Configuration Graph Research Articles

Hidden Semi-Markov Models for Semantic-Graph Language Modeling

Semantic communication is expected to play a critical role in reducing traffic load in future intelligent large-scale sensor networks. With advances in Machine Learning (ML) and Deep Learning (DL) techniques, design of semantically-aware systems has become feasible in recent years. This work focuses on improving the reliability of the semantic information represented in a graph-based language that has been recently proposed. Inaccuracies in the representation of the semantic information can arise due to multiple factors, such as algorithmic shortcomings or sensory errors, decreasing the performance of the semantic extractor. This study aims to model the temporal evolution of semantic information, represented using the graph language, to enhance its reliability. Each unique graph configuration is treated as a distinct state, leading to a Hidden Semi-Markov Model (HSMM) defined over the state space of the graph configurations. The HSMM formulation enables the integration of prior knowledge on the semantic signal into the graph sequences, enhancing the accuracy in identifying semantic innovations. Within the HSMM framework, algorithms designed for graph smoothing, semantic information fusion, and model learning are introduced. The efficacy of these algorithms in improving the reliability of the extracted semantic-graphs is demonstrated through simulations and video streams generated in the CARLA simulation environment.

On a characteristic of a conditional configuration graph

On a characteristic of a conditional configuration graph

A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System

This study introduces an innovative all-in-one malware identification model that significantly enhances convenience and resource efficiency in classifying malware across diverse file types. Traditional malware identification methods involve the extraction of static and dynamic features, followed by comparisons with signature-based databases or machine learning-based classifiers. However, many malware detection applications that rely on transfer learning and image transformation suffer from excessive resource consumption. In recent years, transfer learning has emerged as a powerful tool for developing effective classifiers, leveraging pre-trained neural network models. In this research, we comprehensively explore various pre-trained network architectures, including compact and conventional networks, as well as series and directed acyclic graph configurations for malware classification. Our approach utilizes grayscale transform-based features as a standardized set of characteristics, streamlining malware classification across various file types. To ensure the robustness and generalization of our classification models, we integrate multiple datasets into the training process. Remarkably, we achieve an optimal model with 96% accuracy, while maintaining a modest 5 MB size using the SqueezeNet classifier. Overall, our model efficiently classifies malware across file types, reducing the computational load, which can be useful for cybersecurity professionals and organizations.

Topological Graph Representation and Configuration Synthesis for Power-Split Hybrid Transmissions of Multi-Planetary Gear Trains

Abstract Power-split hybrid transmissions are the core components of power-split hybrid electric vehicles (HEVs), and the quest for a more energy-efficient and higher-performing power-split hybrid transmission has long been the focus of study. In contrast to previously published methodologies, this paper proposes a novel approach for directly synthesizing power-split hybrid transmissions that makes use of the results of previously synthesized planetary gear trains (PGTs) rather than necessitating a resynthesis of their PGTs. A new topological graph that can construct a bridge between the PGTs and power-split hybrid transmission has been developed, reducing the computational complexity in the synthesis process. The new topological graph is obtained by adding topological characteristics of the power-split hybrid transmission to the PGT graph. A standard structure matrix is proposed to further screen out all the isomorphic configurations. The present method can generate various types of multi-PGT hybrid transmissions while avoiding mechanical structural interference. The design process of configurations for power-split hybrid transmission with three-column PGTs (3-PGT) is used as an example to prove the rationality of the method.

Action-Conditioned Generation of Bimanual Object Manipulation Sequences

The generation of bimanual object manipulation sequences given a semantic action label has broad applications in collaborative robots or augmented reality. This relatively new problem differs from existing works that generate whole-body motions without any object interaction as it now requires the model to additionally learn the spatio-temporal relationship that exists between the human joints and object motion given said label. To tackle this task, we leverage the varying degree each muscle or joint is involved during object manipulation. For instance, the wrists act as the prime movers for the objects while the finger joints are angled to provide a firm grip. The remaining body joints are the least involved in that they are positioned as naturally and comfortably as possible. We thus design an architecture that comprises 3 main components: (i) a graph recurrent network that generates the wrist and object motion, (ii) an attention-based recurrent network that estimates the required finger joint angles given the graph configuration, and (iii) a recurrent network that reconstructs the body pose given the locations of the wrist. We evaluate our approach on the KIT Motion Capture and KIT RGBD Bimanual Manipulation datasets and show improvements over a simplified approach that treats the entire body as a single entity, and existing whole-body-only methods.

