Multiclass Semantic Segmentation of Wildland Fire Images Using Context-Aware Centralized Copy-Paste Data Augmentation
为解决野火图像语义分割标注数据稀缺问题，提出了一种上下文感知的集中式复制粘贴数据增强方法，以生成更真实、上下文准确的训练样本。
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Herding End-to-End Autonomous Driving via Neuro-Symbolic Safety Guards
This work addresses the safety and interpretability limitations of end-to-end autonomous driving systems, which often violate traffic rules due to reliance on statistical data patterns and lack verifiable guarantees. The authors propose a lightweight neuro-symbolic safety guard that enforces explicit traffic regulations in real time—without retraining the underlying model or adding new learning components—by validating control commands prior to execution and substituting them with the nearest feasible safe action when necessary. Integrated seamlessly into the TransFuser v6 architecture, this mechanism ensures every intervention is traceable to a specific rule. Experiments on the Fail2Drive and Bench2Drive long-tail benchmarks demonstrate a 53% reduction in severe collisions and a 15% improvement in task success rate, all while preserving the original driving performance.
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Structured Phonological Representations for Audio-Articulatory rtMRI Speech Classification
Mapping real-time magnetic resonance imaging (rtMRI)–derived articulatory movements to phonological categories remains challenging. This work proposes a multimodal modeling approach that, for the first time, transfers audio representations trained with phonological feature supervision—based on PhonoQ, WavLM-large, and HuBERT-large—into purely articulatory modeling, yielding performance gains even during audio-free inference. By fusing synchronized audio and articulatory contours, the model significantly improves macro-F1 scores for phonological classification tasks (e.g., manner and place of articulation, voicing, vowel height, and backness) under both unseen-speech and unseen-speaker conditions, while also enhancing fine-grained accuracy across 39 phoneme classes. Furthermore, the approach reveals interpretable patterns of articulatory variation.
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When Repository Labels Are Not Image-Level Truth: A Supervision Auditing Framework for Chest Radiograph AI
This study addresses the widespread issue of label inaccuracies in public chest X-ray datasets, where annotations derived from radiology reports often misrepresent actual pathological findings, thereby compromising model training with unreliable supervisory signals. To mitigate this, the authors propose the Repository Supervision Auditing (RSA) framework, which employs expert image-level annotations to audit label consistency prior to model development, identify systematic biases, and construct a verified evaluation cohort. Their analysis reveals severe discrepancies between report-based labels and radiological evidence: only 1% of cases with expert-confirmed cardiomegaly were correctly labeled, while nearly half were erroneously annotated as “no abnormality.” A DenseNet121 model trained on the corrected cohort achieved a test ROC-AUC of 0.853, demonstrating that supervision auditing is a critical prerequisite for robust medical imaging AI development.
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An Inflation Model for the Colombian Case. 2001 2025
This study investigates inflation dynamics and the effectiveness of monetary policy in Colombia under an inflation-targeting regime with a floating exchange rate. Utilizing data from 2001 to 2025, the authors develop a structural model that integrates the inflation-targeting framework with a structural Phillips curve incorporating the output gap and inflation expectations, estimating it via generalized method of moments (GMM). The empirical analysis confirms the existence of a Phillips curve conditional on inflation expectations and reveals that while the central bank’s policy stance is directionally appropriate, its response is both delayed and insufficient in magnitude to promptly anchor inflation within the target range. These findings offer new empirical evidence on the importance of timeliness and credibility in monetary policy implementation, particularly for emerging market economies.
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Multiclass Semantic Segmentation of Wildland Fire Images Using Context-Aware Centralized Copy-Paste Data Augmentation
为解决野火图像语义分割标注数据稀缺问题，提出了一种上下文感知的集中式复制粘贴数据增强方法，以生成更真实、上下文准确的训练样本。
0 citationsRead paper
Herding End-to-End Autonomous Driving via Neuro-Symbolic Safety Guards
This work addresses the safety and interpretability limitations of end-to-end autonomous driving systems, which often violate traffic rules due to reliance on statistical data patterns and lack verifiable guarantees. The authors propose a lightweight neuro-symbolic safety guard that enforces explicit traffic regulations in real time—without retraining the underlying model or adding new learning components—by validating control commands prior to execution and substituting them with the nearest feasible safe action when necessary. Integrated seamlessly into the TransFuser v6 architecture, this mechanism ensures every intervention is traceable to a specific rule. Experiments on the Fail2Drive and Bench2Drive long-tail benchmarks demonstrate a 53% reduction in severe collisions and a 15% improvement in task success rate, all while preserving the original driving performance.
0 citationsRead paper
Structured Phonological Representations for Audio-Articulatory rtMRI Speech Classification
Mapping real-time magnetic resonance imaging (rtMRI)–derived articulatory movements to phonological categories remains challenging. This work proposes a multimodal modeling approach that, for the first time, transfers audio representations trained with phonological feature supervision—based on PhonoQ, WavLM-large, and HuBERT-large—into purely articulatory modeling, yielding performance gains even during audio-free inference. By fusing synchronized audio and articulatory contours, the model significantly improves macro-F1 scores for phonological classification tasks (e.g., manner and place of articulation, voicing, vowel height, and backness) under both unseen-speech and unseen-speaker conditions, while also enhancing fine-grained accuracy across 39 phoneme classes. Furthermore, the approach reveals interpretable patterns of articulatory variation.
0 citationsRead paper
When Repository Labels Are Not Image-Level Truth: A Supervision Auditing Framework for Chest Radiograph AI
This study addresses the widespread issue of label inaccuracies in public chest X-ray datasets, where annotations derived from radiology reports often misrepresent actual pathological findings, thereby compromising model training with unreliable supervisory signals. To mitigate this, the authors propose the Repository Supervision Auditing (RSA) framework, which employs expert image-level annotations to audit label consistency prior to model development, identify systematic biases, and construct a verified evaluation cohort. Their analysis reveals severe discrepancies between report-based labels and radiological evidence: only 1% of cases with expert-confirmed cardiomegaly were correctly labeled, while nearly half were erroneously annotated as “no abnormality.” A DenseNet121 model trained on the corrected cohort achieved a test ROC-AUC of 0.853, demonstrating that supervision auditing is a critical prerequisite for robust medical imaging AI development.
0 citationsRead paper
An Inflation Model for the Colombian Case. 2001 2025
This study investigates inflation dynamics and the effectiveness of monetary policy in Colombia under an inflation-targeting regime with a floating exchange rate. Utilizing data from 2001 to 2025, the authors develop a structural model that integrates the inflation-targeting framework with a structural Phillips curve incorporating the output gap and inflation expectations, estimating it via generalized method of moments (GMM). The empirical analysis confirms the existence of a Phillips curve conditional on inflation expectations and reveals that while the central bank’s policy stance is directionally appropriate, its response is both delayed and insufficient in magnitude to promptly anchor inflation within the target range. These findings offer new empirical evidence on the importance of timeliness and credibility in monetary policy implementation, particularly for emerging market economies.
0 citationsRead paper