Tootfinder

Vandaag 166 jaar geleden publiceerde Charles Darwin zijn boek The Origin of Species by means of Natural Selection, dat veel later werd verkozen tot het belangrijkste wetenschappelijke boek ooit geschreven.
Het is een van de weinige boeken dat ik in twee talen heb staan. Naast dat het zeer wetenschappelijk is, leest het lekker weg, ik kan het iedereen aanbevelen.

Inclusive Fitness as a Key Step Towards More Advanced Social Behaviors in Multi-Agent Reinforcement Learning Settings
Andries Rosseau, Rapha\"el Avalos, Ann Now\'e
https://arxiv.org/abs/2510.12555

Inclusive Fitness as a Key Step Towards More Advanced Social Behaviors in Multi-Agent Reinforcement Learning Settings
The competitive and cooperative forces of natural selection have driven the evolution of intelligence for millions of years, culminating in nature's vast biodiversity and the complexity of human minds. Inspired by this process, we propose a novel multi-agent reinforcement learning framework where each agent is assigned a genotype and where reward functions are modelled after the concept of inclusive fitness. An agent's genetic material may be shared with other agents, and our inclusive reward f…

Unsupervised Active Learning via Natural Feature Progressive Framework
Yuxi Liu, Catherine Lalman, Yimin Yang
https://arxiv.org/abs/2510.04939 https://arxi…

Unsupervised Active Learning via Natural Feature Progressive Framework
The effectiveness of modern deep learning models is predicated on the availability of large-scale, human-annotated datasets, a process that is notoriously expensive and time-consuming. While Active Learning (AL) offers a strategic solution by labeling only the most informative and representative data, its iterative nature still necessitates significant human involvement. Unsupervised Active Learning (UAL) presents an alternative by shifting the annotation burden to a single, post-selection step…

Efficient Group Lasso Regularized Rank Regression with Data-Driven Parameter Determination
Meixia Lin, Meijiao Shi, Yunhai Xiao, Qian Zhang
https://arxiv.org/abs/2510.11546 http…

Efficient Group Lasso Regularized Rank Regression with Data-Driven Parameter Determination
High-dimensional regression often suffers from heavy-tailed noise and outliers, which can severely undermine the reliability of least-squares based methods. To improve robustness, we adopt a non-smooth Wilcoxon score based rank objective and incorporate structured group sparsity regularization, a natural generalization of the lasso, yielding a group lasso regularized rank regression method. By extending the tuning-free parameter selection scheme originally developed for the lasso, we introduce …

Decoding Positive Selection in Mycobacterium tuberculosis with Phylogeny-Guided Graph Attention Models
Linfeng Wang, Susana Campino, Taane G. Clark, Jody E. Phelan
https://arxiv.org/abs/2510.08703

Decoding Positive Selection in Mycobacterium tuberculosis with Phylogeny-Guided Graph Attention Models
Positive selection drives the emergence of adaptive mutations in Mycobacterium tuberculosis, shaping drug resistance, transmissibility, and virulence. Phylogenetic trees capture evolutionary relationships among isolates and provide a natural framework for detecting such adaptive signals. We present a phylogeny-guided graph attention network (GAT) approach, introducing a method for converting SNP-annotated phylogenetic trees into graph structures suitable for neural network analysis. Using 500 M…

Developing an information criterion for spatial data analysis through Bayesian generalized fused lasso
Yuko Kakikawa, Yoshiyuki Ninomiya
https://arxiv.org/abs/2510.11172 https:/…

Developing an information criterion for spatial data analysis through Bayesian generalized fused lasso
In the field of spatial data analysis, spatially varying coefficients (SVC) models, which allow regression coefficients to vary by region and flexibly capture spatial heterogeneity, have continued to be developed in various directions. Moreover, the Bayesian generalized fused lasso is often used as a method that efficiently provides estimation under the natural assumption that regression coefficients of adjacent regions tend to take the same value. In most Bayesian methods, the selection of pri…

The InviTE Corpus: Annotating Invectives in Tudor English Texts for Computational Modeling
Sophie Spliethoff, Sanne Hoeken, Silke Schwandt, Sina Zarrie{\ss}, \"Ozge Ala\c{c}am
https://arxiv.org/abs/2509.22345

The InviTE Corpus: Annotating Invectives in Tudor English Texts for Computational Modeling
In this paper, we aim at the application of Natural Language Processing (NLP) techniques to historical research endeavors, particularly addressing the study of religious invectives in the context of the Protestant Reformation in Tudor England. We outline a workflow spanning from raw data, through pre-processing and data selection, to an iterative annotation process. As a result, we introduce the InviTE corpus -- a corpus of almost 2000 Early Modern English (EModE) sentences, which are enriched …

Bifurcation: How to Explore a Tree
Sariel Har-Peled
https://arxiv.org/abs/2510.01939 https://arxiv.org/pdf/2510.01939…

Bifurcation: How to Explore a Tree
Avraham et al. [AFK+15] presented an alternative approach to parametric search, called \emph{bifurcation}, that performs faster under certain circumstances. Intuitively, when the underlying decider execution can be rolled back cheaply and the decider has a near-linear running time. For some problems, this leads to fast algorithms that beat the seemingly natural lower bound arising from distance selection. Bifurcation boils down to a tree exploration problem. You are given a binary (unfortunat…

Graph Neural Network Acceleration on FPGAs for Fast Inference in Future Muon Triggers at HL-LHC
Martino Errico (Sapienza Universit\`a di Roma, INFN Sezione di Roma), Davide Fiacco (Sapienza Universit\`a di Roma, INFN Sezione di Roma), Stefano Giagu (Sapienza Universit\`a di Roma, INFN Sezione di Roma), Giuliano Gustavino (Sapienza Universit\`a di Roma, INFN Sezione di Roma), Valerio Ippolito (INFN Sezione di Roma), Graziella Russo (Sapienza Universit\`a di Roma, INFN Sezione di Roma)

Graph Neural Network Acceleration on FPGAs for Fast Inference in Future Muon Triggers at HL-LHC
The High-Luminosity LHC (HL-LHC) will reach luminosities up to 7 times higher than the previous run, yielding denser events and larger occupancies. Next generation trigger algorithms must retain reliable selection within a strict latency budget. This work explores machine-learning approaches for future muon triggers, using the ATLAS Muon Spectrometer as a benchmark. A Convolutional Neural Network (CNN) is used as a reference, while a Graph Neural Network (GNN) is introduced as a natural model f…