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Gang Wang

Publications and source records attributed to Gang Wang.

At least 217 records · Page 12Linked to original sources
arXiv2023

Insulator-to-metal Mott transition facilitated by lattice deformation in monolayer $α$-RuCl$_3$ on graphite

Creating heterostructures with graphene/graphite is a practical method for charge-doping $α$-RuCl$_3$, but not sufficient to cause the insulator-to-metal transition. In this study, detailed scanning tunneling microscopy/spectroscopy measurements on $α$-RuCl$_3$ with various lattice deformations reveal that both in-plane and out-of-plane lattice distortions may collapse the Mott-gap in the case of monolayer $α$-RuCl$_3$ in proximity to graphite, but have little impact on its bulk form alone. In the Mott-Hubbard framework, the transition is attributed to the lattice distortion-facilitated substantial modulation of the electron correlation parameter. Observation of the orbital textures on a highly compressed monolayer $α$-RuCl$_3$ flake on graphite provides valuable evidence that electrons are efficiently transferred from the heterointerface into Cl3$p$ orbitals under the lattice distortion. It is believed that the splitting of Ru $t_{2g}$ bands within the trigonal distortion of Ru-Cl-Ru octahedra bonds generated the electrons transfer pathways. The increase of the Cl3$p$ states enhance the hopping integral in the Mott-Hubbard bands, resulting in the Mott-transition. These findings suggest a new route for implementing the insulator-to-metal transition upon doping in $α$-RuCl$_3$ by deforming the lattice in addition to the formation of heterostructure.

cond-mat.str-el↗
arXiv2023

On a Foundation Model for Operating Systems

This paper lays down the research agenda for a domain-specific foundation model for operating systems (OSes). Our case for a foundation model revolves around the observations that several OS components such as CPU, memory, and network subsystems are interrelated and that OS traces offer the ideal dataset for a foundation model to grasp the intricacies of diverse OS components and their behavior in varying environments and workloads. We discuss a wide range of possibilities that then arise, from employing foundation models as policy agents to utilizing them as generators and predictors to assist traditional OS control algorithms. Our hope is that this paper spurs further research into OS foundation models and creating the next generation of operating systems for the evolving computing landscape.

cs.OS↗
arXiv2023

Event Shape Selection Method in Search of the Chiral Magnetic Effect in Heavy-ion Collisions

The search for the chiral magnetic effect (CME) in heavy-ion collisions has been impeded by the significant background arising from the anisotropic particle emission pattern, particularly elliptic flow. To alleviate this background, the event shape selection (ESS) technique categorizes collision events according to their shapes and projects the CME observables to a class of events with minimal flow. In this study, we explore two event shape variables to classify events and two elliptic flow variables to regulate the background. Each type of variable can be calculated from either single particles or particle pairs, resulting in four combinations of event shape and elliptic flow variables. By employing a toy model and the realistic event generator, event-by-event anomalous-viscous fluid dynamics (EBE-AVFD), we discover that the elliptic flow of resonances exhibits correlations with both the background and the potential CME signal, making the resonance flow unsuitable for background control. Through the EBE-AVFD simulations of Au+Au collisions at $\sqrt{s_{NN}} = 200$ GeV with various input scenarios, we ascertain that the optimal ESS strategy for background control entails utilizing the single-particle elliptic flow in conjunction with the event shape variable based on particle pairs.

