We investigate the balanced $M=4, p=4$ spin-glass model for a one-dimensional long-range proxy for the finite-dimensional short-range $p$-spin glass model to examine the nature of the glass transition beyond mean-field theory. We perform large-scale Monte Carlo equilibrated simulations for both fully connected and power-law diluted versions of the model. The critical temperatures extracted from the finite-size scaling (FSS) analysis of spin-glass susceptibility are in good agreement with theoretical predictions for $\sigma =0,0.25$ and 0.55. For these values of the long-range exponent $\sigma$ (which is the power of the decrease of the interactions between the spins with their separation), one might have expected that mean-field theory would provide a good description of the system. However, the spin-overlap distribution and the value of the $\lambda$-parameter do not provide numerical evidence for a one-step replica symmetry breaking (1RSB) phase transition. Instead, our results indicate a direct transition from the paramagnetic state to a full replica symmetry broken phase, with a renormalized value of $\lambda \equiv {\omega }_2/{\omega }_1<1$ suggesting a continuous FRSB transition, despite this ratio being equal to 2 at mean-field level. A value of $\lambda >1$ is required for the discontinuous 1RSB transition. We argue that strong finite-size effects and closely spaced transition temperatures remove the expected 1RSB transition for the system sizes which we can study. For values of the exponent $\sigma =0.85$ which roughly correspond to a three-dimensional system, we find that the renormalized value of $\lambda$ is again less than 1, with no signs of either the 1RSB transition or the continuous FRSB transition, suggesting that the Kauzmann temperature $T_K$ in three dimensions might be zero, and the complete absence of phase transitions in structural glasses.

We present a method for analyzing general time series by employing the history state formalism of quantum mechanics. This formalism allows us to describe a complete evolution based on a single quantum state, the history state, which simultaneously includes—also as a quantum system—the reference clock. It naturally leads to the concept of system-time entanglement, with the ensuing entanglement entropy constituting a measure of the effective number of distinguishable states visited in the history. Through a quantum coherent state embedding of the time series data, it is then possible to associate a quantum history state to the series. The Gaussian overlap between these coherent states provides thus a smooth measure of distinguishability between the series data. The eigenvalues of the corresponding overlap matrix determine, in fact, the entanglement spectrum and entropy of the history state, which provide a rigorous characterization of the evolution. As illustration, the formalism is applied to typical financial time-series data. Through the entanglement entropy and spectrum, different evolution regimes can be identified. Entanglement-based volatility indicators are also derived, and compared with standard volatility measures.

A hallmark of aging is loss of information in gene regulatory networks: gene expression becomes less correlated with biological age. These networks are tightly connected, raising the question of whether information could be restored by perturbing single genes. We develop a simple theoretical framework for information transmission in gene regulatory networks that describes the information gained or lost when a gene is “knocked in” (exogenously expressed). Applying the framework to gene expression data from muscle cells in young and old mice, we find that single knock-ins can restore network information by up to 10%. Our work advances the study of information flow in networks and identifies potential gene targets for rejuvenation.

Membrane extrusion experiments offer a powerful framework for investigating cell surface mechanics. However, providing a robust physical interpretation of the observed force-extension curves (FECs) remains a significant challenge. In this work, we introduce a minimal statistical-mechanical model for cellular membrane extrusion that accounts for discrete lipids, adhesion molecules, and the finite-size elasticity of the loading device. Focusing on a peeling geometry to isolate the essential physics of extrusion, we derive exact analytical expressions for the force-extension relations under two loading conditions: isometric (constant extension) and isotensional (constant force). Our results elucidate how lipid-lipid interactions, adhesion energies, and device stiffness govern the emergence of a mechanically induced conformational transition, providing a formal interpretation of the characteristic mechanical signatures reported in membrane-extrusion experiments, including force peaks, plateau regimes, and relaxation patterns. A central outcome of our analysis is that the isometric and isotensional conditions provide qualitatively different FECs of the same membrane system. This finding carries profound physical implications, suggesting that distinct experimental protocols applied to the same membrane may yield different mechanical responses.

