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Tong Liu

Publications and source records attributed to Tong Liu.

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

A Persistent-Excitation-Free Method for System Disturbance Estimation Using Concurrent Learning

Observer-based methods are widely used to estimate the disturbances of different dynamic systems. However, a drawback of the conventional disturbance observers is that they all assume persistent excitation (PE) of the systems. As a result, they may lead to poor estimation precision when PE is not ensured, for instance, when the disturbance gain of the system is close to the singularity. In this paper, we propose a novel disturbance observer based on concurrent learning (CL) with time-variant history stacks, which ensures high estimation precision even in PE-free cases. The disturbance observer is designed in both continuous and discrete time. The estimation errors of the proposed method are proved to converge to a bounded set using the Lyapunov method. A history-sample-selection procedure is proposed to reduce the estimation error caused by the accumulation of old history samples. A simulation study on epidemic control shows that the proposed method produces higher estimation precision than the conventional disturbance observer when PE is not satisfied. This justifies the correctness of the proposed CL-based disturbance observer and verifies its applicability to solving practical problems.

eess.SY↗
arXiv2023

Accurate determination of low-energy eigenspectra with multi-target matrix product states

Determining the low-energy eigenspectra of quantum many-body systems is a long-standing challenge in physics. In this work, we solve this problem by introducing two novel algorithms to determine low-energy eigenstates based on a compact matrix product state (MPS) representation of the multiple targeted eigenstates. The first algorithm utilizes a canonicalization approach that takes advantage of the imaginary-time evolution of multi-target MPS, offering faster convergence and ease of implementation. The second algorithm employs a variational approach that optimizes local tensors on the Grassmann manifold, capable of achieving higher accuracy. These algorithms can be used independently or combined to enhance convergence speed and accuracy. We apply them to the transverse-field Ising model and demonstrate that the calculated low-energy eigenspectra agree remarkably well with the exact solution. Moreover, the eigenenergies exhibit uniform convergence in gapped phases, suggesting that the low-energy excited eigenstates have nearly the same level of accuracy as the ground state. Our results highlight the accuracy and versatility of multi-target MPS-based algorithms for determining low-energy eigenspectra and their potential applications in quantum many-body physics.

cond-mat.str-el↗
arXiv2023

Single-shot spatial instability and electric control of polariton condensates at room temperature

In planar microcavities, the transverse-electric and transverse-magnetic (TE-TM) mode splitting of cavity photons arises due to their different penetration into the Bragg mirrors and can result in optical spin-orbit coupling (SOC). In this work, we find that in a liquid crystal (LC) microcavity filled with perovskite microplates, the pronounced TE-TM splitting gives rise to a strong SOC that leads to the spatial instability of microcavity polariton condensates under single-shot excitation. Spatially varying hole burning and mode competition occurs between polarization components leading to different condensate profiles from shot to shot. The single-shot polariton condensates become stable when the SOC vanishes as the TE and TM modes are spectrally well separated from each other, which can be achieved by application of an electric field to our LC microcavity with electrically tunable anisotropy. Our findings are well reproduced and traced back to their physical origin by our detailed numerical simulations. With the electrical manipulation our work reveals how the shot-to-shot spatial instability of spatial polariton profiles can be engineered in anisotropic microcavities at room temperature, which will benefit the development of stable polariton-based optoeletronic and light-emitting devices.

physics.optics↗
arXiv2023

Simultaneous Recursive Identification of Parameters and Switching Manifolds Identification of Discrete-Time Switched Linear Systems

