We analyse a bi-fluid isentropic compressible Navier-Stokes system with barotropic pressure laws in a two-phase framework with equal pressure and single velocity. We focus on the rigorous analysis of the low Mach number limit under well-prepared initial data. Our main result shows that, as the Mach number tends to zero, the partial densities converge to constant states while the velocity field converges to a divergence-free vector field, and we recover the incompressible non-homogenous fluid system. The volume fractions remain nontrivial and are transported by the limit flow. Our method relies on the introduction of suitable modulated quantities and on two relative entropy functionals adapted to the two-phase structure: a standard entropy commonly used in the literature, and a logarithmic entropy, which is essential here as the former is not sufficient due to the structure of the underlying two-phase system.

The measurements of inclusive electron scattering from nuclear targets carried out at the Thomas Jefferson National Accelerator Facility in the mid 2000s have provided valuable novel information on the $A$-dependence of the modifications of nuclear structure functions known as EMC effect. We argue that these data are best described in terms of the scaling variable $\widetilde{y}$, designed to take into account dynamical effects in interacting many-particle systems, and analyse the $A$-dependence of the slope of the inclusive cross section ratios, $R_A = (σ_A/A)/(σ_2/2)$, providing a measure of the size of the EMC effect in the region where nuclear binding plays a leading role. The results of our study clearly hint at a linear correlation between $dR_A(\widetilde{y})/d\widetilde{y}$ and the average nucleon removal energy $\langle E_A \rangle$. The role of correlation effects in the determination of $\langle E_A \rangle$ is highlighted.

The first generations of stars ionised and enriched their host galaxies and seeded the growth of massive black holes. Models often assume that Pop III stellar masses in different minihaloes are stochastic realisations of a single universal initial mass function (IMF). We use 138 cosmological zoom-in hydrodynamics simulations to test this assumption and to map the first-star IMF across redshift, halo mass, and baryon-dark matter streaming velocity (SV). We construct a dense-cloud merger tree and assign first-star masses by mapping the radial gas accretion-rate profile to stellar mass, yielding per-halo stellar mass functions without imposing any a priori IMF. The high-mass tail and multiplicity increase systematically with redshift, halo mass, and SV. Low-mass, low-SV haloes form only one or a few first stars, whereas massive, high-SV haloes host rich first star clusters and commonly produce very massive ($\gtrsim10^3$-$10^4\,{\rm M}_\odot$) first stars. Even in a fixed environment, halo-to-halo scatter remains substantial. Nevertheless, group-averaged IMFs converge to well-defined forms, ruling out a single universal IMF at the halo level across the range of environments probed here. Mapping our seeds onto the redshift-mass plane, we show that high-SV and massive haloes preferentially populate the heavy-seed regime relevant to luminous high-redshift sources. At the same time, low-SV environments are consistent with single/few-event enrichment signatures in metal-poor stars. Our results deliver a practical, physically motivated prescription for per-halo IMF.

Distributional effects, captured by quantile frameworks, are well-received for characterizing heterogeneous impacts of economic factors across the unobserved relative ranks. Censored outcome, endogenous regressor and heteroskedastic error are prevalent in empirical work, yet challenge the consistency of existing quantile estimation methods. This paper proposes a two-nested-step(TNS) estimation method for distributional effects in censored quantile models with endogeneity and heteroskedasticity. It combines the sequential analysis with the control function approach, adapting for heterogeneous distributional effects. The estimation algorithm is a two-step procedure nested with a sequence of series quantile regressions, thereby providing applied researchers with a computationally tractable and practically feasible tool. Monte Carlo simulation results demonstrate the good performance of our estimator in a finite sample. We apply the proposed method to estimate heterogeneous income elasticities of households across relative ranks of commodity expenditure using data from the UK Family Expenditure Survey.

