The mechanical response of yield-stress materials below the yield point remains a subject of debate. Two of the most widely used constitutive models for these materials offer fundamentally conflicting views: one permits plastic flow at all stress levels, the other assumes entirely recoverable viscoelasticity below yield. Using parallel superposition rheometry, we test the sub-yield behaviour of a microgel and an emulsion. When residual slip effects are properly accounted for, both fluids exhibit bounded, periodic strain responses, offering compelling evidence that they do not flow in the studied regime. Our results indicate that the sub-yield regime is underpinned by nonlinear viscoelasticity and underscore the need for improved constitutive relations that capture such effects without treating yielding as a precursor for nonlinearity.

The aim of this research is to study XRP cryptoasset price dynamics, with a particular focus on forecasting atypical price movements. Recent studies suggest that topological properties of transaction graphs are highly informative for understanding cryptocurrency price behavior. In this work, we show that specific topological properties of the XRP transaction graphs provide important information about extreme XRP price surges, and can be used for more competitive prediction of anomalous price dynamics.

We extend methods of Fontaine, Abrashkin and Schoof to obtain criteria determining number fields K over which no non-zero abelian variety with everywhere good reduction exists. As an application, under the GRH, we find 24744 such fields of various degrees up to 16.

Let $\mathfrak{o}$ be a compact discrete valuation ring and $n\geq 2$. We introduce a method to study the cotype zeta function of subalgebras of $\mathfrak{o}^n$. This multivariable series encodes the number of finite-index subalgebras $Λ$ of the $\mathfrak{o}$-algebra $\mathfrak{o}^n$ of a given elementary divisor type. We express this zeta function as a finite sum of $\mathfrak{o}$-adic integrals and compute these integrals in many cases. As a first application, we recover known results in a natural way from our approach. For instance, we obtain a lower bound for the abscissa of convergence of the subalgebra zeta function of $\mathfrak{o}^n$ by exhibiting an explicit pole. We also determine the number of irreducible subrings of $\mathfrak{o}^n$ of small index. As a second application, we give an explicit formula for the cotype zeta function of subalgebras of $\mathfrak{o}^4$.

We present neural networks to generate redshift-space galaxy power spectrum multipoles for multiple tracer and redshift bins simultaneously given a set of input cosmology and galaxy bias parameters. This emulator utilizes a combination of fully-connected layers and transformer architecture to accurately predict galaxy power spectrum multipoles $900$ times faster than the SPHEREx pipeline. We quantify network performance using both $Δχ^2$, and likelihood contours for simulated SPHEREx analyses, using two correlated tracer bins and two independent redshift bins. After optimizing network architecture, the loss function, and training set sampling strategy, we achieve $\operatorname{Med}\left( Δχ^2\right) = 0.069$ when comparing to our testing set. At the contour-level our emulator agrees with EFT predictions over a realistic parameter range, with an average 1D best-fit shift of $0.078σ$ and $0.82 \%$ change in 1D error bars. These results demonstrate the feasibility of using neural-network emulators to accelerate SPHEREx redshift-space power-spectrum analyses.

Acrylamide, with PubChem identifier CID=6579 is broadcasted to have four stable conformers contrasting with several journal publications characterizing only two or three. In this revision summary the discrepancy is clarified. Through very high precision density functional theory (DFT) calculations, three stable conformers and the three transition state barriers existing between them are verified to exist and validated with our own DFT calculations The most stable conformer is a planar molecular structure termed "sys" or "trans" in the literature. Meanwhile, a less stable structure termed "skew" pertains to two 3-dimensional structures that are energy-degenerate, but differ in their structure for being mirrored images of each other. Vibrational spectra, partial atomic charges, Cartesian coordinates, and Intrinsic Reaction Coordinate paths are summarized and recalculated with DFT at the $ω$B97XD/Def2TZVPP level for the three stable acrylamide isomers: the sys/trans lowest in energy structure, and the two skew mirrored structures.

