The Exploration Into the Influence of Social Unconscious on the Individual Through Cyberpunk

The main purpose of this thesis is to explore the influence of the social unconscious on human beings in a future context. In Fromm\u27s view, the social unconscious refers to Individuals who are afraid of being separated from the society they live in, they often adapt and distort their human needs to meet the requirements of our social system. However, with the strengthening of personality and self-awareness in the present society, how will the relationship between the social subconscious and people\u27s selfawareness change in the future? This is the key issue of this thesis. In the first part, by studying the definition of the social unconscious and related works, this thesis tried to understand the root cause of the influence of the social unconscious and how it affects people\u27s behavior and decision-making. In the second part, this thesis made an in-depth study of Cyberpunk and objective imagery in related works, revealing that Cyberpunk is a projection of the real world, and clues for many problems in the real world can be found in the Cyberpunk world. In the third part, this thesis explored the changes in people\u27s consciousness in the Cyberpunk world. Finally, this thesis concludes people will not rid of the influence of the social unconscious no matter in the real world or the future world. Even as people\u27s selfawareness is constantly enhanced, self-awareness and social unconscious will always exist in contradiction

BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic Segmentation

Active learning strives to reduce the need for costly data annotation, by repeatedly querying an annotator to label the most informative samples from a pool of unlabeled data, and then training a model from these samples. We identify two problems with existing active learning methods for LiDAR semantic segmentation. First, they overlook the severe class imbalance inherent in LiDAR semantic segmentation datasets. Second, to bootstrap the active learning loop when there is no labeled data available, they train their initial model from randomly selected data samples, leading to low performance. This situation is referred to as the cold start problem. To address these problems we propose BaSAL, a size-balanced warm start active learning model, based on the observation that each object class has a characteristic size. By sampling object clusters according to their size, we can thus create a size-balanced dataset that is also more class-balanced. Furthermore, in contrast to existing information measures like entropy or CoreSet, size-based sampling does not require a pretrained model, thus addressing the cold start problem effectively. Results show that we are able to improve the performance of the initial model by a large margin. Combining warm start and size-balanced sampling with established information measures, our approach achieves comparable performance to training on the entire SemanticKITTI dataset, despite using only 5% of the annotations, outperforming existing active learning methods. We also match the existing state-of-the-art in active learning on nuScenes. Our code is available at: https://github.com/Tony-WJR/BaSAL.Comment: ICRA 2024 camera-read

Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels

Developing a deep learning method for medical segmentation tasks heavily relies on a large amount of labeled data. However, the annotations require professional knowledge and are limited in number. Recently, semi-supervised learning has demonstrated great potential in medical segmentation tasks. Most existing methods related to cardiac magnetic resonance images only focus on regular images with similar domains and high image quality. A semi-supervised domain generalization method was developed in [2], which enhances the quality of pseudo labels on varied datasets. In this paper, we follow the strategy in [2] and present a domain generalization method for semi-supervised medical segmentation. Our main goal is to improve the quality of pseudo labels under extreme MRI Analysis with various domains. We perform Fourier transformation on input images to learn low-level statistics and cross-domain information. Then we feed the augmented images as input to the double cross pseudo supervision networks to calculate the variance among pseudo labels. We evaluate our method on the CMRxMotion dataset [1]. With only partially labeled data and without domain labels, our approach consistently generates accurate segmentation results of cardiac magnetic resonance images with different respiratory motions. Code is available at: https://github.com/MAWanqin2002/STACOM2022MaComment: Accepted by International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM2022) of MICCAI202

Multi-Stage Expansion Planning for Decarbonizing Thermal Generation Supported Renewable Power Systems Using Hydrogen and Ammonia Storage

Large-scale centralized development of wind and solar energy and peer-to-grid transmission of renewable energy source (RES) via high voltage direct current (HVDC) has been regarded as one of the most promising ways to achieve goals of peak carbon and carbon neutrality in China. Traditionally, large-scale thermal generation is needed to economically support the load demand of HVDC with a given profile, which in turn raises concerns about carbon emissions. To address the issues above, hydrogen energy storage system (HESS) and ammonia energy storage system (AESS) are introduced to gradually replace thermal generation, which is represented as a multi-stage expansion planning (MSEP) problem. Specifically, first, HESS and AESS are established in the MSEP model with carbon emission reduction constraints, and yearly data with hourly time resolution are utilized for each stage to well describe the intermittence of RES. Then, a combined Dantzig-Wolfe decomposition (DWD) and column generation (CG) solution approach is proposed to efficiently solve the large-scale MSEP model. Finally, a real-life system in China is studied. The results indicate that HESS and AESS have the potential to handle the intermittence of RES, as well as the monthly imbalance between RES and load demand. Especially under the goal of carbon neutrality, the contribution of HESS and AESS in reducing levelized cost of energy (LCOE) reaches 12.28% and 14.59%, respectively, which finally leads to a LCOE of 0.4324 RMB/kWh.Comment: 10 pages, 8 figure