Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and ...diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node's neighbors but do not directly make use of the interactions among it's neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors' influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about Formula: see text nodes, more than 15 times faster than PageRank.
Feature extraction for fault signals is critical and difficult in all kinds of fault detection schemes. A novel simple and effective method of faulty feeder detection in resonant grounding ...distribution systems based on the continuous wavelet transform (CWT) and convolutional neural network (CNN) is presented in this paper. The time-frequency gray scale images are acquired by applying the CWT to the collected transient zero-sequence current signals of the faulty feeder and sound feeders. The features of the gray scale image will be extracted adaptively by the CNN, which is trained by a large number of gray scale images under various kinds of fault conditions and factors. The features extraction and the faulty feeder detection can be implemented by the trained CNN simultaneously. As a comparison, two faulty feeder detection methods based on artificial feature extraction and traditional machine learning are introduced. A practical resonant grounding distribution system is simulated in power systems computer aided design/electromagnetic transients including DC, the effectiveness and performance of the proposed faulty feeder detection method is compared and verified under different fault circumstances.
Due to the difficulty in locating high-resistance grounding faults, this paper proposes a novel fault location method for HVdc transmission lines by considering double-end unsynchronized using ...Hilbert-Huang transform and one-dimensional convolutional neural network (1D-CNN). After the fault signal is collected at both ends, the proposed method can achieve high-precision fault location, requiring only the two ends data transmission without time synchronization. After Empirical Mode Decomposition (EMD), the high-frequency components of the double-terminal fault signals are connected in series to make a characteristic waveform. This waveform contains characteristics of different fault types and distances, which can be learned by CNN. The trained CNN can then be used to achieve fault location effectively. As a comparison, two fault location methods based on traditional traveling wave and machine learning are introduced. Electromagnetic transient simulation software PSCAD/EMTDC has been used to carry out various types of fault simulation on the ± 500 kV HVdc transmission system. The results show that the proposed method can reliably and accurately locate line faults under fault resistance up to 5200 Ω.
Circular RNAs (circRNAs), which are single-stranded closed-loop RNA molecules lacking terminal 5' caps and 3' poly(A) tails, are attracting increasing scientific attention for their crucial ...regulatory roles in the occurrence and development of various diseases. With the rapid development of high-throughput sequencing technologies, increasing numbers of differentially expressed circRNAs have been identified in bladder cancer (BCa) via exploration of the expression profiles of BCa and normal tissues and cell lines. CircRNAs are critically involved in BCa biological behaviours, including cell proliferation, tumour growth suppression, cell cycle arrest, apoptosis, invasion, migration, metastasis, angiogenesis, and cisplatin chemoresistance. Most of the studied circRNAs in BCa regulate cancer biological behaviours via miRNA sponging regulatory mechanisms. CircRNAs have been reported to be significantly associated with many clinicopathologic characteristics of BCa, including tumour size, grade, differentiation, and stage; lymph node metastasis; tumour numbers; distant metastasis; invasion; and recurrence. Moreover, circRNA expression levels can be used to predict BCa patients' survival parameters, such as overall survival (OS), disease-free survival (DFS), and progression-free survival (PFS). The abundance, conservation, stability, specificity and detectability of circRNAs render them potential diagnostic and prognostic biomarkers for BCa. Additionally, circRNAs play crucial regulatory roles upstream of various signalling pathways related to BCa carcinogenesis and progression, reflecting their potential as therapeutic targets for BCa. Herein, we briefly summarize the expression profiles, biological functions and mechanisms of circRNAs and the potential clinical applications of these molecules for BCa diagnosis, prognosis, and targeted therapy.
Although widely used in many applications, accurate and efficient human action recognition remains a challenging area of research in the field of computer vision. Most recent surveys have focused on ...narrow problems such as human action recognition methods using depth data, 3D-skeleton data, still image data, spatiotemporal interest point-based methods, and human walking motion recognition. However, there has been no systematic survey of human action recognition. To this end, we present a thorough review of human action recognition methods and provide a comprehensive overview of recent approaches in human action recognition research, including progress in hand-designed action features in RGB and depth data, current deep learning-based action feature representation methods, advances in human⁻object interaction recognition methods, and the current prominent research topic of action detection methods. Finally, we present several analysis recommendations for researchers. This survey paper provides an essential reference for those interested in further research on human action recognition.
