Based on a consistent quantum microscopic approach, we investigate the peculiarities of collective polyatomic effects in atomic ensembles placed in a waveguide, caused by the presence of evanescent modes of electromagnetic field. We analyze the influence of these modes on the process of cooperative spontaneous decay, as well as on the nature of radiation transfer in the ensembles under consideration. We show that under certain conditions, their influence can be dominant compared to the role of radiation modes, and the mechanism for this influence is the modification of dipole-dipole interatomic interaction.

We recently reported the discovery of prominent features from the thermally pulsing asymptotic giant branch (TP-AGB) phase in the near-IR rest-frame of a massive quiescent galaxy (QG) at z~1 observed with JWST, which set strong constraints on population synthesis models. Here we compare those results against similar measures from a much larger sample of JWST/NIRSpec PRISM spectra for 27 QGs at z>1 from programs GO-5019 and CEERS, with signal-to-noise ratios of ~100 (15/27) and ~50 (12/27), respectively. Each spectrum is modeled with three stellar population synthesis models: the latest Maraston (M13) models with a sizable TP-AGB phase, the Bruzual & Charlot 2003 (BC03) models, and the Conroy & Gunn (2009, C09) models, both of which include TP-AGB contributions of smaller magnitude. The M13 model generally provides the best fit quality. Compared to BC03 and C09, M13 yields systematically younger mass-weighted ages (by <500 Myr) hence lower stellar masses (by >0.2 dex). All models favor super-solar (Z/Z_sun > 1.5) metallicities. Signal-to-noise-weighted stacked spectra reveal that TP-AGB-related features are strongest in galaxies with mass-weighted ages of t = 0.4-1.8 Gyr, consistent with the predicted peak TP-AGB contribution in M13 models. Further sample subdivisions show that these features are most pronounced in high-mass (log M_*/M_sun > 10.445), dusty (A_v > 0.6), and metal-rich (Z/Z_sun > 0.35) systems. These results confirm the prevalence of TP-AGB stars in the NIR spectra of high-redshift, intermediate-age galaxies and pave the way towards improved spectral population synthesis modeling and robust stellar ages and masses.

A classical fluid splitter produces the same patterns of energy redistribution as a Stern-Gerlach quantum device, with rotationally invariant coefficients of correlation between molecular paths. Alternative settings express a cosine squared relationship, leading to Tsirelson-type Bell violations with outcome independence. This result confirms the Correspondence Principle of quantum mechanics, where individual detection events express system-level properties according to Born's Rule. Kochen-Specker contextuality and Bell Locality are not formally contradicted, but their interpretation is in question. Current definitions of Local Realism are limited to intrinsic particle properties. In contrast, quantum-like correlations require the acknowledgement of ensemble effects on dynamically inseparable entities, even when those entities are observed one at a time.

Resorbable magnesium (Mg) alloys are promising candidates for temporary medical devices due to their biodegradability and favorable mechanical properties. To accelerate the design of diluted Mg alloys for implants, we developed a data-driven framework to elucidate the complex relationships between composition, processing, and mechanical properties. The framework screens mechanical properties within biocompatible compositional limits, treating degradation as a design constraint rather than an explicit prediction target. Using a dataset of 410 samples, we trained six different machine learning (ML) models to predict yield strength, ultimate tensile strength, and elongation. Among them, ensemble models, particularly CatBoost, demonstrated high predictive accuracy (R2, YS = 0.950, UTS = 0.916 and El = 0.903). SHapley Additive exPlanation analysis revealed that thermomechanical processing conditions and alloying elements such as Zn, Mn and Gd are the most influential factors governing mechanical behavior in diluted Mg alloys. Validation on the experimental dataset confirmed the models' robustness and generalization capability in capturing process-property relationships. The optimized CatBoost model was further employed to generate predictive property maps visualizing the strength-ductility trade-off as a function of Zn-Mn composition. This work establishes a validated ML framework for rapid in silico screening and targeted design of next-generation resorbable Mg alloys.

