Hadronization Approach for a Quark-Gluon Plasma Formed in Relativistic Heavy Ion Collisions

A transport model is developed to describe hadron emission from a strongly coupled quark-gluon plasma formed in relativistic heavy ion collisions. The quark-gluon plasma is controlled by ideal hydrodynamics, and the hadron motion is characterized by a transport equation with loss and gain terms. The two sets of equations are coupled to each other, and the hadronization hypersurface is determined by both the hydrodynamic evolution and the hadron emission. The model is applied to calculate the transverse momentum distributions of mesons and baryons, and most of the results agree well with the experimental data at RHIC.Comment: 16 pages, 24 figures. Version accepted by PR

On the Bartnik extension problem for the static vacuum Einstein equations

We develop a framework for understanding the existence of asymptotically flat solutions to the static vacuum Einstein equations with prescribed boundary data consisting of the induced metric and mean curvature on a 2-sphere. A partial existence result is obtained, giving a partial resolution of a conjecture of Bartnik on such static vacuum extensions. The existence and uniqueness of such extensions is closely related to Bartnik's definition of quasi-local mass.Comment: 33 pages, 1 figure. Minor revision of v2. Final version, to appear in Class. Quantum Gravit

On the magnetic equation of state in (2+1)-flavor QCD

A first study of critical behavior in the vicinity of the chiral phase transition of (2+1)-flavor QCD is presented. We analyze the quark mass and volume dependence of the chiral condensate and chiral susceptibilities in QCD with two degenerate light quark masses and a strange quark. The strange quark mass (m_s) is chosen close to its physical value; the two degenerate light quark masses (m_l) are varied in a wide range 1/80 \le m_l/m_s \le 2/5, where the smallest light quark mass value corresponds to a pseudo-scalar Goldstone mass of about 75 MeV. All calculations are performed with staggered fermions on lattices with temporal extent Nt=4. We show that numerical results are consistent with O(N) scaling in the chiral limit. We find that in the region of physical light quark mass values, m_l/m_s \simeq 1/20, the temperature and quark mass dependence of the chiral condensate is already dominated by universal properties of QCD that are encoded in the scaling function for the chiral order parameter, the magnetic equation of state. We also provide evidence for the influence of thermal fluctuations of Goldstone modes on the chiral condensate at finite temperature. At temperatures below, but close to the chiral phase transition at vanishing quark mass, this leads to a characteristic dependence of the light quark chiral condensate on the square root of the light quark mass.Comment: 18 pages, 18 EPS-file

Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning

Finding feasible, collision-free paths for multiagent systems can be challenging, particularly in non-communicating scenarios where each agent's intent (e.g. goal) is unobservable to the others. In particular, finding time efficient paths often requires anticipating interaction with neighboring agents, the process of which can be computationally prohibitive. This work presents a decentralized multiagent collision avoidance algorithm based on a novel application of deep reinforcement learning, which effectively offloads the online computation (for predicting interaction patterns) to an offline learning procedure. Specifically, the proposed approach develops a value network that encodes the estimated time to the goal given an agent's joint configuration (positions and velocities) with its neighbors. Use of the value network not only admits efficient (i.e., real-time implementable) queries for finding a collision-free velocity vector, but also considers the uncertainty in the other agents' motion. Simulation results show more than 26% improvement in paths quality (i.e., time to reach the goal) when compared with optimal reciprocal collision avoidance (ORCA), a state-of-the-art collision avoidance strategy.Ford Motor Compan

Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning

Finding feasible, collision-free paths for multiagent systems can be challenging, particularly in non-communicating scenarios where each agent's intent (e.g. goal) is unobservable to the others. In particular, finding time efficient paths often requires anticipating interaction with neighboring agents, the process of which can be computationally prohibitive. This work presents a decentralized multiagent collision avoidance algorithm based on a novel application of deep reinforcement learning, which effectively offloads the online computation (for predicting interaction patterns) to an offline learning procedure. Specifically, the proposed approach develops a value network that encodes the estimated time to the goal given an agent's joint configuration (positions and velocities) with its neighbors. Use of the value network not only admits efficient (i.e., real-time implementable) queries for finding a collision-free velocity vector, but also considers the uncertainty in the other agents' motion. Simulation results show more than 26% improvement in paths quality (i.e., time to reach the goal) when compared with optimal reciprocal collision avoidance (ORCA), a state-of-the-art collision avoidance strategy.Ford Motor Compan

