So-called Berwald-Finsler spacetimes are Finsler spacetimes that are closest to pseudo-Riemannian geometry, as their canonical nonlinear connection defines an affine connection on spacetime. In spherical symmetry, these geometries can be used to describe the gravitational field outside of compact objects. We solve the Finsler gravity vacuum equation for $SO(3)$-symmetric Berwald spacetimes that are asyptotically flat, but not Ricci flat. We find that among all spherically symmetric Berwald spacetimes, only one class is compatible with asymptotic flatness and a well defined causal structure. For this class, we completely solve the Finsler gravity vacuum equation and find three families of non-Ricci flat solutions -- which represent the first non-trivial, exact spherically symmetric vacuum solutions. They are so-called $(α,β)$-Finsler spacetimes that are constructed from a pseudo-Riemannnian metric and a 1-form. In particular, we show, by providing a concrete example, that in Finsler geometry there exist $SO(3)$-symmetric, asymptotically flat vacuum solutions that are not Ricci flat; these solutions are promising candidates to model the gravitational field around compact objects, beyond their Riemannian description.

Magnetic field fluctuations on nanometer length scales manifest in a diverse range of phenomena -- electron and spin dynamics in materials and devices, quantum many-body systems, and molecular chemistry. Measuring these phenomena requires sensors with a challenging combination of broad spectral bandwidth, high sensitivity, and nanoscale spatial resolution. Nitrogen-vacancy (NV) centers, atom-like quantum sensors in diamond, possess the requisite sensitivity and nanoscale sensing volume, but are typically limited in bandwidth by the practical speed of the applied quantum control sequence. Here, we overcome this limitation by exposing the NV qubit to a microwave dressing field during a dynamical decoupling sequence, which both amplifies and frequency-mixes target signals at arbitrary frequencies into the detection band of the dynamical decoupling protocol. We demonstrate this approach by using NV centers to detect both coherent and noisy nanoscale spin wave dynamics in a magnetic yttrium-iron-garnet (YIG) thin film over a broad frequency range. Our technique generalizes to other qubit platforms, providing a versatile framework for nanoscale spectroscopy across diverse physical and chemical systems.

The Ize Conjecture proposed that every absolutely irreducible representation of a compact Lie group admits a maximal isotropy subgroup with an odd-dimensional fixed-point space, which would provide a universal bifurcation guarantee via the equivariant degree. Its disproof by Lauterbach and Matthews necessitates a more targeted criterion. We introduce Ize pairs -- pairs $(G, V)$ for which some maximal isotropy subgroup $H$ satisfies $\dim V^H - \dim V^G \equiv 1 \pmod{2}$ -- and prove that this dimension-parity condition completely captures the algebraic obstruction to a non-trivial equivariant degree change at maximal orbit types. Integrating this criterion with a mod-2 equivariant spectral flow yields local and global bifurcation guarantees without recourse to Burnside ring arithmetic. As an application, we establish unbounded branches of non-stationary periodic solutions in a $Γ$-symmetric coupled oscillator network, where the bifurcation guarantees follow entirely from the crossing parity of the linearization at the boundary of a regular parameter window.

We study non-atomic Bayesian routing games whereby a transportation network is shared by two types of traffic: a coordinated fleet and a mass of selfish users. The links in the network are characterized by travel time functions that depend both on the aggregate flow on the link and on a random state of the world $W$ that, in general, is not directly observable. Rather, we assume that both the fleet coordinator and the selfish users know the prior distribution of $W$, observe (different) private messages that are correlated with $W$ and possibly among themselves, and make routing decisions based on such heterogeneous partial information. Under the assumption that both the state of the world and the private message sets are finite, we prove the existence and uniqueness of a Bayesian equilibrium for the ensuing Bayesian routing game.

