We experimentally explore the morphological evolution of cages in quasi-two-dimensional suspensions of colloidal fluids, uncovering a complex dynamic restructuring in the fluid. Although cages display isotropic evolution in the laboratory frame, we observe a striking anisotropy when analyzed in the displacement frame of the caged particles. Moreover, our findings reveal that particles in specific but distinct regions of the cage predominantly contribute to either its persistence or relaxation. Thus, our study provides a coarse-grained microscopic picture of the structural relaxation of these fluids through cage evolution, which has broader implications for the flow and phase behavior of complex fluids in confined geometry.

Fundamental measure theory (FMT) is a successful and versatile approach for describing the properties of the hard-sphere fluid and hard-sphere mixtures within the framework of classical density functional theory (DFT). Lutsko [Phys. Rev. E 102, 062137 (2020)] introduced a version of FMT containing two free parameters, to be fixed by additional physical constraints. Whereas Lutsko focused on the stability of crystalline phases, we introduce and employ two statistical mechanical sum rules pertinent for the fluid phase that are not automatically satisfied by FMT. By minimizing the relative deviation between different routes to calculate the excess chemical potential and the isothermal compressibility, we determine the two free parameters of the theory. Our results indicate that requiring consistency with these sum rules can improve the quality of predictions of FMT for properties of the hard-sphere fluid phase. We suggest that employing these (test particle) sum rules, which apply for any interparticle pair-potential, might provide a means of testing the performance and accuracy of general DFT approximations.

We experimentally manipulate the structural relaxation of colloidal glass at an atomistically controlled position. We focus a laser beam to eject a particle from the colloidal system. This creates a void that transforms rapidly into a mobile soft spot (quasivoid) composed of a few neighboring free-volume fragments. The transformation and subsequent motions of the quasivoid trigger a sequence of well-connected stringlike motions emanating precisely from the point of laser action with free volumes flowing along the string. Furthermore, the sequence can be terminated by the annihilation of the quasivoid. The observations are supported by molecular dynamics (MD) simulations.

Mutual information is commonly used as a measure of similarity between competing labelings of a given set of objects, for example to quantify performance in classification and community detection tasks. As argued recently, however, the mutual information as conventionally defined can return biased results because it neglects the information cost of the so-called contingency table, a crucial component of the similarity calculation. In principle the bias can be rectified by subtracting the appropriate information cost, leading to the modified measure known as the reduced mutual information, but in practice one can only ever compute an upper bound on this information cost, and the value of the reduced mutual information depends crucially on how good a bound is established. In this paper we describe an improved method for encoding contingency tables that gives a substantially better bound in typical use cases and approaches the ideal value in the common case where the labelings are closely similar, as we demonstrate with extensive numerical results.

Circulating tumor cell clusters play an important role in the metastatic cascade. These clusters can acquire a migratory and more invasive phenotype, and coordinate their motion to migrate as a collective. Before such clusters can form by collectively detaching from a primary tumor, however, the cluster must first aggregate in the tumor interior. The mechanism of this cluster formation process is still poorly understood. One of the possible ways for cells to cluster is by aligning their direction of motion with their neighboring cells. This work aims to investigate the role of this cell-cell alignment interaction on the formation of motile cell clusters inside the bulk of a tumor using computer simulations. We employ a cellular Potts model in which we model a two-dimensional heterogeneous confluent layer containing both motile and nonmotile cells. Our results indicate that the degree of clustering is governed by two distinct processes: the formation of clusters due to the presence of cell-cell alignment interactions among motile cells, and the suppression of clustering due to the presence of the dynamic cellular environment (comprising the nonmotile cells). We find that the largest motile clusters are formed for intermediate alignment strengths, contrary to what is observed for motile cells in free space (that is, unimpeded by a dense cellular environment), in which case stronger cell-cell alignment always leads to larger clustering. Our findings suggest that the presence of a densely packed cellular environment and strong cell-cell alignment inhibits the formation of large migratory clusters within the primary tumor, providing physical insight into potential factors at play during the early stages of metastasis.

