Course Intermediate  11329

Course Introduction："Self-study IT Network Linux Load Balancing Video Tutorial" mainly implements Linux load balancing by performing script operations on web, lvs and Linux under nagin.

Course Advanced  17632

Course Introduction："Shangxuetang MySQL Video Tutorial" introduces you to the process from installing to using the MySQL database, and introduces the specific operations of each link in detail.

Course Advanced  11346

Course Introduction："Brothers Band Front-end Example Display Video Tutorial" introduces examples of HTML5 and CSS3 technologies to everyone, so that everyone can become more proficient in using HTML5 and CSS3.

Course Introduction：Memory is an important part of children's cognitive development and has a vital impact on their academic performance and daily life. This article will introduce some effective methods and techniques to help parents improve their children's memory and make them more confident and successful in learning and life. 1. Set clear learning goals. Setting clear learning goals can help children focus and store information in long-term memory, improving memory effects. Tell children what they will learn today and encourage them to think and ask questions as they learn. 2. Make a reasonable study plan. Making a reasonable study plan can help children allocate time and make full use of the golden time of memory. Divide learning tasks into short-term and long-term goals, and set a clear timetable and mileage for each goal

2024-08-24 comment 0  620

Course Introduction：What is geometric deep learning? In the evolving field of artificial intelligence and machine learning, a powerful paradigm called geometric deep learning (GDL) has emerged that is becoming increasingly prominent. Based on graph theory and geometry, GDL provides a revolutionary method for analyzing data with complex relationships (such as social networks, molecules, and 3D objects) 1. Understanding graphics perspective geometry The core of deep learning is the concept of graphics. A graph consists of nodes and edges and is a model of relationships between entities. GDL uses this structure to capture complex dependencies in data, which are difficult to solve with traditional deep learning models. 2. Embrace spatial and spectral domains GDL works in both spatial and spectral domains. Data and its relationships are encoded directly in the spatial domain of the graph. In the spectral domain, the pattern signal is transformed into

2023-09-20 comment 0  677

Course Introduction：Editor | XSNature published two important research results in November 2023: protein synthesis technology Chroma and crystal material design method GNoME. Both studies adopted graph neural networks as a tool for processing scientific data. In fact, graph neural networks, especially geometric graph neural networks, have always been an important tool for scientific intelligence (AI for Science) research. This is because physical systems such as particles, molecules, proteins, and crystals in the scientific field can be modeled into a special data structure—geometric graphs. Different from general topological diagrams, in order to better describe the physical system, geometric diagrams add indispensable spatial information and need to satisfy the physical symmetry of translation, rotation and flipping. Whereas

2024-03-07 comment 0  887

Course Introduction：Editor | Radish Skin Protein design and engineering are advancing at an unprecedented pace thanks to advances in deep learning. However, current models cannot naturally account for non-protein entities during the design process. Here, researchers at the Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland propose a deep learning method based entirely on a geometric transformer of atomic coordinates and element names that can predict proteins based on backbone scaffolds with constraints imposed by different molecular environments. sequence. Using this method, researchers can produce highly thermostable, catalytically active enzymes with a high success rate. This is expected to increase the versatility of protein design pipelines to achieve desired functions. This study uses "Context-awaregeometricde

2024-08-05 comment 0  978

Course Introduction：Light-realistic simulation plays a key role in applications such as autonomous driving, where advances in neural network radiated fields (NeRFs) may enable better scalability by automatically creating digital 3D assets. However, the reconstruction quality of street scenes suffers due to the high collinearity of camera motion on the streets and sparse sampling at high speeds. On the other hand, the application often requires rendering from a camera perspective that deviates from the input perspective to accurately simulate behaviors such as lane changes. LidaRF presents several insights that allow better utilization of lidar data to improve the quality of NeRF in street views. First, the framework learns geometric scene representations from lidar data, which are combined with an implicit mesh-based decoder to provide stronger geometric information provided by the displayed point cloud.

2024-05-09 comment 0  855