arXiv subjects

Licheng Wang

Publications and source records attributed to Licheng Wang.

30 records · Page 2Linked to original sources

arXiv2024

Second-order topological insulator in Bilayer borophene

As the novel topological states, the higher-order topological insulators have attracted great attentions in the past years. However, their realizations in realistic materials, in particular in two dimensional systems, remains the big challenge due to the lack of adequate candidates. Here, based on the first-principle calculation and tight-binding model simulations, we identify the currently \emph{existing} bilayer $α_{5}$-phase borophenes as the two-dimensional second-order topological insulators, protected by the $C_{2}$-rotational symmetry. The formation of interlayer B-B covalent bonds, stabilizing the bilayer borophenes and opening the large direct bulk gaps ($\sim 0.55-0.62$ eV) at Fermi level, plays the key roles. The second-order topology is characterized by the bulk quantized quadrupole momentum. Our results enriches the candidates for the second-order topological insulators, and also provide a way to study topological states in borophenes.

cond-mat.mtrl-sci↗

arXiv2024

Identifying the Largest RoCoF and Its Implications

The rate of change of frequency (RoCoF) is a critical factor in ensuring frequency security, particularly in power systems with low inertia. Currently, most RoCoF security constrained optimal inertia dispatch methods and inertia market mechanisms predominantly rely on the center of inertia (COI) model. This model, however, does not account for the disparities in post-contingency frequency dynamics across different regions of a power system. Specifically, regional buses can exhibit significantly larger RoCoFs than that predicted by the system's COI, particularly in systems characterized by unevenly distributed inertia. In this letter, a post-contingency nodal RoCoF model is established, and the maximal initial RoCoF is further proven to occur at generator buses equipped with inertia, rather than at inertia-less load buses. This finding facilitates the development of the optimal nodal inertia dispatch method and the nodal inertia market mechanism in a convex and concise form. Our argument is further verified by the simulation results of the South East Australia power system under various scenarios.

eess.SY↗

arXiv2023

Post-Quantum $κ$-to-1 Trapdoor Claw-free Functions from Extrapolated Dihedral Cosets

\emph{Noisy trapdoor claw-free function} (NTCF) as a powerful post-quantum cryptographic tool can efficiently constrain actions of untrusted quantum devices. However, the original NTCF is essentially \emph{2-to-1} one-way function (NTCF$^1_2$). In this work, we attempt to further extend the NTCF$^1_2$ to achieve \emph{many-to-one} trapdoor claw-free functions with polynomial bounded preimage size. Specifically, we focus on a significant extrapolation of NTCF$^1_2$ by drawing on extrapolated dihedral cosets, thereby giving a model of NTCF$^1_κ$ where $κ$ is a polynomial integer. Then, we present an efficient construction of NTCF$^1_κ$ assuming \emph{quantum hardness of the learning with errors (LWE)} problem. We point out that NTCF can be used to bridge the LWE and the dihedral coset problem (DCP). By leveraging NTCF$^1_2$ (resp. NTCF$^1_κ$), our work reveals a new quantum reduction path from the LWE problem to the DCP (resp. extrapolated DCP). Finally, we demonstrate the NTCF$^1_κ$ can naturally be reduced to the NTCF$^1_2$, thereby achieving the same application for proving the quantumness.

cs.CR↗

arXiv2023

A Soft Coordination Method of Heterogeneous Devices in Distribution System Voltage Control

With the continuous increase of photovoltaic (PV) penetration, the voltage control interactions between newly installed PV inverters and previously deployed on-load tap-changer (OLTC) transformers become ever more significant. To achieve coordinated voltage regulation, current methods often rely on a decision-making algorithm to fully take over the control of all devices, requiring OLTC to give up its existing tap switching logic and execute corresponding upgrades. Aiming at bridging this gap, a soft coordination framework is proposed in this paper. Specifically, the decision-making commands are only applied on inverters, and OLTC that retains its own operation rule will be indirectly controlled by the changed system voltage, which is a result of appropriately adjusting inverters' Var output. The proposed method achieves the soft coordination by establishing a modified actor-critic algorithm to train a proxy model of inverters. The well-trained proxy model can properly adjust inverters' Var output to "softly" coordinate OLTC's tap operations, which finally attains coordinated voltage regulation and line loss minimization. Simulation results verify the superiority of our proposed method over traditional ones in coordinating heterogeneous devices for voltage control.

