This paper presents a method for automatically converting raw GPS traces from everyday vehicles into a routable road network. The method begins by smoothing raw GPS traces using a novel aggregation technique. This technique pulls together traces that belong on the same road in response to simulated potential energy wells created around each trace. After the traces are moved in response to the potential fields, they tend to coalesce into smooth paths. To help adjust the parameters of the constituent potential fields, we present a theoretical analysis of the behavior of our algorithm on a few different road configurations. With the resulting smooth traces, we apply a custom clustering algorithm to create a graph of nodes and edges representing the road network. We show how this network can be used to plan reasonable driving routes, much like consumer-oriented mapping Web sites. We demonstrate our algorithms using real GPS data collected on public roads, and we evaluate the effectiveness of our approach by comparing the route planning results suggested by our generated graph to a commercial route planner.
Compelling evidence has demonstrated the potential functions of circular RNAs (circRNAs) in breast cancer (BC) tumorigenesis. Nevertheless, the underlying mechanism by which circRNAs regulate BC progression is still unclear. The purpose of present research was to investigate the novel circRNA circRNF20 (hsa_circ_0087784) and its role in BC. CircRNA microarray sequencing revealed that circRNF20 was one of the upregulated transcripts in BC samples. Increased circRNF20 level predicted the poor clinical outcome in BC specimens. Functionally, circRNF20 promoted the proliferation and Warburg effect (aerobic glycolysis) of BC cells. Mechanistically, circRNF20 harbor miR-487a, acting as miRNA sponge, and then miR-487a targeted the 3'-UTR of hypoxia-inducible factor-1α (HIF-1α). Moreover, HIF-1α could bind with the promoter of hexokinase II (HK2) and promoted its transcription. In conclusion, this finding illustrates the vital roles of circRNF20 via the circRNF20/ miR-487a/HIF-1α/HK2 axis in breast cancer progress and Warburg effect, providing an interesting insight for the BC tumorigenesis.
Abstract-Open Spectrum systems allow fast deployment of wireless technologies by reusing under-utilized pre-allocated spectrum channels, all with minimal impact on existing primary users. However, existing proposals take a reactive sense-andavoid approach to impulsively reconfigure spectrum usage based solely on the latest observations. This can result in frequent disruptions to operations of both primary and secondary users. In this paper, we propose a proactive spectrum access approach where secondary users utilize past channel histories to make predictions on future spectrum availability, and intelligently schedule channel usage in advance. We propose two channel selection and switching techniques to minimize disruptions to primary users and maintain reliable communication at secondary users. Experiments show that the proactive approach effectively reduces the interferences to primary users by up to 30%, and significantly decreases throughput jitters at secondary users.
Access to realistic, complex graph datasets is critical to research on social networking systems and applications. Simulations on graph data provide critical evaluation of new systems and applications ranging from community detection to spam filtering and social web search. Due to the high time and resource costs of gathering real graph datasets through direct measurements, researchers are anonymizing and sharing a small number of valuable datasets with the community. However, performing experiments using shared real datasets faces three key disadvantages: concerns that graphs can be de-anonymized to reveal private information, increasing costs of distributing large datasets, and that a small number of available social graphs limits the statistical confidence in the results.The use of measurement-calibrated graph models is an attractive alternative to sharing datasets. Researchers can "fit" a graph model to a real social graph, extract a set of model parameters, and use them to generate multiple synthetic graphs statistically similar to the original graph. While numerous graph models have been proposed, it is unclear if they can produce synthetic graphs that accurately match the properties of the original graphs. In this paper, we explore the feasibility of measurement-calibrated synthetic graphs using six popular graph models and a variety of real social graphs gathered from the Facebook social network ranging from 30,000 to 3 million edges. We find that two models consistently produce synthetic graphs with common graph metric values similar to those of the original graphs. However, only one produces high fidelity results in our application-level benchmarks. While this shows that graph models can produce realistic synthetic graphs, it also highlights the fact that current graph metrics remain incomplete, and some applications expose graph properties that do not map to existing metrics.
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