Papers

Achieving robust high-dimensional solitons both in optics and Bose-Einstein condensates remains a great challenge, due to unpredictable high-order perturbations. Here we propose and demonstrate a novel topological spatiotemporal mode-locked fiber laser empowered by mode-division multiplexing, for precisely controlling of the spatiotemporal solitons. By introducing time delays between the individual transverse mode paths, a topological spatiotemporal lattice is formed in the multimode cavity, which in turn excites periodic spatiotemporal solitons. These solitons are stable against the perturbations, attributed to periodic potentials induced by the nonreciprocal dissipative coupling interactions. Moreover, under the thermodynamic limit, the solitons condense into nested-type spatiotemporal solitons, a phenomenon analogous to the Bose-Einstein condensate of matter waves. Our results indicate that topological spatiotemporal mode-locked lasers not only constitute a controllable source of robust spatiotemporal light bullets, but also provide a valuable platform for exploring nonlinear high-dimensional soliton dynamics and non-Hermitian topological physics.

Language-model agents favor their own group because they have watched their members favor each other. The group label alone does little once the decision has a cost; what drives favoritism is observed behavior, and an individual's own record can override it. We test this in small societies with arbitrary group labels, ten rounds of point sharing, and matched one-shot decisions across fifteen OpenAI models and three Claude models, about 4,400 societies and 3.3 million audited model calls. First, the large effect of a bare group label reported in earlier work appears only when giving others points costs the agent nothing; once the agent can keep points for itself, that effect collapses on every model that shows it. Second, under a stake, interaction history becomes the main source of favoritism: the history effect is statistically positive on 13 of 15 models, reaches about 3.5-8 points out of 10 on 11, grows with the number of rounds played, and extends to labeled strangers the agent has never met. Third, with scripted histories, favoritism falls to near zero under an egalitarian norm and reverses when the agent's own group is seen favoring the other side; stronger models side with an individual's record when it conflicts with the group. Group favoritism is thus conformity to observed group behavior, carried to strangers by the label and overridden by individual reputation. The same account predicts responses to betrayal, scandal, and a free offer to change group: public reprimand repairs betrayal better than apology or restitution, allocation punishment remains confined to the offending member, and a formed group cannot be bought but can, on weaker models, be invited away.

Reconstructing spatially resolved plasma dynamics from few sensors is essential for diagnostics, reduced-order modelling and control, yet remains difficult because the sparse measurements incompletely constrain multiscale, regime-dependent degrees of freedom. The Shallow Recurrent Decoder (SHRED) partially addresses spatial sparsity by using measurement histories; however, its fully connected decoder provides no explicit mechanism for resolving spatial structure across scales or explicit parametric dependency. We introduce the Recurrent Multiscale Affine-modulated Inference Network (ReMAIN), which preserves SHRED's recurrent temporal encoding but replaces its decoder with a U-Net whose feature hierarchy is conditioned by the recurrent state through feature-wise linear modulation. The temporal representation supplies both a dense prior and scale-specific modulation throughout the U-Net. A parametric extension jointly embeds the operating condition and sensor history, enabling reconstruction to adapt as the governing dynamics change with operating regime. ReMAIN is first benchmarked against SHRED on six one-dimensional nonlinear PDEs representing diverse dynamics. Across all benchmarks, it reduces reconstruction errors on unseen trajectories and more faithfully resolves sharp transitions, localized extrema and fine-scale variations. The parameter-conditioned model is then demonstrated on a collisionless $E \times B$ plasma subject to perpendicular axial electric and radial magnetic fields, with the electric-field strength serving as the operating parameter. ReMAIN reconstructs the high-dimensional, multiscale plasma state and recovers its regime-dependent spatiotemporal dynamics at electric-field strengths withheld from training. Together, ReMAIN improves sparse-sensor full-state reconstruction and, through parameter conditioning, generalizes across plasma operating regimes.