Schlieren imaging is a popular optical technique for visualizing flow in transparent media. In-water high-sensitivity flow visualization, using schlieren imaging, is usually performed with a large-footprint two-mirror z-configuration. Here, we present a small footprint, easy-to-implement, single-mirror schlieren imaging system for in-water flow visualization. The same system is capable of high-sensitivity flow visualization in air as well. At its core, our system uses a concave mirror with water immersion. We present theoretical analysis and experimental results to show that this water immersion helps reduce the system's footprint by 25%. Our water immersion-based single-mirror schlieren imaging method additionally reduces mirror surface artifacts, increasing the sensitivity of flow visualization. This technique enables a low-cost schlieren system, as demonstrated experimentally using an inexpensive concave mirror. We also provide the experimental validation of high sensitivity in-water flow visualization for some transparent chemicals or solutions.

The neutron capture is a process that concerns most of the nuclei used to build our detectors. This process produces protons, alpha particles, and gamma rays which generate background signals. Characterizing this background is important for rare event searches, such as dark matter detection or Coherent Elastic Neutrino-Nucleus Scattering (CEvNS). This paper presents the result of the direct measurement of thermal neutron captures in hydrogen using a new MIcro-TPC MAtrix Chamber (MIMAC-35 cm) detector with a sensitive volume of 35 x 35 x 29 cm3. Data were collected over more than 5 days (443519 sec) with a gas mixture at 30 mbar of 70% isobutane (C4H10) and 30% trifluoromethane (CHF3). Our discrimination method is based on using 3D tracks and released ionization energy, in order to discriminate nuclear recoils (NR) from the dominant electron recoil (ER) background. This method enables the clear identification of 1.3 keV deuteron tracks resulting of nuclear capture reaction 1H(n, γ)2H. We observed 51 neutron capture events among more than 11 million total events mainly produced by muons in the experimental room of our ground laboratory. In parallel we have measured the thermal neutron flux just below the chamber with a BF3 detector and a simulation has been performed to estimate the number of captures expected. This work shows the discrimination power of MIMAC search for low-energy (E < 1 keV) rare event with a huge background without any shielding.

We examine when differentially flat nonlinear control systems with more than two inputs can be rendered static feedback linearizable by a minimal number of prolongations of suitably chosen inputs after applying a static input transformation. We derive sufficient conditions that guarantee such prolongations yield a static feedback linearizable system. For $(x,u)$-flat two-input systems, prior work established precise links between the relative degrees, the highest derivative orders occurring in the flat parameterization, and the minimal dimension of a linearizing dynamic extension, leading to necessary and sufficient criteria for flatness of systems that become static feedback linearizable after at most two prolongations of such suitably chosen inputs. Building on the structure of the time derivatives of a flat output, this work extends this analysis to systems with three inputs.

Evolutionary search is well suited for large-scale biophysical brain modeling, where many parameters with nonlinear interactions and no tractable gradients need to be optimized. Standard evolutionary approaches achieve an excellent fit to MRI data; however, among many possible such solutions, it finds ones that overfit to individual subjects and provide limited predictive power. This paper investigates whether guiding evolution with biological knowledge can help. Focusing on whole-brain Dynamic Mean Field (DMF) models, a baseline where 20 parameters were shared across the brain was compared against a heterogeneous formulation where different sets of 20 parameters were used for the seven canonical brain regions. The heterogeneous model was optimized using four strategies: optimizing all parameters at once, a curricular approach following the hierarchy of brain networks (HICO), a reversed curricular approach, and a randomly shuffled curricular approach. While all heterogeneous strategies fit the data well, only curricular approaches generalized to new subjects. Most importantly, only HICO made it possible to use the parameter sets to predict the subjects' behavioral abilities as well. Thus, by guiding evolution with biological knowledge about the hierarchy of brain regions, HICO demonstrated how domain knowledge can be harnessed to serve the purpose of optimization in real-world domains.

The trigger system of the CMS detector is pivotal in the acquisition of data for physics measurements and searches. Studies of final states characterized by hadronic decays of tau leptons require the reconstruction and the identification of genuine tau leptons against quark- and gluon-initiated jets at the trigger level. This is a difficult task, particularly as improvements to the LHC have resulted in an increased number of interactions per bunch crossing in recent years. To address this challenge, a series of machine-learning algorithms with high identification efficiency and low computational cost have been incorporated into the high-level trigger for hadronically decaying tau leptons. In this paper, these developments and the trigger performance are summarized using data collected by the CMS experiment in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV in 2022$-$2023, corresponding to an integrated luminosity of 62 fb$^{-1}$.