A novel procedure for the online identification of a class of discrete-time switched linear systems, which simultaneously estimates the parameters and switching manifolds of the systems, is proposed in this paper. Firstly, to estimate the parameters of the subsystems, a discrete-time concurrent learning-based recursive parameter estimator is designed to guarantee the exponential convergence of the estimation errors to zero. Secondly, as an assistant procedure of the identification framework, an online switching detection method is proposed by making use of the history stacks produced by the concurrent learning estimators. Thirdly, techniques of incremental support vector machine are applied to develop the recursive algorithm to estimate the system switching manifolds, with its stability proven by a Lynapunov-based method. At the end of the paper, the stability and precision of the proposed identification methods are confirmed by the numerical simulation of a 2-order switched linear system. Compared to the traditional offline identification methods, the proposed online identification framework possesses superior efficiency with respect to large amounts of data, while the limitations and outlook of this framework are also discussed within the conclusion.

eess.SY↗
arXiv2023

Artificial Intelligence System for Detection and Screening of Cardiac Abnormalities using Electrocardiogram Images

The artificial intelligence (AI) system has achieved expert-level performance in electrocardiogram (ECG) signal analysis. However, in underdeveloped countries or regions where the healthcare information system is imperfect, only paper ECGs can be provided. Analysis of real-world ECG images (photos or scans of paper ECGs) remains challenging due to complex environments or interference. In this study, we present an AI system developed to detect and screen cardiac abnormalities (CAs) from real-world ECG images. The system was evaluated on a large dataset of 52,357 patients from multiple regions and populations across the world. On the detection task, the AI system obtained area under the receiver operating curve (AUC) of 0.996 (hold-out test), 0.994 (external test 1), 0.984 (external test 2), and 0.979 (external test 3), respectively. Meanwhile, the detection results of AI system showed a strong correlation with the diagnosis of cardiologists (cardiologist 1 (R=0.794, p<1e-3), cardiologist 2 (R=0.812, p<1e-3)). On the screening task, the AI system achieved AUCs of 0.894 (hold-out test) and 0.850 (external test). The screening performance of the AI system was better than that of the cardiologists (AI system (0.846) vs. cardiologist 1 (0.520) vs. cardiologist 2 (0.480)). Our study demonstrates the feasibility of an accurate, objective, easy-to-use, fast, and low-cost AI system for CA detection and screening. The system has the potential to be used by healthcare professionals, caregivers, and general users to assess CAs based on real-world ECG images.

cs.CV↗
arXiv2023

Robust Digital Watermarking Method Based on Adaptive Feature Area Extraction and Local Histogram Shifting

A new local watermarking method based on histogram shifting has been proposed in this paper to deal with various signal processing attacks (e.g. median filtering, JPEG compression and Gaussian noise addition) and geometric attacks (e.g. rotation, scaling and cropping). A feature detector is used to select local areas for embedding. Then stationary wavelet transform (SWT) is applied on each local area for denoising by setting the corresponding diagonal coefficients to zero. With the implementation of histogram shifting, the watermark is embedded into denoised local areas. Meanwhile, a secret key is used in the embedding process which ensures the security that the watermark cannot be easily hacked. After the embedding process, the SWT diagonal coefficients are used to reconstruct the watermarked image. With the proposed watermarking method, we can achieve higher image quality and less bit error rate (BER) in the decoding process even after some attacks. Compared with global watermarking methods, the proposed watermarking scheme based on local histogram shifting has the advantages of higher security and larger capacity. The experimental results show the better image quality as well as lower BER compared with the state-of-art watermarking methods.

cs.MM↗
arXiv2023

Low-frequency gravitational wave memory from gamma-ray burst afterglows with energy injection

Ultrarelativistic gamma-ray burst (GRB) jets are strong gravitational wave (GW) sources with memory-type signals. The plateau (or shallow decay) phases driven by the energy injection might appear in the early X-ray afterglows of GRBs. In this paper, we investigate the GW signal as well as X-ray afterglow emission in the framework of GRB jets with energy injection, and both short- and long-duration GRBs are considered. We find that, regardless of the case, because of the antibeaming and time delay effects, a rising slope emerging in the waveform of GW signal due to the energy injection lags far behind the energy ejection, and the typical frequency of the characteristic amplitudes falls within a low-frequency region of $\sim10^{-4}-10^{-6} \,{\rm Hz}$; and we consider that the GW memory triggered by GRB jets with energy injection are previously unaware and the nearby GRBs with strong energy injection might disturb the measurement of the stochastic GW background. Such GW memory detection would provide a direct test for models of energy injection in the scenario of GRB jets.

