Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency-domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis.Comment: To appear at IMC 201

TEAD1-dependent expression of the FoxO3a gene in mouse skeletal muscle

<p>Abstract</p> <p>Background</p> <p><it>TEAD1 </it>(TEA domain family member 1) is constitutively expressed in cardiac and skeletal muscles. It acts as a key molecule of muscle development, and trans-activates multiple target genes involved in cell proliferation and differentiation pathways. However, its target genes in skeletal muscles, regulatory mechanisms and networks are unknown.</p> <p>Results</p> <p>In this paper, we have identified 136 target genes regulated directly by TEAD1 in skeletal muscle using integrated analyses of ChIP-on-chip. Most of the targets take part in the cell process, physiology process, biological regulation metabolism and development process. The targets also play an important role in MAPK, mTOR, T cell receptor, JAK-STAT, calcineurin and insulin signaling pathways. TEAD1 regulates <it>foxo3a </it>transcription through binding to the M-CAT element in <it>foxo3a </it>promoter, demonstrated with independent ChIP-PCR, EMSA and luciferase reporter system assay. In addition, results of over-expression and inhibition experiments suggest that <it>foxo3a </it>is positively regulated by TEAD1.</p> <p>Conclusions</p> <p>Our present data suggests that TEAD1 plays an important role in the regulation of gene expression and different signaling pathways may co-operate with each other mediated by TEAD1. We have preliminarily concluded that TEAD1 may regulate <it>FoxO3a </it>expression through calcineurin/MEF2/NFAT and IGF-1/PI3K/AKT signaling pathways in skeletal muscles. These findings provide important clues for further analysis of the role of <it>FoxO3a </it>gene in the formation and transformation of skeletal muscle fiber types.</p

Stimulation Generation Method of STEP-NC Process Routing Based on FBM Constrained Relationship

Abstract. The excitation selection sort method of manufacturing feature was proposed based on the characteristics oriented to manufacturing feature of STEP-NC to realize the process route planning of STEP-NC. First, match the recognizing manufacturing feature, the manufacturing feature with multi machining operation was disintegrated in order that every manufacturing feature has only a processing operation. Then, the rationality constraint problems of machining operation was translated into the processing order problem of manufacturing feature, and the optimal constraint problem of machining operation was translated into the corresponding manufacture feature set. The process route was generated by the operations of excitation, selection, deletion and transfer for the feature of different manufacture feature set. The last, the feasibility and effectiveness of the process route excitation generation method was verified by an example, and show that this method was better solved the problems of process rules and the representation of knowledge

Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network

In the past decade, the heterogeneous information network (HIN) has become an important methodology for modern recommender systems. To fully leverage its power, manually designed network templates, i.e., meta-structures, are introduced to filter out semantic-aware information. The hand-crafted meta-structure rely on intense expert knowledge, which is both laborious and data-dependent. On the other hand, the number of meta-structures grows exponentially with its size and the number of node types, which prohibits brute-force search. To address these challenges, we propose Genetic Meta-Structure Search (GEMS) to automatically optimize meta-structure designs for recommendation on HINs. Specifically, GEMS adopts a parallel genetic algorithm to search meaningful meta-structures for recommendation, and designs dedicated rules and a meta-structure predictor to efficiently explore the search space. Finally, we propose an attention based multi-view graph convolutional network module to dynamically fuse information from different meta-structures. Extensive experiments on three real-world datasets suggest the effectiveness of GEMS, which consistently outperforms all baseline methods in HIN recommendation. Compared with simplified GEMS which utilizes hand-crafted meta-paths, GEMS achieves over $6\%$ performance gain on most evaluation metrics. More importantly, we conduct an in-depth analysis on the identified meta-structures, which sheds light on the HIN based recommender system design.Comment: Published in Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20

Impact-Oriented Contextual Scholar Profiling using Self-Citation Graphs

Quantitatively profiling a scholar's scientific impact is important to modern research society. Current practices with bibliometric indicators (e.g., h-index), lists, and networks perform well at scholar ranking, but do not provide structured context for scholar-centric, analytical tasks such as profile reasoning and understanding. This work presents GeneticFlow (GF), a suite of novel graph-based scholar profiles that fulfill three essential requirements: structured-context, scholar-centric, and evolution-rich. We propose a framework to compute GF over large-scale academic data sources with millions of scholars. The framework encompasses a new unsupervised advisor-advisee detection algorithm, a well-engineered citation type classifier using interpretable features, and a fine-tuned graph neural network (GNN) model. Evaluations are conducted on the real-world task of scientific award inference. Experiment outcomes show that the F1 score of best GF profile significantly outperforms alternative methods of impact indicators and bibliometric networks in all the 6 computer science fields considered. Moreover, the core GF profiles, with 63.6%-66.5% nodes and 12.5%-29.9% edges of the full profile, still significantly outrun existing methods in 5 out of 6 fields studied. Visualization of GF profiling result also reveals human explainable patterns for high-impact scholars

How enlightened self-interest guided global vaccine sharing benefits all: a modelling study

Background: Despite the consensus that vaccines play an important role in combating the global spread of infectious diseases, vaccine inequity is still rampant with deep-seated mentality of self-priority. This study aims to evaluate the existence and possible outcomes of a more equitable global vaccine distribution and explore a concrete incentive mechanism that promotes vaccine equity. Methods: We design a metapopulation epidemiological model that simultaneously considers global vaccine distribution and human mobility, which is then calibrated by the number of infections and real-world vaccination records during COVID-19 pandemic from March 2020 to July 2021. We explore the possibility of the enlightened self-interest incentive mechanism, i.e., improving one's own epidemic outcomes by sharing vaccines with other countries, by evaluating the number of infections and deaths under various vaccine sharing strategies using the proposed model. To understand how these strategies affect the national interests, we distinguish the imported and local cases for further cost-benefit analyses that rationalize the enlightened self-interest incentive mechanism behind vaccine sharing. ...Comment: Accepted by Journal of Global Healt