Privacy regulations such as the General Data Protection Regulation (GDPR) impose strict requirements on how personal data is stored, processed, and audited. While key-value stores (KVS) are widely used in latency-sensitive applications, their simple data model and untrusted cloud deployment environments make GDPR compliance particularly challenging. Existing approaches require invasive code modifications, impose high performance overheads, or overlook the integrity of compliance mechanisms themselves. This paper presents GDPRuler, a trusted middleware system that enables verifiable GDPR compliance for KVS on untrusted clouds without modifying their codebase. GDPRuler deploys a trusted GDPR monitor inside a Confidential Virtual Machine (CVM), which enforces GDPR policies, manages compliance metadata, and maintains tamper-evident audit logs. A declarative policy language translates core GDPR obligations into enforceable runtime rules. To ensure efficiency, GDPRuler encodes metadata compactly within KV records, builds dedicated metadata indexes for GDPR-specific queries, and logs only compliance-relevant events in a space-efficient format. We implement GDPRuler as a transparent proxy for unmodified Redis and RocksDB deployments. Evaluation with YCSB and GDPR-inspired workloads shows that GDPRuler enforces core compliance guarantees with low overheads: GDPRuler achieves ~61% of native KVS throughput with the CVM environment contributing 28%-32% of it, metadata storage overhead remains below 20%, and GDPR queries benefit from 13-182x speedup through metadata indexing. By embedding verifiable policy enforcement into a trusted middleware layer, GDPRuler offers a practical path toward GDPR-compliant KVS on untrusted cloud infrastructures.

We demonstrate a data-driven parameterization for snow albedo using a constrained neural differential equation that directly predicts a range of snow albedo tendencies from standard snow and meteorological inputs. After training with multi-year in-situ and satellite observations from a wide variety of locations, the scheme effectively reproduces daily albedo evolution across diverse climate zones, with median error under 7.5% (RMSE ~0.05), a 10-30% improvement over established models. Furthermore, the model generalizes to sites not seen during training and scales from coarser grids to point locations. The scheme can easily incorporate new features as observational networks expand, offering an adaptive and computationally lightweight framework for next-generation land and climate models.

Decentralized Finance (DeFi) services are usually constructed by composing a variety of smart contracts. While composability is a key driver of the success of DeFi, it also creates security risks: adversaries may exploit interactions between newly deployed contracts and the pre-existing ones to inflict economic losses. We introduce MEV non-interference, a formal security notion for DeFi composability requiring that the maximal extractable value from a set of newly deployed contracts is not increased by interactions with the existing blockchain state. To support this notion, we define local MEV, a novel measure of economic attacks that focusses on the loss of a given set of victim contracts. We study two adversarial models, with bounded and unbounded wealth, and establish sufficient conditions and locality principles that enable modular reasoning about secure composability. We apply the framework to representative DeFi compositions, including exchanges, AMMs, options, lending pools, routers, and arbitrage contracts, showing how it distinguishes secure compositions from vulnerable ones. Our results provide a formal foundation for reasoning about the economic security of DeFi compositions.

Hubble's unique COS G130M+G160M and STIS E140M UV spectral capabilities are essential for characterizing and understanding fundamental properties of main-sequence O-type emission-line (Oe) stars. These are fast rotators, and some are believed to be spun up in binaries. UV medium resolution observations of these stars are crucial for understanding massive binaries and their role in galaxy evolution. Oe stars are more prevalent at low metallicity, where they are highly under-studied, but UV spectra of these stars at all metallicities are needed. Observations of these stars in the 2030's with Hubble are particularly important in the era of ultra wide-field IFU optical and transient astronomy surveys. Ultimately, these observations will inform future UV observations with the Habitable Worlds Observatory.

We study universal deformations in incompressible isotropic Cauchy elastic solids with residual stress, without assuming any specific origin for the residual stress. Starting from the constitutive representation of the Cauchy stress as an isotropic tensor-valued function of strain and residual stress, we derive the universality constraints for residually-stressed incompressible isotropic Cauchy elastic solids. We show that for the six known families of universal deformations the set of universal deformations is identical to that of incompressible isotropic elasticity in the absence of residual stress. We also show that residual stress does not enlarge the space of universal deformations. We then determine the universal residual stress fields corresponding to the six known families of universal deformations. Assuming that the residual stress field has the same symmetry as the corresponding universal deformation, the universality constraints reduce to systems of ordinary differential equations that can be solved explicitly. The resulting universal residual stress fields are characterized and discussed for each family.

