LLMorphism is the biased belief that human cognition works like a large language model. I argue that the rise of conversational LLMs may make this bias increasingly psychologically available. When artificial systems produce human-like language, people may draw a reverse inference: if LLMs can speak like humans, perhaps humans think like LLMs. This inference is biased because similarity at the level of linguistic output does not imply similarity in cognitive architecture. Yet, LLMorphism may spread through two mechanisms: analogical transfer, whereby features of LLMs are projected onto humans, and metaphorical availability, whereby LLM vocabulary becomes a culturally salient vocabulary for describing thought. I distinguish LLMorphism from mechanomorphism, anthropomorphism, computationalism, dehumanization, objectification, and predictive-processing theories of mind. I outline its implications for work, education, responsibility, healthcare, communication, creativity, and human dignity, while also discussing boundary conditions and forms of resistance. I conclude that the public debate may be missing half of the problem: the issue is not only whether we are attributing too much mind to machines, but also whether we are beginning to attribute too little mind to humans.

The present study provides an extension to our recent work on the vector (V) electromagnetic single-nucleon current and associated response functions, both for unpolarized situations and in situations where the target nucleon is polarized. Here the axial-vector (A) single-nucleon current matrix element is developed in detail and the full set of vector and axial-vector currents used to obtain the electroweak VV, AA and VA response functions. Only the unpolarized case is studied in the present work. The general forms for all of these elements are developed together with various approximation schemes in which numerical studies are provided to indicate where these approximations may be expected to be valid. The results of this work provide the basis for a deeper understanding of the roles played by the various single-nucleon form factors in weak interaction reactions on free nucleons and when using the standard ``prescription for nuclear physics'' in reactions involving nucleons in nuclei.

Quadratic response tensors arise naturally in quantum kinetic descriptions, such as the quantum linear Boltzmann equation (QLBE), where they encode the coupled structure of drift and fluctuations beyond simple positive-definite forms. Motivated by this class of systems, we investigate how such response structures are modified under monitoring-induced coarse graining. Within the Gorini--Kossakowski--Sudarshan--Lindblad (GKSL) framework and under time-scale separation, Zeno elimination of fast degrees of freedom generates a subtractive renormalization with Schur-complement structure. As a result, positive definiteness of the response tensor is not preserved: coupling between slow and rapidly damped sectors can induce negative directions even when the microscopic tensor is strictly positive. We formulate a minimal effective flow capturing this mechanism and show that the competition between Schur-induced compression and anisotropic perturbations organizes the dynamics into distinct signature sectors. The resulting structure appears to be robust within the class of models considered and, in appropriate regimes, may be experimentally accessible. Our results establish a general framework for how quadratic response structures, as encountered in QLBE-type dynamics, are dynamically reorganized under Zeno-induced coarse graining.

The rapid growth in the deployment and scale of modern artificial intelligence (AI) systems has intensified concerns regarding their environmental impacts, yet we still lack a comprehensive view of where and how these impacts arise across the AI life cycle. In order to shed more light on this question, we conduct a structured, comprehensive literature review of scientific papers and technical reports that examine different aspects of AI's environmental footprint. Using an eight-stage life cycle framework, spanning hardware manufacturing, infrastructure construction, data gathering and preprocessing, model experimentation, training, post-training adaptation, deployment, inference, and end-of-life, we systematically map which stages are covered, the metrics reported at each stage, and the methodological choices made. We then draw conclusions about the information we gathered, finding that although life cycle language is increasingly common in discussions of "green" or "sustainable" AI, its definition remains unclear -- while some studies focus solely on model training and inference, others encompass broader measurements such as data collection, infrastructure, and embodied emissions. We also find that reporting practices rely predominantly on CO2e estimates derived from coarse proxies, with limited attention dedicated to water usage, materials manufacturing, and multi-impact life cycle assessment, making it difficult to compare and aggregate true results. Building on these findings, we propose measurement and reporting approaches to support more comprehensive, comparable and policy-relevant assessments of AI's environmental impacts.