astro-ph.HE↗
arXiv2023

Light curves and polarizations of gravitationally lensed kilonovae

Kilonovae are generally believed to originate from the ejecta of binary neutron stars (NSs) or black hole and NS mergers. Free neutrons might be retained in the outermost layer of the ejecta to produce a precursor via $β$-decay. During the propagation of kilonovae to observers, a small percentage of them might be gravitationally lensed by foreground objects. In this paper, three lens models, i.e., the point-mass model, the singular isothermal sphere (SIS) model, and the Chang-Refsdal model, were taken into consideration to explore the light curves and polarizations of gravitationally lensed kilonovae. We found that if the time delay between two images exceeds the ejecta heating timescale for the lens mass $\sim 10^{10}~M_\odot$ in the SIS model, a tiny bump-like signal will be generated in the light curve, and the total luminosity will be magnified in all cases. The polarization of lensed kilonovae is significantly enhanced in most cases. Future detections of lensed kilonovae will impose constraints on the morphology of the ejecta and aid in the determination of the nature of compact object mergers and the search for strong gravitational lenses.

astro-ph.HE↗
arXiv2022

Umklapp scattering in the one-dimensional Hubbard model

The Mott metal-insulator transition is a typical strong correlation effect triggered by the Umklapp scattering. However, in a physical system, the Umklapp scattering coexists with the normal scattering, including both forward and backward scattering, which conserves the total momentum of scattered electrons. Therefore, it is not easy to quantify the contribution of the Umklapp scattering in a Mott metal-insulator transition. To resolve this difficulty, we propose to explore these scattering processes separately. We study the contribution of each scattering process in the one-dimensional Hubbard model using the momentum-space density-matrix renormalization group (kDMRG) and bosonization methods. Our kDMRG calculation confirms that the Mott charge gap results from the Umklapp scattering, but the normal scattering processes strongly renormalize its value. Furthermore, we present a scaling analysis of the Mott charge gap in the bosonization theory and show that the interplay between the Umklapp and forward scattering dictates the charge dynamics in the half-filled Hubbard model.

cond-mat.str-el↗
arXiv2022

A Bayesian inference of relativistic mean-field model for neutron star matter from observation of NICER and GW170817/AT2017gfo

The observations of optical and near-infrared counterparts of binary neutron star mergers not only enrich our knowledge about the abundance of heavy elements in the Universe, or help reveal the remnant object just after the merger as generally known, but also can effectively constrain dense nuclear matter properties and the equation of state (EOS) in the interior of the merging stars. Following the relativistic mean-field description of nuclear matter, we perform the Bayesian inference of the EOS and the nuclear matter properties using the first multi-messenger event GW170817/AT2017gfo, together with the NICER mass-radius measurements of pulsars. The kilonova is described by a radiation-transfer model with the dynamical ejecta, and light curves connect with the EOS through the quasi-universal relations between the ejecta properties (the ejected mass, velocity, opacity or electron fraction) and binary parameters (the mass ratio and reduced tidal deformability). It is found that the posterior distributions of the reduced tidal deformability from the AT2017gfo analysis display a bimodal structure, with the first peak enhanced by the GW170817 data, leading to slightly softened posterior EOSs, while the second peak cannot be achieved by a nuclear EOS with saturation properties in their empirical ranges. The inclusion of NICER data in our analyses results in stiffened EOS posterior because of the massive pulsar PSR J0740+6620. We give results at nuclear saturation density for the nuclear incompressibility, the symmetry energy and its slope, as well as the nucleon effective mass, from our analysis of those observational data.

