109 research outputs found

    Keys to Women’s Liberation in Communist China: An Historical Overview

    Get PDF
    Has the Communist Party of China (CPC) fully liberated Chinese women? Is the leadership of the CPC the key to Chinese women’s liberation in the twenty-first century? The CPC has tried to convince the Chinese people and international society to believe that the answer is positive. Having examined the status of Chinese women from an historical perspective, the author has reached the conclusion that women’s problems in present-day China are not only serious but also structural. It is impossible for Chinese women to fully enjoy women’s rights within the current communist system. The future of women’s liberation largely depends on women’s own efforts combined with the process of China’s modernization and the urgent need for democratization

    Solution to an Optimal Control Problem via Canonical Dual Method

    Get PDF
    The analytic solution to an optimal control problem is investigated using the canonical dual method. By means of the Pontryagin principle and a transformation of the cost functional, the optimal control of a nonconvex problem is obtained. It turns out that the optimal control can be expressed by the costate via canonical dual variables. Some examples are illustrated

    Will the Communist Party of China Be Able to Win the Anticorruption Battle?

    Get PDF
    Since the Eighteenth National Congress of the Communist Party of China (CPC) in 2012, the CPC has made great efforts to implement Xi Jinping's blueprint for achieving the “China Dream”. The on-going anticorruption campaign is part of the road map towards the “China Dream”. There has been impressive progress in fighting corruption, but the CPC recognizes that the anticorruption campaign faces a huge challenge and is at a crucial stage. The anticorruption campaign is a life-and-death battle that the CPC cannot afford to lose. The critical question is: How can the CPC win the battle in the current Chinese political system? The intention of this paper is not to offer specific measures, but to discuss policy implications by elucidating why some existing anticorruption measures do not work through examining the relationship between corruption and Chinese market economy and the political system. The basic assumption of this paper is that corruption is universal, but the characteristics of China’s corruption are different from other nations due to the nature of the Chinese economic, political and cultural systems. The key to anticorruption is to find and deal with the real causes of China’s corruption in order to make effective anticorruption measures. There are two opposite perspectives concerning the causes of corruption: While one suggests that the primary source of corruption is the political system, the other contends that corruption has nothing to do with the socialist political system. This paper attempts to argue that either denying or overemphasizing the roles of the current political system in spreading corruption is one-sided

    Discriminative and Robust Online Learning for Siamese Visual Tracking

    Full text link
    The problem of visual object tracking has traditionally been handled by variant tracking paradigms, either learning a model of the object's appearance exclusively online or matching the object with the target in an offline-trained embedding space. Despite the recent success, each method agonizes over its intrinsic constraint. The online-only approaches suffer from a lack of generalization of the model they learn thus are inferior in target regression, while the offline-only approaches (e.g., convolutional siamese trackers) lack the target-specific context information thus are not discriminative enough to handle distractors, and robust enough to deformation. Therefore, we propose an online module with an attention mechanism for offline siamese networks to extract target-specific features under L2 error. We further propose a filter update strategy adaptive to treacherous background noises for discriminative learning, and a template update strategy to handle large target deformations for robust learning. Effectiveness can be validated in the consistent improvement over three siamese baselines: SiamFC, SiamRPN++, and SiamMask. Beyond that, our model based on SiamRPN++ obtains the best results over six popular tracking benchmarks and can operate beyond real-time

    Scene-Conditional 3D Object Stylization and Composition

    Full text link
    Recently, 3D generative models have made impressive progress, enabling the generation of almost arbitrary 3D assets from text or image inputs. However, these approaches generate objects in isolation without any consideration for the scene where they will eventually be placed. In this paper, we propose a framework that allows for the stylization of an existing 3D asset to fit into a given 2D scene, and additionally produce a photorealistic composition as if the asset was placed within the environment. This not only opens up a new level of control for object stylization, for example, the same assets can be stylized to reflect changes in the environment, such as summer to winter or fantasy versus futuristic settings-but also makes the object-scene composition more controllable. We achieve this by combining modeling and optimizing the object's texture and environmental lighting through differentiable ray tracing with image priors from pre-trained text-to-image diffusion models. We demonstrate that our method is applicable to a wide variety of indoor and outdoor scenes and arbitrary objects

