@article{21841,
  abstract     = {The long-standing notion that genotypes map to phenotypes through simple one gene–one trait relationships continues to shape both research in the life sciences and public understanding, with implications for policy and funding priorities. Yet this paradigm is increasingly recognized as inadequate for explaining continuous phenotypic variation and the complex genetic architectures of the genotype–phenotype map. Modern genetics emerged from the early 20th-century synthesis of Mendelian and biometric schools of heredity, with R.A. Fisher demonstrating early on how multiple discrete loci could collectively produce continuous variation. Despite this fundamental insight, Mendelism—with its focus on single genes and standardized genetic backgrounds—became the dominant framework, shaping current genetics research and molecular biology as well as science education. The advent of large-scale genomic data has revealed yet again the limitations of this reductionist approach. Evidence from quantitative genetics now shows that most phenotypes arise from complex networks of many interdependent genes and their dynamic responses to environmental perturbations. Here we trace the historical roots of how Mendelian classical genetics departed from the biometric school to create the current predominant paradigm in genetics, despite fundamentally unresolved issues. Moving on from this one-sided paradigm will require systematic development of integrative, evolutionarily grounded experimental approaches that better capture the multigenic and context-dependent nature of inheritance. Achieving such an extended perspective will require methodological innovation, including advances in large-scale (e.g. automated) phenotyping. Dedicated research programs will be necessary to advance a new era of genetic research into the complex mechanisms underlying phenotypic variation.},
  author       = {Tautz, Diethard and Pallares, Luisa F and Andersson, Leif and Barghi, Neda and Barton, Nicholas H and Bay, Rachael and Chan, Yingguang Frank and Hancock, Angela and Kaiser, Tobias S and Koenig, Daniel and Kontarakis, Zacharias and Liedvogel, Miriam and de Meaux, Juliette and Nordborg, Magnus and Palmer, Abraham A and Purugganan, Michael and Schlötterer, Christian and Schmid, Karl and Stainier, Didier Y R and Weigel, Detlef and Wolf, Jochen B W and Ebert, Dieter and Gibson, Greg},
  issn         = {1943-2631},
  journal      = {Genetics},
  keywords     = {classic genetics, quantitative genetics, genotype–phenotype map},
  number       = {4},
  publisher    = {Oxford University Press},
  title        = {{Beyond Mendel: A call to revisit the genotype–phenotype map through new experimental paradigms}},
  doi          = {10.1093/genetics/iyag024},
  volume       = {232},
  year         = {2026},
}

@article{22291,
  abstract     = {Persistent homology is a fundamental tool in Topological Data Analysis. The associated algebraic structure is the persistence module, a sequence of vector spaces connected by linear maps. Persistence modules admit a complete and fast-to-compute invariant known as the persistence diagram. However, this is no longer the case for maps between persistence modules (i.e. persistence maps). We propose a new invariant for persistence maps, consisting of a partial matching between the persistence diagrams of the domain and codomain modules. We show that this invariant is additive with respect to the direct sum decomposition of persistence maps, is more discriminative than the image module, and is computable in cubic time. Furthermore, we provide an implementation and demonstrate its efficiency by integrating it with edge collapse techniques for flag complexes (e.g., Vietoris–Rips complexes). As a key technical contribution, we describe how to induce a persistence map between two flag complexes that have been independently simplified via edge collapses, even when a direct simplicial map between them is no longer available.},
  author       = {Gonzalez-Diaz, Rocio and Soriano Trigueros, Manuel and Torras-Casas, Alvaro},
  issn         = {1095-855X},
  journal      = {Journal of Symbolic Computation},
  keywords     = {Persistence module, Persistence map, Persistent homology},
  publisher    = {Elsevier},
  title        = {{Additive partial matchings induced by persistence maps}},
  doi          = {10.1016/j.jsc.2026.102598},
  volume       = {138},
  year         = {2026},
}

@inproceedings{22294,
  abstract     = {Modern computer systems store vast amounts of personal data, enabling advances in AI and ML but risking user privacy and trust. For privacy reasons, it is sometimes desired for an ML model to forget part of the data it was trained on. In this paper, we introduce a novel unlearning approach based on Forgetting Neural Networks (FNNs), a neuroscience-inspired architecture that explicitly encodes forgetting through multiplicative decay factors. While FNNs had previously been studied as a theoretical construct, we provide the first concrete implementation and demonstrate their effectiveness for targeted unlearning. We propose several variants with per-neuron forgetting factors, including rank-based assignments guided by activation levels, and evaluate them on MNIST and Fashion-MNIST benchmarks. Our method systematically removes information associated with forget sets while preserving performance on retained data. Membership inference attacks confirm the effectiveness of FNN-based unlearning in erasing information about the training data from the neural network. These results establish FNNs as a promising foundation for efficient and interpretable unlearning. },
  author       = {Hatua, Amartya and Nguyen, Trung and Cano Cordoba, Filip and Sung, Andrew},
  booktitle    = {Proceedings of the 18th International Conference on Agents and Artificial Intelligence},
  isbn         = {9789897587962},
  issn         = {2184-433X},
  keywords     = {Machine Unlearning, Neuroscience-Inspired Machine Learning, Membership Inference Attacks},
  location     = {Marbella, Spain},
  pages        = {1536--1546},
  publisher    = {SciTePress},
  title        = {{Machine unlearning using forgetting neural networks}},
  doi          = {10.5220/0014326500004052},
  volume       = {2},
  year         = {2026},
}

@misc{5561,
  abstract     = {Graph matching problems as described in "Active Graph Matching for Automatic Joint Segmentation and Annotation of C. Elegans." by Kainmueller, Dagmar and Jug, Florian and Rother, Carsten and Myers, Gene, MICCAI 2014. Problems are in OpenGM2 hdf5 format (see http://hciweb2.iwr.uni-heidelberg.de/opengm/) and a custom text format used by the feature matching solver described in "Feature Correspondence via Graph Matching: Models and Global Optimization." by Lorenzo Torresani, Vladimir Kolmogorov and Carsten Rother, ECCV 2008, code at http://pub.ist.ac.at/~vnk/software/GraphMatching-v1.02.src.zip. },
  author       = {Kainmueller, Dagmar and Jug, Florian and Rother, Carsten and Meyers, Gene},
  keywords     = {graph matching, feature matching, QAP, MAP-inference},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Graph matching problems for annotating C. Elegans}},
  doi          = {10.15479/AT:ISTA:57},
  year         = {2017},
}