Papers

Force fields are usually formulated directly in nuclear configuration space, whereas density functional theory is naturally formulated in terms of external potentials, densities, and variational duality. We show that exact force fields are variationally induced by DFT: the Born-Oppenheimer potential-energy surface is the pullback of the external-potential energy functional along the map from nuclear configurations to Coulomb potentials. In the Lieb formulation of density functional theory, the density is the first functional derivative of the energy with respect to the external potential, while the density-density response function is the second. Pulling these derivative objects back to nuclear configuration space yields the force and the nuclear Hessian, together with explicit terms induced by the nuclear-generated potential and the nuclear-nuclear repulsion. The resulting picture places force fields, density functional theory, and response theory within a single derivative hierarchy. The purpose of the present work is conceptual rather than algorithmic.

Truly chiral phonons are lattice eigenmodes that combine broken mirror symmetry with circular atomic motion. They can mediate angular-momentum-selective interactions in quantum materials, yet directly resolving both their chirality and underlying circular motion remains challenging, especially in high-symmetry crystals. Here we show that symmetry-selective terahertz difference-frequency spectroscopy provides a phase- and polarization-resolved route to identifying truly chiral phonons in a tabletop experiment. Using $\alpha$-quartz as a benchmark, we validate this approach by resolving phonon chirality via chiral-sensitive $\chi^{(2)}_{ijk}$ tensor elements ($i \neq j \neq k$), while vector-field detection directly reveals a time-dependent polarization rotation arising from circular ionic motion and thus nonzero angular momentum. Applying the same protocol to tetragonal $\alpha$-TeO$_2$, we isolate chiral $E$-mode resonances below 5~THz and directly verify their circular lattice motion, thereby resolving a symmetry-imposed ambiguity in chiral-phonon identification in fourfold-symmetric crystals. Our results establish symmetry-selective nonlinear terahertz spectroscopy as a general route to identify truly chiral phonons in condensed matter systems.

Campi Flegrei, a large caldera in southern Italy, is among the most hazardous volcanic systems on Earth, directly threatening over one million people. Since 2005, it has entered a phase of accelerating uplift accompanied by intensified seismicity, raising the key question of whether this evolution will culminate in eruption, a bradyseismic peak, or another regime change. Here, we show that the acceleration of seismicity and geodetic deformation is better described by a regularised finite-time singularity than by exponential growth, implying not just a better empirical representation but a different underlying process with potentially dire consequences for the system's subsequent evolution. Independent analyses converge on a critical time $t_c \approx 2030-2034$, with uplift projected to reach about 4 metres by the early 2030s. Geochemical and statistical evidence indicates that deep magmatic volatile input drives this evolution by progressively pressurising the crust. Although no evidence of imminent eruption is found, the system appears to be approaching a critical mechanical threshold whose outcome remains uncertain, requiring sustained high-resolution monitoring and continuously updated forecasts.

Kohn-Sham (KS) eigenvalues are routinely compared with angle-resolved photoemission (ARPES) and used as input for many-body methods, yet density functional theory (DFT) assigns them no physical meaning. For alkali and alkaline-earth metals, KS bandwidths overestimate ARPES measurements by 20-35%, a discrepancy that persists across all exchange-correlation functionals. We construct an effective field theory (EFT) of the inhomogeneous electron gas and show that two conditions imply KS bands are the quasiparticle bands, up to a frozen-core renormalization factor zcore: a scale separation between core excitation energies and the valence Fermi energy, and an approximate Galilean invariance of the uniform electron gas confirmed by diagrammatic Monte Carlo. This factor reflects dynamical core excitations that conventional pseudopotentials freeze out and no static potential can capture. The correction 1-zcore reaches 20-35% for alkali metals but falls below 5% for Al and Si, explaining both the failure and success of KS band theory. We derive a closed-form post-SCF formula and validate it for Li, Na, K, Ca, Mg, Al, and Si; the predicted quasiparticle bands resolve the long-standing ARPES bandwidth discrepancy, matching embedded dynamical mean-field theory at negligible cost. This work also exemplifies first-principles agentic science, a direction particularly suited to the AGI-for-Science paradigm: an LLM-co-developed derivation with controlled approximations, verified symbolically and against a few experiments, becomes a deterministic harness for agentic scale-out, resolving simultaneously the LLM audit bottleneck and the non-falsifiability of fit-based AI-for-science.