We analyze the fine structure and ``fall-apart'' decay patterns of hidden-strangeness tetraquarks within the dynamical diquark model. Several well-established negative-parity resonances listed by the Particle Data Group (PDG) [$ϕ(2170)$, $η(2225)$, $η(2370)$] are examined as potential $q\bar q s\bar s$ tetraquark candidates using a Hamiltonian that incorporates (iso)spin-dependent, spin--orbit, and tensor interactions. We further show that the isovector resonances $ρ(2150)$ and $ρ_3(2250)$ observed by BESIII in $ψ(2S)\rightarrow K^{+}K^{-}η$ are compatible with a tetraquark assignment. Predictions for 28 states, including those with exotic quantum numbers, are also presented. Comparison of the model spectrum with the PDG's unverified ``Further States'' highlights additional promising candidates worth future experimental investigation. The predicted fall-apart decay channels are easily reconstructed by experiment, and may stimulate ongoing searches for hidden-strangeness tetraquarks at GlueX and BESIII.

AI agents dynamically acquire tools, orchestrate sub-agents, and transact across organizational boundaries, yet no existing security layer verifies what an agent can do, whether it executed what it claims, or what happened in a multi-agent interaction. We trace this gap to the capability-context separation: inside a transformer, tool definitions and user context are indistinguishable tokens, but at the orchestration layer they have fundamentally different security semantics. Existing frameworks conflate the two, enabling silent capability escalation and leaving interactions without verifiable provenance. From this principle we derive three Agent Governance Requirements: capability integrity (G1), behavioral verifiability (G2), and interaction auditability (G3), defining what a governed agent ecosystem must enforce, independent of how. We prove two structural results: the Chain Verifiability Theorem (one unverifiable interior agent breaks end-to-end verification for all downstream nodes) and the Bounded Divergence Theorem (replay-based verification yields a probabilistic safety certificate, epsilon <= 1 - alpha^{1/n}). We validate with two crypto-agnostic instantiations -- basic (Ed25519, SHA-256; 97 us verify) and enhanced (BBS+ selective disclosure, Groth16 DV-SNARK; 13.8 ms) -- both satisfying nine security properties. A reproducibility study (9 models, 7 providers) reveals 5.8x variance in inference determinism, connecting model characteristics to governance architecture. End-to-end evaluation over 5-20 agent pipelines confirms <0.02% overhead and detection of all attack scenarios with zero false positives.

In 1998, Richard Gardner and Gaoyong Zhang introduced the radial $p$th mean bodies $R_p K$ of a convex body $K\subset \mathbb{R}^n$ for $p>-1$. Furthermore, they established that $R_p K$ is convex for $p\geq 0$, but the convexity of $R_p K$ for $p\in (-1,0)$ remained unresolved. In this work, we answer this nearly 30-year-old question in the affirmative using Prékopa's theorem. Along the way, we provide a new proof of Keith Ball's theorem on integrals of log-concave functions along rays against the weight $r^{p-1}$ and extend it to $p\in (-1,0)$: if $g$ is an integrable, log-concave function which attains its maximum at the origin, with the origin lying in the interior of its support, then \[ x\mapsto \left(\frac{p}{g(o)}\int_{0}^{+\infty}r^{p-1}(g(rx)-g(o))\mathrm{d}\,r\right)^{-\frac{1}{p}} \] is a finite, positively 1-homogeneous convex function on $\mathbb{R}^n$, i.e. a gauge.

Large-scale MIMO detection remains challenging because exact or near-maximum-likelihood search is difficult to scale, while available quantum resources are insufficient for directly solving full-size detection instances by QAOA. This paper therefore proposes a Block-QAOA-Aware MIMO Detector (BQA-MD), whose primary purpose is to reorganize the detection chain so that it becomes compatible with limited-qubit local quantum subproblems. Specifically, BQA-MD combines block-QAOA-aware preprocessing in the QR domain, a standards-consistent blockwise 5G NR Gray-HUBO interface, an MMSE-induced dynamic regularized blockwise objective, and K-best candidate propagation. Within this framework, fixed-size block construction gives every local subproblem a uniform circuit width and parameter dimension, which in turn enables parameter-transfer QAOA as a practical realization strategy for structurally matched local subproblems. Experiments are conducted on a 16x16 Rayleigh MIMO system with 16QAM using classical simulation of the quantum subroutine. The results show that the regularized blockwise detector improves upon its unregularized counterpart, validating the adopted blockwise objective and the block-QAOA-aware design rationale. They also show that the parameter-transfer QAOA detector nearly matches the regularized blockwise exhaustive reference and clearly outperforms direct-training QAOA in BER, thereby supporting parameter reuse as the preferred QAOA realization strategy within the proposed framework. In the tested setting, MMSE remains slightly better in the low-SNR region, whereas the parameter-transfer QAOA detector becomes highly competitive from the medium-SNR regime onward.

