# pos

id: cord-034824-eelqmzdx
pos: NNP
token:
  https://github.com/YangLiangwei/Influential-nodes-identification-in-complex-networksvia-information-entropy

lemma:
  https://github.com/YangLiangwei/Influential-nodes-identification-in-complex-networksvia-information-entropy


# ent

id: cord-010727-fiukemh3
type: WORK_OF_ART
entity:
  A Survey of Models and Algorithms for Social Influence Analysis Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Networks


id: cord-017590-w5copp1z
type: ORG
entity:
  Distributed Computing Artificial Intelligence Bioinformatics Soft Computing and Ambient Assisted Living


id: cord-005090-l676wo9t
type: ORG
entity:
  the International WIC Institute College of Computer Science and Technology Beijing University of Technology


id: cord-027451-ztx9fsbg
type: ORG
entity:
  Thermal Guidelines for Data Processing Environments Expanded Data Center Classes and Usage Guidance Green Data


id: cord-010751-fgk05n3z
type: EVENT
entity:
  International Conference Networks An Introduction Network Analysis Methodological Foundations Distance


id: cord-119522-2ua8218z
type: GPE
entity:
  the United States A Low Cost Sensing System for Coopertaive Air Quality Monitoring in Urban Areas Evaluation of Low Cost Sensors for Ambient PM2 5


id: cord-284186-zf1w8ksm
type: CELL_LINE
entity:
  Dog Illustrated Veterinary Anatomical Nomenclature Abdominal radiographic and ultrasonographic findings in ferrets Mustela putorius furo with systemic coronavirus infection Histopathology of the lymph nodes Disseminated histologically confirmed Cryptococcus spp infection in a domestic ferret Radiographic kidney measurements in North American pet ferrets Mustela furo Anatomy of the ferret Idiopathic hypersplenism in a ferret Normal clinical and biological parameters Anatomia ultrassonográfica


id: cord-020885-f667icyt
type: GGP
entity:
  metapath m arg max θ v Vm a Am ca Na v log p c a v θ The original Metapath2vec model 6 uses multiple metapaths 29


id: cord-322746-28igib4l
type: DISEASE
entity:
  rhinovirus parainfluenza virus influenza virus respiratory syncytial virus coronavirus adenovirus and reovirus 9


id: cord-276178-0hrs1w7r
type: WORK_OF_ART
entity:
  Futuristic Trends in Network and Communication Technologies Futuristic Trends in Networks and Computing Technologies Communications in Computer and Information Handbook of Wireless Sensor Networks Issues and Challenges in Current Scenario s Lecture Notes in Networks and Systems 121 Proceedings of ICRIC 2019 Introduction on Wireless Sensor Networks Issues and Challenges in Current Era


id: cord-022561-rv5j1201
type: PROTEIN
entity:
  extrinsic tenase factors III VII and X intrinsic tenase factors IX VIII and X and prothrombinase factors X V and II coagulation complexes Platelet GP surface receptors


id: cord-022561-rv5j1201
type: CELL_TYPE
entity:
  reticular space red pulp splenic cords sinuses red pulp sinuses sinus spaces pulp spaces mesh space of the spleen reticular cell


id: cord-022561-rv5j1201
type: CELL_TYPE
entity:
  sinus histiocytes monocyte macrophage system 3 acute or chronic lymphadenitis 4 lymphoma and 5 metastatic neoplasia Macrophage System


id: cord-022561-rv5j1201
type: ORG
entity:
  Lymphoid Lymphatic System Disorders of Domestic Animals Lymph Nodes Pigmentation of Lymph Nodes Dilation


id: cord-022561-rv5j1201
type: ORG
entity:
  Thymomas See Lymphoid Lymphatic System Disorders of Domestic Animals Thymus Neoplasia Lymphomas Lymphoma


id: cord-022561-rv5j1201
type: WORK_OF_ART
entity:
  See Bone Marrow and Blood Cells Disorders of Domestic Animals Types of Hematopoietic Neoplasia Myeloid Neoplasia Mast Cell Neoplasia


# url

id: cord-034824-eelqmzdx
domain: github.com
url:
  http://github.com/YangLiangwei/Influential-nodes-identification-in-complex-networksvia-information-entropy


# bib

id: cord-000196-lkoyrv3s
title: Dynamics and Control of Diseases in Networks with Community Structure
author: Salathé, Marcel
date: 2010-04-08
txt: ./txt/cord-000196-lkoyrv3s.txt
cache: ./cache/cord-000196-lkoyrv3s.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851561/
doi: 10.1371/journal.pcbi.1000736
summary:
  Running standard susceptible-infected-resistant (SIR) epidemic simulations (see Methods) on these networks, we find that the average epidemic size, epidemic duration and the peak prevalence of the epidemic are strongly affected by a change in community structure connectivity that is independent of the overall degree distribution of the full network ( Figure 1 ). While infections are most likely to spread along the shortest paths between any two nodes, the cumulative contribution of other paths can still be important [40] : immunization strategies based on random walk centrality result in the lowest number of infected cases at low vaccination coverage (Figure 4b and 4c ). In practice, identifying immunization targets may be impossible using such algorithms, because the structure of the contact network relevant for the spread of a directly transmissible disease is generally not known.

abstract:
  The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.


id: cord-005090-l676wo9t
title: Network immunization and virus propagation in email networks: experimental evaluation and analysis
author: Gao, Chao
date: 2010-07-14
txt: ./txt/cord-005090-l676wo9t.txt
cache: ./cache/cord-005090-l676wo9t.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7088328/
doi: 10.1007/s10115-010-0321-0
summary:
  For example, computer scientists focus on algorithms and the computational complexities of strategies, i.e. how to quickly search a short path from one "seed" node to a targeted node just based on local information, and then effectively and efficiently restrain virus propagation [42] . Section 4 describes the experiments which are performed to compare different immunization strategies with the measurements of the immunization efficiency, the cost and the robustness in both synthetic networks (including a synthetic community-based network) and two real email networks (the Enron and a university email network), and analyze the effects of network structures and human dynamics on virus propagation. It is readily to observe the microscopic process of worm propagating through this model, and uncover the effects of different factors (e.g. the power-law exponent, human dynamics and the average path length of the network) on virus propagation and immunization strategies.

abstract:
  Network immunization strategies have emerged as possible solutions to the challenges of virus propagation. In this paper, an existing interactive model is introduced and then improved in order to better characterize the way a virus spreads in email networks with different topologies. The model is used to demonstrate the effects of a number of key factors, notably nodes’ degree and betweenness. Experiments are then performed to examine how the structure of a network and human dynamics affects virus propagation. The experimental results have revealed that a virus spreads in two distinct phases and shown that the most efficient immunization strategy is the node-betweenness strategy. Moreover, those results have also explained why old virus can survive in networks nowadays from the aspects of human dynamics.


id: cord-007415-d57zqixs
title: Correlations between structure and random walk dynamics in directed complex networks
author: da Fontoura Costa, Luciano
date: 2007-07-30
txt: ./txt/cord-007415-d57zqixs.txt
cache: ./cache/cord-007415-d57zqixs.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7112555/
doi: 10.1063/1.2766683
summary:
  They establish the necessary conditions for networks to be topologically and dynamically fully correlated (e.g., word adjacency and airport networks), and show that in this case Zipf''s law is a consequence of the match between structure and dynamics. They establish the necessary conditions for networks to be topologically and dynamically fully correlated ͑e.g., word adjacency and airport networks͒, and show that in this case Zipf''s law is a consequence of the match between structure and dynamics. 2766683͔ We address the relationship between structure and dynamics in complex networks by taking the steady-state distribution of the frequency of visits to nodes-a dynamical feature-obtained by performing random walks 1 along the networks. In addition to providing a modeling approach intrinsically compatible with dynamics involving successive visits to nodes by a single or multiple agents, such as is the case with world wide web ͑WWW͒ navigation, text writing, and transportation systems, random walks are directly related to diffusion.

abstract:
  In this letter the authors discuss the relationship between structure and random walk dynamics in directed complex networks, with an emphasis on identifying whether a topological hub is also a dynamical hub. They establish the necessary conditions for networks to be topologically and dynamically fully correlated (e.g., word adjacency and airport networks), and show that in this case Zipf’s law is a consequence of the match between structure and dynamics. They also show that real-world neuronal networks and the world wide web are not fully correlated, implying that their more intensely connected nodes are not necessarily highly active.


id: cord-010727-fiukemh3
title: Three faces of node importance in network epidemiology: Exact results for small graphs
author: Holme, Petter
date: 2017-12-05
txt: ./txt/cord-010727-fiukemh3.txt
cache: ./cache/cord-010727-fiukemh3.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217518/
doi: 10.1103/physreve.96.062305
summary:
  We investigate three aspects of the importance of nodes with respect to susceptible-infectious-removed (SIR) disease dynamics: influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size), and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes). [13] is a bit different from ours-they call important nodes for vaccination "blockers" and important nodes for influence maximization "spreaders".) * holme@cns.pi.titech.ac.jp We will proceed by discussing our setup in greater detail: our implementation of the SIR model, how to analyze the three aspects of importance, network centrality measures that we need for our analysis, and our results, including the smallest networks where different nodes come out as most important. This is the smallest graph where the most important single node (n = 1) is different for influence maximization, vaccination, and sentinel surveillance.

abstract:
  We investigate three aspects of the importance of nodes with respect to susceptible-infectious-removed (SIR) disease dynamics: influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size), and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes). We calculate the exact expressions of these quantities, as functions of the SIR parameters, for all connected graphs of three to seven nodes. We obtain the smallest graphs where the optimal node sets are not overlapping. We find that (i) node separation is more important than centrality for more than one active node, (ii) vaccination and influence maximization are the most different aspects of importance, and (iii) the three aspects are more similar when the infection rate is low.


id: cord-010739-28qfmj9x
title: Bursting endemic bubbles in an adaptive network
author: Sherborne, N.
date: 2018-04-09
txt: ./txt/cord-010739-28qfmj9x.txt
cache: ./cache/cord-010739-28qfmj9x.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217531/
doi: 10.1103/physreve.97.042306
summary:
  Adaptive network models that account for both epidemic and behavioral change have found oscillations, but in an extremely narrow region of the parameter space, which contrasts with intuition and available data. In this paper we propose a simple susceptible-infected-susceptible epidemic model on an adaptive network with time-delayed rewiring, and show that oscillatory solutions are now present in a wide region of the parameter space. [13] showed that cutting links between susceptible and infected individuals can lead to epidemic reemergence, with long periods of low disease prevalence punctuated by large outbreaks. Figure 2 (d) illustrates this behavior both in simulation and in the meanfield model (4) , showing how after the initial outbreak each new wave of infection is preceded by the recovery of network connectivity. Furthermore, since rewiring nodes choose their new neighbors uniformly at random from all available susceptible nodes, the initial network topology itself is transient, as shown in Fig. 4 , and, as a result, over time our model becomes more relevant.