We present sensitive ($\sim$9 $μ$Jy), sub-arcsecond resolution radio continuum observations at 9.0 GHz (3.3 cm) and 14.0 GHz (2.1 cm) obtained with the Karl G. Jansky Very Large Array (VLA) toward the nearby Coronet Cluster in Corona Australis (d $\approx$ 150 pc). We monitored the region from March 2012 to February 2015 using all available VLA configurations, allowing us to construct deep X- and Ku-band maps at multiple angular resolutions. We detected 20 radio sources, including 14 previously known Young Stellar Objects (YSOs), five sources possibly associated with shock emission, and one background galaxy. We resolved IRS 5, previously known to be a binary system, and identified IRS 7A and IRS 7B as multiple systems at centimetre wavelengths. The younger Class 0 and I YSOs exhibit spectral indices $α_{pk}$ ranging from -0.4 to 1.7, while the more evolved Class II YSOs show flatter values between 0 and 0.8, consistent with free-free emission, with minor contributions from non-thermal emission. The Class III source is only constrained by an upper limit. Radio variability, measured as a fraction of the mean intensity peak, is found to be ubiquitous and independent of evolutionary stage. Variability structure functions computed for nine sources indicate no preferred timescales for most of them. We also investigate spectral index variability for six sources and find significant variations in only one object. Finally, we analyse the extended radio emission toward IRS 7B, where some subcomponents exhibit negative spectral indices suggestive of non-thermal processes.

We study the spectrum of the Hodge-Laplacian on $1$-forms for left-invariant metrics on the Lie group $\operatorname{SU}(2) \cong S^3$ and its quotient $\operatorname{SO}(3)\cong P^3(\mathbb{R})$. To the best of our knowledge, we provide the first explicit computation of the full spectrum of the Hodge-Laplacian for a canonical variation by determining the eigenvalues of Berger 3-spheres and analyzing their resulting splitting behavior. Furthermore, we propose and rigorously prove an explicit formula for the first eigenvalue of general homogeneous metrics on $\operatorname{SU}(2)$ and $\operatorname{SO}(3)$. The formal proof of this result was autonomously discovered by an advanced AI model, providing a notable case study for AI-driven mathematical research. Finally, leveraging this explicit formula, we apply these spectral results to the inverse problem, showing that the spectrum on $1$-forms determines the metric up to isometry. The source code for the symbolic computations, visualizations, and a Monte Carlo stress test is provided in the electronic supplementary material [He26].

Explanations are inherently contrastive: E happened rather than E' because of C rather than C'. However, these contrasts, or "foils", are rarely mentioned explicitly but have to be inferred in context. Here, we investigate how people select the intended foil E' of a why-question. Participants read vignettes and judged, for each foil, their prior expectation (what will happen next), closeness (what is most similar to what happened), and hindsight expectation (what could have happened instead), as well as which foil they thought the question asker had in mind when they asked the why-question. We found that foil selections were best predicted by hindsight expectation judgments. This suggests that people infer the foil by considering what a question asker finds surprising after the outcome occurred. Since correct foil selection is relevant not only in human-human interaction but also increasingly in dialogues with large language models, we investigated their performance on the same task. The coupling between LLMs' explicit expectation judgments and their foil selections is inconsistent.

In large observational studies, the case-cohort design is commonly used to reduce the cost associated with covariate measurement. For survival outcomes, literature has suggested that the restricted mean survival time (RMST) be a more appropriate marginal causal effect measure than the hazard ratio. In this paper, we develop a marginal causal effect estimation method for RMST difference under the stratified case-cohort design. We adjust for measured confounders using an innovative template matching design. Compared with conventional matching designs, template matching allows greater flexibility in the sample sizes of the exposed and unexposed groups. We establish the asymptotic properties of the proposed causal effect estimators and develop a bootstrap procedure to estimate their variances. By conducting comprehensive simulation studies, we evaluate the finite sample performance of the proposed estimators, demonstrate the advantage of template matching over conventional matching, and compare between matching on propensity score and matching on covariates. Finally, we apply the proposed methods to the Atherosclerosis Risk in Communities (ARIC) Study to estimate the marginal causal effect of serum hs-CRP level on the coronary heart disease-free survival.

