Valid Randomization Tests in Inexactly Matched Observational Studies via Iterative Convex Programming

In causal inference, matching is one of the most widely used methods to mimic a randomized experiment using observational (non-experimental) data. Ideally, treated units are exactly matched with control units for the covariates so that the treatments are as-if randomly assigned within each matched set, and valid randomization tests for treatment effects can then be conducted as in a randomized experiment. However, inexact matching typically exists, especially when there are continuous or many observed covariates or when unobserved covariates exist. Previous matched observational studies routinely conducted downstream randomization tests as if matching was exact, as long as the matched datasets satisfied some prespecified balance criteria or passed some balance tests. Some recent studies showed that this routine practice could render a highly inflated type-I error rate of randomization tests, especially when the sample size is large. To handle this problem, we propose an iterative convex programming framework for randomization tests with inexactly matched datasets. Under some commonly used regularity conditions, we show that our approach can produce valid randomization tests (i.e., robustly controlling the type-I error rate) for any inexactly matched datasets, even when unobserved covariates exist. Our framework allows the incorporation of flexible machine learning models to better extract information from covariate imbalance while robustly controlling the type-I error rate

Spherical Transformer: Adapting Spherical Signal to CNNs

Convolutional neural networks (CNNs) have been widely used in various vision tasks, e.g. image classification, semantic segmentation, etc. Unfortunately, standard 2D CNNs are not well suited for spherical signals such as panorama images or spherical projections, as the sphere is an unstructured grid. In this paper, we present Spherical Transformer which can transform spherical signals into vectors that can be directly processed by standard CNNs such that many well-designed CNNs architectures can be reused across tasks and datasets by pretraining. To this end, the proposed method first uses locally structured sampling methods such as HEALPix to construct a transformer grid by using the information of spherical points and its adjacent points, and then transforms the spherical signals to the vectors through the grid. By building the Spherical Transformer module, we can use multiple CNN architectures directly. We evaluate our approach on the tasks of spherical MNIST recognition, 3D object classification and omnidirectional image semantic segmentation. For 3D object classification, we further propose a rendering-based projection method to improve the performance and a rotational-equivariant model to improve the anti-rotation ability. Experimental results on three tasks show that our approach achieves superior performance over state-of-the-art methods

Fast Hybrid Cascade for Voxel-based 3D Object Classification

Voxel-based 3D object classification has been frequently studied in recent years. The previous methods often directly convert the classic 2D convolution into a 3D form applied to an object with binary voxel representation. In this paper, we investigate the reason why binary voxel representation is not very suitable for 3D convolution and how to simultaneously improve the performance both in accuracy and speed. We show that by giving each voxel a signed distance value, the accuracy will gain about 30% promotion compared with binary voxel representation using a two-layer fully connected network. We then propose a fast fully connected and convolution hybrid cascade network for voxel-based 3D object classification. This threestage cascade network can divide 3D models into three categories: easy, moderate and hard. Consequently, the mean inference time (0.3ms) can speedup about 5x and 2x compared with the state-of-the-art point cloud and voxel based methods respectively, while achieving the highest accuracy in the latter category of methods (92%). Experiments with ModelNet andMNIST verify the performance of the proposed hybrid cascade network

The Impacts of Emission Control and Regional Transport on PM2.5 Ions and Carbon Components in Nanjing during the 2014 Nanjing Youth Olympic Games

Highly time-resolved measurements of water soluble ions, organic and elemental carbon concentrations in the particle diameter size range D-p <2.5 mu m (PM2.5) were performed at a downwind urban site in Nanjing in the western part of the Yangtze River Delta (YRD) in eastern China during the 2014 Youth Olympic Games (YOG). In this study, we discuss the impacts of emission control in Nanjing and the surrounding areas during the YOG and regional/long-range transport on PM2.5 pollution in Nanjing. The average concentrations of NO3-, SO42-, NH4+ were 12.1 +/- 9.9, 16.5 +/- 9.2, 9.0 +/- 5.4 mu g m(-3) during the YOG, and increased 34.3%, 53.7%, 43.9% after the YOG, respectively. The control of construction or on-road soil dust and control of industry led to the decrease of Ca2+ concentration by 55% and SO2 concentration by 46%. However, SO42- concentrations remained at relatively high levels, suggesting a significant impact of regional pollution to secondary fine particles in Nanjing. Strong correlations between OC and EC were observed during and after the YOG. A higher percentage (41%) of secondary organic carbon in Nanjing during the YOG periods was consistent with high potential photochemistry and low contributions from coal combustion. Lagrangian dispersion modelling results proved that the city clusters along the Nanjing and Shanghai axis were the major source region for high PM2.5 pollution in upwind Nanjing. This work shows that short-term strict control measures could improve the air quality, especially that affected by the primary pollutants; however, regional collaborative control strategy across administrative borders in the YRD is needed for a substantial improvement of air quality.Peer reviewe