31,891 research outputs found
Exotic disordered phases in the quantum model on the honeycomb lattice
We study the ground-state phase diagram of the frustrated quantum
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
(N\'eel) and (spiral). In the intermediate region, we find two magnetically disordered
phases: a gapped spin liquid phase which shows short-range N\'eel correlations
, and a lattice nematic phase
, 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 and the interlayer coupling .
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 , 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
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