Identifying a set of influential spreaders in complex networks plays a crucial role in effective information spreading. A simple strategy is to choose top-r ranked nodes as spreaders according to ...influence ranking method such as PageRank, ClusterRank and k-shell decomposition. Besides, some heuristic methods such as hill-climbing, SPIN, degree discount and independent set based are also proposed. However, these approaches suffer from a possibility that some spreaders are so close together that they overlap sphere of influence or time consuming. In this report, we present a simply yet effectively iterative method named VoteRank to identify a set of decentralized spreaders with the best spreading ability. In this approach, all nodes vote in a spreader in each turn, and the voting ability of neighbors of elected spreader will be decreased in subsequent turn. Experimental results on four real networks show that under Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models, VoteRank outperforms the traditional benchmark methods on both spreading rate and final affected scale. What's more, VoteRank has superior computational efficiency.
A two-marker combination of plastid rbcL and matK has previously been recommended as the core plant barcode, to be supplemented with additional markers such as plastid trnH–psbA and nuclear ribosomal ...internal transcribed spacer (ITS). To assess the effectiveness and universality of these barcode markers in seed plants, we sampled 6,286 individuals representing 1,757 species in 141 genera of 75 families (42 orders) by using four different methods of data analysis. These analyses indicate that (i) the three plastid markers showed high levels of universality (87.1–92.7%), whereas ITS performed relatively well (79%) in angiosperms but not so well in gymnosperms; (ii) in taxonomic groups for which direct sequencing of the marker is possible, ITS showed the highest discriminatory power of the four markers, and a combination of ITS and any plastid DNA marker was able to discriminate 69.9–79.1% of species, compared with only 49.7% with rbcL + matK; and (iii) where multiple individuals of a single species were tested, ascriptions based on ITS and plastid DNA barcodes were incongruent in some samples for 45.2% of the sampled genera (for genera with more than one species sampled). This finding highlights the importance of both sampling multiple individuals and using markers with different modes of inheritance. In cases where it is difficult to amplify and directly sequence ITS in its entirety, just using ITS2 is a useful backup because it is easier to amplify and sequence this subset of the marker. We therefore propose that ITS/ITS2 should be incorporated into the core barcode for seed plants.
Traffic in operator networks is time varying. Conventional network functions implemented by black-boxes should satisfy the peak traffic requirement, and hence result in low resource utilization. ...Thanks to the emergence of Virtual Network Function (VNF), which is realized by running networking software on Virtual Machines (VMs), the operator can dynamically scale in or scale out the VNF instances and hence save the required resources. In this paper, we introduce how the dynamic VNF scaling is implemented in practical operator Data Center Networks (DCNs). First, we analyze the traffic characteristics in our operator networks, and introduce how the VNFs are organized in a common operator DCN. Based on these backgrounds, we not only propose a traffic forecasting method, but also design two VNF placement algorithms to guide the dynamic VNF instance scaling. Through both the implementation in a real operator network and extensive real trace driven simulations, we demonstrate that our dynamic VNF instance scaling system can achieve higher service availability and save the VNF resources (e.g., CPU and memory) by up to 30 percent.
Speeded-Up Robust Features (SURF) is a robust and useful feature detector for various vision-based applications but it is unable to detect symmetrical objects. This paper proposes a new symmetrical ...SURF descriptor to enrich the power of SURF to detect all possible symmetrical matching pairs through a mirroring transformation. A vehicle make and model recognition (MMR) application is then adopted to prove the practicability and feasibility of the method. To detect vehicles from the road, the proposed symmetrical descriptor is first applied to determine the region of interest of each vehicle from the road without using any motion features. This scheme provides two advantages: there is no need for background subtraction and it is extremely efficient for real-time applications. Two MMR challenges, namely multiplicity and ambiguity problems, are then addressed. The multiplicity problem stems from one vehicle model often having different model shapes on the road. The ambiguity problem results from vehicles from different companies often sharing similar shapes. To address these two problems, a grid division scheme is proposed to separate a vehicle into several grids; different weak classifiers that are trained on these grids are then integrated to build a strong ensemble classifier. The histogram of gradient and SURF descriptors are adopted to train the weak classifiers through a support vector machine learning algorithm. Because of the rich representation power of the grid-based method and the high accuracy of vehicle detection, the ensemble classifier can accurately recognize each vehicle.
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