We examine unsupervised machine learning techniques to learn features that best describe configurations of the two-dimensional Ising model and the three-dimensional $XY$ model. The methods range from principal component analysis over manifold and clustering methods to artificial neural-network-based variational autoencoders. They are applied to Monte Carlo–sampled configurations and have, a priori, no knowledge about the Hamiltonian or the order parameter. We find that the most promising algorithms are principal component analysis and variational autoencoders. Their predicted latent parameters correspond to the known order parameters. The latent representations of the models in question are clustered, which makes it possible to identify phases without prior knowledge of their existence. Furthermore, we find that the reconstruction loss function can be used as a universal identifier for phase transitions.

Gene expression is a noisy process that leads to regime shifts between alternative steady states among individual living cells, inducing phenotypic variability. The effects of white noise on the regime shift in bistable systems have been well characterized, however little is known about such effects of colored noise (noise with nonzero correlation time). Here, we show that noise correlation time, by considering a genetic circuit of autoactivation, can have a significant effect on the regime shift between distinct phenotypic states in gene expression. We demonstrate this theoretically, using stochastic potential, stationary probability density function, and first-passage time based on the Fokker-Planck description, where the Ornstein-Uhlenbeck process is used to model colored noise. We find that an increase in noise correlation time in the degradation rate can induce a regime shift from a low to a high protein concentration state and enhance the bistable regime, while an increase in noise correlation time in the basal rate retains the bimodal distribution. We then show how cross-correlated colored noises in basal and degradation rates can induce regime shifts from a low to a high protein concentration state, but reduce the bistable regime. We also validate these results through direct numerical simulations of the stochastic differential equation. In gene expression understanding the causes of regime shift to a harmful phenotype could improve early therapeutic intervention in complex human diseases.

We study a family of generalized elasticalike equilibrium shapes that arise at the interface separating two fluids in a curved rotating Hele-Shaw cell. This family of stationary interface solutions consists of shapes that balance the competing capillary and centrifugal forces in such a curved flow environment. We investigate how the emerging interfacial patterns are impacted by changes in the geometric properties of the curved Hele-Shaw cell. A vortex-sheet formalism is used to calculate the two-fluid interface curvature, and a gallery of possible shapes is provided to highlight a number of peculiar morphological features. A linear perturbation theory is employed to show that the most prominent aspects of these complex stationary patterns can be fairly well reproduced by the interplay of just two interfacial modes. The connection of these dominant modes to the geometry of the curved cell, as well as to the fluid dynamic properties of the flow, is discussed.

We study the $\pm J$ transverse-field Ising spin-glass model at zero temperature on $d$-dimensional hypercubic lattices and in the Sherrington-Kirkpatrick (SK) model, by series expansions around the strong-field limit. In the SK model and in high dimensions our calculated critical properties are in excellent agreement with the exact mean-field results, surprisingly even down to dimension $d=6$, which is below the upper critical dimension of $d=8$. In contrast, at lower dimensions we find a rich singular behavior consisting of critical and Griffiths-McCoy singularities. The divergence of the equal-time structure factor allows us to locate the critical coupling where the correlation length diverges, implying the onset of a thermodynamic phase transition. We find that the spin-glass susceptibility as well as various power moments of the local susceptibility become singular in the paramagnetic phase before the critical point. Griffiths-McCoy singularities are very strong in two dimensions but decrease rapidly as the dimension increases. We present evidence that high enough powers of the local susceptibility may become singular at the pure-system critical point.

Active dynamic processes of cells are largely driven by the cytoskeleton, a complex and adaptable semiflexible polymer network, motorized by mechanoenzymes. Small dimensions, confined geometries, and hierarchical structures make it challenging to probe dynamics and mechanical response of such networks. Embedded semiflexible probe polymers can serve as nonperturbing multiscale probes to detect force distributions in active polymer networks. We show here that motor-induced forces transmitted to the probe polymers are reflected in nonequilibrium bending dynamics, which we analyze in terms of spatial eigenmodes of an elastic beam under steady-state conditions. We demonstrate how these active forces induce correlations among the mode amplitudes, which furthermore break time-reversal symmetry. This leads to a breaking of detailed balance in this mode space. We derive analytical predictions for the magnitude of resulting probability currents in mode space in the white-noise limit of motor activity. We relate the structure of these currents to the spatial profile of motor-induced forces along the probe polymers and provide a general relation for observable currents on two-dimensional hyperplanes.

