arXiv subjects

Wei Jiang

Publications and source records attributed to Wei Jiang.

At least 181 records · Page 10Linked to original sources

arXiv2025

LiveForesighter: Generating Future Information for Live-Streaming Recommendations at Kuaishou

Live-streaming, as a new-generation media to connect users and authors, has attracted a lot of attention and experienced rapid growth in recent years. Compared with the content-static short-video recommendation, the live-streaming recommendation faces more challenges in giving our users a satisfactory experience: (1) Live-streaming content is dynamically ever-changing along time. (2) valuable behaviors (e.g., send digital-gift, buy products) always require users to watch for a long-time (>10 min). Combining the two attributes, here raising a challenging question for live-streaming recommendation: How to discover the live-streamings that the content user is interested in at the current moment, and further a period in the future?

cs.IR↗

arXiv2025

Group Distributionally Robust Dataset Distillation with Risk Minimization

Dataset distillation (DD) has emerged as a widely adopted technique for crafting a synthetic dataset that captures the essential information of a training dataset, facilitating the training of accurate neural models. Its applications span various domains, including transfer learning, federated learning, and neural architecture search. The most popular methods for constructing the synthetic data rely on matching the convergence properties of training the model with the synthetic dataset and the training dataset. However, using the empirical loss as the criterion must be thought of as auxiliary in the same sense that the training set is an approximate substitute for the population distribution, and the latter is the data of interest. Yet despite its popularity, an aspect that remains unexplored is the relationship of DD to its generalization, particularly across uncommon subgroups. That is, how can we ensure that a model trained on the synthetic dataset performs well when faced with samples from regions with low population density? Here, the representativeness and coverage of the dataset become salient over the guaranteed training error at inference. Drawing inspiration from distributionally robust optimization, we introduce an algorithm that combines clustering with the minimization of a risk measure on the loss to conduct DD. We provide a theoretical rationale for our approach and demonstrate its effective generalization and robustness across subgroups through numerical experiments. The source code is available at https://github.com/Mming11/RobustDatasetDistillation.

cs.LG↗

arXiv2024

Singular vectors, characters, and composition series for the N=1 BMS superalgebra

This paper investigates the structure of Verma modules over the N=1 BMS superalgebra. We provide a detailed classification of singular vectors, establish necessary and sufficient conditions for the existence of subsingular vectors, uncover the structure of maximal submodules, present the composition series of Verma modules, and derive character formulas for irreducible highest weight modules. As a byproduct, we also explicitly determine all singular vectors, subsingular vectors, and the composition series of Verma modules over the algebra W(2,2).

math.RT↗

arXiv2024

Degenerate phase-matching for multi-wavelength nonlinear mixing in aperiodic lattice lasers

Holographically-designed aperiodic lattices have proven to be an exciting engineering technique for achieving electrically switchable single- or multi-frequency emissions in terahertz (THz) semiconductor lasers. Here, we employ the nonlinear transfer matrix modeling method to investigate multi-wavelength nonlinear (sum- or difference-) frequency generation within an integrated THz (idler) laser cavity that also supports optical (pump and signal) waves. The laser cavity includes an aperiodic lattice, which engineers the idler photon lifetimes and effective refractive indices. The key findings are: (i) the nonlinear conversion efficiency reveals resonant enhancement at those idler frequencies where the photon lifetime is high; (ii) the resonant phase-matching process between the pump and idler waves has a one-to-one link with absence of any other dispersion, the lowest threshold, multi-wavelength defect modes of the aperiodic lattice laser have degenerate phase-matched pump frequencies. This set of results will potentially have a significant impact on the wavelength multiplexing in electronically switchable THz-over-fiber communication systems [1].

physics.optics↗

arXiv2024

Predictor-corrector, BGN-based parametric finite element methods for surface diffusion

We present a novel parametric finite element approach for simulating the surface diffusion of curves and surfaces. Our core strategy incorporates a predictor-corrector time-stepping method, which enhances the classical first-order temporal accuracy to achieve second-order accuracy. Notably, our new method eliminates the necessity for mesh regularization techniques, setting it apart from previously proposed second-order schemes by the authors (J. Comput. Phys. 514 (2024) 113220). Moreover, it maintains the long-term mesh equidistribution property of the first-order scheme. The proposed techniques are readily adaptable to other geometric flows, such as (area-preserving) curve shortening flow and surface diffusion with anisotropic surface energy. Comprehensive numerical experiments have been conducted to validate the accuracy and efficiency of our proposed methods, demonstrating their superiority over previous schemes.

