The functional renormalization group provides a nonperturbative and systematically improvable route to constructing density functionals for quantum many-body systems from microscopic Hamiltonians. Here we advance this program by benchmarking functional-renormalization-group density functional theory (FRG-DFT) against the exact thermodynamics of the single-site Bose-Hubbard model. This model provides an ideal testing ground because it is analytically solvable, yet remains subtle in the imaginary-time coherent-state path integral, where a naive continuum treatment generates a spurious self-interaction. We show that a careful Hubbard-Stratonovich derivation identifies the self-interaction correction term that must be included in the FRG-DFT flow to recover the exact thermodynamics. We then systematically compare several closures of the resulting hierarchy of flow equations for the free energy, chemical potential, and connected density correlators over broad ranges of density, temperature, and interaction strength. The benchmark shows that the free energy is comparatively robust, whereas the chemical potential and fluctuation observables provide much sharper diagnostics of the hierarchy closure. A maximum-entropy closure gives the most accurate overall description and reproduces even the low-temperature oscillatory structure of the connected two-density correlator. These results identify two general requirements for functional approaches to quantum thermodynamics: the renormalization group flow equation must retain the equal-time contact subtraction to avoid spurious self-interactions, and any closure of the hierarchy must preserve the statistical consistency of density correlators. This work provides a controlled foundation for deriving ab initio density functionals for quantum many-body systems across condensed-matter, ultracold-atom, and nuclear physics, as well as quantum chemistry.

Gas-particle interactions with non-absorbing surfaces are commonly described using the scattering-kernel formalism. In this framework, an operator $\mathbf{K}$ maps incident velocity distributions to reflected velocity distributions. The operator is self-adjoint and has norm $\lVert \mathbf{K} \rVert = 1$ in an $L^2$ space weighted by the three-dimensional Maxwell-Boltzmann distribution, and must satisfy non-negativity, normalisation, and reciprocity. In standard formulations, $\mathbf{K}$ represents the aggregate effect of all gas-surface interaction mechanisms through a single operator, without distinguishing the physical scales at which these mechanisms occur. For gas scattering from a rough surface, however, it is advantageous to separate geometric effects associated with distinct roughness scales from the underlying thermochemical processes occurring at the atomic scale. We therefore introduce a roughness-based extension of the scattering-kernel formalism, in which a local kernel is successively lifted to larger scales via single- and multi-reflection operators associated with statistically defined surface morphologies. We derive sufficient conditions under which the resulting global kernels preserve reciprocity, normalisation, and non-negativity whenever these properties hold for the smallest-scale kernel. We further show that these constructions define operators on the space of scattering kernels, and establish the associated multi-scale composition laws that allow independent roughness contributions to be combined recursively. The resulting framework provides a general basis for modelling gas-surface scattering on rough surfaces with arbitrary scale decompositions.

The ability to identify nanometer-scale nuclei of new phases in atom probe tomography (APT) is often limited by the sensitivity of clustering algorithms to user-defined control parameters. Conventional approaches typically rely on the Euclidean distance metric and consider only solute atoms, thereby discarding the solvent atoms that contain most of the spatial information. Here, we introduce a coordination-number metric based on the composition and apply it to higher-order clustering. Using various metrics, we investigate percolation in typical APT structures. By scaling clustering properties to the corresponding percolation thresholds, we define a self-similar variable that is almost invariant with respect to metrics, clustering parameters, and structural disorder. This variable provides a relevant description of clustering and enables the formal transfer of optimal parameters between clustering methods. We also study the characteristic clustering behavior in small precipitates and quantify how the precipitate-matrix interface alters the composition spectrum and broadens the clustering curve. Finally, using simulations that incorporate finite spatial resolution, detection efficiency, and other APT reconstruction artifacts, we show that the approach based on coordination numbers effectively compensates for heterogeneous dilations and outperforms solute-density-based methods in all tested scenarios.