abstract:
  The spread of an infectious disease is known to change people's behavior, which in turn affects the spread of disease. Adaptive network models that account for both epidemic and behavioral change have found oscillations, but in an extremely narrow region of the parameter space, which contrasts with intuition and available data. In this paper we propose a simple susceptible-infected-susceptible epidemic model on an adaptive network with time-delayed rewiring, and show that oscillatory solutions are now present in a wide region of the parameter space. Altering the transmission or rewiring rates reveals the presence of an endemic bubble—an enclosed region of the parameter space where oscillations are observed.


id: cord-010751-fgk05n3z
title: Objective measures for sentinel surveillance in network epidemiology
author: Holme, Petter
date: 2018-08-15
txt: ./txt/cord-010751-fgk05n3z.txt
cache: ./cache/cord-010751-fgk05n3z.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217546/
doi: 10.1103/physreve.98.022313
summary:
  Then problem of optimizing sentinel surveillance in networks is to identify the nodes to probe such that an emerging disease outbreak can be discovered early or reliably. Furthermore, we do not find one type of network structure that predicts the objective measures, i.e., that depends both on the data set and the SIR parameter values. Finally, if the objective is to stop the disease as early as possible, it makes sense to measure the time to extinction or detection (infection of a sentinel) [13] . Just as for the case of static networks, τ (t x , f d ) is always nonpositive, meaning the time to detection or extinction ranks the nodes in a way positively correlated with the frequency of detection. In Fig. 4 , we show the correlation between our three objective measures and the structural descriptors as a function of β for the Office data set.

abstract:
  Assume one has the capability of determining whether a node in a network is infectious or not by probing it. Then problem of optimizing sentinel surveillance in networks is to identify the nodes to probe such that an emerging disease outbreak can be discovered early or reliably. Whether the emphasis should be on early or reliable detection depends on the scenario in question. We investigate three objective measures from the literature quantifying the performance of nodes in sentinel surveillance: the time to detection or extinction, the time to detection, and the frequency of detection. As a basis for the comparison, we use the susceptible-infectious-recovered model on static and temporal networks of human contacts. We show that, for some regions of parameter space, the three objective measures can rank the nodes very differently. This means sentinel surveillance is a class of problems, and solutions need to chose an objective measure for the particular scenario in question. As opposed to other problems in network epidemiology, we draw similar conclusions from the static and temporal networks. Furthermore, we do not find one type of network structure that predicts the objective measures, i.e., that depends both on the data set and the SIR parameter values.


id: cord-014845-odnlt6fr
title: Reducing Energy Consumption and Overhead Based on Mobile Health in Big Data Opportunistic Networks
author: Wu, Jia
date: 2016-08-13
txt: ./txt/cord-014845-odnlt6fr.txt
cache: ./cache/cord-014845-odnlt6fr.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7088852/
doi: 10.1007/s11277-016-3610-4
summary:
  title: Reducing Energy Consumption and Overhead Based on Mobile Health in Big Data Opportunistic Networks This algorithm can reduce energy consumption and overhead, then improve deliver ratio in big data communication. Compare with Spray and Wait algorithm, Binary spray and wait algorithm in opportunistic networks, this algorithm acquires good results by reduce energy consumption, overhead and deliver ratio. This algorithm can judge routing request and predict data data packets and then can overhead, energy consumption and deliver ratio in transmission. From what has been discussed above, ADTRA needs to solve energy consumption, overhead and deliver ratio when the algorithm is applied of big data environment. In wireless communication, if doctors or hospitals send all pictures to patients, many network resource must be wasted, especially in big data environment, mass of people join in transmission, there are no enough space storing effective information.

abstract:
  A great number of people and non-equalizing medical resources, in developing countries, have become a serious contradiction. Not only does it affects the person’s life, but also causes serious epidemic contagious, because patients can not get help with hospital on time. With the development of wireless communication network, patient may get medical information by wireless network device. It can alleviate contradictions between patients and medical resources. But in developing countries, population quantity is a big data. How to solve data packets in wireless communication network is a big problem when researchers face huge population. In order to solve some problems in big data communication, this paper founds availability data transmission routing algorithm. This algorithm can reduce energy consumption and overhead, then improve deliver ratio in big data communication. Compare with Spray and Wait algorithm, Binary spray and wait algorithm in opportunistic networks, this algorithm acquires good results by reduce energy consumption, overhead and deliver ratio.


id: cord-016196-ub4mgqxb
title: Study on Efficient Complex Network Model
author: Wang, Cheng
date: 2012-11-20
txt: ./txt/cord-016196-ub4mgqxb.txt
cache: ./cache/cord-016196-ub4mgqxb.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7120410/
doi: 10.1007/978-3-642-35398-7_20
summary:
  This paper summarizes the relevant research of the complex network systematically based on Statistical Property, Structural Model, and Dynamical Behavior. An important discover in the complex network researching is that the average path length of the most of the large-scale real networks is much less than our imagine, which we call ''''Small-world Effect''''. Paul Erdös and Alfred Rényi discovered a complete random network model in the late 50s twentieth century, it is made of any two nodes which connected with probability p in the graph made of N nodes, its average degree is \k [ ¼ pðN À 1Þ % PN; the average path length l : ln N= lnð\k [ Þ; the convergence factor C ¼ P; when the value of N is very large, the distribution of the node degree approximately equals poisson distribution. However, the regular network has aggregation, but its average shortest path length is larger, random graph has the opposite property, having small-world and less convergence factor.

abstract:
  This paper summarizes the relevant research of the complex network systematically based on Statistical Property, Structural Model, and Dynamical Behavior. Moreover, it emphatically introduces the application of the complex network in the economic system.


id: cord-017062-dkw2sugl
title: Delivery Systems for Lymphatic Targeting
author: Singh, Indu
date: 2013-10-08
txt: ./txt/cord-017062-dkw2sugl.txt
cache: ./cache/cord-017062-dkw2sugl.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7121534/
doi: 10.1007/978-1-4614-9434-8_20
summary:
  The major purpose of lymphatic targeting is to provide an effective anticancer chemotherapy to prevent the metastasis of cancer cells by accumulating the drug in the regional lymph node. Hydrophilic multiwalled carbon nanotubes (MWNTs) coated with magnetic nanoparticles (MN-MWNTs) have emerged as an effective delivery system for lymphatic targeting following subcutaneous injection of these particles into the left footpad of Sprague Dawley rats; the left popliteal lymph nodes were dyed black. In vivo study, about eight times lymph node uptake of LyP-1-NPs was seen in metastasis than that of NPs, indicated LyP-1-NP as a promising carrier for targetspecifi c drug delivery to lymphatic metastatic tumours [ 129 ] . To improve carrier retention in lymph nodes, a new method of increasing lymphatic uptake of subcutaneously injected liposome utilises the high-affi nity ligands biotin and avidin.

abstract:
  The lymphatic system has a critical role in the immune system’s recognition and response to disease, and it is an additional circulatory system throughout the entire body. Most solid cancers primarily spread from the main site via the tumour’s surrounding lymphatics before haematological dissemination. Targeting drugs to lymphatic system is quite complicated because of its intricate physiology. Therefore, it tends to be an important target for developing novel therapeutics. Currently, nanocarriers have encouraged the lymphatic targeting, but still there are challenges of locating drugs and bioactives to specific sites, maintaining desired action and crossing all the physiological barriers. Lymphatic therapy using drug-encapsulated colloidal carriers especially liposomes and solid lipid nanoparticles emerges as a new technology to provide better penetration into the lymphatics where residual disease exists. Optimising the proper procedure, selecting the proper delivery route and target area and making use of surface engineering tool, better carrier for lymphotropic system can be achieved. Thus, new methods of delivering drugs and other carriers to lymph nodes are currently under investigation.


id: cord-017590-w5copp1z
title: A SIS Epidemiological Model Based on Cellular Automata on Graphs
author: Fresnadillo, María J.
date: 2009
txt: ./txt/cord-017590-w5copp1z.txt
cache: ./cache/cord-017590-w5copp1z.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122184/
doi: 10.1007/978-3-642-02481-8_160
summary:
  The main goal of this work is to introduce a new SIS epidemic model based on a particular type of finite state machines called cellular automata on graphs. The state of each cell stands for the fraction of the susceptible and infected individuals of the cell at a particular time step and the evolution of these classes is given in terms of a local transition function. The model introduced in this paper deals with SIS epidemic diseases (for example the group of those responsible for the common cold), that is, the population is divided into susceptible individuals (S) and infected individuals (I). The main goal of this work is to introduce a new SIS model to simulate the spread of a general epidemic based on cellular automata on graph. Nevertheless, in this paper we will consider a more efficient topology to model an epidemic disease, which is given by an undirected graph where its nodes stand for the cells of the cellular automata.

abstract:
  The main goal of this work is to introduce a new SIS epidemic model based on a particular type of finite state machines called cellular automata on graphs. The state of each cell stands for the fraction of the susceptible and infected individuals of the cell at a particular time step and the evolution of these classes is given in terms of a local transition function.


id: cord-020885-f667icyt
title: Semantic Path-Based Learning for Review Volume Prediction
author: Sharma, Ujjwal
date: 2020-03-17
txt: ./txt/cord-020885-f667icyt.txt
cache: ./cache/cord-020885-f667icyt.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148205/
doi: 10.1007/978-3-030-45439-5_54
summary:
  In this work, we present an approach that uses semantically meaningful, bimodal random walks on real-world heterogeneous networks to extract correlations between nodes and bring together nodes with shared or similar attributes. In this work, -We propose a novel method that incorporates restaurants and their attributes into a multimodal graph and extracts multiple, bimodal low dimensional representations for restaurants based on available paths through shared visual, textual, geographical and categorical features. In this section, we discuss prior work that leverages graph-based structures for extracting information from multiple modalities, focussing on the auto-captioning task that introduced such methods. For each of these sub-networks, we perform random walks and use a variant of the heterogeneous skip-gram objective introduced in [6] to generate low-dimensional bimodal embeddings. Our attention-based model combines separately learned bimodal embeddings using a late-fusion setup for predicting the review volume of the restaurants.

abstract:
  Graphs offer a natural abstraction for modeling complex real-world systems where entities are represented as nodes and edges encode relations between them. In such networks, entities may share common or similar attributes and may be connected by paths through multiple attribute modalities. In this work, we present an approach that uses semantically meaningful, bimodal random walks on real-world heterogeneous networks to extract correlations between nodes and bring together nodes with shared or similar attributes. An attention-based mechanism is used to combine multiple attribute-specific representations in a late fusion setup. We focus on a real-world network formed by restaurants and their shared attributes and evaluate performance on predicting the number of reviews a restaurant receives, a strong proxy for popularity. Our results demonstrate the rich expressiveness of such representations in predicting review volume and the ability of an attention-based model to selectively combine individual representations for maximum predictive power on the chosen downstream task.


