Multilevel Contrastive Graph Masked Autoencoders for Unsupervised Graph-Structure Learning.

Unsupervised graph-structure learning (GSL) which aims to learn an effective graph structure applied to arbitrary downstream tasks by data itself without any labels' guidance, has recently received increasing attention in various real applications. Although several existing unsupervised GSL has achieved superior performance in different graph analytical tasks, how to utilize the popular graph masked autoencoder to sufficiently acquire effective supervision information from the data itself for improving the effectiveness of learned graph structure has been not effectively explored so far. To tackle the above issue, we present a multilevel contrastive graph masked autoencoder (MCGMAE) for unsupervised GSL.

A novel two-way rebalancing strategy for identifying carbonylation sites.

Background: As an irreversible post-translational modification, protein carbonylation is closely related to many diseases and aging. Protein carbonylation prediction for related patients is significant, which can help clinicians make appropriate therapeutic schemes. Because carbonylation sites can be used to indicate change or loss of protein function, integrating these protein carbonylation site data has been a promising method in prediction.

Joint learning sample similarity and correlation representation for cancer survival prediction.

Background: As a highly aggressive disease, cancer has been becoming the leading death cause around the world. Accurate prediction of the survival expectancy for cancer patients is significant, which can help clinicians make appropriate therapeutic schemes. With the high-throughput sequencing technology becoming more and more cost-effective, integrating multi-type genome-wide data has been a promising method in cancer survival prediction.

Corrigendum: Menthol Flavor in E-Cigarette Vapor Modulates Social Behavior Correlated With Central and Peripheral Changes of Immunometabolic Signalings.

[This corrects the article DOI: 10.3389/fnmol.2022.

Menthol Flavor in E-Cigarette Vapor Modulates Social Behavior Correlated With Central and Peripheral Changes of Immunometabolic Signalings.

The use of electronic cigarette (e-cigarette) has been increasing dramatically worldwide. More than 8,000 flavors of e-cigarettes are currently marketed and menthol is one of the most popular flavor additives in the electronic nicotine delivery systems (ENDS). There is a controversy over the roles of e-cigarettes in social behavior, and little is known about the potential impacts of flavorings in the ENDS.

mGlu2/3 receptor in the prelimbic cortex is implicated in stress resilience and vulnerability in mice.

Resilience, referring to "achieving a positive outcome in the face of adversity", is a common phenomenon in daily life. Elucidating the mechanisms of stress resilience is instrumental to developing more effective treatments for stress-related psychiatric disorders such as depression. Metabotropic glutamate receptors (mGlu2/3 and mGlu5) within the medial prefrontal cortex (mPFC) have been recently recognized as promising therapeutic targets for rapid-acting antidepressant treatment.

The GABA(B1) receptor within the infralimbic cortex is implicated in stress resilience and vulnerability in mice.

Resilience is the capacity to maintain normal psychological and physical functions in the face of stress and adversity. Understanding how one can develop and enhance resilience is of great relevance to not only promoting coping mechanisms but also mitigating maladaptive stress responses in psychiatric illnesses such as depression. Preclinical studies suggest that GABA(B) receptors (GABA(B1) and GABA(B2)) are potential targets for the treatment of major depression.

GABA(B) receptors within the lateral habenula modulate stress resilience and vulnerability in mice.

Resilience means "the ability to withstand or recover quickly in the face of adversity". Elucidating the neural and molecular mechanisms underlying stress resilience will facilitate the development of more effective treatments for stress-induced psychiatric disorders such as depression. The habenular nuclei, which consist of the medial and lateral sub-regions (MHb and LHb, respectively), have been described as a critical node in emotional regulations.

Visual-Depth Matching Network: Deep RGB-D Domain Adaptation With Unequal Categories.

Existing domain adaptation (DA) methods generally assume that different domains have identical label space, and the training data are only sampled from a single domain. This unrealistic assumption is quite restricted for real-world applications, since it neglects the more practical scenario, where the source domain can contain the categories that are not shared by the target domain, and the training data can be collected from multiple modalities. In this article, we address a more difficult but practical problem, which recognizes RGB images through training on RGB-D data under the label space inequality scenario.

Dual-regression model for visual tracking.

Existing regression based tracking methods built on correlation filter model or convolution model do not take both accuracy and robustness into account at the same time. In this paper, we propose a dual-regression framework comprising a discriminative fully convolutional module and a fine-grained correlation filter component for visual tracking. The convolutional module trained in a classification manner with hard negative mining ensures the discriminative ability of the proposed tracker, which facilitates the handling of several challenging problems, such as drastic deformation, distractors, and complicated backgrounds.

Modal-Regression-Based Structured Low-Rank Matrix Recovery for Multiview Learning.

Low-rank Multiview Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL-based methods are incapable of handling well view discrepancy and discriminancy simultaneously, which, thus, leads to performance degradation when there is a large discrepancy among multiview data. To circumvent this drawback, motivated by the block-diagonal representation learning, we propose structured low-rank matrix recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of the structured low-rank matrix.

Semi-Supervised Multi-View Deep Discriminant Representation Learning.

