arXiv subjects

Hong Chen

Publications and source records attributed to Hong Chen.

At least 253 records · Page 14Linked to original sources

arXiv2020

Wireless power transfer via topological modes in dimer chains

The topological characteristics, including invariant topological orders, band inversion, and the topological edge mode (TEM) in the photonic insulators, have been widely studied. Whether people can take advantage of intriguing topological modes in simple one-dimensional systems to implement some practical applications is an issue which people are increasingly concerned about. In this work, based on a photonic dimer chain composed of ultra-subwavelength resonators, we verify experimentally that the TEM in the effective second-order parity-time (PT) system is immune to the inner disorder perturbation, and can be used to realize the long-range wireless power transfer (WPT) with high transmission efficiency. To intuitively show the TEM can be used for WPT, a power signal source is used to excite the TEM. It can be clearly seen that two LED lamps with 0.5-W at both ends of the structure are lighted up with the aid of TEMs. In addition, in order to solve the special technical problems of standby power loss and frequency tracking, we further propose that a WPT system with effective third-order PT symmetry can be constructed by using one topological interface mode and two TEMs. Inspired by the long-range WPT with TEMs in this work, it is expected to use more complex topological structures to achieve energy transmission with more functions, such as the WPT devices whose direction can be selected flexibly in the quasiperiodic or trimer topological chains.

physics.app-ph↗

arXiv2020

Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation

Cold-start problem is a fundamental challenge for recommendation tasks. Despite the recent advances on Graph Neural Networks (GNNs) incorporate the high-order collaborative signal to alleviate the problem, the embeddings of the cold-start users and items aren't explicitly optimized, and the cold-start neighbors are not dealt with during the graph convolution in GNNs. This paper proposes to pre-train a GNN model before applying it for recommendation. Unlike the goal of recommendation, the pre-training GNN simulates the cold-start scenarios from the users/items with sufficient interactions and takes the embedding reconstruction as the pretext task, such that it can directly improve the embedding quality and can be easily adapted to the new cold-start users/items. To further reduce the impact from the cold-start neighbors, we incorporate a self-attention-based meta aggregator to enhance the aggregation ability of each graph convolution step, and an adaptive neighbor sampler to select the effective neighbors according to the feedbacks from the pre-training GNN model. Experiments on three public recommendation datasets show the superiority of our pre-training GNN model against the original GNN models on user/item embedding inference and the recommendation task.

cs.IR↗

arXiv2020

Significant enhancement of magnetic shielding effect by using the composite metamaterial composed of mu-near-zero media and ferrite

The magnetic shield plays an important role in magnetic near-field control. However, the requirements of efficient, ultrathin, lightweight and cheap are still the challenges. Here, we firstly propose a composite metamaterial in which the mu-near-zero media is covered with a ferrite slab. We verify that this structure can enhance the shielding effectiveness in a small area. Furthermore, we optimize the magnetic path by changing the bulk ferrite slab into a patterned slab. In this way, significant shielding effectiveness enhancement can be achieved in a large area. Experimental results show that the maximum shielding effectiveness (SE) of the composite metamaterial with a patterned ferrite is 20.56 dB, which is nearly 19 dB higher than that of a single ferrite slab with the same thickness of the composite metamaterial. The results on the composite metamaterial would be very useful in the applications involving magnetic shielding.