Quantum entanglement assistance is known to improve the Shannon capacity of classical communication networks but the largest gains noted thus far are rather modest (less than 6%), motivating the question: are large capacity gains ever possible? It is shown in this work that in the presence of causal channel state information at the transmitters, quantum entanglement assistance provides a multiplicative capacity advantage that grows exponentially with the number of users K for certain classical K-user multiple access channels with fixed size (binary) alphabet for inputs, outputs and states. Similarly, in the presence of causal channel state information at the transmitters, quantum entanglement assistance is shown to provide a multiplicative capacity advantage that is unbounded as the size of the state alphabet grows, while the number of users (K=3) and the input and output alphabet (binary) are held fixed. Even with only a few users and small alphabet sizes, substantial multiplicative gains in capacity are found, e.g., with binary inputs, outputs and states, multiplicative gains by factors exceeding 21 and 88 are noted with K=5 and K=7 users, respectively. The gains are robust in the sense that they persist even with noisy quantum resources, e.g., an exponential (in K) capacity advantage from quantum entanglement assistance remains available even if each entangled qubit independently depolarizes completely with probability $\approx$ 30%. The gains are based on quantum entanglement assistance provided only to the transmitters.

We present version 2 of BSMArt, a powerful yet lightweight scanning tool designed to simplify the exploration of parameter spaces of new physics models. Aside from architectural improvements, simpler installation and expanded documentation with examples, the new version includes additional tools and new machine learning and Monte Carlo scanning algorithms: Affine MC, Contour Finding, MLScanner, DLScanner, MLS, and CMA-ES. We showcase two variants of CMA-ES scans with physics applications relevant for soft lepton excesses at the LHC. We demonstrate that it is easily possible to find diverse and interesting parameter points for future testing.

We introduce LACIA, a verification-driven agentic AI workflow for theoretical physics, and apply it with independent human checks to construct Carrollian conformal bases. We develop the Poincare-Carrollian intertwiner as the central method. It reproduces the celestial and Carrollian conformal bases for massless particles and constructs the missing Carrollian bases for massive and tachyonic particles. The massive basis requires a complex momentum shift in scattering amplitudes.

We propose a unified framework for dimension reduction of partial differential equations posed on thin domains. Our approach combines three complementary ingredients: a careful boundary-condition analysis, an averaging-based splitting for general thin geometries, and a slow-manifold viewpoint for the resulting fast-slow system. Homogeneous Neumann conditions on the thin faces emerge as the most relevant and physical regime because they preserve the transverse zero mode and therefore lead to a genuine lower-dimensional reduced equation. For general thin domains we derive the averaged fast-slow system and isolate the geometry-induced correction term. We then formulate a splitting-based Lyapunov-Perron construction for an exact slow manifold when a suitable spectral decomposition of the slow variable is available, and we construct approximate slow manifolds and corrected reduced dynamics directly from the invariance equation by asymptotic expansion. Moreover, we propose the Schnakenberg reaction-diffusion system as a canonical test problem for comparing the full thin-domain dynamics, the averaged model, and the manifold-corrected reduced dynamics. Finally, we also extend the framework to thin tubular domains and derive the corresponding rescaled fast-slow system in curved geometry.

Fault-tolerant quantum computing requires translating application-level quantum circuits into the Clifford+$T$ gate set, where the $T$ gate is the dominant resource cost. Phase kickback is an ancilla-based technique that can dramatically reduce $T$-count for rotations with dyadic angles, but has previously been limited to highly structured circuit families. We present Dyadic Phase Fixing (DPF), a general multi-qubit synthesis tool that extends phase kickback to general quantum circuits. DPF uses numerical unitary synthesis to greedily extract dyadic angle rotations from any input circuit. Combined with a decision matrix to automatically size the final phase gradient register, our end-to-end workflow achieves up to 70% reduction in $T$-count compared to \texttt{gridsynth} and up to 60% compared to Repeat-Until-Success synthesis on a diverse set of benchmarks. We map these compiled circuits to a surface-code architecture to evaluate space-time volume, demonstrating up to a 60\% reduction in this metric as well. However, for some circuits and mapping strategies the two metrics diverge significantly, demonstrating that $T$-count alone is a useful but incomplete proxy for fault-tolerant program costs.