As User Experience Research (UXR) matures, practitioners face the challenge of moving beyond data collection toward establishing a compelling Point of View (POV) that drives strategic impact. This paper proposes an extension to the UXR POV Playbook, specifically focusing on the transition from the "Insight Generation" layer to the "POV" layer. Drawing on extensive multi-method research in Cloud Developer Tools, spanning AI Agents, Command Line Interfaces (CLI), and Error Messages, we demonstrate how triangulating qualitative and quantitative data facilitates the creation of high-confidence POVs. We introduce three new "Playbook Cards" derived from this research: The Paradigm Shift, Explainability as Trust, and The Cost of Friction. These cards provide a structured mechanism for researchers to translate complex technical findings into irrefutable business narratives.

Searches for new phenomena in complex scientific data are predominantly model-dependent, optimized for specific hypotheses, and therefore limited in their coverage of the space of possible signals. Recently, new AI-based model-agnostic search strategies, many of which have been pioneered in high-energy physics, have been proposed which provide a complementary paradigm, prioritizing broad exploration over tailored analyses. These techniques offer an opportunity to enhance the overall discovery potential of modern experiments, especially in regimes where theoretical guidance is scarce. In this document, we review the conceptual framework behind the main classes of AI-based model-agnostic strategies. We discuss the potential pitfalls of these methods, and strategies for their validation and interpretation. We aim for this document to serve as a useful reference both for practitioners and for researchers interested in learning more about these model-agnostic search strategies.

Flatband eigenstates are widely applied to enhance angle-robust light-matter interactions in metaphotonics. However, controlling the polarization of flatbands remains challenging, as it is usually fixed once the metasurface is fabricated, with no options of post-fabrication modification. Here, we present a rewritable permittivity-asymmetric quasi-bound state in the continuum (ε-qBIC) metasurface platform, where selective polymethyl methacrylate (PMMA) coating of a silicon double-nanorod unit cell establishes a circularly polarized flatband state. By varying the PMMA thickness, the polarization of this state can be further controlled in the range from the right-circular to linear and to left-elliptical. The flatbands maintain stable resonance positions and robust far-field polarizations for the incidence angles up to 10 degrees. Importantly, the PMMA layer can be removed, recoated, and re-patterned on the same nanostructure, providing a pathway to rewrite the optical response. Building on this capability, we experimentally demonstrate the chirality encoding by spatially selective PMMA coating. The results establish a practical strategy for realizing high quality factor flatband metasurfaces with rewritable chirality, thus opening opportunities for applications in chiral encoding and chiroptical photonic devices in general.

In public address systems and hearing aids, the maximally achievable amplification or gain is limited by acoustic feedback. Therefore, in order to be able to apply a higher gain, feedback cancellation methods are required. In addition, it is oftentimes also desirable to dereverberate a recorded signal, that is, remove the late reverberation component of the signal, before playing it back. In this paper, it is shown that under two mild conditions, the acoustic feedback signal can be written as a reverberant version of the source signal. Therefore, it is possible to treat the joint dereverberation and acoustic feedback cancellation problem as a dereverberation-only problem, meaning that dereverberation algorithms can be applied to the joint problem. Simulations corroborate this finding

In this work, we study the interplay between the number of slopes, the number of bends per edge, and the area requirements for planar drawings of bounded-degree graphs. Our motivation stems from the fact that, while numerous algorithms produce planar drawings with few slopes for graphs of relatively small degree in polynomial area, existing approaches for higher-degree graphs often require super-polynomial area. We address this gap in the literature by presenting new constructions that yield polynomial-area drawings with few bends per edge while slightly increasing the required number of slopes, thereby providing the first systematic study of slopes, bends and area trade-offs.

