As deep neural networks (DNNs) are becoming vital to numerous safety-critical applications, ensuring their fault reliability is crucial. This paper presents a hybrid framework that combines genetic algorithms with fault injection and analytical methods to identify vulnerable neurons and layers in DNNs. Validated on LeNet-5, AlexNet, and VGG-11, our approach significantly enhances model accuracy in harsh environments by protecting critical components, achieving reliability improvements (accuracy drop of the model compared to the golden network after fault injection) of $69.86 \%$ for LeNet, and 99.65% for AlexNet at BER=1E-4, and 91.37% improvement for VGG at $\mathrm{BER}=1 \mathrm{E}-6$. Furthermore, our framework reduces computational costs by requiring fewer inferences than traditional analytical methods, highlighting its potential to improve DNN accelerators’ reliability and contribute to their safety.
In 3D reconstruction, creating accurate models from a single 2 D image remains a complex challenge in computer vision and virtual environments. Acknowledging this complexity, our study presents a research approach that aims to advance the field beyond conventional reconstruction methods. We introduce a novel method designed to reconstruct 3D models from single damaged 2D images while tackling the added challenge of restoring missing details. Our method employs an expandedscale stable diffusion model for inpainting the input 2D image, restoring missing information and enhancing depth estimation for sparse-view 3D reconstruction. By integrating inpainting within a scalable diffusion framework, we achieve improved fidelity to the original structure, even with limited sparse mesh data. Additionally, our framework incorporates multi-view stereo processing to optimize available viewpoint information, enhancing reconstruction accuracy while maintaining computational efficiency.Experimental results indicate a significant advancement in transforming single 2 D image inpainting into effective 3 D object reconstruction. By bridging inpainting and 3D reconstruction, our approach recovers missing or occluded details in the 2D image and enhances the accuracy of 3D modeling from sparse viewpoints. The inpainting technique is important here, as it restores essential details within the 2D input, directly improving the quality and fidelity of the 3D reconstruction output. Our integrated method of inpainting and sparse-view reconstruction outperforms existing approaches, achieving higher accuracy in 3D object representation from limited data.
Chemical experimentation remains fundamental to material discovery and compound synthesis, yet conventional methods face critical limitations. Manual laboratory workflows require repetitive operations that prolong research timelines, while robotic automation platforms inadvertently shift the burden to chemists through complex programming requirements. Existing systems also struggle to adapt to novel experimental designs, constraining scientific innovation. To bridge these gaps, we present a large language model (LLM)-driven robotic framework that redefines automated experimentation. Our system interprets natural language instructions through advanced LLM processing, dynamically integrates real-time environmental data, and autonomously generates executable code utilizing a modular action library. A self-correcting validation mechanism ensures operational precision, systematically resolving execution errors through iterative feedback. Empirical validation across two benchmark experiments including the iodine clock reaction kinetics and the salt purification demonstrates transformative advantages: 1) intuitive interaction; 2) extended workflow handling efficiency gain in multi-step protocols; 3) adaptive code generation. This paradigm shift may enable chemists to focus on hypothesis-driven research rather than procedural implementation, establishing a scalable infrastructure for next-generation autonomous laboratories.
With the development of artificial intelligence technology, large language models have shown tremendous potential in professional document generation. However, in document generation tasks with high professionalism and strict content requirements, such as project proposals, ensuring the accuracy and professionalism of generated content remains challenging. This paper proposes a project proposal generation method based on modular knowledge-enhanced large language models. First, this method divides the complex proposal generation task into interrelated submodules such as project background, technical approach, and implementation plan through modular decomposition, and designs a complete intermodule relationship processing mechanism. Second, it constructs a knowledge injection framework based on professional literature, providing professional domain knowledge support for the model through multi-level literature retrieval, knowledge extraction, and organization. Finally, it implements an automatic proposal generation system based on microservice architecture, integrating core functional modules such as literature processing, content generation, and quality control. Experimental results show that this method can significantly improve the professionalism and accuracy of proposal generation, providing new ideas and methods for AI-assisted professional document writing.
Coronary artery disease (CAD) is a leading cause of death worldwide. X-ray coronary angiograms are a diagnostic imaging technique, considered as the benchmark for identifying vascular anomalies. The two-dimensional nature of coronary angiograms limits spatial understanding that can complicate the diagnosis and cause misinformation. This paper presents a multi-stage pipeline leveraging deep learning techniques for reconstructing three-dimensional coronary vasculature from twodimensional X-ray angiograms. The pipeline involves various stages such as pre-processing, keyframe selection, image registration, segmentation and three-dimensional reconstruction, using image processing and deep learning techniques. Additionally, a synthetically generated coronary tree dataset is used to train the neural network for reconstruction and ensure accurate centerline predictions. The method is successful in generating precise reconstructions, overcoming the spatial limitations of twodimensional imaging.
