arXiv subjects

Wei Xiong

Publications and source records attributed to Wei Xiong.

At least 145 records · Page 8Linked to original sources

arXiv2023

ZhichunRoad at Amazon KDD Cup 2022: MultiTask Pre-Training for E-Commerce Product Search

In this paper, we propose a robust multilingual model to improve the quality of search results. Our model not only leverage the processed class-balanced dataset, but also benefit from multitask pre-training that leads to more general representations. In pre-training stage, we adopt mlm task, classification task and contrastive learning task to achieve considerably performance. In fine-tuning stage, we use confident learning, exponential moving average method (EMA), adversarial training (FGM) and regularized dropout strategy (R-Drop) to improve the model's generalization and robustness. Moreover, we use a multi-granular semantic unit to discover the queries and products textual metadata for enhancing the representation of the model. Our approach obtained competitive results and ranked top-8 in three tasks. We release the source code and pre-trained models associated with this work.

cs.CL↗

arXiv2023

Generation of long-lived $W$ states via reservoir engineering in dissipatively coupled systems

Very recently, dissipative coupling was discovered, which develops and broadens methods for controlling and utilizing light-matter interactions. Here, we propose a scheme to generate the tripartite $W$ state in a dissipatively coupled system, where one qubit and two resonators simultaneously interact with a common reservoir. With appropriate parameters, we find the $W$ state is a dark state of the system. By driving the qubit, the dissipatively coupled system will evolve from the ground state to the tripartite $W$ state. Because the initial state is the ground state of the system and no measurement is required, our scheme is easy to implement in experiments. Moreover, the $W$ state decouples from the common reservoir and thus has a very long lifetime. This scheme is applicable to a wide class of dissipatively coupled systems, and we specifically illustrate how to prepare the $W$ state in a hybrid qubit-photon-magnon system by using this scheme.

quant-ph↗

arXiv2023

Entangling spins using cubic nonlinear dynamics

Entangled states with a large number of $N$ atomic spins are a key ingredient for quantum information processing and quantum metrology. Nowadays, the preparation of such states has mainly relied on the quadratic nonlinear dynamics. Here, we investigate the preparation of spin-spin multipartite entanglement, witnessed by quantum Fisher information, by using the cubic nonlinear dynamics. We find that, in the regime of weak coupling, the cubic scheme can greatly speed up the rate of entanglement generation as compared to the quadratic scheme (about $N$ times faster). In the strong coupling regime, the cubic nonlinear dynamics enables the periodic in time generation of a broad variety of new-type macroscopic superposition states, which allow us to realize near-Heisenberg-limit phase sensitivity. In addition, we also reveal an interesting feature that the amount of entanglement generated by the cubic scheme has a macroscopic sensitivity to the parity of $N$, which has no counterpart in quadratic nonlinear dynamics and can be exploited for sensing the parity of $N$ at the single-spin level. We also propose a new approach for a fast and high-fidelity generation of maximally entangled Greenberger-Horne-Zeilinger (GHZ) states. By using an alternative cubic-quadratic-admixture type of nonlinear interaction, we show that one may accelerate the procedure of GHZ-state generation. The realization of the cubic nonlinear dynamics is also considered, showing that the cubic nonlinear dynamics can be realized by either repeatedly using linear- and quadratic-nonlinear dynamics or utilizing light-mediated interactions in just one step. Finally, by taking realistic imperfections into account, we find that the cubic scheme is sensitivity to the single-spin decay in the strong coupling regime, while is robust against the collective dephasing.

quant-ph↗

arXiv2022

Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets

We study episodic two-player zero-sum Markov games (MGs) in the offline setting, where the goal is to find an approximate Nash equilibrium (NE) policy pair based on a dataset collected a priori. When the dataset does not have uniform coverage over all policy pairs, finding an approximate NE involves challenges in three aspects: (i) distributional shift between the behavior policy and the optimal policy, (ii) function approximation to handle large state space, and (iii) minimax optimization for equilibrium solving. We propose a pessimism-based algorithm, dubbed as pessimistic minimax value iteration (PMVI), which overcomes the distributional shift by constructing pessimistic estimates of the value functions for both players and outputs a policy pair by solving NEs based on the two value functions. Furthermore, we establish a data-dependent upper bound on the suboptimality which recovers a sublinear rate without the assumption on uniform coverage of the dataset. We also prove an information-theoretical lower bound, which suggests that the data-dependent term in the upper bound is intrinsic. Our theoretical results also highlight a notion of "relative uncertainty", which characterizes the necessary and sufficient condition for achieving sample efficiency in offline MGs. To the best of our knowledge, we provide the first nearly minimax optimal result for offline MGs with function approximation.