The Evans function is an analytic function that encodes information about the intersection of certain subspaces in ODE boundary value problems. As such it is a useful tool for computing the spectrum of boundary value problems arising in the stability of coherent structures. In typical applications one is interested in the roots of the Evans function, but the overall normalization is somewhat arbitrary. We present a natural normalization of the Evans function on compact domains such that the magnitude of the Evans function provides a lower bound on the distance to the nearest point in the spectrum. In other words the magnitude of the Evans function at a point in the resolvent set implies that a ball about the point in question lies in the resolvent set. Thus, when appropriately normalized, not only does the Evans function $E(λ)$ vanish if and only if $λ$ lies in the spectrum of the operator in question, but a non-zero value for the Evans function guarantees that a disk of radius $|E(λ^*)|$ about the point $λ^*$ lies in the resolvent set. We present some calculations for some common sets of boundary conditions on a compact interval, and present some numerical experiments for 2nd and 4th order self-adjoint operators and for a linearized modified Korteweg-De Vries equation.

Electric power systems are rapidly evolving into deeply digital, cyber-physical infrastructures in which large fleets of distributed energy resources must be coordinated as system-level flexibility across multiple spatial and temporal scales. Despite growing distributed energy resource deployment, existing grid and market architectures lack scalable, interoperable mechanisms to reliably translate device-level flexibility into grid-aware services, creating risks to reliability, affordability, and resilience at high penetration. We propose that scalable and reliable coordination of distributed energy resource-based flexibility in future power systems is fundamentally an architectural problem that can be addressed through laminar cyber-physical design using minimal, standardized interoperability interfaces that link device autonomy with system-level objectives. To assess this claim, we present and discuss a layered cyber-physical systems architecture and explicate its implementation through standards-based interfaces, Flexibility Functions, hierarchical control, and case studies spanning U.S. and Danish regulatory, market, and operational contexts. Empirical evidence from New York's Grid of the Future proceedings, Danish Smart Energy Operating System pilots, and operational aggregator deployments demonstrates that such architecture enables predictable, grid-aware flexibility while preserving device autonomy, interoperability, reliability, and quality of service. These results support a cross-Atlantic research agenda centered on joint testbeds, harmonized interoperability mechanisms, and coordinated policy experiments to accelerate the deployment of resilient, scalable, and flexible clean energy systems.

Heterogeneous Memory Architecture (HMA) aims to optimize memory usage by leveraging a combination of memory types, such as high-bandwidth memory (HBM), commodity DRAM, and non-volatile memory (NVM), when utilized as main memory. To achieve maximum performance benefits, frequently accessed data pages are prioritized for storage in the faster HBM, while less frequently accessed pages are stored in slower memory types like DRAM or NVM. This enables a more efficient allocation of memory resources and improves overall system performance. In a Flat Address Space memory organization, all memory types, both fast and slow, are treated as a unified memory pool. This approach increases the overall memory capacity accessible to the system. In Flat Address Space organization, frequently accessed data pages may need to be remapped from slower memory to faster memory to improve memory access times. Such relocation requires changes to the data/states in the TLB (TLB shootdown) and the processor cache (cache line invalidations), leading to performance degradation. To address these inefficiencies, we propose a novel solution called Duon. The goal of Duon is to eliminate the overheads associated with page migration in systems using Extended TLB and Page Table. Specifically, our approach ensures that the updated mapping information for remapped pages is carefully stored directly in the TLB and page table itself. By doing so, the need for TLB shootdown and cache line invalidation after page migration is eliminated. Consequently, our proposal results in an overall improvement in IPC by 3.87% over existing state-of-the-art techniques, enhancing the efficiency and performance of heterogeneous memory systems. Further, our approach can work with any of the existing page migration policies and improve the performance.

Generating hints for incorrect code is a cognitively demanding task that fosters learning and metacognitive development. This study investigates three designs for personalized, scalable, and reflective hint-writing activities within a data science course: (i) writing a hint independently, (ii) writing a hint with on-demand AI assistance, and (iii) deferred AI assistance, in which students first write a hint independently and then revise it with the help of an AI-generated one. We examine how AI support can scaffold the learning process without diminishing students' productive cognitive effort. Through a randomized controlled experiment with graduate-level students (N=97), we found that deferring AI assistance leads to the highest-quality hints. Further, this design helps students identify a wide range of mistakes they otherwise struggle to identify without any AI assistance. Students valued these activities as opportunities to practice debugging and critically engage with AI outputs--skills that are now critical for learners to acquire as programming becomes increasingly automated and the use of AI for learning grows. Our findings also highlight key considerations for designing student-AI collaborative learning experiences to sustain student engagement, maintain appropriate cognitive load, and mitigate negative effects of AI, such as introducing redundancies and extraneous information into student work.

