427 research outputs found
Degradation and restoration of lake ecosystem in the Changjiang (Yangtze) River Basin
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Triangular BĆ©zier sub-surfaces on a triangular BĆ©zier surface
This paper considers the problem of computing the BĆ©zier representation for a triangular sub-patch on a triangular BĆ©zier surface. The triangular sub-patch is defined as a composition of the triangular surface and a domain surface that is also a triangular BĆ©zier patch. Based on de Casteljau recursions and shifting operators, previous methods express the control points of the triangular sub-patch as linear combinations of the construction points that are constructed from the control points of the triangular BĆ©zier surface. The construction points contain too many redundancies. This paper derives a simple explicit formula that computes the composite triangular sub-patch in terms of the blossoming points that correspond to distinct construction points and then an efficient algorithm is presented to calculate the control points of the sub-patch
Exploring the Style of Korean Girl Group NewJeans in the Context of Contemporary Consumerism
Instead of focusing on the girl crus style commonly adopted by fourth-generation girl groups, i.e. the rebellion against the āmale gazeā and the conceptualization of criticism, the new group NewJeans focused on the emotional growth of women themselves in their teenage years, highlighting the different perceptions of women. It focuses on the emotional growth of women during their adolescence and highlights the attention to the different perceptions of women as individuals. At the same time, the visual image of New Jeans is shaped by the optimistic Y2K aesthetic
A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor Segmentation
Magnetic resonance imaging (MRI) is a commonly used technique for brain tumor
segmentation, which is critical for evaluating patients and planning treatment.
To make the labeling process less laborious and dependent on expertise,
weakly-supervised semantic segmentation (WSSS) methods using class activation
mapping (CAM) have been proposed. However, current CAM-based WSSS methods
generate the object localization map using internal neural network information,
such as gradient or trainable parameters, which can lead to suboptimal
solutions. To address these issues, we propose the confidence-induced CAM
(Cfd-CAM), which calculates the weight of each feature map by using the
confidence of the target class. Our experiments on two brain tumor datasets
show that Cfd-CAM outperforms existing state-of-the-art methods under the same
level of supervision. Overall, our proposed Cfd-CAM approach improves the
accuracy of brain tumor segmentation and may provide valuable insights for
developing better WSSS methods for other medical imaging tasks
Contact Optimization for Non-Prehensile Loco-Manipulation via Hierarchical Model Predictive Control
Recent studies on quadruped robots have focused on either locomotion or
mobile manipulation using a robotic arm. Legged robots can manipulate heavier
and larger objects using non-prehensile manipulation primitives, such as planar
pushing, to drive the object to the desired location. In this paper, we present
a novel hierarchical model predictive control (MPC) for contact optimization of
the manipulation task. Using two cascading MPCs, we split the loco-manipulation
problem into two parts: the first to optimize both contact force and contact
location between the robot and the object, and the second to regulate the
desired interaction force through the robot locomotion. Our method is
successfully validated in both simulation and hardware experiments. While the
baseline locomotion MPC fails to follow the desired trajectory of the object,
our proposed approach can effectively control both object's position and
orientation with minimal tracking error. This capability also allows us to
perform obstacle avoidance for both the robot and the object during the
loco-manipulation task.Comment: 7 pages, 9 figure
Research on the Cultivation of College Studentsā Subjective Consciousness in the New Era: From the Perspective of the Comparative Study of the Ideological and Political Education Between China and the United States
Ideological and political education, as its name implies, has distinctive political and ideological character. It is a process in which governors spread the political concepts and ideologies of a particular society to members of society to maintain the ruling orders of the society. China and the United States, though in different polities and national conditions, use ideological and political education as a tool of political socialization. The contents of American ideological and political education are basically embodied in the connotation and denotation of civic education. Therefore, this article equates the ideological and political education in the United States with American civic education. Based on the history and development of the ideological and political education in China and United States, by analyzing their differences in terms of contents, approaches, and studentsā subject status, this article focuses on highlighting the difference in