cs.LG↗

arXiv2022

End-to-end Recording Device Identification Based on Deep Representation Learning

Deep learning techniques have achieved specific results in recording device source identification. The recording device source features include spatial information and certain temporal information. However, most recording device source identification methods based on deep learning only use spatial representation learning from recording device source features, which cannot make full use of recording device source information. Therefore, in this paper, to fully explore the spatial information and temporal information of recording device source, we propose a new method for recording device source identification based on the fusion of spatial feature information and temporal feature information by using an end-to-end framework. From a feature perspective, we designed two kinds of networks to extract recording device source spatial and temporal information. Afterward, we use the attention mechanism to adaptively assign the weight of spatial information and temporal information to obtain fusion features. From a model perspective, our model uses an end-to-end framework to learn the deep representation from spatial feature and temporal feature and train using deep and shallow loss to joint optimize our network. This method is compared with our previous work and baseline system. The results show that the proposed method is better than our previous work and baseline system under general conditions.

cs.SD↗

arXiv2022

Electronic properties of monolayer copper selenide with one-dimensional moiré patterns

Strain engineering is a vital way to manipulate the electronic properties of two-dimensional (2D) materials. As a typical representative of transition metal mono-chalcogenides (TMMs), a honeycomb CuSe monolayer features with one-dimensional (1D) moiré patterns owing to the uniaxial strain along one of three equivalent orientations of Cu(111) substrates. Here, by combining low-temperature scanning tunneling microscopy/spectroscopy (STM/S) experiments and density functional theory (DFT) calculations, we systematically investigate the electronic properties of the strained CuSe monolayer on the Cu(111) substrate. Our results show the semiconducting feature of CuSe monolayer with a band gap of 1.28 eV and the 1D periodical modulation of electronic properties by the 1D moiré patterns. Except for the uniaxially strained CuSe monolayer, we observed domain boundary and line defects in the CuSe monolayer, where the biaxial-strain and strain-free conditions can be investigated respectively. STS measurements for the three different strain regions show that the first peak in conduction band will move downward with the increasing strain. DFT calculations based on the three CuSe atomic models with different strain inside reproduced the peak movement. The present findings not only enrich the fundamental comprehension toward the influence of strain on electronic properties at 2D limit, but also offer the benchmark for the development of 2D semiconductor materials.

cond-mat.mtrl-sci↗

arXiv2022

Nonlinear multi-frequency phonon lasers with active levitated optomechanics

Phonon lasers, exploiting coherent amplifications of phonons, have been a cornerstone for exploring nonlinear phononics, imaging nanomaterial structures, and operating phononic devices. Very recently, by levitating a nanosphere in an optical tweezer, a single-mode phonon laser governed by dispersive optomechanical coupling has been demonstrated, assisted by alternating mechanical nonlinear cooling and linear heating. Such levitated optomechanical (LOM) devices, with minimal noises in high vacuum, can allow flexible control of large-mass objects without any internal discrete energy levels. However, untill now, it is still elusive to realize phonon lasing with levitated microscale objects, due to much stronger optical scattering losses. Here, by employing a Yb3+-doped active system, we report the first experiment on nonlinear multi-frequency phonon lasers with a micro-size sphere governed instead by dissipative LOM coupling. In this work, active gain plays a key role since not only 3-order enhancement can be achieved for the amplitude of the fundamental-mode phonon lasing, compared with the passive device, but also nonlinear mechanical harmonics can emerge spontaneously above the lasing threshold. Furthermore, for the first time, coherent correlations of phonons are observed for both the fundamental mode and its harmonics. Our work drives the field of LOM technology into a new regime where it becomes promising to engineer collective motional properties of typical micro-size objects, such as atmospheric particulates and living cells, for a wide range of applications in e.g., acoustic sensing, gravimetry, and inertial navigation.

physics.optics↗

arXiv2022

CASA, the Common Astronomy Software Applications for Radio Astronomy

CASA, the Common Astronomy Software Applications, is the primary data processing software for the Atacama Large Millimeter/submillimeter Array (ALMA) and the Karl G. Jansky Very Large Array (VLA), and is frequently used also for other radio telescopes. The CASA software can handle data from single-dish, aperture-synthesis, and Very Long Baseline Interferometery (VLBI) telescopes. One of its core functionalities is to support the calibration and imaging pipelines for ALMA, VLA, VLA Sky Survey (VLASS), and the Nobeyama 45m telescope. This paper presents a high-level overview of the basic structure of the CASA software, as well as procedures for calibrating and imaging astronomical radio data in CASA. CASA is being developed by an international consortium of scientists and software engineers based at the National Radio Astronomical Observatory (NRAO), the European Southern Observatory (ESO), the National Astronomical Observatory of Japan (NAOJ), and the Joint Institute for VLBI European Research Infrastructure Consortium (JIV-ERIC), under the guidance of NRAO.

