecting coherent groups is fundamentally important for crowd behavior analysis. In the past few decades, plenty of works have been conducted on this topic, but most of them have limitations due to the insufficient utilization of crowd properties and the arbitrary processing of individuals. In this study, a Multiview-based Parameter Free framework (MPF) is proposed. Based on the L1-norm and L2-norm, we design two versions of the multiview clustering method, which is the main part of the proposed framework. This paper presents the contributions on three aspects: (1) a new structural context descriptor is designed to characterize the structural properties of individuals in crowd scenes; (2) a self-weighted multiview clustering method is proposed to cluster feature points by incorporating their orientation and context similarities; and (3) a novel framework is introduced for group detection, which is able to determine the group number automatically without any parameter or threshold to be tuned. The effectiveness of the proposed framework is evaluated on real-world crowd videos, and the experimental results show its promising performance on group detection. In addition, the proposed multiview clustering method is also evaluated on a synthetic dataset and several standard benchmarks, and its superiority over the state-of-the-art competitors is demonstrated.

Downregulation of the lncRNA ASB16-AS1 Decreases LARP1 Expression and Promotes Clear Cell Renal Cell Carcinoma Progression via miR-185-5p/miR-214-3p.

Clear cell renal cell carcinoma (ccRCC) comprises approximately 75% of renal cell carcinomas, which is one of the most common and lethal urologic cancers, with poor quality of life for patients and is a huge economic burden to health care systems. It is imperative we find novel prognostic and therapeutic targets for ccRCC clinical intervention. In this study, we found that the expression of the long noncoding RNA (lncRNA) ASB16-AS1 was downregulated in ccRCC tissues compared with non-diseased tissues and was also associated with advanced tumor stage and larger tumors. By constructing cell and mouse models, it was found that downregulated lncRNA ASB16-AS1 enhanced cell proliferation, migration, invasion, and promoted tumor growth and metastasis. Furthermore, by performing bioinformatics analysis, biotinylated RNA pull-downs, AGO2-RIP, and luciferase reporter assays, our findings showed that downregulated ASB16-AS1 decreased La-related protein 1 (LARP1) expression by inhibiting miR-185-5p and miR-214-3p. Furthermore, it was found that overexpression of LARP1 reversed the promotive effects of downregulated ASB16-AS1 on ccRCC cellular progression. Our results revealed that downregulated ASB16-AS1 promotes ccRCC progression via a miR-185-5p-miR-214-3p-LARP1 pathway. We suggest that this pathway could be used to monitor prognosis and presents therapeutic targets for ccRCC clinical management.

Self-Tuned Discrimination-Aware Method for Unsupervised Feature Selection.

Unsupervised feature selection is fundamentally important for processing unlabeled high-dimensional data, and several methods have been proposed on this topic. Most existing embedded unsupervised methods just emphasize the data structure in the input space, which may contain large noise. Therefore, they are limited to perceive the discriminative information implied within the low-dimensional manifold. In addition, these methods always involve several parameters to be tuned, which is time-consuming. In this paper, we present a self-tuned discrimination-aware (STDA) approach for unsupervised feature selection. The main contributions of this paper are threefold: 1) it adopts the advantage of discriminant analysis technique to select the valuable features; 2) it learns the local data structure adaptively in the discriminative subspace to alleviate the effect of data noise; and 3) it performs feature selection and clustering simultaneously with an efficient optimization strategy, and saves the additional efforts to tune parameters. Experimental results on a toy data set and various real-world benchmarks justify the effectiveness of STDA on both feature selection and data clustering, and demonstrate its promising performance against the state of the arts.

ARRDC1 and ARRDC3 act as tumor suppressors in renal cell carcinoma by facilitating YAP1 degradation.

The α-arrestins domain-containing 1 and 3 (ARRDC1 and ARRDC3) are two members of the α-arrestins family. Yes-associated protein 1 (YAP1) is a key downstream transcription co-activator of the Hippo pathway essential for cancer initiation, progression, or metastasis in clear cell renal cell carcinoma (ccRCC). The aim of this work was to elucidate the role of the α-arrestins in ccRCC tumorigenesis by identifying molecular interacting factors and exploring potential mechanisms. In this study, we identified YAP1 as a novel ARRDC3 interacting protein in RCC cells through tandem affinity purification and mass spectrometry. We confirmed that ARRDC1 and ARRDC3, but not other α-arrestin family proteins, interact with YAP1. Binding of ARRDC1/3 to YAP1 is mediated through the WW domains of YAP1 and the PPXY motifs of ARRDC1/3. Functional analysis of ARRDC1/3 by lentiviral shRNA revealed a role for ARRDC1/3 in suppression of cell growth, migration, invasion and epithelial-mesenchymal transition in ccRCC cells, and these effects were mediated, at least in part, through YAP1. Mechanically, ARRDC1/3 negatively regulates YAP1 protein stability by facilitating E3 ubiquitin ligase Itch-mediated ubiquitination and degradation of YAP1. Moreover, ARRDC1/3 mRNA levels were significantly downregulated in ccRCC specimens. A negative correlation was identified between ARRDC3 and YAP1 expression in ccRCC specimens by immunohistochemistry. This study revealed a novel mechanism for ARRDC1/3 in the regulation of YAP1 stability and provided insight in understanding the relationship between ARRDC1/3 downregulation and aberrant Hippo-YAP1 pathway activation in ccRCC.