The GW approximation is a cornerstone of many-body perturbation theory for computing single-particle excitations, yet it fundamentally breaks down in strongly correlated systems where the single-reference picture fails. To overcome this long-standing limitation, we introduce the multi-reference GW (MR-GW) approximation, which incorporates strong correlation effects non-perturbatively into an interacting multi-determinantal zeroth-order reference. While the standard Dyson equation is inapplicable in this setting, we show that the GW approximation can be naturally generalized by developing a rigorous diagrammatic framework with an interacting reference. Specifically, we define the MR-GW self-energy using a diagrammatic expansion based on the generalized Dyson equation, and utilize a multi-reference random phase approximation for the screened interaction, which captures many-body processes absent in standard GW. Applications to challenging strongly correlated molecules demonstrate that MR-GW seamlessly captures both strong and weak correlations, yielding more accurate ionization potentials and recovering complex many-body satellites missed by standard $GW$. This work establishes a rigorous diagrammatic paradigm for extending ab initio Green's function methods into the strongly correlated regime.

In 1981, Erdős and Faudree asked whether there exists an infinite family of graphs $G_N$ on $N$ vertices with $Δ(G_N)<N-1$ and $\sri(G_N)=1$, and whether every family with $|V(G_N)|=N$ and $Δ(G_N)<c$ for some fixed constant $c$ must satisfy $\sri(G_N)\to 0$. We show first that the literal forms of the two questions are controlled entirely by isolated vertices: for every nonempty graph $G$, the whole sequence $\bigl(\sr(tK_2,G)\bigr)_{t\ge 1}$ depends only on the isolate-free core $\core(G)$. Consequently, Problem 1 has a positive answer and Problem~2 has a negative answer in exactly their original form. We then turn to the genuine content behind the two problems. For Problem 1 we study connected graphs and prove a complete limit theorem: for every $α\in[0,1]$ there exists a family of connected bipartite graphs $G_N$ with $|V(G_N)|=N$ and $\sri(G_N)\toα$; in particular there are connected graphs with $Δ(G_N)=N-2$ and $\sri(G_N)\to 1$. For Problem~2 we prove a strengthened positive statement: if $Δ(G_N)<c$ for a fixed constant $c$ and the isolate-free core of $G_N$ has order tending to infinity, then $\sri(G_N)\to 0$. In particular every connected bounded-degree family satisfies $\sri(G_N)\to 0$. Thus the original Erdős-Faudree questions are resolved in their literal form, and the mechanism behind their connected and disconnected behavior is identified precisely.

Emerging agentic LLM workloads are driving rapidly growing demand on both memory capacity and bandwidth, with different phases of inference (e.g., prefill and decode) imposing distinct requirements. Industry is responding by composing heterogeneous accelerators into single interconnected systems, as exemplified by NVIDIA's Vera Rubin platform, where each device brings its own memory architecture. This heterogeneity is further compounded by a widening landscape of available memory technologies: high-density on-chip SRAM, HBM, LPDDR, GDDR, and emerging options such as high-bandwidth flash (HBF), each offering different capacity, bandwidth, and power trade-offs. Identifying the right memory architecture for next-generation inference accelerators requires navigating a vast and rapidly evolving design space, in which the interplay between workload characteristics, NPU design dimensions, and memory system design remains largely underexplored. To address this challenge, we present MemExplorer, a new memory system synthesizer for heterogeneous NPU systems. MemExplorer provides a unified abstraction for modeling diverse memory technologies across different hierarchy levels (e.g., on-chip and off-chip) and automatically determines an efficient heterogeneous memory system together with NPU design choices (e.g., matrix engine size) to balance throughput and power between prefilling and decoding devices in a multi-device NPU system. Experimental results show that, under the same power budget for agentic workloads, MemExplorer achieves up to 2.3x higher energy efficiency than the baseline NPU and 3.23x higher than H100 in the prefill-only setting. Under equivalent performance targets in the decode setting, it further delivers up to 1.93x and 2.72x higher power efficiency over the baseline NPU and H100, respectively.

This paper summarizes theoretical definitions of the relativistic coordinate time scales introduced by the IAU 2000 framework as well as practical aspects of their use. It is argued that the IAU framework already defines relativistic local GCRS-like reference systems and the corresponding TCG-like coordinates times for each body of the Solar system. The interrelations between the coordinate times and the proper time of an observer are discussed. The arguments put forward that any scaling of the local coordinate times like TCL for the Moon is unreasonable. Practical recipes of the transformations between TCB and the local coordinate time scales (TCG, TCL, etc) are then discussed. Time ephemerides giving the transformation between TCB and the local coordinate times at the center of mass of the corresponding body are computed for all major bodies of the Solar system using INPOP19a. Those time ephemerides represented as a standard set of Chebyshev polynomials are available online.