Topology-aware illumination design for volume rendering

© 2016 The Author(s). Background: Direct volume rendering is one of flexible and effective approaches to inspect large volumetric data such as medical and biological images. In conventional volume rendering, it is often time consuming to set up a meaningful illumination environment. Moreover, conventional illumination approaches usually assign same values of variables of an illumination model to different structures manually and thus neglect the important illumination variations due to structure differences. Results: We introduce a novel illumination design paradigm for volume rendering on the basis of topology to automate illumination parameter definitions meaningfully. The topological features are extracted from the contour tree of an input volumetric data. The automation of illumination design is achieved based on four aspects of attenuation, distance, saliency, and contrast perception. To better distinguish structures and maximize illuminance perception differences of structures, a two-phase topology-aware illuminance perception contrast model is proposed based on the psychological concept of Just-Noticeable-Difference. Conclusions: The proposed approach allows meaningful and efficient automatic generations of illumination in volume rendering. Our results showed that our approach is more effective in depth and shape depiction, as well as providing higher perceptual differences between structures

Zika Virus Attenuation by Codon Pair Deoptimization Induces Sterilizing Immunity in Mouse Models.

Zika virus (ZIKV) infection during the large epidemics in the Americas is related to congenital abnormities or fetal demise. To date, there is no vaccine, antiviral drug, or other modality available to prevent or treat Zika virus infection. Here we designed novel live attenuated ZIKV vaccine candidates using a codon pair deoptimization strategy. Three codon pair-deoptimized ZIKVs (Min E, Min NS1, and Min E+NS1) were de novo synthesized and recovered by reverse genetics and contained large amounts of underrepresented codon pairs in the E gene and/or NS1 gene. The amino acid sequence was 100% unchanged. The codon pair-deoptimized variants had decreased replication fitness in Vero cells (Min NS1 ≫ Min E > Min E+NS1), replicated more efficiently in insect cells than in mammalian cells, and demonstrated diminished virulence in a mouse model. In particular, Min E+NS1, the most restrictive variant, induced sterilizing immunity with a robust neutralizing antibody titer, and a single immunization achieved complete protection against lethal challenge and vertical ZIKV transmission during pregnancy. More importantly, due to the numerous synonymous substitutions in the codon pair-deoptimized strains, reversion to wild-type virulence through gradual nucleotide sequence mutations is unlikely. Our results collectively demonstrate that ZIKV can be effectively attenuated by codon pair deoptimization, highlighting the potential of Min E+NS1 as a safe vaccine candidate to prevent ZIKV infections.IMPORTANCE Due to unprecedented epidemics of Zika virus (ZIKV) across the Americas and the unexpected clinical symptoms, including Guillain-Barré syndrome, microcephaly, and other birth defects in humans, there is an urgent need for ZIKV vaccine development. Here we provided the first attenuated versions of ZIKV with two important genes (E and/or NS1) that were subjected to codon pair deoptimization. Compared to parental ZIKV, the codon pair-deoptimized ZIKVs were mammal attenuated and preferred insect to mammalian cells. Min E+NS1, the most restrictive variant, induced sterilizing immunity with a robust neutralizing antibody titer and achieved complete protection against lethal challenge and vertical virus transmission during pregnancy. More importantly, the massive synonymous mutational approach made it impossible for the variant to revert to wild-type virulence. Our results have proven the feasibility of codon pair deoptimization as a strategy to develop live attenuated vaccine candidates against flaviviruses such as ZIKV, Japanese encephalitis virus, and West Nile virus