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ds2-5-body-xs">Universidade Federal de Santa Catarina - UFSC (Federal University of Santa Catarina)</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/KepplerJ"><img class="profile-avatar u-positionAbsolute" alt="Joachim Keppler" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/88279363/20209894/19936752/s200_joachim.keppler.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/KepplerJ">Joachim Keppler</a></div></div></ul></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a 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class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Geza Odor</h3></div><div class="js-work-strip profile--work_container" data-work-id="116627264"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116627264/Synchronization_transitions_on_connectome_graphs_with_external_force"><img alt="Research paper thumbnail of Synchronization transitions on connectome graphs with external force" class="work-thumbnail" src="https://attachments.academia-assets.com/112704272/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116627264/Synchronization_transitions_on_connectome_graphs_with_external_force">Synchronization transitions on connectome graphs with external force</a></div><div class="wp-workCard_item"><span>Frontiers in Physics</span><span>, Mar 9, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We investigate the synchronization transition of the Shinomoto-Kuramoto model on networks of the ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We investigate the synchronization transition of the Shinomoto-Kuramoto model on networks of the fruit-fly and two large human connectomes. This model contains a force term, thus is capable of describing critical behavior in the presence of external excitation. By numerical solution we determine the crackling noise durations with and without thermal noise and show extended non-universal scaling tails characterized by the exponent 2 < τ t < 2.8, in contrast with the Hopf transition of the Kuramoto model, without the force τ t = 3.1(1). Comparing the phase and frequency order parameters we find different synchronization transition points and fluctuation peaks as in case of the Kuramoto model, related to a crossover at Widom lines. Using the local order parameter values we also determine the Hurst (phase) and β (frequency) exponents and compare them with recent experimental results obtained by fMRI. We show that these exponents, characterizing the auto-correlations are smaller in the excited system than in the resting state and exhibit module dependence.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4d55d989ff1544658eb02e18b07a040c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112704272,"asset_id":116627264,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112704272/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116627264"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116627264"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116627264; 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By performing simulations and scaling analyses for the diffusive variant on the two-dimensional lattice, we show that diffusion for all agents, while rendering this symmetry destroyed, constitutes a singular perturbation that induces asymptotically distinct dynamical and stationary critical behavior from the non-diffusive model. In particular, the manifested crossover behavior in the effective mean-square radius exponents reveals that slow crossover behavior in general diffusive multi-species reaction systems may be ascribed to the interference of multiple length scales and timescales at early times.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="40fc208ed1677fcfe10bc8c5db617742" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223511,"asset_id":111792680,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223511/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792680"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792680"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792680; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792680]").text(description); $(".js-view-count[data-work-id=111792680]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792680; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792680']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792676"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/111792676/Scaling_at_First_Order_Phase_Transitions"><img alt="Research paper thumbnail of Scaling at First-Order Phase Transitions" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/111792676/Scaling_at_First_Order_Phase_Transitions">Scaling at First-Order Phase Transitions</a></div><div class="wp-workCard_item"><span>WORLD SCIENTIFIC eBooks</span><span>, May 1, 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792676"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792676"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792676; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792676]").text(description); $(".js-view-count[data-work-id=111792676]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792676; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792676']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=111792676]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111792676,"title":"Scaling at First-Order Phase Transitions","internal_url":"https://www.academia.edu/111792676/Scaling_at_First_Order_Phase_Transitions","owner_id":65671377,"coauthors_can_edit":true,"owner":{"id":65671377,"first_name":"Geza","middle_initials":null,"last_name":"Odor","page_name":"GezaOdor","domain_name":"independent","created_at":"2017-06-19T01:57:01.468-07:00","display_name":"Geza Odor","url":"https://independent.academia.edu/GezaOdor"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792674"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792674/Chimera_states_in_neural_networks_and_power_systems"><img alt="Research paper thumbnail of Chimera states in neural networks and power systems" class="work-thumbnail" src="https://attachments.academia-assets.com/109223418/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792674/Chimera_states_in_neural_networks_and_power_systems">Chimera states in neural networks and power systems</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Jul 5, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Partial, frustrated synchronization and chimera states are expected to occur in Kuramoto-like mod...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Partial, frustrated synchronization and chimera states are expected to occur in Kuramoto-like models if the spectral dimension of the underlying graph is low: d s < 4. We provide numerical evidence that this really happens in case of the high-voltage power grid of Europe (d s < 2) and in case of the largest, exactly known brain network corresponding to the fruit-fly (FF) connectome (d s < 4), even though their graph dimensions are much higher, i.e.: d EU g ≃ 2.6(1) and d FF g ≃ 5.4(1), d KKI113 g ≃ 3.4(1). We provide local synchronization results of the first-and second-order (Shinomoto) Kuramoto models by numerical solutions on the the FF and the European power-grid graphs, respectively, and show the emergence of chimera-like patterns on the graph community level as well as by the local order parameters. We show that Kuramoto oscillator models on large neural connectome graph of the fruit-fly, a human brain, as well as on the power-grid of Europe produce chimera states. This is in agreement with the low spectral dimensions that we calculated by the eigenvalue spectra of the Laplacian of these networks. We compare these results with the topological dimension measurements and previous simulations, strengthening that frustrated synchronization should occur, which can generate slow relaxations, obtained in previous studies within the neighborhood of the synchronization transition point.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="54e927a9feea2bb4cd95a7fc863f8650" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223418,"asset_id":111792674,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223418/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792674"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792674"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792674; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792674]").text(description); $(".js-view-count[data-work-id=111792674]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792674; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792674']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "54e927a9feea2bb4cd95a7fc863f8650" } } $('.js-work-strip[data-work-id=111792674]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111792674,"title":"Chimera states in neural networks and power systems","internal_url":"https://www.academia.edu/111792674/Chimera_states_in_neural_networks_and_power_systems","owner_id":65671377,"coauthors_can_edit":true,"owner":{"id":65671377,"first_name":"Geza","middle_initials":null,"last_name":"Odor","page_name":"GezaOdor","domain_name":"independent","created_at":"2017-06-19T01:57:01.468-07:00","display_name":"Geza Odor","url":"https://independent.academia.edu/GezaOdor"},"attachments":[{"id":109223418,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109223418/thumbnails/1.jpg","file_name":"2307.02216.pdf","download_url":"https://www.academia.edu/attachments/109223418/download_file","bulk_download_file_name":"Chimera_states_in_neural_networks_and_po.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109223418/2307.02216-libre.pdf?1702973374=\u0026response-content-disposition=attachment%3B+filename%3DChimera_states_in_neural_networks_and_po.pdf\u0026Expires=1740465328\u0026Signature=N7j~k9tzPK0ACll1~hcLspPSTsPQgRw6dQfi~amAkGC0QxXUvo3PN-RxmOhmrHTpe2JIehnfl9gKbTYaoYFzkUVvGfXqll~Yh2i3dT0GDrdvxUlRqlC55TwJ9CJCk9r2bhddKfNOazYINPwmG~X9-Zvzk1DZSNxdKFzdZLC8wNBLFlaD4sCpBzW4~xezB0JQ3GIwBDab6t2F1S~ZfzTXYYU6S0G9-SzCi~~DhJLOE~yzDuuBUieiNnQYahkyBEF3mkaQ3iUCXLfjkvLSGdT0T5diEHy3kXVVViZ5thZyMGnPYDpxoN92BD8I3TYve~xUrXdeo0oDlOP2xrWcpD9nLw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":109223419,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109223419/thumbnails/1.jpg","file_name":"2307.02216.pdf","download_url":"https://www.academia.edu/attachments/109223419/download_file","bulk_download_file_name":"Chimera_states_in_neural_networks_and_po.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109223419/2307.02216-libre.pdf?1702973372=\u0026response-content-disposition=attachment%3B+filename%3DChimera_states_in_neural_networks_and_po.pdf\u0026Expires=1740465328\u0026Signature=XJ7Km~cdl5bWTSTeHcVPqkF4uxfJ1o-DMoxWCw2KLgaIuD2xKv9AJ0ZLKYc-yke6FHmCIWIt2ogFWX9C8N4GRSL4nPsBFahrLNYNm9McFrJY74i6giwYYX1rpxKs7VSs1nGIpY9PXyTKClsNqlbCJ4eQWvB2tGiwfeCQL4HJRpPHiiqhIcWGIZ3SN0hl0Cohe6yh2zwjIpDxpdp3xoBh3si-0nNLh~VHgTOTqBJRZld~hBI~4KNI~JgXmrdgLCIuRisIle3nc2BWUM0uQ3uW8pLUnha-3CACe-rPEJiMVyGdDtFJRewviN3bsRlk6qAfJJPYR4XVt7AgXHzZzIznLg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792670"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792670/Suppressing_correlations_in_massively_parallel_simulations_of_lattice_models"><img alt="Research paper thumbnail of Suppressing correlations in massively parallel simulations of lattice models" class="work-thumbnail" src="https://attachments.academia-assets.com/109223409/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792670/Suppressing_correlations_in_massively_parallel_simulations_of_lattice_models">Suppressing correlations in massively parallel simulations of lattice models</a></div><div class="wp-workCard_item"><span>Computer Physics Communications</span><span>, Nov 1, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">For lattice Monte Carlo simulations parallelization is crucial to make studies of large systems a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">For lattice Monte Carlo simulations parallelization is crucial to make studies of large systems and long simulation time feasible, while sequential simulations remain the gold-standard for correlation-free dynamics. Here, various domain decomposition schemes are compared, concluding with one which delivers virtually correlation-free simulations on GPUs. Extensive simulations of the octahedron model for 2 + 1 dimensional Kardar-Parisi-Zhang surface growth, which is very sensitive to correlation in the site-selection dynamics, were performed to show self-consistency of the parallel runs and agreement with the sequential algorithm. We present a GPU implementation providing a speedup of about 30× over a parallel CPU implementation on a single socket and at least 180× with respect to the sequential reference.