id: cord-022561-rv5j1201
title: Bone Marrow, Blood Cells, and the Lymphoid/Lymphatic System
author: Boes, Katie M.
date: 2017-02-17
txt: ./txt/cord-022561-rv5j1201.txt
cache: ./cache/cord-022561-rv5j1201.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7158316/
doi: 10.1016/b978-0-323-35775-3.00013-8
abstract: nan
summary:
  Mechanisms contributing to glucocorticoid-mediated neutrophilia include the following: • Increased release of mature neutrophils from the bone marrow storage pool • Decreased margination of neutrophils within the vasculature, with a resulting increase in the circulating pool • Decreased migration of neutrophils from the bloodstream into tissues The magnitude of neutrophilia tends to be species dependent, with dogs having the most pronounced response (up to 35,000 cells/µL) and in decreasing order of responsiveness, cats (30,000 cells/µL), horses (20,000 cells/µL), and cattle (15,000 cells/µL) having less marked responses. As a result, animals with Chédiak-Higashi 746.e1 CHAPTER 13 Bone Marrow, Blood Cells, and the Lymphoid/Lymphatic System von Willebrand disease (vWD) is the most common canine hereditary bleeding disorder and has also been described in many other domestic species.


id: cord-024437-r5wnz7rq
title: SLGAT: Soft Labels Guided Graph Attention Networks
author: Wang, Yubin
date: 2020-04-17
txt: ./txt/cord-024437-r5wnz7rq.txt
cache: ./cache/cord-024437-r5wnz7rq.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206162/
doi: 10.1007/978-3-030-47426-3_40
summary:
  In this paper, we propose a soft labels guided graph attention network (SLGAT) to improve the performance of node representation learning by leveraging generated pseudo labels. Graph attention networks (GAT) [23] , which is one of the most representative GCNs, learns the weights for neighborhood aggregation via self-attention mechanism [22] and achieves promising performance on semi-supervised node classification problem. In this paper, we propose soft labels guided attention networks (SLGAT) for semi-supervised node representation learning. First, SLGAT aggregates the features of neighbors using convolutional networks and predicts soft labels for each node based on the learned embeddings. The weights for neighborhood aggregation learned by a feedforward neural network based on both label information of central nodes and features of neighboring nodes, which can lead to learning more discriminative node representations for classification. Unlike the prior graph attention networks [23, 28] , we use label information as guidance to learn the weights of neighboring nodes for feature aggregation.

abstract:
  Graph convolutional neural networks have been widely studied for semi-supervised classification on graph-structured data in recent years. They usually learn node representations by transforming, propagating, aggregating node features and minimizing the prediction loss on labeled nodes. However, the pseudo labels generated on unlabeled nodes are usually overlooked during the learning process. In this paper, we propose a soft labels guided graph attention network (SLGAT) to improve the performance of node representation learning by leveraging generated pseudo labels. Unlike the prior graph attention networks, our SLGAT uses soft labels as guidance to learn different weights for neighboring nodes, which allows SLGAT to pay more attention to the features closely related to the central node labels during the feature aggregation process. We further propose a self-training based optimization method to train SLGAT on both labeled and pseudo labeled nodes. Specifically, we first pre-train SLGAT on labeled nodes and generate pseudo labels for unlabeled nodes. Next, for each iteration, we train SLGAT on the combination of labeled and pseudo labeled nodes, and then generate new pseudo labels for further training. Experimental results on semi-supervised node classification show that SLGAT achieves state-of-the-art performance.


id: cord-024499-14jlk5tv
title: SubRank: Subgraph Embeddings via a Subgraph Proximity Measure
author: Balalau, Oana
date: 2020-04-17
txt: ./txt/cord-024499-14jlk5tv.txt
cache: ./cache/cord-024499-14jlk5tv.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206259/
doi: 10.1007/978-3-030-47426-3_38
summary:
  We show that our subgraph embeddings are comprehensive and achieve competitive performance on three important data mining tasks: community detection, link prediction, and cascade growth prediction. A cascade graph is sampled for a set of random walks, which are given as input to a gated neural network to predict the future size of the cascade. In [3] , the authors propose an inductive framework for computing graph embeddings, based on training an attention network to predict a graph proximity measure, such as graph edit distance. Given a graph G = (V, E) and set of subgraphs of G, S = {S 1 , S 2 , · · · , S k }, we learn their representations as dense vectors, i.e. as embeddings.

abstract:
  Representation learning for graph data has gained a lot of attention in recent years. However, state-of-the-art research is focused mostly on node embeddings, with little effort dedicated to the closely related task of computing subgraph embeddings. Subgraph embeddings have many applications, such as community detection, cascade prediction, and question answering. In this work, we propose a subgraph to subgraph proximity measure as a building block for a subgraph embedding framework. Experiments on real-world datasets show that our approach, SubRank, outperforms state-of-the-art methods on several important data mining tasks.


id: cord-024504-p2vxnn9z
title: Node Conductance: A Scalable Node Centrality Measure on Big Networks
author: Lyu, Tianshu
date: 2020-04-17
txt: ./txt/cord-024504-p2vxnn9z.txt
cache: ./cache/cord-024504-p2vxnn9z.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206264/
doi: 10.1007/978-3-030-47436-2_40
summary:
  Moreover, with the help of node embedding techniques, Node Conductance is able to be approximately calculated on big networks effectively and efficiently. Thorough experiments present the differences between existing centralities and Node Conductance, its outstanding ability of detecting influential nodes on both static and dynamic network, and its superior efficiency compared with other global centralities. Random walk, which Node Conductance is based on, is also an effective sampling strategy to capture node neighborhood in the recent network embedding studies [10, 21] . We visualize the special case, football network, in order to have an intuitive sense of the properties of Degree, Betweenness, and Node Conductance (other centralities are presented in the Supplementary Material). Comparing with the existing global centralities, Node Conductance computed by DeepWalk is much more scalable and capable to be performed on big datasets. We also rethink the widely used network representation model, DeepWalk, and calculate Node Conductance approximately by the dot product of the input and output vectors.

abstract:
  Node centralities such as Degree and Betweenness help detecting influential nodes from local or global view. Existing global centrality measures suffer from the high computational complexity and unrealistic assumptions, limiting their applications on real-world applications. In this paper, we propose a new centrality measure, Node Conductance, to effectively detect spanning structural hole nodes and predict the formation of new edges. Node Conductance is the sum of the probability that node i is revisited at r-th step, where r is an integer between 1 and infinity. Moreover, with the help of node embedding techniques, Node Conductance is able to be approximately calculated on big networks effectively and efficiently. Thorough experiments present the differences between existing centralities and Node Conductance, its outstanding ability of detecting influential nodes on both static and dynamic network, and its superior efficiency compared with other global centralities. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this chapter (10.1007/978-3-030-47436-2_40) contains supplementary material, which is available to authorized users.


id: cord-026306-mkmrninv
title: Belief Functions for the Importance Assessment in Multiplex Networks
author: Lepskiy, Alexander
date: 2020-05-15
txt: ./txt/cord-026306-mkmrninv.txt
cache: ./cache/cord-026306-mkmrninv.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274755/
doi: 10.1007/978-3-030-50143-3_22
summary:
  The measures use the combination of "high", "low" and "(high, low)" probabilities of the influence based on weighted and unweighted degrees of nodes via Dempster''s rule. Secondly, one can aggregate connections between pairs of nodes to obtain monoplex network and then apply centrality measures to a new weighted graph. In this Section we describe a graph model with one layer of interaction as well as the construction of centrality measure based on a mass function for a network. Additionally, if we consider a 1-neighborhood of nodes in multiplex acyclic graphs with two layers then the following propositions concerning the aggregated interaction centrality value can be proved. We apply Dempster-Shafer theory in order to reveal key elements in undirected weighted graphs as well as to aggregate interactions between nodes into the total ranking. If nodes cooperate with each other on different levels of interactions then we apply a combination rule to mass functions obtained for different layers of a multiplex structure.

abstract:
  We apply Dempster-Shafer theory in order to reveal important elements in undirected weighted networks. We estimate cooperation of each node with different groups of vertices that surround it via construction of belief functions. The obtained intensities of cooperation are further redistributed over all elements of a particular group of nodes that results in pignistic probabilities of node-to-node interactions. Finally, pairwise interactions can be aggregated into the centrality vector that ranks nodes with respect to derived values. We also adapt the proposed model to multiplex networks. In this type of networks nodes can be differently connected with each other on several levels of interaction. Various combination rules help to analyze such systems as a single entity, that has many advantages in the study of complex systems. In particular, Dempster rule takes into account the inconsistency in initial data that has an impact on the final centrality ranking. We also provide a numerical example that illustrates the distinctive features of the proposed model. Additionally, we establish analytical relations between a proposed measure and classical centrality measures for particular graph configurations.


id: cord-027178-tqj8jgem
title: Modeling of Anti-tracking Network Based on Convex-Polytope Topology
author: Tian, Changbo
date: 2020-06-15
txt: ./txt/cord-027178-tqj8jgem.txt
cache: ./cache/cord-027178-tqj8jgem.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302821/
doi: 10.1007/978-3-030-50417-5_32
summary:
  From the aspects of network stability, network resilience and destroy-resistance, we propose the convex-polytope topology (CPT) applied in the anti-tracking network. From the experimental results, CPT has better robustness, resilience and destroy-resistance confronted with dynamically changed topology, and performs better in the efficiency of network self-optimization. Especially, for the P2P-based anti-tracking network which takes the advantage of the wide distribution of nodes for tracking-resistant communication, it is important to keep a stable, secure and destroy-resistant topology structure. But the convex-polytope structure still has some extreme cases shown in Fig. 2 which may pose a potential threat on the structure stability and communication efficiency of anti-tracking network because of the key nodes. According to the properties of CPT, we can achieve the self-optimization of anti-tracking network to balance the distribution of nodes'' connectivity. For each network constructed by CPT, NN and DHT, we randomly remove p percent of nodes and count the time requirement of network self-optimization as the metric.

abstract:
  Anti-tracking network plays an important role in protection of network users’ identities and communication privacy. Confronted with the frequent network attacks or infiltration to anti-tracking network, a robust and destroy-resistant network topology is an important prerequisite to maintain the stability and security of anti-tracking network. From the aspects of network stability, network resilience and destroy-resistance, we propose the convex-polytope topology (CPT) applied in the anti-tracking network. CPT has three main advantages: (1) CPT can easily avoid the threat of key nodes and cut vertices to network structure; (2) Even the nodes could randomly join in or quit the network, CPT can easily keep the network topology in stable structure without the global view of network; (3) CPT can easily achieve the self-optimization of network topology. Anti-tracking network based on CPT can achieve the self-maintenance and self-optimization of its network topology. We compare CPT with other methods of topology optimization. From the experimental results, CPT has better robustness, resilience and destroy-resistance confronted with dynamically changed topology, and performs better in the efficiency of network self-optimization.


