Electron-electron scattering is one of the most important hot carrier relaxation pathways in plasmonic nanoparticles. Understanding the dynamics of this scattering process and the effects of this on excited state dephasing and relaxation is therefore essential for the design of plasmonic nanostructures, including optical properties and the dynamics of electrons in plasmon-driven catalytic reactions. In this work, we have developed a novel approach that incorporates real-time time-dependent density functional tight-binding (DFTB) simulations with the Lindblad quantum Boltzmann equation (LQBE) based on a screened electron-electron interaction that is determined by the random phase approximation (RPA). This approach enables a self-consistent description of electron-electron scattering effects that occur during and after plasmon excitation in clusters/nanoparticles with hundreds of atoms. With our RT-TDDFTB+LQBE method, we investigate the quasiparticle lifetime as well as population and coherence dynamics in silver, gold and aluminum nanoclusters with sizes between 1.5-2.6 nm. Our results show that the quasiparticle lifetimes and relaxation dynamics are highly energy dependent, becoming much faster at higher energies. For clusters less than 2 nm, quantum effects associated with discrete energy levels can lead to the fluctuating lifetimes and deviation of the dynamics from the typical thermalization process, while for larger nanoparticles, the transition to bulk metallic behavior is found. Decoherence of the initially excited plasmon resonance is observed with a timescale of 10 fs, much faster than population relaxation. For gold, we find that the 5d-band can significantly slow down the relaxation of energetic electrons though Auger scattering, and interband transitions can lead to a secondary decoherence process longer than 50 fs.

We prove a conjecture of Brochier, Jordan, Safronov, and Snyder [BJSS21], first formulated by Lurie [Lur09b], characterizing fully-dualizable and invertible $\mathcal{E}_n$-algebras viewed as objects in the higher Morita categories $\mathbf{Mor}_n(\mathcal{V})$ [Lur09b, Sch14, Hau17b, Hau23]. In other words, we characterize those $\mathcal{E}_n$-algebras which give rise to $(n + 1)$-dimensional topological quantum field theories (TQFT), and those which give rise to invertible theories.

This paper proposes a categorical framework for knowledge graphs linking combinatorial graph structure with topos-theoretic semantics. Knowledge graphs are represented as labelled directed multigraphs and analysed through incidence matrices and line knowledge digraph constructions. The graph induces a free category whose morphisms correspond to relational paths. To model context-dependent meaning, a Grothendieck topology is defined on the free category generated by the graph leading to a topos of sheaves that supports local-to-global semantic reasoning. The framework connects graph-theoretic structure, categorical composition, and sheaf semantics in a unified mathematical model for contextual relational reasoning.

In this paper, we develop a dynamic programming framework for identifying extremal general polyomino chains with respect to degree-based topological indices. As a concrete application, we resolve an open problem posed in 2015 by determining, for any given number of squares, the general polyomino chains that maximize the generalized Randić index with parameter $α=-1$. We show that the extremal configurations depend explicitly on the residue class of the number of squares modulo 4. Beyond this specific result, the proposed dynamic programming approach provides a constructive and systematic methodology for tackling extremal problems in graph theory.

It has been claimed in a series of papers that scalar fields with a fourth-order Lagrangian $\sim(\Boxφ)^2$ can solve the cosmological constant problem by canceling the loop contributions from standard model fields, and that their fluctuations can be the source of the primordial density perturbations of the Universe, without the need for inflation. We dispute these claims. The spectrum of the theory includes a ghost, which leads to classical instabilities and quantum violation of unitarity. We show that the new scalar particles cannot cancel the standard model contributions to the cosmological constant, unless they include a unitarity-violating ghost at the quantum level. Further, the coupling of such scalars to the particles of the standard model induces a confining fifth force which rules it out as a source of density perturbations in the early Universe.

