We present and benchmark a type of variational quantum eigensolver (VQE), which we denote $σ$-VQE. It is designed to target mid-spectrum eigenstates and prepare quantum many-body scar states. The approach leverages the fact that noisy intermediate-scale quantum devices are limited in their ability to generate generic highly entangled states. This modified VQE pairs a low-depth circuit with an energy-selective objective that explicitly penalizes energy variance around a chosen target energy. The cost function exploits the limited expressibility of the shallow circuit as atypical low-entanglement eigenstates such as scar states are preferentially selected. We validate this mechanism across two complementary families of models that contain many-body scar states: the Shiraishi-Mori embedding approach and a matrix product state parent Hamiltonian construction. We define an unbiased estimation scheme for the nonlinear cost function that is compatible with qubit-wise commuting grouping and bitstring reuse. A proof-of-principle demonstration using a small-system instance was performed on IBM Fez (Heron r2 QPU). These results motivate its use as a practical algorithm for detecting quantum many-body scars and variationally generating states with appreciable scar state overlap.

We study quantum criticality in a holographic Einstein--Maxwell--Dilaton--Axion (EMDA) p-wave superconductor exhibiting a superconductor--insulator transition (SIT). By tracking the superconducting energy gap, we show that approaching the quantum critical point (QCP) closes the gap and induces incipient insulating features, indicating that enhanced quantum fluctuations suppress superconducting order and trigger the SIT. We suggest that this behavior occurs only when the condensate orientation is aligned with the direction of translational symmetry breaking. To probe the transition, we employ two holographic indicators: holographic entanglement entropy (HEE) and the entanglement wedge cross-section (EWCS), the latter being a mixed-state entanglement measure. In contrast to HEE, which for sufficiently large configuration is dominated by the thermal entropy and is therefore largely insensitive to entanglement along the temperature direction, EWCS displays pronounced critical scaling and provides a robust diagnostic of the quantum phase transition (QPT). We attribute this contrast to the fact that HEE at large scales is controlled by the infrared (IR) geometry, whereas EWCS is governed by deformations of the entire bulk. Our results establish EWCS as a robust probe of holographic quantum criticality in mixed states.

Secure downlink transmission in UAV-assisted reconfigurable intelligent surface (RIS)-enabled multiuser MISO systems is challenging due to imperfect channel state information (CSI), untrusted energy-harvesting receivers (UEHRs), and the strong coupling among UAV deployment, transmit power control, and RIS configuration. In this paper, we study a secure UAV-assisted RIS-enabled multiuser MISO system with UEHRs, where a hovering UAV-mounted RIS is jointly optimized in terms of its location, transmit power allocation, and discrete RIS phase shifts. The objective is to maximize the worst-case secrecy energy efficiency (WCSEE) under imperfect CSI and practical discrete phase-shift constraints. The resulting problem is highly nonconvex due to the fractional objective, coupled design variables, discrete phase shifts, and CSI uncertainty. To address these challenges, we propose two complementary approaches. First, a block coordinate descent (BCD) framework combined with successive convex approximation (SCA) is developed to solve a secrecy energy efficiency (SEE) formulation, serving as a structured model-based benchmark. Second, for the more general WCSEE problem, we propose a tailored soft actor-critic (SAC) framework that captures the coupling among variables and avoids repeated iterative optimization. Simulation results show that the proposed SAC method consistently outperforms conventional optimization and deep reinforcement learning (DRL)-based benchmarks, including deep deterministic policy gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3), while maintaining robustness to CSI uncertainty and stable performance across system configurations.

The last decade has witnessed a rapid advancement of generative AI technology that significantly scaled the accessibility of AI-generated non-consensual intimate images (AIG-NCII), a form of image-based sexual abuse that disproportionately harms and silences women and girls. There is a patchwork of commendable efforts across industry, policy, academia, and civil society to address AIG-NCII. However, these efforts lack a shared, consistent mental model that clearly situates the technologies they target within the context of a large, interconnected, and ever-evolving technological ecosystem. As a result, interventions remain siloed and are difficult to evaluate and compare, leading to a reactive cycle of whack-a-mole. In this paper, we contribute the first comprehensive AIG-NCII technological ecosystem that maps and taxonomizes 11 categories of technologies facilitating the creation, distribution, proliferation and discovery, infrastructural support, and monetization of AIG-NCII. First, we build and visualize the ecosystem through a synthesis of over a hundred primary sources from researchers, journalists, advocates, policymakers, and technologists. Then, we conduct two detailed walkthroughs to demonstrate the usefulness of the ecosystem in 1) making sense of new AIG-NCII harms using a case study of Grok and 2) mapping a clearer tech policy landscape using U.S. federal law and 63 state laws. We conclude with a vision for future AIG-NCII research that refines the edges of the ecosystem, recommending researchers to study critical relationships between technologies and potential ripple effects from different interventions. Our goal is to produce an AIG-NCII technological ecosystem that provides a clear, shared terminology and framework for stakeholders to move into the future of AIG-NCII prevention with clarity and foresight.

