arXiv subjects

Xin Wang

Publications and source records attributed to Xin Wang.

At least 1,171 records · Page 65Linked to original sources

arXiv2023

Suppression of laser beam's polarization and intensity fluctuation via a Mach-Zehnder interferometer with proper feedback

Long ground-Rydberg coherence lifetime is interesting for implementing high-fidelity quantum logic gates, many-body physics, and other quantum information protocols. However, the potential well formed by a conventional far-off-resonance red-detuned optical-dipole trap that is attractive for ground-state cold atoms is usually repulsive for Rydberg atoms, which will result in the rapid loss of atoms and low repetition rate of the experimental sequence. Moreover, the coherence time will be sharply shortened due to the residual thermal motion of cold atoms. These issues can be addressed by a one-dimensional magic lattice trap, which can form a deeper potential trap than the traveling wave optical dipole trap when the output power is limited. In addition, these common techniques for atomic confinement generally have certain requirements for the polarization and intensity stability of the laser. Here, we demonstrated a method to suppress both the polarization drift and power fluctuation only based on the phase management of the Mach-Zehnder interferometer for a one-dimensional magic lattice trap. With the combination of three wave plates and the interferometer, we used the instrument to collect data in the time domain, analyzed the fluctuation of laser intensity, and calculated the noise power spectral density. We found that the total intensity fluctuation comprising laser power fluctuation and polarization drift was significantly suppressed, and the noise power spectral density after closed-loop locking with a typical bandwidth of 1-3000 Hz was significantly lower than that under the free running of the laser system. Typically, at 1000 Hz, the noise power spectral density after locking was about 10 dB lower than that under the free running of a master oscillator power amplifier system.The intensity-polarization control technique provides potential applications.

physics.optics↗

arXiv2023

Learning-based Intelligent Surface Configuration, User Selection, Channel Allocation, and Modulation Adaptation for Jamming-resisting Multiuser OFDMA Systems

Reconfigurable intelligent surfaces (RISs) can potentially combat jamming attacks by diffusing jamming signals. This paper jointly optimizes user selection, channel allocation, modulation-coding, and RIS configuration in a multiuser OFDMA system under a jamming attack. This problem is non-trivial and has never been addressed, because of its mixed-integer programming nature and difficulties in acquiring channel state information (CSI) involving the RIS and jammer. We propose a new deep reinforcement learning (DRL)-based approach, which learns only through changes in the received data rates of the users to reject the jamming signals and maximize the sum rate of the system. The key idea is that we decouple the discrete selection of users, channels, and modulation-coding from the continuous RIS configuration, hence facilitating the RIS configuration with the latest twin delayed deep deterministic policy gradient (TD3) model. Another important aspect is that we show a winner-takes-all strategy is almost surely optimal for selecting the users, channels, and modulation-coding, given a learned RIS configuration. Simulations show that the new approach converges fast to fulfill the benefit of the RIS, due to its substantially small state and action spaces. Without the need of the CSI, the approach is promising and offers practical value.

cs.IT↗

arXiv2023

Bounding the forward classical capacity of bipartite quantum channels

We introduce various measures of forward classical communication for bipartite quantum channels. Since a point-to-point channel is a special case of a bipartite channel, the measures reduce to measures of classical communication for point-to-point channels. As it turns out, these reduced measures have been reported in prior work of Wang et al. on bounding the classical capacity of a quantum channel. As applications, we show that the measures are upper bounds on the forward classical capacity of a bipartite channel. The reduced measures are upper bounds on the classical capacity of a point-to-point quantum channel assisted by a classical feedback channel. Some of the various measures can be computed by semi-definite programming.

quant-ph↗

arXiv2023

Nonlinear Non-Hermitian Landau-Zener-Stückelberg-Majorana interferometry

In this work, we have studied the non-Hermitian nonlinear LZSM interferometry in a non-Hermitian N-body interacting boson system in which the non-Hermicity is from the nonreciprocal tunnelings between the bosons. By using the mean-field approximation and projective Hilbert space, the effect of nonreciprocity and nonlinearity on the energy spectrum, the dynamics, and the formation of the interference fringes have been studied. The different symmetries and the impact of the two different types of reciprocity, i.e. the in-phase tunneling and anti-phase tunneling, on the energy spectrum and the phase transition between the Josephson oscillation and the self-trapping have been investigated. For the LZSM interferometry, the strength of the nonreciprocity is found to take an essential role in the population of the projective state and the strengths of the interference patterns in the projective space. While the conditions of destructive and constructive interference under the weak-coupling approximation still only depend on the strength of nonlinearity. Our result provides an application of the nonlinear non-Hermitian LZSM interferometry in studying the parameters of a non-Hermitian nonlinear two-level system which related to the nonlinearity and the non-Hermicity.