Inverse design has transformed vast physical parameter spaces into a substrate for emergent functionality, raising the tantalizing prospect of relocating intelligence from the digital domain into the physical world itself. Nowhere is this prospect more consequential than in sensing, where the analog-to-digital interface imposes a fundamental bottleneck: information not captured by the hardware is irrevocably lost to any downstream algorithm. Existing approaches improve information capture through either sensor hardware optimization or adaptive measurement strategies operating on fixed hardware, but rarely both in concert. A principled migration of intelligence from digital to physical demands their joint optimization: the sensing geometry must be co-designed with a policy that determines what to measure next. We formulate this co-design as joint dynamic programming (joint-DP), a unified optimization over sensor geometry and a Bellman-optimal adaptive measurement policy. The outer hardware gradient is obtained through differentiable dynamic programming with a sharp Bellman maximum. A hierarchy of relaxations extends the framework from small discrete POMDPs to freeform photonic topologies with more than $10^5$ design pixels.

Cooperation in groups underpins collective responses to challenges from climate governance to public goods provision, yet how moral evaluation sustains it remains poorly understood. Indirect reciprocity – cooperating to build a good reputation – is well characterized for pairwise interactions, but real collective action requires individuals to be judged against the reputational profile of an entire group. Here we develop a general framework for multiplayer indirect reciprocity and show that stable group cooperation obeys a simple organizing principle: `all good, help; one bad, halt'. This rule is both necessary and sufficient for cooperation to emerge, and it recovers the classical leading eight norms in the pairwise limit. We further show that group structure fundamentally changes reputation dynamics: unlike pairwise models, which are monostable, multiplayer systems exhibit bistability and hysteresis, with a critical tipping point separating cooperative and defective regimes. Assessment of the latent norms of large language models reveals that they shift toward punitive defection when provided with richer social information, yet fail to follow the full logic of `all good, help; one bad, halt'. Our results establish a unifying principle for reputation-based cooperation in groups and provide a benchmark for evaluating cooperative alignment in artificial intelligence.

Precipitation in complex terrain is governed by orographic processes operating at scales of a few kilometers, yet climate models typically run at resolutions of 50–100~km where this topographic detail is absent. Dynamical downscaling with high-resolution regional models such as WRF can resolve these processes, but the computational cost – months of wall-clock time per scenario – precludes the large ensembles needed for uncertainty quantification. We present WxFlow, a conditional generative model based on flow matching that learns to map coarse-resolution climate model output and high-resolution topography to calibrated probabilistic ensembles of fine-scale precipitation fields. Applied to 4~km WRF simulations of maximum 3-day snowfall over southeast Alaska, WxFlow achieves 87.8% improvement in spectral fidelity and dramatically lower Continuous Ranked Probability Scores relative to conventional lapse-rate-corrected bicubic downscaling, while generating 50-member ensembles in seconds on a laptop. Ensemble spread is spatially coherent and governed by topography, reflecting physically plausible uncertainty structure. All code is available at https://github.com/glide-ism/wrf-flow.

