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Non-reciprocal interactions in many-body systems lead to time-dependent states, commonly observed in biological, chemical, and ecological systems. The stability of these states in the thermodynamic limit, as well as the criticality of the phase transition from static to time-dependent states remains an open question. To tackle these questions, we study a minimalistic system endowed with non-reciprocal interactions: an Ising model with two spin species having opposing goals. The mean-field equation predicts three stable phases: disordered, ordered, and a time-dependent swap phase. Large scale numerical simulations support the following: (i) in 2D, the swap phase is destabilized by defects; (ii) in 3D, the swap phase is stable, and has the properties of a time-crystal; (iii) the transition from disorder to swap in 3D is characterized by the critical exponents of the 3D XY model, in agreement with the emerging continuous symmetry of time translation invariance; (iv) when the two species have fully anti-symmetric couplings, the static-order phase is unstable in any dimension due to droplet growth; (v) in the general case of asymmetric couplings, static order can be restored by a droplet-capture mechanism preventing the droplets from growing indefinitely. We provide details on the full phase diagram which includes first- and second-order-like phase transitions and study the coarsening dynamics of the swap as well as the static-order phases., Comment: Companion paper to arXiv:2311.05471 ; see movies at https://home.uchicago.edu/~vitelli/videos.html

Disordered systems generically exhibit aging and a glass transition. Previous studies have long suggested that non-reciprocity tends to destroy glassiness. Here, we show that this is not always the case using a bipartite spherical Sherrington-Kirpatrick model that describes the antagonistic coupling between two identical complex agents modeled as macroscopic spin glasses. Our dynamical mean field theory calculations reveal an exceptional-point mediated transition from a static disorder phase to an oscillating amorphous phase as well as non-reciprocal aging with slow dynamics and oscillations., Comment: 18 pages, 13 figures

Physical learning is an emerging paradigm in science and engineering whereby (meta)materials acquire desired macroscopic behaviors by exposure to examples. So far, it has been applied to static properties such as elastic moduli and self-assembled structures encoded in minima of an energy landscape. Here, we extend this paradigm to dynamic functionalities, such as motion and shape change, that are instead encoded in limit cycles or pathways of a dynamical system. We identify the two ingredients needed to learn time-dependent behaviors irrespective of experimental platforms: (i) learning rules with time delays and (ii) exposure to examples that break time-reversal symmetry during training. After providing a hands-on demonstration of these requirements using programmable LEGO toys, we turn to realistic particle-based simulations where the training rules are not programmed on a computer. Instead, we elucidate how they emerge from physico-chemical processes involving the causal propagation of fields, like in recent experiments on moving oil droplets with chemotactic signalling. Our trainable particles can self-assemble into structures that move or change shape on demand, either by retrieving the dynamic behavior previously seen during training, or by learning on the fly. This rich phenomenology is captured by a modified Hopfield spin model amenable to analytical treatment. The principles illustrated here provide a step towards von Neumann's dream of engineering synthetic living systems that adapt to the environment., Comment: 6 pages, 3 figures

Morphogenesis is the process whereby the body of an organism develops its target shape. The morphogen BMP is known to play a conserved role across bilaterian organisms in determining the dorsoventral (DV) axis. Yet, how BMP governs the spatio-temporal dynamics of cytoskeletal proteins driving morphogenetic flow remains an open question. Here, we use machine learning to mine a morphodynamic atlas of Drosophila development, and construct a mathematical model capable of predicting the coupled dynamics of myosin, E-cadherin, and morphogenetic flow. Mutant analysis shows that BMP sets the initial condition of this dynamical system according to the following signaling cascade: BMP establishes DV pair-rule-gene patterns that set-up an E-cadherin gradient which in turn creates a myosin gradient in the opposite direction through mechanochemical feedbacks. Using neural tube organoids, we argue that BMP, and the signaling cascade it triggers, prime the conserved dynamics of neuroectoderm morphogenesis from fly to humans., Comment: Main text: 22 pages, 7 figures. Supplement: 18 pages, 10 figures

