We present Riccati-ZORO, an algorithm for tube-based optimal control problems (OCP). Tube OCPs predict a tube of trajectories to capture predictive uncertainty. The tube induces a constraint tightening via additional backoff terms. This backoff can significantly affect the performance, and thus implicitly defines a cost of uncertainty. Optimizing the feedback law used to predict the tube can significantly reduce the backoffs, but its online computation is challenging. Riccati-ZORO jointly optimizes the nominal trajectory and uncertainty tube based on a heuristic uncertainty cost design. The algorithm alternates between two subproblems: (i) a nominal OCP with fixed backoffs, (ii) an unconstrained tube OCP, which optimizes the feedback gains for a fixed nominal trajectory. For the tube optimization, we propose a cost function informed by the proximity of the nominal trajectory to constraints, prioritizing reduction of the corresponding backoffs. These ideas are developed for ellipsoidal tubes under linear state feedback. In this case, the decomposition into the two subproblems substantially reduces the computational complexity with respect to the state dimension from $\mathcal{O}(n_x^6)$ to $\mathcal{O}(n_x^3)$, i.e., the complexity of a nominal OCP. We investigate the algorithm in numerical experiments, and provide two open-source implementations: a prototyping version in CasADi and a high-performance implementation integrated into the acados OCP solver.

The experimental data collected by KEDR and BESIII collaborations at the energies below charm quark thresholds are compared with the massless QCD expressions for the $e^+e^-$ annihilation R-ratio truncated at different orders of perturbation theory. The fits demonstrate the dependence of the extracted $α_s(M_Z)$ values on the orders of truncation of the corresponding approximations. The next-to-leading order, next-to-next-to-leading order and next-to-next-to-next-to-leading order fits of the combined KEDR data and BESIII data , truncated at the scale of mass of $J/Ψ$ meson, give the following results $α_s(M_Z)=0.1151_{-0.0069}^{+0.0052}$, $α_s(M_Z)=0.1190_{-0.0081}^{+0.0064}$and $α_s(M_Z)=0.1283_{-0.0075}^{+0.0028}$. The increasing tendency of fitted $α_s(M_Z)$ value is associated with the effects of not totally controlled within asymptotic perturbation theory expansions kinematical $π^2$ contributions to R-ratio coefficients due to analytical continuation from the space-like to time-like energy regions. The applications of the fixed orders of perturbation theory expansions and careful treatment of the analytical continuation effects are commented.

Third-party developers (TPDs) often turn to online communities for support when they can't get immediate responses from the platform. Twitch, as a leading live streaming platform, attracted many TPDs and formed an online support community on Discord. This study explores TPDs' support practices via mixed method (a topic modeling to identify topics related to support seeking and provision first and a follow-up in-depth qualitative analysis with these topics) and found that: (1) TPDs' support-seeking practices around social, technical, and policy matters are highly dependent on Twitch, and this dependence acts as a form of platform labor; (2) TPDs need to switch between Discord and Twitch regarding seeking and provision, exacerbating TPDs' platform labor; (3) TPDs' flexible role practices reflect the community's flourishing on Discord but require roles to bridge the two platforms and transfer informal support seeking to possible formal support from Twitch. We propose implications for effectively managing support seeking and provision between formal and informal spaces to improve the development of TPDs. We also contribute to community support practice and to platform ecology work in CSCW.

Bayesian inference is optimal when the statistical model is well-specified, while outside this setting Bayesian inference can catastrophically fail; accordingly a wealth of post-Bayesian methodologies have been proposed. Predictively oriented (PrO) approaches lift the statistical model $P_θ$ to an (infinite) mixture model $\int P_θ\; \mathrm{d}Q(θ)$ and fit this predictive distribution via minimising an entropy-regularised objective functional. In the well-specified setting one expects the mixing distribution $Q$ to concentrate around the true data-generating parameter in the large data limit, while such singular concentration will typically not be observed if the model is misspecified. Our contribution is to demonstrate that one can empirically detect model misspecification by comparing the standard Bayesian posterior to the PrO `posterior' $Q$, providing a novel and widely-applicable diagnostic tool for the standard Bayesian workflow. To operationalise this, we present an efficient numerical algorithm based on variational gradient descent. A simulation study, and a more detailed case study involving a Bayesian inverse problem in seismology, confirm that model misspecification can be automatically detected using this framework.