id: cord-027451-ztx9fsbg
author: De Chiara, Davide
date: 2020-05-25
txt: ./txt/cord-027451-ztx9fsbg.txt
cache: ./cache/cord-027451-ztx9fsbg.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304773/
doi: 10.1007/978-3-030-50436-6_27
summary:
  This work presents an algorithm that clusters hotspots with the goal of reducing a data centre''s large thermal-gradient due to uneven distribution of server dissipated waste heat followed by increasing cooling effectiveness. Thermal-aware schedulers adopt different thermal-aware approaches (e.g. system-level for work placements [16] ; execute ''hot'' jobs on ''cold'' compute nodes; predictive model for job schedule selection [17] ; ranked node queue based on thermal characteristics of rack layouts and optimisation (e.g. optimal setpoints for workload distribution and supply temperature of the cooling system). Analysis conducted are as follows: hotspots localisation; users categorisations based on submitted jobs to CRESCO6 cluster; compute nodes categorisation based on thermal behaviour of internal and surrounding air temperatures due to workload related waste heat dissipation. Data collected are related to: relevant parameters for each node (e.g. inlet air temperature, internal temperature of each node, energy consumption of CPU, RAM, memory, etc…); environmental parameters (e.g. air temperatures and humidity in both the hot and cold aisles); cooling system related parameters (e.g. fan speed); and finally, individual users who submit their jobs to cluster node.

title:
  Data Mining for Big Dataset-Related Thermal Analysis of High Performance Computing (HPC) Data Center

abstract:
  Greening of Data Centers could be achieved through energy savings in two significant areas, namely: compute systems and cooling systems. A reliable cooling system is necessary to produce a persistent flow of cold air to cool the servers due to increasing computational load demand. Servers’ dissipated heat effects a strain on the cooling systems. Consequently, it is necessary to identify hotspots that frequently occur in the server zones. This is facilitated through the application of data mining techniques to an available big dataset for thermal characteristics of High-Performance Computing ENEA Data Center, namely Cresco 6. This work presents an algorithm that clusters hotspots with the goal of reducing a data centre’s large thermal-gradient due to uneven distribution of server dissipated waste heat followed by increasing cooling effectiveness.


id: cord-028660-hi35xvni
title: Three-Way Decisions Community Detection Model Based on Weighted Graph Representation
author: Chen, Jie
date: 2020-06-10
txt: ./txt/cord-028660-hi35xvni.txt
cache: ./cache/cord-028660-hi35xvni.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338166/
doi: 10.1007/978-3-030-52705-1_11
summary:
  Community detection algorithm based on three-way decisions (TWD) forms a multi-layered community structure by hierarchical clustering and then selects a suitable layer as the community detection result. Therefore, in this paper, we propose a method for three-way decisions community detection based on weighted graph representation (WGR-TWD). In this paper, we propose a three-way decisions community detection model based on weighted graph representation (WGR-TWD). The graph representation can well transform the global structure of the network into vector representation and make the two nodes in the boundary region that appear in the same community more similar by using the weight. (1) We use weighted graph representation to obtain the global structure information of the network to guide the processing of the boundary region, which gets a better three-way decisions community detection method. In this paper, we propose a method for three-way decisions community detection based on weighted graph representation.

abstract:
  Community detection is of great significance to the study of complex networks. Community detection algorithm based on three-way decisions (TWD) forms a multi-layered community structure by hierarchical clustering and then selects a suitable layer as the community detection result. However, this layer usually contains overlapping communities. Based on the idea of TWD, we define the overlapping part in the communities as boundary region (BND), and the non-overlapping part as positive region (POS) or negative region (NEG). How to correctly divide the nodes in the BND into the POS or NEG is a challenge for three-way decisions community detection. The general methods to deal with boundary region are modularity increment and similarity calculation. But these methods only take advantage of the local features of the network, without considering the information of the divided communities and the similarity of the global structure. Therefore, in this paper, we propose a method for three-way decisions community detection based on weighted graph representation (WGR-TWD). The weighted graph representation (WGR) can well transform the global structure into vector representation and make the two nodes in the boundary region more similar by using frequency of appearing in the same community as the weight. Firstly, the multi-layered community structure is constructed by hierarchical clustering. The target layer is selected according to the extended modularity value of each layer. Secondly, all nodes are converted into vectors by WGR. Finally, the nodes in the BND are divided into the POS or NEG based on cosine similarity. Experiments on real-world networks demonstrate that WGR-TWD is effective for community detection in networks compared with the state-of-the-art algorithms.


id: cord-028688-5uzl1jpu
title: Multi-granularity Complex Network Representation Learning
author: Li, Peisen
date: 2020-06-10
txt: ./txt/cord-028688-5uzl1jpu.txt
cache: ./cache/cord-028688-5uzl1jpu.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338194/
doi: 10.1007/978-3-030-52705-1_18
summary:
  In this paper, we propose a multi-granularity complex network representation learning model (MNRL), which integrates topological structure and additional information at the same time, and presents these fused information learning into the same granularity semantic space that through fine-to-coarse to refine the complex network. A series of deep learning-based network representation methods were then proposed to further solve the problems of global topological structure preservation and high-order nonlinearity of data, and increased efficiency. So these location attributes and activity information are inherently indecomposable and interdependence with the suspect, making the two nodes recognize at a finer granularity based on the additional information and relationship structure that the low-dimensional representation vectors learned have certain similarities. To better characterize multiple granularity complex networks and solve the problem of nodes with potential associations that cannot be processed through the relationship structure alone, we refine the granularity to additional attributes, and designed an information fusion method, which are defined as follows:

abstract:
  Network representation learning aims to learn the low dimensional vector of the nodes in a network while maintaining the inherent properties of the original information. Existing algorithms focus on the single coarse-grained topology of nodes or text information alone, which cannot describe complex information networks. However, node structure and attribution are interdependent, indecomposable. Therefore, it is essential to learn the representation of node based on both the topological structure and node additional attributes. In this paper, we propose a multi-granularity complex network representation learning model (MNRL), which integrates topological structure and additional information at the same time, and presents these fused information learning into the same granularity semantic space that through fine-to-coarse to refine the complex network. Experiments show that our method can not only capture indecomposable multi-granularity information, but also retain various potential similarities of both topology and node attributes. It has achieved effective results in the downstream work of node classification and the link prediction on real-world datasets.


id: cord-034545-onj7zpi1
title: Internet of things for healthcare monitoring applications based on RFID clustering scheme
author: Abuelkhail, Abdulrahman
date: 2020-11-03
txt: ./txt/cord-034545-onj7zpi1.txt
cache: ./cache/cord-034545-onj7zpi1.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7607371/
doi: 10.1007/s11276-020-02482-1
summary:
  The mathematical model optimizes the following objective functions: (1) minimizing the total distance between CHs and CMs to improve positioning accuracy; and (2) minimizing the number of clusters which reduces the signal transmission traffic Feature 6 (F-6): two level security is obtained by when a node writes data to its RFID tag, the data is signed with a signature, which is a hash value, the obtained hash is encrypted with a AES 128 bits shared key 

abstract:
  COVID-19 surprised the whole world by its quick and sudden spread. Coronavirus pushes all community sectors: government, industry, academia, and nonprofit organizations to take forward steps to stop and control this pandemic. It is evident that IT-based solutions are urgent. This study is a small step in this direction, where health information is monitored and collected continuously. In this work, we build a network of smart nodes where each node comprises a Radio-Frequency Identification (RFID) tag, reduced function RFID reader (RFRR), and sensors. The smart nodes are grouped in clusters, which are constructed periodically. The RFRR reader of the clusterhead collects data from its members, and once it is close to the primary reader, it conveys its data and so on. This approach reduces the primary RFID reader’s burden by receiving data from the clusterheads only instead of reading every tag when they pass by its vicinity. Besides, this mechanism reduces the channel access congestion; thus, it reduces the interference significantly. Furthermore, to protect the exchanged data from potential attacks, two levels of security algorithms, including an AES 128 bit with hashing, have been implemented. The proposed scheme has been validated via mathematical modeling using Integer programming, simulation, and prototype experimentation. The proposed technique shows low data delivery losses and a significant drop in transmission delay compared to contemporary approaches.


id: cord-034824-eelqmzdx
title: Influential Nodes Identification in Complex Networks via Information Entropy
author: Guo, Chungu
date: 2020-02-21
txt: ./txt/cord-034824-eelqmzdx.txt
cache: ./cache/cord-034824-eelqmzdx.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516697/
doi: 10.3390/e22020242
summary:
  In this paper, we propose the EnRenew algorithm aimed to identify a set of influential nodes via information entropy. Compared with the best state-of-the-art benchmark methods, the performance of proposed algorithm improved by 21.1%, 7.0%, 30.0%, 5.0%, 2.5%, and 9.0% in final affected scale on CEnew, Email, Hamster, Router, Condmat, and Amazon network, respectively, under the Susceptible-Infected-Recovered (SIR) simulation model. The impressive results on the SIR simulation model shed light on new method of node mining in complex networks for information spreading and epidemic prevention. defined the problem of identifying a set of influential spreaders in complex networks as influence maximization problem [57] , and they used hill-climbing based greedy algorithm that is within 63% of optimal in several models. Besides, to make the algorithm practically more useful, we provide EnRenew''s source code and all the experiments details on https://github.com/YangLiangwei/Influential-nodes-identification-in-complex-networksvia-information-entropy, and researchers can download it freely for their convenience.

abstract:
  Identifying a set of influential nodes is an important topic in complex networks which plays a crucial role in many applications, such as market advertising, rumor controlling, and predicting valuable scientific publications. In regard to this, researchers have developed algorithms from simple degree methods to all kinds of sophisticated approaches. However, a more robust and practical algorithm is required for the task. In this paper, we propose the EnRenew algorithm aimed to identify a set of influential nodes via information entropy. Firstly, the information entropy of each node is calculated as initial spreading ability. Then, select the node with the largest information entropy and renovate its l-length reachable nodes’ spreading ability by an attenuation factor, repeat this process until specific number of influential nodes are selected. Compared with the best state-of-the-art benchmark methods, the performance of proposed algorithm improved by 21.1%, 7.0%, 30.0%, 5.0%, 2.5%, and 9.0% in final affected scale on CEnew, Email, Hamster, Router, Condmat, and Amazon network, respectively, under the Susceptible-Infected-Recovered (SIR) simulation model. The proposed algorithm measures the importance of nodes based on information entropy and selects a group of important nodes through dynamic update strategy. The impressive results on the SIR simulation model shed light on new method of node mining in complex networks for information spreading and epidemic prevention.