We exhibit a functor from the category OUS of order unit spaces and positive, unit-preserving mappings into the category $\Prob$ of probabilistic models (test spaces with designated state spaces) and morphisms thereof. Restricted to any subcategory of OUS monoidal with respect to a positive, normalized, bilinear composition rule, our functor is also monoidal. This shows that the convex-operational approach to physical theories can be subsumed by the test-space approach, without resort to ``generalized test spaces''. A second construction, equipping a probabilistic model with tests representing ``weighted coins'', also sheds light on the nature of unsharp observables.

We present SuperSNEC, an accelerated version of the SuperNova Explosion Code (SNEC) designed for rapid production of large radiation-hydrodynamic model grids using low-zone-count simulations ($\sim100$ zones). The main advance is adaptive gridding of the computational grid, which preserves light-curve fidelity relative to a high-resolution SNEC baseline ($\sim1000$ zones) while delivering a runtime improvement of ${\sim}420\times$. SuperSNEC also includes solver optimizations, optimized radioactive-energy deposition and ray-tracing, improved $^{56}$Ni mixing controls, and a smooth photosphere luminosity correction that suppresses low-resolution artifacts. We quantify the speed-accuracy trade-off for a 100-zone configuration against a 1000-zone reference and define baseline settings for efficient large-grid inference of stripped-envelope supernovae. Our optimized 100-zone setup achieves an RMS light-curve residual of $0.022$ mag relative to the 1000-zone reference, at a runtime of $<2$ seconds per model. Applied to SN 2011dh (Type IIb), SN~1993J (Type IIb), and SN 2020oi (Type Ic), SuperSNEC recovers light-curve parameters consistent with the literature; in particular, SN 2020oi is well reproduced by a purely radioactive model, with no clear evidence that an additional power source is required.

In this work, we study the effects of vacuum Cherenkov radiation caused by nonminimal dimension-5 Lorentz-violating (LV) operators in the fermion sector. Explicitly, we focus on two independent isotropic pieces of each set of nonminimal coefficients. Under the assumption that vacuum Cherenkov radiation is an expected phenomenon, experimental data of ultra-high-energy cosmic rays (UHECRs) allow us to put stringent bounds on isotropic coefficients in quarks.

Maxwells Demon is a mythical being, first described by the physicist James Clerk Maxwell (although named Maxwells Demon by Lord Kelvin). Maxwell used it in a thought experiment to potentially violate the Second Law of Thermodynamics by exploiting inhomogeneities existing in a statistically homogeneous system. Blackwells Demon, making (as far as is known) its first appearance in this paper, illustrates a counterintuitive situation occurring in a random walk variation of the Two Envelope problem[1], that it is possible under restrictive conditions to predict with success probability > 1/2 the direction of a random walk generated by the flip of a fair coin. Like Maxwells Demon, Blackwells Demon operates by exploiting inhomogeneities that exist in a statistically homogeneous system. Maxwells Demon achieves its results by knowing when a molecule is moving rapidly and when it is not. Blackwells Demon achieves its results by knowing when a prediction strategy is successful and when it is not. At the time Maxwell proposed his Demon, it confronted a technological Everest, the ability to open and close a gate permitting the passage of a single molecule, and the ability to gauge the speed of an approaching molecule. Blackwells Demon merely has to turn a light on and off, conduct visual observations and keep simple statistical records. It should be noted that the analysis in this paper does not demonstrate the ability to predict the flip of a fair coin ab initio with success probability > 1/2, as it is necessary to embed the fair coin in an environment of some complexity in order to achieve this result.

