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1,890 papers
A study of a Graph Neural Network-based model for track reconstruction in the context of MUonE experiment is presented, using simulated data corresponding to the test-run MUonE detector setup. The fully three-dimensional model successfully addresses both the reconstruction and particle identification challenges essenti...
A self-audit of the authors' own graph attention scorer, MetaGNN, evaluated on TCGA colorectal, in which two independent failure modes produced a near-ceiling benchmark score and a positive architectural result, neither of which survived inspection.
Results confirm that explicitly coupling traffic prediction with online trajectory replanning enables more adaptive and efficient navigation under time-varying traffic conditions.
A hybrid MPPI controller operating over the learned GNN dynamics model improves predictive accuracy over a flat-ground baseline model and achieves superior navigation performance compared to $A^*$-based re-planning and MPPI-only variants.
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A systematic analysis of expressivity and trainability of Pauli Correlation Encoding and a multistage continuation framework that gradually transforms a smooth relaxed objective into a sharper objective that more closely approximates the target discrete problem are proposed.
TeethGNN is developed, a novel graph-based framework designed to combine CBCT image features with morphological information for accurate and efficient malocclusion grading, providing an accurate and reliable solution for vision-based clinical measurement and diagnosis.
This work presents an automated framework that removes redundant checks while provably preserving soundness in zero-knowledge proof systems, and evaluates circuits spanning MLP, CNN, RNN, and transformer architectures generated by two production frameworks.
The PccDiffuser is presented, a conditional diffusion framework for continuum robots that learns a multimodal distribution over complete configuration-space paths and samples multiple candidate solutions in parallel, which are subsequently converted into an executable trajectory by time allocation considering actuator...
The article concludes with a forward-looking roadmap for the future of AI in chemical and materials engineering, defined by a paradigm shift from simply finding new materials to creating viable, economical and scalable pathways to produce them, thereby enabling a new era of synthesis-aware materials innovation.
Due to persistent diagnostic delays, varied disease progression, and unpredictable therapy responses, viral hepatitis remains a major worldwide health concern despite advances in antiviral medication and vaccine. Precision hepatology is a viable strategy for overcoming these limitations through tailored, data-driven cl...
Early prediction of epileptic seizures remains an active research area in scalp electroencephalography (EEG) analysis. Current methods focused on this topic often rely on handcrafted features that insufficiently capture the multiscale nonlinear dynamics of preictal activity, process EEG channels independently without m...
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
Climate & Sustainability
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