DC-cycleGAN: Bidirectional CT-to-MR synthesis from unpaired data.

Magnetic resonance (MR) and computer tomography (CT) images are two typical types of medical images that provide mutually-complementary information for accurate clinical diagnosis and treatment. However, obtaining both images may be limited due to some considerations such as cost, radiation dose and modality missing. Recently, medical image synthesis has aroused gaining research interest to cope with this limitation. In this paper, we propose a bidirectional learning model, denoted as dual contrast cycleGAN (DC-cycleGAN), to synthesize medical images from unpaired data. Specifically, a dual contrast loss is introduced into the discriminators to indirectly build constraints between real source and synthetic images by taking advantage of samples from the source domain as negative samples and enforce the synthetic images to fall far away from the source domain. In addition, cross-entropy and structural similarity index (SSIM) are integrated into the DC-cycleGAN in order to consider both the luminance and structure of samples when synthesizing images. The experimental results indicate that DC-cycleGAN is able to produce promising results as compared with other cycleGAN-based medical image synthesis methods such as cycleGAN, RegGAN, DualGAN, and NiceGAN. Code is available at https://github.com/JiayuanWang-JW/DC-cycleGAN.

D-BIN: A Generalized Disentangling Batch Instance Normalization for Domain Adaptation.

Pattern recognition is significantly challenging in real-world scenarios by the variability of visual statistics. Therefore, most existing algorithms relying on the independent identically distributed assumption of training and test data suffer from the poor generalization capability of inference on unseen testing datasets. Although numerous studies, including domain discriminator or domain-invariant feature learning, are proposed to alleviate this problem, the data-driven property and lack of interpretation of their principle throw researchers and developers off. Consequently, this dilemma incurs us to rethink the essence of networks' generalization. An observation that visual patterns cannot be discriminative after style transfer inspires us to take careful consideration of the importance of style features and content features. Does the style information related to the domain bias? How to effectively disentangle content and style features across domains? In this article, we first investigate the effect of feature normalization on domain adaptation. Based on it, we propose a novel normalization module to adaptively leverage the propagated information through each channel and batch of features called disentangling batch instance normalization (D-BIN). In this module, we explicitly explore domain-specific and domaininvariant feature disentanglement. We maneuver contrastive learning to encourage images with the same semantics from different domains to have similar content representations while having dissimilar style representations. Furthermore, we construct both self-form and dual-form regularizers for preserving the mutual information (MI) between feature representations of the normalization layer in order to compensate for the loss of discriminative information and effectively match the distributions across domains. D-BIN and the constrained term can be simply plugged into state-of-the-art (SOTA) networks to improve their performance. In the end, experiments, including domain adaptation and generalization, conducted on different datasets have proven their effectiveness.

Hierarchical One-Class Model With Subnetwork for Representation Learning and Outlier Detection.

The multilayer one-class classification (OCC) frameworks have gained great traction in research on anomaly and outlier detection. However, most multilayer OCC algorithms suffer from loosely connected feature coding, affecting the ability of generated latent space to properly generate a highly discriminative representation between object classes. To alleviate this deficiency, two novel OCC frameworks, namely: 1) OCC structure using the subnetwork neural network (OC-SNN) and 2) maximum correntropy-based OC-SNN (MCOC-SNN), are proposed in this article. The novelties of this article are as follows: 1) the subnetwork is used to build the discriminative latent space; 2) the proposed models are one-step learning networks, instead of stacking feature learning blocks and final classification layer to recognize the input pattern; 3) unlike existing works which utilize mean square error (MSE) to learn low-dimensional features, the MCOC-SNN uses maximum correntropy criterion (MCC) for discriminative feature encoding; and 4) a brand-new OCC dataset, called CO-Mask, is built for this research. Experimental results on the visual classification domain with a varying number of training samples from 6131 to 513 061 demonstrate that the proposed OC-SNN and MCOC-SNN achieve superior performance compared to the existing multilayer OCC models. For reproducibility, the source codes are available at https://github.com/W1AE/OCC.

A Review of Generalized Zero-Shot Learning Methods.

Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning. To address this challenging task, GZSL leverages semantic information of the seen (source) and unseen (target) classes to bridge the gap between both seen and unseen classes. Since its introduction, many GZSL models have been formulated. In this review paper, we present a comprehensive review on GZSL. First, we provide an overview of GZSL including the problems and challenges. Then, we introduce a hierarchical categorization for the GZSL methods and discuss the representative methods in each category. In addition, we discuss the available benchmark data sets and applications of GZSL, along with a discussion on the research gaps and directions for future investigations.