In this work, we numerically investigate linear thermal noise in a four-terminal system with a finite Berry curvature dipole (BCD) using the nonequilibrium Green's function formalism. By comparing with the semiclassical results for bulk systems, we establish a one-to-one correspondence between terminal-resolved linear noise in multi-terminal systems and direction-resolved noise in bulk transport. Specifically, the auto-correlation function scales as $2 k_B T$ when the driving field is perpendicular to the BCD and vanishes when they are parallel, whereas the cross-correlation scales as $k_B T$. Both the auto- and cross-correlation functions exhibit pronounced peaks near the band edges, consistent with BCD-induced features. In addition, the linear thermal noise increases approximately linearly with $T$ at low temperatures and is suppressed by dephasing effect at high temperatures. Our work bridges semiclassical bulk theory and quantum multi-terminal theory for linear thermal noise, highlighting the symmetry(geometry)-selection rule in quantum transport.

The upcoming High-Luminosity upgrade of the Large Hadron Collider (LHC) necessitates a complete replacement of the ATLAS Inner Detector with the new Inner Tracker (ITk). This upgrade imposes stringent requirements on the associated Detector Control System (DCS), which is responsible for the monitoring, control, and safety of the detector. A critical component of the ITk DCS is the Monitoring of Pixel System (MOPS), which supervises the local voltages and temperatures of the new pixel detector modules. This paper introduces a dedicated testbed and verification methodology for the MOPS readout, defining a structured set of test cases for two DCS-readout architectures: a preliminary Raspberry Pi-based controller, the "MOPS-Hub Mock-up"(MH Mock-up), and the final production FPGA-based "MOPS-Hub" (MH). The methodology specifies the measurement chain for end-to-end latency, jitter, and data integrity across CAN and UART interfaces, including a unified time-stamping scheme, non-intrusive signal taps, and a consistent data-logging and analysis pipeline. This work details the load profiles and scalability scenarios (baseline operation, full-crate stress, and CAN Interface Card channel isolation), together with acceptance criteria and considerations for measurement uncertainty to ensure reproducibility. The objective is to provide a clear, repeatable procedure to qualify the MH architecture for production and deployment in the ATLAS ITk DCS. A companion paper will present the experimental results and the comparative analysis obtained using this testbed.

We build a catalog of massive (M>$8~$M$_\odot$) main sequence stars in the \mbox{metal-poor} ($\sim0.1~$Z$_\odot$) dwarf irregular galaxy Sextans A. HST WFC3 UV photometry in the 275 and 336 nm wideband filters is arranged in a Color-Magnitude Diagram (CMD), and overlaid on top of stellar evolutionary tracks from the MIST library. The star properties (mass, age, etc.) are computed with a Finite Element (FE) interpolation of the stellar tracks. The FE method, originally developed for solid mechanics problems, provides a general framework for interpolating fields inside domains of complex geometry. Besides the interpolated properties, the algorithm computes their gradients with respect to the photometry. These sensitivities provide a direct an efficient estimate of the associated uncertainties. Our catalog contains 655 stars, with the most massive one estimated at $58\pm11~$M$_\odot$. A comparison with a ground-based spectroscopic census of OB stars yields only 8 matches, evidencing the minimal overlap between both datasets. The mass estimates derived from the UV CMD and the spectral classification are in good agreement for the majority of O-type stars found in both datasets. Our catalog provides an extensive list of candidates for followup spectroscopic observation, which could improve our understanding of the early evolutionary stages of massive \mbox{low-metallicity} stars.