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f2ef0edc93510a5d10d0c5d886a425ed" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223409,"asset_id":111792670,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223409/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792670"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792670"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792670; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792664"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792664/Local_scale_invariance_of_the_2_1_dimensional_Kardar_Parisi_Zhang_model"><img alt="Research paper thumbnail of Local scale-invariance of the 2 + 1 dimensional Kardar–Parisi–Zhang model" class="work-thumbnail" src="https://attachments.academia-assets.com/109223403/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792664/Local_scale_invariance_of_the_2_1_dimensional_Kardar_Parisi_Zhang_model">Local scale-invariance of the 2 + 1 dimensional Kardar–Parisi–Zhang model</a></div><div class="wp-workCard_item"><span>Journal of Physics A</span><span>, Feb 20, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Local Scale-Invariance theory is tested by extensive dynamical simulations of the driven dimer la...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Local Scale-Invariance theory is tested by extensive dynamical simulations of the driven dimer lattice gas model, describing the surface growth of the 2+1 dimensional Kardar-Parisi-Zhang surfaces. Very precise measurements of the universal autoresponse function enabled us to perform nonlinear fitting with the scaling forms, suggested by local scale-invariance (LSI). While the simple LSI ansatz does not seem to work, forms based on logarithmic extension of LSI provide satisfactory description of the full (measured) time evolution of the autoresponse function.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8938859334eab5a7a3e03d1148260451" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223403,"asset_id":111792664,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223403/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792664"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792664"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792664; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792664]").text(description); $(".js-view-count[data-work-id=111792664]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792664; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792664']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792652"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792652/Critical_behavior_of_the_one_dimensional_diffusive_pair_contact_process"><img alt="Research paper thumbnail of Critical behavior of the one-dimensional diffusive pair contact process" class="work-thumbnail" src="https://attachments.academia-assets.com/109223398/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792652/Critical_behavior_of_the_one_dimensional_diffusive_pair_contact_process">Critical behavior of the one-dimensional diffusive pair contact process</a></div><div class="wp-workCard_item"><span>Physical review</span><span>, Jan 24, 2003</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The phase transition of the one-dimensional, diffusive pair contact process (PCPD) is investigate...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The phase transition of the one-dimensional, diffusive pair contact process (PCPD) is investigated by N cluster mean-field approximations and high precision simulations. The N = 3, 4 cluster approximations exhibit smooth transition line to absorbing state by varying the diffusion rate D with β2 = 2 mean-field order parameter exponent of the pair density. This contradicts with former N = 2 results, where two different mean-field behavior was found along the transition line. Extensive dynamical simulations on L = 10 5 lattices give estimates for the order parameter exponents of the particles for 0.05 ≤ D ≤ 0.7. These data can support former two distinct class findings. However the gap between low and high D exponents is narrower than estimated previously and the possibility for interpreting numerical data as a single class behavior with exponents α = 0.21(1), β = 0.41(1) assuming logarithmic corrections is shown. Finite size scaling and cluster simulation results are also presented.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c716745e8f1d796f5ba5a43f120eb3fa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223398,"asset_id":111792652,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223398/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792652"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792652"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792652; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792643"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792643/Critical_behavior_of_the_diffusive_susceptible_infected_recovered_model"><img alt="Research paper thumbnail of Critical behavior of the diffusive susceptible-infected-recovered model" class="work-thumbnail" src="https://attachments.academia-assets.com/109223393/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792643/Critical_behavior_of_the_diffusive_susceptible_infected_recovered_model">Critical behavior of the diffusive susceptible-infected-recovered model</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Aug 25, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The critical behavior of the non-diffusive susceptible-infected-recovered model on lattices had b...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The critical behavior of the non-diffusive susceptible-infected-recovered model on lattices had been well established in virtue of its duality symmetry. By performing simulations and scaling analyses for the diffusive variant on the two-dimensional lattice, we show that diffusion for all agents, while rendering this symmetry destroyed, constitutes a singular perturbation that induces asymptotically distinct dynamical and stationary critical behavior from the non-diffusive model. In particular, the manifested crossover behavior in the effective mean-square radius exponents reveals that slow crossover behavior in general diffusive multi-species reaction systems may be ascribed to the interference of multiple length scales and timescales at early times.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e3de64a3f8a4a5144ade465340502136" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223393,"asset_id":111792643,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223393/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792643"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792643"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792643; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792643]").text(description); $(".js-view-count[data-work-id=111792643]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792643; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792643']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792634"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792634/Mapping_of_KPZ_growth_onto_conserved_lattice_gas_a_model_of_dimers_in_two_dimensions"><img alt="Research paper thumbnail of Mapping of KPZ growth onto conserved lattice gas a model of dimers in two dimensions" class="work-thumbnail" src="https://attachments.academia-assets.com/109223391/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792634/Mapping_of_KPZ_growth_onto_conserved_lattice_gas_a_model_of_dimers_in_two_dimensions">Mapping of KPZ growth onto conserved lattice gas a model of dimers in two dimensions</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Oct 10, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We show that a 2+1 dimensional discrete surface growth model exhibiting KPZ class scaling can be ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We show that a 2+1 dimensional discrete surface growth model exhibiting KPZ class scaling can be mapped onto a two dimensional conserved lattice gas model of directed dimers. In case of KPZ height anisotropy the dimers follow driven diffusive motion. We confirm by numerical simulations that the scaling exponents of the dimer model are in agreement with those of the 2+1 dimensional KPZ class. This opens up the possibility of analyzing growth models via reaction-diffusion models, which allow much more efficient computer simulations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cb6d59323dfd6c105d65321b7416e2d1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223391,"asset_id":111792634,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223391/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792634"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792634"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792634; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792634]").text(description); $(".js-view-count[data-work-id=111792634]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792634; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792634']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792625"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792625/Mapping_of_2_1_dimensional_KPZ_growth_onto_driven_lattice_gas_model_of_dimers"><img alt="Research paper thumbnail of Mapping of 2+1 dimensional KPZ growth onto driven lattice gas model of dimers" class="work-thumbnail" src="https://attachments.academia-assets.com/109223388/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792625/Mapping_of_2_1_dimensional_KPZ_growth_onto_driven_lattice_gas_model_of_dimers">Mapping of 2+1 dimensional KPZ growth onto driven lattice gas model of dimers</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Oct 10, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We show that a 2+1 dimensional discrete surface growth model exhibiting KPZ class scaling can be ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We show that a 2+1 dimensional discrete surface growth model exhibiting KPZ class scaling can be mapped onto a two dimensional conserved lattice gas model of directed dimers. In case of KPZ height anisotropy the dimers follow driven diffusive motion. We confirm by numerical simulations that the scaling exponents of the dimer model are in agreement with those of the 2+1 dimensional KPZ class. This opens up the possibility of analyzing growth models via reaction-diffusion models, which allow much more efficient computer simulations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="46fa3f147cbf746622bbe439c3448dae" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223388,"asset_id":111792625,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223388/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792625"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792625"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792625; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792625]").text(description); $(".js-view-count[data-work-id=111792625]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792625; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792625']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792617"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792617/Robustness_of_Griffiths_effects_in_homeostatic_connectome_models"><img alt="Research paper thumbnail of Robustness of Griffiths effects in homeostatic connectome models" class="work-thumbnail" src="https://attachments.academia-assets.com/109223385/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792617/Robustness_of_Griffiths_effects_in_homeostatic_connectome_models">Robustness of Griffiths effects in homeostatic connectome models</a></div><div class="wp-workCard_item"><span>Physical review</span><span>, Jan 10, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">I provide numerical evidence for the robustness of the Griffiths phase (GP) reported previously i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">I provide numerical evidence for the robustness of the Griffiths phase (GP) reported previously in dynamical threshold model simulations on a large human brain network with N = 836733 connected nodes. The model, with equalized network sensitivity, is extended in two ways: introduction of refractory states or by randomized time dependent thresholds. The non-universal power-law dynamics in an extended control parameter region survives these modifications for a short refractory state and weak disorder. In case of temporal disorder the GP shrinks and for stronger heterogeneity disappears, leaving behind a mean-field type of critical transition. Activity avalanche size distributions below the critical point decay faster than in the original model, but the addition of inhibitory interactions sets it back to the range of experimental values.