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Formal foundations for Reowolf: Multi-party sessions via synchronous protocol programming
Get PDFThe Reowolf project developed connectors as a replacement of two-party network sockets for multi-party communication in next-generation internet applications. Users control connectors via protocols in the bespoke protocol description language (PDL), which is based on synchronous languages such as Reo and Esterel. The novelty lies in the emphasis on dynamism: users refine protocols throughout their execution.
We formalise these mantics of PDL, distinguishing dual notions of protocol behaviour: accepted behaviour is highly (de)compositional and specifies what communication is allowed, while constructed behaviour arises from
protocol execution and accounts for how execution steps interdepend and interleave via messages sent and received. Toward machine-checking the correctness of the connector runtime reference implementation, we specify the API and correctness criteria of PDL runtime systems
Physics of the artificial and its beauty
Get PDFValedictory lecture prof. dr. Ton de Kok. In his valedictory lecture, Ton de Kok looks back on 40 years of practice-driven research on the design and control of operational processes. The research and its applications cover production systems, service systems, supply chains and transportation systems. These artificial systems are designed to meet the requirements of users and consumers for products and services. A particular feature of artificial systems is the high level of uncertainty they have to deal with. A supermarket offers its assortment without knowing exactly which customers come to visit the store and what items they will buy. This implies balancing the objective to satisfy the needs of the unknown customer with the objective to limit the investment in items on the shelf. This balancing of conflicting objectives under uncertainty is at the heart of the design and management of artificial systems. Mathematical analysis of artificial systems under uncertainty may reveal surprisingly simple causal relationships, design laws and optimal control mechanisms. The fact that the outcomes may be counterintuitive is even more reason to spend time on the beauty of the physics of the artificial.ts in the International Space Station, carried out by the Dutch astronaut André Kuipers. The entire lecture will be illustrated with live demonstrations
Harnessing equivariance: Modeling turbulence with graph neural networks
Get PDFThis work proposes a novel methodology for turbulence modeling in Large Eddy Simulation (LES) based on Graph Neural Networks (GNNs), which embeds the discrete rotational, reflectional and translational symmetries of the Navier-Stokes equations into the model architecture. In addition, suitable invariant input and output spaces are derived that allow the GNN models to be embedded seamlessly into the LES framework to obtain a symmetry-preserving simulation setup. The suitability of the proposed approach is investigated for two canonical test cases: Homogeneous Isotropic Turbulence (HIT) and turbulent channel flow. For both cases, GNN models are trained successfully in actual simulations using Reinforcement Learning (RL) to ensure that the models are consistent with the underlying LES formulation and discretization. It is demonstrated for the HIT case that the resulting GNN-based LES scheme recovers rotational and reflectional equivariance up to machine precision in actual simulations. At the same time, the stability and accuracy remain on par with non-symmetry-preserving machine learning models that fail to obey these properties. The same modeling strategy translates well to turbulent channel flow, where the GNN model successfully learns the more complex flow physics and is able to recover the turbulent statistics and Reynolds stresses. It is shown that the GNN model learns a zonal modeling strategy with distinct behaviors in the near-wall and outer regions. The proposed approach thus demonstrates the potential of GNNs for turbulence modeling, especially in the context of LES and RL
Computing moment polytopes of tensors, with applications in algebraic complexity and quantum information
Get PDFTensors play a central role in various areas of computer science and mathematics, such as algebraic complexity theory (matrix multiplication), quantum information theory (entanglement), and additive combinatorics (slice rank). Fundamental problems about tensors are strongly tied to well-known questions in computational complexity — such as the problem of determining the matrix multiplication exponent via asymptotic rank, and the stronger Strassen asymptotic rank conjecture, which has recently been intimately linked to a whole range of computational problems.
