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2,658 papers
The findings suggest that AI-powered anomaly detection significantly strengthens observability and security in cloud-based applications, enabling proactive threat mitigation and operational optimization in increasingly complex distributed environments.
The Act2Intention framework is proposed, which builds an active mobile agent by integrating understanding, predicting user intentions, and executing decisions, and establishes a standardized platform for developing and evaluating proactive agents and consequently paves the way for research on intention-driven human-com...
Background Large language models (LLMs) are increasingly used to support statistical analyses in biomedical research. However, their ability to accurately and reproducibly compute descriptive statistics directly from datasets has received limited evaluation. Objective To compare the accuracy, within-modality repeatabil...
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This research proposes a large language model–based approach combined with structured algorithms that process domain-specific competency questions and unstructured construction documents to build ontologies and extract structured knowledge graphs to support the automated generation of quality inspection checklists and...
The research concludes that the DST provides stakeholders with high-level insights regarding the suitable DT maturity levels—essential for stakeholder buy-in—assists them in making data-driven decisions especially during the front-end planning stage, and helps determine realistic technology objectives that contribute t...
Different capacities for mentalization across LLMs are demonstrated, and cognitive computational modeling is highlighted as a formal method for assessing comparative intelligence across humans and machines.
Overall, AI is becoming an integral component of modern oncology, particularly radiation oncology, and its successful integration into routine clinical practice will require robust validation, transparent governance, equitable implementation, and continued clinician oversight to ensure safe, effective, and patient-cent...
A specification-based unit test generation paradigm that replaces the code under test in the prompt with an LLM-generated specification docstring is introduced, showing that this paradigm effectively reduces misguided tests while substantially increasing effective tests, improves multi-round, feedback-driven test gener...
A5 is developed, a fluorogenic substrate for aldehyde dehydrogenase 2 (ALDH2) that exhibits high isoform selectivity and a >240-fold signal enhancement over the standard NADH assay, and enables quantitative imaging of ALDH2 activity across multiple biological scales.
This work proposes LumiCharge, a novel atomic charge prediction framework that incorporates high-order spherical harmonics convolutions and explicitly models multibody interactions, and demonstrates exceptional extrapolation capability and robustness across molecules of varying sizes, effectively overcoming the limitat...
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...
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