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  • Google Scholar
    Scholar Labs update: Search 10x faster, 3x deeper Google Scholar provides a simple way to broadly search for scholarly literature Search across a wide variety of disciplines and sources:
  • Adaptive Graph Refinement and Label Propagation with LLMs for Cost . . .
    Abstract Dirty entity resolution (ER), which identifies records referring to the same real-world entity from a single, messy dataset, is a fundamental task in data management and mining However, the dominant blocking-matching-clustering paradigm for ER suffers from critical flaws Its cascaded, decoupled workflow essentially produces a static, sparse graph plagued by missing edges (due to
  • Rethinking Scientific Modeling: Toward Physically Consistent and . . .
    Graph-R1 is proposed, the first agentic GraphRAG framework via end-to-end reinforcement learning (RL), which introduces lightweight knowledge hypergraph construction, models retrieval as a multi-turn agent-environment interaction, and optimizes the agent process via an end-to-end reward mechanism
  • [2605. 25388] ViroBench: Benchmarking Nucleotide Foundation Models on . . .
    ViroBench evaluates models across two critical dimensions: biological understanding and latent biosecurity risk, covering 18 diverse scenarios within 4 task types Extensive evaluation of 66 NFMs across diverse architectures yields three critical conclusions
  • Resolve a DOI Name
    Type or paste a known DOI name exactly—including its prefix and suffix—into the text box below and then ‘submit’ to resolve it DOIs include a prefix (prefixes always start with 10 ) and a suffix, separated by a forward slash ( )
  • PubMed
    PubMed® comprises more than 40 million citations for biomedical literature from MEDLINE, life science journals, and online books Citations may include links to full text content from PubMed Central and publisher web sites Identification of mechanism of the oncogenic role of FGFR1 in papillary thyroid carcinoma
  • GraphRP-KDD2026 README. md at main - GitHub
    @inproceedings {wen2026graphrp, title = {Defending against Model Extraction for GNNs with Model Reprogramming}, author = {Wen, Yan and Wang, Zhenyi and Huang, Heng}, booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, series = {KDD '26}, year = {2026}, month = {August}, address = {Jeju Island, Republic of Korea}, publisher = {ACM}, doi = {10 1145 3770855 3817983}, url = {https: doi org 10 1145 3770855 3817983} }
  • Advancing Graph Few-Shot Learning via In-Context Learning
    Abstract Graph few-shot learning, which aims to classify nodes from novel classes with only a few labeled examples, is a widely studied problem in graph learning However, existing methods often face two key limitations First, the predominant graph few-shot learning paradigm relies on supervised tasks, failing to leverage the vast number of unlabeled nodes in the graph Second, many approaches
  • Search Publications | ResearchGate
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  • Sci-Hub
    Here, we provide the first report of PD-1 blockade with pembrolizumab in a patient with Lynch syndrome and progressive cortisol-secreting metastatic ACC A 58-year-old female with known Lynch syndrome presented with severe Cushing's syndrome and was diagnosed with a cortisol-secreting ACC





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