Nematic ($N$) liquid crystals made of bent-core molecules exhibit unusual physical properties such as an intermediate phase between the $N$ and isotropic ($I$) phases, a very weak $NI$ transition as inferred from magnetic birefringence measurements in a low field, which is apparently incompatible with a large shift in the $NI$ transition temperature ($T_{ni}$) measured under a high field. Using our conformational studies on the aromatic cores, we propose that only conformers which are more straightened than those in the ground state (GS) form clusters with a few layers, which persist even in the isotropic phase, as inferred from x-ray and rheological experiments. We present a Landau–de Gennes theory of the medium, including an orientational coupling between the clusters and the GS molecules, which accounts for all the unusual properties. The intermediate phase to isotropic transition is predicted to exhibit critical behavior at a very low magnetic field of $<1\mathrm{kG}$.

We consider the Diffusive Epidemic Process (DEP), a two-species reaction-diffusion process originally proposed to model disease spread within a population. This model exhibits a phase transition from an active epidemic to an absorbing state without sick individuals. Field-theoretic analyses suggest that this transition belongs to the universality class of Directed Percolation with a Conserved quantity (DP-C, not to be confused with conserved-directed percolation C-DP, appearing in the study of stochastic sandpiles). However, some exact predictions derived from the symmetries of DP-C seem to be in contradiction with lattice simulations. Here we revisit the field theory of both DP-C and DEP. We discuss in detail the symmetries present in the various formulations of both models. We then investigate the DP-C model using the derivative expansion of the nonperturbative renormalization group formalism. We recover previous results for DP-C near its upper critical dimension $d_c=4$, but show how the corresponding fixed point seems to no longer exist below $d\lesssim 3$. Consequences for the DEP universality class are considered.

A long-standing open problem whether a heat engine with finite power achieves the Carnot efficiency is investgated. We rigorously prove a general trade-off inequality on thermodynamic efficiency and time interval of a cyclic process with quantum heat engines. In a first step, employing the Lieb-Robinson bound we establish an inequality on the change in a local observable caused by an operation far from support of the local observable. This inequality provides a rigorous characterization of the following intuitive picture that most of the energy emitted from the engine to the cold bath remains near the engine when the cyclic process is finished. Using this description, we prove an upper bound on efficiency with the aid of quantum information geometry. Our result generally excludes the possibility of a process with finite speed at the Carnot efficiency in quantum heat engines. In particular, the obtained constraint covers engines evolving with non-Markovian dynamics, which almost all previous studies on this topic fail to address.

We study solutions to second-harmonic-generation equations in two-dimensional media with anomalous dispersion. The analytical solution is obtained in an approximate form of the planar spatiotemporal two-component soliton by means of the averaged Lagrangian method. It is shown that a decrease in the amplitudes of both soliton components and an increase in the value of the transverse coordinate are accompanied by an increase in their temporal duration. Within this variational approach, we have managed to find a stability criterion for the light bullet and a period of oscillations of soliton parameters. Then, we use the obtained form as an initial configuration to carry out the direct numerical simulation of soliton dynamics. We demonstrate stable propagation of spatiotemporal solitons undergoing small oscillations predicted analytically for a long distance. The formation of a two-component light bullet is shown when we launch a pulse only at the fundamental frequency. In addition, we investigate the phase and group-velocity mismatch effects on the propagation of pulses.

We define quasirandom geometric networks using low-discrepancy sequences, such as Halton, Sobol, and Niederreiter. The networks are built in $d$ dimensions by considering the $d$-tuples of digits generated by these sequences as the coordinates of the vertices of the networks in a $d$-dimensional $I^d$ unit hypercube. Then, two vertices are connected by an edge if they are at a distance smaller than a connection radius. We investigate computationally 11 network-theoretic properties of two-dimensional quasirandom networks and compare them with analogous random geometric networks. We also study their degree distribution and their spectral density distributions. We conclude from this intensive computational study that in terms of the uniformity of the distribution of the vertices in the unit square, the quasirandom networks look more random than the random geometric networks. We include an analysis of potential strategies for generating higher-dimensional quasirandom networks, where it is know that some of the low-discrepancy sequences are highly correlated. In this respect, we conclude that up to dimension 20, the use of scrambling, skipping and leaping strategies generate quasirandom networks with the desired properties of uniformity. Finally, we consider a diffusive process taking place on the nodes and edges of the quasirandom and random geometric graphs. We show that the diffusion time is shorter in the quasirandom graphs as a consequence of their larger structural homogeneity. In the random geometric graphs the diffusion produces clusters of concentration that make the process more slow. Such clusters are a direct consequence of the heterogeneous and irregular distribution of the nodes in the unit square in which the generation of random geometric graphs is based on.

A growing body of empirical evidence indicates that social and cooperative behavior can be affected by cognitive and neurological factors, suggesting the existence of state-based decision-making mechanisms that may have emerged by evolution. Motivated by these observations, we propose a simple mechanism of anonymous network interactions identified as a form of generalized reciprocity—a concept organized around the premise “help anyone if helped by someone'—and study its dynamics on random graphs. In the presence of such a mechanism, the evolution of cooperation is related to the dynamics of the levels of investments (i.e., probabilities of cooperation) of the individual nodes engaging in interactions. We demonstrate that the propensity for cooperation is determined by a network centrality measure here referred to as neighborhood importance index and discuss relevant implications to natural and artificial systems. To address the robustness of the state-based strategies to an invasion of defectors, we additionally provide an analysis which redefines the results for the case when a fraction of the nodes behave as unconditional defectors.