Neuromechanics aims to understand the link between an animal's neural activity and its physical behaviors. Recent advances in experimental and machine learning techniques enable simultaneous recordings of neural and locomotion dynamics over long time periods and across multiple behavioral transitions in worms, flies, and other organisms. These high-dimensional datasets present the challenge of inferring interpretable low-dimensional dynamical models that quantitatively connect neural activity and behavioral dynamics. However, despite major experimental and theoretical progress, there is currently no end-to-end model for predicting locomotion and other behaviors from neural activity. Here, we present a theoretical and computational framework for inferring multiscale neuromechanical models from state-of-the-art experimental data. Our data-efficient approach combines interpretable spectral mode representations with Helmholtz-Nambu decompositions and Bayesian inference to identify a predictive stochastic model that converts neural activity time series into behavioral locomotion patterns. We first apply this framework to recently published recordings of neural activity and locomotion in the roundworm Caenorhabditis elegans, showing that it accurately describes experimentally observed dynamics. We further demonstrate how the inferred model can be used to predict neural activation patterns for controlling C. elegans locomotion in real time, providing a basis for future optogenetic experiments. Due to its generic formulation, the framework introduced here is broadly applicable to neuromechanical recordings for a wide range of animal species.

We investigate the axial $w$-mode oscillations of anisotropic neutron stars. Stellar configurations are constructed using two realistic equations of state, BSk21 and SLy4, with two prescriptions for pressure anisotropy, the Horvat ansatz and the Bowers-Liang ansatz. The axial $w$-mode frequencies are computed by solving the linearized perturbation equations using a continued-fraction method. For each fixed anisotropy strength, the axial $w$-mode frequency decreases monotonically with increasing stellar mass along the stable branch, with its magnitude depending on both the equation of state and the nature of the anisotropy. At low stellar masses, configurations with dominant radial pressure ($p_r>p_t$) exhibit higher frequencies than those with dominant tangential pressure, whereas toward the upper end of the stable branch this ordering is reversed, and configurations with $p_t>p_r$ attain higher frequencies at the same mass. The axial $w$-mode frequency displays an approximately linear dependence on compactness, with anisotropy modifying both the slope and the intercept. The Bowers-Liang ansatz produces a wider spread in the frequency values compared to the Horvat ansatz. We also analyze the damping times associated with the axial $w$-modes and find that they increase with stellar mass, with a rapid rise toward the upper end of the stable branch. At a fixed mass, increasing the tangential pressure relative to the radial pressure leads to shorter damping times, while configurations with dominant radial pressure exhibit longer damping times. The sensitivity of the damping time to anisotropy is more pronounced for more compact stars, and the Bowers-Liang ansatz yields systematically larger damping times than the Horvat ansatz. Finally, we provide empirical expressions for the axial $w$-mode frequency and damping time as functions of stellar compactness and anisotropy strength.

In this paper, we discuss a family of highly regular graphs, said to be distance-regular. We are particularly interested in the distance-regular graphs with valency three. It is known that there exist exactly 13 such graphs. Let $Γ$ denote a distance-regular graph with vertex set $X$. For any vertex $x \in X$, the corresponding Terwilliger algebra $T=T(x)$ is generated by the adjacency algebra $M$ of $Γ$ and the dual adjacency algebra $M^*=M^*(x)$ of $Γ$ with respect to $x$. It is known that the algebra $T$ is semisimple. By construction, the vector space $V=\mathbb{C}^X$ is a module for $T$, said to be standard. In this paper we have the following goal. For each of the 13 distance-regular graphs $Γ$ with valency three, we will decompose the standard module $V$ into a direct sum of irreducible $T$-modules. Using this information, we will work out the dimension of $T$.

We consider robust risk measures that arise as worst-case values of convex risk measures evaluated on uncertainty sets. We characterize continuity properties of robust risk measures through their consolidated uncertainty sets, derive dual representations for robust risk measures, and develop a set-valued dual representation for consolidated uncertainty sets. The two dual frameworks rely on distinct geometric assumptions and are therefore complementary rather than interchangeable.

An interaction protocol formalizes how the agents in a multiagent system interact, which facilitates implementing agents. Existing approaches yield agent implementations specific to the selected protocols. How can we engineer intelligent agents that can enact protocols but are programming-free? Our contribution, Ahoy, addresses this question by creating LLM agents that dynamically select and enact declarative protocols to achieve user goals. We demonstrate that an \ahoy agent can correctly and intelligently enact multiple protocols - concurrently if appropriate to the user goal - without specialized training. Ahoy's significance lies in that it brings together declarative protocols and LLMs, both approaches that promise improved knowledge engineering for agents.

