arXiv subjects

Liang Wu

Publications and source records attributed to Liang Wu.

At least 91 records · Page 5Linked to original sources

arXiv2025

Superconducting Diode Effect in Gradiently Strained Nb0.5Ti0.5N Films

The superconducting diode effect (SDE), combining superconductivity with diode-like nonreciprocal current flow, recently emerges as an ideal candidate for zero-dissipation electronic circuits. Such technologically advantageous diodes are achieved by intricate material engineering to disrupt inversion symmetry, which leads to the production challenges as well as a limited pool of viable materials. Here we exploit the gradient interfacial strain to experimentally induce the SDE in Nb0.5Ti0.5N (NTN) films grown on MgO substrates. Additionally, the SDE is tunable with an in-plane magnetic field and can be further enhanced by introducing an interfacial anisotropic pinning potential. Our findings establish interfacial strain gradient as a versatile tool for creating and enhancing tunable SDE.

cond-mat.supr-con↗

arXiv2025

Broad-band THz emission by Spin-to-Charge Conversion in Topological Material -- Ferromagnet Heterostructures

Terahertz spintronic devices combine ultrafast operation with low power consumption, making them strong candidates for next-generation memory technologies. In this study, we use time-domain terahertz emission spectroscopy to investigate spin-to-charge conversion (SCC) in bilayer heterostructures comprising topological insulators (TIs) or Weyl semimetals (WSMs) with ferromagnetic metals (FMs). SCC is studied in TI materials \ce{Bi2Se3}, Pb-doped \ce{Bi2Se3}, and (Bi$_{1-x}$Sb$_x$)$_2$Te$_3$, and the WSM NbP. Our results reveal that the dependence of SCC on TI thickness varies with interface quality, indicating that thickness dependence alone is not a reliable criterion for distinguishing between inverse spin Hall effect and the inverse Rashba--Edelstein effect mechanisms. We find efficient SCC in TIs depends on both \textit{in-situ} growth to prevent surface oxidation and proper composition. In NbP$\vert$FM bilayers, we observe THz emission with efficiency and bandwidth comparable to that of TIs, highlighting the broader potential of topological materials. Finally, broadband spectral measurements demonstrate that both TIs and WSMs can generate THz pulses with frequencies extending up to 8\,THz. These findings underscore the promise of topological materials as efficient platforms for ultrafast, broadband spintronic applications.

cond-mat.mtrl-sci↗

arXiv2025

LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations

Graph recommendation methods, representing a connected interaction perspective, reformulate user-item interactions as graphs to leverage graph structure and topology to recommend and have proved practical effectiveness at scale. Large language models, representing a textual generative perspective, excel at modeling user languages, understanding behavioral contexts, capturing user-item semantic relationships, analyzing textual sentiments, and generating coherent and contextually relevant texts as recommendations. However, there is a gap between the connected graph perspective and the text generation perspective as the task formulations are different. A research question arises: how can we effectively integrate the two perspectives for more personalized recsys? To fill this gap, we propose to incorporate graph-edge information into LLMs via prompt and attention innovations. We reformulate recommendations as a probabilistic generative problem using prompts. We develop a framework to incorporate graph edge information from the prompt and attention mechanisms for graph-structured LLM recommendations. We develop a new prompt design that brings in both first-order and second-order graph relationships; we devise an improved LLM attention mechanism to embed direct the spatial and connectivity information of edges. Our evaluation of real-world datasets demonstrates the framework's ability to understand connectivity information in graph data and to improve the relevance and quality of recommendation results.

cs.AI↗

arXiv2025

A Quadratic Programming Algorithm with $O(n^3)$ Time Complexity

Solving linear systems and quadratic programming (QP) problems are both ubiquitous tasks in the engineering and computing fields. Direct methods for solving systems, such as Cholesky, LU, and QR factorizations, exhibit data-independent time complexity of $O(n^3)$. This raises a natural question: could there exist algorithms for solving QPs that also achieve \textit{data-independent} time complexity of $O(n^3)$? This raises a natural question: could there exist algorithms for solving QPs that also achieve data-independent time complexity of $O(n^3)$? This is critical for offering an execution time certificate for real-time optimization-based applications such as model predictive control. This article first demonstrates that solving real-time strictly convex QPs, Lasso problems, and support vector machine problems can be turned into solving box-constrained QPs (Box-QPs), which support a cost-free initialization strategy for feasible interior-point methods (IPMs). Next, focusing on solving Box-QPs, this article replaces the exact Newton step with an approximated Newton step (substituting the matrix-inversion operation with multiple rank-1 updates) within feasible IPMs. For the first time, this article proposes an implementable feasible IPM algorithm with $O(n^3)$ time complexity, by proving the number of iterations is exact $O(\sqrt{n})$ and the number of rank-1 updates is bounded by $O(n)$. Numerical validations/applications and codes are provided.

