Traditional drug discovery relies on rounds of screening millions of
candidate molecules with low success rates, making drug discovery time and
resource intensive. To overcome this screening bottleneck, we introduce
Latent-X, an all-atom protein de... read more
This study presents a grasping method for objects with uneven mass
distribution by leveraging diffusion models to localize the center of gravity
(CoG) on unknown objects. In robotic grasping, CoG deviation often leads to
postural instability, where... read more
Large Language Diffusion Models (LLDMs) exhibit comparable performance to
LLMs while offering distinct advantages in inference speed and mathematical
reasoning tasks.The precise and rapid generation capabilities of LLDMs amplify
concerns of harmful... read more
Model multiplicity refers to the existence of multiple machine learning
models that describe the data equally well but may produce different
predictions on individual samples. In medicine, these models can admit
conflicting predictions for the same... 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
Drug-drug interactions (DDIs) arise when multiple drugs are administered
concurrently. Accurately predicting the specific mechanisms underlying DDIs
(named DDI events or DDIEs) is critical for the safe clinical use of drugs.
DDIEs are typically rep... read more
BACKGROUND: Artificial intelligence (AI) augmentation of ECG assessment has significant potential to improve patient outcomes in acute coronary syndrome. read more
Reliable medical image classification requires accurate predictions and
well-calibrated uncertainty estimates, especially in high-stakes clinical
settings. This work presents MedSymmFlow, a generative-discriminative hybrid
model built on Symmetrica... read more
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