Application Research on Rank Return Method in Mathematics Achievement Appraisal

AbstractCompared with ordinary least squares, quantile regression can more fully reflect that the dependent variable has different effects in the different parts of the distribution of the independent variables, and has a very wide range of applications. The paper makes a brief introduction to the idea of quantile regression, applying the methods into mathematics achievements achievement and having a comparative analysis of the good or bad results about quantile regression and ordinary least squares under two kinds of external pressures to mathematics achievement

Socialist Political Economy With Chinese Characteristics And Research On The Chinese And Foreign Economies: A Survey of the Viewpoints Expressed by the New Marxian Economics Synthesis School in 2021

In 2021, responding to changes in the world political and economic situation and basing itself on Marxist political economy, the New Marxian Economics Synthesis School led by Professor Enfu Cheng carried forward its traditions and forged ahead into the future. The school conducted active, in-depth research on how to uphold the integrity of socialist political economy with Chinese characteristics and enrich it with new elements, putting forward a series of theoretical innovations in areas that include the ten essentials of socialist political economy with Chinese characteristics, its sources of innovation and logical starting point, the orientation of its practice, and so forth. Based on these theoretical innovations, many of the scholars who make up the school engaged in lively discussion on a range of focal issues of today’s Chinese economy, including common prosperity, the new “dual circulation” development pattern, artificial intelligence and the digital economy, the modernization of national governance and so on. In addition, they made searching criticisms of the financialization of the contemporary capitalist economy and of the new developments seen in liberalism and hegemonism since the COVID-19 pandemic broke out. In sum, they recorded a long series of fruitful theoretical achievements

FAC: 3D Representation Learning via Foreground Aware Feature Contrast

Contrastive learning has recently demonstrated great potential for unsupervised pre-training in 3D scene understanding tasks. However, most existing work randomly selects point features as anchors while building contrast, leading to a clear bias toward background points that often dominate in 3D scenes. Also, object awareness and foreground-to-background discrimination are neglected, making contrastive learning less effective. To tackle these issues, we propose a general foreground-aware feature contrast (FAC) framework to learn more effective point cloud representations in pre-training. FAC consists of two novel contrast designs to construct more effective and informative contrast pairs. The first is building positive pairs within the same foreground segment where points tend to have the same semantics. The second is that we prevent over-discrimination between 3D segments/objects and encourage foreground-to-background distinctions at the segment level with adaptive feature learning in a Siamese correspondence network, which adaptively learns feature correlations within and across point cloud views effectively. Visualization with point activation maps shows that our contrast pairs capture clear correspondences among foreground regions during pre-training. Quantitative experiments also show that FAC achieves superior knowledge transfer and data efficiency in various downstream 3D semantic segmentation and object detection tasks.Comment: 11 pages, IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (CVPR 2023), the work is mainly supported by the Natural Science Foundation Project of Fujian Province (2020J01826

Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning

Image restoration under hazy weather condition, which is called single image dehazing, has been of significant interest for various computer vision applications. In recent years, deep learning-based methods have achieved success. However, existing image dehazing methods typically neglect the hierarchy of features in the neural network and fail to exploit their relationships fully. To this end, we propose an effective image dehazing method named Hierarchical Contrastive Dehazing (HCD), which is based on feature fusion and contrastive learning strategies. HCD consists of a hierarchical dehazing network (HDN) and a novel hierarchical contrastive loss (HCL). Specifically, the core design in the HDN is a hierarchical interaction module, which utilizes multi-scale activation to revise the feature responses hierarchically. To cooperate with the training of HDN, we propose HCL which performs contrastive learning on hierarchically paired exemplars, facilitating haze removal. Extensive experiments on public datasets, RESIDE, HazeRD, and DENSE-HAZE, demonstrate that HCD quantitatively outperforms the state-of-the-art methods in terms of PSNR, SSIM and achieves better visual quality.Comment: 30 pages, 10 figure

Audio-Driven Talking Face Generation with Diverse yet Realistic Facial Animations

Audio-driven talking face generation, which aims to synthesize talking faces with realistic facial animations (including accurate lip movements, vivid facial expression details and natural head poses) corresponding to the audio, has achieved rapid progress in recent years. However, most existing work focuses on generating lip movements only without handling the closely correlated facial expressions, which degrades the realism of the generated faces greatly. This paper presents DIRFA, a novel method that can generate talking faces with diverse yet realistic facial animations from the same driving audio. To accommodate fair variation of plausible facial animations for the same audio, we design a transformer-based probabilistic mapping network that can model the variational facial animation distribution conditioned upon the input audio and autoregressively convert the audio signals into a facial animation sequence. In addition, we introduce a temporally-biased mask into the mapping network, which allows to model the temporal dependency of facial animations and produce temporally smooth facial animation sequence. With the generated facial animation sequence and a source image, photo-realistic talking faces can be synthesized with a generic generation network. Extensive experiments show that DIRFA can generate talking faces with realistic facial animations effectively

Adaptive transmission in heterogeneous networks

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166243/1/cmu2bf00018.pd