The encoding of classical data into quantum states constitutes the primary performance bottleneck in Quantum Machine Learning (qml) on Noisy Intermediate-Scale Quantum (nisq) devices. No existing framework jointly characterises resource cost, expressivity, and noise robustness, nor provides actionable selection guidelines for practitioners. This survey addresses that gap through a systematic review of 66 primary works (2017-2026) assembled via a PRISMA-adapted protocol across five academic databases. Four principal contributions are made. First, a three-axis cost-expressivity-robustness taxonomy classifies all major encoding families - basis, angle, dense-angle, amplitude, data re-uploading, and IQP - along independently measurable axes. Second, closed-form depth-fidelity bounds under nisq decoherence channels identify the critical gate-error rate p* ~ 10^-3 below which amplitude encoding is viable. Third, a unified treatment of Fourier expressivity, barren-plateau onset, and quantum kernel concentration as functions of the encoding circuit provides the first joint trainability analysis. Fourth, a five-regime decision framework maps (D, n, p, tau) - feature dimension, qubit budget, error rate, and task type - to a hardware-grounded encoding recommendation. The central finding is that for p >= 10^-3, shallow angle-based encodings consistently outperform amplitude encoding in practice, despite the latter's exponential qubit advantage.

We consider interacting vertex-reinforced random walks on a finite graph, where each walk transitions at independent geometric random times with parameter $p_i \in (0,1]$. The transition matrix of walk $i$ takes the form $Q^i(x, p_i) = p_i Π^i(x) + (1-p_i)I$, where $π^i(x)$ is the unique invariant measure, independently of $p_i$. Consequently, the limiting points of the occupation measure $X(n)$ coincide with those of the simultaneous-transition model ($p_i = 1$): the solutions of $x = π(x)$. Verifying almost sure convergence to these points is non-trivial, since the stochastic input $U(n+1)$ is not a martingale difference. We address this by decomposing $U(n)$ into a convergent martingale, a geometrically decaying component $(1-p)U(n-1)$, and a controlled correction, allowing us to verify the Clark-Kushner condition and establish almost sure convergence.

We establish the Clark-Kushner condition for a large class of interacting vertex-reinforced random walks on finite graphs, where the transition matrix $Q^i(x)$ of each walk depends on the joint vector $x$ of vertex occupation proportions and may have distinct rows. This allows one to study the dynamics of the vertex occupation measure by using the tools of stochastic approximation theory. However, the standard approach fails because the noise inputs are in our case not a martingale difference: they retain memory of the previous state. Using the solution of the Poisson equation for Markov chains, we decompose the noise into a martingale difference minus the increment of a bounded process -- a structure originating in Gordin's work on limit theorems for stationary processes. The key technical ingredient of our approach is a uniform geometric ergodicity bound derived from the Dobrushin contraction coefficient, which also controls the Lipschitz continuity of the solution of the Poisson equation. Our hypotheses require only that each $Q^i(x)$ be irreducible, aperiodic, and Lipschitz continuous in $x$; in particular, strictly positive entries are not assumed. Our results generalize and simplify previous arguments considered for single self-reinforced vertex-reinforced random walks.

Spatial point processes are a valuable tool for probabilistic modeling to explain location data. However, the data themselves are often observed imperfectly. In order to perform accurate inference, one must account for these imperfections, which we refer to as degradation. We consider two forms of degradation for spatial Poisson processes: thinning and displacement. First, we provide some theoretical results on model identifiability, showing that, under weak conditions, one can jointly learn the scale of the displacement, a parametric form of thinning, and a nonparametric intensity function. The ability to learn all of these components and the resulting improvements for inference compared to the conceptual non-degraded but misspecified model are shown empirically via simulation study. Finally, we apply this approach to North Atlantic right whale call data from Cape Cod Bay.

We study cubic interactions of $\mathcal N=2$ massless integer-spin gauge supermultiplets in harmonic superspace. We construct the complete class of abelian $(\mathbf{s},\mathbf{s_1},\mathbf{s_2})$ cubic vertices with the minimal number of space-time derivatives. Such vertices exist only for $\mathbf{s}\geq \mathbf{s_1}+\mathbf{s_2}$ and universally take the form of the gauge prepotential coupled to the conserved higher-spin supercurrent. For~$\mathbf{s_1}\neq\mathbf{s_2}$, we find the novel complex principal supercurrent, whose real and imaginary parts generate the parity-invariant and the parity-breaking interactions, respectively. The supercurrents are constructed from gauge-invariant $\mathcal N=2$ higher-spin Weyl supertensors associated with the spin-$\mathbf{s_1}$ and spin-$\mathbf{s_2}$ gauge multiplets. These supertensors are defined in terms of unconstrained higher-spin analytic prepotentials. We also derive the complete set of conserved component higher-spin currents associated with the $(s,s_1,s_2)$ vertices, including both traceless currents and currents with the non-vanishing trace.