We present diffHOD-IA, a fully differentiable implementation of a halo occupation distribution (HOD) model that incorporates galaxy intrinsic alignments (IA). Motivated by the diffHOD framework, we create a new implementation that extends differentiable galaxy population modeling to include orientation-dependent statistics crucial for weak gravitational lensing analyses. Our implementation combines this HOD formulation with an IA model, enabling end-to-end automatic differentiation from HOD and IA parameters through to the galaxy field. We additionally extend this framework to differentiably model two-point correlation functions, including galaxy clustering and IA statistics. We validate diffHOD-IA against the reference halotools-IA implementation using the Bolshoi-Planck simulation, demonstrating excellent agreement across both one-point and two-point statistics. We verify the accuracy of gradients computed via automatic differentiation by comparison with finite-difference estimates for both HOD and IA parameters. We present science use cases leveraging gradients in the simulations to recover the IA parameters of a galaxy field representative of the TNG300 simulation. Finally, we apply diffHOD-IA in a Hamiltonian Monte Carlo analysis and compare its performance with halotools-IA and a neural-network-based emulator, IAEmu. Unlike emulator-based approaches for statistics, diffHOD-IA provides differentiability at the galaxy catalog level, enabling integration into field-level inference pipelines and extension to arbitrary summary statistics for next-generation weak-lensing analyses. Our code is publicly available.

Using the nuclear DFT approach with symmetry breaking and restoration, we investigate the electromagnetic properties of the ground and isomeric states in $^{229}$Th. We determine the magnetic dipole transition strength B(M1; $3/2^+_1\rightarrow 5/2^+_1)$ between these two states and discuss the effects of parity breaking, configuration mixing, and time-odd core polarization. We also determine the corresponding spectroscopic magnetic dipole and octupole, and electric quadrupole moments. Because the octupole deformability of the Skyrme functionals used here is not described in sufficient detail, we analyze the results using a set of Skyrme functionals and perform a regression aligned with the measured electric octupole moments of neighboring even-even nuclei. Without parameter adjustment, the results compare favorably with the experimental data but also indicate the need to systematically adjust the octupole degrees of freedom in future functional parametrizations.

Feder-Vardi conjecture, which proposed that every finite-domain Constraint Satisfaction Problem (CSP) is either in P or it is NP-complete, has been solved independently by Bulatov and Zhuk almost ten years ago. Bodirsky-Pinsker conjecture which states a similar dichotomy for countably infinite first-order reducts of finitely bounded homogeneous structures is wide open. In this paper, we prove that CSPs over first-order expansions of finitely bounded homogeneous model-complete cores are either first-order definable (and hence in non-uniform AC$^0$) or L-hard under first-order reduction. It is arguably the most general complexity dichotomy when it comes to the scope of structures within Bodirsky-Pinsker conjecture. Our strategy is that we first give a new proof of Larose-Tesson theorem, which provides a similar dichotomy over finite structures, and then generalize that new proof to infinite structures.

fixest is an R package for fast and flexible econometric estimation. It provides a unified framework for applied research, with comprehensive support for a diverse class of models: ordinary least squares, instrumental variables, generalized linear models, maximum likelihood, and difference-in-differences. The package particularly excels at fixed-effects estimation, supported by a novel fixed-point acceleration algorithm implemented in C++. This algorithm achieves rapid convergence across a variety of data contexts and enables efficient estimation of complex models, including those with varying slopes. An expressive formula interface facilitates multiple estimations, stepwise regressions, and variable interpolation in a single call. Users can adjust inference strategies on the fly, choosing from an array of built-in robust standard errors. The package also provides methods for publication-ready regression tables and coefficient plots. Benchmarks demonstrate that fixest offers best-in-class performance against leading alternatives in R, PYTHON, and JULIA.

The observed masses and radii of sub-Neptunes are typically explained by the gas dwarf and the water world scenarios. While their evolutionary history on a population level has been proposed as a method to distinguish between these compositions, previous evolutionary models, neglected the crucial role of atmosphere-interior chemical interaction. We present a novel evolution framework for sub-Neptunes that combines the thermal evolution with the chemical coupling of the atmosphere and interior. Using this model, we examine how planets formed inside and outside the water-ice line can be observationally distinguished, with an emphasis on their atmospheric properties. Young sub-Neptunes store the majority of their volatile budget in the interior, regardless of formation location. Nevertheless, the atmospheric metallicity is a factor 4 higher for the planet formed outside the water-ice line. During cooling, hydrogen and oxygen exsolve from the interior, increasing the atmospheric mass fraction and counteracting the thermal contraction. Consequently, radius evolution alone cannot distinguish between the two formation scenarios. Instead, the primary discriminators are the abundance of carbon-bearing species and the resulting atmospheric C/O ratio. For sub-Neptunes formed beyond the water-ice line, nearly all carbon resides in the gaseous phase. We find that high molar fractions of CH$_4$ ($>10^{-2}$) and H$_2$O ($> 5\times10^{-2}$), and a high C/O ratio $(> 5\times10^{-1})$ are indicative of formation outside the water-ice line. In contrast, sub-Neptunes formed inside the water-ice line exhibit strongly suppressed CH$_4$ abundances, yielding C/O ratios ranging from $10^{-7}$ to $10^{-1}$.

