Degradation and restoration of lake ecosystem in the Changjiang (Yangtze) River Basin

Article信州大学理学部附属諏訪臨湖実験所報告 9: 35-41(1995)departmental bulletin pape

Triangular Bézier sub-surfaces on a triangular Bézier surface

This paper considers the problem of computing the Bézier representation for a triangular sub-patch on a triangular Bézier surface. The triangular sub-patch is defined as a composition of the triangular surface and a domain surface that is also a triangular Bézier 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 Bézier 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 “male gaze” 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

Research on the Cultivation of College Students’ 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 students’ 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 talents，apart 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 students’ subjective consciousness are mutual cause and effect, promoting each other, and developing together. Therefore, in order to promote the construction and perfection of students’ 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 “reforming the educational pattern by respecting students’ subjective value”, “broadening the educational channels by integrating students’ surroundings” and “enhancing the effectiveness of education by mobilizing the subjective initiative of students” 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

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

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

Hamilton-Jacobi Reachability Analysis for Hybrid Systems with Controlled and Forced Transitions

Hybrid dynamical systems with non-linear dynamics are one of the most general modeling tools for representing robotic systems, especially contact-rich systems. However, providing guarantees regarding the safety or performance of such hybrid systems can still prove to be a challenging problem because it requires simultaneous reasoning about continuous state evolution and discrete mode switching. In this work, we address this problem by extending classical Hamilton-Jacobi (HJ) reachability analysis, a formal verification method for continuous non-linear dynamics in the presence of bounded inputs and disturbances, to hybrid dynamical systems. Our framework can compute reachable sets for hybrid systems consisting of multiple discrete modes, each with its own set of non-linear continuous dynamics, discrete transitions that can be directly commanded or forced by a discrete control input, while still accounting for control bounds and adversarial disturbances in the state evolution. Along with the reachable set, the proposed framework also provides an optimal continuous and discrete controller to ensure system safety. We demonstrate our framework in simulation on an aircraft collision avoidance problem, as well as on a real-world testbed to solve the optimal mode planning problem for a quadruped with multiple gaits