Verification of binary neural network (BNN) robustness is NP-hard, as it can be formulated as a combinatorial search for an adversarial perturbation that induces misclassification. Exact verification methods therefore scale poorly with problem dimension, motivating the use of hardware-accelerated heuristics and unconventional computing platforms, such as Ising solvers, that can efficiently explore complex energy landscapes and discover high-quality solutions. In this work, we reformulate BNN robustness verification as a quadratic unconstrained binary optimization (QUBO) problem and solve it using a digital compute-in-memory (DCIM) SRAM-based Ising machine. Instead of requiring globally optimal solutions, we exploit imperfect solutions produced by the DCIM Ising machine to extract adversarial perturbations and thereby demonstrate the non-robustness of the BNN. The proposed architecture stores quantized QUBO coefficients in approximately 9.1~Mb of SRAM and performs annealing in memory via voltage-controlled pseudo-read dynamics, enabling iterative updates with minimal data movement. Experimental projections indicate that the proposed approach achieves a $178\times$ acceleration in convergence rate and a $1538\times$ improvement in power efficiency relative to conventional CPU-based implementations.

While modern general-purpose computing systems have ample amounts of memory, it is still the case that embedded computer systems, such as in a refrigerator, are memory limited; hence, such embedded systems motivate the need for strictly in-place algorithms, which use only O(1) additional memory besides that used for the input. In this paper, we provide the first comparison-based sorting algorithms that are strictly in-place and have a running time that is optimal in terms of the run-based entropy, H(A), of an input array, A, of size n. In particular, we describe two remarkably simple paradigms for implementing stack-based natural mergesort algorithms to be strictly in-place in O(n(1 + H(A))) time.

Fermionic Gaussian states (FGSs) and the associated matchgate circuits play a central role in quantum information theory and condensed matter physics. Despite being possibly highly entangled, they can still be efficiently simulated on classical computers. We address the question of how to optimally create such states when using matchgate circuits acting on product states. To this end, we derive lower bounds on the number of gates required to prepare an arbitrary pure FGS: We establish both an asymptotic bound on the minimal gate count over general nearest-neighbor gate sets and an exact bound for circuits composed solely of matchgates. We present explicit algorithms whose constructions saturate these bounds, thereby proving their optimality. We furthermore determine when an FGS can be prepared with a circuit of any given depth, and derive an algorithm that constructs such a circuit whenever this condition is satisfied, either exactly or approximately. Our results have direct applications to (approximate) state preparation and to disentangling procedures. Moreover, we introduce a new classical simulation algorithm for matchgate circuits, based entirely on manipulating the generating circuits of the FGSs. Finally, we briefly study an extension of our framework for $t$-doped Gaussian states and circuits.

We introduce SAFT-P, a plaquette-level extension of Statistical Associating Fluid Theory for patchy particles. By treating local clusters as associating superparticles and contracting their free energy back to monomer densities, SAFT-P retains information about patch topology that is lost in conventional SAFT. Grand-canonical Monte Carlo simulations of binary and ternary mixtures show that SAFT-P captures topology-dependent critical points and coexistence curves and discriminates between particles with identical valence but different patch layouts. Beyond topology, incorporating plaquette-scale correlations also improves predictions in regimes where patch specific interactions are absent. Results indicate that resolving correlations at the plaquette scale provides an analytical route to model complex condensates and self-assembly with topology-sensitive local structure.

A reaction-coordinate--resolved information-theoretic analysis of chemical reactivity is developed using mutual information and partial information decomposition (PID). Along an intrinsic reaction coordinate (IRC), a local empirical distribution is constructed at each position $s$ that couples a coarse-grained geometric progress variable (target) to two electronic readouts (sources), and the joint mutual information $I(T;X,Y)$ is decomposed into redundant, unique, and synergistic contributions using the Williams--Beer PID formalism. In the numerical demonstrations, the target is a binned bond-asymmetry coordinate $ξ=d_{\mathrm{C}\!-\!\mathrm{nuc}}-d_{\mathrm{C}\!-\!\mathrm{LG}}$, while the sources are DDEC6 net atomic charges on the nucleophile and leaving-group centres. Application to three prototypical S$_\mathrm{N}$2 reactions (identity exchange $\mathrm{F^-+CH_3F}$, halide substitution $\mathrm{F^-+CH_3Br}$, and hydroxide substitution $\mathrm{OH^-+CH_3CH_2Br}$) yields compact, symmetry-sensitive signatures of bonding evolution: the identity reaction exhibits mirror-related information profiles with exchange of unique-information contributions between equivalent centres, whereas asymmetric reactions show shifted, centre-specific redistribution among redundancy and synergy as C--X cleavage couples to C--Nu formation. This Supplementary Information provides the formal construction, chemically motivated limiting toy models, a solvable analytic symmetric-transfer model, and the computational protocol used to obtain IRC-resolved PID curves.