As semiconductor technology advances to smaller nodes, Negative Bias Temperature Instability (NBTI) under prolonged workloads has emerged as a significant bottleneck constraining reliability-aware Design-Technology Co-Optimization (DTCO). Conventional TCAD simulations incur prohibitive computational overhead when evaluating device aging characteristics, making it difficult to satisfy the demand for efficient iterative design cycles. To address this challenge, this paper proposes an aging evaluation framework based on a physics-aware graph attention network (Physics-Aware RelGAT). By losslessly mapping unstructured device meshes into attributed graphs, this framework constructs a 45-dimensional device encoding scheme that integrates interface trap distributions and macroscopic electro-thermal stresses, achieving a direct mapping from underlying physical quantities to device degradation characteristics. To overcome the challenge of predicting currents that span multiple orders of magnitude, a dual-end normalization strategy and a log-scale loss function optimization are introduced, ensuring the model possesses high-precision fitting capabilities. Experimental results demonstrate that the model achieves a mean error of only 1.27% on an independent test set, achieving an acceleration of approximately 17,000 times compared to traditional TCAD simulations. This framework provides a solution for the assessment of circuit reliability in advanced process nodes that successfully balances physical fidelity with industrial-grade efficiency.

The chemical composition of a protoplanetary disc is sensitive to its thermal structure and dust properties, and can provide insights into the disc evolution. Recent observations with the James Webb Space Telescope (JWST) reveal correlations of the inner disc compositions with disc size, accretion rate and stellar mass, explained by the key role of dust radial drift in redistributing primordial volatiles. We explore how chemical reactions change the composition of ices carried with pebbles and how they affect the inner disc C/O ratios in a disc around a solar mass star. We consider different drift efficiencies set by dust fragmentation velocity and include dust traps at different locations. We vary the incident cosmic ray ionisation rate $ζ$ and the efficiency of cosmic ray dissociation of ices, and consider the effect of carbon grain destruction. We find that methane depletion within $<1$ Myr prevents the delivery of carbon-rich gas to the inner disc and yields $\mathrm{C/O} \lesssim1$ for $ζ\geq10^{-17}$ s$^{-1}$. Dust traps collect water and carbon-rich ices formed via methane destruction, further lowering the inner disc metallicity and C/O ratio. Cosmic-ray driven photodissociation of ices can convert water to O$_2$ and carbon-bearing molecules to CO, allowing ices to escape the trap if $\gtrsim10 \%$ of the dissociated products can participate in surface reactions. We discuss the observational implications and conclude that cosmic rays and their effect on ices are the key factors that determine the impact of chemistry on the inner disc composition.

Extreme gamma-ray transients represent some of the most energetic and physically constraining phenomena in high-energy astrophysics. They are characterized by rapid, large-amplitude variability and by physical conditions approaching fundamental limits on particle acceleration, cooling, and compactness. In this review, we focus on transients detected above around 100 MeV and define extreme events as either those involving catastrophic transformations of astrophysical systems (such as stellar explosions, compact-object mergers, and tidal-disruption events) or those exhibiting evidence for particle acceleration operating in an extreme regime. These systems are powered by the rapid release of gravitational, magnetic, nuclear, or kinetic energy, with shocks and magnetic reconnection playing a central role in producing ultra-relativistic particle populations and non-thermal radiation. We summarize observational and theoretical diagnostics that constrain the size, magnetization, and Lorentz factor of the emitting region, including variability timescales, luminosity-timescale correlations, and spectral evolution across the MeV-TeV domain. We further review the complementary capabilities of space-borne gamma-ray instruments, ground-based Cherenkov and air-shower observatories in detecting short-lived, high-energy outbursts. Extreme transient classes discussed include gamma-ray bursts, novae, rapidly variable emission from extragalactic and Galactic jets. Also, because of its extreme aspects, we include flaring emission detected from the Crab Nebula. While each type of these flares poses interesting challenges for phenomenology and theory of these sources, together, these events form the landscape of extreme gamma-ray variability.