In isolated binary evolution, binary black hole (BBH) mergers are generally formed through stable mass transfer (SMT) or common envelope evolution. In recent years, the SMT channel has received significant attention due to detailed binary models showing increased mass transfer stability compared to previous studies. In this work, we perform a full zero-age-main-sequence to compact object merger analysis using detailed binary models at eight metallicities between $10^{-4}Z_\odot$ and $2Z_\odot$ to self-consistently model the population properties of BBH mergers in the SMT channel, determined their progenitor initial conditional, and investigate the binary physics governing their formation and metallicity dependence. We use the population synthesis code POSYDON to determine the population of BBH mergers from SMT. Using its extended grids of MESA binary models, we determine the essential physics in the formation of BBH mergers. SMT produces BBH mergers predominantly from systems with $P_{ZAMS}\leq10$ days. In these systems, both the initial mass transfer between two stars and the subsequent interaction between the remaining star and the first-born BH take place while the respective donor star is on the main-sequence (Case A). We find a limited contribution from wider Case B/C systems. Without a natal kick, the SMT channel does not produce BBH mergers above $Z>0.2Z_\odot$ due to orbital widening from stellar wind mass loss. The primary BH mass distribution shows a strong dependence on metallicity, while the mass ratio prefers unity independent of metallicity due to mass ratio reversal. Additionally, the $χ_{eff}$ distributions contain peaks at $χ_{eff}=0$ and ~0.15 of which the former disappears at high metallicities. A mass-scaled natal kick leave this sub-population unchanged but introduce a low-mass, unequal mass ratio sub-population that merges due to their high eccentricity.

We consider Bayesian variable selection for binary outcomes under a probit link with a spike-and-slab prior on the regression coefficients. Motivated by the computational challenges encountered by Markov chain Monte Carlo (MCMC) samplers in high-dimensional regimes, we develop a mean-field variational Bayes approximation in which all variational factors admit closed-form updates, and the evidence lower bound is available in closed form. This, in turn, allows the development of an efficient coordinate ascent variational inference algorithm to find the optimal values of the variational parameters. The approach produces posterior inclusion probabilities and parameter estimates, enabling interpretable selection and prediction within a single framework. As shown in both simulated and real data applications, the proposed method successfully identifies the important variables and is orders of magnitude faster than MCMC, while maintaining comparable accuracy.

This work investigates the origin of the TeV emission detected by the Large High Altitude Air Shower Observatory (LHAASO) from NGC~4278, a galaxy hosting a low-luminosity active galactic nucleus (LLAGN). Considering two plausible scenarios, AGN jets and winds, we model the X-ray, GeV, and TeV emission during both TeV-low (quasi-quiet) and TeV-high (active) states. The spectral energy distributions can be explained either by single-zone leptonic emission from moderately relativistic jets or by lepto-hadronic emission from sub-relativistic winds. The best-fit parameters suggest that the transition from the quasi-quiet to the active state may be driven jointly by an enhanced accretion rate and the jet deceleration or wind expansion. We further show that future MeV and very-high-energy $γ$-ray observations can discriminate between the leptonic and lepto-hadronic scenarios. Although the neutrino flux from NGC 4278 predicted by the wind model is too low to be detected with current neutrino observatories, a lepto-hadronic wind scenario can account for the PeV diffuse neutrino background when adopting a local LLAGN density ($n_{\rm L,0}$) corrected for the TeV duty cycle ($ΔT_{\rm TeV}/T$, the fraction of a LLAGN's lifetime spent in a TeV-emitting phase), $n_{\rm L,0}(ΔT_{\rm TeV}/T) \sim 10^{-5}~\rm Mpc^{-3}$, as inferred from the LHAASO detection.

