Papers

How cultural repertoires maintain diversity under selection is a fundamental question in cultural evolution. We address this using thirteen years of weekly popularity data for approximately 20,000 Irish traditional tunes, fitting ecological birth-process models under neutral, frequency-dependent, and per-tune selection hypotheses. We find strong evidence that tunes differ in intrinsic fitness - some are systematically more likely to be learned than others. We find that 29% of the variance in fitness can be explained by a mixture of social and melodic features. Some tunes appear to be carried along via linkage due to the tradition of playing tunes in sets, analogous to selective sweeps in genetics. By measuring changes in fitness over time and comparing this with recordings we precisely identify the mechanism by which a long-dormant tune can become fit through a popular recording. Despite the directional selection, repertoire diversity increases, driven by the continual arrival of new compositions. These results demonstrate that selection and diversity can coexist in a cultural ecosystem, and establish Irish traditional music as a quantitatively tractable system for studying the evolution of cultural variants and understanding what makes a tune stand out.

Purpose: To develop and optimize a 5DCT (3D + cardiac phase + respiratory phase) imaging simulation and reconstruction pipeline, and to compare two sinogram-space interpolation methods for reconstructing images at arbitrary combinations of cardiac and respiratory phase. Methods: Helical CT projections were simulated from the 4D XCAT phantom across a range of cardiac and respiratory motion states, with Poisson and electronic noise added. Ground-truth-matched volumes were generated at 5 cardiac phases and 10 respiratory amplitudes (50 total phase combinations). Because acquired projections are sparsely and unevenly distributed across this joint phase space, each target slice was reconstructed by interpolating rebinned sinogram rows to the target cardiac phase and respiratory amplitude, using either 2D scattered barycentric interpolation or 2D scattered local linear interpolation with a circular kernel for cardiac phase. Reconstructed volumes were compared to phantom ground truth using mean absolute error (MAE), and to conventional respiratory-gated 4DCT (r4DCT) reconstructed from the same simulated data. Results: Both interpolation methods eliminated the severe axial misalignment artifacts present when helical projections were reconstructed without phase-space interpolation. Local linear interpolation achieved lower MAE than barycentric interpolation across most tested conditions, with the largest improvement at low pitch. The 5DCT pipeline also produced respiratory-only volumes with fewer residual cardiac-motion artifacts than conventional r4DCT reconstructed from the same projection data, including at standard clinical pitch (0.1). Conclusions: 5DCT reconstruction using sinogram-space interpolation is feasible and can jointly resolve cardiac and respiratory motion with better accuracy than conventional 4DCT reconstruction.

Critical systems sit near boundaries between qualitatively distinct behaviors. When inferring models of neural activity, this proximity to criticality is thought to require the precise tuning of parameters. Here, we show that as the number of neurons increases, criticality can emerge naturally without fine-tuning. When computing observable statistics from parameters (the forward problem), some small regions in parameter space map to large regions in statistics space. These special parameters are precisely those near criticality. Thus, when inferring parameters from experimental measurements (the inverse problem), models concentrate near critical points, and this concentration becomes stronger as the system grows. We illustrate this flow toward criticality across many large-scale recordings in the mouse brain. In the Curie-Weiss model of Ising spins, we find that all of the recordings collapse to a first-order phase transition, despite substantial differences in the underlying systems. Together, these results suggest a resolution to the tension between criticality and fine-tuning in models of neural activity.

Estimating the preconsolidation pressure ($\sigma'_p$) from one-dimensional consolidation (oedometer) tests is critical in geotechnical engineering for settlement analysis. Traditional graphical methods, such as the Casagrande procedure, may introduce uncertainties, particularly when interpreting rounded $e$-log($P$) curves typical of disturbed specimens of soft clays and silt deposits. This paper introduces PyelogP, an open-source Python library designed to calculate $\sigma'_p$ using the strain-energy method proposed by Becker et al. (1987) as an automated and reproducible alternative. The algorithm combines natural cubic spline interpolation, knee-point detection via the Kneedle algorithm, and split-point linear regression within the work-pressure space. Physically informed thresholds, including overconsolidation ratio limits and second-derivative maxima (${d^2 e}/{d(\log \sigma')^2}$), are incorporated to establish pre-yield and post-yield fitting boundaries. The performance of PyelogP is evaluated against a suite of 22 experimental consolidation datasets covering various clay deposits, including Saint-Alban clay and San Francisco Old Bay Clay. The results demonstrate strong agreement with the published values ($R^2$ = 0.912, RMSE = 0.374, MBE = -0.080), while the $O(N^2)$ algorithm requires only a few milliseconds per curve for typical oedometer datasets and less than 150 milliseconds for the largest datasets.