We analyse screening in multi-field scalar-tensor theories, focusing on systems with a dilaton coupled to matter and an axion with a dilaton-dependent kinetic term, in the presence of both planetary and stellar density profiles. Using analytic arguments and fully coupled numerical solutions, we identify a regime in which full screening for a dark-energy-light, effectively unpinned string-dilaton, can occur without fine-tuning. The backreaction of the dilaton's partnered axion field can suppress the exterior scalar charge by selecting a minimum-energy configuration (the BBQ mechanism), yielding robust screening for generic axion gradients. In this regime screening is achieved by cancelling the dilaton's gradient rather than localising it. This reduces the exterior scalar charge and allows for gravity tests in the solar system to be passed. We then show that the more familiar thin-shell intuition need not apply in the multi-field setting. Axion surface gradients can drastically reshape the dilaton profile and drive a more localised transition without generically suppressing the fifth force. The exterior charge can remain essentially unchanged or even be enhanced as the shell is made thinner by a kinetically coupled field. Multi-field two-derivative dynamics therefore decouple localisation in thin shells from screening, evade single-field no-go arguments, and reopen viable parameter space for cosmologically light dilaton-like scalars with strong couplings to matter.

Given a graph $G$ and a vertex $v\in V(G)$, a local complementation at $v$ on $G$ is an operation that replaces the induced graph on the neighborhood of $v$ by its complement. A graph $H$ is a vertex-minor if $H$ can be obtained from $G$ by a sequence of vertex deletions and local complementation. A graph is said to be $k$-vertex-minor universal if it contains every $k$-vertex graph on any $k$-subset of vertices as a vertex minor. Previously, Ascoli--Fredrickson--Fredrickson--McFarland--Post proved that with high probability $G(n,1/2)$ is $Ω(\sqrt{n})$-vertex-minor universal. Furthermore, they conjectured that with high probability $G(n,p)$ and $G(n,1-p)$ are $Ω(p\sqrt{n})$-vertex-minor universal for all $ω(1/\sqrt{n})\le p\le 1/2$. In this short note, we confirm this conjecture up to an extra logarithm factor and show that this is true with probability $1-2^{-Ω(p^2n)}$ if $Ω(\log n/\sqrt{n})\le p\le 1/2$.

We propose a splitting approach to solve the second-order Hamilton--Jacobi equation, reducing it to a heat step and a purely first-order step. The latter is implemented using a gradient value policy iteration algorithm, enabling efficient characteristic-based machine learning methods. We establish convergence rates for the splitting method. In particular, with $h$ the splitting step, the $L^\infty$ error is bounded between $\mathcal{O}(h)$ and $\mathcal{O}(h^{1/5})$ for Lipschitz data, improving to $\mathcal{O}(h^{1/3})$ for semiconcave data. In the periodic setting, we also obtain an $L^1$ error of order $\mathcal{O}(h^{1/2})$. For the first-order step, we provide a weighted $L^2$ error analysis that shows exponential convergence. Each iteration solves linear characteristic equations and learns the value function by minimizing a weighted value gradient loss. The approach yields stable and accurate numerical results.

Affine Frequency Division Multiplexing (AFDM) has emerged as a promising chirp-based multicarrier technology for high-speed communication systems. To fully exploit the diversity gain offered by AFDM, accurate channel estimation is essential. However, existing studies have mainly focused on the integer-delay-tap scenario and single-symbol pilot-based estimation. Since delay taps in practice are generally fractional, approximating them as integers not only degrades delay estimation accuracy but also severely affects Doppler frequency estimation. To address this problem, in this paper, we investigate channel estimation for multiple-input multiple-output (MIMO)-AFDM systems. A time-affine frequency (T-AF) domain pilot structure is proposed to exploit time-domain phase variations. By leveraging the rotational invariance property in the spatial and temporal domains, a channel estimation algorithm based on Vandermonde-structured tensor-train (TT) decomposition is developed. The proposed algorithm demonstrates superior computational efficiency compared with state-of-the-art parameter estimation methods. Moreover, diverging from current studies, we derive the global Ziv-Zakai bound (ZZB) as an alternative parameter estimation error lower bound to the Cramér-Rao bound (CRB). Numerical results show that the derived ZZB provides tighter global performance characterization and successfully captures the threshold phenomenon in mean square error (MSE) performance in the low-SNR regime. Furthermore, the proposed algorithm achieves superior communication performance relative to the existing schemes, while offering a computational speedup, reducing the execution time by an order of magnitude compared to the state-of-the-art iterative algorithms.