physics.app-ph↗

arXiv2020

Towards the understanding of fully-heavy tetraquark states from various models

We use a color-magnetic interaction model (CMIM), a traditional constituent quark model (CQM) and a multiquark color flux-tube model (MCFTM) to systematically investigate the properties of the states $[Q_1Q_2][\bar{Q}_3\bar{Q}_4]$ ($Q=c,b$). The dynamical investigation indicates that the CMIM can not completely absorb QCD dynamical effects through the effective constituent quark mass and overestimates the color-magnetic interaction in the states under the assumption of the same spatial configurations. The Coulomb interaction plays a critical role in the dynamical model calculations on the heavy hadrons, which induces the fact that none of bound states $[Q_1Q_2][\bar{Q}_3\bar{Q}_4]$ can be found in the dynamical models. The color configuration $\left[[Q_1Q_2]_{\mathbf{6}_c}[\bar{Q}_3\bar{Q}_4]_{\bar{\mathbf{6}}_c}\right]_{\mathbf{1}}$ should be taken seriously in the ground states due to the strong Coulomb attraction between the $[Q_1Q_2]_{\mathbf{6}_c}$ and $[\bar{Q}_3\bar{Q}_4]_{\bar{\mathbf{6}}_c}$. The color configuration $\left[[Q_1Q_2]_{\bar{\mathbf{3}}_c}[\bar{Q}_2\bar{Q}_4]_{\mathbf{3}_c}\right]_{\mathbf{1}}$ is absolutely dominant in the excited states because of the strong Coulomb attraction within the $[Q_1Q_2]_{\bar{\mathbf{3}}_c}$ and $[\bar{Q}_2\bar{Q}_4]_{\mathbf{3}_c}$. The $J/Ψ$-pair resonances recently observed by LHCb are difficult to be accommodated in the CMIM. The broad structure ranging from 6.2 to 6.8 GeV can be described as the ground tetraquark state $[cc][\bar{c}\bar{c}]$ in the various dynamical models. The narrow structure $X(6900)$ can be identified as the excited state $[cc][\bar{c}\bar{c}]$ with $L=1$ ($L=2$) in the CQM (MCFTM).

hep-ph↗

arXiv2020

CONNA: Addressing Name Disambiguation on The Fly

Name disambiguation is a key and also a very tough problem in many online systems such as social search and academic search. Despite considerable research, a critical issue that has not been systematically studied is disambiguation on the fly -- to complete the disambiguation in the real-time. This is very challenging, as the disambiguation algorithm must be accurate, efficient, and error tolerance. In this paper, we propose a novel framework -- CONNA -- to train a matching component and a decision component jointly via reinforcement learning. The matching component is responsible for finding the top matched candidate for the given paper, and the decision component is responsible for deciding on assigning the top matched person or creating a new person. The two components are intertwined and can be bootstrapped via jointly training. Empirically, we evaluate CONNA on two name disambiguation datasets. Experimental results show that the proposed framework can achieve a 1.21%-19.84% improvement on F1-score using joint training of the matching and the decision components. The proposed CONNA has been successfully deployed on AMiner -- a large online academic search system.

cs.SI↗

arXiv2020

Synthetic high-order PT symmetry in a single coil resonator

The exploration of non-Hermitian systems with parity-time (PT) symmetry has witnessed immense research interest both fundamentally and technologically in a wide range of subject areas in physics and engineering. One significant example of the principal emerging fields in this context is the PT symmetric wireless applications using multiple coils that are spatially separated but mutually coupled with position-dependent coupling strength. Such a spatial PT configuration limits the flexibility and miniaturization of the PT symmetric designs. As far as this is concerned, inspired by scattering induced two opposite whispering-gallery (WG) modes in an optical resonator, analogously here we experimentally demonstrate a specially constructed second-order (2-nd order) PT symmetry in a single coil resonator, whose currents with two different directions are excited by internal bypass capacitor. Our proposed structure has the following peculiar feature: First, the bypass capacitor induces coupling in spectral resonances allow us to observe a 2-nd order phase transition between symmetry regimes, without the need of a second coil in the spatial PT case. Under this circumstance, this specially constructed PT symmetry can be regarded as synthetic PT symmetry, which is enabled by coupling modes with different directions. Second, by introducing two or more internal bypass capacitors, the synthetic high-order PT symmetric system bearing such as third-order exceptional point in a single coil resonator can be realized. These results will provide a new paradigm to realize higher-order PT symmetry towards the investigation of non-Hermitian physics in a synthetic perspective, which can be extended to other physical platforms such as optics and acoustics.

physics.app-ph↗

arXiv2020

Miniaturized backward coupler realized by the circuit-based magnetic hyperbolic waveguide

