arXiv subjects

Ze Li

Publications and source records attributed to Ze Li.

At least 37 records · Page 2Linked to original sources

arXiv2025

On Charged Black Holes in Einstein-Weyl-Maxwell Theory

There exist two branches of static and spherically symmetric black hole solutions in Einstein-Weyl theory: one is the Schwarzschild black hole, and the other is a numerically constructed black hole that bifurcates from the Schwarzschild solution. Similarly, there are two branches of charged black holes in Einstein-Weyl-Maxwell theory. We have uncovered the relationships between the charged black holes and the neutral ones. The two charged black holes branch out from the bifurcation point of the neutral ones once charge is added. We found that one of the charged black holes is entirely different from the Reissner-Nordström (RN) black hole, while the other is similar to the RN black hole. In particular, the RN-like black hole approaches the RN black hole as the charge increases. We calculated the charge-to-mass ratio of the RN-like charged black hole in the near-extremal limit, and the value is less than that of the extremal RN black hole. Since we consider the higher-derivative Weyl square term as part of the classical gravity theory, rather than as a quantum effect, our result sets a lower bound on the charge-to-mass ratio in the context of the Weak Gravity Conjecture.

gr-qc↗

arXiv2025

Revisiting holographic dark energy from the perspective of multi-messenger gravitational wave astronomy: future joint observations with short gamma-ray bursts

The advent of third-generation (3G) gravitational-wave (GW) detectors opens new opportunities for multi-messenger observations of binary neutron star merger events, holding significant potential for probing the history of cosmic expansion. In this paper, we investigate the holographic dark energy (HDE) model by using the future GW standard siren data observed from the 3G GW detectors and the short $γ$-ray burst THESEUS-like detector joint observations. We find that GW data alone can achieve a relatively precise estimation of the Hubble constant, with precision of $0.2\%$-$0.6\%$, but its ability to constrain other cosmological parameters remains limited. Nonetheless, since the GW data can break parameter degeneracies generated by the mainstream EM observations, CMB + BAO + SN (CBS), GW standard sirens play a crucial role in enhancing the accuracy of parameter estimation. With the addition of GW data to CBS, the constraints on cosmological parameters $H_0$, $c$ and $Ω_{\rm{m}}$ can be improved by $63\%$-$88\%$, $27\%$-$44\%$ and $55\%$-$70\%$. In summary, observations of GW standard sirens from 3G GW detectors could be pivotal in helping solve the Hubble tension and probe the fundamental nature of dark energy.

astro-ph.CO↗

arXiv2025

Positional encoding is not the same as context: A study on positional encoding for sequential recommendation

The rapid growth of streaming media and e-commerce has driven advancements in recommendation systems, particularly Sequential Recommendation Systems (SRS). These systems employ users' interaction histories to predict future preferences. While recent research has focused on architectural innovations like transformer blocks and feature extraction, positional encodings, crucial for capturing temporal patterns, have received less attention. These encodings are often conflated with contextual, such as the temporal footprint, which previous works tend to treat as interchangeable with positional information. This paper highlights the critical distinction between temporal footprint and positional encodings, demonstrating that the latter offers unique relational cues between items, which the temporal footprint alone cannot provide. Through extensive experimentation on eight Amazon datasets and subsets, we assess the impact of various encodings on performance metrics and training stability. We introduce new positional encodings and investigate integration strategies that improve both metrics and stability, surpassing state-of-the-art results at the time of this work's initial preprint. Importantly, we demonstrate that selecting the appropriate encoding is not only key to better performance but also essential for building robust, reliable SRS models.

cs.IR↗

arXiv2025

Robust Low-Light Human Pose Estimation through Illumination-Texture Modulation

As critical visual details become obscured, the low visibility and high ISO noise in extremely low-light images pose a significant challenge to human pose estimation. Current methods fail to provide high-quality representations due to reliance on pixel-level enhancements that compromise semantics and the inability to effectively handle extreme low-light conditions for robust feature learning. In this work, we propose a frequency-based framework for low-light human pose estimation, rooted in the "divide-and-conquer" principle. Instead of uniformly enhancing the entire image, our method focuses on task-relevant information. By applying dynamic illumination correction to the low-frequency components and low-rank denoising to the high-frequency components, we effectively enhance both the semantic and texture information essential for accurate pose estimation. As a result, this targeted enhancement method results in robust, high-quality representations, significantly improving pose estimation performance. Extensive experiments demonstrating its superiority over state-of-the-art methods in various challenging low-light scenarios.

cs.CV↗

arXiv2024

Deoxys: A Causal Inference Engine for Unhealthy Node Mitigation in Large-scale Cloud Infrastructure