Here we study what we call bounded rough Riemannian metrics $(M,g)$, which are positive definite, symmetric tensors on each tangent space, $T_pM$, which are bounded and measurable as functions in coordinates. This is enough structure to study the length space given by taking the infimum of the length of all piecewise smooth curves connecting points $p,q \in M$. The goal is to find the weakest conditions one can place on $g$ which can guarantee Lipschitz or uniform bounds from above and below. For each condition, an example is given showing that the condition cannot be weakened any further which also explores the geometric intuition.

In this work, temperature dependence on the masses of conventional and hybrid heavy quarkonium systems is investigated. For this, a thermally screened interaction is incorporated through Debye mass $(m_{D}(T))$ into the potential models of conventional and hybrid charmonium ($c\overline{c}$) and bottomonium ($b\overline{b}$) mesons (conventional and hybrid). Mass eigenvalues for S, P, D states of these mesons are computed by power series expansion method at different values of debye mass. Comparison with available lattice-QCD-inspired potentials and previous numerical studies show strong agreement and validate the efficiency of power-series technique for calculations of mass of heavy quarkonium at finite-temperature. Our results can be helpful to explore the recent experimentally determined states of charmonium and bottomonium.

Ethereum's ideals of decentralization and censorship resistance are undermined in practice, motivating ongoing efforts to reestablish these properties. Existing proposals for fairness mechanisms depend on the assumption that a sufficient fraction of block proposers adhere to Ethereum's protocols as intended. We refer to such proposers as altruistic, as this behavior may come at the cost of reduced revenue. Prior analyses indicate that a consistent share of 91 percent of proposers delegate block construction to centralized services, effectively signing externally constructed blocks blindly, and are thus not considered altruistic. To assess whether the remaining 9 percent of proposers genuinely exhibit altruistic behavior, we conducted an empirical analysis and found that an additional 6.1 percent also interact with such external services. Further, we found that less than 1.4 percent of proposers consistently acted in accordance with Ethereum's decentralization and censorship resistance objectives. These findings suggest that relying solely on the mere presence of altruistic proposers is insufficient to ensure that proposed fairness mechanisms reestablish Ethereum's ideals, highlighting the need for additional incentive- or penalty-based mechanisms.

The generalized Property R conjecture (GPRC) predicts that if framed surgery on an $n$-component link $L$ in $S^3$ produces $\#^{n} (S^1\times S^2)$, then $L$ is handleslide equivalent to an unlink, the obvious way to construct such a surgery. Many potential counterexamples to the GPRC are known, but obstructing handleslide equivalence is a tricky proposition. In this vein, we disprove a further generalization of the GPRC. It would be reasonable to expect that if an $n$-component link in $S^3$ surgers to the connected sum of $n$ three-manifolds with the homology of $S^1 \times S^2$, then this link should be handleslide equivalent to an $n$-component split link, the obvious way to construct such a surgery. However, we prove that there are 2-component framed links in $S^3$ that surger to a connected sum of homology $S^1\times S^2$'s but that are not handleslide equivalent, or even weakly handleslide equivalent, to a split link.