Orbital angular momentum (OAM) modes have emerged as an important platform for high-dimensional quantum communication but remain vulnerable to environmental noise and channel imperfections. Here we introduce a logical-qubit representation of OAM propagation in which modes evolve as qubits through noisy quantum channels. In this representation, turbulence manifests as leakage from the logical OAM subspace into turbulence-induced leakage components, providing a unified description of degradation in both single-photon and coherent-beam regimes. We show that turbulence-induced leakage exhibits systematic dependence on turbulence strength and OAM order, revealing structured signatures of information redistribution beyond the logical subspace. Leveraging this insight, we develop a distributed quantum feature-extraction scheme that encodes high-dimensional OAM states into quantum representations and employs quantum convolutional operations to extract turbulence-robust features. Simulations and experimental measurements demonstrate robust extraction of these features across a broad range of turbulence strengths, OAM orders, and propagation conditions. Our results establish a direct link between OAM-channel dynamics, structured leakage, and quantum learning, providing a physics-informed framework for characterizing turbulence-degraded high-dimensional optical states and extracting robust signatures of turbulence-induced information redistribution.

The intermittent and variable nature of precipitation makes its accurate estimation over extended domains difficult, yet its spatiotemporal structure suggests that a low-rank representation may be possible. This work represents daily precipitation over the contiguous United States (CONUS) as spatiotemporal tensors and applies CANDECOMP/PARAFAC factorization, showing that preserving the native spatial and temporal modes yields more accurate reconstruction than factorizing independent daily fields or unfolded space–time matrices. Building on this finding, this work presents TMerge, a tensor-based framework that integrates satellite precipitation with sparse reference observations through shared low-rank spatial and temporal factors. TMerge was applied to correct the IMERG Final Run product with climate prediction center reference observations over CONUS. During 2019-2022, TMerge increased correlation from 0.53 to 0.85 and reduced root-mean-square error and mean absolute error by 48.2% and 29.3%, respectively. TMerge consistently outperformed linear bias correction, quantile mapping, and neural networks across seasons, precipitation-intensity regimes, and regions. Improvements were spatially coherent and largest in coastal regions where IMERG errors were greatest. These results demonstrate that low-rank tensor structure parsimoniously approximates the dominant spatiotemporal variability of precipitation and provides a practical mechanism for improving satellite estimates under limited reference observations over extended domains.

We present the first application of DeepDISC, a scene-level deep-learning framework, to real LSST data, using the Data Preview 1 (DP1) coadded images of the Extended Chandra Deep Field-South and the Euclid Deep Field-South fields. In a single pass, DeepDISC jointly detects, segments, and classifies sources as stars or galaxies, rather than treating these tasks as separate steps as in traditional pipelines. We warm-start from a model pretrained on LSST simulations and fine-tune it on DP1 six-band images, using the DP1 catalog's extendedness flag (point-like versus extended) as provisional class labels. The classification part of the network is then fine-tuned alone on a curated sample of stars and galaxies with spectroscopic, Gaia, and photometric identifications, so the final output is a star/galaxy classification. Fine-tuning on real DP1 observations substantially improves source recovery relative to the un-fine-tuned model, raising galaxy and star completeness from 40.2% and 13% to 84.2% and 49.1% under strict class-aware matching; under class-agnostic matching the fine-tuned model reaches 84.9% and 74.3%. On a common sample of sources that neither classifier saw in training, DeepDISC classifies to within about one percentage point of a published photometry-based Random Forest for the same field (97.4% vs. 98.5%); at fainter magnitudes DeepDISC labels more sources as stars than the Random Forest, and independent labels favor the Random Forest in most of these disagreements. Cross-matching to deeper HST/CANDELS imaging on a held-out footprint gives an unrecognized-blend fraction for DeepDISC of 14.9% (95% CI 12.5-17.8%), consistent with or modestly below the 18.0% we measure for the production DP1 catalog. This work establishes a baseline for DeepDISC on real LSST data and a foundation for self-supervised pretraining and for LSST Data Preview 2.

For the $H^{2|2}$ model on $\mathbb Z^2$, we prove that, at energy zero, fractional moment of Green functions, $\mathbb E[ G(x,y)^s]$ decays exponentially in the distance between $x$ and $y$, for every constant edge weight $W>0$ and every $0<s<1/2$. Here $G$ is the infinite-volume Green function at energy zero of the associated random Schrödinger operator. The main ingredient is a quantitative lower bound on the derivative of $\mathbb E[e^{u_x/2}]$ with respect to the edge weights, where $u$ is the pinned effective field.