In this letter, we address the problem of developing quantum state tomography (QST) methods that remain valid at any time during a sequence of measurements. Specifically, the aim is to provide a rigorous quantification of the uncertainty associated with the current state estimate as data are acquired incrementally. To this end, the proposed framework augments existing QST techniques by associating current point estimates of the state with confidence sets that are guaranteed to contain the true quantum state with a user-defined probability. The methodology is grounded in recent statistical advances in anytime-valid confidence sequences. Numerical results confirm the theoretical coverage properties of the proposed anytime-valid QST.

We report spectropolarimetric and broadband polarimetric observations of the near-Earth asteroid 2025 FA22 during its close approach of 18 September 2025 (about two Moon distances). With a diameter estimated between 130 and 290 m, 2025 FA22 is among the largest NEAs observable at such proximity, prompting an International AsteroidWarning Network (IAWN) rapid-response campaign. Although early orbital solutions indicated a possible impact in 2089, further follow-up astrometric observations ruled out collision hazard. The favourable geometry of this close encounter enabled a dense coverage of the positive part of the phase-polarisation curve, from the high polarisation domain (high phase angles), nearly to the inversion angle where the linear polarisation fraction vanishes. The spectropolarimetric observations provided the wavelength dependence of the polarisation fraction. Using empirical relationships, an estimate of the geometric albedo could be drawn from the slope of the phase-polarisation curve at inversion angle : $ρ_v = 0.17\pm0.04$ in the V band. This value, together with the spectropolarimetric trend, provides constraints on the taxonomic class, with the results being most consistent with an M-type classification. These results demonstrate the interest of polarimetry and spectropolarimetry for rapid characterisation of newly discovered NEAs in planetary defence campaigns.

From February to May of 2024 the Any Light Particle Search II (ALPS II) conducted its first science campaign using the `light-shining-through-a-wall' technique to search for pseudo-Goldstone bosons that lie beyond the Standard Model of particle physics and which are inaccessible by accelerator-based experiments. The experimental setup consists of two strings of superconducting dipole magnets, each more than 100 m long, that are separated by a wall. Laser light is directed through the first magnet string and a heterodyne detection system is used to measure the electromagnetic power that traverses a wall via the conversion to and then from a bosonic field. After the wall, a high-finesse optical cavity resonantly enhances the signal power. Two searches were carried out, one with the laser polarized perpendicular to the magnetic field direction and another with its polarization state aligned parallel to the magnetic field. No evidence for the existence of new bosons was found. In its first science campaign, ALPS II reached photon-boson conversion probability sensitivities of a few $10^{-13}$. The ongoing upgrade of the optical system aims to increase this sensitivity by about four orders of magnitude.

This paper proposes sufficient, yet more general conditions for applying FastTwoSum as an error-free transformation (EFT) under all faithful rounding modes. Additionally, it also identifies guarantees tailored to round-to-odd for establishing FastTwoSum as an EFT. This paper also describes a floating-point splitting tailored for round-to-odd that is an EFT where the distribution of bits is configurable (i.e., ExtractScalar for round-to-odd). Our sufficient conditions are more general than those previously known in the literature (i.e., it applies to a wider operand domain).

We discuss the statistics of additive thermal (internal) noise in systems governed by the generalized Langevin equation with linear dissipation. To assess the equation's validity, it is common to assume that the system is ergodic and to verify that solutions approach correct equilibrium values at asymptotically long times. In this paper, we instead consider the consistency of the generalized Langevin equation with the Jarzynski equality at finite times and do not assume the system's ergodicity. Specifically, we consider a classical Brownian oscillator whose initial stiffness, or frequency, is perturbed by a rectangular pulse of duration $τ$. We find that the Jarzynski equality is satisfied unconditionally only up to the seventh order in $τ$; in higher orders, the Jarzynski equality holds if and only if the noise is Gaussian. These results imply that, unless it is exact, the Langevin equation can only be used to evaluate properties that are linear or quadratic in noise and its derivatives. Such properties are insensitive to the noise statistics, so the Langevin equation with linear dissipation and non-Gaussian noise (though not inconsistent by itself) is superfluous.

