Charge ordering and phase separation in the infinite dimensional extended Hubbard model

We study the extended Hubbard model with both on-site (U) and nearest neighbor (V) Coulomb repulsion using the exact diagonalization method within the dynamical mean field theory. For a fixed U (U=2.0), the T-n phase-diagrams are obtained for V=1.4 and V=1.2, at which the ground states of n=1/2 system is charge-ordered and charge-disordered, respectively. In both cases, robust charge order is found at finite temperature and in an extended filling regime around n=1/2. The order parameter changes non-monotonously with temperature. For V=1.4, phase separation between charge-ordered and charge-disordered phases is observed in the low temperature and n < 0.5 regime. It is described by an "S"-shaped structure of the n-/mu curve. For V=1.2, the ground state is charge-disordered, and a reentrant charge-ordering transition is observed for 0.42 < n < 0.68. Relevance of our results to experiments for doped manganites is discussed.Comment: 9 pages, 7 figures, submitted to Phys. Rev.

Cost-Effective Cache Deployment in Mobile Heterogeneous Networks

This paper investigates one of the fundamental issues in cache-enabled heterogeneous networks (HetNets): how many cache instances should be deployed at different base stations, in order to provide guaranteed service in a cost-effective manner. Specifically, we consider two-tier HetNets with hierarchical caching, where the most popular files are cached at small cell base stations (SBSs) while the less popular ones are cached at macro base stations (MBSs). For a given network cache deployment budget, the cache sizes for MBSs and SBSs are optimized to maximize network capacity while satisfying the file transmission rate requirements. As cache sizes of MBSs and SBSs affect the traffic load distribution, inter-tier traffic steering is also employed for load balancing. Based on stochastic geometry analysis, the optimal cache sizes for MBSs and SBSs are obtained, which are threshold-based with respect to cache budget in the networks constrained by SBS backhauls. Simulation results are provided to evaluate the proposed schemes and demonstrate the applications in cost-effective network deployment

Mott-Hubbard transition in infinite dimensions

We analyze the unanalytical structure of metal-insulator transition (MIT) in infinite dimensions. By introducing a simple transformation into the dynamical mean-field equation of Hubbard model, a multiple-valued structure in Green's function and other thermodynamical quantities with respect to the interaction strength $U$ are found at low temperatures. A unified description of stable, metastable and unstable phases is obtained in the regime $U_{c1}(T)<U<U_{c2}(T)$, and the Maxwell construction is performed to evaluate the MIT line $U^{\ast}(T)$. We show how the first-order MIT at $U^{\ast}(T)$ for $T>0$ evolves into second-order one at $U_{c2}(0)$ for $T=0$. The phase diagram near MIT is presented.Comment: 5 pages with 3 figures, text and figures revise

Deep Learning Topological Invariants of Band Insulators

In this work we design and train deep neural networks to predict topological invariants for one-dimensional four-band insulators in AIII class whose topological invariant is the winding number, and two-dimensional two-band insulators in A class whose topological invariant is the Chern number. Given Hamiltonians in the momentum space as the input, neural networks can predict topological invariants for both classes with accuracy close to or higher than 90%, even for Hamiltonians whose invariants are beyond the training data set. Despite the complexity of the neural network, we find that the output of certain intermediate hidden layers resembles either the winding angle for models in AIII class or the solid angle (Berry curvature) for models in A class, indicating that neural networks essentially capture the mathematical formula of topological invariants. Our work demonstrates the ability of neural networks to predict topological invariants for complicated models with local Hamiltonians as the only input, and offers an example that even a deep neural network is understandable.Comment: 8 pages, 5 figure