Marina Dubova, et al.

PNAS 122 (5) e2401230121

The preference for simple explanations, known as the parsimony principle, has long guided the development of scientific theories, hypotheses, and models. Yet recent years have seen a number of successes in employing highly complex models for scientific inquiry (e.g., for 3D protein folding or climate forecasting). In this paper, we reexamine the parsimony principle in light of these scientific and technological advancements. We review recent developments, including the surprising benefits of modeling with more parameters than data, the increasing appreciation of the context-sensitivity of data and misspecification of scientific models, and the development of new modeling tools. By integrating these insights, we reassess the utility of parsimony as a proxy for desirable model traits, such as predictive accuracy, interpretability, effectiveness in guiding new research, and resource efficiency. We conclude that more complex models are sometimes essential for scientific progress, and discuss the ways in which parsimony and complexity can play complementary roles in scientific modeling practice.

Read the full article at: www.pnas.org

Elias Dritsas and Maria Trigka

Information 2025, 16(1), 8;

The rapid growth of data and the increasing complexity of modern networks have driven the demand for intelligent solutions in the information and communications technology (ICT) domain. Machine learning (ML) has emerged as a powerful tool, enabling more adaptive, efficient, and scalable systems in this field. This article presents a comprehensive survey on the application of ML techniques in ICT, covering key areas such as network optimization, resource allocation, anomaly detection, and security. Specifically, we review the effectiveness of different ML models across ICT subdomains and assess how ML integration enhances crucial performance metrics, including operational efficiency, scalability, and security. Lastly, we highlight the challenges and future directions that are critical for the continued advancement of ML-driven innovations in ICT.

Read the full article at: www.mdpi.com

Ana Maria Jaramillo, Mariana Macedo, Marcos Oliveira, Fariba Karimi, Ronaldo Menezes

The participation of women in academia has increased in the last few decades across many fields (e.g., Computer Science, History, Medicine). However, this increase in the participation of women has not been the same at all career stages. Here, we study how gender participation within different fields is related to gender representation in top-ranking positions in productivity (number of papers), research impact (number of citations), and co-authorship networks (degree of connectivity). We analyzed over 80 million papers published from 1975 to 2020 in 19 academic fields. Our findings reveal that women remain a minority in all 19 fields, with physics, geology, and mathematics having the lowest percentage of papers authored by women at 14% and psychology having the largest percentage at 39%. Women are significantly underrepresented in top-ranking positions (top 10% or higher) across all fields and metrics (productivity, citations, and degree), indicating that it remains challenging for early researchers (especially women) to reach top-ranking positions, as our results reveal the rankings to be rigid over time. Finally, we show that in most fields, women and men with comparable productivity levels and career age tend to attain different levels of citations, where women tend to benefit more from co-authorships, while men tend to benefit more from productivity, especially in pSTEMs. Our findings highlight that while the participation of women has risen in some fields, they remain under-represented in top-ranking positions. Greater gender participation at entry levels often helps representation, but stronger interventions are still needed to achieve long-lasting careers for women and their participation in top-ranking positions.

Read the full article at: arxiv.org

Chris Fields, Michael Levin

Physics of Life Reviews

• An information-theoretic approach to biology renders “objects” and “processes” interchangable at every scale.
• Morphogenesis is a process of memory construction at every scale.
• Life depends on lateral information flows between its component lineages at every scale.
• Viewing living systems as multi-scale competency architectures forefronts communication via scale-appropriate interfaces, as opposed to manipulation of components, as a strategy for both therapuetic intervention and bioengineering.

Read the full article at: www.sciencedirect.com

Giorgio Kaniadakis, Tiziana Di Matteo, Antonio Maria Scarfone & Giampiero Gervino

The European Physical Journal B Volume 97, article number 203, (2024)

This issue contains peer-reviewed papers based on selected contributions presented at the International Conference on Statistical Physics (SigmaPhi) held in Chania-Crete (Greece) from July 10th to July 14th, 2023 (http://sigmaphisrv.polito.it/). The challenge facing statistical physics today is expanding beyond conventional conceptions of physics, bringing together multiple research streams that were thought to be separate and independent for the majority of the 20th century. In this topical issue, we present a collection of papers that demonstrate the current applications of statistical physics in a variety of different fields, including networks, biophysics, statistical mechanics, kinetic theory, and cosmology.

Read the full article at: link.springer.com