This report summarizes the presentations and discussions during the ISO-BREAK 25 Workshop ``Isospin symmetry violation: kaons and beyond'', which was held at Jan Kochanowski University in Kielce on October 23-25, 2025. We address the current status of the isospin-symmetry breaking discovered by NA61/SHINE in nucleus-nucleus collisions at the CERN SPS, its confirmation by other experiments and studies in \ee and deep inelastic scattering. In addition, we discuss the theoretical status as well as we outline experimental and theoretical priorities towards understanding this currently unexplained phenomenon.

Virtual agents have shown promising potential in mental health applications, but current research has predominantly focused on contexts outside of traditional therapy sessions. This paper examines the impact of a virtual supporter in remote psychotherapy sessions conducted via Zoom. We used a two-phase research approach. First we conducted a formative study to understand the roles and functions of human supporters in psychotherapy contexts. Based on these findings, we developed a virtual supporter operating in two modes: Daily Mode (for mood journaling outside therapy) and Therapy Mode (as an additional participant in Zoom therapy sessions). Finally we ran a user study with 14 participants who engaged with the virtual supporter for a week and then joined a remote psychotherapy session together. Our findings revealed that the virtual supporter had positive effects on creating psychological safety, reducing anxiety, and enhancing emotional articulation without disrupting the therapeutic process. We then discussed both the benefits and potential disadvantages of virtual supporters in therapeutic contexts, including concerns about over-reliance and the need for appropriate boundaries. This research contributes to understanding how AI-driven virtual agents could contribute to human-led remote psychotherapy.

A Berry--Esséen theorem for linear combinations of iterates of an inner function is obtained. Our proof, which is based an elementary transfer argument and classical results in martingale theory, also leads to a simple proof of Nicolau and Soler i Gibert's central limit theorem for inner functions.

Code Large Language Models (Code LLMs) have revolutionized software development but raised critical concerns regarding code provenance, copyright protection, and security. Existing code watermarking approaches suffer from two fundamental limitations: black-box methods either exhibit detectable syntactic patterns vulnerable to statistical analysis or rely on implicit neural embedding behaviors that weaken interpretability, auditability, and precise control, while white-box methods lack code-aware capabilities that may compromise functionality. Moreover, current single-layer watermarking schemes fail to address increasingly complex provenance requirements such as multi-level attribution and version tracking. We present MATRIX, a novel code watermarking framework that formulates watermark encoding as solving constrained parity-check matrix equations. MATRIX employs dual-channel watermarking through variable naming and semantic-preserving transformations, enhancing watermark coverage across a wider range of code while ensuring mutual backup for robustness. By integrating BCH error-correction codes with solution space diversity, our approach achieves robustness against statistical analysis. Extensive evaluation on Python code generated by multiple Code LLMs demonstrates that MATRIX achieves an average watermark detection accuracy of 99.20% with minimal functionality loss (0-0.14%), improves robustness by 7.70-26.67% against various attacks, and increases watermarking applicability by 2-6x compared with existing methods. These results establish MATRIX as an effective solution for complex code provenance scenarios while balancing among detectability, fidelity, and robustness.

We prove the existence of global weak entropy solutions for a class of non-symmetric Keyfitz-Kranzer type systems that includes lubrication models for thin-film flow. We identify a family of entropy/entropy-flux pairs for these first-order systems, which is, in particular, admissible for a tailored second-order approximate system. The latter is motivated by higher-order dissipation operators in thin-film flow models. By identifying an invariant region in the state space, it is possible to derive a-priori $L^\infty$-bounds for the sequence of solutions to the approximate system. Exploiting the parabolic and transport structure of the equations associated with the Riemann invariants, we then rigorously justify the vanishing-diffusion limit and establish the existence of weak entropy solutions for the Cauchy problem for the first-order systems.