Brains adapt to the statistical structure of their input. In the visual system, local light intensities change rapidly, the variance of the intensity changes more slowly, and the dynamic range of contrast itself changes more slowly still. We use a motion-sensitive neuron in the fly visual system to probe this hierarchy of adaptation phenomena, delivering naturalistic stimuli that have been simplified to have a clear separation of timescales. We show that the neural response to visual motion depends on contrast and that this dependence itself varies with context. Using the spike-triggered average velocity trajectory as a response measure, we find that this context dependence is confined to a low-dimensional space, dominated by a single principal dimension. To quantify the functional consequences of this structure, we measure the mutual information between stimulus and single-spike responses under models with and without adaptive structure. Across a wide range of conditions, incorporating adaptation consistently increases mutual information, indicating that context-dependent changes in the encoding model improve information transmission.

The mechanical behavior of granular media is strongly influenced by particle geometry, including both size and shape. While critical-state shear strength is widely reported to be insensitive to particle size distribution (PSD) in assemblies of grains sharing a common shape, grain shape itself has a strong impact on strength, with angular particles generally producing higher resistance than rounded ones. Many natural and industrial granular materials, however, combine broad size distributions with significant variability in particle shape, often correlated with particle size. Whether the classical PSD-insensitivity of shear strength still holds when particle shape varies across size classes remains largely unexplored. Here, we present a systematic experimental investigation of sheared granular assemblies composed of injection-molded particles with controlled sizes and shapes. Mono, bi, and polyshaped mixtures built from six characteristic grain geometries are tested under triaxial compression. We show that the classical PSD-insensitivity of critical-state shear strength breaks down when particle shape becomes size dependent. For a given PSD, assemblies with angular small grains and rounded large grains exhibit significantly higher shear strength than those with the opposite size–shape arrangement. This behavior persists over a wide range of PSDs and can be explained by enhanced geometrical interlocking promoted by angular small particles filling the voids between larger grains.

Growing bacterial colonies behave as active matter, with growth providing the activity. These colonies can take on granular properties such as microdomains. Based on this similarity, we perform a statistical analysis of pressure in simulated growing bacterial colonies under confinement, using methods commonly used to describe avalanches in driven granular materials and other intermittent systems. We find that as cells in a confined colony continue to grow, intermittent pressure drops occur alongside cell rearrangements. The statistics of these events have scaling exponents similar to mean-field predictions for avalanches in granular materials. This similarity suggests a new category of avalanche behavior, in which avalanches can be driven by single-particle growth rather than system-wide shearing or compression. These intermittent pressure-relieving cell rearrangements help explain how growing cell colonies can navigate complex environments with full confinement or growth bottlenecks.

The theoretical understanding of pattern formation in active systems remains a central problem of interest. Heterogeneous flocks made up of multiple species can exhibit a remarkable diversity of collective states that cannot be obtained from single-species models. In this paper, we derive a kinetic theory for multispecies systems of self-propelled particles with (anti)alignment interactions. We summarize the numerical results for the binary system before employing linear stability analysis on the coarse-grained system. We find good agreement between theoretical predictions and particle simulations, and our kinetic theory is able to capture the correct lengthscale in the emergent coexistence phases through a Turing-Hopf instability. Extending the kinetic framework to multispecies systems with cyclic alignment interactions, we recover precisely the same emergent ordering as corresponding simulations of the microscopic model. More generally, our kinetic theory provides an extensible framework for analyzing pattern formation and collective order in multispecies active matter systems.

