We investigate the designing of multiple-input and single-output (MISO) logic gates using a periodically driven double-well Duffing oscillator system. Our research focuses on the existence of OR and AND logic gate properties in the output of a single double-well Duffing oscillator with multiple inputs under the influence of external periodic forcing and bias. Through our investigation, we demonstrate that the logic gate can be switched between OR and AND by adjusting the bias value. Our approach successfully scales the number of logical inputs for both the gates up to ten and even more. Additionally, we verify that the logical output of this nonlinear system maintains robustness against noise.

Multicycle experiments performed by slowly tilting granular packings in the range $\pm {\theta }_M$ are analyzed for ${\theta }_M=21$, 22, 23, and $23.5^{\circ }$. Based on optical measurements of the free surface activity, a method is developed to detect numerous bead rearrangements associated to almost regular peaks of activity: on average, 1800 events are detected during an experiment. Occurring ${11}^{\circ }$ after the beginning of the inclination, precursors represent 75% of these events and the interprecursor angle tends to decrease when the surface activity increases. The evolution of the cumulative activity with the half-cycle number can be approximated by a stretched exponential function with an exponent $\beta$. The characteristic time for the system to reach a steady state of activity linearly increases with ${\theta }_M$. The residual activity also increases but reaches a plateau. The energy injected into the system by successive gravity destabilizations decreases with ${\theta }_M$: when ${\theta }_M\le {22}^{\circ }$, $\beta \le 1$ suggests relaxation processes and compaction induced by the precursors themselves increases the stability of the packing. An in-depth force sensor measures the precursor-induced activity at the wall of the grain container: a linear dependency relates wall and surface activities. In-depth acoustic measurements are used to trigger surface high-speed optical signals: during a precursor, which duration is of about 0.18 s, the elastic wave velocity inside the packing drops to few ${\mathrm{m}.\mathrm{s}}^{-1}$ because of weak intergrain contacts that allow beads to rearrange.

We discuss the limit cycle regime of a finite-time quantum Otto cycle with a frictionless two-dimensional anisotropic Ising model as the working fluid. From Onsager's exact equilibrium solution, we first find optimal parameters for the operational modes of work extraction and cooling for infinitely slow cycles. The equilibrium points in these optimal cycles correspond to different phases of the model, such that the nonequilibrium dynamics during the cycle bypasses the phase transition. Finite-time cycles allow for finite power extraction or cooling currents, but for such cycles, we point out that—already within the regime of weak system-reservoir coupling—energetic changes of the system during dissipative strokes may contain a significant portion of coupling and decoupling control work and should thus not be directly identified with heat. For ultrafast cycles, the required control work spoils performance, such that to maximize work extraction or cooling heat per cycle time, there is an optimal cycle duration. We also find that net-zero-energy transitions may lead to undesired reservoir heating.

The double kicked top (DKT) is studied as an extension of the standard quantum kicked top (QKT) model. The model allows us to study the transition from time-reversal symmetric to broken time-reversal symmetric dynamics. A transformation in the kick strength parameter space $(k,k^{\prime })\to (k_r,k_{\theta })$ reveals interesting features. The transformed kicked strength parameter $k_r$ drives a higher growth of chaos and is equivalent to the standard QKT, whereas $k_{\theta }$ leads to a weaker growth. Fixed points and their stability are analyzed both analytically and computationally by using the largest Lyapunov exponent and the Kolmogorov-Sinai entropy. Exact solutions are obtained for 2- to 4-qubit version of the DKT, including eigenvalues, eigenvectors, and entanglement dynamics. Criteria for periodicity of the entanglement dynamics is obtained. Quantum correlations are investigated in both deep quantum and semiclassical regime. Signatures of phase-space structure are numerically shown in the long-time averages of the quantum correlations. The homoclinic point and its associated state are also investigated within the semiclassical regime. Our model can be realised experimentally as an extension of the standard QKT.

In networked systems, stochastic resonance occurs as a collective phenomenon where the entire stochastic network resonates with a weak applied periodic signal. Beyond the interplay among the network coupling, the amplitude of the external periodic signal, and the intensity of stochastic fluctuations, the maintenance of stochastic resonance also crucially depends on the resonance capacity of each oscillator composing the network. This scenario raises the question: Can local defects in the ability of oscillators to resonate break down the stochastic resonance phenomenon in the entire network? Here, we investigate this possibility in complex networks of prototypical bistable oscillators in a double-well potential. We test the sustainability of stochastic resonance by considering a fraction of network oscillators with nonresonant potential landscapes. We find that the number of nonresonant oscillators depends nonlinearly on their dissimilarity from the rest of the network oscillators. In addition, we unravel the role of the network topology and coupling strength in maintaining, or suppressing, the stochastic resonance for different noise levels and number of nonresonant oscillators. Finally, we obtain a low-dimensional deterministic model confirming the results observed for the networks.