High-performance machine learning tools in particle physics rest on two complementary directions: encoding symmetries explicitly in the architecture, and implicitly learning the structure of the data through large-scale (pre-) training. We compare the performance of the representative L-GATr and OmniLearn models on three especially challenging tasks: reweighting-based unfolding, likelihood-ratio estimation, and weakly supervised anomaly detection. Across all benchmarks, both methods achieve comparable performance given the statistical precision of the finetuning datasets, suggesting that the significant efficiency gains from encoding known particle physics structures are largely method-independent.

Collective synchronization is often summarized by a complex order parameter $R e^{iΨ}$, implicitly treating the global phase $Ψ$ as a meaningful macroscopic coordinate. Here we ask when $Ψ$ becomes \emph{operationally well-defined} in oscillator networks whose coupling varies in time. We study damped (and optionally inertial) phase-oscillator models on graphs with time-dependent coupling $K(t)$, covering standard Kuramoto dynamics as a limit and including network and spatial topologies relevant to engineered settings. We propose an operational emergence criterion: a macroscopic phase is emergent only when it is robustly estimable, which we quantify via gauge-fixed phase-lag fluctuations under weak noise and finite sampling. This yields a quantitative threshold controlled by $NR^2$ and makes explicit why $Ψ$ is ill-posed in incoherent states even when formally definable. Nonautonomous coupling introduces a ramp timescale that competes with relaxation. Using a Laplacian-mode reduction near coherence, we derive a graph-spectral rate criterion: ordering tracks the protocol when $K(t)λ_2$ dominates the ramp rate, while faster ramps induce freeze-out. Numerically, we extract an operational freeze-out time from an energy-based tracking diagnostic and show that, for non-spatial networks, the residual incoherence at freeze-out collapses when plotted against the spectral protocol parameter $λ_2τ$ across Erdős--Rényi and small-world graph families. Finally, on periodic lattices we show that topological sectors and defect-mediated ordering obstruct complete alignment, leading to protocol-dependent, long-lived partially synchronized states and systematic deviations from spectral collapse.

Vapor deposition allows for the synthesis of metastable polymorphs with unique properties, yet polymorph selection remains largely empirical due to the lack of predictive guidelines bridging thermodynamics, kinetics, and synthesis conditions. Here, we show that precursor chemistry can control metastable polymorph selection by modulating the reaction driving force governing nucleation. By integrating first-principles reaction energetics and substrate interactions into classical nucleation theory, we establish a quantitative framework that connects precursor-dependent reaction energetics to polymorph accessibility during vapor deposition. Using Ga2O3 as a model system, we demonstrate that highly reactive precursors with large reaction driving forces kinetically stabilize the metastable α phase, whereas low-driving-force precursors permit thermodynamic relaxation to the stable \b{eta} phase. Furthermore, precursor flow rates amplify supersaturation, expanding the kinetic window for stabilizing the elusive \k{appa} phase. The predictive capability of this approach is further validated in the TiO2 system, where precursor-dependent reaction energetics correctly capture the competitive nucleation between rutile and anatase. These results establish precursor chemistry as a tunable chemical lever for controlling nucleation kinetics and provide a predictive design principle for metastable polymorph synthesis in vapor deposition.

We discuss aspects of the Euclidean path integral around axion-supported de Sitter wormholes, at one-loop order. We numerically compute the phase of the path integral around these solutions, as well as for a certain "multiple wormholes" generalization, and interpret this phase in different regimes. When the geometry is well approximated by a sphere with a small handle, the wormhole admits an effective description as a sphere with two local operator insertions, whose positions fluctuate around the antipodal configuration. The antipodal configuration is an extremum of the position integral for the operators, but we show that it is an unstable one. Accordingly, the phase of the wormhole solution can be viewed as the Polchinski phase in the sphere, multiplied by an additional phase from the integral over positions of the effective local operators. Using our expressions for the one-loop determinant, we also estimate the EFT coefficients of the dual bilocal operators in odd spacetime dimensions, to one-loop order. Lastly, we also discuss "maximal flux" solutions, which have $S^{1}\times S^{D-1}$ geometry. Their Lorentzian continuations are Einstein static universes, so we call them "Einstein wormholes". In this limit, we determine the spectrum of fluctuations analytically and show that the phase of the path integral around this solution is entirely accounted for by the well-known instability of the Einstein static universe.

