In this paper, we exploit the concept of Kolmogorov $n$-widths to establish optimality benchmarks for reduced-order methods used in phononic, acoustic, and photonic band structure calculations. The Bloch-transformed operators are entire holomorphic functions of the wave vector~$\kk$, and by Kato's analytic perturbation theory the eigenpairs inherit this holomorphy wherever the spectral gap is positive. The Kolmogorov $n$-width of the solution manifold therefore decays exponentially, at a rate controlled by the minimum spectral gap between the band of interest and its neighbors. For clusters of bands, we show that working with spectral projectors rather than individual eigenvectors renders all internal crossings -- avoided, symmetry-enforced, or conical -- irrelevant: only the gap separating the cluster from the remaining spectrum matters. These results provide a sharp lower bound on the error of any linear reduction method, against which existing approaches can be measured. Numerical experiments on one- and two-dimensional problems confirm the predicted exponential decay and demonstrate that a greedy algorithm achieves near-optimal convergence. It also provides a principled justification for the choice of basis vectors in highly successful reduced-order models like RBME.

Let K be an algebraically closed field of characteristic zero. We study the tame isotropy group Tame_D(K[X,Y]) of locally finite derivations of the polynomial ring K[X,Y], using Van den Essen's classification up to conjugation. For each normal form, we explicitly determine the corresponding tame isotropy group. We then compare Tame_D(K[X,Y]) with the tame isotropy group of the associated exponential automorphism exp(D), and prove that these groups always coincide. This stands in contrast to the behaviour of the full automorphism group, where such an equality may fail for derivations with a nontrivial semisimple part.

We introduce a new family of closed differential forms naturally associated with minimal graphical submanifolds in Euclidean space, defined in arbitrary codimension. For each minimal graph, we construct an explicit closed form whose restriction coincides with the induced volume form. These forms admit a geometric interpretation as pullbacks, via the Gauss map, of tautological differential forms on the Grassmannian. In contrast to most known calibrations, they are generally not parallel and do not arise from special holonomy or symmetry considerations. The calibration problem is thus reduced to estimating the pointwise comass of the constructed forms. We show that the comass bound can be characterized in terms of explicit inequalities involving the singular values of the defining map of the graph, formulated via its two-dilations and we identify precise conditions ensuring that the comass is at most one. As a consequence, any minimal graph satisfying these conditions is calibrated and hence area-minimizing. This yields a broad class of new calibrated minimal graphs, extending the classical codimension-one theory, and provides an effective criterion for determining precisely where a given minimal graph is area-minimizing. As an application of our construction, we confirm a conjecture of Lawson and Osserman under two-dilation conditions, in arbitrary codimesnion.

Human judgment has always been central to conflict and escalation, but how will a world of artificial intelligence (AI) change the role of humans in war? As militaries increasingly adopt AI-enabled decision-support systems (DSS), including the United States in the war against Iran, concerns about automation bias -- over-reliance on algorithmic recommendations -- and algorithm aversion -- premature distrust of automated outputs -- raise fears that relying on AI too much could increase the risk of error, miscalculation, and accidents. Yet existing evidence on how militaries actually interact with AI remains limited. We test theories about the susceptibility of militaries to automation bias by comparing the results from a survey experiment conducted with 236 cadets at the United States Military Academy at West Point to a demographically similar cross-national public sample. Respondents completed a target identification task and then received advice from either an algorithm or a human analyst and had the opportunity to re-assess their initial identification, allowing direct measurement of automation bias and algorithm aversion. Contrary to prominent concerns, we find that West Point cadets are less prone to cognitive distortion than members of the general public, displaying better calibrated trust in algorithmic decision support systems. While the findings are limited, they suggest that military education and exposure to AI can meaningfully shape how AI influences international politics in matters of war and peace.