In this work, we study the spectral form factor of random matrix models which exhibit a large number of degenerate ground states accompanied by a macroscopic gap in the spectrum. The central aim of this work is to understand how the standard narrative about the behavior of the spectral form factor is modified in the presence of these parametrically large number of ground states. We show that, at sufficiently low temperatures, the spectral form factor is dominated by the disconnected contribution, even at arbitrarily late times. Moreover, we demonstrate that the connected form factor only depends on the eigenvalues of the non-degenerate sector, implying that BPS states do not contribute to wormhole calculations in the gravity context. Using the Christoffel-Darboux kernel, we analyze a number of examples including the Bessel model and $\mathcal{N}=2$ Jackiw-Teitelboim supergravity. In these examples, we find damped oscillations in the disconnected form factor, with a period set by the inverse size of the gap. Furthermore, we demonstrate that the slope of the ramp in the connected form factor arises from a universal sine-kernel, which emerges from a truncation of the full non-perturbative kernel in the $\hbar \to 0$ limit, and find agreement with the leading double trumpet result. Finally, we present predictions for how the ramp will transition to a plateau in the connected form factor and demonstrate how the transition depends on the details of the leading spectral density of states.

Communication has become a first-order bottleneck in large-cale GPU workloads, and existing distributed compilers address it mainly by overlapping whole compute and communication kernels at the stream level. This coarse granularity incurs extra kernel launches, forces device-wide synchronizations at kernel boundaries, and leaves substantial slack when the slowest tile or kernel stretches the communication tail. We present Syncopate, a compiler and runtime that enables automatic fine-grained overlap inside a single fused kernel. Syncopate introduces a communication chunk abstraction that decouples communication granularity from kernel structure and backend mechanisms, allowing chunk-level plans to be ported from existing distributed compilers, written directly by users, or instantiated from reusable templates. Given a local Triton kernel and a chunk schedule, Syncopate performs transformations to align computation with chunk availability. Implemented as a source-to-source compiler on Triton, Syncopate delivers an average end-to-end speedup of 1.3$\times$ and up to 4.7$\times$ on multi-GPU workloads.

The fast reaction limit for a nonlinear bulk-surface reaction-diffusion system is investigated. This system describes a reversible reaction with arbitrary stoichiometric coefficients, where one chemical is present in a bounded vessel $Ω$ and the other chemical lies only on the boundary $\partialΩ$ where the reaction takes place. In the limit as the reaction rate constant tends to infinity, we prove that the solution converges in $L^p(0,T;L^p(Ω))$ to the solution of a heat equation with nonlinear dynamical boundary condition. This is obtained by showing a-priori estimates of solutions which are uniform in the reaction rate constants. In order to overcome the difficulty caused by the bulk-surface coupling, we consider the limit in suitable product spaces where the Aubin-Lions lemma is applicable. Moreover, in the case of equal stoichiometric coefficients, we obtain the convergence rate of the fast reaction limit by exploiting suitable estimates of the limiting system.

We present a control framework for stochastic compartmental models in epidemiology. In this framework, rather than directly controlling the stochastic system, we perform optimal control of an associated Fokker-Planck equation, with the goal of steering the distribution of possible solutions of the stochastic system to some desirable state. In particular, this allows for robust control mechanism with uncertainty not only in the dynamics, but also in the initial data. We formulate and fully analyze a partial differential equation constrained optimization problem, including a proof of existence of optimal controls via analysis of the control-to-state map, and a characterization of optimal controls via the Pontryagin minimum principle. We describe the application of the sequential quadratic Hamiltonian method to our problem, which provides numerical approximations of optimal control maps. We demonstrate our method using a minimal stochastic susceptible-infected-recovered model with different choices of cost functionals that represent different policy-maker concerns.

