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Denis Boyer

Publications and source records attributed to Denis Boyer.

At least 19 recordsLinked to original sources
arXiv2026

Stochastic synthesis and degradation of arbitrary diffusive processes

Target search processes are often carried out by diffusive elements that are introduced and removed at random times, causing fluctuations in the number of searchers in the system over time. For instance, proteins are synthesized and degraded stochastically in the cell, and susceptible to react with specific binding sites during their lifetime. Here we derive the density profile generated by non-interacting particles subject to stochastic synthesis and degradation (SSD) at constant rates, given a source location and an arbitrary propagator for the single, non-degrading particle. We also study the first-passage properties of these many-searcher processes, which can be deduced from the knowledge of the first-passage time distribution of a single particle. We analyse the behaviours of the characteristic reaction time and of the mean first-passage time in the limits of small and large rates. Taking into account the costs associated with synthesis leads to the existence of an optimal synthesis rate. We further generalise these results to bursty synthesis, when production events involve more than one particle at a time, as often observed in cell biology. The mean first-passage time in these situations can be significantly reduced at low synthesis or large degradation rates, as it is divided by the mean burst size in these regimes. The results presented here are applied to one-dimensional Brownian searches. Some general connections between SSD and resetting processes are pointed out as well as important differences, since SSD is associated with a distinct queueing protocol.

cond-mat.stat-mech↗
arXiv2026

Encounter times of random walkers with simultaneous resetting on networks

In this work, we study the dynamics of multiple random walkers on networks subject to a simultaneous resetting protocol, whereby all walkers are synchronously returned to their respective initial nodes. For this collective Markovian process, we derive exact analytical expressions for the mean first-encounter time, defined as the average time required for all walkers to meet for the first time at a given node. These results are formulated in terms of the eigenvalues and eigenvectors of the transition matrices governing the dynamics without resetting, providing a clear spectral interpretation of the impact of resetting on encounter processes. We further establish a general criterion for finite networks that determines when the introduction of a nonzero resetting probability reduces the mean first-encounter time and leads to an optimal resetting strategy. The theoretical predictions are illustrated through numerical results on regular and heterogeneous networks, for encounters involving two or more walkers, and for combinations of local and nonlocal dynamics. Our findings demonstrate that simultaneous resetting can significantly reduce encounter times for specific target nodes and initial conditions, while becoming ineffective for highly exploratory dynamics or distant targets. A comparison with independent resetting shows that simultaneous resetting is more efficient in homogeneous networks, whereas independent resetting can outperform it in heterogeneous structures, thereby revealing a trade-off between synchronization and exploration. The framework provides a unified approach to collective search and encounter problems on networks with resetting.

cond-mat.stat-mech↗
arXiv2026

Stochastic synthesis-degradation processes: first-passage properties and connections with resetting

Processes controlled by stochastic synthesis and degradation (SSD) are widespread in biology but their reaction kinetics are not well understood. Using methods borrowed from the theory of resetting processes, we determine the first-passage properties of a collection of independent particles that are synthesized and degraded at constant rates, and follow an arbitrary diffusive motion. At equal synthesis and degradation rates, the mean reaction time with a target site can be minimized as in stochastic resetting, and a $CV$-criterion is derived. When the degradation rate is held fixed and the synthesis costs are taken into account, an optimal synthesis rate is obtained. In bounded domains, despite particle degradation, SSD improves the mean search time compared to a single non-degrading particle if the synthesis rate exceeds a critical value which obeys a universal relation. We illustrate these findings with Brownian diffusion on the infinite line and in an interval.

cond-mat.stat-mech↗
arXiv2026

Ultra slow sub-logarithmic diffusion of a sluggish random walker subject to resetting with memory

