Synthesis of Cadmium Selenide Quantum Dots and Their Cytotoxicity

Cadmium selenide (CdSe) nanoparticles (NPs) have applications in biomedical, biochemistry, bioimaging areas through different methods such as cell labelling and drug delivery (Chapter1). This study aims to test the optical and biological properties of CdSe NPs so that its applications can be improved in these areas in the future. Three types of CdSe NPs have been synthesised using a wet chemical method with the molar ratio of Cd:Se 10:1, 4:1 and 1:1. The observed luminescence of the CdSe NPs was strong and stable. The maxima PL spectrum peak of the CdSe (10:1) nanoparticles was around 590 nm and the ultraviolet-visible absorption (UV-Vis) spectrum showed a peak between 530-550 nm. The photoluminescence peak of CdSe (4:1) was the same as CdSe (10:1) and the UV-Vis spectrum showed a peak at about 550 nm. The aging studies indicate that sodium citrate (a stabiliser) could enhance the stability of the CdSe NPs. For example, the CdSe NPs with 0.2% sodium citrate were more stable than 0.05% (Chapter 3). Using this property, more stable encapsulated drugs could be made in the future to improve the clinical treatment method (Chapter 1). Cell toxicity of the CdSe NPs was evaluated through the use of 3-(4, 5-Dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay. The MTT results show that the more Cadmium ions accumulated in HHL-5 cells, the greater the cell toxicity is. The results (keeping the cadmium level constant) also indicate that CdSe NPs with a cadmium to selenite ratio with 10:1 (CdSe (10:1) had the strongest toxicity in HHL-5 cell of all these three kinds of CdSe NPs tested. Conversely, the CdSe (1:1) has the lowest toxicity among all. The results indicated that the toxicity of the cadmium were very obvious so that we need to avoid accumulation of cadmium in clinical. In addition, the confocal images in MCF-7 cells also reflect the relative toxicities of the CdSe NPs. The results of the confocal images indicated that the higher concentrations of the CdSe NPs in the cells, the greater the observed toxicity is. Moreover, all the experiments in this study (aging, TEM, quantum yield, MTT, confocal) are surrounding 9 kinds of CdSe NPs without any other parameters, which are novel and unique

WHAT AFFECT VTUBER AUDIENCE BEHAVIOR? : Empirical Evidence from China

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GLoRE: Evaluating Logical Reasoning of Large Language Models

Recently, large language models (LLMs), including notable models such as GPT-4 and burgeoning community models, have showcased significant general language understanding abilities. However, there has been a scarcity of attempts to assess the logical reasoning capacities of these LLMs, an essential facet of natural language understanding. To encourage further investigation in this area, we introduce GLoRE, a meticulously assembled General Logical Reasoning Evaluation benchmark comprised of 12 datasets that span three different types of tasks. Our experimental results show that compared to the performance of human and supervised fine-tuning, the logical reasoning capabilities of open LLM models necessitate additional improvement; ChatGPT and GPT-4 show a strong capability of logical reasoning, with GPT-4 surpassing ChatGPT by a large margin. We propose a self-consistency probing method to enhance the accuracy of ChatGPT and a fine-tuned method to boost the performance of an open LLM. We release the datasets and evaluation programs to facilitate future research

2D-Shapley: A Framework for Fragmented Data Valuation

Data valuation -- quantifying the contribution of individual data sources to certain predictive behaviors of a model -- is of great importance to enhancing the transparency of machine learning and designing incentive systems for data sharing. Existing work has focused on evaluating data sources with the shared feature or sample space. How to valuate fragmented data sources of which each only contains partial features and samples remains an open question. We start by presenting a method to calculate the counterfactual of removing a fragment from the aggregated data matrix. Based on the counterfactual calculation, we further propose 2D-Shapley, a theoretical framework for fragmented data valuation that uniquely satisfies some appealing axioms in the fragmented data context. 2D-Shapley empowers a range of new use cases, such as selecting useful data fragments, providing interpretation for sample-wise data values, and fine-grained data issue diagnosis.Comment: ICML 202

Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons

Despite impressive capabilities and outstanding performance, deep neural networks (DNNs) have captured increasing public concern about their security problems, due to their frequently occurred erroneous behaviors. Therefore, it is necessary to conduct a systematical testing for DNNs before they are deployed to real-world applications. Existing testing methods have provided fine-grained metrics based on neuron coverage and proposed various approaches to improve such metrics. However, it has been gradually realized that a higher neuron coverage does \textit{not} necessarily represent better capabilities in identifying defects that lead to errors. Besides, coverage-guided methods cannot hunt errors due to faulty training procedure. So the robustness improvement of DNNs via retraining by these testing examples are unsatisfactory. To address this challenge, we introduce the concept of excitable neurons based on Shapley value and design a novel white-box testing framework for DNNs, namely DeepSensor. It is motivated by our observation that neurons with larger responsibility towards model loss changes due to small perturbations are more likely related to incorrect corner cases due to potential defects. By maximizing the number of excitable neurons concerning various wrong behaviors of models, DeepSensor can generate testing examples that effectively trigger more errors due to adversarial inputs, polluted data and incomplete training. Extensive experiments implemented on both image classification models and speaker recognition models have demonstrated the superiority of DeepSensor.Comment: 32 page

OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding

We introduce OpenShape, a method for learning multi-modal joint representations of text, image, and point clouds. We adopt the commonly used multi-modal contrastive learning framework for representation alignment, but with a specific focus on scaling up 3D representations to enable open-world 3D shape understanding. To achieve this, we scale up training data by ensembling multiple 3D datasets and propose several strategies to automatically filter and enrich noisy text descriptions. We also explore and compare strategies for scaling 3D backbone networks and introduce a novel hard negative mining module for more efficient training. We evaluate OpenShape on zero-shot 3D classification benchmarks and demonstrate its superior capabilities for open-world recognition. Specifically, OpenShape achieves a zero-shot accuracy of 46.8% on the 1,156-category Objaverse-LVIS benchmark, compared to less than 10% for existing methods. OpenShape also achieves an accuracy of 85.3% on ModelNet40, outperforming previous zero-shot baseline methods by 20% and performing on par with some fully-supervised methods. Furthermore, we show that our learned embeddings encode a wide range of visual and semantic concepts (e.g., subcategories, color, shape, style) and facilitate fine-grained text-3D and image-3D interactions. Due to their alignment with CLIP embeddings, our learned shape representations can also be integrated with off-the-shelf CLIP-based models for various applications, such as point cloud captioning and point cloud-conditioned image generation.Comment: Project Website: https://colin97.github.io/OpenShape

Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model

We report Zero123++, an image-conditioned diffusion model for generating 3D-consistent multi-view images from a single input view. To take full advantage of pretrained 2D generative priors, we develop various conditioning and training schemes to minimize the effort of finetuning from off-the-shelf image diffusion models such as Stable Diffusion. Zero123++ excels in producing high-quality, consistent multi-view images from a single image, overcoming common issues like texture degradation and geometric misalignment. Furthermore, we showcase the feasibility of training a ControlNet on Zero123++ for enhanced control over the generation process. The code is available at https://github.com/SUDO-AI-3D/zero123plus