In this paper, we will define generalized critical point, ordered extreme and order monotone property of single-valued mappings in partially ordered Banach spaces. In particular, we will find the explicit formulas of Gateaux and Frechet derivatives of some single-valued mappings on the Banach spaces lp, for and C[0, 1], such as polynomial type operators and trigonometric type operators. By these concepts, we will investigate the connection between generalized critical points and ordered extrema of single-valued mappings in partially ordered Banach spaces that extends the connection between critical points and extrema of real valued functions in calculus. We will prove that in partially ordered Banach spaces, the order monotone of single-valued mappings can be described by its Gateaux derivatives or Frechet derivatives.

High-dimensional spatially correlated covariates are common in regression models encountered in environmental sciences and other fields. In such models, the regression coefficients often exhibit a sparse structure with spatial dependence. Although standard variable selection approaches can help detect the sparse structure, incorporating the dependence into variable selection helps recover spatially contiguous signals and improves prediction accuracy. Motivated by a real-world challenge in hurricane count prediction, we propose a novel neighborhood-structured global-local shrinkage prior for prediction and region selection in Poisson regression with spatial covariates. The proposed prior combines the Conditional Auto-Regressive (CAR) prior with a Super Heavy-tailed prior to introduce spatial dependence among the coefficients while ensuring appropriate shrinkage effects for covariate selection. We develop an efficient Metropolis-within-Gibbs sampler for computation that accommodates the count data. Extensive simulation studies demonstrate that the proposed model excels when signals are weak and adjacent and the spatial dependence in covariates is strong. In the application of hurricane prediction from the north Atlantic, our method outperforms traditional regression-based approaches and rivals the benchmark oracle model.

Atom interferometry generates heterogeneous multivariate temporal streams governed by phase evolution, fringe dynamics, control variables, and auxiliary sensing measurements. Accurate forecasting of these signals is important for predictive monitoring, phase correction, and intelligent quantum sensing, but it requires effective modeling of long-range temporal dependencies and interactions among multiple sensing sources. This paper proposes BARFI-Q, a Quantum-Enhanced Block Attention Residual Fusion framework for multivariate time-series forecasting in atom interferometry. BARFI-Q integrates patch-based embedding, dual-branch temporal modeling, hierarchical fusion, adaptive block-attention residual aggregation, and a quantum feature-mapping module. Unlike conventional Transformer-based forecasting models with fixed additive residual paths, BARFI-Q adaptively reuses cross-depth information and enhances the fused latent representation through quantum feature mapping. To respect phase periodicity, the forecasting target is represented in circular space using sine and cosine components. Experiments show that BARFI-Q consistently outperforms strong baseline models across repeated runs and different historical window sizes. Fusion ablation results further confirm the benefit of jointly modeling channel-wise and spatial feature interactions. These results indicate that multiscale temporal learning, hierarchical fusion, adaptive residual routing, and quantum-enhanced latent transformation provide an effective framework for atom-interferometric time-series forecasting.

Moire superlattices formed by stacking atomically thin two-dimensional materials with a relative twist angle have emerged as a versatile platform for engineering quantum electronic, optical, and ferroic properties. Computational modelling of such systems with periodic boundary conditions requires the identification of commensurate supercells in which the moire periodicity is reproduced exactly, or within a prescribed tolerance. While several codes exist for bilayer systems, extension to three or more layers with independently chosen twist angles remains a significant challenge. Here we present MLM (Multi-Layer Moire), an open-source Python package that constructs periodic, PBC-compatible moire supercells for an arbitrary number of twisted layers with any Bravais lattice type. The package employs a solve-and-round algorithm that reduces the coincidence-site search to an $O(N^2)$ linear-algebra problem per twist angle, compared to the O(N^4) brute-force enumeration required by conventional approaches. We demonstrate the package on bilayer graphene, bilayer and trilayer MoS$_2$, bilayer SrTiO$_3$, and a PbTiO$_3$/SrTiO$_3$ oxide heterostructure, producing simulation-ready structure files for both VASP and LAMMPS. The fractional-coordinate atom-selection algorithm scales to supercells containing millions of atoms and is robust across all twist angles including very small angles below 1 degree.