The presence of unhealthy nodes in cloud infrastructure signals the potential failure of machines, which can significantly impact the availability and reliability of cloud services, resulting in negative customer experiences. Effectively addressing unhealthy node mitigation is therefore vital for sustaining cloud system performance. This paper introduces Deoxys, a causal inference engine tailored to recommending mitigation actions for unhealthy node in cloud systems to minimize virtual machine downtime and interruptions during unhealthy events. It employs double machine learning combined with causal forest to produce precise and reliable mitigation recommendations based solely on limited observational data collected from the historical unhealthy events. To enhance the causal inference model, Deoxys further incorporates a policy fallback mechanism based on model uncertainty and action overriding mechanisms to (i) improve the reliability of the system, and (ii) strike a good tradeoff between downtime reduction and resource utilization, thereby enhancing the overall system performance. After deploying Deoxys in a large-scale cloud infrastructure at Microsoft, our observations demonstrate that Deoxys significantly reduces average VM downtime by 53% compared to a legacy policy, while leading to 49.5% lower VM interruption rate. This substantial improvement enhances the reliability and stability of cloud platforms, resulting in a seamless customer experience.

eess.SY↗

arXiv2024

The Database and Benchmark for the Source Speaker Tracing Challenge 2024

Voice conversion (VC) systems can transform audio to mimic another speaker's voice, thereby attacking speaker verification (SV) systems. However, ongoing studies on source speaker verification (SSV) are hindered by limited data availability and methodological constraints. This paper presents the Source Speaker Tracking Challenge (SSTC) on STL 2024, which aims to fill the gap in the database and benchmark for the SSV task. In this study, we generate a large-scale converted speech database with 16 common VC methods and train a batch of baseline systems based on the MFA-Conformer architecture. In addition, we introduced a related task called conversion method recognition, with the aim of assisting the SSV task. We expect SSTC to be a platform for advancing the development of the SSV task and provide further insights into the performance and limitations of current SV systems against VC attacks. Further details about SSTC can be found in https://sstc-challenge.github.io/.

eess.AS↗

arXiv2024

Encoder-Decoder Framework for Interactive Free Verses with Generation with Controllable High-Quality Rhyming

Composing poetry or lyrics involves several creative factors, but a challenging aspect of generation is the adherence to a more or less strict metric and rhyming pattern. To address this challenge specifically, previous work on the task has mainly focused on reverse language modeling, which brings the critical selection of each rhyming word to the forefront of each verse. On the other hand, reversing the word order requires that models be trained from scratch with this task-specific goal and cannot take advantage of transfer learning from a Pretrained Language Model (PLM). We propose a novel fine-tuning approach that prepends the rhyming word at the start of each lyric, which allows the critical rhyming decision to be made before the model commits to the content of the lyric (as during reverse language modeling), but maintains compatibility with the word order of regular PLMs as the lyric itself is still generated in left-to-right order. We conducted extensive experiments to compare this fine-tuning against the current state-of-the-art strategies for rhyming, finding that our approach generates more readable text and better rhyming capabilities. Furthermore, we furnish a high-quality dataset in English and 12 other languages, analyse the approach's feasibility in a multilingual context, provide extensive experimental results shedding light on good and bad practices for lyrics generation, and propose metrics to compare methods in the future.

cs.CL↗

arXiv2024

Dynamics for the corotational energy-critical wave map equation with quantized blow-up rates

We consider the wave maps from $\mathbb{R}^{1+2}$ into $\mathbb{S}^2\subset \mathbb{R}^3.$ Under an additional assumption of $k$-corotational symmetry, the problem reduces to the one dimensional semilinear wave equation: \begin{equation*} \partial_t^2 u-\partial_r^2 u-\frac{\partial_r u}{r}+k^2 \frac{\sin(2u)}{2r^2}=0. \end{equation*} Given any integer $k\ge 1$ and any integer $m\ge 2k,$ we exhibit a set of initial data $(u_0,u_1)$ with energy arbitrarily close to that of the ground state solution $Q$, such that the corresponding solution $u$ blows up in finite time by concentrating its energy. To be precise, the solution $u$ satisfies \begin{equation*} \lim\limits_{t\rightarrow T} \left\|\left(u(t,r)-Q\left(\frac{r}{λ(t)}\right)-u_1^*(r), \partial_t u-u_2^*(r)\right)\right\|_{H\times L^2}=0 \end{equation*} with a quantized speed \begin{equation*} λ(t)=c(u_0,u_1)(1+o_{t\to T}(1))\frac{(T-t)^{\frac{m}{k}}}{|\log(T-t)|^{\frac{m}{k(m-k)}}}, \end{equation*} where $\|u\|_{H}:=\int_{\mathbb{R}^2}\left(|\partial_r u|^2+\frac{|u|^2}{r^2}\right).$

math.AP↗

arXiv2024

Why does Prediction Accuracy Decrease over Time? Uncertain Positive Learning for Cloud Failure Prediction

