670 research outputs found
Criticality in Translation-Invariant Parafermion Chains
In this work we numerically study critical phases in translation-invariant
parafermion chains with both nearest- and next-nearest-neighbor
hopping terms. The model can be mapped to a spin model with
nearest-neighbor couplings via a generalized Jordan-Wigner transformation and
translation invariance ensures that the spin model is always self-dual. We
first study the low-energy spectrum of chains with only nearest-neighbor
coupling, which are mapped onto standard self-dual clock models.
For we match the numerical results to the known conformal field
theory(CFT) identification. We then analyze in detail the phase diagram of a
chain with both nearest and next-nearest neighbor hopping and six
critical phases with central charges being , 1 or 2 are found. We find
continuous phase transitions between and phases, while the phase
transition between and is conjectured to be of
Kosterlitz-Thouless type.Comment: published versio
Topology and Criticality in Resonating Affleck-Kennedy-Lieb-Tasaki loop Spin Liquid States
We exploit a natural Projected Entangled-Pair State (PEPS) representation for
the resonating Affleck-Kennedy-Lieb-Tasaki loop (RAL) state. By taking
advantage of PEPS-based analytical and numerical methods, we characterize the
RAL states on various two-dimensional lattices. On square and honeycomb
lattices, these states are critical since the dimer-dimer correlations decay as
a power law. On kagome lattice, the RAL state has exponentially decaying
correlation functions, supporting the scenario of a gapped spin liquid. We
provide further evidence that the RAL state on the kagome lattice is a
spin liquid, by identifying the four topological sectors and
computing the topological entropy. Furthermore, we construct a one-parameter
family of PEPS states interpolating between the RAL state and a short-range
Resonating Valence Bond state and find a critical point, consistent with the
fact that the two states belong to two different phases. We also perform a
variational study of the spin-1 kagome Heisenberg model using this
one-parameter PEPS.Comment: 10 pages, 14 figures, published versio
Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approach
Exploiting the computational heterogeneity of mobile devices and edge nodes,
mobile edge computation (MEC) provides an efficient approach to achieving
real-time applications that are sensitive to information freshness, by
offloading tasks from mobile devices to edge nodes. We use the metric
Age-of-Information (AoI) to evaluate information freshness. An efficient
solution to minimize the AoI for the MEC system with multiple users is
non-trivial to obtain due to the random computing time. In this paper, we
consider multiple users offloading tasks to heterogeneous edge servers in a MEC
system. We first reformulate the problem as a Restless Multi-Arm-Bandit (RMAB)
problem and establish a hierarchical Markov Decision Process (MDP) to
characterize the updating of AoI for the MEC system. Based on the hierarchical
MDP, we propose a nested index framework and design a nested index policy with
provably asymptotic optimality. Finally, the closed form of the nested index is
obtained, which enables the performance tradeoffs between computation
complexity and accuracy. Our algorithm leads to an optimality gap reduction of
up to 40%, compared to benchmarks. Our algorithm asymptotically approximates
the lower bound as the system scalar gets large enough
PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting
When predicting PM2.5 concentrations, it is necessary to consider complex
information sources since the concentrations are influenced by various factors
within a long period. In this paper, we identify a set of critical domain
knowledge for PM2.5 forecasting and develop a novel graph based model,
PM2.5-GNN, being capable of capturing long-term dependencies. On a real-world
dataset, we validate the effectiveness of the proposed model and examine its
abilities of capturing both fine-grained and long-term influences in PM2.5
process. The proposed PM2.5-GNN has also been deployed online to provide free
forecasting service.Comment: Pre-print version of a ACM SIGSPATIAL 2020 poster
[paper](https://dl.acm.org/doi/10.1145/3397536.3422208). The code is
available at [Github](https://github.com/shawnwang-tech/PM2.5-GNN), and the
talk is available at [YouTube](https://www.youtube.com/watch?v=VX93vMthkGM
Development and application of an advection-dispersion model for data analysis of electromigration experiments with intact rock cores
An advection-dispersion model was developed for interpreting the experimental results of electromigration in granitic rock cores. The most important mechanisms governing the movement of the tracer ions, i.e. electromigration, electroosmosis and dispersion were taken into account by the advection-dispersion model, but the influence of aqueous chemistry was ignored. An analytical solution in the Laplace domain was derived and then applied to analyze the measured results of a series of experiments, performed in an updated device with different applied voltages. The modelling results suggested that both studied tracers, i.e. iodide and selenite, are effectively non-sorbing in the intact rock investigated. The effective diffusivities and formation factors evaluated from the model were also found to be in good agreement with data reported in literature and the associated uncertainties are much smaller than those obtained from the classical ideal plug-flow model, which accounts only for the dominant effect of electromigration on ionic transport. To explore further how the quality of parameter identifications would be influenced by neglect of aqueous chemistry, a reactive transport model was also implemented, which may be regarded as a multi-component version of the advection-dispersion model. The analysis showed that the advection-dispersion model works equally well as the reactive transport model but requires much less computational demanding. It can, therefore, be used with great confidence to interpret the experimental results of electromigration for studies of diffusion and sorption behavior of radionuclides in intact rock cores.Peer reviewe
Deep learning forecasts of cosmic acceleration parameters from DECi-hertz Interferometer Gravitational-wave Observatory
Validating the accelerating expansion of the universe is an important issue
for understanding the evolution of the universe. By constraining the cosmic
acceleration parameter , we can discriminate between the (cosmological constant plus cold dark matter) model and LTB (the
Lema\^itre-Tolman-Bondi) model. In this paper, we explore the possibility of
constraining the cosmic acceleration parameter with the inspiral gravitational
waveform of neutron star binaries (NSBs) in the frequency range of 0.1Hz-10Hz,
which can be detected by the second-generation space-based gravitational wave
detector DECIGO. We use a convolutional neural network (CNN), a long short-term
memory (LSTM) network combined with a gated recurrent unit (GRU), and Fisher
information matrix to derive constraints on the cosmic acceleration parameter
. Based on the simulated gravitational wave data with a time duration of 1
month, we conclude that CNN can limit the relative error to 14.09%, while LSTM
network combined with GRU can limit the relative error to 13.53%. Additionally,
using Fisher information matrix for gravitational wave data with a 5-year
observation can limit the relative error to 32.94%. Compared with the Fisher
information matrix method, deep learning techniques will significantly improve
the constraints on the cosmic acceleration parameters at different redshifts.
Therefore, DECIGO is expected to provide direct measurements of the
acceleration of the universe, by observing the chirp signals of coalescing
binary neutron stars
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