Time resolution is an important parameter in particle detectors, as it determines their ability to distinguish between two temporally separated events. It can be decomposed into electronic and intrinsic contributions, the latter being influenced by scintillation, optical transport, detector geometry, particle type and energy, and the sensitive area of the photosensor. In this work, a Monte Carlo simulation based on Geant4 is used to investigate the intrinsic time resolution of a hexagonal BC404 plastic scintillator. The analysis includes the evaluation of the optical-photon distribution over the hexagonal surfaces and the characterization of the intrinsic time resolution for different interaction configurations. The intrinsic time resolution is investigated using muons as cosmic-ray proxies with energies ranging from 4~GeV to 1~TeV. In addition, the effect of surface reflectivity on optical-photon transport and timing performance is studied for reflectivities ranging from 0% to 100%. The results show that the intrinsic time resolution exhibits little dependence on the incident muon energy, while a clear dependence on the interaction position is observed. Furthermore, although the number of detected photons varies with the interaction position and surface reflectivity, the intrinsic time resolution is not solely determined by photon statistics, indicating that optical transport and geometrical effects play an important role in the timing performance. Across the investigated configurations, intrinsic time resolutions ranging from approximately 15 to 100~ps are obtained. These findings highlight the importance of the interaction position, optical transport, photon statistics, and surface reflectivity in determining the timing performance of hexagonal plastic scintillators and provide guidance for the optimization of their optical properties and geometry for timing-sensitive applications.

Ion cyclotron radio-frequency heating (ICRH) is a key auxiliary heating system in ITER, but high-power RF operation can enhance plasma-material interactions through rectified RF sheath potentials on antenna structures and nearby plasma-facing components. We present the first predictive application of the STRIPE (Simulated Transport of RF Impurity Production and Emission) framework to assess RF sheath-driven tungsten (W) erosion and global impurity transport from the ITER ICRH antenna under ITER-relevant neon-seeded conditions. STRIPE couples SOLPS-ITER plasma backgrounds, full-wave RF sheath calculations, geometry-specific ion energy-angle distributions, sputtering physics, and three-dimensional impurity transport. Simulations predict RF sheath potentials of 1 to 3 kV on antenna limiter sidewalls, increasing gross W erosion by about a factor of 64 relative to thermal sheath conditions and producing a gross source of 3.34e18 W atoms per second. Erosion is governed by RF-modified ion energy-angle distributions together with local plasma flux rather than sheath voltage alone. About 10 percent of sputtered W is locally redeposited, giving a net source of 3.01e18 W atoms per second. The RF-induced antenna source remains about three orders of magnitude smaller than the thermal divertor source and more than two orders of magnitude smaller than the integrated thermal main-chamber source. After 100 ms, about 22 percent of the mobile W inventory resides within the SOLPS-covered confined-plasma region, corresponding to an annular W concentration of 1.70e-6. These results indicate that the ITER ICRH antenna is unlikely to dominate the total W source budget under the conditions considered and demonstrate the need for coupled modeling of RF waves, sheaths, sputtering, redeposition, and global impurity transport.

Dense granular flows are a common occurrence throughout nature and industry, but still exhibit phenomena that are not fully understood. One of these phenomena is the segregation of particles that constitute a dense granular flow, where particles separate due to differences in properties such as size and density. This work approaches segregation from a new perspective by developing a force model for a single particle based on the observation that there are two opposing drag forces acting on the particle. Validation of the model in mono-disperse flows reveals a family of velocity profiles, including a Bagnold profile. Further validation is done by comparing with known particle scaling laws in literature. The force model shows that the mechanism of segregation is always present in dense granular flows and that mono-disperse flows are a unique case where it is not distinctly visible. The insights in this paper prove a fundamental new way of thinking about dense granular flows and its individual particles, opening the field for new research directions.