Advances in accelerator technology have led to significant improvements in the quality of muon beams over the past decades. Investigations of the muon and muonium enable precise measurements of fundamental constants, as well as searches for new physics beyond the Standard Model. Furthermore, by utilizing muon beams with high intensity and polarization, studies of the dynamics of the muon and muonium within atomic level can offer valuable insights into materials science. This review presents recent progress and prospects at the frontiers of muon beams and high-precision muonium physics. It also provides an overview of novel methods and detection techniques to achieve high sensitivities in different areas, including particle physics, nuclear physics, materials science and beyond.

Elastoinertial turbulence (EIT) is a self-sustaining chaotic state resulting from the interplay between inertia and elasticity in the flow of dilute polymeric solutions, and its emergence is believed to limit the achievable drag reduction in turbulence flow using polymer additives. In the present study, we introduce a viscoelastic variant of spectral proper orthogonal decomposition (VESPOD) that decomposes velocity and polymeric stress fields of EIT together into well-defined orthogonal oscillating modes such that the decomposition is optimal in the terms of the total mechanical energy of the flow. Using this technique, we investigate the dominant coherently evolving structures underlying the dynamics of EIT in axisymmetric pipe flow. By analyzing distinct peaks in the leading eigenvalue of the VESPOD eigenvalue spectrum, we find that the dynamics of EIT in pipe flow is dominated by three distinct families of traveling waves, where the higher wavenumber structures of each family are simple harmonics of their respective fundamental waves. The radial velocity fields of the traveling waves are characterized by the formation of large-scale structures spanning the pipe radial direction. However, the polymeric stress fields corresponding to them are characterized by the formation of thin inclined sheets of high stress fluctuations at the critical layers of the respective waves, i.e.~ the locations where the wave speed of the VESPOD mode matches the mean streamwise velocity. Additionally, these sheets exhibit nested structures, where the polymeric sheets of faster waves are confined by those of the immediately slower waves.

The aim of this exposé is to make explicit the analogy between the classical notion of non-independent probability distribution and the quantum notion of entangled state. To bring that analogy forth, we consider a classical systems with two dependent random variables and a quantum system with two components. In the classical case, afet observing one of the random variables, the underlying sample space and the probability distribution change. In the quantum case, when and event pertaining to one of the components is observed, the post-measurement state captures, both, the change in the state of the system and implicitly the new probability distribution. The predictions after a measurement in the classical case and in the quantum case, have to be computed with the conditional distribution given the value of the observed variable.

Chain-like macromolecules in solution, whether biological or synthetic, transform from an extended conformation to a compact one when temperature or other system parameters change. This collapse transition is relevant in various phenomena, including DNA condensation, protein folding, and the behavior of polymers in solution. We investigate the interplay of chain stiffness and range of attraction between monomers in the collapse of a single polymer chain. We use Monte Carlo simulations based on the pruned-enriched Rosenbluth method. We demonstrate that the competition between the persistence length, l_p, and the range of attraction, r_c, determines whether the chain's collapse behavior resembles that of flexible chains or stiff ones. When l_p is larger than r_c, the chain collapses sharply with decreasing temperature, whereas if l_p is smaller than r_c, it contracts gradually. Notably, in the regime of small l_p and large r_c, this rounding into a gradual compaction persists upon increasing the chain length and may remain in place in the limit of infinite chain length. Furthermore, for small r_c, the transition temperature (theta-temperature) increases with l_p, whereas for large r_c the theta-temperature decreases with l_p. Thus, stiffness promotes collapse for small r_c but suppresses it for large r_c. Our findings are in agreement with recent experiments on the contraction of single-stranded RNA as compared to double-stranded DNA, and provide valuable insights for understanding polymer collapse and the essential polymer parameters affecting it.