Intermittency refers to the broken self-similarity of turbulent flows caused by anomalous spatio-temporal fluctuations. In this Letter, we ask how intermittency is affected by a non-dissipative viscosity, known as odd viscosity (also Hall or gyro-viscosity), which appears in parity-breaking fluids such as magnetized polyatomic gases, electron fluids under magnetic field and spinning colloids or grains. Using a combination of Navier-Stokes simulations and theory, we show that intermittency is suppressed by odd viscosity at small scales. This effect is caused by parity-breaking waves, induced by odd viscosity, that break the multiple scale invariances of the Navier-Stokes equations. Building on this insight, we construct a two-channel helical shell model that reproduces the basic phenomenology of turbulent odd-viscous fluids including the suppression of anomalous scaling. Our findings illustrate how a fully developed direct cascade that is entirely self-similar can emerge below a tunable length scale, paving the way for designing turbulent flows with adjustable levels of intermittency.

We present a data-driven pipeline for model building that combines interpretable machine learning, hydrodynamic theories, and microscopic models. The goal is to uncover the underlying processes governing nonlinear dynamics experiments. We exemplify our method with data from microfluidic experiments where crystals of streaming droplets support the propagation of nonlinear waves absent in passive crystals. By combining physics-inspired neural networks, known as neural operators, with symbolic regression tools, we generate the solution, as well as the mathematical form, of a nonlinear dynamical system that accurately models the experimental data. Finally, we interpret this continuum model from fundamental physics principles. Informed by machine learning, we coarse grain a microscopic model of interacting droplets and discover that non-reciprocal hydrodynamic interactions stabilise and promote nonlinear wave propagation., Comment: Main text: 6 pages, 4 figures. Supplement: 11 pages, 4 figures

A novel mixture cure frailty model is introduced for handling censored survival data. Mixture cure models are preferable when the existence of a cured fraction among patients can be assumed. However, such models are heavily underexplored: frailty structures within cure models remain largely undeveloped, and furthermore, most existing methods do not work for high-dimensional datasets, when the number of predictors is significantly larger than the number of observations. In this study, we introduce a novel extension of the Weibull mixture cure model that incorporates a frailty component, employed to model an underlying latent population heterogeneity with respect to the outcome risk. Additionally, high-dimensional covariates are integrated into both the cure rate and survival part of the model, providing a comprehensive approach to employ the model in the context of high-dimensional omics data. We also perform variable selection via an adaptive elastic-net penalization, and propose a novel approach to inference using the expectation-maximization (EM) algorithm. Extensive simulation studies are conducted across various scenarios to demonstrate the performance of the model, and results indicate that our proposed method outperforms competitor models. We apply the novel approach to analyze RNAseq gene expression data from bulk breast cancer patients included in The Cancer Genome Atlas (TCGA) database. A set of prognostic biomarkers is then derived from selected genes, and subsequently validated via both functional enrichment analysis and comparison to the existing biological literature. Finally, a prognostic risk score index based on the identified biomarkers is proposed and validated by exploring the patients' survival., Comment: 43 pages, 5 tables, 40 figures

We consider the problem of assessing the non-equilibrium behavior of a system from the study of time series. In particular, we analyze experimental data from a piezoelectric energy harvester driven by broadband random vibrations where the extracted power and the relative tip displacement can be simultaneously measured. We compute autocorrelation and cross-correlation functions of these quantities in order to investigate the system properties under time reversal. We support our findings with numerical simulations of a linear underdamped Langevin equation, which very well describes the dynamics and fluctuations of the energy harvester. Our study shows that, due to the linearity of the system, from the analysis of a single variable, it is not possible to evidence the non-equilibrium nature of the dynamics. On the other hand, when cross-correlations are considered, the irreversible nature of the dynamics can be revealed., Comment: 8 pages, 7 figures

Living systems display complex behaviors driven not only by physical forces, but also decision-making guided by information processing and molded by cultural and/or biological evolution. Hydrodynamic theories hold promise for simplified, universal descriptions of these collective behaviors. However, incorporating the individual preferences of decision-making organisms into a hydrodynamic theory is an open problem. Here, we develop a data-driven pipeline that links micromotives to macrobehavior by augmenting hydrodynamics with utility functions that describe individual preferences in microeconomics. We show how to systematically validate the hypotheses underlying this construction from data using statistical tools based on neural networks. We illustrate this pipeline on the case study of human residential dynamics in the United States, for which census and sociological data is available, and show how trends in sociological surveys can be related to trends seen in racial segregation. In particular, we highlight that a history-dependence in the segregation-integration transition can arise even when agents have no memory. Beyond residential segregation, our work paves the way for systematic investigations of social-driven motility in real space from micro-organisms to humans, as well as fitness-mediated motion in more abstract genomic spaces.

