This paper presents a meta-synthesis that draws together four constituent studies covering adversarial machine learning, AI-powered anomaly detection in cloud environments, automated vulnerability patching by multi-agent large language model (LLM) pipelines, and the broader landscape of securing AI systems across their...
Harsh Verma· International Journal of Sci...· 0 citations
Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near-surface conditions also depend on unresolved terrain and land-surface properties. Existing probabilistic downscalers address this gap using...
Pedro Sousa, Will Tebbutt, S. Jaffer et al.· 0 citations
The model, Laxmi, improves global probabilistic accuracy by 19% and resolves systematic distributional biases in ERA5 and mitigates extreme rainfall underprediction, improving the global 95th percentile Brier skill score by 57%.
Julian Schmitt, B. Delorme, Robert C. King et al.· 0 citations
Accurate cloud-cover forecasts are important for temperature prediction, radiation forecasting, and solar-power operations. Short-range forecasting methods can preserve observed cloud placement during the first forecast hours, but their skill decreases when cloud fields evolve through formation, dissipation and deforma...
Mikko Partio, Leila Hieta, Ossi Laine· 0 citations
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Geostationary atmospheric motion vectors (AMVs) provide the dense horizontal wind vectors (u,v) and heights ingested into data assimilation systems. Traditional AMVs track features using window-based cross-correlation and estimate heights via infrared brightness temperatures paired with numerical weather prediction (NW...
T. Vandal, Dong L. Wu, J. Carr et al.· 0 citations
Spatio-Temporal U-DeepONet (GenONet), a novel architecture for long-range precipitation forecasting up to 3 hours, specifically designed to produce sharp and physically consistent results, is introduced.
Mohammad Kian Golkar, Luciano Alves de Oliveira, Mohammad Khanjani· 0 citations
A practical checklist that ML/AI and Earth system science practitioners can use to assess and reduce the environmental footprint of their own applications, organised around the successive stages of the model development pipeline.
Filippo Dainelli, A. Mozaffari, Marina Castaño et al.· 0 citations
CDEP Agent is presented, an auditable LLM-agent framework that tests this mismatch directly by linking CDEP candidates detected from meteorological reanalysis to real-world hazard and impact evidence across sources with different spatial scales, temporal resolutions, and reporting conventions.
PepBAN is introduced, a deep learning framework for modeling PepPI predictions that effectively learns the pattern of pairwise local interactions, enables the identification of key residues participating in the peptide-protein interactions, and offers an intuitive approach to interpret the underlying mechanisms of PepP...
Shuaiyan Li, Xiaorui Wang, Yuchen Zhu et al.· Journal of Chemical Informat...· 4 citations
This study proposes SynGFN, which models molecular design as a cascade of simulated chemical reactions, enabling the assembly of molecules from synthesizable building blocks, as a bridge linking molecular design and synthesis, accelerating exploration and producing diverse, synthesizable, high-performance molecules.
Results indicate that BF3 antagonists can synergize with the LBP antagonist to amplify conformational flexibility in H12 and induce anticorrelated dynamics of H12 with H3 and H4, suggesting a promising avenue for enhancing the efficacy and overcoming the resistance in castration-resistant prostate cancer treatment.
Xiaotian Kong, Yushan Zou, Peng Cao et al.· Journal of Chemical Informat...· 1 citation
This work introduces an advanced equivariant graph attention neural network specifically engineered to model long-range atomic electrostatic interactions with high precision, and improves the model's accuracy, generalization, and robustness in complex scenarios.
Qiaolin Gou, Qun Su, Ji-Ke Wang et al.· Journal of Chemical Informat...· 1 citation
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