Digraph-defined external difference families were recently introduced as a natural generalization of several well-studied combinatorial objects motivated by cryptography (e.g. external difference families (EDFs) and circular external difference families (CEDFs)). In this paper, we develop a systematic framework for using various types of vertex-labellings for graphs and digraphs to create digraph-defined external difference families. The approach is to combine suitable vertex-labellings (generalizations of $α$-valuations, namely near $α$-valuations and oriented near $α$-valuations) with a graph blow-up technique. Many new families are produced, including the first explicit construction for an infinite family of $2$-CEDFs, achieving all parameter sets for $(n,m,l;1)$-$2$-CEDFs with $m \equiv 0 \mod 4$ sets. Further, new results arise for graph labellings themselves (e.g. cyclotomy-based near $α$-valuations for a family of trees without $α$-valuations, and an $α$-valuation for sun graphs).

Predicting the output of a dynamical system from streaming data is fundamental to real-time feedback control and decision-making. We first derive an autoregressive representation that relates future local outputs to asynchronous past outputs. Building on this structure, we propose an online least-squares algorithm to learn this autoregressive model for real-time prediction. We then establish a regret bound of O(log^3 N) relative to the optimal model-based predictor, which holds for marginally stable systems. Moreover, we provide a sufficient condition characterized via a symplectic matrix, under which the proposed cooperative online learning method provably outperforms the optimal model-based predictor that relies solely on local observations. From a technical standpoint, our analysis exploits the orthogonality of the innovation process under asynchronous data structure and the persistent excitation of the Gram matrix despite delay-induced asymmetries. Overall, these results offer both theoretical guarantees and practical algorithms for model-free cooperative prediction with asynchronous observations, thereby enriching the theory of online learning for dynamical systems.

The central question of this paper is: how do algebraic invariants of edge ideals change under natural graph operations? We study this question through the lens of suspensions. The (full) suspension of a graph is obtained by adjoining a new vertex adjacent to every vertex of the original graph; this construction is well-understood in the literature. Motivated by the fact that regularity is preserved under full suspension while projective dimension becomes maximal, we refine the construction to selective suspensions, where the new vertex is joined only to a prescribed subset of vertices. We focus on two extremal choices: minimal vertex covers and maximal independent sets. For suspensions over minimal vertex covers of an arbitrary graph, regularity is preserved and projective dimension increases by one. Moreover, the independence polynomial changes in a controlled way, allowing us to track $\mathfrak a$-invariants under cover suspension. In contrast, the analogous uniform behavior fails in general for suspensions over maximal independent sets. We therefore analyze paths and cycles and give a complete description: projective dimension always increases by one, and regularity and the $\mathfrak a$-invariant are preserved except for a unique extremal family of paths, where both invariants increase by one.

The advent of neural-network-based deep learning techniques has led to the emergence of increasingly sophisticated numerical interatomic potentials, including graph neural networks and large language-motivated foundation models. Parameterized to reproduce large, precomputed quantum mechanical training datasets for molecules and materials, models can be fine-tuned for greater accuracy on specific problems. Despite notable successes, machine learning (ML) models of potentials still face intrinsic challenges due to the combinatoric complexity of the underlying quantum chemical interactions, the existence of as-yet-undiscovered but potentially relevant bonding motifs absent from training datasets, and the need for post-prediction interpretability analysis. Drawing inspiration from autoencoder methods, we propose a constructive approach to interatomic potential design. In standard autoencoder architectures, a ML model self-organizes numerical training data in an unsupervised manner to discover underlying patterns and construct a compressed representation or latent space embedding model of the data, which is then used for prediction and inference. In the present work, we describe how latent space patterns and associated quantum embeddings can be constructed using first-principles methods based on theorems of density functional theory (DFT) and known, analytic constraints. This enables a parsimonious, physics-based representation of energies and densities, formally coupling the electronic and atomic length scales through the electron density, and linking ground, excited, and charge-transfer states of the interacting atoms. We describe the complete set of latent space components providing the foundation for a recently-proposed ensemble charge-transfer potential, and discuss opportunities for synergy in the design and explainability of contemporary machine-learned interatomic potentials.