Public transit is a critical component of urban mobility and equity, yet mobility and air-quality linkages are rarely operationalized in reproducible smart-city analytics workflows. This study develops a transparent, multi-source monitoring dataset that integrates agency-reported transit ridership with ambient fine particulate matter PM2.5 from the U.S. EPA Air Quality System (AQS) for four U.S. metropolitan areas - New York City, Chicago, Las Vegas, and Phoenix, using two seasonal snapshots (March and October 2024). We harmonize heterogeneous ridership feeds (daily and stop-level) to monthly system totals and pair them with monthly mean PM2.5 , reporting both absolute and per-capita metrics to enable cross-city comparability. Results show pronounced structural differences in transit scale and intensity, with consistent seasonal shifts in both ridership and PM2.5 that vary by urban context. A set of lightweight regression specifications is used as a descriptive sensitivity analysis, indicating that apparent mobility-PM2.5 relationships are not uniform across cities or seasons and are strongly shaped by baseline city effects. Overall, the paper positions integrated mobility and environment monitoring as a practical smart-city capability, offering a scalable framework for tracking infrastructure utilization alongside exposure-relevant air-quality indicators to support sustainable communities and public-health-aware urban resilience.

We introduce the notion of the $\infty$-category of (complete) derived $G$-graded modules over a $G$-graded ring $R$ for a torsion-free abelian group $G$, and we study its foundational properties. Moreover, we prove a categorical equivalence between (complete) derived $G$-graded modules over $R$ and derived (formal) comodules over a certain comonad constructed from the group ring $R[G]$ of $G$ over $R$.

We investigate quasinormal modes, late-time tails, and grey-body factors for massive scalar perturbations in the background of the Dymnikova regular black hole. By applying both the time-domain integration and the WKB method with Padé improvements, we show that the spectrum of massive fields differs qualitatively from the massless case. The oscillation frequency of the dominant mode grows with the field mass $μ$, while the damping rate decreases, suggesting the existence of quasi-resonances at sufficiently large $μ$. In the time domain, the late-time signal exhibits oscillatory tails with a power-law envelope, whose decay rate matches analytic expectations. Grey-body factors are also computed, showing strong suppression of radiation when mass is increased. Taken together, these results indicate that massive fields provide distinctive signatures of regular black holes and may serve as probes of near-horizon quantum corrections in the Dymnikova geometry.

We study the space of all bimodule homomorphisms $R_x\otimes_R R(\underline{t})\otimes_R R_y\to R_z\otimes_R R(\underline{t}')\otimes_R R_w$ as a one-sided module, where $R_x,R_y,R_z,R_w$ are standard twisted bimodules and $R(\underline{t})$ and $R(\underline{t}')$ are the Bott-Samelson bimodules corresponding to sequences of reflections $\underline{t}$ and $\underline{t}'$ respectively. We prove that this module is always reflexive under some reasonable restrictions on the representation of the underlying Coxeter group. However, unlike the case where $\underline{t}$ and $\underline{t}'$ contain only simple reflections, this module does not need any longer to be free. We provide a series of counterexamples already for the symmetric groups $S_n$, where $n\ge4$. The projective dimension of the modules dual to them is $n-3$ and thus serves to measure the deviation from the free modules. When placed within a geometric framework, these examples show how to find fibers of points fixed by the compact torus in the Bott-Samelson resolutions (as in the original definition by Raoul Bott and Hans Samelson) with non-vanishing odd cohomology.

Reconstructing images of the radio sky from incomplete Fourier information is a key challenge in radio astronomy. In this work, we present a method for radio interferometric image reconstruction using a data-driven prior for the radio sky based on denoising diffusion probabilistic models (DDPMs). We train a DDPM on radio galaxy observations from the VLA FIRST survey, then create simulated VLA, EHT, and ALMA observations of radio galaxies. We use an unsupervised posterior sampling method called Denoising Diffusion Restoration Models (DDRM) to reconstruct the corresponding images using our DDPM as a prior. Our approach is agnostic to the measured radio interferometric data and naturally incorporates the physics of the measurement process. We are able to reconstruct images with very high fidelity and demonstrate a marked improvement over image reconstruction techniques that work on gridded visibilities like CLEAN.

