Hyperparameter transfer allows extrapolating optimal optimization hyperparameters from small to large scales, making it critical for training large language models (LLMs). This is done either by fitting a scaling law to the hyperparameters or by a judicious choice of parameterization, such as Maximal Update ($μ$P), that renders optimal hyperparameters approximately scale invariant. In this paper, we first develop a framework to quantify hyperparameter transfer through three metrics: (1) the quality of the scaling law fit, (2) the robustness to extrapolation errors, and (3) the asymptotic loss penalty due to choice of parameterization. Next, we investigate through a comprehensive series of ablations why $μ$P appears to offer high-quality learning rate transfer relative to standard parameterization (SP), as existing theory is inadequate. We find that the overwhelming benefit of $μ$P relative to SP when training with AdamW arises simply from maximizing the learning rate of the embedding layer. In SP, the embedding layer learning rate acts as a bottleneck that induces training instabilities; increasing it by a factor of width to match $μ$P dramatically smooths out training while improving hyperparameter transfer. We also find that weight decay improves the scaling law fits, while, in the fixed token-per-parameter setting, it hurts the robustness of the extrapolation.

Precise measurement of the kinematic Sunyaev-Zel'dovich (kSZ) effect - a probe of the large-scale distribution of baryonic matter, a key observable for cosmological inference - requires accurate reconstruction of galaxy velocities from spectroscopic surveys. The signal-to-noise ratio (SNR) of kSZ measurements scales directly with the correlation coefficient $r$ between reconstructed and true velocities. We introduce Velocityformer, an equivariant graph transformer architecture designed to match the specific symmetry of the observational data. While the underlying physics is equivariant with respect to translations and rotations, observational effects break this symmetry due to the preferred line-of-sight direction. Matching the model's inductive bias to the data's broken symmetry consistently improves performance across all model sizes and training volumes, with Velocityformer improving $r$ by 35% over the standard linear theory baseline and outperforming ML baselines at every data volume. By matching the model's inductive bias to the data and conditioning on the physics-based long-wavelength solution, Velocityformer is highly data-efficient, training to high accuracy on as few as 4 low-fidelity simulations, and generalises zero-shot across input geometry, cosmological parameters, and galaxy sample. On high-fidelity simulated galaxy catalogues, this yields a 30% improvement in $r$ over the physical baseline, directly translating to the same SNR gain on observational data.

The hot Big Bang may have emerged from evaporation of primordial black holes (PBHs) lighter than $10^9$g. Standard monochromatic treatments predict nearly simultaneous evaporation, abrupt reheating, and a large Poltergeist scalar-induced gravitational wave signal. We confront this expectation with the irreducible collapse mass tail predicted by general relativity, $df_{\rm PBH}/d\ln M\propto M^{3.78}$, which smooths reheating, suppresses the signal by orders of magnitude, and reopens the ultra-light PBH window.

The reheating of the universe by the evaporation of light primordial black holes (PBHs) can leave a stochastic gravitational-wave (GW) background in the early Universe. In the monochromatic limit, their simultaneous evaporation produces an abrupt matter-to-radiation transition, triggering the so-called Poltergeist GW signal, usually predicted to be dominant and observable. We revisit this result by including the irreducible mass spread implied by gravitational collapse in General Relativity, whose infrared tail scales as $d f_{\rm PBH}/d\ln M_{\rm PBH}\propto M_{\rm PBH}^{3.78}$. We show that this minimal width smooths reheating enough to suppress the Poltergeist background by orders of magnitude, down to the level of the scalar-induced GW signal produced during a generic early matter era, such as one driven by the decay of a heavy relic. We provide a complete decomposition of the scalar-induced spectrum into eight production channels and find that none, except the one from PBH formation, reaches either the $ΔN_{\rm eff}$ bound or the projected sensitivity of future GW observatories. This reopens regions of ultra-light PBH parameter space previously thought to be excluded by these constraints.

