569 research outputs found
Topology-driven phase transitions in the classical monomer-dimer-loop model
In this work, we investigate the classical loop models doped with monomers
and dimers on a square lattice, whose partition function can be expressed as a
tensor network (TN). In the thermodynamic limit, we use the boundary matrix
product state technique to contract the partition function TN, and determine
the thermodynamic properties with high accuracy. In this monomer-dimer-loop
model, we find a second-order phase transition between a trivial
monomer-condensation and a loop-condensation (LC) phases, which can not be
distinguished by any local order parameter, while nevertheless the two phases
have distinct topological properties. In the LC phase, we find two degenerate
dominating eigenvalues in the transfer-matrix spectrum, as well as a
non-vanishing (nonlocal) string order parameter, both of which identify the
\textit{topological ergodicity breaking} in the LC phase and can serve as the
order parameter for detecting the phase transitions.Comment: 6 pages, 8 figures, supplementary materials adde
The Trajectory of Voice Onset Time with Vocal Aging
Vocal aging, a universal process of human aging, can largely affect one's
language use, possibly including some subtle acoustic features of one's
utterances like Voice Onset Time. To figure out the time effects, Queen
Elizabeth's Christmas speeches are documented and analyzed in the long-term
trend. We build statistical models of time dependence in Voice Onset Time,
controlling a wide range of other fixed factors, to present annual variations
and the simulated trajectory. It is revealed that the variation range of Voice
Onset Time has been narrowing over fifty years with a slight reduction in the
mean value, which, possibly, is an effect of diminishing exertion, resulting
from subdued muscle contraction, transcending other non-linguistic factors in
forming Voice Onset Time patterns over a long time.Comment: conferenc
Kosterlitz-Thouless transitions and phase diagrams of the interacting monomer-dimer model on a checkerboard lattice
Using the tensor network approach, we investigate the monomer-dimer models on
a checkerboard lattice, in which there are interactions (with strength )
between the parallel dimers on half of the plaquettes. For the fully packed
interacting dimer model, we observe a Kosterlitz-Thouless (KT) transition
between the lowtemperature symmetry breaking and the high-temperature critical
phases; for the doped monomer-dimer casewith finite chemical potential ,
we also find an order-disorder phase transition which is of second order
instead. We use the boundary matrix product state approach to detect the KT and
second-order phase transitions and obtain the phase diagrams and
. Moreover, for the noninteracting monomer-dimer model (setting ), we get an extraordinarily accurate determination of the free energy
per site (negative of the monomer-dimer constant ) as with the dimer density , both
of 15 correct digits.Comment: 8 pages, 15 figure
PT-ISABB: A Hybrid Tree-based Complete Algorithm to Solve Asymmetric Distributed Constraint Optimization Problems
Asymmetric Distributed Constraint Optimization Problems (ADCOPs) have emerged
as an important formalism in multi-agent community due to their ability to
capture personal preferences. However, the existing search-based complete
algorithms for ADCOPs can only use local knowledge to compute lower bounds,
which leads to inefficient pruning and prohibits them from solving large scale
problems. On the other hand, inference-based complete algorithms (e.g., DPOP)
for Distributed Constraint Optimization Problems (DCOPs) require only a linear
number of messages, but they cannot be directly applied into ADCOPs due to a
privacy concern. Therefore, in the paper, we consider the possibility of
combining inference and search to effectively solve ADCOPs at an acceptable
loss of privacy. Specifically, we propose a hybrid complete algorithm called
PT-ISABB which uses a tailored inference algorithm to provide tight lower
bounds and a tree-based complete search algorithm to exhaust the search space.
We prove the correctness of our algorithm and the experimental results
demonstrate its superiority over other state-of-the-art complete algorithms
Planecell: Representing the 3D Space with Planes
Reconstruction based on the stereo camera has received considerable attention
recently, but two particular challenges still remain. The first concerns the
need to aggregate similar pixels in an effective approach, and the second is to
maintain as much of the available information as possible while ensuring
sufficient accuracy. To overcome these issues, we propose a new 3D
representation method, namely, planecell, that extracts planarity from the
depth-assisted image segmentation and then projects these depth planes into the
3D world. An energy function formulated from Conditional Random Field that
generalizes the planar relationships is maximized to merge coplanar segments.