Dust plays a crucial role in galaxy evolution by shaping the spectral energy distribution (SED) and star formation history. However, standard models often underestimate the infrared luminosity of high-redshift galaxies ($z \sim 8$), leading to the so-called dust budget crisis. In this work, we modify the theoretical framework by focusing on compact star-forming clumps in the interstellar medium. Motivated by the observed compactness of high-z galaxies, we treat the cold neutral medium density as a free parameter. Our analysis reveals that the ISM must reach extreme densities ($n_{\text{H,CNM}} \sim 7.5 \times 10^3 \, \mathrm{cm}^{-3}$). This enhances UV photon trapping, accelerates dust processing in dense gas, and reduces dust destruction by supernova shocks. Our model successfully reproduces the observed UV-to-FIR SED of MACS0416_Y1 ($z = 8.312$). A grain-size-resolved treatment further shows that the warm IR emission is dominated by intermediate-size grains ($a = 0.01$ - $0.1\,μ$m), which contribute about 89% of the luminosity near the SED peak and in the ALMA Band~9 continuum. These grains are nearly in thermal equilibrium at characteristic temperatures of $\sim 70$ K, while the largest grains remain cooler and the smallest grains exhibit a high-temperature tail with low probability. We conclude that extreme ISM densities can alleviate the dust budget crisis by promoting efficient UV photon trapping and rapid dust evolution, thereby increasing dust mass and producing a multi-temperature grain population.

We report the experimental observation of highly nonlinear coherent structures in a linear magnettized plasma characterized by a strong background density gradient and significant ExB velocity shear under high ion-neutral collisionality. These structures, identified as drift acoustic waves, exhibit large normalized density fluctuations reaching amplitudes of up to ~10% and show periodic sawtooth-like waveforms. These observed waveforms are well described by cnoidal functions, corresponding to stationary nonlinear wave trains. Cnoidal waves are exact solutions of the Korteweg-de Vries (KdV)-type equations, alongside the more commonly studied soliton solutions. To the best of our knowledge, this work presents the first controlled experimental observation of cnoidal wave trains in a highly collisional magnetized plasma through systematic variation of profile gradients. These findings provide important new insights into the nonlinear evolution and saturation of drift acoustic waves in inhomogeneous, sheared, and collisional magnetized plasmas.

In this paper, we study fluctuation identities for spectrally negative Lévy processes killed by a general class of additive functionals. We consider positive co-natural additive functionals (PcNAFs), which include as special cases both absolutely continuous functionals and finite mixtures of local times. Our main result shows that the associated fluctuation identities, such as two-sided exit problems and resolvent measures, retain the same structure as in the classical case and can be expressed in terms of generalized scale functions. These scale functions are characterized as the unique solutions to Volterra-type integral equations driven by Radon measures, thereby extending the results of Li and Palmowski and Li and Zhou. Our approach is based on representing the additive functional as a mixture of local times with respect to its Revuz measure, combined with classical fluctuation identities and an approximation scheme for general Radon measures using Poisson random measures.

Cosmic neutrinos are among the most abundant fermions in the Universe, yet the values of their masses and chemical potentials remain uncertain. In this Letter, we present the first constraints on the total neutrino mass $M_ν$ and the neutrino asymmetry parameter $η^2$ derived from the mean galaxy pairwise peculiar velocity in the quasi-linear and nonlinear regimes. We develop a simulation-based analysis pipeline that connects neutrino properties to predictions of galaxy pairwise velocity, and apply it to galaxy data from the Cosmicflows-4 grouped catalog. Our analysis is performed within two independent cosmological frameworks, based on cosmological parameters derived from Cosmic microwave background (CMB) and local distance ladder measurements, respectively. By performing fits to the galaxy pairwise velocity, we obtain consistent constraints from both frameworks. Quoting posterior means with 68% CL, we find $M_ν= 0.24^{+0.34}_{-0.18}\ \mathrm{eV}$ and $η^2 = 2.14^{+0.30}_{-0.32}$ in the CMB framework, and $M_ν= 0.37^{+0.34}_{-0.26}\ \mathrm{eV}$ and $η^2 = 2.4^{+2.1}_{-1.6}$ in the local framework. In particular, we find a 7$σ$ measurement of a non-zero neutrino asymmetry in the CMB framework. These neutrino parameters are consistent with those, in our previous work, obtained from the Planck CMB temperature power spectrum. These results demonstrate that galaxy pairwise velocities provide an independent and sensitive probe of neutrino properties, opening a new avenue for testing neutrino physics with large-scale structure observations.