astro-ph.IM↗

arXiv2022

A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games

Existing studies on provably efficient algorithms for Markov games (MGs) almost exclusively build on the "optimism in the face of uncertainty" (OFU) principle. This work focuses on a different approach of posterior sampling, which is celebrated in many bandits and reinforcement learning settings but remains under-explored for MGs. Specifically, for episodic two-player zero-sum MGs, a novel posterior sampling algorithm is developed with general function approximation. Theoretical analysis demonstrates that the posterior sampling algorithm admits a $\sqrt{T}$-regret bound for problems with a low multi-agent decoupling coefficient, which is a new complexity measure for MGs, where $T$ denotes the number of episodes. When specialized to linear MGs, the obtained regret bound matches the state-of-the-art results. To the best of our knowledge, this is the first provably efficient posterior sampling algorithm for MGs with frequentist regret guarantees, which enriches the toolbox for MGs and promotes the broad applicability of posterior sampling.

cs.LG↗

arXiv2022

Higher-order exceptional point in a blue-detuned non-Hermitian cavity optomechanical system

Higher-order exceptional points (EPs) in non-Hermitian systems have attracted great interest due to their advantages in sensitive enhancement and distinct topological features. However, realization of such EPs is still challenged because more fine-tuning parameters is generically required in quantum systems, compared to the second-order EP (EP2). Here, we propose a non-Hermitian three-mode optomechanical system in the blue-sideband regime for predicting the third-order EP (EP3). By deriving the pseudo-Hermitian condition for the proposed system, one cavity with loss and the other one with gain must be required. Then we show EP3 or EP2 can be observed when the mechanical resonator (MR) is neutral, loss or gain. For the neutral MR, we find both two degenerate or two non-degenerate EP3s can be predicted by tuning system parameters in the parameter space, while four non-degenerate EP2s can be observed when the system parameters derivate from EP3s, which is distinguished from the previous study in the red-detuned optomechanical system. For the gain (loss) MR, we find only two degenerate EP3s or EP2s can be predicted by tuning enhanced coupling strength. Our proposal provides a potential way to predict higher-order EPs or multiple EP2s and study multimode quantum squeezing around EPs using the blue-detuned non-Hermitian optomechanical systems.

quant-ph↗

arXiv2022

Dynamical descalarization in Einstein-Maxwell-scalar theory

For an asymptotically flat hairy black hole in the Einstein-Maxwell-scalar (EMS) theory, we study the possibility of shedding off its scalar hair via nonlinear scalar perturbation fully interacting with the background spacetime. We examine the effect of the perturbation strength on the descalarization. The results show that the effective charge to mass ratio of the black hole plays the key role in the dynamical descalarization. The descalarization at the threshold is continuous. This indicates a second order phase transition.

gr-qc↗

arXiv2022

Simulation of classical Ising-like magnetism with a Mott insulator of paired atoms

Quantum simulation of the XXZ model with a two-component Bose or Fermi Hubbard model based on a Mott insulator background has been widely used in the investigations of quantum magnetism with ultracold neutral atoms. In most cases, the diagonal spin-spin interaction is always accompanied by a large spin-exchange interaction which hinders the formation of long-range magnetic order at low temperature. Here we show that the spin-exchange interaction can be strongly reduced in a Mott insulator of paired atoms, while the diagonal spin-spin interaction remains unaffected. Thus, the effective magnetic model is quite close to an exact classical Ising model in the textbook. And we analysed an experimentally achievable three-component Fermi-Hubbard model of $\mathrm{{}^{6}Li}$ with two hyperfine levels of atoms paired in the lattice. We find the long-range antiferromagnetic order of such a three-component Fermi-Hubbard model can be much stronger than that of a typical two-component Fermi-Hubbard model at low temperature. And we discussed the possiblity of simulating an exact two-dimensional ferromagnetic Ising model in a Mott insulator of paired bosonic atoms. Our results may be useful for experimental investigation of the long-range Ising-like magnetism with ultracold neutral atoms under thermal equilibrium.

cond-mat.quant-gas↗

arXiv2022

Strong Long-Range Spin-Spin Coupling via a Kerr Magnon Interface

Strong long-range coupling between distant spins is crucial for spin-based quantum information processing. However, achieving such a strong spin-spin coupling remains challenging. Here we propose to realize a strong coupling between two distant spins via the Kerr effect of magnons in a yttrium-iron-garnet nanosphere. By applying a microwave field on this nanosphere, the Kerr effect of magnons can induce the magnon squeezing, so that the coupling between the spin and the squeezed magnons can be exponentially enhanced. This in turn allows the spin-magnon distance to increase from nano- to micrometer scale. By considering the virtual excitation of the squeezed magnons in the dispersive regime, strong spin-spin coupling mediated by the squeezed magnons can be achieved, and a remote quantum-state transfer, as well as the nonlocal two-qubit ISWAP gate with high fidelity becomes implementable. Our approach offers a feasible scheme to perform quantum information processing among distant spins.

