Zero mode in a strongly coupled quark gluon plasma

In connection with massless two-flavour QCD, we analyse the chiral symmetry restoring phase transition using three distinct gluon-quark vertices and two different assumptions about the long-range part of the quark-quark interaction. In each case, we solve the gap equation, locate the transition temperature T_c, and use the maximum entropy method to extract the dressed-quark spectral function at T>T_c. Our best estimate for the chiral transition temperature is T_c=(147 +/- 8)MeV; and the deconfinement transition is coincident. For temperatures markedly above T_c, we find a spectral density that is consistent with those produced using a hard thermal loop expansion, exhibiting both a normal and plasmino mode. On a domain T\in[T_c,T_s], with T_s approximately 1.5T_c, however, with each of the six kernels we considered, the spectral function contains a significant additional feature. Namely, it displays a third peak, associated with a zero mode, which is essentially nonperturbative in origin and dominates the spectral function at T=T_c. We suggest that the existence of this mode is a signal for the formation of a strongly-coupled quark-gluon plasma and that this strongly-interacting state of matter is likely a distinctive feature of the QCD phase transition.Comment: 11 pages, 5 figures, 1 tabl

Phase diagram and thermal properties of strong-interaction matter

We introduce a novel procedure for computing the (mu,T)-dependent pressure in continuum QCD; and therefrom obtain a complex phase diagram and predictions for thermal properties of the system, providing the in-medium behaviour of the trace anomaly, speed of sound, latent heat and heat capacity.Comment: 6 pages, 4 figures. Minor amendments in the version accepted for publicatio

Effect of Different Water-Binder Ratios and Fiber Contents on the Fluidity and Mechanical Properties of PVA-ECC Materials

With the development of fiber-reinforced cement composites, the diversity and complexity of application scenarios require enhanced strength and ductility and tough materials in practical engineering. To explore the effects of different water-binder ratios and fiber contents on the fluidity, bending resistance, tensile properties, fracture toughness, and fracture behavior of polyvinyl alcohol (PVA) fiber cement composites, several groups of high ductility test blocks (PVA-engineering cementitious composites (ECC)) with different mixing ratios were designed in this study. Based on the expansion degree, the mechanical experimental data, and the electron microscopy scanning image results, K-value analysis was performed on the strain hardening strength criterion. The effect of the water–binder ratio and the fiber dosing on the PVA-ECC material was determined. Results show that the greater the water-binder ratio is, the better the fluidity of the ECC matrix is. In the same cement system and at the same water-binder ratio, the fluidity of the ECC paste gradually deteriorates with the increase of the fiber content. The water-binder ratio significantly affects the flexural tensile strength of the composite. The flexural and tensile strengths of the PVA-ECC gradually increase as the water-binder ratio decreases, but the ductility gradually decreases. The water-binder ratio of the substrate directly influences the damage behavior of the fibers within the substrate. With the gradual increase of the water-binder ratio, the fiber at the crack interface gradually changes from pull-out morphology to fracture morphology. The strain capacity and the multi-crack cracking performance decrease. To achieve improved working performance in the actual project, the matrix water-binder ratio should be controlled at approximately 0.45, and the PVA fiber dose of 1.7% is optimal. This study can provide a good reference for the optimization of practical engineering components

Complex Dynamics of an Adnascent-Type Game Model

The paper presents a nonlinear discrete game model for two oligopolistic firms whose products are adnascent. (In biology, the term adnascent has only one sense, “growing to or on something else,” e.g., “moss is an adnascent plant.” See Webster's Revised Unabridged Dictionary published in 1913 by C. & G. Merriam Co., edited by Noah Porter.) The bifurcation of its Nash equilibrium is analyzed with Schwarzian derivative and normal form theory. Its complex dynamics is demonstrated by means of the largest Lyapunov exponents, fractal dimensions, bifurcation diagrams, and phase portraits. At last, bifurcation and chaos anticontrol of this system are studied

Climatic change controls productivity variation in global grasslands.

Detection and identification of the impacts of climate change on ecosystems have been core issues in climate change research in recent years. In this study, we compared average annual values of the normalized difference vegetation index (NDVI) with theoretical net primary productivity (NPP) values based on temperature and precipitation to determine the effect of historic climate change on global grassland productivity from 1982 to 2011. Comparison of trends in actual productivity (NDVI) with climate-induced potential productivity showed that the trends in average productivity in nearly 40% of global grassland areas have been significantly affected by climate change. The contribution of climate change to variability in grassland productivity was 15.2-71.2% during 1982-2011. Climate change contributed significantly to long-term trends in grassland productivity mainly in North America, central Eurasia, central Africa, and Oceania; these regions will be more sensitive to future climate change impacts. The impacts of climate change on variability in grassland productivity were greater in the Western Hemisphere than the Eastern Hemisphere. Confirmation of the observed trends requires long-term controlled experiments and multi-model ensembles to reduce uncertainties and explain mechanisms

Adversarial AutoMixup

Data mixing augmentation has been widely applied to improve the generalization ability of deep neural networks. Recently, offline data mixing augmentation, e.g. handcrafted and saliency information-based mixup, has been gradually replaced by automatic mixing approaches. Through minimizing two sub-tasks, namely, mixed sample generation and mixup classification in an end-to-end way, AutoMix significantly improves accuracy on image classification tasks. However, as the optimization objective is consistent for the two sub-tasks, this approach is prone to generating consistent instead of diverse mixed samples, which results in overfitting for target task training. In this paper, we propose AdAutomixup, an adversarial automatic mixup augmentation approach that generates challenging samples to train a robust classifier for image classification, by alternatively optimizing the classifier and the mixup sample generator. AdAutomixup comprises two modules, a mixed example generator, and a target classifier. The mixed sample generator aims to produce hard mixed examples to challenge the target classifier, while the target classifier's aim is to learn robust features from hard mixed examples to improve generalization. To prevent the collapse of the inherent meanings of images, we further introduce an exponential moving average (EMA) teacher and cosine similarity to train AdAutomixup in an end-to-end way. Extensive experiments on seven image benchmarks consistently prove that our approach outperforms the state of the art in various classification scenarios. The source code is available at https://github.com/JinXins/Adversarial-AutoMixup.Comment: ICLR 2024 Camera Ready.(19 pages) with the source code at https://github.com/JinXins/Adversarial-AutoMixu

Decreased modulation by the risk level on the brain activation during decision making in adolescents with internet gaming disorder

Greater impulse and risk-taking and reduced decision-making ability were reported as the main behavioral impairments in individuals with internet gaming disorder (IGD), which has become a serious mental health issue worldwide. However, it is not clear to date how the risk level modulates brain activity during the decision-making process in IGD individuals. In this study, 23 adolescents with IGD and 24 healthy controls (HCs) without IGD were recruited, and the balloon analog risk task (BART) was used in a functional magnetic resonance imaging experiment to evaluate the modulation of the risk level (the probability of balloon explosion) on brain activity during risky decision making in IGD adolescents. Reduced modulation of the risk level on the activation of the right dorsolateral prefrontal cortex (DLPFC) during the active BART was found in IGD group compared to the HCs. In the IGD group, there was a significant negative correlation between the risk-related DLPFC activation during the active BART and the Barratt impulsivity scale (BIS-11) scores, which were significantly higher in IGD group compared with the HCs. Our study demonstrated that, as a critical decision-making-related brain region, the right DLPFC is less sensitive to risk in IGD adolescents compared with the HCs, which may contribute to the higher impulsivity level in IGD adolescents