math.NA↗

arXiv2024

Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction

Sign stochastic gradient descent (signSGD) is a communication-efficient method that transmits only the sign of stochastic gradients for parameter updating. Existing literature has demonstrated that signSGD can achieve a convergence rate of $\mathcal{O}(d^{1/2}T^{-1/4})$, where $d$ represents the dimension and $T$ is the iteration number. In this paper, we improve this convergence rate to $\mathcal{O}(d^{1/2}T^{-1/3})$ by introducing the Sign-based Stochastic Variance Reduction (SSVR) method, which employs variance reduction estimators to track gradients and leverages their signs to update. For finite-sum problems, our method can be further enhanced to achieve a convergence rate of $\mathcal{O}(m^{1/4}d^{1/2}T^{-1/2})$, where $m$ denotes the number of component functions. Furthermore, we investigate the heterogeneous majority vote in distributed settings and introduce two novel algorithms that attain improved convergence rates of $\mathcal{O}(d^{1/2}T^{-1/2} + dn^{-1/2})$ and $\mathcal{O}(d^{1/4}T^{-1/4})$ respectively, outperforming the previous results of $\mathcal{O}(dT^{-1/4} + dn^{-1/2})$ and $\mathcal{O}(d^{3/8}T^{-1/8})$, where $n$ represents the number of nodes. Numerical experiments across different tasks validate the effectiveness of our proposed methods.

cs.LG↗

arXiv2024

Orbital torque switching of room temperature two-dimensional van der Waals ferromagnet Fe3GaTe2

Efficiently manipulating the magnetization of van der Waals ferromagnets has attracted considerable interest in developing room-temperature two-dimensional material-based memory and logic devices. Here, taking advantage of the unique properties of the van der Waals ferromagnet as well as promising characteristics of the orbital Hall effect, we demonstrate the room-temperature magnetization switching of van der Waals ferromagnet Fe3GaTe2 through the orbital torque generated by the orbital Hall material, Titanium (Ti). The switching current density is estimated to be around 1.6 x 10^6 A/cm^2, comparable to that achieved in Fe3GaTe2 using spin-orbit torque from spin Hall materials. The efficient magnetization switching arises from the combined effects of the large orbital Hall conductivity of Ti and the strong spin-orbit correlation of the Fe3GaTe2, as confirmed through theoretical calculations. Our findings advance the understanding of orbital torque switching and pave the way for exploring material-based orbitronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Magnetic ground state of monolayer CeI$_{2}$: occupation matrix control and DFT+U calculations

The magnetic ground state is crucial for the applications of the two-dimension magnets as it decides fundamental magnetic properties of the material, such as magnetic order, magnetic transition temperature, and low-energy excitation of the spin waves. However, the simulations for magnetism of local-electron systems are challenging due to the existence of metastable states. In this study, occupation matrix control (OMC) and density functional theory plus Hubbard $U$ calculations are applied to investigate the magnetic ground state of monolayer CeI$_{2}$. Following the predicted ferromagnetic (FM) order, the FM ground state and the FM metastable states are identified and found to have different values of the magnetic parameters. Based on the calculated magnetic parameters of the FM ground state, the Curie temperature is estimated to be $128$ K for monolayer CeI$_{2}$. When spin-orbit coupling (SOC) is considered, the FM ground state is further confirmed to contain both off-plane and in-plane components of magnetization. SOC is shown to be essential for reasonably describing not only magnetic anisotropy but also local electronic orbital state of monolayer CeI$_{2}$.

