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Interpretable factorization of clinical questionnaires to identify latent factors of psychopathology
arXiv:2312.07762v3 Announce Type: replace Abstract: Psychiatry research seeks to understand the manifestations of psychopathology in behavior, as measured in questionnaire data, by identifying a small number of latent factors that explain them. While factor analysis is the traditional tool for this purpose, the resulting factors may not be interpretable, and may also be subject to confounding variables. Moreover, missing data are common, and explicit imputation is often required.
On solving symmetric multi-type orthogonal non-negative matrix tri-factorization problem
arXiv:2606.08291v1 Announce Type: new Abstract: We study the symmetric multi-type orthogonal non-negative matrix tri-factorization problem, where several symmetric non-negative matrices are simultaneously approximated by factors of the form $GS_{i}G^{\top}$, with a shared non-negative and orthogonal factor $G$. This model is motivated by clustering and network analysis, where non-negativity improves interpretability and orthogonality gives a natural assignment-type structure to the latent...
Human Factors in Cybersecurity in Icelandic Small and Medium-sized Enterprises
arXiv:2606.02839v1 Announce Type: new Abstract: Cybersecurity threats are increasing in all aspects of society due to the integration of digital systems into modern-day life and a volatile geo-political landscape. Technical factors are an ongoing arms race; however, the threat surface from human and social factors is still present, often providing malicious actors the means to bypass complex technical security controls. Understanding human factors in light of technical evolution is essential...
Rethinking Bregman Divergences in Kronecker-Factored Optimizers
arXiv:2606.00542v2 Announce Type: replace Abstract: Shampoo-style optimizers approximate gradient covariance matrices using Kronecker-factored structures. Recent work~\cite{lin2026understanding} showed that such approximations can be viewed as projections under Bregman matrix divergences, leading to different Kronecker-factored preconditioners. However, it remains unclear what role the choice of divergence plays when the covariance is not exactly Kronecker-factored.
Simulation of transcription factor clustering in nuclei from molecular kinetics
Transcription factors form clusters often described as condensates that exhibit emergent biophysical properties. Here we present a software package to simulate transcription factor spatial distributions from molecular diffusion and binding kinetics alone. The software simulates microscopy data and FRAP experiments and recapitulates the clustering behavior of experimentally characterized transcription factors.
An Ensembled Latent Factor Model via Differential Evolution and Gradient Descent Optimization
arXiv:2606.04408v1 Announce Type: new Abstract: High-dimensional and incomplete (HDI) data are prevalent in many real-world big data scenarios. Latent factor models serve as a common representation learning approach, capable of uncovering informative latent factors from such data.
Diff-CA: Separating Common and Salient Factors with Diffusion Models
arXiv:2606.06120v1 Announce Type: new Abstract: Contrastive Analysis aims to separate factors that are common between two data distributions from those that are salient to only one of them. Existing contrastive methods are based on generative models (e.g., VAEs or GANs) that often suffer from limited reconstruction and image quality, which hampers effective latent factor separation and limits their applicability to high-fidelity image generation and edition. We propose a novel conditioning...
Graphical and algebraic methods for Boolean factoring
arXiv:2606.04038v1 Announce Type: cross Abstract: The problem of factoring Boolean polynomials has significant applications in both classical and quantum computing technology. In this paper we have developed novel algorithms for factoring both ESOP and SOP expressions. Our aim is to optimize the AND-count.
ConTraIRL: Factorized Contrastive Abstractions for Transferable IRL
arXiv:2606.03017v1 Announce Type: new Abstract: Reward transfer in Inverse Reinforcement Learning (IRL) is unreliable when policies must generalize to unseen combinations of environment dynamics and task goals. We propose Factorized Contrastive Abstractions for Transferable IRL (ConTraIRL), a framework that enables compositional reward transfer by learning decoupled latent representations of these two factors. ConTraIRL uses a dual-encoder architecture that maps observations into separate...
Graph Regularized Non-negative Reduced Biquaternion Matrix Factorization for Color Image Recognition
arXiv:2606.03654v1 Announce Type: new Abstract: Non-negative reduced biquaternion matrix factorization (NRBMF) uses the product of reduced biquaternion (RB) matrices to incorporate the non-negativity constraints of color image pixels into the factorization process. However, NRBMF mainly focuses on reconstruction accuracy and does not exploit the local geometric structure of image data, which may limit the discriminative ability of the learned low-dimensional features. To address this issue,...