Optimizing slender elastic structures for renewable energy applications requires non-classical continuum formulations capable of accounting for spatial micro-interactions without sacrificing analytical tractability. Here, we extend beam vibration mechanics by replacing standard spatial derivatives with the Dunkl differential operator. This modification introduces a reflection-coupled mathematical structure that accounts for spatial parity effects across the beam domain. We formulate the governing dynamic equations into a generalized eigenvalue problem and derive exact analytical expressions for modal characteristics under standard boundary conditions. The classical limit confirms exact convergence to classical Euler-Bernoulli formulations. Parametric analyses reveal that the Dunkl parameter acts as a reflection-induced modulation parameter, significantly shifting natural frequencies and altering the modal characteristics of higher modes. These results provide an analytical baseline for dynamic optimization in lightweight structural components.

Dissimilar Al/Cu joints are essential to battery-pack assemblies; however, their mechanical strength is limited by brittle Al-Cu intermetallic compounds (IMCs) such as Al2Cu and Al4Cu9. Interlayer strategies to suppress these phases remain largely empirical, lacking a predictive framework linking interlayer chemistry to the phases that form and to their intrinsic mechanical character. Here an Ag interlayer is introduced and combines computational thermodynamics, first-principles calculations, microstructural characterization, and mechanical testing into a single self-consistent description of the joint. CALculation of PHAse Diagrams (CALPHAD) equilibrium and Scheil simulations predict the solidification path of the Al-rich Al-Ag fusion zone and explain why Cu incorporation is limited when Ag is present; energy-dispersive X-ray spectroscopy (EDS) and electron backscatter diffraction (EBSD) confirm an FCC Al-Ag solid solution as the dominant constituent. First-principles phonon calculations within the quasiharmonic approximation yield finite-temperature entropy and Gibbs energy, benchmarked against CALPHAD, while elastic constants assess ductility via the Pugh criterion (i.e., the bulk/shear (B/G) modulus ratio). All Al-Ag phases, including the observed solid solution, exceed the Pugh threshold of 1.75, whereas the targeted Al-Cu IMCs do not, giving a mechanistic basis for the interlayer's effectiveness. This microstructural change translates into improved performance: nominal strength rises from 47.9 to 67.4 MPa. Nanoindentation gives a fusion-zone reduced modulus of 82.8 GPa (Young's modulus 82.0 GPa), versus a calculated 0 K Voigt-Reuss-Hill value of 71.4 GPa. The present work establishes a transferable CALPHAD, first-principles, and experiment workflow for rational interlayer selection in dissimilar-metal joining.

We investigate turbulent boundary layers at Mach 2.5 over sinusoidal roughness at matched $Re_\tau=784$. We considered three wall temperatures, $T_w/T_r=$ 1.0, 0.7, and 0.4. Each wall temperature was repeated with a smooth and rough surface, the latter following a three-dimensional sinusoidal profile with effective slope 0.5 and matched $k^+=79.1$. In total six direct numerical simulations were completed. Analysis of mean wall shear and heat transfer detailed the augmentation in skin friction and heat transfer coefficient between smooth and rough cases; resulting in the classical failure of the Reynolds analogy for rough walls. We also show that differences in shear stress across wall temperatures are driven by the viscous component even though the pressure component dominates. The roughness sublayer was found to be $R_{RSL}=3k-6k$, consistent with the literature. Moreover, the wall offset $d=d_\Theta\approx0.5k$ for both momentum and thermal boundary layers, signifying the offset represents the half peak-to-valley height rather than the "mean roughness height." For the mean momentum boundary layer, we find the present conditions recover the incompressible roughness function $\Delta u_1^+$ when matched via the semi-local roughness Reynolds number $k^*$. For this reason, an equivalent sand-grain roughness of $k_s^*\approx3.7k^*$ is proposed. The existing compressible transformations hold regardless of wall temperature or roughness - contradicting recent claims otherwise. The generalized Reynolds analogy works for smooth walls but fails for rough walls; a roughness correction term is proposed and validated. Compressible mean temperature transformations fail for cold walls, especially when $(T_w-T_e)/(T_r-T_e)<0$. The thermal roughness function, $\Delta\Theta^+$, is reported with caution given transformation singularities.