Spiking neural networks (SNNs) are rapidly gaining momentum as an alternative to conventional artificial neural networks in resource constrained edge systems. In this work, we continue a recent research line on recurrent SNNs where axonal delays are learned at runtime along with the other network parameters. The first proposed approach, dubbed DelRec, demonstrated the benefit of recurrent delay learning in SNNs. Here, we extend it by advocating the use of convolutional recurrent connections in conjunction with the DelRec delay learning mechanism. According to our tests on an audio classification task, this leads to a streamlined architecture with smaller memory footprint (around 99% savings in terms of number of recurrent parameters) and a much faster (52x) inference time, while retaining DelRec's accuracy. Our code is available at: https://github.com/luciozebendo/delrec_snn/tree/conv_delays

This paper studies the vehicle bicycle model under three classes of stealthy cyber-attacks: replay attacks, zero dynamics attacks, and covert attacks. Using a system-theoretic framework, we analyze the feasibility and impact of these attacks on vehicle lateral dynamics. The investigation considers different measurement configurations, including yaw rate, lateral acceleration, and longitudinal acceleration outputs, to evaluate how sensor selection influences attack detectability and system vulnerability. Each attack class is characterized in terms of required system knowledge, communication access, and impact. The analysis shows that replay attacks remain largely model-agnostic, while zero dynamics attacks are fundamentally constrained by control-oriented design choices, particularly output selection, which can eliminate unstable zero dynamics and limit the attack impact. In contrast, covert attacks, enabled by coordinated actuator and sensor manipulation, allow sustained and stealthy deviation of lateral states when sufficient access and system knowledge are available. The effects of actuator and tire saturation are also examined, revealing attack-dependent impacts on stealthiness and effectiveness. Finally, simulation case studies are conducted by using CarSim-Simulink co-simulation to validate and verify the theoretical results.

Neutrino masses provide one of the clearest indications of physics beyond the Standard Model. In this brief review, I discuss the main theoretical frameworks developed to account for them, with particular emphasis on scenarios in which neutrinos are Majorana particles. After a short overview of the current landscape of neutrino mass models, I focus on constructions featuring the spontaneous breaking of global lepton number and examine the phenomenological implications of a massless Goldstone boson, the majoron.

This paper is about hypersurfaces with boundary lying in the Euclidean unit ball, which meet the unit sphere at a fixed angle $θ\in(0,\fracπ{2}]$. Such hypersurfaces are called $θ$-capillary hypersurfaces and for those we introduce a new notion of convexity, which we call $θ$-horocap-convexity. For such hypersurfaces, we prove the convergence of a curvature flow of Guan/Li type with capillary boundary. Remarkably, we prove this result for a class of curvature functions which include all quotients of symmetric polynomials and, as a consequence, we obtain the full set of quermassintegral inequalities in the $θ$-horocap-convex case. In the strictly horocap-convex setting, we employ the flow to prove the geometric inequalities, while for the horocap-convex case and the characterization of the equality case, we develop new arguments which are interesting in their own right.

This paper introduces SATFuL, a SAT solver for fuzzy logics. In contrast to the Boolean case, for which numerous SAT solvers exist, the SAT problem for fuzzy logics has attracted less attention, even though these tools have interesting applications. Unlike existing SAT solvers for fuzzy logics, SATFuL uses MINLP (Mixed Integer Non-Linear Programming) solvers to check the satisfiability of fuzzy formulas. This approach offers certain benefits; for instance, our tool can handle all major variations of fuzzy propositional logic, whereas other fuzzy solvers are usually tailored to specific versions of fuzzy logic. We conduct some experiments and demonstrate that the performance of our tool is comparable with state-of-the-art fuzzy solvers for Lukasiewicz logic, and outperforms available solvers for Product logic. The approach is sound and complete and can be easily extended to accommodate new fuzzy operators.

In this work, we initiate the study of the biharmonic heat equation in a spatial bounded domain subject to dynamic boundary conditions involving the bi-Laplace-Beltrami operator on the boundary. The boundary heat equation is coupled to the interior one via a normal derivative term. By combining the sesquilinear form method and semigroup theory, we establish substantial qualitative properties of the fourth-order parabolic equation; in particular, the self-adjointness of the associated operator, compactness of its resolvent, and further spectral properties. We also investigate the generation of a $C_0$-semigroup and analyze its main properties: analyticity, compactness, eventual positivity, and eventual $L^\infty$-contractivity.