Data in the form of rankings, ratings, pair comparisons or clicks are frequently collected in diverse fields, from marketing to politics, to understand assessors' individual preferences. Combining such preference data with features associated with the assessors can lead to a better understanding of the assessors' behaviors and choices. The Mallows model is a popular model for rankings, as it flexibly adapts to different types of preference data, and the previously proposed Bayesian Mallows Model (BMM) offers a computationally efficient framework for Bayesian inference, also allowing capturing the users' heterogeneity via a finite mixture. We develop a Bayesian Mallows-based finite mixture model that performs clustering while also accounting for assessor-related features, called the Bayesian Mallows model with covariates (BMMx). BMMx is based on a similarity function that a priori favours the aggregation of assessors into a cluster when their covariates are similar, using the Product Partition models (PPMx) proposal. We present two approaches to measure the covariate similarity: one based on a novel deterministic function measuring the covariates' goodness-of-fit to the cluster, and one based on an augmented model as in PPMx. We investigate the performance of BMMx in both simulation experiments and real-data examples, showing the method's potential for advancing the understanding of assessor preferences and behaviors in different applications.

The development of synthetic microswimmers has advanced our understanding of the fundamental self-propelled mechanisms of living systems. However, there are scarce studies at the microscale within highly structured anisotropic media, such as bacteria or cellular receptors that swim in concentrated solutions of filamentous proteins or lipids with viscoelastic properties. Synthetic liquid crystals (LCs) have the potential to serve as biomimetic surrogates to study the structure and dynamics of living systems. Nevertheless, studies on thermotropic LCs have mainly focused on electro and magneto-phoretic effects, with a few others on diffusiophoresis or light-driven distortions of the LC nematic director. To the best of our knowledge, here we report self-thermophoretic experiments on thermotropic LCs for the first time. Our system consists of 2D confined Janus particles in 5CB with homeotropic anchoring on the particle and LC cell surfaces. The Janus particles include a conductive titanium coating that, upon exposure to an LED source, is heated and induces a local steady nematic-isotropic phase transition, leading to the self-propulsion of the particles orthogonally to the LC director. The trajectories of the Janus particles were tracked at different intensities of the applied light. A model is developed to describe the mean-squared displacement of a Janus particle suspended in a nematic LC. The model assumes that the Janus particle feels the LC as a continuum with the anisotropic viscosity of the bulk nematic phase. Moreover, the viscoelasticity of the LC is also considered. The model describes the experimental data well, and the fitting parameter related to the magnitude of the swimming force increases with the intensity of the applied light. Moreover, our approach represents an important step for developing a platform for highly structured anisotropic active materials., Comment: 15 pages, 6 figures

Model reduction is the construction of simple yet predictive descriptions of the dynamics of many-body systems in terms of a few relevant variables. A prerequisite to model reduction is the identification of these relevant variables, a task for which no general method exists. Here, we develop a systematic approach based on the information bottleneck to identify the relevant variables, defined as those most predictive of the future. We elucidate analytically the relation between these relevant variables and the eigenfunctions of the transfer operator describing the dynamics. Further, we show that in the limit of high compression, the relevant variables are directly determined by the slowest-decaying eigenfunctions. Our information-based approach indicates when to optimally stop increasing the complexity of the reduced model. Furthermore, it provides a firm foundation to construct interpretable deep learning tools that perform model reduction. We illustrate how these tools work in practice by considering uncurated videos of atmospheric flows from which our algorithms automatically extract the dominant slow collective variables, as well as experimental videos of cyanobacteria colonies in which we discover an emergent synchronization order parameter., Comment: 39 pages, 19 figures