Observations of periodic stellar activity near the transit phase of a close-in exoplanet provide evidence of star-planet magnetic interactions (SPMI), similar to the magnetic coupling between Jupiter and its moons. Comparing the power associated with SPMI signals to analytical theories offers a way to constrain exoplanetary magnetic fields, but models based on moon-magnetosphere analogs often underpredict observed energy fluxes. Unlike moons, many close-in exoplanets are extended, highly irradiated gas giants undergoing significant photoevaporation. However, it is not known how atmospheric escape influences the star-planet magnetic coupling. Here, we present three-dimensional radiation magneto-hydrodynamic simulations that simultaneously model planetary evaporation and SPMI in a hot Jupiter planet embedded in a magnetised stellar wind. Our simulations reveal the formation of magnetic structures known as Alfvén wings, which transport magnetic energy away from the planet. When the dayside mass-loss rate $\dot{M}_d$ of the planet lies below a threshold $\dot{M}_0$ defined by pressure balance between the planetary and stellar winds ($\dot{M}_d \leq \dot{M}_0$), the maximal power delivered to the star matches predictions from the Alfvén wing model. For higher escape rates, the planetary outflow opens additional magnetic flux, and the SPMI power increases proportionally with $(\dot{M}_d / \dot{M}_0)^{1/2}$. Applying this scaling law to the HD18973 system, we find that a $50$ G planet could reproduce the observed power if $\dot{M}_d \sim 5 \times 10^{11}$ g/s. Although this signal likely represents only a fraction of the total power, additional mechanisms could amplify the energy budget. These results show that photoevaporating exoplanets in sub-Alfvénic orbits constitute promising targets for SPMI observations.

Understanding the long-term transport of hydrogen isotopes in plasma-facing materials, such as tungsten, is critical for the steady-state operation of magnetic confinement fusion reactors. However, dynamically updating the transition parameters for kinetic Monte Carlo (kMC) simulations as the atomic structure evolves under continuous plasma irradiation remains a severe computational bottleneck. Conventionally, calculating these migration barriers requires the iterative and computationally expensive Nudged Elastic Band (NEB) method. To overcome this limitation, this article presents a highly efficient surrogate model for predicting migration barriers using a three-dimensional Convolutional Neural Network (3D-CNN), establishing the final component necessary to realize on-the-fly molecular dynamics (MD) and kMC hybrid simulations. The proposed deep learning model takes a two-channel volumetric input, the local three-dimensional potential energy distribution and the voxelized spatial coordinates of the initial and final trapping sites, to directly output the migration barrier as a scalar value. Trained on a comprehensive dataset of tungsten-hydrogen configurations evaluated using the Embedded Atom Method (EAM) potential, the model demonstrated robust predictive accuracy, achieving a Mean Absolute Error (MAE) of 0.124 eV and a high coefficient of determination of 0.890. Furthermore, utilizing GPU acceleration, the inference time is reduced to approximately 2.7 milliseconds per barrier, achieving a speed-up ratio of over 23,000 compared to conventional NEB calculations. This extraordinary acceleration effectively resolves the computational barrier of transition rate evaluations, paving the way for large-scale, dynamic modeling of plasma-wall interactions.

It is only in exceptional cases that a $_2F_1(z)$-series with rational parameters and a rational argument, apart from the cases for $z \in \{ \pm 1, \frac{1}{2} \}$ associated with classical hypergeometric identities, admits an evaluation given by a combination of $Γ$-values with rational arguments. In this paper, we present a new and integration-based approach toward the construction of special values for $_2F_1$-series of the desired form. We apply this approach using a $_2F_1\big(\frac{1}{4}\big)$-identity originally due to Gosper and later considered by Vidunas, Ebisu, and Zudilin, to evaluate a ${}_{2}F_{1}$-series of convergence rate $\big(\frac{172872}{185039}\big)^2$. With regard to extant research on so-called ``strange'' ${}_{2}F_{1}$-evaluations, as in the work of Ebisu and Zeilberger, our new series seems to have the largest numerator/denominator in its argument.