With the rapid growth of cloud computing, a variety of software services have been deployed in the cloud. To ensure the reliability of cloud services, prior studies focus on failure instance (disk, node, and switch, etc.) prediction. Once the output of prediction is positive, mitigation actions are taken to rapidly resolve the underlying failure. According to our real-world practice in Microsoft Azure, we find that the prediction accuracy may decrease by about 9% after retraining the models. Considering that the mitigation actions may result in uncertain positive instances since they cannot be verified after mitigation, which may introduce more noise while updating the prediction model. To the best of our knowledge, we are the first to identify this Uncertain Positive Learning (UPLearning) issue in the real-world cloud failure prediction scenario. To tackle this problem, we design an Uncertain Positive Learning Risk Estimator (Uptake) approach. Using two real-world datasets of disk failure prediction and conducting node prediction experiments in Microsoft Azure, which is a top-tier cloud provider that serves millions of users, we demonstrate Uptake can significantly improve the failure prediction accuracy by 5% on average.

cs.DC↗

arXiv2023

Time-optimal universal quantum gates on superconducting circuits

Decoherence is inevitable when manipulating quantum systems. It decreases the quality of quantum manipulations and thus is one of the main obstacles for large-scale quantum computation, where high-fidelity quantum gates are needed. Generally, the longer a gate operation is, the more decoherence-induced gate infidelity will be. Therefore, how to shorten the gate time becomes an urgent problem to be solved. To this end, time-optimal control based on solving the quantum brachistochrone equation is a straightforward solution. Here, based on time-optimal control, we propose a scheme to realize universal quantum gates on superconducting qubits in a two-dimensional square lattice configuration, and the two-qubit gate fidelity approaches 99.9\%. Meanwhile, we can further accelerate the Z-axis gate considerably by adjusting the detuning of the external driving. Finally, in order to reduce the influence of the dephasing error, decoherence-free subspace encoding is also incorporated in our physical implementation. Therefore, we present a fast quantum scheme which is promising for large-scale quantum computation.

quant-ph↗

arXiv2023

Multi-objective Progressive Clustering for Semi-supervised Domain Adaptation in Speaker Verification

Utilizing the pseudo-labeling algorithm with large-scale unlabeled data becomes crucial for semi-supervised domain adaptation in speaker verification tasks. In this paper, we propose a novel pseudo-labeling method named Multi-objective Progressive Clustering (MoPC), specifically designed for semi-supervised domain adaptation. Firstly, we utilize limited labeled data from the target domain to derive domain-specific descriptors based on multiple distinct objectives, namely within-graph denoising, intra-class denoising and inter-class denoising. Then, the Infomap algorithm is adopted for embedding clustering, and the descriptors are leveraged to further refine the target domain's pseudo-labels. Moreover, to further improve the quality of pseudo labels, we introduce the subcenter-purification and progressive-merging strategy for label denoising. Our proposed MoPC method achieves 4.95% EER and ranked the 1$^{st}$ place on the evaluation set of VoxSRC 2023 track 3. We also conduct additional experiments on the FFSVC dataset and yield promising results.

eess.AS↗

arXiv2023

The DKU-MSXF Speaker Verification System for the VoxCeleb Speaker Recognition Challenge 2023

This paper is the system description of the DKU-MSXF System for the track1, track2 and track3 of the VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC-23). For Track 1, we utilize a network structure based on ResNet for training. By constructing a cross-age QMF training set, we achieve a substantial improvement in system performance. For Track 2, we inherite the pre-trained model from Track 1 and conducte mixed training by incorporating the VoxBlink-clean dataset. In comparison to Track 1, the models incorporating VoxBlink-clean data exhibit a performance improvement by more than 10% relatively. For Track3, the semi-supervised domain adaptation task, a novel pseudo-labeling method based on triple thresholds and sub-center purification is adopted to make domain adaptation. The final submission achieves mDCF of 0.1243 in task1, mDCF of 0.1165 in Track 2 and EER of 4.952% in Track 3.