We study a two-species Vicsek model with intraspecies alignment and asymmetric interspecies couplings, where one species aligns with the other while the latter antialigns. Motivated by recent results showing that globally coherent chiral motion is not a generic large-scale state of finite-range nonreciprocal flocking, we ask whether a chiral state can nevertheless be stabilized in the discrete-time, metric, nonreciprocal two-species Vicsek model, and if so, under what conditions. For equal populations and motilities, we find that the global chiral state is a long-lived finite-time state rather than an asymptotically stable phase at nonzero motility. Its breaking time increases rapidly as the self-propulsion speed is reduced or the strength of the nonreciprocal coupling is enhanced, explaining why systems with very low motility can appear stably chiral over conventional simulation timescales. This finite-time chiral regime is further limited to high-density systems and to system sizes that are small relative to the interaction range. Within this window, we also find that chirality appears primarily when aligning interactions dominate over antialignment, whereas stronger antialignment leads to species segregation and suppresses chirality. Conversely, introducing species asymmetry through population imbalance drives transitions from the finite-time chiral regime to porous parallel-flocking or antiparallel-flocking liquids; motility imbalance induces asynchronous oscillations and, in extreme cases, leads to segregation into moving clusters of the faster species within a more dispersed background of slower particles. Overall, these results indicate that chirality in the nonreciprocal two-species Vicsek model arises within a restricted regime set by density, motility, interspecies coupling, and system size, rather than being a generic outcome of nonreciprocal interactions.

The aggregation or clustering of proteins plays an important role in the formation of postsynaptic domains (PSDs) at excitatory and inhibitory synapses in neurons. PSDs are rich in scaffolding proteins that can transiently trap transmembrane neurotransmitter receptors, allowing them to regulate the strength of synaptic connections during learning and memory. Recently, a two-dimensional diffusion-mediated aggregation model of PSD formation was developed in which the spatial locations of the clusters are determined by a set of fixed anchoring sites. The system is kept out of equilibrium by the recycling of particles between the cell membrane and interior. This results in a nontrivial stationary state consisting of multiple stable protein clusters. In this paper, we use matched asymptotic methods to reduce the underlying reaction-diffusion model with moving interior boundaries to a corresponding finite-dimensional nonlinear system. The latter couples the cluster radii to the spatially averaged protein concentration in the bulk domain. We assume that the diffusivity $D$ is $O(1/\nu )$, where $\nu =-1/ln\epsilon$ and $\epsilon$ is a small parameter that characterizes the size of the clusters relative to the size of the bulk domain. The reduced dynamical system allows us to explore both the existence and stability of the multicluster stationary state while maintaining the effects of diffusion-mediated interactions between clusters.

Many morphogenetic processes in living systems are driven by active stresses within epithelial sheets that undergo simultaneous changes in thickness, curvature, and internal order. Existing theoretical descriptions of active nematics have largely focused on effectively two-dimensional layers of fixed thickness or prescribed geometry, limiting their ability to capture the coupled thickness-shape dynamics that are central to epithelial morphogenesis. Here, starting from a three-dimensional active nematohydrodynamic description, we derive a hierarchy of low-dimensional continuum models for thin active layers with finite thickness that can deform, flow, and reorient. Using an asymptotic long-wavelength theory, we show how activity couples nematic order to thickness variations and surface geometry in both flat and curved films. In flat layers, allowing thickness to evolve qualitatively modifies classical bend and splay instabilities, producing activity-driven modes that are not directionally constrained. In curved geometries, such as cylindrical films relevant to tubular epithelia and cnidarian body columns, thickness and shape instabilities become intrinsically coupled. Notably, we find that in isotropic phases both extensile and contractile activity can induce nematic order when thickness and shape are dynamic—contrasting sharply with active nematics on fixed substrates. Our results suggest experimentally testable mechanisms by which active stresses in epithelia can drive invagination, thickening, and curvature generation during development. More broadly, this framework provides a minimal physical theory linking active nematic order, thickness regulation, and morphogenesis in living tissues.

Non-neutral plasmas can be confined for long times in electromagnetic traps. Adiabatic cooling of such plasmas can be achieved by slowly decreasing the effective trap stiffness and letting the plasma occupy a larger volume. Here, we propose an optimal control strategy to cool an antiproton plasma in a time much shorter than that required by an adiabatic process. The optimal cooling protocol is found by minimizing an appropriate cost functional that measures the distance of the system from the desired “cold” state, while keeping the stiffness amplitude within prescribed limits. The proposed optimal protocol is shown to outperform other expansion-based cooling methods in terms of stability of the plasma, duration of the process, and volume occupied by the final plasma.