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2441f8afcbd6d938518cf586ed391822" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223385,"asset_id":111792617,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223385/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792617"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792617"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792617; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792617]").text(description); $(".js-view-count[data-work-id=111792617]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792617; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792617']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792605"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792605/Heterogeneity_effects_in_power_grid_network_models"><img alt="Research paper thumbnail of Heterogeneity effects in power grid network models" class="work-thumbnail" src="https://attachments.academia-assets.com/109223381/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792605/Heterogeneity_effects_in_power_grid_network_models">Heterogeneity effects in power grid network models</a></div><div class="wp-workCard_item"><span>Physical review</span><span>, Aug 8, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We have compared the phase synchronization transition of the second order Kuramoto model on 2D la...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We have compared the phase synchronization transition of the second order Kuramoto model on 2D lattices and on large, synthetic power grid networks, generated from real data. The latter are weighted, hierarchical modular networks. Due to the inertia the synchronization transitions are of first order type, characterized by fast relaxation and hysteresis by varying the global coupling parameter K. Finite size scaling analysis shows that there is no real phase transition in the thermodynamic limit, unlike in the mean-field model. The order parameter and its fluctuations depend on the network size without any real singular behavior. In case of power grids the phase synchronization breaks down at lower global couplings, than in case of 2D lattices of the same sizes, but the hysteresis is much narrower or negligible due to the low connectivity of the graphs. The temporal behavior of de-synchronization avalanches after a sudden quench to low K values, has been followed and duration distributions with power-law tails have been detected. This suggests rare region effects, caused by frozen disorder, resulting in heavy tailed distributions, even without a self organization mechanism as a consequence of a catastrophic drop event in the couplings.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f70fe04ec82385368494791da97f90c0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223381,"asset_id":111792605,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223381/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792605"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792605"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792605; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792605]").text(description); $(".js-view-count[data-work-id=111792605]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792605; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792605']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792594"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792594/Revisiting_and_modeling_power_law_distributions_in_empirical_outage_data_of_power_systems"><img alt="Research paper thumbnail of Revisiting and modeling power-law distributions in empirical outage data of power systems" class="work-thumbnail" src="https://attachments.academia-assets.com/109223368/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792594/Revisiting_and_modeling_power_law_distributions_in_empirical_outage_data_of_power_systems">Revisiting and modeling power-law distributions in empirical outage data of power systems</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Mar 22, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The size distribution of planned and forced outages and following restoration times in power syst...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The size distribution of planned and forced outages and following restoration times in power systems have been studied for almost two decades and has drawn great interest as they display heavy tails. Understanding of this phenomenon has been done by various threshold models, which are self-tuned at their critical points, but as many papers pointed out, explanations are intuitive, and more empirical data is needed to support hypotheses. In this paper, the authors analyze outage data collected from various public sources to calculate the outage energy and outage duration exponents of possible power-law fits. Temporal thresholds are applied to identify crossovers from initial shorttime behavior to power-law tails. We revisit and add to the possible explanations of the uniformness of these exponents. By performing power spectral analyses on the outage event time series and the outage duration time series, it is found that, on the one hand, while being overwhelmed by white noise, outage events show traits of self-organized criticality (SOC), which may be modeled by a crossover from random percolation to directed percolation branching process with dissipation, coupled to a conserved density. On the other hand, in responses to outages, the heavy tails in outage duration distributions could be a consequence of the highly optimized tolerance (HOT) mechanism, based on the optimized allocation of maintenance resources.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ebc76cd4b6b39c167189aa7ef9bc1db7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223368,"asset_id":111792594,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223368/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792594"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792594"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792594; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792594]").text(description); $(".js-view-count[data-work-id=111792594]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792594; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792594']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ebc76cd4b6b39c167189aa7ef9bc1db7" } } $('.js-work-strip[data-work-id=111792594]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111792594,"title":"Revisiting and modeling power-law distributions in empirical outage data of power systems","internal_url":"https://www.academia.edu/111792594/Revisiting_and_modeling_power_law_distributions_in_empirical_outage_data_of_power_systems","owner_id":65671377,"coauthors_can_edit":true,"owner":{"id":65671377,"first_name":"Geza","middle_initials":null,"last_name":"Odor","page_name":"GezaOdor","domain_name":"independent","created_at":"2017-06-19T01:57:01.468-07:00","display_name":"Geza Odor","url":"https://independent.academia.edu/GezaOdor"},"attachments":[{"id":109223368,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109223368/thumbnails/1.jpg","file_name":"2303.12714.pdf","download_url":"https://www.academia.edu/attachments/109223368/download_file","bulk_download_file_name":"Revisiting_and_modeling_power_law_distri.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109223368/2303.12714-libre.pdf?1702973393=\u0026response-content-disposition=attachment%3B+filename%3DRevisiting_and_modeling_power_law_distri.pdf\u0026Expires=1740605849\u0026Signature=gf8b3hN8NyRZPPlK~SfaHc0a3nbzJ003O8RiQFKc7apCaYHBjH8JaK74DgQ3xFw22nuIhKQSRaTKJwlgtiCDcV7ASaIsKhxgmj0UlviTQB66cpKOGrwBXna6Cq9DqJHIL1UIhZH9nc-toTbvar1FbfoXocuQpbrwk48eaRPDn3OroU6pcNXG~sf6lQQRFFiBccYfdH3iVkJZpycUWWqCVDivn--CtusicEWicDb-Nr481Y-JTEl8Gt0L-ccJ68UZSTlmWuRNxKkbZKAxQcjf~SCZ3FJTsjCahZZwTKrLUQGn3Xo-hJosximDrODnWQPtMcp1YqEyt6MtwsDzGhZYwA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792581"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792581/Synchronization_transition_of_the_second_order_Kuramoto_model_on_lattices"><img alt="Research paper thumbnail of Synchronization transition of the second-order Kuramoto model on lattices" class="work-thumbnail" src="https://attachments.academia-assets.com/109223361/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792581/Synchronization_transition_of_the_second_order_Kuramoto_model_on_lattices">Synchronization transition of the second-order Kuramoto model on lattices</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Nov 28, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The second-order Kuramoto equation describes synchronization of coupled oscillators with inertia,...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The second-order Kuramoto equation describes synchronization of coupled oscillators with inertia, which occur in power grids for example. Contrary to the first-order Kuramoto equation it's synchronization transition behavior is much less known. In case of Gaussian self-frequencies it is discontinuous, in contrast to the continuous transition for the first-order Kuramoto equation. Here we investigate this transition on large 2d and 3d lattices and provide numerical evidence of hybrid phase transitions, that the oscillator phases θi, exhibit a crossover, while the frequency spread a real phase transition in 3d. Thus a lower critical dimension d O l = 2 is expected for the frequencies and d R l = 4 for the phases like in the massless case. We provide numerical estimates for the critical exponents, finding that the frequency spread decays as ∼ t −d/2 in case of aligned initial state of the phases in agreement with the linear approximation. However in 3d, in the case of initially random distribution of θi, we find a faster decay, characterized by ∼ t −1.8(1) as the consequence of enhanced nonlinearities which appear by the random phase fluctuations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="adea683d486d7307078f1b0b8d2b815b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223361,"asset_id":111792581,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223361/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792581"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792581"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792581; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792581]").text(description); $(".js-view-count[data-work-id=111792581]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792581; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792581']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792575"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/111792575/Kuramoto_Model_on_KKI18_connectome"><img alt="Research paper thumbnail of Kuramoto Model on KKI18 connectome" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/111792575/Kuramoto_Model_on_KKI18_connectome">Kuramoto Model on KKI18 connectome</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Results of fourth-order Runge--Kutta integration of the first-order Kuramoto model in brain conne...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Results of fourth-order Runge--Kutta integration of the first-order Kuramoto model in brain connectome graph. Awr.dat.gz : connectome graph Awri.dat.gz : connectome graph with inhibitory links ccdata.tgz : simulations data for different configurations (averages at top-level, single runs in folders) eERll*.dat: cube graph with random long-range links o.ocp-kur_{lambda}_*.dat: connectome with coupling lambda o.ocp-kur_{lambda}I_*.dat: connectome with inhibitory links, coupling lambda elo-Thr: smoothed probability distributions of avalance times</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792575"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792575"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792575; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792575]").text(description); $(".js-view-count[data-work-id=111792575]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792575; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792575']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=111792575]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111792575,"title":"Kuramoto Model on KKI18 connectome","internal_url":"https://www.academia.edu/111792575/Kuramoto_Model_on_KKI18_connectome","owner_id":65671377,"coauthors_can_edit":true,"owner":{"id":65671377,"first_name":"Geza","middle_initials":null,"last_name":"Odor","page_name":"GezaOdor","domain_name":"independent","created_at":"2017-06-19T01:57:01.468-07:00","display_name":"Geza Odor","url":"https://independent.academia.edu/GezaOdor"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792561"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792561/Synchronization_transitions_on_connectome_graphs_with_external_force"><img alt="Research paper thumbnail of Synchronization transitions on connectome graphs with external force" class="work-thumbnail" src="https://attachments.academia-assets.com/109223356/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792561/Synchronization_transitions_on_connectome_graphs_with_external_force">Synchronization transitions on connectome graphs with external force</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Jan 12, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We investigate the synchronization transition of the Shinomoto-Kuramoto model on networks of the ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We investigate the synchronization transition of the Shinomoto-Kuramoto model on networks of the fruit-fly and two large human connectomes. This model contains a force term, thus is capable of describing critical behavior in the presence of external excitation. By numerical solution we determine the crackling noise durations with and without thermal noise and show extended non-universal scaling tails characterized by 2 < τt < 2.8, in contrast with the Hopf transition of the Kuramoto model, without the force τt = 3.1(1). Comparing the phase and frequency order parameters we find different transition points and fluctuations peaks as in case of the Kuramoto model. Using the local order parameter values we also determine the Hurst (phase) and β (frequency) exponents and compare them with recent experimental results obtained by fMRI. We show that these exponents, characterizing the auto-correlations are smaller in the excited system than in the resting state and exhibit module dependence.