astro-ph.HE↗
arXiv2022

Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming

Deep Reinforcement Learning (DRL) is regarded as a potential method for car-following control and has been mostly studied to support a single following vehicle. However, it is more challenging to learn a stable and efficient car-following policy when there are multiple following vehicles in a platoon, especially with unpredictable leading vehicle behavior. In this context, we adopt an integrated DRL and Dynamic Programming (DP) approach to learn autonomous platoon control policies, which embeds the Deep Deterministic Policy Gradient (DDPG) algorithm into a finite-horizon value iteration framework. Although the DP framework can improve the stability and performance of DDPG, it has the limitations of lower sampling and training efficiency. In this paper, we propose an algorithm, namely Finite-Horizon-DDPG with Sweeping through reduced state space using Stationary approximation (FH-DDPG-SS), which uses three key ideas to overcome the above limitations, i.e., transferring network weights backward in time, stationary policy approximation for earlier time steps, and sweeping through reduced state space. In order to verify the effectiveness of FH-DDPG-SS, simulation using real driving data is performed, where the performance of FH-DDPG-SS is compared with those of the benchmark algorithms. Finally, platoon safety and string stability for FH-DDPG-SS are demonstrated.

eess.SY↗
arXiv2022

Deep Intention-Aware Network for Click-Through Rate Prediction

E-commerce platforms provide entrances for customers to enter mini-apps that can meet their specific shopping requirements. Trigger items displayed on entrance icons can attract more entering. However, conventional Click-Through-Rate (CTR) prediction models, which ignore user instant interest in trigger item, fail to be applied to the new recommendation scenario dubbed Trigger-Induced Recommendation in Mini-Apps (TIRA). Moreover, due to the high stickiness of customers to mini-apps, we argue that existing trigger-based methods that over-emphasize the importance of trigger items, are undesired for TIRA, since a large portion of customer entries are because of their routine shopping habits instead of triggers. We identify that the key to TIRA is to extract customers' personalized entering intention and weigh the impact of triggers based on this intention. To achieve this goal, we convert CTR prediction for TIRA into a separate estimation form, and present Deep Intention-Aware Network (DIAN) with three key elements: 1) Intent Net that estimates user's entering intention, i.e., whether he/she is affected by the trigger or by the habits; 2) Trigger-Aware Net and 3) Trigger-Free Net that estimate CTRs given user's intention is to the trigger-item and the mini-app respectively. Following a joint learning way, DIAN can both accurately predict user intention and dynamically balance the results of trigger-free and trigger-based recommendations based on the estimated intention. Experiments show that DIAN advances state-of-the-art performance in a large real-world dataset, and brings a 9.39% lift of online Item Page View and 4.74% CTR for Juhuasuan, a famous mini-app of Taobao.

cs.LG↗
arXiv2022

Effects of vertical advection on multimessenger signatures of black hole neutrino-dominated accretion flows in compact binary coalescences

In the coalescence events of binary neutron star (NS) or a black hole (BH) and an NS, a BH hyperaccretion disk might be eventually formed. At very high mass accretion rates, MeV neutrinos will be emitted from this disk, which is called a neutrino-dominated accretion flow (NDAF). Neutrino annihilation in the space out of the disk is energetic enough to launch ultrarelativistic jets to power gamma-ray bursts. Moreover, vertical advection might exist in NDAFs, which can generate the magnetic buoyancy bubbles to release gamma-ray photons. In this paper, we visit the effects of the vertical advection in NDAFs on the disk structure and gamma-ray and neutrino luminosities for different accretion rates. Then we study the anisotropic emission of kilonovae and the following gravitational waves (GWs) driven by the gamma-ray photons and neutrinos from NDAFs. Comparing NDAFs without vertical advection, the neutrino luminosity and GW strains slightly decrease for the case with vertical advection, and the kilonovae will be brightened by the injected gamma-ray photons. The future joint multimessenger observations might distinguish whether the vertical advection exists in NDAFs or not after compact binary coalescences.

