On the maxima of suprema of dependent Gaussian models

In this paper, we study the asymptotic distribution of the maxima of suprema of dependent Gaussian processes with trend. For different scales of the time horizon we obtain different normalizing functions for the convergence of the maxima. The obtained results not only have potential applications in estimating the delay of certain Gaussian fork-join queueing systems but also provide interesting insights to the extreme value theory for triangular arrays of random variables with row-wise dependence.Comment: 21 page

Interpretable Graph Anomaly Detection using Gradient Attention Maps

Detecting unusual patterns in graph data is a crucial task in data mining. However, existing methods often face challenges in consistently achieving satisfactory performance and lack interpretability, which hinders our understanding of anomaly detection decisions. In this paper, we propose a novel approach to graph anomaly detection that leverages the power of interpretability to enhance performance. Specifically, our method extracts an attention map derived from gradients of graph neural networks, which serves as a basis for scoring anomalies. In addition, we conduct theoretical analysis using synthetic data to validate our method and gain insights into its decision-making process. To demonstrate the effectiveness of our method, we extensively evaluate our approach against state-of-the-art graph anomaly detection techniques. The results consistently demonstrate the superior performance of our method compared to the baselines

USL-Net: Uncertainty Self-Learning Network for Unsupervised Skin Lesion Segmentation

Unsupervised skin lesion segmentation offers several benefits, including conserving expert human resources, reducing discrepancies due to subjective human labeling, and adapting to novel environments. However, segmenting dermoscopic images without manual labeling guidance presents significant challenges due to dermoscopic image artifacts such as hair noise, blister noise, and subtle edge differences. To address these challenges, we introduce an innovative Uncertainty Self-Learning Network (USL-Net) designed for skin lesion segmentation. The USL-Net can effectively segment a range of lesions, eliminating the need for manual labeling guidance. Initially, features are extracted using contrastive learning, followed by the generation of Class Activation Maps (CAMs) as saliency maps using these features. The different CAM locations correspond to the importance of the lesion region based on their saliency. High-saliency regions in the map serve as pseudo-labels for lesion regions while low-saliency regions represent the background. However, intermediate regions can be hard to classify, often due to their proximity to lesion edges or interference from hair or blisters. Rather than risk potential pseudo-labeling errors or learning confusion by forcefully classifying these regions, we consider them as uncertainty regions, exempting them from pseudo-labeling and allowing the network to self-learn. Further, we employ connectivity detection and centrality detection to refine foreground pseudo-labels and reduce noise-induced errors. The application of cycle refining enhances performance further. Our method underwent thorough experimental validation on the ISIC-2017, ISIC-2018, and PH2 datasets, demonstrating that its performance is on par with weakly supervised and supervised methods, and exceeds that of other existing unsupervised methods.Comment: 14 pages, 9 figures, 71 reference

SHA-SCP: A UI Element Spatial Hierarchy Aware Smartphone User Click Behavior Prediction Method

Predicting user click behavior and making relevant recommendations based on the user's historical click behavior are critical to simplifying operations and improving user experience. Modeling UI elements is essential to user click behavior prediction, while the complexity and variety of the UI make it difficult to adequately capture the information of different scales. In addition, the lack of relevant datasets also presents difficulties for such studies. In response to these challenges, we construct a fine-grained smartphone usage behavior dataset containing 3,664,325 clicks of 100 users and propose a UI element spatial hierarchy aware smartphone user click behavior prediction method (SHA-SCP). SHA-SCP builds element groups by clustering the elements according to their spatial positions and uses attention mechanisms to perceive the UI at the element level and the element group level to fully capture the information of different scales. Experiments are conducted on the fine-grained smartphone usage behavior dataset, and the results show that our method outperforms the best baseline by an average of 10.52%, 11.34%, and 10.42% in Top-1 Accuracy, Top-3 Accuracy, and Top-5 Accuracy, respectively

The emerging role of deubiquitylating enzymes as therapeutic targets in cancer metabolism.

Cancer cells must rewire cellular metabolism to satisfy the unbridled proliferation, and metabolic reprogramming provides not only the advantage for cancer cell proliferation but also new targets for cancer treatment. However, the plasticity of the metabolic pathways makes them very difficult to target. Deubiquitylating enzymes (DUBs) are proteases that cleave ubiquitin from the substrate proteins and process ubiquitin precursors. While the molecular mechanisms are not fully understood, many DUBs have been shown to be involved in tumorigenesis and progression via controlling the dysregulated cancer metabolism, and consequently recognized as potential drug targets for cancer treatment. In this article, we summarized the significant progress in understanding the key roles of DUBs in cancer cell metabolic rewiring and the opportunities for the application of DUBs inhibitors in cancer treatment, intending to provide potential implications for both research purpose and clinical applications

The miR167-OsARF12 module regulates grain filling and grain size downstream of miR159

Grain weight and quality are always determined by the grain filling. Plant miRNAs have drawn attention as key targets for regulating grain size and yield. Yet the mechanisms underlying the regulation of grain size are largely unclear due to the complex networks controlling this trait. Our earlier studies proved that the suppressed expression of miR167 (STTM/MIM167) substantially increased grain weight. In a field test, the increased yield up to 12.90%-21.94% due to the significantly enhanced grain filling rate. Biochemical and genetic analyses reveal the regulatory effects of miR159 on miR167 expression. Further analysis indicates that OsARF12 is the major mediator of miR167 in regulating rice grain filling. Expectedly, over expressing OsARF12 could resemble the phenotype of STTM/MIM167 plants with respect to grain weight and grain filling rate. Upon in-depth analysis, we found that OsARF12 activates OsCDKF;2 expressions by directly binding to the TGTCGG motif in the promoter region. Flow cytometric analysis in young panicles of plants overexpressing OsARF12 and cell number examination of cdkf;2 mutants verify that OsARF12 positively regulates grain filling and grain size by targeting OsCDKF;2. Moreover, RNA-seq result suggests that miR167-OsARF12 module is involved in the cell development process and hormone pathways. Additionally, plants overexpressing OsARF12 or cdkf;2 mutants present enhanced or reduced sensitivity to exogenous auxin and brassinosteroid (BR) treatments, confirming that OsCDKF;2 targeting by OsARF12 mediates auxin and BR signaling. Our results reveal that miR167-OsARF12 module works downstream of miR159 to regulate rice grain filling and grain size by OsCDKF;2 through controlling cell division and mediating auxin and BR signals