Understanding the nature of quasihole excitations, i.e., anyons that have fractional charge and statistics, has been a challenging problem in condensed matter physics. Our theoretical approach to this problem has been to consider a quantum dot, containing a few charged particles, coupled to the incompressible fluid. It has provided important insights into the energetics of Laughlin quasiholes. Photoluminescence (PL) spectroscopy studies of this system have been able to probe these quasiholes that have confirmed our expectations. Turning to the Pfaffian state, we now observe that such a system is also able to provide valuable information about the Pfaffian quasiholes, viz., the energy dispersion, the charge density distribution and the quasihole creation energy. The energy dispersion of e/4 quasiholes derived here, clearly reflect the interaction between the quantum dot and the incompressible Pfaffian state. PL spectroscopy experiments on the 5/2 Pfaffian-Laughlin state could perhaps shed light on the energetics of these elusive quasiparticles.

Standard quantum inference converts quantum data into classical outputs. We study an alternative inference setting in which the desired output is quantum, preserving coherence. Such settings include quantum purity amplification (QPA), mixed-state approximate purification or cloning, and density matrix exponentiation. We show that such protocols can achieve exponentially lower sample complexity than incoherent, measurement-mediated protocols. For QPA with principal eigenstate targets and $d$-dimensional inputs, coherent processing achieves error $\varepsilon$ using $O(1/\varepsilon)$ copies, versus the $Ω(d/\varepsilon)$ copies required by any incoherent protocol. Together, these sharp coherent-incoherent separations seed a theory of coherent quantum inference, with an entanglement-breaking limit identifying the optimal incoherent counterpart of each coherent protocol.

Recent cosmological analyses based on DESI and CMB data have revealed a tension between the inferred sum of neutrino masses and the minimum value allowed by neutrino oscillation experiments, when assuming an underlying $Λ$CDM model of cosmology. In this work, we perform a systematic exploration of alternative dark energy models, including models that can supply a negative dark energy density capable of reproducing the cosmological effects of negative effective neutrino masses. We argue that dark energy models can alleviate the tension by modifying the cosmic expansion rate over a specific redshift range relevant for CMB lensing, while matching BAO distance measurements from DESI at lower redshifts. Among the models considered, we find that a sign-switching cosmological constant model, $Λ_\mathrm{s}$CDM, is uniquely capable of recovering positive neutrino masses by modifying the expansion history in this way. For the combination of DESI DR2 BAO, CMB, and DES-Dovekie supernova data, the constraint on the effective neutrino mass shifts from $\sum m_{ν,\mathrm{eff}}=-0.075^{+0.039}_{-0.053}$ eV (68%) for $Λ$CDM to $\sum m_{ν,\mathrm{eff}}=0.055\pm0.050$ eV (68%) for $Λ_\mathrm{s}$CDM, with a 95% lower bound on the dark energy transition redshift, $z_\dagger>2.4$. Although $Λ_\mathrm{s}$CDM does not have the strongest overall statistical support among the models considered, when the $\sum m_{ν,\mathrm{eff}}$ parameter is allowed to vary, our findings point toward a specific sign- and redshift-structured contribution to the late-time expansion history as a viable way to alleviate the neutrino mass tension.

The sensitivity of the proposed FCC-ee collider to axion-like particles (ALPs) is investigated at all planned center-of-mass energies, with focus on the case where the ALP couples primarily to electroweak gauge bosons at leading order. We study the associated production of the ALP with a photon, with the ALP decaying in turn to two photons, yielding a three-photon final state. The ALP coupling to the photon is evaluated for ALP masses of 5 to 320 GeV. In this effective model, the FCC-ee will be able to detect ALPs for couplings down to a few $10^{-6} \mathrm{GeV}^{-1}$ ($10^{-5} \mathrm{GeV}^{-1}$) during the Z pole run (WW, ZH, and $t\bar{t}$ threshold runs). Additionally, this final state has the potential to probe the underlying electroweak structure of ALP couplings for ALP masses below the $Z$-boson mass.

