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Yu Ye

Publications and source records attributed to Yu Ye.

At least 73 records · Page 4Linked to original sources
arXiv2021

Generalized Knörrer's Periodicity Theorem

Let $A$ be a noetherian Koszul Artin-Schelter regular algebra, and let $f\in A_2$ be a central regular element of $A$. The quotient algebra $A/(f)$ is usually called a (noncommutative) quadric hypersurface. In this paper, we use the Clifford deformation to study the quadric hypersurfaces obtained from the tensor products. We introduce a notion of simple graded isolated singularity and proved that, if $B/(g)$ is a simple graded isolated singularity of 0-type, then there is an equivalence of triangulated categories $\underline{\text{mcm}}\,A/(f)\cong\underline{\text{mcm}}\,(A\otimes B)/(f+g)$ of the stable categories of maximal Cohen-Macaulay modules. This result may be viewed as a generalization of Knörrer's periodicity theorem. As an application, we study the double branch cover $(A/(f))^\#=A[x]/(f+x^2)$ of a noncommutative conic $A/(f)$.

math.RA↗
arXiv2021

Clifford deformations of Koszul Frobenius algebras and noncommutative quadrics

Let $E$ be a Koszul Frobenius algebra. A Clifford deformation of $E$ is a finite dimensional $\mathbb Z_2$-graded algebra $E(θ)$, which corresponds to a noncommutative quadric hypersurface $E^!/(z)$, for some central regular element $z\in E^!_2$. It turns out that the bounded derived category $D^b(\text{gr}_{\mathbb Z_2}E(θ))$ is equivalent to the stable category of the maximal Cohen-Macaulay modules over $E^!/(z)$ provided that $E^!$ is noetherian. As a consequence, $E^!/(z)$ is a noncommutative isolated singularity if and only if the corresponding Clifford deformation $E(θ)$ is a semisimple $\mathbb Z_2$-graded algebra. The preceding equivalence of triangulated categories also indicates that Clifford deformations of trivial extensions of a Koszul Frobenius algebra are related to the Knörrer Periodicity Theorem for quadric hypersurfaces. As an application, we recover Knörrer Periodicity Theorem without using of matrix factorizations.

math.RA↗
arXiv2021

Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT

Edge computing provides a promising paradigm to support the implementation of Industrial Internet of Things (IIoT) by offloading tasks to nearby edge nodes. Meanwhile, the increasing network size makes it impractical for centralized data processing due to limited bandwidth, and consequently a decentralized learning scheme is preferable. Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes. For RL in a decentralized setup, edge nodes (agents) connected through a communication network aim to work collaboratively to find a policy to optimize the global reward as the sum of local rewards. However, communication costs, scalability and adaptation in complex environments with heterogeneous agents may significantly limit the performance of decentralized RL. Alternating direction method of multipliers (ADMM) has a structure that allows for decentralized implementation, and has shown faster convergence than gradient descent based methods. Therefore, we propose an adaptive stochastic incremental ADMM (asI-ADMM) algorithm and apply the asI-ADMM to decentralized RL with edge-computing-empowered IIoT networks. We provide convergence properties for proposed algorithms by designing a Lyapunov function and prove that the asI-ADMM has $O(\frac{1}{k}) +O(\frac{1}{M})$ convergence rate where $k$ and $ M$ are the number of iterations and batch samples, respectively. Then, we test our algorithm with two supervised learning problems. For performance evaluation, we simulate two applications in decentralized RL settings with homogeneous and heterogeneous agents. The experiment results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability, and can well adapt to complex IoT environments.

cs.LG↗
arXiv2021

Optically and magnetically addressable valley pseudospin of interlayer excitons in bilayer MoS2

Interlayer valley excitons in bilayer MoS2 feature concurrently large oscillator strength and long lifetime, and hence represent an advantageous scenario for valleytronic applications. However, control of valley pseudospin of interlayer excitons in pristine bilayer MoS2, which lies at the heart of valleytronics, has remained elusive. Here we report the observation of highly circularly polarized photoluminescence from interlayer excitons of bilayer MoS2 with both optical and magnetic addressability. Under excitation of circularly polarized light near exciton resonance, interlayer excitons of bilayer MoS2 show a near-unity, but negative circular polarization. Significantly, by breaking time-reversal symmetry with an out-of-plane magnetic field, a record level of spontaneous valley polarization (7.7%/Tesla) is identified for interlayer excitons in bilayer MoS2. The giant valley polarization of the interlayer excitons in bilayer MoS2, together with the feasibility of electrical/optical/magnetic control and strong oscillator strength, provides a firm basis for the development of next-generation electronic and optoelectronic applications.