Frontend code, replicated across millions of page views, consumes significant energy and contributes directly to digital emissions. Yet current AI coding assistants, such as GitHub Copilot and Amazon CodeWhisperer, emphasize developer speed and convenience, with energy impact not yet a primary focus. At the same time, existing energy-focused guidelines and metrics have seen limited adoption among practitioners, leaving a gap between research and everyday coding practice. To address this gap, we introduce EcoAssist, an energy-aware assistant integrated into an IDE that analyzes AI-generated frontend code, estimates its energy footprint, and proposes targeted optimizations. We evaluated EcoAssist through benchmarks of 500 websites and a controlled study with 20 developers. Results show that EcoAssist reduced per-website energy by 13-16% on average, increased developers' awareness of energy use, and maintained developer productivity. This work demonstrates how energy considerations can be embedded directly into AI-assisted coding workflows, supporting developers as they engage with energy implications through actionable feedback.

Deploying Vision Transformers (ViTs) on near-sensor analog accelerators demands training pipelines that are explicitly aligned with device-level noise and energy constraints. We introduce a compact framework for silicon-photonic execution of ViTs that integrates measured hardware noise, robust attention training, and an energy-aware processing flow. We first characterize bank-level noise in microring-resonator (MR) arrays, including fabrication variation, thermal drift, and amplitude noise, and convert these measurements into closed-form, activation-dependent variance proxies for attention logits and feed-forward activations. Using these proxies, we develop Chance-Constrained Training (CCT), which enforces variance-normalized logit margins to bound attention rank flips, and a noise-aware LayerNorm that stabilizes feature statistics without changing the optical schedule. These components yield a practical ``measure $\rightarrow$ model $\rightarrow$ train $\rightarrow$ run'' pipeline that optimizes accuracy under noise while respecting system energy limits. Hardware-in-the-loop experiments with MR photonic banks show that our approach restores near-clean accuracy under realistic noise budgets, with no in-situ learning or additional optical MACs.

Diffusion quantum Monte Carlo (DMC) and coupled cluster theory [CCSD(T)] are widely-employed benchmark methods for noncovalent interactions (NCIs). However, recent studies have reported notable discrepancies across several hydrogen-bonded and dispersion-dominated systems, raising questions on the accuracy of the approximations underlying each approach. In DMC, the dominant error is expected to stem from the fixed-node approximation, where the nodal surface is typically taken from a single Slater determinant derived from a density functional theory or Hartree-Fock calculation. In this work, we assess the impact of nodal surface optimization on DMC predictions for 12 compounds spanning diverse NCIs, using a recently proposed antisymmetrized geminal power ansatz with natural orbitals. We find improved agreement with CCSD(T) for hydrogen-bonded systems, while having negligible effect for dispersion-dominated systems. These results provide a practical and computationally efficient route to resolving discrepancies in hydrogen-bonded interactions, while offering insight into the remaining differences in dispersion-dominated systems.

We consider the problem of simultaneous control and parameter estimation when the model is available only as a differentiable physics simulator. We propose a receding-horizon control framework in which a model predictive control (MPC) objective is optimized using gradients obtained by differentiating through the simulator, while physical parameters are updated online using measurement data. Unlike classical MPC, which relies on explicit algebraic models, our approach treats the dynamics as a computational object and performs simulation-based optimization using automatic differentiation. A shared differentiable model enables joint, real-time optimization of control inputs and physical parameters. We present two preliminary examples to demonstrate the proposed framework on two challenging applications: a fluid flow problem and a particle accelerator.

We study the set $W_{r,e,w}\ $ of dominant weights of $\mathfrak{sl}_r$ arising from partitions of fixed $e$-weight $w$. For $e$-cores, we show that $W_{r,e,0}\ $ decomposes as a disjoint union of simplices indexed by compositions of $r$. For general $w$, we prove that $W_{r,e,w}\ $ is a disjoint union of copies of these simplices, with multiplicities determined by the corresponding quotient data, yielding in particular a closed counting formula for $|W_{r,e,w}\ |\ $. The geometry gives rise to the stingray patterns appearing in the title. More generally, it yields a natural labeling of the dominant $e$-alcoves meeting $W_{r,e,w}\ $ by weak compositions of $w$, together with a compatible partial action of the affine Weyl group via wall crossing. Finally, we give an explicit alcove-geometric proof of the empty runner removal theorem for Iwahori-Hecke algebras.

