Search Results (2)

Information asymmetry between complex source texts and general-audience comprehension remains a critical challenge in Artificial Intelligence. However, existing supervised simplification methods suffer from the scarcity of parallel training data, while standard text summarization methods often discard essential details to reduce length. Furthermore, zero-shot large language models frequently lack fine-grained controllability over linguistic complexity. To address these technical limitations, we present a framework to resolve information asymmetry by casting text simplification as a controllable denoising language modeling task. Unlike summarization, our approach preserves full semantic coverage while reducing difficulty. Our algorithm targets the problem of identifying and rewriting complex spans without labeled data through three mechanisms: (1) Asymmetry-Aware Masking, which uses model-based reconstruction difficulty (Negative Log-Likelihood) to isolate high-complexity terms; (2) paraphrase context prompting to enforce semantic invariance; and (3) an adaptive decoding strategy that dynamically penalizes complex tokens based on input difficulty. On ASSET (Abstractive Sentence Simplification Evaluation and Tuning dataset), our best setting reaches SARI (System output Against References and against the Input) 42.90 with FKGL (Flesch–Kincaid Grade Level) 7.10 (Sentence Similarity 0.948), and performs consistently on TurkCorpus (SARI 41.10), while requiring no parallel data or fine-tuning. Full article

The efficient separation of coal and gangue remains a critical challenge in modern coal mining, directly impacting energy efficiency, environmental protection, and sustainable development. Current machine vision-based sorting methods face significant challenges in dense scenes, where label rewriting problems severely affect model performance, particularly when coal and gangue are closely distributed in conveyor belt images. This paper introduces CGDet (Coal and Gangue Detection), a novel compact convolutional neural network that addresses these challenges through two key innovations. First, we proposed an Object Distribution Density Measurement (ODDM) method to quantitatively analyze the distribution density of coal and gangue, enabling optimal selection of input and feature map resolutions to mitigate label rewriting issues. Second, we developed a Relative Resolution Object Scale Measurement (RROSM) method to assess object scales, guiding the design of a streamlined feature fusion structure that eliminates redundant components while maintaining detection accuracy. Experimental results demonstrate the effectiveness of our approach; CGDet achieved superior performance with AP50 and AR50 scores of 96.7% and 99.2% respectively, while reducing model parameters by 46.76%, computational cost by 47.94%, and inference time by 31.50% compared to traditional models. These improvements make CGDet particularly suitable for real-time coal and gangue sorting in underground mining environments, where computational resources are limited but high accuracy is essential. Our work provides a new perspective on designing compact yet high-performance object detection networks for dense scene applications. Full article