arXiv subjects

Ming Xie

Publications and source records attributed to Ming Xie.

At least 37 records · Page 2Linked to original sources

arXiv2023

Nematic excitonic insulator in transition metal dichalcogenide moiré heterobilayers

We study the effect of inter-electron Coulomb interactions on the displacement field induced topological phase transition in transition metal dichalcogenide (TMD) moiré heterobilayers. We find a nematic excitonic insulator (NEI) phase that breaks the moiré superlattice's three-fold rotational symmetry and preempts the topological phase transition in both AA and AB stacked heterobilayers when the interlayer tunneling is weak, or when the Coulomb interaction is not strongly screened. The nematicity originates from the frustration between the nontrivial spatial structure of the interlayer tunneling, which is crucial to the existence of the topological Chern band, and the interlayer coherence induced by the Coulomb interaction that favors uniformity in layer pseudo-spin orientations. We construct a unified effective two-band model that captures the physics near the band inversion and applies to both AA and AB stacked heterobilayers. Within the two-band model, the competition between the NEI phase and the Chern insulator phase can be understood as the switching of the energetic order between the $s$-wave and the $p$-wave excitons upon increasing the interlayer tunneling.

cond-mat.str-el↗

arXiv2023

A universal meta-heuristic framework for influence maximization in hypergraphs

Influence maximization (IM) aims to select a small number of nodes that are able to maximize their influence in a network and covers a wide range of applications. Despite numerous attempts to provide effective solutions in ordinary networks, higher-order interactions between entities in various real-world systems are not usually taken into account. In this paper, we propose a versatile meta-heuristic approach, hyper genetic algorithm (HGA), to tackle the IM problem in hypergraphs, which is based on the concept of genetic evolution. Systematic validations in synthetic and empirical hypergraphs under both simple and complex contagion models indicate that HGA achieves universal and plausible performance compared to baseline methods. We explore the cause of the excellent performance of HGA through ablation studies and correlation analysis. The findings show that the solution of HGA is distinct from that of other prior methods. Moreover, a closer look at the local topological features of the seed nodes acquired by different algorithms reveals that the selection of seed nodes cannot be based on a single topological characteristic, but should involve a combination of multiple topological features to address the IM problem.

physics.soc-ph↗

arXiv2023

Magic-Angle Twisted Symmetric Trilayer Graphene as Topological Heavy Fermion Problem

Recently, Ref. [1] reformulated magic-angle twisted bilayer graphene (MATBG) as a topological heavy fermion problem, and used this reformulation to provide a deeper understanding for the correlated phases at integer fillings. In this work, we generalize this heavy-fermion paradigm to magic-angle twisted symmetric trilayer graphene (MATSTG), and propose a low-energy $f-c-d$ model that reformulates MATSTG as heavy localized $f$ modes coupled to itinerant topological semimetalic $c$ modes and itinerant Dirac $d$ modes. Our $f-c-d$ model well reproduces the single-particle band structure of MATSTG at low energies for displacement field $\mathcal{E}\in[0,300]$meV. By performing Hartree-Fock calculations with the $f-c-d$ model for $ν=0,-1,-2$ electrons per Moiré unit cell, we reproduce all the correlated ground states obtain from the previous numerical Hartree-Fock calculations with the Bistritzer-MacDonald-type (BM-type) model, and we find additional new correlated ground states at high displacement field. Based on the numerical results, we propose a simple rule for the ground states at high displacement fields by using the $f-c-d$ model, and provide analytical derivation for the rule at charge neutrality. We also provide analytical symmetry arguments for the (nearly-)degenerate energies of the high-$\mathcal{E}$ ground states at all the integer fillings of interest, and make experimental predictions of which charge-neutral states are stabilized in magnetic fields. Our $f-c-d$ model provides a new perspective for understanding the correlated phenomena in MATSTG, suggesting that the heavy fermion paradigm of Ref. [1] should be the generic underpinning of correlated physics in multilayer moire graphene structures.