The quasielastic cross-sections of charged-current neutrino and antineutrino scattering on $^{12}$C are calculated using the coherent density fluctuation model with a relativistic effective mass $m_N^* =0.8 m_N$ (CDFM$_{M^*}$). The model explicitly considers the modification of the relativistic effective mass of the nucleon within the relativistic mean field (RMF) model of nuclear matter. In addition, our calculations include neutrino-induced two-particle emission processes, which are evaluated within the RMF model of nuclear matter. Utilizing the CDFM$_{M^*}$, we provide predictions for the neutrino and antineutrino cross sections of $^{12}$C, which have been observed in accelerator experiments, such as MiniBooNE, T2K, and MINERvA. Also, we analyze the axial form factor value for the excitation of the $Δ$ at zero momentum transfer (commonly denoted as $C^A_5 (0)$) which is important for the treatment of the $Δ$ current in the meson-exchange currents (MEC) calculation. In addition, the quasielastic results obtained within CDFM$_{M^*}$ model are thoroughly evaluated for different regions of the momentum transfer.

This paper proposes a Radio Environment Map (REM) for energy-efficient (EE) serving cluster formulation in a user-centric cell-free network. By incorporating the location of the user and the characteristics of the power amplifier, REM enables EE to be improved by up to 19%.

In this paper, we study the Chevalley property of Cayley-Hamilton Hopf algebras in the sense of De Concini-Procesi-Reshetikhin-Rosso using discriminant ideals. For any affine Cayley-Hamilton Hopf algebra $(H,C,\text{tr})$ whose identity fiber algebra has the Chevalley property, we prove that an irreducible $H$-module $V$ has the property that $V\otimes W$ is a completely reducible $H$-module for every irreducible $H$-module $W$ if and only if $V$ is annihilated by the lowest discriminant ideal of $(H,C,\text{tr})$, which establishes a bridge between the tensor-nondegenerate behaviour of the irreducible representations of $H$ and the lowest discriminant ideal of $(H,C,\text{tr})$. Using discriminant ideals, we prove that an affine Cayley-Hamilton Hopf algebra $(H,C,\text{tr})$ has the Chevalley property if and only if its identity fiber algebra $H/\mathfrak{m}_{\overline{\varepsilon}}H$ has the Chevalley property and all the discriminant ideals of $(H,C,\text{tr})$ are trivial, thereby resolving a question posed by Huang-Mi-Qi-Wu. Moreover, it is shown that the lowest discriminant subvariety $\mathcal{V}_{\ell}$ of the algebraic group $\operatorname{maxSpec}C$ is a closed subgroup, which reflects the rigid nature of $\mathcal{V}_{\ell}$ and is effective in determining the lowest discriminant subvarieties in certain examples of low GK dimension. This rigidity property provides a method, via the lowest discriminant ideals, for constructing a large family of Hopf algebras with the Chevalley property and finite GK dimension. The results are illustrated through applications to the big quantized Borel subalgebras at roots of unity and to certain Artin-Schelter Gorenstein Hopf algebras of low GK dimension. In particular, the framework yields (non-finite) tensor categories with the Chevalley property arising from some big quantum groups at roots of unity.

The landscape of condensed matter physics is facing an unprecedented data surge driven by high-throughput ab initio workflows and rapidly expanding experimental datasets. Traditional first-principles methods such as Density Functional Theory (DFT), despite their foundational role, suffer from cubic scaling, creating a major bottleneck when exploring the vast chemical space of quantum materials. This review analyzes how Machine Learning (ML) and Deep Learning (DL) are overcoming these limitations and accelerating the discovery of exotic phases of matter. We examine the shift from rigid descriptor-based models to flexible, symmetry-aware architectures, particularly E(3)-equivariant Graph Neural Networks (GNNs) that respect rotational and translational invariance. A central focus is the automated identification of topological phases, where ML models exploit symmetry indicators and elementary band representations to diagnose non-trivial topology without costly band structure integrations. The discussion culminates in a case study of the Altermagnet, a recently identified third class of magnetism beyond the ferromagnetic, antiferromagnetic dichotomy. We highlight how specialized AI search engines, combining graph theory with crystallographic symmetry analysis, have uncovered d-wave, g-wave, and even i-wave altermagnets, expanding the known landscape of magnetic order. The review concludes by addressing the interpretability gap and advocates for symbolic regression and active-learning frameworks to connect black-box predictions with experimentally verifiable mechanisms.

Topological insulators are typically characterized by particularly stable properties, such as global invariants, and can be identified by probing their robust surface states. A recently discovered novel form of band topology, delicate topology, challenges this paradigm: its defining property, multicellularity, can be removed by introducing a coupling to local orbitals anywhere in the spectrum, even far above the relevant band gap. This makes it hard to diagnose delicate topology with conventional probes that access only low-energy degrees of freedom. Here, we introduce strong local impurities as a spectroscopic probe of a delicate topological insulator which we realize in a phononic metamaterial. By tuning the impurity strength and performing orbital-resolved readout, we observe recently proposed indicators of topology: ring states, in-gap bound states whose frequencies remain pinned in the strong-impurity limit while their real-space profiles form a pronounced ring around the impurity site. We find that these ring states persist even when the multicellularity in our system is removed by a weakly hybridizing additional orbital. Our results establish impurity-induced ring states as probes of complex multiband physics, including delicate topological phases.

