Motivated by a question of L. Robert, asking whether $\rm L(T(A)) = Lsc_{C}(T(A))$ for any separable C*-algebra A, we introduce and initiate the study of \emph{tracially reflexive C*-algebras}. We first prove that commutative C*-algebras are tracially reflexive. We also prove that tracial reflexiveness satisfies permanence properties, such as being preserved under inductive limits. Subsequently, we expose two criteria for tracial reflexiveness, using the Cuntz semigroup and a weak version of the Schröder-Simpson theorem, respectively. In particular, separable topological dimension zero C*-algebras are tracially reflexive. We end the manuscript by closing remarks that could lead to further lines of investigation involving tracial reflexiveness.

We assess the dynamical feasibility of redirecting small volatile-bearing trans-Neptunian objects (TNOs) onto Mars-impacting orbits using continuous low-thrust propulsion and a single gravity-assist encounter. The study considers two representative dynamical classes: classical Kuiper Belt--like and Scattered Disk--like initial orbits, and determines the minimum characteristic velocity increment $ΔV$ required to drive the objects onto a Mars-impacting trajectory within a specified transfer time $ΔT$. The dynamics is modelled in the two-body problem with a fixed maximum low thrust included, allowing the computed $ΔV$ to represent a dynamical lower bound independent of specific propulsion-technical implementation. Three trajectory classes are investigated: (i) inward spiral transfer, (ii) time-dependent thrust-direction steering optimized via global evolutionary algorithms, and (iii) hybrid transfers combining low thrust with a single Neptune flyby. Pure spiral trajectories yield very high velocity expenditures ($ΔV \gtrsim 22~\mathrm{km~s^{-1}}$) and millennia durations, confirming that monotonic inward migration is dynamically inefficient for TNO redirection. In contrast, optimized steering strategies systematically increase orbital eccentricity and achieve Mars-impacting geometries with $ΔV \approx 2.5$--$3.2~\mathrm{km~s^{-1}}$ over 380--540 yr timescales. A single Neptune encounter further reduces the total $ΔV$ in favourable cases, with minimum values falling below those of direct optimized transfers. These results establish a quantitative lower bound on the energy cost of importing volatiles from the outer Solar System to Mars, showing that controlled redirection is feasible under modest $ΔV$ budgets when target bodies are chosen from favourable regions of orbital phase space.

Using a simple circular tokamak geometry, we show the well-known `second stability region' of MHD-ballooning modes exists for linear gyrokinetics too -- whether electrostatic or electromagnetic -- and we suggest that the plasma enters this region in H-mode as a consequence of the bootstrap current and Shafranov shift altering the magnetic field, which may occur if the normalised pressure gradient is $α_{\rm MHD} \simgt 1$ and collisionality is low. By performing simulations in more realistic magnetic geometries, we demonstrate a large reduction in collisionless, electrostatic turbulent transport when going from density and temperature profiles typical of L- and H-mode, respectively. This reduction is shown to be a consequence of both the bootstrap current lowering the global magnetic shear, and the pressure gradient altering the local magnetic shear, pushing the plasma towards the second-stability region. A path connecting the L- and H-mode equilibria is constructed, along which the energy and particle fluxes exhibit non-monotonic behaviour as a function of the pressure gradient.

Nowcasting and forecasting of infectious diseases have become increasingly important since the SARS-CoV-2 pandemic. In particular, methods for modeling the composition of circulating variants at a given time have seen more use in part due to a large increase in the frequency of genomic sequencing conducted as a part of routine surveillance. However, methods must take into account that locations have different amounts of data and sometimes have different trends. We discuss hierarchical multinomial logistic regression (HMLR), a commonly used method for forecasting SARS-CoV-2 variants, which allows for data sharing across locations. We show how it has been used in the literature, and define a class of HMLR models for SARS-CoV-2 variant nowcasting and forecasting. We rigorously test a subset of this class of models using the framework of the US SARS-CoV-2 Variant Nowcast Hub, a collaborative modeling project that launched in 2024. We created two years of weekly predictions based on retrospective datasets, with the prediction dates ranging from Wednesday, August 3, 2022, to Wednesday, August 7, 2024. We tested 12 HMLR models against a baseline model on these datasets. We found that the HMLR models outperformed the baseline both in terms of probabilistic accuracy, as measured by the energy score, as well as point accuracy, as measured by the Brier score. Overall, we find that HMLR models perform best with respect to the baseline model in locations with more data, and more complex HMLR models also showed more improvement in those high-data locations; however, there was no one best model across all metrics, and simpler HMLR models perform better in low-data locations. We find that HMLR models perform well in practice for nowcasting and forecasting SARS-CoV-2 variants.

