STUDY OF GAS PHASE ION MUTUAL-NEUTRALIZATION THROUGH MOLECULAR AND LANGEVIN DYNAMICS TRAJECTORY SIMULATION

Mutual-neutralization (MN) of cation-anion pairs is one of the key processes that determines the chemical compositions of reacting gas mixtures found in combustion, plasmas, and cosmic gas clouds. In the last few decades, experiments and modeling investigations have been conducted in free-molecular limit. However, the MN process is less extensively studied at near or beyond atmospheric pressure for relevant applications and the measured rate constant shows a clear pressure dependency. The present work focuses on developing a method based on trajectory simulation for a pressure range from the free molecular limit to atmospheric pressure. Langevin dynamics is incorporated to capture the effect of neutral atoms and the Landau-Zener formula will be used to calculate the state transition probability based on the instantaneous crossing velocities, the product branching ratio and the reaction cross-section are evaluated by Monte-Carlo method. Over the pressure range tested, simulation results show reasonable agreement with experimental results

Elastic pion-proton and pion-pion scattering via the holographic Pomeron and Reggeon exchange

The elastic pion-proton and pion-pion scattering are studied in a holographic QCD model, focusing on the Regge regime. Taking into account the Pomeron and Reggeon exchange, which are described by the Reggeized $2^{++}$ glueball and vector meson propagator respectively, the total and differential cross sections are calculated. The adjustable parameters involved in the model are determined with the experimental data of the pion-proton total cross sections. The differential cross sections can be predicted without any additional parameters, and it is shown that our predictions are consistent with the data. The energy dependence of the Pomeron and Reggeon contribution is also discussed.Comment: 18 pages, 9 figure

CEN34 -- High-Mass YSO in M17 or Background Post-AGB Star?

We investigate the proposed high-mass young stellar object (YSO) candidate CEN34, thought to be associated with the star forming region M17. Its optical to near-infrared (550-2500 nm) spectrum reveals several photospheric absorption features, such as H{\alpha}, Ca triplet and CO bandheads but lacks any emission lines. The spectral features in the range 8375-8770{\AA} are used to constrain an effective temperature of 5250\pm250 (early-/mid-G) and a surface gravity of 2.0\pm0.3 (supergiant). The spectral energy distribution of CEN34 resembles the SED of a high-mass YSO or an evolved star. Moreover, the observed temperature and surface gravity are identical for high-mass YSOs and evolved stars. The radial velocity relative to LSR (V_LSR) of CEN34 as obtained from various photospheric lines is of the order of -60 km/s and thus distinct from the +25 km/s found for several OB stars in the cluster and for the associated molecular cloud. The SED modeling yields ~ 10^{-4} M_sun of circumstellar material which contributes only a tiny fraction to the total visual extinction (11 mag). In the case of a YSO, a dynamical ejection process is proposed to explain the V_LSR difference between CEN34 and M17. Additionally, to match the temperature and luminosity, we speculate that CEN34 had accumulated the bulk of its mass with accretion rate > 4x10^{-3} M_sun/yr in a very short time span (~ 10^3 yrs), and currently undergoes a phase of gravitational contraction without any further mass gain. However, all the aforementioned characteristics of CEN34 are compatible with an evolved star of 5-7 M_sun and an age of 50-100 Myrs, most likely a background post-AGB star with a distance between 2.0 kpc and 4.5 kpc. We consider the latter classification as the more likely interpretation. Further discrimination between the two possible scenarios should come from the more strict confinement of CEN34's distance.Comment: 8 pages, 8 figures, 2 tables; accepted by A&

A Generative Adversarial Network for AI-Aided Chair Design

We present a method for improving human design of chairs. The goal of the method is generating enormous chair candidates in order to facilitate human designer by creating sketches and 3d models accordingly based on the generated chair design. It consists of an image synthesis module, which learns the underlying distribution of training dataset, a super-resolution module, which improve quality of generated image and human involvements. Finally, we manually pick one of the generated candidates to create a real life chair for illustration.Comment: 6 pages, 5 figures, accepted at MIPR201

PHYFU: Fuzzing Modern Physics Simulation Engines

A physical simulation engine (PSE) is a software system that simulates physical environments and objects. Modern PSEs feature both forward and backward simulations, where the forward phase predicts the behavior of a simulated system, and the backward phase provides gradients (guidance) for learning-based control tasks, such as a robot arm learning to fetch items. This way, modern PSEs show promising support for learning-based control methods. To date, PSEs have been largely used in various high-profitable, commercial applications, such as games, movies, virtual reality (VR), and robotics. Despite the prosperous development and usage of PSEs by academia and industrial manufacturers such as Google and NVIDIA, PSEs may produce incorrect simulations, which may lead to negative results, from poor user experience in entertainment to accidents in robotics-involved manufacturing and surgical operations. This paper introduces PHYFU, a fuzzing framework designed specifically for PSEs to uncover errors in both forward and backward simulation phases. PHYFU mutates initial states and asserts if the PSE under test behaves consistently with respect to basic Physics Laws (PLs). We further use feedback-driven test input scheduling to guide and accelerate the search for errors. Our study of four PSEs covers mainstream industrial vendors (Google and NVIDIA) as well as academic products. We successfully uncover over 5K error-triggering inputs that generate incorrect simulation results spanning across the whole software stack of PSEs.Comment: This paper is accepted at The 38th IEEE/ACM International Conference on Automated Software Engineering, a.k.a. ASE 2023. Please cite the published version as soon as this paper appears in the conference publication