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Kai Liu

Publications and source records attributed to Kai Liu.

At least 235 records · Page 13Linked to original sources
arXiv2023

Nahida: In-Band Distributed Tracing with eBPF

Microservices are commonly used in modern cloud-native applications to achieve agility. However, the complexity of service dependencies in large-scale microservices systems can lead to anomaly propagation, making fault troubleshooting a challenge. To address this issue, distributed tracing systems have been proposed to trace complete request execution paths, enabling developers to troubleshoot anomalous services. However, existing distributed tracing systems have limitations such as invasive instrumentation, trace loss, or inaccurate trace correlation. To overcome these limitations, we propose a new tracing system based on eBPF (extended Berkeley Packet Filter), named Nahida, that can track complete requests in the kernel without intrusion, regardless of programming language or implementation. Our evaluation results show that Nahida can track over 92% of requests with stable accuracy, even under the high concurrency of user requests, while the state-of-the-art non-invasive approaches can not track any of the requests. Importantly, Nahida can track requests served by a multi-threaded application that none of the existing invasive tracing systems can handle by instrumenting tracing codes into libraries. Moreover, the overhead introduced by Nahida is negligible, increasing service latency by only 1.55%-2.1%. Overall, Nahida provides an effective and non-invasive solution for distributed tracing.

cs.OS↗
arXiv2023

Rethinking Symmetric Matrix Factorization: A More General and Better Clustering Perspective

Nonnegative matrix factorization (NMF) is widely used for clustering with strong interpretability. Among general NMF problems, symmetric NMF is a special one that plays an important role in graph clustering where each element measures the similarity between data points. Most existing symmetric NMF algorithms require factor matrices to be nonnegative, and only focus on minimizing the gap between similarity matrix and its approximation for clustering, without giving a consideration to other potential regularization terms which can yield better clustering. In this paper, we explore factorizing a symmetric matrix that does not have to be nonnegative, presenting an efficient factorization algorithm with a regularization term to boost the clustering performance. Moreover, a more general framework is proposed to solve symmetric matrix factorization problems with different constraints on the factor matrices.

cs.LG↗
arXiv2023

A Spatial-Temporal Transformer based Framework For Human Pose Assessment And Correction in Education Scenarios

Human pose assessment and correction play a crucial role in applications across various fields, including computer vision, robotics, sports analysis, healthcare, and entertainment. In this paper, we propose a Spatial-Temporal Transformer based Framework (STTF) for human pose assessment and correction in education scenarios such as physical exercises and science experiment. The framework comprising skeletal tracking, pose estimation, posture assessment, and posture correction modules to educate students with professional, quick-to-fix feedback. We also create a pose correction method to provide corrective feedback in the form of visual aids. We test the framework with our own dataset. It comprises (a) new recordings of five exercises, (b) existing recordings found on the internet of the same exercises, and (c) corrective feedback on the recordings by professional athletes and teachers. Results show that our model can effectively measure and comment on the quality of students' actions. The STTF leverages the power of transformer models to capture spatial and temporal dependencies in human poses, enabling accurate assessment and effective correction of students' movements.

cs.CV↗
arXiv2023

Observation of Chern insulator in crystalline ABCA-tetralayer graphene with spin-orbit coupling

Degeneracies in multilayer graphene, including spin, valley, and layer degrees of freedom, are susceptible to Coulomb interactions and can result into rich broken-symmetry states. In this work, we report a ferromagnetic state in charge neutral ABCA-tetralayer graphene driven by proximity-induced spin-orbit coupling from adjacent WSe2. The ferromagnetic state is further identified as a Chern insulator with Chern number of 4, and its Hall resistance reaches 78% and 100% quantization of h/4e2 at zero and 0.4 tesla, respectively. Three broken-symmetry insulating states, layer-antiferromagnet, Chern insulator and layer-polarized insulator and their transitions can be continuously tuned by the vertical displacement field. Remarkably, the magnetic order of the Chern insulator can be switched by three knobs, including magnetic field, electrical doping, and vertical displacement field.