cultivating the subjective consciousness of college students between the two modes of education.In addition to possessing the scientific knowledge and technological abilities necessary for the development of China, the talents serving the socialist modernization construction should also be equipped with the qualities of modern citizens. As the important institution for cultivating modern talentsļ¼apart from undertaking the responsibility of imparting professional knowledge, universities should also play the role of modern civic education and integrate modern civic education into the ideological and political education of college students. The modern civic education in universities and the cultivation of university studentsā subjective consciousness are mutual cause and effect, promoting each other, and developing together. Therefore, in order to promote the construction and perfection of studentsā subjective consciousness and the development of modern civic education in universities, by drawing lessons from the relevant experience in cultivating the subject consciousness of college students in the United States, this article puts forward the three approaches of āreforming the educational pattern by respecting studentsā subjective valueā, ābroadening the educational channels by integrating studentsā surroundingsā and āenhancing the effectiveness of education by mobilizing the subjective initiative of studentsā to cultivate students to become socialist modernization builders who are not only knowledgeable and competent, but also independent and socially responsible, embracing both individualistic and collectivist values
CasIL: Cognizing and Imitating Skills via a Dual Cognition-Action Architecture
Enabling robots to effectively imitate expert skills in longhorizon tasks
such as locomotion, manipulation, and more, poses a long-standing challenge.
Existing imitation learning (IL) approaches for robots still grapple with
sub-optimal performance in complex tasks. In this paper, we consider how this
challenge can be addressed within the human cognitive priors. Heuristically, we
extend the usual notion of action to a dual Cognition (high-level)-Action
(low-level) architecture by introducing intuitive human cognitive priors, and
propose a novel skill IL framework through human-robot interaction, called
Cognition-Action-based Skill Imitation Learning (CasIL), for the robotic agent
to effectively cognize and imitate the critical skills from raw visual
demonstrations. CasIL enables both cognition and action imitation, while
high-level skill cognition explicitly guides low-level primitive actions,
providing robustness and reliability to the entire skill IL process. We
evaluated our method on MuJoCo and RLBench benchmarks, as well as on the
obstacle avoidance and point-goal navigation tasks for quadrupedal robot
locomotion. Experimental results show that our CasIL consistently achieves
competitive and robust skill imitation capability compared to other
counterparts in a variety of long-horizon robotic tasks
Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation
Recent advances in denoising diffusion probabilistic models have shown great
success in image synthesis tasks. While there are already works exploring the
potential of this powerful tool in image semantic segmentation, its application
in weakly supervised semantic segmentation (WSSS) remains relatively
under-explored. Observing that conditional diffusion models (CDM) is capable of
generating images subject to specific distributions, in this work, we utilize
category-aware semantic information underlied in CDM to get the prediction mask
of the target object with only image-level annotations. More specifically, we
locate the desired class by approximating the derivative of the output of CDM
w.r.t the input condition. Our method is different from previous diffusion
model methods with guidance from an external classifier, which accumulates
noises in the background during the reconstruction process. Our method
outperforms state-of-the-art CAM and diffusion model methods on two public
medical image segmentation datasets, which demonstrates that CDM is a promising
tool in WSSS. Also, experiment shows our method is more time-efficient than
existing diffusion model methods, making it practical for wider applications
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor
Magnetic resonance imaging (MRI) is commonly used for brain tumor
segmentation, which is critical for patient evaluation and treatment planning.
To reduce the labor and expertise required for labeling, weakly-supervised
semantic segmentation (WSSS) methods with class activation mapping (CAM) have
been proposed. However, existing CAM methods suffer from low resolution due to
strided convolution and pooling layers, resulting in inaccurate predictions. In
this study, we propose a novel CAM method, Attentive Multiple-Exit CAM
(AME-CAM), that extracts activation maps from multiple resolutions to
hierarchically aggregate and improve prediction accuracy. We evaluate our
method on the BraTS 2021 dataset and show that it outperforms state-of-the-art
methods.Comment: arXiv admin note: text overlap with arXiv:2306.0547
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