eess.AS↗

arXiv2023

On holographic time-like entanglement entropy

In order to study the pseudo entropy of time-like subregions holographically, the previous smooth space-like extremal surface was recently generalized to mix space-like and time-like segments and the area becomes complex value. This paper finds that, if one tries to use such kind of piecewise smooth extremal surfaces to compute time-like entanglement entropy holographically, the complex area is not unique in general. We then generalize the original holographic proposal of space-like entanglement entropy to pick up a unique area from all allowed ``space-like+time-like'' piecewise smooth extremal surfaces for a time-like subregion. We will give some concrete examples to show the correctness of our proposal.

hep-th↗

arXiv2023

Effect of AC current annealing on the microstructure, magnetism and magnetoimpedance of CoFeSiBNb$_3$ microfibers

This paper systematically studies the changes in the microstructure and magnetic properties of CoFeSiBNb$_3$ metallic microfibers before and after AC annealing. The influence of current intensity on the magneto-impedance (MI) effect of the microfibers was analyzed and the microstructure changes of the microfibers before and after annealing were explored by means of high-resolution transmission electron microscopy. Using this data, the mechanism of current annealing to improve the MI characteristics was further revealed. The results show that the surface of the CoFeSiBNb$_3$ metallic microfibers after AC current annealing is smooth and continuous; its general magnetic properties and MI ratio increase at first with current intensity and then decrease at higher intensities. During the current annealing process, Joule heat eliminates residual stresses in the microfibers while forming atomically ordered micro-domains, which improves the degree of its organizational order. Meanwhile, a stable toroidal magnetic field is generated, which promotes the distribution of the magnetic domain structure of the microfibers, thereby improving the MI effect.

physics.app-ph↗

arXiv2022

Global regularity of Skew mean curvature flow for small data in $d\geq 4$ dimensions

The skew mean curvature flow is an evolution equation for a $d$ dimensional manifold immersed into $\mathbb{R}^{d+2}$, and which moves along the binormal direction with a speed proportional to its mean curvature. In this article, we prove small data global regularity in low-regularity Sobolev spaces for the skew mean curvature flow in dimensions $d\geq 4$. This extends the local well-posedness result in \cite{HT}.

math.AP↗

arXiv2022

Design of Core-Shell Structured Magnetic Microwires with Desirable Properties for Multifunctional Applications

Amorphous Co-rich microwires with excellent soft magnetic and mechanical properties produced by melt-extraction technique are emerging as a multifunctional material for a variety of applications ranging from ultrasensitive magnetic field sensors to structural health self-monitoring composites. There is a pressing need for enhancing these properties to make the microwires practical for integration into new technologies. Conventional heat treatments at temperature below crystallization may improve the magnetic softness of an as-quenched amorphous wire, but usually deteriorate the good mechanical characteristic of the wire due to crystallization. To overcome this, we propose a new approach that utilizes the advantages of a multi-step Joule current annealing method to design novel (nanocrystal, amorphous)/amorphous core/shell structures directly from as-quenched amorphous microwires. These results show that the density and size of nanocrystals in the core can be optimized by controlling the Joule current intensity, resulting in the large enhancement of soft magnetic and giant magneto-impedance properties, while the amorphous shell preserves the excellent mechanical strength of the microwire. This study also provides a new pathway for the design of novel core/shell structures directly from rapidly quenched amorphous magnetic materials that are currently exploited in high frequency transformers, sensing and cooling devices.

cond-mat.mtrl-sci↗

arXiv2021

Global and local theory of skew mean curvature flows

In this paper, we study the skew mean curvature flow. The results are threefold. First, we prove the global regularity of solutions with initial data which are small perturbations of planes in Sobolev spaces. Second, we prove the modified scattering and the existence of wave operators for small data, which completely determines the set of asymptotic states. Third, we study the Cauchy problem for arbitrary large data.

math.AP↗

arXiv2021

On long time dynamics of 1D Schrödinger map flows

In this paper, we study the long time dynamics of small solutions to Schrödinger map flows from $\Bbb R$ to Riemannian surfaces. The results are threefold. (i) We prove that for general Riemannian surface targets the points with some geometric condition can be completely divided into two categories according to the sectional curvature so that the long time dynamics of small solutions of 1D Schrödinger map flow near them are described by modified scattering and scattering respectively for the two categories. (ii) If the geometric condition fails, we prove that solutions with slow time growth in frequency space and sharp time decay in physical space, which scatter or scatter by a phrase correction, must be trivial. (iii) We also prove the asymptotic completeness in $L^2$ spaces for 1D SMF into general Riemannian surface near points without any geometric assumptions. Compared with our previous works [26,27] on higher dimensional Schrödinger map flows where resolution to finite numbers of radiation terms in energy space was proved for small solutions, the results of this work reveal the essentially different and diverse dynamical behaviors of 1D Schrödinger map flows.

math.AP↗