Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks

Quantized Neural Networks (QNNs), which use low bitwidth numbers for representing parameters and performing computations, have been proposed to reduce the computation complexity, storage size and memory usage. In QNNs, parameters and activations are uniformly quantized, such that the multiplications and additions can be accelerated by bitwise operations. However, distributions of parameters in Neural Networks are often imbalanced, such that the uniform quantization determined from extremal values may under utilize available bitwidth. In this paper, we propose a novel quantization method that can ensure the balance of distributions of quantized values. Our method first recursively partitions the parameters by percentiles into balanced bins, and then applies uniform quantization. We also introduce computationally cheaper approximations of percentiles to reduce the computation overhead introduced. Overall, our method improves the prediction accuracies of QNNs without introducing extra computation during inference, has negligible impact on training speed, and is applicable to both Convolutional Neural Networks and Recurrent Neural Networks. Experiments on standard datasets including ImageNet and Penn Treebank confirm the effectiveness of our method. On ImageNet, the top-5 error rate of our 4-bit quantized GoogLeNet model is 12.7\%, which is superior to the state-of-the-arts of QNNs

Ungerpricing [sic] and long-run performance of Chinese IPOs

This study examines the underpricing and long-term performance of A-share initial public offerings (IPOs) issued in China between January 1996 and December 2004. The sample is divided into State Owned Enterprises (SOEs) and non-State Owned Enterprises (non-SOEs) to investigate the difference in IPO underpricing and long-term performance between these two groups. I find that non-SOEs are significantly less underpriced than SOEs. In addition, I find that the changes in government policies do have an impact on IPO underpricing. My study confirms the results of previous studies that the long-term stock returns of Chinese IPOs is positive using a market index as a benchmark, while the long-term operating performance of these IPO firms exhibits negative changes. However, the SOE and non-SOE sub-samples do not show any significant differences in either long-term stock returns or operating performance when size- and book-to-market-matched portfolios are used as benchmarks. Finally, my findings are also consistent with the signaling theory of IPOs

Localization in the Incommensurate Systems: A Plane Wave Study via Effective Potentials

In this paper, we apply the effective potentials in the localization landscape theory (Filoche et al., 2012, Arnold et al., 2016) to study the spectral properties of the incommensurate systems. We uniquely develop a plane wave method for the effective potentials of the incommensurate systems and utilize that, the localization of the electron density can be inferred from the effective potentials. Moreover, we show that the spectrum distribution can also be obtained from the effective potential version of Weyl's law. We perform some numerical experiments on some typical incommensurate systems, showing that the effective potential provides an alternative tool for investigating the localization and spectrum distribution of the systems.Comment: 14page

EAST: An Efficient and Accurate Scene Text Detector

Previous approaches for scene text detection have already achieved promising performances across various benchmarks. However, they usually fall short when dealing with challenging scenarios, even when equipped with deep neural network models, because the overall performance is determined by the interplay of multiple stages and components in the pipelines. In this work, we propose a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes. The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images, eliminating unnecessary intermediate steps (e.g., candidate aggregation and word partitioning), with a single neural network. The simplicity of our pipeline allows concentrating efforts on designing loss functions and neural network architecture. Experiments on standard datasets including ICDAR 2015, COCO-Text and MSRA-TD500 demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods in terms of both accuracy and efficiency. On the ICDAR 2015 dataset, the proposed algorithm achieves an F-score of 0.7820 at 13.2fps at 720p resolution.Comment: Accepted to CVPR 2017, fix equation (3

Neoproterozoic subduction along the Ailaoshan zone, South China : geochronological and geochemical evidence from amphibolite

This study was supported by China Natural Science Foundation (41190073 and 41372198), National Basic Research Program of China (2014CB440901) and Natural Environment Research Council (grant NE/J021822/1).Lenses of amphibolites occur along the Ailaoshan suture zone at the southwestern margin of the Yangtze Block, South China. Petrological, geochemical and zircon U-Pb geochronological data indicate that they are divisible into two coeval groups. Group 1, represented by the Jinping amphibolite, has mg-number of 71-76 and (La/Yb)cn ratios of 7.2-7.7, and displays a geochemical affinity to island arc volcanic rocks. Group 2 amphibolites occur at Yuanyang and are characterized by high Nb contents (14.3-18.4 ppm), resembling Nb-enriched basalts. The epsilon(Nd)(t) values for Group 1 range from -3.45 to -2.04 and for Group 2 from +4.08 to +4.39. A representative sample for Group 1 yields a U-Pb zircon age of 803 7 Ma, whereas two samples for Group 2 give U-Pb zircon ages of 813 +/- 11 Ma and 814 +/- 12 Ma. Petrogenetic analysis suggests that Group 1 originated from an orthopyroxene-rich source and Group 2 from a mantle wedge modified by slab-derived melt. In combination with other geological observations, these amphibolites are inferred to constitute part of an early Neoproterozoic (similar to 815-800 Ma) arc-back-arc basin system. The Neoproterozoic amphibolites and related rocks along the Ailaoshan zone may be the southward extension of the Neoproterozoic supra-subduction zone that developed along the western margin of the Yangtze Block. (C) 2014 Elsevier B.V. All rights reserved.PostprintPeer reviewe

Physics-guided Noise Neural Proxy for Low-light Raw Image Denoising

Low-light raw image denoising plays a crucial role in mobile photography, and learning-based methods have become the mainstream approach. Training the learning-based methods with synthetic data emerges as an efficient and practical alternative to paired real data. However, the quality of synthetic data is inherently limited by the low accuracy of the noise model, which decreases the performance of low-light raw image denoising. In this paper, we develop a novel framework for accurate noise modeling that learns a physics-guided noise neural proxy (PNNP) from dark frames. PNNP integrates three efficient techniques: physics-guided noise decoupling (PND), physics-guided proxy model (PPM), and differentiable distribution-oriented loss (DDL). The PND decouples the dark frame into different components and handles different levels of noise in a flexible manner, which reduces the complexity of the noise neural proxy. The PPM incorporates physical priors to effectively constrain the generated noise, which promotes the accuracy of the noise neural proxy. The DDL provides explicit and reliable supervision for noise modeling, which promotes the precision of the noise neural proxy. Extensive experiments on public low-light raw image denoising datasets and real low-light imaging scenarios demonstrate the superior performance of our PNNP framework

Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation

In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising. Compared with traditional RGB image denoising, performing this task on direct camera sensor readings presents new challenges such as how to effectively handle various Bayer patterns from different data sources, and subsequently how to perform valid data augmentation with raw images. To address the first problem, we propose a Bayer pattern unification (BayerUnify) method to unify different Bayer patterns. This allows us to fully utilize a heterogeneous dataset to train a single denoising model instead of training one model for each pattern. Furthermore, while it is essential to augment the dataset to improve model generalization and performance, we discovered that it is error-prone to modify raw images by adapting augmentation methods designed for RGB images. Towards this end, we present a Bayer preserving augmentation (BayerAug) method as an effective approach for raw image augmentation. Combining these data processing technqiues with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969 in NTIRE 2019 Real Image Denoising Challenge, demonstrating the state-of-the-art performance. Our code is available at https://github.com/Jiaming-Liu/BayerUnifyAug.Comment: Accepted by CVPRW 201