Most of the intracyclic variability in the large-scale solar magnetic field comes from the equatorial dipole component of the solar magnetic field. The equatorial dipole component is highly sensitive to the longitude distribution of the active regions. We quantify the effect of individual active regions on the large-scale solar magnetic field of the solar cycle 24. We study the effect of the longitude distribution of active regions on the strength of the large-scale dipole component. We used a surface flux transport (SFT) model to simulate the evolution of individual active regions and quantified their effect on the large-scale magnetic field using the recently developed vector sum method. We took advantage of the longitudinal translational invariance of the SFT model and compared the observed solar cycle 24 to the 10 000 simulations of the solar cycle 24 using randomized longitudinal source locations, but otherwise identical flux emergence. We find that taking into account both the axial and equatorial components of the vector sum characterizing the global solar magnetic field sets better constraints on the parameter space of the SFT model than, for example, using the axial dipole moment alone as an optimization metric. We studied the maximum of cycle 24 and identified the recurrent and localized flux emergence in the southern hemisphere as the main culprit behind the rapid strengthening of the large-scale magnetic field in late 2014. We find that during the declining phase of the solar cycle, the strength of the large-scale magnetic field stayed above the median level of randomized simulations (p < 0.027) for 42 subsequent. This indicates that the longitudinal distribution of active regions is not random and, rather, that it demonstrates a tendency for some regions to emerge at longitudes where their equatorial components reinforce the large-scale equatorial field.

Active polymeric systems exhibit a rich spectrum of non-equilibrium phenomena arising from stochastic forces that explicitly break detailed balance. Despite the rapid growth of experimental and numerical studies, analytical progress remains limited. To date, theoretical understanding relies largely on variants of the active Rouse model, whose formal solutions, though exact, are often obscured by summations over Rouse modes and therefore provide limited direct physical insight. In this work, we develop a transparent scaling theory that captures the tagged-monomer mean-squared displacement (MSD) in active polymers through a compounding formula: the MSD of a monomer in the chain is expressed as that of an isolated active particle, modulated by a connectivity factor encoding tension propagation along the polymer backbone. This approach isolates the role of activity from that of polymer connectivity and reveals the emergent dynamical regimes in a physically intuitive manner. We test the scaling predictions against exact calculations for a broad class of generalized active polymer models driven by diverse noise statistics. The agreement demonstrates the robustness of the scaling framework across microscopic details. Our results provide a simple and extensible theoretical structure that can be applied to complex and analytically intractable active polymer systems, thereby offering a unifying perspective on non-equilibrium polymer dynamics.

Magnetic spectroscopy at the nanoscale provides unique insights into material properties and dynamics, with quantum sensors like nitrogen-vacancy (NV) centers being ideally suited for these scales. However, detecting low-frequency signals remains a challenge due to finite coherence times ($T_2^*$), as signals oscillating slower than $1/T_2^*$ decay before sufficient phase accumulation occurs. We present RESOLUTE (Ramsey corrElation SpectroscOpy puLse seqUence wiTh phasE cycling), a protocol that overcomes these limitations by combining Ramsey measurements with correlation spectroscopy. By storing accumulated phase as a population imbalance during a correlation period ($T_\mathrm{corr} < T_1$) between two sensing periods, RESOLUTE generates an effective coherence time $T_2^p > T_2^*$. This shifts the frequency-matching condition to the correlation time, enabling detection in the previously inaccessible spectral region between $1/T_1$ and $1/T_2^p$. We experimentally demonstrate an extension of the effective coherence time from $T_2^* = 0.38\,μs$ to $T_2^p = 5.1\,μs$, surpassing Hahn Echo measurements. The technique successfully detects $^{13}$C nuclear spin Larmor precession at fields as low as 49$\,$G ($\sim$50$\,$kHz). We further provide theoretical insight using Fisher information to characterize RESOLUTE's frequency estimation capabilities compared to existing protocols. Finally, by integrating adiabatic pulses and phase cycling, we demonstrate robust spin control and effective DC signal extraction. These advancements provide enhanced sensitivity to weak dipolar interactions, essential for single-molecule imaging and quantum sensing applications.