    Exploring Target Representations for Masked Autoencoders

    Full text link
    Masked autoencoders have become popular training paradigms for self-supervised visual representation learning. These models randomly mask a portion of the input and reconstruct the masked portion according to the target representations. In this paper, we first show that a careful choice of the target representation is unnecessary for learning good representations, since different targets tend to derive similarly behaved models. Driven by this observation, we propose a multi-stage masked distillation pipeline and use a randomly initialized model as the teacher, enabling us to effectively train high-capacity models without any efforts to carefully design target representations. Interestingly, we further explore using teachers of larger capacity, obtaining distilled students with remarkable transferring ability. On different tasks of classification, transfer learning, object detection, and semantic segmentation, the proposed method to perform masked knowledge distillation with bootstrapped teachers (dBOT) outperforms previous self-supervised methods by nontrivial margins. We hope our findings, as well as the proposed method, could motivate people to rethink the roles of target representations in pre-training masked autoencoders.The code and pre-trained models are publicly available at https://github.com/liuxingbin/dbot.Comment: The first two authors contributed equall

    Decomposition Ascribed Synergistic Learning for Unified Image Restoration

    Full text link
    Learning to restore multiple image degradations within a single model is quite beneficial for real-world applications. Nevertheless, existing works typically concentrate on regarding each degradation independently, while their relationship has been less exploited to ensure the synergistic learning. To this end, we revisit the diverse degradations through the lens of singular value decomposition, with the observation that the decomposed singular vectors and singular values naturally undertake the different types of degradation information, dividing various restoration tasks into two groups,\ie, singular vector dominated and singular value dominated. The above analysis renders a more unified perspective to ascribe the diverse degradations, compared to previous task-level independent learning. The dedicated optimization of degraded singular vectors and singular values inherently utilizes the potential relationship among diverse restoration tasks, attributing to the Decomposition Ascribed Synergistic Learning (DASL). Specifically, DASL comprises two effective operators, namely, Singular VEctor Operator (SVEO) and Singular VAlue Operator (SVAO), to favor the decomposed optimization, which can be lightly integrated into existing convolutional image restoration backbone. Moreover, the congruous decomposition loss has been devised for auxiliary. Extensive experiments on blended five image restoration tasks demonstrate the effectiveness of our method, including image deraining, image dehazing, image denoising, image deblurring, and low-light image enhancement.Comment: 13 page

    FPM-WSI: Fourier ptychographic whole slide imaging via feature-domain backdiffraction

    Full text link
    Fourier ptychographic microscopy (FPM), characterized by high-throughput computational imaging, theoretically provides a cunning solution to the trade-off between spatial resolution and field of view (FOV), which has a promising prospect in the application of digital pathology. However, block reconstruction and then stitching has currently become an unavoidable procedure due to vignetting effects. The stitched image tends to present color inconsistency in different image segments, or even stitching artifacts. In response, we reported a computational framework based on feature-domain backdiffraction to realize full-FOV, stitching-free FPM reconstruction. Different from conventional algorithms that establish the loss function in the image domain, our method formulates it in the feature domain, where effective information of images is extracted by a feature extractor to bypass the vignetting effect. The feature-domain error between predicted images based on estimation of model parameters and practically captured images is then digitally diffracted back through the optical system for complex amplitude reconstruction and aberration compensation. Through massive simulations and experiments, the method presents effective elimination of vignetting artifacts, and reduces the requirement of precise knowledge of illumination positions. We also found its great potential to recover the data with a lower overlapping rate of spectrum and to realize automatic blind-digital refocusing without a prior defocus distance
    • …
    corecore