Lacking a universal law for granular flows across gravitational environments, fundamental processes such as hopper discharge remain vulnerable to failure in low gravity environments. A central challenge is clogging, the spontaneous arrest of flow through a constriction; yet previous studies report contradictory results on its dependence on gravitational acceleration. We identify the granular Bond number as the missing control parameter, defined as the ratio of intrinsic cohesive interactions among particles to gravity. Based on an in-bulk measurement of this quantity, we propose to rescale Earth-measured data for predicting granular behavior in low gravity. We present experiments of granular flow through an orifice under true reduced gravity (Moon and Mars), using an active drop tower, and extraterrestrial soil simulants as model cohesive materials. Our experiments reveal substantial increases in clogging probability, contrary to previously predictions, which depends on the properties of the material itself. When rescaled by the Bond number, seemingly conflicting results can be explained and collapse into a unified state diagram, predicting clogging across materials and gravitational accelerations. This establishes a general framework for the cohesion-to-gravity competition. Future space missions to the Moon, Mars, and asteroids will rely on such predictions of granular behavior in low gravity.

We employ a variational neural annealing approach for solving large-scale constrained portfolio optimization problems, mapped onto classical Ising-like Hamiltonians, and study its performance under finite annealing times. The method enables efficient exploration of high-dimensional feasible spaces and achieves competitive solutions on standard financial indices. To characterize the underlying optimization dynamics, we analyze the decay of the residual energy as a function of annealing steps and observe a power-law behavior for fixed problem size. Extending this analysis across different portfolio sizes, we examine whether the optimization dynamics are consistent with a dynamical finite-size scaling form and find that the residual energy data collapse onto a single scaling curve within the studied range. This scaling analysis yields an effective annealing-time exponent that quantifies how computational effort grows with problem size. Our results demonstrate that finite-size scaling concepts provide a useful framework for understanding and forecasting the performance of neural annealing-based optimization methods in realistic, constrained settings.

Suspensions of paramagnetic colloids exhibit kinetic arrest in strong magnetic fields. Through a dissipative process of toggling the field on and off, suspensions self-assemble into dense and dynamic steady-state phases. We construct a phase diagram using $k$-means clustering analysis based on domain elongation, α-shapes, contour metrics, and the degree of phase separation. We identify six characteristic structural regimes: a structureless phase, an arrested structure, sheets, ribbons, a spiky phase, and a transient fluid-fluid regime. We further report the distribution and alignment of domains and the generality of the results. We model self-assembled domain shapes using an equilibrium mean-field magnetostatic energy calculation, which predicts the surprising emergence of highly anisotropic structures driven by the sample's confinement.

The ability to optimize the properties of a material is particularly advantageous for applications. Here the tuneable properties of side-chain liquid crystal elastomers (LCEs) are investigated. LCEs were fabricated using the same acrylate monomers, varying the proportion of mesogenic groups to influence the phase, density, and mechanical properties. The density increases with mesogenic content, varying from $1.11\mathrm{g}/\mathrm{c}{\mathrm{m}}^3$ to $1.19\mathrm{g}/\mathrm{c}{\mathrm{m}}^3$ as the mesogenic content is increased from 51 mol% to 99 mol%. The 7% increase in density was attributed to closer packing of the nanoscale network, observed directly through Small- and Wide-Angle X-ray Scattering. A 22% reduction in the network end-to-end spacing was measured for LCEs as the mesogenic content was changed from 56 mol% to 75 mol%. LCEs with above 70 mol% mesogenic content were revealed to exhibit a frustrated smectic phase, with nematic phases confirmed for the lower mesogenic concentrations. The frustrated smectic materials exhibit a higher correlation length parallel to the director ($>125\AA$) than the nematic LCEs ($<100\AA$) and demonstrate significantly higher Young's moduli, with the greatest moduli perpendicular to the director measured at $21{}^{\circ }\mathrm{C}$ as 8.3 MPa for the smectic LCEs and as a constant value of 2.1 MPa for the nematic LCEs. Crucially, this demonstrates that only a small change in the mesogenic content can induce a transition from a nematic LCE to a frustrated smectic LCE. This series of materials reveals how the nanoscale packing, the macroscopic material properties, and the phase can be controlled via the mesogenic content in side-chain acrylate LCEs, suggesting design approaches that allow for the potential customization of the material properties for applications.

