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2022 ◽  
Vol 6 (POPL) ◽  
pp. 1-28
Author(s):  
Matthias Eichholz ◽  
Eric Hayden Campbell ◽  
Matthias Krebs ◽  
Nate Foster ◽  
Mira Mezini

Programming languages like P4 enable specifying the behavior of network data planes in software. However, with increasingly powerful and complex applications running in the network, the risk of faults also increases. Hence, there is growing recognition of the need for methods and tools to statically verify the correctness of P4 code, especially as the language lacks basic safety guarantees. Type systems are a lightweight and compositional way to establish program properties, but there is a significant gap between the kinds of properties that can be proved using simple type systems (e.g., SafeP4) and those that can be obtained using full-blown verification tools (e.g., p4v). In this paper, we close this gap by developing Π4, a dependently-typed version of P4 based on decidable refinements. We motivate the design of Π4, prove the soundness of its type system, develop an SMT-based implementation, and present case studies that illustrate its applicability to a variety of data plane programs.


2022 ◽  
Vol 19 (3) ◽  
pp. 2700-2719
Author(s):  
Siyuan Yin ◽  
◽  
Yanmei Hu ◽  
Yuchun Ren

<abstract> <p>Many systems in real world can be represented as network, and network analysis can help us understand these systems. Node centrality is an important problem and has attracted a lot of attention in the field of network analysis. As the rapid development of information technology, the scale of network data is rapidly increasing. However, node centrality computation in large-scale networks is time consuming. Parallel computing is an alternative to speed up the computation of node centrality. GPU, which has been a core component of modern computer, can make a large number of core tasks work in parallel and has the ability of big data processing, and has been widely used to accelerate computing. Therefore, according to the parallel characteristic of GPU, we design the parallel algorithms to compute three widely used node centralities, i.e., closeness centrality, betweenness centrality and PageRank centrality. Firstly, we classify the three node centralities into two groups according to their definitions; secondly, we design the parallel algorithms by mapping the centrality computation of different nodes into different blocks or threads in GPU; thirdly, we analyze the correlations between different centralities in several networks, benefited from the designed parallel algorithms. Experimental results show that the parallel algorithms designed in this paper can speed up the computation of node centrality in large-scale networks, and the closeness centrality and the betweenness centrality are weakly correlated, although both of them are based on the shortest path.</p> </abstract>


2021 ◽  
Author(s):  
Chenye Wang ◽  
Junhan Shi ◽  
Jiansheng Cai ◽  
Yusen Zhang ◽  
Xiaoqi Zheng ◽  
...  

Abstract Background: Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data. A critical challenge in cancer genomics is identification of a few driver mutation genes from a much larger number of passenger mutation genes. However, majority of existing computational approaches underuse the co-occurrence information of the individuals, which deems to be important in tumorigenesis and tumor progression. Driver gene list predicted from these tools are prone to be false positive, recent research is far from achieving the ultimate goal of discovering a complete catalog of driver genes. Results: To make full use of co-mutation information, we present a random walk algorithm referred to as DriverRWH on a weighted gene mutation hypergraph model, using somatic mutation data and molecular interaction network data to prioritize candidate driver genes. Applied to tumor samples of different cancer types from The Cancer Genome Atlas (TCGA), DriverRWH shows significantly better performance than state-of-art prioritization methods in terms of the area under the curve (AUC) scores and the cumulative number of known driver genes recovered in top-ranked candidate genes. DriverRWH recovers approximately 50% known driver genes in the top 30 ranked candidate genes for more than half of the cancer types. In addition, DriverRWH is also highly robust to perturbations in the mutation data and gene functional network data. Conclusion: DriverRWH is effective among various cancer types in prioritizes cancer driver genes and provides considerable improvement over other tools with a better balance of precision and sensitivity. It can be a useful tool for detecting potential driver genes and facilitate targeted cancer therapies.


