Efficient matrix exponential method based on extended Krylov subspace for transient simulation of large-scale linear circuits

Paper 3C-3Matrix exponential (MEXP) method has been demonstrated to be a competitive candidate for transient simulation of very large-scale integrated circuits. Nevertheless, the performance of MEXP based on ordinary Krylov subspace is unsatisfactory for stiff circuits, wherein the underlying Arnoldi process tends to oversample the high magnitude part of the system spectrum while undersampling the low magnitude part that is important to the final accuracy. In this work we explore the use of extended Krylov subspace to generate more accurate and efficient approximation for MEXP. We also develop a formulation that allows unequal positive and negative dimensions in the generated Krylov subspace for better performance. Numerical results demonstrate the efficacy of the proposed method. © 2014 IEEE.published_or_final_versio

Quark model predictions for K∗K^* photoproduction on the proton

The photoproduction of $K^*$ vector mesons is investigated in a quark model with an effective Lagrangian. Including both baryon resonance excitations and {\it t}-channel exchanges, observables for the reactions $\gamma p\to K^{*0}\Sigma^+$ and $\gamma p\to K^{*+}\Sigma^0$ are predicted, using the SU(3)-flavor-blind assumption of non-perturbative QCD.Comment: Revtex, 3 eps figures, revised version accepted by PRC Rapid Comm

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page

Nucleonic resonance excitations with linearly polarized photon in γp→ωp\gamma p\to \omega p

In this work, an improved quark model approach to the $\omega$ meson photo-production with an effective Lagrangian is presented. The {\it t}-channel {\it natural}-parity exchange is taken into account through the Pomeron exchange, while the {\it unnatural}-parity exchange is described by the $\pi^0$ exchange. With a very limited number of parameters, the available experimental data in the low energy regime can be consistently accounted for. We find that the beam polarization observables show sensitivities to some {\it s}-channel individual resonances in the $SU(6)\otimes O(3)$ quark model symmetry limit. Especially, the two resonances $P_{13}(1720)$ and $F_{15}(1680)$, which belong to the representation $[{\bf 56, ^2 8}, 2, 2, J]$, have dominant contributions over other excited states. Concerning the essential motivation of searching for "missing resonances" in meson photo-production, this approach provides a feasible framework, on which systematic investigations can be done.Comment: 16 pages, Revtex, 9 eps figures, to appear in PR

Doping evoluton of antiferromagnetic order and structural distortion in LaFeAsO1−x_{1-x}Fx_x

We use neutron scattering to study the structural distortion and antiferromagnetic (AFM) order in LaFeAsO$_{1-x}$F$_{x}$ as the system is doped with fluorine (F) to induce superconductivity. In the undoped state, LaFeAsO exhibits a structural distortion, changing the symmetry from tetragonal (space group $P4/nmm$) to orthorhombic (space group $Cmma$) at 155 K, and then followed by an AFM order at 137 K. Doping the system with F gradually decreases the structural distortion temperature, but suppresses the long range AFM order before the emergence of superconductivity. Therefore, while superconductivity in these Fe oxypnictides can survive in either the tetragonal or the orthorhombic crystal structure, it competes directly with static AFM order.Comment: reference update

Plaquette order and deconfined quantum critical point in the spin-1 bilinear-biquadratic Heisenberg model on the honeycomb lattice

We have precisely determined the ground state phase diagram of the quantum spin-1 bilinear-biquadratic Heisenberg model on the honeycomb lattice using the tensor renormalization group method. We find that the ferromagnetic, ferroquadrupolar, and a large part of the antiferromagnetic phases are stable against quantum fluctuations. However, around the phase where the ground state is antiferroquadrupolar ordered in the classical limit, quantum fluctuations suppress completely all magnetic orders, leading to a plaquette order phase which breaks the lattice symmetry but preserves the spin SU(2) symmetry. On the evidence of our numerical results, the quantum phase transition between the antiferromagnetic phase and the plaquette phase is found to be either a direct second order or a very weak first order transition.Comment: 6 pages, 9 figures, published versio

Open charm scenarios

We discuss possibilities of identifying open charm effects in direct production processes, and propose that direct evidence for the open charm effects can be found in $e^+ e^-\to J/\psi\pi^0$. A unique feature with this process is that the $D\bar{D^*}+c.c.$ open channel is located in a relatively isolated energy, i.e. $\sim 3.876$ GeV, which is sufficiently far away from the known charmonia $\psi(3770)$ and $\psi(4040)$. Due to the dominance of the isospin-0 component at the charmonium energy region, an enhanced model-independent cusp effect between the thresholds of $D^0\bar{D^{*0}}+c.c.$ and $D^+ D^{*-}+c.c.$ can be highlighted. An energy scan over this energy region in the $e^+e^-$ annihilation reaction can help us to understand the nature of X(3900) recently observed by Belle Collaboration in $e^+ e^-\to D\bar{D}+c.c.$, and establish the open charm effects as an important non-perturbative mechanism in the charmonium energy region.Comment: 6 pages, Proceeding contribution to the Rutherford Centennial Conference, Aug. 8-12, 2011, Manchester, U.