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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August 12, 2020

New Paper: Experimental Realization of Diffusion with Stochastic Resetting

Diffusion with stochastic resetting serves as a paradigmatic model to study resetting phenomena, but a well-controlled platform by which this process can be studied experimentally has so far been lacking. We report the experimental realization of colloidal particle diffusion and resetting via holographic optical tweezers. We provide the first experimental corroboration of central theoretical results and go on to measure the energetic cost of resetting in steady-state and first-passage scenarios. In both cases, we show that this cost cannot be made arbitrarily small because of fundamental constraints on realistic resetting protocols.

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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.

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July 8, 2020

New Paper: Ribosome Composition Maximizes Cellular Growth Rates in E. coli

We show that the composition of the ribosome, the cell’s protein-synthesis machinery, is tuned to optimize the cell’s reproduction rate. A previously unrecognized growth law, and an invariant of bacterial growth, also follow from our analysis. Quantitative predictions from the growth law and invariant are shown to be in excellent agreement with E. coli data despite having no fitting parameters. Our analysis can be readily extended to other bacteria once data become available.

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September 3, 2020

Reuveni Group Wins ERC Starting Grant

We thank the European Research Council for providing us with this generous support. We also thank the School of Chemistry at Tel Aviv University for creating a perfect atmosphere for scientific research. 

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June 17, 2020

New Paper: Diffusion with resetting in a logarithmic potential

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We study the effect of resetting on diffusion in a logarithmic potential which arises as an effective potential in a large variety of problems in chemical, statistical, and biological physics. We show that this analytically tractable model system exhibits a series of transitions as a function of a single parameter: the ratio between the strength of the potential and the thermal energy. Specifically, we show that as the latter ratio exceeds the value of five, resetting can no longer expedite first-passage to the origin.