## >> DIEP seminars and talks

###### DIEP is a broad interdisciplinary endeavour with the aim of bringing scientists together and fostering collaborations in order to progress the science of emergence. For this purpose, DIEP organises a wide variety of events, ranging from brainstorming sessions, community building events, workshops, conferences, public lectures, technical talks and cocktail parties. Regular events include workshops and DIEP seminars (see also the events page).

To register for the DIEP seminars taking place every Thursday please fill out the form here. See full calendar of events here.See recordings of all talks here.

## >> DIEP Seminar: Andrea Luppi (Cambridge University)

Modelling how brain function emerges from network architecture in space and time | 11am, 19th of September 2024

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Disentangling how cognitive functions emerge from the interplay of brain dynamics and network architecture is among the major challenges faced by neuroscientists. Addressing these complex challenges requires a concerted integration of theory and data. In this talk, I will show how we can investigate the spatial and temporal dimensions of structure-function relationships in the brain, by combining the formal tools of network science with pharmacological and pathological perturbations.

I will show how perturbations of consciousness induce convergent reconfigurations of the brain’s unimodal-transmodal functional architecture. However, loss of consciousness increases structural constraints on brain dynamics across scales, whereas psychedelics decouple brain function from anatomy. Computational models provide a formal way to integrate our understanding.

## >> DIEP Seminar: Frederik de Laender (University of Namur)

Persistence and coexistence of species in spatial networks | 11am, 12th of September 2024

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A classic result in community ecology is that dispersal between patches (e.g. islands, forest fragments) promotes local biodiversity in those patches. However, the mechanisms causing this result are not well understood. Available patch-occupancy models predict the fraction of patches a given species occupies, but either only consider a single species, or imply extinction of all species across all patches in absence of dispersal. Available simulation models are more realistic but have been impossible to mathematically analyse beyond species pairs, making it unclear how general their simulation results really are.

During my talk, I will present new results that shed light on the effect of dispersal on local biodiversity. I will show how the assumption of small dispersal leads to an analytical treatment of an otherwise impalpable model. A first analysis focuses on the species level, showing how dispersal, local species interactions, and the size of the spatial network (i.e. the number of patches) jointly influence persistence. Interestingly, the effect of local interactions is fully captured by the focal species invasion growth rate, a main variable of interest in modern coexistence theory. A second analysis focus on the community level, introducing the idea of community-level patch occupancy: the fraction of patches occupied by all species and thus the community as a whole. I show how patch occupancy is intimately linked to feasibility, another key concept in coexistence theory. I end by showing how our predictions of patch occupancy match simulations, relaxing all our assumptions.

## >> DIEP Seminar: Monique de Jager (Utrecht University)

Solving the SLoSS debate: Scale-dependent effects of habitat fragmentation on biodiversity loss | 11am, 5th of September 2024

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Should what is left of nature be contained in a Single Large or Several Small (SLoSS) areas? What would minimize the severe impact of habitat destruction on biodiversity loss is much debated, mainly because studies generally focus on different spatial and temporal scales. Using a semi-spatially explicit, (near-)neutral, individual-based model, we investigate the effects of fragmentation on biodiversity loss at two spatial (landscape- versus subcommunity level) and two temporal scales (static versus dynamic effects). Our results show that the role of spatial configuration of habitat destruction depends on when and at what scale we measure biodiversity loss. When considering the more realistic assumption that species differ in dispersal capacity, differences between spatial configurations are amplified. Our results indicate that the spatial configuration of habitat loss needs to be considered when evaluating the risks of further habitat destruction.

## >> DIEP Seminar: Stefania Sardellitti (Universitas Mercatorum)

Topological Signal Processing with Applications to Brain Networks | 11am, 27th of June 2024

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Recently, we are witnessing a vibrant and fast-growing interest in the signal processing and machine learning communities in extracting and analyzing multiway relationships from complex data.

Topological Signal processing (TSP) is an emerging and powerful framework combining signal processing and topological tools to represent, analyse and process signals defined over topological domains. Topological domains, such as simplicial and cell complexes, enable the extraction and capture of higher-order relationships among data, overcoming the limitations of graph-based representations that only extract dyadic relationships between pairs of vertices.

In this talk, I will present methods for filtering, sampling and estimation of signals defined over simplicial and cell complexes. These topological spaces exhibit a rich hierarchical structure represented through a powerful algebraic framework that allows learning of global properties of the data by using local operators. Efficient methods to learn the structure of the topological domain from data will be presented, with a focus on interesting applications to brain networks. In brain networks, a fundamental issue is learning multiway relationships between groups, such as neurons or brain regions, that encode electrical or chemical coupling. Using TSP tools for identifying cycles and cavities can provide useful insights into the complex interactions and structures that underlie brain functions and diseases.

## >> DIEP Seminar: Subodh Patil (Leiden University)

It's a small world! (and how complex networks help us understand why) | 11am, 20th of June 2024

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Networks are a useful mathematical abstraction for any system where the relations between any two elements (or agents) are as significant to our understanding as the elements themselves. A remarkable feature emerges in a wide class of networks: that on average, one can hop from any two elements with a remarkably small number of hops -- the so-called small world property. In this talk I will review this idea illustrated with some familiar examples and present a novel mean field method to study this, and other properties of complex networks where heterogeneity of interactions and disorder and may be present. Each italicized term in this abstract will be explained in simple terms that require little to no prior or formal knowledge to accompany more formal aspects of the discussion. I will conclude with some potential applications of the work presented towards social, epidemiological, and biological systems.

## >> DIEP Seminar: Matteo Marsili (ICTP)

What is abstraction? | 11am, 13th of June 2024

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Deep neural networks develop representations of the data they are trained with, with a level of abstraction that increases with depth, in a similar way as our brain does. But what is abstraction? Abstraction is qualitatively assessed by looking at the features of the data to which internal nodes respond to. Can the level of abstraction be quantified in terms of the statistical properties of the activity of internal nodes without reference to what is represented (i.e. the data)?

We address these questions analysing the internal representation of deep belief networks trained on benchmark datasets.

## >> DIEP Seminar: Tobias Stark (Utrecht University)

How dual identifiers affect interethnic relations in social networks| 11am, 30th of May 2024

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Improving relations between ethnic minority and ethnic majority groups is one of the most pressing needs in modern societies. During this talk, I will present research from my group in which we test a new theory: that such relations can be improved by descendants of immigrants who identify with both the ethnic group of their parents and the national majority group, because these dual identifiers can create social bridges between communities. However, not all dual identifiers are recognized as such by others, as many descendants of immigrants experience being labeled, for instance, "Turks" or "Moroccans" instead of (also) "Dutch". This may undermine the amount of bridging that dual identifiers can accomplish.