Coulomb explosion imaging (CEI) is a powerful tool for determining static geometries of isolated gas-phase molecules. In this work, taking allene and propyne as prototype, we demonstrate sub-angstrom resolution achieved with highly charged ion-induced CEI. Using 41.9 keV/u Xe$^{20+}$ beam, minute variations of CC single, double and triple bond lengths are clearly visualized, indicating that an ultrahigh resolution of 0.1 $\mathring{\mathrm{A}}$ is achieved. This unprecedented resolution not only makes it possible to distinguish CC bonds with different bond orders, but also lays a solid foundation for direct observation of quantum fluctuations inherent to 3D structure of a ground-state molecule. Notably, the coupled collective motion of the two CH$_2$ moieties spaced by a central carbon within allene is unambiguously visualized in our measurements.

Spontaneous collapse models provide a possible, testable solution to the quantum measurement problem, but constructing a relativistic version applicable to quantum field theory remains an important open challenge. Challenges in constructing relativistic collapse models include avoiding superluminal signaling and foliation dependence, controlling energy production, and ensuring vacuum stability. Here, we present a relativistic collapse framework that addresses these problems at the cost of introducing a new space of physical states, whose complete characterization is left open. A modified, history-dependent Aharonov–Albert prescription describes instantaneous nonlocal state updates on arbitrary spacelike foliations while preserving foliation independence. To ensure vacuum stability, we modify the standard unitary evolution through a prescription that we name dynamical vacuum subtraction. The resulting dynamical map is linear and trace-preserving but not positive on the full set of standard density operators. We establish no-signaling by proving microscopic parameter independence, and identify conditions under which standard nonrelativistic collapse dynamics are recovered. Finally, we apply our framework to a free massive scalar field, obtaining finite particle-number and energy-increase rates at leading order in the long-time regime, together with further promising evidence for the consistency of the framework.

Finite photon momentum allows second-order photocurrents in centrosymmetric space groups, where the conventional bulk photovoltaic effect is forbidden. Here we study the photon-drag response of altermagnets and show that its angular dependence reveals both the symmetry of the magnetic order and the Néel domain of the sample. For example, when a crystal rotation exchanges the two domains, the domain-even and domain-odd parts of the current appear in different angular harmonics, $4n$ and $4n+2$ for tetragonal $d$-wave altermagnets, so that two measurements rotated by $90^\circ$ on a single domain isolate the domain-odd current. We also find that these microscopic contributions exhibit opposite domain parities depending on light polarization. Under linear light, only the shift term is domain odd, while under circular light, the domain-odd channel switches to the injection, Fermi-surface, and contact principal-value terms. Mirror symmetries fix the angular phase of each harmonic and distinguish $d_{x^2-y^2}$, $d_{xy}$, and $g$-wave order. In contrast, conventional collinear antiferromagnets show no domain-odd photon-drag current, and in tetragonal ferromagnets the two parts share the same harmonics. Although the altermagnetic spin splitting does not require spin-orbit coupling (SOC), SOC is essential to observe the domain-odd charge current. Our work uncovers distinct signatures of altermagnetism in photon drag and provides a route to all optical magnetic readouts of altermagnets.

Massive discrete Dirac-type operators on periodic planar graphs are investigated. Using Floquet–Bloch decomposition, we first show that neglecting the faces leads to spurious flat bands that are unrelated to the graph's geometry. Then, for the correct Dirac–Hodge operator $D_m$, we establish a spectral reduction expressing $\sigma(D_m)$ through the graph Laplacian on vertices $-\Delta_V$ and the Laplacian on faces $-\Delta_F$. As a consequence, $D_m$ carries a flat band precisely when one of these scalar Laplacians does. We finally apply this reduction to the study of flat bands of the operator $D_m$ on Archimedean lattices and contrast the outcome with that on the Lieb lattice.

We propose that the path to realizing the full potential of two-dimensional (2D) electronics lies in understanding and mastering their energy band tail states (BTS). Unlike silicon, which benefits from sharp band edges (Urbach energy $E_\rm U$ ~ 10 meV), today's monolayer 2D semiconductors have rough band edges ($E_\rm U$ ~ 100 meV) caused by spatial potential inhomogeneity. Comparing a physics-based electrical model to experiments, we demonstrate that BTS cause reduced effective mobility and "slow" transistor turn-on with excessive gate voltage in monolayer 2D transistors, which sets a fundamental limit for their energy-efficient operation. We also show that BTS effects in 2D transistors could be mitigated with higher gate capacitance, with strain engineering, and by using bilayer instead of monolayer 2D channels, as long as $E_\rm U$ is reduced below ~ 50 meV. This work provides a theoretical framework and design guidelines to navigate these fundamental limits, and to enable successful integration of 2D semiconductors into low-power nanoelectronics.