cond-mat.mes-hall↗
arXiv2020

Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge Computing

Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones and vehicles. Due to the limitations of communication costs and security requirements, it is of paramount importance to extract information in a decentralized manner instead of aggregating data to a fusion center. To train large-scale machine learning models, edge/fog computing is often leveraged as an alternative to centralized learning. We consider the problem of learning model parameters in a multi-agent system with data locally processed via distributed edge nodes. A class of mini-batch stochastic alternating direction method of multipliers (ADMM) algorithms is explored to develop the distributed learning model. To address two main critical challenges in distributed networks, i.e., communication bottleneck and straggler nodes (nodes with slow responses), error-control-coding based stochastic incremental ADMM is investigated. Given an appropriate mini-batch size, we show that the mini-batch stochastic ADMM based method converges in a rate of $O(\frac{1}{\sqrt{k}})$, where $k$ denotes the number of iterations. Through numerical experiments, it is revealed that the proposed algorithm is communication-efficient, rapidly responding and robust in the presence of straggler nodes compared with state of the art algorithms.

cs.DC↗
arXiv2020

Fully Decentralized Federated Learning Based Beamforming Design for UAV Communications

To handle the data explosion in the era of internet of things (IoT), it is of interest to investigate the decentralized network, with the aim at relaxing the burden to central server along with keeping data privacy. In this work, we develop a fully decentralized federated learning (FL) framework with an inexact stochastic parallel random walk alternating direction method of multipliers (ISPW-ADMM). Performing more communication efficient and enhanced privacy preservation compared with the current state-of-the-art, the proposed ISPW-ADMM can be partially immune to the impacts from time-varying dynamic network and stochastic data collection, while still in fast convergence. Benefits from the stochastic gradients and biased first-order moment estimation, the proposed framework can be applied to any decentralized FL tasks over time-varying graphs. Thus to further demonstrate the practicability of such framework in providing fast convergence, high communication efficiency, and system robustness, we study the extreme learning machine(ELM)-based FL model for robust beamforming (BF) design in UAV communications, as verified by the numerical simulations.

eess.SP↗
arXiv2020

On simple-minded systems over representation-finite self-injective algebras

Let $A$ be a representation-finite self-injective algebra over an algebraically closed field $k$. We give a new characterization for an orthogonal system in the stable module category $A$-$\stmod$ to be a simple-minded system. As a by-product, we show that every Nakayama-stable orthogonal system in $A$-$\stmod$ extends to a simple-minded system.

math.RT↗
arXiv2020

Decentralized Beamforming Design for Intelligent Reflecting Surface-enhanced Cell-free Networks

Cell-free networks are considered as a promising distributed network architecture to satisfy the increasing number of users and high rate expectations in beyond-5G systems. However, to further enhance network capacity, an increasing number of high-cost base stations (BSs) are required. To address this problem and inspired by the cost-effective intelligent reflecting surface (IRS) technique, we propose a fully decentralized design framework for cooperative beamforming in IRS-aided cell-free networks. We first transform the centralized weighted sum-rate maximization problem into a tractable consensus optimization problem, and then an incremental alternating direction method of multipliers (ADMM) algorithm is proposed to locally update the beamformer. The complexity and convergence of the proposed method are analyzed, and these results show that the performance of the new scheme can asymptotically approach that of the centralized one as the number of iterations increases. Results also show that IRSs can significantly increase the system sum-rate of cell-free networks and the proposed method outperforms existing decentralized methods.

eess.SP↗
arXiv2020

Odd-even layer-number effect and layer-dependent magnetic phase diagrams in MnBi2Te4

The intrinsic magnetic layered topological insulator MnBi2Te4 with nontrivial topological properties and magnetic order has become a promising system for exploring exotic quantum phenomena such as quantum anomalous Hall effect. However, the layer-dependent magnetism of MnBi2Te4, which is fundamental and crucial for further exploration of quantum phenomena in this system, remains elusive. Here, we use polar reflective magnetic circular dichroism spectroscopy, combined with theoretical calculations, to obtain an in-depth understanding of the layer-dependent magnetic properties in MnBi2Te4. The magnetic behavior of MnBi2Te4 exhibits evident odd-even layer-number effect, i.e. the oscillations of the coercivity of the hysteresis loop (at μ0Hc) and the spin-flop transition (at μ0H1), concerning the Zeeman energy and magnetic anisotropy energy. In the even-number septuple layers, an anomalous magnetic hysteresis loop is observed, which is attributed to the thickness-independent surface-related magnetization. Through the linear-chain model, we can clarify the odd-even effect of the spin-flop field and determine the evolution of magnetic states under the external magnetic field. The mean-field method also allows us to trace the experimentally observed magnetic phase diagrams to the magnetic fields, layer numbers and especially, temperature. Overall, by harnessing the unusual layer-dependent magnetic properties, our work paves the way for further study of quantum properties of MnBi2Te4.