Zoology of non-Hermitian spectra and their graph topology

We uncover the very rich graph topology of generic bounded non-Hermitian spectra, distinct from the topology of conventional band invariants and complex spectral winding. The graph configuration of complex spectra are characterized by the algebraic structures of their corresponding energy dispersions, drawing new intimate links between combinatorial graph theory, algebraic geometry, and non-Hermitian band topology. Spectral graphs that are conformally related belong to the same equivalence class, and are characterized by emergent symmetries not necessarily present in the physical Hamiltonian. The simplest class encompasses well-known examples such as the Hatano-Nelson and non-Hermitian SSH models, while more sophisticated classes represent novel multicomponent models with interesting spectral graphs resembling stars, flowers, and insects. With recent rapid advancements in metamaterials, ultracold atomic lattices, and quantum circuits, it is now feasible to not only experimentally realize such esoteric spectra, but also investigate the non-Hermitian flat bands and anomalous responses straddling transitions between different spectral graph topologies.

Survival probability of the Grover walk on the ladder graph

We provide a detailed analysis of the survival probability of the Grover walk on the ladder graph with an absorbing sink. This model was discussed in Mareš et al (2020 Phys. Rev. A 101 032113), as an example of counter-intuitive behaviour in quantum transport where it was found that the survival probability decreases with the length of the ladder L, despite the fact that the number of dark states increases. An orthonormal basis in the dark subspace is constructed, which allows us to derive a closed formula for the survival probability. It is shown that the course of the survival probability as a function of L can change from increasing and converging exponentially quickly to decreasing and converging like L −1 simply by attaching a loop to one of the corners of the ladder. The interplay between the initial state and the graph configuration is investigated.

Graphic Characterization and Clustering Configuration Descriptors of Determinant Space for Molecules.

Quantum Monte Carlo approaches based on stochastic sampling of determinant space have evolved to be powerful methods to compute the electronic states of molecules. These methods not only calculate the correlation energy at an unprecedented accuracy but also provide insightful information on the electronic structures of computed states, for example, the population, connection, and clustering of determinants, which have not been fully explored. In this work, we devise a configuration graph for visualizing determinant space, revealing the nature of the molecule's electronic structure. In addition, we propose two analytical descriptors to quantify the extent of configuration clustering of multideterminant wave functions. The graph and descriptors provide us with a fresh perspective of the electronic structures of molecules and can assist with further development of configuration interaction-based electronic structure methods.

Dense Graphs Have Rigid Parts

While the problem of determining whether an embedding of a graph G in $${\mathbb {R}}^2$$ is infinitesimally rigid is well understood, specifying whether a given embedding of G is rigid or not is still a hard task that usually requires ad hoc arguments. In this paper, we show that every embedding (not necessarily generic) of a dense enough graph (concretely, a graph with at least $$C_0n^{3/2}(\log n)^{\beta }$$ edges, for some absolute constants $$C_0>0$$ and $$\beta $$ ), which satisfies some very mild general position requirements (no three vertices of G are embedded to a common line), must have a subframework of size at least three which is rigid. For the proof we use a connection, established in Raz (Discrete Comput. Geom. 58(4), 986–1009 (2017)), between the notion of graph rigidity and configurations of lines in $${\mathbb {R}}^3$$ . This connection allows us to use properties of line configurations established in Guth and Katz (Ann. Math. 181(1), 155–190 (2015)). In fact, our proof requires an extended version of Guth and Katz result; the extension we need is proved by János Kollár in an appendix to our paper. We do not know whether our assumption on the number of edges being $$\Omega (n^{3/2}\log n)$$ is tight, and we provide a construction that shows that requiring $$\Omega (n\log n)$$ edges is necessary.

