LLatrieval: LLM-Verified Retrieval for Verifiable Generation

Verifiable generation aims to let the large language model (LLM) generate text with corresponding supporting documents, which enables the user to flexibly verify the answer and makes it more trustworthy. Its evaluation not only measures the correctness of the answer, but also the answer's verifiability, i.e., how well the answer is supported by the corresponding documents. In typical, verifiable generation adopts the retrieval-read pipeline, which is divided into two stages: 1) retrieve relevant documents of the question. 2) according to the documents, generate the corresponding answer. Since the retrieved documents can supplement knowledge for the LLM to generate the answer and serve as evidence, the retrieval stage is essential for the correctness and verifiability of the answer. However, the widely used retrievers become the bottleneck of the entire pipeline and limit the overall performance. They often have fewer parameters than the large language model and have not been proven to scale well to the size of LLMs. Since the LLM passively receives the retrieval result, if the retriever does not correctly find the supporting documents, the LLM can not generate the correct and verifiable answer, which overshadows the LLM's remarkable abilities. In this paper, we propose LLatrieval (Large Language Model Verified Retrieval), where the LLM updates the retrieval result until it verifies that the retrieved documents can support answering the question. Thus, the LLM can iteratively provide feedback to retrieval and facilitate the retrieval result to sufficiently support verifiable generation. Experimental results show that our method significantly outperforms extensive baselines and achieves new state-of-the-art results

Stochastic Simulation of Controlled Radical Polymerization Forming Dendritic Hyperbranched Polymers

Stochastic simulation of the formation process of hyperbranched polymers (HBPs) based on the reversible deactivation radical polymerization (RDRP) using a branch-inducing monomer, evolmer, has been carried out. The simulation program successfully reproduced the change of dispersities (Đs) during the polymerization process. Furthermore, the simulation suggested that the observed Đs (=1.5–2) are due to the distribution of the number of branches instead of undesired side reactions, and that the branch structures are well controlled. In addition, the analysis of the polymer structure reveals that the majority of HBPs have structures close to the ideal one. The simulation also suggested the slight dependence of branch density on molecular weight, which was experimentally confirmed by synthesizing HBPs with an evolmer having phenyl group

Aesthetic Enhancement via Color Area and Location Awareness

Choosing a suitable color palette can typically improve image aesthetic, where a naive way is choosing harmonious colors from some pre-defined color combinations in color wheels. However, color palettes only consider the usage of color types without specifying their amount in an image. Also, it is still challenging to automatically assign individual palette colors to suitable image regions for maximizing image aesthetic quality. Motivated by these, we propose to construct a contribution-aware color palette from images with high aesthetic quality, enabling color transfer by matching the coloring and regional characteristics of an input image. We hence exploit public image datasets, extracting color composition and embedded color contribution features from aesthetic images to generate our proposed color palettes. We consider both image area ratio and image location as the color contribution features to extract. We have conducted quantitative experiments to demonstrate that our method outperforms existing methods through SSIM (Structural SIMilarity) and PSNR (Peak Signal to Noise Ratio) for objective image quality measurement and no-reference image assessment (NIMA) for image aesthetic scoring

A Survey on Visual Mamba

State space models (SSM) with selection mechanisms and hardware-aware architectures, namely Mamba, have recently shown significant potential in long-sequence modeling. Since the complexity of transformers’ self-attention mechanism is quadratic with image size, as well as increasing computational demands, researchers are currently exploring how to adapt Mamba for computer vision tasks. This paper is the first comprehensive survey that aims to provide an in-depth analysis of Mamba models within the domain of computer vision. It begins by exploring the foundational concepts contributing to Mamba’s success, including the SSM framework, selection mechanisms, and hardware-aware design. Then, we review these vision Mamba models by categorizing them into foundational models and those enhanced with techniques including convolution, recurrence, and attention to improve their sophistication. Furthermore, we investigate the widespread applications of Mamba in vision tasks, which include their use as a backbone in various levels of vision processing. This encompasses general visual tasks, medical visual tasks (e.g., 2D/3D segmentation, classification, image registration, etc.), and remote sensing visual tasks. In particular, we introduce general visual tasks from two levels: high/mid-level vision (e.g., object detection, segmentation, video classification, etc.) and low-level vision (e.g., image super-resolution, image restoration, visual generation, etc.). We hope this endeavor will spark additional interest within the community to address current challenges and further apply Mamba models in computer vision

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to their great ability in modeling graph-structured data, GNNs are vastly used in various applications, including high-stakes scenarios such as financial analysis, traffic predictions, and drug discovery. Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society. For example, existing works demonstrate that attackers can fool the GNNs to give the outcome they desire with unnoticeable perturbation on training graph. GNNs trained on social networks may embed the discrimination in their decision process, strengthening the undesirable societal bias. Consequently, trustworthy GNNs in various aspects are emerging to prevent the harm from GNN models and increase the users' trust in GNNs. In this paper, we give a comprehensive survey of GNNs in the computational aspects of privacy, robustness, fairness, and explainability. For each aspect, we give the taxonomy of the related methods and formulate the general frameworks for the multiple categories of trustworthy GNNs. We also discuss the future research directions of each aspect and connections between these aspects to help achieve trustworthiness

Theoretical model and practical exploration of digital technology-empowered green management of water resource in semiconductor manufacturing industry

[Objective] This study focused on the green management of water resources in the semiconductor manufacturing industry based on digital technology. It analyzed the establishment of a comprehensive framework for green management of water resources from the perspective of technological empowerment, providing theoretical paradigm and practical guidance for the application of digital technology and the green development in the semiconductor manufacturing industry. [Methods] By systematically reviewing relevant literature on the domestic and international levels and incorporating multidisciplinary solutions, an innovative circular economy model was constructed. The article established a water resource green intelligent management system for semiconductor manufacturing enterprises, and used a case study method to select a typical semiconductor manufacturing enterprise, then deeply analyzed the application process of the technological framework. [Results] (1) Based on the “reduction, reuse, recycle” 3R principle of the circular economy model, and utilized the systematic analysis method, a theoretical model for the digital technology-empowered green management of water resources framework was built. (2) The theoretical model explained the connotation, characteristics, and functions of digital technology empowerment from the source to the end of each production process. It analyzed the specific measures of each enterprise under the 3R principle, and revealed the action path of data technology in empowering green management of water resources. (3) Through a case study of a semiconductor company in East China, the practical exploration of intelligent water resources green management technology solutions was achieved. The trend of data change over the past three years indicated a basic positive correlation between digital technology innovation and the benefits of green management of water resources, with higher benefits corresponding to higher innovation levels. [Conclusion] Digital technology can provide innovative green water resources management solutions for semiconductor manufacturing companies, enhancing their competitiveness. For the new development stage of the Chinese semiconductor manufacturing industry, it has significant implications for strengthening the dominant position of digital technology innovation and improving intelligent green management of water resources