nucl-th↗
arXiv2023

Strain mediated phase crossover in Ruddlesden Popper nickelates

Recent progress on the signatures of pressure-induced high temperature superconductivity in Ruddlesden Popper (RP) nickelates (Lan+1NinO3n+1) has attracted growing interest in both theoretical calculations and experimental efforts. The fabrication of high-quality single crystalline RP nickelate thin films is critical for possible reducing the superconducting transition pressure and advancing applications in microelectronics in the future. In this study, we report the observations of an active phase transition in RP nickelate films induced by misfit strain. We found that RP nickelate films favor the perovskite structure (n = infinite) under tensile strains, while compressive strains stabilize the La3Ni2O7 (n = 2) phase. The selection of distinct phases is governed by the strain dependent formation energy and electronic configuration. In compressively strained La3Ni2O7, we experimentally determined splitting energy is ~0.2 eV and electrons prefer to occupy in-plane orbitals. First principles calculations unveil a robust coupling between strain effects and the valence state of Ni ions in RP nickelates, suggesting a dual driving force for the inevitable phase co-existence transition in RP nickelates. Our work underscores the sensitivity of RP nickelate formation to epitaxial strain, presenting a significant challenge in fabricating pure-phase RP nickelate films. Therefore, special attention to stacking defects and grain boundaries between different RP phases is essential when discussing the pressure-induced superconductivity in RP nickelates.

cond-mat.supr-con↗
arXiv2023

Robust Control of Unknown Switched Linear Systems from Noisy Data

This paper investigates the problem of data-driven stabilization for linear discrete-time switched systems with unknown switching dynamics. In the absence of noise, a data-based state feedback stabilizing controller can be obtained by solving a semi-definite program (SDP) on-the-fly, which automatically adapts to the changes of switching dynamics. However, when noise is present, the persistency of excitation condition based on the closed-loop data may be undermined, rendering the SDP infeasible. To address this issue, an auxiliary function-based switching control law is proposed, which only requires intermittent SDP solutions when its feasibility is guaranteed. By analyzing the relationship between the controller and the system switching times, it is shown that the proposed controller guarantees input-to-state practical stability (ISpS) of the closed-loop switched linear system, provided that the noise is bounded and the dynamics switches slowly enough. Two numerical examples are presented to verify the effectiveness of the proposed controller.

eess.SY↗
arXiv2023

Observation of high-temperature superconductivity in the high-pressure tetragonal phase of La2PrNi2O7-δ

The recent discovery of high-temperature superconductivity in the Ruddlesden-Popper phase La3Ni2O7 under high pressure marks a significant breakthrough in the field of 3d transition-metal oxide superconductors. For an emerging novel class of high-Tc superconductors, it is crucial to find more analogous superconducting materials with a dedicated effort toward broadening the scope of nickelate superconductors. Here, we report on the observation of high-Tc superconductivity in the high-pressure tetragonal I4/mmm phase of La2PrNi2O7 above ~10 GPa, which is distinct from the reported orthorhombic Fmmm phase of La3Ni2O7 above 14 GPa. For La2PrNi2O7, the onset and the zero-resistance temperatures of superconductivity reach Tconset = 78.2 K and Tczero = 40 K at 15 GPa. This superconducting phase shares the samilar structural symmetry as many cuprate superconductors, providing a fresh platform to investigate underlying mechanisms of nickelate superconductors.

cond-mat.supr-con↗
arXiv2023

Magnetic-coupled electronic landscape in bilayer-distorted titanium-based kagome metals

Quantum materials whose atoms are arranged on a lattice of corner-sharing triangles, $\textit{i.e.}$, the kagome lattice, have recently emerged as a captivating platform for investigating exotic correlated and topological electronic phenomena. Here, we combine ultra-low temperature angle-resolved photoemission spectroscopy (ARPES) with scanning tunneling microscopy and density functional theory calculations to reveal the fascinating electronic structure of the bilayer-distorted kagome material $\textit{Ln}$Ti${_3}$Bi${_4}$, where $\textit{Ln}$ stands for Nd and Yb. Distinct from other kagome materials, $\textit{Ln}$Ti${_3}$Bi${_4}$ exhibits two-fold, rather than six-fold, symmetries, stemming from the distorted kagome lattice, which leads to a unique electronic structure. Combining experiment and theory we map out the electronic structure and discover double flat bands as well as multiple van Hove singularities (VHSs), with one VHS exhibiting higher-order characteristics near the Fermi level. Notably, in the magnetic version NdTi${_3}$Bi${_4}$, the ultra-low base temperature ARPES measurements unveil an unconventional band splitting in the band dispersions which is induced by the ferromagnetic ordering. These findings reveal the potential of bilayer-distorted kagome metals $\textit{Ln}$Ti${_3}$Bi${_4}$ as a promising platform for exploring novel emergent phases of matter at the intersection of strong correlation and magnetism.

