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Basic Theoretical Research in the Mathematical & Natural Sciences 



April 19, 2022

Inspection Paradox Approach to Stochastic Resetting

Shlomi Reuveni speaks at the Statistical Physics and Complexity Webinar Series, School of Physics and Astronomy, The University of Edinburgh, April 19, 2022.

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November 17, 2021

Restart: The Science of Starting Anew

Shlomi Reuveni speaks at the APCTP Workshop: Frontiers in Theoretical Biophysics, Nov 17-19, 2021.


January 27, 2022

New Paper: The Inspection Paradox in Stochastic Resetting

Passengers arriving at a bus stop at a random time may on average wait longer than the mean time between bus arrivals—a counter-intuitive result, since one expects to wait less when coming some time after the previous bus departed. We review the origins of this phenomenon, a.k.a. the inspection paradox, and use the insight gained to explain why, and under which conditions, stochastic resetting expedites the completion of random processes: from search to chemical reactions.

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August 6, 2021

New Paper: Thermodynamic Uncertainty Relation for First-Passage Times on Markov Chains

We derive a thermodynamic uncertainty relation that poses a lower bound on first-passage time fluctuations on Markov chains.

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November 22, 2021

New Paper: Gated Reactions in Discrete Time and Space

A unified, continuous-time, approach to gated reactions on networks was presented in Phys. Rev. Lett. 127, 018301, (2021). We build on this recent advancement to present an analogous discrete-time version of the theory. We show that the discretization of time gives rise to resonances and anti-resonances, which were absent from the continuous-time picture. These features are illustrated using two case studies that are also used to demonstrate how the developed approach greatly simplifies the analysis of gated reactions.


August 10, 2021

New Paper: Mean-performance of Sharp Restart II: Inequality Roadmap

Inequality indices are widely applied in economics and in the social sciences to measure income and wealth disparities. We borrow this methodology to quantify the statistical heterogeneity in the random completion time of a stochastic process, and show how this information can then be utilized to predict the effect of sharp restart.