Semisupervised Manifold Regularization via a Subnetwork-Based Representation Learning Model.

Semisupervised classification with a few labeled training samples is a challenging task in the area of data mining. Moore-Penrose inverse (MPI)-based manifold regularization (MR) is a widely used technique in tackling semisupervised classification. However, most of the existing MPI-based MR algorithms can only generate loosely connected feature encoding, which is generally less effective in data representation and feature learning. To alleviate this deficiency, we introduce a new semisupervised multilayer subnet neural network called SS-MSNN. The key contributions of this article are as follows: 1) a novel MPI-based MR model using the subnetwork structure is introduced. The subnet model is utilized to enrich the latent space representations iteratively; 2) a one-step training process to learn the discriminative encoding is proposed. The proposed SS-MSNN learns parameters by directly optimizing the entire network, accepting input from one end, and producing output at the other end; and 3) a new semisupervised dataset called HFSWR-RDE is built for this research. Experimental results on multiple domains show that the SS-MSNN achieves promising performance over the other semisupervised learning algorithms, demonstrating fast inference speed and better generalization ability.

Sequential Order-Aware Coding-Based Robust Subspace Clustering for Human Action Recognition in Untrimmed Videos.

Human action recognition (HAR) is one of most important tasks in video analysis. Since video clips distributed on networks are usually untrimmed, it is required to accurately segment a given untrimmed video into a set of action segments for HAR. As an unsupervised temporal segmentation technology, subspace clustering learns the codes from each video to construct an affinity graph, and then cuts the affinity graph to cluster the video into a set of action segments. However, most of the existing subspace clustering schemes not only ignore the sequential information of frames in code learning, but also the negative effects of noises when cutting the affinity graph, which lead to inferior performance. To address these issues, we propose a sequential order-aware coding-based robust subspace clustering (SOAC-RSC) scheme for HAR. By feeding the motion features of video frames into multi-layer neural networks, two expressive code matrices are learned in a sequential order-aware manner from unconstrained and constrained videos, respectively, to construct the corresponding affinity graphs. Then, with the consideration of the existence of noise effects, a simple yet robust cutting algorithm is proposed to cut the constructed affinity graphs to accurately obtain the action segments for HAR. The extensive experiments demonstrate the proposed SOAC-RSC scheme achieves the state-of-the-art performance on the datasets of Keck Gesture and Weizmann, and provides competitive performance on the other 6 public datasets such as UCF101 and URADL for HAR task, compared to the recent related approaches.

Multimodal Moore-Penrose Inverse-Based Recomputation Framework for Big Data Analysis.

Most multilayer Moore-Penrose inverse (MPI)-based neural networks, such as deep random vector functional link (RVFL), are structured with two separate stages: unsupervised feature encoding and supervised pattern classification. Once the unsupervised learning is finished, the latent encoding is fixed without supervised fine-tuning. However, in complex tasks such as handling the ImageNet dataset, there are often many more clues that can be directly encoded, while unsupervised learning, by definition, cannot know exactly what is useful for a certain task. There is a need to retrain the latent space representations in the supervised pattern classification stage to learn some clues that unsupervised learning has not yet been learned. In particular, the residual error in the output layer is pulled back to each hidden layer, and the parameters of the hidden layers are recalculated with MPI for more robust representations. In this article, a recomputation-based multilayer network using Moore-Penrose inverse (RML-MP) is developed. A sparse RML-MP (SRML-MP) model to boost the performance of RML-MP is then proposed. The experimental results with varying training samples (from 3k to 1.8 million) show that the proposed models provide higher Top-1 testing accuracy than most representation learning algorithms. For reproducibility, the source codes are available at https://github.com/W1AE/Retraining.

CLRNet: A Cross Locality Relation Network for Crowd Counting in Videos.

In this article, we propose a new cross locality relation network (CLRNet) to generate high-quality crowd density maps for crowd counting in videos. Specifically, a cross locality relation module (CLRM) is proposed to enhance feature representations by modeling local dependencies of pixels between adjacent frames with an adapted local self-attention mechanism. First, different from the existing methods which measure similarity between pixels by dot product, a new adaptive cosine similarity is advanced to measure the relationship between two positions. Second, the traditional self-attention modules usually integrate the reconstructed features with the same weights for all the positions. However, crowd movement and background changes in a video sequence are uneven in real-life applications. As a consequence, it is inappropriate to treat all the positions in reconstructed features equally. To address this issue, a scene consistency attention map (SCAM) is developed to make CLRM pay more attention to the positions with strong correlations in adjacent frames. Furthermore, CLRM is incorporated into the network in a coarse-to-fine way to further enhance the representational capability of features. Experimental results demonstrate the effectiveness of our proposed CLRNet in comparison to the state-of-the-art methods on four public video datasets. The codes are available at: https://github.com/Amelie01/CLRNet.