The Jeans instability in a magnetized dusty plasma is considered a fundamental process in space, where magnetic fields are common. We investigate the Jeans instability in a magnetized dusty plasma using 1D and 2D particle-in-cell simulations, in which dust grains are treated as particles and the Poisson's equation for the corresponding gravitational and electrostatic potentials is solved in a self-consistent manner. We first confirm that when the magnitude of the dust cyclotron frequency is larger than the Jeans frequency, the Jeans instability is completely stabilized, and the PIC simulation of the stable case shows the existence of cyclotron harmonics in the dispersion relation as predicted by the kinetic linear theory. In unstable cases, we find that the linear growth obtained by the simulations and the fluid and kinetic linear theory are consistent with each other, showing that the linear growth rate decreases as the magnetic field becomes stronger. The magnetized Jeans instability is also qualitatively modified due to the gyromotion of dust grains: (1) a dipole-like structure is observed in phase space and the density peak at the bottom of the gravitational potential is 3 times larger than the nonmagnetized case in the 1D simulation; and (2) dust grains form a filament, which corresponds to the direction in which the gyrating dust grains are accelerated by the gravitational potential, during the linear stage of the instability in the 2D simulation while dust grains condensate at a point in the nonmagnetized case.

Recently, deep neural networks have been proven capable of predicting output expectation values of certain random quantum circuits via a supervised learning approach. Here we investigate the potential of this possible approach to the emulation of quantum circuits, testing both its limitations and the successful applications where it might outperform conventional direct simulation methods. The testbeds we consider are circuits often employed in variational quantum algorithms, featuring layers of cnot gates alternated with single-qubit random rotations. On the one hand, we find that the computational cost of supervised learning scales exponentially with the interlayer variance of the random angles. This allows for entering a promising regime for quantum advantage, where quantum computers could easily outperform classical neural networks. On the other hand, circuits featuring only interqubit angle variations are easily emulated. In fact, thanks to a suitable scalable design, the trained networks accurately predict the expectation values of larger and deeper circuits than those used for training, even reaching circuit sizes, which, as we numerically show, are computationally intractable for the most common simulation libraries of state-vector and tensor-network algorithms. A repository of test data in the intractable regime is provided. We also analyze the most common metrics for the entanglement content, the expressibility, and the classical computational cost of quantum circuits, finding that they do not distinguish the easy from the hard circuit configurations.

Establishing a robust link between microscopic structure and effective forces in active systems remains a fundamental challenge. Recently, Paul et al. [Phys. Rev. Lett. 129, 058001 (2022)] achieved a significant theoretical breakthrough by developing a framework based on active stress. This framework enables the computation of forces exerted by active particles on a tightly constrained probe. Nevertheless, the pronounced sensitivity of depletionlike forces in active baths to external constraints offers an opportunity to extend this theoretical framework. Using simulations, we systematically investigate this constraint dependence and uncover key discrepancies between theory and numerical results. By incorporating a constraint-dependent correction factor into the existing theory, our results align remarkably well with simulations across extensive parameters. Our findings advance the theoretical understanding of depletionlike forces in active matter and offer direct implications for experimental validation.

Observations of glassy dynamics in dense cellular tissues have inspired a wealth of research modeling their collective behavior. Initial studies of the physical properties of several geometric cell models have highlighted anomalous sub-Arrhenius, or “ultrastrong,” scaling of the dynamics with temperature. Here we show that the dynamics in this sub-Arrhenius regime deviate even further from the standard glassforming paradigm, displaying unusual scaling of the viscosity with temperature, a lack of breakdown of the Stokes-Einstein-Sutherland relation, and strongly suppressed dynamical heterogeneities. The dynamics in this regime, despite not possessing these hallmarks of glassy behavior, are also distinct from those of simple liquids at low temperatures. These unusual dynamical behaviors can be tuned by controlling the characteristic cell shape index of the model: decreasing this parameter tunes the model between an anomalous and a standard set of glassforming dynamics. Our results add to the growing evidence that these geometric cell models display universal features distinct from those observed in standard glassformers.

We generate point configurations (PCs) on a square lattice by thresholding the local energy of the Ashkin-Teller model in two dimensions (2D) and study the percolation transition at different values of $\lambda$ along the critical Baxter line by varying the threshold that controls the particle density $\rho$. For all values of $\lambda$, the PCs exhibit power-law correlations with a decay exponent $a$ that remains independent of $\rho$ and varies continuously with $\lambda$. For $\lambda <0$, where the PCs are hyperuniform, the percolation critical behavior is identical to that of ordinary percolation. In contrast, for $\lambda >0$, the configurations exhibit giant number fluctuations, and all critical exponents vary continuously, but form a superuniversality class of percolation transition in 2D.