Biological tissues grow by continuously producing, transporting, and reorganizing multiple interacting constituents. These processes are intrinsically coupled to finite deformation and residual stress. Existing models typically capture either finite growth kinematics or multi-constituent transport, but rarely both within a thermodynamically consistent setting. In particular, existing approaches do not consistently link the volume created by finite growth to the mass produced for each individual constituent. In this work, we develop a general continuum framework that unifies finite growth kinematics and multiphase mixture theory for fully saturated multi-constituent mixtures containing an arbitrary number of dilute dissolved solutes. Formulated in a solid-skeleton-based description, the framework rests on constituent-wise balance laws and a free-energy dissipation principle, from which thermodynamically admissible constitutive closures are derived for all mass-exchange, transport, reaction, and growth processes. The central novelty of the framework is a coupling between growth-induced volume creation and constituent mass production, expressed through volume accumulation fractions that distribute the newly created volume among the constituents while preserving saturation. We cast the resulting model in a total Lagrangian mixed weak form and specialize the general theory to a four-constituent, two-solute model of avascular tumor growth that couples nutrient transport, waste production, phenotype transitions between proliferative, hypoxic, and necrotic cells, volume growth, elastic deformation, and growth-induced residual stress. The model is implemented within a finite element setting and its capabilities are demonstrated on representative benchmark problems.

Directional amplification, in which signals are amplified selectively depending on their propagation direction, is a key resource for quantum information processing and stands in one-to-one correspondence with non-trivial non-Hermitian topology. So far, this correspondence has concerned the mean fields, and thus classical response. Here we turn to the quantum fluctuations, giving access to correlations and entanglement. For phase-preserving amplifiers, we derive analytic expressions for the normal and anomalous correlations, showing that non-trivial topology produces correlations that grow exponentially with the distance between modes and approach the largest values compatible with the uncertainty relations. The associated correlation length diverges at the topological phase transition. Entanglement nonetheless remains local, set by the competition between normalised anomalous correlations and the asymmetry of the mode occupations. For the bosonic Kitaev chain, a phase-sensitive amplifier, the system instead splits into two halves that are internally fully correlated yet mutually uncorrelated. Our work prepares the ground for exploring the quantum properties of non-Hermitian topological systems with state-of-the-art platforms such as cavity optomechanics and superconducting circuits.

N-body simulations are an essential tool for modelling the evolution of gravitating systems. Differentiable N-body simulations allow solving complicated reconstruction problems by connecting observations of evolved systems to simple informative priors on their initial conditions. While so far most reconstruction efforts focus on mildly non-linear large scale dynamics, the internal dynamics of galaxies and clusters should exhibit rich information about their formation. The exploration of reconstruction possibilities in this highly non-linear regime naturally requires highly performant differentiable N-body simulations with small scale forces. Here, we present JZ-FMM, a GPU-native differentiable implementation of the fast multipole method for N-body simulations. We show that the GPU oriented design of tree structure, dual tree traversal and multipole translation operators improves performance over established CPU-native and hybrid N-body codes by notably more than an order of magnitude. Further, we show that gradients can be evaluated efficiently and accurately so that JZ-FMM can be used as an essential building block in future field-level reconstruction efforts. We apply the new code to the reconstruction problem of a tidally stripped satellite to show that deeply non-linear problems can indeed be solved efficiently with simulation gradients, although appropriate care must be taken to navigate the complicated optimization landscape.

Electronic rearrangements associated with bond forming/breaking in catalytic enzymes require quantum mechanical (QM) treatment beyond classical molecular mechanics (MM). Hybrid QM/MM methods enable tractable simulations but require system-specific setup and are sensitive to the QM region choice and treatment of the QM/MM interface. We demonstrate quantum-accurate treatment of all-atom, complete enzymes in explicit solvent comprising up to 54k atoms and 1 microsecond of total simulation time using the machine-learned interatomic potential (MLIP) eSEN-omol. We reproduce experimental barrier trends for Claisen rearrangement in chorismate mutase, resolve critical intermediate states in PETase catalyzed polymer depolymerization, and distinguish mechanistic alternatives for metal-activated phosphoryl transfer in nucleoside diphosphate kinase. We realize 1000x speedups relative to typical QM/MM calculations without system-specific tuning. These results establish MLIPs as a practical route to QM-accurate simulations of enzyme catalysis.

