Why T_c is too high when antiferromagnetism is underestimated? --- An understanding based on the phase string effect

It is natural for a Mott antiferromagnetism in RVB description to become a superconductor in doped metallic regime. But the issue of superconducting transition temperature is highly nontrivial, as the AF fluctuations in the form of RVB pair-breaking are crucial in determining the phase coherence of the superconductivity. Underestimated AF fluctuations in a fermionic RVB state are the essential reason causing an overestimate of T_c in the same system. We point out that by starting with a {\it bosonic} RVB description where both the long-range and short-range AF correlations can be accurately described, the AF fluctuations can effectively reduce T_c to a reasonable value through the phase string effect, by controlling the phase coherence of the superconducting order parameter.Comment: Latex; two figure

Upper Pseudogap Phase: Magnetic Characterizations

It is proposed that the upper pseudogap phase (UPP) observed in the high-Tc cuprates correspond to the formation of spin singlet pairing under the bosonic resonating-valence-bond (RVB) description. We present a series of evidence in support of such a scenario based on the calculated magnetic properties including uniform spin susceptibility, spin-lattice and spin-echo relaxation rates, which consistently show that strong spin correlations start to develop upon entering the UPP, being enhanced around the momentum (\pi, \pi) while suppressed around (0, 0). The phase diagram in the parameter space of doping concentration, temperature, and external magnetic field, is obtained based on the the bosonic RVB theory. In particular, the competition between the Zeeman splitting and singlet pairing determines a simple relation between the "critical" magnetic field, H_{PG}, and characteristic temperature scale, T0, of the UPP. We also discuss the magnetic behavior in the lower pseudogap phase at a temperature Tv lower than T0, which is characterized by the formation of Cooper pair amplitude where the low-lying spin fluctuations get suppressed at both (0, 0) and (\pi, \pi). Properties of the UPP involving charge channels will be also briefly discussed.Comment: 11 pages, 5 figures, final version to appear in PR

Superfluid-Mott-Insulator Transition in a One-Dimensional Optical Lattice with Double-Well Potentials

We study the superfluid-Mott-insulator transition of ultracold bosonic atoms in a one-dimensional optical lattice with a double-well confining trap using the density-matrix renormalization group. At low density, the system behaves similarly as two separated ones inside harmonic traps. At high density, however, interesting features appear as the consequence of the quantum tunneling between the two wells and the competition between the "superfluid" and Mott regions. They are characterized by a rich step-plateau structure in the visibility and the satellite peaks in the momentum distribution function as a function of the on-site repulsion. These novel properties shed light on the understanding of the phase coherence between two coupled condensates and the off-diagonal correlations between the two wells.Comment: 5 pages, 7 figure

Semantic Communication Systems for Speech Transmission

Semantic communications could improve the transmission efficiency significantly by exploring the semantic information. In this paper, we make an effort to recover the transmitted speech signals in the semantic communication systems, which minimizes the error at the semantic level rather than the bit or symbol level. Particularly, we design a deep learning (DL)-enabled semantic communication system for speech signals, named DeepSC-S. In order to improve the recovery accuracy of speech signals, especially for the essential information, DeepSC-S is developed based on an attention mechanism by utilizing a squeeze-and-excitation (SE) network. The motivation behind the attention mechanism is to identify the essential speech information by providing higher weights to them when training the neural network. Moreover, in order to facilitate the proposed DeepSC-S for dynamic channel environments, we find a general model to cope with various channel conditions without retraining. Furthermore, we investigate DeepSC-S in telephone systems as well as multimedia transmission systems to verify the model adaptation in practice. The simulation results demonstrate that our proposed DeepSC-S outperforms the traditional communications in both cases in terms of the speech signals metrics, such as signal-to-distortion ration and perceptual evaluation of speech distortion. Besides, DeepSC-S is more robust to channel variations, especially in the low signal-to-noise (SNR) regime