id: cord-102394-vk4ag44m
title: Impact of asymptomatic infection on coupled disease-behavior dynamics in complex networks
author: Zhang, Hai-Feng
date: 2016-08-14
txt: ./txt/cord-102394-vk4ag44m.txt
cache: ./cache/cord-102394-vk4ag44m.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/1608.04049v1.pdf
doi: 10.1209/0295-5075/114/38004
summary:
  The analytic results emph{quantitatively} describe the influence of different factors, such as asymptomatic infection, the awareness rate, the network structure, and so forth, on the epidemic thresholds. Since the manner of the diffusion of awareness is quite different from the mechanism of epidemic spreading, the coupled disease-behavior interaction models in multiplex networks were also investigated [18] [19] [20] . Therefore, the SAUIR model is an irreversible process but the SAUIS model is a reversible process, which yields different theoretical methods to deal with their epidemic thresholds and the results. For our SAUIR model, on one hand, the epidemic threshold is very difficult or impossible to obtain by solving the meanfield based differential equations; on the other hand, in Ref. Near the epidemic threshold, the number of infected nodes is very few, indicating that the value of θ is a very small too. Then the epidemic thresholds for the improved SIS model and SIR model were obtained by using different theoretical methods.

abstract:
  Studies on how to model the interplay between diseases and behavioral responses (so-called coupled disease-behavior interaction) have attracted increasing attention. Owing to the lack of obvious clinical evidence of diseases, or the incomplete information related to the disease, the risks of infection cannot be perceived and may lead to inappropriate behavioral responses. Therefore, how to quantitatively analyze the impacts of asymptomatic infection on the interplay between diseases and behavioral responses is of particular importance. In this Letter, under the complex network framework, we study the coupled disease-behavior interaction model by dividing infectious individuals into two states: U-state (without evident clinical symptoms, labelled as U) and I-state (with evident clinical symptoms, labelled as I). A susceptible individual can be infected by U- or I-nodes, however, since the U-nodes cannot be easily observed, susceptible individuals take behavioral responses emph{only} when they contact I-nodes. The mechanism is considered in the improved Susceptible-Infected-Susceptible (SIS) model and the improved Susceptible-Infected-Recovered (SIR) model, respectively. Then, one of the most concerned problems in spreading dynamics: the epidemic thresholds for the two models are given by two methods. The analytic results emph{quantitatively} describe the influence of different factors, such as asymptomatic infection, the awareness rate, the network structure, and so forth, on the epidemic thresholds. Moreover, because of the irreversible process of the SIR model, the suppression effect of the improved SIR model is weaker than the improved SIS model.


id: cord-102588-vpu5w9wh
title: treeheatr: an R package for interpretable decision tree visualizations
author: Le, Trang T.
date: 2020-07-10
txt: ./txt/cord-102588-vpu5w9wh.txt
cache: ./cache/cord-102588-vpu5w9wh.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.07.10.196352
doi: 10.1101/2020.07.10.196352
summary:
  Summary treeheatr is an R package for creating interpretable decision tree visualizations with the data represented as a heatmap at the tree''s leaf nodes. Implemented in an easily installed package with a detailed vignette, treeheatr can be a useful teaching tool to enhance students'' understanding of a simple decision tree model before diving into more complex tree-based machine learning methods. We have developed the treeheatr package to incorporate the functionality of ggparty but also utilize the leaf node space to display the data as a heatmap, a popular visualization that uncovers groups of samples and features in a dataset (Wilkinson and Friendly, 2009, Galili,T. The following lines of code compute and visualize the conditional decision tree along with the heatmap containing features that are important for constructing this model ( Fig. 1) : When the first argument x is a data.frame object representing the dataset instead of the decision tree, treeheatr automatically computes a conditional tree with default parameters for visualization.

abstract:
  Summary treeheatr is an R package for creating interpretable decision tree visualizations with the data represented as a heatmap at the tree’s leaf nodes. The integrated presentation of the tree structure along with an overview of the data efficiently illustrates how the tree nodes split up the feature space and how well the tree model performs. This visualization can also be examined in depth to uncover the correlation structure in the data and importance of each feature in predicting the outcome. Implemented in an easily installed package with a detailed vignette, treeheatr can be a useful teaching tool to enhance students’ understanding of a simple decision tree model before diving into more complex tree-based machine learning methods. Availability The treeheatr package is freely available under the permissive MIT license at https://trang1618.github.io/treeheatr and https://cran.r-project.org/package=treeheatr. It comes with a detailed vignette that is automatically built with GitHub Actions continuous integration. Contact ttle@pennmedicine.upenn.edu


id: cord-102935-cx3elpb8
author: Hassani-Pak, Keywan
date: 2020-04-24
txt: ./txt/cord-102935-cx3elpb8.txt
cache: ./cache/cord-102935-cx3elpb8.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.04.02.017004
doi: 10.1101/2020.04.02.017004
summary:
  Here we report the main design principles behind KnetMiner and provide use cases for mining public datasets to identify unknown links between traits such grain colour and pre-harvest sprouting in Triticum aestivum, as well as, an evidence-based approach to identify candidate genes under an Arabidopsis thaliana petal size QTL. KnetMiner is the first open-source gene discovery platform that can leverage genome-scale knowledge graphs, generate evidence-based biological networks and be deployed for any species with a sequenced genome. Even when 38 the task of gathering information is complete, it is demanding to assemble a coherent view of how 39 each piece of evidence might come together to "tell a story" about the biology that can explain how 40 multiple genes might be implicated in a complex trait or disease.

title:
  KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species

abstract:
  Generating new ideas and scientific hypotheses is often the result of extensive literature and database reviews, overlaid with scientists’ own novel data and a creative process of making connections that were not made before. We have developed a comprehensive approach to guide this technically challenging data integration task and to make knowledge discovery and hypotheses generation easier for plant and crop researchers. KnetMiner can digest large volumes of scientific literature and biological research to find and visualise links between the genetic and biological properties of complex traits and diseases. Here we report the main design principles behind KnetMiner and provide use cases for mining public datasets to identify unknown links between traits such grain colour and pre-harvest sprouting in Triticum aestivum, as well as, an evidence-based approach to identify candidate genes under an Arabidopsis thaliana petal size QTL. We have developed KnetMiner knowledge graphs and applications for a range of species including plants, crops and pathogens. KnetMiner is the first open-source gene discovery platform that can leverage genome-scale knowledge graphs, generate evidence-based biological networks and be deployed for any species with a sequenced genome. KnetMiner is available at http://knetminer.org.


id: cord-119522-2ua8218z
title: Improving Spatio-Temporal Understanding of Particulate Matter using Low-Cost IoT Sensors
author: Reddy, C. Rajashekar
date: 2020-05-12
txt: ./txt/cord-119522-2ua8218z.txt
cache: ./cache/cord-119522-2ua8218z.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2005.05936v1.pdf
doi: nan
summary:
  This paper focuses on studying the dense deployment based air pollution monitoring using IoT enabled low-cost sensor nodes. For this, total nine low-cost IoT nodes monitoring particulate matter (PM), which is one of the most dominant pollutants, are deployed in a small educational campus in Indian city of Hyderabad. This paper focuses on studying the dense deployment based air pollution monitoring using IoT enabled low-cost sensor nodes in Indian urban conditions. For spatial interpolation, inverse distance weighing (IDW) scheme is used on these nodes for the data collected before and during the bursting of firecrackers on the main night of Diwali (one of the most popular festivals in India) to show the variability pattern in a small campus, hot spot detection and need for a dense deployment to provide better local pollution indicators.

abstract:
  Current air pollution monitoring systems are bulky and expensive resulting in a very sparse deployment. In addition, the data from these monitoring stations may not be easily accessible. This paper focuses on studying the dense deployment based air pollution monitoring using IoT enabled low-cost sensor nodes. For this, total nine low-cost IoT nodes monitoring particulate matter (PM), which is one of the most dominant pollutants, are deployed in a small educational campus in Indian city of Hyderabad. Out of these, eight IoT nodes were developed at IIIT-H while one was bought off the shelf. A web based dashboard website is developed to easily monitor the real-time PM values. The data is collected from these nodes for more than five months. Different analyses such as correlation and spatial interpolation are done on the data to understand efficacy of dense deployment in better understanding the spatial variability and time-dependent changes to the local pollution indicators.


id: cord-125089-1lfmqzmc
title: Interacting Regional Policies in Containing a Disease
author: Chandrasekhar, Arun G.
date: 2020-08-24
txt: ./txt/cord-125089-1lfmqzmc.txt
cache: ./cache/cord-125089-1lfmqzmc.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2008.10745v3.pdf
doi: nan
summary:
  We show that a regional quarantine policy''s effectiveness depends upon whether (i) the network of interactions satisfies a balanced-growth condition, (ii) infections have a short delay in detection, and (iii) the government has control over and knowledge of the necessary parts of the network (no leakage of behaviors). As these conditions generally fail to be satisfied, especially when interactions cross borders, we show that substantial improvements are possible if governments are proactive: triggering quarantines in reaction to neighbors'' infection rates, in some cases even before infections are detected internally. In the SI (Theorem 1) we prove that, with no delays in detection and no leakage, a (k, x)regional policy halts infection among all nodes beyond distance k +1 from i 0 with probability approaching 1 (as the population grows) if and only if the network satisfies growth-balance. Given that real-world networks have short average distances between nodes [32], non-trivial delays in detection allow the disease to escape a regional quarantine.

abstract:
  Regional quarantine policies, in which a portion of a population surrounding infections are locked down, are an important tool to contain disease. However, jurisdictional governments -- such as cities, counties, states, and countries -- act with minimal coordination across borders. We show that a regional quarantine policy's effectiveness depends upon whether (i) the network of interactions satisfies a balanced-growth condition, (ii) infections have a short delay in detection, and (iii) the government has control over and knowledge of the necessary parts of the network (no leakage of behaviors). As these conditions generally fail to be satisfied, especially when interactions cross borders, we show that substantial improvements are possible if governments are proactive: triggering quarantines in reaction to neighbors' infection rates, in some cases even before infections are detected internally. We also show that even a few lax governments -- those that wait for nontrivial internal infection rates before quarantining -- impose substantial costs on the whole system. Our results illustrate the importance of understanding contagion across policy borders and offer a starting point in designing proactive policies for decentralized jurisdictions.


id: cord-155475-is3su3ga
title: Winning the competition: enhancing counter-contagion in SIS-like epidemic processes
author: Kalogeratos, Argyris
date: 2020-06-24
txt: ./txt/cord-155475-is3su3ga.txt
cache: ./cache/cord-155475-is3su3ga.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2006.13395v1.pdf
doi: nan
summary:
  Motivated by social epidemics, we apply this method to a generic continuous-time SIS-like diffusion model where we allow for: i) arbitrary node transition rate functions that describe the dynamics of propagation depending on the network state, and ii) competition between the healthy (positive) and infected (negative) states, which are both diffusive at the same time, yet mutually exclusive on each node. Among them, the Largest Reduction in Infectious Edges (LRIE) [9] results to be the optimal greedy algorithm for resource allocation under limitations in the resource budget, in the N -intertwined Susceptible-Infected-Susceptible (SIS) epidemic model. In this study, we propose the Generalized Largest Reduction in Infectious Edges (gLRIE) strategy, which is adapted for the diffusion competition of recurrent epidemics, as well as nonlinearity and saturation of the functions of node transition rates.