Despite careful design involving classifiers, parameters, and safeguarding, errors during human/AI interaction are not rare. Poor error recovery can disrupt interaction flow, damage user trust, and decrease user engagement. Whilst existing work has explored LLM recovery, tone, context, and personality as separate design dimensions, no existing work has combined these variables into a structured guidance framework. This paper presents a recovery code that maps four common LLM chatbot task contexts to associated personality traits (four Big Five personalities: Conscientiousness, Agreeableness, Openness, and Extraversion), tones, and three-stage recovery instructions. A recovery evaluation rubric was also designed, comprising three dimensions (Recovery quality, Tone alignment, and Appropriateness) and nine sub-dimensions. The methodology is exploratory, with no participants used. A between-subjects design was employed across two conditions: Condition A (baseline, uncoded), four separate Claude Sonnet 4.6 agents received no recovery code training; Condition B (coded), four separate Claude Sonnet 4.6 models were trained on the recovery code. Identical 'user' prompts and error scenarios were used across both conditions. Eight LLM evaluator agents assessed the recovery responses using the evaluation rubric, producing scores out of 5 for each sub-dimension. Results found a 27.8% average performance increase in coded recovery responses (76.7%) compared to baseline responses (48.9%). Condition B performed strongest in the appropriateness dimension (83.3%), with notable improvement in personality appropriateness (75% versus 50%) and providing explanation (60% versus 20%). These findings suggest that structured personality, context, and tone-informed recovery codes can be successfully learnt and applied by LLM chatbots to improve error recovery quality across varying contextual tasks.

We prove that the classical Efron--Stein inequality holds for independent exchangeable pairs \((X_i,Y_i)\). The same inequality fails for independent identically distributed pairs; a simple trigonometric counterexample shows that the trivial Cauchy--Schwarz bound of factor \(n\) is sharp. When each random variable takes at most \(k_i\) values, a useful bound still holds with explicit constant \(ρ(k)\le\max_i k_i/2\).

We study the geometry of the mean fitness surface of replicator systems and its relationship to evolutionary trajectory dynamics. Using the symmetric--antisymmetric decomposition of the fitness landscape matrix, we derive an explicit formula for the rate of change of mean fitness and establish necessary conditions for its monotonicity along trajectories. In general, replicator trajectories do not reach the maximum of the fitness surface, even in the presence of a unique asymptotically stable equilibrium. We characterise, in terms of the symmetric and antisymmetric parts of the fitness matrix, the precise conditions under which an equilibrium coincides with a local extremum of the fitness surface. Circulant matrices are identified as a natural and nontrivial class satisfying these conditions. We establish a two-way connection between fitness surface maxima and evolutionarily stable states: evolutionary stability implies a local fitness maximum, and the converse holds under the identified structural conditions. When the unique asymptotically stable equilibrium is a local maximum, it is evolutionarily stable and realises the global maximum of the fitness surface; an unstable equilibrium forces the global maximum to the boundary of the simplex. The framework is extended to general Lotka--Volterra systems, where an analogue of mean fitness is shown to share the same extremal properties. Results are illustrated through six examples spanning autocatalytic and hypercyclic replication, a parametric family exhibiting Andronov--Hopf bifurcation and heteroclinic cycles, and the Eigen quasispecies model.

Sampling geographically dispersed minority populations poses substantial challenges when individual group membership cannot be directly observed. Although stratified sampling can offer efficiency gains, these gains are typically modest unless the minority population is highly concentrated within a small number of strata. In this paper, we propose using Bayesian Improved Surname Geocoding (BISG) to enhance the efficiency of minority population sampling. BISG generates individual-level probabilities of minority group membership based on names and residential addresses. We incorporate these probabilities into a stratified Poisson probability sampling design. Applying the proposed approach to a national survey of Jewish Americans, we find that our estimates closely align with those from a large-scale Pew Research Center survey of the same population, which relied on a substantially more expensive sampling strategy involving geographic stratification and screening. At a fraction of the cost, our survey reproduces nearly identical patterns observed by Pew, including estimates of religious denominations and participation in specific religious activities.