Gold monocarbide (AuC) has been produced and characterized using laser spectroscopy, representing the first reported observation of AuC. We recorded the optical spectrum of gas-phase AuC between 400 nm and 700 nm, assigning excitations from the $\mathrm{X}\,^2Π_{1/2}( (2σ)^2 (2π)^1 )$ ground state to states arising from the $(2σ)^2 (3σ^\ast)^1 $ and $(2σ)^1 (2π)^2 $ configurations. Dispersed-fluorescence spectra are used to study the vibrational and spin-orbit structure of the ground state, branching ratios and radiative lifetimes of the excited states, and the Au--C bond dissociation energy. A molecular orbital diagram is used to rationalize the nature of AuC's low-lying electronic states. The data serve as valuable benchmarks of relativistic theory and are relevant to quantum science and precision measurements with cold molecules.

The kinetics of the $κ$ to $β$-Ga$_2$O$_3$ phase transformation were investigated in five batches of nominally phase-pure $κ$-Ga2O3 thin films heteroepitaxially grown on c-plane sapphire, with film thickness ranging from 700 to 1100 nm, using in-situ high-temperature X-ray diffraction. Phase fractions were quantitatively extracted through modified Rietveld refinement that accounts for preferred orientation, and the transformation kinetics were analyzed using the Johnson-Mehl-Avrami-Kolmogorov (JMAK) model. The applicability of the JMAK model to thin-film materials was evaluated and its lower and upper bounds for thin films and bulk materials were established. Based on this analysis, a method specifically suited for thin-film kinetic studies was developed and yielded reproducible and robust results across all five sample batches. The results indicate that the $κ$ to $β$ phase transformation in ~700-1100 nm films is best described as an interface-controlled, site-saturated nucleation with thickness-limited or effectively two-dimensional growth.

Using the relative de Rham stack for a family $X \to S$ in characteristic $p,$ we reprove the (local and global) Ogus-Vologodsky equivalence. Moreover, we observe that a lift of $S$ is not necessary. Instead, we use a lift of $X$ to the second Witt vectors of $S.$ The main ingredient is that, for a quasi-syntomic family $X/S,$ the relative de Rham stack admits a structure of a torsor over $X'$ which is the analogue of the Azumaya property of the algebra of differential operators. This can be applied to families of (reasonable) algebraic stacks, which gives rise to a logarithmic version of the Cartier equivalence. Along the way, we also obtain a decompleted version of the global Cartier equivalence.

We propose a variance-penalized formulation of Bayesian optimal experimental design for nonlinear models that augments the classical expected utility criterion with a penalty on utility variability, yielding a mean--variance objective that promotes robust experimental performance. To evaluate this objective, we develop Monte Carlo estimators for the expected utility, its second moment, and the resulting utility variance using prior sampling, thereby avoiding explicit posterior sampling. We then derive leading-order bias and variance expressions using conditional delta-method arguments. The objective is optimized using Bayesian optimization with common random samples to reduce noise. Numerical examples, including a linear-Gaussian benchmark, a nonlinear test problem, and contaminant source inversion in diffusion fields, demonstrate that the proposed approach identifies designs with substantially reduced variability while maintaining competitive expected utility.

An $N$-point FFT admits many valid implementations that differ in radix choice, stage ordering, and register-blocking strategy. These alternatives use different SIMD instruction mixes with different latencies, yet produce the same mathematical result. We show that finding the fastest implementation is a shortest-path problem on a directed acyclic graph. We formalize two variants of this graph. In the \emph{context-free} model, nodes represent computation stages and edge weights are independently measured instruction costs. In the \emph{context-aware} model, nodes are expanded to encode the \emph{predecessor edge type}, so that edge weights capture inter-operation correlations such as cache warming -- the cost of operation~B depends on which operation~A preceded it. This addresses a limitation identified but deliberately bypassed by FFTW \citep{FrigoJohnson1998}: that optimal-substructure assumptions break down ``because of the different states of the cache.'' Applied to Apple M1 NEON, the context-free Dijkstra finds an arrangement at 22.1~GFLOPS (74\% of optimal). The context-aware Dijkstra discovers $\text{R4} \to \text{R2} \to \text{R4} \to \text{R4} \to \text{Fused-8}$ at 29.8~GFLOPS -- a $5.2\times$ improvement over pure radix-2 and 34\% faster than the context-free result. This arrangement includes a radix-2 pass \emph{sandwiched between} radix-4 passes, exploiting cache residuals that only exist in context. No context-free search can discover this.