abstract:
  In this paper we consider the epidemic competition between two generic diffusion processes, where each competing side is represented by a different state of a stochastic process. For this setting, we present the Generalized Largest Reduction in Infectious Edges (gLRIE) dynamic resource allocation strategy to advantage the preferred state against the other. Motivated by social epidemics, we apply this method to a generic continuous-time SIS-like diffusion model where we allow for: i) arbitrary node transition rate functions that describe the dynamics of propagation depending on the network state, and ii) competition between the healthy (positive) and infected (negative) states, which are both diffusive at the same time, yet mutually exclusive on each node. Finally we use simulations to compare empirically the proposed gLRIE against competitive approaches from literature.


id: cord-168862-3tj63eve
title: Nonlinearity + Networks: A 2020 Vision
author: Porter, Mason A.
date: 2019-11-09
txt: ./txt/cord-168862-3tj63eve.txt
cache: ./cache/cord-168862-3tj63eve.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/1911.03805v1.pdf
doi: nan
summary:
  However, recent uses of the term "network" have focused increasingly on connectivity patterns that are more general than graphs [98] : a network''s nodes and/or edges (or their associated weights) can change in time [70, 72] (see Section 3), nodes and edges can include annotations [26] , a network can include multiple types of edges and/or multiple types of nodes [90, 140] , it can have associated dynamical processes [142] (see Sections 3, 4, and 5) , it can include memory [152] , connections can occur between an arbitrary number of entities [127, 131] (see Section 6) , and so on. Following a long line of research in sociology [37] , two important ingredients in the study of networks are examining (1) the importances ("centralities") of nodes, edges, and other small network structures and the relationship of measures of importance to dynamical processes on networks and (2) the large-scale organization of networks [121, 193] .

abstract:
  I briefly survey several fascinating topics in networks and nonlinearity. I highlight a few methods and ideas, including several of personal interest, that I anticipate to be especially important during the next several years. These topics include temporal networks (in which the entities and/or their interactions change in time), stochastic and deterministic dynamical processes on networks, adaptive networks (in which a dynamical process on a network is coupled to dynamics of network structure), and network structure and dynamics that include"higher-order"interactions (which involve three or more entities in a network). I draw examples from a variety of scenarios, including contagion dynamics, opinion models, waves, and coupled oscillators.


id: cord-196353-p05a8zjy
title: Virus spread and voter model on random graphs with multiple type nodes
author: Backhausz, ''Agnes
date: 2020-02-17
txt: ./txt/cord-196353-p05a8zjy.txt
cache: ./cache/cord-196353-p05a8zjy.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2002.06926v1.pdf
doi: nan
summary:
  The two processes can be connected by the following idea: We can see virus spread as a special case of the voter model with two different opinions (healthy and infected), but only one of the opinions (infected) can be transmitted, while any individuals with infected opinion switch to healthy opinion after a period of time. We examined the virus spread with vaccination and the voter model on random graphs of different structures, where in some cases the nodes of the graph corresponding to the individuals of the network are divided into groups representing significantly distinct properties for the process. To study the result on the discretized model, we generated 5 random graphs with N = 10000 nodes for each graph structure, and run the process 20 times on a random graph with independent In case of most of the structures we can derive rather different outcomes on the same graph with different initial values concerning the peak of the virus.

abstract:
  When modelling epidemics or spread of information on online social networks, it is crucial to include not just the density of the connections through which infections can be transmitted, but also the variability of susceptibility. Different people have different chance to be infected by a disease (due to age or general health conditions), or, in case of opinions, ones are easier to be convinced by others, or stronger at sharing their opinions. The goal of this work is to examine the effect of multiple types of nodes on various random graphs such as ErdH{o}s--R'enyi random graphs, preferential attachment random graphs and geometric random graphs. We used two models for the dynamics: SEIR model with vaccination and a version of voter model for exchanging opinions. In the first case, among others, various vaccination strategies are compared to each other, while in the second case we studied sevaral initial configurations to find the key positions where the most effective nodes should be placed to disseminate opinions.


id: cord-199630-2lmwnfda
author: Ray, Sumanta
date: 2020-07-05
txt: ./txt/cord-199630-2lmwnfda.txt
cache: ./cache/cord-199630-2lmwnfda.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2007.02338v1.pdf
doi: nan
summary:
  Therefore, host-(1) We link existing high-quality, long-term curated and refined, large scale drug/protein -protein interaction data with (2) molecular interaction data on SARS-CoV-2 itself, raised only a handful of weeks ago, (3) exploit the resulting overarching network using most advanced, AI boosted techniques (4) for repurposing drugs in the fight against SARS-CoV-2 (5) in the frame of HDT based strategies. As for (3)-(5), we will highlight interactions between SARS-Cov-2-host protein and human proteins important for the virus to persist using most advanced deep learning techniques that cater to exploiting network data. As per our simulation study, a large fraction, if not the vast majority of the predictions establish true, hence actionable interactions between drugs on the one hand and SARS-CoV-2 associated human proteins (hence of use in HDT) on the other hand.

title:
  Predicting potential drug targets and repurposable drugs for COVID-19 via a deep generative model for graphs

abstract:
  Coronavirus Disease 2019 (COVID-19) has been creating a worldwide pandemic situation. Repurposing drugs, already shown to be free of harmful side effects, for the treatment of COVID-19 patients is an important option in launching novel therapeutic strategies. Therefore, reliable molecule interaction data are a crucial basis, where drug-/protein-protein interaction networks establish invaluable, year-long carefully curated data resources. However, these resources have not yet been systematically exploited using high-performance artificial intelligence approaches. Here, we combine three networks, two of which are year-long curated, and one of which, on SARS-CoV-2-human host-virus protein interactions, was published only most recently (30th of April 2020), raising a novel network that puts drugs, human and virus proteins into mutual context. We apply Variational Graph AutoEncoders (VGAEs), representing most advanced deep learning based methodology for the analysis of data that are subject to network constraints. Reliable simulations confirm that we operate at utmost accuracy in terms of predicting missing links. We then predict hitherto unknown links between drugs and human proteins against which virus proteins preferably bind. The corresponding therapeutic agents present splendid starting points for exploring novel host-directed therapy (HDT) options.


id: cord-248848-p7jv79ae
author: Lee, Kookjin
date: 2020-10-28
txt: ./txt/cord-248848-p7jv79ae.txt
cache: ./cache/cord-248848-p7jv79ae.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.14685v1.pdf
doi: nan
summary:
  We emphasize that our approach is closely related to [63] , where neural networks are trained to approximate the action of the first-order time-integration scheme applied to latent dynamics and, at each time step, the neural networks take a set of problem-specific parameters as well as reduced state as an input. PNODEs still can learn multiple trajectories, which are characterized by the ODE parameters, even if the same initial states are given for different ODE parameters, which is not achievable with NODEs. Furthermore, the proposed framework is significantly simpler than the common neural network settings for NODEs when they are used to learn latent dynamics: the sequence-to-sequence architectures as in [9, 61, 74, 45, 46] , which require that a (part of) sequence is fed into the encoder network to produce a context vector, which is then fed into the NODE decoder network as an initial condition.

title:
  Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics Problems

abstract:
  This work proposes an extension of neural ordinary differential equations (NODEs) by introducing an additional set of ODE input parameters to NODEs. This extension allows NODEs to learn multiple dynamics specified by the input parameter instances. Our extension is inspired by the concept of parameterized ordinary differential equations, which are widely investigated in computational science and engineering contexts, where characteristics of the governing equations vary over the input parameters. We apply the proposed parameterized NODEs (PNODEs) for learning latent dynamics of complex dynamical processes that arise in computational physics, which is an essential component for enabling rapid numerical simulations for time-critical physics applications. For this, we propose an encoder-decoder-type framework, which models latent dynamics as PNODEs. We demonstrate the effectiveness of PNODEs with important benchmark problems from computational physics.


id: cord-269711-tw5armh8
title: The importance of contact network topology for the success of vaccination strategies
author: Ma, Junling
date: 2013-05-21
txt: ./txt/cord-269711-tw5armh8.txt
cache: ./cache/cord-269711-tw5armh8.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/23376579/
doi: 10.1016/j.jtbi.2013.01.006
summary:
  Abstract The effects of a number of vaccination strategies on the spread of an SIR type disease are numerically investigated for several common network topologies including random, scale-free, small world, and meta-random networks. These strategies, namely, prioritized, random, follow links and contact tracing, are compared across networks using extensive simulations with disease parameters relevant for viruses such as pandemic influenza H1N1/09. (2006) compared the efficacy of contact tracing on random and scale-free networks and found that for transmission rates greater than a certain threshold, the final epidemic size is smaller on a scale-free network than on a corresponding random network, while they considered the effects of degree correlations in Kiss et al. We investigate numerically whether network topologies affect the effectiveness of vaccination strategies started with a delay after the disease is widespread; for example, a 40 day delay as in the second wave of the 2009 influenza pandemic in British Columbia, Canada (Office of the Provincial Health Officer, 2010).

abstract:
  Abstract The effects of a number of vaccination strategies on the spread of an SIR type disease are numerically investigated for several common network topologies including random, scale-free, small world, and meta-random networks. These strategies, namely, prioritized, random, follow links and contact tracing, are compared across networks using extensive simulations with disease parameters relevant for viruses such as pandemic influenza H1N1/09. Two scenarios for a network SIR model are considered. First, a model with a given transmission rate is studied. Second, a model with a given initial growth rate is considered, because the initial growth rate is commonly used to impute the transmission rate from incidence curves and to predict the course of an epidemic. Since a vaccine may not be readily available for a new virus, the case of a delay in the start of vaccination is also considered in addition to the case of no delay. It is found that network topology can have a larger impact on the spread of the disease than the choice of vaccination strategy. Simulations also show that the network structure has a large effect on both the course of an epidemic and the determination of the transmission rate from the initial growth rate. The effect of delay in the vaccination start time varies tremendously with network topology. Results show that, without the knowledge of network topology, predictions on the peak and the final size of an epidemic cannot be made solely based on the initial exponential growth rate or transmission rate. This demonstrates the importance of understanding the topology of realistic contact networks when evaluating vaccination strategies.


