A Hybrid Method with Deviational Particles for Spatial Inhomogeneous Plasma

In this work we propose a Hybrid method with Deviational Particles (HDP) for a plasma modeled by the inhomogeneous Vlasov-Poisson-Landau system. We split the distribution into a Maxwellian part evolved by a grid based fluid solver and a deviation part simulated by numerical particles. These particles, named deviational particles, could be both positive and negative. We combine the Monte Carlo method proposed in \cite{YC15}, a Particle in Cell method and a Macro-Micro decomposition method \cite{BLM08} to design an efficient hybrid method. Furthermore, coarse particles are employed to accelerate the simulation. A particle resampling technique on both deviational particles and coarse particles is also investigated and improved. The efficiency is significantly improved compared to a PIC-MCC method, especially near the fluid regime.Comment: 26 pages, 13 figure

An asymptotic preserving scheme for kinetic models with singular limit

We propose a new class of asymptotic preserving schemes to solve kinetic equations with mono-kinetic singular limit. The main idea to deal with the singularity is to transform the equations by appropriate scalings in velocity. In particular, we study two biologically related kinetic systems. We derive the scaling factors and prove that the rescaled solution does not have a singular limit, under appropriate spatial non-oscillatory assumptions, which can be verified numerically by a newly developed asymptotic preserving scheme. We set up a few numerical experiments to demonstrate the accuracy, stability, efficiency and asymptotic preserving property of the schemes.Comment: 24 pages, 6 figure

Non-invasive assessment of intracranial wall shear stress using high-resolution magnetic resonance imaging in combination with computational fluid dynamics technique

In vivo studies on association between wall shear stress (WSS) and intracranial plaque are deficient. Based on the three-dimensional T1-weighted high-resolution magnetic resonance imaging (3DT1 HR-MRI) data of patients with low-grade stenotic (<50%) atherosclerotic middle cerebral artery (MCA) and subjects with normal MCA, we built a three-dimensional reconstructed WSS model by computational fluid dynamics (CFD) technique. Three-dimensional registration of the CFD model to the HR-MRI was performed with projections based on the resolution and thickness of the images. The relationships between the WSS at each side of the vessel wall and plaque location were analyzed. A total of 94 MCA plaques from 43 patients and 50 normal MCAs were analyzed. In the normal MCAs, WSS was lower at the ventral-inferior wall than at the dorsal-superior wall (proximal segment, p < 0.001; middle segment, p < 0.001) and lower at the inner wall than at the outer wall of the MCA curve (p < 0.001). In atherosclerotic MCAs, similar low WSS regions were observed where plaques developed. The WSS ratio of the ventral-inferior wall to the dorsal-superior wall in atherosclerotic MCAs was lower than that in normal MCAs (p = 0.002). The WSSinner-outer ratio in atherosclerotic MCAs was lower than that in normal MCAs (p = 0.002). Low WSS was associated with MCA atherosclerosis formation and occurred mainly at the ventral-inferior wall, which was anatomically opposite the orifices of penetrating arteries, and at the inner wall of the MCA curve. Overall, the results were well consistent with the low WSS theory in atherosclerosis formation. The reconstructed WSS model is a promising novel method for assessing an individualized vascular profile once validated by further studies

Evidence for Ag participating the electrochemical migration of 96.5Sn-3Ag-0.5Cu alloy

Ag participating the electrochemical migration (ECM) of Sn-Ag based alloys is still controversial. In this work,Ag+concentration in electrolyte layer and Ag distribution in dendrites formed during the ECM of 96.5Sn-3Ag-0.5Cu alloy were investigated using Inductively Coupled Plasma Source Mass Spectrometer and ScanningTransmission Electron Microscopy, respectively. Results show that Ag+can only be detected when Ag can re-lease from Ag3Sn during the anodic polarization of 96.5Sn-3Ag-0.5Cu alloy. Under such a condition, Ag couldalso be found in dendrites. Therefore, it can be concluded that Ag participates the ECM of 96.5Sn-3Ag-0.5Cualloy, but it is potential-dependent

Tool Learning with Foundation Models

Humans possess an extraordinary ability to create and utilize tools, allowing them to overcome physical limitations and explore new frontiers. With the advent of foundation models, AI systems have the potential to be equally adept in tool use as humans. This paradigm, i.e., tool learning with foundation models, combines the strengths of specialized tools and foundation models to achieve enhanced accuracy, efficiency, and automation in problem-solving. Despite its immense potential, there is still a lack of a comprehensive understanding of key challenges, opportunities, and future endeavors in this field. To this end, we present a systematic investigation of tool learning in this paper. We first introduce the background of tool learning, including its cognitive origins, the paradigm shift of foundation models, and the complementary roles of tools and models. Then we recapitulate existing tool learning research into tool-augmented and tool-oriented learning. We formulate a general tool learning framework: starting from understanding the user instruction, models should learn to decompose a complex task into several subtasks, dynamically adjust their plan through reasoning, and effectively conquer each sub-task by selecting appropriate tools. We also discuss how to train models for improved tool-use capabilities and facilitate the generalization in tool learning. Considering the lack of a systematic tool learning evaluation in prior works, we experiment with 18 representative tools and show the potential of current foundation models in skillfully utilizing tools. Finally, we discuss several open problems that require further investigation for tool learning. In general, we hope this paper could inspire future research in integrating tools with foundation models