🍩 Database of Original & NonTheoretical Uses of Topology
(found 19 matches in 0.003871s)


Severe Slugging Flow Identification From Topological Indicators (2022)
Simone CasoloAbstract
In this work, topological data analysis is used to identify the onset of severe slug flow in offshore petroleum production systems. Severe slugging is a multiphase flow regime known to be very inefficient and potentially harmful to process equipment and it is characterized by large oscillations in the production fluid pressure. Time series from pressure sensors in subsea oil wells are processed by means of Takens embedding to produce point clouds of data. Embedded sensor data is then analyzed using persistent homology to obtain topological indicators capable of revealing the occurrence of severe slugging in a conditionbased monitoring approach. A large dataset of well events consisting of both real and simulated data is used to demonstrate the possibilty of authomatizing severe slugging detection from live data via topological data analysis. Methods based on persistence diagrams are shown to accurately identify severe slugging and to classify different flow regimes from pressure signals of producing wells with supervised machine learning. 
A Topological Perspective on Regimes in Dynamical Systems (2021)
Kristian Strommen, Matthew Chantry, Joshua Dorrington, Nina OtterAbstract
The existence and behaviour of socalled `regimes' has been extensively studied in dynamical systems ranging from simple toy models to the atmosphere itself, due to their potential of drastically simplifying complex and chaotic dynamics. Nevertheless, no agreedupon and clearcut definition of a `regime' or a `regime system' exists in the literature. We argue here for a definition which equates the existence of regimes in a system with the existence of nontrivial topological structure. We show, using persistent homology, a tool in topological data analysis, that this definition is both computationally tractable, practically informative, and accounts for a variety of different examples. We further show that alternative, more strict definitions based on clustering and/or temporal persistence criteria fail to account for one or more examples of dynamical systems typically thought of as having regimes. We finally discuss how our methodology can shed light on regime behaviour in the atmosphere, and discuss future prospects. 
Contagion Dynamics for Manifold Learning (2020)
Barbara I. MahlerAbstract
Contagion maps exploit activation times in threshold contagions to assign vectors in highdimensional Euclidean space to the nodes of a network. A point cloud that is the image of a contagion map reflects both the structure underlying the network and the spreading behaviour of the contagion on it. Intuitively, such a point cloud exhibits features of the network's underlying structure if the contagion spreads along that structure, an observation which suggests contagion maps as a viable manifoldlearning technique. We test contagion maps as a manifoldlearning tool on a number of different realworld and synthetic data sets, and we compare their performance to that of Isomap, one of the most wellknown manifoldlearning algorithms. We find that, under certain conditions, contagion maps are able to reliably detect underlying manifold structure in noisy data, while Isomap fails due to noiseinduced error. This consolidates contagion maps as a technique for manifold learning. 
Finding Universal Structures in Quantum ManyBody Dynamics via Persistent Homology (2020)
Daniel Spitz, Jürgen Berges, Markus K. Oberthaler, Anna WienhardAbstract
Inspired by topological data analysis techniques, we introduce persistent homology observables and apply them in a geometric analysis of the dynamics of quantum field theories. As a prototype application, we consider simulated data of a twodimensional Bose gas far from equilibrium. We discover a continuous spectrum of dynamical scaling exponents, which provides a refined classification of nonequilibrium universal phenomena. A possible explanation of the underlying processes is provided in terms of mixing wave turbulence and vortex kinetics components in point clouds. We find that the persistent homology scaling exponents are inherently linked to the geometry of the system, as the derivation of a packing relation reveals. The approach opens new ways of analyzing quantum manybody dynamics in terms of robust topological structures beyond standard field theoretic techniques. 
Model Comparison via Simplicial Complexes and Persistent Homology (2020)
Sean T. Vittadello, Michael P. H. StumpfAbstract
In many scientific and technological contexts we have only a poor understanding of the structure and details of appropriate mathematical models. We often need to compare different models. With available data we can use formal statistical model selection to compare and contrast the ability of different mathematical models to describe such data. But there is a lack of rigorous methods to compare different models \emph\a priori\. Here we develop and illustrate two such approaches that allow us to compare model structures in a systematic way. Using welldeveloped and understood concepts from simplicial geometry we are able to define a distance based on the persistent homology applied to the simplicial complexes that captures the model structure. In this way we can identify shared topological features of different models. We then expand this, and move from a distance between simplicial complexes to studying equivalences between models in order to determine their functional relatedness. 