The classical double copy provides a powerful bridge between gravity and gauge theory, but its most explicit realizations remain concentrated in stationary or highly symmetric settings. We show that trapped regions of black-hole geometries furnish an exact setting for time-dependent classical double copy. In the static, spherically symmetric case, each trapped interval admits a local single-copy description on the associated Kantowski--Sachs patch that is intrinsically time dependent, although it can be derived from static Kerr--Schild data and does not require knowledge of any exterior black-hole completion. We prove that this class is characterized intrinsically by a distinguished relation between the Kantowski--Sachs scale factors, equivalently by the longitudinal relation \(p_{\parallel}=-ρ\), and that the Kerr--Schild scalar and single-copy field are uniquely reconstructible from interior cosmological data. Schwarzschild provides the singular benchmark, for which the single-copy electric field diverges along the interior evolution, while the regular Bardeen solution yields a finite single-copy field throughout the trapped region and a smooth extension into a regular static core. The Bardeen core violates the strong energy condition in a compact region, whereas the corresponding single-copy Maxwell field remains regular and satisfies the standard classical energy conditions. We further show that the Bardeen horizon phase structure is encoded in the single-copy scalar. These results identify trapped Kerr--Schild interiors as an exact local laboratory for time-dependent classical double copy.

Detached eclipsing binaries (DEBs) enable direct inference of stellar and orbital properties across diverse stellar populations. However, inference typically requires computationally intensive forward modeling and radial velocity (RV) measurements, limiting homogeneous analyses to relatively small samples. The growing number of photometrically identified DEBs from modern time-domain surveys motivates scalable methods for extracting physical parameters without RVs. We present multimodal amortized neural posterior estimation for DEB inference that combines survey-realistic light curves, broadband SEDs, and Gaia parallaxes within a physically motivated hierarchical prior framework. The generative model enforces broad stellar evolution consistency through MIST isochrones and geometric eclipse prior constraints while incorporating empirically derived survey cadence patterns and flux-dependent noise models to produce realistic training data. A conditional normalizing flow, informed by modality-specific encoders, approximates the full 16-dimensional posterior distribution. Across nearly 5000 held-out simulations, the amortized posterior recovers parameters accurately and yields statistically calibrated uncertainties, verified through simulation-based calibration and empirical coverage tests. Parameters tied directly to eclipse geometry and flux scale are tightly constrained, while quantities intrinsically degenerate in broadband photometry (e.g., age and metallicity) exhibit broader posteriors consistent with expectations. Generating the training set requires computational effort similar to a traditional MCMC analysis of only a single system, and posterior inference for new systems is effectively instantaneous. This framework enables scalable, statistically calibrated inference for large DEB samples, providing a pathway toward population-level analysis in the era of large time-domain surveys.

Understanding how dust evolves in protoplanetary disks is crucial to constraining the initial conditions of planet formation. The apparent "mass budget problem", which stems from the comparison of the observed disk masses to the ones inferred for exoplanets, remains debated, as it is unclear whether the discrepancy arises from limitations in interpreting disk observations, from evolutionary processes that rapidly deplete dust, or from incorrect assumptions about the initial disk mass distribution. This work is build on the analysis presented in Savvidou and Bitsch (2025) by separating the cumulative distribution functions of dust masses at different evolutionary stages into different populations according to the initial disk masses and embryo injection times. The best match to observations comes from disks with intermediate initial disk masses around 4-7% solar mass. The largest discrepancy between the total dust mass in the models and the estimated through an "optically thin" approximation comes from the models that have the most favorable conditions for giant planet formation and thus contain a large fraction of giants and subsequently trapped "optically thick" dust mass because of the pressure bumps they generate. However, the final dust masses remain higher compared to the estimates from the observed evolved disks. Example cases in this work including planetesimal formation show that the pressure bumps that giant planets form can be prime locations for planetesimal formation and the conversion to planetesimals significantly decreases the dust mass, as expected. However, (giant) planet formation is not influenced showing that the mass in evolved protoplanetary disks can be estimated to be quite low but it can be a natural consequence of planetesimal and planet formation along with depletion due to radial drift.