In modern density functional theory, effective Hamiltonians are constructed to reproduce densities and forces of electrons at equilibrium. However, their nonlocal potentials leave systematic errors in spectral properties and real-time dynamics. Access to local optimized or inverted effective potentials would remove this limitation, but the numerical fragility in finite orbital bases has long prevented their wide adoption. Here, we introduce multipole splats, a class of trial potentials that carry the correct asymptotic decay required to support the unoccupied spectrum. By connecting the computation of effective potentials to variational and supervised variants of Hamiltonian learning, we recast both problems as stable nonlinear optimization formulated directly in standard orbital basis sets and applicable to any hybrid functional approximation. The resulting solver allows us to resolve spatial profiles of exchange-correlation potential errors during molecular dissociation and accurately reconstruct key excited states without empirical asymptotic corrections. We also show the deviation from the ionization potential theorem for different exchange-correlation approximations on a dataset of molecular systems. Multipole splats extract insights from established approximations and provide capacity for robust dataset generation for downstream processing and learning.

We present an analytical, statistical solution to the binary-binary (2+2) outcome of the chaotic non-hierarchical four-body problem. The solution is based on the density-of-states formulation pioneered by J. J. Monaghan. The method skips the computationally expensive integration of the equations of motion, and instead samples the outcome from the chaotic phase-space, subject to conservation of energy, momentum, and angular momentum. From the joint distribution, we extract marginal distributions of several key parameters using Monte-Carlo integration, and numerically verify them by comparing to an identical ensemble of scattering experiments produced by the FEWBODY code. From the comparison, we identify a regime not represented by the density-of-states formulation: the hard binary regime, where one binary is much harder then the four-body energy scale, and the system acts as an effective three-body system. We hypothesize that at this regime the system's probability distribution spreads over an effective reduced three-body chaotic phase space. The process of transfer from four-body to three-body phase-space is still not understood, and represents the next natural extension of density-of-states methods.

The energetic effect of a force error depends on atomic motion. We establish a directional residual-work coefficient combining directional curvature mismatch with the spatial distribution of the residual response. For conservative potentials force-matched at an anchor, it determines the leading signed work at the first crossing of a small force-error budget. At a 474-atom lithium-electrolyte interface, predictions fixed before future reference evaluations differ from measurements by less than 4.6% of predicted work across 24 prescribed endpoints. Changing only the initial velocity direction at fixed structure and initial total kinetic energy reverses the force-work ranking. At the same admitted time of 0.25 fs, one direction gives an 11.6% larger maximum force residual but 36.1% less work. The reversal recurs at a second structure. The framework connects force tolerances to reference-energy transfer, providing a physical basis for potential assessment and adaptive reference allocation.

Local heatwave hazard depends on fine-scale air temperature, but ground stations are sparse and reanalysis products such as ERA5 cannot resolve the terrain and land-surface contrasts that shape real heat exposure. We present a conditional diffusion emulator for high-resolution 2-m temperature downscaling, conditioned on static geography, a training climatology, exact-time ERA5 temperature, and solar and temporal features, guided at inference by score-based data assimilation (SDA): a differentiable Gaussian observation likelihood steers the diffusion score toward sparse revealed temperature observations without any retraining. On a controlled 32-case synthetic-grid protocol over AORC, guidance improves hidden-cell reconstruction over both ERA5 and a strong observation-proximal nearest-neighbor baseline once observation density reaches 1% (RMSE 0.318 vs.\ 0.431~K, winning all 32 cases), while sparser regimes still favor direct interpolation. We further map the full guidance-strength landscape across three observation densities, showing that the optimal strength shifts systematically with density and that over-guiding causes sharp, predictable degradation – giving a concrete operating recipe rather than a single untuned setting. The resulting fields are intended as a temperature layer for downstream heatwave-hazard products such as threshold exceedance and cumulative heat-burden. The present evidence is a controlled synthetic-grid validation; station-network and held-out-year evaluations are the next steps toward deployment.