quant-ph↗

arXiv2022

Out-Of-Distribution Generalization on Graphs: A Survey

Graph machine learning has been extensively studied in both academia and industry. Although booming with a vast number of emerging methods and techniques, most of the literature is built on the in-distribution hypothesis, i.e., testing and training graph data are identically distributed. However, this in-distribution hypothesis can hardly be satisfied in many real-world graph scenarios where the model performance substantially degrades when there exist distribution shifts between testing and training graph data. To solve this critical problem, out-of-distribution (OOD) generalization on graphs, which goes beyond the in-distribution hypothesis, has made great progress and attracted ever-increasing attention from the research community. In this paper, we comprehensively survey OOD generalization on graphs and present a detailed review of recent advances in this area. First, we provide a formal problem definition of OOD generalization on graphs. Second, we categorize existing methods into three classes from conceptually different perspectives, i.e., data, model, and learning strategy, based on their positions in the graph machine learning pipeline, followed by detailed discussions for each category. We also review the theories related to OOD generalization on graphs and introduce the commonly used graph datasets for thorough evaluations. Finally, we share our insights on future research directions. This paper is the first systematic and comprehensive review of OOD generalization on graphs, to the best of our knowledge.

cs.LG↗

arXiv2022

Communication-Efficient Local SGD with Age-Based Worker Selection

A major bottleneck of distributed learning under parameter-server (PS) framework is communication cost due to frequent bidirectional transmissions between the PS and workers. To address this issue, local stochastic gradient descent (SGD) and worker selection have been exploited by reducing the communication frequency and the number of participating workers at each round, respectively. However, partial participation can be detrimental to convergence rate, especially for heterogeneous local datasets. In this paper, to improve communication efficiency and speed up the training process, we develop a novel worker selection strategy named AgeSel. The key enabler of AgeSel is utilization of the ages of workers to balance their participation frequencies. The convergence of local SGD with the proposed age-based partial worker participation is rigorously established. Simulation results demonstrate that the proposed AgeSel strategy can significantly reduce the number of training rounds needed to achieve a targeted accuracy, as well as the communication cost. The influence of the algorithm hyper-parameter is also explored to manifest the benefit of age-based worker selection.

cs.IT↗

arXiv2022

Compositionally Complex Perovskite Oxides as a New Class of Li-Ion Solid Electrolytes

Compositionally complex ceramics (CCCs), including high-entropy ceramics (HECs) as a subclass, offer new opportunities of materials discovery beyond the traditional methodology of searching new stoichiometric compounds. Herein, we establish new strategies of tailoring CCCs via a seamless combination of (1) non-equimolar compositional designs and (2) controlling microstructures and interfaces. Using oxide solid electrolytes for all-solid-state batteries as an exemplar, we validate these new strategies via discovering a new class of compositionally complex perovskite oxides (CCPOs) to show the possibility of improving ionic conductivities beyond the limit of conventional doping. As an example (amongst the 28 CCPOs examined), we demonstrate that the ionic conductivity can be improved by >60% in (Li0.375Sr0.4375)(Ta0.375Nb0.375Zr0.125Hf0.125)O3-δ, in comparison with the state-of-art (Li0.375Sr0.4375)(Ta0.75Zr0.25)O3-δ (LSTZ) baseline, via maintaining comparable electrochemical stability. Furthermore, the ionic conductivity can be improved by another >70% via grain boundary (GB) engineering, achieving >270% of the LSTZ baseline. This work suggests transformative new strategies for designing and tailoring HECs and CCCs, thereby opening a new window for discovering materials for energy storage and many other applications.

cond-mat.mtrl-sci↗

arXiv2022

Uhlmann phase of coherent states and the Uhlmann-Berry correspondence

We first compare the geometric frameworks behind the Uhlmann and Berry phases in a fiber-bundle language and then evaluate the Uhlmann phases of bosonic and fermionic coherent states. The Uhlmann phases of both coherent states are shown to carry geometric information and decrease smoothly with temperature. Importantly, the Uhlmann phases approach the corresponding Berry phases as temperature decreases. Together with previous examples in the literature, we propose a correspondence between the Uhlmann and Berry phases in the zero-temperature limit as a general property except some special cases and present a conditional proof of the correspondence.

quant-ph↗

arXiv2022

Probabilistic activity driven model of temporal simplicial networks and its application on higher-order dynamics

Network modeling characterizes the underlying principles of structural properties and is of vital significance for simulating dynamical processes in real world. However, bridging structure and dynamics is always challenging due to the multiple complexities in real systems. Here, through introducing the individual's activity rate and the possibility of group interaction, we propose a probabilistic activity driven (PAD) model that could generate temporal higher-order networks with both power-law and high-clustering characteristics, which successfully links the two most critical structural features and a basic dynamical pattern in extensive complex systems. Surprisingly, the power-law exponents and the clustering coefficients of the aggregated PAD network could be tuned in a wide range by altering a set of model parameters. We further provide an approximation algorithm to select the proper parameters that can generate networks with given structural properties, the effectiveness of which is verified by fitting various real-world networks. Lastly, we explore the co-evolution of PAD model and higher-order contagion dynamics, and analytically derive the critical conditions for phase transition and bistable phenomenon. Our model provides a basic tool to reproduce complex structural properties and to study the widespread higher-order dynamics, which has great potential for applications across fields.

physics.soc-ph↗

arXiv2022

Twisted Elliptic Genera

We study the twisted elliptic genera of 2d $(0,4)$ SCFTs associated with the BPS strings in the twisted circle compactification of 6d rank-one $(1,0)$ SCFTs. Such objects can arise when the 6d gauge algebra allows outer automorphism, thus are classified by twisted affine Lie algebras. We study several fascinating aspects of the twisted elliptic genera including 2d localization, twisted elliptic blowup equations, Higgsing and spectral flow symmetry. We derive a recursion formula with respect to the number of strings to exactly compute the twisted elliptic genera. We also investigate the modular bootstrap of twisted one-string elliptic genera and find the modularity of congruence subgroups $Γ_1(N)$ naturally appears with possible $N=2,3,4$. Geometrically, our study solves the refined BPS partition of the underlying genus-one fibered Calabi-Yau threefolds with $N$-section.

hep-th↗

arXiv2022

Upper Bounds on the Distillable Randomness of Bipartite Quantum States

The distillable randomness of a bipartite quantum state is an information-theoretic quantity equal to the largest net rate at which shared randomness can be distilled from the state by means of local operations and classical communication. This quantity has been widely used as a measure of classical correlations, and one version of it is equal to the regularized Holevo information of the ensemble that results from measuring one share of the state. However, due to the regularization, the distillable randomness is difficult to compute in general. To address this problem, we define measures of classical correlations and prove a number of their properties, most importantly that they serve as upper bounds on the distillable randomness of an arbitrary bipartite state. We then further bound these measures from above by some that are efficiently computable by means of semi-definite programming, we evaluate one of them for the example of an isotropic state, and we remark on the relation to quantities previously proposed in the literature.

quant-ph↗

arXiv2022

Nonlinear eco-evolutionary games with global environmental fluctuations and local environmental feedbacks

Environmental changes play a critical role in determining the evolution of social dilemmas in many natural or social systems. Generally, the environmental changes include two prominent aspects: the global time-dependent fluctuations and the local strategy-dependent feedbacks. However, the impacts of these two types of environmental changes have only been studied separately, a complete picture of the environmental effects exerted by the combination of these two aspects remains unclear. Here we develop a theoretical framework that integrates group strategic behaviors with their general dynamic environments, where the global environmental fluctuations are associated with a nonlinear factor in public goods game and the local environmental feedbacks are described by the `eco-evolutionary game'. We show how the coupled dynamics of local game-environment evolution differs in static and dynamic global environments. In particular, we find the emergence of cyclic evolutions of group cooperation and local environment, which forms an interior irregular loop in the phase plane, depending on the relative changing speed of both global and local environments compared to the strategic change. Our results provide important insights toward how diverse evolutionary outcomes could emerge from the nonlinear interactions between strategies and the changing environments.

physics.soc-ph↗

arXiv2022

BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement

Multimodal groupwise registration aligns internal structures in a group of medical images. Current approaches to this problem involve developing similarity measures over the joint intensity profile of all images, which may be computationally prohibitive for large image groups and unstable under various conditions. To tackle these issues, we propose BInGo, a general unsupervised hierarchical Bayesian framework based on deep learning, to learn intrinsic structural representations to measure the similarity of multimodal images. Particularly, a variational auto-encoder with a novel posterior is proposed, which facilitates the disentanglement learning of structural representations and spatial transformations, and characterizes the imaging process from the common structure with shape transition and appearance variation. Notably, BInGo is scalable to learn from small groups, whereas being tested for large-scale groupwise registration, thus significantly reducing computational costs. We compared BInGo with five iterative or deep learning methods on three public intrasubject and intersubject datasets, i.e. BraTS, MS-CMR of the heart, and Learn2Reg abdomen MR-CT, and demonstrated its superior accuracy and computational efficiency, even for very large group sizes (e.g., over 1300 2D images from MS-CMR in each group).

cs.CV↗

arXiv2022

Early Results from GLASS-JWST. I: Confirmation of Lensed $z\geqslant7$ Lyman-Break Galaxies Behind the Abell 2744 Cluster With NIRISS

