Unsupervised cell segmentation by fast Gaussian Processes

Cell boundary information is crucial for analyzing cell behaviors from time-lapse microscopy videos. Existing supervised cell segmentation tools, such as ImageJ, require tuning various parameters and rely on restrictive assumptions about the shape of the objects. While recent supervised segmentation tools based on convolutional neural networks enhance accuracy, they depend on high-quality labelled images, making them unsuitable for segmenting new types of objects not in the database. We developed a novel unsupervised cell segmentation algorithm based on fast Gaussian processes for noisy microscopy images without the need for parameter tuning or restrictive assumptions about the shape of the object. We derived robust thresholding criteria adaptive for heterogeneous images containing distinct brightness at different parts to separate objects from the background, and employed watershed segmentation to distinguish touching cell objects. Both simulated studies and real-data analysis of large microscopy images demonstrate the scalability and accuracy of our approach compared with the alternatives.

HAUR: Human Annotation Understanding and Recognition Through Text-Heavy Images

Vision Question Answering (VQA) tasks use images to convey critical information to answer text-based questions, which is one of the most common forms of question answering in real-world scenarios. Numerous vision-text models exist today and have performed well on certain VQA tasks. However, these models exhibit significant limitations in understanding human annotations on text-heavy images. To address this, we propose the Human Annotation Understanding and Recognition (HAUR) task. As part of this effort, we introduce the Human Annotation Understanding and Recognition-5 (HAUR-5) dataset, which encompasses five common types of human annotations. Additionally, we developed and trained our model, OCR-Mix. Through comprehensive cross-model comparisons, our results demonstrate that OCR-Mix outperforms other models in this task. Our dataset and model will be released soon .

MathLearner: A Large Language Model Agent Framework for Learning to Solve Mathematical Problems

With the development of artificial intelligence (AI), large language models (LLM) are widely used in many fields. However, the reasoning ability of LLM is still very limited when it comes to mathematical reasoning. Mathematics plays an important role in all aspects of human society and is a technical guarantee in the fields of healthcare, transport and aerospace, for this reason, the development of AI big language models in the field of mathematics has great potential significance. To improve the mathematical reasoning ability of large language models, we proposed an agent framework for learning to solve mathematical problems based on inductive reasoning. By emulating the human learning process of generalization of learned information and effective application of previous knowledge in new reasoning tasks, this framework has great performance in the mathematical reasoning process. It improves global accuracy over the baseline method (chain-of-thought) by 20.96% and solves 17.54% of the mathematical problems that the baseline cannot solve. Benefiting from the efficient RETRIEVAL method, our model improves the ability of large language models to efficiently use external knowledge, i.e., the mathematical computation of the model can be based on written procedures. In education, our model can be used as a personalised learning aid, thus reducing the inequality of educational resources.