2021 ◽  
Vol 12 (1) ◽  
pp. 45-50
Author(s):  
Masood Mohammed Abdul Aziz ◽  
Masud Imtiaz ◽  
Choudhury Habibur Rasul

Background: Medical institutes remained on complete shut down during the coronavirus disease of 2019 (COVID-19) pandemic while Information Technology (IT) bridged the teaching learning between the students and teachers. The study objectives were to determine the opportunities and obstacles of teaching learning process and overall effectiveness of online classes over traditional classes. Methods: A cross-sectional study was conducted from July 2020 to December 2020 between students and teachers of Khulna city Medical College, Khulna, Bangladesh. An online questionnaire was developed using google form containing four sections about different aspects of IT and teaching learning process. Results: Around 87% (160 vs 48) students and teachers responded to the questionnaire. The most preferred online teaching learning platform for students and teachers was zoom (84.4% vs 83.4%) and the favored devices were smart phone (96.2% vs 87.5%) followed by laptop computer (90.6% vs 83.3%). The majority felt connected to each other (82.5% vs 62.5%) and could work faster and effectively (75% vs 66.7%). Conversely, students (65.6%) felt online learning was more enjoyable than teachers (29.2%), where significant difference was found; (p= 0.006). Poor network (98.1% vs 79.1%) and affordability of mobile data (81.2% vs 66.7%) were the two main barriers among them. On-line teaching-learning neither increased student- teacher interaction (51.9% vs 66.7%), nor had better scope of asking question (52.5% vs 70.8%) and there was less scope of explaining details (52.5% vs 66.7%). Oral assessment was the most preferable (89.4% vs 83.3%), but the practical assessment (78.1% vs 83.3%) was the least preferable method for assessing students’ knowledge and skill online. Overall effectiveness of online classes over traditional classes was scored around 50% (±10%) by students and teachers (58.1% vs 62.5%). Conclusion: Despite having barriers like poor network, data affordability and limited computer and net usability, online classes played a pivotal role to continue the academic activities in a medical college during Corona pandemic. BIRDEM Med J 2022; 12(1): 45-50


Author(s):  
Benjamin Choat ◽  
Amber Pulido ◽  
Aditi S. Bhaskar ◽  
Rebecca L. Hale ◽  
Harry X. Zhang ◽  
...  

2021 ◽  
Author(s):  
Bernard C Silenou ◽  
Carolin Verset ◽  
Basil B Kaburi ◽  
Olivier Leuci ◽  
Juliane Doerrbecker ◽  
...  

BACKGROUND The Surveillance Outbreak Response Management and Analysis System (SORMAS) contains a management module to support countries in epidemic response. It consists of documentation, linkage and follow-up of cases, contacts, and events. To allow SORMAS users to visualise, compute key surveillance indicators and estimate epidemiological parameters from such a network data in real time, we developed the SORMAS Statistics (SORMAS-Stats) application. OBJECTIVE The aim of this study is to describe the key visualisations, surveillance indicators and epidemiological parameters implemented in the SORMAS-Stats application, and illustrate the application of SORMAS-Stats to COVID-19 outbreak response. METHODS Based on literature review and user requests, we included the following visualisation and estimation of parameters in SORMAS-Stats: transmission network diagram, serial interval (SI), time-varying reproduction number (Rt), dispersion parameter (k) and additional surveillance indicators presented in graphs and tables. We estimated SI by fitting a lognormal, gamma, and Weibull distributions to the observed distribution of the number of days between symptoms onset dates of infector-infectee pairs. We estimated k by fitting a negative binomial distribution to the observed number of infectees per infector. We applied the Markov Chain Monte Carlo approach and estimated Rt using the incidence data and the observed SI data, computed from the transmission network data. RESULTS Using COVID-19 contact tracing data of confirmed cases reported between July 31, and October 29, 2021 in Bourgogne-Franche-Comté region of France, we constructed a network diagram containing 63570 nodes comprising 1.75% (1115/63570) events, 19.59% (12452/63570) case persons, and 78.66% (50003/63570) exposed persons, 1238 infector-infectee pairs, 3860 transmission chains with 24.69% (953/3860) having events as the index infector. The distribution with best fit to the observed SI data was lognormal distribution with mean 4.32 days (95% CI, 4.10–4.53 days). We estimated the dispersion parameter, k of 21.11 (95% CI, 7.57–34.66) and a reproductive number, R of 0.9 (95% CI, 0.58–0.60). The weekly estimated Rt values ranged from 0.80 to 1.61. CONCLUSIONS We provide an application for real-time estimation of epidemiological parameters, which are essential for informing outbreak response strategies. These estimates are commensurate with findings from previous studies. SORMAS-Stats application would greatly assist public health authorities in the regions using SORMAS or similar applications by providing extensive visualisations and computation of surveillance indicators.