In this work, we develop a linear robust minimum mean-square error (RMMSE) precoder to mitigate the effects of imperfect channel state information (CSI) and the intra-cluster (ICL) and out-of-cluster (OCL) interference in cell-free (CF) multiple-antenna systems. The proposed precoder includes statistical information of the OCL interference in its derivation, allowing a more effective interference mitigation. An analysis of the sum-rate that can be obtained by the CF system is carried out and an expression quantifying the theoretical gains of mitigating OCL interference are derived. Simulation results corroborate that the proposed RMMSE precoder effectively mitigates ICL and OCL interference.

Standard chance constrained control algorithms typically rely on the assumption that uncertainties in vehicle states obey Gaussian statistics. Highly nonlinear systems tend to disrupt Gaussianity, challenging standard chance-constrained control methods. This paper develops a non-Gaussian confidence boundary parameterization technique for such cases where the problem departs appreciably from the Gaussian assumption. The approach is to consider the true confidence boundary as a perturbation of the one predicted from covariance, deriving perturbed boundary geometry from computed higher-order statistical moments. Applying this technique to so-called "banana-shaped distributions" (found e.g. in orbital mechanics problems) enables a simple parameterization of the confidence boundary using the skew and kurtosis tensors. The method is then applied to an impulsive stochastic spacecraft maneuver targeting problem in two-body dynamics. An algorithmic implementation outperforms a standard linear covariance-based approach in computing control parameters satisfying certain probabilistic bounds on the non-Gaussian distribution.

Most barred galaxies exhibit symmetric structures. However, recent studies have shown that a subset of barred galaxies exhibit lopsided morphologies. To quantify their occurrence and investigate their physical origins, we analyze barred galaxies in the IllustrisTNG TNG50 simulation. We select 519 clearly barred galaxies in their stellar mass maps out of 770 barred galaxies from the TNG50-1 catalog. We classify the bar morphologies into four subgroups - `Lopsided', `Perturbed', `Symmetric', and `Indeterminate' - and perform a comparative analysis of their physical properties. We find that galaxies hosting asymmetric bars (`Lopsided' and `Perturbed') tend to have higher gas densities around the bar region, enhanced star formation activity, and more recent bar-formation epochs than galaxies with symmetric bars. However, the factor that most consistently distinguishes the four subgroups is the stellar mass distribution of the host galaxy, and there appears to be no physical correlation with bar size. These findings suggest that asymmetric bars form preferentially in less massive galaxies and may evolve into symmetric bars over time through secular processes. However, this conclusion should be considered with caution, as the fraction of asymmetric bars in the TNG50 simulation is systematically higher than that observed in the local universe.

We investigate regularity properties of generalized conjugate functions induced by a general coupling function and the associated generalized proximal mapping. Our main results provide verifiable conditions ensuring local single-valuedness, continuity, Lipschitz continuity, and differentiability of the generalized proximal mapping, and transfer these properties to generalized conjugates providing explicit derivative formulas. These results are based on a nonsmooth implicit function theorem for generalized equations, relying on graphical localizations and second-order variational tools. Beyond first-order regularity, we also derive conditions under which generalized conjugates are strictly twice differentiable.

Discrete Choice Experiments (DCEs) investigate participants' preferences by observing their choice behavior in hypothetical scenarios and are widely used in the domain of healthcare. To reduce participants' cognitive burden, especially when dealing with a large number of attributes, researchers often employ partial profile designs. In these designs, certain attributes within each choice set are kept constant. Current literature on partial profile designs mainly focuses on main-effects models rather than interaction-effect models, with certain partial profile designs even incapable of estimating interaction effects. To address this issue, this paper introduces an Simulated Annealing (SA) algorithm to construct partial profile designs based on an interaction-effects model. During the experimental design phase, the existence and magnitude of interaction effects are often unknown. Therefore, this paper proposes a model-robust experimental design strategy. Through extensive simulation experiments and a real-life case study, we demonstrate that our SA model-robust partial profile design performs relatively well regardless of the underlying model.