Exploration drill targeting in structurally complex terranes is hindered by sparse sampling, heterogeneous datasets, and the ambiguity of geophysical inversions. Here, we present an uncertainty-aware 3D workflow for the acceleration of time-to-discovery in brownfield explorations and apply it to the Kogodai prospect in the Rudny Altai metallogenic province. We jointly analyse existing drilling and geophysical data in a comprehensive approach, revealing hidden patterns in already available data. Drillholes and trenches were desurveyed to a common 3D reference frame, and assays were composited to a consistent spatial support to facilitate joint modelling with geophysical inputs. We develop Bayesian deep-learning models to predict 3D fields of Cu grade together with chargeability and apparent resistivity while quantifying epistemic uncertainty via Monte Carlo sampling. The original contribution of this work is to treat the problem not as pointwise regression between co-located observations, but as joint learning of spatially continuous 3D fields from sparse, heterogeneous exploration evidence. The resulting 3D predictions delineate a principal mineralized trend and several localized candidate zones that coincide with elevated induced polarization (IP) responses, while uncertainty mapping highlights where predictions are robust versus where additional drilling would be most informative. The continuous Cu-grade field can also be thresholded to produce binary prospectivity maps, allowing the sensitivity of target delineation to the chosen cutoff to be evaluated. The outputs are intended for qualitative interpretation and risk-aware drill targeting rather than resource estimation, and we discuss key limitations arising from incomplete provenance metadata for geophysical products and heterogeneity of historical sampling.

A latent space network model places the nodes in a metric space and lets the probability of a tie decrease with distance. In a space of constant curvature, pairwise distances determine the positions up to an isometry. In the products and in the three remaining three-dimensional model geometries they do not. We study the three geometries that are neither of constant curvature nor products: the Heisenberg group, the solvable group and the universal cover of the unit tangent bundle of the hyperbolic plane. We ask what one network identifies about positions in them and when their geometry is detectable. Two anchors remove the isometry ambiguity. Small configurations are not determined by their distances, and generic local identification holds beyond a finite threshold, certified in the Heisenberg group. We derive the posterior on the quotient by the isometry group. For small configurations, the divergence to the nearest product or constant-curvature competitor is the stress component of the curvature difference and vanishes at high order in the scale; undirected ties therefore detect the geometry only in large networks with large enclosed areas. Directed ties expose it at first order: in the two twisted geometries, asymmetric preferences circulate around triangles in proportion to enclosed area, which no additive ranking produces. On dense competitive-game counter networks, the coupled model beats rankings on every geometry, degree-corrected rankings and free antisymmetric terms. A Euclidean model with the same coupled term matches it, so the gain is the coupling of similarity and circulation through shared coordinates. An additive-and-multiplicative-effects model predicts better still.

In the Path Integral Monte Carlo (PIMC) simulation of fermions in a harmonic trap, with and without pairwise harmonic interactions, the partition functions for any discrete number of imaginary time slices (or beads) and for any choice of the short-time propagator can be analytically obtained from the contracted determinant form of the propagator. This work shows that the resulting recursion relation can be reformulated in the $\lambda$-ring language, yielding a closed-form finite-bead partition function in two dimensions in terms of permutation statistics. This closed-form partition function can be evaluated by a special algorithm in $O(n^2)$ time, providing an exact and numerically stable scheme for reproducing the energies of the original (undoable) fermion PIMC simulation for $n=10^4$ or more fermions. This result provides a concrete framework in which the numerical instability of the sign problem is completely bypassed, and serves as a counterexample to the prevailing view that all truly fermionic PIMC sign problems are NP-hard.

We present a quotient-space Bayesian formulation for DESI-era anomaly interpretation. Given a pattern-labeled catalog with map \(i\mapsto \Act(i)\), the induced posterior \(p(\Act\mid D)\), sector inclusion probabilities \(P_\alpha\), co-activation probabilities \(P_{\alpha\beta}\), and grouped Bayes factors \(B_\alpha(D)\) are exact summaries over predeclared physical activation events. Pairwise comparisons such as \(\lcdm\) versus \(\wacdm\) remain ordinary Bayes-factor tests between specified families; the quotient construction addresses the coarser question of which physical sector carries posterior support when different sectors are represented by unequal numbers of catalog elements. We derive a sector-resolved DESI-CMB-SN likelihood specification for late-time background, early-time ruler, supernova calibration, perturbation, and gravitational-wave propagation sectors. The construction includes an Alcock-Paczynski/isotropic-scale BAO decomposition, a pure-ruler projection, analytic marginalization of low-rank supernova calibration modes, Fisher-normalized sector priors, inactive-sector leakage tests, log-evidence uncertainty propagation, and prior/sector-partition diagnostics. The result is a quantitative procedure for reporting model-comparison support at the level of physically interpretable sectors.