Planar waveguides can limit the transmission of electromagnetic waves in a specific direction and have a wide range of applications in filters, sensors, and energy-transfer devices. However, given the increasing demand for planar integrated photonics, new waveguides are required with excellent characteristics such as more functionality, greater efficiency, and smaller size. In this work, we report the experimental results for a subwavelength planar microwave-regime waveguide fabricated from circuit-based magnetic hyperbolic metamaterial (HMM) and develop the associated theory. HMM is a special type of anisotropic metamaterial whose isofrequency contour (IFC) takes the form of an open hyperboloid. Because of the open-dispersion IFCs, HMMs support propagating high-k modes with large effective refractive indices, which allows planar hyperbolic waveguides to be miniaturized. In particular, as opposed to the traditional dielectric slab waveguide, the group velocity of the hyperbolic guided modes is negative-a characteristic that can be exploited to realize the backward propagation of electromagnetic waves. Furthermore, we study the abnormal photonic spin Hall effect and experimentally demonstrate an ultracompact backward coupler based on hyperbolic waveguides. The experimental results are consistent with numerical simulations. This work not only reveals the significant potential of circuit-based platforms for the experimental study of the propagation and coupling of guided modes but also promotes the use of HMMs in the microwave regime for numerous integrated functional devices.

physics.app-ph↗

arXiv2020

One-dimensional topological quasiperiodic chain for directional wireless power transfer

As an important class of systems with unique topological effects beyond the periodic lattices, quasiperiodic topological structures have attracted much attention in recent years. Due to the quasiperiodic modulation, the topological states in the quasiperiodic topological structures have the characteristics of self-similarity, which can be used to observe the charming Hofstadter butterfly. In addition, because of the asymmetric distribution, the edge states in quasiperiodic chain can be used to realize the adiabatic pumping. When the topological parameters in quasiperiodic topological lattices are considered as synthetic dimensions, they can also be used to study the topological properties with higher dimensions. Here, by using ultra-subwavelength resonators, we design and fabricate a type of one-dimensional quasiperiodic Harper chain with asymmetric topological edge states for the directional wireless power transfer (WPT). By further introducing a power source into the system, we selectively light up two Chinese characters which is composed of LED lamps at both ends of the chain. Moreover, the directional WPT implemented by the topological quasiperiodic chain has the property of topological protection, which is immune to the internal disorder perturbation of the structure. Not only do we apply the asymmetric edge state for directional WPT, but also may further actively control the directional WPT by using the external voltage. In addition, this work provides a flexible platform for designing new WPT devices, such as using the corner states in high-order topological structures or the skin effect in the non-Hermitian topological lattices.

physics.app-ph↗

arXiv2020

Abnormal wave propagation of high-k modes in tilted linear-crossing metamaterials

In this work, we treat the rotation of the optical axis as a new degree of freedom and theoretically propose a tilted linear-crossing metamaterials (TLCMM). Specifically, the conical dispersions of the NLCMM and TLCMM have the same shapes as type-I and type-II Dirac cones, respectively, in condensed matter physics. Upon rotating the optical axis angle such that it is equal to the cone angle, we find that this special TLCMM has the shape of a type-III Dirac cone. This critical TLCMM can have many unique properties and is of a fundamentally different nature than neighboring phases. When EM waves with large wave vectors are incident to a metamaterial with an open IFC to free space, the incident EM wave is strongly reflected due to wave-vector mismatch. Here, we use boundary conditions and the causality law to reveal that TLCMM high-k modes can achieve abnormal refraction without reflection and filtering. Moreover, these phenomenon are observed experimentally in a planar circuit-based system. The circuit-based TLCMM not only provides a versatile platform for the study of robust negative refraction phenomena in metamaterials, but also has a planar structure that is easier to integrate. Our results regarding the manipulation of EM waves may enable their use in planar-integrated photonics including for directional propagation, cloaking, and switching.