We solve a model of sluggish stochastic motion in which a Brownian particle diffuses with a diffusion coefficient that decays algebraically with the distance to the origin, as $|x|^{-α}$. Additionally, the particle resets with a constant rate $r$ to positions previously visited in the past, so that frequently visited regions are more likely to be revisited. An exact expression is obtained at all times for the position distribution in arbitrary spatial dimensions. At late times, the typical displacement of the walker from the origin grows extremely slowly, as $[\ln(rt)]^{1/(α+2)}$, and the position distribution tends to a scaling law. For any $α>0$, the scaling function has a bimodal shape with a minimum at $x=0$ and non-Gaussian tails. Although the mean square displacement is hard to compute, some generalized moments of this process can be calculated exactly at all times in one dimension, and are shown to be closely related to the moments of the well-studied model with a constant diffusion coefficient.

cond-mat.stat-mech↗
arXiv2026

Mirror transitions in diffusion with stochastic resetting confined on a ring

Diffusion with an incorporated resetting mechanism provides a reference framework for modeling a wide range of natural phenomena. Within this framework, the optimal resetting rate is a key quantity that arises from the optimization of the mean first-passage time. While substantial work has focused on the study of the optimal resetting rate in unbounded one dimensional domains, little is still known about the optimization of the mean first-passage time in bounded systems, in particular when multiple resetting sites are available. In this work, we consider a particle diffusing along a circular circumference and under resetting, with an absorbing target site at a fixed location. Using the appropriate free propagator for this system, we compute the Laplace transform of the survival probability when resetting occurs to multiple sites drawn from an arbitrary probability density function. We also calculate the mean first-passage time at the target site, and study the dependence of the optimal resetting rate in terms of the relevant parameters of the system in a two-resetting site configuration. Depending on the arc length between one of the resetting sites and the absorbing target site, and the weight of the remaining resetting site, the optimal resetting rate can exhibit abrupt ("first order'') and continuous ("second order'') transitions. Moreover, the behavior of the mean first-passage time is rich enough to allow both critical and tri-critical points to exist in the parameter space. All the transitions have "mirror symmetry'' around the selected target site and its corresponding diametrically opposite site.

cond-mat.stat-mech↗
arXiv2026

Emerging correlations between diffusing particles evolving via simultaneous resetting with memory

We study the emergence of correlations between $N$ components of the position of a diffusive walker in $N$ dimensions that starts at the origin and resets to previously visited sites with certain probabilities. This is equivalent to $N$ independent one-dimensional diffusive processes starting from the origin and being subject to simultaneous resetting to positions visited in the past. Resetting follows a memory kernel that interpolates between resetting to the origin only, and the preferential relocation model, a path-dependent process which is highly non-Markov. For weak memory, the correlation coefficient between two components of the $N$-dimensional process grows monotonously with time and tends at late times to a constant bounded by $1/5$, the value corresponding to the non-equilibrium steady state of resetting to the origin. When memory is sufficiently long-ranged, the correlation is non-monotonous and reaches a maximum at a finite time before converging to its asymptotic value. These two regimes are separated by a critical memory parameter value. In the limiting case of the preferential relocation model, the components become uncorrelated at both short and long times, but the correlation vanishes logarithmically slowly at late times. The emergence of correlations through resetting can be described in a unified way in all cases by noticing that the processes are conditionally independent and identically distributed, even in the presence of memory. In the non-Markovian case, the conditioning parameter is the duration of a Brownian path composed of several parts of the full trajectory of a fixed duration $t$.

cond-mat.stat-mech↗
arXiv2025

Universal criterion for selective outcomes under stochastic resetting

Resetting plays a pivotal role in optimizing the completion time of complex first passage processes with single or multiple outcomes/exit possibilities. While it is well established that the coefficient of variation -- a statistical dispersion defined as a ratio of the fluctuations over the mean of the first passage time -- must be larger than unity for resetting to be beneficial for any outcome averaged over all the possibilities, the same can not be said while conditioned on a particular outcome. The purpose of this letter is to derive a universal condition which reveals that two statistical metric -- the mean and coefficient of variation of the conditional times -- come together to determine when resetting can expedite the completion of a selective outcome, and furthermore can govern the biasing between preferential and non-preferential outcomes. The universality of this result is demonstrated for a one dimensional diffusion process subjected to resetting with two absorbing boundaries.