math.OC↗

arXiv2025

Large Topological Magnetic Optical Effects and Imaging of Antiferromagnetic Octupole Domains of an Altermagnet-like Weyl Semimetal

Pyrochlore iridates have attracted significant interest due to their complex phase behavior arising from the interplay among electron correlations, quantum metric in flat bands, geometrically frustrated lattices, and topology induced by strong spin-orbit coupling. In this study, we focus on Eu$_2$Ir$_2$O$_7$ thin films oriented along the (111) crystallographic direction. This quantum material, identified as an antiferromagnetic Weyl semimetal, exhibits a large anomalous Hall effect in transport experiments. Here we employ optical circular dichroism microscopy, to directly image ferroic octupole order and resolve all-in--all-out and all-out--all-in antiferromagnetic domains below the Néel temperature. Remarkably, despite the absence of a detectable net magnetic moment at zero applied magnetic field, we detected a large magnetic circular dichroism signal ($\sim 10^{-4}$) and Kerr effect ($\sim 10^{-4}$ radians) in zero magnetic field attributable to Berry curvature effects from Weyl nodes. Eu$_2$Ir$_2$O$_7$ is a non-collinear magnet with vanishing net moment and magnetic octupole order, similar to the recently proposed collinear d-wave altermagnets, allowing for magneto-optical responses and anomalous Hall effect. This finding likely represents the first demonstration of magnetic circular dichroism and Kerr effect in a topologically non-trivial quantum antiferromagnet with a vanishing net magnetization. Our work opens up the possibility of ultrafast domain switching in the terahertz frequency and the domain wall dynamics in the magnetic Weyl systems, which establishes the foundation for topological antiferromagnetic spintronics.

cond-mat.mtrl-sci↗

arXiv2025

Probing massive neutrinos and modified gravity with redshift-space morphologies and anisotropies of large-scale structure

Strong degeneracy exists between some modified gravity (MG) models and massive neutrinos because the enhanced structure growth produced by modified gravity can be suppressed due to the free-streaming massive neutrinos. Previous works showed this degeneracy can be broken with non-Gaussian or velocity information. Therefore in this work, we focus on the large-scale structure (LSS) in redshift space and investigate for the first time the possibility of using the non-Gaussian information and velocity information captured by the 3D scalar Minkowski functionals (MFs) and the 3D Minkowski tensors (MTs) to break this degeneracy. Based on the Quijote and Quijote-MG simulations, we find the imprints on redshift space LSS left by the Hu-Sawicki $f(R)$ gravity can be discriminated from those left by massive neutrinos with these statistics. With the Fisher information formalism, we first show how the MTs extract information with their perpendicular and parallel elements for both low- and high-density regions; then we compare constraints from the power spectrum monopole and MFs in real space with those in redshift space, and investigate how the constraining power is further improved with anisotropies captured by the quadrupole and hexadecapole of the power spectrum and the MTs; finally, we combine the power spectrum multipoles with MFs plus MTs and find the constraints from the power spectrum multipoles on $Ω_{\mathrm{m}}, h, σ_8$, $M_ν$, and $f_{R_0}$ can be improved, because they are complemented with non-Gaussian information, by a factor of 3.4, 3.0, 3.3, 3.3, and 1.9 on small scales ($k_{\rm{max}}=0.5~h\rm{Mpc}^{-1},\ R_G=5~h^{-1}\rm{Mpc}$), and 2.8, 2.2, 3.4, 3.4, and 1.5 on large scales ($k_{\rm{max}}=0.25~h\rm{Mpc}^{-1},\ R_G=10~h^{-1}\rm{Mpc}$).

astro-ph.CO↗

arXiv2025

Quantized Magneto-Terahertz Effects in the Antiferromagnetic Topological Insulator MnBi$_2$Te$_4$ Thin Films

MnBi$_2$Te$_4$ (MBT) is an ideal platform for studying the interplay between magnetism and topology. Many exotic topological phenomena, such as the quantum anomalous Hall effect and the axion insulator, have been observed in few-layer MBT. A key feature in MBT is the emergence of the surface exchange gap, which lies in the milli-electron-volt range (1 THz corresponds to 4.14 meV). This makes THz spectroscopy a powerful tool to probe the associated topological physics. In this study, we report the THz spectra of Faraday and Kerr rotations in MBT thin films grown by molecular beam epitaxy. By varying the external magnetic field, we observe three magnetic states: the antiferromagnetic state, the canted antiferromagnetic state, and the ferromagnetic state. Our terahertz results show a quantized Hall state under 6 T in both 6 SL and 7 SL samples without gate voltage. These findings provide new insights into the magneto-terahertz properties of MBT and its potential for topological spintronic applications.