physics.optics↗

arXiv2020

Sensitivity of topological edge states in a non-Hermitian dimer chain

Photonic topological edge states in one-dimensional dimer chains have long been thought to be robust to structural perturbations by mapping the topological Su-Schrieffer-Heeger model of a solid-state system. However, the edge states at the two ends of a finite topological dimer chain will interact as a result of near-field coupling. This leads to deviation from topological protection by the chiral symmetry from the exact zero energy, weakening the robustness of the topological edge state. With the aid of non-Hermitian physics, the splitting frequencies of edge states can be degenerated again and topological protection recovered by altering the gain or loss strength of the structure. This point of coalescence is known as the exceptional point (EP). The intriguing physical properties of EPs in topological structures give rise to many fascinating and counterintuitive phenomena. In this work, based on a finite non-Hermitian dimer chain composed of ultra-subwavelength resonators, we propose theoretically and verify experimentally that the sensitivity of topological edge states is greatly affected when the system passes through the EP. Using the EP of a non-Hermitian dimer chain, we realize a new sensor that is sensitive to perturbation at the end of the structure and yet topologically protected from internal perturbation. Our demonstration of a non-Hermitian topological structure with an EP paves the way for the development of novel sensors that are not sensitive to internal manufacturing errors but are highly sensitive to changes in the external environment.

physics.app-ph↗

arXiv2020

Nonlinear nanophotonic devices in the Ultraviolet to Visible wavelength range

Although the first lasers invented operated in the visible, the first on-chip devices were optimized for near-infrared (IR) performance driven by demand in telecommunications. However, as the applications of integrated photonics has broadened, the wavelength demand has as well, and we are now returning to the visible (Vis) and pushing into the ultraviolet (UV). This shift has required innovations in device design and in materials as well as leveraging nonlinear behavior to reach these wavelengths. This review discusses the key nonlinear phenomena that can be used as well as presents several emerging material systems and devices that have reached the UV-Vis wavelength range.

physics.optics↗

arXiv2020

Experimental demonstration of an anomalous Floquet topological insulator based on negative-index media

Photonic and acoustic topological insulators exhibiting one-way transportation that is robust against defects and impurities are typically realized in coupled arrays of two-dimensional ring resonators. These systems have produced a series of applications, including optical isolators, delay lines, and lasers. However, the structures are complicated because an additional coupler ring between neighboring rings is needed to construct photonic pseudo-spin. In this work, a photonic analogue of the quantum spin Hall effect is proposed and experimentally demonstrated in an anomalous Floquet topological insulator in the microwave regime. This improved design takes advantage of the efficient and backward coupling of negative-index media. The results contribute to the understanding of topological structures in metamaterials and point toward a new direction for constructing useful topological photonic devices.

physics.app-ph↗

arXiv2020

Experimental Realization of Near-Field Photonic Routing with All-Electric Metasources

The spatially confined evanescent microwave photonics have been proved to be highly desirable in broad practical scenarios ranging from robust information communications to efficient quantum interactions. However, the feasible applications of these photonics modes are limited due to the lack of fundamental understandings and feasible directional coupling approaches at sub-wavelengths. Here, we experimentally demonstrate the efficient near-field photonic routing achieved in waveguides composed of two kinds of single-negative metamaterials. Without mimicking the polarization features, we propose all-electric near-field metasource in subwavelength scale and exemplify its near-field functions like Janus, Huygens and spin sources, corresponding to time-reversal, parity-time and parity symmetries of its inner degree of freedom. Our work furthers the understandings about optical near-field symmetry and feasible engineering approaches of directional couplings, which would pave the way for promising integrated mircrowave photonics devices.