Hyperspectral imagery represents the best contemporary technology to remotely detect anomalous objects. Nevertheless, hyperspectral anomaly detection (HAD) technique makes ground facilities/situations completely exposed. For the first time, we develop the first anti-HAD (AHAD) technique rendering the key objects undetected, without perfect coordinate/position state information (CSI) of the detectors (e.g., reconnaissance aircraft). Our AHAD algorithm is generally applicable to defend against almost all the existing benchmark data-driven and model-driven HAD methods. AHAD is fundamentally different from conventional adversarial attacks, so novel theory is needed. We customize novel regularizers for assimilating real anomalies into the backgrounds (ARAB) and fooling the detectors with pseudo-anomalies, thereby optimizing an energy-efficient stealthy perturbation signal for AHAD. The ARAB regularization is mathematically interpretable as flattening the topology-enhanced anomaly/background structures in the feature space, hence termed Lipschitz-forcing perturbations. Considering the imperfect CSI, we further develop a robust AHAD criterion, where the uncertainty is mathematically described as matrix-shifting misalignment for statistically generating the robust perturbation. Comprehensive experiments demonstrate the effectiveness and robustness of our AHAD algorithm across diverse real-world datasets. Remarkably, our algorithm generates a single AHAD perturbation signal that can simultaneously evade almost all benchmark detectors, greatly enhancing its practicality, given that the reconnaissance detector type is usually unknown. To the best of our knowledge, this is the first formal AHAD study. As a side contribution, we propose a new quantitative performance index, ArmCBA, to evaluate the robustness of an HAD method against our AHAD signal.

The Habitable Worlds Observatory (HWO) will provide the first opportunity to directly image and spectrally characterize terrestrial exoplanets in the habitable zones of nearby stars. Maximizing its scientific return requires a comprehensive understanding of the high-energy radiation environments of target stars, which shape planetary atmospheres and govern the production, destruction, and detectability of biosignatures. Ultraviolet (UV) radiation plays a particularly critical role in atmospheric chemistry. Far-ultraviolet (FUV) and near-ultraviolet (NUV) photons regulate key photochemical pathways, influence ozone stability, and drive the formation of prebiotic molecules. However, the majority of high-priority HWO target stars lack high-quality UV observations. Existing datasets are sparse, heterogeneous, or limited by calibration uncertainties, and no comparable UV observatory is expected for at least 5-10 years (with UVEX offering more limited spectral resolution, wavelength coverage, and sensitivity). The Hubble Space Telescope (HST) remains the only observatory capable of acquiring high-resolution FUV and NUV spectra for these targets over the next 10-15 years. We therefore advocate for a coordinated HST program to systematically obtain UV spectra of high-priority HWO targets, ideally in conjunction with X-ray observations. This effort is essential for enabling accurate target prioritization, constraining stellar radiation environments, and ensuring robust interpretation of future HWO observations.

Magnetohydrodynamic (MHD) waves in coronal loops provide key seismological diagnostics through their characteristic time signatures. While fast and slow magnetoacoustic modes are routinely exploited, the entropy mode, despite being another eigenmode of the system, remains largely inaccessible due to its non-propagating and non-oscillatory nature. We identify possible observable time-domain signatures of the entropy mode and its indirect effects. Our approach exploits the intrinsically non-adiabatic conditions of the solar corona, under which the entropy mode is closely linked to the compressive slow mode. We consider a one-dimensional coronal loop model with field-aligned thermal conduction, where standing slow and entropy modes are simultaneously excited. We show that the entropy mode leaves distinct imprints on the total loop temperature and density perturbations. Specifically, its rapid decay relative to the slow mode produces a non-exponential damping profile during the initial oscillation cycles and introduces a pronounced asymmetry between the upper and lower temperature and density envelopes. These effects arise naturally from the superposition of two exponentially decaying components with different damping timescales. Furthermore, deviations from the canonical quarter-period phase shift between temperature/density and velocity perturbations in the standing slow mode are explained by the entropy-mode effect. We conclude that the entropy mode may be detected through its impact on compressive oscillations. Revealing its role in non-exponential damping, envelope asymmetry, and phase shifts of compressive oscillations makes the entropy mode potentially accessible to observations and lays the foundation for solar and stellar seismological applications.