cond-mat.mes-hall↗

arXiv2023

Flavor symmetry breaking in spin-orbit coupled bilayer graphene

Recent experimental discovery of flavor symmetry breaking metallic phases in Bernal-stacked bilayer graphene points to the strongly interacting nature of electrons near the top (bottom) of its valence (conduction) band. Superconductivity was also observed in between these symmetry breaking phases when the graphene bilayer is placed under a small in-plane magnetic field or in close proximity to a monolayer WSe$_2$ substrate. Here we address the correlated nature of the band edge electrons and obtain the quantum phase diagram of their many-body ground states incorporating the effect of proximity induced spin-orbit coupling. We find that in addition to the spin/valley flavor polarized half and quarter metallic states, two types of intervalley coherent phases emerge near the phase boundaries between the flavor polarized metals. Both spin-orbit coupling and in-plane magnetic field disfavor the spin-unpolarized valley coherent phase. Our findings suggest possible competition between intervalley coherence and superconducting orders, arising from the intriguing correlation effects in bilayer graphene in the presence of spin-orbit coupling.

cond-mat.str-el↗

arXiv2023

Giant optical nonlinearity of Fermi polarons in atomically thin semiconductors

Realizing strong nonlinear optical responses is a long-standing goal of both fundamental and technological importance. Recently significant efforts have focused on exploring excitons in solids as a pathway to achieving nonlinearities even down to few-photon levels. However, a crucial tradeoff arises as strong light-matter interactions require large oscillator strength and short radiative lifetime of the excitons, which limits their interaction strength and nonlinearity. Here we experimentally demonstrate strong nonlinear optical responses by exploiting the coupling between excitons and carriers in an atomically thin semiconductor of trilayer tungsten diselenide. By controlling the electric field and electrostatic doping of the trilayer, we observe the hybridization between intralayer and interlayer excitons along with the formation of Fermi polarons due to the interactions between excitons and free carriers. We find substantial optical nonlinearity can be achieved under both continuous wave and pulsed laser excitation, where the resonance of the hole-doped Fermi polaron blueshifts by as much as ~10 meV. Intriguingly, we observe a remarkable asymmetry in the optical nonlinearity between electron and hole doping, which is tunable by the applied electric field. We attribute these features to the strong interactions between excitons and free charges with optically induced valley polarization. Our results establish that atomically thin heterostructures are a highly versatile platform for engineering nonlinear optical response with applications to classical and quantum optoelectronics, and open avenues for exploring many-body physics in hybrid Fermionic-Bosonic systems.

cond-mat.mes-hall↗

arXiv2023

Align, Perturb and Decouple: Toward Better Leverage of Difference Information for RSI Change Detection

Change detection is a widely adopted technique in remote sense imagery (RSI) analysis in the discovery of long-term geomorphic evolution. To highlight the areas of semantic changes, previous effort mostly pays attention to learning representative feature descriptors of a single image, while the difference information is either modeled with simple difference operations or implicitly embedded via feature interactions. Nevertheless, such difference modeling can be noisy since it suffers from non-semantic changes and lacks explicit guidance from image content or context. In this paper, we revisit the importance of feature difference for change detection in RSI, and propose a series of operations to fully exploit the difference information: Alignment, Perturbation and Decoupling (APD). Firstly, alignment leverages contextual similarity to compensate for the non-semantic difference in feature space. Next, a difference module trained with semantic-wise perturbation is adopted to learn more generalized change estimators, which reversely bootstraps feature extraction and prediction. Finally, a decoupled dual-decoder structure is designed to predict semantic changes in both content-aware and content-agnostic manners. Extensive experiments are conducted on benchmarks of LEVIR-CD, WHU-CD and DSIFN-CD, demonstrating our proposed operations bring significant improvement and achieve competitive results under similar comparative conditions. Code is available at https://github.com/wangsp1999/CD-Research/tree/main/openAPD

cs.CV↗

arXiv2023

AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Enhancing Autonomous Driving Research and Education