Systems with non-reciprocal interactions generically display time-dependent states. These are routinely observed in finite systems, from neuroscience to active matter, in which globally ordered oscillations exist. However, the stability of these uniform non-reciprocal phases in noisy spatially-extended systems, their fate in the thermodynamic limit, and the criticality of the corresponding phase transitions are not fully understood. Here, we address these questions by introducing a non-reciprocal generalization of the Ising model and study its critical behavior by means of numerical and analytical approaches. While the mean-field equations predict three stable homogeneous phases (disordered, ordered and a time-dependent swap phase), our large scale numerical simulations reveal a more complex picture. Static order is destroyed in any finite dimension due to the growth of rare droplets unless the symmetry between the two spins types is broken triggering a stabilizing droplet-capture mechanism. The swap phase is destroyed by fluctuations in two dimensions through the proliferation of spiral defects, but stabilized in three dimensions where non-reciprocity changes the critical exponents from Ising to XY, thus giving rise to a robust spatially-distributed clock., Comment: See movies at https://home.uchicago.edu/~vitelli/videos.html

Atomically thin films, like transition metal dichalcogenides, can now be synthesized at wafer scale, achieving the same extreme aspect ratio (~10^8) that a sheet of paper would have if it covered an entire city. Yet, the intrinsic (i.e. unconfined) three-dimensional shape of these extreme membranes remains a mystery because of the very fundamentals of mechanical measurements: to measure such an ultra-thin film, one first needs to simultaneously free it and stabilize it without introducing confining boundaries. Here, we introduce a counter-intuitive solution to this problem: place atomically thin films on water. Using atomic force microscopy (AFM) and Raman spectroscopy adapted to water's surface, we reveal that large-scale freestanding membranes spontaneously self-wrinkle into a universal mechanical state with long emergent length scales that follow robust scaling trends. Our analytical and numerical models suggest that these universal trends are controlled by mesoscopic parameters of the polycrystalline domains instead of atomistic details. Moreover, we demonstrate experimentally that the wrinkles result in a large and tunable reduction of elastic stiffness by up to 2 orders of magnitude. The present work illuminates the physical properties of the world's thinnest materials at length scales never probed before and highlights their potential for tunable strain-controlled nanomechanical devices., Comment: Includes main text with 4 figures and supplementary text with 10 supplementary figures

Chiral fluids - such as fluids under rotation or a magnetic field as well as synthetic and biological active fluids - flow in a different way than ordinary ones. Due to symmetries broken at the microscopic level, chiral fluids may have asymmetric stress and viscosity tensors, for example giving rise to a hydrostatic torque or non-dissipative (odd) and parity-violating viscosities. In this article, we investigate the motion of rigid bodies in such an anisotropic fluid in the incompressible Stokes regime through the mobility matrix, which encodes the response of a solid body to forces and torques. We demonstrate how the form of the mobility matrix, which is usually determined by particle geometry, can be analogously controlled by the symmetries of the fluid. By computing the mobility matrix for simple shapes in a three-dimensional anisotropic chiral fluid, we predict counter-intuitive phenomena such as motion perpendicular to applied forces and spinning under the force of gravity., Comment: 33 pages, 8 figures

In active materials, uncoordinated internal stresses lead to emergent long-range flows. An understanding of how the behavior of active materials depends on mesoscopic (hydrodynamic) parameters is developing, but there remains a gap in knowledge concerning how hydrodynamic parameters depend on the properties of microscopic elements. In this work, we combine experiments and multiscale modeling to relate the structure and dynamics of active nematics composed of biopolymer filaments and molecular motors to their microscopic properties, in particular motor processivity, speed, and valency. We show that crosslinking of filaments by both motors and passive crosslinkers not only augments the contributions to nematic elasticity from excluded volume effects but dominates them. By altering motor kinetics we show that a competition between motor speed and crosslinking results in a nonmonotonic dependence of nematic flow on motor speed. By modulating passive filament crosslinking we show that energy transfer into nematic flow is in large part dictated by crosslinking. Thus motor proteins both generate activity and contribute to nematic elasticity. Our results provide new insights for rationally engineering active materials.