Topological Analysis Reveals State Transitions in Human Gut and Marine Bacterial Communities (2020)
William K. Chang, David VanInsberghe, Libusha KellyAbstract
Microbiome dynamics influence the health and functioning of human physiology and the environment and are driven in part by interactions between large numbers of microbial taxa, making largescale prediction and modeling a challenge. Here, using topological data analysis, we identify states and dynamical features relevant to macroscopic processes. We show that gut disease processes and marine geochemical events are associated with transitions between community states, defined as topological features of the data density. We find a reproducible twostate succession during recovery from cholera in the gut microbiomes of multiple patients, evidence of dynamic stability in the gut microbiome of a healthy human after experiencing diarrhea during travel, and periodic state transitions in a marine Prochlorococcus community driven by water column cycling. Our approach bridges smallscale fluctuations in microbiome composition and largescale changes in phenotype without details of underlying mechanisms, and provides an assessment of microbiome stability and its relation to human and environmental health. 
Capturing Dynamics of TimeVarying Data via Topology (2020)
Lu Xian, Henry Adams, Chad M. Topaz, Lori ZiegelmeierAbstract
One approach to understanding complex data is to study its shape through the lens of algebraic topology. While the early development of topological data analysis focused primarily on static data, in recent years, theoretical and applied studies have turned to data that varies in time. A timevarying collection of metric spaces as formed, for example, by a moving school of fish or flock of birds, can contain a vast amount of information. There is often a need to simplify or summarize the dynamic behavior. We provide an introduction to topological summaries of timevarying metric spaces including vineyards [17], crocker plots [52], and multiparameter rank functions [34]. We then introduce a new tool to summarize timevarying metric spaces: a crocker stack. Crocker stacks are convenient for visualization, amenable to machine learning, and satisfy a desirable stability property which we prove. We demonstrate the utility of crocker stacks for a parameter identification task involving an influential model of biological aggregations [54]. Altogether, we aim to bring the broader applied mathematics community uptodate on topological summaries of timevarying metric spaces. 
WeightedPersistentHomologyBased Machine Learning for RNA Flexibility Analysis (2020)
Chi Seng Pun, Brandon Yung Sin Yong, Kelin XiaAbstract
With the great significance of biomolecular flexibility in biomolecular dynamics and functional analysis, various experimental and theoretical models are developed. Experimentally, DebyeWaller factor, also known as Bfactor, measures atomic meansquare displacement and is usually considered as an important measurement for flexibility. Theoretically, elastic network models, Gaussian network model, flexibilityrigidity model, and other computational models have been proposed for flexibility analysis by shedding light on the biomolecular inner topological structures. Recently, a topologybased machine learning model has been proposed. By using the features from persistent homology, this model achieves a remarkable high Pearson correlation coefficient (PCC) in protein Bfactor prediction. Motivated by its success, we propose weightedpersistenthomology (WPH)based machine learning (WPHML) models for RNA flexibility analysis. Our WPH is a newlyproposed model, which incorporate physical, chemical and biological information into topological measurements using a weight function. In particular, we use local persistent homology (LPH) to focus on the topological information of local regions. Our WPHML model is validated on a wellestablished RNA dataset, and numerical experiments show that our model can achieve a PCC of up to 0.5822. The comparison with the previous sequenceinformationbased learning models shows that a consistent improvement in performance by at least 10% is achieved in our current model. 
Topological Portraits of Multiscale Coordination Dynamics (2020)
Mengsen Zhang, William D. Kalies, J. A. Scott Kelso, Emmanuelle TognoliAbstract
Living systems exhibit complex yet organized behavior on multiple spatiotemporal scales. To investigate the nature of multiscale coordination in living systems, one needs a meaningful and systematic way to quantify the complex dynamics, a challenge in both theoretical and empirical realms. The present work shows how integrating approaches from computational algebraic topology and dynamical systems may help us meet this challenge. In particular, we focus on the application of multiscale topological analysis to coordinated rhythmic processes. First, theoretical arguments are introduced as to why certain topological features and their scaledependency are highly relevant to understanding complex collective dynamics. Second, we propose a method to capture such dynamically relevant topological information using persistent homology, which allows us to effectively construct a multiscale topological portrait of rhythmic coordination. Finally, the method is put to test in detecting transitions in real data from an experiment of rhythmic coordination in ensembles of interacting humans. The recurrence plots of topological portraits highlight collective transitions in coordination patterns that were elusive to more traditional methods. This sensitivity to collective transitions would be lost if the behavioral dynamics of individuals were treated as separate degrees of freedom instead of constituents of the topology that they collectively forge. Such multiscale topological portraits highlight collective aspects of coordination patterns that are irreducible to properties of individual parts. The present work demonstrates how the analysis of multiscale coordination dynamics can benefit from topological methods, thereby paving the way for further systematic quantification of complex, highdimensional dynamics in living systems. 
