UGrAD: A graph-theoretic framework for classification of activity with complementary graph boundary detection

Author(s):  
Tamal Batabyal ◽  
Scott T. Acton ◽  
Andrea Vaccari
Author(s):  
V. P. Agrawal ◽  
J. N. Yadav ◽  
C. R. Pratap

Abstract A new graph theoretic concept of link-centre of a kinematic chain is introduced. The link-centre of a kinematic chain is defined as a subset of set of links of the kinematic chain using a hierarchy of criteria based on distance concept. A number of structural invariants are defined for a kinematic chain which may be used for identification and classification of kinematic chains and mechanisms. An algorithm is developed on the basis of the concept of distance and the link-centre for optimum selection of input, output and fixed links in a multi-degree-of-freedom function generator.


2019 ◽  
Vol 34 (18) ◽  
pp. 1950085 ◽  
Author(s):  
S. James Gates ◽  
Yangrui Hu ◽  
Kory Stiffler

An adinkra is a graph-theoretic representation of space–time supersymmetry. Minimal four-color valise adinkras have been extensively studied due to their relations to minimal 4D, [Formula: see text] supermultiplets. Valise adinkras, although an important subclass, do not encode all the information present when a 4D supermultiplet is reduced to 1D. Eigenvalue equivalence classes for valise adinkra matrices exist, known as [Formula: see text] equivalence classes, where valise adinkras within the same [Formula: see text] equivalence class are isomorphic in the sense that adinkras within a [Formula: see text]-equivalence class can be transformed into each other via field redefinitions of the nodes. We extend this to non-valise adinkras, via Python code, providing a complete eigenvalue classification of “node-lifting” for all 36,864 valise adinkras associated with the Coxeter group [Formula: see text]. We term the eigenvalues associated with these node-lifted adinkras Height Yielding Matrix Numbers (HYMNs) and introduce HYMN equivalence classes. These findings have been summarized in a Mathematica notebook that can be found at the HEPTHools Data Repository on GitHub.


2018 ◽  
Vol 7 (2.14) ◽  
pp. 536
Author(s):  
Salim Chavan ◽  
M Narayana ◽  
L Koteswara Rao,

Transition detection is the necessary step in retrieval and investigation of videos on the basis of contents. Since last two decades, most of the researchers are involved in developing algorithms for detection of gradual transitions. However, the features of all gradual transitions are different and hence this issue still needs to be addressed precisely. After identifying this issue, an integrated shot boundary detection method is proposed in this paper. Wipe transition is the effect which is mostly used in the video making industries .Since there are different types of wipe transitions, it becomes very difficult to detect such transitions. Due to complexity in detection of wipes due to noise, object and camera motion earlier methods have less focus on this issue. In this paper an efficient wipe detection method is presented which gives better results even in the presence of object and camera movements.


2022 ◽  
pp. 1-108 ◽  
Author(s):  
Pedro Conceição ◽  
Dejan Govc ◽  
Jānis Lazovskis ◽  
Ran Levi ◽  
Henri Riihimäki ◽  
...  

Abstract A binary state on a graph means an assignment of binary values to its vertices. A time dependent sequence of binary states is referred to as binary dynamics. We describe a method for the classification of binary dynamics of digraphs, using particular choices of closed neighbourhoods. Our motivation and application comes from neuroscience, where a directed graph is an abstraction of neurons and their connections, and where the simplification of large amounts of data is key to any computation. We present a topological/graph theoretic method for extracting information out of binary dynamics on a graph, based on a selection of a relatively small number of vertices and their neighbourhoods. We consider existing and introduce new real-valued functions on closed neighbourhoods, comparing them by their ability to accurately classify different binary dynamics. We describe a classification algorithm that uses two parameters and sets up a machine learning pipeline. We demonstrate the effectiveness of the method on simulated activity on a digital reconstruction of cortical tissue of a rat, and on a non-biological random graph with similar density.


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