cond-mat.mtrl-sci↗
arXiv2023

pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing

As a privacy-preserving collaborative machine learning paradigm, federated learning (FL) has attracted significant interest from academia and the industry alike. To allow each data owner (a.k.a., FL clients) to train a heterogeneous and personalized local model based on its local data distribution, system resources and requirements on model structure, the field of model-heterogeneous personalized federated learning (MHPFL) has emerged. Existing MHPFL approaches either rely on the availability of a public dataset with special characteristics to facilitate knowledge transfer, incur high computation and communication costs, or face potential model leakage risks. To address these limitations, we propose a model-heterogeneous personalized Federated learning approach based on feature Extractor Sharing (pFedES). It incorporates a small homogeneous feature extractor into each client's heterogeneous local model. Clients train them via the proposed iterative learning method to enable the exchange of global generalized knowledge and local personalized knowledge. The small local homogeneous extractors produced after local training are uploaded to the FL server and for aggregation to facilitate easy knowledge sharing among clients. We theoretically prove that pFedES can converge over wall-to-wall time. Extensive experiments on two real-world datasets against six state-of-the-art methods demonstrate that pFedES builds the most accurate model, while incurring low communication and computation costs. Compared with the best-performing baseline, it achieves 1.61% higher test accuracy, while reducing communication and computation costs by 99.6% and 82.9%, respectively.

cs.LG↗
arXiv2023

Study of Baryon Number Transport Dynamics and Strangeness Conservation Effects Using $Ω$-hadron Correlations

In nuclear collisions at RHIC energies, an excess of $Ω$ hyperons over $\barΩ$ is observed, indicating that $Ω$ carries a net baryon number despite $s$ and $\bar{s}$ quarks being produced in pairs. The baryon number in $Ω$ could have been transported from the incident nuclei and/or produced in baryon-pair production of $Ω$ with other types of anti-hyperons, such as $\barΞ$. To investigate these two scenarios, we propose to measure correlations between $Ω$ and $K$, as well as between $Ω$ and anti-hyperons. We will use two versions, the default and string-melting, of a multiphase transport (AMPT) model to illustrate the method to measure the correlation and to demonstrate the general shape of the correlation. We will present the $Ω$-hadron correlations from simulated $\mathrm{Au}$+$\mathrm{Au}$ collisions at $\sqrt{s_{NN}} = 7.7$ and $14.6 \ \mathrm{GeV}$, and discuss the dependence on collision energy and on the hadronization scheme in these two AMPT versions. These correlations can be used to explore the mechanism of baryon number transport and the effects of baryon number and strangeness conservation in nuclear collisions.

hep-ph↗
arXiv2023

Learning Agility and Adaptive Legged Locomotion via Curricular Hindsight Reinforcement Learning

Agile and adaptive maneuvers such as fall recovery, high-speed turning, and sprinting in the wild are challenging for legged systems. We propose a Curricular Hindsight Reinforcement Learning (CHRL) that learns an end-to-end tracking controller that achieves powerful agility and adaptation for the legged robot. The two key components are (I) a novel automatic curriculum strategy on task difficulty and (ii) a Hindsight Experience Replay strategy adapted to legged locomotion tasks. We demonstrated successful agile and adaptive locomotion on a real quadruped robot that performed fall recovery autonomously, coherent trotting, sustained outdoor speeds up to 3.45 m/s, and tuning speeds up to 3.2 rad/s. This system produces adaptive behaviours responding to changing situations and unexpected disturbances on natural terrains like grass and dirt.