eess.SY↗

arXiv2023

Improving Covariance-Regularized Discriminant Analysis for EHR-based Predictive Analytics of Diseases

Linear Discriminant Analysis (LDA) is a well-known technique for feature extraction and dimension reduction. The performance of classical LDA, however, significantly degrades on the High Dimension Low Sample Size (HDLSS) data for the ill-posed inverse problem. Existing approaches for HDLSS data classification typically assume the data in question are with Gaussian distribution and deal the HDLSS classification problem with regularization. However, these assumptions are too strict to hold in many emerging real-life applications, such as enabling personalized predictive analysis using Electronic Health Records (EHRs) data collected from an extremely limited number of patients who have been diagnosed with or without the target disease for prediction. In this paper, we revised the problem of predictive analysis of disease using personal EHR data and LDA classifier. To fill the gap, in this paper, we first studied an analytical model that understands the accuracy of LDA for classifying data with arbitrary distribution. The model gives a theoretical upper bound of LDA error rate that is controlled by two factors: (1) the statistical convergence rate of (inverse) covariance matrix estimators and (2) the divergence of the training/testing datasets to fitted distributions. To this end, we could lower the error rate by balancing the two factors for better classification performance. Hereby, we further proposed a novel LDA classifier De-Sparse that leverages De-sparsified Graphical Lasso to improve the estimation of LDA, which outperforms state-of-the-art LDA approaches developed for HDLSS data. Such advances and effectiveness are further demonstrated by both theoretical analysis and extensive experiments on EHR datasets.

cs.LG↗

arXiv2022

Multimodal Pre-training Based on Graph Attention Network for Document Understanding

Document intelligence as a relatively new research topic supports many business applications. Its main task is to automatically read, understand, and analyze documents. However, due to the diversity of formats (invoices, reports, forms, etc.) and layouts in documents, it is difficult to make machines understand documents. In this paper, we present the GraphDoc, a multimodal graph attention-based model for various document understanding tasks. GraphDoc is pre-trained in a multimodal framework by utilizing text, layout, and image information simultaneously. In a document, a text block relies heavily on its surrounding contexts, accordingly we inject the graph structure into the attention mechanism to form a graph attention layer so that each input node can only attend to its neighborhoods. The input nodes of each graph attention layer are composed of textual, visual, and positional features from semantically meaningful regions in a document image. We do the multimodal feature fusion of each node by the gate fusion layer. The contextualization between each node is modeled by the graph attention layer. GraphDoc learns a generic representation from only 320k unlabeled documents via the Masked Sentence Modeling task. Extensive experimental results on the publicly available datasets show that GraphDoc achieves state-of-the-art performance, which demonstrates the effectiveness of our proposed method. The code is available at https://github.com/ZZR8066/GraphDoc.

cs.CV↗

arXiv2020

Four-Dimensional Higher-Order Chern Insulator and Its Acoustic Realization

We present a theoretical study and experimental realization of a system that is simultaneously a four-dimensional (4D) Chern insulator and a higher-order topological insulator (HOTI). The system sustains the coexistence of (4-1)-dimensional chiral topological hypersurface modes (THMs) and (4-2)-dimensional chiral topological surface modes (TSMs). Our study reveals that the THMs are protected by second Chern numbers, and the TSMs are protected by a topological invariant composed of two first Chern numbers, each belonging a Chern insulator existing in sub-dimensions. With the synthetic coordinates fixed, the THMs and TSMs respectively manifest as topological edge modes (TEMs) and topological corner modes (TCMs) in the real space, which are experimentally observed in a 2D acoustic lattice. These TCMs are not related to quantized polarizations, making them fundamentally distinctive from existing examples. We further show that our 4D topological system offers an effective way for the manipulation of the frequency, location, and the number of the TCMs, which is highly desirable for applications.