Interacting supernovae probe the twilight years of massive stars, exhibiting signatures of interaction between the supernova ejecta and surrounding material expelled from the progenitor. We present the peculiar interacting supernova, SN\,2025ngs in NGC5961 (37.8 Mpc). This transient toes the line between strongly interacting supernovae (type IIn) and type IIP supernovae. SN 2025ngs presents photometrically as a short-plateau supernova, with a plateau duration, t$_{\mathrm{PT}}^{}\approx70$ days. Interaction features subside within a week post-explosion, consistent with the growing number of flash supernovae, giving way to a short period where a typical IIP spectrum is exhibited. Towards the drop off the plateau, interaction features re-emerge, exhibiting complex H$α$ profiles throughout the rest of the transient evolution. We compare with models of early spectra, finding the abundances generally consistent with a supergiant progenitor with a high mass-loss rate (10$^{-3}$ M$_\odot$ yr$^{-1}$). Early, high-resolution spectra reveal a double-horned H$α$ profile, providing strong evidence for shock interaction with a proximate disk-like circumstellar medium. Spectroscopically, SN 2025ngs closely resembles the luminous SN 1998S, despite photometric differences, with SN 2025ngs having a relatively modest peak magnitude of $M_\mathrm{V}=-17.9$ mag, adding another member to the surprisingly diverse 98S-like group.

We introduce the Frustrated Distance Matrix (FDM) model, a dynamic extension of the static distance-matrix ensemble on S^2 analyzed by Bogomolny, Bohigas, and Schmit (BBS). Its entries are pairwise geodesic distances between N Brownian particles on the sphere evolving under quenched random pairwise couplings linear in those distances. Where the static BBS theory recovers geometric information about the underlying manifold from spectra of distance matrices on i.i.d.\ samples, the time-resolved FDM spectrum carries information about structural changes of the underlying point process. The particle dynamics realize one such change: a fast collapse from a uniform configuration onto a one-dimensional ring, followed by slow rotational drift of the ring orientation; the particle-level picture provides the ground truth against which spectral diagnostics are calibrated. We find that the static BBS template is preserved at every time, with the dynamics entering as a redistribution of spectral mass within that template, sharp enough to flag ring formation. We propose self-averaging of the bulk density as the mechanism behind this preservation, verified by an i.i.d.-resample comparison, and extract a small set of spectral diagnostics of the structural change computable from the distance matrix alone. We suggest that our diagnostics can be applied in other similar inverse-problem settings: financial correlation matrices, graph and network adjacency spectra, similarity matrices in molecular dynamics, and dynamics on parameter manifolds.

We study fluctuations of the metric in the postquantum theory of classical gravity, a covariant theory which couples a classical spacetime with quantum matter fields. Mathematical consistency requires spacetime to evolve stochastically. Starting from the classical-quantum path integral, we linearize around Minkowski space and perform a scalar-vector-tensor decomposition, identifying the stochastic modes: a classical spin-2 field and spin-0 scalar, both diffusing around their respective wave equations. There is also a non-dynamical vector and scalar field. These are related to the degrees of freedom found in quadratic gravity, but here interpreted as stochastic contributions to spacetime. We show that the action is positive semi-definite (PSD) on all dynamical modes, which is a necessary condition for the theory to consistently treat spacetime classically. We compute the two-point function and power spectral density corresponding to fluctuations of the Newtonian potential, and compare it to the excess noise found in LISA Pathfinder. This sets a bound on one combination of the two dimensionless coupling constants of the theory, while bounds on the stochastic gravitational wave energy density in a FLRW background constrain another combination. We derive the effective action for matter distributions, and find that bounds from decoherence experiments are constrained by fluctuations in the Newtonian potential $Φ$ and the curvature perturbation $ψ$. Finally, we show consistency between different formulations of the pure gravity theory, the Onsager-Machlup form of the action, the Martin-Siggia-Rose form, and that given by stochastic differential equations.

Two-phase clocking offers significant advantages in timing margin and clock flexibility, yet its adoption remains limited due to the absence of automation in modern design flows. Managing strict non-overlap and 180$^\circ$ phase separation introduces complexity in RTL implementation and timing closure, leaving two-phase clocking rare in practice. This paper presents the first fully automated two-phase clocking flow integrated into OpenROAD Flow Scripts (ORFS). Our methodology automatically transforms flip-flop-based RTL into two-phase latch-based designs using Yosys technology mapping, ABC retiming, dual clock tree synthesis, two-phase correctness validation, and full physical design from RTL-to-GDS. We implement clock-gated and recirculation mux variants, where clock-gated achieves an average 29.2\% power reduction and 50\% latch count reduction over recirculation mux. Both variants are compared against flip-flop baselines, demonstrating timing closure through time borrowing on a design that failed timing with flip-flops.