We develop a framework for localized source detection in dynamical systems governed by nonlinear partial differential equations based on first and second-order sensitivity analysis. Building on the standard adjoint formulation, which relates multiple measurements to external sources through a linear duality relation, we first introduce a linear positional embedding that identifies the source location by aligning the measurement vector with the embedding. To capture weakly nonlinear effects that arise when the source intensity is finite, we then incorporate a quadratic correction represented as a symmetric bilinear operator and approximated via a truncated eigen-expansion obtained with Krylov subspace iterations. This yields quadratic positional embeddings that augment the linear adjoint field, enabling measurement data to be projected onto a higher-dimensional hyperplane, spanned by the linear and quadratic embeddings. A source search algorithm is formulated based on principal angle minimization between this hyperplane and the observation vector, providing a natural probabilistic interpretation of source location. The method operates in a one-shot fashion without iterative updates of candidate source positions, and it can be readily extended to scenarios involving multiple sources. Demonstrations on benchmark inverse problems include perturbation-source identification in the viscous Burgers equation and heat-source detection in a two-dimensional laminar stratified channel. The results with quadratic embeddings show significant improvements in localization accuracy compared with linear adjoint-based sensitivity methods, especially in the region where linear adjoint sensitivity vanishes.

Wave-like dark matter composed of spin-1 particles, known as dark photons, is theorized to form clumps called "vector solitons". These solitons are compact astrophysical objects that exhibit coherent oscillations and a high concentration relative to the local dark matter density. A significant portion of dark matter in galactic halos today may consist of these solitons. This study explores how photons can be produced from these vector solitons by the influence of external electromagnetic fields or charge densities, via a dimension-6 dark photon-photon coupling and a kinetic mixing, respectively. We further explore the astrophysical implications of these phenomena, highlighting a novel avenue for dark matter discovery that our research provides.

Previous work of the author and N. Reshetikhin defines an invariant $\operatorname{Z}_{N}^ψ(K, ρ, μ)$ of a knot $K$, a representation $ρ: π_{1}(S^{3} \setminus K) \to \operatorname{SL}_2(\mathbb{C})$, and a logarithm $μ$ of a meridian eigenvalue of $ρ$. It can be interpreted as a geometric twist of the Kasahev invariant or as a quantization of the $\operatorname{SL}(\mathbb{C})$ Chern-Simons invariant and is defined using a discrete state-sum involving quantum dilogarithms. In this paper we show how to express $\operatorname{Z}_{N}^ψ(K, ρ, μ)$ as a sum over contour integrals in a space parametrizing hyperbolic structures on the knot complement. Such integral presentations are an important step in determining the asymptotics of quantum invariants as predicted by the Volume Conjecture. We discuss this perspective and the remaining obstacles to establishing exponential growth of $\operatorname{Z}_{N}^ψ$.

The slitless spectroscopic method employed by missions such as Euclid and the Chinese Space-station Survey Telescope (CSST) faces a fundamental challenge: spectroscopic redshifts derived from their data are susceptible to emission-line misidentification due to the limited spectral resolution and signal-to-noise ratio. This effect systematically introduces interloper galaxies into the sample. Conventional strict selection not only struggles to secure high redshift purity but also drastically reduces completeness by discarding valuable data. To overcome this limitation, we develop an XGBoost classifier that leverages photometric properties and spectroscopic diagnostics to construct a high-purity redshift catalog while maximizing completeness. We validate this method on a simulated sample with spectra generated by the CSST emulator for slitless spectroscopy. Of the $\sim$62 million galaxies that obtain valid redshifts (parent sample), approximately 43% achieve accurate measurements, defined as $|Δz| \leqslant 0.002(1+z)$. From this parent sample, the XGBoost classifier selects galaxies with a selection efficiency of 42.3% on the test set and 42.2% when deployed on the entire parent sample. Crucially, among the retained galaxies, 96.6% (parent sample: 96.5%) achieve accurate measurements, while the outlier fraction ($|Δz|>0.01(1+z)$) is constrained to 0.13% (0.11%). We verified that simplified configurations that exclude either spectroscopic diagnostics (except the measured redshift) or photometric data yield significantly higher outlier fractions, increasing by factors of approximately 3.5 and 6.3, respectively, with the latter case also introducing notable catastrophic interloper contamination. This framework effectively resolves the purity-completeness trade-off, enabling robust large-scale cosmological studies with CSST and similar surveys.

We investigate the inverse Edelstein effect in a non-homogeneous system consisting of a ferromagnetic layer coupled to a Rashba two-dimensional electron gas. Within a semiclassical Boltzmann framework, we derive analytical expressions for the charge and spin currents and analyze their dependence on key parameters such as the chemical potential and the Rashba coupling strength. We show how interfacial exchange and spin-orbit interactions jointly control the efficiency of spin-to-charge conversion, leading to distinct regimes characterized by qualitatively different transport responses. A central outcome of our work is the availability of closed-form analytical results, which provide direct physical insight and enable a transparent and quantitative benchmarking with experiments on complex oxide interfaces, such as LaAlO$_3$/SrTiO$_3$.

