Publications by authors named "Max Falkenberg"

Existing studies of political polarization are often limited to a single country and one form of polarization, hindering a comprehensive understanding of the phenomenon. Here we investigate patterns of polarization online across nine countries (Canada, France, Germany, Italy, Poland, Spain, Turkey, UK, USA), focusing on the structure of political interaction networks, the use of toxic language targeting out-groups, and how these factors relate to user engagement. First, we show that political interaction networks are structurally polarized on Twitter (currently X).

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Deplatforming, or banning malicious accounts from social media, is a key tool for moderating online harms. However, the consequences of deplatforming for the wider social media ecosystem have been largely overlooked so far, due to the difficulty of tracking banned users. Here, we address this gap by studying the ban-induced platform migration from Twitter to Gettr.

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Article Synopsis
  • Micro-anatomical reentry has been recognized as a key factor contributing to atrial fibrillation (AF), prompting the study of atrial regions most vulnerable to it.
  • The authors propose a new computational method that uses modeling to understand how interstitial fibrosis affects micro-reentry, focusing on spatial network phenomena.
  • Results indicate that densely clustered micro-reentrant substrates usually occur in areas with thin, convex atrial walls, but as fibrosis increases, this clustering diminishes, potentially impacting the success of localized treatments like ablation.
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Automated ECG classification is a standard feature in many commercial 12-Lead ECG machines. As part of the Physionet/CinC Challenge 2020, our team, "Mad-hardmax", developed an XGBoost based classification method for the analysis of 12-Lead ECGs acquired from four different countries. Our aim is to develop an interpretable classifier that outputs diagnoses which can be traced to specific ECG features, while also testing the potential of information theoretic features for ECG diagnosis.

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Atrial fibrillation (AF) is the most common cardiac arrhytmia, characterized by the chaotic motion of electrical wavefronts in the atria. In clinical practice, AF is classified under two primary categories: paroxysmal AF, short intermittent episodes separated by periods of normal electrical activity; and persistent AF, longer uninterrupted episodes of chaotic electrical activity. However, the precise reasons why AF in a given patient is paroxysmal or persistent is poorly understood.

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Re-entrant circuits have been identified as potential drivers of atrial fibrillation (AF). In this paper, we develop a novel computational framework for finding the locations of re-entrant circuits from high resolution fibre orientation data. The technique follows a statistical approach whereby we generate continuous fibre tracts across the tissue and couple adjacent fibres stochastically if they are within a given distance of each other.

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The mechanisms of atrial fibrillation (AF) are poorly understood, resulting in disappointing success rates of ablative treatment. Different mechanisms defined largely by different atrial activation patterns have been proposed and, arguably, this dispute has slowed the progress of AF research. Recent clinical evidence suggests a unifying mechanism of local drivers based on sustained reentrant circuits in the complex atrial architecture.

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Most animal traps are constructed from self-secreted silk, so antlions are rare among trap builders because they use only materials found in the environment. We show how antlions exploit the properties of the substrate to produce very effective structures in the minimum amount of time. Our modelling demonstrates how antlions: (i) exploit self-stratification in granular media differentially to expose deleterious large grains at the bottom of the construction trench where they can be ejected preferentially, and (ii) minimize completion time by spiral rather than central digging.

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