Do as I can, not as I get: Topology-aware multi-hop reasoning on multi-modal knowledge graphs

Multi-modal knowledge graph (MKG) includes triplets that consist of entities and relations and multi-modal auxiliary data. In recent years, multi-hop multi-modal knowledge graph reasoning (MMKGR) based on reinforcement learning (RL) has received extensive attention because it addresses the intrinsic incompleteness of MKG in an interpretable manner. However, its performance is limited by empirically designed rewards and sparse relations. In addition, this method has been designed for the transductive setting where test entities have been seen during training, and it works poorly in the inductive setting where test entities do not appear in the training set. To overcome these issues, we propose TMR (Topology-aware Multi-hop Reasoning), which can conduct MKG reasoning under inductive and transductive settings. Specifically, TMR mainly consists of two components. (1) The topology-aware inductive representation captures information from the directed relations of unseen entities, and aggregates query-related topology features in an attentive manner to generate the fine-grained entity-independent features. (2) After completing multi-modal feature fusion, the relation-augment adaptive RL conducts multi-hop reasoning by eliminating manual rewards and dynamically adding actions. Finally, we construct new MKG datasets with different scales for inductive reasoning evaluation. Experimental results demonstrate that TMP outperforms state-of-the-art MKGR methods under both inductive and transductive settings

DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning

Temporal knowledge graphs (TKGs) model the temporal evolution of events and have recently attracted increasing attention. Since TKGs are intrinsically incomplete, it is necessary to reason out missing elements. Although existing TKG reasoning methods have the ability to predict missing future events, they fail to generate explicit reasoning paths and lack explainability. As reinforcement learning (RL) for multi-hop reasoning on traditional knowledge graphs starts showing superior explainability and performance in recent advances, it has opened up opportunities for exploring RL techniques on TKG reasoning. However, the performance of RL-based TKG reasoning methods is limited due to: (1) lack of ability to capture temporal evolution and semantic dependence jointly; (2) excessive reliance on manually designed rewards. To overcome these challenges, we propose an adaptive reinforcement learning model based on attention mechanism (DREAM) to predict missing elements in the future. Specifically, the model contains two components: (1) a multi-faceted attention representation learning method that captures semantic dependence and temporal evolution jointly; (2) an adaptive RL framework that conducts multi-hop reasoning by adaptively learning the reward functions. Experimental results demonstrate DREAM outperforms state-of-the-art models on public datasetComment: 11 page

The Evolving Epidemiology of Elderly with Degenerative Valvular Heart Disease: The Guangzhou (China) Heart Study

Aim. The present study was aimed at investigating the prevalence, incidence, progression, and prognosis of degenerative valvular heart disease (DVHD) in permanent residents aged ≥65 years from Guangzhou, China. Methods. This was a prospective study based on community population. Over a 3-year span, we conducted repeated questionnaires, blood tests, and echocardiographic and electrocardiogram examinations (2018) of a random sample of initially 3538 subjects. Results. The prevalence of DVHD increased with age, average values being 30.6%, 49.2%, and 62.9% in 65-74, 75-84, and ≥85 years of age, respectively. The incidence rate was 1.7%/year. Aortic stenosis was the result of DVHD, and the mean transvalvular pressure gradient increased by 5.6 mmHg/year. The increase of mild aortic stenosis was lower than that of more severe disease, showing a nonlinear development of gradient, but with great individual variations. Mortality was significantly increased in the DVHD group (HR=2.49). Risk factors for higher mortality included age (χ2=1.9, P<0.05), renal insufficiency (χ2=12.5, P<0.01), atrial fibrillation (χ2=12.2, P<0.01), mitral regurgitation (χ2=1.8, P<0.05), and tricuspid regurgitation (χ2=6.7, P<0.05) in a DVHD population. Conclusions. DVHD was highly prevalent among residents in southern China. With the progression of the disease, the mean transvalvular pressure gradient accelerated. DVHD was an independent predictor of death, and the mortality was higher in those with older age, renal insufficiency, atrial fibrillation, mitral regurgitation, and tricuspid regurgitation

ET White Paper: To Find the First Earth 2.0

We propose to develop a wide-field and ultra-high-precision photometric survey mission, temporarily named "Earth 2.0 (ET)". This mission is designed to measure, for the first time, the occurrence rate and the orbital distributions of Earth-sized planets. ET consists of seven 30cm telescopes, to be launched to the Earth-Sun's L2 point. Six of these are transit telescopes with a field of view of 500 square degrees. Staring in the direction that encompasses the original Kepler field for four continuous years, this monitoring will return tens of thousands of transiting planets, including the elusive Earth twins orbiting solar-type stars. The seventh telescope is a 30cm microlensing telescope that will monitor an area of 4 square degrees toward the galactic bulge. This, combined with simultaneous ground-based KMTNet observations, will measure masses for hundreds of long-period and free-floating planets. Together, the transit and the microlensing telescopes will revolutionize our understandings of terrestrial planets across a large swath of orbital distances and free space. In addition, the survey data will also facilitate studies in the fields of asteroseismology, Galactic archeology, time-domain sciences, and black holes in binaries.Comment: 116 pages,79 figure