An analysis of event management best practice

This research will investigate what makes a successful event, and the Waikato talent show will be adopted as a case study. The purpose of this research is to ensure the importance of event management. In addition, there will be many literature reviews in this research to support this topic and show different opinions. In order to obtain accurate results, this study uses questionnaires and interviews to collect information. Also, researchers will investigate the participation of 60 people, 60 people of different ages and genders, respectively. The researcher also will interview two people for further information. When the Waikato Chinese Students Association organizes events, good event management is essential. The selection of the geographic location of the event, the determination of the number of participants and the promotion measures are all very important components of organizing a successful event. After analysing the results of the questionnaire and the interview results, the Waikato Chinese Students Association will change their incident management based on these results. Finally, the study will provide Waikato Chinese Student Association with some advice to help their organization's activities become well

Too Large; Data Reduction for Vision-Language Pre-Training

This paper examines the problems of severe image-text misalignment and high redundancy in the widely-used large-scale Vision-Language Pre-Training (VLP) datasets. To address these issues, we propose an efficient and straightforward Vision-Language learning algorithm called TL;DR, which aims to compress the existing large VLP data into a small, high-quality set. Our approach consists of two major steps. First, a codebook-based encoder-decoder captioner is developed to select representative samples. Second, a new caption is generated to complement the original captions for selected samples, mitigating the text-image misalignment problem while maintaining uniqueness. As the result, TL;DR enables us to reduce the large dataset into a small set of high-quality data, which can serve as an alternative pre-training dataset. This algorithm significantly speeds up the time-consuming pretraining process. Specifically, TL;DR can compress the mainstream VLP datasets at a high ratio, e.g., reduce well-cleaned CC3M dataset from 2.82M to 0.67M ($\sim$24\%) and noisy YFCC15M from 15M to 2.5M ($\sim$16.7\%). Extensive experiments with three popular VLP models over seven downstream tasks show that VLP model trained on the compressed dataset provided by TL;DR can perform similar or even better results compared with training on the full-scale dataset. The code will be made available at \url{https://github.com/showlab/data-centric.vlp}.Comment: Work in progress. Code: https://github.com/showlab/data-centric.vl

Measuring the practical particle-in-a-box: orthorhombic perovskite nanocrystals

A connection between condensed matter physics and basic quantum mechanics is demonstrated as we use the fundamental 3D particle-in-a-box model to explain the optical properties of semiconductor nanocrystals, which are substantially modified due to quantum confinement. We also discuss recent advances in the imaging and measurement capabilities of transmission electron microscopy, which have made it possible to directly image single nanocrystals while simultaneously measuring their characteristic absorption energies. We introduce the basic theory of nanocrystals and derive a simplified expression to approximate the optical bandgap energy of an orthorhombic nanocrystal. CsPbBr3 perovskite nanocrystals are used to demonstrate this model due to their cubic crystal structure, large absorption cross-section, and favourable dielectric properties, which make them ideal for exploring the applications of this simple classroom problem. Various orthorhombic shapes are explored, and the predicted values of the optical bandgap energies using the proposed model are shown to be in good agreement with the experimentally determined values