Tootfinder

You Only Train Once: Differentiable Subset Selection for Omics Data
Daphn\'e Chopard, Jorge da Silva Gon\c{c}alves, Irene Cannistraci, Thomas M. Sutter, Julia E. Vogt
https://arxiv.org/abs/2512.17678 https://arxiv.org/pdf/2512.17678 https://arxiv.org/html/2512.17678
arXiv:2512.17678v1 Announce Type: new
Abstract: Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling. However, most existing feature selection approaches either operate as multi-stage pipelines or rely on post hoc feature attribution, making selection and prediction weakly coupled. In this work, we present YOTO (you only train once), an end-to-end framework that jointly identifies discrete gene subsets and performs prediction within a single differentiable architecture. In our model, the prediction task directly guides which genes are selected, while the learned subsets, in turn, shape the predictive representation. This closed feedback loop enables the model to iteratively refine both what it selects and how it predicts during training. Unlike existing approaches, YOTO enforces sparsity so that only the selected genes contribute to inference, eliminating the need to train additional downstream classifiers. Through a multi-task learning design, the model learns shared representations across related objectives, allowing partially labeled datasets to inform one another, and discovering gene subsets that generalize across tasks without additional training steps. We evaluate YOTO on two representative single-cell RNA-seq datasets, showing that it consistently outperforms state-of-the-art baselines. These results demonstrate that sparse, end-to-end, multi-task gene subset selection improves predictive performance and yields compact and meaningful gene subsets, advancing biomarker discovery and single-cell analysis.
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S-D-RSM: Stochastic Distributed Regularized Splitting Method for Large-Scale Convex Optimization Problems
Maoran Wang, Xingju Cai, Yongxin Chen
https://arxiv.org/abs/2511.10133 https://arxiv.org/pdf/2511.10133 https://arxiv.org/html/2511.10133
arXiv:2511.10133v1 Announce Type: new
Abstract: This paper investigates the problems large-scale distributed composite convex optimization, with motivations from a broad range of applications, including multi-agent systems, federated learning, smart grids, wireless sensor networks, compressed sensing, and so on. Stochastic gradient descent (SGD) and its variants are commonly employed to solve such problems. However, existing algorithms often rely on vanishing step sizes, strong convexity assumptions, or entail substantial computational overhead to ensure convergence or obtain favorable complexity. To bridge the gap between theory and practice, we integrate consensus optimization and operator splitting techniques (see Problem Reformulation) to develop a novel stochastic splitting algorithm, termed the \emph{stochastic distributed regularized splitting method} (S-D-RSM). In practice, S-D-RSM performs parallel updates of proximal mappings and gradient information for only a randomly selected subset of agents at each iteration. By introducing regularization terms, it effectively mitigates consensus discrepancies among distributed nodes. In contrast to conventional stochastic methods, our theoretical analysis establishes that S-D-RSM achieves global convergence without requiring diminishing step sizes or strong convexity assumptions. Furthermore, it achieves an iteration complexity of $\mathcal{O}(1/\epsilon)$ with respect to both the objective function value and the consensus error. Numerical experiments show that S-D-RSM achieves up to 2--3$\times$ speedup compared to state-of-the-art baselines, while maintaining comparable or better accuracy. These results not only validate the algorithm's theoretical guarantees but also demonstrate its effectiveness in practical tasks such as compressed sensing and empirical risk minimization.
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Game Theory Analysis of Third-Party Regulation in Organic Supply Chains
Jo\~ao Zambujal-Oliveira, Andr\'e Silva, Rui Vasconcelos
https://arxiv.org/abs/2510.12420 https://

Game Theory Analysis of Third-Party Regulation in Organic Supply Chains
As people become more conscious of their health and the environment, the demand for organic food is expected to increase. However, distinguishing organic products from conventionally produced ones can be hard, creating a problem where producers may have the incentive to label their conventional products as organic to sell them at a higher price. Game theory can help to analyze the strategic interactions between producers and consumers in order to help consumers verifying these claims. Through a…

Introspection in Learned Semantic Scene Graph Localisation
Manshika Charvi Bissessur, Efimia Panagiotaki, Daniele De Martini
https://arxiv.org/abs/2510.07053 https://

Introspection in Learned Semantic Scene Graph Localisation
This work investigates how semantics influence localisation performance and robustness in a learned self-supervised, contrastive semantic localisation framework. After training a localisation network on both original and perturbed maps, we conduct a thorough post-hoc introspection analysis to probe whether the model filters environmental noise and prioritises distinctive landmarks over routine clutter. We validate various interpretability methods and present a comparative reliability analysis. …

Self-lensing binaries in globular clusters - predictions for ELT
Grzegorz Wiktorowicz, Matthew Middleton, Mirek Giersz, Adam Ingram, Adam McMaster, Abbas Askar, Lucas Hellstr\"om
https://arxiv.org/abs/2510.02751

Self-lensing binaries in globular clusters - predictions for ELT
Self-lensing (SL) represents a powerful technique for detecting compact objects in binary systems through gravitational microlensing effects, when a compact companion transits in front of its luminous partner. We present the first comprehensive study of SL probability within globular cluster (GC) environments, utilizing synthetic stellar populations from MOCCA simulations to predict detection rates for the Extremely Large Telescope (ELT). Our analysis incorporates finite-size lens effects for w…

Effect of Tube Radius on the Adsorption of Chlorothalonil on Single-Walled Carbon and Boron Nitride Nanotube Surfaces: A Theoretical Study for Environmental Remediation
Francisco Gleidson de S. Ferreira, Caio V. C. Ribeiro da Silva, Silvete Guerini, Kleuton A. L. Lima, Douglas Soares Galv\~ao, Jos\'e Milton Elias de Matos, Alexandre Araujo de Souza
https://…

Effect of Tube Radius on the Adsorption of Chlorothalonil on Single-Walled Carbon and Boron Nitride Nanotube Surfaces: A Theoretical Study for Environmental Remediation
We investigated the interaction of the pesticide chlorothalonil (CLT) with single-walled carbon nanotubes (CNTs) and boron nitride nanotubes (BNNTs) using density functional theory (DFT) with the SIESTA code. Structural, energetic, and electronic analyses were used to characterize adsorption on nanotube surfaces. The adsorption energy becomes more favorable as the tube radius increases, consistent with enhanced pi-pi stacking on less curved surfaces. Both CNTs and BNNTs physisorb CLT; however, …