In-situ measurements in space plasmas indicate that the velocity distributions of charged particles are not in thermal equilibrium, deviating from a standard Maxwellian mainly due to anisotropies and suprathermal populations which enhance high-energy tails. Although the Standard ąppa}-Distribution (SKD) is well established in modeling these non-equilibrium distributions, its application is often viewed controversially due to certain unphysical implications, in particular divergent velocity moments. To address these issues, the Regularized ąppa}-Distribution (RKD) was introduced. Such advanced, in general anisotropic RKDs are invoked here for the first time to investigate oblique firehose instabilities, including those induced by the temperature anisotropy of electrons and protons, the dominant species in space plasmas. In weakly collisional plasmas, both of these instabilities are expected to play significant roles in the self-regulation of the macroscopic properties of space plasmas (e.g. the expanding solar wind) reported by observations. Is not yet possible to resort to a general dispersion tensor related to RKD plasmas (whose derivation is still a challenge), instead the Arbitrary Linear Plasma Solver (ALPS) is exploited here. The unstable solutions obtained for the already established Maxwellian & SKDs successfully validate the capability of ALPS. For RKDs, the instabilities confirm the stimulating effect of suprathermal populations, with lower ąppa} values generally enhancing the firehose growth rates. For the oblique electron firehose instability, RKDs substantially modify the competition between periodic & aperiodic branches only at intermediate angles & can sustain significantly increased growth rates of especially aperiodic modes at highly oblique propagation beyond both the Maxwellian unstable regime & the one that is accessible with SKDs.

Conventional semi-local density functional approximations for the exchange-correlation energy systematically underestimate fundamental band gaps. Advanced meta-generalized gradient approximations (meta-GGAs) can partially overcome this limitation through their orbital dependence via the kinetic energy density. In principle, the flexible meta-GGA form should allow for balanced performance across diverse properties. However, atomization energies, reaction barrier heights, and lattice constants, as well as band gaps, exhibit different sensitivities to the large parameter space of meta-GGA functionals. In this work, we explore the space of appropriate norms that guide the r$^2$SCAN-type functional construction through the interpolation function connecting two fundamental paradigms: the one-electron limit and the uniform electron gas limit. We introduce local modifications to a strongly-smoothed interpolation function and investigate their impact on functional performance. To further probe the limits of the meta-GGA framework, we include spin-unpolarized bonded systems among the set of norms. The resulting bonded-norm bn-r$^2$SCAN meta-GGA achieves balance: band-gap predictions approaching the accuracy of the ultra-nonlocal LAK meta-GGA and lattice constants of nearly r$^2$SCAN accuracy.

In aquatic environments, gradients in flow velocity and turbulence define a constantly shifting landscape that sets the physical constraints on propulsion and energy expenditure in fish. Successfully navigating these complex flows depends on the ability to sense and respond to subtle hydrodynamic cues in an energy-efficient manner. Revealing these mechanisms is central to understanding how fish identify and exploit favorable flow conditions. To this end, we designed a hydrodynamic maze with spatially varying mean flow and turbulence, creating a controlled heterogeneous flow landscape for understanding fish movement strategies. We found that fish escape energetically unfavorable regions by yawing their bodies relative to the flow. These yawed orientations appear to facilitate lateral migration through lift forces and may improve sensitivity to local flow variations. Notably, fish maintain body orientations near, but below, predicted stall conditions to exploit lift without a significant increase in drag. These findings provide insight into how fish navigate heterogeneous hydrodynamic environments, with potential relevance for both natural and engineered flow systems.

Stimulated Brillouin Scattering (SBS) is a nonlinear interaction between optical and acoustic waves in solids. First regarded as a parasitic process in optical fibers, it has gathered significant interest in microwave photonic applications such as optical signal processing and narrow linewidth lasers. While many Brillouin system demonstrations have been done on photonic chips, they struggle to provide simultaneous high Brillouin gain and narrow linewidth in a scalable platform, capable of integration with established photonic integrated circuits. Here, we present a novel integrated InGaP-on-SiO$_2$ platform, where a single crystalline InGaP waveguide layer offers superior material properties and strong nonlinearities to support SBS near 1550 nm wavelength. We demonstrate backward SBS with a measured high Brillouin gain coefficient of $588\, \mathrm{W}^{-1}\,\mathrm{m}^{-1}$ and a record narrow linewidth of 5.2 MHz at 9.346 GHz frequency shift. This work establishes a wafer-scale complementary metal-oxide semiconductor compatible fabrication process, paving a scalable path for high gain and narrow linewidth Brillouin photonics with applications such as precision signal processing.