The paper \cite{M0} studied, for a \emph{complex} linear ordinary differential equation $y^\prime(t)=Ay(t)$, the long-time propagation to the solution $y(t)$ of a perturbation of the initial value. By measuring the perturbations with relative errors, this paper introduced a directional pointwise condition number, defined for a specific initial value and for a specific direction of perturbation of this initial value, and a pointwise condition number, defined for a specific initial value and the worst-case scenario for the direction of perturbation. The asymptotic (long-time) behaviors of these two condition numbers were determined. The present paper analyzes such asymptotic behaviors in depth, for a \emph{real} linear ordinary differential equation in a generic case.

Text-based explainable recommendation aims to generate natural-language explanations that justify item recommendations, to improve user trust and system transparency. Although recent advances leverage LLMs to produce fluent outputs, a critical question remains underexplored: are these explanations factually consistent with the available evidence? We introduce a comprehensive framework for evaluating the factual consistency of text-based explainable recommenders. We design a prompting-based pipeline that uses LLMs to extract atomic explanatory statements from reviews, thereby constructing a ground truth that isolates and focuses on their factual content. Applying this pipeline to five categories from the Amazon Reviews dataset, we create augmented benchmarks for fine-grained evaluation of explanation quality. We further propose statement-level alignment metrics that combine LLM- and NLI-based approaches to assess both factual consistency and relevance of generated explanations. Across extensive experiments on six state-of-the-art explainable recommendation models, we uncover a critical gap: while models achieve high semantic similarity scores (BERTScore F1: 0.81-0.90), all our factuality metrics reveal alarmingly low performance (LLM-based statement-level precision: 4.38%-32.88%). These findings underscore the need for factuality-aware evaluation in explainable recommendation and provide a foundation for developing more trustworthy explanation systems.

For a simple graph G = (V, E), a coloring of vertices of G using two colors, say red and blue, is called a quasi neighborhood balanced coloring if, for every vertex of the graph, the number of red neighbors and the number of blue neighbors differ by at most one. In addition, there must be at least one vertex in G for which this difference is exactly one. If a graph G admits such a colouring, then G is said to be a quasi-neighbourhood balanced colored graph. We also define variants of such a coloring, like uniform quasi neighborhood balanced coloring, positive quasi neighborhood balanced coloring and negative quasi neighborhood balanced coloring based on the color of the extra neighbor of every vertex of odd degree of the graph G. We present several examples of graph classes that admit the various variants of quasi neighborhood balanced coloring. We also discuss various graph operations involving such graphs. Furthermore, we prove that there is no forbidden subgraph characterization for the class of quasi neighborhood balanced coloring and show that the problem of determining whether a given graph has such a coloring is NP-complete.

We present a theory of the graphene nanoslide, a fundamental device for graphene straintronics that realizes a single pseudogauge barrier. We solve the scattering problem in closed form and demonstrate that the nanoslide gives rise to a hybrid pseudogauge and electrostatic cavity in the bipolar regime, and hosts one-dimensional transverse channels. The latter can be tuned using a bottom gate between valley-chiral or counterpropagating modes, as well as one-dimensional flatbands. Hence, the local density of states near the barrier depends strongly on the gate voltage with a tunable sublattice and electron-hole asymmetry. In the presence of electron-electron interactions, the nanoslide allows for \textit{in-situ} tuning between a chiral and ordinary Tomonaga-Luttinger liquid.

We present an ensemble machine-learning approach for composition-based, structure-agnostic screening of candidate superconductors among ternary hydrides under high pressure. Hydrogen-rich hydrides are known to exhibit high superconducting transition temperatures, and ternary or multinary hydrides can stabilize superconducting phases at reduced pressures through chemical compression. To systematically explore this vast compositional space, we construct an ensemble of 30 XGBoost regression models trained on a curated dataset of approximately 2000 binary and ternary hydride entries. The model ensemble is used to screen a broad set of A-B-H compositions at pressures of 100, 200, and 300 GPa, with screening outcomes evaluated statistically based on prediction consistency across ensemble members. This analysis highlights several high-scoring compositional systems, including Ca-Ti-H, Li-K-H, and Na-Mg-H, which were not explicitly included in the training dataset. In addition, feature-importance analysis indicates that elemental properties such as ionization energy and atomic radius contribute significantly to the learned composition-level trends in superconducting transition temperature. Overall, these results demonstrate the utility of ensemble-based machine learning as a primary screening tool for identifying promising regions of chemical space in superconducting hydrides.