Measurements of baryon acoustic oscillations (BAO) by the Dark Energy Spectroscopic Instrument (DESI) have revealed evidence for dark energy that evolves. If local distance measurements are analyzed with the $w_0w_a$ models preferred by the DESI measurements, the value for the Hubble constant can be as much as $2.5\,\mathrm{km\,s^{-1}Mpc^{-1}}$ smaller than the value obtained assuming $Λ$CDM. When these $w_0w_a$ models are further constrained by cosmic microwave background (CMB) and type Ia supernova (SNIa) data, the downward shift is $1.1 \pm 0.38\,\mathrm{km\,s^{-1}Mpc^{-1}}$ (DESI + CMB) and $0.5 \pm 0.1\,\mathrm{km\,s^{-1}Mpc^{-1}}$ (DESI + CMB + SNIa). The dependence of local determinations of $H_0$ on the background cosmology, combined with the fact that the low-redshift cosmology is not well constrained, is relevant to the Hubble tension.

Chiral superconductivity, in which the phase of the superconducting order parameter winds in momentum space, has long been sought for its close link to topological superconductivity. Recent work reported a superconductor in rhombohedral multilayer graphene emerging from a time-reversal symmetry broken normal state, suggesting that it could be a chiral superconductor. However, the possibility of chirality depends on the symmetry and structure of the normal-state Fermi surface, which have not been directly measured. Here we measure quantum oscillations in rhombohedral tetralayer graphene over a broad range of the phase diagram, including the superconducting region. At densities well above the onset of superconductivity, we reproduce previously-reported oscillations consistent with a spin- and valley-polarized quarter metal with a single simply-connected Fermi pocket. As the carrier density is reduced, we find a transition to a complex "multitone" state that persists through the superconducting region. This state's spectrum of quantum oscillations is incompatible with a simply-connected quarter metal. The next-simplest candidate normal states suggested by our microscopic modeling (fully-polarized annular, nematic, and three-pocket states) are inconsistent with our measurements, albeit difficult to rule out entirely. The normal state is thus seen to be richer than previously envisaged, reshaping the search for the superconducting mechanism and the possible chirality of the pairing channel.

The highly dynamic intracluster medium (ICM) influences cluster thermodynamic evolution and probes key physical processes. Quantifying the non-thermal motions is therefore essential for understanding cluster physics and interpreting high spectral-resolution X-ray observations from telescopes like {\it XRISM}. We quantify bulk and turbulent gas motions in 352 galaxy clusters at $z=0$ (${\rm M_{200c}=10^{14.3-15.4}\, M_\odot}$) from the TNG-Cluster suite of magneto-hydrodynamical galaxy simulations. We use a multi-scale filtering Reynolds decomposition to separate total gas velocities into bulk (coherent) and turbulent (small-scale fluctuations) components. We primarily focus on the hot X-ray emitting gas in the central core regions. According to TNG-Cluster, majority of the ICM has subsonic turbulence but with broad velocity distributions reaching $\mathcal{M}_{\rm Turb}\sim 10$ and large cluster-to-cluster variations. In cluster centres, turbulence contributes less than half of the total velocity dispersion $(σ_{v\rm,Turb } \sim 0.5 ~σ_{v,\rm Total})$ for most clusters, with typical turbulent velocity dispersions of $50-75$ km s$^{-1}$ across the mass range, and with sub per cent levels of turbulent pressure support. Clusters that are strong cool cores, or have X-ray cavities, or experienced recent SMBH feedback energy injections exhibit systematically larger turbulent velocity dispersions and more prominent turbulent velocity tails. On average, the turbulent velocity dispersion peaks in cluster centres, decreases slightly to a minimum at $0.1-0.2 \, R_{\rm500c}$, then rises again. Our analysis shows that SMBH feedback is a key driver of turbulence in cluster cores, generating strong but short-lived motion alongside high-velocity outflows. It also calls for caution for interpreting {\it XRISM} observations.