In this talk, I will present a series of studies in which we explored why ethnic majority members do (not) ascribe co-national belonging to descendants of immigrants and how descendants of immigrants can signal their national identification. Our hypothesis is that dual identifiers' relationships with members of both groups are seen as signals of their dual belonging, but that the degree to which these signals are picked up depends on people?s perception of the structure of their social networks. To test this hypothesis, we have developed a novel open-source software that allows collecting data on perceived social networks and the perception of others' ethnic/national belonging.

## >> DIEP Seminar: Rak Kim (Utrecht University)

The complexity of law and governance| 11am, 23rd of May 2024

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The concept of complexity has received considerable attention in the study of legal and governance institutions. Its relevance stems from the inherent complexity of both the issues being regulated (e.g., climate change) and the institutional frameworks themselves (e.g., international climate regime). The challenge lies in navigating this dual complexity, or managing complex institutions in order to manage other complex social-ecological systems. This is a relatively new and ongoing area of research, and this talk will provide a broad overview aimed at an interdisciplinary audience who may not be familiar with the specific literature. We will explore various conceptualisations, operationalisations, and explanations of institutional complexity, as well as its political and systemic implications for governance outcomes. We will conclude with a reflection on the potential for interdisciplinary collaboration to enhance our capacity to harness complexity in law and governance.

## >> DIEP Seminar: Ginestra Bianconi (Queen Mary University of London)

Topology shapes dynamics of higher-order networks| 11am, 2nd of May 2024

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Higher-order networks capture the interactions among two or more nodes and they are raising increasing interest in the study of brain networks. Here we show that higher-order interactions are responsible for new non-linear dynamical processes that cannot be observed in pairwise networks.

We reveal how non-linear dynamical processes can be used to learn the topology, by defining Topological Kuramoto model and Topological global synchronization. These critical phenomena capture the synchronization of topological signals, i.e. dynamical signals defined not only on nodes but also on links, triangles and higher-dimensional simplices in simplicial complexes. Moreover, will discuss how the Dirac operator can be used to couple and process topological signal of different dimensions, formulating Dirac signal processing. Finally, we will reveal how non-linear dynamics can shape topology by formulating triadic percolation. In triadic percolation triadic interactions can turn percolation into a fully-fledged dynamical process in which nodes can turn on and off intermittently in a periodic fashion or even chaotically leading to period doubling and a route to chaos of the percolation order parameter. Triadic percolation changes drastically our understanding of percolation and can describe real systems in which the giant component varies significantly in time such as in brain functional networks and in climate.

We show that in many situations the statistics is determined by the details of the driving process and does not depend on the specific relaxation dynamics. Simple (homogeneous) driving strategies universally lead to Zipf’s law and exact power laws. Other driving processes results in exponential, Gamma, normal, Weibull, Gompertz, and Pareto distributions. We discuss a number of examples of SRR processes, including fragmentation processes, language formation, cascading and search processes, as well as a classic in physics: inelastical collisions.

## >> DIEP Seminar: Wilson Poon (University of Edinburgh)

Active Self Disassembly: Towards a Physics of Death| 11am, 25th of April 2024

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The concept of self-assembly was first invented by two physicists in 1962 to explain the construction of viral capsids. Since then, the idea that biology is self-assembled soft matter has become commonplace. However, biology at all length scales from molecules through cells and organisms to ecosystems also depends vitally on processes of active self-disassembly. Living systems have evolved to use energy to deconstruct part or all of themselves in highly-organised ways, and feedback building blocks to self-assembly processes. Programmed (or regulated) cell death in our tissues is a good example, but death at all levels has been highly-evolved to enable life to function. In this talk, I consider what such a physics of death may look like, report briefly some ongoing work in this direction, and explain why such research may have a vital role to play in the drive towards a more sustainable circular economy.

## >> DIEP Seminar: Jacopo Grilli (International Centre for Theoretical Physics)

Competition without contingency and functional convergence| 3pm, 18th of April 2024

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Microbial communities are taxonomically diverse and variable: species presence and their abudances widely fluctuate over time and space, and even across biological replicates in experimental controlled conditions. On the other hand, environmental conditions exert a strong selection on the traits of community members and the function they perform, and similar environmental conditions are expected to correspond to functionally similar communities. The consequence of this environmental selection, together with taxonomic variability, lead to the influental concept of functional redundancy: the same function is performed by many species, so that one may assemble communities with different species but the same functional profile.

The centrality of the concept of functional redundancy in microbial ecology does not parallel with a theoretical understanding of its origin. In this talk I will describe the eco-evolutionary dynamics of communities interacting through competition and cross-feeding. I will show that the eco-evolutionary trajectories rapidly converge to a "functional attractor'', characterized by a functional composition uniquely determined by environmental conditions. The taxonomic composition instead follows non-reproducible dynamics, constrained by the conservation of the functional composition. This framework provides a deep theoretical foundation to the concepts of functional robustness and redundancy.

## >> DIEP Seminar: Stefan Thurner (Complexity Science Hub Vienna)

Towards a statistics of driven complex systems| 11am, 18th of April 2024

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Most complex systems are not in equilibrium but driven. We argue that driven systems can be understood by so-called sample space reducing (SSR) processes. They provide an intuitive understanding of the origin and ubiquity of fat-tailed distributions in complex systems, power-laws in particular. SSR processes are mathematically simple and offer an alternative to Boltzmann-equation based approaches to non-equilibrium systems.

We show that in many situations the statistics is determined by the details of the driving process and does not depend on the specific relaxation dynamics. Simple (homogeneous) driving strategies universally lead to Zipf’s law and exact power laws. Other driving processes results in exponential, Gamma, normal, Weibull, Gompertz, and Pareto distributions. We discuss a number of examples of SRR processes, including fragmentation processes, language formation, cascading and search processes, as well as a classic in physics: inelastical collisions.

## >> DIEP Seminar: Harsha Honnappa (Purdue University)

Pathwise Relaxed Optimal Control of Non-Markovian Systems| 11am, 11th of April 2024

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This talk presents a theoretical framework for a definition of differential systems that model reinforcement learning or simulation-based control in continuous time non-Markovian rough environments. The desideratum for such a framework arises, in part, from rare event estimation for non-Markovian stochastic systems in the Friedlin-Wentzell small noise setting for instance. Specifically, I will focus on optimal relaxed control of rough equations (the term relaxed referring to the fact that controls have to be considered as measure valued objects).