This note is a follow up on our recent paper with L. Malý (to appear in Rev. Mat. Complut.). We provide a simple example of a compact metric space $\mathcal{P}$ for which $L^\infty(\mathcal{P})$ has the Vitali-Carathéodory property, the Sobolev $C_\infty$-capacity is an outer capacity, but the Newtonian space $N^{1,\infty}(\mathcal{P})$ contains functions which are not weakly quasicontinuous. The novelty here is that the Vitali-Carathéodory property is satified. We also obtain some related results about quasicontinuous functions in $N^{1,\infty}(\mathcal{P})$ and a characterization of when $L^\infty(\mathcal{P})$ has the Vitali-Carathéodory property.

We prove that all lattice path matroids are Ehrhart positive. This unifies and generalizes numerous results on the Ehrhart positivity of matroids developed over the last two decades. We rely on our previous work on the positivity of order polynomials of fences. Our main result supports the conjecture by Ferroni, Jochemko, and Schröter (2022) on the Ehrhart positivity of positroids. Furthermore, our main result implies that all Schubert matroids are Ehrhart positive, which thus settles a conjecture by Fan and Li (2024), and supports a conjecture by Monical, Tokcan, and Yong (2019) on the Ehrhart positivity of Schubitopes.

A new family of local-global conjectures in the representation theory of finite groups has recently been proposed by Moretó. We show that one of the strongest of these conjectures, the strong subnormalizer conjecture, holds for $p$-solvable groups when $p$ is odd, under the condition that the subnormalizer subset is a subgroup. We also prove it in general when $p$ is odd and the $p$-length of the group is 1 and, in the process, obtain new properties related to the Glauberman correspondence.

The phase of Quasi-Linkage Equilibrium (QLE) in evolutionary populations is analogous to the thermal equilibrium state in statistical mechanics, a concept pioneered by Kimura in 1965 for two-locus two-allele models. QLE describes a stationary state maintained by the interplay of selection, mutation, recombination and genetic drift. Here we extend QLE theory to populations connected by migration, a fundamental evolutionary force that couples the evolutionary dynamics of interacting subpopulations. Specifically, we examine two populations interacting via symmetric or asymmetric migration while evolving under multi-locus selection. Using whole-genome time-series data generated through FFPopSim, we demonstrate that the QLE phase persists under conditions of sufficiently low migration rates. In this regime, we derive analytical inference relations that allow for the accurate and quantitative estimation of both additive fitness and epistatic interactions.

With the rising number of interactions between autonomous or sensor-assisted vehicles -- especially in poor weather conditions -- come the need and opportunity for a new class of bicycle safety reflectors designed to enhance cyclist visibility to radars. To this effect, the first retrodirective planar metalens-based tag operating in the millimeter-wave automotive frequency range is proposed. The compact, lightweight ($0.61~\mathrm{g}$) design consists of two layers: a metalens layer and a patch antenna pixel layer. The metalens focuses incoming plane waves from different incidence angles onto corresponding patch antenna pixels on the second layer, which re-radiate the signal back through the metalens, enabling retrodirective operation. The proposed tag was thoroughly evaluated, demonstrating reliable detection beyond 70 m and a peak monostatic radar cross section (RCS) of $3.54~\mathrm{dBsm}$ with stable retrodirectivity over $\pm 40^\circ$, providing an average gain improvement of $7.58~\mathrm{dB}$ and an RCS enhancement of $15.16~\mathrm{dB}$ relative to a lens-less reference. A realistic deployment scenario on a metallic bicycle demonstrated up to a 110x improvement in its detectability at broadside. These results highlight the potential of the proposed passive tag to operate as a low-cost, lightweight, and easily integrable bicycle safety reflector for next-generation autonomous vehicle radar systems.

