Analysis of the X(1576) as a tetraquark state with the QCD sum rules

In this letter, we take the point of view that the X(1576) be tetraquark state which consists of a scalar-diquark and an anti-scalar-diquark in relative $P$-wave, and calculate its mass in the framework of the QCD sum rules approach. The numerical value of the mass $m_X=(1.66\pm 0.14) GeV$ is consistent with the experimental data, there may be some tetraquark component in the vector meson X(1576).Comment: 6 pages, 1 figure, second version, typos correcte

Creation of Entanglement between Two Electron Spins Induced by Many Spin Ensemble Excitations

We theoretically explore the possibility of creating spin entanglement by simultaneously coupling two electronic spins to a nuclear ensemble. By microscopically modeling the spin ensemble with a single mode boson field, we use the time-dependent Fr\"{o}hlich transformation (TDFT) method developed most recently [Yong Li, C. Bruder, and C. P. Sun, Phys. Rev. A \textbf{75}, 032302 (2007)] to calculate the effective coupling between the two spins. Our investigation shows that the total system realizes a solid state based architecture for cavity QED. Exchanging such kind effective boson in a virtual process can result in an effective interaction between two spins. It is discovered that a maximum entangled state can be obtained when the velocity of the electrons matches the initial distance between them in a suitable way. Moreover, we also study how the number of collective excitations influences the entanglement. It is shown that the larger the number of excitation is, the less the two spins entangle each other.Comment: 8 pages, 4 figure

Local spin fluctuations in iron-based superconductors: 77Se and 87Rb NMR measurements of Tl0.47Rb0.34Fe1.63Se2

We report nuclear magnetic resonance (NMR) studies of the intercalated iron selenide superconductor (Tl, Rb)$_{y}$Fe$_{2-x}$Se$_2$ ($T_c = 32$ K). Single-crystal measurements up to 480 K on both $^{77}$Se and $^{87}$Rb nuclei show a superconducting phase with no magnetic order. The Knight shifts $K$ and relaxation rates $1/T_1T$ increase very strongly with temperature above $T_c$, before flattening at 400 K. The quadratic $T$-dependence and perfect proportionality of both $K$ and $1/T_1T$ data demonstrate their origin in paramagnetic moments. A minimal model for this pseudogap-like response is not a missing density of states but two additive contributions from the itinerant electronic and local magnetic components, a framework unifying the $K$ and $1/T_1 T$ data in many iron-based superconductors

Microscopic theory of surface-enhanced Raman scattering in noble-metal nanoparticles

We present a microscopic model for surface-enhanced Raman scattering (SERS) from molecules adsorbed on small noble-metal nanoparticles. In the absence of direct overlap of molecular orbitals and electronic states in the metal, the main enhancement source is the strong electric field of the surface plasmon resonance in a nanoparticle acting on a molecule near the surface. In small particles, the electromagnetic enhancement is strongly modified by quantum-size effects. We show that, in nanometer-sized particles, SERS magnitude is determined by a competition between several quantum-size effects such as the Landau damping of surface plasmon resonance and reduced screening near the nanoparticle surface. Using time-dependent local density approximation, we calculate spatial distribution of local fields near the surface and enhancement factor for different nanoparticles sizes.Comment: 8 pages, 6 figures. Considerably extended final versio

Multi-modality sensor data classification with selective attention

© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment. However, most existing work in this field employs domain-specific approaches and is thus ineffective in complex situations where multi-modality sensor data are collected. Moreover, the wearable sensor data are less informative than the conventional data such as texts or images. In this paper, to improve the adaptability of such classification methods across different application domains, we turn this classification task into a game and apply a deep reinforcement learning scheme to deal with complex situations dynamically. Additionally, we introduce a selective attention mechanism into the reinforcement learning scheme to focus on the crucial dimensions of the data. This mechanism helps to capture extra information from the signal and thus it is able to significantly improve the discriminative power of the classifier. We carry out several experiments on three wearable sensor datasets and demonstrate the competitive performance of the proposed approach compared to several state-of-the-art baselines

Superelastic Hybrid CNT/Graphene Fibers for Wearable Energy Storage

The demands for wearable technologies continue to grow and novel approaches for powering these devices are being enabled by the advent of new electromaterials and novel fabrication strategies. Herein, a novel approach is reported to develop superelastic wet-spun hybrid carbon nanotube graphene fibers followed by electrodeposition of polyaniline to achieve a high-performance fiber-based supercapacitor. It is found that the specific capacitance of hybrid carbon nanotube (CNT)/graphene fiber is enhanced up to ≈39% using a graphene to CNT fiber ratio of 1:3. Fabrication of spring-like coiled fiber coated with an elastic polymer shows an extraordinary elasticity capable of 800% strain while affording a specific capacitance of ≈138 F g -1 . The elastic rubber coating enables extreme stretchability and enabling cycles with up to 500% strain for thousands of cycles with no significant change in its performance. Multiple supercapacitors can be easily assembled in series or parallel to meet specific energy and power needs