In 2008, Halman proved a discrete Helly-type theorem for axis-parallel boxes in $\mathbb R^d$. Very recently, this result was extended to the $(p,q)$ setting with $p \geq q \geq d+1$ by Edwards and Soberón, and subsequently to the case $p \geq q \geq 2$ by Gangopadhyay, Polyanskii, and the author of this paper. In this paper, we obtain improved bounds for the $(p,q)$ problem in the case $q=2$ and $d=2$. More precisely, our main result asserts that for any integer $p \geq 2$, any set $P \subseteq \mathbb R^2$, and any finite family $\mathcal B$ of axis-parallel rectangles in $\mathbb R^2$ such that every rectangle contains a point of $P$, if among every $p$ rectangles there exist two whose intersection contains a point of $P$, then there exists a subset $S \subseteq P$ of size at most $O\!\bigl( (p \log \log p)^2 \bigr)$ such that every rectangle contains a point of $S$. Moreover, when $p=2$, the size of $S$ can be bounded by $4$.

Extreme weather events in electric power systems can cause line trips or physical damage to transmission infrastructure, potentially leading to large-scale load shedding. To mitigate this risk, we propose a framework that strategically pre-positions the commitment of generation units--particularly slow-start units--to adapt to transmission topologies that may arise following such events. The objective is to minimize load shedding under worst-case conditions. This paper makes two main contributions. First, we provide a more accurate representation of the underlying physical laws than those used in prior studies. Second, we develop a highly efficient solution algorithm that outperforms state-of-the-art, off-the-shelf solvers. The proposed framework is formulated as a two-stage robust optimization model. In the first stage, generation units are scheduled in anticipation of disruptions. In the second stage, power dispatch decisions are optimized to minimize load shedding under the worst-case transmission topology. To ensure system reliability and security, we incorporate convexified AC power flow constraints. The resulting model is a tri-level mixed-integer nonlinear optimization problem. To address its computational complexity, we design a problem-specific outer approximation algorithm embedded within a column-and-constraint generation framework. Computational results show that the proposed model and solution approach can achieve solutions within a standard optimality gap in a reasonable time for moderately large instances.

Graph neural networks (GNNs) are designed to extract latent patterns from graph-structured data, making them particularly well suited for crystal representation learning. Here, we propose a GNN model tailored for estimating electronic transport coefficients in inorganic thermoelectric crystals. The model encodes crystal structures and physicochemical properties in a multiscale manner, encompassing global, atomic, bond, and angular levels. It achieves state-of-the-art performance on benchmark datasets with remarkable extrapolative capability. By combining the proposed GNN with \textit{ab initio} calculations, we successfully identify compounds exhibiting outstanding electronic transport properties and further perform interpretability analyses from both global and atomic perspectives, tracing the origins of their distinct transport behaviors. Interestingly, the decision process of the model naturally reveals underlying physical patterns, offering new insights into computer-assisted materials design.

To solve the mystery of flavor structure, we demonstrate two implications emerging from the hierarchical masses of quarks: one for the mass matrix itself and one for the CKM mixing. These implications naturally lead to a non-redundant, ordered, and family-unified quark flavor structure, which serves as a candidate to replace the unclear Yukawa interactions of the Standard Model.

We investigate the conditions for triggering the plasmoid instability in a dynamically forming current sheet in the resistive magnetohydrodynamic framework, using a pseudo-spectral code applied to the Orszag-Tang vortex at Lundquist number $S \sim 10^5$. Following García Morillo \& Alexakis (2025), we use the power spectrum of the current density $E_J(k)$, complemented by the vorticity spectrum $E_ω(k)$, to assess the convergence of our simulations, and show that this diagnostic remains valid even in the presence of physical plasmoids, allowing us to unambiguously distinguish them from spurious ones. We then show that physical plasmoids can be triggered in a well-resolved spectral simulation when three conditions are simultaneously met: a perturbation applied near the time of maximum current density, with amplitude above a critical threshold $\varepsilon_c \sim 10^{-5}$ for our numerical scheme, and with spectral content containing the unstable wavenumbers. These conditions are confirmed using continuous noise injection, which yields similar results at amplitudes one to two orders of magnitude lower. The resulting growth rates and plasmoid numbers are in good agreement with the theory of \citet{Comisso2017}. These results resolve the apparent paradox raised by García Morillo \& Alexakis (2025) and also clarify the role of numerical noise in the triggering of the plasmoid instability.