id: cord-276178-0hrs1w7r
author: Bangotra, Deep Kumar
date: 2020-07-13
txt: ./txt/cord-276178-0hrs1w7r.txt
cache: ./cache/cord-276178-0hrs1w7r.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.3390/s20143887
doi: 10.3390/s20143887
summary:
  In this paper, we propose an intelligent opportunistic routing protocol (IOP) using a machine learning technique, to select a relay node from the list of potential forwarder nodes to achieve energy efficiency and reliability in the network. The problem of energy efficiency during the routing of data packets from source to target in case of IoToriented WSN is significantly addressed by another network-based routing protocol known as GreeDi [6] . The problem of energy efficiency during the routing of data packets from source to target in case of IoT-oriented WSN is significantly addressed by another network-based routing protocol known as GreeDi [6] . The proposed method of relay node selection using IOP could be understood by considering an example of WSN shown in Figure 2 and using the naïve Baye''s algorithm on the generic data available in Table 4 , to find the optimal path in terms of energy efficiency and reliability from source node S to destination node D.

title:
  An Intelligent Opportunistic Routing Algorithm for Wireless Sensor Networks and Its Application Towards e-Healthcare

abstract:
  The lifetime of a node in wireless sensor networks (WSN) is directly responsible for the longevity of the wireless network. The routing of packets is the most energy-consuming activity for a sensor node. Thus, finding an energy-efficient routing strategy for transmission of packets becomes of utmost importance. The opportunistic routing (OR) protocol is one of the new routing protocol that promises reliability and energy efficiency during transmission of packets in wireless sensor networks (WSN). In this paper, we propose an intelligent opportunistic routing protocol (IOP) using a machine learning technique, to select a relay node from the list of potential forwarder nodes to achieve energy efficiency and reliability in the network. The proposed approach might have applications including e-healthcare services. As the proposed method might achieve reliability in the network because it can connect several healthcare network devices in a better way and good healthcare services might be offered. In addition to this, the proposed method saves energy, therefore, it helps the remote patient to connect with healthcare services for a longer duration with the integration of IoT services.


id: cord-284186-zf1w8ksm
author: Suran, J. N.
date: 2017-04-17
txt: ./txt/cord-284186-zf1w8ksm.txt
cache: ./cache/cord-284186-zf1w8ksm.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/28414856/
doi: 10.1111/jsap.12680
summary:
  The goal of this prospective, cross-sectional study was to describe the characteristics of the spleen and abdominal lymph nodes on radiographs and with ultrasound in a sample of clientowned, clinically healthy domestic ferrets ( Mustela putorius furo ). For the calculation of sample sizes, the standard deviations of previously reported measurements of abdominal viscera in clinically healthy ferrets were compared, including gross renal measurements (1·25, 1·5 mm), ultrasonographic adrenal thickness (0·5, 0·6 mm) and ultrasonographic jejunal lymph node thickness (1·39, 2.0 mm) (O '' Brien et al . The results presented in this study provide the most comprehensive evaluation of the spleen and abdominal lymph nodes with radiographs and ultrasound in clinically healthy ferrets to date. Based on the results of this study, the jejunal, pancreaticoduodenal, hepatic and caudal mesenteric lymph nodes can be routinely detected with ultrasound in most ferrets.

title:
  Radiographic and ultrasonographic findings of the spleen and abdominal lymph nodes in healthy domestic ferrets

abstract:
  OBJECTIVE: To describe the radiographic and ultrasonographic characteristics of the spleen and abdominal lymph nodes in clinically healthy ferrets. MATERIALS AND METHODS: Fifty‐five clinically healthy ferrets were prospectively recruited for this cross‐sectional study. Three‐view whole body radiographs and abdominal ultrasonography were performed on awake (23 out of 55) or sedated (32 out of 55) ferrets. On radiographs splenic and abdominal lymph node visibility was assessed. Splenic thickness and echogenicity and lymph node length, thickness, echogenicity, number and presence of cyst‐like changes were recorded. RESULTS: The spleen was radiographically detectable in all ferrets. On ultrasound the spleen was hyperechoic to the liver (55 out of 55) and mildly hyperechoic (28 out of 55), isoechoic (15 out of 55) or mildly hypoechoic (12 out of 55) to the renal cortices. Mean splenic thickness was 11.80 ±0.34 mm. Lymph nodes were radiographically discernible in 28 out of 55 ferrets and included caudal mesenteric and sublumbar nodes. An average of 9 ±2 lymph nodes (mean± standard deviation; mode 10) were identified in each ferret using ultrasound. A single large jejunal lymph node was identified in all ferrets and had a mean thickness of 5.28 ± 1.66 mm. For other lymph nodes the mean thickness measurements plus one standard deviation were less than 4.4 mm (95% confidence interval: ≤ 3.72 mm). CLINICAL SIGNIFICANCE: The information provided in this study may act as a baseline for evaluation of the spleen and lymph nodes in ferrets.


id: cord-285350-64mzmiv3
title: An integrated P2P framework for E-learning
author: Bhagatkar, Nikita
date: 2020-06-29
txt: ./txt/cord-285350-64mzmiv3.txt
cache: ./cache/cord-285350-64mzmiv3.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1007/s12083-020-00919-0
doi: 10.1007/s12083-020-00919-0
summary:
  The focus of this paper is to design and develop a Peer-to-Peer Presentation System (P2P-PS) that supports E-learning through live media streaming coupled with a P2P shared whiteboard. In current situation of pandemic outbreak of COVID-19, our P2P-PS may, in fact, be an ideal complementary system for offloading pressure on Zoom on WebEx. To organize the stored contents, we used an efficient implementation of Distributed Hash Table ( DHT) based on de Bruijn graphs. -It incorporates an efficient DHT-based sharing and searching of media and document files, which is implemented using de Bruijn graph-based overlays. We use a de Bruijn graph-based overlay for implementing distributed hash table (DHT) organization for stored Fig. 2 Overall system architecture of P2P-PS materials. The bootstrap server randomly selects a set of required number of active peers from the entire active peer list and returns the same to the requesting node.

abstract:
  The focus of this paper is to design and develop a Peer-to-Peer Presentation System (P2P-PS) that supports E-learning through live media streaming coupled with a P2P shared whiteboard. The participants use the “ask doubt” feature to raise and resolve doubts during a session of ongoing presentation. The proposed P2P-PS system preserves causality between ask doubt and its resolution while disseminating them to all the participants. A buffered approach is employed to enhance the performance of P2P shared whiteboard, which may be used either in tandem with live media streaming or in standalone mode. The proposed system further extends P2P interactions on stored contents (files) built on top of a P2P file sharing and searching module with additional features. The added features allow the creation of mash-up presentations with annotations, posts, comments on audio, video, and PDF files as well as a discussion forum. We have implemented the P2P file sharing and searching system on the de Bruijn graph-based overlay for low latency. Extensive experiments were carried out on Emulab to validate the P2P-PS system using 200 physical nodes.


id: cord-306727-2c1m04je
title: Promoting Trustless Computation Through Blockchain Technology
author: Pandey, Prateek
date: 2020-05-20
txt: ./txt/cord-306727-2c1m04je.txt
cache: ./cache/cord-306727-2c1m04je.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32836612/
doi: 10.1007/s40009-020-00978-0
summary:
  The paper explains the blockchain technology and a variety of its implementation through five different use cases in the field of drug supply chain, health insurance, land record management, courier services, and immigration records. Therefore, virtually all the use cases, from money transaction to mushroom supply chain, and from agricultural commodity market place to electronic health records (EHR) are a potential client for blockchain technology. In this article, the authors propose the use of Blockchain in five different use cases, namely drug supply chain, health insurance, land record management, courier services, and immigration records. Therefore, Drug supply, health insurance, and land records blockchains are implemented over the Ethereum network, whereas immigration and courier blockchains are implemented on hyper ledger fabric. While implementing the private Blockchain for immigration records and courier tracking, we observe the performance of the built network in terms of throughput, which is the number of transactions written in the ledger per second.

abstract:
  Records, irrespective of their nature (whether electronic or paper-based), are vulnerable to fraud. People's hard-earned money, their personal information, identity, and health are at a higher risk than ever due to the misuse of technology in doing forgery. However, the technology can also be used as an answer to counteracting against fraudulence prevalent in affairs from every walk of life. This short paper attempts to present the blockchain technology as a solution to overcome the menace of forgery by promoting trustless computing in business transactions. The paper explains the blockchain technology and a variety of its implementation through five different use cases in the field of drug supply chain, health insurance, land record management, courier services, and immigration records. The immigration blockchain is also proposed as a solution to check pandemic like the coronavirus (COVID-19) effectively. The implementation of the Blockchain is performed using a locally built IBM’s hyper-ledger fabric-based platform, and Ethereum public platform. The results are encouraging enough to substitute existing business operations using Blockchain-based solutions.


id: cord-319291-6l688krc
title: Alignment using genetic programming with causal trees for identification of protein functions
author: Hung, Chun-Min
date: 2006-09-01
txt: ./txt/cord-319291-6l688krc.txt
cache: ./cache/cord-319291-6l688krc.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0362546X05009028
doi: 10.1016/j.na.2005.09.048
summary:
  Particularly, the model has three characteristics: (i) it is a hybrid evolutionary model with multiple fitness functions that uses genetic programming to predict protein functions on a distantly related protein family, (ii) it incorporates modified robust point matching to accurately compare all feature points using the moment invariant and thin-plate spline theorems, and (iii) the hierarchical homologies holding up a novel protein sequence in the form of a causal tree can effectively demonstrate the relationship between proteins. The hybrid model, namely Alignment using Genetic programming with Causal Tree (AGCT), is a heuristic evolutionary method that contains three basic components: (i) genetic programming with innerexchanged individual strategy, (ii) causal trees [4, 28, 31] with probabilistic reasoning, and (iii) construction of hierarchical homologies with local block-to-block alignment using the methods of moment invariant and robust points matching (RPM) [24] .

abstract:
  A hybrid evolutionary model is used to propose a hierarchical homology of protein sequences to identify protein functions systematically. The proposed model offers considerable potentials, considering the inconsistency of existing methods for predicting novel proteins. Because some novel proteins might align without meaningful conserved domains, maximizing the score of sequence alignment is not the best criterion for predicting protein functions. This work presents a decision model that can minimize the cost of making a decision for predicting protein functions using the hierarchical homologies. Particularly, the model has three characteristics: (i) it is a hybrid evolutionary model with multiple fitness functions that uses genetic programming to predict protein functions on a distantly related protein family, (ii) it incorporates modified robust point matching to accurately compare all feature points using the moment invariant and thin-plate spline theorems, and (iii) the hierarchical homologies holding up a novel protein sequence in the form of a causal tree can effectively demonstrate the relationship between proteins. This work describes the comparisons of nucleocapsid proteins from the putative polyprotein SARS virus and other coronaviruses in other hosts using the model.


