Investigation and Improvement Strategies of College Students\u27 Self-cognition

Professor Howard Gardner\u27s theory of multiple intelligences believes that human intelligence is composed of at least eight abilities such as language intelligence, mathematical logic intelligence, and introspective intelligence. Introspective intelligence is the individual\u27s recognition of self\u27s behavior and psychological state. It is very important for personal self-understanding and constructing a correct self. It plays a significant role in human learning, employment and development The development level of self-cognition is different at different stages. The article compiles a questionnaire based on the characteristics of Campbell\u27s self-cognition.By collating and analyzing the data collected in the questionnaire, the basic status of self-knowledge of Chinese college students in the emerging stage can be obtained: College students\u27 self-awareness is maturing, but self-awareness is high; College students have average emotional management skills, poor emotional expression, and emotional fluctuations; Undergraduates have clear learning goals, but their self-fulfilling channels are confused; College students have their own value system, but the values are immature; Career ideals are seriously ahead of schedule, not in line with professional abilities. The countermeasures to improve college students\u27 self-cognition are: Educate students to build good interpersonal relationships; Strengthen the education of college students\u27 self-awareness and strengthen career guidance; Create a good and positive mood; Educate students to strengthen self-improvement strategies in multiple ways and promote the healthy and harmonious development of college students

Visual Thinking Methods and Training in Video Production

"A picture is worth a thousand words". Internet plus has brought people into the era of picture reading. Pictures and videos are everywhere. And dynamic video has the characteristics of sound, sound and documentary. It has become a popular media form for the public. Therefore, mobile phone video shooting and production are convenient, and the popularization of video production and dissemination has become inevitable. However, the creation of artistic and innovative video works requires producers to master certain visual thinking methods in addition to film montage theories and techniques. The article briefly outlines the forming process of the concept of visual thinking, and proposes advanced methods of visual thinking: intuitive method, selection method, discovery method, and inquiry method. In the process of video production, some methods of visual thinking are analyzed through a case, such as the visualization of textual information, the figuration of image, the logic of concreteness, and the systematization of logic. We have studied practical visual thinking training methods, from the three stages of video production: script creation, shooting practice, and video packaging

Mechanisms of copper protrusion in through-silicon-via structures at the nanoscale

Thermal stress-induced copper protrusion is frequently observed in through-silicon-vias (TSVs) based three-dimensional (3D) system integration. In this study, the detailed process of Cu protrusion is reproduced on the atomic scale using a two-mode phase-field-crystal (PFC) model, and the mechanisms of protrusion are identified. To simulate thermal loading, a “penalty term” is added to the governing equation of the PFC model. The application of loading on the TSVs induces copper grain deformation and grain boundary migration at the nanoscale. Furthermore, the simulation results suggest that the Cu protrusion is resulted from diffusional creep, involving both Nabarro-Herring creep and Coble creep. The obtained power index of diffusional creep is around 2.16, suggesting that lattice diffusion contributes more to protrusion than grain boundary diffusion does. The protrusion height in micron-scale TSVs predicted by extrapolating the relationship between the protrusion height and diameter of nanoscale TSVs agrees with the experimental data

Nanoporous Structure of Sintered Metal Powder Heat Exchanger in Dilution Refrigeration: A Numerical Study

We use LAMMPS to randomly pack hard spheres to simulate the heat exchanger, where the hard spheres represent sintered metal particles in the heat exchanger. We simulated the heat exchanger under different sphere radii and different packing fractions of the metal particle and researched pore space. To improve the performance of the heat exchanger, we adopted this simulation method to explore when the packing fraction is 65%, the optimal sintering particle radius in the heat exchanger is 30~35nm.Comment: 5 pages,3 figures, one tabl

Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization

In this work, we decouple the iterative bi-level offline RL (value estimation and policy extraction) from the offline training phase, forming a non-iterative bi-level paradigm and avoiding the iterative error propagation over two levels. Specifically, this non-iterative paradigm allows us to conduct inner-level optimization (value estimation) in training, while performing outer-level optimization (policy extraction) in testing. Naturally, such a paradigm raises three core questions that are not fully answered by prior non-iterative offline RL counterparts like reward-conditioned policy: (q1) What information should we transfer from the inner-level to the outer-level? (q2) What should we pay attention to when exploiting the transferred information for safe/confident outer-level optimization? (q3) What are the benefits of concurrently conducting outer-level optimization during testing? Motivated by model-based optimization (MBO), we propose DROP (design from policies), which fully answers the above questions. Specifically, in the inner-level, DROP decomposes offline data into multiple subsets, and learns an MBO score model (a1). To keep safe exploitation to the score model in the outer-level, we explicitly learn a behavior embedding and introduce a conservative regularization (a2). During testing, we show that DROP permits deployment adaptation, enabling an adaptive inference across states (a3). Empirically, we evaluate DROP on various tasks, showing that DROP gains comparable or better performance compared to prior methods.Comment: NeurIPS 202

Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning

Offline reinforcement learning (RL) aims to learn a policy using only pre-collected and fixed data. Although avoiding the time-consuming online interactions in RL, it poses challenges for out-of-distribution (OOD) state actions and often suffers from data inefficiency for training. Despite many efforts being devoted to addressing OOD state actions, the latter (data inefficiency) receives little attention in offline RL. To address this, this paper proposes the cross-domain offline RL, which assumes offline data incorporate additional source-domain data from varying transition dynamics (environments), and expects it to contribute to the offline data efficiency. To do so, we identify a new challenge of OOD transition dynamics, beyond the common OOD state actions issue, when utilizing cross-domain offline data. Then, we propose our method BOSA, which employs two support-constrained objectives to address the above OOD issues. Through extensive experiments in the cross-domain offline RL setting, we demonstrate BOSA can greatly improve offline data efficiency: using only 10\% of the target data, BOSA could achieve {74.4\%} of the SOTA offline RL performance that uses 100\% of the target data. Additionally, we also show BOSA can be effortlessly plugged into model-based offline RL and noising data augmentation techniques (used for generating source-domain data), which naturally avoids the potential dynamics mismatch between target-domain data and newly generated source-domain data