Insight into Rice Resistance to the Brown Planthopper: Gene Cloning, Functional Analysis, and Breeding Applications.

This review provides a comprehensive overview of the current understanding of rice resistance to the brown planthopper (BPH), a major pest that poses significant threats to rice production through direct feeding damage and by transmitting viruses such as Rice grassy stunt virus (RGSV) and Rice ragged stunt virus (RRSV). We highlight the emergence of various BPH biotypes that have overcome specific resistance genes in rice. Advances in genetic mapping and cloning have identified 17 BPH resistance genes, classified into typical R genes encoding nucleotide-binding leucine-rich repeat (NLR) proteins and atypical R genes such as lectin receptor kinases and proteins affecting cell wall composition.

Differentiable self-supervised clustering with intrinsic interpretability.

Self-supervised clustering has garnered widespread attention due to its ability to discover latent clustering structures without the need for external labels. However, most existing approaches on self-supervised clustering lack of inherent interpretability in the data clustering process. In this paper, we propose a differentiable self-supervised clustering method with intrinsic interpretability (DSC2I), which provides an interpretable data clustering mechanism by reformulating clustering process based on differentiable programming.

Identification and candidate analysis of a new brown planthopper resistance locus in an Indian landrace of rice, paedai kalibungga.

Unlabelled: The brown planthopper ( Stål, BPH) is the most destructive pest of rice ( L.). Utilizing resistant rice cultivars that harbor resistance gene/s is an effective strategy for integrated pest management.

A Survey on Information Bottleneck.

This survey is for the remembrance of one of the creators of the information bottleneck theory, Prof. Naftali Tishby, passing away at the age of 68 on August, 2021. Information bottleneck (IB), a novel information theoretic approach for pattern analysis and representation learning, has gained widespread popularity since its birth in 1999.

Deep Multiview Clustering by Pseudo-Label Guided Contrastive Learning and Dual Correlation Learning.

Deep multiview clustering (MVC) is to learn and utilize the rich relations across different views to enhance the clustering performance under a human-designed deep network. However, most existing deep MVCs meet two challenges. First, most current deep contrastive MVCs usually select the same instance across views as positive pairs and the remaining instances as negative pairs, which always leads to inaccurate contrastive learning (CL).

Cross-Modal Clustering With Deep Correlated Information Bottleneck Method.

Cross-modal clustering (CMC) intends to improve the clustering accuracy (ACC) by exploiting the correlations across modalities. Although recent research has made impressive advances, it remains a challenge to sufficiently capture the correlations across modalities due to the high-dimensional nonlinear characteristics of individual modalities and the conflicts in heterogeneous modalities. In addition, the meaningless modality-private information in each modality might become dominant in the process of correlation mining, which also interferes with the clustering performance.

Multitask Image Clustering via Deep Information Bottleneck.

Multitask image clustering approaches intend to improve the model accuracy on each task by exploring the relationships of multiple related image clustering tasks. However, most existing multitask clustering (MTC) approaches isolate the representation abstraction from the downstream clustering procedure, which makes the MTC models unable to perform unified optimization. In addition, the existing MTC relies on exploring the relevant information of multiple related tasks to discover their latent correlations while ignoring the irrelevant information between partially related tasks, which may also degrade the clustering performance.

Multiview Clustering With Propagating Information Bottleneck.

In many practical applications, massive data are observed from multiple sources, each of which contains multiple cohesive views, called hierarchical multiview (HMV) data, such as image-text objects with different types of visual and textual features. Naturally, the inclusion of source and view relationships offers a comprehensive view of the input HMV data and achieves an informative and correct clustering result. However, most existing multiview clustering (MVC) methods can only process single-source data with multiple views or multisource data with single type of feature, failing to consider all the views across multiple sources.

Learning From the Future: Light Cone Modeling for Sequential Recommendation.

Modeling sequential behaviors is the core of sequential recommendation. As users visit items in chronological order, existing methods typically capture a user's present interests from his/her past-to-present behaviors, i.e.