physics.app-ph↗

arXiv2019

SecureGBM: Secure Multi-Party Gradient Boosting

Federated machine learning systems have been widely used to facilitate the joint data analytics across the distributed datasets owned by the different parties that do not trust each others. In this paper, we proposed a novel Gradient Boosting Machines (GBM) framework SecureGBM built-up with a multi-party computation model based on semi-homomorphic encryption, where every involved party can jointly obtain a shared Gradient Boosting machines model while protecting their own data from the potential privacy leakage and inferential identification. More specific, our work focused on a specific "dual--party" secure learning scenario based on two parties -- both party own an unique view (i.e., attributes or features) to the sample group of samples while only one party owns the labels. In such scenario, feature and label data are not allowed to share with others. To achieve the above goal, we firstly extent -- LightGBM -- a well known implementation of tree-based GBM through covering its key operations for training and inference with SEAL homomorphic encryption schemes. However, the performance of such re-implementation is significantly bottle-necked by the explosive inflation of the communication payloads, based on ciphertexts subject to the increasing length of plaintexts. In this way, we then proposed to use stochastic approximation techniques to reduced the communication payloads while accelerating the overall training procedure in a statistical manner. Our experiments using the real-world data showed that SecureGBM can well secure the communication and computation of LightGBM training and inference procedures for the both parties while only losing less than 3% AUC, using the same number of iterations for gradient boosting, on a wide range of benchmark datasets.

cs.LG↗

arXiv2016

A Miniature CCA2 Public key Encryption scheme based on non-Abelian factorization problems in Lie Groups

Since 1870s, scientists have been taking deep insight into Lie groups and Lie algebras. With the development of Lie theory, Lie groups have got profound significance in many branches of mathematics and physics. In Lie theory, exponential mapping between Lie groups and Lie algebras plays a crucial role. Exponential mapping is the mechanism for passing information from Lie algebras to Lie groups. Since many computations are performed much more easily by employing Lie algebras, exponential mapping is indispensable while studying Lie groups. In this paper, we first put forward a novel idea of designing cryptosystem based on Lie groups and Lie algebras. Besides, combing with discrete logarithm problem(DLP) and factorization problem(FP), we propose some new intractable assumptions based on exponential mapping. Moreover, in analog with Boyen's sceme(AsiaCrypt 2007), we disign a public key encryption scheme based on non-Abelian factorization problems in Lie Groups. Finally, our proposal is proved to be IND-CCA2 secure in the random oracle model.

cs.CR↗

arXiv2016

Public Key Encryption in Non-Abelian Groups

In this paper, we propose a brand new public key encryption scheme in the Lie group that is a non-abelian group. In particular, we firstly investigate the intractability assumptions in the Lie group, including the non-abelian factoring assumption and non-abelian inserting assumption. After that, by using the FO technique, a CCA secure public key encryption scheme in the Lie group is proposed. At last, we present the security proof in the random oracle based on the non-abelian inserting assumption.

cs.CR↗

arXiv2015

Minimal Logarithmic Signatures for Sporadic Groups

cs.CR↗

arXiv2015

Minimal Logarithmic Signatures for one type of Classical Groups

As a special type of factorization of finite groups, logarithmic signature (LS) is used as the main component of cryptographic keys for secret key cryptosystems such as PGM and public key cryptosystems like MST1, MST2 and MST3. An LS with the shortest length, called a minimal logarithmic signature (MLS), is even desirable for cryptographic applications. The MLS conjecture states that every finite simple group has an MLS. Recently, the conjecture has been shown to be true for general linear groups GLn(q), special linear groups SLn(q), and symplectic groups Spn(q) with q a power of primes and for orthogonal groups On(q) with q as a power of 2. In this paper, we present new constructions of minimal logarithmic signatures for the orthogonal group On(q) and SOn(q) with q as a power of odd primes. Furthermore, we give constructions of MLSs for a type of classical groups projective commutator subgroup.

cs.CR↗