cond-mat.mtrl-sci↗
arXiv2020

Pre-resolutions of noncommutative isolated singularities

We introduce the notion of right pre-resolutions (quasi-resolutions) for noncommutative isolated singularities, which is a weaker version of quasi-resolutions introduced by Qin-Wang-Zhang. We prove that right quasi-resolutions for noetherian bounded below and locally finite graded algebra with right injective dimension 2 are always Morita equivalent. When we restrict to noncommutative quadric hypersurfaces, we prove that a noncommutative quadric hypersurface, which is a noncommutative isolated singularity, always admits a right pre-resolution. Besides, we provide a method to verify whether a noncommutative quadric hypersurface is an isolated singularity. An example of noncommutative quadric hypersurfaces with detailed computations of indecomposable maximal Cohen-Macaulay modules and right pre-resolutions is included as well.

math.RA↗
arXiv2020

Learning Based Hybrid Beamforming for Millimeter Wave Multi-User MIMO Systems

Hybrid beamforming (HBF) design is a crucial stage in millimeter wave (mmWave) multi-user multi-input multi-output (MU-MIMO) systems. However, conventional HBF methods are still with high complexity and strongly rely on the quality of channel state information. We propose an extreme learning machine (ELM) framework to jointly optimize transmitting and receiving beamformers. Specifically, to provide accurate labels for training, we first propose an factional-programming and majorization-minimization based HBF method (FP-MM-HBF). Then, an ELM based HBF (ELM-HBF) framework is proposed to increase the robustness of beamformers. Both FP-MM-HBF and ELM-HBF can provide higher system sum-rate compared with existing methods. Moreover, ELM-HBF cannot only provide robust HBF performance, but also consume very short computation time.

eess.SP↗
arXiv2020

Deep Reinforcement Learning Based Spectrum Allocation in Integrated Access and Backhaul Networks

We develop a framework based on deep reinforce-ment learning (DRL) to solve the spectrum allocation problem inthe emerging integrated access and backhaul (IAB) architecturewith large scale deployment and dynamic environment. The avail-able spectrum is divided into several orthogonal sub-channels,and the donor base station (DBS) and all IAB nodes have thesame spectrum resource for allocation, where a DBS utilizes thosesub-channels for access links of associated user equipment (UE)as well as for backhaul links of associated IAB nodes, and anIAB node can utilize all for its associated UEs. This is one ofkey features in which 5G differs from traditional settings wherethe backhaul networks were designed independently from theaccess networks. With the goal of maximizing the sum log-rateof all UE groups, we formulate the spectrum allocation probleminto a mix-integer and non-linear programming. However, itis intractable to find an optimal solution especially when theIAB network is large and time-varying. To tackle this problem,we propose to use the latest DRL method by integrating anactor-critic spectrum allocation (ACSA) scheme and deep neuralnetwork (DNN) to achieve real-time spectrum allocation indifferent scenarios. The proposed methods are evaluated throughnumerical simulations and show promising results compared withsome baseline allocation policies.

cs.IT↗
arXiv2020

Learning Based Hybrid Beamforming Design for Full-Duplex Millimeter Wave Systems

Millimeter Wave (mmWave) communications with full-duplex (FD) have the potential of increasing the spectral efficiency, relative to those with half-duplex. However, the residual self-interference (SI) from FD and high pathloss inherent to mmWave signals may degrade the system performance. Meanwhile, hybrid beamforming (HBF) is an efficient technology to enhance the channel gain and mitigate interference with reasonable complexity. However, conventional HBF approaches for FD mmWave systems are based on optimization processes, which are either too complex or strongly rely on the quality of channel state information (CSI). We propose two learning schemes to design HBF for FD mmWave systems, i.e., extreme learning machine based HBF (ELM-HBF) and convolutional neural networks based HBF (CNN-HBF). Specifically, we first propose an alternating direction method of multipliers (ADMM) based algorithm to achieve SI cancellation beamforming, and then use a majorization-minimization (MM) based algorithm for joint transmitting and receiving HBF optimization. To train the learning networks, we simulate noisy channels as input, and select the hybrid beamformers calculated by proposed algorithms as targets. Results show that both learning based schemes can provide more robust HBF performance and achieve at least 22.1% higher spectral efficiency compared to orthogonal matching pursuit (OMP) algorithms. Besides, the online prediction time of proposed learning based schemes is almost 20 times faster than the OMP scheme. Furthermore, the training time of ELM-HBF is about 600 times faster than that of CNN-HBF with 64 transmitting and receiving antennas.

