Corruption, productivity, and import liberalization in China : a firm-level analysis

Understanding whether and how corruption impacts firm productivity in China is crucial for promoting good governance of economic development. Based on our econometric model developed with China’s firm-level data, including detailed firm heterogeneity information and provincial records of government official-related corruption, we confirm that corruption acts as “sand” rather than “grease” in the wheels of firm productivity improvement. The hampering effect of corruption on firm productivity is not obvious for state-owned, relatively large-sized, and low productive firms, but it is quite significant for private, relatively small-sized, and high productive ones. More importantly, we find that a firm’s productivity gains from import liberalization are significantly inhibited by corruption. Therefore, if the institutional environment can be improved, firms in China possess great potential—especially private and small-sized firms—to be more efficient or be able to obtain more productivity gains from import liberalization

DAISY filter flow: A generalized discrete approach to dense correspondences

Establishing dense correspondences reliably between a pair of images is an important vision task with many ap-plications. Though significant advance has been made to-wards estimating dense stereo and optical flow fields for two images adjacent in viewpoint or in time, building re-liable dense correspondence fields for two general images still remains largely unsolved. For instance, two given im-ages sharing some content exhibit dramatic photometric and geometric variations, or they depict different 3D scenes of similar scene characteristics. Fundamental challenges to such an image or scene alignment task are often mul-tifold, which render many existing techniques fall short of producing dense correspondences robustly and efficiently. This paper presents a novel approach called DAISY filter flow (DFF) to address this challenging task. Inspired by the recent PatchMatch Filter technique, we leverage and extend a few established methods: 1) DAISY descriptors, 2) filter-based efficient flow inference, and 3) the Patch-Match fast search. Coupling and optimizing these mod-ules seamlessly with image segments as the bridge, the pro-posed DFF approach enables efficiently performing dense descriptor-based correspondence field estimation in a gen-eralized high-dimensional label space, which is augmented by scales and rotations. Experiments on a variety of chal-lenging scenes show that our DFF approach estimates spa-tially coherent yet discontinuity-preserving image align-ment results both robustly and efficiently. 1

ViFi-Loc: Multi-modal Pedestrian Localization using GAN with Camera-Phone Correspondences

In Smart City and Vehicle-to-Everything (V2X) systems, acquiring pedestrians' accurate locations is crucial to traffic safety. Current systems adopt cameras and wireless sensors to detect and estimate people's locations via sensor fusion. Standard fusion algorithms, however, become inapplicable when multi-modal data is not associated. For example, pedestrians are out of the camera field of view, or data from camera modality is missing. To address this challenge and produce more accurate location estimations for pedestrians, we propose a Generative Adversarial Network (GAN) architecture. During training, it learns the underlying linkage between pedestrians' camera-phone data correspondences. During inference, it generates refined position estimations based only on pedestrians' phone data that consists of GPS, IMU and FTM. Results show that our GAN produces 3D coordinates at 1 to 2 meter localization error across 5 different outdoor scenes. We further show that the proposed model supports self-learning. The generated coordinates can be associated with pedestrian's bounding box coordinates to obtain additional camera-phone data correspondences. This allows automatic data collection during inference. After fine-tuning on the expanded dataset, localization accuracy is improved by up to 26%