Early diagnosis of battery thermal anomalies is crucial to ensure safe and reliable battery operation by preventing catastrophic thermal failures. Battery diagnostics primarily rely on battery surface temperature measurements and/or estimation of core temperatures. However, aging-induced changes in the battery model and limited training data remain major challenges for model-based and machine-learning based battery state estimation and diagnostics. To address these issues, we propose a Kolomogorov-Arnold network (KAN) in conjunction with a Koopman-based detection algorithm that leverages the unique advantages of both methods. Firstly, the lightweight KAN provides a model-free estimation of the core temperature to ensure rapid detection of battery thermal anomalies. Secondly, the Koopman operator is learned in real time using the estimated core temperature from KAN and the measured surface temperature of the battery to provide the core and surface temperature prediction for diagnostic residual generation. This online learning approach overcomes the challenges of model changes. Furthermore, we derive analytical conditions to obtain diagnostic guarantees on our KAN-Koopman detection scheme. Our simulation results illustrate a significant reduction in detection time with the proposed algorithm compared to the baseline Koopman-only algorithm.

Symmetry is one of the most beautiful yet mysterious concepts in science. In chemical systems, presence of local symmetries at specific fragments often serve as driving forces behind many physicochemical properties, including stability, spectroscopy, and reactivity. Moreover, degree of symmetry varies continuously with molecular dynamics and intermolecular interactions, making it a hidden but decisive factor. In this study, we propose a theoretical framework to quantify continuous degrees of symmetry and chirality localized within constrained regions of a molecular environment. Application of this method to reaction sites of dendralene molecules reveals strong correlations between local symmetry and molecular stability, parity-dependent behavior, and Diels--Alder reactivity. Additionally, representations of local chirality (chirotopicity) fields in porphyrins uncover unique signatures accounting for the chirality recognition power. Overall, these findings highlight the potential of local symmetries within a molecular framework on predicting chemical properties.

Passkeys -- discoverable WebAuthn credentials synchronised across devices are widely promoted as the future of passwordless authentication. Built on the FIDO2 standard, they eliminate shared secrets and resist phishing while offering usability through platform credential managers. Since their introduction in 2022, major vendors have integrated passkeys into operating systems and browsers, and prominent websites have announced support. Yet the true extent of adoption across the broader web remains unknown. Measuring this is challenging because websites implement passkeys in heterogeneous ways. Some expose explicit "Sign in with passkey" buttons, others hide options under multi-step flows or rely on conditional mediation, and many adopt external mechanisms such as JavaScript libraries or OAuth-based identity providers. There is no standardised discovery endpoint, and dynamic, JavaScript-heavy pages complicate automated detection. This paper makes two contributions. First, we present Fidentikit, a browser-based crawler implementing 43 heuristics across five categories - UI elements, DOM structures, WebAuthn API calls, network patterns, and library detection - developed through iterative refinement over 1,500 sites. Second, we apply Fidentikit to the top 100,000 Tranco-ranked domains, producing the first large-scale census of passkey adoption. Our results show adoption strongly correlates with site popularity and often depends on external identity providers rather than native implementations.

This study treats transmission scheduling for remote state estimation over unreliable channels with a hidden mode. A local Kalman estimator selects scheduling actions, such as power allocation and resource usage, and communicates with a remote estimator based on acknowledgement feedback, balancing estimation performance and communication cost. The resulting problem is naturally formulated as a partially observable Markov decision process (POMDP). In settings with observable channel modes, it is well known that monotonicity of the value function can be established via investigating order-preserving property of transition kernels. In contrast, under partial observability, the transition kernels generally lack this property, which prevents the direct application of standard monotonicity arguments. To overcome this difficulty, we introduce a novel technique, referred to as state-space folding, which induces transformed transition kernels recovering order preservation on the folded space. This transformation enables a rigorous monotonicity analysis in the partially observable setting. As a representative implication, we focus on an associated optimal stopping formulation and show that the resulting optimal scheduling policy admits a threshold structure.