Background and Context. Computer programming often involves extended periods of sustained activity and mobile phone notifications introduce frequent opportunities for interruption. Prior work demonstrates that suppressing phone notifications may reduce these disruptions. Objectives. Our primary research question is: How does suppressing phone notifications affect students' task engagement and productivity while programming? Method. We report on a replication and methodological extension study conducted in a CS1 course involving 22 students. Using a within-subject design, selected programming assignments were randomly designated for enabling notification suppression. Phone state logs were synchronized with millisecond-resolution IDE keystroke data to measure student attention and focus when in the control and notification-suppression conditions. Findings. Assignments completed with notification suppression enabled significantly lower break rates and longer intervals of focus compared to assignments completed in the control condition for many, but not all, students. This study provides evidence that notification suppression is associated with measurable differences in programming engagement and behavior. We also find a remarkable bimodality in the effect across students -- many students are positively affected, a small number are negatively affected, and very few experience little or no effect. This finding is consistent with other studies in diverse disciplines. Implications. Our results show that, for many students, phone notification suppression tools, such as Do Not Disturb, can improve attention and focus. Implications apply to educational settings (do-not-disturb as an intervention) and scholarship (understanding the effects of phone distraction).

We investigate the emergence of approximate neutrino texture structures in the minimal scotogenic model through large-scale Casas--Ibarra parameter scans subject to lepton flavor violation and dark matter constraints. We demonstrate that approximate suppressions can dynamically emerge from phenomenological consistency conditions. The interplay between relic density requirements, radiative neutrino mass generation, and lepton flavor violating observables induces a nontrivial flavor geometry in parameter space. Particular suppressions in the $(eμ)$ and $(eτ)$ sectors arise naturally, while diagonal entries strongly resist cancellation. We further compare normal and inverted mass hierarchies, analyze reduced versus full Casas--Ibarra geometries, and identify approximate scaling relations linking dark matter and flavor observables. Our results suggest that emergent flavor structures may represent dynamical consequences of radiative neutrino mass generation rather than externally imposed flavor symmetries.

This study experimentally examines the impact of spacing between two pulsed jets and their strengths on the fluid dynamics within an elastic hemispherical cavity. Such interactions between multiple pulsed jets are observed in various natural and industrial contexts, including cardiovascular flows, where they occur naturally within the atria or result from medical interventions (e.g., mitral valve repair, mechanical heart valves, paravalvular leaks) or diseases (e.g., aortic or pulmonary valve regurgitation). Fundamentally, these flows usually feature two or more pulsed jets interacting in an expanding, elastic environment. In this investigation, the experimental setup features two parallel pulsed jets entering the cavity, with jet strength varied across five formation times (1, 2, 3, 4, 5) and four spacing ratios (1.5, 2.0, 2.5, 3.0). Time-resolved particle image velocimetry is used to capture the instantaneous velocity fields. The results reveal three distinct flow regimes: short-time decay, decay at the wall, and wall rebound with or without the formation of secondary vortices. These findings uncover rare aspects of twin vortex ring behavior, including symmetry breaking, trajectory shifts, and wall-induced rebound mechanisms, with direct relevance to cardiac fluid dynamics in both healthy and pathological conditions.

The advent of 5G virtualized Radio Access Networks (vRANs) brings a new challenge with regards to computer architectures. It requires to select or design computing technologies that provide a sufficient level of performance while maximizing the flexibility and efficiency of the implemented networks. Several solutions addressing this challenge were proposed, relying on general purpose processors as well as hardware accelerators. This work describes our effort to enable an intensive vRAN deployment using the 5G software stack OpenAirInterface on top of these computer architectures. We had to adapt the software stack to leverage the capabilities of hardware and to find how to scale up the vRAN deployment with several vRAN instances sharing a server. We describe in this work our improvements to the stack and their effect on performance. We also share our observations on the behavior of the computer architectures and how they affect our deployment. We finally discuss the limitations of our deployment and further efforts to implement better vRAN deployments.

Liquid biopsy can detect tumor-derived biomarkers such as circulating tumor DNA (ctDNA), but ultra-low-fraction assays remain costly, slow, and difficult to scale. This motivates interest in intravascular in vivo sensing in the context of intrabody nanonetworks, where nanosensors could support local biomarker monitoring. BioFET-based nanosensors are relevant here because they are label-free, highly miniaturizable, and have shown strong ctDNA sensitivity in controlled media. We examine whether this sensitivity still yields reliable ctDNA detection in whole blood using a reduced-order stochastic simulation model that links operating-point selection, Debye-screened charge transduction, stochastic finite-capacity binding, nonspecific adsorption, background fluctuations, and intrinsic electronic noise to blank-threshold detection. Monte Carlo evaluation with physiologically grounded parameters shows that short Debye length and several-nanometer charge-to-channel separation attenuate the current shift, while low-frequency noise and background fluctuations reduce the margin between target-present and blank responses. Under the tested quasi-static charge-gating regime, the simulated current shifts do not reliably exceed the blank-derived threshold at low ctDNA concentrations. The model therefore provides a whole-blood boundary analysis that identifies which interface configurations and operating conditions most strongly limit reliable BioFET-based intravascular ctDNA detection.

