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This paper aims to develop a valid navigation strategy that allows tetromino-based self-reconfigurable robots to perform complete coverage tasks. To this end, a novel graph theory-based model to simulate the workspace coverage and make use of dynamic programming technique for optimal path searching and adaptive robot morphology shifting algorithms is propos…

However, current research neglects the relationships between the local and global traffic flow data. Additionally, the predefined static graph structure fails to adequately capture the dynamic spatial features of traffic flow.

Graph embeddings map graph-structured data into vector spaces for machine learning tasks. In Graph Neural Networks (GNNs), these embeddings are computed through message passing and support tasks such as node classification, link prediction and community detection across several application domains.

In this setting, namely multi-view unlabeled sensing under local permutations, previous results and algorithms are not directly applicable. In this paper, we propose a computationally efficient algorithm, R-local unlabeled sensing (RLUS), that creatively exploits the machinery of indefinite relaxations of the graph matching problem to estimate the local per…

The local metric dimension of G is the minimum cardinality of a local resolving set of G. A connected graph G is called a split graph if V(G) can be partitioned into two subsets V1 and V2 where an induced subgraph of G by V1 and V2 is a complete graph and an independent set, respectively.

Given a collection of n objects equipped with a distance function d(·, ·), the Nearest Neighbor Graph (NNG) consists in finding the nearest neighbor of each object in the collection.

Knowledge of weaponry and equipment is a crucial basis for formulating equipment utilization strategies and development pathways. To address issues such as data redundancy, high interaction difficulty, and low match accuracy of question answers, this paper constructs a Q&A system based on a knowledge graph for weaponry and equipment.

This preprint investigates a deliberately minimal relational graph model in which a real scalar variable is attached to each vertex and adjacent vertices interact through a bare edge-local quartic functional.

For every positive n and d, a simple graph on a fixed labelled vertex set is determined by its full connected coordinate-sign self-stress partition of (R^d)^n, with all vertex collisions retained.

Abstract The existing static voltage stability margin evaluation methods cannot meet the actual demand of current power grid well in terms of calculation speed and accuracy. Thus, this paper proposes a static voltage stability margin prediction method based on a graph attention network (GAT) and a long short‐term memory network (LSTM) to predict the static…

In this paper we present a provenance abstraction operator that achieves this goal. It maps a graphical representation of a PROV document PG1 to a new abstract version PG2, ensuring that (i) PG2 is a valid PROV graph, and (ii) the dependencies that appear in PG2 are justified by those that appear in PG1.

Many combinatorial problems can be solved in time $\mathcal{O}^*(c^{\mathrm{tw}})$ on graphs of treewidth $\mathrm{tw}$, for a problem-specific constant $c$. In several cases, matching upper and lower bounds on $c$ are known based on the Strong Exponential Time Hypothesis (SETH).

Using diffusion MRI, we applied correlational tractography, differential tractography, and graph theoretical analysis to explore the potential connectivity associated with 1p/19q co-deletion.ResultsThe study revealed that the quantitative anisotropy (QA) of key deep medial fiber tracts, including the anterior thalamic radiation, superior thalamic radiation,…

Identifying the modular structure in multilayer brain networks represents a gateway toward a deeper understanding of neural processes underlying cognition. Electroencephalographic (EEG) signals, thanks to their high temporal resolution, can give rise to multilayer networks able to follow the dynamics of brain activity.

Background/Objectives: Knowledge Graphs (KGs) are often incomplete, which can significantly impact the performance of downstream applications. Manual completion of KGs is time-consuming and costly, emphasizing the importance of developing automated methods for KGC.

All amino H atoms and nitrate O atoms are involved in the three-dimensional hydrogen-bond network. There are two graph-set motifs R22(8), which include the amino groups connected to the N atoms in the biguanide 3-, 4- and 5-positions, and the O atoms of a nitrate ion.

From the analysis of the results, we propose research guidelines and process development strategies, and construct an MD module performance curve. From this graph, permeate flow rate, thermal energy consumption and outlet temperatures can be determined for given feed inlet conditions (temperature and concentration).

The model employs a Graph Attention Network (GAT) to extract complex topological features from graph-structured data derived from received signal strength, dynamically highlighting the most relevant information.

To address this gap, we propose an integrative computational modeling framework that combines complex network analysis with cognitive taxonomy. Specifically, textbook knowledge networks (TKNs) are constructed by representing knowledge points as nodes, with links defined by their co-occurrence within paragraphs.