Accurate and real-time sensing of targets in three-dimensional (3D) environments is essential for modern machine vision, underpinning emerging technologies such as autonomous systems, robotic manipulation, augmented reality, and intelligent surveillance. However, state-of-the-art 3D sensing approaches typically rely on complex postprocessing of multi-view images or LiDAR point clouds, resulting in considerable computational load, power consumption, and latency. To address these challenges, we propose a metasurface-encoder integrated optoelectronic neural network architecture that compresses 3D information into two-dimensional images by encoding depth using double-helix point spread function generated by a metasurface. The depth-encoded images are captured with a conventional monocular camera and subsequently processed by a lightweight shadow ResNet neural network. We experimentally validate the proposed architecture on the MNIST and Vehicle-Image datasets, achieving high accuracy simultaneously in target classification and depth estimation, thereby enabling real-time target tracking. The framework is readily extendable to other depth- or angle-encoding metasurfaces for multidimensional compression and detection. Our results demonstrate the effectiveness of the meta-optic-encoder/electronic-decoder paradigm in significantly reducing network complexity and computational burden while maintaining strong performance for smart vision sensory applications.

Data assimilation (DA) integrates observational information with model predictions to improve state estimation in complex systems. While filtering provides the basis for online forecasts by using only past and present observations, it can exhibit delays and biases when the underlying dynamics evolve rapidly or undergo regime transitions. Smoothing, which additionally incorporates future observations, provides a natural pipeline for hindcasting and reanalysis that yields an uncertainty reduction beyond the filter. This paper introduces an ensemble Kalman–Bucy smoother (EnKBS) for continuous-time DA of nonlinear dynamical systems, where the smoother's conditional distributions are reconstructed using ensemble moments. The result is a derivative-free framework that does not require explicit computation of tangent-linear or adjoint models, which recovers the exact smoothing mean and covariance equations in the infinite-ensemble limit for linear–Gaussian systems. Incorporating standard regularization techniques for high-dimensional systems, such as covariance localization and inflation, the skill of the EnKBS is demonstrated in various important scientific problems. By integrating future observations, which reveal the underlying causal mechanisms for retrospective state updates, the EnKBS is used for Bayesian-based inference of causal relationships and their temporal influence range in a dyadic trigger-feedback model and the development of a causality-driven iterative learning algorithm that identifies the structure and recovers the hidden parameters of a nonlinear reduced-order model mimicking midlatitude atmospheric circulation. Notably, both tasks remain effective with an ensemble size of $O(10)$ under partial observations, suggesting that EnKBS can support the instantaneous discovery of high-dimensional complex systems over time.

We solve the time-dependent Schrödinger equation by learning the score function, the gradient of the log-probability density, on Bohmian trajectories. In Bohm's formulation of quantum mechanics, particles follow deterministic paths under the classical potential supplemented by a quantum potential depending on the score function of the evolving density. These non-crossing Bohmian trajectories form a continuous normalizing flow governed by the score. We parametrize the score with a neural network and minimize a self-consistent Fisher divergence between the network and the score of the resulting density. We prove that the zero-loss minimizer of this self-consistent objective recovers Schrödinger dynamics for nodeless wave functions, a condition naturally met in quantum vibrations of atoms. We demonstrate the approach on wavepacket splitting in a double-well potential and anharmonic vibrations of a Morse chain. By recasting real-time quantum dynamics as a self-consistent score-driven normalizing flow, this framework opens the time-dependent Schrödinger equation to the rapidly advancing toolkit of modern generative modeling.

To grow the quantum information science and technology workforce, opportunities for students to gain experiential learning and build a sense of belonging in the broader community are essential. The Undergraduate School on Experimental Quantum Information Processing (USEQIP) is a two-week summer school for undergraduate students that has been held since 2009 with the goal of introducing undergraduate students from around the world to the tools of quantum information research, paired with a summer internship program. Here we report on the structure, impact, and outlook of the program, including hands-on laboratory activities refined over many iterations of the program. We highlight the career trajectories of program alumni, many of whom have made significant contributions to the quantum field.

