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Development and external validation of a multivariate model for predicting pneumonia in patients receiving maintenance hemodialysis: a retrospective study
CONCLUSION: The created predictive model provided a precise, individualized evaluation of pneumonia risk in patients with MHD. It can be used to identify individuals at high risk of pulmonary infection in patients undergoing MHD and guide their treatment and prognosis follow-up. →
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Phase II study of dual epigenetic targeting with chidamide and azacitidine in patients with high-risk acute myeloid leukemia after allo-HSCT
CONCLUSIONS: Dual epigenetic targeting maintenance treatment with CHI-AZA has an acceptable toxicity profile and might potentially be effective to prevent relapse in high-risk AML patients after allo-HSCT. →
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NVIDIA Researchers Propose Reinforcement Learning Pretraining (RLP): Reinforcement as a Pretraining Objective for Building Reasoning During Pretraining
Understanding the Target Audience for NVIDIA’s Reinforcement Learning Pretraining (RLP) The target audience for NVIDIA’s Reinforcement Learning Pretraining (RLP) includes AI researchers, machine learning engineers, data scientists, and business leaders in technology-focused industries. These individuals are typically engaged in developing AI models or implementing AI solutions in their organizations. Their primary pain points include: Difficulty… →
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7 LLM Generation Parameters—What They Do and How to Tune Them?
«`html 7 LLM Generation Parameters—What They Do and How to Tune Them In the realm of language model fine-tuning, understanding the seven parameters that govern large language model (LLM) generation is crucial for achieving desired outputs. These parameters influence aspects such as response length, randomness, novelty, and termination. Below, we analyze each parameter, its function,… →
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ServiceNow AI Research Releases DRBench, a Realistic Enterprise Deep-Research Benchmark
ServiceNow AI Research Releases DRBench, a Realistic Enterprise Deep-Research Benchmark Understanding the Target Audience The primary audience for the DRBench benchmark includes AI researchers, enterprise software developers, data scientists, and business analysts, particularly those focused on improving the capabilities of AI agents in enterprise contexts. Their pain points often revolve around the challenge of accurately… →
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From Employee-Owners to Environmental Champions
Tania Yakunova/Ikon Images A North Carolina engineering company slashed its waste to near-zero levels thanks to the active involvement and support of its employees. An Indiana consulting firm pioneered eco-friendly business practices, driven by the commitment and engagement of its employees. In Rhode Island, a local coffee shop enhanced its sustainability practices from bean to… →
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Podcast: When We Get Too Comfortable, It’s Tough to Take Flight
Scary as it is, it’s important for leaders to try new—and unproven—things. “Everyone falls. Great leaders get up and start running again,” says Sanjay Khosla, an executive coach and adjunct professor at Kellogg. In this episode of The Insightful Leader: clinging to comfort yields diminishing returns. We hear how three leaders broke the habit. Podcast… →
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Meta’s ARE + Gaia2 Set a New Bar for AI Agent Evaluation under Asynchronous, Event-Driven Conditions
«`html Understanding the Target Audience for Meta’s ARE + Gaia2 The target audience for Meta’s Agents Research Environments (ARE) and Gaia2 includes AI researchers, business managers, and technology decision-makers. These individuals are typically involved in the development, evaluation, and deployment of AI agents in various business contexts. Pain Points Difficulty in evaluating AI agents under… →
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Microsoft AI Debuts MAI-Image-1: An In-House Text-to-Image Model that Enters LMArena’s Top-10
«`html Microsoft AI Debuts MAI-Image-1: An In-House Text-to-Image Model that Enters LMArena’s Top-10 Microsoft AI has introduced MAI-Image-1, its first image generation model developed entirely in-house. As of October 13, 2025, this model has debuted in the Top-10 of the LMArena text-to-image leaderboard. MAI-Image-1 is currently being tested publicly to collect community feedback, with plans… →
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How to Evaluate Your RAG Pipeline with Synthetic Data?
«`html How to Evaluate Your RAG Pipeline with Synthetic Data Evaluating LLM applications, particularly those using RAG (Retrieval-Augmented Generation), is crucial but often neglected. Without proper evaluation, it’s almost impossible to confirm if your system’s retriever is effective, if the LLM’s answers are grounded in the sources (or hallucinating), and if the context size is… →