Mobility and Trajectory-Based Technique for Monitoring Asymptomatic Patients

Asymptomatic patients (AP) travel through neighborhoods in communities. The mobility dynamics of the AP makes it hard to tag them with specific interests. The lack of efficient monitoring systems can enable the AP to infect several vulnerable people in the communities. This article studied the monitoring of AP through their mobility and trajectory towards reducing the stress of socio-economic complications in the case of pandemics. Mobility and Trajectory based Technique for Monitoring Asymptomatic Patients (MTT-MAP) was established. The time-ordered spatial and temporal trajectory records of the AP were captured through their activities. A grid-based index data structure was designed based on network topology, graph theory and trajectory analysis to cater for the continuous monitoring of the AP over time. Also, concurrent object localisation and recognition, branch and bound, and multi-object instance strategies were adopted. The MTT-MAP has shown efficient when experimented with GeoLife dataset and can be integrated with state-of-the-art patients monitoring systems.

VTG-Net: A CNN Based Vessel Topology Graph Network for Retinal Artery/Vein Classification

From diagnosing cardiovascular diseases to analyzing the progression of diabetic retinopathy, accurate retinal artery/vein (A/V) classification is critical. Promising approaches for A/V classification, ranging from conventional graph based methods to recent convolutional neural network (CNN) based models, have been known. However, the inability of traditional graph based methods to utilize deep hierarchical features extracted by CNNs and the limitations of current CNN based methods to incorporate vessel topology information hinder their effectiveness. In this paper, we propose a new CNN based framework, VTG-Net (vessel topology graph network), for retinal A/V classification by incorporating vessel topology information. VTG-Net exploits retinal vessel topology along with CNN features to improve A/V classification accuracy. Specifically, we transform vessel features extracted by CNN in the image domain into a graph representation preserving the vessel topology. Then by exploiting a graph convolutional network (GCN), we enable our model to learn both CNN features and vessel topological features simultaneously. The final predication is attained by fusing the CNN and GCN outputs. Using a publicly available AV-DRIVE dataset and an in-house dataset, we verify the high performance of our VTG-Net for retinal A/V classification over state-of-the-art methods (with ~2% improvement in accuracy on the AV-DRIVE dataset).

ON INTEGRATION OF EVOLVING INFRASTRUCTURE TOPOLOGY GRAPHS AND METRIC DATA STREAMS IN INFORMATION TECHNOLOGY INFRASTRUCTURE MANAGEMENT

Modern cloud-based information technology (IT) infrastructure monitoring context and data are gathered from various systems. Typical monitoring systems provide a set of metrics characterizing the performance and health of a variety of infrastructure components. To understand the dependencies and relations among these measurements, the infrastructure topology can be analysed to provide context to the monitoring metrics. However, the metrics and the topology are updated at different time intervals and providing continuous merging and analysis of both data sets is a challenging task which is rarely addressed in the scientific literature. The paper elaborates a method for integration of infrastructure topology graph and monitoring metric data streams. The method is intended for application in the identification of anomalies in IT infrastructure.

The Airflow Reversal Law in Ventilation System after Coal and Gas Outburst in Tunneling Roadway

Considering the coal and gas outburst phenomenon in the mining space, this paper analyzes the main characteristics of coal and gas outburst accidents, defines the outburst airflow reversal degree, and constructs a simplified topology graph of tunneling ventilation system, while the air door is not destroyed. Using the numerical simulation method, this paper elaborates on the relationship between the outburst pressure and airflow reversal degree. The results indicate that the inlet pressure increases to 264 hPa and the outlet pressure increases to 289 hPa when the outburst pressure increases from 1 hPa to 1 MPa, and the relative variation coefficient of pressure decreases from 1501.5 to 1.62 in the inlet of return airway and decreases from 2002 to 1.65 in the outlet of return airway. Furthermore, the air velocity decreases from −1.38 to −284.44 m/s in the inlet and increases from 3.10 to 297.38 m/s in the outlet. Moreover, the gas concentration of the inlet and outlet in return airway increases rapidly with the increase of outburst pressure. When the outburst pressure is greater than 0.15 MPa, the gas concentration will be over 98% in tunneling ventilation system. This paper also finds out a cubic polynomial relationship existing between the reversal degree and the outburst pressure. It provides the prediction of coal and gas outburst and serves as a guidance in case mine ventilation disturbances occur.

Welfare Guarantees in Schelling Segregation

Schelling’s model is an influential model that reveals how individual perceptions and incentives can lead to residential segregation. Inspired by a recent stream of work, we study welfare guarantees and complexity in this model with respect to several welfare measures. First, we show that while maximizing the social welfare is NP-hard, computing an assignment of agents to the nodes of any topology graph with approximately half of the maximum welfare can be done in polynomial time. We then consider Pareto optimality, introduce two new optimality notions based on it, and establish mostly tight bounds on the worst-case welfare loss for assignments satisfying these notions as well as the complexity of computing such assignments. In addition, we show that for tree topologies, it is possible to decide whether there exists an assignment that gives every agent a positive utility in polynomial time; moreover, when every node in the topology has degree at least 2, such an assignment always exists and can be found efficiently.

Mesh repairing using topology graphs

Geometrical and topological inconsistencies, such as self-intersections and non-manifold elements, are common in triangular meshes, causing various problems across all stages of geometry processing. In this paper, we propose a method to resolve these inconsistencies using a graph-based approach. We first convert geometrical inconsistencies into topological inconsistencies and construct a topology graph. We then define local pairing operations on the topology graph, which is guaranteed not to introduce new inconsistencies. The final output of our method is an oriented manifold with all geometrical and topological inconsistencies fixed. Validated against a large data set, our method overcomes chronic problems in the relevant literature. First, our method preserves the original geometry and it does not introduce a negative volume or false new data, as we do not impose any heuristic assumption (e.g. watertight mesh). Moreover, our method does not introduce new geometric inconsistencies, guaranteeing inconsistency-free outcome.

The Zariski topology-graph of modules over commutative rings II

Let M be a module over a commutative ring R. In this paper, we continue our study about the Zariski topology-graph $$G(\tau _T)$$ G ( τ T ) which was introduced in Ansari-Toroghy et al. (Commun Algebra 42:3283–3296, 2014). For a non-empty subset T of $$\mathrm{Spec}(M)$$ Spec ( M ) , we obtain useful characterizations for those modules M for which $$G(\tau _T)$$ G ( τ T ) is a bipartite graph. Also, we prove that if $$G(\tau _T)$$ G ( τ T ) is a tree, then $$G(\tau _T)$$ G ( τ T ) is a star graph. Moreover, we study coloring of Zariski topology-graphs and investigate the interplay between $$\chi (G(\tau _T))$$ χ ( G ( τ T ) ) and $$\omega (G(\tau _T))$$ ω ( G ( τ T ) ) .