Figure 1: We introduce datasets for 3D tracking and motion forecasting with rich maps for autonomous driving. Our 3D tracking dataset contains sequences of LiDAR measurements, 360 • RGB video, front-facing stereo (middle-right), and 6-dof localization. All sequences are aligned with maps containing lane center lines (magenta), driveable region (orange), and ground height. Sequences are annotated with 3D cuboid tracks (green). A wider map view is shown in the bottom-right. AbstractWe present Argoverse -two datasets designed to support autonomous vehicle machine learning tasks such as 3D tracking and motion forecasting. Argoverse was collected by a fleet of autonomous vehicles in Pittsburgh and Miami. The Argoverse 3D Tracking dataset includes 360 • images from 7 cameras with overlapping fields of view, 3D point clouds from long range LiDAR, 6-DOF pose, and 3D track annotations. Notably, it is the only modern AV dataset that provides forward-facing stereo imagery. The Argoverse Motion Forecasting dataset includes more than 300,000 5 second tracked scenarios with a particular vehicle identified for trajectory forecasting. Argoverse is the first autonomous vehicle dataset to include "HD maps" with 290 km of mapped lanes with geometric and semantic metadata. All data is released under a Creative Commons license at *Equal contribution www.argoverse.org. In our baseline experiments, we illustrate how detailed map information such as lane direction, driveable area, and ground height improves the accuracy of 3D object tracking and motion forecasting. Our tracking and forecasting experiments represent only an initial exploration of the use of rich maps in robotic perception. We hope that Argoverse will enable the research community to explore these problems in greater depth.

We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The database covers a wide range of realistic tokamak core parameters. Physical features such as the existence of a critical gradient for the onset of turbulent transport were integrated into the neural network training methodology. We have coupled QLKNN to the tokamak modelling framework JINTRAC and rapid control-oriented tokamak transport solver RAPTOR. The coupled frameworks are demonstrated and validated through application to three JET shots covering a representative spread of H-mode operating space, predicting turbulent transport of energy and particles in the plasma core. JINTRAC-QLKNN and RAPTOR-QLKNN are able to accurately reproduce JINTRAC-QuaLiKiz T i,e and n e profiles, but 3 to 5 orders of magnitude faster. Simulations which take hours are reduced down to only a few tens of seconds. The discrepancy in the final source-driven predicted profiles between QLKNN and QuaLiKiz is on the order 1%-15%. Also the dynamic behaviour was well captured by QLKNN, with differences of only 4%-10% compared to JINTRAC-QuaLiKiz observed at mid-radius, for a study of density buildup following the L-H transition. Deployment of neural network surrogate models in multi-physics integrated tokamak modelling is a promising route towards enabling accurate and fast tokamak scenario optimization, Uncertainty Quantification, and control applications.

Urban infrastructure has been substantially upgraded in reform-era China. This paper explains, contextually and empirically, how Chinese cities finance their infrastructure. It demonstrates that China has succeeded in addressing urban infrastructure backlogs by opening up new venues for financing, but simultaneously, heavily relying on unconventional sources. The paper also argues that urban infrastructure financing has much to do with the country’s transition to a market-oriented economy that fosters the pro-growth role of city governments as well as the redistribution of fiscal power between the levels of the urban hierarchy that produces significant variation of financial capacities among the different administrative ranks of cities.

Abstract-With an average of 80% length reduction, the URL shorteners have become the norm for sharing URLs on Twitter, mainly due to the 140-character limit per message. Unfortunately, spammers have also adopted the URL shorteners to camouflage and improve the user click-through of their spam URLs. In this paper, we measure the misuse of the short URLs and analyze the characteristics of the spam and non-spam short URLs. We utilize these measurements to enable the detection of spam short URLs. To achieve this, we collected short URLs from Twitter and retrieved their click traffic data from Bitly, a popular URL shortening system. We first investigate the creators of over 600,000 Bitly short URLs to characterize short URL spammers. We then analyze the click traffic generated from various countries and referrers, and determine the top click sources for spam and non-spam short URLs. Our results show that the majority of the clicks are from direct sources and that the spammers utilize popular websites to attract more attention by cross-posting the links. We then use the click traffic data to classify the short URLs into spam vs. non-spam and compare the performance of the selected classifiers on the dataset. We determine that the Random Tree algorithm achieves the best performance with an accuracy of 90.81% and an F-measure value of 0.913.