The hard-core model can be used to understand the number of independent sets in graphs in extremal graph theory. The occupancy fraction, defined as the logarithmic derivative of the independence polynomial of a graph, is a key quantity in hard-core model. Davies \textit{et al.} (2017) established an upper bound on the occupancy fraction for $d$-regular graphs, and Perarnau and Perkins (2018) derived a corresponding bound on it for graphs with given girth. Inspired by their work, we provide the tight upper and lower bounds on occupancy fraction in $n$-vertex graphs with independence number $α$, extending the classical results on bounds for independence polynomials. We also prove a relevant conjecture posed by Davies \textit{et al.} (2025) on this topic.

This paper investigates the optical images of neutron stars within the framework of the radiatively inefficient accretion flow model, taking into account a polytropic equation of state. After obtaining the numerical solutions of the neutron star, we solved numerically the geodesic equations together with the radiative transfer equation. We mainly examine the effects of the polytropic index $N$ and the observer inclination angle $θ_o$ on the image morphology. The obtained images are also compared with the shadow of a Schwarzschild black hole. It is shown that, under the assumption that photon trajectories are terminated at the neutron star surface, the image exhibits a bright higher order structure surrounding an inner dark region. As $N$ increases, the size of the higher-order image gradually expands. As $θ_o$ increases, the obscuration of the neutron star silhouette by radiation originating outside the equatorial plane becomes more pronounced. Compared with the black hole shadow obtained under the same parameter configuration, the neutron star exhibits a larger higher order image and a more extended obscured inner dark region, whereas the higher order image of the black hole is more readily distinguishable. These results indicate significant differences in the optical appearance of neutron stars and black holes, and thus provide a theoretical basis for distinguishing between them through high resolution imaging.

Growing energy demands of modern digital devices necessitate alternative, low-power computing mechanisms. When incident loads take the form of acoustic or vibrational waves, the ability to mechanically process information eliminates the need for transduction, paving the way for passive computing. Recent studies have proposed systems that learn and execute mechanical logic through buckling, bistability, and origami-inspired lattices. However, owing to the large timescales of shape morphing, such concepts suffer from slow operation or require active stimulation of adaptive materials. To address these limitations, we present a novel approach to mechanical logic, leveraging the rich dynamics of wave propagation in elastic structures. In lieu of traditional forward-design tools, such as band diagrams and transmission spectra, we employ a multi-faceted topology optimization approach, enabling us to identify candidate waveguide configurations within an extremely large design space. By incorporating voids within an otherwise uniform substrate, the optimized waveguides are able to precisely manipulate wave propagation paths, triggering desirable interferences of the scattered wavefield that culminate in energy localization at readouts corresponding to a given logic function. An experimental setup is used to demonstrate the efficacy of such logic gates and their resilience to non-uniform loading. By implementing these building blocks into a mechanical adder, we demonstrate the scalable deployment of more sophisticated mechanical computing circuits, opening up new avenues in mechanical signal processing and physical computing.

High-velocity fluid flow through porous media is modeled by prescribing a nonlinear relationship between the flow rate and the pressure gradient, called the Darcy--Forchheimer equation. This paper is concerned with the analysis of parallel multilevel methods for solving the Darcy--Forchheimer model. We begin by reformulating the Darcy--Forchheimer model as a nearly semicoercive convex optimization problem via the augmented Lagrangian method. Building on this formulation, we develop a parallel multilevel method, also known as a multilevel additive Schwarz method, within the framework of subspace correction for nearly semicoercive convex problems, yielding a theoretically supported and computationally efficient solver for the Darcy--Forchheimer model. The convergence analysis establishes robustness with respect to the augmented Lagrangian parameter $ε$. To further enhance convergence, we incorporate a backtracking line search and a full approximation scheme. Numerical results support the theoretical findings and demonstrate the effectiveness of the proposed approach.

A sharp lower bound for the first Dirichlet eigenvalue of the $p$-laplacian in Gaussian space is derived for sets with prescribed generalized torsional rigidity. The result provides an extension of the classical spectral inequality due to Kohler-Jobin. The proof is based on a careful analysis of the generalized torsional rigidity and on a sharp mass comparison result. Furthermore, a Payne-Rayner type inequality is established.