Nonlinear Dynamic Approaches to Identify Atrial Fibrillation Progression Based on Topological Methods (2019)
Bahareh Safarbali, Seyed Mohammad Reza Hashemi GolpayeganiAbstract
In recent years, atrial fibrillation (AF) development from paroxysmal to persistent or permanent forms has become an important issue in cardiovascular disorders. Information about AF pattern of presentation (paroxysmal, persistent, or permanent) was useful in the management of algorithms in each category. This management is aimed at reducing symptoms and stopping severe problems associated with AF. AF classification has been based on time duration and episodes until now. In particular, complexity changes in Heart Rate Variation (HRV) may contain clinically relevant signals of imminent systemic dysregulation. A number of nonlinear methods based on phase space and topological properties can give more insight into HRV abnormalities such as fibrillation. Aiming to provide a nonlinear tool to qualitatively classify AF stages, we proposed two geometrical indices (fractal dimension and persistent homology) based on HRV phase space, which can successfully replicate the changes in AF progression. The study population includes 38 lone AF patients and 20 normal subjects, which are collected from the PhysioBank database. “Time of Life (TOL)” is proposed as a new feature based on the initial and final Čech radius in the persistent homology diagram. A neural network was implemented to prove the effectiveness of both TOL and fractal dimension as classification features. The accuracy of classification performance was 93%. The proposed indices provide a signal representation framework useful to understand the dynamic changes in AF cardiac patterns and to classify normal and pathological rhythms. 
Persistent Homology Analysis of Osmolyte Molecular Aggregation and Their HydrogenBonding Networks (2019)
Kelin Xia, D. Vijay Anand, Saxena Shikhar, Yuguang MuAbstract
Dramatically different properties have been observed for two types of osmolytes, i.e., trimethylamine Noxide (TMAO) and urea, in a protein folding process. Great progress has been made in revealing the potential underlying mechanism of these two osmolyte systems. However, many problems still remain unsolved. In this paper, we propose to use the persistent homology to systematically study the osmolytes’ molecular aggregation and their hydrogenbonding network from a global topological perspective. It has been found that, for the first time, TMAO and urea show two extremely different topological behaviors, i.e., an extensive network and local clusters, respectively. In general, TMAO forms highly consistent large loop or circle structures in high concentrations. In contrast, urea is more tightly aggregated locally. Moreover, the resulting hydrogenbonding networks also demonstrate distinguishable features. With a concentration increase, TMAO hydrogenbonding networks vary greatly in their total number of loop structures and largesized loop structures consistently increase. In contrast, urea hydrogenbonding networks remain relatively stable with slight reduction of the total loop number. Moreover, the persistent entropy (PE) is, for the first time, used in characterization of the topological information of the aggregation and hydrogenbonding networks. The average PE systematically increases with the concentration for both TMAO and urea, and decreases in their hydrogenbonding networks. But their PE variances have totally different behaviors. Finally, topological features of the hydrogenbonding networks are found to be highly consistent with those from the ion aggregation systems, indicating that our topological invariants can characterize intrinsic features of the “structure making” and “structure breaking” systems. 
Liquid Structures Characterized by a Combination of the Persistent Homology Analysis and Molecular Dynamics Simulation (2018)
Kohei Sasaki, Ryo Okajima, Takefumi Yamashita 
Persistent Homology Analysis of Ion Aggregations and HydrogenBonding Networks (2018)
Kelin XiaAbstract
Despite the great advancement of experimental tools and theoretical models, a quantitative characterization of the microscopic structures of ion aggregates and their associated water hydrogenbonding networks still remains a challenging problem. In this paper, a newlyinvented mathematical method called persistent homology is introduced, for the first time, to quantitatively analyze the intrinsic topological properties of ion aggregation systems and hydrogenbonding networks. The two most distinguishable properties of persistent homology analysis of assembly systems are as follows. First, it does not require a predefined bond length to construct the ion or hydrogenbonding network. Persistent homology results are determined by the morphological structure of the data only. Second, it can directly measure the size of circles or holes in ion aggregates and hydrogenbonding networks. To validate our model, we consider two wellstudied systems, i.e., NaCl and KSCN solutions, generated from molecular dynamics simulations. They are believed to represent two morphological types of aggregation, i.e., local clusters and extended ion networks. It has been found that the two aggregation types have distinguishable topological features and can be characterized by our topological model very well. Further, we construct two types of networks, i.e., Onetworks and H2Onetworks, for analyzing the topological properties of hydrogenbonding networks. It is found that for both models, KSCN systems demonstrate much more dramatic variations in their local circle structures with a concentration increase. A consistent increase of largesized local circle structures is observed and the sizes of these circles become more and more diverse. In contrast, NaCl systems show no obvious increase of largesized circles. Instead a consistent decline of the average size of the circle structures is observed and the sizes of these circles become more and more uniform with a concentration increase. As far as we know, these unique intrinsic topological features in ion aggregation systems have never been pointed out before. More importantly, our models can be directly used to quantitatively analyze the intrinsic topological invariants, including circles, loops, holes, and cavities, of any networklike structures, such as nanomaterials, colloidal systems, biomolecular assemblies, among others. These topological invariants cannot be described by traditional graph and network models. 