Gradual typing has gained popularity as a design choice for integrating static and dynamic typing within a single language. Several practical languages have adopted gradual typing to offer programmers the flexibility to annotate their programs as needed. Meanwhile there is a key challenge of unexpected performance degradation in partially typed programs. The execution speed may significantly decrease when simply adding more type annotations. Prior studies have investigated strategies of selectively adding type annotations for better performance. However, they are restricted in substantial compilation time, which impedes the practical usage. This paper presents a new technique to select a subset of type annotations derived by type inference for improving the execution performance of gradually typed programs. The advantage of the proposal is shorter compilation time by employing a lightweight, amortized approach. It selects type annotations along the data flows, which is expected to avoid expensive runtime casts caused by a value repeatedly crossing the boundaries between untyped and typed code. We demonstrate the applicability of our proposal, and conduct experiments to validate its effectiveness of improving the execution time on Reticulated Python. Our implementation supports a Python subset to select type annotations derived by an implemented, external type inference engine. Experiment results show that our proposal outperforms a naive strategy of using all type annotations derived by type inference among the benchmark programs. In comparison with an existing approach, the proposal achieves comparable execution speed and shows advantage of maintaining a more stable compilation time of deriving and selecting type annotations. Our results empirically indicate that the proposed technique is practical within Reticulated Python for mitigating the performance bottleneck of gradually typed programs.

Join patterns are a high-level programming construct for message-passing applications. They offer an intuitive and declarative approach for specifying how concurrent and distributed components coordinate, possibly depending on complex conditions over combinations of messages. Join patterns have inspired many implementations -- but most of them are not available as libraries: rather, they are domain-specific languages that can be hard to integrate into pre-existing ecosystems. Moreover, all implementations ship with a predefined matching algorithm, which may not be optimal depending on the application requirements. These limitations are addressed by `JoinActors`, a recently published library which integrates join patterns in the off-the-shelf Scala 3 programming language, and is designed to be modular w.r.t. the matching algorithm in use. In this work we address the problem of designing, developing, and evaluating a modular join pattern matching toolkit that (1) can be used as a regular library with a developer-friendly syntax within a pre-existing programming language, and (2) has an extensible design that supports the use and comparison of different matching algorithms. We analyse how `JoinActors` achieves goals (1) and (2) above. The paper that introduced `JoinActors` only briefly outlined its design and implementation (as its main goal was formalising its novel fair matching semantics*). In this work we present and discuss in detail an improved version of `JoinActors`, focusing on its use of metaprogramming (which enables an intuitive API resembling standard pattern matching) and on its modular design. We show how this enables the integration of multiple matching algorithms with different optimisations and we evaluate their performance via benchmarks covering different workloads. We illustrate a sophisticated use of Scala 3's metaprogramming for the integration of an advanced concurrent programming construct within a pre-existing language. In addition, we discuss the insights and "lessons learned" in optimising join pattern matching, and how they are facilitated by `JoinActors`'s modularity -- which allows for the systematic comparison of multiple matching algorithm implementations. We adopt the fair join pattern matching semantics and the benchmark suite from the paper that originally introduced `JoinActors`. Through extensive testing we ensure that our new optimised matching algorithms produce exactly the same matches as the original `JoinActors` library, while achieving significantly better performance. The improved version of `JoinActors` is the companion artifact of this paper. This work showcases the expressiveness, effectiveness, and usability of join patterns for implementing complex coordination patterns in distributed message-passing systems, within a pre-existing language. It also demonstrates promising performance results, with significant improvements over previous work. Besides the practical promise, `JoinActors`'s modular design offers a research playground for exploring and comparing new join pattern matching algorithms, possibly based on entirely different semantics.