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ff145d17c988ea423f1888a1dc84aeec" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223356,"asset_id":111792561,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223356/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792561"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792561"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792561; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792561]").text(description); $(".js-view-count[data-work-id=111792561]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792561; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792561']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ff145d17c988ea423f1888a1dc84aeec" } } $('.js-work-strip[data-work-id=111792561]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111792561,"title":"Synchronization transitions on connectome graphs with external force","internal_url":"https://www.academia.edu/111792561/Synchronization_transitions_on_connectome_graphs_with_external_force","owner_id":65671377,"coauthors_can_edit":true,"owner":{"id":65671377,"first_name":"Geza","middle_initials":null,"last_name":"Odor","page_name":"GezaOdor","domain_name":"independent","created_at":"2017-06-19T01:57:01.468-07:00","display_name":"Geza Odor","url":"https://independent.academia.edu/GezaOdor"},"attachments":[{"id":109223356,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109223356/thumbnails/1.jpg","file_name":"2301.04951.pdf","download_url":"https://www.academia.edu/attachments/109223356/download_file","bulk_download_file_name":"Synchronization_transitions_on_connectom.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109223356/2301.04951-libre.pdf?1702973385=\u0026response-content-disposition=attachment%3B+filename%3DSynchronization_transitions_on_connectom.pdf\u0026Expires=1740605849\u0026Signature=hItJveUnyvTbZRSwxXF1yWdn3tkSQjhQiCcBjHwmG8fXuJkX1kBHlIAuXhY6l4x5TwE3NbvJ9T9xXpEyX5AtwlsuXj55TKkUosu28i-7Qwhj6F67596y~yt4LhjKAfg6OCyO3j-tZbsryd~ZNHbOCZ-6H9jILvn23-Iufu8EZ40L0-t04BV6dNvS3aZRJRzLw0tlEvObtabrlaJFb1THiVvj-5QU5oFoUn2vOAD-vIMw3224BsaoxqKe96wBNbrc57Umnlp1ChbIrYvOWMea~cfI2jyh6t-IHkEWyqlikkHskuvU8gCxyIA-bekDoSyVQJ5jIIMjA6nTIM3fMuG4fA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792554"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/111792554/Non_universal_power_laws_of_SIR_models_on_hierarchical_modular_networks"><img alt="Research paper thumbnail of Non-universal power-laws of SIR models on hierarchical modular networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/111792554/Non_universal_power_laws_of_SIR_models_on_hierarchical_modular_networks">Non-universal power-laws of SIR models on hierarchical modular networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Power-law (PL) time dependent infection growth has been reported in many Covid statistics. In sim...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Power-law (PL) time dependent infection growth has been reported in many Covid statistics. In simple SIR models the number of infections grows at the outbreak as I(t) ∝ t^d-1 on d-dimensional Euclidean lattices in the endemic phase or follow a slower universal PL at the critical point, until finite sizes cause immunity and a crossover to an exponential decay. Heterogeneity may alter the dynamics of spreading models, spatially inhomogeneous infection rates can cause slower decays, posing a threat of a long recovery from a pandemic. Covid statistics have also provided epidemic size distributions with PL tails in several countries. Here I investigate SIR like models on hierarchical modular networks, embedded in 2d lattices with the addition of long-range links. I show that if the topological dimension of the network is finite, average degree dependent PL growth of prevalence emerges. Supercritically the same exponents as of regular graphs occurs, but the topological disorder alters the critical behavior. This is also true for the epidemic size distributions. Mobility of individuals does not affect the form of the scaling behavior, except for the d=2 lattice, but increases the magnitude of the epidemic. Addition of a super-spreader hot-spot also does not changes the growth exponent and the exponential decay in the heard immunity regime.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792554"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792554"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792554; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792554]").text(description); $(".js-view-count[data-work-id=111792554]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792554; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792554']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=111792554]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111792554,"title":"Non-universal power-laws of SIR models on hierarchical modular networks","internal_url":"https://www.academia.edu/111792554/Non_universal_power_laws_of_SIR_models_on_hierarchical_modular_networks","owner_id":65671377,"coauthors_can_edit":true,"owner":{"id":65671377,"first_name":"Geza","middle_initials":null,"last_name":"Odor","page_name":"GezaOdor","domain_name":"independent","created_at":"2017-06-19T01:57:01.468-07:00","display_name":"Geza Odor","url":"https://independent.academia.edu/GezaOdor"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792546"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792546/Desynchronization_dynamics_of_the_Kuramoto_model_on_connectome_graphs"><img alt="Research paper thumbnail of Desynchronization dynamics of the Kuramoto model on connectome graphs" class="work-thumbnail" src="https://attachments.academia-assets.com/109223344/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792546/Desynchronization_dynamics_of_the_Kuramoto_model_on_connectome_graphs">Desynchronization dynamics of the Kuramoto model on connectome graphs</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Mar 1, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The hypothesis, that cortical dynamics operates near criticality also suggests, that it exhibits ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The hypothesis, that cortical dynamics operates near criticality also suggests, that it exhibits universal critical exponents which marks the Kuramoto equation, a fundamental model for synchronization, as a prime candidate for an underlying universal model. Here, we determined the synchronization behavior of this model by solving it numerically on a large, weighted human connectome network, containing 804 092 nodes, in an assumed homeostatic state. Since this graph has a topological dimension d < 4, a real synchronization phase transition is not possible in the thermodynamic limit, still we could locate a transition between partially synchronized and desynchronized states. At this crossover point we observe power-law-tailed synchronization durations, with τ t ≃ 1.2(1), away from experimental values for the brain. For comparison, on a large twodimensional lattice, having additional random, long-range links, we obtain a mean-field value: τ t ≃ 1.6(1). However, below the transition of the connectome we found global coupling control-parameter dependent exponents 1 < τ t ≤ 2, overlapping with the range of human brain experiments. We also studied the effects of random flipping of a small portion of link weights, mimicking a network with inhibitory interactions, and found similar results. The control-parameter dependent exponent suggests extended dynamical criticality below the transition point.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fc24085a3be2aad909a817b869547edd" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223344,"asset_id":111792546,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223344/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792546"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792546"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792546; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792541"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792541/Effects_of_heterogeneity_in_power_grid_network_models"><img alt="Research paper thumbnail of Effects of heterogeneity in power-grid network models" class="work-thumbnail" src="https://attachments.academia-assets.com/109223343/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792541/Effects_of_heterogeneity_in_power_grid_network_models">Effects of heterogeneity in power-grid network models</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Jan 29, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We have compared the phase synchronization transition of the second order Kuramoto model on 2D la...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We have compared the phase synchronization transition of the second order Kuramoto model on 2D lattices and on large, synthetic power grid networks, generated from real data. The latter are weighted, hierarchical modular networks. Due to the inertia the synchronization transitions are of first order type, characterized by fast relaxation and hysteresis by varying the global coupling parameter K. Finite size scaling analysis shows that there is no real phase transition in the thermodynamic limit, unlike in the mean-field model. The order parameter and its fluctuations depend on the network size without any real singular behavior. In case of power grids the phase synchronization breaks down at lower global couplings, than in case of 2D lattices of the same sizes, but the hysteresis is much narrower or negligible due to the low connectivity of the graphs. The temporal behavior of de-synchronization avalanches after a sudden quench to low K values, has been followed and duration distributions with power-law tails have been detected. This suggests rare region effects, caused by frozen disorder, resulting in heavy tailed distributions, even without a self organization mechanism as a consequence of a catastrophic drop event in the couplings.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ec2a018e78111636e140a148b88fadf3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223343,"asset_id":111792541,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223343/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792541"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792541"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792541; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792538"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792538/Universality_class_split_in_directed_percolation"><img alt="Research paper thumbnail of Universality class split in directed percolation" class="work-thumbnail" src="https://attachments.academia-assets.com/109223342/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792538/Universality_class_split_in_directed_percolation">Universality class split in directed percolation</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Apr 18, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The crossover behavior of various models exhibiting phase transition to absorbing phase with pari...