2,509 research outputs found

    Magnetic Borophenes from an Evolutionary Search

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    A computational methodology based on ab initio evolutionary algorithms and spin-polarized density functional theory was developed to predict two-dimensional magnetic materials. Its application to a model system borophene reveals an unexpected rich magnetism and polymorphism. A metastable borophene with nonzero thickness is an antiferromagnetic semiconductor from first-principles calculations, and can be further tuned into a half-metal by finite electron doping. In this borophene, the buckling and coupling among three atomic layers are not only responsible for magnetism, but also result in an out-of-plane negative Poisson\u27s ratio under uniaxial tension, making it the first elemental material possessing auxetic and magnetic properties simultaneously

    A peripherally inserted central vein catheter fractured and slid into the right pulmonary artery: A case report

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    AbstractCatheter fracture is a rare but serious complication of a peripherally inserted central catheter (PICC). An adolescent patient was sent to Tianjin Medical University General Hospital (Tianjin, China) because the PICC was fractured when removed by a nurse. Chest X-ray showed that the PICC fragment slid into the right pulmonary artery. Through emergency surgery, the remainder of the PICC was successfully retrieved by an interventional operation percutaneously via the right femoral vein. PICC fracture is less common and always without significant discomfort if not found timely, and it may lead to serious complications, such as pulmonary embolism, and even death. Thus, nurses, patients and their family members should pay enough attention to the daily maintenance of PICC and have a deep understanding of the reasons associated with PICC fracture as well as how to prevent it. Interventional operation is minimally invasive, which is a good choice for the removal of intravascular foreign bodies, leading to fewer complications and a good prognosis

    FLM-101B: An Open LLM and How to Train It with $100K Budget

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    Large language models (LLMs) have achieved remarkable success in NLP and multimodal tasks, among others. Despite these successes, two main challenges remain in developing LLMs: (i) high computational cost, and (ii) fair and objective evaluations. In this paper, we report a solution to significantly reduce LLM training cost through a growth strategy. We demonstrate that a 101B-parameter LLM with 0.31T tokens can be trained with a budget of 100K US dollars. Inspired by IQ tests, we also consolidate an additional range of evaluations on top of existing evaluations that focus on knowledge-oriented abilities. These IQ evaluations include symbolic mapping, rule understanding, pattern mining, and anti-interference. Such evaluations minimize the potential impact of memorization. Experimental results show that our model, named FLM-101B, trained with a budget of 100K US dollars, achieves performance comparable to powerful and well-known models, e.g., GPT-3 and GLM-130B, especially on the additional range of IQ evaluations. The checkpoint of FLM-101B is released at https://huggingface.co/CofeAI/FLM-101B
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