Prototyping and validating hardware-software components, sub-systems and systems within the intelligent transportation system-of-systems framework requires a modular yet flexible and open-access ecosystem. This work presents our attempt towards developing such a comprehensive research and education ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and deploying cyber-physical solutions pertaining to autonomous driving as well as smart city management. AutoDRIVE features both software as well as hardware-in-the-loop testing interfaces with openly accessible scaled vehicle and infrastructure components. The ecosystem is compatible with a variety of development frameworks, and supports both single and multi-agent paradigms through local as well as distributed computing. Most critically, AutoDRIVE is intended to be modularly expandable to explore emergent technologies, and this work highlights various complementary features and capabilities of the proposed ecosystem by demonstrating four such deployment use-cases: (i) autonomous parking using probabilistic robotics approach for mapping, localization, path planning and control; (ii) behavioral cloning using computer vision and deep imitation learning; (iii) intersection traversal using vehicle-to-vehicle communication and deep reinforcement learning; and (iv) smart city management using vehicle-to-infrastructure communication and internet-of-things.

cs.RO↗

arXiv2023

Multi-Center Federated Learning: Clients Clustering for Better Personalization

Federated learning has received great attention for its capability to train a large-scale model in a decentralized manner without needing to access user data directly. It helps protect the users' private data from centralized collecting. Unlike distributed machine learning, federated learning aims to tackle non-IID data from heterogeneous sources in various real-world applications, such as those on smartphones. Existing federated learning approaches usually adopt a single global model to capture the shared knowledge of all users by aggregating their gradients, regardless of the discrepancy between their data distributions. However, due to the diverse nature of user behaviors, assigning users' gradients to different global models (i.e., centers) can better capture the heterogeneity of data distributions across users. Our paper proposes a novel multi-center aggregation mechanism for federated learning, which learns multiple global models from the non-IID user data and simultaneously derives the optimal matching between users and centers. We formulate the problem as a joint optimization that can be efficiently solved by a stochastic expectation maximization (EM) algorithm. Our experimental results on benchmark datasets show that our method outperforms several popular federated learning methods.

cs.LG↗

arXiv2022

Topological Phases in AB-Stacked MoTe$_2$/WSe$_2$: $\mathbb{Z}_2$ Topological Insulators, Chern Insulators, and Topological Charge Density Waves

We present a theory on the quantum phase diagram of AB-stacked MoTe$_2$/WSe$_2$ using a self-consistent Hartree-Fock calculation performed in the plane-wave basis, motivated by the observation of topological states in this system. At filling factor $ν=2$ (two holes per moiré unit cell), Coulomb interaction can stabilize a $\mathbb{Z}_2$ topological insulator by opening a charge gap. At $ν=1$, the interaction induces three classes of competing states, spin density wave states, an in-plane ferromagnetic state, and a valley polarized state, which undergo first-order phase transitions tuned by an out-of-plane displacement field. The valley polarized state becomes a Chern insulator for certain displacement fields. Moreover, we predict a topological charge density wave forming a honeycomb lattice with ferromagnetism at $ν=2/3$. Future directions on this versatile system hosting a rich set of quantum phases are discussed.

cond-mat.str-el↗

arXiv2022

Influence Maximization in Hypergraphs

Influence maximization in complex networks, i.e., maximizing the size of influenced nodes via selecting K seed nodes for a given spreading process, has attracted great attention in recent years. However, the influence maximization problem in hypergraphs, in which the hyperedges are leveraged to represent the interactions among more than two nodes, is still an open question. In this paper, we propose an adaptive degree-based heuristic algorithm, i.e., Heuristic Degree Discount (HDD), which iteratively selects nodes with low influence overlap as seeds, to solve the influence maximization problem in hypergraphs. We further extend algorithms from ordinary networks as baselines and compare the performance of the proposed algorithm and baselines on both real data and synthetic hypergraphs. Results show that HDD outperforms the baselines in terms of both effectiveness and efficiency. Moreover, the experiments on synthetic hypergraphs indicate that HDD shows high performance, especially in hypergraphs with heterogeneous degree distribution.

