相关论文: PC-JeDi: Diffusion for Particle Cloud Generation i…
In this paper, we learn a diffusion model to generate 3D data on a scene-scale. Specifically, our model crafts a 3D scene consisting of multiple objects, while recent diffusion research has focused on a single object. To realize our goal,…
In challenging low light and adverse weather conditions,thermal vision algorithms,especially object detection,have exhibited remarkable potential,contrasting with the frequent struggles encountered by visible vision algorithms.…
Mesh generation is of great value in various applications involving computer graphics and virtual content, yet designing generative models for meshes is challenging due to their irregular data structure and inconsistent topology of meshes…
Autonomous driving demands high-quality LiDAR data, yet the cost of physical LiDAR sensors presents a significant scaling-up challenge. While recent efforts have explored deep generative models to address this issue, they often consume…
Accurate weather forecasting is critical for science and society. Yet, existing methods have not managed to simultaneously have the properties of high accuracy, low uncertainty, and high computational efficiency. On one hand, to quantify…
We investigate the effects of jet production on the following parameters: pseudorapidity, transverse momentum and transverse mass distributions of secondary charged particles produced in pp-collisions at 1.8 TeV, using the HIJING code.…
This work introduces MiDi, a novel diffusion model for jointly generating molecular graphs and their corresponding 3D arrangement of atoms. Unlike existing methods that rely on predefined rules to determine molecular bonds based on the 3D…
High-quality power flow datasets are essential for training machine learning models in power systems. However, security and privacy concerns restrict access to real-world data, making statistically accurate and physically consistent…
We expand the off-resonant scattering theory for particle diffusion in magnetized current filaments that can be typically compared to astrophysical jets, including active galactic nucleus jets. In a high plasma beta region where the…
In recent years traditional numerical methods for accurate weather prediction have been increasingly challenged by deep learning methods. Numerous historical datasets used for short and medium-range weather forecasts are typically organized…
An earlier developed $k_T$-factorization framework to calculate the associated prompt photon and hadronic jets production cross sections at high energies is extended now to the electron-proton deep inelastic scattering. The proposed method…
Jet point cloud images are high dimensional data structures that needs to be transformed to a separable feature space for machine learning algorithms to distinguish them with simple decision boundaries. In this article, the authors focus on…
We study a novel property of large rapidity-gap events in deep inelastic scattering at HERA: splitting the pomeron into two jets in the photon-pomeron fusion reaction $\gamma^{*}\Pom \rightarrow q\bar{q}$. It gives rise to the diffraction…
In the double-cone ignition (DCI) inertial confinement fusion scheme, head-on collision of high density plasma jets is one of the most distinguished feature when compared with other schemes. However, the application of traditional…
We investigated the formation of granular focused jets defined as narrow upward ejections of grains triggered by impulsive forces and shaped by kinematic focusing on a concave free surface. The jets were generated from nonfluidized granular…
The precipitation of cloud particles in brown dwarf and exoplanet atmospheres establishes an ongoing downward flux of condensable elements. To understand the efficiency of cloud formation, it is therefore crucial to quantify the…
Precise measurements of the energy of jets emerging from particle collisions at the LHC are essential for a vast majority of physics searches at the CMS experiment. In this study, we leverage well-established deep learning models for point…
Modern video generative models based on diffusion models can produce very realistic clips, but they are computationally inefficient, often requiring minutes of GPU time for just a few seconds of video. This inefficiency poses a critical…
Diffusionmodels(DMs)havedemonstratedremarkableachievements in synthesizing images of high fidelity and diversity. However, the extensive computational requirements and slow generative speed of diffusion models have limited their widespread…
Recent advancements in 3D object generation using diffusion models have achieved remarkable success, but generating realistic 3D urban scenes remains challenging. Existing methods relying solely on 3D diffusion models tend to suffer a…