The L-Band Digital Aviation Communication System (LDACS) aims to modernize communications between the aircraft and the tower. Besides digitizing this type of communication, the contributors also focus on protecting them against cyberattacks. There are several proposals regarding LDACS security, and a recent one suggests the use of physical unclonable functions (PUFs) for the authentication module. This work demonstrates this PUF-based authentication mechanism along with its potential vulnerabilities. Sophisticated models are able to predict PUFs, and, on the other hand, quantum computers are capable of threatening current cryptography, consisting factors that jeopardize the authentication mechanism giving the ability to perform impersonation attacks. In addition, aging is a characteristic that affects the stability of PUFs, which may cause instability issues, rendering the system unavailable. In this context, this work proposes the well-established Public Key Infrastructure (PKI), as an alternative solution.

Parametrised quantum circuits are a central framework for near term quantum machine learning. However, it remains challenging to determine in advance how architectural choices, such as encoding strategies, gate placement, and entangling structure, influence both the expressive capacity of the model and its trainability during optimisation. We introduce a data-agnostic framework, one requiring no knowledge of a training dataset or optimisation trajectory, that maps a broad family of circuits into a single architecture matrix built over learnable features and parameters. We show that this framework provides an explicit link between circuit structure, the correlations among learnable features, and the geometry of training kernels through the factorisation of each of these objects as quadratic forms in terms of these matrices. We show how correlations between learnable features arise from shared parameter-induced harmonics generated by non-commuting gate-observable interactions during Heisenberg back-propagation, and how these correlations are encoded directly in the architecture matrix. From this perspective, kernel structure and coefficient statistics can be reconstructed analytically from circuit design alone, without reference to a dataset or optimisation trajectory. The resulting framework makes circuit-induced structure explicit, separating architectural effects from data-dependent ones, and provides a principled foundation for analysing and comparing parametrised quantum circuits based on intrinsic, design-level signatures.

We present a search for strong H$β$+[O III] emitters at $z=9.4-11.3$ in the HUDF using ultra-deep JWST/MIRI F560W imaging from the MIDIS survey. Three galaxies are identified via pronounced F560W flux excesses, consistent with strong rest-frame optical line emission. SED modelling yields rest-frame H$β$+[O III] equivalent widths of $\sim 600-1300$AA (median $\simeq 1260$AA), placing these sources among the most extreme known at these epochs. Combining these with a literature sample of 16 spectroscopically confirmed galaxies at $z\geq 9$, we find a median ${\rm EW}^{\rm Hβ+[O III]}_{\rm rest}\simeq 1300$AA, similar to values at $z\sim6-9$. We find no evidence for either a strong increase or decline in EW beyond $z\sim9$. A tentative trend of higher EW with increasing UV luminosity is observed, while no statistically significant anti-correlation with stellar mass is found. We place a first constraint on the H$β$+[O III] luminosity function at $z\simeq9-11$ ($Φ\sim10^{-3.4}\,{\rm Mpc^{-3}\,dex^{-1}}$ at $\log( L_{\rm Hβ+[OIII]}/{\rm erg\,s^{-1}})=42.5$), consistent with a decline relative to $z\sim7-8$. The MIDIS sources have $\log(ξ_{\rm ion}/{\rm Hz\,erg^{-1}})=25.1-25.4$. We find significant correlations between $ξ_{\rm ion}$ and EW and $β$, but not with UV luminosity, consistent with trends at lower redshift. These results suggest that the physical conditions governing nebular emission and ionising efficiency are already in place at $z\sim9-11$, extending trends established at $z\sim6-9$.

