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

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

Aluminum impairs rat neural cell mitochondria in vitro.

Exposure to aluminum has been reported to lead to neurotoxicity. Mitochondria are important organelles involved in maintaining cell function. This study investigates the effect of aluminum on mitochondria in rat neural cells. The ultrastructure of mitochondria was observed, and the cell death rate (CDR), reactive oxygen species (ROS), mitochondrial membrane potential (MMP) and 3-[4,5demethyl-2-thiazalyl]-2,-5diphenyl-2H-tetrazolium bromide (MTT) were measured to investigate the effect of aluminum on the mitochondrial structure and its function in neural cells. Results observed from the mitochondrial ultrastructure show that aluminum may impair the mitochondrial membrane and cristae. Increased CDR, enhanced ROS, decreased MMP, and decreased enzyme activity in mitochondria were observed in the Al-exposed neurons (100 – 500 μM). The present study demonstrates that alteration in the mitochondrial structure and function plays an important role in neurotoxic mechanisms induced by aluminum

Multi-Task Recommendations with Reinforcement Learning

In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications. However, current MTL-based recommendation models tend to disregard the session-wise patterns of user-item interactions because they are predominantly constructed based on item-wise datasets. Moreover, balancing multiple objectives has always been a challenge in this field, which is typically avoided via linear estimations in existing works. To address these issues, in this paper, we propose a Reinforcement Learning (RL) enhanced MTL framework, namely RMTL, to combine the losses of different recommendation tasks using dynamic weights. To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks. Experiments on two real-world public datasets demonstrate the effectiveness of RMTL with a higher AUC against state-of-the-art MTL-based recommendation models. Additionally, we evaluate and validate RMTL's compatibility and transferability across various MTL models.Comment: TheWebConf202

GeneCAI: Genetic Evolution for Acquiring Compact AI

In the contemporary big data realm, Deep Neural Networks (DNNs) are evolving towards more complex architectures to achieve higher inference accuracy. Model compression techniques can be leveraged to efficiently deploy such compute-intensive architectures on resource-limited mobile devices. Such methods comprise various hyper-parameters that require per-layer customization to ensure high accuracy. Choosing such hyper-parameters is cumbersome as the pertinent search space grows exponentially with model layers. This paper introduces GeneCAI, a novel optimization method that automatically learns how to tune per-layer compression hyper-parameters. We devise a bijective translation scheme that encodes compressed DNNs to the genotype space. The optimality of each genotype is measured using a multi-objective score based on accuracy and number of floating point operations. We develop customized genetic operations to iteratively evolve the non-dominated solutions towards the optimal Pareto front, thus, capturing the optimal trade-off between model accuracy and complexity. GeneCAI optimization method is highly scalable and can achieve a near-linear performance boost on distributed multi-GPU platforms. Our extensive evaluations demonstrate that GeneCAI outperforms existing rule-based and reinforcement learning methods in DNN compression by finding models that lie on a better accuracy-complexity Pareto curve

Association between ecological risks and ecosystem services in an urban agglomeration in arid China

Rapid urbanization leads to changes in ecosystem services and may exacerbate ecological risks. Clarifying the relationship between these two factors in a specific context is essential to explore the integrated management model and achieve sustainable regional development. However, previous studies mainly lack an integrated analysis, fail to clearly explain the mechanism of ecosystem change, and can neither support landscape ecological security construction nor spatial planning and management. This study, using the urban agglomeration on the northern slope of the Tianshan Mountains (UANSTM) as an example, applied multi-source data from 2010 to 2020, investigated the changes and relationships between ecological risks and ecosystem services, and proposes an assessment framework. The total ecosystem services (TES) of the studied agglomeration showed a decreasing trend, with an overall loss of 0.43%. Corresponding to the decrease of ecosystem services, the ecological risk was higher in the south and north of the UANSTM and lower in the northwestern, central, and eastern regions. The proportion of ecological high-risk areas was expanding. The key to the relationship between ecological risks and ecosystem services is the change in hydrological conditions. Therefore, we suggest that the UANSTM actively transforms the development and use mode of water resources and coordinates their allocation, aiming to reduce regional ecological risks and optimize the pattern of ecosystem services

Role of emission controls in reducing the 2050 climate change penalty for PM2.5 in China

Previous studies demonstrated that global warming can lead to deteriorated air quality even when anthropogenic emissions were kept constant, which has been called a climate change penalty on air quality. It is expected that anthropogenic emissions will decrease significantly in the future considering the aggressive emission control actions in China. However, the dependence of climate change penalty on the choice of emission scenario is still uncertain. To fill this gap, we conducted multiple independent model simulations to investigate the response of PM2.5 to future (2050) climate warming (RCP8.5) in China but with different emission scenarios, including the constant 2015 emissions, the 2050 CLE emissions (based on Current Legislation), and the 2050 MTFR emissions (based on Maximum Technically Feasible Reduction). For each set of emissions, we estimate climate change penalty as the difference in PM2.5 between a pair of simulations with either 2015 or 2050 meteorology. Under 2015 emissions, we find a PM2.5 climate change penalty of 1.43 μg m−3 in Eastern China, leading to an additional 35,000 PM2.5-related premature deaths [95% confidence interval (CI), 21,000-40,000] by 2050. However, the PM2.5 climate change penalty weakens to 0.24 μg m−3 with strict anthropogenic emission controls under the 2050 MTFR emissions, which decreases the associated PM2.5-related deaths to 17,000. The smaller MTFR climate change penalty contributes 14% of the total PM2.5 decrease when both emissions and meteorology are changed from 2015 to 2050, and 24% of total health benefits associated with this PM2.5 decrease in Eastern China. This finding suggests that controlling anthropogenic emissions can effectively reduce the climate change penalty on PM2.5 and its associated premature deaths, even though a climate change penalty still occurs even under MTFR. Strengthened controls on anthropogenic emissions are key to attaining air quality targets and protecting human health in the context of future global climate change

Surface functionalization of vertical graphene significantly enhances the energy storage capability for symmetric supercapacitors

Vertical graphene (VG) sheets, which consist of few-layer graphene vertically aligned on the substrate with three dimensionally interconnected porous network, make them become one of the most promising energy storage electrodes, especially for SCs. Nevertheless, the intrinsic hydrophobic nature of pristine VG sheets severely limited its application in aqueous SCs. Here, electrochemical oxidation strategy is adopted to increase the hydrophilicity of VG sheets by introducing oxygen functional groups so that the aqueous electrolyte can fully be in contact with the VG sheets to improve charge storage performance. Our work demonstrated that the introduction of oxygen functional groups not only greatly improved the hydrophilicity but also generated a pseudo capacitance to increase the specific capacitance. The resulting capacitance of electrochemically oxidized VG for 7 min (denoted as EOVG-7) exhibited three orders of magnitude higher (1605 mF/cm²) compared to pristine VG sheets. Through assembled two EOVG-7 electrodes, a symmetric supercapacitor demonstrated high specific capacitance of 307.5 mF/cm², high energy density of 138.3 μWh/cm2 as well as excellent cyclic stability (84% capacitance retention after 10000 cycles). This strategy provides a promising way for designing and engineering carbon-based aqueous supercapacitors with high performance