We have used new magnetotelluric data collected in the Curnamona Province and the adjacent part of the Delamerian Orogen margin to image electrical conductivity structures and to inform the understanding of the crustal architecture within the regional geological context. The preferred 3D resistivity model confirms, and resolves in greater detail, crustal-scale conductive features that have been mapped by the long-period data collected at half-degree spacing as part of the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP), that is, the prominent Curnamona Province Conductor and the two Nackara Arc conductors. The new model reveals that the eastern Nackara Arc (ENAC) conductor continues as the Broken Hill Conductor (BHC) into the Curnamona Province. Regional geological considerations suggest that its formation is possibly linked to rifting/extension in the early Cambrian. Although we recognise that the east-west trending Wilcannia Conductor could be a possible continuation of the ENAC-BHC zone, integration with recently acquired deep seismic reflection data and evaluation of the geological setting lead us to suggest that they are not genetically linked. We suggest that the Wilcannia Conductor is younger and most likely is related to late Delamerian (~500 Ma) or Siluro-Devonian magmatism. Finally, these conductivity anomalies may represent large-scale trans-crustal structures that control the emplacement of low volume alkaline ultramafic magmas, and show a spatial relationship with certain mineral deposit types, suggesting a possible control on the distribution and formation of metallogenic provinces/belts in the region. This will be further investigated in future work.

Fluid models offer crucial computational efficiency for plasma simulations, yet accurately capturing kinetic effects like Landau damping remains a fundamental challenge. While conventional closures (e.g., Hammett-Perkins and Hunana) are widely used, their fidelity relative to exact kinetic response degrades significantly depending on the perturbation wave number. Here, we propose a novel wave-number-dependent closure condition for the three-moment fluid equations that explicitly preserves the primary dispersion relation. By mapping Padé approximant coefficients directly to the kinetic roots of the collisionless Vlasov-Poisson system, we derive an analytical closure that rigorously embeds exact kinetic scaling across all spatial scales. We further demonstrate that this framework readily extends to collisional plasmas via the BGK model. This deterministic approach precisely captures the long-term macroscopic evolution of fluid moments and field energy, offering a rigorous foundation for high-fidelity fluid modeling.

Action-detected two-dimensional electronic spectroscopy (A-2DES) could potentially be a versatile chemical tool with applicability across a range of photophysical observables such as photocurrent, photoionization, or fluorescence. However, a prominent absence of excited state energy/charge transfer dynamics signals in archetypal photosynthetic proteins has suggested severe limitations of A-2DES in probing large aggregates where sensitivity to excited state dynamics is proposed to go down as 1/N, where N is the aggregate size. We report measurements of energy transfer dynamics in a cyanobacterial protein through both conventional and fluorescence 2DES (F-2DES), where the dynamics reported by F-2DES is quite prominent and comparable to that measured by conventional 2DES. Analysis of our experiments combined with coarse-grained simulations of the spectra suggest that the 1/N limit argument, which assumes infinitely fast intra-exciton manifold equilibration, is modified in case of cyanobacterial proteins because of slow annihilation. Our results suggest that action detection may in fact be well-suited to probe exciton diffusion across weakly coupled systems.

Inertial microfluidic devices (IMDs) offer low-cost, high-throughput alternative techniques for many traditional particle- (or cell-) manipulation tasks, but simulating them requires being able to predict particle migration, and thus particle lift forces, under a variety of possible channel geometries. Recent work has demonstrated that machine learning models can be used to drastically speed up these numerical simulations, but doing so required training individual models for every unique channel cross-section type (e.g., rectangular, triangular) – shifting the burden from the simulation step to the training step. In this paper, we develop a novel approach for predicting particle lift forces that contains no explicit geometric parameters. We train a neural network model using a new parameter set and show that while it performs comparably to existing models on channel geometries in the training set, it is able to generalize to unseen channel geometries far more effectively. We show that the lift force model developed herein can be easily transferred to particle tracing simulation software, where it is capable of predicting particle migration patterns consistent with the literature across a variety of channel designs.