Unlike matrices, which are often well understood through their rank, tensors have such intricate structure that understanding them (and aforementioned problems) requires information of a more subtle nature. The moment polytope, going back decades to work in symplectic geometry, invariant theory, and representation theory, is a mathematical object associated to any tensor that collects such "rank-like" information. Their relevance has become apparent in several areas: (1) through applications in geometric complexity theory (GCT), (2) in the construction of functions in Strassen’s asymptotic spectrum of tensors, (3) as entanglement polytopes in quantum information theory, and (4) in optimization via scaling algorithms.
Despite their fundamental role and interest from many angles, little is known about these polytopes, and in particular for tensors beyond and only sporadically have they been computed. Even less is known about the polytopes' inclusions and separations (which are particularly relevant for applications).
We give a new algorithm for computing moment polytopes of tensors (and in fact moment polytopes for a natural general class of reductive algebraic groups) based on a mathematical characterization of moment polytopes by Franz. This algorithm enables us to compute moment polytopes of tensors of dimension an order of magnitude larger than previous methods, allowing us to compute with certainty, for the first time, all moment polytopes of tensors in , and with high probability those in .
Towards an open problem in geometric complexity theory, we prove (guided by moment polytopes computed with our algorithm) separations between the moment polytopes of matrix multiplication tensors and unit tensors, showing in particular that the matrix multiplication moment polytopes are not maximal (i.e., not equal to the corresponding Kronecker polytopes).
As a consequence of the above, we obtain a no-go result for a certain operational characterization of moment polytope inclusion, by proving that Strassen's asymptotic restriction on tensors does not imply moment polytope inclusion.
Finally, based on our algorithmic observations, we construct explicit (concise) non-free tensors in every format , thus solving a "hay in a haystack" problem for this generic property that plays an important role in Strassen's theory of asymptotic spectra
Nested sampling for discrete spaces
No full textEstimators are developed for application to nested sampling over a discrete probability space
A quantum time-space tradeoff for directed st-connectivity
Get PDFDirected -connectivity (DSTCON) is the problem of deciding if there exists a directed path between a pair of distinguished vertices and in an input directed graph. This problem appears in many algorithmic applications, and is also a fundamental problem in complexity theory, due to its -completeness. We show that for any , there is a quantum algorithm for DSTCON using space and time , which is an (up to quadratic) improvement over the best classical algorithm for any . Of the total space used by our algorithm, only is quantum space - the rest is classical. This effectively means that we can trade off classical space for quantum time
Community detection: between heuristics and statistics
Get PDFIn this article Martijn Gösgens explains the community detection method 'modularity' for networks. He describes an interpretation of modularity in terms of Bayesian statistics and explains a phase transition in the corresponding prior distribution. Gösgens finished his PhD supervised by Remco van der Hofstad and Nelly Litvak. This article was written on the occasion of the VVS-OR Van Zwet PhD thesis award Gösgens received on 20 March 2025
Pitfalls in understanding how multiple long-term conditions cluster: whole population and age-stratified associations in 7,490,874 people in England
Get PDFStudies of how multiple long-term conditions (MLTC) cluster together in individuals vary in the populations studied, and whether they age and/or sex stratify, which limits comparison between studies and reproducibility. This study uses a large, UK primary-care dataset to examine how pairwise strength of association between 74 conditions varies by age in both men and women aged 30-99 years, and to explore implications for MLT cluster analyses. Joint prevalence of conditions was lowest in younger age-groups and progressively increased with age, whereas Association Beyond Chance (ABC) was highest in younger age-groups and progressively decreased with age. Condition clustering based on ABC identified different clusters in all men and all women aged 30-99 years, and these clusters differed from those identified in each age-group. Researchers examining how MLTC cluster should consider whether age and sex stratification is appropriate given their study aims and/or would improve comparability and reproducibility, and explicitly justify their choices
Terug naar Toen: Nederland verbonden met het internet
No full textOp 17 november 1988 werd voor het eerst een email ontvangen uit Amerika: Nederland was op het internet aangesloten. Onderzoeker Steven Pemberton van Centrum Wiskunde en Informatica in Amsterdam vertelt in Villa VdB over deze bijzondere dag