This paper introduces a novel, multi-source framework for the relational validation of Large Language Models (LLMs). While existing benchmarks have demonstrated LLMs' proficiency at factual recall, their ability to understand and reproduce the intricate web of relationships that defines a domain's conceptual structure remains largely unexplored. Our three-layer analytical framework provides a scalable and robust methodology for assessing the depth of an LLM's knowledge across diverse academic domains. By comparing LLM-generated knowledge graphs to expert-curated encyclopedias, we reveal a consistent and significant ``relational deficit'': LLMs recognize domain-specific concepts but consistently fail to reproduce their relational structure. Our findings highlight the need for more sophisticated evaluation metrics that go beyond simple accuracy and assess the relational integrity of an LLM's knowledge. We demonstrate that this deficit is highly domain-dependent, with performance varying significantly across ten specialized encyclopedias spanning sociology, political science, philosophy, and other fields. The cases of complete relational failure in the most specialized domains are particularly revealing, suggesting that the LLM's internal knowledge representation is not aligned with the conceptual structures of these fields. This has significant implications for the deployment of LLMs in high-stakes applications that require a deep, nuanced understanding of domain-specific knowledge.

Industry-academia ML collaborations routinely fail to launch -- not for scientific reasons, but because academics must publish while companies must protect models trained on proprietary data, and no standard contract framework resolves this tension. Because contracts are negotiated by legal departments alone, many apparent legal disputes are incentive misalignment problems that only scientists at the table can correctly diagnose. We propose PBOS (Protect-the-Business / Open-Source-the-Science), a community-adoptable contract template anchored to a single technically-grounded boundary: pre-training artifacts (architectures, training code, benchmarks, untrained weights) are open science; post-training artifacts (weights trained on proprietary data) are business IP. This boundary is technically meaningful, legally clean, and auditable -- and could not have been drawn correctly without scientists at the negotiating table. We argue the ML community should adopt PBOS as its default contract for such collaborations.

Gravitational lensing by large-scale structure imprints secondary anisotropies on the Cosmic Microwave Background (CMB) that can be exploited to probe cosmology. In particular, cosmic voids produce a characteristic lensing signature detectable through Void x CMB cross-correlations. This signal has been robustly measured in the past but its cosmological constraining power remains limited by the incomplete knowledge of how methodological choices affect its measurement and by its uncertain dependence on cosmological parameters. Using a set of validated Roman mock catalogs, we first quantify how mock construction impacts the measured signal and then forecast the capabilities of Roman, in combination with current and upcoming CMB surveys such as Planck, SO and CMB-S4-like experiments. We analyze the signal-to-noise ratio (S/N) for different void definitions (2D and 3D), stacking approaches (rescaled versus non-rescaled profiles), CMB map filtering schemes and noise levels. In contrast to galaxy and void statistics, we find that the Void x CMB lensing signal is less sensitive to the choice of mock catalog, indicating that future tensions with data are unlikely to stem from mock inaccuracies alone. The highest S/N is achieved for 2D voids with rescaled profiles. We forecast S/N ~13$σ$ (8$σ$) for 2D (3D) Roman voids combined with Planck, increasing to 22$σ$ (13$σ$) for SO and 31$σ$ (18$σ$) for CMB-S4-like surveys. While the cosmological dependence of this observable remains to be quantified, Roman together with next-generation of LSS and CMB surveys opens a path toward the first direct cosmological constraints from Void x CMB lensing.