Federated Learning (FL) is a promising approach for multiparty collaboration as a privacy-preserving technique in hardware assurance, but its security against adversaries with domain-specific knowledge is underexplored. This paper demonstrates a critical vulnerability where available standard cell library layouts (SCLL) can be exploited to compromise the privacy of sensitive integrated circuit (IC) training data. We introduce DECIFR, a novel two-stage Membership Inference Attack (MIA) that requires no auxiliary dataset. The attack employs a guided Gradient Inversion Attack (GIA) to reconstruct a client's training images from intercepted model updates. Our findings reveal that the fidelity of these reconstructions directly correlates with membership status, allowing an adversary to reliably distinguish members from non-members based on image quality. This work exposes a practical threat that overcomes the limitations of conventional attacks and underscores that standard FL protocols are insufficient for securing domains with extensive knowledge. We conclude that robust defenses are essential for the secure application of FL in hardware assurance.

Artificial intelligence systems are now deployed at scale across sectors, accompanied by a growing number of real-world incidents ranging from misinformation and cybercrime to autonomous-system failures. Databases of AI incidents index these events, but they cannot measure ``risk'' (i.e., a joint measure of likelihood and severity) without additional data regarding the prevalence of risk-associated systems and their incident reporting rates. As a result, policymakers, companies, and the general public lack a means to weigh the benefits of AI against their in-context risks. Inspired by public-health processes, which presume noisy and incomplete disease surveillance, we identify six phases of incident emergence. We demonstrate the framework through a detailed case study of autonomous vehicles, whose mandatory reporting requirements produces reliable incident-rate ground truth expressed in distance traveled. The case study shows that an informed panel of domain experts (e.g., self-driving experts) can combine their domain expertise, incident data, and a collection of statistical and visualization tools to arrive at incident phase determinations serving public needs. We further demonstrate the approach with a deepfake incident case study and chart a path for future research in incident phase determination.

Soliton microcombs provide compact, broadband, coherent light sources for precision metrology, spectroscopy, communications, and microwave photonics. Extending their spectral span while retaining useful output power remains challenging and often requires impractically high pump power. Existing approaches mainly tailor the dispersion and pumping conditions, but they do not exploit the coupling spectrum as a programmable aspect of soliton operation. Here we introduce a meta-coupler whose lithographically programmed coupling spectrum concentrates strong pump access near the pumped resonance while leaving most comb lines close to the intrinsic loss rate. Si$_3$N$_4$ microresonators incorporating a meta-coupler exhibit broader circulating soliton spectra, nearly twofold larger 3 dB soliton bandwidths, up to about 12 dB higher central comb-line power, and up to about fivefold greater emitted comb power, without an additional pump-power penalty. Our work unlocks gain and loss as simultaneous programmable knobs for realizing high-performance soliton microcombs.

Dipolar fluids are known to exhibit complex self-assembly at low temperatures, yet a compact thermodynamic description of their aggregate statistics has remained elusive. Using molecular dynamics simulations of Stockmayer particles with a purely repulsive WCA core, we show that over broad regions of the ($ρ$, $T$) phase space the chain-size distribution follows an exponential decay with characteristic size $s_0$. Within this regime, we find that $s_0$ can be accurately described by an effective thermodynamic potential $ϕ$ that incorporates bonding energy, a crowding penalty, and translational entropy. Identifying deviations from this ideal scaling provides a further division of the phase space into four regions. Therefore, our results locate a regime of relatively simple chain statistics and offer an alternative regime-based perspective on dipolar self-assembly.

Variational methods based on optimization strategies are proposed to numerically solve a large family of nonlinear partial differential equations. They are all particular instances of gradient flows with general costs, including the $p$-Laplace equation and flux-limited equations such as the relativistic heat equation. This is achieved by computing explicit formulas for proximal operators with general costs amenable to efficient numerical approximation. We showcase our numerical approach via validation of the results by recovering the qualitative behavior of particular known cases of this large family of steepest descent evolutions.