cs.SI↗

arXiv2022

Spontaneous emission modulation in biaxial hyperbolic van der Waals material

As a natural van der Waals crystal, α-MoO3 has excellent in-plane hyperbolic properties and essential nanophotonics applications. However, its actively tunable properties are generally neglected. In this work, we achieved active modulation of spontaneous emission from a single-layer flat plate using the rotation method for the first time. Numerical results and theoretical analysis show that α-MoO3 exhibits good tunability when rotated in the y-z or x-y plane. A modulation factor of more than three orders of magnitude can be obtained at 634 cm-1. However, when the rotation is in the x-z plane, the spontaneous emission of the material exhibits strong angle independence. The theoretical formulation and the physical mechanism analysis explain the above phenomenon well. In addition, for the semi-infinite α-MoO3 flat structure, we give the distribution of the modulation factor of spontaneous emission with wavenumber and rotation angle. Finally, we extended the calculation results from semi-infinite media to finite thickness films. We obtained the general evolution law of the peak angle of the modulation factor with thickness, increasing the modulation factor to about 2000. We believe that the results of this paper can guide the active modulation of spontaneous emission based on anisotropic materials.

physics.optics↗

arXiv2022

Learning Distinctive Margin toward Active Domain Adaptation

Despite plenty of efforts focusing on improving the domain adaptation ability (DA) under unsupervised or few-shot semi-supervised settings, recently the solution of active learning started to attract more attention due to its suitability in transferring model in a more practical way with limited annotation resource on target data. Nevertheless, most active learning methods are not inherently designed to handle domain gap between data distribution, on the other hand, some active domain adaptation methods (ADA) usually requires complicated query functions, which is vulnerable to overfitting. In this work, we propose a concise but effective ADA method called Select-by-Distinctive-Margin (SDM), which consists of a maximum margin loss and a margin sampling algorithm for data selection. We provide theoretical analysis to show that SDM works like a Support Vector Machine, storing hard examples around decision boundaries and exploiting them to find informative and transferable data. In addition, we propose two variants of our method, one is designed to adaptively adjust the gradient from margin loss, the other boosts the selectivity of margin sampling by taking the gradient direction into account. We benchmark SDM with standard active learning setting, demonstrating our algorithm achieves competitive results with good data scalability. Code is available at https://github.com/TencentYoutuResearch/ActiveLearning-SDM

cs.LG↗

arXiv2022

Euler Obstructed Cooper Pairing in Twisted Bilayer Graphene: Nematic Nodal Superconductivity and Bounded Superfluid Weight

Magic-angle twisted bilayer graphene (MATBG) hosts normal-state nearly-flat bands with nonzero Euler numbers and shows superconductivity. In this work, we study the effects of the nontrivial normal-state band topology on the intervalley $C_{2z}\mathcal{T}$-invariant mean-field Cooper pairing order parameter in MATBG. We show that the pairing order parameter can always be split into a trivial channel and an Euler obstructed channel in all gauges for the normal-state basis, generalizing the previously-studied channel splitting in the Chern gauge. The nonzero normal-state Euler numbers require the pairing gap function of the Euler obstructed channel to have zeros, while the trivial channel can have a nonvanishing pairing gap function. When the pairing is spontaneously nematic, we find that a sufficiently-dominant Euler obstructed channel with two zeros typically leads to nodal superconductivity. Under the approximation of exactly-flat bands, we find that the mean-field zero-temperature superfluid weight is generally bounded from below, no matter whether the Euler obstructed channel is dominant or not, generalizing the previously-derived bound for the uniform s-wave pairing. We numerically verify these statements for pairings derived from a local attractive interaction. Our work suggests that Euler obstructed Cooper pairing may play an essential role in the superconducting MATBG.

cond-mat.supr-con↗

arXiv2021

Weak-field Hall Resistivity and Spin/Valley Flavor Symmetry Breaking in Magic-Angle Twisted Bilayer Graphene

Near a magic twist angle, the lowest energy conduction and valence bands of bilayer graphene moiré superlattices become extremely narrow. The band dispersion that remains is sensitive to the moiré's strain pattern, nonlocal tunneling between layers, and filling-factor dependent Hartree and exchange band renormalizations. In this article we analyze the influence of these band-structure details on the pattern of flavor-symmetry-breaking observed in this narrow band system, and on the associated pattern of Fermi surface reconstructions revealed by weak-field-Hall and Schubnikov-de Haas magneto-transport measurements.