In a general context, our contribution focuses on a careful definition of the corresponding relaxed Hamilton-Jacobi-Bellman (HJB)-type equation. A substantial part of our endeavor consists in a precise definition of the notion of test function and viscosity solution for the rough relaxed PDE obtained in this framework. Note that this task is often merely sketched in the rough viscosity literature, in spite of the fact that it gives a proper meaning to the differential system at stake. We show that a natural value function solves a rough HJB equation in the viscosity sense. With reinforcement learning in view, our reward functions encompass forms that involve an entropy-type term favoring exploration. I will demonstrate that, in this setting, closed-form expressions for the optimal relaxed control are obtainable.

## >> DIEP Seminar: Ricardo Martinez-Garcia (Center for Advanced Systems and Understanding (CASUS), Germany)

Causes and consequences of imperfect coordination in collective behaviors across scales: from microbial aggregates to ungulate migration| 11am, 4th of April 2024

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Collective behaviors, in which many individuals exhibit some degree of behavioral coordination, are frequent in nature and observed across a continuum of scales, from microbial aggregates to ungulate migrations. Intriguingly, however, such coordination is sometimes imperfect, and out-of-sync individuals exist in many of these systems. The roots of such imperfect coordination, and hence the mechanisms underlying the emergence of out-of-sync individuals, will undoubtedly differ across systems. Nevertheless, the occurrence of imperfect coordination across such different systems and scales raises fundamental questions about its causes and consequences. Are ?out-of-sync? individuals merely inevitable byproducts of large-scale coordination attempts, or can they, at least in some systems, be a variable trait that selection can shape with potential ecological consequences?

I will address this question by combining empirical data on slime-mold imperfect aggregation and observed patterns of partial migration observed within three ungulate specie. In each of these systems, we find that the number of individuals that do not engage in the collective behavior is unrelated to the total population size, suggesting that a complex individual decision-making process underlies the onset of the collective behavior. Using a minimalistic modeling framework, we propose that imperfectly synchronized collective behaviors are, in fact, a dynamic population partition process that originates from each individual making a stochastic signal-based decision. The parallelisms between these two seemingly different systems suggest that imperfectly synchronized collective behaviors could be critical to understanding social behaviors and ecological dynamics across scales.

## >> DIEP Seminar: Swinda Falkena (Utrecht University)

From coupled Earth System Models to a conceptual climate model and back; the case of the subpolar gyre| 11am, 14th of March 2024

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Conceptual climate models are based on a physical understanding of the system of interest. What processes are at play? What are the interactions between different components? The results of studying a conceptual climate model in isolation, knowing the processes are reasonably realistic, are often used to infer aspects of the real system. The question is how well these results extend from a relatively simple system to a highly complex one. In this talk I will discuss this question using the example of the subpolar gyre.

The subpolar gyre in the North-Atlantic ocean is a key area for convection, which contributes to the Atlantic Meridional Overturning Circulation (AMOC). The circulation itself is wind-driven, but the convection in its center is the result of different positive and negative feedback mechanisms. There exists a conceptual model representing the dynamics of this convection, which is based on studies of a limited number of coupled earth system models (ESMs). I will discuss how well the dynamics this model describes are represented in a large set of ESMs. Which parts of the dynamics are accurately captured? Which are not? And what are the consequences if we want to extend results from the conceptual model to the real system? I will end with an outlook and discussion of next steps.

## >> DIEP Seminar: Andrey Bagrov (Radboud University Nijmegen)

Multi-scale structural complexity of natural patterns| 11am, 7th of March 2024

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Complexity of a pattern or a system is a very intuitive concept that, at the same time, is very elusive when one attempts to give a formal definition of it. In nature, a distinctive feature of complex systems is the multi-level hierarchy of organization, with different levels being drastically different from each other (think, e.g., of the levels of organelles, cells, tissues, and organs in a human body). In this talk, I will review the measure of structural complexity of complex systems based on the idea of their inter-scale self-dissimilarity introduced by us a few years ago [Bagrov et. al, 2020)]. I will show how this measure can be used to quantify perceptive psychological complexity of patterns, to describe phase transitions in classical and quantum systems, and to address the problem of verification of quantum many-body states. If time allows, I will cover some of recent applications of this concept in diverse contexts such as time series analysis and quantum simulators.

## >> DIEP Seminar: Frederike Oetker (University of Amsterdam)

Navigating Cocaine Networks: Computational Tools and Data-Driven Insights | 11am, 29th of February 2024

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We present an innovative approach to understanding and intervening in criminal cocaine networks, employing three validated functional computational models created using Agent-Based Models (ABMs). These models aim to simulate network dynamics post-node removal, intervention strategies, and a large-scale digital twin of the criminal cocaine network in the Netherlands. To process law enforcement data inputs, the FREIDA (Framework for Expert-Informed Data-driven Agent-based models) framework facilitates the creation of validated computational models through integrating qualitative and quantitative data input such as case files and arrest data bases to understand emergent behaviour in a criminal context. We enrich the model by applying a tie strength classification to investigate network composition and potential link prediction between agents and keeping in mind the value network of cocaine trade.

An interactive dashboard is being developed for end-user manipulation of data and models.

## >> DIEP Seminar: Bernadette Stolz (EPFL)

Applications of global and local persistent homology for the shape and classification of biological data | 11am, 22th of February 2024

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Topological data analysis (TDA) is an emerging mathematical field that uses topological and geometric approaches to quantify the “shape” of data. In the first part of this talk, I will showcase how persistent homology, a method from TDA, can be used to spatially characterise structural abnormality in tumour blood vessel networks reconstructed from experimental data. More specifically, I will show that the number of vessel loops and their spatial distribution in these networks change over time when tumours undergo treatment with vascular targeting agents and radiation therapy. I will also show what insight TDA can give when applied to synthetic data generated from mathematical models of tumour-induced vascular growth. In the second part of the talk, I will demonstrate applications of local persistent homology. I will show how local persistent homology can be used to select landmarks from large and noisy data sets. In contrast to existing methods, this subsampling process is robust to outliers and is developed specifically as a preprocessing step for persistent homology. Based on similar ideas, I will introduce a novel method that can detect geometric anomalies, such as intersections or boundaries, in point cloud data sampled from intersecting surfaces. This detection is based on the computation of persistent homology in local annular neighbourhoods around points and is less sensitive to the size of the local neighbourhood and surface curvature than local principal component analysis.