This paper reports on the use of coherent microwave scattering (CMS) for spatially resolved electron number density measurements of elongated plasma structures induced at mid-IR femtosecond filamentation in air. The presented studies comprise one-dimensional mapping of laser filaments induced via 3.9 µm, 127.3 fs laser pulses at output energies up to 15 mJ. The axial electron number density was measured to be invariant (about $2\times {10}^{15}\mathrm{c}{\mathrm{m}}^{-3}$) along the entire filament length and for all tested laser pulse energies 5–15 mJ, and the corresponding laser intensity in the middle portion of the filament was estimated to be nearly constant for 5–15 mJ pulse energies (about $30-40\mathrm{TW}/\mathrm{c}{\mathrm{m}}^2$). These findings support that intensity clamping conditions were achieved in the experiments. The proposed approach enables capabilities that are currently unavailable to perform absolute and longitudinally resolved measurements of electron number density in laser filaments and to precisely characterize conditions associated with self-focusing and intensity clamping.

The imperfect narrow escape problem considers the mean first passage time (MFPT) of a Brownian particle through one of several small, partially reactive traps on an otherwise reflecting boundary within a confining domain. Mathematically, this problem is equivalent to Poisson's equation with mixed Neumann-Robin boundary conditions. Here, we obtain this MFPT in general three-dimensional domains by using strong localized perturbation theory in the small trap limit. These leading-order results involve factors, which are analogous to electrostatic capacitances, and we use Brownian local time theory and kinetic Monte Carlo (KMC) algorithms to rapidly compute these factors. Furthermore, we use a heuristic approximation of such a capacitance to obtain a simple, approximate MFPT, which is valid for any trap reactivity. In addition, we develop KMC algorithms to efficiently simulate the full problem and find excellent agreement with our asymptotic approximations.

Models for viral populations with high replication error rates (such as RNA viruses) rely on the quasispecies concept, in which mutational pressure beyond the so-called “error threshold” leads to a loss of essential genetic information and population collapse, an effect known as the “error catastrophe.” We explain how crossing this threshold, as a result of increasing mutation rates, can be understood as a second-order phase transition, even in the presence of lethal mutations. In particular, we show that, in fitness landscapes with a single peak, this collapse is equivalent to a ferroparamagnetic transition, where the back-mutation rate plays the role of the external magnetic field. We then generalize this framework to rugged fitness landscapes, like the ones that arise from epistatic interactions, and provide numerical evidence that there is a transition from a high average fitness regime to a low average fitness one, similarly to single-peaked landscapes. The onset of the transition is heralded by a sudden change in the susceptibility to variations in the mutation rate. We use insight from replica symmetry breaking mechanisms in spin glasses, in particular by considering the fluctuations of the genotype similarity distribution as the order parameter.

Inspired by studies of legislative-executive conflict in modern presidential democracies in South America, we present an agent-based statistical mechanics exploration of the collective, coordinated action of strategic political actors in the legislative chamber and the conditions that may result in premature changes in the executive officeholder, such as a president's impeachment or a motion of no confidence in a prime minister. The legislative actors are represented by information processing agents equipped with a neural network, and emit opinions about issues in the presidential agenda. We construct a Hamiltonian which is the sum of the costs for the agents to hold a specific set of political positions. We use replica methods for two types of disorder, in the space of weights and in the network of agents' interactions. We obtain the phase diagram of the model, where the control parameters may be loosely described as indices measuring the strategic legislative support, the presidential polling popularity and the volume of the presidential agenda under discussion. It shows an intermediate phase of coexistence of pro and con strategic behavior. This region is surrounded by pure phases where the strategic vote falls completely in the pro or con camps. Driven by external forces, the change of these indices may lead the system out of the coexistence region into the purely anti-executive phase, triggering a phase transition into a state opposing the executive leader and supporting their removal from office by constitutional means. We use data from Brazil, and show the presidential trajectories that led to impeachment or not during the democratic period which began in 1989. These trajectories ended in the region of the phase diagram in accordance to the president being removed or not from office.

The statistical mechanics of equilibrium interfaces has been well-established for over a half century. In the past decade, a wealth of observations have made increasingly clear that a new perspective is required to describe interfaces arbitrarily far from equilibrium. In this work, beginning from microscopic particle dynamics that break time-reversal symmetry, we derive the linear interfacial dynamics of coexisting motility-induced phases. Doing so allows us to identify the athermal energy scale that excites interfacial fluctuations and the nonequilibrium surface tension that resists these excitations. Our theory identifies that, in contrast to equilibrium fluids, this active surface tension contains contributions arising from nonconservative forces which act to suppress interfacial fluctuations and, crucially, is distinct from the mechanical surface tension of Kirkwood and Buff. We find that the interfacial stiffness scales linearly with the intrinsic persistence length of the constituent active particle trajectories, in agreement with simulation data. We demonstrate that at wavelengths much larger than the persistence length, the interface obeys surface-area minimizing Boltzmann statistics with our derived nonequilibrium interfacial stiffness playing a role identical to that of equilibrium systems.