We present a systematic X-ray spectral analysis of the 2019 and 2022 outbursts of the accreting millisecond pulsar SAX J1808.4-3658 with extit{NICER} with a particular emphasis on the nature of the longstanding $\sim$1\,keV spectral feature. We report here the first clear evidence that the 0.9-1.0 keV feature varies systematically with source brightness, exhibiting strong correlations with both continuum and blackbody flux. This establishes that the feature is directly coupled to the accretion process. The scaling and energy distribution of the feature differ between outbursts, corresponding to a $\sim$2\,dex change in reprocessor density (log\,$n_e \approx 19.1$ in 2019 vs. $\sim$17.3 in 2022), which naturally explains the stronger, broader feature in 2019 and the weaker, more localized $\sim$1\,keV feature in 2022, in agreement with high-density reflection models. These results provide the strongest constraints to date on the origin of the $\sim$1\,keV feature in SAX J1808.4-3658.

Quantum fault tolerance is a prerequisite to harness the full potential of quantum computers. However, achieving fault tolerance remains a major challenge since it requires optimizing tradeoffs between several intertwined theoretical and experimental parameters, including the encoding rate, code distance, parity-check weights, code planarity, and routing. Surface codes remain strong candidates for fault tolerance due to their simple geometric structure, established decoding methods, locality, and experimental attainability. In this work, we present finite families of hyperbolic surface codes that leverage the optimal efficiency scaling $kd^2/n = C (\log{k})^2$ attainable by surface codes with bounded local geometry. We show that optimizing the periodic identifications can double the code distance and thereby increase the code's efficiency by a factor of four, at fixed qubit count, encoding rate, and parity-check weight. We also address the routing problem of hyperbolic surface codes by introducing a topology-aware algorithm that partitions a hyperbolic code into bounded planar modules while reducing long-range communications demand. Circuit-level Monte Carlo simulations yield finite-size threshold estimates for both modular and monolithic layouts, showing that tripling the long-range gate error probability mildly lowers, but does not eliminate, the estimated error-correction thresholds. Together, these results strengthen the case for hyperbolic surface codes as structured and highly efficient large-scale quantum error correction codes for future fault-tolerant architectures.

Polaritons and quantum vacuum dressing are usually regarded as intertwined consequences of ultrastrong light-matter coupling. Here we show that they can be controlled independently through helicity. We consider cyclotron resonance coupled to a chiral cavity supporting a single circular mode. When the helicity of the cavity field matches the cyclotron motion, energy is exchanged resonantly between light and matter, producing a large Rabi splitting and conventional polaritons, while the ground state remains the bare vacuum. Reversing the magnetic field reverses the cyclotron motion without changing the cavity. The same mode then couples through anomalous processes that create and annihilate light and matter excitations in pairs: the conventional polaritonic anticrossing disappears, while the ground state acquires squeezing and virtual excitations. These two regimes show that spectral hybridization and quantum vacuum dressing are distinct manifestations of ultrastrong coupling, and that a large Rabi splitting alone does not imply a dressed quantum vacuum. Helicity therefore provides a route to controlling polariton formation and quantum correlations as distinct resources in cavity quantum materials.

Dark matter subhaloes are a fundamental prediction of the standard cosmological model, but their low-mass population remains difficult to constrain observationally. Recent work has shown that interactions between subhaloes and dwarf spheroidal galaxies can induce large-scale, out-of-equilibrium fluctuations in their stellar surface density fields, identifiable through Fourier-based statistics. We apply this formalism to deep Subaru photometry of the Draco dwarf, extending previous tests to $\gtrsim\mathcal{O}(10^{4})$ resolved stars. We use artificial-star tests to assign completeness weights to individual stars and incorporate them into a discrete likelihood-based framework. Relative to a cored axisymmetric Plummer model, Draco shows radial corrugations that appear in the filtered normalised residuals as a central stellar depletion surrounded by a ring-like excess. The same feature appears in Fourier space as a low-frequency peak with a signal-to-noise ratio ${\rm S/N}=6.0$. Independently, fitting the surface density $\Sigma$ with an $\alpha\beta\gamma$ model yields ${\rm d}\Sigma/{\rm d}R>0$ at $>99.9\%$ confidence – i.e., a profile that decreases towards the centre. Although these signatures cannot yet be regarded as robust evidence for dark subhaloes, they suggest that central stellar depletion in dwarf galaxies may reflect genuine physical structure rather than crowding alone.

