Unravelling the origins of anomalous diffusion: from molecules to migrating storks
Anomalous diffusion or, more generally, anomalous transport, with nonlinear dependence of the mean-squared displacement on the measurement time, is ubiquitous in nature. It has been observed in processes ranging from microscopic movement of molecules to macroscopic, large-scale paths of migrating bi...
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creator | Vilk, Ohad Aghion, Erez Avgar, Tal Beta, Carsten Nagel, Oliver Sabri, Adal Sarfati, Raphael Schwartz, Daniel K Weiss, Matthias Krapf, Diego Nathan, Ran Metzler, Ralf Assaf, Michael |
description | Anomalous diffusion or, more generally, anomalous transport, with nonlinear
dependence of the mean-squared displacement on the measurement time, is
ubiquitous in nature. It has been observed in processes ranging from
microscopic movement of molecules to macroscopic, large-scale paths of
migrating birds. Using data from multiple empirical systems, spanning 12 orders
of magnitude in length and 8 orders of magnitude in time, we employ a method to
detect the individual underlying origins of anomalous diffusion and transport
in the data. This method decomposes anomalous transport into three primary
effects: long-range correlations ("Joseph effect"), fat-tailed probability
density of increments ("Noah effect"), and non-stationarity ("Moses effect").
We show that such a decomposition of real-life data allows to infer nontrivial
behavioral predictions, and to resolve open questions in the fields of single
particle tracking in living cells and movement ecology. |
doi_str_mv | 10.48550/arxiv.2109.04309 |
format | Article |
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dependence of the mean-squared displacement on the measurement time, is
ubiquitous in nature. It has been observed in processes ranging from
microscopic movement of molecules to macroscopic, large-scale paths of
migrating birds. Using data from multiple empirical systems, spanning 12 orders
of magnitude in length and 8 orders of magnitude in time, we employ a method to
detect the individual underlying origins of anomalous diffusion and transport
in the data. This method decomposes anomalous transport into three primary
effects: long-range correlations ("Joseph effect"), fat-tailed probability
density of increments ("Noah effect"), and non-stationarity ("Moses effect").
We show that such a decomposition of real-life data allows to infer nontrivial
behavioral predictions, and to resolve open questions in the fields of single
particle tracking in living cells and movement ecology.</description><identifier>DOI: 10.48550/arxiv.2109.04309</identifier><language>eng</language><subject>Physics - Data Analysis, Statistics and Probability ; Physics - Statistical Mechanics ; Quantitative Biology - Quantitative Methods</subject><creationdate>2021-09</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2109.04309$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2109.04309$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Vilk, Ohad</creatorcontrib><creatorcontrib>Aghion, Erez</creatorcontrib><creatorcontrib>Avgar, Tal</creatorcontrib><creatorcontrib>Beta, Carsten</creatorcontrib><creatorcontrib>Nagel, Oliver</creatorcontrib><creatorcontrib>Sabri, Adal</creatorcontrib><creatorcontrib>Sarfati, Raphael</creatorcontrib><creatorcontrib>Schwartz, Daniel K</creatorcontrib><creatorcontrib>Weiss, Matthias</creatorcontrib><creatorcontrib>Krapf, Diego</creatorcontrib><creatorcontrib>Nathan, Ran</creatorcontrib><creatorcontrib>Metzler, Ralf</creatorcontrib><creatorcontrib>Assaf, Michael</creatorcontrib><title>Unravelling the origins of anomalous diffusion: from molecules to migrating storks</title><description>Anomalous diffusion or, more generally, anomalous transport, with nonlinear
dependence of the mean-squared displacement on the measurement time, is
ubiquitous in nature. It has been observed in processes ranging from
microscopic movement of molecules to macroscopic, large-scale paths of
migrating birds. Using data from multiple empirical systems, spanning 12 orders
of magnitude in length and 8 orders of magnitude in time, we employ a method to
detect the individual underlying origins of anomalous diffusion and transport
in the data. This method decomposes anomalous transport into three primary
effects: long-range correlations ("Joseph effect"), fat-tailed probability
density of increments ("Noah effect"), and non-stationarity ("Moses effect").
