How Perfect a Gluon Plasma Can Be in Perturbative QCD?

The shear viscosity to entropy density ratio, \eta /s, characterizes how perfect a fluid is. We calculate the leading order \eta /s of a gluon plasma in perturbation using the kinetic theory. The leading order contribution only involves the elastic gg -> gg (22) process and the inelastic ggggg (23) process. The Hard-Thermal-Loop (HTL) treatment is used for the 22 matrix element, while the exact matrix element in vacuum is supplemented by the gluon Debye mass insertion for the 23 process. Also, the asymptotic mass is used for the external gluons in the kinetic theory. The errors from not implementing HTL and the Landau-Pomeranchuk-Migdal effect in the 23 process, and from the uncalculated higher order corrections, are estimated. Our result for \eta /s lies between that of Arnold, Moore and Yaffe (AMY) and Xu and Greiner (XG). Our result shows that although the finite angle contributions are important at intermediate \alpha_s (\alpha_s \sim 0.01-0.1), the 22 process is still more important than 23 when \alpha_s < 0.1. This is in qualitative agreement with AMY's result. We find no indication that the proposed perfect fluid limit \eta /s \simeq 1/(4\pi) can be achieved by perturbative QCD alone.Comment: ReVTex 4, 11 pages, 5 figures. A coding error in the exact matrix element for the 23 process is corrected. Results in Fig. 2,3 and Table I are re-calculated, and relevant discussions are adjusted. Part of the conclusion is change

Policy-Based Reinforcement Learning for Assortative Matching in Human Behavior Modeling

Human behavior is the potential and expressive capacity (mental, physical, and social) of human individuals or groups to respond to internal and external stimuli. We explore assortative matching as a typical human behavior in virtual networked communities. We propose a modeling approach based on MAS(Multi-Agent System) and policy-based reinforcement learning to simulate human behavior through various environmental parameter settings and agent action strategies. In our experiment, reinforcement learning serves specific agents who learn from the environment status and competitor behaviors, then optimize strategy to achieve better results. This work simulates both the individual and group level, showing some possible paths for forming relative competitive advantages. This modeling approach can help further analyze the evolutionary dynamics of human behavior, communities, and organizations on various socioeconomic topics.Comment: 2 pages, 800 words, Extended abstract for DHM of HCI International 202

BCS-BEC crossover in a relativistic boson-fermion model beyond mean field approximation

We investigate the fluctuation effect of the di-fermion field in the crossover from Bardeen-Cooper-Schrieffer (BCS) pairing to a Bose-Einstein condensate (BEC) in a relativistic superfluid. We work within the boson-fermion model obeying a global U(1) symmetry. To go beyond the mean field approximation we use Cornwall-Jackiw-Tomboulis (CJT) formalism to include higher order contributions. The quantum fluctuations of the pairing condensate is provided by bosons in non-zero modes, whose interaction with fermions gives the two-particle-irreducible (2PI) effective potential. It changes the crossover property in the BEC regime. With the fluctuations the superfluid phase transition becomes the first order in grand canonical ensemble. We calculate the condensate, the critical temperature $T_{c}$ and particle abundances as functions of crossover parameter the boson mass.Comment: The model Lagrangian is re-formulated by decomposing the complex scalar field into its real and imaginary parts. The anomalous propagators of the complex scalar are then included at tree level. All numerical results are updated. ReVTex 4, 13 pages, 10 figures, PRD accepted versio

Shear and Bulk Viscosities of a Gluon Plasma in Perturbative QCD: Comparison of Different Treatments for the gg<->ggg Process