cond-mat.stat-mech↗
arXiv2025

Intermittent localization and fast spatial learning by non-Markov random walks with decaying memory

Random walks on lattices with preferential relocation to previously visited sites provide a simple framework for modeling the displacements of animals and humans. When the lattice contains a few impurities or resource sites where the walker spends more time on average at each visit than on the other sites, the long range memory can suppress diffusion and induce by reinforcement a steady state localized around a resource. This phenomenon can be identified with a spatial learning process. Here we study theoretically and numerically how the decay of memory impacts learning in a model with one impurity. If memory decays as $1/τ$ or slower, where $τ$ is the time backward into the past, the localized solution is the same as with perfect, non-decaying memory and it is linearly stable. If forgetting is faster than $1/τ$, for instance exponential, an unusual regime of intermittent localization is observed, where well localized periods of exponentially distributed duration are disrupted by possibly long intervals of diffusive motion. At the transition between the two regimes, for a kernel in $1/τ$, the approach to the stable localized state is the fastest, opposite to the expected critical slowing down effect. Hence, forgetting can allow the walker to save a lot of memory without compromising learning and to achieve a faster learning process. These findings agree with biological evidence on the benefits of forgetting.

cond-mat.stat-mech↗
arXiv2025

Abrupt transitions in the optimization of diffusion with distributed resetting

Brownian diffusion subject to stochastic resetting to a fixed position has been widely studied for applications to random search processes. In an unbounded domain, the mean first-passage time at a target site can be minimized for a convenient choice of the resetting rate. Here we study this optimization problem in one dimension when resetting occurs to random positions, chosen from a probability density function with compact support that does not include the target. Depending on the shape of this distribution, the optimal resetting rate either varies smoothly with the mean distance to the target, as in single-site resetting, or exhibits a discontinuity caused by the presence of a second local minimum in the mean first-passage time. These two regimes are separated by a critical line containing a singular point that we characterize through a Ginzburg-Landau theory. To quantify how useful is a given resetting point for the search, we calculate the probability density function of the last resetting position before absorption. The discontinuous transition separates two markedly different optimal strategies: one with a small resetting rate where the last path before absorption starts from a rather distant but likely position, while the other strategy has a large resetting rate, favoring last paths starting from not-so-likely points but which are closer to the target.

cond-mat.stat-mech↗
arXiv2025

Uncovering complementary information sharing in spider monkey collective foraging using higher-order spatial networks

Collectives are often able to process information in a distributed fashion, surpassing each individual member's processing capacity. In fission-fusion dynamics, where group members come together and split from others often, sharing complementary information about uniquely known foraging areas could allow a group to track a heterogenous foraging environment better than any group member on its own. We analyse the partial overlaps between individual spider monkey core ranges, which we assume represent the knowledge of an individual during a given season. Sets of individuals with complementary overlaps are identified, showing a balance between redundantly and uniquely known portions, and we use simplicial complexes to represent these higher-order interactions. The structure of the simplicial complexes shows holes in various dimensions, revealing complementarity in the foraging information that is being shared. We propose that the complex spatial networks arising from fission-fusion dynamics allow for adaptive, collective processing of foraging information in dynamic environments.

q-bio.QM↗
arXiv2025

Modeling revolutions in networked societies: learning from the Tunisian spring

Economic competition and deregulation have led to a polarization of societies between a small, increasingly powerful elite and a majority of socially excluded individuals, marginalized and unconnected to political representations. This is the breeding ground for protest movements, relayed by local ties and amplified by social networks. Based on the characteristics revealed by socio-economic research into the Arab revolutions of the 2010s, this article proposes a formalization inspired by the theory of complex systems. We discuss the conditions under which an initial localized event - for example, the suicide of a street vendor condemned to ruin in a small Tunisian town - can trigger an explosion of the number of opponents to the regime, typical of a revolutionary episode. We consider a network model of agents and oppressors where pair interactions are controlled by a fear parameter, or the inclination of the individuals to rebel despite of repression. The model exhibits a phase transition at a critical threshold above which the quiescent state becomes unstable. Furthermore, the ability of individuals to forge triadic relationships accelerates the process of joining a rising rebellion, making the transition more abrupt, in the form of a brutal discontinuity that can be described as revolutionary. The imposition of a counter-revolution by the hardening of repression can be explained by the hysteresis property displayed by the model: when mobilization has extended the initial network and made it partially permanent, repression by the authorities must be tightened to higher levels than before to regain control.