cond-mat.mes-hall↗

arXiv2025

Stability-based Generalization Analysis of Randomized Coordinate Descent for Pairwise Learning

Pairwise learning includes various machine learning tasks, with ranking and metric learning serving as the primary representatives. While randomized coordinate descent (RCD) is popular in various learning problems, there is much less theoretical analysis on the generalization behavior of models trained by RCD, especially under the pairwise learning framework. In this paper, we consider the generalization of RCD for pairwise learning. We measure the on-average argument stability for both convex and strongly convex objective functions, based on which we develop generalization bounds in expectation. The early-stopping strategy is adopted to quantify the balance between estimation and optimization. Our analysis further incorporates the low-noise setting into the excess risk bound to achieve the optimistic bound as $O(1/n)$, where $n$ is the sample size.

cs.LG↗

arXiv2025

EIQP: Execution-time-certified and Infeasibility-detecting QP Solver

Solving real-time quadratic programming (QP) is a ubiquitous task in control engineering, such as in model predictive control and control barrier function-based QP. In such real-time scenarios, certifying that the employed QP algorithm can either return a solution within a predefined level of optimality or detect QP infeasibility before the predefined sampling time is a pressing requirement. This article considers convex QP (including linear programming) and adopts its homogeneous formulation to achieve infeasibility detection. Exploiting this homogeneous formulation, this article proposes a novel infeasible interior-point method (IPM) algorithm with the best theoretical $O(\sqrt{n})$ iteration complexity that feasible IPM algorithms enjoy. The iteration complexity is proved to be \textit{exact} (rather than an upper bound), \textit{simple to calculate}, and \textit{data independent}, with the value $\left\lceil\frac{\log(\frac{n+1}ε)}{-\log(1-\frac{0.414213}{\sqrt{n+1}})}\right\rceil$ (where $n$ and $ε$ denote the number of constraints and the predefined optimality level, respectively), making it appealing to certify the execution time of online time-varying convex QPs. The proposed algorithm is simple to implement without requiring a line search procedure (uses the full Newton step), and its C-code implementation (offering MATLAB, Julia, and Python interfaces) and numerical examples are publicly available at https://github.com/liangwu2019/EIQP.

eess.SY↗

arXiv2024

ChatGPT-Assisted Visualization of Atomic Orbitals: Understanding Symmetry, Mixed State, and Superposition

For undergraduate students newly introduced to quantum mechanics, solving simple Schrödinger equations is relatively straightforward. However, the more profound challenge lies in comprehending the underlying physical principles embedded in the solutions. During my academic experience, a recurring conceptual difficulty was understanding why only $s$ orbitals, and not others like $p$ orbitals, exhibit spherical symmetry. At first glance, this seems paradoxical, given that the potential energy function itself is spherically symmetric. Specifically, why do $p$ orbitals adopt a dumbbell shape instead of a spherical one? For a hydrogen atom with an electron in the $2p$ state, which specific $2p$ orbital does the electron occupy, and how do the $x$, $y$, and $z$ axes in $2p_x$, $2p_y$, and $2p_z$ connect to the real world? Additionally, is the atom still spherically symmetric in such a state? These questions relate to core concepts of quantum mechanics concerning symmetry, mixed state, and superposition. This paper delves into these questions by investigating this specific case, utilizing the advanced visualization capabilities offered by ChatGPT. This paper underscores the importance of emerging AI tools in enhancing students' understanding of abstract principles.

physics.ed-ph↗

arXiv2024

Time-resolved magneto-optical effects in the altermagnet candidate MnTe

$α$-MnTe is an antiferromagnetic semiconductor with above room temperature $T_N$ = 310 K, which is promising for spintronic applications. Recently, it was reported to be an altermagnet, containing bands with momentum-dependent spin splitting; time-resolved experimental probes of MnTe are therefore important both for understanding novel magnetic properties and potential device applications. We investigate ultrafast spin dynamics in epitaxial MnTe(001)/InP(111) thin films using pump-probe magneto-optical measurements in the Kerr configuration. At room temperature, we observe an oscillation mode at 55 GHz that does not appear at zero magnetic field. Combining field and polarization dependence, we identify this mode as a magnon, likely originating from inverse stimulated Raman scattering. Magnetic field-dependent oscillations persist up to at least 335 K, which could reflect coupling to known short-range magnetic order in MnTe above $T_N$. Additionally, we observe two optical phonons at 3.6 THz and 4.2 THz, which broaden and redshift with increasing temperature.