physics.optics↗

arXiv2020

FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection

As massive data are produced from small gadgets, federated learning on mobile devices has become an emerging trend. In the federated setting, Stochastic Gradient Descent (SGD) has been widely used in federated learning for various machine learning models. To prevent privacy leakages from gradients that are calculated on users' sensitive data, local differential privacy (LDP) has been considered as a privacy guarantee in federated SGD recently. However, the existing solutions have a dimension dependency problem: the injected noise is substantially proportional to the dimension $d$. In this work, we propose a two-stage framework FedSel for federated SGD under LDP to relieve this problem. Our key idea is that not all dimensions are equally important so that we privately select Top-k dimensions according to their contributions in each iteration of federated SGD. Specifically, we propose three private dimension selection mechanisms and adapt the gradient accumulation technique to stabilize the learning process with noisy updates. We also theoretically analyze privacy, accuracy and time complexity of FedSel, which outperforms the state-of-the-art solutions. Experiments on real-world and synthetic datasets verify the effectiveness and efficiency of our framework.

cs.LG↗

arXiv2020

JarKA: Modeling Attribute Interactions for Cross-lingual Knowledge Alignment

Abstract. Cross-lingual knowledge alignment is the cornerstone in building a comprehensive knowledge graph (KG), which can benefit various knowledge-driven applications. As the structures of KGs are usually sparse, attributes of entities may play an important role in aligning the entities. However, the heterogeneity of the attributes across KGs prevents from accurately embedding and comparing entities. To deal with the issue, we propose to model the interactions between attributes, instead of globally embedding an entity with all the attributes. We further propose a joint framework to merge the alignments inferred from the attributes and the structures. Experimental results show that the proposed model outperforms the state-of-art baselines by up to 38.48% HitRatio@1. The results also demonstrate that our model can infer the alignments between attributes, relationships and values, in addition to entities.

cs.CL↗

arXiv2020

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.

cond-mat.dis-nn↗

arXiv2019

Can the state $Y(4626)$ be a $P$-wave tetraquark state $[cs][\bar{c}\bar{s}]$?

Stimulated by the state $Y(4626)$ recently reported by Belle Collaboration, we utilize a multiquark color flux-tube model with a multibody confinement potential and one-glue-exchange interaction to make an exhaustive investigation on the diquark-antidiquark state $[cs][\bar{c}\bar{s}]$. Numerical results indicate that the appearance of the states $[cs][\bar{c}\bar{s}]$ like a dumb-bell, the larger the orbital excitation $L$, the more distinguished the shape. The mixing of the color configurations $\left[[cs]_{\bar{\mathbf{3}}_c}[\bar{c}\bar{s}]_{\mathbf{3}_c}\right]_{\mathbf{1}}$ and $\left[[cs]_{\mathbf{6}_c}[\bar{c}\bar{s}]_{\bar{\mathbf{6}}_c}\right]_{\mathbf{1}}$ in the ground states is strong while the color configuration $\left[[cs]_{\bar{\mathbf{3}}_c}[\bar{c}\bar{s}]_{\mathbf{3}_c}\right]_{\mathbf{1}}$ is absolutely predominant in the excited states. The main component of the state $Y(4626)$ can be interpreted as a $P$-wave state $[cs][\bar{c}\bar{s}]$. Its hidden-bottom partner is predicted in the model calculation. The states $X(4140)$, $X(4274)$, $X(4350)$, $X(4500)$ and $X(4700)$ are also discussed.

hep-ph↗

arXiv2019

Cascaded LSTMs based Deep Reinforcement Learning for Goal-driven Dialogue

This paper proposes a deep neural network model for joint modeling Natural Language Understanding (NLU) and Dialogue Management (DM) in goal-driven dialogue systems. There are three parts in this model. A Long Short-Term Memory (LSTM) at the bottom of the network encodes utterances in each dialogue turn into a turn embedding. Dialogue embeddings are learned by a LSTM at the middle of the network, and updated by the feeding of all turn embeddings. The top part is a forward Deep Neural Network which converts dialogue embeddings into the Q-values of different dialogue actions. The cascaded LSTMs based reinforcement learning network is jointly optimized by making use of the rewards received at each dialogue turn as the only supervision information. There is no explicit NLU and dialogue states in the network. Experimental results show that our model outperforms both traditional Markov Decision Process (MDP) model and single LSTM with Deep Q-Network on meeting room booking tasks. Visualization of dialogue embeddings illustrates that the model can learn the representation of dialogue states.

cs.CL↗