cs.RO↗
arXiv2023

Self-triggered Consensus Control of Multi-agent Systems from Data

This paper considers self-triggered consensus control of unknown linear multi-agent systems (MASs). Self-triggering mechanisms (STMs) are widely used in MASs, thanks to their advantages in avoiding continuous monitoring and saving computing and communication resources. However, existing results require the knowledge of system matrices, which are difficult to obtain in real-world settings. To address this challenge, we present a data-driven approach to designing STMs for unknown MASs building upon the model-based solutions. Our approach leverages a system lifting method, which allows us to derive a data-driven representation for the MAS. Subsequently, a data-driven self-triggered consensus control (STC) scheme is designed, which combines a data-driven STM with a state feedback control law. We establish a data-based stability criterion for asymptotic consensus of the closed-loop MAS in terms of linear matrix inequalities, whose solution provides a matrix for the STM as well as a stabilizing controller gain. In the presence of external disturbances, a model-based STC scheme is put forth for $\mathcal{H}_{\infty}$-consensus of MASs, serving as a baseline for the data-driven STC. Numerical tests are conducted to validate the correctness of the data- and model-based STC approaches. Our data-driven approach demonstrates a superior trade-off between control performance and communication efficiency from finite, noisy data relative to the system identification-based one.

eess.SY↗
arXiv2023

Analysis of Geometric Phases in Particle Diffusion Systems: Insights from Non-Hermitian Heat Transfer

Geometric phases in particle diffusion systems, an intriguing aspect enlightened from thermal systems, offer a different understanding beyond traditional Brownian motion and Fick's laws. This concept introduces a phase factor with significant implications for particle behavior, central to which is the non-Hermitian nature of the Hamiltonian in the diffusion system. A unique structure composed of two rings moving in opposite directions and a stationary intermediate layer plays multifunctional roles in controlling particle diffusion. For the real-world application, a bilayer particle-diffusion cloak is demonstrated, showcasing the extensive control and adaptability achievable through mastering geometric phases. This system has potential applications in industries like healthcare and environmental management, thus expanding the understanding of the geometric phase and offering insights for the design of particle-diffusion metamaterials.

cond-mat.stat-mech↗
arXiv2023

ZoomTrack: Target-aware Non-uniform Resizing for Efficient Visual Tracking

Recently, the transformer has enabled the speed-oriented trackers to approach state-of-the-art (SOTA) performance with high-speed thanks to the smaller input size or the lighter feature extraction backbone, though they still substantially lag behind their corresponding performance-oriented versions. In this paper, we demonstrate that it is possible to narrow or even close this gap while achieving high tracking speed based on the smaller input size. To this end, we non-uniformly resize the cropped image to have a smaller input size while the resolution of the area where the target is more likely to appear is higher and vice versa. This enables us to solve the dilemma of attending to a larger visual field while retaining more raw information for the target despite a smaller input size. Our formulation for the non-uniform resizing can be efficiently solved through quadratic programming (QP) and naturally integrated into most of the crop-based local trackers. Comprehensive experiments on five challenging datasets based on two kinds of transformer trackers, \ie, OSTrack and TransT, demonstrate consistent improvements over them. In particular, applying our method to the speed-oriented version of OSTrack even outperforms its performance-oriented counterpart by 0.6% AUC on TNL2K, while running 50% faster and saving over 55% MACs. Codes and models are available at https://github.com/Kou-99/ZoomTrack.

cs.CV↗
arXiv2023

STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning

Recently, model-based reinforcement learning algorithms have demonstrated remarkable efficacy in visual input environments. These approaches begin by constructing a parameterized simulation world model of the real environment through self-supervised learning. By leveraging the imagination of the world model, the agent's policy is enhanced without the constraints of sampling from the real environment. The performance of these algorithms heavily relies on the sequence modeling and generation capabilities of the world model. However, constructing a perfectly accurate model of a complex unknown environment is nearly impossible. Discrepancies between the model and reality may cause the agent to pursue virtual goals, resulting in subpar performance in the real environment. Introducing random noise into model-based reinforcement learning has been proven beneficial. In this work, we introduce Stochastic Transformer-based wORld Model (STORM), an efficient world model architecture that combines the strong sequence modeling and generation capabilities of Transformers with the stochastic nature of variational autoencoders. STORM achieves a mean human performance of $126.7\%$ on the Atari $100$k benchmark, setting a new record among state-of-the-art methods that do not employ lookahead search techniques. Moreover, training an agent with $1.85$ hours of real-time interaction experience on a single NVIDIA GeForce RTX 3090 graphics card requires only $4.3$ hours, showcasing improved efficiency compared to previous methodologies.