quant-ph↗

arXiv2022

Dynamical spontaneous scalarization in Einstein-Maxwell-scalar theory

We study the linear instability and the nonlinear dynamical evolution of the Reissner-Nordström (RN) black hole in the Einstein-Maxwell-scalar theory in asymptotic flat spacetime. We focus on the coupling function $f(ϕ)=e^{-bϕ^2}$ which allows both the scalar-free RN solution and scalarized black hole solution. We first present the evolution of system parameters during dynamic scalarization. For parameter regions where spontaneous scalarization occurs, we find that the evolution of the scalar field at the horizon is dominated by the fundamental unstable mode from linear analysis at early times. At late times, the nonlinear evolution can be viewed as the perturbation of scalarized black holes.

gr-qc↗

arXiv2022

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of $\times$4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution.

cs.CV↗

arXiv2022

Unsupervised Low-light Image Enhancement with Decoupled Networks

In this paper, we tackle the problem of enhancing real-world low-light images with significant noise in an unsupervised fashion. Conventional unsupervised learning-based approaches usually tackle the low-light image enhancement problem using an image-to-image translation model. They focus primarily on illumination or contrast enhancement but fail to suppress the noise that ubiquitously exists in images taken under real-world low-light conditions. To address this issue, we explicitly decouple this task into two sub-tasks: illumination enhancement and noise suppression. We propose to learn a two-stage GAN-based framework to enhance the real-world low-light images in a fully unsupervised fashion. To facilitate the unsupervised training of our model, we construct samples with pseudo labels. Furthermore, we propose an adaptive content loss to suppress real image noise in different regions based on illumination intensity. In addition to conventional benchmark datasets, a new unpaired low-light image enhancement dataset is built and used to thoroughly evaluate the performance of our model. Extensive experiments show that our proposed method outperforms the state-of-the-art unsupervised image enhancement methods in terms of both illumination enhancement and noise reduction.

eess.IV↗

arXiv2022

Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Auto-Encoders

Objective and Impact Statement. We adopt a deep learning model for bone osteolysis prediction on computed tomography (CT) images of murine breast cancer bone metastases. Given the bone CT scans at previous time steps, the model incorporates the bone-cancer interactions learned from the sequential images and generates future CT images. Its ability of predicting the development of bone lesions in cancer-invading bones can assist in assessing the risk of impending fractures and choosing proper treatments in breast cancer bone metastasis. Introduction. Breast cancer often metastasizes to bone, causes osteolytic lesions, and results in skeletal related events (SREs) including severe pain and even fatal fractures. Although current imaging techniques can detect macroscopic bone lesions, predicting the occurrence and progression of bone lesions remains a challenge. Methods. We adopt a temporal variational auto-encoder (T-VAE) model that utilizes a combination of variational auto-encoders and long short-term memory networks to predict bone lesion emergence on our micro-CT dataset containing sequential images of murine tibiae. Given the CT scans of murine tibiae at early weeks, our model can learn the distribution of their future states from data. Results. We test our model against other deep learning-based prediction models on the bone lesion progression prediction task. Our model produces much more accurate predictions than existing models under various evaluation metrics. Conclusion. We develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone lesions. It will assist in planning and evaluating treatment strategies to prevent SREs in breast cancer patients.

eess.IV↗

arXiv2022

Phase Transformations During Continuous Cooling in Inconel 718 Alloys Manufactured by Laser Powder Bed Fusion and Suction Casting

Understanding alloy phase transformations during continuous cooling is important for post-processing design and optimization. In this work, continuous-cooling-transformation (CCT) diagrams of Inconel 718 alloys manufactured by laser powder bed fusion (LPBF) and suction casting are developed under different homogenization conditions. Unlike the available CCT diagrams in the reported studies, no gamma double prime and gamma prime precipitates can be observed. NbC and delta are determined to be the precipitates after cooling from the gamma matrix. Importantly, homogenization time and manufacturing methods are found to affect the Nb homogeneity in the matrix near NbC particles and thus significantly influence the precipitation process of the delta phase, which has a high content in Nb. In the alloys with high Nb homogeneity, the nucleation process mainly contributes to the precipitation, whereas in the alloys with low Nb homogeneity, the precipitation is primarily associated with the growth process. Subgrains are found to form after cooling at 0.1 K/s and can cause the highest hardness in samples. This work provides a new viewpoint on the study of processing-structure-property relationships during cooling in Inconel 718 and is beneficial to the development of alloy post-processing strategies.

cond-mat.mtrl-sci↗