We introduce a comprehensive data structure, tangle structure trees, which simultaneously displays all the $\mathcal{F}$-tangles of an abstract separation system for very general obstruction sets $\mathcal{F}$. It simultaneously also displays certificates $σ\in\mathcal{F}$ for any non-existence of such tangles, or for the non-extendability of low-order tangles to higher-order ones. Our theorem can be applied to produce the structures of the classical tree-of-tangles and tangle-tree duality theorems, both for graph tangles and for their known generalizations to more general separation systems. It extends those theorems to obstruction sets $\mathcal{F}$ that need not define profiles (as they must in all known trees of tangles) or consist of stars of separations (as they must in traditional tangle-tree duality). Our existence proof for these structure trees is constructive. The construction has been implemented in open-source software available for tangle detection and further analysis.

The booksize $ \mathrm{bk}(G) $ of a graph $ G $, introduced by Erdős, refers to the maximum integer $ r $ for which $G$ contains the book $ B_r $ as a subgraph. This paper investigates two open problems in spectral graph theory related to the booksize of graphs. First, we prove that for any positive integer $r$ and any $ B_{r+1} $-free graph $ G $ with $ m \geq (9r)^2 $ edges, the spectral radius satisfies $ ρ(G) \leq \sqrt{m} $. Equality holds if and only if $ G $ is a complete bipartite graph. This result improves the lower bound on the booksize of Nosal graphs (i.e., graphs with $ ρ(G) > \sqrt{m} $) from the previously established $ \mathrm{bk}(G) > \frac{1}{144}\sqrt{m} $ to $ \mathrm{bk}(G) > \frac{1}{9}\sqrt{m} $, presenting a significant advancement in the booksize conjecture proposed Li, Liu, and Zhang. Second, we show that for any positive integer $r$ and any non-bipartite $ B_{r+1} $-free graph $ G $ with $ m \geq (240r)^2 $ edges, the spectral radius $ρ$ satisfies $ρ^2<m-1+\frac{2}{ρ-1}$, unless $G$ is isomorphic to $S^+_{m,s}$ for some $s\in\{1,\ldots,r\}$. This resolves Liu and Miao's conjecture and further reveals an interesting phenomenon: even with a weaker spectral condition, $ρ^2\geq m-1+\frac2{ρ-1}$, we can still derive the supersaturation of the booksize for non-bipartite graphs.

Cerebellar-like networks, in which input activity patterns are separated by projection to a much higher-dimensional space before classification, are a recurring neurobiological motif, present in the cerebellum, dentate gyrus, insect olfactory system, and electrosensory system of the electric fish. Their relatively well-understood design presents a promising test-case for probing principles of biological learning. The circuits' expansive projections have long been modelled as random, enabling effective general purpose pattern separation. However, electron-microscopy studies have discovered interesting hints of structure in both the fly mushroom body and mouse cerebellum. Recent numerical work suggested that this non-random connectivity enables the circuit to prioritise learning of some, presumably natural, tasks over others. Here, rather than numerical results, we present a robust mathematical link between the observed connectivity patterns and the cerebellar circuit's learning ability. In particular, we extend a simplified kernel regression model of the system and use recent machine learning theory results to relate connectivity to learning. We find that the reported structure in the projection weights shapes the network's inductive bias in intuitive ways: functions are easier to learn if they depend on inputs that are oversampled, or on collections of neurons that tend to connect to the same hidden layer neurons. Our approach is analytically tractable and pleasingly simple, and we hope it continues to serve as a model for understanding the functional implications of other processing motifs in cerebellar-like networks.

Einstein-bumblebee gravity, as a class of massive non-minimally coupled vector-tensor theories, provides a useful framework for constraining Lorentz symmetry breaking through astrophysical observations, largely due to the existence of exact static and spherically symmetric black hole solutions. These solutions are typically obtained under the assumption that the vector-field potential vanishes everywhere once the vector field acquires a nonzero radial vacuum expectation value. However, imposing this assumption globally obstructs the construction of self-consistent compact-star solutions. In this work, we elucidate the origin of this inconsistency through a detailed analysis of the field equations and construct neutron-star configurations by abandoning the global vanishing-potential assumption. Crucially, we show that even without enforcing this condition everywhere, it is violated only in the strong-field interior region and is dynamically restored in the weak-field regime by asymptotic boundary conditions at spatial infinity. As a result, consistency with existing black-hole solutions and observational constraints is preserved. Our results establish massive vector-tensor gravity as a unified, natural, and self-consistent framework for compact objects, significantly extending its astrophysical viability beyond black holes and Solar System tests.