The leading order contribution to the shear and bulk viscosities, \eta and \zeta, of a gluon plasma in perturbative QCD includes the gg -> gg (22) process, gg ggg (23) process and multiple scattering processes known as the Landau-Pomeranchuk-Migdal (LPM) effect. Complete leading order computations for \eta and \zeta were obtained by Arnold, Moore and Yaffe (AMY) and Arnold, Dogan and Moore (ADM), respectively, with the inelastic processes computed by an effective g gg gluon splitting. We study how complementary calculations with 22 and 23 processes and a simple treatment to model the LPM effect compare with the results of AMY and ADM. We find that our results agree with theirs within errors. By studying the contribution of the 23 process to \eta, we find that the minimum angle \theta among the final state gluons in the fluid local rest frame has a distribution that is peaked at \theta \sim \sqrt{\alpha_{s}}, analogous to the near collinear splitting asserted by AMY and ADM. However, the average of \theta is much bigger than its peak value, as its distribution is skewed with a long tail. The same \theta behavior is also seen if the 23 matrix element is taken to the soft gluon bremsstrahlung limit in the center-of-mass (CM) frame. This suggests that the soft gluon bremsstrahlung in the CM frame still has some near collinear behavior in the fluid local rest frame. We also generalize our result to a general SU(N_c) pure gauge theory and summarize the current viscosity computations in QCD.Comment: ReVTex 4, 18 pages, 7 figures, accepted version in Phys. Rev.

An improved approach for the segmentation of starch granules in microscopic images

<p>Abstract</p> <p>Background</p> <p>Starches are the main storage polysaccharides in plants and are distributed widely throughout plants including seeds, roots, tubers, leaves, stems and so on. Currently, microscopic observation is one of the most important ways to investigate and analyze the structure of starches. The position, shape, and size of the starch granules are the main measurements for quantitative analysis. In order to obtain these measurements, segmentation of starch granules from the background is very important. However, automatic segmentation of starch granules is still a challenging task because of the limitation of imaging condition and the complex scenarios of overlapping granules.</p> <p>Results</p> <p>We propose a novel method to segment starch granules in microscopic images. In the proposed method, we first separate starch granules from background using automatic thresholding and then roughly segment the image using watershed algorithm. In order to reduce the oversegmentation in watershed algorithm, we use the roundness of each segment, and analyze the gradient vector field to find the critical points so as to identify oversegments. After oversegments are found, we extract the features, such as the position and intensity of the oversegments, and use fuzzy c-means clustering to merge the oversegments to the objects with similar features. Experimental results demonstrate that the proposed method can alleviate oversegmentation of watershed segmentation algorithm successfully.</p> <p>Conclusions</p> <p>We present a new scheme for starch granules segmentation. The proposed scheme aims to alleviate the oversegmentation in watershed algorithm. We use the shape information and critical points of gradient vector flow (GVF) of starch granules to identify oversegments, and use fuzzy c-mean clustering based on prior knowledge to merge these oversegments to the objects. Experimental results on twenty microscopic starch images demonstrate the effectiveness of the proposed scheme.</p

Eye-Tracking Signals Based Affective Classification Employing Deep Gradient Convolutional Neural Networks

Utilizing biomedical signals as a basis to calculate the human affective states is an essential issue of affective computing (AC). With the in-depth research on affective signals, the combination of multi-model cognition and physiological indicators, the establishment of a dynamic and complete database, and the addition of high-tech innovative products become recent trends in AC. This research aims to develop a deep gradient convolutional neural network (DGCNN) for classifying affection by using an eye-tracking signals. General signal process tools and pre-processing methods were applied firstly, such as Kalman filter, windowing with hamming, short-time Fourier transform (SIFT), and fast Fourier transform (FTT). Secondly, the eye-moving and tracking signals were converted into images. A convolutional neural networks-based training structure was subsequently applied; the experimental dataset was acquired by an eye-tracking device by assigning four affective stimuli (nervous, calm, happy, and sad) of 16 participants. Finally, the performance of DGCNN was compared with a decision tree (DT), Bayesian Gaussian model (BGM), and k-nearest neighbor (KNN) by using indices of true positive rate (TPR) and false negative rate (FPR). Customizing mini-batch, loss, learning rate, and gradients definition for the training structure of the deep neural network was also deployed finally. The predictive classification matrix showed the effectiveness of the proposed method for eye moving and tracking signals, which performs more than 87.2% inaccuracy. This research provided a feasible way to find more natural human-computer interaction through eye moving and tracking signals and has potential application on the affective production design process