Large-Language Models (LLMs) are changing the way learners acquire knowledge outside the classroom setting. Previous studies have shown that LLMs seem effective in generating to short and simple questions in introductory CS courses using high-resource programming languages such as Java or Python. In this paper, we evaluate the effectiveness of LLMs in the context of a low-resource programming language -- OCaml, in an educational setting. In particular, we built three benchmarks to comprehensively evaluate 9 state-of-the-art LLMs: 1) $λ$CodeGen (a benchmark containing natural-language homework programming problems); 2) $λ$Repair (a benchmark containing programs with syntax, type, and logical errors drawn from actual student submissions); 3) $λ$Explain (a benchmark containing natural language questions regarding theoretical programming concepts). We grade each LLMs responses with respect to correctness using the OCaml compiler and an autograder. And our evaluation goes beyond common evaluation methodology by using manual grading to assess the quality of the responses. Our study shows that the top three LLMs are effective on all tasks within a typical functional programming course, although they solve much fewer homework problems in the low-resource setting compared to their success on introductory programming problems in Python and Java. The strength of LLMs lies in correcting syntax and type errors as well as generating answers to basic conceptual questions. While LLMs may not yet match dedicated language-specific tools in some areas, their convenience as a one-stop tool for multiple programming languages can outweigh the benefits of more specialized systems. We hope our benchmarks can serve multiple purposes: to assess the evolving capabilities of LLMs, to help instructors raise awareness among students about the limitations of LLM-generated solutions, and to inform programming language researchers about opportunities to integrate domain-specific reasoning into LLMs and develop more powerful code synthesis and repair tools for low-resource languages.

CHERI (Capability Hardware Enhanced RISC Instructions) is a novel hardware designed to address memory safety issues. By replacing traditional pointers with hardware capabilities, it enhances security in modern software systems. A Virtual Machine (VM) is one such system that can benefit from CHERI's protection, as it may contain latent memory vulnerabilities. However, developing and porting VMs to CHERI is a non-trivial task. There are many subtle pitfalls from the assumptions on the undefined behaviors of the C language made based on conventional architectures. Those assumptions conflict with CHERI's stricter memory safety model, causing unexpected failures. Although several prior works have discussed the process of porting VMs, they focus on the overall porting process instead of the pitfalls for VM implementation on CHERI. The guide for programming in CHERI exists, but it is for general programming, not addressing VM-specific issues. We have ported CRuby to CHERI as a case study and surveyed previous works on porting VMs to CHERI. We categorized and discussed the issues found based on their causes. In this paper, we illustrate the VM-specific pitfalls for each category. Most of the pitfalls arise from the undefined behaviors in the C language; in particular, implementation techniques and idioms of VMs often assume behaviors of traditional architectures that are invalid on CHERI. We also discuss workarounds for them and the impacts of those workarounds. We verified the validity of the workarounds by applying them to our CRuby port and by surveying the codebases of prior case studies. This work contributes to the body of knowledge on developing and porting VMs to CHERI and will help guide efforts toward constructing safer VMs.

In programming, better tools often yield better results. For that, modern programming environments offer mechanisms to allow for their extensibility. The closer those tools are to the code, the easier it is for programmers to map the information provided by a tool to the code this information is about. However, existing extension mechanisms do not facilitate the close integration of tools with textual source code. Tools must be able to track program structures across edits to appear at the right positions but the parsing step of text complicates tracking structures. We propose hybrid structured editing, an approach that supports tool builders by providing structural guarantees while providing tool users with a familiar and consistent text editing interface. Hybrid structured editing allows tool builders to declare constraints on the structure that a program must conform to and ensures their observance. We present an implementation and several case studies of tools based on hybrid structured editing to demonstrate its effectiveness. Hybrid structured editing supports the safe extension of programming environments with tools that work on a structured representation of code and provide a consistent and reliable user experience.