Let $N$ be a Riemannian, neutral or Lorentzian $4$-dimensional space form. In this paper, the expressions of the equations of Gauss, Codazzi and Ricci of a space-like or time-like surface in $N$ given in [7] are naturally understood in terms of the induced connection (of the complexification) of the two-fold exterior power of the pull-back bundle on the surface. Moreover, based on such expressions, we characterize several classes of surfaces related to the covariant derivatives of the twistor lifts and so on.

In two dimensions, the non-unitary class of conformal minimal models, $\mathcal{M}(2,2m+1)$, has been recently conjectured to arise as renormalization-group fixed points of scalar field theories with complex $iφ^{2m-1}$ interaction, $m\in \mathbb{N}$, $m\ge2$. We test a variation of this conjecture through the perturbative study of two separate long-range constructions based on respectively the minimal model and its potential Landau-Ginzburg formalism. For $m>2$, inconsistencies are found when subsequently relating both constructions. In contrast, the long-range Lee-Yang model, the $m=2$ case, is shown to be analogue to the long-range Ising model.

In this paper, part of a series on the effects of X-ray sources in promoting Population III (Pop III) star formation, we investigate the ionisation and heating of the intergalactic medium (IGM) and the consequent enhancement of molecular hydrogen (H$_{2}$) and Pop III formation using cosmological zoom-in simulations. We adopt a minimal X-ray feedback model in which X-rays originate solely from Pop III supernovae, and compute the global X-ray and Lyman-Werner (LW) radiation backgrounds on-the-fly during the simulation of a mean-density region of the Universe. This approach self-consistently captures the feedback loop between Pop III stars and the radiation backgrounds they produce. Pop III supernovae generate a weak X-ray background (J$_{\mathrm{X,21}} \sim 10^{-5}$) and a moderate LW background (J$_{\mathrm{LW,21}} \sim 10^{-1}$); the latter intensifies below $z \approx 12$ (J$_{\mathrm{LW,21}} \sim 10^{1}-10^{2}$) with the onset of Pop II star formation. Applying these backgrounds to regions of varying mean density produces a net positive X-ray feedback that increases the Pop III number density, with stronger enhancement in underdense regions. The positive feedback is more pronounced when the X-ray background is computed on-the-fly rather than by post-processing, demonstrating the importance of the feedback loop. The X-ray background also raises the Thomson scattering optical depth at high redshift, while the total optical depth remains consistent with Planck 2018 constraints. Because our model includes only Pop III supernovae as X-ray sources, it represents the most conservative scenario; stronger X-ray feedback is expected when additional sources are included, as will be explored in future work.

We develop an unfitted compatible finite element discretisation for the Darcy problem based on $H(\mathrm{div})$-conforming flux spaces and discontinuous pressure spaces. The method is designed to preserve pointwise discrete mass conservation while remaining robust in the presence of arbitrarily small cut cells arising from unfitted meshes. Robustness is achieved by combining an $L^2$-stabilisation of the flux with an additional mixed-term stabilisation that enhances pressure control without destroying the local conservation structure. We consider both cell-wise (bulk) and face-based ghost-penalty realisations of the stabilisation. Mixed boundary conditions are handled by weak imposition of both flux and pressure traces on unfitted boundaries. We prove stability and a priori error estimates with constants independent of the cut configuration, and establish pressure-robust flux error bounds in the case of pure pressure boundary conditions. We also introduce an augmented Lagrangian variant that improves control of the conservation constraint and is amenable to efficient preconditioning strategies. Numerical experiments for a range of cut configurations, boundary-condition regimes and parameter choices confirm the theoretical results, demonstrating optimal convergence, cut-independent conditioning and mass conservation up to solver tolerance.

