🍩 Database of Original & NonTheoretical Uses of Topology
(found 8 matches in 0.00187s)


PINet: A Deep Learning Approach to Extract Topological Persistence Images (2020)
Anirudh Som, Hongjun Choi, Karthikeyan Natesan Ramamurthy, Matthew Buman, Pavan TuragaAbstract
Topological features such as persistence diagrams and their functional approximations like persistence images (PIs) have been showing substantial promise for machine learning and computer vision applications. This is greatly attributed to the robustness topological representations provide against different types of physical nuisance variables seen in realworld data, such as viewpoint, illumination, and more. However, key bottlenecks to their large scale adoption are computational expenditure and difﬁculty incorporating them in a differentiable architecture. We take an important step in this paper to mitigate these bottlenecks by proposing a novel onestep approach to generate PIs directly from the input data. We design two separate convolutional neural network architectures, one designed to take in multivariate time series signals as input and another that accepts multichannel images as input. We call these networks Signal PINet and Image PINet respectively. To the best of our knowledge, we are the ﬁrst to propose the use of deep learning for computing topological features directly from data. We explore the use of the proposed PINet architectures on two applications: human activity recognition using triaxial accelerometer sensor data and image classiﬁcation. We demonstrate the ease of fusion of PIs in supervised deep learning architectures and speed up of several orders of magnitude for extracting PIs from data. Our code is available at https://github.com/anirudhsom/PINet. 
A Topological Approach to Selecting Models of Biological Experiments (2019)
M. Ulmer, Lori Ziegelmeier, Chad M. TopazAbstract
We use topological data analysis as a tool to analyze the fit of mathematical models to experimental data. This study is built on data obtained from motion tracking groups of aphids in [Nilsen et al., PLOS One, 2013] and two random walk models that were proposed to describe the data. One model incorporates social interactions between the insects via a functional dependence on an aphid’s distance to its nearest neighbor. The second model is a control model that ignores this dependence. We compare data from each model to data from experiment by performing statistical tests based on three different sets of measures. First, we use time series of order parameters commonly used in collective motion studies. These order parameters measure the overall polarization and angular momentum of the group, and do not rely on a priori knowledge of the models that produced the data. Second, we use order parameter time series that do rely on a priori knowledge, namely average distance to nearest neighbor and percentage of aphids moving. Third, we use computational persistent homology to calculate topological signatures of the data. Analysis of the a priori order parameters indicates that the interactive model better describes the experimental data than the control model does. The topological approach performs as well as these a priori order parameters and better than the other order parameters, suggesting the utility of the topological approach in the absence of specific knowledge of mechanisms underlying the data. 
Analyzing Collective Motion With Machine Learning and Topology (2019)
Dhananjay Bhaskar, Angelika Manhart, Jesse Milzman, John T. Nardini, Kathleen M. Storey, Chad M. Topaz, Lori ZiegelmeierAbstract
We use topological data analysis and machine learning to study a seminal model of collective motion in biology [M. R. D’Orsogna et al., Phys. Rev. Lett. 96, 104302 (2006)]. This model describes agents interacting nonlinearly via attractiverepulsive social forces and gives rise to collective behaviors such as flocking and milling. To classify the emergent collective motion in a large library of numerical simulations and to recover model parameters from the simulation data, we apply machine learning techniques to two different types of input. First, we input time series of order parameters traditionally used in studies of collective motion. Second, we input measures based on topology that summarize the timevarying persistent homology of simulation data over multiple scales. This topological approach does not require prior knowledge of the expected patterns. For both unsupervised and supervised machine learning methods, the topological approach outperforms the one that is based on traditional order parameters. 
Synthesis of EnergyBounded Planar Caging Grasps Using Persistent Homology (2018)
Jeffrey Mahler, Florian T. Pokorny, Sherdil Niyaz, Ken Goldberg 
Use of Topological Data Analysis in Motor Intention Based BrainComputer Interfaces (2018)
Fatih Altindis, Bulent Yilmaz, Sergey Borisenok, Kutay Icoz 
Zebrafish Behavior: Opportunities and Challenges (2017)
Michael B. Orger, Gonzalo G. de Polavieja 
Branching and Circular Features in High Dimensional Data (2011)
B. Wang, B. Summa, V. Pascucci, M. VejdemoJohanssonAbstract
Large observations and simulations in scientific research give rise to highdimensional data sets that present many challenges and opportunities in data analysis and visualization. Researchers in application domains such as engineering, computational biology, climate study, imaging and motion capture are faced with the problem of how to discover compact representations of highdimensional data while preserving their intrinsic structure. In many applications, the original data is projected onto lowdimensional space via dimensionality reduction techniques prior to modeling. One problem with this approach is that the projection step in the process can fail to preserve structure in the data that is only apparent in high dimensions. Conversely, such techniques may create structural illusions in the projection, implying structure not present in the original highdimensional data. Our solution is to utilize topological techniques to recover important structures in highdimensional data that contains nontrivial topology. Specifically, we are interested in highdimensional branching structures. We construct local circlevalued coordinate functions to represent such features. Subsequently, we perform dimensionality reduction on the data while ensuring such structures are visually preserved. Additionally, we study the effects of global circular structures on visualizations. Our results reveal neverbeforeseen structures on realworld data sets from a variety of applications.