Covariance matrix outcomes arise naturally in neuroimaging experiments to study brain functional connectivity. It is also of interest to understand how brain network organization varies with subject-level covariates. Existing covariance regression methods operate in a single-level framework and do not accommodate the hierarchically nested data structure in which subjects are grouped into clusters, such as age cohorts in lifespan studies. A Multilevel Covariate-Assisted Principal Regression (MCAP) framework is introduced, which identifies, for each cluster, a linear projection such that a generalized linear mixed effects model can be formulated with the covariates. The cluster-specific projections are modeled on the unit sphere via a von Mises-Fisher distribution, enabling principled borrowing of information across clusters. Model parameters are estimated by maximizing a hierarchical likelihood. For inference, a two-stage bootstrap procedure is proposed. Asymptotic properties of the estimators are established. Simulation studies demonstrate that MCAP substantially outperforms single-level competitors in estimating regression coefficients. Applied to the Human Connectome Project Lifespan Study spanning ages from five to ninety, MCAP identifies a dominant spectral brain network capturing age and sex effects on functional connectivity, and reveals findings including the convergence of neural reorganization patterns in late adulthood and the coordinated lifespan modulation of cross-network regions linked to language and executive function.

Quantum data-center networks must distribute entanglement between QPUs over paths whose length grows with system scale, but each entanglement-swapping step reduces the quality of the raw end-to-end state. Topology, multiplexing, and repeated connection attempts can increase the number of raw end-to-end copies available for a request, yet they do not answer the central resource question: whether those copies are sufficient to remove, via entanglement purification, the fidelity loss caused by multi-hop distribution. We study this question through a topology-independent black-box model of the network. Each elementary link is modeled as a Werner state with parameter $w_0$, so ideal swapping over an $\ell$-link path produces equal-quality raw copies with Werner parameter $w_0^\ell$; purification succeeds if it outputs at least one state with Werner parameter at least $w_0$ with probability at least $p_{\mathrm{th}}$. We compare recursive BBPSSW purification with higher-order $r$-to-$1$ bilocal-Clifford purification protocols of Jansen \emph{et al.}, using an all-in recursive schedule whose success probability is computed by exact dynamic programming. The resulting resource landscapes show a threshold structure governed by the Werner entanglement condition $w_0^\ell>1/3$ and demonstrate that multi-copy purification substantially improves both feasibility and copy efficiency. Across the evaluated grid, the Jansen family requires fewer copies than BBPSSW at more than $96\%$ of shared feasible points; at $p_{\mathrm{th}}=0.70$, the median copy budget drops from $268$ to $30$. These results provide a quantitative purification-resource benchmark for assessing whether future quantum data-center architectures can practically support hop-independent end-to-end entanglement quality.

We develop a statistical field theory that describes the large-N limit of a system of Brownian particles with quenched random pairwise interactions on a compact two-dimensional Riemannian manifold. The resulting Frustrated Fields (F2) model is a non-linear field theory for a smooth self-interacting density field $ρ$ on the manifold, with local and non-local (in space and time) self-interactions characteristic of spin-glass dynamics. Particle simulations show \emph{adiabatic dimension reduction}: on $S^2$, the density concentrates on a slowly precessing great-circle ring whose orientation is a director ($\hat{\mathbf{n}} \sim -\hat{\mathbf{n}}$, even profile). Conditioned on this simulation-supported ring saddle and on a Born-Oppenheimer separation between the slow orientation and the gapped density fluctuations, symmetry fixes the low-energy dynamics to be the nonlinear sigma model (NLSM) on the real projective plane $S^2/\mathbb{Z}_2 = \mathbb{RP}^2$ (the $\mathbb{RP}^2$ NLSM on the projective rotor space) in $(0+1)$ dimensions, governed by a single low-energy constant, the rotational diffusion coefficient $D_{\text{rot}}$. With $D_{\text{rot}}$ and the static ring profile $f_0$ measured from particle simulations, the resulting effective theory reproduces multiple independent orientation- and density-sector diagnostics with no further adjustable parameters.