We study transient behavior in the dynamics of complex systems described by a set of nonlinear ordinary differential equations. Destabilizing nature of transient trajectories is discussed and its connection with the eigenvalue-based linearization procedure. The complexity is realized as a random matrix drawn from a modified May-Wigner model. Based on the initial response of the system, we identify a novel stable-transient regime. We calculate exact abundances of typical and extreme transient trajectories finding both Gaussian and Tracy-Widom distributions known in extreme value statistics. We identify degrees of freedom driving transient behavior as connected to the eigenvectors and encoded in a nonorthogonality matrix $T_0$. We accordingly extend the May-Wigner model to contain a phase with typical transient trajectories present. An exact norm of the trajectory is obtained in the vanishing $T_0$ limit where it describes a normal matrix.

Directed networks have various topologically different extensive components, in contrast to a single giant component in undirected networks. We study the sensitivity (response) of the sizes of these extensive components in directed complex networks to the addition and pruning of edges and vertices. We introduce the susceptibility, which quantifies this sensitivity. We show that topologically different parts of a directed network have different sensitivity to the addition and pruning of edges and vertices and, therefore, they are characterized by different susceptibilities. These susceptibilities diverge at the critical point of the directed percolation transition, signaling the appearance (or disappearance) of the giant strongly connected component in the infinite size limit. We demonstrate this behavior in randomly damaged real and synthetic directed complex networks, such as the World Wide Web, Twitter, the Caenorhabditis elegans neural network, directed Erdős-Rényi graphs, and others. We reveal a nonmonotonic dependence of the sensitivity to random pruning of edges or vertices in the case of C. elegans and Twitter that manifests specific structural peculiarities of these networks. We propose the measurements of the susceptibilities during the addition or pruning of edges and vertices as a new method for studying structural peculiarities of directed networks.

Typical wild-type bacteria swimming in sparse suspensions exhibit a movement pattern called “run and tumble,” characterized by straight trajectories (runs) interspersed by shorter, random reorientation (tumbles). This is achieved by rotating their flagella counterclockwise, or clockwise, respectively. The chemotaxis signaling network operates in controlling the frequency of tumbles, enabling navigation toward or away from desired regions in the medium. In contrast, while in dense populations, flagellated bacteria exhibit collective motion and form large dynamic clusters, whirls, and jets, with intricate dynamics that is fundamentally different than trajectories of sparsely swimming cells. Although collectively swarming cells do change direction at the level of the individual cell, often exhibiting reversals, it has been suggested that chemotaxis does not play a role in multicellular colony expansion, but the change in direction stems from clockwise flagellar rotation. In this paper, the effects of cell rotor switching (i.e., the ability to tumble) and chemotaxis on the collective statistics of swarming bacteria are studied experimentally in wild-type Bacillus subtilis and two mutants—one that does not tumble and one that tumbles independently of the chemotaxis system. We show that while several of the parameters examined are similar between the strains, other collective and individual characteristics are significantly different. The results demonstrate that tumbling and/or flagellar directional rotor switching has an important role on the dynamics of swarming, and imply that swarming models of self-propelled rods that do not take tumbling and/or rotor switching into account may be oversimplified.

Surface-energy minimization is used to study capillary effects that determine the stable configurations of (liquid or solid) particles atop tubes or wires. The results give allowable ranges for the volume ($V_c$) of the particles as a function of the inner and outer radii of the tubes, $R_I$ and $R_o$, respectively. When $R_I/R_o=0$, the object is a nanowire. When $R_I/R_o=1$, it is a single-wall carbon nanotube. When $0<R_I/R_o<1$, it can be thought of as a multiwall carbon nanotube. Moreover, the transition paths among different configurations are studied. These results suggest possible mechanisms for the reshaping of the “pear-like” catalyst particles in carbon nanotubes that oscillate across local energy-maximum points with respect to the position of the lower interface in the inner wall.