Within a simple unified framework of classical electrodynamics that incorporates background bound charges and currents, we have analytically determined the attenuation constant, phase constant, and reflectivity of Drude metals across the full frequency spectrum ($0 < \omega < \infty$).Our result for reflectivity agrees well with existing experimental data for both plasmas and seawater. Notably, for highly conductive Drude metals ($\omega_p \tau \gg 1$), the attenuation constant simplifies to $k_- \simeq \sqrt{\frac{\mu \epsilon}{2}} \sqrt{\omega_p^2 - \omega^2 + \vert{}\omega_p^2 - \omega^2\vert{}}$ across a broad high-frequency range spanning the plasma frequency $\omega_p$. The branch-point singularity at $\omega=\omega_p$ in this relation reveals opto-critical behavior near $\omega=\omega_p$ in properties such as the attenuation constant, group velocity, and complex dielectric constant, for which we have determined the corresponding opto-critical exponents. Furthermore, we have incorporated quantum corrections into these optical properties via the Drude-Sommerfeld model with Thomas-Fermi screening.

In wall-modelled large-eddy simulations of hypersonic boundary-layer transition, Hoffmann, Chamarthi and Frankel reported that characteristic reconstruction based on conservative-variable eigenvectors produced markedly better results than the corresponding primitive-variable implementation. The observation was empirical. A subsequent wave-appropriate conservative reconstruction (WA-CR) algorithm used a rank-one entropy correction based on the premise that contact-discontinuity error lies in a single conservative entropy/contact direction. This note gives the algebraic foundation for both observations. For the standard conservative curvilinear eigenvectors, the density row of the right-eigenvector matrix contains exact, metric-free zeros in the shear columns, so shear waves carry no density perturbation and a contact discontinuity is represented by the conservative entropy eigenvector alone. The conservative left eigenvectors provide the dual projection property: the entropy amplitude is obtained with a metric-independent left eigenvector and has unit contact scaling, while total-energy perturbations have zero projection onto the shear amplitudes. In the standard primitive curvilinear eigenvectors, by contrast, shear right eigenvectors contain metric-dependent density components and the primitive entropy left eigenvector contains metric-weighted tangential-velocity terms. Thus the conservative formulation supplies the two algebraic requirements for an exact, sufficient, metric-invariant, rank-one entropy correction: metric-independent entropy projection and a metric-independent entropy update direction. Curvilinear metrics make the distinction explicit, but the conservative state-space contact direction is already the natural direction underlying WA-CR even on Cartesian grids.

The single-ionization rate coefficient of a plasma neutral depends both on the microscopic electron-impact cross section and on the macroscopic shape of the electron energy distribution function (EEDF). We present a reproducible benchmark and sensitivity study – not a new theory – of these two effects for the three lightest neutrals He, Li, and Be, combining the recommended Bell~et~al.\ (1983) cross sections with a properly normalized two-temperature Tsallis $q$-generalized EEDF and varying $q$ on both sides of the Maxwellian limit and the hot-electron fraction $f_{\mathrm{hot}}$ at $T_{\mathrm{hot}}=10\,T_{\mathrm{bulk}}$. The calculation cleanly separates two independent uncertainty axes – cross-section model (Bell vs.\ Lotz) and EEDF shape (Maxwellian vs.\ Tsallis). The Bell–Lotz spread on $\tau_M$ is small for He (within about $7\%$), moderate for Be ($\lesssim 17\%$), and largest for Li (up to $+95\%$ at $T=1$~keV); sub-extensive distributions ($q<1$) suppress ionization through a hard tail cut-off, while super-extensive distributions ($q>1$) enhance low-temperature ionization through a $\kappa$-like power-law tail with $\kappa=1/(q-1)$. The quantitatively safest non-Maxwellian cases are $q=1$ and $q=1.2$ ($\kappa=5$), which lie inside the finite-mean-energy regime; the cases $q=1.4$ and $q=1.6$ are retained as heavy-tail stress tests and should be read as qualitative trends rather than as quantitatively reliable predictions. Both EEDF effects scale with $I_p/k_BT$, so He responds most strongly and Li least. The full numerical pipeline is released as a persistent reproducibility package, intended as a drop-in non-Maxwellian ionization module for collisional-radiative and ionization-balance modelling of light-neutral plasmas.