Line intensity mapping (LIM) is an emerging technique for probing the large-scale structure (LSS) in the post-reionisation era. This captures the integrated flux of a particular spectral line emission from multiple sources within a patch of the sky without resolving them. Mapping different galaxy line emissions, such as the HI $21$-cm and CO rotational lines via LIM, can reveal complementary information about the bias with which the line emitters trace the underlying matter distribution and how different astrophysical phenomena affect the clustering pattern of these signals. The stage at which the structures in the "cosmic web" merge to form a single connected structure is known as the percolation transition. Using mock HI $21$-cm and CO($1-0$) LIM signals in the post-reionisation universe, we explore the connectivity of structures through percolation analysis and compare it with the underlying galaxy distribution. We probe the relative contributions of voids, filaments, and sheets to the galaxy density and line intensity maps using a morphological measure known as the local dimension. The CO($1-0$) map exhibits an increased filamentary behaviour and larger contribution from sheets than the $21$-cm map. We attempt to explain such an emission of the CO($1-0$) line from biased environments. The upcoming SKA-Mid will produce tomographic intensity maps of the $21$-cm signal at $z \lesssim 3$ in Band-1. CO maps can be produced at these redshifts in phase 2 of SKA-Mid, where the frequency coverage is expected to increase up to $\sim 50$ GHz. We present forecasts for the recovery of the local dimensions of these line intensity maps contaminated by thermal noise and line interlopers in SKA-Mid surveys.

We study positional statistics for four families of pattern-avoiding permutations counted by the large Schröder numbers. Specifically, we focus on the pairs of patterns $\{2413,3142\}$ (separable permutations), $\{1324,1423\}$, $\{1423,2413\}$, and $\{1324,2134\}$. For each class, we derive multivariate generating functions that track the relative positions of specific entries. Our approach combines structural decompositions with the kernel method to obtain explicit formulas involving the generating function for the Schröder numbers. As a byproduct, we obtain alternative proofs that each of these classes is enumerated by the Schröder numbers. We also identify several known triangular arrays arising from our positional refinements, including connections to the central binomial coefficients and sequences appearing in the work of Kreweras on covering hierarchies.

By profiting from recent developments in detector technologies, making it possible to access a stream of detection events with few-ns time resolutions, a new ptychographic workflow is established. This methodological framework, referred to as guided progressive reconstructive imaging, relies on a quantization-based description of the acquired intensity, through an elementary derivation. Established direct phase retrieval solutions, such as the Wigner distribution deconvolution approach, can then be adapted to a continuous treatment of received counts, with no need for a dense data representation. Consequently, the result is obtained in the form of a progressively improving estimate, while providing immediate user feedback thanks to a processing speed high enough to surpass the acquisition bandwidth. This fast measurement is enabled by the cumulative usage of a pre-calculated library of kernel-limited functions, accumulating count-wise contributions as a function of the triggered detector pixel. Hence, the reconstruction offers the same advantages of direct phase retrieval methods, in particular a high dose-efficiency and the absence of complex convergence dynamics, with much less stringent restrictions on the field of view than is typical in current alternatives. Its implementation is also significantly more straightforward and flexible. Overall, this work constitutes a major evolution in the state-of-the-art, facilitating repeatable and low-dose experiments with high accessibility, and being applicable to electron-based imaging, X-ray diffraction and optical microscopy.

The partial trace is commonly introduced in quantum mechanics as an algebraic operation used to define reduced states of composite systems. However, the probabilistic origin of this operation goes systematically unnoticed in the literature. Here, we show that the partial trace emerges naturally from the requirement of consistency between the Born rule for measurement probabilities and the classical marginalization of probability mass functions. Starting from the classical marginalization rule relating joint and marginal probability distributions, we impose that the reduced density operator of a subsystem must reproduce the local measurement statistics derived from the global state. We show that this requirement directly leads to the standard expression of the partial trace. From this perspective, the reduced density operator appears not as an ad hoc algebraic construction, but as a natural consequence of the probabilistic structure of quantum mechanics.

We generalize the logarithmic purity theorem of Fujiwara-Kato-Mochizuki to torsors which arise in the Kummer log flat topology under finite flat linearly reductive group schemes. We then give a stack-theoretic interpretation of our purity theorem via root stacks, relating it to the valuative criterion of properness for tame algebraic stacks of Bresciani-Vistoli. Finally, we construct a logarithmic Nori fundamental group scheme of a log regular log scheme classifying such torsors, and compare it with the classical Nori fundamental group and the tame fundamental group.