Modern 5G user equipment (UE) processes Radio Resource Control (RRC) configuration messages during early control-plane exchanges, before authentication and integrity protection are established. Prior work for testing 5G UEs has largely focused on constructing syntactically invalid inputs. In contrast, we show that syntactically valid but semantically inconsistent messages, which violate specification-level field constraints or cross-field dependencies, can drive baseband implementations into invalid states, triggering assertion failures or modem crashes. These findings reveal semantic inconsistencies in pre-authentication signaling as a critical yet underexplored attack surface in 5G UE implementations. To address this gap, we present Constraint-Guided Semantic Testing (ConSeT), a framework that systematically extracts specification-level constraints and leverages them to generate targeted semantic violations for testing 5G UEs. ConSeT decodes RRC messages into structured fields, derives schema-based rules, infers cross-field dependencies using a Large Language Model (LLM) in an evidence-bounded manner, and produces syntactically valid test cases that intentionally violate semantic constraints. We evaluate ConSeT on both commercial and open-source 5G UEs. On commercial smartphones, it uncovers 7 previously unknown vulnerabilities through responsible disclosure, including 3 high-severity CVEs, affecting 64 chipset models and over 542 commercially available smartphone models. On the open-source OAI UE, ConSeT additionally triggers 29 distinct crash sites.

In the Rectangle Stabbing problem, input is a set ${\cal R}$ of axis-parallel rectangles and a set ${\cal L}$ of axis parallel lines in the plane. The task is to find a minimum size set ${\cal L}^* \subseteq {\cal L}$ such that for every rectangle $R \in {\cal R}$ there is a line $\ell \in {\cal L}^*$ such that $\ell$ intersects $R$. Gaur et al. [Journal of Algorithms, 2002] gave a polynomial time $2$-approximation algorithm, while Dom et al. [WALCOM 2009] and Giannopolous et al. [EuroCG 2009] independently showed that, assuming FPT $\neq$ W[1], there is no algorithm with running time $f(k)(|{\cal L}||{\cal R}|)^{O(1)}$ that determines whether there exists an optimal solution with at most $k$ lines. We give the first parameterized approximation algorithm for the problem with a ratio better than $2$. In particular we give an algorithm that given ${\cal R}$, ${\cal L}$, and an integer $k$ runs in time $k^{O(k)}(|{\cal L}||{\cal R}|)^{O(1)}$ and either correctly concludes that there does not exist a solution with at most $k$ lines, or produces a solution with at most $\frac{7k}{4}$ lines. We complement our algorithm by showing that unless FPT $=$ W[1], the Rectangle Stabbing problem does not admit a $(\frac{5}{4}-ε)$-approximation algorithm running in $f(k)(|{\cal L}||{\cal R}|)^{O(1)}$ time for any function $f$ and $ε> 0$.

We develop new methods for constructing confidence sets and intervals in linear instrumental variables (IV) models based on tests that remain valid under weak identification and under heteroskedastic, autocorrelated, or clustered errors. In practice, researchers typically recover such sets by grid search, a procedure that can miss parts of the confidence region, truncate unbounded sets, and deliver misleading inference. We replace grid inversion with exact and approximation-based methods that are both reliable and computationally efficient. Our approach exploits the polynomial and rational structure of the Anderson-Rubin and Lagrange multiplier statistics to obtain exact confidence sets via polynomial root finding. For the conditional quasi-likelihood ratio test, we derive an exact inversion algorithm based on the geometry of the statistic and its critical value function. For more general conditional tests, we construct polynomial approximations whose coverage error vanishes with approximation degree, allowing numerical accuracy to be made arbitrarily high. In many empirical applications with weak instruments, standard grid methods produce incorrect confidence regions, while our procedures reliably recover sets with correct nominal coverage. The framework extends beyond linear IV to models with piecewise polynomial or rational moment conditions, offering a general tool for reliable weak-identification robust inference.

Sequential estimators are proposed for the relative risk, odds ratio, log relative risk or log odds ratio of a dichotomous attribute in two populations. The estimators take the same number of observations from each population, and guarantee that the relative mean-square error for the relative risk or odds ratio, or the mean-square error for their logarithmic versions, is less than a given target. The efficiency of the estimators, defined in terms of the Cramér-Rao bound, is high when the considered attribute is rare or moderately rare.