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The crossover behavior of various models exhibiting phase transition to absorbing phase with parity conserving class has been investigated by numerical simulations and cluster mean-field method. In case of models exhibiting Z_2 symmetric absorbing phases (the NEKIMCA and Grassberger's A stochastic cellular automaton) the introduction of an external symmetry breaking field causes a crossover to dynamical scaling of the directed percolation (DP) with the crossover exponent's: 1/\phi = 0.53(2). In case an even offspringed branching and annihilating random walk model (dual to NEKIMCA) the introduction of spontaneous particle decay destroys the parity conservation and results in a crossover to the DP class characterized by the crossover exponent: 1/\phi = 0.205(5). The two different kinds of crossover operator can't be mapped onto each other and a split of the DP universality class can be observed in one dimension.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="61856154e1c79734d49c52903a582e56" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223342,"asset_id":111792538,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223342/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792538"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792538"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792538; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="7138743" id="papers"><div class="js-work-strip profile--work_container" data-work-id="116627264"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116627264/Synchronization_transitions_on_connectome_graphs_with_external_force"><img alt="Research paper thumbnail of Synchronization transitions on connectome graphs with external force" class="work-thumbnail" src="https://attachments.academia-assets.com/112704272/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116627264/Synchronization_transitions_on_connectome_graphs_with_external_force">Synchronization transitions on connectome graphs with external force</a></div><div class="wp-workCard_item"><span>Frontiers in Physics</span><span>, Mar 9, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We investigate the synchronization transition of the Shinomoto-Kuramoto model on networks of the ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We investigate the synchronization transition of the Shinomoto-Kuramoto model on networks of the fruit-fly and two large human connectomes. This model contains a force term, thus is capable of describing critical behavior in the presence of external excitation. By numerical solution we determine the crackling noise durations with and without thermal noise and show extended non-universal scaling tails characterized by the exponent 2 < τ t < 2.8, in contrast with the Hopf transition of the Kuramoto model, without the force τ t = 3.1(1). Comparing the phase and frequency order parameters we find different synchronization transition points and fluctuation peaks as in case of the Kuramoto model, related to a crossover at Widom lines. Using the local order parameter values we also determine the Hurst (phase) and β (frequency) exponents and compare them with recent experimental results obtained by fMRI. We show that these exponents, characterizing the auto-correlations are smaller in the excited system than in the resting state and exhibit module dependence.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4d55d989ff1544658eb02e18b07a040c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112704272,"asset_id":116627264,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112704272/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116627264"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116627264"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116627264; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792680"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792680/Critical_behavior_of_the_diffusive_susceptible_infected_recovered_model"><img alt="Research paper thumbnail of Critical behavior of the diffusive susceptible-infected-recovered model" class="work-thumbnail" src="https://attachments.academia-assets.com/109223511/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792680/Critical_behavior_of_the_diffusive_susceptible_infected_recovered_model">Critical behavior of the diffusive susceptible-infected-recovered model</a></div><div class="wp-workCard_item"><span>Physical Review E</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The critical behavior of the non-diffusive susceptible-infected-recovered model on lattices had b...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The critical behavior of the non-diffusive susceptible-infected-recovered model on lattices had been well established in virtue of its duality symmetry. By performing simulations and scaling analyses for the diffusive variant on the two-dimensional lattice, we show that diffusion for all agents, while rendering this symmetry destroyed, constitutes a singular perturbation that induces asymptotically distinct dynamical and stationary critical behavior from the non-diffusive model. In particular, the manifested crossover behavior in the effective mean-square radius exponents reveals that slow crossover behavior in general diffusive multi-species reaction systems may be ascribed to the interference of multiple length scales and timescales at early times.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="40fc208ed1677fcfe10bc8c5db617742" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223511,"asset_id":111792680,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223511/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792680"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792680"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792680; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792676"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/111792676/Scaling_at_First_Order_Phase_Transitions"><img alt="Research paper thumbnail of Scaling at First-Order Phase Transitions" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/111792676/Scaling_at_First_Order_Phase_Transitions">Scaling at First-Order Phase Transitions</a></div><div class="wp-workCard_item"><span>WORLD SCIENTIFIC eBooks</span><span>, May 1, 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792676"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792676"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792676; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=111792676]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111792676,"title":"Scaling at First-Order Phase Transitions","internal_url":"https://www.academia.edu/111792676/Scaling_at_First_Order_Phase_Transitions","owner_id":65671377,"coauthors_can_edit":true,"owner":{"id":65671377,"first_name":"Geza","middle_initials":null,"last_name":"Odor","page_name":"GezaOdor","domain_name":"independent","created_at":"2017-06-19T01:57:01.468-07:00","display_name":"Geza Odor","url":"https://independent.academia.edu/GezaOdor"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792674"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792674/Chimera_states_in_neural_networks_and_power_systems"><img alt="Research paper thumbnail of Chimera states in neural networks and power systems" class="work-thumbnail" src="https://attachments.academia-assets.com/109223418/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792674/Chimera_states_in_neural_networks_and_power_systems">Chimera states in neural networks and power systems</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Jul 5, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Partial, frustrated synchronization and chimera states are expected to occur in Kuramoto-like mod...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Partial, frustrated synchronization and chimera states are expected to occur in Kuramoto-like models if the spectral dimension of the underlying graph is low: d s < 4. We provide numerical evidence that this really happens in case of the high-voltage power grid of Europe (d s < 2) and in case of the largest, exactly known brain network corresponding to the fruit-fly (FF) connectome (d s < 4), even though their graph dimensions are much higher, i.e.: d EU g ≃ 2.6(1) and d FF g ≃ 5.4(1), d KKI113 g ≃ 3.4(1). We provide local synchronization results of the first-and second-order (Shinomoto) Kuramoto models by numerical solutions on the the FF and the European power-grid graphs, respectively, and show the emergence of chimera-like patterns on the graph community level as well as by the local order parameters. We show that Kuramoto oscillator models on large neural connectome graph of the fruit-fly, a human brain, as well as on the power-grid of Europe produce chimera states. This is in agreement with the low spectral dimensions that we calculated by the eigenvalue spectra of the Laplacian of these networks. We compare these results with the topological dimension measurements and previous simulations, strengthening that frustrated synchronization should occur, which can generate slow relaxations, obtained in previous studies within the neighborhood of the synchronization transition point.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="54e927a9feea2bb4cd95a7fc863f8650" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223418,"asset_id":111792674,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223418/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792674"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792674"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792674; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792670"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792670/Suppressing_correlations_in_massively_parallel_simulations_of_lattice_models"><img alt="Research paper thumbnail of Suppressing correlations in massively parallel simulations of lattice models" class="work-thumbnail" src="https://attachments.academia-assets.com/109223409/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792670/Suppressing_correlations_in_massively_parallel_simulations_of_lattice_models">Suppressing correlations in massively parallel simulations of lattice models</a></div><div class="wp-workCard_item"><span>Computer Physics Communications</span><span>, Nov 1, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">For lattice Monte Carlo simulations parallelization is crucial to make studies of large systems a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">For lattice Monte Carlo simulations parallelization is crucial to make studies of large systems and long simulation time feasible, while sequential simulations remain the gold-standard for correlation-free dynamics. Here, various domain decomposition schemes are compared, concluding with one which delivers virtually correlation-free simulations on GPUs. Extensive simulations of the octahedron model for 2 + 1 dimensional Kardar-Parisi-Zhang surface growth, which is very sensitive to correlation in the site-selection dynamics, were performed to show self-consistency of the parallel runs and agreement with the sequential algorithm. We present a GPU implementation providing a speedup of about 30× over a parallel CPU implementation on a single socket and at least 180× with respect to the sequential reference.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f2ef0edc93510a5d10d0c5d886a425ed" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223409,"asset_id":111792670,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223409/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792670"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792670"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792670; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792664"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792664/Local_scale_invariance_of_the_2_1_dimensional_Kardar_Parisi_Zhang_model"><img alt="Research paper thumbnail of Local scale-invariance of the 2 + 1 dimensional Kardar–Parisi–Zhang model" class="work-thumbnail" src="https://attachments.academia-assets.com/109223403/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792664/Local_scale_invariance_of_the_2_1_dimensional_Kardar_Parisi_Zhang_model">Local scale-invariance of the 2 + 1 dimensional Kardar–Parisi–Zhang model</a></div><div class="wp-workCard_item"><span>Journal of Physics A</span><span>, Feb 20, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Local Scale-Invariance theory is tested by extensive dynamical simulations of the driven dimer la...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Local Scale-Invariance theory is tested by extensive dynamical simulations of the driven dimer lattice gas model, describing the surface growth of the 2+1 dimensional Kardar-Parisi-Zhang surfaces. Very precise measurements of the universal autoresponse function enabled us to perform nonlinear fitting with the scaling forms, suggested by local scale-invariance (LSI). While the simple LSI ansatz does not seem to work, forms based on logarithmic extension of LSI provide satisfactory description of the full (measured) time evolution of the autoresponse function.