109 research outputs found
Keys to Women’s Liberation in Communist China: An Historical Overview
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
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?
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
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
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
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
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
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
- …