Galaxy clusters offer powerful laboratories for studying galaxy evolution in dense environments. In this context, the Constrained LOcal and Nesting Environment (CLONE) project provides a zoom-in hydrodynamical simulation of the Virgo cluster, including active galactic nucleus and supernova feedback, with a resolution down to 350~pc, designed to mirror Virgo's observed properties. Previous work showed that this replica and Virgo share the same history, mass, and luminosity distributions including the central M87. This study examines several observational relations extending to lower stellar masses than previous synthetic-population studies: star formation density, (specific) star formation rate, metallicity, and quenched fraction of galaxies as a function of stellar mass and cluster-centric distance. Despite the slightly low metallicity and the sufficiently high quenched fraction, simulated galaxies reproduce key observational trends even without averaging or accounting for observational uncertainties, aside from the consideration of projection effects: At fixed stellar mass, cluster galaxies form fewer stars than field counterparts; Most galaxies are quenched, except for intermediate-mass or isolated galaxies; Low-mass galaxies are highly quenched, thus implying a sharp metallicity drop, and low metallicity does not imply youth; Quenching occurs earlier for the most massive and the smallest galaxies than for those of intermediate mass, at least until they enter the cluster; Quenched galaxies have undergone dark matter stripping; Gas depletion drives quenching, especially in low-mass galaxies and the farther from the cluster center they are. Overall, the synthetic population jointly reproduces multiple observational trends, making it a valuable tool to probe processes from jellyfish galaxies to cluster-core gas dynamics.[Abridged]

Magneto-optic photonic systems can violate Kirchhoff's law of thermal emission by breaking Lorentz reciprocity. We develop a dispersive perturbation theory yielding an analytical expression for magneto-optical resonance frequency shifts in plasmonic semiconductors under applied magnetic fields. This expression shows the shift is governed by the overlap of the mode's optical spin density with the magneto-optical material. We use this expression to design a III-V metasurface that achieves nonreciprocal emissivity contrast of 0.8 at only 0.1 T, and demonstrate that the theory can explain order-of magnitude differences in magnetic field sensitivity between different photonic structures.

High-quality LLM request scheduling requires meeting two key objectives: ensuring the routed instance has KVCache to accelerate request execution, and ensuring that the workload is balanced across instances. Achieving both objectives is challenging because pursuing one may compromise the other. Current approaches use various combinators (e.g., linear combinations) to compute a scheduling score that combines indicators for the two objectives. These approaches are complex: they either require significant workload-specific hyperparameter tuning or model-hardware-aware simulator development, yet could still lead to suboptimal performance. In this paper, we show that using a simple multiplication of two carefully chosen indicators: one KVCache-aware (new prefill tokens if routed to an instance) and one load-balancing-aware (current batch size of the instance), as the scheduling score (LMETRIC) can achieve both objectives simultaneously without any hyperparameter tuning. The key idea is that the simply multiplied score considers both objectives in a manner similar to a linear combination, but the original hyperparameters cancel out during comparison, so no tuning is needed to find the best parameters. The two indicators are chosen based on our analysis of LLM characteristics. Our extensive experiments show that this simple approach can reduce TTFT by 92% and 39%, and TPOT by 24% and 51%, compared to vLLM-v1 and an in-production scheduler on real-world workloads covering chatbots and coding agents. We also derive the mathematical conditions under which multiplication may fail, and find that such conditions are extremely rare in practice and can be detected (and mitigated) beforehand. LMETRIC has been deployed in production and canary release confirms its effectiveness

Classical actuator disk theory, developed more than a century ago, provides an idealised description of turbine rotor performance. It treats a rotor as an infinitesimally-thin permeable disk and applies the governing flow equations over a streamtube encompassing the disk. A well-known limitation of the theory is its assumption of ideal flow downstream of the disk, which restricts its applicability to short downwind distances before turbulence and mixing processes governing the wake evolution take hold. The classical theory also leads to unphysical predictions of thrust and power coefficients for highly-loaded rotors. Turbulent axisymmetric wakes, by contrast, represent an extensively-studied canonical free shear flow with much of the progress and its applications to wind turbines limited to the far-wake dynamics. In this work, we introduce a generalised actuator disk theory based on a hybrid stream-tube and wake control volume, that seamlessly integrates classical actuator disk analysis with wake turbulence modelling at arbitrary distances from the rotor. The resulting model, while still idealised, can be used to predict variations in velocity, pressure, and cross-sectional flow area as function of position, both upstream and downstream of the rotor disk. Furthermore, by accounting for turbulent entrainment in the wake development, it provides more realistic predictions of thrust and power coefficients for highly-loaded disks.