Hierarchical Interpolating MeshGraphNets (HI-MGN), a multiscale extension of MeshGraphNets for efficient long-range communication on unstructured meshes that replaces the flat processor with a hierarchical multiscale processor that coarsens graphs using farthest-point sampling and Voronoi partitioning while preserving...
Sihun Lee, D. Park, Taesoo Bang et al.· 0 citations
A method combining graph neural networks (GNNs) to construct a propagation path modeling framework that integrates topological structure and node status time series characteristics and is directly applicable to reliability analysis of electromagnetic communication links in power networks.
L.-N. Hu, Y. Tang, L. Ye· Advanced Electromagnetics· 0 citations
Two structural ablations indicate that both the sparse connectivity and the estimated grouping of characteristics contribute to the ranking advantage, and both effects remain significant after correcting for multiple testing.
Hongyu Lin, Yulin Chen, Yuanrong Wang et al.· 0 citations
Investigation of when and why critical-class-first scheduling is effective in CITO and a GNN-D3QN joint framework is proposed to unify critical class identification and test order optimization suggests that the effectiveness of critical-class prioritization is structurally conditioned by the topological roles and distr...
Jia-Yi Wang, Fan Chen, Miao Zhang et al.· Journal of King Saud Univers...· 0 citations
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A Self-Supervised Graph Vision Transformer Network (SS-GVTNet) in detecting and classifying pulmonary nodules and malignancies through CT scanning and offers credible classification of malignancy, which help clinicians in early diagnosis and treatment planning is suggested.
Koventhan G. M., R. Ramkumar· Adolescência e Saúde· 0 citations
An intelligent employment decision support system that integrates multi-source heterogeneous data with GNNs (Graph Neural Networks), which can effectively identify structurally mismatched groups, provide quantitative basis for professional adjustment and regional talent policies, and demonstrate good technical performa...
Wen-Bo Jing, Jianhua Zhao· International Conference on...· 0 citations
Although the framework performed slightly better than the best baseline under the tested conditions, the improvement was not statistically significant enough to establish consistent superiority and emphasize the need for further evaluation of deployment time, base-rate estimation, and real-world cybersecurity scenarios...
N. Deotale, Annaluri Sreenivasa Rao, E. Sreedevi et al.· International Journal for Gl...· 0 citations
Overall, the proposed AI–DFT framework provides a physically interpretable computational approach for climate-aware prioritization of perovskite photovoltaic materials by connecting quantum-derived descriptors, ML predictions, environmental factors, sensitivity-based robustness assessment, and device-level performance...
Douglas Yeboah, Claudia Asare, Prince Gaka· Engineering Research Express· 0 citations
This work observes that predictive uncertainty decreases as latent representations shift toward lower-variance posterior directions, even though the posterior variance does not contract, and proposes Alignment-Guided Learning (AGL), which explicitly promotes this alignment during training.
Suk-Hoon Choi, Damdae Park, Junhyuk Choi et al.· 0 citations
Antenna selection is investigated as a means of reducing radio-frequency (RF) chain power in massive multiple-input multiple-output (MIMO) base stations under realistic channel state information (CSI) impairments. The study is motivated by the mismatch between conventional selection objectives and multi-cell operation...
Iacovos I. Ioannou, V. Vassiliou· Network· 0 citations
ReCoG (Reciprocal Co-Evolution for Multimodal Graph Learning) is proposed, a new learning paradigm that tightly couples graph structure learning and multimodal representation learning through end-to-end reciprocal interaction and yields greater expressiveness than decoupled or two-stage formulations.
Experiments show that prediction must be coupled with autonomous optimization to deliver practical resilience, and show that the learned policy sustains service levels, shortens recovery time, and reduces total disruption cost relative to base-stock and single-agent baselines.
Sohail Sayed, Nauman Sayed· Iconic research and engineer...· 0 citations
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
MIT News · Artificial Intelligence· news.mit.eduJul 15, 2026
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.
Microsoft Research Blog· microsoft.comJul 13, 2026
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.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
