Showing papers by "Boris Mirkin published in 2021"

Traffic flow modeling and feedback control for future Low-Altitude Air city Transport: An MFD-based approach

Abstract: The imminent penetration of low-altitude passenger and delivery aircraft into the urban airspace will give rise to new urban air transport systems, which we call low-altitude air city transport (LAAT) systems. As the urban mobility revolution approaches, we must investigate (i) the collective behavior of LAAT aircraft in cities, and (ii) ways of controlling LAAT systems. Future LAAT systems exemplify a new class of modern large scale engineering systems — networked control systems. They are spatially distributed, consist of many interconnected elements with control loops through digital communication networks such that the system signals can be exchanged among all components through a common network. Therefore, a decentralized controller design in framework of the unilateral event-driven paradigm is considered. Inspired by controlled urban road networks, in this paper we first establish the concept of Macroscopic Fundamental Diagram (MFD) for LAAT systems and develop a collective and aggregate aircraft traffic flow model. Then, based on that, we design an adaptive boundary feedback flow control which is robust to various anomalies in technical devices and network communication links for LAAT systems.

Summable and nonsummable data-driven models for community detection in feature-rich networks

Abstract: A feature-rich network is a network whose nodes are characterized by categorical or quantitative features. We propose a data-driven model for finding a partition of the nodes to approximate both the network link data and the feature data. The model involves summary quantitative characteristics of both network links and features. We distinguish between two modes of using the network link data. One mode postulates that the link values are comparable and summable across the network (summability); the other assumption models the case in which different nodes represent different measurement systems so that the link data are neither comparable, nor summable, across different nodes (nonsummability). We derive a Pythagorean decomposition of the combined data scatter involving our data recovery least-squares criterion. We address an equivalent problem of maximizing its complementary part, the contribution of a found partition to the combined data scatter. We follow a doubly greedy strategy in maximizing that. First, communities are found one-by-one, and second, entities are added one-by-one in the process of identifying a community. Our algorithms determine the number of clusters automatically. The nonsummability version proves to have a niche of its own; also, it is faster than the other version. In our experiments, they appear to be competitive over generated synthetic data sets and six real-world data sets from the literature.

Least-squares community extraction in feature-rich networks using similarity data

Abstract: We explore a doubly-greedy approach to the issue of community detection in feature-rich networks. According to this approach, both the network and feature data are straightforwardly recovered from the underlying unknown non-overlapping communities, supplied with a center in the feature space and intensity weight(s) over the network each. Our least-squares additive criterion allows us to search for communities one-by-one and to find each community by adding entities one by one. A focus of this paper is that the feature-space data part is converted into a similarity matrix format. The similarity/link values can be used in either of two modes: (a) as measured in the same scale so that one may can meaningfully compare and sum similarity values across the entire similarity matrix (summability mode), and (b) similarity values in one column should not be compared with the values in other columns (nonsummability mode). The two input matrices and two modes lead us to developing four different Iterative Community Extraction from Similarity data (ICESi) algorithms, which determine the number of communities automatically. Our experiments at real-world and synthetic datasets show that these algorithms are valid and competitive.

GOT: Generalization over Taxonomies, a Software Toolkit for Content Analysis with Taxonomies

Abstract: GOT is a Python3 software toolkit for taxonomic content analysis of text collections. The structure of the toolkit follows an in-house methodology for processing a collection of texts using a domain taxonomy. The method includes the following steps: (1) computing matrix of relevance between texts and taxonomy leaf topics using a purely structural string-to-text relevance measure based on suffix trees representing the texts and annotated by substring frequencies, (2) obtaining fuzzy clusters of taxonomy leaf topics using a method involving both additive and spectral properties, and (3) finding most specific generalizations of the fuzzy clusters in a rooted tree of the taxonomy. Such a generalization parsimoniously lifts a cluster to its “head subject” in the higher ranks of the taxonomy, to tightly cover the cluster by minimizing the number of errors, “gaps” and “offshoots”. The efficiency of this methodology was illustrated in the analysis of research tendencies in Data Science: our findings led to insights on the tendencies of research that could not be derived by using more conventional techniques. The toolkit can be used either as a whole or with its individual modules including a visualization module. GOT toolkit provides for two usage scenarios: (a) console mode for using via command line and (b) import mode for using in Python3 source codes.