Robust stabilization conditions for uncertain switched affine systems subject to a unitary input delay are presented. They are obtained through the Lyapunov framework and a min-switching state-feedback predictive control law. The result relies on a prediction scheme considering nominal system parameters. By constructing a Lyapunov function that considers the prediction error, we demonstrate the exponential convergence of the system trajectories and system prediction to a robust limit cycle. An example is provided to validate the obtained result.

Laser nitriding represents a versatile approach for tailoring the surface properties of metals. Up to now, its effect on the superconducting response of niobium nitrides remains largely unexplored. In this work, the nitriding process of niobium by laser irradiation under a controlled nitrogen atmosphere up to 2.50 bar, using a nanosecond pulsed laser with wavelength of 1064 nm has been investigated. By independently tuning the nitrogen pressure, the two-dimensional accumulated fluence ($F_{2D}$) and the laser irradiance, a laser-processing map for the formation of either a combination of $β$-Nb$_2$N (hexagonal) and $γ$-Nb$_4$N$_{3\pm x}$ (tetragonal) phases or only the $β$-phase has been established. Systematic analysis by X-ray diffraction, scanning electron microscopy and electron backscatter diffraction revealed that the nitrogen-rich $γ$-phase forms in the near-surface layer through melting when $F_{2D}$ exceeds a certain value ($> 50 \,\mathrm{kJ/cm^2}$ at 2.50 bar). A $β$-layer is observed underneath, and further inside, there is a band of embedded $β$-grains in the Nb matrix. Their size gradually decreases with increasing distance to surface, suggesting thermal gradients and a diffusion formation mechanism. When the $γ$-phase becomes predominant, a significant increase in the superconducting critical temperature is observed, up to $T_c \approx 15\,\mathrm{K}$, and magnetic irreversibility. For low $F_{2D}$ values ($\approx 7.5 \,\mathrm{kJ/cm^2}$ at 1.50-2.50 bar), the formation of a uniform nitride layer composed of sub-micron-sized $β$-Nb$_2$N grains results in a ca. fourfold enhancement in surface microhardness. These findings provide fundamental insights into laser-induced nitriding of niobium to engineer mechanically robust and superconducting Nb-N layers.

This paper examines a stochastic deconvolution problem on compact symmetric spaces which is referred to as decompounding. This involves estimating the step distributions of a random walk, where in addition the number of steps between observations is unknown. The harmonic analysis of symmetric spaces is used to construct an estimator to the problem which converges in mean squared error, extending and improving on the analogous problem on compact Lie groups. The rates of convergence are shown to coincide with asymptotic lower bounds of density estimation in Euclidean space. We provide proofs that while the same rates hold for general density estimation problems in compact symmetric spaces, the decompounding problem lies in a subclass of these with different lower bounds depending on the rank of the space. Consequently, the optimality of the estimator depends on the rank of the symmetric space. Decompounding is a broad problem which appears in applications ranging from mathematical finance to wave optics, and the extension to compact symmetric spaces covers manifolds that commonly appear in the statistics literature.

Folklore is often saying "The Java memory model is broken." Therefore, several approaches have proposed repairs, only to find new programs exhibiting unexpected, unintuitive behavior or the model forbidding standard compiler optimizations. The complexity of defining a memory model for concurrent Java lies in the fact that it requires a multi-execution model. Multi-execution models need to inspect the many potential executions of a program in order to find the valid ones. Tools automatically validating novel proposals of Java memory models are, however, largely lacking. To alleviate this problem, we introduce jMT, a novel tool for constructing multi-execution semantics for concurrent Java programs. jMT relies on single-execution models defining well-formed execution graphs, based on which it builds valid multi-execution semantics via causality checking. Thereby, jMT supports evaluating new proposals of Java memory models (JMMs) on a per-program basis. jMT can furthermore be employed for testing the conformance of JMMs to existing compilation schemes and compilers. Our evaluation of jMT on 169 litmus tests reveals a number of interesting insights into existing JMMs.