For the kinetic Langevin diffusion and its splitting discretizations, the hypoelliptic noise structure makes the relationship between couplings and total variation (TV) bounds more subtle than in the elliptic case. We establish that, for the kinetic Langevin equation with quadratic potential, no Markovian coupling (continuous or discrete) captures the asymptotic decay rate of the TV distance between two solutions with different initial values; the canonical iterated one-shot (or sticky) coupling, for which we derive an exact contraction formula, saturates this lower bound. On the constructive side, we show that the recent sharp TV bounds obtained by Chak and Monmarché admit a natural interpretation through an explicit non-Markovian coupling, built from an optimal coalescence trajectory characterized by a classical minimum-energy control problem. For the OBABO splitting scheme, this approach additionally eliminates the Hessian-Lipschitz, step-size, and final-time assumptions in the work of Chak and Monmarché.

This work introduces a novel optical setup based on a Mach-Zehnder interferometer, enabling spatial phase shifting shearography with rotational shear. The optical concept employs a virtual double slit configuration, which decouples the adjustment of the shear amount from the generation of the spatial carrier frequency, thereby enabling flexible control of the measurement sensitivity. Rotational shear is generated within the optical setup by means of image rotation using Dove prisms. Since the proposed system is based on spatial phase shifting shearography, full field measurements can be performed at the camera frame rate, making the method suitable for fast non destructive testing under industrial conditions. In contrast to conventional linear shear configurations, the rotational shear approach is sensitive to tangential displacement gradients around the centre of rotation. In this paper, the proposed rotational shear approach is compared with linear shear configurations, with the results demonstrating reliable detection of relevant defects. Spatial phase shifting shearography combined with rotational shear offers significant potential for industrial non destructive testing applications, particularly in sealing technology and for the inspection of rotationally symmetric components.

For Z d actions equipped with a completely commuting filtration, normalized partial sums of martingale differences converge in distribution (see [V19]) but even if the action is ergodic, the limit law need not be normal. Explication of this phenomenon has been studied in [GLV] but often in dimension 2 only. It turned out that the non normality is related to the factor of product type I. Here we extend the necessary and sufficient condition of normality from dimension 2 to all finite dimensions, as well as a sufficient condition of normality for martingale differences in the orthocomplement of L 2 (I). At the same time an improved and corrected version of the central limit theorem for random fields from [V19] with dimension d > 2 is given.

Service meshes have recently emerged as the de-facto standard for deploying microservices. Conceptually, they provide a uniform abstraction for inter-process communication (IPC) between services by implementing common networking mechanisms -- such as encryption, routing, and load balancing -- and by allowing these mechanisms to be configured and composed through high-level policies. Supporting these policies, however, comes with a significant performance cost, since service meshes interpose proxies (``sidecars'') on the data path, leading to numerous context switches. This paper presents L7FP, a fast path for service meshes which can enforce the vast majority of application-layer policies seen in the wild directly in kernel space. Given high-level policies, L7FP automatically synthesizes an eBPF-based data plane which enforces them in the kernel. L7FP accelerates existing microservices without any code modification, and transparently falls back to existing service proxies (the slow path) for the few unsupported policies. We fully implemented L7FP, with support for both TLS and HTTP/2. Compared to state-of-the-art service meshes, L7FP reduces the median request latency of realistic applications by up to $6\times$ while sustaining $3\times$ more throughput.

Extending megaelectronvolt ultrafast electron diffraction (MeV UED) into the attosecond regime is essential for resolving intrinsic structural dynamics, yet requires simultaneously controlling electron-pulse duration and arrival-time stability. Here, we propose a generalized harmonic laser-electron interaction that extends beam modulation into a continuous harmonic regime. We demonstrate that highly detuned, non-integer harmonic modulation via a single-period undulator achieves stronger coupling efficiency than conventional integer-harmonic resonance. Driven by a mid-infrared seed laser whose wavelength is a small fraction of the nominal resonant wavelength, this mechanism enables effective longitudinal phase space manipulation. It facilitates attosecond compression with minimal laser-induced energy spread, preserving the beam quality required for high-fidelity diffraction. Furthermore, deriving both the modulation and experimental pump lasers from a common source intrinsically locks their relative timing. Simulations demonstrate 680-as pulse durations and 470-as arrival-time jitter, establishing a viable route to attosecond MeV UED for resolving coupled electron-nuclear dynamics.

