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- VisionGuard: Secure and Robust Visual Perception of Autonomous Vehicles in Practice10
The key of VisionGuard is to leverage the spatiotemporal inconsistency property of PAEs to detect anomalies and it predicts the motion states from historical ones and compares them with the current driving states to identify any motion inconsistency caused by physical attacks.
- Testing the Fault-Tolerance of Multi-sensor Fusion Perception in Autonomous Driving Systems9
This work presents FADE, the first testing methodology to comprehensively assess the fault tolerance of MSF perception-based ADSs, and designs a feedback-guided differential fuzzer to uncover safety violations of ADSs caused by the injected faults.
- MRI-based clinical-radiomics nomogram to predict early neurological deterioration in isolated acute pontine infarction: a two-center study in Northeast China9
The clinical-radiomics model combined MRI-derived radiomics and clinical metrics and may serve as a scoring tool for early prediction of END among patients with isolated API.
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- Adaptive Blurring Estimation for Learning-Based Super Resolution–
This paper proposes to firstly estimate the blurring degree of an input image, and then generate the adaptive codebook (dictionary) for learning-based SR, which can simultaneously achieve the de-blurring and high-resolution image in the learning framework.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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