Bayesian Inference of Online Social Network Statistics via Lightweight Random Walk Crawls

Online social networks (OSN) contain extensive amount of information about the underlying society that is yet to be explored. One of the most feasible technique to fetch information from OSN, crawling through Application Programming Interface (API) requests, poses serious concerns over the the guarantees of the estimates. In this work, we focus on making reliable statistical inference with limited API crawls. Based on regenerative properties of the random walks, we propose an unbiased estimator for the aggregated sum of functions over edges and proved the connection between variance of the estimator and spectral gap. In order to facilitate Bayesian inference on the true value of the estimator, we derive the approximate posterior distribution of the estimate. Later the proposed ideas are validated with numerical experiments on inference problems in real-world networks

Temporal Ordered Clustering in Dynamic Networks: Unsupervised and Semi-supervised Learning Algorithms

In temporal ordered clustering, given a single snapshot of a dynamic network in which nodes arrive at distinct time instants, we aim at partitioning its nodes into $K$ ordered clusters $\mathcal{C}_1 \prec \cdots \prec \mathcal{C}_K$ such that for $i<j$, nodes in cluster $\mathcal{C}_i$ arrived before nodes in cluster $\mathcal{C}_j$, with $K$ being a data-driven parameter and not known upfront. Such a problem is of considerable significance in many applications ranging from tracking the expansion of fake news to mapping the spread of information. We first formulate our problem for a general dynamic graph, and propose an integer programming framework that finds the optimal clustering, represented as a strict partial order set, achieving the best precision (i.e., fraction of successfully ordered node pairs) for a fixed density (i.e., fraction of comparable node pairs). We then develop a sequential importance procedure and design unsupervised and semi-supervised algorithms to find temporal ordered clusters that efficiently approximate the optimal solution. To illustrate the techniques, we apply our methods to the vertex copying (duplication-divergence) model which exhibits some edge-case challenges in inferring the clusters as compared to other network models. Finally, we validate the performance of the proposed algorithms on synthetic and real-world networks.Comment: 14 pages, 9 figures, and 3 tables. This version is submitted to a journal. A shorter version of this work is published in the proceedings of IEEE International Symposium on Information Theory (ISIT), 2020. The first two authors contributed equall

Distribution and Dependence of Extremes in Network Sampling Processes

We explore the dependence structure in the sampled sequence of large networks. We consider randomized algorithms to sample the nodes and study extremal properties in any associated stationary sequence of characteristics of interest like node degrees, number of followers or income of the nodes in Online Social Networks etc, which satisfy two mixing conditions. Several useful extremes of the sampled sequence like $k$th largest value, clusters of exceedances over a threshold, first hitting time of a large value etc are investigated. We abstract the dependence and the statistics of extremes into a single parameter that appears in Extreme Value Theory, called extremal index (EI). In this work, we derive this parameter analytically and also estimate it empirically. We propose the use of EI as a parameter to compare different sampling procedures. As a specific example, degree correlations between neighboring nodes are studied in detail with three prominent random walks as sampling techniques

Distributed Spectral Decomposition in Networks by Complex Diffusion and Quantum Random Walk

International audienceIn this paper we address the problem of finding top k eigenvalues and corresponding eigenvectors of symmetric graph matrices in networks in a distributed way. We propose a novel idea called complex power iterations in order to decompose the eigenvalues and eigenvectors at node level, analogous to time-frequency analysis in signal processing. At each node, eigenvalues correspond to the frequencies of spectral peaks and respective eigenvector components are the amplitudes at those points. Based on complex power iterations and motivated from fluid diffusion processes in networks, we devise distributed algorithms with different orders of approximation. We also introduce a Monte Carlo technique with gossiping which substantially reduces the computational overhead. An equivalent parallel random walk algorithm is also presented. We validate the algorithms with simulations on real-world networks. Our formulation of the spectral decomposition can be easily adapted to a simple algorithm based on quantum random walks. With the advent of quantum computing, the proposed quantum algorithm will be extremely useful

Perceived responsibility for mechanical ventilation and weaning decisions in intensive care units in the Kingdom of Saudi Arabia

Background: Optimizing patient outcomes and reducing complications require constant monitoring and effective collaboration among critical care professionals. The aim of the present study was to describe the perceptions of physician directors, respiratory therapist managers and nurse managers regarding the key roles, responsibilities and clinical decision-making related to mechanical ventilation and weaning in adult Intensive Care Units (ICUs) in the Kingdom of Saudi Arabia (KSA). Methods: A multi-centre, cross-sectional self-administered survey was sent to physician directors, respiratory therapist managers and nurse managers of 39 adult ICUs at governmental tertiary referral hospitals in 13 administrative regions of the KSA. The participants were advised to discuss the survey with the frontline bedside staff to gather feedback from the physicians, respiratory therapists and nurses themselves on key mechanical ventilation and weaning decisions in their units. We performed T-test and non-parametric Mann-Whitney U tests to test the physicians, respiratory therapists, and nurses’ autonomy and influence scores, collaborative or single decisions among the professionals. Moreover, logistic regressions were performed to examine organizational variables associated with collaborative decision-making. Results: The response rate was 67% (14/21) from physician directors, 84% (22/26) from respiratory therapist managers and 37% (11/30) from nurse managers. Physician directors and respiratory therapist managers agreed to collaborate significantly in most of the key decisions with limited nurses’ involvement (P<0.01). We also found that physician directors were perceived to have greater autonomy and influence in ventilation and waning decision-making with a mean of 8.29 (SD±1.49), and 8.50 (SD±1.40), respectively. Conclusion: The key decision-making was implemented mainly by physicians and respiratory therapists in collaboration. Nurses had limited involvement. Physician directors perceived higher autonomy and influence in ventilatory and weaning decision-making than respiratory therapist managers and nurse managers. A critical care unit’s capacity to deliver effective and safe patient care may be improved by increasing nurses’ participation and acknowledging the role of respiratory therapists in clinical decision-making regarding mechanical ventilation and weaning