Multimodal Vigilance Estimation Using Deep Learning.

The phenomenon of increasing accidents caused by reduced vigilance does exist. In the future, the high accuracy of vigilance estimation will play a significant role in public transportation safety. We propose a multimodal regression network that consists of multichannel deep autoencoders with subnetwork neurons (MCDAE sn ). After we define two thresholds of "0.35" and "0.70" from the percentage of eye closure, the output values are in the continuous range of 0-0.35, 0.36-0.70, and 0.71-1 representing the awake state, the tired state, and the drowsy state, respectively. To verify the efficiency of our strategy, we first applied the proposed approach to a single modality. Then, for the multimodality, since the complementary information between forehead electrooculography and electroencephalography features, we found the performance of the proposed approach using features fusion significantly improved, demonstrating the effectiveness and efficiency of our method.

3MNet: Multi-task, multi-level and multi-channel feature aggregation network for salient object detection.

Salient object detection is a hot spot of current computer vision. The emergence of the convolutional neural network (CNN) greatly improves the existing detection methods. In this paper, we present 3MNet, which is based on the CNN, to make the utmost of various features of the image and utilize the contour detection task of the salient object to explicitly model the features of multi-level structures, multiple tasks and multiple channels, so as to obtain the final saliency map of the fusion of these features. Specifically, we first utilize contour detection task for auxiliary detection and then utilize use multi-layer network structure to extract multi-scale image information. Finally, we introduce a unique module into the network to model the channel information of the image. Our network has produced good results on five widely used datasets. In addition, we also conducted a series of ablation experiments to verify the effectiveness of some components in the network.

MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection.

Anomaly detection refers to the identification of cases that do not conform to the expected pattern, which takes a key role in diverse research areas and application domains. Most of existing methods can be summarized as anomaly object detection-based and reconstruction error-based techniques. However, due to the bottleneck of defining encompasses of real-world high-diversity outliers and inaccessible inference process, individually, most of them have not derived groundbreaking progress. To deal with those imperfectness, and motivated by memory-based decision-making and visual attention mechanism as a filter to select environmental information in human vision perceptual system, in this paper, we propose a Multi-scale Attention Memory with hash addressing Autoencoder network (MAMA Net) for anomaly detection. First, to overcome a battery of problems result from the restricted stationary receptive field of convolution operator, we coin the multi-scale global spatial attention block which can be straightforwardly plugged into any networks as sampling, upsampling and downsampling function. On account of its efficient features representation ability, networks can achieve competitive results with only several level blocks. Second, it's observed that traditional autoencoder can only learn an ambiguous model that also reconstructs anomalies "well" due to lack of constraints in training and inference process. To mitigate this challenge, we design a hash addressing memory module that proves abnormalities to produce higher reconstruction error for classification. In addition, we couple the mean square error (MSE) with Wasserstein loss to improve the encoding data distribution. Experiments on various datasets, including two different COVID-19 datasets and one brain MRI (RIDER) dataset prove the robustness and excellent generalization of the proposed MAMA Net.

Context-Aware Correlation Filter Learning Toward Peak Strength for Visual Tracking.

Recently, the correlation filter (CF) has been catching significant attention in visual tracking for its high efficiency in most state-of-the-art algorithms. However, the tracker easily fails when facing the distractions caused by background clutter, occlusion, and other challenging situations. These distractions commonly exist in the visual object tracking of real applications. Keep tracking under these circumstances is the bottleneck in the field. To improve tracking performance under complex interference, a combination of least absolute shrinkage and selection operator (LASSO) regression and contextual information is introduced to the CF framework through the learning stage in this article to ignore these distractions. Moreover, an elastic net regression is proposed to regroup the features, and an adaptive scale method is implemented to deal with the scale changes during tracking. Theoretical analysis and exhaustive experimental analysis show that the proposed peak strength context-aware (PSCA) CF significantly improves the kernelized CF (KCF) and achieves better performance than other state-of-the-art trackers.

Hierarchical One-Class Classifier With Within-Class Scatter-Based Autoencoders.