Two hallmarks of non-equilibrium systems, from active colloids to animal herds, are agents motility and nonreciprocal interactions. Their interplay creates feedback loops leading to complex spatiotemporal dynamics crucial to understand and control the nonlinear response of active systems. Here, we introduce a minimal model that captures these two features at the microscopic scale, while admitting an exact hydrodynamic theory valid also in the fully-nonlinear regime. Our goal is to account for the fact that animal herds and colloidal swarms are rarely in the thermodynamic limit where particle number fluctuations can be completely ignored. Using statistical mechanics techniques we exactly coarse-grain a nonreciprocal microscopic model into a fluctuating hydrodynamics and use dynamical systems insights to analyze the resulting equations. In the absence of motility, we find two transitions to oscillatory phases occurring via distinct mechanisms: a Hopf bifurcation and a Saddle-Node on Invariant Circle (SNIC) bifurcation. In the presence of motility, this rigorous approach, complemented by numerical simulations, allows us to quantitatively assess the hitherto neglected impact of inter-species nonreciprocity on a paradigmatic transition in active matter: the emergence of collective motion. When nonreciprocity is weak, we show that flocking is accelerated and bands tend to synchronize with a spatial overlap controlled by nonlinearities. When nonreciprocity is strong, flocking is superseded by a Chase & Rest dynamical phase where each species alternates between a chasing state, when they propagate, and a resting state, when they stand still. Finally, we demonstrate how fluctuations in finite systems can be harnessed to characterize microscopic non-reciprocity from macroscopic time-correlation functions, even in phases where nonreciprocal interactions do not affect the thermodynamic steady-state.

Control of particle motion is generally achieved by applying an external field that acts directly on each particle. Here, we propose a global way to manipulate the motion of a particle by dynamically changing the properties of the fluid in which it is immersed. We exemplify this principle by considering a small particle sinking in an anisotropic fluid whose viscosity depends on the shear axis. In the Stokes regime, the motion of an immersed object is fully determined by the viscosity of the fluid through the mobility matrix, which we explicitly compute for a pushpin-shaped particle. Rather than falling upright under the force of gravity, as in an isotropic fluid, the pushpin tilts to the side, sedimenting at an angle determined by the viscosity anisotropy axis. By changing this axis, we demonstrate control over the pushpin orientation as it sinks, even in the presence of noise, using a closed feedback loop. This strategy to control particle motion, that we dub viscous tweezers, could be experimentally realized in systems ranging from polyatomic fluids under external fields to chiral active fluids of spinning particles by suitably changing their direction of global alignment or anisotropy., Comment: 5 pages, 4 figures + appendices

Fully developed turbulence is a universal and scale-invariant chaotic state characterized by an energy cascade from large to small scales where the cascade is eventually arrested by dissipation. In this article, we show how to harness these seemingly structureless turbulent cascades to generate patterns. Pattern formation entails a process of wavelength selection, which can usually be traced to the linear instability of a homogeneous state. By contrast, the mechanism we propose here is fully non-linear. It is triggered by a non-dissipative arrest of turbulent cascades: energy piles up at an intermediate scale, which is neither the system size nor the smallest scales at which energy is usually dissipated. Using a combination of theory and large-scale simulations, we show that the tunable wavelength of these cascade-induced patterns can be set by a non-dissipative transport coefficient called odd viscosity, ubiquitous in chiral fluids ranging from bio-active to quantum systems. Odd viscosity, which acts as a scale-dependent Coriolis-like force, leads to a two-dimensionalization of the flow at small scales, in contrast with rotating fluids where a two-dimensionalization occurs at large scales. Beyond odd-viscosity fluids, we discuss how cascade-induced patterns can arise in natural systems including atmospheric flows, stellar plasma such as the solar wind~, or the pulverization and coagulation of objects or droplets where mass rather than energy cascades., Comment: 23 pages, 8 figures