Network geometry, characterized by nodes with associated latent variables, is a fundamental feature of real-world networks. Still, when only the network edges are given, it may be difficult to assess whether the network contains an underlying source of geometry. This paper investigates the limits of geometry detection in networks in a wide class of models that contain geometry and power-law degrees, which include the popular hyperbolic random graph model. We specifically focus on the regime in which the geometric signal is weak. We show that the dependencies between edges in these models can be tackled through mixed-integer linear problems, which lift the nonlinear nature of network analysis into an exponential space in which simple linear optimization techniques can be employed. This approach allows us to investigate which subgraph and degree-based statistic is most effective at detecting the presence of an underlying geometric space. Interestingly, we show that even when the geometric effect is extremely weak, our mixed-integer programming approach identifies a network statistic that efficiently distinguishes geometric and nongeometric random graph models.

We present an approach combining reservoir computing and nonlinear dynamics to replicate the behavior of stochastic systems, even when only partial observations are available. Unlike conventional RC applications, our approach systematically evaluates the conditions under which a system can be “strongly” cloned (exact trajectory prediction) versus “weakly” cloned (statistical replication), leveraging external noise excitation to infer hidden dynamics. By applying external noise and analyzing the system response, we demonstrate the feasibility of our approach both theoretically and experimentally. We show that strong cloning is achievable only when a deterministic functional relationship exists between the external noise and the system's response. Using the FitzHugh-Nagumo neuron model and a diode-pumped erbium-doped fiber laser as test cases, we show that a “strong” clone—capable of accurately predicting system dynamics—can be constructed for the neuron model, whereas only a “weak” clone, capable of statistical prediction, is achievable for the laser system. These findings underscore the potential of leveraging machine learning and nonlinear dynamics for system identification and prediction in complex real-world scenarios.

We present a series of experiments investigating the flow regimes and repose angles of highly concave particle packings in a rotating drum. By varying grain geometry from spherical to highly nonconvex shapes, adjusting frictional properties and the particle number of branches, we examine how these parameters and the drum speed influence the flow behavior. Our study identifies two distinct flow regimes: the rolling regime, where granular matter exhibits solid-like behavior near the walls and flows like a liquid near the free surface, and the slumping regime, characterized by cyclic avalanches and solid body rotations. Using quantitative criteria such as the repose angle difference and the area ratio of particle packings, we construct phase diagrams delineating the crossover between these regimes. Our findings highlight the significant effects of particle concavity, friction, and rotation speed on the flow dynamics of granular materials, providing new insights into the mechanical behaviors of metagranular matter.

We combine the theory of slow spectral closure for linearized Boltzmann equations with Maxwell's kinetic boundary conditions to derive optimal hydrodynamics with arbitrary accommodation. Focusing on shear-mode dynamics, we obtain explicit steady state solutions in terms of Fourier integrals and closed-form expressions for the mean flow and the stress. We demonstrate that the exact nonlocal fluid model correctly predicts several rarefaction effects with accommodation, including the Couette flow and thermal creep in a plane channel.

A chain of harmonic oscillators with nonreciprocal coupling exhibits characteristic amplification behavior that serves as a classical analog of the non-Hermitian skin effect (NHSE). We extend this concept of nonreciprocal amplification to nonlinear dynamics by employing double-well Duffing oscillators arranged in ring-structured units. The addition of units induces bifurcations of attractors, driving transitions from limit cycles to tori, chaos, and hyperchaos. Unidirectional couplings between units enable the decomposition of attractors in phase space into projected subspaces corresponding to each unit. In the chaotic regime, amplitude saturation emerges, characterized by monotonically decreasing amplitudes within a unit, in sharp contrast to the increasing profiles seen in the linear NHSE. This work uncovers alternative bifurcation behavior resulting from the intricate interplay between nonreciprocity and nonlinearity.

Wounds in epithelial tissues compromise their vital role in homeostasis. A rapid and efficient wound healing encompasses different mechanisms, which includes the formation of a contractile actin-myosin cable around its edge, known as the purse-string mechanism. We combine mean-field calculations and numerical simulations of the Vertex model to study the interplay between tissue properties and the purse-string mechanism and its impact on the healing process. We find different regimes, where the wound opens or closes partially or completely. We also derive an analytic expression for the closure time which is validated by numerical simulations. This study establishes under which conditions the purse-string mechanism suffices for closure, providing an analytical mean-field expression for the respective thresholds.

New experimental methods make it possible to measure the expression levels of many genes, simultaneously, in snapshots from thousands or even millions of individual cells. Current approaches to analyze these experiments involve clustering or low-dimensional projections, and often start with the assumption that distinct cell types exist. Here we use the principle of maximum entropy to obtain a probabilistic description that captures the observed presence or absence of mRNAs from hundreds of genes in cells from the mammalian brain. We construct the Ising model compatible with experimental means and pairwise correlations, and validate it by showing that it gives good predictions for higher-order statistics. We find that the probability distribution of cell states has many local maxima. Grouping cells according to these maxima (or energy minima) gives a classification in good agreement with currently assigned cell types. We show that when assignments disagree our model is dividing cell types into subtypes with clearly distinguishable expression patterns. These results make concrete the intuition that types or classes of cells are emergent behaviors.