Exotic disordered phases in the quantum J1−J2J_1-J_2 model on the honeycomb lattice

We study the ground-state phase diagram of the frustrated quantum $J_1-J_2$ Heisenberg antiferromagnet on the honeycomb lattice using a mean field approach in terms of the Schwinger boson representation of the spin operators. We present results for the ground-state energy, local magnetization, energy gap and spin-spin correlations. The system shows magnetic long range order for $0\leq J_{2}/J_{1}\lesssim 0.2075$ (N\'eel) and $0.398\lesssim J_{2}/J_{1}\leq 0.5$ (spiral). In the intermediate region, we find two magnetically disordered phases: a gapped spin liquid phase which shows short-range N\'eel correlations $(0.2075 \lesssim J_{2}/J_{1} \lesssim 0.3732)$, and a lattice nematic phase $(0.3732 \lesssim J_{2}/J_{1}\lesssim 0.398)$, which is magnetically disordered but breaks lattice rotational symmetry. The errors in the values of the phase boundaries which are implicit in the number of significant figures quoted, correspond purely to the error in the extrapolation of our finite-size results to the thermodynamic limit.Comment: 11 pages, 9 figures, to appear in Phys. Rev.

Implementation of Sink Particles in the Athena Code

We describe implementation and tests of sink particle algorithms in the Eulerian grid-based code Athena. Introduction of sink particles enables long-term evolution of systems in which localized collapse occurs, and it is impractical (or unnecessary) to resolve the accretion shocks at the centers of collapsing regions. We discuss similarities and differences of our methods compared to other implementations of sink particles. Our criteria for sink creation are motivated by the properties of the Larson-Penston collapse solution. We use standard particle-mesh methods to compute particle and gas gravity together. Accretion of mass and momenta onto sinks is computed using fluxes returned by the Riemann solver. A series of tests based on previous analytic and numerical collapse solutions is used to validate our method and implementation. We demonstrate use of our code for applications with a simulation of planar converging supersonic turbulent flow, in which multiple cores form and collapse to create sinks; these sinks continue to interact and accrete from their surroundings over several Myr.Comment: 39 pages, 14 figures, Accepted to ApJ

Quantum phases in the frustrated Heisenberg model on the bilayer honeycomb lattice

We use a combination of analytical and numerical techniques to study the phase diagram of the frustrated Heisenberg model on the bilayer honeycomb lattice. Using the Schwinger boson description of the spin operators followed by a mean field decoupling, the magnetic phase diagram is studied as a function of the frustration coupling $J_{2}$ and the interlayer coupling $J_{\bot}$. The presence of both magnetically ordered and disordered phases is investigated by means of the evaluation of ground-state energy, spin gap, local magnetization and spin-spin correlations. We observe a phase with a spin gap and short range N\'eel correlations that survives for non-zero next-nearest-neighbor interaction and interlayer coupling. Furthermore, we detect signatures of a reentrant behavior in the melting of N\'eel phase and symmetry restoring when the system undergoes a transition from an on-layer nematic valence bond crystal phase to an interlayer valence bond crystal phase. We complement our work with exact diagonalization on small clusters and dimer-series expansion calculations, together with a linear spin wave approach to study the phase diagram as a function of the spin $S$, the frustration and the interlayer couplings.Comment: 10 pages, 9 figure

Multiple Instance Curriculum Learning for Weakly Supervised Object Detection

When supervising an object detector with weakly labeled data, most existing approaches are prone to trapping in the discriminative object parts, e.g., finding the face of a cat instead of the full body, due to lacking the supervision on the extent of full objects. To address this challenge, we incorporate object segmentation into the detector training, which guides the model to correctly localize the full objects. We propose the multiple instance curriculum learning (MICL) method, which injects curriculum learning (CL) into the multiple instance learning (MIL) framework. The MICL method starts by automatically picking the easy training examples, where the extent of the segmentation masks agree with detection bounding boxes. The training set is gradually expanded to include harder examples to train strong detectors that handle complex images. The proposed MICL method with segmentation in the loop outperforms the state-of-the-art weakly supervised object detectors by a substantial margin on the PASCAL VOC datasets.Comment: Published in BMVC 201