cond-mat.mes-hall↗
arXiv2023

On Regularized Sparse Logistic Regression

Sparse logistic regression is for classification and feature selection simultaneously. Although many studies have been done to solve $\ell_1$-regularized logistic regression, there is no equivalently abundant work on solving sparse logistic regression with nonconvex regularization term. In this paper, we propose a unified framework to solve $\ell_1$-regularized logistic regression, which can be naturally extended to nonconvex regularization term, as long as certain requirement is satisfied. In addition, we also utilize a different line search criteria to guarantee monotone convergence for various regularization terms. Empirical experiments on binary classification tasks with real-world datasets demonstrate our proposed algorithms are capable of performing classification and feature selection effectively at a lower computational cost.

cs.LG↗
arXiv2023

Proton-mediated reversible switching of metastable ferroelectric phases with low operation voltages

The exploration of ferroelectric phase transitions enables an in-depth understanding of ferroelectric switching and promising applications in information storage. However, controllably tuning the dynamics of ferroelectric phase transitions remains challenging owing to inaccessible hidden phases. Here, using protonic gating technology, we create a series of metastable ferroelectric phases and demonstrate their reversible transitions in van der Waals ferroelectric α-In2Se3 transistors. By varying the gate bias, protons can be incrementally injected or extracted, achieving controllable tuning of the ferroelectric α-In2Se3 protonic dynamics across the channel and obtaining numerous intermediate phases. Combining piezoelectric force microscopy and electrical hysteresis measurements, we surprisingly discover that the gate tuning of α-In2Se3 protonation is volatile, unlike the conventional gating effect, and the created new phases remain polar. Their origin, revealed by first-principles calculations, is related to the formation of metastable hydrogen-stabilized α-In2Se3 phases with switchable dipoles. Furthermore, our approach enables ultralow gate-voltage switching of different phases (below 0.4 V), and easy integration with transistor components. This work provides fundamental insights into the interactions between protons and ferroelectric phase transitions, providing a possible avenue for accessing hidden phases in ferroelectric switching and for developing energy-efficient, multilevel-switching memory devices.

cond-mat.mtrl-sci↗
arXiv2023

Realization of multiple topological states and topological phase transitions in (4,0) carbon nanotube derivatives

Exploring various topological states (TS) and topological phase transitions (TPT) has attracted great attention in condensed matter physics. However, so far, there is rarely a typical material system that can be used as a platform to study the TS and TPT as the system transforms from one-dimensional (1D) nanoribbons to two-dimensional (2D) sheet then to three-dimensional (3D) bulk. Here, we first propose that some typical TS in 1D, 2D, and 3D systems can be realized in a tight-binding (TB) model. Following the TB model and further based on first-principles electronic structure calculations, we demonstrate that the structurally stable (4,0) carbon nanotube derivatives are an ideal platform to explore the semiconductor/nodal-point semimetal states in 1D nanoribbons [1D-(4,0)-C16H4 and 1D-(4,0)-C32H4], nodal-ring semimetal state in 2D sheet [2D-(4,0)-C16], and nodal-cage semimetal state in 3D bulk [3D-(4,0)-C16]. Furthermore, we calculate the characteristic band structures and the edge/surface states of 2D-(4,0)-C16 and 3D-(4,0)-C16 to confirm their nontrivial topological properties. Our work not only provides new excellent 2D and 3D members for the topological carbon material family, but also serves as an ideal template for the study of TS and TPT with the change of system dimension.

cond-mat.mtrl-sci↗
arXiv2023

GraphText: Graph Reasoning in Text Space

Large Language Models (LLMs) have gained the ability to assimilate human knowledge and facilitate natural language interactions with both humans and other LLMs. However, despite their impressive achievements, LLMs have not made significant advancements in the realm of graph machine learning. This limitation arises because graphs encapsulate distinct relational data, making it challenging to transform them into natural language that LLMs understand. In this paper, we bridge this gap with a novel framework, GraphText, that translates graphs into natural language. GraphText derives a graph-syntax tree for each graph that encapsulates both the node attributes and inter-node relationships. Traversal of the tree yields a graph text sequence, which is then processed by an LLM to treat graph tasks as text generation tasks. Notably, GraphText offers multiple advantages. It introduces training-free graph reasoning: even without training on graph data, GraphText with ChatGPT can achieve on par with, or even surpassing, the performance of supervised-trained graph neural networks through in-context learning (ICL). Furthermore, GraphText paves the way for interactive graph reasoning, allowing both humans and LLMs to communicate with the model seamlessly using natural language. These capabilities underscore the vast, yet-to-be-explored potential of LLMs in the domain of graph machine learning.