We show that such a decomposition of real-life data allows to infer nontrivial
behavioral predictions, and to resolve open questions in the fields of single
particle tracking in living cells and movement ecology.</description><subject>Physics - Data Analysis, Statistics and Probability</subject><subject>Physics - Statistical Mechanics</subject><subject>Quantitative Biology - Quantitative Methods</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz8tKxDAYhuFsXMjoBbgyN9CaU5vEnQyeYGBAxnX5m0MNJo0k7aB3LzO6-lbvBw9CN5S0QnUduYPyHY4to0S3RHCiL9Hb-1zg6GIM84SXD4dzCVOYK84ew5wTxLxWbIP3aw15vse-5IRTjs6s0VW8ZJzCVGA59XXJ5bNeoQsPsbrr_92gw9PjYfvS7PbPr9uHXQO91A1oqUQH2jMCjve8o4Y5ooxx1hAteylGS-QopNTUSia9cpz3YMxotVWU8Q26_bs9o4avEhKUn-GEG844_gufLUtY</recordid><startdate>20210909</startdate><enddate>20210909</enddate><creator>Vilk, Ohad</creator><creator>Aghion, Erez</creator><creator>Avgar, Tal</creator><creator>Beta, Carsten</creator><creator>Nagel, Oliver</creator><creator>Sabri, Adal</creator><creator>Sarfati, Raphael</creator><creator>Schwartz, Daniel K</creator><creator>Weiss, Matthias</creator><creator>Krapf, Diego</creator><creator>Nathan, Ran</creator><creator>Metzler, Ralf</creator><creator>Assaf, Michael</creator><scope>ALC</scope><scope>GOX</scope></search><sort><creationdate>20210909</creationdate><title>Unravelling the origins of anomalous diffusion: from molecules to migrating storks</title><author>Vilk, Ohad ; Aghion, Erez ; Avgar, Tal ; Beta, Carsten ; Nagel, Oliver ; Sabri, Adal ; Sarfati, Raphael ; Schwartz, Daniel K ; Weiss, Matthias ; Krapf, Diego ; Nathan, Ran ; Metzler, Ralf ; Assaf, Michael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a679-a97845a9f20ae36351c2e08ccedc097674bd07b47791d727f8e336accbd9d8123</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Physics - Data Analysis, Statistics and Probability</topic><topic>Physics - Statistical Mechanics</topic><topic>Quantitative Biology - Quantitative Methods</topic><toplevel>online_resources</toplevel><creatorcontrib>Vilk, Ohad</creatorcontrib><creatorcontrib>Aghion, Erez</creatorcontrib><creatorcontrib>Avgar, Tal</creatorcontrib><creatorcontrib>Beta, Carsten</creatorcontrib><creatorcontrib>Nagel, Oliver</creatorcontrib><creatorcontrib>Sabri, Adal</creatorcontrib><creatorcontrib>Sarfati, Raphael</creatorcontrib><creatorcontrib>Schwartz, Daniel K</creatorcontrib><creatorcontrib>Weiss, Matthias</creatorcontrib><creatorcontrib>Krapf, Diego</creatorcontrib><creatorcontrib>Nathan, Ran</creatorcontrib><creatorcontrib>Metzler, Ralf</creatorcontrib><creatorcontrib>Assaf, Michael</creatorcontrib><collection>arXiv Quantitative Biology</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Vilk, Ohad</au><au>Aghion, Erez</au><au>Avgar, Tal</au><au>Beta, Carsten</au><au>Nagel, Oliver</au><au>Sabri, Adal</au><au>Sarfati, Raphael</au><au>Schwartz, Daniel K</au><au>Weiss, Matthias</au><au>Krapf, Diego</au><au>Nathan, Ran</au><au>Metzler, Ralf</au><au>Assaf, Michael</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Unravelling the origins of anomalous diffusion: from molecules to migrating storks</atitle><date>2021-09-09</date><risdate>2021</risdate><abstract>Anomalous diffusion or, more generally, anomalous transport, with nonlinear
dependence of the mean-squared displacement on the measurement time, is
ubiquitous in nature. It has been observed in processes ranging from
microscopic movement of molecules to macroscopic, large-scale paths of
migrating birds. Using data from multiple empirical systems, spanning 12 orders
of magnitude in length and 8 orders of magnitude in time, we employ a method to
detect the individual underlying origins of anomalous diffusion and transport
in the data. This method decomposes anomalous transport into three primary
effects: long-range correlations ("Joseph effect"), fat-tailed probability
density of increments ("Noah effect"), and non-stationarity ("Moses effect").
We show that such a decomposition of real-life data allows to infer nontrivial
behavioral predictions, and to resolve open questions in the fields of single
particle tracking in living cells and movement ecology.</abstract><doi>10.48550/arxiv.2109.04309</doi><oa>free_for_read</oa></addata></record> |
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subjects | Physics - Data Analysis, Statistics and Probability Physics - Statistical Mechanics Quantitative Biology - Quantitative Methods |
title | Unravelling the origins of anomalous diffusion: from molecules to migrating storks |
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