Reply To "Comment on 'Quantum String Seal Is Insecure' "

In Phys. Rev. A. 76, 056301 (2007), He claimed that the proof in my earlier paper [Phys. Rev. A 75, 012327 (2007)] is insufficient to conclude the insecurity of all quantum string seals because my measurement strategy cannot obtain non-trivial information on the sealed string and escape detection at the same time. Here, I clarify that our disagreement comes from our adoption of two different criteria on the minimum amount of information a quantum string seal can reveal to members of the public. I also point out that He did not follow my measurement strategy correctly.Comment: 2 page

Quark Recombination and Heavy Quark Diffusion in Hot Nuclear Matter

We discuss resonance recombination for quarks and show that it is compatible with quark and hadron distributions in local thermal equilibrium. We then calculate realistic heavy quark phase space distributions in heavy ion collisions using Langevin simulations with non-perturbative T-matrix interactions in hydrodynamic backgrounds. We hadronize the heavy quarks on the critical hypersurface given by hydrodynamics after constructing a criterion for the relative recombination and fragmentation contributions. We discuss the influence of recombination and flow on the resulting heavy meson and single electron R_AA and elliptic flow. We will also comment on the effect of diffusion of open heavy flavor mesons in the hadronic phase.Comment: Contribution to Quark Matter 2011, submitted to J.Phys.G; 4 pages, 5 figure

Centers and Cocenters of 00-Hecke algebras

In this paper, we give explicit descriptions of the centers and cocenters of $0$-Hecke algebras associated to finite Coxeter groups.Comment: 13 pages, a mistake in 4.2 is correcte

Stabilized Schemes for the Hydrostatic Stokes Equations

Some new stable finite element (FE) schemes are presented for the hydrostatic Stokes system or primitive equations of the ocean. It is known that the stability of the mixed formulation ap- proximation for primitive equations requires the well-known Ladyzhenskaya–Babuˇska–Brezzi condi- tion related to the Stokes problem and an extra inf-sup condition relating the pressure and the vertical velocity. The main goal of this paper is to avoid this extra condition by adding a residual stabilizing term to the vertical momentum equation. Then, the stability for Stokes-stable FE combinations is extended to the primitive equations and some error estimates are provided using Taylor–Hood P2 –P1 or miniele- ment (P1 +bubble)–P1 FE approximations, showing the optimal convergence rate in the P2 –P1 case. These results are also extended to the anisotropic (nonhydrostatic) problem. On the other hand, by adding another residual term to the continuity equation, a better approximation of the vertical derivative of pressure is obtained. In this case, stability and error estimates including this better approximation are deduced, where optimal convergence rate is deduced in the (P 1 +bubble)–P1 case. Finally, some numerical experiments are presented supporting previous results

Structural and vibrational properties of two-dimensional MnxOy\rm Mn_xO_y nanolayers on Pd(100)

Using different experimental techniques combined with density functional based theoretical methods we have explored the formation of interface-stabilized manganese oxide structures grown on Pd(100) at (sub)monolayer coverage. Amongst the multitude of phases experimentally observed we focus our attention on four structures which can be classified into two distinct regimes, characterized by different building blocks. Two oxygen-rich phases are described in terms of MnO(111)-like O-Mn-O trilayers, whereas the other two have a lower oxygen content and are based on a MnO(100)-like monolayer structure. The excellent agreement between calculated and experimental scanning tunneling microscopy images and vibrational electron energy loss spectra allows for a detailed atomic description of the explored models.Comment: 14 pages, 11 figure

Group-level Emotion Recognition using Transfer Learning from Face Identification

In this paper, we describe our algorithmic approach, which was used for submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017) group-level emotion recognition sub-challenge. We extracted feature vectors of detected faces using the Convolutional Neural Network trained for face identification task, rather than traditional pre-training on emotion recognition problems. In the final pipeline an ensemble of Random Forest classifiers was learned to predict emotion score using available training set. In case when the faces have not been detected, one member of our ensemble extracts features from the whole image. During our experimental study, the proposed approach showed the lowest error rate when compared to other explored techniques. In particular, we achieved 75.4% accuracy on the validation data, which is 20% higher than the handcrafted feature-based baseline. The source code using Keras framework is publicly available.Comment: 5 pages, 3 figures, accepted for publication at ICMI17 (EmotiW Grand Challenge