4,818 research outputs found
Synchronization reveals correlation between oscillators on networks
The understanding of synchronization ranging from natural to social systems
has driven the interests of scientists from different disciplines. Here, we
have investigated the synchronization dynamics of the Kuramoto dynamics
departing from the fully synchronized regime. We have got the analytic
expression of the dynamical correlation between pairs of oscillators that
reveals the relation between the network dynamics and the underlying topology.
Moreover, it also reveals the internal structure of networks that can be used
as a new algorithm to detect community structures. Further, we have proposed a
new measure about the synchronization in complex networks and scrutinize it in
small-world and scale-free networks. Our results indicate that the more
heterogeneous and "smaller" the network is, the more closely it would be
synchronized by the collective dynamics.Comment: 4 pages, 3 figure
Topological Landau-Zener Bloch Oscillations in Photonic Floquet Lieb Lattices
The Lieb Lattice exhibits intriguing properties that are of general interest
in both the fundamental physics and practical applications. Here, we
investigate the topological Landau-Zener Bloch oscillation in a photonic
Floquet Lieb lattice, where the dimerized helical waveguides is constructed to
realize the synthetic spin-orbital interaction through the Floquet mechanism,
rendering us to study the impacts of topological transition from trivial gaps
to non-trivial ones. The compact localized states of flat bands supported by
the local symmetry of Lieb lattice will be associated with other bands by
topological invariants, Chern number, and involved into Landau-Zener transition
during Bloch oscillation. Importantly, the non-trivial geometrical phases after
topological transitions will be taken into account for constructive and
destructive interferences of wave functions. The numerical calculations of
continuum photonic medium demonstrate reasonable agreements with theoretical
tight-binding model. Our results provide an ongoing effort to realize designed
quantum materials with tailored properties.Comment: 5 pages, 4 figure
Asymmetric Nonlinear System is Not sufficient for Non-Reciprocal Quantum Wave Diode
We demonstrate symmetric wave propagations in asymmetric nonlinear quantum
systems. By solving the nonlinear Sch\"ordinger equation, we first analytically
prove the existence of symmetric transmission in asymmetric systems with a
single nonlinear delta-function interface. We then point out that a finite
width of the nonlinear interface region is necessary to produce non-reciprocity
in asymmetric systems. However, a geometrical resonant condition for breaking
non-reciprocal propagation is then identified theoretically and verified
numerically. With such a resonant condition, the nonlinear interface region of
finite width behaves like a single nonlinear delta-barrier so that wave
propagations in the forward and backward directions are identical under
arbitrary incident wave intensity. As such, reciprocity re-emerges periodically
in the asymmetric nonlinear system when changing the width of interface region.
Finally, similar resonant conditions of discrete nonlinear Sch\"ordinger
equation are discussed. Therefore, we have identified instances of Reciprocity
Theorem that breaking spatial symmetry in nonlinear interface systems is not
sufficient to produce non-reciprocal wave propagation.Comment: 6 pages, 5 figures, submittin
Unsupervised Manifold Clustering of Topological Phononics
Classification of topological phononics is challenging due to the lack of
universal topological invariants and the randomness of structure patterns.
Here, we show the unsupervised manifold learning for clustering topological
phononics without any priori knowledge, neither topological invariants nor
supervised trainings, even when systems are imperfect or disordered. This is
achieved by exploiting the real-space projection operator about finite phononic
lattices to describe the correlation between oscillators. We exemplify the
efficient unsupervised manifold clustering in typical phononic systems,
including one-dimensional Su-Schrieffer-Heeger-type phononic chain with random
couplings, amorphous phononic topological insulators, higher-order phononic
topological states and non-Hermitian phononic chain with random dissipations.
The results would inspire more efforts on applications of unsupervised machine
learning for topological phononic devices and beyond.Comment: 6 pages, 4 figure
Next-to-leading order QCD corrections to production at 14 TeV LHC
Since the precise study of Higgs gauge couplings is important to test the
Standard Model (SM), we calculate the complete next-to-leading order QCD(NLO
QCD) correction to the production in the SM at 14 TeV LHC.