## >> DIEP Seminar: Els Weinans (Utrecht University)

Drivers of Polarization and consensus| 11am, 15th of February 2024

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Our ability to deal with external changes such as climate change, pandemics, and geopolitical developments, does not solely depend on the development of new ideas or technologies, but also on our willingness to adopt new ideas. Opinion dynamics models are one way to explore how people may adopt new ideas. In this talk, I will introduce a recently published model on opinion dynamics that helps to explore the possible pathways towards consensus and polarization. We find that under most parameter settings, both consensus as well as polarization and an in-between co-existence state are possible outcomes of the model. Sensitivity analysis reveal that the most important determinant of model outcome, is the amount of stochasticity, where consensus is found for intermediate levels of stochasticity.

## >> DIEP Seminar: Irene Ferri (University of Barcelona)

Opinion Modeling from Statistical Physics| 2pm, 1st of February 2024

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In an increasingly globalized world, society confronts complex challenges with numerous interconnected variables. Statistical physics offers a framework to study human behavior by simplifying general situations to the interplay of a few essential parameters. This approach is valuable for comprehending and addressing practical issues, including achieving consensus in areas such as energy production, resource management and poverty reduction. Moreover, it can contribute to the enhancement of deliberative spaces. Our proposal introduces an agent-based three-state opinion model, emphasizing the relationship between neutral and extremist opinions and examining how opinions evolve in tandem with the rearrangement of social ties.

## >> DIEP Seminar: Frank Pijpers (University of Amsterdam)

Statistics Netherlands' datasets as a resource for complexity |

11am, 1st of February 2024

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Statistics Netherlands (SN) primary role is as the Dutch National Statistical Institute, and it is known for its output in the form enormous numbers of tables on topics ranging from the potato harvest to Dutch GDP and from demographic change to inflation. Perhaps less widely known is that the microdata, out of which such tables are compiled, can be accessed (for a modest cost to keep IT systems running) by researchers at Dutch universities for research purposes. While some data is survey-based, a lot of datasets are extracted from administrative registers; which means that data about the entire population are available at microlevel. For the purpose of calibrating models for emergent social phenomena especially, such integral datasets are invaluable. In this talk I will discuss some of the benefits and applications of the data, some projects currently being pursued in the field of complexity science by SN researchers and others, but also some pitfalls associated with these datasets.

## >> DIEP Seminar: Laura Alessandretti (TU Denmark)

The Dynamics of Online Coordination in the GameStop Stock Surge| 11am, 25th of January 2024

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In this talk, I will present a systematic investigation of the GameStop stock surge, a paradigmatic example of collective action facilitated by social media platforms. The focal point of this analysis is the r/wallstreetbets community on Reddit, which contributed a dramatic financial event through an unprecedented form of online coordination. Using a data driven approach, I will demonstrate the key role played by a committed minority within the online community and that of the community's social identity as events unfolded. I will unveil the linkage between online discourse and market movements, revealing distinct phases of activity impacting the stock behaviour. The presentation aims to provide a nuanced understanding of the interplay between social media and financial markets, offering insights into the emergent collective actions in digital spaces and their implications for market behavior.

## >> DIEP Seminar: Tommaso Gili (IMT School for Advanced Studies)

Laplacian Renormalization Group for heterogeneous networks | 11am, 18th of January 2024

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Complex networks usually exhibit a rich architecture organized over multiple intertwined scales. Information pathways are expected to pervade these scales, reflecting structural insights not manifested from network topology analyses. Moreover, small-world effects correlate with network hierarchies, complicating identifying coexisting mesoscopic structures and functional cores. To shed further light on these issues, we present a communicability analysis of effective information pathways throughout complex networks based on information diffusion. This leads us to formulate a new renormalization group scheme for heterogeneous networks. The Renormalization Group is the cornerstone of the modern theory of universality and phase transitions, a powerful tool to scrutinize symmetries and organizational scales in dynamical systems. However, its network counterpart is particularly challenging due to correlations between intertwined scales. The Laplacian RG picture for complex networks defines the supernodes concept à la Kadanoff and the equivalent momentum space procedure à la Wilson for graphs. A direct application of the Laplacian Renormalization Group (LRG) framework is the multi-scale Laplacian (MSL) community detection algorithm. Based on inter-node communicability, our definition provides a unifying framework for multiple partitioning measures.

## >> DIEP Seminar: Eric Dignum (University of Amsterdam)

Computational modelling of school choice and school segregation: from theoretical to data-driven agent-based models | 11am, 14th of December 2023

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Many educational systems still consist of substantial levels of school segregation along various lines, such as race, ethnicity, household income levels, parental educational attainment and ability. This means that, globally, pupils with similar characteristics cluster together in the same schools. This is widely acknowledged to reproduce or even exacerbate inequalities and result in unequal outcomes. Hence, even despite decades of research and policies aimed to counteract school segregation, it is still a persistent societal problem. Existing literature shows that factors affecting school segregation and individual components in the system of school choice interact with each other. These interactions are reasoned to be an important mechanism through which the levels of school segregation emerge on the macro-level, but commonly used qualitative and quantitative analysis methodologies (e.g. discrete choice models, interviews, surveys) often ignore these dependencies between the components and their consequences (e.g. feedback loops, non-linearity). Using methodologies that ignore these interactions could be an explanation why school segregation is still plaguing our educational systems. However, tools from complex systems such as Agent-Based Models (ABMs), can potentially be a complementary methodology that can explicitly account for such interactions and could therefore complement our understanding of the effect of them on school segregation. In this talk, two examples of ABMs are presented which illustrate that a complexity perspective on school choice dynamics can meaningfully contribute to the field of school choice and school segregation. Firstly, using a highly stylised ABM, an alternative explanation of why schools are often more segregated relative to neighbourhoods is given. Households do not have to be less tolerant for school compositions compared to neighbourhood compositions. Instead, this ABM demonstrates that asymmetric preferences are not a requirement for excess school segregation, but that residential segregation combined with distance preferences and interacting components can also play a key role. However, these highly stylised models have limited applicability to reality and actual policy scenarios. Therefore, we also present ongoing work on a methodology to empirically calibrate large-scale ABMs on empirical data in the context of school choice. We show that this methodology is able to retrieve the (artificial) ground truth within reasonable accuracy. Additionally, (computational) challenges and open questions are discussed, focusing on the usage of household-level register data.

## >> DIEP Seminar: Christian List (LMU Munich)

Do group agents have free will | 11am, 30th of November 2023

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It is common to ascribe agency to some organized collectives, such as corporations, courts, and states, and to treat them as loci of responsibility, over and above their individual members. But since responsibility is often assumed to require free will, should we also think that group agents have freewill? Surprisingly, the literature contains very few in-depth discussions of this question (a notable exception is Hess 2014). I will argue that group agents can have free will not only in a relatively undemanding compatibilist sense, but also in a recognizably libertarian sense, which includes the possibility of doing otherwise. In developing this account of corporate free will, I will bring together recent work on group agency and recent work on free will.