cs.CL↗
arXiv2023

Quality-Cost Trade-off on Constructing Logical Views for Vehicular Cyber-Physical Systems: A Deep Reinforcement Learning Approach

With the development of sensing technologies, vehicle-to-everything (V2X) communications, edge computing paradigm, vehicular cyber-physical systems (VCPS) are emerging as the most fundamental platform for realizing future intelligent transportation systems (ITSs). In particular, the construction of logical views at the edge nodes based on heterogeneous information sensing and uploading are critical to the realization of VCPS. However, a higher-quality view in terms of timeliness and accuracy may require higher cost on sensing and uploading. In view of this, this paper is dedicated to striking a balance between the quality and the cost for constructing logical views of VCPS. Specifically, we first derive an information sensing model based on multi-class M/G/1 priority queue and a data uploading model based on reliability-guaranteed vehicle-to-infrastructure (V2I) communications. On this basis, we design two metrics, namely, age of view (AoV) and cost of view (CoV), simultaneously. Then, we formulate a bi-objective problem to maximize the AoV and minimize the CoV. Further, we propose a distributed distributional deep deterministic policy gradient (D4PG) solution to determine sensing information, frequency, uploading priority, transmission power, and V2I bandwidth. Finally, we build a simulation model and give a comprehensive performance evaluation, and the simulation results conclusively demonstrate the superiority of the proposed solution.

cs.NI↗
arXiv2023

Machine-Learning Recognition of Dzyaloshinskii-Moriya Interaction from Magnetometry

The Dzyaloshinskii-Moriya interaction (DMI), which is the antisymmetric part of the exchange interaction between neighboring local spins, winds the spin manifold and can stabilize non-trivial topological spin textures. Since topology is a robust information carrier, characterization techniques that can extract the DMI magnitude are important for the discovery and optimization of spintronic materials. Existing experimental techniques for quantitative determination of DMI, such as high-resolution magnetic imaging of spin textures and measurement of magnon or transport properties, are time consuming and require specialized instrumentation. Here we show that a convolutional neural network can extract the DMI magnitude from minor hysteresis loops, or magnetic "fingerprints" of a material. These hysteresis loops are readily available by conventional magnetometry measurements. This provides a convenient tool to investigate topological spin textures for next-generation information processing.

cond-mat.mtrl-sci↗
arXiv2023

Ionically-Driven Synthesis and Exchange Bias in Mn$_{4}$N/MnN$_{x}$ Heterostructures

Ferrimagnets have received renewed attention as a promising platform for spintronic applications. Of particular interest is the Mn4N from the $ε$-phase of the manganese nitride as an emergent rare-earth-free spintronic material due to its perpendicular magnetic anisotropy, small saturation magnetization, high thermal stability, and large domain wall velocity. We have achieved high-quality (001)-ordered Mn$_{4}$N thin film by sputtering Mn onto $η$-phase Mn$_{3}$N$_{2}$ seed layers on Si substrates. As the deposited Mn thickness varies, nitrogen ion migration across the Mn$_{3}$N$_{2}$/Mn layers leads to a continuous evolution of the layers to Mn$_{3}$N$_{2}$/Mn$_{2}$N/Mn$_{4}$N, Mn$_{2}$N/Mn$_{4}$N, and eventually Mn$_{4}$N alone. The ferrimagnetic Mn$_{4}$N indeed exhibits perpendicular magnetic anisotropy, and forms via a nucleation-and-growth mechanism. The nitrogen ion migration is also manifested in a significant exchange bias, up to 0.3 T at 5 K, due to the interactions between ferrimagnetic Mn$_{4}$N and antiferromagnetic Mn$_{3}$N$_{2}$ and Mn$_{2}$N. These results demonstrate a promising all-nitride magneto-ionic platform with remarkable tunability for device applications.

