Cross sections for pentaquark baryon production from protons in reactions induced by hadrons and photons

Using hadronic Lagrangians that include the interaction of pentaquark $\Theta^+$ baryon with $K$ and $N$, we evaluate the cross sections for its production from meson-proton, proton-proton, and photon-proton reactions near threshold. With empirical coupling constants and form factors, the predicted cross sections are about 1.5 mb in kaon-proton reactions, 0.1 mb in rho-nucleon reactions, 0.05 mb in pion-nucleon reactions, 20 $\mu$b in proton-proton reactions, and 40 nb in photon-proton reactions.Comment: 14 pages, 7 figure

Mass in anti-de Sitter spaces

The boundary stress tensor approach has proven extremely useful in defining mass and angular momentum in asymptotically anti-de Sitter spaces with CFT duals. An integral part of this method is the use of boundary counterterms to regulate the gravitational action and stress tensor. In addition to the standard gravitational counterterms, in the presence of matter we advocate the use of a finite counterterm proportional to phi^2 (in five dimensions). We demonstrate that this finite shift is necessary to properly reproduce the expected mass/charge relation for R-charged black holes in AdS_5.Comment: 15 pages, late

Quantum Statistical Entropy and Minimal Length of 5D Ricci-flat Black String with Generalized Uncertainty Principle

In this paper, we study the quantum statistical entropy in a 5D Ricci-flat black string solution, which contains a 4D Schwarzschild-de Sitter black hole on the brane, by using the improved thin-layer method with the generalized uncertainty principle. The entropy is the linear sum of the areas of the event horizon and the cosmological horizon without any cut-off and any constraint on the bulk's configuration rather than the usual uncertainty principle. The system's density of state and free energy are convergent in the neighborhood of horizon. The small-mass approximation is determined by the asymptotic behavior of metric function near horizons. Meanwhile, we obtain the minimal length of the position $\Delta x$ which is restrained by the surface gravities and the thickness of layer near horizons.Comment: 11pages and this work is dedicated to the memory of Professor Hongya Li

A mathematical simulation model of the CH-47B helicopter, volume 1

A nonlinear simulation model of the CH-47B helicopter was adapted for use in the NASA Ames Research Center (ARC) simulation facility. The model represents the specific configuration of the ARC variable stability CH-47B helicopter and will be used in ground simulation research and to expedite and verify flight experiment design. Modeling of the helicopter uses a total force approach in six rigid body degrees of freedom. Rotor dynamics are simulated using the Wheatlely-Bailey equations including steady-state flapping dynamics. Also included in the model is the option for simulation of external suspension, slung-load equations of motion

Exchange Field-Mediated Magnetoresistance in the Correlated Insulator Phase of Be Films

We present a study of the proximity effect between a ferromagnet and a paramagnetic metal of varying disorder. Thin beryllium films are deposited onto a 5 nm-thick layer of the ferromagnetic insulator EuS. This bilayer arrangement induces an exchange field, $H_{ex}$, of a few tesla in low resistance Be films with sheet resistance $R\ll R_Q$, where $R_Q=h/e^2$ is the quantum resistance. We show that $H_{ex}$ survives in very high resistance films and, in fact, appears to be relatively insensitive to the Be disorder. We exploit this fact to produce a giant low-field magnetoresistance in the correlated insulator phase of Be films with $R\gg R_Q$.Comment: To be published in Physical Review Letter

LoANs: Weakly Supervised Object Detection with Localizer Assessor Networks

Recently, deep neural networks have achieved remarkable performance on the task of object detection and recognition. The reason for this success is mainly grounded in the availability of large scale, fully annotated datasets, but the creation of such a dataset is a complicated and costly task. In this paper, we propose a novel method for weakly supervised object detection that simplifies the process of gathering data for training an object detector. We train an ensemble of two models that work together in a student-teacher fashion. Our student (localizer) is a model that learns to localize an object, the teacher (assessor) assesses the quality of the localization and provides feedback to the student. The student uses this feedback to learn how to localize objects and is thus entirely supervised by the teacher, as we are using no labels for training the localizer. In our experiments, we show that our model is very robust to noise and reaches competitive performance compared to a state-of-the-art fully supervised approach. We also show the simplicity of creating a new dataset, based on a few videos (e.g. downloaded from YouTube) and artificially generated data.Comment: To appear in AMV18. Code, datasets and models available at https://github.com/Bartzi/loan