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Multiple Hypothesis Testing To Estimate The Number Of Communities in Stochastic Block Models
Chetkar Jha, Mingyao Li, Ian Barnett
https://arxiv.org/abs/2507.15471

Multiple Hypothesis Testing To Estimate The Number Of Communities in Stochastic Block Models
Clustering of single-cell RNA sequencing (scRNA-seq) datasets can give key insights into the biological functions of cells. Therefore, it is not surprising that network-based community detection methods (one of the better clustering methods) are increasingly being used for the clustering of scRNA-seq datasets. The main challenge in implementing network-based community detection methods for scRNA-seq datasets is that these methods \emph{apriori} require the true number of communities or blocks f…

Attention-Enhanced Deep Learning Ensemble for Breast Density Classification in Mammography
Peyman Sharifian, Xiaotong Hong, Alireza Karimian, Mehdi Amini, Hossein Arabi
https://arxiv.org/abs/2507.06410

Attention-Enhanced Deep Learning Ensemble for Breast Density Classification in Mammography
Breast density assessment is a crucial component of mammographic interpretation, with high breast density (BI-RADS categories C and D) representing both a significant risk factor for developing breast cancer and a technical challenge for tumor detection. This study proposes an automated deep learning system for robust binary classification of breast density (low: A/B vs. high: C/D) using the VinDr-Mammo dataset. We implemented and compared four advanced convolutional neural networks: ResNet18, …

Influence and Connectivity in Networks: A Generating Function Approach
Yang Sun, Wei Zhao, Junjie Zhou
https://arxiv.org/abs/2508.09492 https://arxiv.org/p…

Influence and Connectivity in Networks: A Generating Function Approach
Many widely used network centralities are based on counting walks that meet specific criteria. This paper introduces a systematic framework for walk enumeration using generating functions. We introduce a first-passage decomposition that uniquely divides any walk passing through specified nodes or links into two components: a first-reaching walk and a subsequent walk. This decomposition yields a system of interconnected equations that relate three disjoint categories of walks: unrestricted walks…

Structural and rigidity properties of Lie skew braces
Marco Damele, Andrea Loi
https://arxiv.org/abs/2507.06214 https://arxiv.org/pdf…

Structural and rigidity properties of Lie skew braces
We investigate structural and rigidity properties of \emph{Lie skew braces} (LSBs), objects essentially known in the literature as \emph{post--Lie groups}, obtained by endowing a manifold with two compatible group laws that share the same identity element. LSBs extend skew left braces, which are central to the study of non-involutive set-theoretic solutions of the Yang--Baxter equation, to the smooth category. Our first main result shows that, for every connected LSB $(G,\cdot,\circ)$, linearit…

This https://arxiv.org/abs/2504.07884 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

CatCMA with Margin: Stochastic Optimization for Continuous, Integer, and Categorical Variables
This study focuses on mixed-variable black-box optimization (MV-BBO), addressing continuous, integer, and categorical variables. Many real-world MV-BBO problems involve dependencies among these different types of variables, requiring efficient methods to optimize them simultaneously. Recently, stochastic optimization methods leveraging the mechanism of the covariance matrix adaptation evolution strategy have shown promising results in mixed-integer or mixed-category optimization. However, such …