Knowledge mining of unstructured information: application to cyber domain

Information on cyber-related crimes, incidents, and conflicts is abundantly available in numerous open online sources. However, processing large volumes and streams of data is a challenging task for the analysts and experts, and entails the need for newer methods and techniques. In this article we present and implement a novel knowledge graph and knowledge mining framework for extracting the relevant information from free-form text about incidents in the cyber domain. The computational framework includes a machine learning-based pipeline for generating graphs of organizations, countries, industries, products and attackers with a non-technical cyber-ontology. The extracted knowledge graph is utilized to estimate the incidence of cyberattacks within a given graph configuration. We use publicly available collections of real cyber-incident reports to test the efficacy of our methods. The knowledge extraction is found to be sufficiently accurate, and the graph-based threat estimation demonstrates a level of correlation with the actual records of attacks. In practical use, an analyst utilizing the presented framework can infer additional information from the current cyber-landscape in terms of the risk to various entities and its propagation between industries and countries.

Frequency of Scoliosis among School Going Students Carrying Heavy Bags: A Cross-Sectional Study in Lahore, Pakistan

Objective: Scoliosis is structural change that occurs in a diversity of conditions. Development of the scoliosis during time periods of rapid child growth may consequence in severe abnormality. This survey had the purpose to find out the scoliosis frequency among school going students with age 11-16 years and determine possible associations among scoliosis and bodyweight and the bagpacks weight.
 Method: A Cross-Sectional study was conducted on 374 students with age of 11-16 years old boys and girls were having no abnormality or any MSK deformity was involved in the survey. All students were monitored by Adam's forward bend test and physical inspection test and for this purpose SRS-22r patient questionnaire was used. All data collected via a questionnaire which coded it into SPSS version 26. Data was constituted in configuration of bar charts, graphs and tables.
 Results: This inquiry contains 374 participants, and means weight of students 43.83±11.98, and mean weight of bags 5.21±1.99. The frequency of scoliosis among respondents was 48.66%, 103(27.54%) students were suffering from right Scoliotic hump, and 79(21.12%) have left Scoliotic hump. The frequency of shoulder & scapular asymmetry 73%, uneven hips 19.7%, head deviation 50.8%, body overweight 1.9%, carrying immoderate heavy school bag packs material 4%.
 Conclusion: The scoliosis frequency among the sample of school going students was high. The statistical analysis showed that scoliosis was positively correlated with weight of students with statistically significant (p=0.000). No association was found among frequency of scoliosis and bags weigh.

On one characteristic of a conditional distribution of configuration graph

Рассматриваются конфигурационные графы с $N$ вершинами. Степени вершин являются независимыми одинаково распределенными случайными величинами, и для любой вершины распределение распределение ее степени $\eta$ удовлетворяет следующему условию: при $k\to\infty$ $$\mathbf{P}\{\eta=k\}\sim \frac{d}{k^{g}\ln^h k},$$ где $d>0$, $h\geqslant 0$, $2< g<3$. Найдены предельные распределения максимальной степени вершины в таком конфигурационном графе, когда $N,n\to\infty$ так, что $ n/N^{(3g-4)/(2g-2)}\to\infty$, при условии, что сумма степеней всех вершин равна $n$.

Altered single-subject gray matter structural networks in social anxiety disorder.

Previous fMRI studies have reported more random brain functional graph configurations in social anxiety disorder (SAD). However, it is still unclear whether the same configurations would occur in gray matter (GM) graphs. Structural MRI was performed on 49 patients with SAD and on 51 age- and gender-matched healthy controls (HC). Single-subject GM networks were obtained based on the areal similarities of GM, and network topological properties were analyzed using graph theory. Group differences in each topological metric were compared, and the structure-function coupling was examined. These network measures were further correlated with the clinical characteristics in the SAD group. Compared with controls, the SAD patients demonstrated globally decreased clustering coefficient and characteristic path length. Altered topological properties were found in the fronto-limbic and sensory processing systems. Altered metrics were associated with the illness duration of SAD. Compared with the HC group, the SAD group exhibited significantly decreased structural-functional decoupling. Furthermore, structural-functional decoupling was negatively correlated with the symptom severity in SAD. These findings highlight less-optimized topological configuration of the brain structural networks in SAD, which may provide insights into the neural mechanisms underlying the excessive fear and avoidance of social interactions in SAD.