The role of non-invasive ventilation in weaning and decannulating critically ill patients with tracheostomy: A narrative review of the literature

Abstract Introduction Invasive mechanical ventilation (IMV) is associated with several complications. Placement of a long-term airway (tracheostomy) is also associated with short and long-term risks for patients. Nevertheless, tracheostomies are placed to help reduce the duration of IMV, facilitate weaning and eventually undergo successful decannulation. Methods We performed a narrative review by searching PubMed, Embase and Medline databases to identify relevant citations using the search terms (with synonyms and closely related words) "non-invasive ventilation", "tracheostomy" and "weaning". We identified 13 publications comprising retrospective or prospective studies in which non-invasive ventilation (NIV) was one of the strategies used during weaning from IMV and/or tracheostomy decannulation. Results In some studies, patients with tracheostomies represented a subgroup of patients on IMV. Most of the studies involved patients with underlying cardiopulmonary comorbidities and conditions, and primarily involved specialized weaning centres. Not all studies provided data on decannulation, although those which did, report high success rates for weaning and decannulation when using NIV as an adjunct to weaning patient off ventilatory support. However, a significant percentage of patients still needed home NIV after discharge. Conclusions The review supports a potential role for NIV in weaning patients with a tracheostomy either off the ventilator and/or with its decannulation. Additional research is needed to develop weaning protocols and better characterize the role of NIV during weaning

Perspectives, practices, and challenges of online teaching during COVID-19 pandemic: A multinational survey

The result of the movement restrictions during the COVID-19 pandemic was an impromptu and abrupt switch from in-person to online teaching. Most focus has been on the perception and experience of students during the process. The aim of this international survey is to assess staffs' perspectives and challenges of online teaching during the COVID-19 lockdown. Cross-sectional research using a validated online survey was carried out in seven countries (Brazil, Saudi Arabia, Jordan, Indonesia, India, the United Kingdom, and Egypt) between the months of December 2021 and August 2022, to explore the status of online teaching among faculty members during the COVID-19 pandemic. Variables and response are presented as percentages while logistic regression was used to assess the factors that predict levels of satisfaction and the challenges associated with online instruction. A total of 721 response were received from mainly male (53%) staffs. Most respondents are from Brazil (59%), hold a Doctorate degree (70%) and have over 10 years of working experience (62%). Although, 67% and 79% have relevant tools and received training for online teaching respectively, 44% report that online teaching required more preparation time than face-to-face. Although 41% of respondents were uncertain about the outcome of online teaching, 49% were satisfied with the process. Also, poor internet bandwidth (51%), inability to track students' engagement (18%) and Lack of technical skills (11.5%) were the three main observed limitations. Having little or no prior experience of online teaching before the COVID-19 pandemic [OR, 1.58 (95% CI, 1.35–1.85)], and not supporting the move to online teaching mode [OR, 0.56 (95% CI,0.48–0.64)] were two main factors independently linked with dissatisfaction with online teaching. While staffs who support the move to online teaching were twice likely to report no barriers [OR, 2.15 (95% CI, 1.61–2.86)]. Although, relevant tools and training were provided to support the move to online teaching during COVID-19 lockdown, barriers such as poor internet bandwidth, inability to track students’ engagement and lack of technical skills were main limitations observed internationally by teaching staffs. Addressing these barriers should be the focus of higher education institution in preparation for future disruptions to traditional teaching modes

Spectrum sensing using distributed sequential detection via noisy reporting MAC

International audienceThis paper considers cooperative spectrum sensing algorithms for Cognitive Radios which focus on reducing the number of samples to make a reliable detection. We propose algorithms based on decentralized sequential hypothesis testing in which the Cognitive Radios sequentially collect the observations, make local decisions and send them to the fusion center for further processing to make a final decision on spectrum usage. The reporting channel between the Cognitive Radios and the fusion center is assumed more realistically as a Multiple Access Channel (MAC) with receiver noise. Furthermore the communication for reporting is limited, thereby reducing the communication cost. We start with an algorithm where the fusion center uses an SPRT-like (Sequential Probability Ratio Test) procedure and theoretically analyse its performance. Asymptotically, its performance is close to the optimal centralized test without fusion center noise. We further modify this algorithm to improve its performance at practical operating points. Later we generalize these algorithms to handle uncertainties in SNR and fading