End-to-End Reinforcement Learning for Automatic Taxonomy Induction

We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. While prior methods treat the problem as a two-phase task (i.e., detecting hypernymy pairs followed by organizing these pairs into a tree-structured hierarchy), we argue that such two-phase methods may suffer from error propagation, and cannot effectively optimize metrics that capture the holistic structure of a taxonomy. In our approach, the representations of term pairs are learned using multiple sources of information and used to determine \textit{which} term to select and \textit{where} to place it on the taxonomy via a policy network. All components are trained in an end-to-end manner with cumulative rewards, measured by a holistic tree metric over the training taxonomies. Experiments on two public datasets of different domains show that our approach outperforms prior state-of-the-art taxonomy induction methods up to 19.6\% on ancestor F1.Comment: 11 Pages. ACL 2018 Camera Read

Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast

In this paper, we present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For this purpose, we establish a data-driven environment by downloading $43$ years of hourly global weather data from the 5th generation of ECMWF reanalysis (ERA5) data and train a few deep neural networks with about $256$ million parameters in total. The spatial resolution of forecast is $0.25^\circ\times0.25^\circ$, comparable to the ECMWF Integrated Forecast Systems (IFS). More importantly, for the first time, an AI-based method outperforms state-of-the-art numerical weather prediction (NWP) methods in terms of accuracy (latitude-weighted RMSE and ACC) of all factors (e.g., geopotential, specific humidity, wind speed, temperature, etc.) and in all time ranges (from one hour to one week). There are two key strategies to improve the prediction accuracy: (i) designing a 3D Earth Specific Transformer (3DEST) architecture that formulates the height (pressure level) information into cubic data, and (ii) applying a hierarchical temporal aggregation algorithm to alleviate cumulative forecast errors. In deterministic forecast, Pangu-Weather shows great advantages for short to medium-range forecast (i.e., forecast time ranges from one hour to one week). Pangu-Weather supports a wide range of downstream forecast scenarios, including extreme weather forecast (e.g., tropical cyclone tracking) and large-member ensemble forecast in real-time. Pangu-Weather not only ends the debate on whether AI-based methods can surpass conventional NWP methods, but also reveals novel directions for improving deep learning weather forecast systems.Comment: 19 pages, 13 figures: the first ever AI-based method that outperforms traditional numerical weather prediction method

A novel CT-guided technique using medical adhesive for localization of small pulmonary ground-glass nodules and mixed ground-glass nodules (≤20 mm) before video-assisted thoracoscopic surgery

PURPOSE:We aimed to evaluate the success rate and complication occurrence of CT-guided localization of small pure ground-glass nodules (pGGNs) and mixed ground-glass nodules (mGGNs) with medical adhesive injection before video-assisted thoracoscopic surgery (VATS).METHODS:From March 2015 to May 2017, 41 patients with 44 small pGGNs and mGGNs underwent CT-guided percutaneous localization with medical adhesive prior to wedge resection by VATS.RESULTS:Localization with medical adhesive was successful in all patients (100%). The nodules (13 pGGNs, 31 mGGNs) had a mean maximal long-axis diameter of 9±4 mm and a mean distance of 10±7 mm from the most superficial edge of the nodule to the visceral pleura. The localization time was 16±8 minutes. There was a moderate inverse relationship between localization time and the nodule diameter (r= -0.42, P = 0.005). Thirty-three nodules with primary lung cancer were pathologically confirmed. There were 3 cases of pneumothorax (7%), 3 cases of parenchyma hemorrhage (7%) and 2 cases of irritable cough (5%), respectively. No conversion to thoracotomy was necessary in any patient.CONCLUSION:CT-guided percutaneous localization with medical adhesive can label small pGGNs and mGGNs prior to VATS, with high success and low complication rates