cond-mat.mtrl-sci↗

arXiv2024

Magneto-optical effects of an artificially-layered ferromagnetic topological insulator with T$_C$ of 160 K

Magnetic topological insulator is a fertile platform to study the interplay between magnetism and topology. The unique electronic band structure can induce exotic transport and optical properties. However, a comprehensive optical study in both near-infrared frequency and terahertz frequency has been lacking. Here, we report magneto-optical effects from a heterostructure of Cr-incorporated topological insulator, CBST. We use 800 nm magneto-optical Kerr effect to reveal a ferromagnetic order in the CBST film with a high transition temperature at 160 K. We also use time-domain terahertz polarimetry to reveal a terahertz Faraday rotation of 1.5 mrad and Kerr rotation of 5.1 mrad at 2 K. The calculated terahertz Hall conductance is 0.42 $e^2/h$. Our work shows the optical responses of an artificially layered magnetic topological insulator, paving the way towards high-temperature quantum anomalous Hall effect via heterostructure engineering.

cond-mat.mtrl-sci↗

arXiv2024

Merging Multiple Datasets for Improved Appearance-Based Gaze Estimation

Multiple datasets have been created for training and testing appearance-based gaze estimators. Intuitively, more data should lead to better performance. However, combining datasets to train a single esti-mator rarely improves gaze estimation performance. One reason may be differences in the experimental protocols used to obtain the gaze sam-ples, resulting in differences in the distributions of head poses, gaze an-gles, illumination, etc. Another reason may be the inconsistency between methods used to define gaze angles (label mismatch). We propose two innovations to improve the performance of gaze estimation by leveraging multiple datasets, a change in the estimator architecture and the intro-duction of a gaze adaptation module. Most state-of-the-art estimators merge information extracted from images of the two eyes and the entire face either in parallel or combine information from the eyes first then with the face. Our proposed Two-stage Transformer-based Gaze-feature Fusion (TTGF) method uses transformers to merge information from each eye and the face separately and then merge across the two eyes. We argue that this improves head pose invariance since changes in head pose affect left and right eye images in different ways. Our proposed Gaze Adaptation Module (GAM) method handles annotation inconsis-tency by applying a Gaze Adaption Module for each dataset to correct gaze estimates from a single shared estimator. This enables us to combine information across datasets despite differences in labeling. Our experi-ments show that these innovations improve gaze estimation performance over the SOTA both individually and collectively (by 10% - 20%). Our code is available at https://github.com/HKUST-NISL/GazeSetMerge.

cs.CV↗

arXiv2024

A Parallel Vector-form $LDL^\top$ Decomposition for Accelerating Execution-time-certified $\ell_1$-penalty Soft-constrained MPC

Handling possible infeasibility and providing an execution time certificate are two pressing requirements of real-time Model Predictive Control (MPC). To meet these two requirements simultaneously, this paper proposes an $\ell_1$-penalty soft-constrained MPC formulation that is globally feasible and solvable with an execution time certificate using our proposed algorithm. This paper proves for the first time that $\ell_1$-penalty soft-constrained MPC problems can be equivalently transformed into a box-constrained quadratic programming (Box-QP) and then our previous execution-time-certified algorithm \cite{wu2023direct} (only limited to Box-QP) can be applied. However, our previous Box-QP algorithm \cite{wu2023direct}, which provides a theoretical execution-time certificate, is conservative in its iteration analysis, thus sacrificing computation efficiency. To this end, this paper proposes a novel $LDL^\top$ decomposition for the first time, to accelerate the computation of Newton step at each iteration. The speedup of our $LDL^\top$ decomposition comes from two-fold: \textit{i)} exploitation of the fact that the number of inequality constraints is generally larger than the number of variables in condensed MPC formulations, \textit{ii)} vectorized and parallel implementation based on based on its vector-wise operations, instead of element-wise operations of previous decomposition methods. Numerical experiments demonstrate great speedups of the proposed $LDL^\top$ decomposition (even up to 1000-fold, compared to the standard Choleksky method), which thus helps our solver achieve comparable computation performance to the state-of-the-art solvers such as IPOPT and OSQP. Code is available at \url{https://github.com/liangwu2019/L1-penalty-QP}.