We compare three secrecy-coding schemes for the degraded wiretap binary symmetric channel (BSC) in the finite-blocklength regime: (i) polar wiretap coset codes, (ii) PAC codes used as wiretap coset codes, and (iii) the invertible-extractor (IE) framework of Bellare-Tessaro. Our comparison is empirical and uses a common semantic-secrecy metric (distinguishing advantage). For polar coset codes, we compute Eve's polarized bit-channel capacities (via the Tal-Vardy construction) to obtain explicit finite-length upper bounds on mutual-information leakage, yielding strong secrecy bounds. For PAC coset codes, we prove that Eve's synthesized bit-channels are equivalent to those of polar codes (up to a permutation), so the same leakage bounds apply; we then convert these strong-secrecy bounds into semantic-secrecy guarantees for symmetric wiretap channels. For the IE scheme, we use the closed-form semantic-secrecy bounds given in the reference work. Finally, we report finite-length results that jointly characterize (a) semantic-secrecy guarantees against Eve and (b) frame-error-rate performance at Bob, illustrating that PAC codes can significantly improve reliability without changing the secrecy bounds inherited from polar coding. Moreover, under the finite-length bounds considered in this work, polar/PAC secrecy codes provide tighter security guarantees than the invertible-extractor framework.

We present a new class of control pulses designed to transfer co-located ensembles without relying on frequency selectivity, thereby allowing much faster state-transitions. A geometric approach allows us to construct sequences which are robust to changes in the background magnetic field along multiple axes, and errors in the pulse area. \red{These pulses are extremely fast, with robustness to pulse area shown at half the quantum speed limit.} We demonstrate these sequences on nuclear-dipole states, showing milliradian precision over several hours, 30-fold beyond the previous state of the art. This provides a path for extending the coherent integration time of ultra-long-lived nuclear-spin states to the fundamental limit set by their $>$10000 second lifetimes, as the limiting self-interactions of the nuclei are suppressed in the symmetric superposition. The state-preparation quality demonstrated here directly opens up 30-fold improvements in next generation tests of the standard model, especially tests of the symmetries of QCD and searches for dark matter; it is also crucial for the development of nuclear-spin based quantum memories and may be useful in other scenarios demanding extremely fast but robust transitions.

Bug reports play a critical role in software maintenance by helping users convey encountered issues to developers. Recently, GUI screen capture videos have gained popularity as a bug reporting artifact due to their ease of use and ability to retain rich contextual information. However, automatically reproducing bugs from such recordings remains a significant challenge. Existing methods often rely on fragile image-processing heuristics, explicit touch indicators, or pre-constructed UI transition graphs, which require non-trivial instrumentation and app-specific setup. This paper presents ViBR, a lightweight and fully automated approach that reproduces bugs directly from GUI recordings. Specifically, ViBR combines CLIP-based embedding similarity for action boundary segmentation with Vision-Language Models (VLMs) for region-aware GUI state comparison and guided bug replay. Experimental results show that ViBR successfully reproduces 72% of bug recordings, significantly outperforming state-of-the-art baselines and ablation variants.

This paper provides novel insights into channel and subspace codes in nonadaptive channel sensing with a single RF chain. Observing that this problem naturally maps to a noncoherent decoding problem, we show that the sensing performance of the maximum likelihood (ML) angle estimator, which does not require knowledge of the typically unknown channel coefficient, is governed by two key terms: the minimum subspace distance and beam gain of the used beamformers. We derive an exact expression for the subspace distance of binary linear channel codes mapped to BPSK, which illuminates the relationship between subspace and Hamming distance, used to design subspace and channel codes, respectively. Our result also reveals why good Hamming distance alone is insufficient for sensing, and shows that well-known families of channel codes such as Reed-Muller codes, yield zero subspace distance and thereby poor sensing performance when used naively without proper codebook pruning. Finally, we introduce so-called beamspace subspace codes based on sparse antenna selection patterns (Golomb rulers), which we show provide near-optimal subspace distance. We demonstrate that this property of judiciously designed sparse arrays can be leveraged together with beamforming gain via convolutional beamspaces, enabling hardware- and sample-efficient channel sensing with theoretical guarantees in large-scale multiantenna communications.