Our results show that the NLO QCD correction can enhance the leading-order
cross section of by 45%, when = 125.3 GeV. We also
study the dependence of the LO and NLO corrected cross sections on the
renormalization and factorization scale . Besides, due to the unbalance of
parton distribution functions, we investigate the charge asymmetry of
in the production of , which can reach 32.94% for
at 14 TeV LHC.Comment: discussions added, accepted by Physics Letters
Directional Design of Materials Based on the Pareto Optimization: Application to Two-Dimensional Thermoelectric SnSe
Increasing the efficiency of directional design of functional materials is a
challenging work in theory, whose performance and stability are determined by
different factors entangled with each other complicatedly. In this work, we
apply the Pareto Optimization based on the Pareto Efficiency and Particle-Swarm
Optimization to design new functional materials directionally. As a
demonstration, we apply the method to the thermoelectric design of 2D SnSe
materials and identify several novel structures with lower free energy and
better thermoelectric performance than the experimental monolayer structure in
theory. We hope the multi-objective Pareto Optimization method can make the
integrative design of multi-objective and multi-functional materials a reality.Comment: 5 pages, 4 figures, under revie
3D Human Pose Estimation for Free-from and Moving Activities Using WiFi
This paper presents GoPose, a 3D skeleton-based human pose estimation system
that uses WiFi devices at home. Our system leverages the WiFi signals reflected
off the human body for 3D pose estimation. In contrast to prior systems that
need specialized hardware or dedicated sensors, our system does not require a
user to wear or carry any sensors and can reuse the WiFi devices that already
exist in a home environment for mass adoption. To realize such a system, we
leverage the 2D AoA spectrum of the signals reflected from the human body and
the deep learning techniques. In particular, the 2D AoA spectrum is proposed to
locate different parts of the human body as well as to enable
environment-independent pose estimation. Deep learning is incorporated to model
the complex relationship between the 2D AoA spectrums and the 3D skeletons of
the human body for pose tracking. Our evaluation results show GoPose achieves
around 4.7cm of accuracy under various scenarios including tracking unseen
activities and under NLoS scenarios
Liquid Sensing Using WiFi Signals
The popularity of Internet-of-Things (IoT) has provided us with unprecedented
opportunities to enable a variety of emerging services in a smart home
environment. Among those services, sensing the liquid level in a container is
critical to building many smart home and mobile healthcare applications that
improve the quality of life. This paper presents LiquidSense, a liquid-level
sensing system that is low-cost, high accuracy, widely applicable to different
daily liquids and containers, and can be easily integrated with existing smart
home networks. LiquidSense uses an existing home WiFi network and a low-cost
transducer that attached to the container to sense the resonance of the
container for liquid level detection. In particular, our system mounts a
low-cost transducer on the surface of the container and emits a well-designed
chirp signal to make the container resonant, which introduces subtle changes to
the home WiFi signals. By analyzing the subtle phase changes of the WiFi
signals, LiquidSense extracts the resonance frequency as a feature for liquid
level detection. Our system constructs prediction models for both continuous
and discrete predictions using curve fitting and SVM respectively. We evaluate
LiquidSense in home environments with containers of three different materials
and six types of liquids. Results show that LiquidSense achieves an overall
accuracy of 97% for continuous prediction and an overall F-score of 0.968 for
discrete prediction. Results also show that our system has a large coverage in
a home environment and works well under non-line-of-sight (NLOS) scenarios
Exploring supersymmetry with machine learning
Investigation of well-motivated parameter space in the theories of Beyond the
Standard Model (BSM) plays an important role in new physics discoveries.
However, a large-scale exploration of models with multi-parameter or equivalent
solutions with a finite separation, such as supersymmetric models, is typically
a time-consuming and challenging task. In this paper, we propose a
self-exploration method, named Machine Learning Scan (MLS), to achieve an
efficient test of models. As a proof-of-concept, we apply MLS to investigate
the subspace of MSSM and CMSSM and find that such a method can reduce the
computational cost and may be helpful for accelerating the exploration of
supersymmetry.Comment: 7 pages, 8 figures. Discussions, comments and CMSSM model are added.
Accepted for publication in Nuclear Physics
Simulating Quantum Spin Hall Effect in Topological Lieb Lattice of Linear Circuit
Inspired by the topological insulator circuit proposed and experimentally
verified by Jia., et al. \cite{1}, we theoretically realized the topological
Lieb lattice, a line centered square lattice with rich topological properties,
in a radio-frequency circuit. We open the topological nontrivial band-gap
through specific capacitor-inductor network, which resembles adding intrinsic
spin orbit coupling term into the tight binding model. Finally, we discuss the
extension of the phase change of hopping between sites to
arbitrary value, and investigate the topological phase transition of the band
structure vary with capacitance, thereby paving the way for designing tunable
lattices using the presented framework.Comment: 9 pages, 5 figures, see also
https://journals.aps.org/prb/abstract/10.1103/PhysRevB.97.07531
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