In quantum mechanics of genus-1 potentials, perturbation theory around the topologically trivial saddle and non-perturbative instanton saddle are constructively related via the P/NP relation. Whether such a relation persists for higher-genus potentials has remained an open problem. In this work, we resolve it using tools from complex algebraic geometry combined with exact WKB. The classical energy conservation relation $p^2 = 2(E-V(q))$ defines a genus-g Riemann surface $X$, whose periods are naturally organized by its Jacobian variety $J(X) = \mathbb{C}^{\text{g}}/\Lambda$ via the Abel-Jacobi map, and independently by the solution basis of the associated Picard-Fuchs equations. We show that the physically relevant, WKB-active cycles are generally related to the Picard-Fuchs basis by a linear transformation that lies in $\text{SL}(2\text{g},\mathbb{Z})$ but not in the symplectic group $\text{Sp}(2\text{g},\mathbb{Z})$ once $\text{g}\geq2$, in contrast to the genus-1 case, where the two groups coincide. This mismatch is the obstruction that has made an explicit higher-genus P/NP relation elusive. We show that it can be resolved by passing to a modified Riemann bilinear identity, built from a suitably transformed intersection matrix, which the WKB-active periods do satisfy exactly. The result is an all-orders quantum P/NP relation valid for potentials of arbitrary genus, which we derive explicitly for genus-2 (quintic and sextic) and genus-3 (septic and octic) anharmonic oscillators.

The purpose of this work is to introduce and discuss a general framework for performing a linear instability analysis on multiplex networks, in the absence of inter-layer diffusion, with the specific motivation of detecting the Turing instability. The formalism itself is presented in full generality for $m$-unknowns, each of which arise as solutions of non-autonomous reaction-diffusion equations, on $n$-node temporal multiplex networks. In addition, we identify a class of multiplex networks for which the Laplacian matrices commute. In this setting analytical progress can be made and the explicit inequalities, governing instability, are derived. In order to explore our formalism we apply it to the specific example of a two-species predator-prey model on archipelagos. Here nodes are interpreted as islands and edges correspond to the transportation network for each species. Moreover, the non-autonomous setting is used to model a variety of natural phenomena.

The conditional colour-magnitude distribution (CCMD) model connects the galaxy colour-luminosity distribution with dark matter halo mass, which has been constrained in Xu et al. by jointly modelling the two-point correlation functions of a large galaxy sample of the Sloan Digital Sky Survey (SDSS) in fine bins of galaxy colour and luminosity. We test the CCMD model of the galaxy-halo connection using counts-in-cells (CIC) statistics by comparing CCMD model predictions with SDSS measurements for 16 subsamples of galaxies in various luminosity and colour bins. In addition, we compare SDSS measurements with three analytic models of CIC distribution. The CCMD model provides a good description of the SDSS CIC measurements for most subsamples. The exceptions are the void probability functions and CIC distributions with cell radius 5 Mpc/h for a few subsamples in the luminous bins, which are not well described by the analytic models, either. For these subsamples, in general, removing the Sloan Great Wall from SDSS or introducing velocity bias to the CCMD model does not resolve the disagreement. Finally, with the help of the CCMD mocks, we investigate the cause of the variations of the galaxy counts in cells. For small cells or cells with a low mean galaxy count, the variation reflects effects from fluctuations in both the matter environment and the halo occupation distribution, while for large cells or cells with a high mean galaxy count, the environment effect dominates. We discuss the implications of our work for investigating galaxy assembly bias.