eess.SP↗
arXiv2020

Diagonalizable Higher Degree Forms and Symmetric Tensors

We provide simple criteria and algorithms for expressing homogeneous polynomials as sums of powers of independent linear forms, or equivalently, for decomposing symmetric tensors into sums of rank-1 symmetric tensors of linearly independent vectors. The criteria rely on two facets of higher degree forms, namely Harrison's algebraic theory and some algebro-geometric properties. The proposed algorithms are elementary and based purely on solving linear and quadratic equations. Moreover, as a byproduct of our criteria and algorithms one can easily decide whether or not a homogeneous polynomial or symmetric tensor is orthogonally or unitarily decomposable.

math.RA↗
arXiv2020

Extension of elementary $p$-groups and its application in classification of groups of prime exponent

Let $p$ be a prime number and $\mathbb{Z}_p=\mathbb{Z}/p\mathbb{Z}$. We study finite groups with abelian derived subgroup and exponent $p$ in terms of group extension data and their matrix presentations. We show a one-to-one correspondence between the following two sets: (i) the isoclasses of class 2 groups of exponent $p$ and order $p^{m+n}$ and with derived subgroup $\mathbb{Z}_p^n$, and (ii) the set $\text{Gr}(n,\text{AS}_m(\mathbb{Z}_p))/\text{GL}_m(\mathbb{Z}_p)$ of orbits of $\text{Gr}(n,\text{AS}_m(\mathbb{Z}_p))$ under the congruence action by $\text{GL}_m(\mathbb{Z}_p)$, where $\text{Gr}(n,\text{AS}_m(\mathbb{Z}_p))$ is the set of $n$-dimensional subspaces of anti-symmetric matrices of order $m$ over $\mathbb{Z}_p$. We give a description of the orbit spaces $\text{Gr}(2, \text{AS}_m(\mathbb{Z}_p))/\text{GL}_m(\mathbb{Z}_p)$ for all $m$ and $p$ by applying the theory of pencils of anti-symmetric matrices. Based on this, we show complete sets of representatives of orbits of $\text{Gr}(3,\text{AS}_4(\mathbb{Z}_3))/\text{GL}_4(\mathbb{Z}_3)$, $\text{Gr}(4, \text{AS}_4(\mathbb{Z}_3))/\text{GL}_4(\mathbb{Z}_3)$ and $\text{Gr}(3, \text{AS}_5(\mathbb{Z}_3))/\text{GL}_5(\mathbb{Z}_3)$. As a consequence, we obtain a classification of corresponding class 2 groups of exponent $p$. In particular, we recover the classification of groups with exponent 3 and order $\le 3^8$.

math.GR↗
arXiv2020

Privacy-preserving Incremental ADMM for Decentralized Consensus Optimization

The alternating direction method of multipliers (ADMM) has been recently recognized as a promising optimizer for large-scale machine learning models. However, there are very few results studying ADMM from the aspect of communication costs, especially jointly with privacy preservation, which are critical for distributed learning. We investigate the communication efficiency and privacy-preservation of ADMM by solving the consensus optimization problem over decentralized networks. Since walk algorithms can reduce communication load, we first propose incremental ADMM (I-ADMM) based on the walk algorithm, the updating order of which follows a Hamiltonian cycle instead. However, I-ADMM cannot guarantee the privacy for agents against external eavesdroppers even if the randomized initialization is applied. To protect privacy for agents, we then propose two privacy-preserving incremental ADMM algorithms, i.e., PI-ADMM1 and PI-ADMM2, where perturbation over step sizes and primal variables is adopted, respectively. Through theoretical analyses, we prove the convergence and privacy preservation for PI-ADMM1, which are further supported by numerical experiments. Besides, simulations demonstrate that the proposed PI-ADMM1 and PI-ADMM2 algorithms are communication efficient compared with state-of-the-art methods.

math.OC↗
arXiv2020

Spin-Valley Locking Effect in Defect States of Monolayer MoS$_2$

Valley pseudospin in two-dimensional (2D) transition-metal dichalcogenides (TMDs) allows optical control of spin-valley polarization and intervalley quantum coherence. Defect states in TMDs give rise to new exciton features and theoretically exhibit spin-valley polarization; however, experimental achievement of this phenomenon remains challenges. Here, we report unambiguous valley pseudospin of defect-bound localized excitons in CVD-grown monolayer MoS2; enhanced valley Zeeman splitting with an effective g-factor of -6.2 is observed. Our results reveal that all five d-orbitals and the increased effective electron mass contribute to the band shift of defect states, demonstrating a new physics of the magnetic responses of defect-bound localized excitons, strikingly different from that of A excitons. Our work paves the way for the manipulation of the spin-valley degrees of freedom through defects toward valleytronic devices.

cond-mat.mtrl-sci↗