The present-day solar system comprises meteorites with varying oxidation levels, derived from different parent bodies. Previous studies (e.g. Rubie et al., 2011) of the partitioning of siderophile elements between mantle and core during planetary growth and differentiation showed that Earth must accrete reduced bodies first and oxidised bodies later. Here we show that, if the terrestrial planets formed from a narrow ring of planetesimals, this condition is not fulfilled, whatever heliocentric gradient of oxidation is assumed in the ring. The reason is that planetary embryos quickly accrete planetesimals from the whole width of the ring, incorporating both reduced and oxidised material. The partially oxidised state of all planetary embryos leads to mismatches with the composition of the bulk silicate Earth (BSE) because oxygen fugacity strongly affects the partitioning of siderophile elements. We demonstrate that reproducing the BSE composition requires reduced and oxidised reservoirs to remain segregated until embryo formation is almost complete. The delivery of oxidised material to the terrestrial planet-forming ring towards the end of the disc's lifetime is therefore a key requirement of any successful dynamical model of terrestrial planet formation.

This study compares two methodological approaches for predicting, at a given site, threshold exceedances of atmospheric variables such as temperature and wind speed: (i) direct probabilistic methods, which treat exceedance as a binary classification problem, and (ii) full distribution probabilistic methods, which model the complete conditional probability law of the target variable. Using theoretical analysis and numerical simulations on a toy model, alongside real-world data from the MeteoNet dataset (2016--2018) for southeastern France, we demonstrate that the full distribution approach consistently outperforms the direct method for rare, extreme events. This advantage arises because the full distribution approach effectively learns the parameters of the conditional distribution from moderate and mild intensity events, thereby achieving better calibration and discrimination in the tails. We find that the specific parametric shape of the chosen distribution plays a secondary role compared to accurately capturing predictable shifts in its bulk properties (i.e., mean and variance). This empirical indistinguishability is also informative about the physical mechanics driving atmospheric extremes, suggesting that extreme exceedances are primarily driven by significant conditional displacements of the entire distribution rather than by unpredictable, fat-tailed anomalies within a static climatology. Our results are validated for both strong surface wind speeds and intense hourly rainfall, with performance evaluated using proper scoring rules (Brier score, logarithmic score) and deterministic skill scores (Peirce Skill Score, CSI, HSS). These findings highlight the critical importance of modeling the full probability distribution for rare-event forecasting and provide practical guidance for improving extreme weather prediction in operational meteorology.

In a recent paper, Chen et al. developed a framework for modeling the seeding and growth of supermassive black holes (BHs) in the context of $Λ$CDM cosmogony. Here, we use a set of physically motivated criteria to select a population of predicted BHs and link them to Little Red Dots (LRDs) discovered by JWST. We show that the LRD population at high redshift ($z$) emerges naturally from a subset of BHs with super-Eddington accretion during nuclear bursts. The model suggests that the observed LRDs are the "tip of the iceberg" of a much larger population of less luminous BHs in the same subset. The model makes specific predictions for the LRD population, such as the mass distributions of their BHs and host galaxies/halos, and the piece-wise redshift evolution of their number density. The cosmological context of the model also allows us to link the observed LRD population to their progenitors (their BH seeds) and lower-$z$ descendant BHs, galaxies and halos. Most LRDs at $z\sim 5$ are seeded at $z \gtrsim 20$ through direct-collapse BHs or pair-instability supernovae from Pop-III stars, and have grown to $M_{\rm BH} \approx 10^5$--$10^7\,{\rm M}_\odot$ through nuclear bursts by their observed redshift. LRDs are predicted to have diverse descendants, ranging from compact dwarf galaxies to brightest cluster galaxies (BCGs) at $z=0$. These predictions are consistent with current observations and can be further tested. The success of this model indicates that the results presented here provide a robust foundation for building detailed models of the LRD population.