Autoencoding is a vital branch of representation learning in deep neural networks (DNNs). The extreme learning machine-based autoencoder (ELM-AE) has been recently developed and has gained popularity for its fast learning speed and ease of implementation. However, the ELM-AE uses random hidden node parameters without tuning, which may generate meaningless encoded features. In this brief, we first propose a within-class scatter information constraint-based AE (WSI-AE) that minimizes both the reconstruction error and the within-class scatter of the encoded features. We then build stacked WSI-AEs into a one-class classification (OCC) algorithm based on the hierarchical regularized least-squared method. The effectiveness of our approach was experimentally demonstrated in comparisons with several state-of-the-art AEs and OCC algorithms. The evaluations were performed on several benchmark data sets.

Multimodel Feature Reinforcement Framework Using Moore-Penrose Inverse for Big Data Analysis.

Fully connected representation learning (FCRL) is one of the widely used network structures in multimodel image classification frameworks. However, most FCRL-based structures, for instance, stacked autoencoder encode features and find the final cognition with separate building blocks, resulting in loosely connected feature representation. This article achieves a robust representation by considering a low-dimensional feature and the classifier model simultaneously. Thus, a new hierarchical subnetwork-based neural network (HSNN) is proposed in this article. The novelties of this framework are as follows: 1) it is an iterative learning process, instead of stacking separate blocks to obtain the discriminative encoding and the final classification results. In this sense, the optimal global features are generated; 2) it applies Moore-Penrose (MP) inverse-based batch-by-batch learning strategy to handle large-scale data sets, so that large data set, such as Place365 containing 1.8 million images, can be processed effectively. The experimental results on multiple domains with a varying number of training samples from  âˆ¼  1 K to  âˆ¼ 2 M show that the proposed feature reinforcement framework achieves better generalization performance compared with most state-of-the-art FCRL methods.

CASNet: A Cross-Attention Siamese Network for Video Salient Object Detection.

Recent works on video salient object detection have demonstrated that directly transferring the generalization ability of image-based models to video data without modeling spatial-temporal information remains nontrivial and challenging. Considering both intraframe accuracy and interframe consistency of saliency detection, this article presents a novel cross-attention based encoder-decoder model under the Siamese framework (CASNet) for video salient object detection. A baseline encoder-decoder model trained with Lovász softmax loss function is adopted as a backbone network to guarantee the accuracy of intraframe salient object detection. Self- and cross-attention modules are incorporated into our model in order to preserve the saliency correlation and improve intraframe salient detection consistency. Extensive experimental results obtained by ablation analysis and cross-data set validation demonstrate the effectiveness of our proposed method. Quantitative results indicate that our CASNet model outperforms 19 state-of-the-art image- and video-based methods on six benchmark data sets.

miR-34a and miR-125a-5p inhibit proliferation and metastasis but induce apoptosis in hepatocellular carcinoma cells via repressing the MACC1-mediated PI3K/AKT/mTOR pathway.

microRNA-34a (miR-34a) and microRNA-1251-5p (miR-125a-5p) were considered as tumor suppressors in hepatocellular carcinoma (HCC). Nevertheless, the modulatory mechanisms of miR-34a and miR-125a-5p in HCC haven't been completely understood. The levels of metastasis-associated with colon cancer 1 (MACC1) and miRNAs (miR-34a and miR-125a-5p) were determined by quantitative real-time polymerase chain reaction (qRT-PCR), and the levels of associated proteins were detected by western blot assay. Cell proliferation and metastasis were examined via Cell Counting Kit-8 (CCK-8) and transwell assays, respectively. Cell apoptosis was measured through flow cytometry. The effect of MACC1 on HCC in vivo was explored via xenograft assay. Dual-luciferase reporter assay and RNA Immunoprecipitation (RIP) assay were implemented to explore the target correlation. The expression of MACC1 was upregulated in HCC tissues and cells. Knockdown of MACC1 inhibited proliferation and metastasis but expedited apoptosis of HCC cells and the repression of tumor growth in vivo was evoked by MACC1 downregulation. Both miR-34a and miR-125a-5p directly targeted MACC1 and repressed the expression of MACC1 in HCC cells. Overexpression of miR-34a or miR-125a-5p restrained cell proliferation and metastasis while induced apoptosis by downregulating MACC1 in HCC cells. miR-34a and miR-125a-5p repressed phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signal pathway via reducing MACC1 in HCC cells. miR-34a and miR-125a-5p refrained proliferation and metastasis while motivated apoptosis in HCC cells through the PI3K/AKT/mTOR pathway by repressing MACC1. miR-34a and miR-125a-5p might be splendid biomarkers in the therapeutic strategies for HCC.

Temperature decline is a trigger of subarachnoid hemorrhage: case-crossover study with distributed lag model.