Excitable media, ranging from bioelectric tissues and chemical oscillators to forest fires and competing populations, are nonlinear, spatially extended systems capable of spiking. Most investigations of excitable media consider situations where the amplifying and suppressing forces necessary for spiking coexist at every point in space. In this case, spiking requires a fine-tuned ratio between local amplification and suppression strengths. But, in Nature and engineered systems, these forces can be segregated in space, forming structures like interfaces and boundaries. Here, we show how boundaries can generate and protect spiking if the reacting components can spread out: even arbitrarily weak diffusion can cause spiking at the edge between two non-excitable media. This edge spiking is a robust phenomenon that can occur even if the ratio between amplification and suppression does not allow spiking when the two sides are homogeneously mixed. We analytically derive a spiking phase diagram that depends on two parameters: (i) the ratio between the system size and the characteristic diffusive length-scale, and (ii) the ratio between the amplification and suppression strengths. Our analysis explains recent experimental observations of action potentials at the interface between two non-excitable bioelectric tissues. Beyond electrophysiology, we highlight how edge spiking emerges in predator-prey dynamics and in oscillating chemical reactions. Our findings provide a theoretical blueprint for a class of interfacial excitations in reaction-diffusion systems, with potential implications for spatially controlled chemical reactions, nonlinear waveguides and neuromorphic computation, as well as spiking instabilities, such as cardiac arrhythmias, that naturally occur in heterogeneous biological media., Comment: 31 pages. 15 figures. Supplemental videos available at: https://home.uchicago.edu/~vitelli/videos.html

Cellular form and function emerge from complex mechanochemical systems within the cytoplasm. No systematic strategy currently exists to infer large-scale physical properties of a cell from its many molecular components. This is a significant obstacle to understanding biophysical processes such as cell adhesion and migration. Here, we develop a data-driven biophysical modeling approach to learn the mechanical behavior of adherent cells. We first train neural networks to predict forces generated by adherent cells from images of cytoskeletal proteins. Strikingly, experimental images of a single focal adhesion protein, such as zyxin, are sufficient to predict forces and generalize to unseen biological regimes. This protein field alone contains enough information to yield accurate predictions even if forces themselves are generated by many interacting proteins. We next develop two approaches - one explicitly constrained by physics, the other more agnostic - that help construct data-driven continuum models of cellular forces using this single focal adhesion field. Both strategies consistently reveal that cellular forces are encoded by two different length scales in adhesion protein distributions. Beyond adherent cell mechanics, our work serves as a case study for how to integrate neural networks in the construction of predictive phenomenological models in cell biology, even when little knowledge of the underlying microscopic mechanisms exist., Comment: 30 pages, 7 figures

Many biomolecular systems can be viewed as ratchets that rectify environmental noise through measurements and information processing. As miniaturized robots cross the scale of unicellular organisms, on-board sensing and feedback open new possibilities for propulsion strategies that exploit fluctuations rather than fight them. Here, we study extended media in which many constituents display a feedback control loop between measurement of their microstates and the capability to bias their noise-induced transitions. We dub such many body systems informational active matter and show how information theoretic arguments and kinetic theory derivations yield their macroscopic properties starting from microscopic agent strategies. These include the ability to self-propel without applying work and to print patterns whose resolution improves as noise increases. We support our analytical results with extensive simulations of a fluid of `thinker' type particles that can selectively change their diameters to bias scattering transitions. This minimal model can be regarded as a non-equilibrium analogue of entropic elasticity that exemplifies the key property of this class of systems: self-propulsive forces grow ever stronger as environmental noise increases thanks to measurements and control actions undertaken by the microscopic constituents. We envision applications of our ideas ranging from noise induced patterning performed by collections of microrobots to reinforcement learning aided identification of migration strategies for collections of organisms that exploit turbulent flows or fluctuating chemotactic fields.

Odd elasticity encompasses active elastic systems whose stress-strain relationship is not compatible with a potential energy. As the requirement of energy conservation is lifted from linear elasticity, new anti-symmetric (odd) components appear in the elastic tensor. In this work, we study the odd elasticity and non-Hermitian wave dynamics of active surfaces, specifically plates of moderate thickness. We find that a free-standing moderately thick, isotropic plate can exhibit two odd-elastic moduli, both of which are related to shear deformations of the plate. These odd moduli can endow the vibrational modes of the plate with a nonzero topological invariant known as the first Chern number. Within continuum elastic theory, we show that the Chern number is related to the presence of unidirectional shearing waves that are hosted at the plate's boundary. We show that the existence of these chiral edge waves hinges on a distinctive two-step mechanism: the finite thickness of the sample gaps the shear modes and the odd elasticity endows them with chirality., Comment: 13 pages, 6 figures