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8938859334eab5a7a3e03d1148260451" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223403,"asset_id":111792664,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223403/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792664"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792664"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792664; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792652"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792652/Critical_behavior_of_the_one_dimensional_diffusive_pair_contact_process"><img alt="Research paper thumbnail of Critical behavior of the one-dimensional diffusive pair contact process" class="work-thumbnail" src="https://attachments.academia-assets.com/109223398/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792652/Critical_behavior_of_the_one_dimensional_diffusive_pair_contact_process">Critical behavior of the one-dimensional diffusive pair contact process</a></div><div class="wp-workCard_item"><span>Physical review</span><span>, Jan 24, 2003</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The phase transition of the one-dimensional, diffusive pair contact process (PCPD) is investigate...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The phase transition of the one-dimensional, diffusive pair contact process (PCPD) is investigated by N cluster mean-field approximations and high precision simulations. The N = 3, 4 cluster approximations exhibit smooth transition line to absorbing state by varying the diffusion rate D with β2 = 2 mean-field order parameter exponent of the pair density. This contradicts with former N = 2 results, where two different mean-field behavior was found along the transition line. Extensive dynamical simulations on L = 10 5 lattices give estimates for the order parameter exponents of the particles for 0.05 ≤ D ≤ 0.7. These data can support former two distinct class findings. However the gap between low and high D exponents is narrower than estimated previously and the possibility for interpreting numerical data as a single class behavior with exponents α = 0.21(1), β = 0.41(1) assuming logarithmic corrections is shown. Finite size scaling and cluster simulation results are also presented.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c716745e8f1d796f5ba5a43f120eb3fa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223398,"asset_id":111792652,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223398/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792652"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792652"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792652; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792643"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792643/Critical_behavior_of_the_diffusive_susceptible_infected_recovered_model"><img alt="Research paper thumbnail of Critical behavior of the diffusive susceptible-infected-recovered model" class="work-thumbnail" src="https://attachments.academia-assets.com/109223393/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792643/Critical_behavior_of_the_diffusive_susceptible_infected_recovered_model">Critical behavior of the diffusive susceptible-infected-recovered model</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Aug 25, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The critical behavior of the non-diffusive susceptible-infected-recovered model on lattices had b...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The critical behavior of the non-diffusive susceptible-infected-recovered model on lattices had been well established in virtue of its duality symmetry. By performing simulations and scaling analyses for the diffusive variant on the two-dimensional lattice, we show that diffusion for all agents, while rendering this symmetry destroyed, constitutes a singular perturbation that induces asymptotically distinct dynamical and stationary critical behavior from the non-diffusive model. In particular, the manifested crossover behavior in the effective mean-square radius exponents reveals that slow crossover behavior in general diffusive multi-species reaction systems may be ascribed to the interference of multiple length scales and timescales at early times.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e3de64a3f8a4a5144ade465340502136" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223393,"asset_id":111792643,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223393/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792643"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792643"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792643; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792634"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792634/Mapping_of_KPZ_growth_onto_conserved_lattice_gas_a_model_of_dimers_in_two_dimensions"><img alt="Research paper thumbnail of Mapping of KPZ growth onto conserved lattice gas a model of dimers in two dimensions" class="work-thumbnail" src="https://attachments.academia-assets.com/109223391/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792634/Mapping_of_KPZ_growth_onto_conserved_lattice_gas_a_model_of_dimers_in_two_dimensions">Mapping of KPZ growth onto conserved lattice gas a model of dimers in two dimensions</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Oct 10, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We show that a 2+1 dimensional discrete surface growth model exhibiting KPZ class scaling can be ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We show that a 2+1 dimensional discrete surface growth model exhibiting KPZ class scaling can be mapped onto a two dimensional conserved lattice gas model of directed dimers. In case of KPZ height anisotropy the dimers follow driven diffusive motion. We confirm by numerical simulations that the scaling exponents of the dimer model are in agreement with those of the 2+1 dimensional KPZ class. This opens up the possibility of analyzing growth models via reaction-diffusion models, which allow much more efficient computer simulations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cb6d59323dfd6c105d65321b7416e2d1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223391,"asset_id":111792634,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223391/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792634"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792634"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792634; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792625"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792625/Mapping_of_2_1_dimensional_KPZ_growth_onto_driven_lattice_gas_model_of_dimers"><img alt="Research paper thumbnail of Mapping of 2+1 dimensional KPZ growth onto driven lattice gas model of dimers" class="work-thumbnail" src="https://attachments.academia-assets.com/109223388/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792625/Mapping_of_2_1_dimensional_KPZ_growth_onto_driven_lattice_gas_model_of_dimers">Mapping of 2+1 dimensional KPZ growth onto driven lattice gas model of dimers</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Oct 10, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We show that a 2+1 dimensional discrete surface growth model exhibiting KPZ class scaling can be ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We show that a 2+1 dimensional discrete surface growth model exhibiting KPZ class scaling can be mapped onto a two dimensional conserved lattice gas model of directed dimers. In case of KPZ height anisotropy the dimers follow driven diffusive motion. We confirm by numerical simulations that the scaling exponents of the dimer model are in agreement with those of the 2+1 dimensional KPZ class. This opens up the possibility of analyzing growth models via reaction-diffusion models, which allow much more efficient computer simulations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="46fa3f147cbf746622bbe439c3448dae" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223388,"asset_id":111792625,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223388/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792625"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792625"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792625; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792617"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792617/Robustness_of_Griffiths_effects_in_homeostatic_connectome_models"><img alt="Research paper thumbnail of Robustness of Griffiths effects in homeostatic connectome models" class="work-thumbnail" src="https://attachments.academia-assets.com/109223385/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792617/Robustness_of_Griffiths_effects_in_homeostatic_connectome_models">Robustness of Griffiths effects in homeostatic connectome models</a></div><div class="wp-workCard_item"><span>Physical review</span><span>, Jan 10, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">I provide numerical evidence for the robustness of the Griffiths phase (GP) reported previously i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">I provide numerical evidence for the robustness of the Griffiths phase (GP) reported previously in dynamical threshold model simulations on a large human brain network with N = 836733 connected nodes. The model, with equalized network sensitivity, is extended in two ways: introduction of refractory states or by randomized time dependent thresholds. The non-universal power-law dynamics in an extended control parameter region survives these modifications for a short refractory state and weak disorder. In case of temporal disorder the GP shrinks and for stronger heterogeneity disappears, leaving behind a mean-field type of critical transition. Activity avalanche size distributions below the critical point decay faster than in the original model, but the addition of inhibitory interactions sets it back to the range of experimental values.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2441f8afcbd6d938518cf586ed391822" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223385,"asset_id":111792617,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223385/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792617"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792617"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792617; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792617]").text(description); $(".js-view-count[data-work-id=111792617]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792617; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792617']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792605"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792605/Heterogeneity_effects_in_power_grid_network_models"><img alt="Research paper thumbnail of Heterogeneity effects in power grid network models" class="work-thumbnail" src="https://attachments.academia-assets.com/109223381/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792605/Heterogeneity_effects_in_power_grid_network_models">Heterogeneity effects in power grid network models</a></div><div class="wp-workCard_item"><span>Physical review</span><span>, Aug 8, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We have compared the phase synchronization transition of the second order Kuramoto model on 2D la...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We have compared the phase synchronization transition of the second order Kuramoto model on 2D lattices and on large, synthetic power grid networks, generated from real data. The latter are weighted, hierarchical modular networks. Due to the inertia the synchronization transitions are of first order type, characterized by fast relaxation and hysteresis by varying the global coupling parameter K. Finite size scaling analysis shows that there is no real phase transition in the thermodynamic limit, unlike in the mean-field model. The order parameter and its fluctuations depend on the network size without any real singular behavior. In case of power grids the phase synchronization breaks down at lower global couplings, than in case of 2D lattices of the same sizes, but the hysteresis is much narrower or negligible due to the low connectivity of the graphs. The temporal behavior of de-synchronization avalanches after a sudden quench to low K values, has been followed and duration distributions with power-law tails have been detected. This suggests rare region effects, caused by frozen disorder, resulting in heavy tailed distributions, even without a self organization mechanism as a consequence of a catastrophic drop event in the couplings.