Universal approximation theorems provide a mathematical explanation for the expressive power of neural networks. They assert that, under mild conditions on the activation function, feedforward neural networks are dense in broad function classes, such as continuous functions on compact subsets of $\mathbb{R}^d$, $L^p$ spaces, or Sobolev spaces. Over the past four decades, these qualitative universality results have evolved into a rich quantitative theory addressing approximation rates, parameter efficiency, and the role of architectural features such as depth and width. This survey presents several glimpses into this theory. We review classical density results for single-hidden-layer networks, as well as quantitative bounds that relate approximation error to network size and smoothness assumptions on target functions. Particular emphasis is placed on depth--width trade-offs and on results demonstrating that deeper architectures can achieve superior parameter efficiency for structured function classes. In addition to standard feedforward neural networks, we also review recent developments on Kolmogorov--Arnold Networks (KANs), which offer an alternative architectural paradigm and whose approximation-theoretic properties have begun to attract significant theoretical attention.

2D Ruddlesden-Popper perovskites are compelling platforms for quantum-confined optoelectronics. However, polar order in iodide composition remains rare under ambient conditions, and the mechanistic origin of anomalous photoluminescence in this class of perovskite is still speculative. Here, we demonstrate that solution-grown $(PA)_2FAPb_2I_7$ single crystals develop an inadvertent moiré superlattice through pseudo-merohedral twinning, driven by hybrid improper ferroelectricity in which trilinear mode coupling between two primary zone-boundary modes ($X_2^+$ and $X_3^-$) and a secondary $Γ_4^-$ polar displacement simultaneously breaks inversion symmetry and imposes a ca. 5.17° rotational misalignment between adjacent layers. This symmetry breaking activates one of the highest piezoelectric coefficients $d_{33}$ (ca. 20 pm/V) reported among 2D perovskites. This misalignment generates a moiré superlattice that undergoes a thermally driven commensurate-incommensurate transition, switching between a periodic confinement potential that quantizes excitons into an equidistant photoluminescence ladder at 123 K and a disordered incommensurate phase with broadened emission at 298 K. These emissions are attributed to moiré-confined excitons, resolving a longstanding debate on anomalous secondary photoluminescence in layered 2D perovskites and opening pathways to twistronics, photoferroelectrics and piezo-optoelectronic devices.

Hybrid tensor networks offer a promising route to enhance the expressivity of classical tensor network methods by incorporating quantum states prepared on a quantum computer. Existing approaches are limited by the variational optimization of the quantum component of the tensor network. In this work, we introduce an alternative strategy that combines a non-parametric quantum state prepared through quantum annealing and a classical isometric tensor network. The latter is variationally optimized while the former is used as a fixed, boundary tensor resource in the form of classical shadows. We demonstrate the feasibility of this approach through extensive numerical simulations on the transverse-field Ising model, showing that the optimization procedure remains robust under statistical and hardware noise. Moreover, our results indicate that our newly proposed setup improves the accuracy of the obtained ground state approximation compared to the original quantum simulation, without increasing the depth of the applied quantum circuits. Therefore, this setup offers a practical route to scale variational quantum algorithms towards the quantum utility scale.

The disordered quantum world hosts three fundamental types of states: extended, localized, and critical, of which the critical states are confined to fine-tuned critical points or mobility edges in randomly disordered systems. The tripartite phase, with all three types of states coexisting over finite spectral windows, represents a hallmark distinction between quasiperiodic and truly random systems in the localization physics. Here, we report the realization of this exotic phase in a quasi-periodically driven orbital optical lattice with ultracold atoms. The optical lattice with a quasiperiodic Floquet modulation coupling s and p orbitals is realized in experiment and shown to host the tripartite phase from exact theory. We develop a two-stage protocol to precisely prepare and detect the three types of quantum states. The characteristic exponents of these states are determined from expansion dynamics, showing their distinct universal transport properties. Our study marks a significant advancement in exploring unconventional critical phenomena and localization physics with ultracold atoms.

