DeepInf: Social Influence Prediction with Deep Learning

Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access friends' behaviors and are in turn influenced by them. Consequently, an effective social influence prediction for each user is critical for a variety of applications such as online recommendation and advertising. Conventional social influence prediction approaches typically design various hand-crafted rules to extract user- and network-specific features. However, their effectiveness heavily relies on the knowledge of domain experts. As a result, it is usually difficult to generalize them into different domains. Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence. In general, DeepInf takes a user's local network as the input to a graph neural network for learning her latent social representation. We design strategies to incorporate both network structures and user-specific features into convolutional neural and attention networks. Extensive experiments on Open Academic Graph, Twitter, Weibo, and Digg, representing different types of social and information networks, demonstrate that the proposed end-to-end model, DeepInf, significantly outperforms traditional feature engineering-based approaches, suggesting the effectiveness of representation learning for social applications.Comment: 10 pages, 5 figures, to appear in KDD 2018 proceeding

Attention Is All You Need

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.Comment: 15 pages, 5 figure

Embedding Principal Component Analysis for Data Reductionin Structural Health Monitoring on Low-Cost IoT Gateways

Principal component analysis (PCA) is a powerful data reductionmethod for Structural Health Monitoring. However, its computa-tional cost and data memory footprint pose a significant challengewhen PCA has to run on limited capability embedded platformsin low-cost IoT gateways. This paper presents a memory-efficientparallel implementation of the streaming History PCA algorithm.On our dataset, it achieves 10x compression factor and 59x memoryreduction with less than 0.15 dB degradation in the reconstructedsignal-to-noise ratio (RSNR) compared to standard PCA. More-over, the algorithm benefits from parallelization on multiple cores,achieving a maximum speedup of 4.8x on Samsung ARTIK 710

Cooking practices, air quality, and the acceptability of advanced cookstoves in Haryana, India: an exploratory study to inform large-scale interventions.

BackgroundIn India, approximately 66% of households rely on dung or woody biomass as fuels for cooking. These fuels are burned under inefficient conditions, leading to household air pollution (HAP) and exposure to smoke containing toxic substances. Large-scale intervention efforts need to be informed by careful piloting to address multiple methodological and sociocultural issues. This exploratory study provides preliminary data for such an exercise from Palwal District, Haryana, India.MethodsTraditional cooking practices were assessed through semi-structured interviews in participating households. Philips and Oorja, two brands of commercially available advanced cookstoves with small blowers to improve combustion, were deployed in these households. Concentrations of particulate matter (PM) with a diameter <2.5 μm (PM2.5) and carbon monoxide (CO) related to traditional stove use were measured using real-time and integrated personal, microenvironmental samplers for optimizing protocols to evaluate exposure reduction. Qualitative data on acceptability of advanced stoves and objective measures of stove usage were also collected.ResultsTwenty-eight of the thirty-two participating households had outdoor primary cooking spaces. Twenty households had liquefied petroleum gas (LPG) but preferred traditional stoves as the cost of LPG was higher and because meals cooked on traditional stoves were perceived to taste better. Kitchen area concentrations and kitchen personal concentrations assessed during cooking events were very high, with respective mean PM2.5 concentrations of 468 and 718 µg/m3. Twenty-four hour outdoor concentrations averaged 400 µg/m3. Twenty-four hour personal CO concentrations ranged between 0.82 and 5.27 ppm. The Philips stove was used more often and for more hours than the Oorja.ConclusionsThe high PM and CO concentrations reinforce the need for interventions that reduce HAP exposure in the aforementioned community. Of the two stoves tested, participants expressed satisfaction with the Philips brand as it met the local criteria for usability. Further understanding of how the introduction of an advanced stove influences patterns of household energy use is needed. The preliminary data provided here would be useful for designing feasibility and/or pilot studies aimed at intervention efforts locally and nationally

Confocal Laser Scanning Microscopic Evaluation of Sealer Penetration in Root Canals of Teeth with the butterfly and Non-butterfly Effect: An In vitro Study

AIM: The study aimed to investigate the penetration depth of calcium hydroxide-based root canal sealer into buccolingual and mesiodistal aspects of roots with and without the butterfly effect at coronal and middle root sections. METHODS AND MATERIALS: Twenty single-rooted maxillary premolars were decoronated at the cementoenamel junction and viewed under a light microscope and grouped as Group 1 – butterfly (B) and Group 2 – non-butterfly according to the presence or absence of the effect. Canals were prepared till working length followed with copious irrigation. Canals were finally rinsed with 5 ml of 17% ethylenediaminetetraacetic acid solution and activated using EndoActivator followed by obturation using gutta-percha (warm vertical compaction technique) with Sealapex sealer. To provide fluorescence for confocal laser scanning microscopy (CLSM), the Sealapex was mixed with rhodamine B dye. Root sectioning yielded coronal and middle sections. CLSM was used to assess the penetration of the sealer. STATISTICAL ANALYSIS: Shapiro–Wilk test, unpaired “t-test.” RESULTS: Teeth with the butterfly effect had greater mean penetration buccolingually (905.2 μm) than mesiodistally (182.1 μm; p < 0.001). Coronal sections had greater penetration (517.4 μm) compared with the middle (354.6 μm). CONCLUSION: Sealapex sealer exhibited maximum tubular penetration in teeth with butterfly effect in buccolingual direction at the coronal third level

IL RUOLO DEI PUNTI VENDITA COME STRUMENTO DI IMMAGINE DI MARCA NEL MERCATO CINESE

Preeclampsia (PE) and intrauterine growth restriction (IUGR) are major obstetric health problems. Higher levels of T-helper (Th) 1 (proinflammatory) cytokines have been observed in pregnancies complicated with PE and IUGR; this is in contrast to the predominant Th2 (anti-inflammatory) cytokine environment found in uncomplicated pregnancies. Myostatin is best known as a negative regulator of muscle development and reportedly has a role in fat deposition, glucose metabolism, and cytokine modulation (outside the placenta). Myostatin concentrations in plasma and protein expression in placental tissue are significantly higher in women with PE. Expression of myostatin in IUGR and PE-IUGR and the effect of this protein on the cytokine production from the placenta is unknown. In the current study, significant differences were identified in the expression of myostatin in pregnancies complicated with IUGR, PE, and PE with IUGR. Furthermore, cytokine production by first-trimester placental tissues was altered following myostatin treatment.</p