physics.soc-ph↗
arXiv2025

Power-law relaxation of a confined diffusing particle subject to resetting with memory

We study the relaxation of a Brownian particle with long range memory under confinement in one dimension. The particle diffuses in an arbitrary confining potential and resets at random times to previously visited positions, chosen with a probability proportional to the local time spent there by the particle since the initial time. This model mimics an animal which moves erratically in its home range and returns preferentially to familiar places from time to time, as observed in nature. The steady state density of the position is given by the equilibrium Gibbs-Boltzmann distribution, as in standard diffusion, while the transient part of the density can be obtained through a mapping of the Fokker-Planck equation of the process to a Schrödinger eigenvalue problem. Due to memory, the approach at late times toward the steady state is critically self-organised, in the sense that it always follows a sluggish power-law form, in contrast to the exponential decay that characterises Markov processes. The exponent of this power-law depends in a simple way on the resetting rate and on the leading relaxation rate of the Brownian particle in the absence of resetting. We apply these findings to several exactly solvable examples, such as the harmonic, V-shaped and box potentials.

cond-mat.stat-mech↗
arXiv2025

Diffusion with preferential relocation in a confining potential

We study the relaxation of a diffusive particle confined in an arbitrary external potential and subject to a non-Markovian resetting protocol. With a constant rate $r$, a previous time $τ$ between the initial time and the present time $t$ is chosen from a given probability distribution $K(τ,t)$, and the particle is reset to the position that it occupied at time $τ$. Depending on the shape of $K(τ,t)$, the particle either relaxes toward the Gibbs-Boltzmann distribution or toward a non-trivial stationary distribution that breaks ergodicity and depends on the initial position and the resetting protocol. From a general asymptotic theory, we find that if the kernel $K(τ,t)$ is sufficiently localized near $τ=0$, i.e., mostly the initial part of the trajectory is remembered and revisited, the steady state is non-Gibbs-Boltzmann. Conversely, if $K(τ,t)$ decays slowly enough or increases with $τ$, i.e., recent positions are more likely to be revisited, the probability distribution of the particle tends toward the Gibbs-Boltzmann state at large times. In the latter case, however, the temporal approach to the stationary state is generally anomalously slow, following for instance an inverse power law or a stretched exponential, if $K(τ,t)$ is not too strongly peaked at the current time $t$. These findings are verified by the analysis of several exactly solvable cases and by numerical simulations.

cond-mat.stat-mech↗
arXiv2024

Random walks with long-range memory on networks

We study an exactly solvable random walk model with long-range memory on arbitrary networks. The walker performs unbiased random steps to nearest-neighbor nodes and intermittently resets to previously visited nodes in a preferential way, such that the most visited nodes have proportionally a higher probability to be chosen for revisit. The occupation probability can be expressed as a sum over the eigenmodes of the standard random walk matrix of the network, where the amplitudes slowly decay as power-laws at large time, instead of exponentially. The stationary state is the same as in the absence of memory and detailed balance is fulfilled. However, the relaxation of the transient part becomes critically self-organized at late times, as it is dominated by a single power-law whose exponent depends on the second largest eigenvalue and on the resetting probability. We apply our findings to finite networks such as rings, complete graphs, Watts-Strogatz and Barabási-Albert networks, and to Barbell and comb-like graphs. Our study could be of interest for modeling complex transport phenomena, such as human mobility, epidemic spreading, or animal foraging.

cond-mat.stat-mech↗
arXiv2024

Transition path time over a barrier of a colloidal particle in a viscoelastic bath

