While Large Language Models (LLMs) demonstrate immense potential for automating integrated circuit (IC) development, their practical deployment is fundamentally limited by restricted context windows. Existing context-extension methods struggle to achieve effective semantic modeling and thorough multi-hop reasoning over extensive, intricate circuit specifications. To address this, we introduce ChipMind, a novel knowledge graph-augmented reasoning framework specifically designed for lengthy IC specifications. ChipMind first transforms circuit specifications into a domain-specific knowledge graph (ChipKG) through the Circuit Semantic-Aware Knowledge Graph Construction methodology. It then leverages the ChipKG-Augmented Reasoning mechanism, combining information-theoretic adaptive retrieval to dynamically trace logical dependencies with intent-aware semantic filtering to prune irrelevant noise, effectively balancing retrieval completeness and precision. Evaluated on an industrial-scale specification reasoning benchmark, ChipMind significantly outperforms state-of-the-art baselines, achieving an average improvement of 34.59% (up to 72.73%). Our framework bridges a critical gap between academic research and practical industrial deployment of LLM-aided Hardware Design (LAD).

Ground penetrating radar (GPR) frequency attributes are commonly used to describe subsurface structures and characterize anomalous media. Single-frequency slices face challenges in capturing the broadband characteristics of GPR data, so the fusion of extracted multi-frequency components using a fusion algorithm can be effective. However, selecting appropriate attributes and mapping them to media characterization remain unresolved challenges. In this study, we propose a workflow based on adaptive sensitive frequency attribute analysis (ASFAA) to address these issues. First, the generalized S-transform (GST) is used to calculate the multi-frequency attributes of GPR data. Then, a sensitive feature analysis method combining hierarchical clustering and correlation analysis is employed to reduce redundancy in frequency attributes. Multi-frequency data are fused using the potential of heat-diffusion for affinity-based transition embedding (PHATE), which performs affinity-based diffusion embedding. The workflow is tested with synthetic and field data, yielding characterization results consistent with both the forward model results and actual leak extents. Therefore, the proposed workflow effectively integrates multi-frequency components, demonstrating its capability to delineate leak extents.

Detecting 3D lane lines from images is a fundamental challenge and an ill-posed problem in autonomous driving. Existing methods are limited by scene robustness and computational efficiency. This paper introduces an innovative 3D lane detection method that addresses the challenges of lane detection in autonomous driving. Our approach is based on a simple yet efficient view transformation module and layer-by-layer refined bird’s-eye-view (BEV) features. First, we introduce a module for dual-branch view transformation that combines deformable convolutions and view relation modules to convert front view features into BEV features. This enhances scene robustness across various data scenarios. Additionally, we suggest an auxiliary training head for inverse view transformation that offers supplementary supervisory information. Moreover, we progressively refine the BEV features, making use of features from different levels. The results of our experiment indicate the supremacy of our approach on two datasets, as it achieved a considerable increase in F1-score in comparison to preexisting methods.

Automatic source code summarization system aims to generate a valuable natural language description for a program, which can facilitate software development and maintenance, code categorization, and retrieval. However, previous sequence-based research did not consider the long-distance dependence and highly structured characteristics of source code simultaneously. In this article, we present a Transformer-based Graph-Augmented Source Code Summarization (GA-SCS), which can effectively incorporate inherent structural and textual features of source code to generate an effective code description. Specifically, we develop a graph-based structure feature extraction scheme leveraging abstract syntax tree and graph attention networks to mine global syntactic information. And then, to take full advantage of the lexical and syntactic information of code snippets, we extend the original attention to a syntax-informed self-attention mechanism in our encoder. In the training process, we also adopt a reinforcement learning strategy to enhance the readability and informativity of generated code summaries. We utilize the Java dataset and Python dataset to evaluate the performance of different models. Experimental results demonstrate that our GA-SCS model outperforms all competitive methods on BLEU, METEOR, ROUGE, and human evaluations.

Information diffusion prediction aims to predict the tendency of information spreading in the network. Previous methods focus on extracting chronological features from diffusion paths and leverage relations in social graph as side information to facilitate diffusion prediction. However, abundant high-order social relations in information diffusion have not been sufficiently utilized, such as co-repose and co-following which can further mine potential user common preferences. In this paper, we construct a heterogeneous diffusion network (HDN) from the social graph and information cascades to model the high-order social relations in information diffusion. Then, we design a novel model named Multi-Channel Recurrent Graph Convolutional Network (MC-RGCN), which can extract high-order social relation semantics from the channels of HDN to promote prediction performance. In each channel, we depict a specific social relations from the views of global topology, pairwise strength, and local structure. Finally, we conduct extensive experiments on three real-world datasets, and the results show that our proposed method outperforms the state-of-the-art models on diffusion prediction.