## >> DIEP Seminar: Renee Hoekzema (VU Amsterdam)

Multiscale methods for gene selection in single cell transcriptomics data | 11am, 23rd of November 2023

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Single cell transcriptomics is a revolutionary technique in biology that allows for the measurement of gene expression levels in many individual cells simultaneously. Analysis of these large datasets reveals complex variation in expression patterns between cells. Current methods for analysis assume that cell types are discrete. However, in practice there is also continuous variation between cells: subtypes of subtypes, differentiation pathways, responses to environment or treatment, et cetera. The complexity found in modern single cell transcriptomics datasets calls for intricate methods to biologically interpret both discrete clusters as well as continuous variations. We propose topologically-inspired data analysis methods that identify coherent gene expression patterns on multiple scales, considering discrete and continuous patterns on equal footing. As well as finding new biologically meaningful genes, the methodology allows one to visualise and explore the space of gene expression patterns in the dataset.

## >> DIEP Seminar: Meike Wortel (University of Amsterdam)

Eco-evolutionary mechanisms to explain species and strain diversity | 11am, 16th of November 2023

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I will discuss a recent paper https://onlinelibrary.wiley.com/doi/full/10.1111/jeb.14158 where I investigated whether fluctuating nutrients can (partly) explain diversity observed in microbial communities, such as oceans or the human gut. When nutrient levels fluctuate over time, one possibly relevant mechanism is coexistence between specialists on low and specialists on high nutrient levels. The relevance of this process is supported by the observations of coexistence in the laboratory, and by simple models, which show that negative frequency dependence of two such specialists can stabilize coexistence. However, as microbial populations are often large and fast growing, they evolve rapidly. I will discuss how we determine what happens when species can evolve; whether evolutionary branching can create diversity or whether evolution will destabilize coexistences. We derive an analytical expression of the invasion fitness in fluctuating environments and use adaptive dynamics techniques to find that evolutionarily stable coexistence requires a special type of trade-off between growth at low and high nutrients. I will also discuss current work on a different mechanism: When one phenotype degrades a toxin which allo another phenotype to grow (cross-protection) and ideas how to generalize results from both of these studies to study the interaction of more mechanisms in larger networks.

## >> DIEP Seminar: Christian Hamster (University of Amsterdam)

Journal club on modelling evolutionary dynamics | 11am, 9th of November 2023

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Real world ecosystems can be very complex, with many species coexisting that are functionally very similar. For example, there are many different plankton species in the oceans competing for a small number of resources. Mathematical modelling, however, indicates that only a few species should survive, the 'most fit' species. This discrepancy is known as the paradox of the plankton. Our approach starts with a simple resource-consumer model, and at a random timepoint, we introduce a new species that is a small random perturbation of an already existing species. This allows us to numerically mimic evolution and build complex ecosystems. This approach raises a lot of questions, both mathematically and biologically. From a mathematical perspective, can we understand and describe the numerical solutions? And from a biological perspective, what constitutes a species in our system? Is every perturbation a new species, or just genetic variation within a species and should we look at clusters of species? These and many other questions we can discuss during the meeting.

## >> DIEP Seminar: Han van der Maas (University of Amsterdam)

Sociophysics: an overview and new ideas | 11am, 2nd of November 2023

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To develop a mathematical and computational toolbox for studying the interdependencies between polarization, segregation, and inequality, an overview of modeling in sociophysics is needed. In this talk I will try to provide such an overview. I will also present some new research directions in the field of polarization.

## >> DIEP Seminar: Liang Li (Max Planck Institute of Animal Behavior)

Schooling fish: from biology to robotics and back | 11am, 26th of October 2023

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With over half a billion years behind them, fish have evolved to swim with remarkable efficiency, agility, and stealth in their three-dimensional aquatic world. Given this, it's natural that engineers often look to fish for inspiration when developing underwater propulsion systems. Over the years, roboticists have been inspired by these biological marvels to design fish-like robots that mimic real fish in terms of morphology, locomotion, and movement. Interestingly, the trend has recently shifted from merely drawing inspiration from biology to using robotics as a tool for better understanding biological processes. In this talk, I will first discuss our approach to designing and controlling these robotic fish, rooted in the concept of bio-inspiration. I will then provide examples of how we employ both real and virtual robots to investigate the mechanisms of collective behaviour in schooling fish. To conclude, I'll offer a glimpse into my current and future endeavors in the realms of robotics and biology.

## >> DIEP Seminar: Alexandru Baltag & Sonja Smets

(University of Amsterdam)

Logic Meets Wigner’s Friend(s): the epistemology of quantum observers | 11am, 19th of October 2023

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This presentation is about Wigner's Friend thought-experiment [1], and its more recent variations [2] and extensions such as the Frauchiger-Renner (FR) Paradox [3], that have recently reignited the debates in the foundations of quantum theory. Such thought experiments seem to indicate that, if quantum theory is assumed to be universally valid (and hence can be applied to multi-partite systems that may include classical observers), then different agents are rationally entitled to ascribe different (mutually inconsistent) states to the same system, and as a result they cannot share their information in a consistent manner. More precisely, the result in [3] is stated in the format of a no-go theorem. To analyze this problem, we focus on a few questions: what is the correct epistemic interpretation of the multiplicity of state assignments in these scenarios?; under which conditions can one include classical observers into the quantum state descriptions, in a way that is still compatible with Quantum Mechanics?; under which conditions can one system be admitted as an additional ‘observer’ from the perspective of another (background) observer?; when can the standard axioms of multi-agent Epistemic Logic (that allow for “knowledge transfer” between agents) be applied to quantum-physical observers? After discussing some of the various answers to these questions proposed in the literature, we propose a new such answer, sketch a particular formal implementation of it, and apply it to obtain a principled solution to Wigner Friend-type paradoxes. The presentation is based on recent joint work [4].

[1] E.P. Wigner, Remarks on the mind-body question, in I.J. Good, The Scientist Speculates, London Heinemann,1961.

[2] D. Deutsch, Quantum theory as a universal physical theory, International Journal of Theoretical Physics, 24, I, 1985.

[3] D. Frauchiger and R. Renner, Quantum theory cannot consistently describe the use of itself, Nature Communications, 9(1):3711. Preprint, arXiv:1604.07422, 2018.

[4] A. Baltag and S. Smets, Logic meets Wigner’s Friend (and their Friends), preprint, arXiv:2307.01713 [quant-ph], July 2023.