We propose a quasilinear (QL) flux model in which the saturation amplitude is uniquely determined using multiscale gyrokinetic ordering relations. The model is fully self-contained within a linear framework and does not rely on calibration against nonlinear simulations or mixing-length estimates. The wavenumber-dependent flux is given in ion gyro-Bohm units with a weighting factor of $|k_\theta \rho_i|$, such that its area integral in the log-linear scale yields the total flux, as employed in multiscale simulations. In systems with comparable ion and electron temperature gradients, the QL ion energy flux reproduces nonlinear simulation results in both its wavenumber dependence and absolute magnitude. In contrast, the QL electron flux is predominantly generated at electron scales, indicating that the shift of electron-scale transport toward ion scales observed in nonlinear Gsimulations is not captured within the present linear framework. We argue that the relation $Q_i\sim Q_e$, obtained as a closed conclusion of the QL model, may be predictive of simulation results if the area-integrated flux is conserved in nonlinear energy cascade process.

Cellular differentiation is governed by gene regulatory networks, the high-dimensional stochastic biochemical systems that determine the transcriptional landscape and mediate cellular responses to signals and perturbations. Although single-cell RNA sequencing provides quantitative snapshots of the transcriptome, current methods for inferring gene-regulatory dynamics often lack mechanistic interpretability and fail to generalize to unseen conditions. Here we introduce Probability Flow Matching (PFM), a scalable framework for learning biophysically consistent stochastic processes directly from time-resolved single-cell measurements. Applying PFM to three hematopoiesis datasets, we show that models with similar interpolation accuracy can encode fundamentally different dynamics, with only biophysically consistent formulations accurately capturing mechanisms of lineage transitions, fate specification, and gene perturbation responses. We further demonstrate that PFM accommodates unbalanced populations, enabling simultaneous inference of cellular proliferation and death dynamics. Together, these results establish PFM as a flexible, scalable framework for integrating mechanistic modeling with single-cell omics.

Tensor network (TN) methods are well established for computing partition functions in statistical mechanics, though this use has traditionally been limited to lattice models. We extend the scope of TN methodology to interacting particle systems in continuous space. Through a real-space discretization combined with a cell-based coarse-graining scheme, we formulate an effective lattice model that explicitly preserves spatial locality. The partition function of this model is represented as a TN, and the thermodynamic quantities are computed via boundary contraction. We apply this framework to the two-dimensional hard-disk problem and demonstrate the strengths of the TN formulation compared to existing Monte Carlo simulations.

Thermal conductivity of aligned polymer molecules can be exceptionally high along the alignment direction due to energy transport through strong covalent bonds. At the same time, it is highly sensitive to molecular conformation, varying by orders of magnitude as a result of gauche kinks. Here, we theoretically investigate phonon transport in kinked polymers by numerically evaluating thermal conductivity and interpreting the results in terms of phonon scattering from randomly distributed kinks. For strongly aligned polymers with restricted deviations from a linear backbone, we find that heat transport becomes superdiffusive at long lengths, with thermal conductivity scaling as $\kappa \propto L^{1/3}$. At shorter lengths, thermal conductivity exhibits non-monotonic behavior: it increases at very short scales due to ballistic transport of almost all phonons, then decreases at intermediate lengths due to the Anderson localization of most phonon modes. These results are consistent with experiments and molecular dynamics simulations, and they elucidate the microscopic mechanisms governing heat transport in polymers.