Neuromodulatory levels in the brain change moment to moment, yet are typically ignored in studies of neural criticality, where empirical support remains varied. Here we show in a biophysical network of bursting neurons that arousal acts as a mechanistic control parameter for a directed percolation phase transition. At intermediate arousal, neuronal activity exhibits peaked susceptibility, power-law avalanche size and duration distributions, universal shape collapse, and anomalous diffusion, jointly satisfying the hyperscaling relation of the 2+1-dimensional directed percolation universality class. Reanalyzing extracellular recordings in awake mice, we find that time-resolved susceptibility tracks pupil-inferred arousal as the model predicts. These results identify arousal as a physiological control parameter for neural criticality, offering a statistical-physics basis for the Yerkes-Dodson effect, and imply that arousal should be tracked at fine temporal resolution to obtain reliable empirical estimates of neural criticality.

Numerical implementation of a theory yields acoustic wave packets whose peak-to-peak speeds from source to receiver are supersonic in a dispersionless medium due to temporal interference between direct and boundary-reflected paths. The effect occurs when the source and receiver are near each other and at least one is within $c\widetilde{\delta t}/2$ of the boundary, where $c$ is the phase speed of propagation in the medium, and $\widetilde{\delta t}$ is the smallest temporal separation between the paths at which interference first occurs. This $\mathrm{direct}+\mathrm{reflected}$ path mechanism is distinct from mechanisms yielding superluminal speeds for electromagnetic waves (EM) including quantum tunneling and anomalous dispersion. For temporally interfering $\mathrm{direct}+\mathrm{reflected}$ acoustic paths, simulations yield a speed of information less than $c$. The speed of information from the interfering paths can exceed the speed derived from propagation only along the direct path. We conjecture these results will also hold for EM waves. If so, we prove the speed of information is less than or equal to the speed of light in a vacuum, so the effect does not violate special relativity. These theoretical and simulation results, as well as their conjectured EM extension, should be readily accessible to experimental verification.

Granular gases are commonly characterized through their velocity distribution, which provides access to the granular temperature. In experiments, velocity distributions are typically obtained by particle tracking, which becomes, however, limited at moderate and high particle densities. As a way forward, we propose a new technique for measuring particle velocities in situ by using a fiber Bragg grating (FBG) sensor, which remains applicable at significantly higher particle densities. The FBG sensor detects strain pulses induced by particle-fiber collisions, from which the velocity of the impacting particle can be derived. Applying this method to an ensemble of granular particles allows us to extract its velocity distributions, as we present for a granular system excited by a vibrational shaker. We validate the extracted velocity distribution against conventional particle-tracking measurements, confirming the reliability of the FBG-based technique.

We demonstrate that the thermodynamic friction metric governing dissipation in slowly driven continuous-time Markov chains is equivalent to the commute-time embedding and the resistance distance. This equivalence yields complementary insights: The commute-time embedding demonstrates the intrinsic cost of transporting probability across dynamical bottlenecks, while the resistance distance maps thermodynamic dissipation to Joule heating in an electrical network. We further demonstrate that the linear-response thermodynamic distance is a discrete $L^2$-Wasserstein optimal transport cost evaluated along paths of equilibrium distributions, extending a continuous-state correspondence to discrete networks. This conceptual synthesis of linear-response thermodynamics, random walks on graphs, electrical circuits, and optimal-transport theory connects independently developed geometric frameworks, reduces complex metric calculations to simple circuit algebra, and provides a clear physical picture of dissipation as the energetic cost of routing probability through the state space network.