Standard approaches to forecasting the weekly number of earthquakes on a spatial grid rely on the Poisson distribution with a single global dispersion assumption. We show that this assumption is systematically violated in seismic data from Central Asia (2010-2024), where a likelihood-ratio test with boundary correction strongly rejects the Poisson hypothesis (p < 10^{-179}). The main contribution of this work is the EarthquakeNet architecture, which provides an endogenous per-cell estimate of the overdispersion parameter alpha via a neural network (spatial embeddings + MLP), without explicit spatial covariance specification. In contrast to existing negative binomial regression approaches in seismological forecasting, which typically assume a single global alpha, the proposed per-cell formulation allows the model to identify spatial heterogeneity in seismic clustering and to construct probabilistic risk-aware alerts via quantiles of the predicted distribution. A walk-forward evaluation (2018-2023) over four systems shows an 8.6 percent reduction in mean pinball deviation (MPD) relative to a negative binomial GLM baseline. The strongest improvements are observed in the tail regime (Y >= 5), where the continuous ranked probability score (CRPS) of the proposed model is 12.5 percent lower than that of the baseline, indicating improved calibration in extreme-event forecasting.

Primordial non-Gaussianity (PNG) is a powerful probe of the origin of cosmic structure. Stage-IV surveys like \Euclid will measure galaxy $2$- and $3$-point clustering at high signal-to-noise, whose exploitation requires robust joint analysis. We prepare for Euclid's spectroscopic sample by validating a redshift-space power-spectrum and bispectrum pipeline (one-loop $P_\ell$, tree-level $B_\ell$) on Euclid-like mocks from Abacus-PNG $N$-body simulations with Gaussian and local-PNG initial conditions, using a halo occupation distribution (HOD) tuned to Euclid Flagship 2. We stress-test analysis choices -- PNG-bias parametrisation, priors, and scale cuts -- and perform null tests without PNG. In a `prior-agnostic setup', detection of the dominant PNG term $\propto f_{\rm NL} \, b_ϕ$ in single redshift bins is difficult; nevertheless, the bispectrum provides constraints on other PNG combinations that partially lift degeneracies. We propose a physically motivated prior on $b_ϕ$ that yields unbiased $f_{\rm NL}$ while accounting for theory uncertainty, and determine scale cuts that give unbiased $Λ$CDM and $f_{\rm NL}$. With $V_{\rm eff}=16\,h^{-3}\,{\rm Gpc}^3$ across four snapshots ($0.8\le z\le1.7$), our likelihood analyses recover $<1σ$ bias in $f_{\rm NL}$ and $Λ$CDM. At fixed cuts, $B_\ell$ alone reduces $σ({f_{\rm NL}})$ by $\sim29$--$46\%$ relative to $P_\ell$, and joint power spectrum-bispectrum analysis tightens a further $\sim8$--$13\%$; the cumulative gain from $z=0.8$ to $1.7$ is $\sim2.3$ for the joint case. The bispectrum quadrupole is key. Our strongest results are at $z=1.7$: $1.9σ$ for $f_{\rm NL} \, b_ϕ$ (prior-agnostic) and $2.35σ$ for $f_{\rm NL}$ (prior-based). Joint analyses thus offer strong prospects for testing multi-field inflation, pending end-to-end validation in the full Euclid geometry with observational systematics.

Transparent conducting oxides near their epsilon-near-zero frequency exhibit near-unity ultrafast modulations of the refractive index which have enabled the field of time-varying metamaterials, yet the underlying carrier dynamics at high driving fluences remain poorly understood. Here, we report ultrafast modulations in the reflectivity and transmissivity of indium tin oxide, and a non-monotonic oscillatory behavior. This is especially evident in the time evolution of the complex Fresnel coefficients retrieved directly from pump-probe spectrograms using a optical gating technique, GRUMPY FROG. The dynamics of the retrieved plasma frequency and damping coefficient are well captured by an extended two-temperature model incorporating a competing nonlinear interband process: at high fluences, Auger-type scattering of hot conduction electrons promotes valence band carriers, increasing the plasma frequency while accelerating hot-electron cooling and raising the damping coefficient. These results clarify the origin of anomalous high-fluence dynamics in indium tin oxide and identify a fluence-tuneable modulation dynamic with direct implications for ultrafast refractive index engineering in time-varying photonic devices and optical switching