cond-mat.mtrl-sci↗
arXiv2023

Highly Tunable Perpendicular Magnetic Anisotropy and Anisotropic Magnetoresistance in Ru-doped La0.67Sr0.33MnO3 Epitaxial Films

As a prototypical half-metallic ferromagnet, La0.67Sr0.33MnO3 (LSMO) has been extensively studied due to its versatile physical properties and great potential in spintronic applications. However, the weak perpendicular magnetic anisotropy (PMA) limits the controllability and detection of magnetism in LSMO, thus hindering the realization of oxide-based spintronic devices with low energy consumption and high integration level. Motivated by this challenge, we develop an experimental approach to enhance the PMA of LSMO epitaxial films. By cooperatively introducing 4d Ru doping and a moderate compressive strain, the maximum uniaxial magnetic anisotropy in Ru-doped LSMO can reach 3.0 to 1E5 J/m3 at 10 K. Furthermore, we find a significant anisotropic magnetoresistance effect in these Ru-doped LSMO films, which is dominated by the strong PMA. Our findings offer an effective pathway to harness and detect the orientations of magnetic moments in LSMO films, thus promoting the feasibility of oxide-based spintronic devices, such as spin valves and magnetic tunnel junctions.

cond-mat.mtrl-sci↗
arXiv2023

Randomized Rank-Revealing QLP for Low-Rank Matrix Decomposition

The pivoted QLP decomposition is computed through two consecutive pivoted QR decompositions, and provides an approximation to the singular value decomposition. This work is concerned with a partial QLP decomposition of low-rank matrices computed through randomization, termed Randomized Unpivoted QLP (RU-QLP). Like pivoted QLP, RU-QLP is rank-revealing and yet it utilizes random column sampling and the unpivoted QR decomposition. The latter modifications allow RU-QLP to be highly parallelizable on modern computational platforms. We provide an analysis for RU-QLP, deriving bounds in spectral and Frobenius norms on: i) the rank-revealing property; ii) principal angles between approximate subspaces and exact singular subspaces and vectors; and iii) low-rank approximation errors. Effectiveness of the bounds is illustrated through numerical tests. We further use a modern, multicore machine equipped with a GPU to demonstrate the efficiency of RU-QLP. Our results show that compared to the randomized SVD, RU-QLP achieves a speedup of up to 7.1 times on the CPU and up to 2.3 times with the GPU.

math.NA↗
arXiv2023

Strictly Low Rank Constraint Optimization -- An Asymptotically $\mathcal{O}(\frac{1}{t^2})$ Method

We study a class of non-convex and non-smooth problems with \textit{rank} regularization to promote sparsity in optimal solution. We propose to apply the proximal gradient descent method to solve the problem and accelerate the process with a novel support set projection operation on the singular values of the intermediate update. We show that our algorithms are able to achieve a convergence rate of $O(\frac{1}{t^2})$, which is exactly same as Nesterov's optimal convergence rate for first-order methods on smooth and convex problems. Strict sparsity can be expected and the support set of singular values during each update is monotonically shrinking, which to our best knowledge, is novel in momentum-based algorithms.

math.OC↗
arXiv2023

Kagome surface states and weak electronic correlation in vanadium-kagome metals

RV6Sn6 (R = Y and lanthanides) with two-dimensional vanadium-kagome surface states is an ideal platform to investigate kagome physics and manipulate the kagome features to realize novel phenomena. Utilizing the micron-scale spatially resolved angle-resolved photoemission spectroscopy and first-principles calculations, we report a systematical study of the electronic structures of RV6Sn6 (R = Gd, Tb, and Lu) on the two cleaved surfaces, i.e., the V- and RSn1-terminated (001) surfaces. The calculated bands without any renormalization match well with the main ARPES dispersive features, indicating the weak electronic correlation in this system. We observe 'W'-like kagome surface states around the Brillouin zone corners showing R-element-dependent intensities, which is probably due to various coupling strengths between V and RSn1 layers. Our finding suggests an avenue for tuning electronic states by interlayer coupling based on two-dimensional kagome lattices.