2022 ◽  
Vol 16 (2) ◽  
pp. 1-21
Author(s):  
Michael Nelson ◽  
Sridhar Radhakrishnan ◽  
Chandra Sekharan ◽  
Amlan Chatterjee ◽  
Sudhindra Gopal Krishna

Time-evolving web and social network graphs are modeled as a set of pages/individuals (nodes) and their arcs (links/relationships) that change over time. Due to their popularity, they have become increasingly massive in terms of their number of nodes, arcs, and lifetimes. However, these graphs are extremely sparse throughout their lifetimes. For example, it is estimated that Facebook has over a billion vertices, yet at any point in time, it has far less than 0.001% of all possible relationships. The space required to store these large sparse graphs may not fit in most main memories using underlying representations such as a series of adjacency matrices or adjacency lists. We propose building a compressed data structure that has a compressed binary tree corresponding to each row of each adjacency matrix of the time-evolving graph. We do not explicitly construct the adjacency matrix, and our algorithms take the time-evolving arc list representation as input for its construction. Our compressed structure allows for directed and undirected graphs, faster arc and neighborhood queries, as well as the ability for arcs and frames to be added and removed directly from the compressed structure (streaming operations). We use publicly available network data sets such as Flickr, Yahoo!, and Wikipedia in our experiments and show that our new technique performs as well or better than our benchmarks on all datasets in terms of compression size and other vital metrics.


2021 ◽  
pp. jmedgenet-2021-108193
Author(s):  
Ido Shalev ◽  
Judith Somekh ◽  
Alal Eran

BackgroundLoss of tectonin β-propeller repeat-containing 2 (TECPR2) function has been implicated in an array of neurodegenerative disorders, yet its physiological function remains largely unknown. Understanding TECPR2 function is essential for developing much needed precision therapeutics for TECPR2-related diseases.MethodsWe leveraged considerable amounts of functional data to obtain a comprehensive perspective of the role of TECPR2 in health and disease. We integrated expression patterns, population variation, phylogenetic profiling, protein-protein interactions and regulatory network data for a minimally biased multimodal functional analysis. Genes and proteins linked to TECPR2 via multiple lines of evidence were subject to functional enrichment analyses to identify molecular mechanisms involving TECPR2.ResultsTECPR2 was found to be part of a tight neurodevelopmental gene expression programme that includes KIF1A, ATXN1, TOM1L2 and FA2H, all implicated in neurological diseases. Functional enrichment analyses of TECPR2-related genes converged on a role in late autophagy and ribosomal processes. Large-scale population variation data demonstrated that this role is non-redundant.ConclusionsTECPR2 might serve as an indicator for the energy balance between protein synthesis and autophagy, and a marker for diseases associated with their imbalance, such as Alzheimer’s disease and Huntington’s disease. Specifically, we speculate that TECPR2 plays an important role as a proteostasis regulator during synaptogenesis, highlighting its importance in developing neurons. By advancing our understanding of TECPR2 function, this work provides an essential stepping stone towards the development of precision diagnostics and targeted treatment options for TECPR2-related disorders.


2021 ◽  
Vol 7 (4) ◽  
pp. 91-98
Author(s):  
Ivan Tkachev ◽  
Roman Vasilyev ◽  
Elena Belousova

Monitoring thunderstorm activity can help you solve many problems such as infrastructure facility protection, warning of hazardous phenomena associated with intense precipitation, study of conditions for the occurrence of thunderstorms and the degree of their influence on human activity, as well as the influence of thunderstorm activity on the formation of near-Earth space. We investigate the characteristics of thunderstorm cells by the method of cluster analysis. We take the Vereya-MR network data accumulated over a period from 2012 to 2018 as a basis. The Vereya-MR network considered in this paper is included in networks operating in the VLF-LF range (long and super-long radio waves). Reception points equipped with recording equipment, primary information processing systems, communication systems, precision time and positioning devices based on global satellite navigation systems are located throughout Russia. In the longitudinal-latitudinal thunderstorm distributions of interest, the dependence on the location of recording devices might be manifested. We compare the behavior of thunderstorms on the entire territory of the Russian Federation with those in the Baikal natural territory. We have established the power of thunderstorms over the Baikal region is lower. The daily variation in thunderstorm cells we obtained is consistent with the data from other works. There are no differences in other thunderstorm characteristics between the regions under study. This might be due to peculiarities of the analysis method. On the basis of the work performed, we propose sites for new points of our own lightning location network, as well as additional methods of cluster analysis.


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