Let $D$ be a principal ideal domain with infinite spectrum such that for every nonzero prime ideal $M$ of $D$, the residue field $D/M$ is finite. Let $K$ be the quotient field of $D$. We investigate sets of lengths in the ring of integer-valued polynomials on $M$, $\text{Int}(M, D) = \{f \in K[x] ~ \vert ~ f(M) \subseteq D\}$. For every multiset of integers $1 < z_1 \leq z_2 \leq \cdots \leq z_n$, we explicitly construct an element of $\text{Int}(M, D)$ with exactly $n$ essentially different factorizations into irreducible elements of $\text{Int}(M, D)$ whose lengths are $z_1, z_2, \ldots, z_n$. Furthermore, we show that $\text{Int}(M, D)$ is not a transfer Krull domain. These results spark off the study of sets of lengths in the rings $\text{Int}(S, D) \neq \text{Int}(D)$, where $S$ is an infinite subset of $D$.

The Architecture, Engineering, and Construction (AEC) industry is undergoing rapid digital transformation, producing diverse digital assets such as datasets, computational models, use cases, and educational materials across the built environment lifecycle. However, these resources are often fragmented across repositories and inconsistently documented, limiting their discoverability, interpretability, and reuse in research, education, and practice. This study introduces OpenConstruction, a community-driven open-science ecosystem that aggregates, organizes, and contextualizes openly accessible AEC digital resources. The ecosystem is structured into four catalogs, including datasets, models, use cases, and educational resources, supported by consistent descriptors, curator-led validation, and transparent governance. As of December 2025, the platform hosts 94 datasets, 65 models, and a growing collection of use cases and educational materials. Two case studies demonstrate how the ecosystem supports benchmarking, curriculum development, and broader adoption of open-science practices in the AEC sector. The platform is publicly accessible at https://www.openconstruction.org/.

We study the ergodic properties of a unitary Floquet dynamics arising from the repeated application of a translationally-invariant Clifford Quantum Cellular Automata to an infinite system of qubits in d dimensions. One expects that if the QCA does not exhibit any periodicity, a generic initial state of qubits will thermalize, that is, approach the infinite-temperature state. We show that this is true for many classes of states, both pure and mixed. In particular, this is true for all initial states that are short-range entangled and close to the equilibrium state. We also point out a subtle distinction between weak and strong thermalization.

Reproducibility in simulation-based computer architecture research requires coordinating artifacts like disk images, kernels, and benchmarks, but existing workflows are inconsistent. We improve gem5, an open-source simulator with over 1600 forks, and gem5 Resources, a centralized repository of over 2000 pre-packaged artifacts, to address these issues. While gem5 Resources enables artifact sharing, researchers still face challenges. Creating custom disk images is complex and time-consuming, with no standardized process across ISAs, making it difficult to extend and share images. gem5 provides limited guest-host communication features through a set of predefined exit events that restrict researchers' ability to dynamically control and monitor simulations. Lastly, running simulations with multiple workloads requires researchers to write custom external scripts to coordinate multiple gem5 simulations which creates error-prone and hard-to-reproduce workflows. To overcome this, we introduce several features in gem5 and gem5 Resources. We standardize disk-image creation across x86, ARM, and RISC-V using Packer, and provide validated base images with pre-annotated benchmark suites (NPB, GAPBS). We provide 12 new disk images, 6 new kernels, and over 200 workloads across three ISAs. We refactor the exit event system to a class-based model and introduce hypercalls for enhanced guest-host communication that allows researchers to define custom behavior for their exit events. We also provide a utility to remotely monitor simulations and the gem5-bridge driver for user-space m5 operations. Additionally, we implemented Suites and MultiSim to enable parallel full-system simulations from gem5 configuration scripts, eliminating the need for external scripting. These features reduce setup complexity and provide extensible, validated resources that improve reproducibility and standardization.