Research on the Integration and Development Path of College Students’ Labor Education and College Student Associations

Student organizations in colleges and universities are important carriers for implementing the fundamental task of cultivating morality and cultivating people and promoting quality education, and college and university organizations have a good foundation for the masses of students and play an important role in educating people in ideological and political education. Therefore, it is necessary to combine the educational platform of college student clubs to explore and analyze the reality of labor education integrated into college associations, clarify the community groups in college clubs that can effectively integrate labor education, and drive the implementation of labor education for college students from multiple perspectives

A Game of Simulation: Modeling and Analyzing the Dragons of Game of Thrones

This paper outlines two approaches for mathematical, simulation, modeling, and analysis of hypothetical creatures, in particular, the dragons of HBO's television series Game of Thrones (GOT). Our first approach, the forward model, utilizes quasi-empirical observations of various features of GOT dragons. We then mathematically derive the growth rate, other dimensions, energy consumption, etc. In the backward model, we use projected energy consumption by given ecological impact to model an expected dragon in terms of physical features. We compare and contrast both models to examine the plausibility of a real-world existence for our titular dragons and provide brief analyses of potential impacts on ecology.Comment: 16 page

K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment

The problem of how to assess cross-modality medical image synthesis has been largely unexplored. The most used measures like PSNR and SSIM focus on analyzing the structural features but neglect the crucial lesion location and fundamental k-space speciality of medical images. To overcome this problem, we propose a new metric K-CROSS to spur progress on this challenging problem. Specifically, K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location, together with a tumor encoder for representing features, such as texture details and brightness intensities. To further reflect the frequency-specific information from the magnetic resonance imaging principles, both k-space features and vision features are obtained and employed in our comprehensive encoders with a frequency reconstruction penalty. The structure-shared encoders are designed and constrained with a similarity loss to capture the intrinsic common structural information for both modalities. As a consequence, the features learned from lesion regions, k-space, and anatomical structures are all captured, which serve as our quality evaluators. We evaluate the performance by constructing a large-scale cross-modality neuroimaging perceptual similarity (NIRPS) dataset with 6,000 radiologist judgments. Extensive experiments demonstrate that the proposed method outperforms other metrics, especially in comparison with the radiologists on NIRPS

Action Sensitivity Learning for Temporal Action Localization

Temporal action localization (TAL), which involves recognizing and locating action instances, is a challenging task in video understanding. Most existing approaches directly predict action classes and regress offsets to boundaries, while overlooking the discrepant importance of each frame. In this paper, we propose an Action Sensitivity Learning framework (ASL) to tackle this task, which aims to assess the value of each frame and then leverage the generated action sensitivity to recalibrate the training procedure. We first introduce a lightweight Action Sensitivity Evaluator to learn the action sensitivity at the class level and instance level, respectively. The outputs of the two branches are combined to reweight the gradient of the two sub-tasks. Moreover, based on the action sensitivity of each frame, we design an Action Sensitive Contrastive Loss to enhance features, where the action-aware frames are sampled as positive pairs to push away the action-irrelevant frames. The extensive studies on various action localization benchmarks (i.e., MultiThumos, Charades, Ego4D-Moment Queries v1.0, Epic-Kitchens 100, Thumos14 and ActivityNet1.3) show that ASL surpasses the state-of-the-art in terms of average-mAP under multiple types of scenarios, e.g., single-labeled, densely-labeled and egocentric.Comment: Accepted to ICCV 202

A New Method for Estimation of the Sensible Heat Flux Under Unstable Conditions Using Satellite Vector Winds

It has been difficult to estimate the sensible heat flux at the air - sea interface using satellite data because of the difficulty in remotely observing the sea level air temperature. In this study, a new method is developed for estimating the sensible heat flux using satellite observations under unstable conditions. The basic idea of the method is that the air - sea temperature difference is related to the atmospheric convergence. Employed data include the wind convergence, sea level humidity, and sea surface temperature. These parameters can be derived from the satellite wind vectors, Special Sensor Microwave Imager (SSM/I) precipitable water, and Advanced Very High Resolution Radiometer (AVHRR) observations, respectively. The authors selected a region east of Japan as the test area where the atmospheric convergence appears all year. Comparison between the heat fluxes derived from the satellite data and from the National Centers for Environmental Prediction (NCEP) data suggests that the rms difference between the two kinds of sensible heat fluxes has low values in the sea area east of Japan with a minimum of 10.0 W m(-2). The time series of the two kinds of sensible heat fluxes at 10 locations in the area are in agreement, with rms difference ranging between 10.0 and 14.1 W m(-2) and correlation coefficient being higher than 0.7. In addition, the National Aeronautics and Space Administration ( NASA) Goddard Satellite-Based Surface Turbulent Flux (GSSTF) was used for a further comparison. The low-rms region with high correlation coefficient (\u3e0.7) was also found in the region east of Japan with a minimum of 12.2 W m(-2). Considering the nonlinearity in calculation of the sensible monthly means, the authors believe that the comparison with GSSTF is consistent with that with NCEP data