Extreme ultraviolet (EUV) lithography is the cornerstone of the fabrication of advanced integrated circuits at the 7-nm node and beyond, but its reliance on multi-element reflective projection optics makes it inaccessible for small-scale research and prototyping. EUV interference lithography (EUV-IL) provides a lensless alternative but is intrinsically restricted to periodic structures. Here we demonstrate EUV holographic lithography (EUV-HL) as a lensless route to arbitrary, non-periodic, curvilinear patterning at the EUV wavelength of 13.5 nm. We introduce an inverse-design framework for computer-generated holograms that captures the dominant physical effects of EUV mask diffraction within a shift-invariant convolution model that is tractable for full mask layouts. Using this framework, we design and fabricate transmissive holographic masks by direct-write electron-beam lithography in hydrogen silsesquioxane, expose them with synchrotron-generated EUV radiation, and print target layouts with critical dimensions down to 40 nm, nearly an order of magnitude finer than the previous state of the art in EUV-HL. The demonstrated combination of sub-50 nm resolution, curvilinear design freedom, and a lensless optical setup establishes EUV-HL as a uniquely flexible tool for nanostructure prototyping at EUV wavelengths, and provides a natural pathway to non-periodic pattern prototyping at beyond-EUV (BEUV) wavelengths, which is currently inaccessible to interference-based methods.

Quasiperiodic systems are an intermediate class of systems between periodic crystals and disordered systems, famously exhibiting metal-insulator transitions (MITs) even in one dimension. While their transport properties have been studied extensively, a systematic analysis of the finite-frequency optical conductivity near the critical point has been lacking. In this work, we carry out a detailed study of the optical conductivity in the paradigmatic Aubry-André model. We find that the zero-temperature low-frequency optical signal is strongly restructured by the quasiperiodic potential, exhibiting an optical gap that closes discontinuously as the system approaches the MIT. Most strikingly, we uncover a mechanism for a strong enhancement of the low-frequency finite temperature optical conductivity at certain resonant frequencies. This enhancement stems from the thermal activation of Pauli-blocked transitions between strongly resonant van Hove singularities. This mechanism provides new insight into finite-frequency transport in quasiperiodic systems and a new pathway for manipulating optical properties near a localization transition. Furthermore, our findings establish the optical response as a powerful, experimentally accessible tool for probing non-trivial quasiperiodicity effects.

We study cosmological collider (CC) signatures generated by right-handed neutrino loops in the setup of inflation combined with neutrino seesaw mechanism. We formulate the inflaton interaction with the right-handed neutrino through a unique dimension-5 operator respecting shift symmetry, which induces an effective chemical potential in the slow-roll background, leading to helicity-dependent right-handed-neutrino production and enhancing the CC signatures. The right-handed neutrino is described by two-component Weyl spinor with Majorana mass term. Using the Schwinger-Keldysh (SK) formalism, we derive a set of seed integrals for fermion propagators of the right-handed Majorana neutrino. With these we compute the factorized nonlocal contributions to the three-point inflaton correlator generated by the right-handed-neutrino triangle loop. We show that the chemical potential can substantially soften the heavy-mass Boltzmann suppression and amplify the oscillatory non-Gaussianity signatures associated with the dominant helicity mode. These provide a systematic framework for analyzing the fermion loop signatures in the cosmological collider physics and demonstrate that the heavy right-handed neutrinos associated with seesaw mechanism may leave observable imprints in the primordial non-Gaussianities.