cond-mat.mtrl-sci↗
arXiv2023

Reverse Doping Asymmetry in Semiconductor Thin Films Using External Voltage

Doping asymmetry is a notable phenomenon with semiconductors and a particularly longstanding challenge limiting the applications of most wide-band-gap semiconductors, which are inherent of spontaneous heavy n- or p-type doping because of their extreme band edges. This study theoretically shows that by applying a proper external voltage on materials during their growth or doping processes, we can largely tune the band edges and consequently reverse the doping asymmetry in semiconductor thin films. We take zinc oxide as a touchstone and computationally demonstrate that this voltage-assisted-doping approach efficiently suppresses the spontaneous n-type defects by around four orders under three distinct growth conditions and successfully generates p-type zinc oxide up to the lowest acceptor levels. The proposed approach is insensitive to materials, growth conditions, or defects origins, and thus offers a general solution to the doping asymmetry in semiconductor thin films.

cond-mat.mtrl-sci↗
arXiv2023

Interaction-driven spontaneous broken-symmetry insulator and metals in ABCA tetralayer graphene

Interactions among charge carriers in graphene can lead to the spontaneous breaking of multiple degeneracies. When increasing the number of graphene layers following rhombohedral stacking, the dominant role of Coulomb interactions becomes pronounced due to the significant reduction in kinetic energy. In this study, we employ phonon-polariton assisted near-field infrared imaging to determine the stacking orders of tetralayer graphene devices. Through quantum transport measurements, we observe a range of spontaneous broken-symmetry states and their transitions, which can be finely tuned by carrier density n and electric displacement field D. Specifically, we observe a layer antiferromagnetic insulator at n = D = 0 with a gap of approximately 15 meV. Increasing D allows for a continuous phase transition from a layer antiferromagnetic insulator to a layer polarized insulator. By simultaneously tuning n and D, we observe isospin polarized metals, including spin-valley-polarized and spin-polarized metals. These transitions are associated with changes in Fermi surface topology and are consistent with the Stoner criteria. Our findings highlight the efficient fabrication of specially stacked multilayer graphene devices and demonstrate that crystalline multilayer graphene is an ideal platform for investigating a wide range of broken symmetries driven by Coulomb interactions.

cond-mat.mes-hall↗
arXiv2023

Exploring charge and spin fluctuations in infinite-layer cuprate SrCuO$_{2}$ from a phonon perspective

The infinite-layer cuprate $A$CuO$_2$ ($A=$ Ca, Sr, Ba) has the simplest crystal structure among numerous cuprate superconductors and can serve a prototypical system to explore the unconventional superconductivity. Based on the first-principles electronic structure calculations, we have studied the electronic and magnetic properties of the infinite-layer cuprate SrCuO$_{2}$ from a phonon perspective. We find that interesting fluctuations of charges, electrical dipoles, and local magnetic moments can be induced by the zero-point vibrations of phonon modes in SrCuO$_{2}$ upon the hole doping. Among all optical phonon modes of SrCuO$_{2}$ in the antiferromagnetic Néel state, only the $A_{1}$$_g$ mode that involves the full-breathing O vibrations along the Cu-O bonds can cause significant fluctuations of local magnetic moments on O atoms and dramatic charge redistributions between Cu and O atoms. Notably, due to the zero-point vibration of the $A_{1g}$ mode, both the charge fluctuations on Cu and the electrical dipoles on O show a dome-like evolution with increasing hole doping, quite similar to the experimentally observed behavior of the superconducting $T_c$; in comparison, the fluctuations of local magnetic moments on O display a monotonic enhancement along with the hole doping. Further analyses indicate that around the optimal doping, there exist a large softening in the frequency of the $A_{1g}$ phonon mode and a van Hove singularity in the electronic structure close to the Fermi level, suggesting potential electron-phonon coupling. Our work reveals the important role of the full-breathing O phonon mode playing in the infinite-layer SrCuO$_{2}$, which may provide new insights in understanding the cuprate superconductivity.

cond-mat.supr-con↗