The current work has investigated the effect of co-doping 3Y-Zirconia (3YSZ) with Sc, Mg and Y by wet infiltration. Pre-sintered discs of 3YSZ were immersed in diluted nitric acid solutions containing Sc, Mg or Y, and combinations of the three, trapping liquid within the porosities of the samples. Upon drying, the cations are maintained inside the pellet, making the basis for the co-doping. After sintering, mass increase confirms the co-doping effect and X-ray diffraction analysis show clear variations in atomic structure depending on the doping element. Rietveld refinements show that the wet-infiltrated samples contain the tetragonal t, t double prime and cubic c-phase in various fractions depending on the doping elements. Sc-infiltrated samples show a tendency to higher tetragonality, while the Mg-infiltrated sample obtained a single cubic phase. The multi-phase wet-infiltrated samples have a similar phase separation after sintering as 5Y-Zirconia (5YSZ), as calculated by the tetragonality deviation parameter. 3YSZ and 5YSZ sintered for 0 hours and 2 hours at 1500 degrees C show the effect of sintering time on the phase segregation. To evaluate the material properties in an application-based perspective, the Knoop hardness, translucency and grain size was measured. We conclude that liquid infiltration is a viable route to perform co-doping of Zirconia with various co-doping elements.

In an earlier paper [arXiv:2511.03669] we extracted the OPE of celestial CFT operator duals of positive helicity graviton and scalar particles from the Mellin transformed relevant MHV amplitudes of conformal gravity, realised as the bosonic subsector of the Berkovits-Witten theory. A soft theorem analysis of bulk MHV amplitudes established that this conformal gravity exhibits a chiral $\mathfrak{bms}_4$ symmetry on the celestial sphere with the associated $\mathfrak{sl}(2,\mathbb{R})$ current algebra, which acquires a non-trivial central extension, unlike the Einstein gravity. Here we construct a $2d$ chiral CFT free-field realisation of the relevant chiral $\mathfrak{bms}_4$ algebra in terms of three free scalars ($ϕ_i$) and three $(β_i,γ_i)$ ghost pairs, and propose vertex operators for the positive-helicity graviton primary $G^{++}_Δ(z,\bar{z})$ as well as the scalar primary $Φ_Δ(z,\bar{z})$, and compute their OPEs. These OPEs reproduce exactly those obtained from the bulk conformal gravity MHV amplitudes, providing a concrete celestial dual description of its MHV sector.

Understanding the atomic-scale structure and dynamics of amorphous oxide surfaces is essential for interpreting their chemical reactivity, mechanical stability, and interfacial behavior, yet direct experimental characterization remains challenging. We employ Deep Potential (DP) molecular dynamics to generate large-scale, ab initio-quality models of amorphous Al$_2$O$_3$ bulk glasses and melt-quenched free surfaces, enabling a quantitative analysis of both structure and relaxation dynamics with statistical confidence inaccessible to direct ab initio simulation. The trained DP model reproduces experimental liquid and glass structure, captures the cooling-rate dependence of the bulk glass transition, and corrects systematic biases in the polyhedral populations predicted by widely used classical force fields. At the free surface, mass density recovers to bulk values over ~10 $\unicode{x212B}$, while local coordination requires a slightly wider subsurface region to fully converge. The outermost layer is oxygen-enriched, exhibits altered polyhedral connectivity with contracted Al-O bonds, and hosts a broad population of under-coordinated motifs (notably AlO$_3$ and OAl$_2$) whose abundances are governed by glass stability. These reactive Lewis acid and Br$\unicode{x00F8}$nsted base sites are locally paired in a manner consistent with bond-valence compensation, yet remain spatially dispersed rather than aggregating into extended clusters. Despite this pronounced structural heterogeneity, the surface relaxes on the same timescale as the bulk and exhibits a comparable glass transition temperature, suggesting that the disordered surface is kinetically stable once formed. Together, these results establish a molecular-level picture of amorphous alumina surfaces and demonstrate the capability of machine-learned potentials to resolve structure-property relationships in disordered oxide interfaces.

