Tootfinder

Quantifying topological features and irregularities in zebrafish patterns using the sweeping-plane filtration
Nour Khoudari, John Nardini, Alexandria Volkening
https://arxiv.org/abs/2509.11023

Quantifying topological features and irregularities in zebrafish patterns using the sweeping-plane filtration
Complex patterns emerge across a wide range of biological systems. While such patterns often exhibit remarkable robustness, variation and irregularity exist at multiple scales and can carry important information about the underlying agent interactions driving collective dynamics. Many methods for quantifying biological patterns focus on large-scale, characteristic features (such as stripe width or spot number), but questions remain on how to characterize messy patterns. In the case of cellular …

SafeMT: Multi-turn Safety for Multimodal Language Models
Han Zhu, Juntao Dai, Jiaming Ji, Haoran Li, Chengkun Cai, Pengcheng Wen, Chi-Min Chan, Boyuan Chen, Yaodong Yang, Sirui Han, Yike Guo
https://arxiv.org/abs/2510.12133

SafeMT: Multi-turn Safety for Multimodal Language Models
With the widespread use of multi-modal Large Language models (MLLMs), safety issues have become a growing concern. Multi-turn dialogues, which are more common in everyday interactions, pose a greater risk than single prompts; however, existing benchmarks do not adequately consider this situation. To encourage the community to focus on the safety issues of these models in multi-turn dialogues, we introduce SafeMT, a benchmark that features dialogues of varying lengths generated from harmful quer…

Elevating Medical Image Security: A Cryptographic Framework Integrating Hyperchaotic Map and GRU
Weixuan Li, Guang Yu, Quanjun Li, Junhua Zhou, Jiajun Chen, Yihang Dong, Mengqian Wang, Zimeng Li, Changwei Gong, Lin Tang, Xuhang Chen
https://arxiv.org/abs/2510.12084

Elevating Medical Image Security: A Cryptographic Framework Integrating Hyperchaotic Map and GRU
Chaotic systems play a key role in modern image encryption due to their sensitivity to initial conditions, ergodicity, and complex dynamics. However, many existing chaos-based encryption methods suffer from vulnerabilities, such as inadequate permutation and diffusion, and suboptimal pseudorandom properties. This paper presents Kun-IE, a novel encryption framework designed to address these issues. The framework features two key contributions: the development of the 2D Sin-Cos Pi Hyperchaotic Ma…

Integrable Model of a Superconductor with non-Fermi liquid and Mott Phases
Santhosh M, Jorge Dukelsky, Gerardo Ortiz
https://arxiv.org/abs/2510.10684 https://

Integrable Model of a Superconductor with non-Fermi liquid and Mott Phases
We present and analyze an exactly solvable interacting fermionic pairing model, which features interactions that entangle states at momenta $\mathbf{k}$ and $-\mathbf{k}$. These interactions give rise to novel correlated ground states, leading to a rich phase diagram that includes superconducting, multiple metallic, and Mott-insulating phases. At finite interaction strengths, we observe the emergence of multiple many-body Fermi surfaces, which violate Luttinger's theorem and challenge the conve…

Repulsive Mixture Model with Projection Determinantal Point Process
Ziyi Song, Federico Camerlenghi, Weining Shen, Michele Guindani, Mario Beraha
https://arxiv.org/abs/2510.08838

Repulsive Mixture Model with Projection Determinantal Point Process
In many scientific domains, clustering aims to reveal interpretable latent structure that reflects relevant subpopulations or processes. Widely used Bayesian mixture models for model-based clustering often produce overlapping or redundant components because priors on cluster locations are specified independently, hindering interpretability. To mitigate this, repulsive priors have been proposed to encourage well-separated components, yet existing approaches face both computational and theoretica…

Accelerated Aggregated D-Optimal Designs for Estimating Main Effects in Black-Box Models
Chih-Yu Chang, Ming-Chung Chang
https://arxiv.org/abs/2510.08465 https://

Accelerated Aggregated D-Optimal Designs for Estimating Main Effects in Black-Box Models
Recent advances in supervised learning have driven growing interest in explaining black-box models, particularly by estimating the effects of input variables on model predictions. However, existing approaches often face key limitations, including poor scalability, sensitivity to out-of-distribution sampling, and instability under correlated features. To address these issues, we propose A2D2E, an $\textbf{E}$stimator based on $\textbf{A}$ccelerated $\textbf{A}$ggregated $\textbf{D}$-Optimal $\te…

HPQEA: A Scalable and High-Performance Quantum Emulator with High-Bandwidth Memory for Diverse Algorithms Support
Tran Van Duy, Tuan Hai Vu, Vu Trung Duong Le, Hoai Luan Pham, Yasuhiko Nakashima
https://arxiv.org/abs/2510.07110

HPQEA: A Scalable and High-Performance Quantum Emulator with High-Bandwidth Memory for Diverse Algorithms Support
In recent years, there has been a growing interest in the development of quantum emulation. However, existing studies often struggle to achieve broad applicability, high performance, and efficient resource and memory utilization. To address these challenges, we provide HPQEA, a quantum emulator based on the state-vector emulation approach. HPQEA includes three main features: a high-performance computing core, an optimized controlled-NOT gate computation strategy, and effective utilization of hi…

Fast Witness Persistence for MRI Volumes via Hybrid Landmarking
Jorge Leonardo Ruiz Williams
https://arxiv.org/abs/2510.04553 https://arxiv.org/pdf/2510.04…

Fast Witness Persistence for MRI Volumes via Hybrid Landmarking
We introduce a scalable witness-based persistent homology pipeline for full-brain MRI volumes that couples density-aware landmark selection with a GPU-ready witness filtration. Candidates are scored by a hybrid metric that balances geometric coverage against inverse kernel density, yielding landmark sets that shrink mean pairwise distances by 30-60% over random or density-only baselines while preserving topological features. Benchmarks on BrainWeb, IXI, and synthetic manifolds execute in under …

PRESOL: a web-based computational setting for feature-based flare forecasting
Chiara Curletto, Paolo Massa, Valeria Tagliafico, Cristina Campi, Federico Benvenuto, Michele Piana, Andrea Tacchino
https://arxiv.org/abs/2510.01799

PRESOL: a web-based computational setting for feature-based flare forecasting
Solar flares are the most explosive phenomena in the solar system and the main trigger of the events' chain that starts from Coronal Mass Ejections and leads to geomagnetic storms with possible impacts on the infrastructures at Earth. Data-driven solar flare forecasting relies on either deep learning approaches, which are operationally promising but with a low explainability degree, or machine learning algorithms, which can provide information on the physical descriptors that mostly impact the …

A Unified Framework for Diffusion Model Unlearning with f-Divergence
Nicola Novello, Federico Fontana, Luigi Cinque, Deniz Gunduz, Andrea M. Tonello
https://arxiv.org/abs/2509.21167

A Unified Framework for Diffusion Model Unlearning with f-Divergence
Machine unlearning aims to remove specific knowledge from a trained model. While diffusion models (DMs) have shown remarkable generative capabilities, existing unlearning methods for text-to-image (T2I) models often rely on minimizing the mean squared error (MSE) between the output distribution of a target and an anchor concept. We show that this MSE-based approach is a special case of a unified $f$-divergence-based framework, in which any $f$-divergence can be utilized. We analyze the benefits…