On spectral stability of solitary waves of nonlinear Dirac equation on a line

We study the spectral stability of solitary wave solutions to the nonlinear Dirac equation in one dimension. We focus on the Dirac equation with cubic nonlinearity, known as the Soler model in (1+1) dimensions and also as the massive Gross-Neveu model. Presented numerical computations of the spectrum of linearization at a solitary wave show that the solitary waves are spectrally stable. We corroborate our results by finding explicit expressions for several of the eigenfunctions. Some of the analytic results hold for the nonlinear Dirac equation with generic nonlinearity.Comment: 20 pages with figure

On a Penrose Inequality with Charge

We construct a time-symmetric asymptotically flat initial data set to the Einstein-Maxwell Equations which satisfies the inequality: m - 1/2(R + Q^2/R) < 0, where m is the total mass, R=sqrt(A/4) is the area radius of the outermost horizon and Q is the total charge. This yields a counter-example to a natural extension of the Penrose Inequality to charged black holes.Comment: Minor revision: some typos; author's address updated; bibliographical reference added; journal information: to appear in Comm. Math. Phy

Instanton interpolating current for σ\sigma--tetraquark

We perform a QCD sum rule analysis for the light scalar meson $\sigma$ ($f_0(600)$) with a tetraquark current related to the instanton picture for QCD vacuum. We demonstrate that instanton current, including equal weights of scalar and pseudoscalar diquark-antidiquarks, leads to a strong cancelation between the contributions of high dimension operators in the operator product expansion (OPE). Furthermore, in the case of this current direct instanton contributions do not spoil the sum rules. Our calculation, obtained from the OPE up to dimension 10 operators, gives the mass of $\sigma$--meson around 780MeV.Comment: 11 pages, 7 figures, final version to be appeared in Phys. Lett.

The Design of RFID Convey or Belt Gate Systems Using an Antenna Control Unit

This paper proposes an efficient management system utilizing a Radio Frequency Identification (RFID) antenna control unit which is moving along with the path of boxes of materials on the conveyor belt by manipulating a motor. The proposed antenna control unit, which is driven by a motor and is located on top of the gate, has an array structure of two antennas with parallel connection. The array structure helps improve the directivity of antenna beam pattern and the readable RFID distance due to its configuration. In the experiments, as the control unit follows moving materials, the reading time has been improved by almost three-fold compared to an RFID system employing conventional fixed antennas. The proposed system also has a recognition rate of over 99% without additional antennas for detecting the sides of a box of materials. The recognition rate meets the conditions recommended by the Electronic Product Code glbal network (EPC)global for commercializing the system, with three antennas at a 20 dBm power of reader and a conveyor belt speed of 3.17 m/s. This will enable a host of new RFID conveyor belt gate systems with increased performance

O(k)O(k)-Equivariant Dimensionality Reduction on Stiefel Manifolds

Many real-world datasets live on high-dimensional Stiefel and Grassmannian manifolds, $V_k(\mathbb{R}^N)$ and $Gr(k, \mathbb{R}^N)$ respectively, and benefit from projection onto lower-dimensional Stiefel (respectively, Grassmannian) manifolds. In this work, we propose an algorithm called Principal Stiefel Coordinates (PSC) to reduce data dimensionality from $V_k(\mathbb{R}^N)$ to $V_k(\mathbb{R}^n)$ in an $O(k)$-equivariant manner ($k \leq n \ll N$). We begin by observing that each element $\alpha \in V_n(\mathbb{R}^N)$ defines an isometric embedding of $V_k(\mathbb{R}^n)$ into $V_k(\mathbb{R}^N)$. Next, we optimize for such an embedding map that minimizes data fit error by warm-starting with the output of principal component analysis (PCA) and applying gradient descent. Then, we define a continuous and $O(k)$-equivariant map $\pi_\alpha$ that acts as a ``closest point operator'' to project the data onto the image of $V_k(\mathbb{R}^n)$ in $V_k(\mathbb{R}^N)$ under the embedding determined by $\alpha$, while minimizing distortion. Because this dimensionality reduction is $O(k)$-equivariant, these results extend to Grassmannian manifolds as well. Lastly, we show that the PCA output globally minimizes projection error in a noiseless setting, but that our algorithm achieves a meaningfully different and improved outcome when the data does not lie exactly on the image of a linearly embedded lower-dimensional Stiefel manifold as above. Multiple numerical experiments using synthetic and real-world data are performed.Comment: 26 pages, 8 figures, comments welcome