We prove operator-norm and gradient Lipschitz bounds for exponential-map parameterizations on the special Euclidean algebra se(3), providing an explicit example of intermediate polynomial growth behavior. Using the contraction property of the SO(3) left Jacobian, we show that ||exp(theta)||_op <= 1 + ||theta||_F for all theta in se(3). We then derive a self-contained O(R^2) upper bound for the gradient Lipschitz constant, with explicit constant 4.02, and construct an objective J* satisfying L_J*(R; se(3)) >= 0.0505 R^2 for R >= 2. These results place se(3) between compact Lie algebras, where the Lipschitz constant remains bounded, and Lie algebras with hyperbolic elements, where it grows exponentially. The upper and lower bounds are obtained for different objective classes; no minimax optimality claim is made.

Many advancements in optics have relied on the tight-binding approximation, which simplifies the description and prediction of complex system behaviors. This approximation describes the dynamics of the total light field by examining the coupling between the guided modes of individual single-mode substructures -- also known as coupled mode theory. However, the underlying assumption, that the guided modes of individual waveguides form an orthogonal basis, breaks down when waveguides are brought into close proximity or when larger arrays are considered. In this work, we systematically analyze the consequences of this non-orthogonality and show that it leads to a generalized eigenvalue problem involving an overlap matrix, causing a fundamental mismatch between the standard TB model and solutions of the paraxial wave equation. To resolve this issue, we introduce a modified TB framework based on the Löwdin orthogonalization, which constructs an orthonormal basis from the non-orthogonal guided modes while minimally altering their physical shape and preserving their symmetry properties. The resulting Löwdin-TB method restores the standard eigenvalue problem and yields excellent agreement with exact beam propagation simulations across a wide range of system sizes and waveguide separations. Furthermore, it captures important physical effects, such as enhanced long-range coupling and nontrivial hopping phases, that are absent in the standard approach.

We develop a game-theoretic framework that compares welfare efficiency across three market mechanisms: continuous double auctions with transparent order books (lit exchanges), opaque order books (dark pools), and periodic batch auctions. Each mechanism is modeled as a queuing system where heterogeneous traders face trade-offs between the execution price, waiting costs, and transaction costs. Our main result establishes that under moderate arrival rates and bounded adverse selection, dark pools dominate both alternatives in aggregate ex-ante welfare. Observable order books create costly strategic timing games in which traders delay or rush submissions to optimize their position in the queue, generating wasteful social waiting costs. Opaque order books eliminate these timing games through information design. We formally characterize the equilibrium strategies in each mechanism and prove the welfare ranking $W^{DARK} > W^{LIT} > W^{BATCH}$. Extensions incorporate asymmetric information and endogenous venue choice. The results demonstrate how the information structure and the discipline of the service jointly determine efficiency in strategic matching environments.

The HD 81809 system presents peculiar chemical composition with a large metallicity difference between its two components: the primary has low metallicity ([Fe/H]=-0.57 dex), while the secondary has approximately solar metallicity ([Fe/H]=0.0 dex). This study investigates whether the chemical enrichment of HD 81809B can be reconciled by a planetary engulfment event, consistent with the star's evolutionary constraints. Using Modules for Experiments in Stellar Astrophysics (MESA) code, we model HD 81809B introducing accretion events with different ranges of masses and chemical mixtures to understand their impact in the surface chemical composition. Our simulation shows that reproducing the observed surface [Fe/H] in HD 81809B requires a substantial accretion event. To reach the observed metallicity, the star must engulf 25 to 75 $M_\oplus$ of metals near its current age. Accretion brings the effective temperature of stellar models closer to the observed one; however, the lithium results over-enriched. In order to reproduce the abundance of this element, the star must accrete less than 6 $M_\oplus$. These results suggest that a planetary accretion event is plausible in the evolution of HD 81809B; however, the chemical composition of the accreted material may differ from the assumptions adopted here. Nevertheless, this study emphasizes the importance of considering such external events when modeling chemically anomalous binary systems.