eess.SY↗

arXiv2024

Understanding and Modeling Job Marketplace with Pretrained Language Models

Job marketplace is a heterogeneous graph composed of interactions among members (job-seekers), companies, and jobs. Understanding and modeling job marketplace can benefit both job seekers and employers, ultimately contributing to the greater good of the society. However, existing graph neural network (GNN)-based methods have shallow understandings of the associated textual features and heterogeneous relations. To address the above challenges, we propose PLM4Job, a job marketplace foundation model that tightly couples pretrained language models (PLM) with job market graph, aiming to fully utilize the pretrained knowledge and reasoning ability to model member/job textual features as well as various member-job relations simultaneously. In the pretraining phase, we propose a heterogeneous ego-graph-based prompting strategy to model and aggregate member/job textual features based on the topological structure around the target member/job node, where entity type embeddings and graph positional embeddings are introduced accordingly to model different entities and their heterogeneous relations. Meanwhile, a proximity-aware attention alignment strategy is designed to dynamically adjust the attention of the PLM on ego-graph node tokens in the prompt, such that the attention can be better aligned with job marketplace semantics. Extensive experiments at LinkedIn demonstrate the effectiveness of PLM4Job.

cs.IR↗

arXiv2024

Is quasar variability regulated by the close environment of accretion?

UV/optical variability in quasars is a well-observed phenomenon, yet its primeval origins remain unclear. This study investigates whether the accretion disk turbulence, which is responsible for UV/optical variability, is influenced by the close environment of the accretion by analyzing the correlation between variability and infrared emission for two luminous SDSS quasar samples. The first sample includes light curves from SDSS, Pan-STARRS, and ZTF $g$ band photometry, while the second sample utilizes SDSS Stripe 82 $g$ band light curves. We explore the correlation between the $g$ band excess variance ($σ_{rms}$) and the wavelength-dependent infrared covering factor ($L_{\rm IR}(λ)/L_{\rm bol}$), controlling for the effects of redshift, luminosity, and black hole mass. An anti-correlation between two variables is observed in both samples, which is strongest at wavelengths of 2-3$μ$m but gradually weakens towards longer wavelength. This suggests the equatorial dusty torus (which dominates near-infrared emission) plays a significant role in influencing the UV/optical variability, while the cooler polar dust (which contributes significantly to mid-infrared emission) does not. The findings indicate that quasar variability may be connected to the physical conditions within the dusty torus which feeds the accretion, and support the notion that the close environment of the accretion plays an important role in regulating the accretion disk turbulence.

astro-ph.GA↗

arXiv2024

Imprinting spin patterns by local strain control in a van der Waals antiferromagnet

Van der Waals magnets provide opportunities for exploring low-dimensional magnetism and spintronic phenomena. The Mermin-Wagner theorem states that long-range correlations in reduced dimensions are stabilized and controlled by magnetic anisotropy. In this study, we meticulously create and control the in-plane easy-axis magnetic anisotropy within two-dimensional (2D) van der Waals antiferromagnet MnPSe3 via a novel method involving topography and therefore strain control by using a micro-patterned substrate. By transposing the MnPSe3 thin flakes onto a substrate patterned with micro-scale grooves, we introduce local uniaxial strain pattern, which not only locks the spin direction to the strain direction but also replicates the groove pattern in the spin orientation distribution. Our approach generates spin orientations that correspond to the substrate patterns, therefore having the potential to significantly advance spintronic devices by offering a unique method for manipulating and designing spin textures in easy-plane magnets.

cond-mat.mtrl-sci↗

arXiv2024

ERD: Exponential Retinex decomposition based on weak space and hybrid nonconvex regularization and its denoising application

The Retinex theory models the image as a product of illumination and reflection components, which has received extensive attention and is widely used in image enhancement, segmentation and color restoration. However, it has been rarely used in additive noise removal due to the inclusion of both multiplication and addition operations in the Retinex noisy image modeling. In this paper, we propose an exponential Retinex decomposition model based on hybrid non-convex regularization and weak space oscillation-modeling for image denoising. The proposed model utilizes non-convex first-order total variation (TV) and non-convex second-order TV to regularize the reflection component and the illumination component, respectively, and employs weak $H^{-1}$ norm to measure the residual component. By utilizing different regularizers, the proposed model effectively decomposes the image into reflection, illumination, and noise components. An alternating direction multipliers method (ADMM) combined with the Majorize-Minimization (MM) algorithm is developed to solve the proposed model. Furthermore, we provide a detailed proof of the convergence property of the algorithm. Numerical experiments validate both the proposed model and algorithm. Compared with several state-of-the-art denoising models, the proposed model exhibits superior performance in terms of peak signal-to-noise ratio (PSNR) and mean structural similarity (MSSIM).

cs.CV↗