Real critical systems, such as uniaxial ferromagnets in the 3d Ising universality class, are constrained by boundaries and subject to random couplings. We consider the Wilson-Fisher fixed point in $4-ε$ dimensions subject to a random magnetic field localized on a two-dimensional surface, which becomes co-dimension 1 in the physical $ε\to1$ limit. Using the replica method for the disordered field, we find that the ordinary boundary condition is stable under disorder but also discover a non-trivial ``dirty" boundary condition which can be reached by tuning the disorder strength or the local temperature. We also investigate the logarithmic structure of the defect spectrum and how it emerges via the replica formalism.

Digital Image Correlation (DIC) enables dense, time-resolved measurement of surface strain in deforming materials, providing insight into strain localization and failure mechanisms. However, the resulting strain fields are typically explored frame-by-frame through spatial visualizations, making global temporal patterns difficult to discern. We present a visual summarization approach that represents the evolution of high-strain regions as a single Sankey diagram constructed from superlevel sets of the von Mises equivalent strain field. By tracking connected components over time via spatial overlap, the diagram encodes the birth, persistence, merging, and disappearance of strain concentrations. Applied to four tensile test datasets with varying notch geometries, the approach compactly captures differences in deformation regimes and qualitative precursors to failure, complementing traditional spatial strain visualizations with a global temporal overview.

In this work, we simulate the functionality of artificial neuron and synapse using spin-orbit torque-based spintronic devices and implemented a fully connected artificial neural netwrok (ANN). These neuro-synaptic devices are emulated using transverse domain wall dynamics in a rectangular magnetic nanotrack comprised of heavy metal/ferromagnet (HM/FM) heterostructures. The ReLU activation function of the neuron has been mimicked using the domain wall motion induced by a 3 ns current pulse. The synapse has been modelled using current-induced domain wall (DW) dynamics through a corrugated HM/FM nanotrack under the influence of a 10 ns current pulse with varying current density. The semicircular corrugations are in the form of notches, which are symmetrically located on both sides of the nanotrack. By applying 10 ns current pulses of varying densities, we achieve controlled DW pinning, revealing a step-like motion caused by temporary pauses at each pinning center. The electrical conductance of the pinned DW across various pinning points, act as stable synaptic weights for our ANN. Furthermore, we observe a threshold-dependent delay effect where each depinning event is influenced by previous ones, successfully mimicking synaptic memory and adaptability in neuromorphic systems. The fully connected ANN has been modeled using the conventional float32 synaptic weights for the MNIST and Fashion MNIST datasets with an accuracy of ~97 % and ~86 %, respectively, which serves as a test bed of our neuromorphic simulations. With the aim of implementing a sparse and low memory footprint ANN, we quantize the trained synaptic weights into discrete quantized level and tested the network, which demonstrate an accuracy of ~95% and ~62%, for the MNIST and Fashion-MNIST dataset, respectively.

Let $p$ be a prime, let $d \geq 1$ be an integer and $A$ be the algebra of square matrices of size $d$ over the field of order $p$. Let $P, Q \in A[x_1, \dots x_n]$ be polynomials in $n$ indeterminates with coefficients in $A$, such that $Q$ is invertible in $ A[\![x_1, \dots, x_n]\!]$. Let also $\mathcal M \colon \mathbf Z^n \to A$ be the map associating to the $n$-tuple of integers $(α_1, \dots, α_n)$ the coefficient of the monomial $x_1^{α_1} \dots x_n^{α_n}$ in the development of the rational fraction $PQ^{-1}$ as a power series (the support of $\mathcal M$ is contained in $\mathbf N^n$). Our main result ensures that the map $\mathcal M$, viewed as a tiling of $\mathbf R^n$ by unit cubes with color set $A$, is self-similar. The self-similarity is expressed in terms of invariance under substitutions. By specializing to $d=1$, $n=2$, $P=1$ and $Q =1-x_1-x_2$, we recover the well-known self-similarity feature of the binomial coefficients modulo $p$.

We study if there is an opportunity to describe quantum particles in the vicinity of three types of cosmological singularities, big bang-big crunch, big rip and big brake. Writing down the Dirac equation for spinors, and choosing a convenient parametrization for basis functions of the spinor field, we show that the corresponding second-order differential equation has two independent solutions which are non-singular in the case of all three types of singularities. That permits us to construct the Fock space for the spinor particles and to interprete this fact as their opportunity to cross these cosmological singularities. We show also that this is impossible to do for scalar particles and changing the parametrization does not help. Thus, fermions look more resilient to the passage of the cosmological singularities than bosons.