The interplay between charge density waves (CDWs) and superconductivity is a central theme in quantum materials, yet how CDW phase textures govern vortex topology remains poorly understood. We develop a theoretical framework showing that the phase of a stripe CDW can switch a magnetic vortex between topological and trivial regimes. Motivated by recent experiments, we propose two candidate mechanisms enabling phase-controlled switching of vortex topology. In a direct-modulation scenario, the CDW acts as a periodic potential that locally renormalizes band parameters and can induce topological transitions, but it generally cannot reproduce the symmetric node/antinode trend without fine tuning. In contrast, in an inversion-symmetry-breaking (ISB) scenario, a CDW node pinned to the vortex center breaks local inversion and allows for the mixture of spin-triplet pairing of Cooper pairs, producing a robust topological transition when this component dominates. Our results suggests CDW phase as a possible local handle to tune and test vortex topology.

The double Schwarzschild solution in the equal mass case is studied in bispherical coordinates. An explicit conformal transformation from cylindrical Weyl coordinates to bispherical coordinates is given in terms of elliptic functions. A multi-domain spectral method for spacetimes in bispherical coordinates is presented to numerically reconstruct this solution.

In this artcle, we introduce and investigate a subclass of Bazilevi{č} functions, denoted by $\mathcal{B}_{φ_{A,B}}(α^{(m)})$. We determine the Hardy space to which this subclass of Bazilevi{č} functions belong to. Additionally, we provide a necessary condition for a particular case of this subclass. Finally, we obtain a sharp coefficient estimate for the functions associated with $\mathcal{B}_1(α).$

A central question of the Ethereum ecosystem is where Maximal Extractable Value (MEV)revenue originates and to what extent it stems from harming unsuspecting users. It is acceptable if MEV arises from arbitrages between centralised and decentralised exchanges (CEX-DEX). Yet theoretical models have significantly underestimated the scale of these arbitrages, while empirical studies have highlighted their importance - though these remain conservative estimates, constrained by numerous debatable heuristic assumptions. Revisiting the theoretical model, we found that CEX-DEX arbitrages require trading volumes on the order of the total activity of major liquidity pools and yield profits comparable to MEV. Most prior AMM models utilised the Black-Scholes (BS) stochastic differential equation (SDE) - i.e., geometric Brownian motion - and assumed continuous price trajectories where asset prices move in small increments only.We argue that BS underestimates arbitrage profits by ignoring price jumps, which are precisely the points at which arbitrage opportunities tend to arise. To address this gap, we present an extended discrete-time AMM model in which the price process is the sum of a diffusive component and stochastic jumps that can have arbitrary noise distributions. Although mathematically more involved this framework allows us to employ a general discrete-time SDE and compute the stationary probability distribution via function iteration with geometric convergence. We further prove that the resulting mispricing process is an ergodic Markov chain. We implement our model in C++, collect spot prices and AMM exchange data from the Ethereum blockchain and fit the model parameters to the observed prices. The estimates derived from our model closely match empirical observations and provide a natural theoretical explanation for several fundamental questions in the blockchain ecosystem.

Quantum technologies promise a radically new way to solve classically intractable computing problems. Superconducting circuits as a platform are at the forefront of this field. The cryogenic operation temperatures of superconducting circuits however impose challenges for the further scaling to many connected quantum information processing units into a local area or global network. In this work, we present a hardware solution for connecting quantum devices operating at microwave frequencies into local area networks, which enable the exchange of quantum information between spatially separated parties. Specifically, we demonstrate a modular system spanning distances of 5, 10 and 30 meters operated at cryogenic temperatures and connecting two superconducting circuit systems, located in individual dilution refrigerators, through a quantum communication channel. We develop a thermal model to evaluate the heat transfer processes in the setup, optimize the design and select appropriate materials for its construction. The assembled 30-meter-long system achieves operating temperatures of below 50 mK after a cooldown time of about six and a half days. This link enables the execution of distributed quantum computing and communication algorithms. It also adds the resource of non-locality, certified by a loophole-free Bell test, to the field of quantum science and technology with superconducting circuits.

I was interested in the work of Solomon Marcus in Mathematical Linguistics as a high-school student. Later, I had the opportunity to discuss with him about many topics. He was a polymath. We wrote a paper together, and I refereed an editorial paper about his work in 2021. Samples of (possible) discussions are presented: some topology conjectures, a self-dual theorem in geometry, results about Boolean algebras, a B-ring Euler formula, Yang-Baxter maps and a discussion on sequences and series. A short appendix on poetry is also included.