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Jacobi Set Simplification for Tracking Topological Features in Time-Varying Scalar Fields
No full textThe Jacobi set of a bivariate scalar field is the set of points where the gradients of the two constituent scalar fields align with each other. It captures the regions of topological changes in the bivariate field. The Jacobi set is a bivariate analog of critical points, and may correspond to features of interest. In the specific case of time-varying fields and when one of the scalar fields is time, the Jacobi set corresponds to temporal tracks of critical points, and serves as a feature-tracking graph. The Jacobi set of a bivariate field or a time-varying scalar field is complex, resulting in cluttered visualizations that are difficult to analyze. This paper addresses the problem of Jacobi set simplification. Specifically, we use the time-varying scalar field scenario to introduce a method that computes a reduced Jacobi set. The method is based on a stability measure called robustness that was originally developed for vector fields and helps capture the structural stability of critical points. We also present a mathematical analysis for the method, and describe an implementation for 2D time-varying scalar fields. Applications to both synthetic and real-world datasets demonstrate the effectiveness of the method for tracking features
Geometric localization of homology cycles
No full textComputing an optimal cycle in a given homology class, also referred to as the homology localization problem, is known to be an NP-hard problem in general. Furthermore, there is currently no known optimality criterion that localizes classes geometrically and admits a stability property under the setting of persistent homology. We present a geometric optimization of the cycles that is computable in polynomial time and is stable in an approximate sense. Tailoring our search criterion to different settings, we obtain various optimization problems like optimal homologous cycle, minimum homology basis, and minimum persistent
homology basis. In practice, the (trivial) exact algorithm is computationally expensive despite having a worst case polynomial runtime. Therefore, we design approximation algorithms for the above problems and study their performance experimentally. These algorithms have reasonable runtimes for moderate sized datasets and the cycles computed by these algorithms
are consistently of high quality as demonstrated via experiments on multiple datasets
Time-varying Extremum Graphs
No full textWe introduce time-varying extremum graph (TVEG), a topological structure to support visualization and analysis of a time- varying scalar field. The extremum graph is a substructure of the Morse-Smale complex. It captures the adjacency relationship between cells in the Morse decomposition of a scalar field. We define the TVEG as a time-varying extension of the extremum graph and demonstrate how it captures salient feature tracks within a dynamic scalar field. We formulate the construction of the TVEG as an optimization problem and describe an algorithm for computing the graph. We also demonstrate the capabilities of TVEG towards identification and exploration of topological events such as deletion, generation, split, and merge within a dynamic scalar field via comprehensive case studies including a viscous fingers and a 3D von Kármán vortex street dataset
Fast and unified path gradient estimators for normalizing flows
No full textRecent work shows that path gradient estimators for normalizing flows have lower
variance compared to standard estimators for variational inference, resulting in
improved training. However, they are often prohibitively more expensive from a
computational point of view and cannot be applied to maximum likelihood train-
ing in a scalable manner, which severely hinders their widespread adoption. In
this work, we overcome these crucial limitations. Specifically, we propose a fast
path gradient estimator which improves computational efficiency significantly and
works for all normalizing flow architectures of practical relevance. We then show
that this estimator can also be applied to maximum likelihood training for which
it has a regularizing effect as it can take the form of a given target energy func-
tion into account. We empirically establish its superior performance and reduced
variance for several natural sciences applications
The gradient projection method: Is the Polyak adaptive stepsize rule optimal?
No full textNot always! This is our answer to the question of whether the Polyak adaptive stepsize rule in the gradient projection method is optimal. The answer is based on revisiting the subgradient projection method by Polyak [USSR Computational Mathematics and Mathematical Physics 9 (1969)] for smooth and convex minimization problems where the objective function possesses a geometric property called flatness. Our results show that the method can be more flexible (the effective range for the parameter controlling the stepsize can be wider) and have sharper convergence rates. Applications to split feasibility/equality problems are presented, deriving for the first time the O(1/k) rate of convergence for the adaptive CQ method. A theoretical guarantee of the linear convergence of the gradient descent method with adaptive stepsizes for Google PageRank is provided. At the same time, numerical experiments are designed to spot the ``optimal" stepsize and to compare with other basic gradient methods
Mathematical modeling and analysis of neurotransmission
No full textModeling and simulation of neurotransmission plays a central role in the scientific investigation of synaptic functioning and neuronal communication. Understanding the neurotransmission process is crucial due to its fundamental significance in cognitive function, with impairments in this process potentially giving rise to severe pathological conditions. Existing modeling approaches capture the dynamic behavior and the probabilistic properties of the synaptic machinery utilizing results from stochastic chemical kinetics. However, the underlying mathematical theory rarely receives detailed consideration. In this thesis, we explore the neurotransmission process from a mathematical perspective and provide important insights that improve efficiency in its modeling and simulation. We introduce a new method for the direct and exact computation of first- and second-order moments of the postsynaptic current for the case of linear reaction networks, circumventing previously employed stochastic simulations. The approach hinges on the computation of the cross-correlation functions for the associated Markov jump process and their characterization by a partial differential equation. Our method can be applied to efficiently compare coarse-graining strategies for neurotransmission models, which we demonstrate for a significant recent model of synaptic vesicle release. We also numerically analyze the feasibility of reducing this particular model and show that a reduction can preserve the characteristics of the mean output current and its standard deviation to a satisfactory degree under typical stimulation conditions, where the approximation error can even be neglected depending on release site position. As the dependency on the local calcium concentration is preserved in the reduced model, it can be subjected to arbitrary types of signals in future studies. Moreover, we investigate the recovery processes of synaptic vesicles and release sites by introducing a nonlinear model with explicit recovery reactions. A sensitivity analysis provides the novel insight that the widely debated identity of the limiting recovery process during sustained stimulation is time-dependent. For our model, the output current’s first-order moment and its deterministic approximation show an exceptionally high level of agreement. We determine this to be an inherent consequence of
the model structure due to the independence of the two recovery processes, supporting
future use of the deterministic approximation. Parameter estimations confirm our model’s
capability of reproducing experimental data