We present results from a planar shear experiment in which a two-dimensional horizontal granular assembly of pentagonal particles sheared between two parallel walls is subjected to external vibration. Particle tracking and photoelastic measurements are used to quantify both grain scale motion and interparticle stresses with and without imposed vibrations. We characterize the particle motion in planar shear and find that flow of these strongly interlocking particles consists of transient vortex motion with a mean flow given by the sum of exponential profiles imposed by the shearing walls. Vibration is applied either through the shearing surface or as bulk vertical vibration of the entire shearing region with dimensionless accelerations $\mathrm{\Gamma }=A{\left(2\pi f\right)}^2/g\approx 0–2$. In both cases, increasing amplitude of vibration $A$ at fixed frequency $f$ leads to failure of the force network, reduction in mean stress, and a corresponding reduction in imposed strain. Vibration of the shearing surface is shown to induce the preferential slipping of large-angle force chains. These effects are insensitive to changes in frequency in the range studied ($f=30–120$ Hz), as sufficiently large displacements are required to relieve the geometrical frustration of the jammed states.

We study the yielding transition of a two-dimensional amorphous system under shear by using a mesoscopic elasto-plastic model. The model combines a full (tensorial) description of the elastic interactions in the system and the possibility of structural reaccommodations that are responsible for the plastic behavior. The possible structural reaccommodations are encoded in the form of a “plastic disorder” potential, which is chosen independently at each position of the sample to account for local heterogeneities. We observe that the stress must exceed a critical value ${\sigma }_c$ in order for the system to yield. In addition, when the system yields a flow curve (relating stress $\sigma$ and strain rate $\dot{\gamma }$) of the form $\dot{\gamma }\sim {(\sigma -{\sigma }_c)}^{\beta }$ is obtained. Remarkably, we observe the value of $\beta$ to depend on some details of the plastic disorder potential. For smooth potentials a value of $\beta \simeq 2.0$ is obtained, whereas for potentials obtained as a concatenation of smooth pieces a value $\beta \simeq 1.5$ is observed in the simulations. This indicates a dependence of critical behavior on details of the plastic behavior. In addition, by integrating out nonessential, harmonic degrees of freedom, we derive a simplified scalar version of the model that represents a collection of interacting Prandtl-Tomlinson particles. A mean-field treatment of this interaction reproduces the difference of $\beta$ exponents for the two classes of plastic disorder potentials and provides values of $\beta$ that compare favorably with those found in the full simulations.

We present a detailed self-consistent model of a positive column in argon glow discharge at moderate pressures and currents. This model describes the discharge transition between diffuse and constricted states. The model includes an extensive set of plasma chemical reactions and equation for inhomogeneous gas heating. The nonequilibrium behavior of an electron distribution function is also considered. One of the main features of the model is an accurate treatment of radiation trapping by solving the Holstein-Biberman equation directly. Influence of the radiation trapping on macroscopic parameters of the constricted positive column is studied. We propose a method for solving a boundary-value problem, including particle and energy balance equations for electrons, ground state atoms, atomic and molecular ions, and excited species. Unlike traditional solution approaches for similar systems, the method provides continuous Z- and S-shaped characteristics of discharge parameters, describing hysteresis in transition between diffuse and constricted discharge regimes. Performed experiments include measurements of volt-ampere characteristics and spectroscopic study of radial density profiles of excited atoms by measuring line emission and absorption, and electrons by measuring bremsstrahlung intensity. The role of resonance radiation trapping in spatial redistribution of $1s$ and $2p\mathrm{states}$ of argon is demonstrated. Results of modeling are compared to the experimental data.

Obtaining structural information from tomographic images of porous materials is a critical component of porous media research. Extracting pore networks is particularly valuable since it enables pore network modeling simulations which can be useful for a host of tasks from predicting transport properties to simulating performance of entire devices. This work reports an efficient algorithm for extracting networks using only standard image analysis techniques. The algorithm was applied to several standard porous materials ranging from sandstone to fibrous mats, and in all cases agreed very well with established or known values for pore and throat sizes, capillary pressure curves, and permeability. In the case of sandstone, the present algorithm was compared to the network obtained using the current state-of-the-art algorithm, and very good agreement was achieved. Most importantly, the network extracted from an image of fibrous media correctly predicted the anisotropic permeability tensor, demonstrating the critical ability to detect key structural features. The highly efficient algorithm allows extraction on fairly large images of ${500}^3$ voxels in just over 200 s. The ability for one algorithm to match materials as varied as sandstone with 20% porosity and fibrous media with 75% porosity is a significant advancement. The source code for this algorithm is provided.

We investigate the performance at a given power of a thermoelectric heat engine with broken time-reversal symmetry, and derive analytically the efficiency at a given power of a thermoelectric generator within linear irreversible thermodynamics. A universal bound on the efficiency of the thermoelectric heat engine is achieved under a strong constraint on the Onsager coefficients, and some interesting features are further revealed. Our results demonstrate that there exists a trade-off between efficiency and power output, and the efficiency at a given power may surpass the Curzon-Ahlborn limit due to broken time-reversal symmetry. Moreover, optimal efficiency at a given power can be achieved, which indicates that broken time-reversal symmetry offers physically allowed ways to optimize the performance of heat engines. Our study may contribute to the interesting guidelines for optimizing actual engines.