We identify materially defined regions in unsteady two-dimensional flows that combine finite-time contraction with elevated accumulated intrinsic rotation along trajectories, which we term Lagrangian rotating contracting structures (LRCS). These regions are detected using existing objective diagnostics – the Lagrangian-averaged vorticity deviation (LAVD) together with direct tests of material contraction – without relying on the geometry of LAVD level sets. In strongly deforming flows, LAVD maxima need not correspond to vortical regions or be enclosed by regular level sets, rendering geometry-based identification unreliable. Nevertheless, regions exhibiting inward spiraling motion and contraction can be extracted by combining LAVD with a contraction criterion. Applications to atmospheric and oceanic flows show that such behavior arises both in twisted LAVD fields generated at submesoscales and in mesoscale flows where it is enhanced by inertial effects, with finite-time contraction providing the dynamical constraint that isolates materially organized regions with elevated intrinsic rotation.

We present case studies of two impulsive solar energetic electron (SEE) events during which particles at energies from 1-600 keV were detected by THEMIS-ARTEMIS orbiting the Moon, Wind at Earth's first Lagrange point, and (for one event) STEREO-A located at 1 AU, off the Sun-Earth line. The SEEs were initially highly anisotropic, traveling outward along the magnetic field with distinct energy-time dispersion. For one event, the spectra contained inverse velocity dispersion (IVD) signatures, whereby electrons at intermediate energies arrived to the spacecraft before those at higher energies. Similar features were recently discovered within 1 AU for energetic protons; this represents the first IVD detection for energetic electrons at Earth's orbital distance. During both events, a second beam of counter-streaming electrons was detected after a short time. Based on the time-delay in the detections at various energies, the path traveled by these counter-streaming electrons was on the order of 1-2 AU. We show that an interplanetary coronal mass ejection (ICME) passed the spacecraft a few days prior to the onset of each event and was located beyond 1 AU when the SEEs were detected, suggesting that the electrons were part of the same population, but reflected off the shock front of these precursor ICMEs. In the context of solar system exploration, this represents an unidentified hazard for astronaut safety beyond low-Earth orbit: although the initial phase of impulsive SEE events typically stream anti-Sunward, ICMEs located beyond Earth provide a mechanism for hazardous particles to travel Sunward during extreme events.

Classical optical frameworks such as the discrete dipole approximation (DDA) assume that the linear spectrum of coupled quantum emitters can be computed solely from the linear susceptibilities of individual constituents. However, recent polariton studies show that cavity linear response can encode nonlinear optical susceptibilities. Here, we demonstrate that this phenomenon is more general: emitter-emitter interactions allow nonlinearities of individual emitters to emerge in the linear response of arrays, without cavities or permutational symmetry. To illustrate this phenomenon, we show linear spectra for coupled heterodimers and linear chains, and demonstrate that Raman features of individual monomers show up as vibrational sidebands of collective resonances. Moreover, tuning Raman-type anharmonicities enables systematic control of spectral features, establishing a genuine quantum optical effect in molecular aggregates and quantum emitter arrays, which goes beyond mean-field descriptions in light-matter interactions.

In this work, we propose and analyze a novel Schelling-type metapopulation model that examines how random relocations of families between neighborhoods can lead to segregation. The model consists of a large number of houses organized into $N$ neighborhoods with $L$ houses each, without any spatial structure. Houses can be occupied by either a blue or a red family, and families relocate – to an empty house selected uniformly at random – at a rate that depends only on the number of families of the other type within the same neighborhood. We study two mean-field regimes: the large $N$ limit with fixed $L$, and the large $L$ limit with fixed $N$. The associated mean-field systems of ODEs are derived, and their long-time behavior is investigated. As is often the case with Schelling-type models, we find a rich interplay between the model parameters and the social structure of the equilibrium distribution, which exhibits segregation in some parameter ranges. Our work demonstrates that segregation patterns can emerge even when the relocation mechanism is destination-agnostic.