cond-mat.mtrl-sci↗

arXiv2024

DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression

Scalable coding, which can adapt to channel bandwidth variation, performs well in today's complex network environment. However, most existing scalable compression methods face two challenges: reduced compression performance and insufficient scalability. To overcome the above problems, this paper proposes a learned fine-grained scalable image compression framework, namely DeepFGS. Specifically, we introduce a feature separation backbone to divide the image information into basic and scalable features, then redistribute the features channel by channel through an information rearrangement strategy. In this way, we can generate a continuously scalable bitstream via one-pass encoding. For entropy coding, we design a mutual entropy model to fully explore the correlation between the basic and scalable features. In addition, we reuse the decoder to reduce the parameters and computational complexity. Experiments demonstrate that our proposed DeepFGS outperforms previous learning-based scalable image compression models and traditional scalable image codecs in both PSNR and MS-SSIM metrics.

eess.IV↗

arXiv2024

Hybrid Local-Global Context Learning for Neural Video Compression

In neural video codecs, current state-of-the-art methods typically adopt multi-scale motion compensation to handle diverse motions. These methods estimate and compress either optical flow or deformable offsets to reduce inter-frame redundancy. However, flow-based methods often suffer from inaccurate motion estimation in complicated scenes. Deformable convolution-based methods are more robust but have a higher bit cost for motion coding. In this paper, we propose a hybrid context generation module, which combines the advantages of the above methods in an optimal way and achieves accurate compensation at a low bit cost. Specifically, considering the characteristics of features at different scales, we adopt flow-guided deformable compensation at largest-scale to produce accurate alignment in detailed regions. For smaller-scale features, we perform flow-based warping to save the bit cost for motion coding. Furthermore, we design a local-global context enhancement module to fully explore the local-global information of previous reconstructed signals. Experimental results demonstrate that our proposed Hybrid Local-Global Context learning (HLGC) method can significantly enhance the state-of-the-art methods on standard test datasets.

cs.MM↗

arXiv2024

Machine-Learning based photon counting for PMT waveforms and its application to the improvement of the energy resolution in large liquid scintillator detectors

Photomultiplier tubes (PMTs) are widely used in particle experiments for photon detection. PMT waveform analysis is crucial for high-precision measurements of the position and energy of incident particles in liquid scintillator (LS) detectors. A key factor contributing to the energy resolution in large liquid scintillator detectors with PMTs is the charge smearing of PMTs. This paper presents a machine-learning-based photon counting method for PMT waveforms and its application to the energy reconstruction, using the JUNO experiment as an example. The results indicate that leveraging the photon counting information from the machine learning model can partially mitigate the impact of PMT charge smearing and lead to a relative 2.0% to 2.8% improvement on the energy resolution in the energy range of [1, 9] MeV.

physics.ins-det↗

arXiv2024

A weighted scalar auxiliary variable method for solving gradient flows: bridging the nonlinear energy-based and Lagrange multiplier approaches

Two primary scalar auxiliary variable (SAV) approaches are widely applied for simulating gradient flow systems, i.e., the nonlinear energy-based approach and the Lagrange multiplier approach. The former guarantees unconditional energy stability through a modified energy formulation, whereas the latter preserves original energy stability but requires small time steps for numerical solutions. In this paper, we introduce a novel weighted SAV method which integrates these two approaches for the first time. Our method leverages the advantages of both approaches: (i) it ensures the existence of numerical solutions for any time step size with a sufficiently large weight coefficient; (ii) by using a weight coefficient smaller than one, it achieves a discrete energy closer to the original, potentially ensuring stability under mild conditions; and (iii) it maintains consistency in computational cost by utilizing the same time/spatial discretization formulas. We present several theorems and numerical experiments to validate the accuracy, energy stability and superiority of our proposed method.

math.NA↗

arXiv2024

Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions

This paper explores adaptive variance reduction methods for stochastic optimization based on the STORM technique. Existing adaptive extensions of STORM rely on strong assumptions like bounded gradients and bounded function values, or suffer an additional $\mathcal{O}(\log T)$ term in the convergence rate. To address these limitations, we introduce a novel adaptive STORM method that achieves an optimal convergence rate of $\mathcal{O}(T^{-1/3})$ for non-convex functions with our newly designed learning rate strategy. Compared with existing approaches, our method requires weaker assumptions and attains the optimal convergence rate without the additional $\mathcal{O}(\log T)$ term. We also extend the proposed technique to stochastic compositional optimization, obtaining the same optimal rate of $\mathcal{O}(T^{-1/3})$. Furthermore, we investigate the non-convex finite-sum problem and develop another innovative adaptive variance reduction method that achieves an optimal convergence rate of $\mathcal{O}(n^{1/4} T^{-1/2} )$, where $n$ represents the number of component functions. Numerical experiments across various tasks validate the effectiveness of our method.