This paper characterizes the performance limits of optimal array designs using orthogonal and coherent waveforms for both linear and planar arrays. For orthogonal waveforms, we show that the single-target Cramér-Rao Bound (CRB) depends on the sum of the so-called spatial variances of the transmit (Tx) and receive (Rx) arrays, or equivalently, the spatial variance of the sum co-array weighted by the multiplicities of the virtual sensors. This reveals that CRB-optimal geometries are inherently redundant, highlighting a fundamental trade-off between mean squared error (MSE) and identifiability in parameter estimation. Moreover, we derive optimal Tx-Rx sensor allocations given a total sensor budget and show that unequal allocation (favoring the Rx) is optimal even for nonredundant arrays, questioning conventional designs. We extend our results to planar arrays, providing a new general condition that the spatial covariances of the Tx and Rx arrays should satisfy for the optimal waveforms to direct power in the target direction. Additionally, we establish a connection between Diophantine equations and array geometries with equal CRB, along with a constructive method for designing such arrays. Our work provides new guidelines for and insights into optimal array and waveform design with relevance in emerging active sensing multiple-input multiple-output systems.

A novel collocation scheme is presented for elliptic-type differential equations with degenerate coefficients and homogeneous Dirichlet boundary conditions. The use of weighted orthogonal Chebyshev polynomials for the basis functions leads to stiffness matrices with sparse structure, enabling efficient direct calculations. By an orthogonal projection, rigorous analyses are devoted to deriving a-priori error estimates of spectral accuracy in two norms. Furthermore, ample numerical experiments are conducted and compared with error data, convergence rates, condition numbers and $N$-$\log$ curves to confirm the theoretical analyses results. Our proposed method achieves spectral accuracy and handles boundary singularities efficiently, as demonstrated by theoretical analyses and numerical experiments.

Early ($z \gtrsim 2$) Massive ($M_{\star} \gtrsim 10^{10}\,\mathrm{M_{\odot}}$), Quenched Galaxies (MQGs) challenge current galaxy formation models. In this series, we study these systems using the new COLIBRE cosmological hydrodynamical simulations. Following the broad agreement between its predictions and observations found in the first paper, this second paper explores the processes driving galaxies to become massive and quenched in COLIBRE, identifying Active Galactic Nucleus (AGN) feedback as the primary quenching mechanism in both the thermal (L200m6 simulation) and hybrid (thermal+jet, L200m7h simulation) AGN feedback models implemented. However, the two models behave differently: while the thermal model efficiently quenches massive galaxies at $z>3$, the hybrid model is less effective because black holes (BHs) grow more slowly in the early Universe, and the jet component, which dominates the feedback energy, acts on longer timescales to impact galaxies. Both models predict quasar-like MQGs (AGN with $L_{\rm bol}\gtrsim10^{45}\,\mathrm{erg\,s^{-1}}$), with the most luminous systems associated with more recently quenched galaxies. Compared to star-forming galaxies of similar mass, MQGs host more massive BHs and exhibit higher star formation efficiencies. These differences arise primarily from their environments before quenching, particularly at local ($\rm 0.3\,cMpc$) to intermediate scales ($\rm 1.0\,cMpc$), where overdense regions are associated with enhanced gas inflows, higher BH accretion and, hence, feedback power. We find that about $54\%$ ($20\%$) of the $z=3$ MQGs survive as the main progenitors of $z=0$ galaxies, although up to $56\%$ ($60\%$) experience rejuvenation episodes at $z<3$ in L200m6 (L200m7h). Our results highlight the central role of BH growth, AGN feedback and environment in driving rapid quenching in the early Universe.