There is a growing consensus that the exceptional optoelectronic properties of metal halide perovskites (MHPs) are largely due to the peculiar interplay between the inorganic cage lattice, composed of a labile network of corner-sharing metal halide octahedra, and the A-site cationic sublattice. This interaction significantly affects the vibrational spectrum of MHPs (phonon frequencies, linewidths, and lifetimes), resulting from the effects of lattice potential anharmonicity and/or static/dynamic disorder. Raman scattering is a suitable technique to probe phonon interactions in solids, allowing for the in-situ characterization of chemical environments, revealing the nature of lattice vibrations. In this perspective, the available experimental evidence of the aforementioned interplay will be reviewed with special emphasis on understanding Raman signatures depending on whether the coupling is principally mediated by hydrogen bonding or steric hindrance. The controversy about the origin of a strong Raman background, steeply rising towards zero Raman shift and called central peak, will be specifically addressed. This background signal, which is typically observed in the temperature range of stability of cubic and tetragonal phases when the A-site cation dynamics unfold, will be shown to be mostly due to disorder-induced second-order acoustic-phonon Raman scattering. This interpretation receives support from other semiconductor systems with nanoscale structural disorder, where the central Raman peak arises either from the vertical misalignment of Ge quantum dots in multi-stack heterostructures or from the interface roughness exhibited by short-period GaAs/AlAs superlattices. In this way, a unifying picture of phonon interactions in MHPs and how they impact different Raman processes is provided, which is key to interpreting their Raman spectra.

Theoretical models predict that subgiants within a narrow mass regime can retain detectable lithium enrichment signatures from planetary engulfment. We test this prediction using TOI-5882, selected because it occupies this favorable subgiant parameter space and hosts a massive brown dwarf ($22 \, M_{ \rm J}$, $P=7.1 \,{\rm d}$) companion capable of dynamically perturbing inner planets. We investigate whether: (1) TOI-5882 exhibits lithium enhancement among similar subgiants, (2) planetary material would be deposited in the convective zone, and (3) the required engulfed mass lies within a plausible range for planetary engulfment. Using spectra from the Tillinghast Reflector Echelle Spectrograph, we measured a Li I equivalent width of $75.39 \pm 3.58$ mA and an abundance of A(Li) $=2.49 \pm 0.12$ dex. Comparing these values to a control sample of 61 subgiants from the GALactic Archaeology with HERMES (GALAH) DR4 survey, we find that TOI-5882 ranks in the 98.4th percentile in both metrics, confirming significant lithium enrichment. We evaluate the engulfment scenario by modeling convective zone deposition and estimating the mass required to reproduce the observed enhancement relative to the control sample. We perform an estimate of the engulfed planetary mass incorporating CI chondritic Li abundances, as planets formed via core accretion are enriched in heavy elements and lithium partitions with these metals. This yields a required engulfed mass of $9$-$95\,M_\oplus$--an order of magnitude lower than the $5.6 \, M_{\rm J}$ implied by proto-solar assumptions. TOI-5882's lithium excess can plausibly result from the ingestion of a super-Earth to Neptune-mass planet, motivating further studies to test this scenario.

Generative neural networks learn how to produce highly realistic images from a large, but finite number of examples - or do they simply memorise their training set? To settle this question, Kadkhodaie, Guth, Simoncelli and Mallat (ICLR '24) trained diffusion models independently on disjoint subsets of a dataset and showed that they converge to nearly the same density when the number of training images is large enough. This result raises two basic questions: how much data do you need for convergence, and what does convergence capture about learning the data distribution? Here, we address these questions by providing an exact analytical characterisation of the transition from memorisation to generalisation in linear generative models. We find that these models memorise at small load, while convergence emerges continuously when the number of samples is linear in the input dimension. Strikingly, we find that convergence is insensitive to recovery of the principal latent factors of the data, which are recovered in a sharp transition. After extending our approach to data with power-law spectra, we find the same distinction between convergence and latent recovery in our experiments with convolutional denoisers and in the data of Kadkhodaie et al. We thus show that generalisation in generative models decomposes into at least two distinct objectives: matching the bulk of the data distribution and recovering the principal latent factors. These objectives correspond to two different distances between true and learnt data distribution, and only the first one is captured by convergence.

We develop a physics-facing version of the persistence/transition-variety framework for scalar-field cosmology, tailored to inflationary dynamics. The guiding idea is that observationally viable inflationary models are often best understood not as single asymptotic phases but as concatenations of persistent regimes separated by universal transition episodes. In this picture, slow roll appears as a robust persistent balance, ultra-slow roll as a bottleneck passage near a nonhyperbolic organising set, and oscillatory post-inflationary behaviour as a recurrent exit sector. Using the exponential model as a reference regime atlas and the massive case as a dynamical realisation of slope drift, we show how such histories may be organised and read geometrically. The resulting framework makes explicit that the relevant regime transitions are organised precisely where hyperbolicity is lost or the spectrum crosses the imaginary axis, and are therefore invisible to a purely hyperbolic or asymptotic treatment.