It has been an open question in deep learning if fault-tolerant computation is possible: can arbitrarily reliable computation be achieved using only unreliable neurons? In the grid cells of the mammalian cortex, analog error correction codes have been observed to protect states against neural spiking noise, but their role in information processing is unclear. Here, we use these biological error correction codes to develop a universal fault-tolerant neural network that achieves reliable computation if the faultiness of each neuron lies below a sharp threshold; remarkably, we find that noisy biological neurons fall below this threshold. The discovery of a phase transition from faulty to fault-tolerant neural computation suggests a mechanism for reliable computation in the cortex and opens a path towards understanding noisy analog systems relevant to artificial intelligence and neuromorphic computing.

Nonequilibrium Green's function theory and related methods are widely used to describe transport phenomena in many-body systems, but they often require a costly inversion of a large matrix. We show here that the shift-invert Lanczos method can dramatically reduce the computational effort. We apply the method to two test problems, namely a simple model Hamiltonian and to a more realistic Hamiltonian for nuclear fission. For a Hamiltonian of dimension 66 103 we find that the computation time is reduced by a factor of 33 compared to the direct calculation of the Green's function.

In this paper, we study a simple model of a diffusive particle on a line, undergoing a stochastic resetting with rate $r$, via rescaling its current position by a factor $a$, which can be either positive or negative. For $\left|a\right|<1$, the position distribution becomes stationary at long times and we compute this limiting distribution exactly for all $\left|a\right|<1$. This symmetric distribution has a Gaussian shape near its peak at $x=0$, but decays exponentially for large $\left|x\right|$. We also studied the mean first-passage time (MFPT) $T\left(0\right)$ to a target located at a distance $L$ from the initial position (the origin) of the particle. As a function of the initial position $x$, the MFPT $T\left(x\right)$ satisfies a nonlocal second order differential equation and we have solved it explicitly for $0\le a<1$. For $-1<a\le 0$, we also solved it analytically but up to a constant factor $\kappa$ whose value can be determined independently from numerical simulations. Our results show that, for all $-1<a<1$, the MFPT $T\left(0\right)$ (starting from the origin) shows a minimum at $r=r^{*}\left(a\right)$. However, the optimized MFPT $T_{\mathrm{opt}}\left(a\right)$ turns out to be a monotonically increasing function of $a$ for $-1<a<1$. This demonstrates that, compared to the standard resetting to the origin $(a=0)$, while the positive rescaling is not beneficial for the search of a target, the negative rescaling is. Thus resetting via rescaling followed by a reflection around the origin expedites the search of a target in one dimension.

Advances in microarray and sequencing technologies have made possible the interrogation of biological processes at increasing levels of complexity. The underlying biomolecular networks contain large numbers of nodes, yet interactions within the networks are not known precisely. In the absence of accurate models, one may inquire if it is possible to find relationships between the states of such networks under external changes, and in particular, if such relationships can be model-independent. In this paper we introduce a class of such relationships. The results are based on the observation that changes to the equilibrium state of a network due to an alteration in an external input are “small” compared to the change in the input, a phenomenon we refer to as network modulation. It relies on the stability of the state. One consequence of network modulation is that response surfaces containing expression profiles of different mutants of an organism are low-dimensional linear subspaces. As an example, the expression profile of a double-knockout mutant generally lies close to the plane defined by the expression profiles of the wild-type and those of the two single-knockout mutants. This assertion is validated using experimental data from the sleep-deprivation network of Drosophila and the oxygen-deprivation network of Escherichia coli. The linearity of response surfaces is crucial in the design of a feedback control algorithm to move the underlying network from an initial state to a prespecified target state.

