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 HZW±HZW^{\pm} 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 $pp \to HZW^{\pm}$ production in the SM at 14 TeV LHC. Our results show that the NLO QCD correction can enhance the leading-order cross section of $pp \to HZW^{\pm}$ by 45%, when $m_H$ = 125.3 GeV. We also study the dependence of the LO and NLO corrected cross sections on the renormalization and factorization scale $\mu$. Besides, due to the unbalance of parton distribution functions, we investigate the charge asymmetry of $W^{\pm}$ in the production of $pp\to HZW^{\pm}$, which can reach 32.94% for $\mu=(m_H+m_Z+m_W)/2$ 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 $\phi=\pi/2$ 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