id: cord-322746-28igib4l
title: Acute, subacute, and chronic cervical lymphadenitis in children
author: Gosche, John R.
date: 2007-06-06
txt: ./txt/cord-322746-28igib4l.txt
cache: ./cache/cord-322746-28igib4l.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/16616313/
doi: 10.1053/j.sempedsurg.2006.02.007
summary:
  Involvement of superficial or deep cervical lymph nodes is also frequently indicative of the site of entry since superficial nodal enlargement usually reflects invasion through an epithelial surface (eg, buccal mucosa, skin, scalp), whereas deep nodal enlargement results from an infectious process involving more central structures (eg, middle ear, posterior pharynx). Finally, serologic testing for human immunodeficiency virus (HIV) should be considered in any patient with at-risk behaviors, generalized lymphadenitis, and unusual or recurrent infections caused by opportunistic organisms. Plain radiographs are seldom necessary in patients with acute cervical lymphadenitis, but may occasionally document the primary site of an infection (eg, pneumonia, sinusitis, or dental caries). 8 Acute viral associated cervical lymphadenitis typically develops following an upper respiratory tract infection. 11 Bilateral, acute cervical lymphadenitis associated with a viral upper respiratory tract infection rarely requires additional diagnostic testing or specific therapy.

abstract:
  Lymphadenopathy refers to any disease process involving lymph nodes that are abnormal in size and consistency. Lymphadenitis specifically refers to lymphadenopathies that are caused by inflammatory processes. Cervical lymphadenopathy is a common problem in the pediatric age group and is largely inflammatory and infectious in etiology. Although most patients are treated successfully by their primary care physician, surgical consultation is frequently required for patients who fail to respond to initial therapy or for those in whom there is an index of suspicion for a neoplastic process. This article addresses current approaches to the diagnosis and management of cervical lymphadenitis in children.


id: cord-322890-w78tftva
title: IMAGING FINDINGS IN 14 DOMESTIC FERRETS (MUSTELA PUTORIUS FURO) WITH LYMPHOMA
author: Suran, Jantra Ngosuwan
date: 2013-06-06
txt: ./txt/cord-322890-w78tftva.txt
cache: ./cache/cord-322890-w78tftva.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1111/vru.12068
doi: 10.1111/vru.12068
summary:
  Radiographs, CT, MRI, static ultrasound images, and when available, ultrasound cine loops were retrospectively evaluated, and abnormal findings were recorded by J.N.S. The imaging reports generated by a board-certified veterinary radiologist at the time the study was performed were also reviewed. Cytology of the right renal mass in the first ferret was not performed; however, the patient received chemotherapy for the treatment of lymphoma, confirmed from aspiration and biopsy of an enlarged lymph node, and the mass was seen to decrease in size during follow-up studies (Fig. 5) . Concurrent abdominal imaging findings considered incidental to the diagnosed lymphoma included renal cysts (8) , cystic lymph nodes (7), adrenomegaly in ferrets with diagnosed adrenal disease (5), and pancreatic nodules in ferrets diagnosed with insulinoma (2). Additional radiographic findings in this ferret included splenomegaly, hepatomegaly, and abdominal masses consistent with enlarged lymph nodes.

abstract:
  Lymphoma is the most common malignant neoplasia in domestic ferrets, Mustela putorius furo. However, imaging findings in ferrets with lymphoma have primarily been described in single case reports. The purpose of this retrospective study was to describe imaging findings in a group of ferrets with confirmed lymphoma. Medical records were searched between 2002 and 2012. A total of 14 ferrets were included. Radiographs (n = 12), ultrasound (n = 14), computed tomography (CT; n = 1), and magnetic resonance imaging (MRI; n = 1) images were available for review. Median age at the time of diagnosis was 5.2 years (range 3.25–7.6 years). Clinical signs were predominantly nonspecific (8/14). The time between the first imaging study and lymphoma diagnosis was 1 day or less in most ferrets (12). Imaging lesions were predominantly detected in the abdomen, and most frequently included intra‐abdominal lymphadenopathy (12/14), splenomegaly (8/14), and peritoneal effusion (11/14). Lymphadenopathy and mass lesions were typically hypoechoic on ultrasound. Mild peritoneal effusion was the only detected abnormality in two ferrets. Mild pleural effusion was the most common thoracic abnormality (3/12). Expansile lytic lesions were present in the vertebrae of two ferrets with T3‐L3 myelopathy and the femur in a ferret with lameness. Hyperattenuating, enhancing masses with secondary spinal cord compression were associated with vertebral lysis in CT images of one ferret. The MRI study in one ferret with myelopathy was inconclusive. Findings indicated that imaging characteristics of lymphoma in ferrets are similar to those previously reported in dogs, cats, and humans.


id: cord-328875-fgeudou6
title: Cervical lymphadenitis: Etiology, diagnosis, and management
author: Leung, Alexander K. C.
date: 2009-04-18
txt: ./txt/cord-328875-fgeudou6.txt
cache: ./cache/cord-328875-fgeudou6.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/19366560/
doi: 10.1007/s11908-009-0028-0
summary:
  Acute bilateral cervical lymphadenitis is usually caused by a viral upper respiratory tract infection or streptococcal pharyngitis. Generalized lymphadenopathy is often caused by a viral infection, and less frequently by malignancies, collagen vascular diseases, and medications. Offending organisms usually fi rst infect the upper respiratory tract, anterior nares, oral cavity, or skin in the head and neck area before spreading to the cervical lymph nodes. Bartonella henselae (cat-scratch disease), nontuberculosis mycobacteria (eg, Mycobacterium avium-intracellulare, Mycobacterium scrofulaceum ), and Mycobacterium tuberculosis ("scrofula") are important causes of subacute or chronic cervical lymphadenopathy [ 8 ] . Kikuchi-Fujimoto disease (histocytic necrotizing lymphadenitis) is a benign cause of lymph node enlargement, usually in the posterior cervical triangle [ 9 ] . Acute bilateral cervical lymphadenitis is usually caused by a viral upper respiratory tract infection or pharyngitis due to S.

abstract:
  Cervical lymphadenopathy is a common problem in children. The condition most commonly represents a transient response to a benign local or generalized infection. Acute bilateral cervical lymphadenitis is usually caused by a viral upper respiratory tract infection or streptococcal pharyngitis. Acute unilateral cervical lymphadenitis is caused by streptococcal or staphylococcal infection in 40% to 80% of cases. Common causes of subacute or chronic lymphadenitis include cat-scratch disease and mycobacterial infection. Generalized lymphadenopathy is often caused by a viral infection, and less frequently by malignancies, collagen vascular diseases, and medications. Laboratory tests are not necessary in most children with cervical lymphadenopathy. Most cases of cervical lymphadenitis are self-limited and require no treatment. The treatment of acute bacterial cervical lymphadenitis without a known primary source should provide adequate coverage for both Staphylococcus aureus and Streptococcus pyogenes.


id: cord-349724-yq4dphmb
title: A Multi-Tier Fog Content Orchestrator Mechanism with Quality of Experience Support
author: Santos, Hugo
date: 2020-05-06
txt: ./txt/cord-349724-yq4dphmb.txt
cache: ./cache/cord-349724-yq4dphmb.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S1389128620304448?v=s5
doi: 10.1016/j.comnet.2020.107288
summary:
  For the analysis phase, the Fog4Video collects information about available bandwidth, delay, stall duration, number of stall events, and monetary cost from the network, user client, and fog node. Based on our analysis of the state-of-the-art, we conclude that VoD services deployed in a multi-tier fog architecture improve the QoE by efficiently orchestrating fog nodes resources, while reducing delay and amount of data uploading/downloading to the cloud in a cost efficient fashion. Based on such architecture, Fog4Video classifies the connectivity and resources of each available fog node into a multi-criteria rank, where it considers the AHP method to assign different degrees of importance for each criterion to provide better QoE for each user. Based on these values, Fog4Video implemented in the Orchestrator considers network, fog node, and user information to perform real-time content orchestration, i.e., it chooses an appropriate Streaming Unit from a given tier for the client to download the video.

abstract:
  Abstract Video-on-Demand (VoD) services create a demand for content orchestrator mechanisms to support Quality of Experience (QoE). Fog computing brings benefits for enhancing the QoE for VoD services by caching the content closer to the user in a multi-tier fog architecture, considering their available resources to improve QoE. In this context, it is mandatory to consider network, fog node, and user metrics to choose an appropriate fog node to distribute videos with QoE support properly. In this article, we introduce a content orchestrator mechanism, called of Fog4Video, which chooses an appropriate fog node to download video content. The mechanism considers the available bandwidth, delay, and cost, besides the QoE metrics for VoD, namely number of stalls and stalls duration, to deploy VoD services in the opportune fog node. Decision-making acknowledges periodical reports of QoE from the clients to assess the video streaming from each fog node. These values serve as inputs for a real-time Analytic Hierarchy Process method to compute the influence factor for each parameter and compute the QoE improvement potential of the fog node. Fog4Video is executed in fog nodes organized in multiple tiers, having different characteristics to provide VoD services. Simulation results demonstrate that Fog4Video transmits adapted videos with 30% higher QoE and reduced monetary cost up to 24% than other content request mechanisms.


id: cord-355393-ot7hztyk
title: Community-based immunization in opportunistic social networks
author: Yuan, Peiyan
date: 2015-02-15
txt: ./txt/cord-355393-ot7hztyk.txt
cache: ./cache/cord-355393-ot7hztyk.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S037843711400942X
doi: 10.1016/j.physa.2014.10.087
summary:
  More interestingly, we find that high local importance but non-central nodes play a big role in epidemic spreading process, removing them improves the immunization efficiency by 25% to 150% at different scenarios. To this end, we investigate the evolution of community structure in opportunistic social networks, and analyze the effect of community-based immunization strategy on epidemic spreading. We observe that the most efficient immunization strategy on epidemic spreading is to remove nodes with high local importance in communities. Although many random mobility models, such as Random Walk and Random Way Point, have been widely used in opportunistic social networks for evaluating routing performance or even the epidemic dynamics [30, 31] , they cannot reflect the main features of human mobility, including the truncated power-law flights and pause-times, the heterogeneously bounded mobility areas of different nodes, etc.

abstract:
  Abstract Immunizing important nodes has been shown to be an effective solution to suppress the epidemic spreading. Most studies focus on the globally important nodes in a network, but neglect the locally important nodes in different communities. We claim that given the temporal community feature of opportunistic social networks (OSN), this strategy has a biased understanding of the epidemic dynamics, leading us to conjecture that it is not “the more central, the better” for the implementation of control strategy. In this paper, we track the evolution of community structure and study the effect of community-based immunization strategy on epidemic spreading. We first break the OSN traces down into different communities, and find that the community structure helps to delay the outbreak of epidemic. We then evaluate the local importance of nodes in communities, and show that immunizing nodes with high local importance can remarkably suppress the epidemic. More interestingly, we find that high local importance but non-central nodes play a big role in epidemic spreading process, removing them improves the immunization efficiency by 25% to 150% at different scenarios.