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f70fe04ec82385368494791da97f90c0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223381,"asset_id":111792605,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223381/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792605"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792605"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792605; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792605]").text(description); $(".js-view-count[data-work-id=111792605]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792605; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792605']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f70fe04ec82385368494791da97f90c0" } } $('.js-work-strip[data-work-id=111792605]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111792605,"title":"Heterogeneity effects in power grid network models","internal_url":"https://www.academia.edu/111792605/Heterogeneity_effects_in_power_grid_network_models","owner_id":65671377,"coauthors_can_edit":true,"owner":{"id":65671377,"first_name":"Geza","middle_initials":null,"last_name":"Odor","page_name":"GezaOdor","domain_name":"independent","created_at":"2017-06-19T01:57:01.468-07:00","display_name":"Geza Odor","url":"https://independent.academia.edu/GezaOdor"},"attachments":[{"id":109223381,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109223381/thumbnails/1.jpg","file_name":"1801.09492.pdf","download_url":"https://www.academia.edu/attachments/109223381/download_file","bulk_download_file_name":"Heterogeneity_effects_in_power_grid_netw.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109223381/1801.09492-libre.pdf?1702973374=\u0026response-content-disposition=attachment%3B+filename%3DHeterogeneity_effects_in_power_grid_netw.pdf\u0026Expires=1740605849\u0026Signature=gjz9ASKclfHz-ZRIdsrZIZomVmsV8PPuUujlO3Ua3ZLdbcsprFXcja6xrpyhFJnk15qtIhDLGbAy8NnlrSGU4c3lg5TUTuJQcurV-PgPsLc4LiqZn5W2faz0Vv4HF2y2tLRcPBv-mXZ~47V4P4EhLid6Yv9pDbufTS8BYWLautZjXMtiUZkZQfPR0gwbXkspi3ll5ppGZLfkk6vNTmZTiT-rSZn49LDK96ar0JVrUiTsSiYuj3AMnVUv6~MhLCV9~UCLCa0Of9qAkHqffOSzuOtp1X1vmxjMl-qgnDW6DbjocmyZEpG0RyolTVCvqr7F5Qe4kxQmFJPGyIzqV7Sgig__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792594"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792594/Revisiting_and_modeling_power_law_distributions_in_empirical_outage_data_of_power_systems"><img alt="Research paper thumbnail of Revisiting and modeling power-law distributions in empirical outage data of power systems" class="work-thumbnail" src="https://attachments.academia-assets.com/109223368/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792594/Revisiting_and_modeling_power_law_distributions_in_empirical_outage_data_of_power_systems">Revisiting and modeling power-law distributions in empirical outage data of power systems</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Mar 22, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The size distribution of planned and forced outages and following restoration times in power syst...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The size distribution of planned and forced outages and following restoration times in power systems have been studied for almost two decades and has drawn great interest as they display heavy tails. Understanding of this phenomenon has been done by various threshold models, which are self-tuned at their critical points, but as many papers pointed out, explanations are intuitive, and more empirical data is needed to support hypotheses. In this paper, the authors analyze outage data collected from various public sources to calculate the outage energy and outage duration exponents of possible power-law fits. Temporal thresholds are applied to identify crossovers from initial shorttime behavior to power-law tails. We revisit and add to the possible explanations of the uniformness of these exponents. By performing power spectral analyses on the outage event time series and the outage duration time series, it is found that, on the one hand, while being overwhelmed by white noise, outage events show traits of self-organized criticality (SOC), which may be modeled by a crossover from random percolation to directed percolation branching process with dissipation, coupled to a conserved density. On the other hand, in responses to outages, the heavy tails in outage duration distributions could be a consequence of the highly optimized tolerance (HOT) mechanism, based on the optimized allocation of maintenance resources.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ebc76cd4b6b39c167189aa7ef9bc1db7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223368,"asset_id":111792594,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223368/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792594"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792594"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792594; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792581"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792581/Synchronization_transition_of_the_second_order_Kuramoto_model_on_lattices"><img alt="Research paper thumbnail of Synchronization transition of the second-order Kuramoto model on lattices" class="work-thumbnail" src="https://attachments.academia-assets.com/109223361/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792581/Synchronization_transition_of_the_second_order_Kuramoto_model_on_lattices">Synchronization transition of the second-order Kuramoto model on lattices</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Nov 28, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The second-order Kuramoto equation describes synchronization of coupled oscillators with inertia,...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The second-order Kuramoto equation describes synchronization of coupled oscillators with inertia, which occur in power grids for example. Contrary to the first-order Kuramoto equation it's synchronization transition behavior is much less known. In case of Gaussian self-frequencies it is discontinuous, in contrast to the continuous transition for the first-order Kuramoto equation. Here we investigate this transition on large 2d and 3d lattices and provide numerical evidence of hybrid phase transitions, that the oscillator phases θi, exhibit a crossover, while the frequency spread a real phase transition in 3d. Thus a lower critical dimension d O l = 2 is expected for the frequencies and d R l = 4 for the phases like in the massless case. We provide numerical estimates for the critical exponents, finding that the frequency spread decays as ∼ t −d/2 in case of aligned initial state of the phases in agreement with the linear approximation. However in 3d, in the case of initially random distribution of θi, we find a faster decay, characterized by ∼ t −1.8(1) as the consequence of enhanced nonlinearities which appear by the random phase fluctuations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="adea683d486d7307078f1b0b8d2b815b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223361,"asset_id":111792581,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223361/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792581"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792581"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792581; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792575"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/111792575/Kuramoto_Model_on_KKI18_connectome"><img alt="Research paper thumbnail of Kuramoto Model on KKI18 connectome" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/111792575/Kuramoto_Model_on_KKI18_connectome">Kuramoto Model on KKI18 connectome</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Results of fourth-order Runge--Kutta integration of the first-order Kuramoto model in brain conne...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Results of fourth-order Runge--Kutta integration of the first-order Kuramoto model in brain connectome graph. Awr.dat.gz : connectome graph Awri.dat.gz : connectome graph with inhibitory links ccdata.tgz : simulations data for different configurations (averages at top-level, single runs in folders) eERll*.dat: cube graph with random long-range links o.ocp-kur_{lambda}_*.dat: connectome with coupling lambda o.ocp-kur_{lambda}I_*.dat: connectome with inhibitory links, coupling lambda elo-Thr: smoothed probability distributions of avalance times</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792575"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792575"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792575; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=111792575]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111792575,"title":"Kuramoto Model on KKI18 connectome","internal_url":"https://www.academia.edu/111792575/Kuramoto_Model_on_KKI18_connectome","owner_id":65671377,"coauthors_can_edit":true,"owner":{"id":65671377,"first_name":"Geza","middle_initials":null,"last_name":"Odor","page_name":"GezaOdor","domain_name":"independent","created_at":"2017-06-19T01:57:01.468-07:00","display_name":"Geza Odor","url":"https://independent.academia.edu/GezaOdor"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792561"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792561/Synchronization_transitions_on_connectome_graphs_with_external_force"><img alt="Research paper thumbnail of Synchronization transitions on connectome graphs with external force" class="work-thumbnail" src="https://attachments.academia-assets.com/109223356/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792561/Synchronization_transitions_on_connectome_graphs_with_external_force">Synchronization transitions on connectome graphs with external force</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Jan 12, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We investigate the synchronization transition of the Shinomoto-Kuramoto model on networks of the ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We investigate the synchronization transition of the Shinomoto-Kuramoto model on networks of the fruit-fly and two large human connectomes. This model contains a force term, thus is capable of describing critical behavior in the presence of external excitation. By numerical solution we determine the crackling noise durations with and without thermal noise and show extended non-universal scaling tails characterized by 2 < τt < 2.8, in contrast with the Hopf transition of the Kuramoto model, without the force τt = 3.1(1). Comparing the phase and frequency order parameters we find different transition points and fluctuations peaks as in case of the Kuramoto model. Using the local order parameter values we also determine the Hurst (phase) and β (frequency) exponents and compare them with recent experimental results obtained by fMRI. We show that these exponents, characterizing the auto-correlations are smaller in the excited system than in the resting state and exhibit module dependence.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ff145d17c988ea423f1888a1dc84aeec" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223356,"asset_id":111792561,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223356/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792561"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792561"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792561; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792561]").text(description); $(".js-view-count[data-work-id=111792561]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792561; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792561']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ff145d17c988ea423f1888a1dc84aeec" } } $('.js-work-strip[data-work-id=111792561]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111792561,"title":"Synchronization transitions on connectome graphs with external force","internal_url":"https://www.academia.edu/111792561/Synchronization_transitions_on_connectome_graphs_with_external_force","owner_id":65671377,"coauthors_can_edit":true,"owner":{"id":65671377,"first_name":"Geza","middle_initials":null,"last_name":"Odor","page_name":"GezaOdor","domain_name":"independent","created_at":"2017-06-19T01:57:01.468-07:00","display_name":"Geza Odor","url":"https://independent.academia.edu/GezaOdor"},"attachments":[{"id":109223356,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109223356/thumbnails/1.jpg","file_name":"2301.04951.pdf","download_url":"https://www.academia.edu/attachments/109223356/download_file","bulk_download_file_name":"Synchronization_transitions_on_connectom.