The Ornstein-Zernike equation is a powerful tool in liquid state theory for predicting structural and thermodynamic properties of fluids. Combined with a suitable closure, it has been shown to reproduce, e.g., the static structure factor, pressure, and compressibility of liquids to a great degree of accuracy. However, out of the multitude of closures that exist for the Ornstein-Zernike equation, it is hard to predict a priori which closure will give the most accurate predictions for the system at hand. To alleviate this problem, we compare the predictive power of many closures on a curated set of representative benchmark systems, including those with hard-sphere, inverse power-law, Gaussian core, and Lennard-Jones particles, in three and two dimensions. For example, we find that the well-known and highly used Percus-Yevick closure gives significantly worse predictions than lesser-known closures of equal complexity in all cases studied. We anticipate that the trends observed in our results will aid in making more informed decisions regarding closure choices. To facilitate the adoption of more modern closure theories, we also have packaged, documented, and distributed the code necessary to numerically solve the equations for a given closure and pair interaction potential.

It is well known that two permanent magnets of fixed orientation will either always repel or attract one another regardless of the distance between them. However, if one magnet is rotated at sufficient speed, a stable position at a given equilibrium distance can exist for a second free magnet. The equilibrium is produced by magnetic forces alone, which are strong enough to maintain a levitating state under gravity. We show that a stable levitation can be obtained when the rotating magnet is tilted from the rotation axis, with no offset in its position. In this regime, the levitating magnet remains centered and its spinning rate remains negligible, while its magnetic moment precesses in synchronization with the driving magnet. We provide a physical explanation of the levitation through a model relying on static dipolar interactions between the two magnets and present experimental results which validate the proposed theory.

The reptation Monte Carlo algorithm is a simple, physically motivated and efficient method for equilibrating semidilute solutions of linear polymers. Here, we propose two simple generalizations for the analog Amoeba algorithm for randomly branching chains, which allow us to efficiently deal with random trees with controlled branching activity. We analyze the rich relaxation dynamics of Amoeba algorithms and demonstrate the existence of an unexpected scaling regime for the tree relaxation. Our results suggest that the equilibration time for Amoeba algorithms scales in general like $N^2{\langle n_{\mathrm{lin}}\rangle }^{\mathrm{\Delta }}$, where $N$ denotes the number of tree nodes, $\langle n_{\mathrm{lin}}\rangle$ the mean number of linear segments the trees are composed of, and $\mathrm{\Delta }\simeq 0.4$.

We adapted the SWAP molecular dynamics algorithm for use in lattice Ising spin models. We dressed the spins with a randomly distributed length and we alternated long-range spin exchanges with conventional single spin flip Monte Carlo updates, both accepted with a stochastic rule which respects detailed balance. We show that this algorithm, when applied to the bidimensional Edwards-Anderson model, speeds up significantly the relaxation at low temperatures and manages to find ground states with high efficiency and little computational cost. The exploration of spin models should help in understanding why SWAP accelerates the evolution of particle systems and sheds light on relations between dynamics and free-energy landscapes.

Magnetic skyrmions, topologically stabilized chiral magnetic textures with particlelike properties, have so far primarily been studied statically. Here, we experimentally investigate the dynamics of skyrmion ensembles in metallic thin film conduits where they behave as quasiparticle fluids. By exploiting our access to the full trajectories of all fluid particles by means of time-resolved magneto-optical Kerr microscopy, we demonstrate that boundary conditions of skyrmion fluids can be tuned by modulation of the channel geometry. We observe as a function of channel width deviations from classical flow profiles even into the no- or partial-slip regime. Unlike conventional colloids, the skyrmion Hall effect can also introduce transversal flow asymmetries and even local motion of single skyrmions against the driving force which we explore with particle-based simulations, demonstrating the unique properties of skyrmion liquid flow that uniquely deviates from previously known behavior of other quasiparticles.

In data mining, density-based clustering, which entails classifying datapoints according to their distributions in some space, is an essential method to extract information from large datasets. With the advent of software-based radio, ionospheric radars are capable of producing unprecedentedly large datasets of plasma turbulence backscatter observations, and new automatic techniques are needed to sift through them. We present an algorithm to automatically identify and track clusters of radar echoes through time, using dbscan, a celebrated density-based clustering method for noisy point clouds. We demonstrate our algorithm's efficiency by tracking turbulent structures in the E-region ionosphere, the so-called radar aurora. Through conjugate auroral imagery, as well as in situ satellite observations, we demonstrate that the observed turbulent structures generally track the motion of auroras. What is more, the radar aurora bulk motions exhibit key qualities of auroral electric field enhancements that have previously been observed with various instruments. We present preliminary statistical results using our method, and briefly discuss the method's limitations and potential future adaptations.