## >> DIEP Seminar: Charlotte Hemelrijk (University of Groningen)

Self-organized motion and collective escape in schools of fish and flocks of birds | 11am, 12th of October 2023

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It is mysterious how schools of fish and flocks of birds, such as the huge flocks of starlings, coordinate their members particularly when the shape and density of the flock change miraculously, for instance during collective escape from a predator by collective turning, flock-splitting, flash expansion and the so-called wave of agitation. These fascinating questions need to be solved by combining computational models with empirical data. Empirical data have been collected on many aspects, such as the shapes of the flock, the degree of motion of individuals within a flock and the collective patterns of evasion of a predator, for different species of birds when they are chased by a robotic bird. In the present talk, we will use computational models based on self-organization, such as StarDisplay and others, because flocks and schools in these models resemble empirical data in many respects. We will show what causes the shape of a fish school to be on average oblong and that of huge flocks of starlings to be continuously changing; whether or not starlings keep close to a familiar neighbour and what may be underlying in schools and flocks the internal motion, flock splitting and the wave agitation.

## >> DIEP Seminar: Alexandre Genin (University of Utrecht)

Spatial self-organization and its consequences in simple and complex ecosystems | 11am, 5th of October 2023

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Because species interact with each other, the spatial organisation of ecosystems is seldom random, but most often self-organized into specific spatial patterns. A seminal example is the case of positive interactions between plants producing emergent patches, bands, or fractal patterns in a landscape, with important consequences on its resilience to perturbations. In this seminar, we will first focus on simple, but widespread examples of such ecological interactions leading to spatial-self organisation. We will show how the non-random, emergent spatial structure can be quantified to help us understand and predict ecosystem collapse, showcasing a recent application to the coral reefs of Rapa Nui (Easter Island, Chile). We will then discuss how these relatively simple principles can be extended to species-rich systems, to understand the link between ecological interactions and spatial self-organisation, this time in more complex settings.

## >> DIEP Seminar: Conor Finn (Max Planck Institute for Mathematics in the Sciences)

Pointwise information decomposition for complex systems | 11am, 28th of September 2023

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The aim of information decomposition is to provide a mathematical framework that partitions the total information provided by a set of source variables about a target variable into i) the unique information that is provided by each individual source variable, ii) the shared information that is redundantly provided by two or more source variables, and iii) the synergistic information that is only attainable from simultaneous knowledge of two or more source variables. Such a decomposition has many potential applications in the sciences: for instance, quantifying the synergistic interaction between multiple incoming neural stimuli that are fused to create some output signal, or for determining the extent to which a particular phenotypic trait depends uniquely on each individual source gene, redundantly on two or more genes or is determined by some synergistic combination of several genes. Most approaches to information decomposition focus on decomposition the average information provided the variables involved. In this talk, I will discuss why this is unsatisfactory, especially when in comes to analysing complex systems. I will provide a brief overview of the pointwise perspective on information theory and then discuss the challenges associated with determining a pointwise information decomposition. I will then discuss the pointwise partial information decomposition that we introduced before closing by demonstrating how this theory can be used to quantify emergent intrinsic computation in canonical complex systems, namely elementary cellular automata.

## >> DIEP Seminar: Daniel Miranda (Federal University of Pernambuco)

Phenomenological renormalization group analysis of cortical spiking data | 10am, 21st of September 2023

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The critical brain hypothesis has emerged in the last decades as a fruitful theoretical framework for understanding collective neuronal phenomena. Lending support to the idea that the brain operates near a phase transition, Beggs and Plenz were the first to report experimentally recorded neuronal avalanches, whose distributions coincide with the mean-field directed percolation (DP) universality class, which comprises a variety of models in which a phase transition occurs between an absorbing (silent) and an active phase. However, this hypothesis is highly debated, as neuronal avalanches analyses and other common statistical mechanics tools may struggle with challenges ubiquitous in living systems, such as subsampling, long range correlations and the absence of an explicit model for the complete neuronal dynamics. In this context, Meshulam et al. recently proposed a phenomenological renormalization group (PRG) method to deal with neural networks typical long range interactions with a model independent analysis. The procedure consists of repeatedly manipulating the data, obtaining an increasingly coarse-grained description of the activity after each iteration. Under a critical regime, non-trivial correlations and scale-free behavior should be unveiled as we simplify our description. This can be inferred from a series of statistical features of the data, which lead us to different scaling relations. Here, we apply this phenomenological renormalization group (PRG) in different experimental setups. Additionally, we investigate how the scaling exponents found via PRG behave as we parse our data by its coefficient of variation (CV); this measurement has appeared in recent literature as a means of tracking different cortical states through spiking variability.

## >> DIEP Seminar: Ro Jefferson (Utrecht University)

Physics ∩ Machine Learning | 11am, 14th of September 2023

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Machine learning has become both powerful and ubiquitous, but remains a black box whose internal workings are still largely unclear. In this talk, I will discuss some interesting connections between ideas in physics (in particular QFT and the renormalization group) and deep neural networks in particular, which collectively motivate a physics-based approach towards a theory of deep learning.

## >> DIEP Seminar: Daniele Marinazzo (University of Ghent)

###### (Higher-order) informational interactions: ideas, implementations, and applications in neurosciences and behavioral sciences” | 11am, 7th of September 2023

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Systems composed of many units, whose behavior goes beyond the sum of the individual behaviors of the singles, are ubiquitous. Examples relevant to what we do are the brain, the body as a whole, and the social systems we live in. When it comes to analyzing collective behavior we are often stuck with pairwise dependencies (often correlations). In this talk, I will describe a framework rooted in information theory to mine multiplets of variables sharing common information about the variability of complex systems, and provide some examples in neuroscience, physiological, and psychometrics.

## >> DIEP Seminar: Pierre Baudot (Median Technologies)

###### Information cohomology, higher-order statistical interactions, complexity and deep networks | 11am, 29th June 2023

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Information theory, probability and statistical dependencies, and algebraic topology provide different views of a unified theory yet currently in development, where uncertainty goes as deep as Galois's ambiguity theory, topos and motivs. I will review some foundations, that characterize uniquely entropy as the first group of cohomology, on random variable complexes and probability laws. This framework allows to retrieve most of the usual information functions, like KL divergence, cross entropy, and was extended by Juan Pablo Vigneaux to Tsallis entropies, differential entropy. Multivariate interaction/Mutual information (I_k and J_k) appear as coboundaries, and their negative minima, also called synergy, corresponds to homotopical link configurations, which at the image of Borromean links, illustrate what purely collective interactions or emergence can be. Those functions refine and characterize statistical independence in the multivariate case, in the sens that (X1,...,Xn) are independent iff all the I_k=0 (with 1<k<n+1, whereas for Total correlations G_k, it is sufficient that G_n=0), generalizing correlation coefficient. Concerning data analysis, restricting to the simplicial random variable structure sub-case, the application of the formalism to genetic transcription or to some classical benchmark dataset using open access infotopo library, unravels that higher statistical interactions are nonetheless omnipresent but also constitutive of biologically relevant assemblies. On the side of deep networks, information cohomology provides a topological and combinatorial formalization of deep networks' supervised and unsupervised learning, where the depth of the layers is the simplicial dimension, derivation-propagation is forward (co-homological). Recently, Leon Lang could generalize higher-order mutual informations notably to Tsallis entropy and Kolmogorov complexity, and Tom Mainiero further established a general associated index, or “Euler characteristic”, given by the Tsallis mutual informations on a weighted simplicial complex whose topology retains information about the correlations between various subsystems. Tsallis mutual informations hence open a totally new and unexplored territory for higher order interactions studies, both theoretically and in application to data-natural sciences.