math.OC↗

arXiv2024

Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement

In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node's local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through $3$-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only $3$-bits). Our proposed algorithm respects the network's bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm's operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm's unique characteristics.

eess.SY↗

arXiv2024

A Visual Cooperative Localization Method for Airborne Magnetic Surveying Based on a Manifold Sensor Fusion Algorithm Using Lie Groups

Recent advancements in UAV technology have spurred interest in developing multi-UAV aerial surveying systems for use in confined environments where GNSS signals are blocked or jammed. This paper focuses airborne magnetic surveying scenarios. To obtain clean magnetic measurements reflecting the Earth's magnetic field, the magnetic sensor must be isolated from other electronic devices, creating a significant localization challenge. We propose a visual cooperative localization solution. The solution incorporates a visual processing module and an improved manifold-based sensor fusion algorithm, delivering reliable and accurate positioning information. Real flight experiments validate the approach, demonstrating single-axis centimeter-level accuracy and decimeter-level overall 3D positioning accuracy.

eess.SP↗

arXiv2024

PSF Calibration of DAMPE for gamma-ray Observations

The DArk Matter Particle Explorer (DAMPE) is dedicated to exploring critical scientific domains including the indirect detection of dark matter, cosmic ray physics, and gamma ray astronomy. This study introduces a novel method for calibrating the Point Spread Function (PSF) of DAMPE, specifically designed to enhance the accuracy of gamma-ray observations. By leveraging data from regions near pulsars and bright Active Galactic Nuclei (AGNs), we have refined the PSF calibration process, resulting in an improved angular resolution that closely matches our observational data. This advancement significantly boosts the precision of gamma-ray detection by DAMPE, thereby contributing to its mission objectives in dark matter detection and gamma ray astronomy.

astro-ph.IM↗

arXiv2024

The calibrations of DAMPE $γ$-ray effective area

The DArk Matter Particle Explorer (DAMPE) is a cosmic-ray detector as well as a pair-converting $γ$-ray telescope. The effective area, reflecting the geometrical cross-section area, the $γ$-ray conversion probability and the photon selection efficiency, is important in the $γ$-ray analyses. In the work, we find a significant time variation in the effective area, as large as $\sim -4\%/{\rm yr}$ at 2 GeV for the high-energy trigger. We derive the data-based correction factors to the effective areas and apply corrections to both the effective areas and the exposure maps. The calibrated exposure can be $\sim 12\%$ smaller than the Monte Carlo one on average at 2 GeV. The calibration is further verified using the observation of the Vela pulsar, showing the spectral parameters with the correction are more consistent with those in the Fermi-LAT catalog than the ones without correction. All the corrections are now implemented in the latest version of the DAMPE $γ$-ray analysis toolkit DmpST.

astro-ph.IM↗

arXiv2024

Data Poisoning-based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks

Spiking Neural Networks (SNNs), the third generation neural networks, are known for their low energy consumption and high robustness. SNNs are developing rapidly and can compete with Artificial Neural Networks (ANNs) in many fields. To ensure that the widespread use of SNNs does not cause serious security incidents, much research has been conducted to explore the robustness of SNNs under adversarial sample attacks. However, many other unassessed security threats exist, such as highly stealthy backdoor attacks. Therefore, to fill the research gap in this and further explore the security vulnerabilities of SNNs, this paper explores the robustness performance of SNNs trained by supervised learning rules under backdoor attacks. Specifically, the work herein includes: i) We propose a generic backdoor attack framework that can be launched against the training process of existing supervised learning rules and covers all learnable dataset types of SNNs. ii) We analyze the robustness differences between different learning rules and between SNN and ANN, which suggests that SNN no longer has inherent robustness under backdoor attacks. iii) We reveal the vulnerability of conversion-dependent learning rules caused by backdoor migration and further analyze the migration ability during the conversion process, finding that the backdoor migration rate can even exceed 99%. iv) Finally, we discuss potential countermeasures against this kind of backdoor attack and its technical challenges and point out several promising research directions.

cs.CR↗