astro-ph.HE↗
arXiv2022

Hybrid HMM Decoder For Convolutional Codes By Joint Trellis-Like Structure and Channel Prior

The anti-interference capability of wireless links is a physical layer problem for edge computing. Although convolutional codes have inherent error correction potential due to the redundancy introduced in the data, the performance of the convolutional code is drastically degraded due to multipath effects on the channel. In this paper, we propose the use of a Hidden Markov Model (HMM) for the reconstruction of convolutional codes and decoding by the Viterbi algorithm. Furthermore, to implement soft-decision decoding, the observation of HMM is replaced by Gaussian mixture models (GMM). Our method provides superior error correction potential than the standard method because the model parameters contain channel state information (CSI). We evaluated the performance of the method compared to standard Viterbi decoding by numerical simulation. In the multipath channel, the hybrid HMM decoder can achieve a performance gain of 4.7 dB and 2 dB when using hard-decision and soft-decision decoding, respectively. The HMM decoder also achieves significant performance gains for the RSC code, suggesting that the method could be extended to turbo codes.

cs.IT↗
arXiv2022

System size dependence of particle production and collectivity from the STAR experiment at RHIC

Medium modification of particle spectra and the origin of collectivity in small collision systems are widely debated topics in the heavy-ion community. To address these open questions, we propose the study of particle production and collectivity for varying system sizes,Au+Au $>$ Ru+Ru/Zr+Zr $>$ Cu+Cu $>$ d+Au $>$ $γ$+Au, available at RHIC using the STAR detector. We present the first measurements of centrality dependent charged hadron production in Isobar (Ru+Ru and Zr+Zr) collisions, including the nuclear modification factors ($R_\mathrm{AA}$) at high transverse momentom ($p_\mathrm{T}$), and identified particle spectra at low $p_\mathrm{T}$\ at mid-rapidity. Combined with existing results from other systems, they probe system size and collision geometry dependences of the medium modification to particle production. In addition, we present the measurement of particle production and long-range di-hadron correlations in $γ$+Au events using ultra-peripheral Au+Au collisions at RHIC.

nucl-ex↗
arXiv2022

Variational Quantum Computation of Molecular Linear Response Properties on a Superconducting Quantum Processor

Simulating response properties of molecules is crucial for interpreting experimental spectroscopies and accelerating materials design. However, it remains a long-standing computational challenge for electronic structure methods on classical computers. While quantum computers hold the promise to solve this problem more efficiently in the long run, existing quantum algorithms requiring deep quantum circuits are infeasible for near-term noisy quantum processors. Here, we introduce a pragmatic variational quantum response (VQR) algorithm for response properties, which circumvents the need for deep quantum circuits. Using this algorithm, we report the first simulation of linear response properties of molecules including dynamic polarizabilities and absorption spectra on a superconducting quantum processor. Our results indicate that a large class of important dynamical properties such as Green's functions are within the reach of near-term quantum hardware using this algorithm in combination with suitable error mitigation techniques.

quant-ph↗
arXiv2022

Observation of entanglement negativity transition of pseudo-random mixed states

Multipartite entanglement is a key resource for quantum computation. It is expected theoretically that entanglement transition may happen for multipartite random quantum states, however, which is still absent experimentally. Here, we report the observation of entanglement transition quantified by negativity using a fully connected 20-qubit superconducting processor. We implement multi-layer pseudo-random circuits to generate pseudo-random pure states of 7 to 15 qubits. Then, we investigate negativity spectra of reduced density matrices obtained by quantum state tomography for 6 qubits.Three different phases can be identified by calculating logarithmic negativities based on the negativity spectra. We observe the phase transitions by changing the sizes of environment and subsystems. The randomness of our circuits can be also characterized by quantifying the distance between the distribution of output bit-string probabilities and Porter-Thomas distribution. Our simulator provides a powerful tool to generate random states and understand the entanglement structure for multipartite quantum systems.

quant-ph↗