Advances in sensing technology have made it possible to collect large volumes of high-dimensional time-series data. In fields like genetics and neuroscience, key questions concern whether directed relationships between variables can be learned from these data. To this end, graphical vector autoregressions are a popular tool because zeros among the autoregressive coefficients and error precision matrix have natural interpretations in terms of Granger non-causality and contemporaneous conditional independence. In applications where system dynamics are subject to functional or structural constraints, assuming the process is stable can be advantageous. However, enforcing stability demands restricting the autoregressive coefficients to lie in a constrained space with a complex geometry called the stationary region. The resulting inferential challenges are compounded when sparsity is also a requirement. Working in the Bayesian paradigm, we tackle the problem of developing a prior that simultaneously enforces stationarity and sparsity through parameter expansion, constructing a spike-and-slab prior with support constrained to the stationary region. A mixture of G-Wishart distributions provides a sparse prior for the error precision matrix. Computational inference is carried out using Metropolis-within-Gibbs, exploiting the No-U-Turn Sampler and reversible-jump steps. We demonstrate the inferential and predictive benefits of our approach through simulations and applications in macroeconomics and neuroscience.

We propose a simplified model for the cosmological constant in string theory flux vacua motivated by type IIB and F-theory compactifications. Relative to the Bousso-Polchinski model, small vacuum energy spacing occurs in thin wafers rather than thin shells. The model is applied to the entire Schöller-Skarke database of Calabi-Yau fourfolds, which exhibit $532,600,483$ distinct sets of Hodge numbers. The overwhelming majority of those ($99.95\%$ percent for some choices of parameters) exhibit a vacuum energy spacing of~$10^{-120}$ in Planck units or smaller. Brown-Teitelboim membrane nucleation transitions can populate this landscape of flux vacua. In the thin-wall approximation, and ignoring gravitational corrections, we find that the bubble transitions are always dominated by giant leaps in flux space. The age of the universe places a bound on Calabi-Yau topology that is satisfied for the entire Schöller-Skarke database.

Silver is an important light neutron-capture element whose stellar abundances help constrain the origin of the weak r-process. The Sun is an important reference point for such studies; moreover, being a moderately volatile element in CI chondrites, the solar silver abundance is interesting as a diagnostic for the debated Sun-CI abundance vs. condensation temperature trend. These studies require accurate silver abundances that go beyond the commonly used assumptions of 1D atmospheres and local thermodynamic equilibrium (LTE); however, no consistent 3D non-LTE analysis of silver has been available to date. We present a new Ag I model atom built from carefully curated radiative and collisional data, including newly computed oscillator strengths using an ab initio multi-configurational Hartree-Fock method and inelastic hydrogen collision rates based on a combined asymptotic and free-electron model approach. We assess modelling uncertainties via targeted sensitivity tests, finding the results most sensitive to hydrogen collision data. Applying the model to the solar Ag I 328 and 338 nm resonance lines, we find severe positive abundance corrections from coupled 3D and non-LTE effects. Using revised equivalent width measurements, we derive a recommended solar 3D non-LTE silver abundance of 1.15 +/- 0.08. This is an increase of 0.19 dex relative to the current reference value. Our ab initio model significantly reduces the discrepancy with the meteoritic value from 0.25 to 0.06 dex; moreover, this residual offset is consistent with recent results for other moderately volatile elements. The Sun provides the benchmark test for the first Ag I non-LTE model atom presented here. In subsequent work, this model will be applied to determine 3D non-LTE silver abundances in metal-poor dwarfs and giants, enabling improved constraints on Galactic chemical evolution and weak r-process nucleosynthesis.

X-ray polarimetry is now providing a new way to look at the high energy sky. The addition of two observables, polarization fraction and angle, reveals crucial new information on the structure of accretion flows and magnetic fields in astrophysical systems. Here, we review the basic physical processes that produce polarized X-rays in astrophysical contexts. Then, we briefly describe the physical processes used to measure X-ray polarization and the detectors that have been flown or are under construction.