Analysis of Kolmogorov Flow and Rayleigh–Bénard Convection Using Persistent Homology (2016)
Miroslav Kramár, Rachel Levanger, Jeffrey Tithof, Balachandra Suri, Mu Xu, Mark Paul, Michael F. Schatz, Konstantin MischaikowAbstract
We use persistent homology to build a quantitative understanding of large complex systems that are driven farfromequilibrium. In particular, we analyze image time series of flow field patterns from numerical simulations of two important problems in fluid dynamics: Kolmogorov flow and Rayleigh–Bénard convection. For each image we compute a persistence diagram to yield a reduced description of the flow field; by applying different metrics to the space of persistence diagrams, we relate characteristic features in persistence diagrams to the geometry of the corresponding flow patterns. We also examine the dynamics of the flow patterns by a second application of persistent homology to the time series of persistence diagrams. We demonstrate that persistent homology provides an effective method both for quotienting out symmetries in families of solutions and for identifying multiscale recurrent dynamics. Our approach is quite general and it is anticipated to be applicable to a broad range of open problems exhibiting complex spatiotemporal behavior. 
Using Persistent Homology and Dynamical Distances to Analyze Protein Binding (2016)
Violeta KovacevNikolic, Peter Bubenik, Dragan Nikolić, Giseon HeoAbstract
Persistent homology captures the evolution of topological features of a model as a parameter changes. The most commonly used summary statistics of persistent homology are the barcode and the persistence diagram. Another summary statistic, the persistence landscape, was recently introduced by Bubenik. It is a functional summary, so it is easy to calculate sample means and variances, and it is straightforward to construct various test statistics. Implementing a permutation test we detect conformational changes between closed and open forms of the maltosebinding protein, a large biomolecule consisting of 370 amino acid residues. Furthermore, persistence landscapes can be applied to machine learning methods. A hyperplane from a support vector machine shows the clear separation between the closed and open proteins conformations. Moreover, because our approach captures dynamical properties of the protein our results may help in identifying residues susceptible to ligand binding; we show that the majority of active site residues and allosteric pathway residues are located in the vicinity of the most persistent loop in the corresponding filtered VietorisRips complex. This finding was not observed in the classical anisotropic network model. 
A Collaborative Visual Analytics Suite for Protein Folding Research (2014)
William Harvey, InHee Park, Oliver Rübel, Valerio Pascucci, PeerTimo Bremer, Chenglong Li, Yusu Wang 
Topological Extraction and Tracking of Defects in Crystal Structures (2011)
Sebastian Grottel, Carlos A. Dietrich, João L. D. Comba, Thomas ErtlAbstract
Interfaces between materials with different mechanical properties play an important role in technical applications. Nowadays molecular dynamics simulations are used to observe the behavior of such compound materials at the atomic level. Due to different atom crystal sizes, dislocations in the atom crystal structure occur once external forces are applied, and it has been observed that studying the change of thesedislocations can provide further understanding of macroscopic attributes like elasticity and plasticity. Standard visualization techniques such as the rendering of individual atoms work for 2D data or sectional views; however, visualizingdislocations in 3D using such methods usually fail due to occlusion and clutter. In this work we propose to extract and visualize the structure ofdislocations, which summarizes the commonly employed filtered atomistic renderings into a concise representation. The benefits of our approach are clearer images while retaining relevant data and easier visual tracking of topological changes over time. 
Construction of Symbolic Dynamics From Experimental Time Series (1999)
K. Mischaikow, M. Mrozek, J. Reiss, A. SzymczakAbstract
Symbolic dynamics play a central role in the description of the evolution of nonlinear systems. Yet there are few methods for determining symbolic dynamics of chaotic data. One difficulty is that the data contains random fluctuations associated with the experimental process. Using data obtained from a magnetoelastic ribbon experiment we show how a topological approach that allows for experimental error and bounded noise can be used to obtain a description of the dynamics in terms of subshift dynamics on a finite set of symbols.