Subpicosecond laser excitation of ferromagnetic metals induces strongly nonequilibrium dynamics involving scattering and transport of electrons, phonons, and magnons. Widely used theoretical approaches, such as the three-temperature model and diffusion equations, are ill-suited to capture these processes on ultrafast timescales. Here, we present an ab initio-parameterized microscopic framework that incorporates nonthermal magnon scattering and transport via the quantum Boltzmann equation. We apply this approach to simulate ultrafast laser-induced demagnetization in bcc Fe films. The model predicts an ultrafast spin Seebeck effect, characterized by a strong burst of fast-moving magnonic spin current reaching technologically relevant amplitudes. Furthermore, we identify a superdiffusive transport regime: a crossover from initially ballistic magnon transport to a diffusive regime at later times. To connect our theoretical predictions to experimentally accessible observables, we calculate the magneto-optical Kerr angles resulting from the predicted depth-resolved magnetization profiles. Our framework provides a route to describe ultrafast nonthermal magnon transport beyond diffusive models and will aid in the design and interpretation of time-resolved spin-transport experiments.

Strange metallicity, characterized by a linear temperature dependence of the resistivity, is observed in a broad range of correlated materials, including heavy-fermion compounds and cuprate superconductors. It has also recently been reported for the Kagome metal Ni$_3$In, where almost localized and itinerant electronic degrees of freedom coexist as a result of a partially flat band. We investigate the correlated electronic structure and transport properties of Ni$_3$In with dynamical mean field theory (DMFT) calculations performed on a minimal single-band Hubbard model, constructed from compact molecular orbitals. Despite the large band filling, even for moderate Hubbard repulsion, we observe a non-Fermi-liquid like frequency dependence of the self-energy, as well as the formation of local magnetic moments. With increased hole doping, a crossover to a heavy Fermi-liquid regime is found. We interpret these results in terms of an effective model for the partially filled narrow band near $k_z=0$.

The majority of white dwarfs that host periodic transiting planetary debris do so at distances that exceed the rubble-pile Roche limit, in disagreement with canonical formation models that focus on the tidal disruption of minor planets. Here, we quantify the conditions by which rotational fission due to sublimative outgassing ("SYORP" break-up) can occur outside of the Roche sphere in the distance range of 1-5 Roche radii. We use the Many Materials Orbital Sublimation (MaMOS) model to quantify the outgassing properties of three representative types of planetary materials: cores (iron), mantles (forsterite olivine) and comets (water ice), and characterise the resulting spin-up rate analytically by adopting SYORP coefficients in the range of $10^{-5}-10^{-3}$. We then compare this rate to that generated by the radiative YORP effect with YORP coefficients of $10^{-3}-10^{-2}$, and focus on planetesimals with radii of 0.1, 1.0, and 10 km. We find that for white dwarf cooling ages of up to $\sim$ 1 Gyr, sublimative fission of planetesimals and fragments $\lesssim$ 0.1 km in size due to water ice outgassing occur on observable timescales (within 10 yr), regardless if the spin-up is monotonic or stochastic. Further, these timescales are orders of magnitude shorter than the corresponding YORP fission timescales. For drier planetesimals, both iron and forsterite outgassing can be effective at 10-100 Myr cooling ages. Our results do not substantially differ for strengthless rubble piles versus objects with 1 kPa of internal strength. These findings add to growing evidence that gravitationally scattered comets and asteroids do not need to adopt pericentres within a white dwarf's Roche radius to eventually enrich, or pollute, the star with metals.