The logarithmic law of the wall is one of the most robust scaling laws of classical turbulence, yet whether it survives in a quantum fluid such as superfluid $^4$He (He II) remains unknown. At a solid wall, the viscous normal-fluid and inviscid superfluid components of He II obey fundamentally different boundary conditions, yet their motions can be coupled through quantized vortices. How these competing effects organize the near-wall flow remains an open question in quantum-fluid hydrodynamics. Here we visualize the near-wall velocity field in mechanically driven He II pipe flows at Reynolds numbers up to $1.26\times10^6$ using molecular tagging velocimetry and independently determine the wall shear stress from pressure-drop measurements. We find that the normal-fluid velocity develops a robust logarithmic profile, showing that a law-of-the-wall description persists despite the two-fluid nature of He II. Strikingly, however, the resulting wall law is distinctly nonclassical: the effective Kármán constant is less than half its classical value and remains nearly unchanged across the temperature range studied. This invariance, despite substantial changes in the relative fractions of the two components, indicates strong dynamical coupling between them. These results not only reveal how a universal law of classical turbulence is transformed by quantum hydrodynamics, but also provide valuable guidance for the design and operation of cryogenic cooling systems involving He II transport.

Motivated by the recent finding of Ji and Abbot (2025) that at moderate eccentricity (0.2–0.3) the planetary Snowball bistability vanishes, we have introduced stochasticity of the orbital eccentricity into a simplified version of the energy balance model of Eisenman and Wettlaufer (2009) to study a general Earth-like planet. The stochastic eccentricity is approximated by two dominant time scales, annual and daily, and is introduced through an analysis of the Kepler equation. We find that, apart from mean eccentricities less than 0.1, the stochastic dynamics are quantitatively and qualitatively different than the deterministic dynamics. For a fixed mean eccentricity, fluctuations in eccentricity amplify the asymmetry and intermittency in radiative flux forcing, thereby increasing the time it takes to reach a stable seasonal cycle. As the mean eccentricity increases above 0.1, the stochastic transition from the Snowball to non-Snowball state occurs over a range of mean stellar flux $\Delta S^*$ rather that at a particular stellar flux $S^*$ for the deterministic case. Moreover, as the mean eccentricity increases from 0.1 and the variance of the annual eccentricity fluctuations increases, $\Delta S^*$ increases and the probability of the system reaching a steady state and experiencing a Snowball to non-Snowball bifurcation decreases. This implies that there may be many high eccentricity planetary systems in a perpetually transient state.

Using the Mellin-Barnes representation of de Sitter propagators, we reduce tree-level heavy-particle exchange to finite integrals that converge at physical kinematics on mass-independent contours, and one-loop diagrams to the same integrand times a flat-space momentum factor. We evaluate the triple-exchange bispectrum without analytic continuation and compute the complete one-loop triangle at general mass. These give cosmological collider predictions, the oscillatory signatures of heavy particles during inflation, for multiple exchanges and loops at arbitrary masses.

The radar cross section (RCS) is a fundamental parameter in radar engineering, as it determines the detectability of a given target by a radar system. Increasing interest in very low observability (VLO) targets has arisen recently. As a result, especial effort is put in developing numerical tools to accurately predict the electromagnetic scattering from perfect electric conductor objects. Integral equation methods are commonly used in this task. However, full-wave computation of the monostatic RCS at a single angle can be rather time-consuming, nevermind computing the RCS in a specific angular interval with a fine sampling. In fact, this is what is needed in industrial applications. A reduced-order model for fast full-wave monostatic RCS evaluation is proposed to easily achieve fine details in the desired angular domain. An open source integral equation solver, taken into account as a black box, is used to get the electromagnetic scattering in non-penetrable objects at some specific incident angles. Several radar targets will show the possibilities and capabilities of these model order reduction approaches.

Anterior-posterior polarization in the Drosophila oocyte requires posterior localization of oskar mRNA. oskar mRNA is produced in the anteriorly situated nurse cells, moved into the oocyte, and assembled into oskar-Staufen ribonucleoprotein particles (RNPs). These RNPs then move through the oocyte and interact with the cortex, where myosin-V binds them to the actin network and kinesin-1 removes them along cortically anchored microtubules. This cortical tug-of-war takes place against an anterior-to-posterior decrease in microtubule density. How the interplay between cytoplasmic transport and cortical binding and unbinding produces a stable posterior cap is unclear. Here, we combine quantitative microscopy, photoconversion experiments, experimental perturbations, and biophysical modeling to show that posterior localization emerges from position-dependent cortical exchange coupled to cytoplasmic diffusion. Our model, which accounts for the tendency of RNPs to demix, suggests that two key parameters that determine the observed localization pattern are the total amount of oskar and the ratio of cortical unbinding to binding rates (the exchange equilibrium constant). Given oskar volume fractions in wild-type, the measured exchange constant is such that oskar localizes to the posterior cortex without demixing. If the balance is broken, say by increasing unbinding rates, oskar irreversibly demixes into cytoplasmic droplets. This is consistent with observations from our experimental perturbations. Our results show how bulk diffusion and cortical exchange, in the face of oskar RNPs' self-attraction, convert a cytoskeletal asymmetry into robust developmental polarity.