Side-scan sonar (SSS) images are susceptible to motion-induced geometric distortion, which degrades their reliability for seabed interpretation and downstream tasks. Existing correction methods either exploit image-domain consistency without adequately preserving global geometric referencing, or rely on navigation-based geocoding whose effectiveness is limited when recorded attitude and motion fail to capture ping-scale perturbations. To address this issue, we propose a geometric correction method for SSS images with image-consistent attitude refinement. The core idea is to refine the yaw-pitch sequence used in geocoding by explicitly linking stripe-wise distortion patterns in dual-sided waterfall images to geometric deformation modes. Specifically, a navigation-derived macro-scale attitude baseline is fused with image-inferred microscopic perturbations, where port-starboard symmetry is used to separate pitch-related common-mode responses from yaw-related differential-mode responses. The refined attitude is then incorporated into a physically geocoding framework with track-aligned gridding and normalized-convolution-based hole completion to generate the corrected image. Experiments on real SSS datasets from different sonar platforms and environments show that the proposed method reduces inter-ping misalignment, local stretching, and structural discontinuity, and improves local geometric consistency under both degraded-attitude and cross-dataset evaluation settings, demonstrating its effectiveness for geometrically consistent SSS correction.

We present a high-order space-time discretization equipped with fully-discrete entropy stability properties for general choices of volume and surface quadrature rules. The formulation uses flux reconstruction (FR) in the spatial dimension paired with a discontinuous Galerkin (DG) method in the temporal dimension. The result is a fully-implicit system using polynomial bases in space and time. An energy-stable discretization is applied to the linear advection equation, yielding optimal $p+1$ convergence for small FR correction parameters and $p$ convergence at the same filter strength as method-of-lines implementations. We can thus recover the space-time equivalent to schemes such as DG, Huynh's FR, or spectral difference through a single parameter $c$. We follow with a similar space-time nonlinearly-stable flux reconstruction (ST-NSFR) scheme, which uses skew-symmetric stiffness operators in both space and time. The ST-NSFR scheme is fully-discretely entropy preserving using the $c_{DG}$ parameter or entropy-stable for small $c$. Numerical experiments using the linear advection and Euler equations confirm convergence orders and stability properties. The advantage of FR in a space-time context is demonstrated by a reduction in computational cost up to about $70\%$ as $c$ is increased.

Recently, Retrieval Augmented Generation (RAG) has shifted focus to multi-retrieval approaches to tackle complex tasks such as multi-hop question answering. However, these systems struggle to decide when to stop searching once enough information has been gathered. To address this, \citet{zhou2024metacognitive} introduced Metacognitive Retrieval Augmented Generation (MetaRAG), a framework inspired by metacognition that enables Large Language Models to critique and refine their reasoning. In this reproducibility paper, we reproduce MetaRAG following its original experimental setup and extend it in two directions: (i) by evaluating the effect of PointWise and ListWise rerankers, and (ii) by comparing with SIM-RAG, which employs a lightweight critic model to stop retrieval. Our results confirm MetaRAG's relative improvements over standard RAG and reasoning-based baselines, but also reveal lower absolute scores than reported, reflecting challenges with closed-source LLM updates, missing implementation details, and unreleased prompts. We show that MetaRAG is partially reproduced, gains substantially from reranking, and is more robust than SIM-RAG when extended with additional retrieval features.

For an integer $n\geq 1$, consider the $n$-th metallic number $ϕ_n=\frac{n+\sqrt{n^2+4}}{2}$ (e.g. $ϕ_1$ is the golden number) and denote by $[ϕ_n]_q$ its $q$-deformation in the sense of S. Morier-Genoud and V. Ovsienko. This is an algebraic continued fraction which admits an expansion into a power series $[ϕ_n]_q =\sum_{l=0}^{+\infty} κ_l(ϕ_n) q^l$ around $q=0$, with integral coefficients. By using techniques from analytic combinatorics, we establish several properties of the sequence $( κ_l(ϕ_n))_{l\geq 0}$ of Taylor coefficients: characterisation by recurrences or by differential equations, closed-form expressions when $n=1,2,3$, and asymptotics. We also present some remarkable identities induced by the action of the modular group $PSL(2,Z)$ and address, mainly through computer experimentations, the question of the logarithmic behaviour of the sequence $( κ_l(ϕ_n))_{l\geq 0}$. A particular accent is put on the comparison between the $q$-deformation $[ϕ_1]_q$ of the golden ratio and RNA secondary structures, the former being actually a signed version of the latter. By doing so, we would be pleased to bring the interest of combinatoricians to the newly discovered world of $q$-numbers.