Learning an expressive representation from multi-view data is a key step in various real-world applications. In this paper, we propose a semi-supervised multi-view deep discriminant representation learning (SMDDRL) approach. Unlike existing joint or alignment multi-view representation learning methods that cannot simultaneously utilize the consensus and complementary properties of multi-view data to learn inter-view shared and intra-view specific representations, SMDDRL comprehensively exploits the consensus and complementary properties as well as learns both shared and specific representations by employing the shared and specific representation learning network.

Adaptive Weighted Sparse Principal Component Analysis for Robust Unsupervised Feature Selection.

Current unsupervised feature selection methods cannot well select the effective features from the corrupted data. To this end, we propose a robust unsupervised feature selection method under the robust principal component analysis (PCA) reconstruction criterion, which is named the adaptive weighted sparse PCA (AW-SPCA). In the proposed method, both the regularization term and the reconstruction error term are constrained by the l -norm: the l -norm regularization term plays a role in the feature selection, while the l -norm reconstruction error term plays a role in the robust reconstruction.

Multiset Feature Learning for Highly Imbalanced Data Classification.

With the expansion of data, increasing imbalanced data has emerged. When the imbalance ratio (IR) of data is high, most existing imbalanced learning methods decline seriously in classification performance. In this paper, we systematically investigate the highly imbalanced data classification problem, and propose an uncorrelated cost-sensitive multiset learning (UCML) approach for it.

Multiview Hybrid Embedding: A Divide-and-Conquer Approach.

We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multiview subspace learning (MvSL) that aims to learn a latent subspace shared by multiview data. Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multiview data is sampled from nonlinear manifolds or suffers from heavy outliers.

Involvement of oxytocin and GABA in consolation behavior elicited by socially defeated individuals in mandarin voles.

Consolation, which entails comforting contact directed toward a distressed party, is a common empathetic response in humans and other species with advanced cognition. Here, using the social defeat paradigm, we provide empirical evidence that highly social and monogamous mandarin voles (Microtus mandarinus) increased grooming toward a socially defeated partner but not toward a partner who underwent only separation. This selective behavioral response existed in both males and females.

Single Sample Face Recognition under Varying Illumination via QRCP Decomposition.

In this paper, we present a novel high-frequency facial feature and a high-frequency based sparse representation classification to tackle single sample face recognition (SSFR) under varying illumination. Firstly, we propose the assumption that QRCP bases can represent intrinsic face surface features with different frequencies, and their corresponding energy coefficients describe illumination intensities. Based on this assumption, we take QRCP bases with corresponding weighting coefficients (i.

Intraspectrum Discrimination and Interspectrum Correlation Analysis Deep Network for Multispectral Face Recognition.

Multispectral images contain rich recognition information since the multispectral camera can reveal information that is not visible to the human eye or to the conventional RGB camera. Due to this characteristic of multispectral images, multispectral face recognition has attracted lots of research interest. Although some multispectral face recognition methods have been presented in the last decade, how to fully and effectively explore the intraspectrum discriminant information and the useful interspectrum correlation information in multispectral face images for recognition has not been well studied.

Video-based Person Re-identification by Simultaneously Learning Intra-video and Inter-video Distance Metrics.

Video-based person re-identification (re-id) is an important application in practice. Since large variations exist between different pedestrian videos, as well as within each video, it's challenging to conduct re-identification between pedestrian videos. In this paper, we propose a simultaneous intra-video and inter-video distance learning (SI2DL) approach for video-based person re-id.

A joint space-angle regularization approach for single 4D diffusion image super-resolution.

Purpose: Low signal-to-noise-ratio and limited scan time of diffusion magnetic resonance imaging (dMRI) in current clinical settings impede obtaining images with high spatial and angular resolution (HSAR) for a reliable fiber reconstruction with fine anatomical details. To overcome this problem, we propose a joint space-angle regularization approach to reconstruct HSAR diffusion signals from a single 4D low resolution (LR) dMRI, which is down-sampled in both 3D-space and q-space.

Methods: Different from the existing works which combine multiple 4D LR diffusion images acquired using specific acquisition protocols, the proposed method reconstructs HSAR dMRI from only a single 4D dMRI by exploring and integrating two key priors, that is, the nonlocal self-similarity in the spatial domain as a prior to increase spatial resolution and ridgelet approximations in the diffusion domain as another prior to increase the angular resolution of dMRI.

Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal.

Nonlocal image representation methods, including group-based sparse coding and block-matching 3-D filtering, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on intensity similarity, however, gives rise to disturbance and inaccuracy in estimation of the true images.

Super-Resolution Person Re-Identification With Semi-Coupled Low-Rank Discriminant Dictionary Learning.

Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high resolution (HR), while probe images are usually low resolution (LR) in the identification scenarios with large variation of illumination, weather, or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied.

Chronic Co-species Housing Mice and Rats Increased the Competitiveness of Male Mice.

Rats are predators of mice in nature. Nevertheless, it is a common practice to house mice and rats in a same room in some laboratories. In this study, we investigated the behavioral and physiological responsively of mice in long-term co-species housing conditions.