![]() A coalescent-based isolation-with-migration (IM) model was used to estimate lineage divergence times and population demographic parameters. arguta populations to infer current patterns of molecular structure and diversity in relation to past (Last Interglacial and Last Glacial Maximum) and present distributions based on ecological niche modelling (ENM). Here, we investigate the effects of Quaternary changes in climate and sea level on the evolutionary and demographic history of Platycrater arguta, a rare temperate understorey shrub with disjunct distributions in East China (var. However, it is less clear when and how lineages diverged in this region, whether in full isolation or in the face of post-divergence gene flow. Sign up for the free insideBIGDATA newsletter.In East Asia, an increasing number of studies on temperate forest tree species find evidence for migration and gene exchange across the East China Sea (ECS) land bridge up until the last glacial maximum (LGM). TensorRT, with its real-time inference capabilities, improves the performance of the view detection algorithm and it also shortened our time to market during the R&D project.” “The cardiac view recognition algorithm selects appropriate images for analysis of cardiac wall motion. During the R&D project leading up to the Vivid Patient Care Elevated Release, we wanted to make the process more efficient by implementing automated cardiac view detection on our Vivid E95 scanner,” said Erik Steen, chief engineer of Cardiovascular Ultrasound at GE Healthcare. ![]() “When it comes to ultrasound, clinicians spend valuable time selecting and measuring images. This enables clinicians to deliver the highest quality of care through its intelligent healthcare solutions. GE Healthcare, a leading global medical technology, diagnostics and digital solutions innovator, is using TensorRT to help accelerate computer vision applications for ultrasounds, a critical tool for the early detection of diseases. With TensorRT 8, Hugging Face achieved 1ms inference latency on BERT, and we’re excited to offer this performance to our customers later this year.” “The Hugging Face Accelerated Inference API already delivers up to 100x speedup for transformer models powered by NVIDIA GPUs. “We’re closely collaborating with NVIDIA to deliver the best possible performance for state-of-the-art models on NVIDIA GPUs,” said Jeff Boudier, product director at Hugging Face. The company is working closely with NVIDIA to introduce groundbreaking AI services that enable text analysis, neural search and conversational applications at scale. ![]() Hugging Face is an open-source AI leader relied on by the world’s largest AI service providers across multiple industries. Industry leaders have embraced TensorRT for their deep learning inference applications in conversational AI and across a range of other fields. This significantly reduces compute and storage overhead for efficient inference on Tensor Cores. Quantization aware training enables developers to use trained models to run inference in INT8 precision without losing accuracy. Sparsity is a new performance technique in NVIDIA Ampere architecture GPUs to increase efficiency, allowing developers to accelerate their neural networks by reducing computational operations. In addition to transformer optimizations, TensorRT 8’s breakthroughs in AI inference are made possible through two other key features. “The latest version of TensorRT introduces new capabilities that enable companies to deliver conversational AI applications to their customers with a level of quality and responsiveness that was never before possible.” That makes it imperative for enterprises to deploy state-of-the-art inferencing solutions,” said Greg Estes, vice president of developer programs at NVIDIA. ![]() “AI models are growing exponentially more complex, and worldwide demand is surging for real-time applications that use AI. Now, with TensorRT 8, companies can double or triple their model size to achieve dramatic improvements in accuracy. In the past, companies had to reduce their model size which resulted in significantly less accurate results. TensorRT 8’s optimizations deliver record-setting speed for language applications, running BERT-Large, one of the world’s most widely used transformer-based models, in 1.2 milliseconds. NVIDIA today launched TensorRT™ 8, the eighth generation of the company’s AI software, which slashes inference time in half for language queries - enabling developers to build the world’s best-performing search engines, ad recommendations and chatbots and offer them from the cloud to the edge.
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