Quantum computing has demonstrated its significant advantage over supercomputing for specific applications and shown promising prospect, such as machine learning, cryptography, finance, etc.. Quantum oracles are very common in many quantum algorithms and oracle resource consumption directly affects algorithm performance. However, existing oracle designs often exhibit high resource overhead and limited compatibility. Moreover, structured description tools and complexity analysis methods are lacked. In this work, we introduces a Hierarchical Recursive Synthesis-Evaluation (HRSE) model, enabling formal description and precise quantum gate complexity analysis of oracles. Based on this model, we propose an Adaptive Space-depth Trade-off (ASDT) algorithm for generating oracle structures under a fixed qubit constraint. We provide a theoretical proof showing that the ASDT algorithm achieves the optimal gate count for a given number of qubits. Experimental results show that the ASDT algorithm reduces the average quantum circuit depth by 53.99% compared with the W-cycle approach, with the number of variables being 10, 15, and 20, respectively.

Relativistic hydrodynamics successfully provides an effective field theory description for the low energy regime of many out-of-equilibrium systems. On the other hand, in this paper we proof that any stand-alone hydrodynamic EFT is inherently acausal and therefore requires the addition of transient UV modes in order to restore causality. This is made possible by the exponential decay of dissipative hydrodynamics in a majority of the lightcone, allowing the possibility of a causal description that still reduces to the hydrodynamic one at late timescales. We then investigate the emergence and possible restrictions of the non-hydrodynamic modes in these causal UV completions.

Astrometry - the precise measurement of celestial positions and motions - is entering the micro-arcsecond ($μ$as) era at multiple wavelengths, enabling new insights on compact objects across all mass scales. Here we review how high-precision astrometry is advancing our understanding of compact objects - neutron stars (NSs) and black holes (BHs). We provide the context for high precision astrometry before discussing natal kicks and the latest results from Gaia Data Release 3 (DR3). We highlight the evidence for mass-dependent peculiar velocities of accreting binaries, and also reveal a close similarity between NSs and BHs. Next-generation surveys will find recoiling supermassive BHs (SMBHs) in galactic nuclei, exploring how gravitational-wave-induced kicks operate. Exploitation of scientific opportunities on the lunar surface could facilitate much larger collecting areas and astrometric precision in X-rays than currently feasible.

Polar topological textures in ferroelectrics can host internal structure beyond a single integer topological charge. Here, effective-Hamiltonian molecular-dynamics simulations are used to examine whether such internal fractional topology can be reconfigured by local electric excitation in ordered 12.5% Zr-substituted barium titanate. Chemical doubling along the polar axis stabilizes a coupled nanodomain texture consisting of alternating Q = -2 antiskyrmionic and Q = +4 skyrmionic slices, in which the local topological charge fragments into six -1/3 and six +2/3 localized contributions, denoted here as topological quarks, separated by Bloch-point-like singular conversion regions. Picosecond local electric-field pulses applied to selected vortex-core columns drive reconfiguration of the internal dipolar texture of a 2.6 nm nanodomain. Under a binary pulse-mask protocol addressing the six vortex cores, all 64 masks lead, within the chosen low-temperature simulation protocol, to distinct relaxed metastable configurations. The switching calculations are performed in a cryogenic regime, and the programmed states remain stable over at least 1 ns of field-free evolution on the simulation timescale. The resulting configurations are distinguishable both by sector-resolved topological fingerprints and by their real-space polarization fields. These results provide a computational proof of concept that fractional polar topology in a ferroelectric nanodomain can be locally reconfigured by ultrafast electric excitation and used as a multistate configurational degree of freedom in an idealized low-temperature setting.

Despite the knowledge that social, economical, and ecological networks are often of a small-world nature with inter-nodal distance growing even slower than logarithmically with system size, we often assume theoretical systems to be outside of this regime, to make them easier to treat analytically. Here we derive a framework to apply the powerful dynamical mean-field theory on highly heterogeneous networks that is able to account for more of the degree correlations naturally arising from network constraints, known as structural cut-offs. We apply this framework to the well-studied and understood disordered Lotka-Volterra model, and show typically reported observables such as survival rates and stability for these systems on ultra small-world networks. We find much better agreement for these variables for all ranges of exponents for simulated power-law networks as well as empirically sourced networks.