Dalian Maritime University

Ancient-book pages often carry paper ageing, water-induced distortion, insect holes and mold at once, while existing public benchmarks are limited in scale and stack isolated degradations, which weakens supervised restoration. AncBench supplies 100,379 pixel-aligned clean–degraded pairs in which chemical, physical and biological damage are composed in sequence; trained on it, AncRestore first estimates page-wide and local degradation, then dynamically selects and distributes visual priors according to those cues. On the AncBench test set AncRestore reaches 26.70 dB PSNR, 0.9191 SSIM and 0.1346 LPIPS, with 89.58% character-recognition accuracy.

Ancient-book pages often carry paper ageing, water-induced distortion, insect holes and mold at once, while existing public benchmarks are limited in scale and stack isolated degradations, which weakens supervised restoration. AncBench supplies 100,379 pixel-aligned clean–degraded pairs in which chemical, physical and biological damage are composed in sequence; trained on it, AncRestore first estimates page-wide and local degradation, then dynamically selects and distributes visual priors according to those cues. On the AncBench test set AncRestore reaches 26.70 dB PSNR, 0.9191 SSIM and 0.1346 LPIPS, with 89.58% character-recognition accuracy.

The authors propose CircuitGate, a function-aware representation learning framework for And-Inverter Graphs that explicitly encodes polarity-aware global primary-input (PI) support, modulates fanin updates with support-overlap-aware reconvergence gating, and adds logic-inspired Boolean constraints; across ForgeEDA, EPFL and ITC'99 it outperforms the compared GNN and circuit-specific methods on equivalent-gate identification and signal-probability prediction, reducing MAE by 21.7% and 13.8% on ForgeEDA, and it attains the lowest equivalent-gate MAE under zero-shot ForgeEDA-to-OpenABC transfer.

The authors propose CircuitGate, a function-aware representation learning framework for And-Inverter Graphs that explicitly encodes polarity-aware global primary-input (PI) support, modulates fanin updates with support-overlap-aware reconvergence gating, and adds logic-inspired Boolean constraints; across ForgeEDA, EPFL and ITC'99 it outperforms the compared GNN and circuit-specific methods on equivalent-gate identification and signal-probability prediction, reducing MAE by 21.7% and 13.8% on ForgeEDA, and it attains the lowest equivalent-gate MAE under zero-shot ForgeEDA-to-OpenABC transfer.

The work proposes MITE, which reformulates biomedical named entity recognition as a structure-to-structure generation task by rendering each training instance in multiple programming-language formats such as Python, C++, and Java to supply structurally diverse supervision without external biomedical knowledge or extra annotations, and at inference aggregates predictions from the different code formats through entity-level voting; on six widely used BioNER datasets MITE consistently outperforms representative BERT-based and LLM-based baselines and shows strong cross-dataset generalization, with ablation and parameter analyses further supporting the effectiveness and robustness of the proposed components.

The work proposes MITE, which reformulates biomedical named entity recognition as a structure-to-structure generation task by rendering each training instance in multiple programming-language formats such as Python, C++, and Java to supply structurally diverse supervision without external biomedical knowledge or extra annotations, and at inference aggregates predictions from the different code formats through entity-level voting; on six widely used BioNER datasets MITE consistently outperforms representative BERT-based and LLM-based baselines and shows strong cross-dataset generalization, with ablation and parameter analyses further supporting the effectiveness and robustness of the proposed components.