Since its launch in 2010, the Solar Dynamics Observatory (SDO) has provided continuous, high-cadence, multi-wavelength observations of the Sun, capturing thousands of solar flares and offering new insights into coronal dynamics. Among the discoveries enabled by SDO is the EUV late-phase (ELP), characterised by a secondary enhancement in warm coronal emission occurring tens of minutes after the main flare. While recent work has demonstrated the relevance of the ELP for space weather, the statistical behaviours and physical origins are not fully understood. Here, we present the most comprehensive statistical analysis of the ELP to date, based on 15-years of Fe xvi (335 angstrom) observations from the Atmospheric Imaging Assembly onboard SDO (SDO/AIA). From a sample of 5335 isolated flares between 2010 and 2025, we identify and validate 467 ELP events. The overall ELP occurrence rate was found to be 9 percent, with no significant dependence on the solar cycle and only a modest enhancement in the low-mid M-class range. The ELP typically exhibited an onset delay of 19 minutes, a peak-to-peak delay of 88 minutes, and a duration of 93 minutes. Strong correlations were found between ELP rise and decay rates (p = 0.76), and between flare and ELP impulsivity (p = 0.61). However, a comprehensive pairwise analysis revealed no significant correlation between the flare and ELP phases. A Principal Component Analysis of flare and ELP properties identified several semi-independent axes of variability, broadly associated with late-phase temporal scale, impulsive heating characteristics, and the relative prominence of flare and late-phase intensity measures. These results highlight the continuing importance of SDO's long-term, high-resolution observations for uncovering new aspects of solar flare evolution and improving understanding of the Sun-Earth connection.

We consider the problem of writing an effective, linearised theory in small derivatives that reproduces the Mittag-Leffler expansion of a charge current correlator with an arbitrary number of simple poles. We demonstrate how such a framework: can be compatible with hydrostaticity without modification of thermodynamics, properly accounts for the differing notions of smallness in time and space derivatives including setting the lowest order effective equation of motion, and corrects the effective equations in derivatives. As an application, we apply the results to charge fluctuations of the D3/D5 probe brane and quantify how the transport coefficients behave when quasihydrodynamics emerges at large charge density.

Efficiently harnessing GPU compute is critical to improving user experience and reducing operational costs in large language model (LLM) services. However, current inference engine schedulers overlook the attention backend's sensitivity to request-length heterogeneity within a batch. As state-of-the-art models now support context windows exceeding 128K tokens, this once-tolerable inefficiency has escalated into a primary system bottleneck, causing severe performance degradation through GPU underutilization and increased latency. We present CascadeInfer, a runtime system that dynamically reschedules requests across multiple instances serving the same LLM to mitigate per-instance length heterogeneity. CascadeInfer partitions these instances into length-specialized groups, each handling requests within a designated length range, naturally forming a pipeline as requests flow through them. CascadeInfer devises a dynamic programming algorithm to efficiently find the stage partition with the best QoE, employs runtime range refinement together with decentralized load (re)balance both across and within groups, achieving a balanced and efficient multi-instance service. Our evaluation shows that, under the same configuration, CascadeInfer reduces end-to-end latency by up to 67% and tail latency by up to 69%, while improving overall system throughput by up to 2.89 times compared to the state-of-the-art multi-instance scheduling systems.

The dynamics of a degenerate spherically symmetric wormhole in a vacuum is considered. An extension of the equivalence principle to matter free objects that are the source of a gravitational field is proposed. Using the Klinkhamer metric as an example, it is shown that a degenerate wormhole is precisely such an object. Application of the extended equivalence principle reduces the radial dynamics of the Klinkhamer wormhole to the dynamics of the radial fall of a test particle in a Schwarzschild gravitational field. It is proven that any bound state of the traversable Klinkhamer wormhole eventually collapses into a nontraversable Einstein-Rosen wormhole. An estimate is presented showing that the traversable Klinkhamer wormhole, although nonstationary, is a longlived state.