Does Using ChatGPT Really Change Your Brain Activity? | Scientific American
https://www.nature.com/articles/d41586-025-02005-y

What Is a Macrostate? Subjective Observations and Objective Dynamics

We consider the question of whether thermodynamic macrostates are objective consequences of dynamics, or subjective reflections of our ignorance of a physical system. We argue that they are both; more specifically, that the set of macrostates forms the unique maximal partition of phase space which (1) is consistent with our observations (a subjective fact about our ability to observe the system) and (2) obeys a Markov process (an objective fact about the system’s dynamics). We review the ideas of computational mechanics, an information-theoretic method for finding optimal causal models of stochastic processes, and argue that macrostates coincide with the “causal states” of computational mechanics. Defining a set of macrostates thus consists of an inductive process where we start with a given set of observables, and then refine our partition of phase space until we reach a set of states which predict their own future, i.e. which are Markovian. Macrostates arrived at in this way are provably optimal statistical predictors of the future values of our observables.

Micro-, meso-, macroscales: The effect of triangles on communities in networks

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.100.022315

Life ≠ alive
A cat is alive, a sofa is not: that much we know. But a sofa is also part of life. Information theory tells us why

https://aeon.co/essays/what-can-schrodingers-cat-say-about-3d-printers-on-mars

#PhD Fellowship in Interdisciplinary Research on Misinformation and Pandemics

https://candidate.hr-manager.net/ApplicationInit.aspx?cid=1310&ProjectId=147740&DepartmentId=18971&MediaId=5&SkipAdvertisement=False

#PhD openings in Network Science – Minimal Subtraction
https://minimalsubtraction.net/ph-d-openings-in-network-science/

What is emergence, after all?
https://arxiv.org/abs/2507.04951

We hear the word #emergence a lot—in science, philosophy, even everyday conversation—but what does it really mean? In this perspective paper, we take a clear-eyed look at emergence as it manifests in real systems, ranging from flocking birds to magnets to herd immunity in social networks. We explain how complex behaviors and patterns can emerge from simple parts interacting locally, and why these large-scale phenomena often can’t be easily understood just by looking at the pieces alone. Instead of getting lost in buzzwords, we break down the idea using concrete examples, showing that emergence isn’t magic—it’s measurable, physical, and beneficial for making sense of the multi-layered complex world we live in.

Springer Nature book on machine learning is full of made-up citations

https://retractionwatch.com/2025/06/30/springer-nature-book-on-machine-learning-is-full-of-made-up-citations/

two #PhD studentships in real-time infectious disease modelling
https://www.lshtm.ac.uk/study/fees-and-funding/funding-scholarships/research-degree-funding/phd-studentships-real-time-infectious-disease-modelling

#Postdoc Researcher on alignment of MMFM with ethical, legal, and social values. Specifically, Oxford Internet Institute

https://my.corehr.com/pls/uoxrecruit/erq_jobspec_version_4.display_form?p_company=10&p_internal_external=E&p_display_in_irish=N&p_process_type=&p_applicant_no=&p_form_profile_detail=&p_display_apply_ind=Y&p_refresh_search=Y&p_recruitment_id=180757

Strength and weakness of disease-induced herd immunity in networks
https://www.pnas.org/doi/10.1073/pnas.2421460122

What if #herd_immunity isn’t just about how many people are immune, but how they’re 'spatially' connected? Our new PNAS paper explores this concept. We show how the topology and geometry of social networks influence the dynamics of herd immunity, whether it arises from infection or #vaccination.

Here is a less technical blog post for a more general reader: abbas.sitpor.org/2025/07/10/the-spatial-puzzle-of-herd-immunity

Strength and Weakness of Disease-induced Herd Immunity in Networks

During the COVID-19 pandemic, several studies suggested that the spread of infection might induce herd immunity more easily than previously thought due to population heterogeneity. However, these studies relied on differential equation-based epidemic models, which cannot account for correlations between individuals. We reexamine the effect of disease-induced herd immunity using individual-based contact network models. We find that herd immunity is weaker when such correlations are taken into account, so much so that the conclusions of the previous studies may be overturned. This effect is especially pronounced when the contact network is spatially embedded. Our results highlight the importance of considering network effects in policy decisions that affect the lives and well-being of millions in future pandemics.

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