cs.LG↗
arXiv2023

Pressure-induced superconductivity in polycrystalline La3Ni2O7

We synthesized polycrystalline La3Ni2O7 samples by using the sol-gel method without post-annealing under high oxygen pressure, and then measured temperature-dependent resistivity under various hydrostatic pressures up to 14.5 GPa in a cubic anvil cell apparatus. We find that the density-wave-like anomaly in resistivity is progressively suppressed with increasing pressure and the resistivity drop corresponding to the onset of superconductivity emerges at pressure as low as 7 GPa. Zero resistivity is achieved at 9 GPa below 6.6 K, which increases quickly with pressure to 35.6 K at 14.5 GPa. The observation of zero-resistance state in the polycrystalline La3Ni2O7 samples under high pressures not only corroborates the recent report of superconductivity in the pressurized La3Ni2O7 crystals but also facilitates further studies on this emerging family of nickelate high-Tc superconductors.

cond-mat.supr-con↗
arXiv2023

A topological attractor of vortices as a clock generator based on polariton superfluids

We reveal a topologically protected persistent oscillatory dynamics of a polariton superfluid, which is driven non-resonantly by a super-Gaussian laser beam in a planar semiconductor microcavity subjected to an external C-shape potential. We find persistent oscillations, characterized by a topological attractor, that are based on the dynamical behavior of small Josephson vortices rotating around the outside edge of the central vortex. The attractor is being formed due to the inverse energy cascade accompanied by the growth of the incompressible kinetic energy. The attractor displays a remarkable stability towards perturbations and it may be tuned by the pump laser intensity to two distinct frequency ranges: 20.16$\pm$0.14 GHz and 48.4$\pm$1.2 GHz. This attractor is bistable due to the chirality of the vortex. The switching between two stable states is achieved by altering the pump power or by adding an extra incoherent Gaussian pump beam.

cond-mat.mes-hall↗
arXiv2023

Robust Sensor Fusion for Indoor Wireless Localization

Location knowledge in indoor environment using Indoor Positioning Systems (IPS) has become very useful and popular in recent years. Indoor wireless localization suffers from severe multi-path fading and non-line-of-sight conditions. This paper presents a novel indoor localization framework based on sensor fusion of Zigbee Wireless Sensor Networks (WSN) using Received Signal Strength (RSS). The unknown position is equipped with two or more mobile nodes. The range between two mobile nodes is fixed as priori. The attitude (roll, pitch, and yaw) of the mobile node are measured by inertial sensors (ISs). Then the angle and the range between any two nodes can be obtained, and thus the path between the two nodes can be modeled as a curve. Through an efficient cooperation between two or more mobile nodes, this framework effectively exploits the RSS techniques. This constraint help improve the positioning accuracy. Theoretical analysis on localization distortion and Monte Carlo simulations shows that the proposed cooperative strategy of multiple nodes with extended Kalman filter (EKF) achieves significantly higher positioning accuracy than the existing systems, especially in heavily obstructed scenarios.

eess.SY↗
arXiv2023

Variational Bayesian Approximations Kalman Filter Based on Threshold Judgment

The estimation of non-Gaussian measurement noise models is a significant challenge across various fields. In practical applications, it often faces challenges due to the large number of parameters and high computational complexity. This paper proposes a threshold-based Kalman filtering approach for online estimation of noise parameters in non-Gaussian measurement noise models. This method uses a certain amount of sample data to infer the variance threshold of observation parameters and employs variational Bayesian estimation to obtain corresponding noise variance estimates, enabling subsequent iterations of the Kalman filtering algorithm. Finally, we evaluate the performance of this algorithm through simulation experiments, demonstrating its accurate and effective estimation of state and noise parameters.

eess.SY↗