We experimentally study the statistics of the transition path time taken by a submicron bead to successfully traverse an energy barrier created by two optical tweezers in two prototypical viscoelastic fluids, namely, aqueous polymer and micellar solutions. We find a very good agreement between our experimental distributions and a theoretical expression derived from the generalized Langevin equation for the particle motion. Our results reveal that the mean transition path time measured in such viscoelastic fluids have a non-trivial dependence on the barrier curvature and they can be significantly reduced when compared with those determined in Newtonian fluids of the same zero-shear viscosity. We verify that the decrease of the mean transition path time can be described in terms of an effective viscosity that quantitatively coincides with that measured by linear microrheology at a frequency determined by the reactive mode that gives rise to the unstable motion over the barrier. Therefore, our results uncover the linear response of the particle during its thermally activated escape from a metastable state even when taking place in a non-Markovian bath.

cond-mat.soft↗
arXiv2024

Channel-facilitated transport under resetting dynamics

The transport of particles through channels holds immense significance in physics, chemistry, and biological sciences. For instance, the motion of solutes through biological channels is facilitated by specialized proteins that create water-filled channels and valuable insights can be obtained by studying the transition paths of particles through a channel and gathering statistics on their lifetimes within the channel or their exit probabilities. In a similar vein, we consider a one-dimensional model of channel-facilitated transport where a diffusive particle is subject to attractive interactions with the walls within a limited region of the channel. We study the statistics of conditional and unconditional escape times, in the presence of resetting--an intermittent dynamics that brings the particle back to its initial coordinate randomly. We determine analytically the physical conditions under which such resetting mechanism can become beneficial for faster escape of the particles from the channel thus enhancing the transport. Our theory has been verified with the aid of Brownian dynamics simulations for various interaction strengths and extent. The overall results presented herein highlight the scope of resetting-based strategies to be universally promising for complex transport processes of single or long molecules through biological membranes.

cond-mat.stat-mech↗
arXiv2024

Active particle in one dimension subjected to resetting with memory

The study of diffusion with preferential returns to places visited in the past has attracted an increased attention in recent years. In these highly non-Markov processes, a standard diffusive particle intermittently resets at a given rate to previously visited positions. At each reset, a position to be revisited is randomly chosen with a probability proportional to the accumulated amount of time spent by the particle at that position. These preferential revisits typically generate a very slow diffusion, logarithmic in time, but still with a Gaussian position distribution at late times. Here we consider an active version of this model, where between resets the particle is self-propelled with constant speed and switches direction in one dimension according to a telegraphic noise. Hence there are two sources of non-Markovianity in the problem. We exactly derive the position distribution in Fourier space, as well as the variance of the position at all times. The crossover from the short-time ballistic regime, dominated by activity, to the large-time anomalous logarithmic growth induced by memory is studied. We also analytically derive a large deviation principle for the position, which exhibits a logarithmic time-scaling instead of the usual algebraic form. Interestingly, at large distances, the large deviations become independent of time and match the non-equilibrium steady state of a particle under resetting to its starting position only.

cond-mat.stat-mech↗
arXiv2024

Diffusion with two resetting points

We study the problem of a target search by a Brownian particle subject to stochastic resetting to a pair of sites. The mean search time is minimized by an optimal resetting rate which does not vary smoothly, in contrast with the well-known single site case, but exhibits a discontinuous transition as the position of one resetting site is varied while keeping the initial position of the particle fixed, or vice-versa. The discontinuity vanishes at a "liquid-gas" critical point in position space. This critical point exists provided that the relative weight $m$ of the further site is comprised in the interval $[2.9028...,8.5603...]$. When the initial position follows the resetting point distribution, a discontinuous transition also exists for the optimal rate as the distance between the resetting points is varied, provided that $m$ exceeds the critical value $m_c=6.6008...$ This setup can be mapped onto an intermittent search problem with switching diffusion coefficients and represents a minimal model for the study of distributed resetting.

cond-mat.stat-mech↗