A major goal of the Habitable Worlds Observatory (HWO) is to precisely characterize exoplanets and their atmospheres. However, magnetic activity from an exoplanet's host star can complicate measurements of both the stellar and planetary properties, and stellar activity can be an important factor in our interpretation of the evolutionary history of an exoplanet. In this work, we assess the extent to which magnetic activity has been characterized for potential HWO target stars by collating archival measurements of relevant observables as published in a broad range of photometric and spectroscopic datasets. We describe our data collection strategy, provide an overview of currently known activity and rotation properties in the Activity and Rotation Catalog (ARC) for potential HWO target stars, and briefly review known relationships between stellar inclination, rotation, activity, and age. Overall, we find that stellar activity (S-index and R'HK) and rotation (v sin i and Prot) properties have been measured for at least 70% systems that are currently of high interest as potential HWO atmospheric characterization targets. However, stellar activity is temporal in nature, such that activity properties should be regularly monitored in order to remain up-to-date for informing future observations. In particular, we find that stellar activity cycles are measured for fewer than 20% of high interest potential HWO target stars. Measuring a star's activity cycle is critical for anticipating times when higher levels of magnetic activity may occur during planned HWO observations, which may interfere with measuring precise exoplanet atmospheric characteristics.

Particle-accelerating colliding-wind binaries (PACWBs) are multiple systems of massive stars in which strong stellar winds collide, accelerating particles to relativistic energies. This population of relativistic particles emits NT radiation, including synchrotron radiation in the radio domain. This emission is expected to be linearly polarized, but the polarization signature has not yet been detected for a PACWB. Our objective is to quantify the linear polarization of synchrotron radiation in two well-known PACWBs and to interpret our measurements within the framework of the physics of these specific NT emitters. We observed the PACWBs WR 147 and HD 167971 with the Very Large Array (VLA) radio interferometer in the frequency bands L and C (1-2 and 4-8 GHz, respectively), where synchrotron emission is expected to be more prominent. We performed polarization calibration and analyzed the resulting Stokes maps. We did not detect any polarization signature for either of the two targets in either of the two bands, even when considering narrower bands to mitigate the effect of bandpass depolarization. The most conservative upper limit on the polarization degree is on the order of 1% for both targets. The lack of linear polarization for the two targets is likely attributable to a combination of effects, including the turbulent nature of the magnetic field in the synchrotron-emitting region, and depolarization processes based on Faraday rotation that are certainly active in these sources. Their complex geometry, unresolved by the VLA at these frequencies, is most likely to lead to beam depolarization. We emphasize that, in contrast to other canonical synchrotron sources, PACWBs are also subject to thermal dilution. This is especially relevant for systems with stars whose winds are strong enough to contribute copiously to thermal emission, such as those harboring a Wolf-Rayet component.

We model the solar radial velocity (RV) signal induced by faculae, the dominant contributor to RV variability in Sun-like stars. We use a representative case of a facular patch transiting the visible solar disk as the Sun rotates to disentangle various physical effects contributing to the RV signal. Our approach is based on 3D radiative magnetohydrodynamic (MHD) simulations of the solar photosphere and upper convection zone with the MURaM code and spectral synthesis with the MPS-ATLAS code. We show that the faculae-induced RV strongly depends on the facular position on the solar disk. Near disk centre, facular magnetic fields inhibit the convective blueshift and thus produce a relative redshift of the solar spectrum. Surprisingly, when located closer to the limb, namely at heliocentric angles greater than about $60^\circ$, faculae produce a relative blueshift. This transition from redshift to blueshift is caused by the effect of magnetic fields on horizontal flows, which dominate the signal near the limb, and on the visibility of these flows. In combination with solar rotation, this centre-to-limb dependence of the facular effect leads to a complex RV profile during the facular transit and, in particular, to a phase lag between the maximum of the RV signal and the facular crossing of the central meridian. We further show that, in contrast to stellar reflex motion, the facular signal strongly depends on the spectral line in which it is measured.

We study the spin excitations in the frustrated coplanar 120-degree ground state of the triangular-lattice Heisenberg antiferromagnet and demonstrate that they carry a magnetic moment perpendicular to the plane in which the spins order, despite the ground-state sublattice moments having no out-of-plane component. The symmetry of the momentum dependence of the magnetic moment and energy of the magnons renders the system an odd-parity f-wave magnet. Extending this model to a stack of antiferromagnetically coupled triangular layers provides a realization of magnons in a three-dimensional f-wave antialtermagnet. We show that nonlinear thermal transport effects of magnons, such as Edelstein and spin-splitter effects, provide clear experimental signatures of magnons in f-wave antialtermagnets.