G310.7-5.4 is a supernova remnant (SNR) candidate identified as a faint shell in the second epoch Molonglo Galactic Plane Survey (MGPS-2), but this has not been followed up with multi-wavelength observations until now. It is an example of an SNR at high Galactic latitude showing spatially coinciding $γ$-ray emission. Here, we make the first detailed investigation of the radio emission from the G310.7-5.4 region, aiming to characterise the radio structure, polarisation measurements and the coinciding GeV emission. We used recent radio continuum observations at 943.5 MHz from the EMU and the POSSUM surveys with ASKAP, as well as 16.5 years of Fermi-LAT observations. We furthermore considered the multiwavelength context of the object by investigating observations previously conducted with other instruments, such as infrared and X-ray surveys. We confirm the SNR candidate as a new supernova remnant, dubbed Abeona. We detect the presence of a faint, extended, bilateral radio shell of the size of around 30' diameter and ASKAP radio flux density of $1.5^{+1.5}_{-0.1}$ Jy with no obvious infrared counterparts. With a radio surface brightness of about $2.4^{+2.4}_{-0.1}\times10^{-22}$ W m$^{-2}$ Hz$^{-1}$ sr$^{-1}$, this SNR is one of the faintest radio SNRs known. The northern part of the shell shows linearly polarised radio emission, characteristic of synchrotron emission in SNRs. The physical size of the SNR is estimated to be around $42^{+42}_{-21}$ pc, which would give a distance of around $4.9^{+4.9}_{-2.5}$ kpc. Furthermore, the spatially coincident $γ$-ray source 4FGL J1413.9-6705 shows an energy flux of $1.26\pm0.35\times 10^{-6}$ MeV cm$^{-2}$ s$^{-1}$ with a significance of 5.7 $σ$ between 100 MeV and 100 GeV. The SNR is also put in context with known high-latitude SNRs with $γ$-ray counterparts and compared with their observational properties.

We present results from a search of cold neutral gas associated with radio-loud active galactic nuclei (AGNs) at $z < 0.5$ using HI 21-cm absorption measurements from the MeerKAT Absorption Line Survey (MALS). Cross-matching the MALS 1006 MHz and SDSS DR18 catalogs yields 1908 radio sources at $z < 0.5$. Of these, 613 are classified as AGNs using BPT diagnostics and radio luminosity criteria. We further classify 426 AGNs into 327 low-excitation radio galaxies (LERGs) and 99 high-excitation radio galaxies (HERGs). We observe a significant ($>3σ$) difference in $k$-corrected $g-r$ color, consistent with LERGs residing in older galaxies with quenched star formation. We searched a radio-bright subsample of 79 LERGs and 20 HERGs ($S_{\mathrm{1.4\,GHz}} > 4$ mJy) for associated HI 21-cm absorption. This spans six decades in radio luminosity ($\log L_{\mathrm{1.4\,GHz}}$ (WHz$^{-1}$) $\sim 21.1-27.0$), probing an order of magnitude fainter than previous targeted HI surveys. We report five new detections (4 LERGs, 1 HERG) at $0.29 < z < 0.47$. The overall detection rate of $3^{+3}_{-2}$% (at a $3σ$ threshold of 10.0 kms$^{-1}$) is consistent with sensitivity-matched low-$z$ ($<0.2$) samples, suggesting no significant redshift evolution out to $z \sim 0.5$ or dependence on radio luminosity. Evaluating velocity offset, asymmetry, and width reveals three systems with entirely redshifted absorption and two with predominantly blueshifted absorption. HI profiles in LERGs show diverse asymmetries and velocity offsets exceeding 350 kms$^{-1}$, indicating disturbed cold-gas kinematics likely driven by lobe expansion or jet activity.