The modeling of genomic sequences presents unique challenges due to their
length and structural complexity. Traditional sequence models struggle to
capture long-range dependencies and biological features inherent in DNA. In
this work, we propose Tr... read more
The rapid advancement of image-generation technologies has made it possible
for anyone to create photorealistic images using generative models, raising
significant security concerns. To mitigate malicious use, tracing the origin of
such images is e... read more
Autonomous vehicles generate massive volumes of point cloud data, yet only a
subset is relevant for specific tasks such as collision detection, traffic
analysis, or congestion monitoring. Effectively querying this data is essential
to enable target... read more
Differentiated thyroid cancer DTC recurrence is a major public health
concern, requiring classification and predictive models that are not only
accurate but also interpretable and uncertainty aware. This study introduces a
comprehensive framework f... read more
A pronounced imbalance in GPU resources exists on campus, where some
laboratories own underutilized servers while others lack the compute needed for
AI research. GPU sharing can alleviate this disparity, while existing platforms
typically rely on c... read more
Rapid advances in medical imaging technology underscore the critical need for
precise and automated image quality assessment (IQA) to ensure diagnostic
accuracy. Existing medical IQA methods, however, struggle to generalize across
diverse modalitie... read more
Early detection of non-small cell lung cancer (NSCLC) is critical for
improving patient outcomes, and novel approaches are needed to facilitate early
diagnosis. In this study, we explore the use of automatic cough analysis as a
pre-screening tool f... read more
Fine-grained traffic management and prediction are fundamental to key
applications such as autonomous driving, lane change guidance, and traffic
signal control. However, obtaining lane-level traffic data has become a
critical bottleneck for data-dr... read more
In medical imaging, generative models are increasingly relied upon for two
distinct but equally critical tasks: reconstruction, where the goal is to
restore medical imaging (usually inverse problems like inpainting or
superresolution), and generati... read more
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