Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction

Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C$^2$SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or mismatched sentences, while the correlation among relations were captured to improve the quality of attention weights. Moreover, instead of treating all entity-pairs equally, we try to pay more attention to entity-pairs with a higher quality. Similarly, we adopt the selective attention mechanism to achieve this goal. Experiments with two types of relation extractor demonstrate the superiority of the proposed approach over the state-of-the-art, while further ablation studies verify our intuitions and demonstrate the effectiveness of our proposed two techniques.Comment: AAAI 201

Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition

Handwritten mathematical expression recognition (HMER) has attracted extensive attention recently. However, current methods cannot explicitly study the interactions between different symbols, which may fail when faced similar symbols. To alleviate this issue, we propose a simple but efficient method to enhance semantic interaction learning (SIL). Specifically, we firstly construct a semantic graph based on the statistical symbol co-occurrence probabilities. Then we design a semantic aware module (SAM), which projects the visual and classification feature into semantic space. The cosine distance between different projected vectors indicates the correlation between symbols. And jointly optimizing HMER and SIL can explicitly enhances the model's understanding of symbol relationships. In addition, SAM can be easily plugged into existing attention-based models for HMER and consistently bring improvement. Extensive experiments on public benchmark datasets demonstrate that our proposed module can effectively enhance the recognition performance. Our method achieves better recognition performance than prior arts on both CROHME and HME100K datasets.Comment: 12 Page

Pan-Arctic land–atmospheric fluxes of methane and carbon dioxide in response to climate change over the 21st century

Future changes of pan-Arctic land–atmospheric methane (CH[subscript 4]) and carbon dioxide (CO[subscript 2]) depend on how terrestrial ecosystems respond to warming climate. Here, we used a coupled hydrology–biogeochemistry model to make our estimates of these carbon exchanges with two contrasting climate change scenarios (no-policy versus policy) over the 21st century, by considering (1) a detailed water table dynamics and (2) a permafrost-thawing effect. Our simulations indicate that, under present climate conditions, pan-Arctic terrestrial ecosystems act as a net greenhouse gas (GHG) sink of −0.2 Pg CO[subscript 2]-eq. yr[superscript −1], as a result of a CH[subscript 4] source (53 Tg CH4 yr[superscript −1]) and a CO[subscript 2] sink (−0.4 Pg C yr[superscript −1]). In response to warming climate, both CH[subscript 4] emissions and CO[subscript 2] uptakes are projected to increase over the century, but the increasing rates largely depend on the climate change scenario. Under the non-policy scenario, the CH[subscript 4] source and CO[subscript 2] sink are projected to increase by 60% and 75% by 2100, respectively, while the GHG sink does not show a significant trend. Thawing permafrost has a small effect on GHG sink under the policy scenario; however, under the no-policy scenario, about two thirds of the accumulated GHG sink over the 21st century has been offset by the carbon losses as CH[subscript 4] and CO[subscript 2] from thawing permafrost. Over the century, nearly all CO[subscript 2]-induced GHG sink through photosynthesis has been undone by CH[subscript 4]-induced GHG source. This study indicates that increasing active layer depth significantly affects soil carbon decomposition in response to future climate change. The methane emissions considering more detailed water table dynamics continuously play an important role in affecting regional radiative forcing in the pan-Arctic.United States. Dept. of Energy. SciDAC Institute on Quantum Simulation of Materials and NanostructuresUnited States. Dept. of Energy (Abrupt Climate Change)United States. National Aeronautics and Space Administration (Land Use and Land Cover Change Program NASA-NNX09AI26G)United States. Dept. of Energy (DE-FG02-08ER64599)National Science Foundation (U.S.). Division of Information and Intelligent Systems (NSF-1028291)National Science Foundation (U.S.) (Carbon and Water in the Earth Program (NSF-0630319)United States. Dept. of Energy. Office of Biological and Environmental Research (Contract DE-AC02-05CH11231

Cell phone–based health education messaging improves health literacy

Background: The ubiquity of cell phones, which allow for short message service (SMS), provides new and innovative opportunities for disease prevention and health education.Objective: To explore the use of cell phone–based health education SMS to improve the health literacy of community residents in China.Methods: A multi-stage random sampling method was used to select representative study communities and participants ≥ 18 years old. Intervention participants were sent health education SMSs once a week for 1 year and controls were sent conventional, basic health education measures. Health literacy levels of the residents before and after the intervention were evaluated between intervention and control groups.Results: Public health literacy scores increased 1.5 points, from 61.8 to 63.3, after SMS intervention for 1 year (P<0.01); the increase was greater for males than females (2.01 vs. 1.03; P<0.01) and for Shenzhen local residents than non permanent residents (2.56 vs. 1.14; P<0.01). The frequency of high health literacy scores was greater for the intervention than control group (22.03% to 30.93% vs. 22.07% to 20.82%). With health literacy as a cost-effective index, the cost-effectiveness per intervention was 0.54.Conclusion: SMS may be a useful tool for improving health literacy.Keywords: Health literacy, intervention, community residents, cell phone, short message servic