We evaluate our method with a variety of reconstruction baselines on both KITTI
and Middlebury datasets, and the results indicate the superiorities compared to
other 3D space representation methods in accuracy, memory requirements and
further applications
AsymDPOP: Complete Inference for Asymmetric Distributed Constraint Optimization Problems
Asymmetric distributed constraint optimization problems (ADCOPs) are an
emerging model for coordinating agents with personal preferences. However, the
existing inference-based complete algorithms which use local eliminations
cannot be applied to ADCOPs, as the parent agents are required to transfer
their private functions to their children. Rather than disclosing private
functions explicitly to facilitate local eliminations, we solve the problem by
enforcing delayed eliminations and propose AsymDPOP, the first inference-based
complete algorithm for ADCOPs. To solve the severe scalability problems
incurred by delayed eliminations, we propose to reduce the memory consumption
by propagating a set of smaller utility tables instead of a joint utility
table, and to reduce the computation efforts by sequential optimizations
instead of joint optimizations. The empirical evaluation indicates that
AsymDPOP significantly outperforms the state-of-the-arts, as well as the
vanilla DPOP with PEAV formulation
From Multiple Nodal Chain to Dirac/Weyl Semimetal and Topological Insulator in Ternary Hexagonal Materials
Dirac semimetal (DSM) hosts four-fold degenerate isolated band-crossing
points with linear dispersion, around which the quasiparticles resemble the
relativistic Dirac Fermions. It can be described by a 4 * 4 massless Dirac
Hamiltonian which can be decomposed into a pair of Weyl points or gaped into an
insulator. Thus, crystal symmetry is critical to guarantee the stable
existence. On the contrary, by breaking crystal symmetry, a DSM may transform
into a Weyl semimetal (WSM) or a topological insulator (TI). Here, by taking
hexagonal LiAuSe as an example, we find that it is a starfruit shaped multiple
nodal chain semimetal in the absence of spin-orbit coupling(SOC). In the
presence of SOC, it is an ideal DSM naturally with the Dirac points locating at
Fermi level exactly, and it would transform into WSM phase by introducing
external Zeeman field or by magnetic doping with rare-earth atom Sm. It could
also transform into TI state by breaking rotational symmetry. Our studies show
that DSM is a critical point for topological phase transition, and the
conclusion can apply to most of the DSM materials, not limited to the hexagonal
material LiAuSe.Comment: 21 pages, 7 figure
Short-term Load Forecasting with Deep Residual Networks
We present in this paper a model for forecasting short-term power loads based
on deep residual networks. The proposed model is able to integrate domain
knowledge and researchers' understanding of the task by virtue of different
neural network building blocks. Specifically, a modified deep residual network
is formulated to improve the forecast results. Further, a two-stage ensemble
strategy is used to enhance the generalization capability of the proposed
model. We also apply the proposed model to probabilistic load forecasting using
Monte Carlo dropout. Three public datasets are used to prove the effectiveness
of the proposed model. Multiple test cases and comparison with existing models
show that the proposed model is able to provide accurate load forecasting
results and has high generalization capability.Comment: This paper is currently accepted by IEEE Transactions on Smart Gri
Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images
Segmentation of ultra-high resolution images is increasingly demanded, yet
poses significant challenges for algorithm efficiency, in particular
considering the (GPU) memory limits. Current approaches either downsample an
ultra-high resolution image or crop it into small patches for separate
processing. In either way, the loss of local fine details or global contextual
information results in limited segmentation accuracy. We propose collaborative
Global-Local Networks (GLNet) to effectively preserve both global and local
information in a highly memory-efficient manner. GLNet is composed of a global
branch and a local branch, taking the downsampled entire image and its cropped
local patches as respective inputs. For segmentation, GLNet deeply fuses
feature maps from two branches, capturing both the high-resolution fine
structures from zoomed-in local patches and the contextual dependency from the
downsampled input. To further resolve the potential class imbalance problem
between background and foreground regions, we present a coarse-to-fine variant
of GLNet, also being memory-efficient. Extensive experiments and analyses have
been performed on three real-world ultra-high aerial and medical image datasets
(resolution up to 30 million pixels). With only one single 1080Ti GPU and less
than 2GB memory used, our GLNet yields high-quality segmentation results and
achieves much more competitive accuracy-memory usage trade-offs compared to
state-of-the-arts.Comment: CVPR2019 ora
Thermal Tensor Renormalization Group Simulations of Square-Lattice Quantum Spin Models
In this work, we benchmark the well-controlled and numerically accurate
exponential thermal tensor renormalization group (XTRG) in the simulation of
interacting spin models in two dimensions. Finite temperature introduces a
thermal correlation length, which justifies the analysis of finite system size
for the sake of numerical efficiency. In this paper we focus on the square
lattice Heisenberg antiferromagnet (SLH) and quantum Ising models (QIM) on open
and cylindrical geometries up to width . We explore various
one-dimensional mapping paths in the matrix product operator (MPO)
representation, whose performance is clearly shown to be geometry dependent. We
benchmark against quantum Monte Carlo (QMC) data, yet also the series-expansion
thermal tensor network results. Thermal properties including the internal
energy, specific heat, and spin structure factors, etc., are computed with high
precision, obtaining excellent agreement with QMC results. XTRG also allows us
to reach remarkably low temperatures. For SLH we obtain at low temperature an
energy per site and a spontaneous magnetization
, which is already consistent with the ground state
properties. We extract an exponential divergence vs. of the structure
factor , as well as the correlation length , at the ordering wave
vector , which represents the renormalized classical behavior and
can be observed over a narrow but appreciable temperature window, by analysing
the finite-size data by XTRG simulations. For the QIM with a finite-temperature
phase transition, we employ several thermal quantities, including the specific
heat, Binder ratio, as well as the MPO entanglement to determine the critical
temperature .Comment: 16 pages, 16 figure
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