## >> DIEP Seminar: Abel Jansma (University of Edinburg)

###### The information theory of higher-order interactions | 11am, 22nd June 2023

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Information-theoretic quantities reveal dependencies among variables in the structure of joint, marginal, and conditional entropies, but leave some fundamentally different systems indistinguishable. Furthermore, there is no consensus on how to construct and interpret a higher-order generalisation of mutual information (MI). In this talk, I will show that a recently proposed model-free definition of higher-order interactions amongst binary variables (MFIs), like mutual information, is a Möbius inversion on a Boolean algebra, but of surprisal instead of entropy. This gives an information-theoretic interpretation to the MFIs, and by extension to Ising interactions. We will study the dual objects to MI and MFIs on the order-reversed lattice, and find that dual MI corresponds to conditional mutual information, while dual interactions (outeractions) are interactions with respect to a different background state. Unlike mutual information, in- and outeractions uniquely identify all six 2-input logic gates, the dy- and triadic distributions, and different causal dynamics that are identical in terms of their Shannon-information content.

## >> DIEP Seminar: Mike Lees (UvA)

###### Measuring, Modelling and Simulating Crowd Dynamics: Mobility to Epidemics | 11am, 15th June 2023

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In this talk I will present the challenges of understanding and modelling the multi-disciplinary problem of human mobility and crowd dynamics. I’ll highlight our attempts to conduct empirical experiments of human crowds and demonstrate the technological and technical challenges that this presents. I’ll show the classical approaches used by computational scientists when modelling crowds and the challenges of connecting measurement to models. I’ll showcase two ongoing projects where we attempt to measure and model crowd dynamics to understand the spread of infectious disease. Firstly, the Kumbh Mela Experiment where we measure and model pilgrims in Ujjain, India, to estimate the spread of Tuberculosis. Secondly, A project in the Johan Cruyff arena where we use Wi-Fi data to try and connect movement human movement ecology (e.g., Levy Flight Dynamics) to the exposure and spread of infectious diseases.

## >> DIEP Seminar: Jay Armas (DIEP)

###### Risk aversion promotes cooperation | 11am, 8th June 2023

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Cooperation is at the heart of many phenomena in living and complex systems, including multicellularity, eusociality in insect societies, human communities and financial markets. Many mechanisms that lead to the emergence of cooperation in small groups have been proposed and studied in depth in the past decades. Yet, little is known about the existence of potential mechanisms that can sustain large-scale cooperation involving a very large group of individuals. I will combine chemical reaction networks with evolutionary game theory and stochastic methods to study simple models of interacting groups and show, under certain assumptions, that if individuals are risk averse, cooperation can emerge in large groups.

## >> DIEP Seminar: Diego Garlaschelli (University of Leiden and the IMT School of Advanced Studies, Lucca, Italy)

###### Multiscale network renormalization: scale-invariance without geometry | 11am, 1st June 2023

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Systems with lattice geometry can be renormalized exploiting their coordinates in metric space, which naturally define the coarse-grained nodes. By contrast, complex networks defy the usual techniques, due to their small-world character and lack of explicit geometric embedding. Current network renormalization approaches require strong assumptions (e.g. community structure, hyperbolicity, scale-free topology), thus remaining incompatible with generic graphs and ordinary lattices. Here we introduce a graph renormalization scheme valid for any hierarchy of coarse-grainings, thereby allowing for the definition of `block-nodes' across multiple scales. This approach reveals a necessary and specific dependence of network topology on additive hidden variables attached to nodes, plus optional dyadic factors. Renormalizable networks turn out to be consistent with a unique specification of the fitness model, while they are incompatible with preferential attachment, the configuration model or the stochastic blockmodel. These results highlight a deep conceptual distinction between scale-free and scale-invariant networks, and provide a geometry-free route to renormalization. If the hidden variables are annealed, they lead to realistic scale-free networks with density-dependent cut-off, assortatitivy and finite local clustering, even in the sparse regime and in absence of geometry. If they are quenched, they can guide the renormalization of real-world networks with node attributes and distance-dependence or communities. As an application, we derive an accurate multiscale model of the International Trade Network applicable across hierarchically nested geographic partitions.

## >> DIEP Seminar: Leonardo di Gaetano (Central European University)

###### Percolation and Topological properties of temporal higher-order networks | 11am, 25th May 2023

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Hypergraphs provide a more accurate representation of complex systems with non-pairwise interactions, such as social networks and cellular networks. However, analyzing and characterizing hypergraphs remains a challenge. To address this, we present a hidden variables formalism to analyze higher-order networks. We apply this framework to a higher-order activity-driven model and provide analytical expressions for the main topological properties of the time-integrated hypergraphs. Our analysis demonstrates the importance of considering higher-order interactions and shows that neglecting them can lead to underestimating the percolation threshold. Overall, our work contributes to a better understanding of the interplay between group dynamics and the unfolding dynamical processes over them in complex systems.