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109223356/2301.04951-libre.pdf?1702973385=\u0026response-content-disposition=attachment%3B+filename%3DSynchronization_transitions_on_connectom.pdf\u0026Expires=1740605849\u0026Signature=hItJveUnyvTbZRSwxXF1yWdn3tkSQjhQiCcBjHwmG8fXuJkX1kBHlIAuXhY6l4x5TwE3NbvJ9T9xXpEyX5AtwlsuXj55TKkUosu28i-7Qwhj6F67596y~yt4LhjKAfg6OCyO3j-tZbsryd~ZNHbOCZ-6H9jILvn23-Iufu8EZ40L0-t04BV6dNvS3aZRJRzLw0tlEvObtabrlaJFb1THiVvj-5QU5oFoUn2vOAD-vIMw3224BsaoxqKe96wBNbrc57Umnlp1ChbIrYvOWMea~cfI2jyh6t-IHkEWyqlikkHskuvU8gCxyIA-bekDoSyVQJ5jIIMjA6nTIM3fMuG4fA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792554"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/111792554/Non_universal_power_laws_of_SIR_models_on_hierarchical_modular_networks"><img alt="Research paper thumbnail of Non-universal power-laws of SIR models on hierarchical modular networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/111792554/Non_universal_power_laws_of_SIR_models_on_hierarchical_modular_networks">Non-universal power-laws of SIR models on hierarchical modular networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Power-law (PL) time dependent infection growth has been reported in many Covid statistics. In sim...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Power-law (PL) time dependent infection growth has been reported in many Covid statistics. In simple SIR models the number of infections grows at the outbreak as I(t) ∝ t^d-1 on d-dimensional Euclidean lattices in the endemic phase or follow a slower universal PL at the critical point, until finite sizes cause immunity and a crossover to an exponential decay. Heterogeneity may alter the dynamics of spreading models, spatially inhomogeneous infection rates can cause slower decays, posing a threat of a long recovery from a pandemic. Covid statistics have also provided epidemic size distributions with PL tails in several countries. Here I investigate SIR like models on hierarchical modular networks, embedded in 2d lattices with the addition of long-range links. I show that if the topological dimension of the network is finite, average degree dependent PL growth of prevalence emerges. Supercritically the same exponents as of regular graphs occurs, but the topological disorder alters the critical behavior. This is also true for the epidemic size distributions. Mobility of individuals does not affect the form of the scaling behavior, except for the d=2 lattice, but increases the magnitude of the epidemic. Addition of a super-spreader hot-spot also does not changes the growth exponent and the exponential decay in the heard immunity regime.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792554"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792554"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792554; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792554]").text(description); $(".js-view-count[data-work-id=111792554]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792554; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792554']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=111792554]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111792554,"title":"Non-universal power-laws of SIR models on hierarchical modular networks","internal_url":"https://www.academia.edu/111792554/Non_universal_power_laws_of_SIR_models_on_hierarchical_modular_networks","owner_id":65671377,"coauthors_can_edit":true,"owner":{"id":65671377,"first_name":"Geza","middle_initials":null,"last_name":"Odor","page_name":"GezaOdor","domain_name":"independent","created_at":"2017-06-19T01:57:01.468-07:00","display_name":"Geza Odor","url":"https://independent.academia.edu/GezaOdor"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792546"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792546/Desynchronization_dynamics_of_the_Kuramoto_model_on_connectome_graphs"><img alt="Research paper thumbnail of Desynchronization dynamics of the Kuramoto model on connectome graphs" class="work-thumbnail" src="https://attachments.academia-assets.com/109223344/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792546/Desynchronization_dynamics_of_the_Kuramoto_model_on_connectome_graphs">Desynchronization dynamics of the Kuramoto model on connectome graphs</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Mar 1, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The hypothesis, that cortical dynamics operates near criticality also suggests, that it exhibits ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The hypothesis, that cortical dynamics operates near criticality also suggests, that it exhibits universal critical exponents which marks the Kuramoto equation, a fundamental model for synchronization, as a prime candidate for an underlying universal model. Here, we determined the synchronization behavior of this model by solving it numerically on a large, weighted human connectome network, containing 804 092 nodes, in an assumed homeostatic state. Since this graph has a topological dimension d < 4, a real synchronization phase transition is not possible in the thermodynamic limit, still we could locate a transition between partially synchronized and desynchronized states. At this crossover point we observe power-law-tailed synchronization durations, with τ t ≃ 1.2(1), away from experimental values for the brain. For comparison, on a large twodimensional lattice, having additional random, long-range links, we obtain a mean-field value: τ t ≃ 1.6(1). However, below the transition of the connectome we found global coupling control-parameter dependent exponents 1 < τ t ≤ 2, overlapping with the range of human brain experiments. We also studied the effects of random flipping of a small portion of link weights, mimicking a network with inhibitory interactions, and found similar results. The control-parameter dependent exponent suggests extended dynamical criticality below the transition point.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fc24085a3be2aad909a817b869547edd" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223344,"asset_id":111792546,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223344/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792546"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792546"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792546; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792546]").text(description); $(".js-view-count[data-work-id=111792546]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792546; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792546']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "fc24085a3be2aad909a817b869547edd" } } $('.js-work-strip[data-work-id=111792546]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":111792546,"title":"Desynchronization dynamics of the Kuramoto model on connectome graphs","internal_url":"https://www.academia.edu/111792546/Desynchronization_dynamics_of_the_Kuramoto_model_on_connectome_graphs","owner_id":65671377,"coauthors_can_edit":true,"owner":{"id":65671377,"first_name":"Geza","middle_initials":null,"last_name":"Odor","page_name":"GezaOdor","domain_name":"independent","created_at":"2017-06-19T01:57:01.468-07:00","display_name":"Geza Odor","url":"https://independent.academia.edu/GezaOdor"},"attachments":[{"id":109223344,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109223344/thumbnails/1.jpg","file_name":"1903.00385.pdf","download_url":"https://www.academia.edu/attachments/109223344/download_file","bulk_download_file_name":"Desynchronization_dynamics_of_the_Kuramo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109223344/1903.00385-libre.pdf?1702973377=\u0026response-content-disposition=attachment%3B+filename%3DDesynchronization_dynamics_of_the_Kuramo.pdf\u0026Expires=1740605849\u0026Signature=FIDKSeA203OSEeU8EsIYX59rGe46rbHxMcKqDs5uTVyIV99IWRTSXYuyoAStE-Ir2fEMBryMLPin1O7gUpI402Pni4KH6tqZJo529Q8lg-Mq1nmaDdR5VFbJGyJ4oBh3GnlYn-leqib0LzgdlubNpsrthXS316BMxl2YSBC1SXvAw9YstHi~zVRnKJAHwTq3k4j~urKPfR5H8cL7l0R-2HBgX7d6DdndinzQK68lIYMVUt8VbTMbixjmKIyc3M4IdVdaRuDfpppZDM4jA4qmLK8rKOXhOpy0-qWNHdQPKms9u3i~JtfQBwFKo~gXLt2BF1qlC~RqRcsnXy6kMmU84A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792541"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792541/Effects_of_heterogeneity_in_power_grid_network_models"><img alt="Research paper thumbnail of Effects of heterogeneity in power-grid network models" class="work-thumbnail" src="https://attachments.academia-assets.com/109223343/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792541/Effects_of_heterogeneity_in_power_grid_network_models">Effects of heterogeneity in power-grid network models</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Jan 29, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We have compared the phase synchronization transition of the second order Kuramoto model on 2D la...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We have compared the phase synchronization transition of the second order Kuramoto model on 2D lattices and on large, synthetic power grid networks, generated from real data. The latter are weighted, hierarchical modular networks. Due to the inertia the synchronization transitions are of first order type, characterized by fast relaxation and hysteresis by varying the global coupling parameter K. Finite size scaling analysis shows that there is no real phase transition in the thermodynamic limit, unlike in the mean-field model. The order parameter and its fluctuations depend on the network size without any real singular behavior. In case of power grids the phase synchronization breaks down at lower global couplings, than in case of 2D lattices of the same sizes, but the hysteresis is much narrower or negligible due to the low connectivity of the graphs. The temporal behavior of de-synchronization avalanches after a sudden quench to low K values, has been followed and duration distributions with power-law tails have been detected. This suggests rare region effects, caused by frozen disorder, resulting in heavy tailed distributions, even without a self organization mechanism as a consequence of a catastrophic drop event in the couplings.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ec2a018e78111636e140a148b88fadf3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223343,"asset_id":111792541,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223343/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792541"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792541"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792541; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=111792541]").text(description); $(".js-view-count[data-work-id=111792541]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 111792541; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='111792541']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="111792538"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/111792538/Universality_class_split_in_directed_percolation"><img alt="Research paper thumbnail of Universality class split in directed percolation" class="work-thumbnail" src="https://attachments.academia-assets.com/109223342/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/111792538/Universality_class_split_in_directed_percolation">Universality class split in directed percolation</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Apr 18, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The crossover behavior of various models exhibiting phase transition to absorbing phase with pari...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The crossover behavior of various models exhibiting phase transition to absorbing phase with parity conserving class has been investigated by numerical simulations and cluster mean-field method. In case of models exhibiting Z_2 symmetric absorbing phases (the NEKIMCA and Grassberger's A stochastic cellular automaton) the introduction of an external symmetry breaking field causes a crossover to dynamical scaling of the directed percolation (DP) with the crossover exponent's: 1/\phi = 0.53(2). In case an even offspringed branching and annihilating random walk model (dual to NEKIMCA) the introduction of spontaneous particle decay destroys the parity conservation and results in a crossover to the DP class characterized by the crossover exponent: 1/\phi = 0.205(5). The two different kinds of crossover operator can't be mapped onto each other and a split of the DP universality class can be observed in one dimension.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="61856154e1c79734d49c52903a582e56" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109223342,"asset_id":111792538,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109223342/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="111792538"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="111792538"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111792538; 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