Vehicle-to-everything (V2X) communications impose stringent physical-layer integrity requirements, particularly under short-packet transmission and mobility-induced channel variation. This paper studies whether standard-compliant online selection of Cyclic Redundancy Check (CRC) polynomials and Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) coding rates can reduce silent (undetected) errors in 5G New Radio (5G-NR) V2X links. The joint configuration problem is formulated as a lightweight Contextual Bandit (CB) with a small, discrete action space, and a discounted LinUCB policy is evaluated against greedy online adaptation and a conservative fixed baseline. A 5G-NR-compliant physical-layer simulation is developed using Sionna, modeling mobility through time-correlated Rayleigh fading, where vehicle speed governs channel correlation, and non-stationary interference via a two-state Markov process. The learning agent operates on coarse receiver feedback, including a noisy Signal-to-Noise Ratio (SNR) estimate and indicators of burst interference and deep fades, and targets minimization of the Undetected Error Probability ((P{UE})) while accounting for the Detected Error Probability ((P{DE})). Overall, our objective is to delineate the mobility regimes in which learning-assisted CRC-QC-LDPC configuration improves physical-layer integrity in 5G-NR V2X systems. Our results indicate that learning-assisted adaptation is most effective at low to moderate mobility, reducing (P_UE) by up to 50-70% relative to greedy selection in the low-SNR regime ((-5) to 5~dB) and approaching the best fixed configuration at higher (E_b/N_0). At high mobility (>= 180~km/h), fast channel decorrelation weakens temporal predictability, limiting the effectiveness of online learning and reducing performance differences across policies.

We present fully kinetic simulations of driven 2D turbulence in a relativistic plasma, designed for the first time to induce a fast magnetosonic cascade. As the driving strength increases, turbulence transitions from a weak wave-dominated regime to strong shock-driven dynamics. Using spatiotemporal Fourier analysis, we identify fast modes, finding that the weak turbulence regime exhibits spectral properties that are in excellent agreement with theoretical expectations. Our results are relevant for the modeling of turbulence in high-energy astrophysical plasmas.

We report the detection of weak high-velocity Na$\;$I absorption at V$_{\odot}$ = $-61.0\pm0.2$ km s$^{-1}$ in the spectrum of the stellar remnant at the center of the Galactic supernova remnant of 1181 AD. This velocity is not unlike that seen in old, more evolved SN remnants, but is much less than the remnant's $\simeq10^{3}$ km s$^{-1}$ expanding optical nebula. We briefly discuss its possible nature and origin.

The rapid growth of nature-inspired metaheuristics has exposed a persistent gap between metaphorical novelty and genuine algorithmic advancement. Motivated by the biophysics of chromatin loop extrusion -- a well-characterized genome-folding process driven by SMC motor complexes and conditional barriers -- we introduce the Loop-Extrusion Linkage (LEL) operator, a structure-learning wrapper that combines online variable-interaction estimation, spectral seriation via the Fiedler vector, and adaptive interval-based subspace search. LEL constructs a sparse interaction graph from successful optimization steps, derives a heuristic one-dimensional variable ordering, and generates overlapping evaluation subsets through stochastic interval growth modulated by learned boundary-crossing probabilities. We evaluate LEL on six synthetic diagnostic functions at d=96 designed to probe specific structural hypotheses -- contiguous blocks, permuted blocks, overlapping windows, banded chains, separable controls, and dense rotated couplings -- across 10^4 and 5 x 10^4 evaluation budgets with 15 independent seeds. Results are assessed via the Wilcoxon signed-rank test with Holm-Bonferroni correction and Vargha-Delaney A12 effect sizes. At 10^4 evaluations, Full LEL achieves the best median log-gap on 3 of 6 functions significantly outperforming all ablations and jSO on the structured tasks. At 5 x 10^4 evaluations, simpler ablations and baselines often surpass the full method, indicating that the adaptive barrier mechanism may over-constrain late-stage search on uniformly partitioned landscapes. The strongest supported finding is that learned spectral ordering consistently improves over graph-only grouping and random variable ordering, suggesting that interaction-graph seriation is the most valuable component of the proposed framework.