Decomposition of Triebel-Lizorkin and Besov spaces in the context of Laguerre expansions

A pair of dual frames with almost exponentially localized elements (needlets) are constructed on \RR_+^d based on Laguerre functions. It is shown that the Triebel-Lizorkin and Besov spaces induced by Laguerre expansions can be characterized in terms of respective sequence spaces that involve the needlet coefficients.Comment: 42 page

Role of Disorder in Mn:GaAs, Cr:GaAs, and Cr:GaN

We present calculations of magnetic exchange interactions and critical temperature T_c in Mn:GaAs, Cr:GaAs and Cr:GaN. The local spin density approximation is combined with a linear-response technique to map the magnetic energy onto a Heisenberg hamiltonion, but no significant further approximations are made. Special quasi-random structures in large unit cells are used to accurately model the disorder. T_c is computed using both a spin-dynamics approach and the cluster variation method developed for the classical Heisenberg model. We show the following: (i) configurational disorder results in large dispersions in the pairwise exchange interactions; (ii) the disorder strongly reduces T_c; (iii) clustering in the magnetic atoms, whose tendency is predicted from total-energy considerations, further reduces T_c. Additionally the exchange interactions J(R) are found to decay exponentially with distance R^3 on average; and the mean-field approximation is found to be a very poor predictor of T_c, particularly when J(R) decays rapidly. Finally the effect of spin-orbit coupling on T_c is considered. With all these factors taken into account, T_c is reasonably predicted by the local spin-density approximation in MnGaAs without the need to invoke compensation by donor impurities.Comment: 10 pages, 3 figure

Quantum Phase Transitions beyond the Landau's Paradigm in Sp(4) Spin System

We propose quantum phase transitions beyond the Landau's paradigm of Sp(4) spin Heisenberg models on the triangular and square lattices, motivated by the exact Sp(4)$\simeq$ SO(5) symmetry of spin-3/2 fermionic cold atomic system with only $s-$wave scattering. On the triangular lattice, we study a phase transition between the $\sqrt{3}\times\sqrt{3}$ spin ordered phase and a $Z_2$ spin liquid phase, this phase transition is described by an O(8) sigma model in terms of fractionalized spinon fields, with significant anomalous scaling dimensions of spin order parameters. On the square lattice, we propose a deconfined critical point between the Neel order and the VBS order, which is described by the CP(3) model, and the monopole effect of the compact U(1) gauge field is expected to be suppressed at the critical point.Comment: 6 pages, 3 figure

The perfect spin injection in silicene FS/NS junction

We theoretically investigate the spin injection from a ferromagnetic silicene to a normal silicene (FS/NS), where the magnetization in the FS is assumed from the magnetic proximity effect. Based on a silicene lattice model, we demonstrated that the pure spin injection could be obtained by tuning the Fermi energy of two spin species, where one is in the spin orbit coupling gap and the other one is outside the gap. Moreover, the valley polarity of the spin species can be controlled by a perpendicular electric field in the FS region. Our findings may shed light on making silicene-based spin and valley devices in the spintronics and valleytronics field.Comment: 6 pages, 3 figure

Learning-aided Stochastic Network Optimization with Imperfect State Prediction

We investigate the problem of stochastic network optimization in the presence of imperfect state prediction and non-stationarity. Based on a novel distribution-accuracy curve prediction model, we develop the predictive learning-aided control (PLC) algorithm, which jointly utilizes historic and predicted network state information for decision making. PLC is an online algorithm that requires zero a-prior system statistical information, and consists of three key components, namely sequential distribution estimation and change detection, dual learning, and online queue-based control. Specifically, we show that PLC simultaneously achieves good long-term performance, short-term queue size reduction, accurate change detection, and fast algorithm convergence. In particular, for stationary networks, PLC achieves a near-optimal $[O(\epsilon)$, $O(\log(1/\epsilon)^2)]$ utility-delay tradeoff. For non-stationary networks, \plc{} obtains an $[O(\epsilon), O(\log^2(1/\epsilon)$ $+ \min(\epsilon^{c/2-1}, e_w/\epsilon))]$ utility-backlog tradeoff for distributions that last $\Theta(\frac{\max(\epsilon^{-c}, e_w^{-2})}{\epsilon^{1+a}})$ time, where $e_w$ is the prediction accuracy and $a=\Theta(1)>0$ is a constant (the Backpressue algorithm \cite{neelynowbook} requires an $O(\epsilon^{-2})$ length for the same utility performance with a larger backlog). Moreover, PLC detects distribution change $O(w)$ slots faster with high probability ($w$ is the prediction size) and achieves an $O(\min(\epsilon^{-1+c/2}, e_w/\epsilon)+\log^2(1/\epsilon))$ convergence time. Our results demonstrate that state prediction (even imperfect) can help (i) achieve faster detection and convergence, and (ii) obtain better utility-delay tradeoffs