Decisions that will shape future fusion facilities, including the production of waste, the management of tritium, the achievement of safety, and impacts on land, water, and local communities, are increasingly becoming sociotechnical in nature. For prior energy technologies, fission among them, such decisions were made top-down and met sustained public opposition. Research shows such opposition is rarely a deficit of public understanding and is instead rooted in place-specific environmental, health, sociocultural, and economic concerns and in distrust of how technologies are selected and sited. We argue that fusion developers have an opportunity to design differently, and we propose an ecology of participation: multiple, complementary modalities of engagement sustained over time, replacing the one-off public consultations now typical of large infrastructure projects. We synthesize human- and environment-centered design frameworks and introduce a framework that reinterprets opposition as a divergence between communities and technology developers in values, norms, or design characteristics. We present six case studies from our work: participatory technology assessment focus groups, participatory design workshops, immersive virtual-reality models of a fusion facility, the Global Fusion Forum platform, the Imaginary Energies speculative design platform, and Heartbeat, an arts-based sound installation. Mapping these onto the values-norms-design framework shows how each interrogates or closes a different part of the expert-public gap, and exposes two limitations of fusion public engagement efforts writ large: developers are seldom asked to articulate their own values and norms, and the environment is represented only indirectly. We close with five guiding questions for fusion researchers building their own participatory engagements.

The magnetic field of the Sun is a key driver of coronal heating and solar wind acceleration. In tandem, these influence the observable morphology of the corona, along with defining the magnetic connectivity and magnitude of open magnetic flux. Here,we investigate how coronal heating affects the magnetothermal state of the 3D global corona by analyzing a series of data-constrained, thermodynamic magnetohydrodynamic (MHD) simulations for solar minimum conditions. We vary the overall heat deposited in the corona via modifying the input Poynting flux of a wave-turbulence-driven (WTD) model for coronal heating and by scaling the inner boundary magnetic field. We quantify the magnetic, thermodynamic, and combined magnetothermal effects on our simulations. We find that increasing the overall amount of heat leads to a higher magnitude of open flux and a more visibly open white-light morphology in the K-Corona between 1.5 and 6 Rsun. We quantify how plasma beta evolves and drives morphological differences between simulations. We also compare these runs to an extremely high-resolution reference solution that includes small-scale magnetic information in the surface boundary condition. This latter comparison provides insight into the further development of 'subgrid' corrections for low(er) resolution models to best represent the structure and observables of the corona and inner heliosphere.

We study the stellar, black hole, and satellite galaxy populations of group- and cluster-scale halos in the IllustrisTNG, TNG-Cluster, and FLAMINGO cosmological hydrodynamical simulations. We characterize the mass-conditioned scaling relations, covariance, conditional likelihoods, and mass proxy quality (MPQ) of integral properties within $R_{\rm 500c}$: total stellar mass ($M_{\star,\,\rm tot}$), brightest central galaxy stellar mass ($M_{\star,\,\rm BCG}$), intracluster light mass ($M_{\star,\,\rm ICL}$), total black hole mass ($M_{\rm BH}$), satellite occupation ($N_{\rm sat}$), and satellite velocity dispersion ($\sigma_{\rm sat,\,1D}$), at $z\in \{0,0.5,1,2\}$. While normalization differences of up to a factor of two emerge among simulations, many properties exhibit remarkably similar mass-localized slopes, intrinsic scatters, and MPQs across halo samples and redshifts. Correlations among stellar, satellite, and gas properties are generally consistent in sign and their dependence on halo mass and redshift, whereas those involving $M_{\rm BH}$ and $N_{\rm sat}$ show greater variation, particularly in TNG-Cluster. Conditional likelihoods are nearly lognormal for many properties, including $M_{\star,\,\rm tot}$, $N_{\rm sat}$, and $\sigma_{\rm sat,\,1D}$, while $M_{\star,\,\rm BCG}$ and $M_{\star,\,\rm ICL}$ are negatively skewed. Among the properties considered here, total stellar mass is the best mass proxy, with a halo mass scatter of $7$-$14\%$, followed by $N_{\rm sat}$ with a mass scatter of $9$-$18\%$. The consistent correlation behavior of $M_{\star,\,\rm ICL}$ highlights its potential for next-generation observational studies. Finally, $\sigma_{\rm sat,\,1D}$ exhibits highly consistent scaling, with mass-independent slope and scatter and a largely neutral correlation structure, suggesting that it is relatively insensitive to astrophysical complexity.

