A Multiple Source based Transfer Learning Framework for Marketing Campaigns

© 2018 IEEE. The rapid growing number of marketing campaigns demands an efficient learning model to identify prospective customers to target. Transfer learning is widely considered as a major way to improve the learning performance by using the generated knowledge from previous learning tasks. Most recent studies focused on transferring knowledge from source domains to target domains which may result in knowledge missing. To avoid this, we proposed a multiple source based transfer learning framework to do it reversely. The data in target domains is transferred into source domains by normalizing them into the same distributions and then improving the learning task in target domains by its generated knowledge in source domains. The proposed method is general and can deal with supervised and unsupervised inductive and transductive learning simultaneously with a compatibility to work with different machine learning models. The experiments on real-world campaign data demonstrate the performance of the proposed method

Knowledge and attitude on maternal health care among rural-to-urban migrant women in Shanghai, China

<p>Abstract</p> <p>Background</p> <p>In China, with the urbanization, women migrated from rural to big cities presented much higher maternal mortality rates than local residents. Health knowledge is one of the key factors enabling women to be aware of their rights and health status in order to seek appropriate health services. This study aims to assess the knowledge and attitude on maternal health care and the contributing factors to being knowledgeable among rural-to-urban migrant women in Shanghai.</p> <p>Methods</p> <p>A cross-sectional study was conducted in a district center hospital in Shanghai where migrants gathered. Totally 475 rural-to-urban migrant pregnant women were interviewed and completed the self-administered questionnaire after obtaining informed consent.</p> <p>Results</p> <p>The mean score of knowledge on maternal health care was 8.28 out of 12. However, only 36.6% women had attended the required 5 antenatal checks, and 58.3% of the subjects thought financial constrains being the main reason for not attending antenatal care. It was found that higher level of education (OR = 3.3, 95%CI: 1.8–3.8), husbands' Shanghai residence (OR = 4.0, 95%CI: 1.3–12.1) and better family income (OR = 3.3, 95%CI: 1.4–8.2) were associated with better knowledge.</p> <p>Conclusions</p> <p>Rural-to-urban migrant women's unawareness of maternal health service, together with their vulnerable living status, influences their utilization of maternal health care. Tailored maternal health education and accessible services are in demands for this population.</p

Search for neutral MSSM Higgs bosons decaying to a pair of tau leptons in pp collisions

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Measurement of top quark–antiquark pair production in association with a W or Z boson in pp collisions at √s=8 TeV

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Measurement of prompt J/ψ pair production in pp collisions at √s = 7 Tev

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Constraints on parton distribution functions and extraction of the strong coupling constant from the inclusive jet cross section in pp collisions at √s=7 TeV

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Study of hadronic event-shape variables in multijet final states in pp collisions at √s=7 TeV

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Searches for electroweak production of charginos, neutralinos, and sleptons decaying to leptons and W, Z, and Higgs bosons in pp collisions at 8 TeV

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Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe