arXiv subjects

Ping Li

Publications and source records attributed to Ping Li.

At least 127 records · Page 7Linked to original sources

arXiv2024

Last-iterate Convergence Separation between Extra-gradient and Optimism in Constrained Periodic Games

Last-iterate behaviors of learning algorithms in repeated two-player zero-sum games have been extensively studied due to their wide applications in machine learning and related tasks. Typical algorithms that exhibit the last-iterate convergence property include optimistic and extra-gradient methods. However, most existing results establish these properties under the assumption that the game is time-independent. Recently, (Feng et al, 2023) studied the last-iterate behaviors of optimistic and extra-gradient methods in games with a time-varying payoff matrix, and proved that in an unconstrained periodic game, extra-gradient method converges to the equilibrium while optimistic method diverges. This finding challenges the conventional wisdom that these two methods are expected to behave similarly as they do in time-independent games. However, compared to unconstrained games, games with constrains are more common both in practical and theoretical studies. In this paper, we investigate the last-iterate behaviors of optimistic and extra-gradient methods in the constrained periodic games, demonstrating that similar separation results for last-iterate convergence also hold in this setting.

cs.LG↗

arXiv2024

CoopHash: Cooperative Learning of Multipurpose Descriptor and Contrastive Pair Generator via Variational MCMC Teaching for Supervised Image Hashing

Leveraging supervised information can lead to superior retrieval performance in the image hashing domain but the performance degrades significantly without enough labeled data. One effective solution to boost performance is to employ generative models, such as Generative Adversarial Networks (GANs), to generate synthetic data in an image hashing model. However, GAN-based methods are difficult to train, which prevents the hashing approaches from jointly training the generative models and the hash functions. This limitation results in sub-optimal retrieval performance. To overcome this limitation, we propose a novel framework, the generative cooperative hashing network, which is based on energy-based cooperative learning. This framework jointly learns a powerful generative representation of the data and a robust hash function via two components: a top-down contrastive pair generator that synthesizes contrastive images and a bottom-up multipurpose descriptor that simultaneously represents the images from multiple perspectives, including probability density, hash code, latent code, and category. The two components are jointly learned via a novel likelihood-based cooperative learning scheme. We conduct experiments on several real-world datasets and show that the proposed method outperforms the competing hashing supervised methods, achieving up to 10\% relative improvement over the current state-of-the-art supervised hashing methods, and exhibits a significantly better performance in out-of-distribution retrieval.

cs.CV↗

arXiv2024

Evidence for a close-in tertiary orbiting around the Algol-type system HZ Dra with tidal splitting and spots activities

We reported a cyclic variation of $O-C$ diagram with a semi-amplitude of 0.0033 days and a period of 1.05 years for the pulsating eclipsing binary HZ Dra. The cyclic variation can be explained by the light travel-time effect via the presence of a close-in third body orbiting around HZ Dra in an elliptical orbit with a maximum semi-major axis of 0.92 au. Based on the W-D code, the contribution of the third light to the total system is determined to be 29 $\%$, which is in agreement with the estimated value. Our light curve modelling indicates an evolving hot and cool spot on the surface of the primary and secondary components, respectively. Their positions are roughly symmetrical to the inner Lagrangian point L1, which could be used to explain the variation in the O$^{'}$Connell effect. Our frequency analysis detects 1 radial p-mode, 7 non-radial p-modes and 1 non-radial g-mode. In addition, a total of 6 multiplets are identified, spaced by the orbital frequency, which can be explained as a tidally split mode caused by the equilibrium tides of the close binary system with a circular orbit. These pulsating features suggest that the primary of HZ Dra is a $δ$ Scuti star, pulsating in both p- and g-mode and influenced by tidal forces.

astro-ph.SR↗

arXiv2024

An eco-friendly passivation strategy of resveratrol for highly efficient and antioxidative perovskite solar cells

The stability of perovskite solar cells is closely related to the defects in perovskite crystals, and there are a large number of crystal defects in the perovskite thin films prepared by the solution method, which is not conducive to the commercial production of PSCs. In this study, resveratrol(RES), a green natural antioxidant abundant in knotweed and grape leaves, was introduced into perovskite films to passivate the defect. RES achieves defect passivation by interacting with uncoordinated Pb2+ in perovskite films. The results show that the quality of the perovskite film is significantly improved, and the energy level structure of the device is optimized, and the power conversion efficiency of the device is increased from 21.62% to 23.44%. In addition, RES can hinder the degradation of perovskite structures by O2- and CO2- free radicals, and the device retained 88% of its initial PCE after over 1000 hours in pure oxygen environment. The device retains 91% of the initial PCE after more than 1000 hours at 25°C and 50+5% relative humidity. This work provides a strategy for the use of natural and environmentally friendly additives to improve the efficiency and stability of devices, and provides an idea for the development of efficient, stable and environmentally friendly PSCs.

cond-mat.mtrl-sci↗

arXiv2024

Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection

Recent self-training techniques have shown notable improvements in unsupervised domain adaptation for 3D object detection (3D UDA). These techniques typically select pseudo labels, i.e., 3D boxes, to supervise models for the target domain. However, this selection process inevitably introduces unreliable 3D boxes, in which 3D points cannot be definitively assigned as foreground or background. Previous techniques mitigate this by reweighting these boxes as pseudo labels, but these boxes can still poison the training process. To resolve this problem, in this paper, we propose a novel pseudo label refinery framework. Specifically, in the selection process, to improve the reliability of pseudo boxes, we propose a complementary augmentation strategy. This strategy involves either removing all points within an unreliable box or replacing it with a high-confidence box. Moreover, the point numbers of instances in high-beam datasets are considerably higher than those in low-beam datasets, also degrading the quality of pseudo labels during the training process. We alleviate this issue by generating additional proposals and aligning RoI features across different domains. Experimental results demonstrate that our method effectively enhances the quality of pseudo labels and consistently surpasses the state-of-the-art methods on six autonomous driving benchmarks. Code will be available at https://github.com/Zhanwei-Z/PERE.

cs.CV↗

arXiv2024

Analytical calculation of Kerr and Kerr-Ads black holes in $f(R)$ theory

In this paper, we extend Chandrasekhar's method of calculating rotating black holes into $f(R)$ theory. We consider the Ricci scalar is a constant and derive the Kerr and Kerr-Ads metric by using the analytical mathematical method. Suppose that the spacetime is a 4-dimensional Riemannian manifold with a general stationary axisymmetric metric, we calculate Cartan's equation of structure and derive the Einstein tensor. In order to reduce the solving difficulty, we fix the gauge freedom to transform the metric into a more symmetric form. We solve the field equations in the two cases of the Ricci scalar $R=0$ and $R\neq 0$. In the case of $R=0$, the Ernst's equations are derived. We give the elementary solution of Ernst's equations and show the way to obtain more solutions including Kerr metric. In the case of $R\neq 0$, we reasonably assume that the solution to the equations consists of two parts: the first is Kerr part and the second is introduced by the Ricci scalar. Giving solution to the second part and combining the two parts, we obtain the Kerr-Ads metric. The calculations are carried out in a general $f(R)$ theory, indicating the Kerr and Kerr-Ads black holes exist widely in general $f(R)$ models. Furthermore, the whole solving process can be treated as a standard calculation procedure to obtain rotating black holes, which can be applied to other modified gravities.

gr-qc↗

arXiv2024

Beacon-enabled TDMA Ultraviolet Communication Network System Design and Realization

Nonline of sight (NLOS) ultraviolet (UV) scattering communication can serve as a good candidate for outdoor optical wireless communication (OWC) in the cases of non-perfect transmitter-receiver alignment and radio silence. We design and demonstrate a NLOS UV scattering communication network system in this paper, where a beacon-enabled time division multiple access (TDMA) scheme is adopted. In our system, LED and PMT are employed for transmitter and receiver devices, repectivey. Furthermore, we design algorithms for beacon transmission, beacon reception, time compensation, and time slot transition for hardware realization in field-programmable gate array (FPGA) board based on master-slave structure, where master node periodically transmits beacon signals to slave nodes. Experimental results are provided to evaluate the time synchronization error and specify the system key parameters for real-time implementation. We perform field tests for real-time communication network with the transmission range over 110 multiplied by 90 square meters, where the system throughput reaches 800kbps.

eess.SY↗

arXiv2024

Stochastic Controlled Averaging for Federated Learning with Communication Compression

Communication compression, a technique aiming to reduce the information volume to be transmitted over the air, has gained great interests in Federated Learning (FL) for the potential of alleviating its communication overhead. However, communication compression brings forth new challenges in FL due to the interplay of compression-incurred information distortion and inherent characteristics of FL such as partial participation and data heterogeneity. Despite the recent development, the performance of compressed FL approaches has not been fully exploited. The existing approaches either cannot accommodate arbitrary data heterogeneity or partial participation, or require stringent conditions on compression. In this paper, we revisit the seminal stochastic controlled averaging method by proposing an equivalent but more efficient/simplified formulation with halved uplink communication costs. Building upon this implementation, we propose two compressed FL algorithms, SCALLION and SCAFCOM, to support unbiased and biased compression, respectively. Both the proposed methods outperform the existing compressed FL methods in terms of communication and computation complexities. Moreover, SCALLION and SCAFCOM accommodates arbitrary data heterogeneity and do not make any additional assumptions on compression errors. Experiments show that SCALLION and SCAFCOM can match the performance of corresponding full-precision FL approaches with substantially reduced uplink communication, and outperform recent compressed FL methods under the same communication budget.

math.OC↗

arXiv2024

The complex genera, symmetric functions and multiple zeta values

We examine the coefficients in front of Chern numbers for complex genera, and pay special attention to the $\text{Td}^{\frac{1}{2}}$-genus, the $Γ$-genus as well as the Todd genus. Some related geometric applications to hyper-Kähler and Calabi-Yau manifolds are discussed. Along this line and building on the work of Doubilet in 1970s, various Hoffman-type formulas for multiple-(star) zeta values and transition matrices among canonical bases of the ring of symmetric functions can be uniformly treated in a more general framework.

math.DG↗

arXiv2024

Adapprox: Adaptive Approximation in Adam Optimization via Randomized Low-Rank Matrices

As deep learning models exponentially increase in size, optimizers such as Adam encounter significant memory consumption challenges due to the storage of first and second moment data. Current memory-efficient methods like Adafactor and CAME often compromise accuracy with their matrix factorization techniques. Addressing this, we introduce Adapprox, a novel approach that employs randomized low-rank matrix approximation for a more effective and accurate approximation of Adam's second moment. Adapprox features an adaptive rank selection mechanism, finely balancing accuracy and memory efficiency, and includes an optional cosine similarity guidance strategy to enhance stability and expedite convergence. In GPT-2 training and downstream tasks, Adapprox surpasses AdamW by achieving 34.5% to 49.9% and 33.8% to 49.9% memory savings for the 117M and 345M models, respectively, with the first moment enabled, and further increases these savings without the first moment. Besides, it enhances convergence speed and improves downstream task performance relative to its counterparts.

cs.LG↗

arXiv2024

Adaptive Target Detection for FDA-MIMO Radar with Training Data in Gaussian noise

This paper addresses the problem of detecting a moving target embedded in Gaussian noise with an unknown covariance matrix for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. To end it, assume that obtaining a set of training data is available. Moreover, we propose three adaptive detectors in accordance with the one-step generalized likelihood ratio test (GLRT), two-step GLRT, and Rao criteria, namely OGLRT, TGLRT, and Rao. The LH adaptive matched filter (LHAMF) detector is also introduced when decomposing the Rao test. Next, all provided detectors have constant false alarm rate (CFAR) properties against the covariance matrix. Besides, the closed-form expressions for false alarm probability (PFA) and detection probability (PD) are derived. Finally, this paper substantiates the correctness of the aforementioned algorithms through numerical simulations.

eess.SP↗

arXiv2024

Improving Visual Quality and Transferability of Adversarial Attacks on Face Recognition Simultaneously with Adversarial Restoration

Adversarial face examples possess two critical properties: Visual Quality and Transferability. However, existing approaches rarely address these properties simultaneously, leading to subpar results. To address this issue, we propose a novel adversarial attack technique known as Adversarial Restoration (AdvRestore), which enhances both visual quality and transferability of adversarial face examples by leveraging a face restoration prior. In our approach, we initially train a Restoration Latent Diffusion Model (RLDM) designed for face restoration. Subsequently, we employ the inference process of RLDM to generate adversarial face examples. The adversarial perturbations are applied to the intermediate features of RLDM. Additionally, by treating RLDM face restoration as a sibling task, the transferability of the generated adversarial face examples is further improved. Our experimental results validate the effectiveness of the proposed attack method.

cs.CV↗

arXiv2024

Coexisting Magnetism, Ferroelectric, and Ferrovalley Multiferroic in Stacking-Dependent Two-Dimensional Materials

The two-dimensional (2D) multiferroic materials have widespread of application prospects in facilitating the integration and miniaturization of nanodevices. However, it is rarely coupling between the magnetic, ferroelectric, and ferrovalley in one 2D material. Here, we propose a mechanism for manipulating magnetism, ferroelectric, and valley polarization by interlayer sliding in 2D bilayer material. Monolayer GdI2 exhibits a ferromagnetic semiconductor with the valley polarization up to 155.5 meV. More interestingly, the magnetism and valley polarization of bilayer GdI2 can be strongly coupled by sliding ferroelectricity, appearing these tunable and reversible. In addition, we uncover the microscopic mechanism of magnetic phase transition by spin Hamiltonian and electron hopping between layers. Our findings offer a new direction for investigating 2D multiferroic in the implication for next-generation electronic, valleytronic, and spintronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Improving the JPEG-resistance of Adversarial Attacks on Face Recognition by Interpolation Smoothing

JPEG compression can significantly impair the performance of adversarial face examples, which previous adversarial attacks on face recognition (FR) have not adequately addressed. Considering this challenge, we propose a novel adversarial attack on FR that aims to improve the resistance of adversarial examples against JPEG compression. Specifically, during the iterative process of generating adversarial face examples, we interpolate the adversarial face examples into a smaller size. Then we utilize these interpolated adversarial face examples to create the adversarial examples in the next iteration. Subsequently, we restore the adversarial face examples to their original size by interpolating. Throughout the entire process, our proposed method can smooth the adversarial perturbations, effectively mitigating the presence of high-frequency signals in the crafted adversarial face examples that are typically eliminated by JPEG compression. Our experimental results demonstrate the effectiveness of our proposed method in improving the JPEG-resistance of adversarial face examples.

cs.CV↗

arXiv2024

Detection of Tidally Excited Oscillations in Kepler Heartbeat Stars

Heartbeat stars (HBSs) with tidally excited oscillations (TEOs) are ideal laboratories for studying the effect of equilibrium and dynamical tides. However, studies of TEOs in Kepler HBSs are rare due to the need for better modeling of the equilibrium tide in light curves. We revisit the HBSs in our previous work and study the TEOs in these HBSs based on the derived orbital parameters that could express the equilibrium tide. We also compile a set of analytic procedures to examine the harmonic and anharmonic TEOs in their Fourier spectra. The TEOs of 21 HBSs have been newly analyzed and presented. Twelve of these HBSs show prominent TEOs (signal-to-noise ratio of the harmonics $S/N \ge 10$). The relation between the orbital eccentricities and the harmonic number of the TEOs shows a positive correlation. The relation between the orbital periods and the harmonic number also shows a positive correlation. Furthermore, the distribution of HBSs with TEOs in the Hertzsprung-Russell (H-R) diagram shows that TEOs are more visible in hot stars with surface effective temperatures $T$ $\gtrsim$ 6500 K. These samples may also be valuable targets for future studies of the effect of tidal action in eccentric orbits.

astro-ph.SR↗

arXiv2024

Ramsey and Gallai-Ramsey numbers for linear forests and kipas

For two graphs $G,H$, the \emph{Ramsey number} $r(G,H)$ is the minimum integer $n$ such that any red/blue edge-coloring of $K_n$ contains either a red copy of $G$ or a blue copy of $H$. For two graphs $G,H$, the \emph{Gallai-Ramsey number} $\operatorname{gr}_k(G:H)$ is defined as the minimum integer $n$ such that any $k$-edge-coloring of $K_n$ must contain either a rainbow copy of $G$ or a monochromatic copy of $H$. In this paper, the classical Ramsey numbers of linear forest versus kipas are obtained. We obtain the exact values of $\operatorname{gr}_k(G:H)$, where $H$ is either a path or a kipas and $G\in\{K_{1,3},P_4^+,P_5\}$ and $P_4^+$ is the graph consisting of $P_4$ with one extra edge incident with inner vertex.

math.CO↗

arXiv2024

Origin of zigzag antiferromagnetic orders in XPS3 (X= Fe, Ni) monolayers

Recently, two monolayer magnetic materials, i.e., FePS3 and NiPS3, have been successfully fabricated. Despite that they have the same atomic structure, the two monolayers exhibit distinct magnetic properties. FePS3 holds an out-of-plane zigzag antiferromagnetic (AFM-ZZ) structure, while NiPS3 exhibits an in-plane AFM-ZZ structure. However, there is no theoretical model which can properly describe its magnetic ground state due to the lack of a full understanding of its magnetic interactions. Here, by combining the first-principles calculations and the newly developed machine learning method, we construct an exact spin Hamiltonian of the two magnetic materials. Different from the previous studies which failed to fully consider the spin-orbit coupling effect, we find that the AFM-ZZ ground state in FePS3 is stabilized by competing ferromagnetic nearest-neighbor and antiferromagnetic third nearest-neighbor exchange interactions, and combining single-ion anisotropy. Whereas, the often ignored nearest-neighbor biquadratic exchange is responsible for the in-plane AFM-ZZ ground state in NiPS3. We additionally calculate spin-wave spectrum of AFM-ZZ structure in the two monolayers based on the exact spin Hamiltonian, which can be directly verified by the experimental investigation. Our work provides a theoretical framework for the origin of AFM-ZZ ground state in two-dimensional materials.

cond-mat.mtrl-sci↗

arXiv2023

GUITAR: Gradient Pruning toward Fast Neural Ranking

With the continuous popularity of deep learning and representation learning, fast vector search becomes a vital task in various ranking/retrieval based applications, say recommendation, ads ranking and question answering. Neural network based ranking is widely adopted due to its powerful capacity in modeling complex relationships, such as between users and items, questions and answers. However, it is usually exploited in offline or re-ranking manners for it is time-consuming in computations. Online neural network ranking--so called fast neural ranking--is considered challenging because neural network measures are usually non-convex and asymmetric. Traditional Approximate Nearest Neighbor (ANN) search which usually focuses on metric ranking measures, is not applicable to these advanced measures. In this paper, we introduce a novel graph searching framework to accelerate the searching in the fast neural ranking problem. The proposed graph searching algorithm is bi-level: we first construct a probable candidate set; then we only evaluate the neural network measure over the probable candidate set instead of evaluating the neural network over all neighbors. Specifically, we propose a gradient-based algorithm that approximates the rank of the neural network matching score to construct the probable candidate set; and we present an angle-based heuristic procedure to adaptively identify the proper size of the probable candidate set. Empirical results on public data confirm the effectiveness of our proposed algorithms.

cs.IR↗