Optimizing Hartree-Fock orbitals by the density-matrix renormalization group

We have proposed a density-matrix renormalization group (DMRG) scheme to optimize the one-electron basis states of molecules. It improves significantly the accuracy and efficiency of the DMRG in the study of quantum chemistry or other many-fermion system with nonlocal interactions. For a water molecule, we find that the ground state energy obtained by the DMRG with only 61 optimized orbitals already reaches the accuracy of best quantum Monte Carlo calculation with 92 orbitals.Comment: published version, 4 pages, 4 figure

Energy and momentum deposited into a QCD medium by a jet shower

Hard partons moving through a dense QCD medium lose energy by radiative emissions and elastic scatterings. Deposition of the radiative contribution into the medium requires rescattering of the radiated gluons. We compute the total energy loss and its deposition into the medium self-consistently within the same formalism, assuming perturbative interaction between probe and medium. The same transport coefficients that control energy loss of the hard parton determine how the energy is deposited into the medium; this allows a parameter free calculation of the latter once the former have been computed or extracted from experimental energy loss data. We compute them for a perturbative medium in hard thermal loop (HTL) approximation. Assuming that the deposited energy-momentum is equilibrated after a short relaxation time, we compute the medium's hydrodynamical response and obtain a conical pattern that is strongly enhanced by showering.Comment: 4 pages, 3 figures, revtex4, intro modified, typos correcte

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

Effect of disorder with long-range correlation on transport in graphene nanoribbon

Transport in disordered armchair graphene nanoribbons (AGR) with long-range correlation between quantum wire contact is investigated by transfer matrix combined with Landauer's formula. Metal-insulator transition is induced by disorder in neutral AGR. Thereinto, the conductance is one conductance quantum for metallic phase and exponentially decays otherwise when the length of AGR is infinity and far longer than its width. Similar to the case of long-range disorder, the conductance of neutral AGR first increases and then decreases while the conductance of doped AGR monotonically decreases, as the disorder strength increases. In the presence of strong disorder, the conductivity depends monotonically and non-monotonically on the aspect ratio for heavily doped and slightly doped AGR respectively.Comment: 6 pages, 8 figures; J. Phys: Condensed Matter (May 2012

Deep learning for cardiac image segmentation: A review

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research