Machine learningGranular gasParticle trackingObject detectionMask-CNNDilute ensembles of granular matter (so-called granular gases) are nonlinear systems which exhibit fascinating dynamical behavior far from equilibrium, including non-Gaussian distributions of velocities and rotational velocities, clustering, and...
We present a machine learning technique to discover and distinguish relevant ordered structures from molecular simulation snapshots or particle tracking data. Unlike other popular methods for structural identification, our technique requires no a priori description of the target structures. Instead, we use...
experiments that are not restricted by the properties of biological samples. In this study, we developed a method for label-free motion tracking to elucidate the mechanism by which leptospirosis spirochetes move on host cells. Our label-free bacterial tracking method uses machine learning-based image...
Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers 2024 Cha et al. AAAI instance-wise unlearning [Code] Parameter-tuning-free data entry error unlearning with adaptive selective synaptic dampening 2024 Schoepf et al. arXiv ASSD [Code] Zero-Shot Machine Unlearning at Scale...
做Sampling, particle filtering的,不妨看看统计物理学(Statistical Physics),他们对于蒙特卡罗方法已经应用数十年,积累极深,很可能在vision或者learning提出的一些新方法,已经是被他们以另外一种形式或者名称提出过了。 做Tracking, video, 和optimization的,可以看看控制论(Control theory)。控制科学对于动态系统(或者其它...
Machine learning is a research area of artificial intelligence that enables computers to learn and improve from large datasets without being explicitly programmed. It involves creating algorithms that can analyze patterns in data and generate models for specific tasks, allowing for accurate predictions and...
Flow visualization technologies such as particle tracking velocimetry are broadly used for studying three-dimensional turbulent flow in natural and industrial processes. Despite the advances in three-dimensional acquisition techniques, it is challenging
Dig deep into the data with a hands-on guide to machine learningMachine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book ...
no existing models are capable of faithfully reproducing statistical and topological properties exhibited by particle trajectories in turbulence. We propose a machine learning approach, based on a state-of-the-art diffusion model, to generate single-particle trajectories in three-dimensional turbulence at...
A Framework for Machine Learning for Charged Particle Tracking in the CLAS Detector a lithography subsystem (316) including a maskless pattern writer such as a charged particle multi-beamlet lithography machine (1) or ebeam machine. The... Z Zhou - 《Theory of Computing Systems》 被引量: 0发...