We report simultaneous sub-Doppler cooling and optical dipole trapping of $^{39}$K-Cs and $^{41}$K-Cs mixtures. Both mixtures are cooled to temperatures $\sim10$ $\mathrm{\mu}$K, achieving performance comparable to that obtained with each species individually. To the best of our knowledge, this constitutes the first realization of a laser-cooled and optically trapped $^{41}$K-Cs mixture. For the $^{39}$K-Cs mixture, we additionally implement parallel spin-resolved Feshbach spectroscopy enabled by Stern-Gerlach separation. For potassium isotopes, we implement sub-Doppler cooling with $D_1$-line gray molasses, while for cesium the entire cooling sequence is implemented using $D_2$ transitions. We spin-polarize the atoms and confine Bose-Bose mixtures in a 1064 nm optical dipole trap. Using the resulting $^{39}$K-Cs samples, we observe 14 heteronuclear Feshbach loss features. Five agree with resonances reported previously, while nine have, to the best of our knowledge, not been observed experimentally before, including $p$-wave features and resonances in additional spin channels. The shorter lifetime of the $^{41}$K-Cs mixture in the optical dipole trap currently hinders systematic Feshbach spectroscopy, which we therefore do not pursue in this work. To characterize this limitation quantitatively, we study the decay dynamics of $^{39}$K-Cs and $^{41}$K-Cs mixtures under comparable temperature and density conditions, revealing a substantially stronger nonexponential loss in the $^{41}$K-Cs mixture. The demonstrated preparation of ultracold $^{41}$K-Cs mixtures provides a starting point for Feshbach-resonance and photoassociation spectroscopy of this previously unexplored isotopologue. Such measurements are essential for identifying suitable pathways for magnetoassociation and coherent optical transfer, and ultimately for the production of ultracold ground-state $^{41}$KCs molecules.

We prove that, for any given sufficiently regular one-particle density, there exists a unique external potential for which the solution to the many-body Schrödinger equation has this prescribed density. This implies in particular that one can exactly reproduce the time-dependent density of an interacting system using non-interacting electrons, which is at the heart of time-dependent density functional theory. Our result covers the Coulomb interaction and densities arising from extended nuclei.

We provide the first fully rigorous justification of time-dependent density functional theory in the continuum: Given a time-dependent density, we prove that there exists an external potential, unique up to a time-dependent constant, such that the corresponding Schrödinger equation reproduces the given density. This potential can be obtained with an iteration scheme. Our argument covers Coulomb interactions and does not need any Taylor expansion in time. It instead requires analyticity in space for the density and potential, which is compatible with extended nuclei.

Graph-based surrogate models offer a promising route to accelerate computational fluid dynamics (CFD) simulations on unstructured meshes. However, their development is limited by the scarcity of benchmark datasets spanning multiple flow regimes and standardized protocols for long-horizon autoregressive prediction. We introduce ONECYL (ONE CYLinder), a new benchmark for unsteady flow past a circular cylinder across laminar, transitional, and high-Reynolds-number regimes. The benchmark comprises 450 high-fidelity Variational Multiscale finite-element simulations (270,000 flow snapshots) with randomized cylinder geometries, providing time-resolved velocity and pressure fields together with mesh connectivity, geometric descriptors, Reynolds numbers, and integrated aerodynamic quantities. Beyond the dataset, ONECYL establishes a unified evaluation framework combining full-field rollout errors, virtual probes, and drag and lift predictions to assess numerical accuracy and physical fidelity. To accompany the benchmark, we develop a Graph Transformer as a reference baseline predicting velocity and pressure fields autoregressively on unstructured meshes. Using ONECYL, we investigate geometric representations and physics-based regularization across the three Reynolds-number regimes. The results show that explicitly encoding the cylinder geometry through a level-set representation consistently improves long-horizon prediction accuracy and generalization to unseen geometries, while divergence-based regularization becomes increasingly beneficial as flow complexity increases. The ONECYL benchmark and its Graph Transformer baseline provide a reproducible framework for evaluating graph-based surrogate models and establish a foundation for future research on long-horizon prediction of unsteady bluff-body flows.