This paper describes the design and implementation of a two-day quantum hackathon for underrepresented high school students in Nova Scotia, Canada. The first day of the hackathon is spent introducing students to quantum computing through hands-on activities, whereas the second day teaches students to apply this knowledge through guided challenges. Both days are informed by the theory of mastery learning and specification grading, with the full curriculum being crafted within the Integrated Course Design framework. This requires identifying situational factors unique to our target demographics, from which we develop learning outcomes, and then work backwards to a full curriculum with educative assessments. A novel aspect of our hackathon is that all circuit simulations are performed within Quirk: a decision based on best practices in computer science education. Based on feedback from students, we conclude that our hackathon successfully introduced students to the basics of quantum computing, and was able to reach most of our target demographics.

Non-Maxwellian distributions and their origins in space plasma have attracted significant attention due to their prevalence and impact on various astrophysical and space-related phenomena. This paper presents a theoretical study of the consequences of incorporating a Skew-Kappa distribution to describe the non-thermal electron distribution in the solar wind. By introducing a Krook-like term into the Boltzmann equation to represent collision effects, we investigate the dependence of the skewness parameter on plasma macro-dynamics. Our analysis focuses on understanding the departure from thermal equilibrium and the statistical behavior of the plasma under the influence of collisional processes. By analyzing the Boltzmann transport equation adapted to space plasma, we derive expressions for the skewness parameter as a function of plasma parameters and the collision effect. Our results provide valuable information on the relationship between skewness, collisional dynamics, and the statistical properties of space plasmas, namely $\delta \sim K_N$, the relationship between the skewness parameter and the effective Knudsen number. This study contributes to a deeper understanding of non-Maxwellian distributions and their role in astrophysical and space plasma phenomena.

Water exhibits an anomalous nonlinear density equation of state (EOS) as it approaches freezing, along with an increase in viscosity ($\mu$) and a decrease in thermal conductivity ($k$). Here we ask: how do these temperature-dependent material properties affect thermal convection in coldwater? We examine these effects within the canonical Rayleigh-Bénard convection (RBC) system, performing direct numerical simulations with coldwater between $0^{\circ}$C and the temperature of maximum density $3.98^{\circ}$C. To disentangle the effects of these properties, we consider two main cases: RBC with a quadratic EOS and constant $\mu$ and $k$; and RBC with a quadratic EOS and temperature-dependent $\mu(T)$ and $k(T)$. We show that the most significant consequence is a shift in the mean fluid temperature relative to `standard RBC'. The nonlinear EOS and the temperature-dependent $\mu$ and $k$ drive this shift in opposite directions, with the former dominating and producing a net decrease in the mean temperature. This decrease varies with the Rayleigh number $Ra$, reaching up to $0.08^{\circ}$C at $Ra=10^{8}$. Additionally, the onset of convection is affected, but the influence from the EOS and $\mu(T)$ and $k(T)$ compensate, resulting in a negligible net effect. Despite these changes, the Nusselt and the Reynolds numbers follow classical scalings in all cases. Our results establish how the anomalous material properties of coldwater affect local and global features of convection, evidencing the implications of model selection for studies of cryospheric water bodies.

Monodisperse spherical colloidal particles confined within emulsion droplets can crystallize into icosahedral clusters. Experimentally it was observed that a few large colloidal particles added as defects preferentially migrate to the vertices of the icoshedral clusters. To understand this structure formation phenomenon, we simulate the confined self-assembly of hard spheres in the presence of a small number of larger particles. The results demonstrate that large spheres are significantly influenced by concentric shells of small spheres near the crystallization transition. Entropic forces drive the large spheres to the cluster surface, where they settle into free energy minima at the icosahedron vertices. Notably, the addition of twelve large spheres results in the formation of a perfect icosahedral frame. Free energy calculations via umbrella sampling are used to quantify this process and show that both the migration to the cluster surface and the trapping at the vertices with trapping strength of multiple $k_\text{B}T$ results from free energy minimization. Moreover, our study reveals that the crystallization pathway and dynamics of large spheres are consistent across different systems, suggesting robustness of entropic trapping.