In this paper, we investigate the notion of the \textit{$λ$-chromatic polynomial} of a graph, which enumerates the number of distinct $L(2,1)$-colorings using colors from a prescribed finite set. We prove that the $λ$-chromatic polynomial of a graph with $n$ vertices is a monic polynomial of degree $n$ and provide a combinatorial interpretation via lattice point enumeration within the framework of inside-out polytopes. Moreover, we compute the $λ$-chromatic polynomial of complete graphs $K_n$ using lattice path enumeration, and we develop a block-gap technique to derive the $λ$-chromatic polynomials for complete bipartite and multipartite graphs. Our approach unifies geometric, combinatorial, and algebraic methods to provide a systematic treatment of $λ$-colorings across various families of graphs.

Considering the increasing availability of digital tools and measurement data in physics classrooms, students are more frequently confronted with larger datasets. While existing literature often assumes that working with large amounts of data is more complex, recent work by Benz, Ludwig, and Vorholzer [Sci. Ed. 119, 1669-1700 (2025)] suggests that this is not necessarily the case when data are presented in diagrams. Building on this, the present study investigates how students evaluate small and large datasets in diagrammatic representations. Using a process-oriented approach, we analyzed the data evaluation processes of $N=20$ university physics students via eye-tracking and concurrent think-aloud. The results indicate that dataset size, in interaction with the visibility of patterns in the data, systematically shapes how students reason with measurement data. Larger datasets support more pattern- and trend-based evaluation and can lead to more unambiguous conclusions. In contrast, smaller datasets are associated with a stronger focus on single measurement points and increased expressions of uncertainty, including an articulated "need for data," alongside an overinterpretation/overweighting of single measurements. The observed shift from uncertainty-related and locally focused evaluation toward more integrative and trend/pattern-based reasoning suggests that larger datasets may support students in integrating multiple data points into more coherent interpretations of measurement data. Overall, the study contributes to a process-oriented understanding of how students engage with measurement data in physics education.

Conventional image-based microfluidic systems face an inherent trade-off between throughput, imaging speed, and data bandwidth, limiting their ability to monitor high-velocity flows without significant motion blur or prohibitive data generation. Event-based sensing has emerged as a high-speed, low-power alternative, but has so far been largely restricted to tracking monodisperse, spherical particles. In this work, we introduce a microfluidic sensing platform that enables the simultaneous extraction of kinematic and spectral information from polydisperse microparticles using a neuromorphic imaging approach. By integrating a spatially multiplexed RGB filter mask with an asynchronous event-based sensor, spectral signature and motion are encoded directly at the sensing stage, eliminating the need for image reconstruction or learning-based inference. The system achieves sub-millisecond temporal resolution and maintains robust classification performance across a broad range of particle sizes and flow velocities, including under non-laminar conditions, reaching up to 82% accuracy for classification of colored particles within the 0.08-0.18 mm range. The event-driven architecture reduces data bandwidth by >240x compared to conventional high-speed imaging, while sustaining an area throughput of 460 mm^2/s. By providing a computationally efficient and low-latency particle characterization, this framework paves the way for a scalable solution towards high-speed, label-free screening of heterogeneous analytes in clinical diagnostics and environmental monitoring.

Ultrafast light-driven magnetization, a frontier in quantum magneto-optics, has traditionally relied on circularly polarized lasers to provide external angular momentum. While increasing efforts have aimed to achieve light-polarization-robust (LPR) magnetization that is insensitive to the form of external light excitation, the underlying mechanism remains largely unclear. Here, we establish the symmetry-constrained rule for LPR magnetization in antiferromagnetic systems. Through real-time time-dependent density functional theory calculations, we observe the strong LPR magnetization in spin-spiral magnets and its suppression in collinear antiferromagnets, confirming our theory. Strikingly, laser excitation induces real-space demagnetization, rotation, and oscillation of atomic spins in spin-spiral monolayer NiI$_2$, whereas rotation is largely suppressed in conventional collinear antiferromagnets. Our work reveals a novel microscopic pathway for ultrafast magnetization that is independent of light polarization, paving the way for advanced femtosecond spin control.