Entropy of Weighted Graph by Edge Weights by Using Sombor Reduced Index

This paper delves into the exploration of entropy in weighted graphs, where edge weights are determined by the Sombor Reduced Index. The primary aim is to establish and substantiate results by examining various graph types, including Connected Graphs, Star Graphs, Unicyclic Graphs, Regular Graphs, Complete Graphs, Complete Bipartite Graphs, Chemical Graphs, and Tree Graphs. Through rigorous analysis, we unveil the implications of these weighted structures on information content and structural complexities. Additionally, we extend our study to compute weighted entropies for molecular graphs representing specific dendrimer structures. This research contributes to a nuanced understanding of the informational intricacies embedded in diverse graph configurations, particularly emphasizing their significance in the molecular domain.

Invariants of links and 3-manifolds that count graph configurations

Invariants of links and 3-manifolds that count graph configurations

A meta-learning configuration framework for graph-based similarity search indexes

Similarity searches retrieve elements in a dataset with similar characteristics to the input query element. Recent works show that graph-based methods have outperformed others in the literature, such as tree-based and hash-based methods. However, graphs are highly parameter-sensitive for indexing and searching, which usually demands extra time for finding a suitable trade-off for specific user requirements. Current approaches to select parameters rely on observing published experimental results or Grid Search procedures. While the former has no guarantees that good settings for a dataset will also perform well on a different one, the latter is computationally expensive and limited to a small range of values. In this work, we propose a meta-learning-based recommender framework capable of providing a suitable graph configuration according to the characteristics of the input dataset. We present two instantiations of the framework: a global instantiation that uses the whole meta-database to train meta-models and a dataset-similarity-based instantiation that relies on clustering to generate meta-models tailored to datasets with similar characteristics. We also developed generic and tuned versions of the instantiations. The generic versions can satisfy user requirements in orders of magnitude faster than the traditional Grid Search. The tuned versions provide more accurate predictions at a higher cost. Our results show that the tuned methods outperform the Grid Search for most cases, providing recommendations close to the optimal one and being a suitable alternative, particularly for more challenging datasets.

The contraction category of graphs

We study the category whose objects are graphs of fixed genus and whose morphisms are contractions. We show that the corresponding contravariant module categories are Noetherian and we study two families of modules over these categories. The first takes a graph to a graded piece of the homology of its unordered configuration space and the second takes a graph to an intersection homology group whose dimension is given by a Kazhdan–Lusztig coefficient; in both cases we prove that the module is finitely generated. This allows us to draw conclusions about torsion in the homology groups of graph configuration spaces, and about the growth of Betti numbers of graph configuration spaces and Kazhdan–Lusztig coefficients of graphical matroids. We also explore the relationship between our category and outer space, which is used in the study of outer automorphisms of free groups.

Minimum and maximum of the average distance in power-law configuration graphs

The article studies configuration graphs with a discrete fixed-parameter power-law distribution of node degrees. Simulation is applied to estimate the average distance (the arithmetic mean of all distances between all pairs of graph vertices), and two characteristics are considered: the minimum and the maximum average distance in the graph. The dependences of these characteristics on the graph size and the vertex degree distribution parameter are obtained and the empirical confidence interval of the average distance in the power-law configuration graph is constructed

Connectivity conditions of Internet graphs

We consider a configuration graph with N vertices whose degrees are independent and identically distributed according to the power law which depends on a slowly varying function. They are equal to the number of each vertex’s numbered semi-edges. The graph is constructed by joining all the semi-edges pairwise equiprobably to form edges. Such models can be used to adequately describe various communication networks and Internet topologies. The paper investigates the conditions under which the random configuration graph is asymptotically connected as N → ∞. In addition, the rate at which the probability that the graph is not connected convergences to zero was estimated

Ideal spatial graph configurations

Graphs are typically represented in published research literature as two-dimensional images, for obvious reasons. With the increased accessibility of 3D rendering software and 3D printing hardware, we can now represent graphs in three dimensions more easily. Years of published work in the field have led to certain ‘standard’ two-dimensional configurations of well-known graphs such as the Petersen graph or , but there is no such standard for illustrations of graphs in three-dimensional space. Ideally, a spatial graph configuration should highlight the primary properties and features of the graph, as well as be aesthetically pleasing to view. In this paper, we will suggest and realize standard ideal spatial configurations for a variety of well-known graphs and families of graphs. These configurations can help provide fresh three-dimensional intuition about certain families of graphs, in particular the relationships between graphs in the Petersen family.