Learning social networks from text data using covariate information

Accurately describing the lives of historical figures can be challenging, but unraveling their social structures perhaps is even more so. Historical social network analysis methods can help in this regard and may even illuminate individuals who have been overlooked by historians, but turn out to be influential social connection points. Text data, such as biographies, are a useful source of information for learning historical social networks but the identifcation of links based on text data can be challenging. The Local Poisson Graphical Lasso model models social networks by conditional independence structures, and leverages the number of name co-mentions in the text to infer relationships. However, this method does not take into account the abundance of covariate information that is often available in text data. Conditional independence structure like Poisson Graphical Model, which makes use name mention counts in the text can be useful tools to avoid false positive links due to the co-mentions but given historical tendency of frequently used or common names, without additional distinguishing information, we may introduce incorrect connections. In this work, we therefore extend the Local Poisson Graphical Lasso model with a (multiple) penalty structure that incorporates covariates, opening up the opportunity for similar individuals to have a higher probability of being connected. We propose both greedy and Bayesian approaches to estimate the penalty parameters. We present results on data simulated with characteristics of historical networks and show that this type of penalty structure can improve network recovery as measured by precision and recall. We also illustrate the approach on biographical data of individuals who lived in early modern Britain between 1500 and 1575. We will show how these covariates affect the statistical model’s performance using simulations, discuss how it helps to better identify links for the people with common names and those who are traditionally underrepresented in the biography text data.

ANALYSIS OF THE ATTACKER AND DEFENDER GAN MODELS FOR THE INDOOR NAVIGATION NETWORK

Evacuation research relies heavily on the efficiency analysis of the study navigation networks, and this principle also applies to indoor scenarios. One crucial type of these scenarios is the attacker and defender topic, which discusses the paralyzing and recovering operations for a specific indoor navigation network. Our approach is to apply the Generative-Adversarial-Neural network (GAN) model to optimize both reduction and increase operations for a specific indoor navigation network. In other words, the proposed model utilizes GAN both in the attacking behavior efficiency analysis and the recovering behavior efficiency analysis. To this purpose, we design a black box of training the generative model and adversarial model to construct the hidden neural networks to mimic the human selection of choosing the critical nodes in the studying navigation networks. The experiment shows that the proposed model could alleviate the selection of nodes that significantly influence network transportation efficiency. Therefore, we could apply this model to disaster responding scenarios like fire evacuation and communication network recovery operations.

Unifying Node Labels, Features, and Distances for Deep Network Completion

Collected network data are often incomplete, with both missing nodes and missing edges. Thus, network completion that infers the unobserved part of the network is essential for downstream tasks. Despite the emerging literature related to network recovery, the potential information has not been effectively exploited. In this paper, we propose a novel unified deep graph convolutional network that infers missing edges by leveraging node labels, features, and distances. Specifically, we first construct an estimated network topology for the unobserved part using node labels, then jointly refine the network topology and learn the edge likelihood with node labels, node features and distances. Extensive experiments using several real-world datasets show the superiority of our method compared with the state-of-the-art approaches.

Neurosensory Rehabilitation and Olfactory Network Recovery in Covid-19-Related Olfactory Dysfunction

Non-conductive olfactory dysfunction (OD) is an important extra-pulmonary manifestation of coronavirus disease 2019 (COVID-19). Olfactory bulb (OB) volume loss and olfactory network functional connectivity (FC) defects were identified in two patients suffering from prolonged COVID-19-related OD. One patient received olfactory treatment (OT) by the combination of oral vitamin A and smell training via the novel electronic portable aromatic rehabilitation (EPAR) diffusers. After four-weeks of OT, clinical recuperation of smell was correlated with interval increase of bilateral OB volumes [right: 22.5 mm3 to 49.5 mm3 (120%), left: 37.5 mm3 to 42 mm3 (12%)] and improvement of mean olfactory FC [0.09 to 0.15 (66.6%)].

Enhanced Bit Repair IP Fast Reroute Mechanism for Rapid Network Recovery

The massive development of virtualized infrastructures, Internet of Things (IoT), and Wireless Sensor Network (WSN) in recent years has led to an increase in quality requirements for the management and reliability of underlay communication networks. Existing converged networks must therefore guarantee specific quantitative and qualitative parameters of different network communication services to meet customer requirements. However, the quality of the services operated is very negatively affected by an unpredictable failure of a communication link or a network node. In such situations, communication is typically interrupted for a period that is difficult to predict, and which can lead to significant financial losses and other negative effects. Internet Protocol Fast Reroute (IP FRR) technology was developed for these reasons. The paper presents the proposal of the new Enhanced Bit Repair (EB-REP) IP FRR mechanism, which offers significant improvements over its predecessor, the B-REP mechanism. The B-REP offers protection against a single failure and only for selected critical IP flows. The EB-REP provides advanced protection against multiple failures in a protected network domain and the protection can be provided for all network flows. The EB-REP calculates alternative paths in advance based on link metrics, but also allows the construction of alternative paths independently of them. The construction of alternative FRR paths uses a standardized tunneling approach via a unique field Bit-String. Thanks to these features, EB-REP is an advanced contribution to solving IP FRR-related problems, which enables the use of EB-REP in many network deployments, but especially in network solutions that require reliable data transmission.

Survivability of Optical Networks

Telecommunication networks with their unprecedented penetration in today’s society must deliver services that can survive unpredictable failures across the network. Recovery from failure can be made in various ways, broadly categorized in two types: protection and restoration schemes. Realization of these schemes varies for different network segments: access, metro, and long-haul networks. Protection schemes are proactive in nature and need more resources, while ensuring fast recovery from failure. However, restoration schemes are reactive in nature, as in such schemes a network starts exploring the possible alternate connections after a failure occurs, and hence they offer slower recovery while needing fewer resources. We present various protection and restoration schemes, as applicable to the respective network segments: PON, SONET/SDH and WDM-over-SONET/SDH rings, and long-haul WRONs, and we discuss the underlying issues for the implementation of these schemes. (135 words)

Neurosensory Rehabilitation and Olfactory Network Recovery in COVID-19-related Olfactory Dysfunction

BackgroundNon-conductive olfactory dysfunction (OD) is an important extra-pulmonary manifestation of coronavirus disease 2019 (COVID-19). Prolonged COVID-19-related OD is a serious neurosensory disability. Treatment for the restoration of smell is urgently needed.Case presentationTwo patients presenting with prolonged COVID-19-related OD underwent structural and resting-state functional magnetic resonance imaging (rs-fMRI) brain scans. Two healthy controls were recruited for radiological comparison. One patient received olfactory treatment (OT) by the combination of oral vitamin A and smell training via the novel electronic portable aromatic rehabilitation (EPAR) diffusers. After four-weeks of OT, clinical recuperation of smell was correlated with interval increase of bilateral OB volumes [right: 22.5mm3 to 49.5mm3 (120%), left: 37.5mm3 to 42mm3 (12%)] and the enhancement of mean olfactory functional connectivity [0.09 to 0.15 (66.6%)]. ConclusionsOlfactory network functional defects and OB volume loss were identified in patients presenting with prolonged COVID-19-related OD. Preliminary evidence demonstrated that the combination of oral vitamin A and smell training may induce neurogenesis at the olfactory apparatus and achieve olfactory neurosensory rehabilitation. This observation should be validated in large scale randomized–controlled trials.