In the present work, we revisit the Adlam-Allen (AA) model in order to investigate its numerically observed rarefaction and dispersive shock waves that arise in numerical simulations of the Riemann problem associated with the model. On the one hand, we perform a direct analysis of the rarefaction and dispersive shock waves of the AA model via examining its corresponding dispersionless system and leveraging the DSW-fitting method to obtain theoretical predictions on various edge features of the dispersive shock waves. On the other hand, we review the KdV reduction of the AA model and utilize the KdV dispersive shock wave to approximate that of the AA model. Relevant numerical comparisons demonstrate the good performance of not only the direct analysis on the AA dispersive shock wave, but also of the approximation via the KdV DSW. These methodologies provide a systematic toolbox for analyzing the outcome of Riemann problems in not only this fundamental setting of cold plasmas but also potentially in related plasma-physics problems.

Local Markovian noise cannot bring entanglement back, but it can bring magic back. Unlike separability, stabilizer membership is not preserved by local channels, allowing dissipation to push states out of the stabilizer polytope as well as in. Under local amplitude damping, the $n$-qubit GHZ family $α|0^n\rangle+β|1^n\rangle$ ($0<α<β$) loses its magic at a lower damping strength $γ_-$ and regains it at a higher one $γ_+$, while entanglement is irreversibly lost at $γ_e$. This magic-entanglement complementarity, $γ_e+γ_+=1$ for every $n$, reflects a system-environment duality of amplitude damping and persists for a broader class of dissipative channels. For small $α$, the reborn magic resides in a fully separable state with all proper marginals stabilizer, yet parity-syndrome extraction concentrates it onto a single qubit for magic-state distillation. Local dissipation further divides pure stabilizer states into magic-generators and magic-insulators: at two qubits, the Bell state $|Φ^+\rangle$ generates magic immediately, while its Bell-state partner $|Ψ^+\rangle$ remains stabilizer. Together, magic and entanglement reveal a symmetry invisible to either alone.

We designed a procedure to train a machine learning interatomic potential (MLIP) at benchmark-quality quantum Monte Carlo (QMC) accuracy. To avoid the complexities of high-quality atomic force determination with the stochastic QMC methods, we use a multi-fidelity approach wherein high-level QMC energies are used alongside suitably processed low-level DFT atomic forces to train a QMC fine-tuned MLIP which significantly improves both the energetics and atomic forces over the baseline DFT-based MLIP. Fine-tuning is only applied to the readout layers of an equivariant message-passing MACE MLIP. We used sulfur mono- and multiple vacancies in monolayer MoS$_2$ as a testbed and demonstrate a near QMC accuracy of the model in a number of in- and out-of-domain tests. We show that a fairly limited dataset of QMC energies suffice to significantly improve the baseline DFT MLIP. The accuracy of our approach is demonstrated on energy and free energy migration barriers of mono- and multiple S-vacancy defects. The results open the window to large-scale near QMC quality simulations with large numbers of atoms and/or molecular dynamics configurations which would not be possible by a direct brute-force application of QMC methods.

Measurements from the Dark Energy Survey (DES) Year 3 data have shown that the Weyl potential -- the sum of the spatial and temporal distortions of the geometry -- evolves more slowly than predicted by General Relativity, assuming a $Λ$CDM background evolution. An evolving dark energy with a phantom crossing, as preferred by the Dark Energy Spectroscopic Instrument (DESI), is expected to decrease the depth of the gravitational potentials through a stronger acceleration than in $Λ$CDM, potentially solving the tension with General Relativity. In this paper, we show that $w_0w_a$CDM models indeed reduce the tension with respect to $Λ$CDM, down to a level of $1.6-2.2σ$, depending on the treatment of CMB lensing. This reduction is not due to an increase in the Weyl potential's uncertainties, but truly to the impact of the evolving background on the theoretical predictions in General Relativity. More data are needed to robustly determine if evolving dark energy fully explains the low value of the Weyl potential at intermediate redshifts, or if modifications of gravity or interactions in the dark sector are needed, which could simultaneously stabilize the phantom crossing indicated by DESI.