In this paper, we define non-parabolic spatial hybrid framed curves in the spatial hybrid number space, which may have singularities, and prove the existence and uniqueness theorem for non-parabolic spatial hybrid framed curves. As applications, we define evolutes, involutes, pedal and contrapedal curves of non-parabolic spatial hybrid framed curves and discuss their relations.

We investigate the effective quantum evolution of the Bianchi type I cosmological model within the Brans-Dicke framework, using an effective Hamiltonian approach including expectation values, quantum dispersions, and cross-correlation terms between different degrees of freedom. We show that cross-correlation terms are essential for a physically consistent effective dynamics: neglecting them leads to spurious divergences, violation of the Heisenberg uncertainty relations, and unphysical behavior. For w < -3/2, where bouncing solutions exist already classically, quantum backreaction smooths the bounce, suppresses shear anisotropy, and the energy density peak is slightly enhanced and remains well-behaved, in contrast to the unphysical divergences that arise when cross-correlations are neglected. For the conformally invariant case w = -3/2, quantum corrections accelerate the approach to de Sitter expansion while enhancing shear anisotropy. When correlations are included, small-amplitude damped oscillations appear shortly after the bounce, which we interpret as quantum remnant effects encoding Planck scale correlation information between gravitational and scalar field degrees of freedom. The effective energy density remains finite for both regimes. Our results demonstrate that cross-correlations carry crucial quantum information influencing cosmological dynamics, with implications for quantum gravity phenomenology, inflationary model building, and primordial observational signatures.

Photometric light curves suffer from fundamental degeneracies that limit surface information recovery. We demonstrate that astrometry enables access to complementary information through photocentre variations induced by rotating surface features. The forthcoming commissioning of microarcsecond-precision astrometric missions presents an opportunity to improve stellar surface mapping. This paper extends a previous theoretical framework for stellar surface mapping, along three primary directions: (1) we derive analytical selection rules showing that astrometry is sensitive to spherical harmonic modes not detectable via photometry, particularly odd-$\ell$ modes that encode north-south asymmetries; (2) we quantify the information content of combined photometric and astrometric observations, showing that the rank of observable modes grows faster for combined observations than for either technique alone, though the fraction of recoverable modes still decreases asymptotically with increasing spatial resolution; and (3) we reframe astrometric jitter-traditionally treated as noise in exoplanet studies-as a signal encoding stellar surface structure. Given the limited proposed target lists of high-precision astrometric missions, this capability is particularly valuable: understanding host star surfaces is crucial for both removing stellar signals from exoplanet detections and characterising star-planet interactions. We show that while Sun-like stars require sub-microarcsecond precision, evolved stars with angular diameter and larger spots present immediate opportunities with current technology, such as the Gaia mission.

High-dimensional portfolio optimization faces significant computational challenges under complex constraints, with traditional optimization methods struggling to balance convergence speed and global exploration capability. To address this, firstly, we introduce an enhanced Sharpe ratio-based model that incorporates all constraints into the objective function using adaptive penalty terms, transforming the original constrained problem into an unconstrained single-objective formulation. This approach preserves financial interpretability while simplifying algorithmic implementation. To efficiently solve the resulting high-dimensional optimization problem, we develop a Quantum Hybrid Differential Evolution (QHDE) algorithm, which introduces a dynamic quantum tunneling mechanism that enables individuals to probabilistically escape local optima, dramatically enhancing global exploration and solution flexibility. To further improve performance, a good point set-chaos reverse learning strategy generates a well-dispersed initial population, providing a robust and diverse starting point. Meanwhile, a dynamic elite pool combined with Cauchy-Gaussian hybrid perturbations maintains population diversity and mitigates premature convergence, ensuring stable and high-quality solutions. Experimental validation on CEC benchmarks and real-world portfolios involving 20 to 80 assets demonstrates that QHDE's performance improves by up to 96.6%. It attains faster convergence, higher solution precision, and greater robustness than seven state-of-the-art counterparts, thereby confirming its suitability for complex, high-dimensional portfolio optimization.