OBJECTIVE: The aim was to use a novel statistical test to predict the trend of subarachnoid hemorrhage (SAH) incidence in response to temperature change and demonstrate its delayed effect in a short hazard period. PATIENTS AND METHODS: In a retrospective study, data collected between January 2005 and September 2019 were analyzed and 1682 consecutive SAH patients from one hospital were enrolled. Meteorological data in this period including temperature, atmospheric pressure, and humidity were obtained from the China Surface Meteorological Station. Using a case-crossover analysis and distributed lag linear model (DLM) with 4 days lag period to assess the association of temperature change from the previous day (TCP) and risk of SAH. Results were presented as overall cumulative odds ratios (ORs) and 95% CI. RESULTS: Temperature decline was associated with increased risks of SAH: overall cumulative OR was 1.14 (95% CI: 1.05-1.23) for -1.1°C; 2.11 (95% CI: 1.37-3.25) for -6.2°C, as compared with a reference TCP of 0°C. Temperature decline on the day of SAH onset was significantly associated with SAH incidence days, ORs 1.34 (95% CI: 1.19-1.52). In addition, December, ORs 1.49 (95% CI: 1.17-1.90) in winter was the ictus peak in Rizhao throughout the year. CONCLUSIONS: Temperature decline from the previous day is a trigger for the occurrence of SAH. Its effect was most apparent on the day of exposure.

Distributed fusion estimation for multisensor systems with non-Gaussian but heavy-tailed noises.

Student's t distribution is a useful tool that can model heavy-tailed noises appearing in many practical systems. Although t distribution based filter has been derived, the information filter form is not presented and the data fusion algorithms for dynamic systems disturbed by heavy-tailed noises are rarely concerned. In this paper, based on multivariate t distribution and variational Bayesian estimation, the information filter, the centralized batch fusion, the distributed fusion, and the suboptimal distributed fusion algorithms are derived, respectively. The centralized fusion is given in two forms, namely, from t distribution based filter and the proposed t distribution based information filter, respectively. The distributed fusion is deduced by the use of the newly derived information filter, and it has been demonstrated to be equivalent to the centralized batch fusion. The suboptimal distributed fusion is obtained by a parameter approximation from the derived distributed fusion to decrease the computation complexity. The presented algorithms are shown to be the generalization of the classical Kalman filter based traditional algorithms. Theoretical analysis and exhaustive experimental analysis by a target tracking example show that the proposed algorithms are feasible and effective.

MiR-7a ameliorates spinal cord injury by inhibiting neuronal apoptosis and oxidative stress.

OBJECTIVE: The aim of this study was to investigate the therapeutic effect of microRNA-7a (miR-7a) on spinal cord injured rats and to explore its underlying mechanism in vivo. MATERIALS AND METHODS: The spinal cord injury (SCI) model was first established in adult rats. The epicenter of the lesion was treated with miR-7a mimics via intrathecal injection. The Basso-Beattie-Bresnahan (BBB) locomotor rating scale was used to evaluate the functional recovery of hindlimbs in rats within 4 weeks following SCI. Western blotting and qPCR were utilized to detect the apoptosis and oxidative stress in rats treated with or without miR-7a. In addition, the neuron survival and neuro-filament amount were determined using immunofluorescence. RESULTS: After SCI and miR-7a treatment, the locomotor recovery of treated rats was significantly improved when compared with rats without treatment. The mitochondrial disorder and cell death were significantly reduced in miR-7a treated rats. Meanwhile, the nuclear transcription factor-κB (NF-κB) pathway was significantly reduced as well. Contrarily, the expression of anti-apoptotic protein Bcl-2 and NF-κB inhibitor I-κB was remarkably elevated in miR-7a treated rats. In addition, up-regulation of miR-7a rescued neurons and maintained the neural structure. CONCLUSIONS: The up-regulation of miR-7a alleviated the injury-induced oxidative stress and inhibited apoptosis by down-regulating NF-κB pathway in SCI rats.

Bridging User Interest to Item Content for Recommender Systems: An Optimization Model.

Recommender systems are currently utilized widely in e-commerce for product recommendations and within content delivery platforms. Previous studies usually use independent features to represent item content. As a result, the relationship hidden among the content features is overlooked. In fact, the reason that an item attracts a user may be attributed to only a few set of features. In addition, these features are often semantically coupled. In this paper, we present an optimization model for extracting the relationship hidden in content features by considering user preferences. The learned feature relationship matrix is then applied to address the cold-start recommendations and content-based recommendations. It could also easily be employed for the visualization of feature relation graphs. Our proposed method was examined on three public datasets: 1) hetrec-movielens-2k-v2; 2) book-crossing; and 3) Netflix. The experimental results demonstrated the effectiveness of our method in comparison to the state-of-the-art recommendation methods.