We report a systematic ab initio investigation of the excited states and absorption spectra of small mercury clusters (Hg$_{\mathrm{2-5}}$). Benchmarking multi-reference configuration interaction (MR-CISD) and equation-of-motion coupled-cluster (EOM-CCSD) methods shows that EOM-CCSD/aug-cc-PVTZ-PP reproduces atomic excitation energies within $\pm$500 cm$^{-1}$ of experiment, whereas MR-CISD deviates by about $\pm$1100 cm$^{-1}$. For the mercury dimer, EOM-CCSD potential energy curves yield absorption bands in excellent agreement with experimental transitions, while asymptotic excitation energies closely match atomic references. Extending this methodology, we present the first systematic ab initio study of absorption spectra of Hg$_{\mathrm{3}}$, Hg$_{\mathrm{4}}$, Hg$_{\mathrm{5}}$, which display broad bands in the ranges of 37000-60000 cm$^{-1}$. The consistent agreement with experimental benchmarks and diagnostics confirming single-reference character validates EOM-CCSD as a robust approach for describing excited states and optical spectra of mercury clusters.

Physics-Informed Neural Networks (PINNs) are a widely used data-free method for solving Partial Differential Equations (PDEs) using machine learning. With recent advances in Generative Adversarial Networks (GANs), adversarial learning has shown strong capabilities for modeling complex data-driven problems; however, the use of GANs in deterministic physics-informed PDE solutions remains limited. In this work, we first identify limitations of standard PINNs for solving PDEs, including spectral bias, loss imbalance, and optimizer stagnation. We then propose Generative Adversarial Physics-Informed Neural Networks (Gen-PINNs), a unified deterministic residual-adversarial framework designed to improve data-free solutions of PDEs with sharp or shock-front behavior. The generator learns the underlying PDE solution using dynamically weighted physics-informed loss components, while separate discriminators evaluate complementary PDE residual features against ideal zero-residual states. The framework further develops and adapts several methodological components, including a Fourier representation for resolving high-frequency spatial content, an orthonormal spectral diagnostic for quantifying frequency-dependent solution errors, and a modified gradient-based dynamic weighting system for physics, initial-condition, boundary-condition, and adversarial loss objectives. Gen-PINNs is tested against standard PINNs on nonlinear and higher-order PDEs, including the Burgers, Allen-Cahn, and Kuramoto-Sivashinsky equations. The results demonstrate substantial improvements in accuracy and convergence across sharp-front, stiff, and higher-order PDE solutions, highlighting the potential of deterministic residual-adversarial learning as an effective approach for solving challenging nonlinear PDEs.

Femtoscopic correlations in proton-proton and heavy ion collisions are commonly calculated using emission sources from microscopic transport models, where emitted particles are represented by classical phase-space points with simultaneously specified positions and momenta. This point-emitter picture neglects the finite phase-space localization required by quantum mechanics, which can become relevant when the localization scale approaches the femtoscopic source size, particularly in small collision systems. We formulate a quantum treatment by replacing each transport phase-space point with a minimum-uncertainty Gaussian phase-space distribution. Finite localization then induces both spatial and momentum smearing; in the presence of coordinate-momentum correlations, the latter produces a nontrivial modification of the emission source. Using proton–proton collisions at $\sqrt{s}=7~\mathrm{TeV}$ simulated with the microscopic Parton–Hadron–String Dynamics transport approach, we show that this effect significantly modifies the proton-pair source and its momentum correlation. Our results expose a quantum localization effect overlooked in transport-based femtoscopy and establish the regime in which the classical point-emitter approximation is valid.