We present the first search for $z\geqslant7$, continuum-confirmed sources with NIRISS/WFS spectroscopy over the Abell 2744 Frontier Fields cluster, as part of the GLASS-JWST ERS survey. With $\sim15$ hrs of pre-imaging and multi-angle grism exposures in the F115W, F150W, and F200W filters, we describe the general data handling (i.e., reduction, cleaning, modeling, and extraction processes) and analysis for the GLASS-JWST survey. We showcase the power of JWST to peer deep into reionization, when most intergalactic hydrogen is neutral, by confirming two galaxies at $z=8.04\pm0.15$ and $z=7.90\pm0.13$ by means of their Lyman breaks. Fainter continuum spectra are observed in both the F150W and F200W bands, indicative of blue ($-1.69$ and $-1.33$) UV slopes and moderately-bright absolute magnitudes ($-20.37$ and $-19.68$ mag). We do not detect strong Ly$α$ in either galaxy, but do observe tentative ($\sim2.7-3.8σ$) HeII$λ$1640 A, OIII]$λλ$1661,1666 A, and NIII]$λλ$1747,1749 A line emission in one, suggestive of low metallicity, star-forming systems with possible non-thermal contributions. These novel observations provide a first look at the extraordinary potential of JWST/NIRISS for confirming representative samples of bright $z\geqslant7$ sources in the absence of strong emission lines, and gain unprecedented insight into their contributions towards cosmic reionization.

astro-ph.GA↗

arXiv2022

On the Kinematic Morphology around Halos

In this paper, we report an interesting kinematic phenomenon around the halos' edge related to the splashback radius. After the shell-crossing, cosmic flow exhibits various rotational morphologies via stream-mixing. Vorticity is generated in a particular way that coincides with the large-scale structure. Notably, one specific flow morphology, which is spiraling inward and compressing in the third direction, concentrates around halos. A detailed examination that reveals a sharp change in the logarithmic derivative of its volume fraction, coincides with the location of the splashback radius defined as the outermost caustic structure. Such a feature encodes valuable phase space information and provides a new perspective on understanding the dynamical evolution of halos. As a volume-weighted quantity, the profile of flow morphology is purely kinematic. And unlike other related studies, the rotational flow morphologies capture the anisotropic phase structure in the multi-stream region.

astro-ph.CO↗

arXiv2022

Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation

Most existing domain adaptation (DA) methods align the features based on the domain feature distributions and ignore aspects related to fog, background and target objects, rendering suboptimal performance. In our DA framework, we retain the depth and background information during the domain feature alignment. A consistency loss between the generated depth and fog transmission map is introduced to strengthen the retention of the depth information in the aligned features. To address false object features potentially generated during the DA process, we propose an encoder-decoder framework to reconstruct the fog-free background image. This reconstruction loss also reinforces the encoder, i.e., our DA backbone, to minimize false object features.Moreover, we involve our target data in training both our DA module and our detection module in a semi-supervised manner, so that our detection module is also exposed to the unlabeled target data, the type of data used in the testing stage. Using these ideas, our method significantly outperforms the state-of-the-art method (47.6 mAP against the 44.3 mAP on the Foggy Cityscapes dataset), and obtains the best performance on multiple real-image public datasets. Code is available at: https://github.com/VIML-CVDL/Object-Detection-in-Foggy-Scenes

cs.CV↗

arXiv2022

FedSiam-DA: Dual-aggregated Federated Learning via Siamese Network under Non-IID Data

Federated learning is a distributed learning that allows each client to keep the original data locally and only upload the parameters of the local model to the server. Despite federated learning can address data island, it remains challenging to train with data heterogeneous in a real application. In this paper, we propose FedSiam-DA, a novel dual-aggregated contrastive federated learning approach, to personalize both local and global models, under various settings of data heterogeneity. Firstly, based on the idea of contrastive learning in the siamese network, FedSiam-DA regards the local and global model as different branches of the siamese network during the local training and controls the update direction of the model by constantly changing model similarity to personalize the local model. Secondly, FedSiam-DA introduces dynamic weights based on model similarity for each local model and exercises the dual-aggregated mechanism to further improve the generalization of the global model. Moreover, we provide extensive experiments on benchmark datasets, the results demonstrate that FedSiam-DA achieves outperforming several previous FL approaches on heterogeneous datasets.

cs.LG↗

arXiv2022

Accidental symmetries in the scalar potential of the Standard Model extended with two Higgs triplets

The two-Higgs-triplet model (2HTM) provides us with an attractive way to simultaneously account for tiny neutrino masses and the observed matter-antimatter asymmetry in our Universe. In this talk, we study the accidental symmetries of the scalar potential in the 2HTM using the bilinear-field formalism. Based on the group-theoretical arguments, we prove that the maximal symmetry group is ${\rm SO(4)}$. We carefully analyze all the subgroups of ${\rm SO(4)}$, and find that there are eight distinct kinds of accidental symmetries in the scalar potential of the 2HTM.

hep-ph↗