8 research outputs found
Approximating Persistent Homology in Euclidean Space Through Collapses
The \v{C}ech complex is one of the most widely used tools in applied
algebraic topology. Unfortunately, due to the inclusive nature of the \v{C}ech
filtration, the number of simplices grows exponentially in the number of input
points. A practical consequence is that computations may have to terminate at
smaller scales than what the application calls for.
In this paper we propose two methods to approximate the \v{C}ech persistence
module. Both are constructed on the level of spaces, i.e. as sequences of
simplicial complexes induced by nerves. We also show how the bottleneck
distance between such persistence modules can be understood by how tightly they
are sandwiched on the level of spaces. In turn, this implies the correctness of
our approximation methods.
Finally, we implement our methods and apply them to some example point clouds
in Euclidean space
Topological exploration of artificial neuronal network dynamics
One of the paramount challenges in neuroscience is to understand the dynamics
of individual neurons and how they give rise to network dynamics when
interconnected. Historically, researchers have resorted to graph theory,
statistics, and statistical mechanics to describe the spatiotemporal structure
of such network dynamics. Our novel approach employs tools from algebraic
topology to characterize the global properties of network structure and
dynamics.
We propose a method based on persistent homology to automatically classify
network dynamics using topological features of spaces built from various
spike-train distances. We investigate the efficacy of our method by simulating
activity in three small artificial neural networks with different sets of
parameters, giving rise to dynamics that can be classified into four regimes.
We then compute three measures of spike train similarity and use persistent
homology to extract topological features that are fundamentally different from
those used in traditional methods. Our results show that a machine learning
classifier trained on these features can accurately predict the regime of the
network it was trained on and also generalize to other networks that were not
presented during training. Moreover, we demonstrate that using features
extracted from multiple spike-train distances systematically improves the
performance of our method
A Notion of Harmonic Clustering in Simplicial Complexes
We outline a novel clustering scheme for simplicial complexes that produces
clusters of simplices in a way that is sensitive to the homology of the
complex. The method is inspired by, and can be seen as a higher-dimensional
version of, graph spectral clustering. The algorithm involves only sparse
eigenproblems, and is therefore computationally efficient. We believe that it
has broad application as a way to extract features from simplicial complexes
that often arise in topological data analysis
FORLORN: A Framework for Comparing Offline Methods and Reinforcement Learning for Optimization of RAN Parameters
The growing complexity and capacity demands for mobile networks necessitate
innovative techniques for optimizing resource usage. Meanwhile, recent
breakthroughs have brought Reinforcement Learning (RL) into the domain of
continuous control of real-world systems. As a step towards RL-based network
control, this paper introduces a new framework for benchmarking the performance
of an RL agent in network environments simulated with ns-3. Within this
framework, we demonstrate that an RL agent without domain-specific knowledge
can learn how to efficiently adjust Radio Access Network (RAN) parameters to
match offline optimization in static scenarios, while also adapting on the fly
in dynamic scenarios, in order to improve the overall user experience. Our
proposed framework may serve as a foundation for further work in developing
workflows for designing RL-based RAN control algorithms
Dynamics of CLIMP-63 S-acylation control ER morphology
A key player in the formation of endoplasmic reticulum sheets is CLIMP-63, but mechanistic details remained elusive. Here authors combined cellular experiments and mathematical modelling to show that S-acylation of CLIMP-63 regulates its function by mediating its oligomerisation, turnover, and localisation