Many surfactant-based formulations are utilised in industry as they produce desirable visco-elastic properties at low-concentrations. These properties are due to the presence of worm-like micelles (WLM) and, as a result, understanding the processes that

In their seminal work on homological sensor networks, de Silva and Ghrist showed the surprising fact that it's possible to certify the coverage of a coordinate-free sensor network even with very minimal knowledge of the space to be covered. Here, coverage means that every point in the domain (except possibly those very near the boundary) has a nearby sensor. More generally, their algorithm takes a pair of nested neighborhood graphs along with a labeling of vertices as either boundary or interior and computes the relative homology of a simplicial complex induced by the graphs. This approach, called the Topological Coverage Criterion (TCC), requires some assumptions about the underlying geometric domain as well as some assumptions about the relationship of the input graphs to the domain. The goal of this paper is to generalize these assumptions and show how the TCC can be applied to both much more general domains as well as very weak assumptions on the input. We give a new, simpler proof of the de Silva-Ghrist Topological Coverage Criterion that eliminates any assumptions about the smoothness of the boundary of the underlying space, allowing the results to be applied to much more general problems. The new proof factors the geometric, topological, and combinatorial aspects, allowing us to provide a coverage condition that supports thick boundaries, k-coverage, and weighted coverage, in which sensors have varying radii. From Sensor Coverage to Data CoverageProblems in Homological Sensor Networks (HSNs) are usually stated in the vocabulary of sensor networks. There are sensors and coverage regions, and an important problem is to determine if the sensing region of a collection of sensors covers a given domain, given only the neighborhood relationships between the sensors and some indication of which sensors are near the boundary. The locations (coordinates) of the points is not assumed, nor is the shape (topology) of the domain to be covered. Although phrased in the language of sensor networks, the problem may be understood more generally as one of data coverage; it answers when a data set * Supported by NSF grants CCF-1464379 and CCF-1525978.† University of Connecticut. ‡ University of Connecticut. § University of Connecticut.sufficiently covers a domain, and holes in coverage can be viewed as gaps in the data. A surprising result by de Silva & Ghrist is that there exists a computable, sufficient condition called the Topological Coverage Criterion (TCC) to certify coverage without knowing the locations of the sensors when the domain's boundary is smooth [4]. All that is required is that the sensors have unique identifiers, can detect nearby sensors, can differentiate whether neighboring sensors are "close" or "very close" (in a technical sense to be defined below), and can detect if the boundary of the domain is close. Developed over a series of papers [8,17,5,4], the most general version of the TCC can be understood as a purely geometric problem on point sets P in R d with unknown coordinates in an unknown d...

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From the work of Bauer and Lesnick, it is known that there is no functor from the category of pointwise finite-dimensional persistence modules to the category of barcodes and overlap matchings. In this work, we introduce sub-barcodes and show that there is a functor from the category of factorizations of persistence module homomorphisms to a poset of barcodes ordered by the sub-barcode relation. Sub-barcodes and factorizations provide a looser alternative to bottleneck matchings and interleavings that can give strong guarantees in a number of settings that arise naturally in topological data analysis. The main use of sub-barcodes is to make strong claims about an unknown barcode in the absence of an interleaving. For example, given only upper and lower bounds g ≥ f ≥ of an unknown real-valued function f , a sub-barcode associated with f can be constructed from and g alone. We propose a theory of sub-barcodes and observe that the subobjects in the functor category Fun(Int op , Mch) naturally correspond to sub-barcodes.