Editor |KX To this day, the structural detail and precision determined by crystallography, from simple metals to large membrane proteins, are unmatched by any other method. However, the biggest challenge, the so-called phase problem, remains retrieving phase information from experimentally determined amplitudes. Researchers at the University of Copenhagen in Denmark have developed a deep learning method called PhAI to solve crystal phase problems. A deep learning neural network trained using millions of artificial crystal structures and their corresponding synthetic diffraction data can generate accurate electron density maps. The study shows that this deep learning-based ab initio structural solution method can solve the phase problem at a resolution of only 2 Angstroms, which is equivalent to only 10% to 20% of the data available at atomic resolution, while traditional ab initio Calculation

In 2023, almost every field of AI is evolving at an unprecedented speed. At the same time, AI is constantly pushing the technological boundaries of key tracks such as embodied intelligence and autonomous driving. Under the multi-modal trend, will the situation of Transformer as the mainstream architecture of AI large models be shaken? Why has exploring large models based on MoE (Mixed of Experts) architecture become a new trend in the industry? Can Large Vision Models (LVM) become a new breakthrough in general vision? ...From the 2023 PRO member newsletter of this site released in the past six months, we have selected 10 special interpretations that provide in-depth analysis of technological trends and industrial changes in the above fields to help you achieve your goals in the new year. be prepared. This interpretation comes from Week50 2023

Local fine-tuning of DeepSeek class models faces the challenge of insufficient computing resources and expertise. To address these challenges, the following strategies can be adopted: Model quantization: convert model parameters into low-precision integers, reducing memory footprint. Use smaller models: Select a pretrained model with smaller parameters for easier local fine-tuning. Data selection and preprocessing: Select high-quality data and perform appropriate preprocessing to avoid poor data quality affecting model effectiveness. Batch training: For large data sets, load data in batches for training to avoid memory overflow. Acceleration with GPU: Use independent graphics cards to accelerate the training process and shorten the training time.

Editor | KX Retrosynthesis is a critical task in drug discovery and organic synthesis, and AI is increasingly used to speed up the process. Existing AI methods have unsatisfactory performance and limited diversity. In practice, chemical reactions often cause local molecular changes, with considerable overlap between reactants and products. Inspired by this, Hou Tingjun's team at Zhejiang University proposed to redefine single-step retrosynthetic prediction as a molecular string editing task, iteratively refining the target molecular string to generate precursor compounds. And an editing-based retrosynthetic model EditRetro is proposed, which can achieve high-quality and diverse predictions. Extensive experiments show that the model achieves excellent performance on the standard benchmark data set USPTO-50 K, with a top-1 accuracy of 60.8%.

Editor | ScienceAI Based on limited clinical data, hundreds of medical algorithms have been approved. Scientists are debating who should test the tools and how best to do so. Devin Singh witnessed a pediatric patient in the emergency room suffer cardiac arrest while waiting for treatment for a long time, which prompted him to explore the application of AI to shorten wait times. Using triage data from SickKids emergency rooms, Singh and colleagues built a series of AI models that provide potential diagnoses and recommend tests. One study showed that these models can speed up doctor visits by 22.3%, speeding up the processing of results by nearly 3 hours per patient requiring a medical test. However, the success of artificial intelligence algorithms in research only verifies this

LaravelEloquent Model Retrieval: Easily obtaining database data EloquentORM provides a concise and easy-to-understand way to operate the database. This article will introduce various Eloquent model search techniques in detail to help you obtain data from the database efficiently. 1. Get all records. Use the all() method to get all records in the database table: useApp\Models\Post;$posts=Post::all(); This will return a collection. You can access data using foreach loop or other collection methods: foreach($postsas$post){echo$post->

Peking University and the EVLO innovation team jointly proposed DriveWorld, a four-dimensional space-time pre-training algorithm for autonomous driving. This method uses a world model for pre-training, designs a memory state space model for four-dimensional spatio-temporal modeling, and reduces the random uncertainty and knowledge uncertainty faced by autonomous driving by predicting the occupation grid of the scene. This paper has been accepted by CVPR2024. Paper title: DriveWorld: 4DPre-trainedSceneUnderstandingviaWorldModelsforAutonomousDriving Paper link: https://arxiv.org/abs/2405.04390 1. Motion

Improve HDFS performance on CentOS: A comprehensive optimization guide to optimize HDFS (Hadoop distributed file system) on CentOS requires comprehensive consideration of hardware, system configuration and network settings. This article provides a series of optimization strategies to help you improve HDFS performance. 1. Hardware upgrade and selection resource expansion: Increase the CPU, memory and storage capacity of the server as much as possible. High-performance hardware: adopts high-performance network cards and switches to improve network throughput. 2. System configuration fine-tuning kernel parameter adjustment: Modify /etc/sysctl.conf file to optimize kernel parameters such as TCP connection number, file handle number and memory management. For example, adjust TCP connection status and buffer size