## >> DIEP Seminar: Ana Millan Vidal (U. Granada)

###### The role of epidemic spreading in seizure propagation and epilepsy surgery | 11am, 11th May 2023

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Computational models of brain dynamics can provide new insights into the prognosis of neurological disorders such as epilepsy. Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but up to 50% of the patients continue to have seizures one year after the resection. The propagation of seizures over the brain can be regarded as an epidemic spreading process taking place on the patient's connectome. We show in a retrospective study (N=15) that this simple dynamic -namely the Susceptible-Infected-Recover (SIR) model- is enough to reproduce the main aspects of seizure propagation as recorded via invasive electroencephalography (iEEG) [2,3]. Remarkably, the SIR model parameters that best describe the iEEG seizure patterns correspond to the critical transition between the percolating and absorbing phases of the SIR model [2,3,4], and the similarity between the iEEG and modelled seizure predicted surgical outcome (area under the curve AUC = 0.73). We validated the use of the model in the clinic with a blind, independent pseudo-prospective study (N=34) using the parameters as in the retrospective study to avoid over-fitting. As a consequence iEEG data (highly invasive and not always part of the presurgical evaluation) was not required. Using the model to find optimal resection strategies [1], we found smaller resections (AUC=0.65) for patients with good outcome, indicating intrinsic differences in the presurgical data of patients with good and bad outcome [4]. The actual resection also overlapped more with the optimal one (AUC=0.64) and had a larger effect decreasing modelled seizure propagation (AUC=0.78) for patients with good outcome [4]. Individualised computational models may inform surgical planning by suggesting optimal resection strategies and informing on the likelihood of a good outcome after a proposed resection. This is the first time that such a model is validated on a fully independent cohort without the need for iEEG recordings.

## >> DIEP Seminar: Swarnendu Banerjee (U. Utrecht)

###### Rethinking tipping points in spatial ecosystems | 11am, 4th May 2023

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The theory of alternative stable states and tipping points has garnered a lot of attention in the last decades. It predicts potential critical transition from one ecosystem state to a completely different state under increasing environmental stress. However, typically ecosystem models that predict tipping do not resolve space explicitly. As ecosystems are inherently spatial, it is important to understand the effects of incorporating spatial processes in models, and how those insights translate to the real world. Moreover, spatial ecosystem structures, such as vegetation patterns, are important to predict ecosystem response in the face of environmental change. Models and observations from real savanna ecosystems and drylands have suggested that they may exhibit both tipping behavior as well as spatial pattern formations. Hence, in this talk, I will use mathematical models of humid savannas and drylands to illustrate several pattern formation phenomena that may arise when incorporating spatial dynamics in models that exhibit tipping. I will argue that such mechanisms challenge the notion of large-scale critical transitions in response to global change and reveal a more resilient nature of spatial ecosystems.

## >> DIEP Seminar: Christian Hamster (Wageningen University)

###### Understanding Stochastic Waves in Cell Movement Models, from Gillespie Algorithms to (S)PDEs | 11am, 20th April 2023

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Single-cell organisms are remarkably good at sensing food, especially if you consider that they lack our sensing organs and have to measure a gradient in the food supply over the length of a single cell. The precise mechanisms behind this gradient sensing are not fully understood yet, but scientists

have determined many relevant molecules that are relevant in the motion of the cell and we can see how these molecules are activated in wavelike patterns. These processes can be used to build stochastic models for cell movement, where individual molecules are modeled. These models are complex, both numerically

and analytically, so we often summarise everything into 'simpler' PDEs. In this talk, I would like to introduce (and explain) an in-between option, so-called Chemical Langevin Equations, effectively a Stochastic PDE approximation of the underlying stochastic algorithms. This approach allows us to use all the insights from the deterministic PDE, without throwing away the stochastic nature of the models.

## >> DIEP Seminar: Sandro Sozzo (University of Udine)

###### Quantum nature of energy and entropy in cognition: Towards a Non—classical Thermodynamic Theory of Human Culture | 11am, 13th April 2023

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Convincing evidence reveals that quantum structures model cognitive phenomena better and more efficiently than classical structures. This has led to the development of a novel research programme called quantum cognition. Inspired by a two–decade research on the field, we extend here the range of applicability of quantum cognition and prove that the notions of energy and entropy can be consistently introduced in human language and, more generally, in human culture. More explicitly, if energy is attributed to words according to their frequency of appearance in a text, then the ensuing energy levels are distributed non–classically, namely, they obey Bose–Einstein, rather than Maxwell–Boltzmann, statistics, because of the genuine quantum indistinguishability of the words that appear in the text. Secondly, the quantum entanglement due to the way meaning is carried by a text reduces the (von Neumann) entropy of the words that appear in the text, a behaviour which cannot be explained within classical (thermodynamic or information) entropy. We claim that this ‘quantum–type behaviour holds in general in human language', namely, any text is conceptually more concrete than the words composing it, which entails that the entropy of the overall text decreases as a result of composition. In addition, we provide examples taken from cognition, where quantization of energy appears in categorical perception, and from culture, where entities collaborate, thus entangle, to decrease overall entropy. We use these findings to propose the development of a non–classical thermodynamic theory for human cognition, which also covers broad parts of human culture and its artefacts, and bridges concepts with quantum physics entities.

## >> DIEP Seminar: Rodrigo Cofre (Paris-Saclay University)

###### Novel perspectives on the structure-function dynamics of the primate and human brain under diverse consciousness states | 11am, 6th April 2023

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The analysis of brain network dynamics provides an insightful perspective to analyze the dynamic reconfiguration in brain network structure across species to investigate its role during the loss of consciousness. Recent work has highlighted the importance of the dynamical aspect in understanding the functional relevance of alterations in this network structure to investigate how the brain supports consciousness. In this talk, my idea is to introduce the topic of research of consciousness from the STEM perspective and then fly over a set of ideas and recent results in order to discuss the problems associated with data analysis in the temporal dimension.

I will show convergent and complementary results between different methods of investigating the dynamical aspects of the structure-function relationship during the loss of consciousness in the primate and human brain. If time permits, I would like to present different ideas and datasets that we may use in collaboration to explore more in-depth the relationship between the different states of consciousness and recorded brain activity under those states.

## >> DIEP Seminar: Chase Broedersz (VU Amsterdam)

###### The dynamics of cell migration in flat and curved geometries | 11am, 30th March 2023

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In many biological phenomena, cells migrate through flat or curved confining environments. However, a quantitative framework to describe the stochastic dynamics of such multicellular confined cell migration remains elusive. We employ a data-driven approach to infer the dynamics of cell movement, morphology and interactions of cells confined in micropatterns. By inferring a stochastic equation of motion directly from the experimentally determined short time-scale dynamics, we show that cells exhibit intricate non-linear deterministic dynamics that adapt to the geometry of confinement. We extend this approach to interacting systems, by tracking the repeated collisions of confined pairs of cells. By inferring an interacting equation of motion for this system, we find that non-cancerous (MCF10A) cells exhibit repulsive and frictional interactions. In contrast, cancerous (MDA-MB-231) cells exhibit attraction and a novel and surprising anti-friction interaction, causing cells to accelerate upon collision. Based on the inferred interactions, we show how our framework may generalize to provide a unifying theoretical description of diverse cellular interaction behaviors. Finally, I will discuss the collective dynamics cells migrating in 3D curved confining geometries in multicellular spheroids.