Cutting Across Disciplines
REUVENI GROUP

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Basic Theoretical Research in the Mathematical & Natural Sciences 

THE REUVENI GROUP

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May 3, 2021

New Paper: Resetting transition is governed by an interplay between thermal and potential energy

A dynamical process that takes a random time to complete, e.g., a chemical reaction, may either be accelerated or hindered due to resetting. Tuning system parameters, such as temperature, viscosity, or concentration, can invert the effect of resetting on the mean completion time of the process, which leads to a resetting transition. We show that this transition is governed by a simple interplay between the thermal and potential energy.

January 8, 2021

New Paper: Growth laws and invariants from ribosome biogenesis in lower Eukarya

We construct a theoretical framework for the growth of unicellular eukaryotes based on kinetics of ribosome biogenesis.  The predictions are consistent with available data for the model organism S. cerevisiae. 

 

   

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March 23, 2021

New Paper: Thermodynamic uncertainty relation for systems with unidirectional transitions

We derive a thermodynamic uncertainty relation for stochastic processes with unidirectional transitions and apply it to a random walk with stochastic resetting, and to the Michaelis-Menten model of enzymatic catalysis.

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November 3, 2020

New Paper: Search with home returns provides advantage under high uncertainty

Many search processes are conducted in the vicinity of a favored location, i.e., a home, which is visited repeatedly. Foraging animals return to their dens and nests to rest, scouts return to their bases to resupply, and drones return to their docking stations to recharge or refuel. We show that this type of behaviour allows a searcher to locate hidden targets faster and more efficiently in conditions of high uncertainty. Our findings have applications to all branches of search research which range from animal foraging to stochastic optimization.

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March 5, 2021

New Paper: Diffusion with Local Resetting and Exclusion

We introduce the notion of local stochastic resetting in interacting many-body systems and study its effect on the symmetric simple exclusion process in one dimension.

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September 3, 2020

New Paper: Mean-performance of sharp restart I

Restart is a general framework, of prime importance and wide applicability, for expediting first-passage times and completion times of general stochastic processes. Restart protocols can use either deterministic or stochastic timers. Restart protocols with deterministic timers—'sharp restart'—assume a principal role: if there exists a restart protocol that improves mean-performance, then there exists a sharp-restart protocol that performs as good or better. In this paper, the first of a duo, we presents a comprehensive mean-performance analysis of sharp restart.