Author: cxdig

The Third Law of Evolution and The Future of Life: A systems approach to natural philosophy, by Gerard Jagers op Akkerhuis

* Offers an integrating framework for natural philosophy
* Connects biological and physical evolution through novel theory, elaborating an extended evolutionary synthesis
* Analyses science from a philosophical perspective and looks at philosophy from a scientific perspective

More at: link.springer.com

School on Biological Physics and Biomolecular Simulations in the Machine Learning Era

The re-emergence of Machine Learning (ML) in the last decade has started to revolutionize the way we think about science, technology, and even our everyday lives. ML has rapidly become a significant part of research across all scientific areas, including the physical sciences. This school attempts to capture the recent excitement about ML in general and for biophysical and biomolecular systems in particular, addressing participants with various backgrounds ranging from biology or biotechnology to physics.

The school’s purpose is threefold: a) to provide a theoretical foundation from the physicists’ perspective, b) to cross-pollinate different theoretical, experimental, and computational approaches, and c) to develop an overarching perspective that would tie together the various phenomena from biomolecular simulation and electrostatic interactions on the molecular scale to collective behavior of macroscopic biological entities in a unified approach within an ML framework.

There is no registration fee and limited funds are available for travel and local expenses.

This school will be preceded by the II Brazilian Workshop on Soft Matter from April 7-11.

More at: www.ictp-saifr.org

Network community detection via neural embeddings

Sadamori Kojaku, Filippo Radicchi, Yong-Yeol Ahn & Santo Fortunato 

Nature Communications volume 15, Article number: 9446 (2024)

Recent advances in machine learning research have produced powerful neural graph embedding methods, which learn useful, low-dimensional vector representations of network data. These neural methods for graph embedding excel in graph machine learning tasks and are now widely adopted. However, how and why these methods work—particularly how network structure gets encoded in the embedding—remain largely unexplained. Here, we show that node2vec—shallow, linear neural network—encodes communities into separable clusters better than random partitioning down to the information-theoretic detectability limit for the stochastic block models. We show that this is due to the equivalence between the embedding learned by node2vec and the spectral embedding via the eigenvectors of the symmetric normalized Laplacian matrix. Numerical simulations demonstrate that node2vec is capable of learning communities on sparse graphs generated by the stochastic blockmodel, as well as on sparse degree-heterogeneous networks. Our results highlight the features of graph neural networks that enable them to separate communities in the embedding space.

Read the full article at: www.nature.com

Reimagining Life. Emergent Complexity from Non-Living to Living

Gordana Dodig-Crnkovic 

The development of naturalistic approaches to complexity of life continues a lineage of thought from Prigogine’s thermodynamics to contemporary complexity science. The paper highlights the central themes of self-organization, emergence, and the interplay between physical, informational, and biological processes. Prigogine’s concept of dissipative structures and irreversibility provided a foundation for understanding complexity in physical systems, which later expanded into biology through Kauffman’s models of creativity and evolution. Margulis’s endosymbiosis theory illuminate the cooperative dynamics underpinning life’s complexity, while Walker’s work integrates thermodynamics and information theory to bridge the gap between chemistry and biology through multiscale interactions and adaptive dynamics. By synthesizing these perspectives, this article situates life as an emergent phenomenon shaped by interactions across scales, proposing a unified framework for understanding complexity in the natural world.

Read the full article at: www.preprints.org