cond-mat.mes-hall↗

arXiv2021

Exact Diagonalization for Magic-Angle Twisted Bilayer Graphene

We report on finite-size exact-diagonalization calculations in a Hilbert space defined by the continuum-model flat moiré bands of magic angle twisted bilayer graphene (MATBG). For moiré band filling $3>|ν|>2$, where superconductivity is strongest, we obtain evidence that the ground state is a spin ferromagnet. Near $|ν|=3$, we find Chern insulator ground states that have spontaneous spin, valley, and sublattice polarization, and demonstrate that the anisotropy energy in this order-parameter space is strongly band-filling-factor dependent. We emphasize that inclusion of the remote band self-energy is necessary for a reliable description of MATBG flat band correlations.

cond-mat.str-el↗

arXiv2021

AutoDRIVE Simulator: A Simulator for Scaled Autonomous Vehicle Research and Education

AutoDRIVE is envisioned to be an integrated research and education platform for scaled autonomous vehicles and related applications. This work is a stepping-stone towards achieving the greater goal of realizing such a platform. Particularly, this work introduces the AutoDRIVE Simulator, a high-fidelity simulator for scaled autonomous vehicles. The proposed simulation ecosystem is developed atop the Unity game engine, and exploits its features in order to simulate realistic system dynamics and render photorealistic graphics. It comprises of a scaled vehicle model equipped with a comprehensive sensor suite for redundant perception, a set of actuators for constrained motion control and a fully functional lighting system for illumination and signaling. It also provides a modular environment development kit, which comprises of various environment modules that aid in reconfigurable construction of the scene. Additionally, the simulator features a communication bridge in order to extend an interface to the autonomous driving software stack developed independently by the users. This work describes some of the prominent components of this simulation system along with some key features that it has to offer in order to accelerate education and research aimed at autonomous driving.

cs.RO↗

arXiv2021

Lattice Collective Modes from a Continuum Model of Magic-Angle Twisted Bilayer Graphene

We show that the insulating states of magic-angle twisted bilayer graphene support a series of collective modes corresponding to local particle-hole excitations on triangular lattice sites. Our theory is based on a continuum model of the magic angle flat bands. When the system is insulating at moiré band filling $ν=-3$, our calculations show that the ground state supports seven low-energy modes that lie well below the charge gap throughout the moiré Brillouin zone, one of which couples strongly to THz photons. The low-energy collective modes are faithfully described by a model with a local $SU(8)$ degree of freedom in each moiré unit cell that we identify as the direct product of spin, valley, and an orbital pseudospin. Apart from spin and valley-wave modes, the collective mode spectrum includes a low-energy intra-flavor exciton mode associated with transitions between flat valence and conduction band orbitals.

cond-mat.str-el↗

arXiv2021

A Two-Kind-Boson Mixture Honeycomb Hamiltonian of Bloch Exciton-Polaritons

The electronic bandstructure of a solid is a collection of allowed bands separated by forbidden bands, revealing the geometric symmetry of the crystal structures. Comprehensive knowledge of the bandstructure with band parameters explains intrinsic physical, chemical and mechanical properties of the solid. Here we report the artificial polaritonic bandstructures of two-dimensional honeycomb lattices for microcavity exciton-polaritons using GaAs semiconductors in the wide-range detuning values, from cavity-photon-like (red-detuned) to exciton-like (blue-detuned) regimes. In order to understand the experimental bandstructures and their band parameters, such as gap energies, bandwidths, hopping integrals and density of states, we originally establish a polariton band theory within an augmented plane wave method with two-kind-bosons, cavity photons trapped at the lattice sites and freely moving excitons. In particular, this two-kind-band theory is absolutely essential to elucidate the exciton effect in the bandstructures of blue-detuned exciton-polaritons, where the flattened exciton-like dispersion appears at larger in-plane momentum values captured in our experimental access window. We reach an excellent agreement between theory and experiments in all detuning values.

cond-mat.mes-hall↗