MHADTI: predicting drug-target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms.

Motivation: Discovering the drug-target interactions (DTIs) is a crucial step in drug development such as the identification of drug side effects and drug repositioning. Since identifying DTIs by web-biological experiments is time-consuming and costly, many computational-based approaches have been proposed and have become an efficient manner to infer the potential interactions. Although extensive effort is invested to solve this task, the prediction accuracy still needs to be improved.

GOGCN: Graph Convolutional Network on Gene Ontology for Functional Similarity Analysis of Genes.

The measurement of gene functional similarity plays a critical role in numerous biological applications, such as gene clustering, the construction of gene similarity networks. However, most existing approaches still rely heavily on traditional computational strategies, which are not guaranteed to achieve satisfactory performance. In this study, we propose a novel computational approach called GOGCN to measure gene functional similarity by modeling the Gene Ontology (GO) through Graph Convolutional Network (GCN).

GCRec: Graph-Augmented Capsule Network for Next-Item Recommendation.

Next-item recommendation has been a hot research, which aims at predicting the next action by modeling users' behavior sequences. While previous efforts toward this task have been made in capturing complex item transition patterns, we argue that they still suffer from three limitations: 1) they have difficulty in explicitly capturing the impact of inherent order of item transition patterns; 2) only a simple and crude embedding is insufficient to yield satisfactory long-term users' representations from limited training sequences; and 3) they are incapable of dynamically integrating long-term and short-term user interest modeling. In this work, we propose a novel solution named graph-augmented capsule network (GCRec), which exploits sequential user behaviors in a more fine-grained manner.

A novel gene functional similarity calculation model by utilizing the specificity of terms and relationships in gene ontology.

Background: Recently, with the foundation and development of gene ontology (GO) resources, numerous works have been proposed to compute functional similarity of genes and achieved series of successes in some research fields. Focusing on the calculation of the information content (IC) of terms is the main idea of these methods, which is essential for measuring functional similarity of genes. However, most approaches have some deficiencies, especially when measuring the IC of both GO terms and their corresponding annotated term sets.

View-Wise Versus Cluster-Wise Weight: Which Is Better for Multi-View Clustering?

Weighted multi-view clustering (MVC) aims to combine the complementary information of multi-view data (such as image data with different types of features) in a weighted manner to obtain a consistent clustering result. However, when the cluster-wise weights across views are vastly different, most existing weighted MVC methods may fail to fully utilize the complementary information, because they are based on view-wise weight learning and can not learn the fine-grained cluster-wise weights. Additionally, extra parameters are needed for most of them to control the weight distribution sparsity or smoothness, which are hard to tune without prior knowledge.

DMIB: Dual-Correlated Multivariate Information Bottleneck for Multiview Clustering.

Multiview clustering (MVC) has recently been the focus of much attention due to its ability to partition data from multiple views via view correlations. However, most MVC methods only learn either interfeature correlations or intercluster correlations, which may lead to unsatisfactory clustering performance. To address this issue, we propose a novel dual-correlated multivariate information bottleneck (DMIB) method for MVC.

A Joint Symbol-Detection, Channel-Estimation and Decoding Scheme under Few-Bit ADCs in mmWave Communications.

Few-bit analog-to-digital converter (ADC) is regarded as a promising technique to greatly reduce power consumption of Internet of Things (IoT) devices in millimeter-wave (mmWave) communications. In this work, based on the recently proposed parametric bilinear generalized approximate message passing (PBiGAMP), we propose a new scheme to perform joint symbol detection, channel estimation and decoding. The proposed scheme is flexible to deal with discrete prior on symbols, Gaussian mixture prior on channels and quantized likelihood on observations.

Rank-based pooling for deep convolutional neural networks.

Pooling is a key mechanism in deep convolutional neural networks (CNNs) which helps to achieve translation invariance. Numerous studies, both empirically and theoretically, show that pooling consistently boosts the performance of the CNNs. The conventional pooling methods are operated on activation values.