arXiv subjects

Zhenhua Li

Publications and source records attributed to Zhenhua Li.

33 records · Page 2Linked to original sources

arXiv2024

Scalable and Versatile Linear Computation with Minimalistic Photonic Matrix Processor

The advancement of artificial intelligence demands flexible multimodal data processing with high throughput and energy efficiency. Photonic integrated circuits (PIC) has demonstrated promising potentials in terms of low latency and low power consumption per operation for linear operations such as matrix-vector multiplication. However, the existing schemes face challenges in their scalability due to the use of photonic circuits that expand with the scale of the operants, despite efforts of exploiting the multiple optical parameter dimensions such as time, wavelength and spatial parallelism. They also lacked flexibility and efficiency in switching between different types of operations or tasks and adapting to multimodal data. In this article, we introduce an optical matrix processor (MP) with a minimalistic recursive structure for both multiplications and accumulations. The MP consists of an eletro-optic ring-modulator implemented as a thin-film lithium niobate PIC that allows flexible configurability and time-division multiplexed scheduling. The MP supports not only versatile linear operations including vector/matrix-vector multiplication and single/multi-kernel convolution but also ultrafast task switching and adaptability to data of different sizes, by simply adjusting the data baud rate relative to the ring delay without structural modifications. We demonstrate its capabilities in a optic-electronic convolutional neural network with a computing throughput up to 73.4 billion operations per second. The MP further supports high scalability through appropriate allocation of wavelength and space resources,extending computing parallelism to handle higher data volumes with higher energy efficiency. This novel scheme paves the way for a new class of photonic processorscapable of managing escalating data workloads with unprecedented flexibility, efficiency and scalability.

physics.optics↗

arXiv2024

High Spectral-Efficiency, Ultra-low MIMO SDM Transmission over a Field-Deployed Multi-Core OAM Fiber

Few-mode multi-core fiber (FM-MCF) based Space-Division Multiplexing (SDM) systems possess the potential to maximize the number of multiplexed spatial channels per fiber by harnessing both the space (fiber cores) and mode (optical mode per core) dimensions. However, to date, no SDM transmissions over field-deployed FM-MCFs in realistic outdoor settings have been reported, which contrasts with SDM schemes demonstrated using single-mode multi-core fibers (SM-MCFs) installed in practical fiber cable ducts. In this paper, we present the successful demonstration of bidirectional SDM transmission over a 5-km field-deployed seven ring-core fiber (7-RCF) with a cladding diameter of 178 $μ$m, achieving a Spectral Efficiency (SE) of 2$\times$201.6 bit/s/Hz. This work establishes a new record for the highest SE attained in SDM demonstrations utilizing field-deployed fiber cables, achieving an approximate 10x increase compared to the SE of reported field-deployed optical fiber cable transmission systems. Notably, these results are realized through the utilization of small-scale modular 4$\times$4 multiple-input multiple-output (MIMO) processing with a time-domain equalization (TDE) tap number not exceeding 15, maintaining a complexity per unit capacity comparable to that of MIMO equalization in SDM demonstrations employing weakly coupled SM-MCF cables. These results underscore the significant potential for achieving heightened SE and expanding capacity per individual fiber using SDM techniques in practical applications.

cs.NI↗

arXiv2023

Microwave spectroscopy and Zeeman effect of cesium $(n+2)D_{5/2}\rightarrow nF_{J}$ Rydberg transitions

We report on high-resolution microwave spectroscopy of cesium Rydberg $(n+2)D_{5/2}\rightarrow nF_{J}$ transitions in a cold atomic gas. Atoms laser-cooled and trapped in a magnetic-optical trap are prepared in the $D$ Rydberg state using a two-photon laser excitation scheme. A microwave field transmitted into the chamber with a microwave horn drives the Rydberg transitions, which are probed via state selective field ionization. Varying duration and power of the microwave pulse, we observe Fourier side-band spectra as well as damped, on-resonant Rabi oscillations with pulse areas up to $\gtrsim 3 π$. Furthermore, we investigate the Zeeman effect of the clearly resolved $nF_J$ fine-structure levels in fields up to 120~mG, where the transition into $nF_{7/2}$ displays a thee-peak Zeeman pattern, while $nF_{5/2}$ shows a two-peak pattern. Our theoretical models explain all observed spectral characteristics, showing good agreement with the experiment. Our measurements provide a pathway for the study of high-angular-momentum Rydberg states, initialization and coherent manipulation of such states, Rydberg-atom macrodimers, and other Rydberg-atom interactions. Furthermore, the presented methods are suitable for calibration of microwave radiation as well as for nulling and calibration of DC magnetic fields in experimental chambers for cold atoms.

physics.atom-ph↗

arXiv2023

Deep Richardson-Lucy Deconvolution for Low-Light Image Deblurring

Images taken under the low-light condition often contain blur and saturated pixels at the same time. Deblurring images with saturated pixels is quite challenging. Because of the limited dynamic range, the saturated pixels are usually clipped in the imaging process and thus cannot be modeled by the linear blur model. Previous methods use manually designed smooth functions to approximate the clipping procedure. Their deblurring processes often require empirically defined parameters, which may not be the optimal choices for different images. In this paper, we develop a data-driven approach to model the saturated pixels by a learned latent map. Based on the new model, the non-blind deblurring task can be formulated into a maximum a posterior (MAP) problem, which can be effectively solved by iteratively computing the latent map and the latent image. Specifically, the latent map is computed by learning from a map estimation network (MEN), and the latent image estimation process is implemented by a Richardson-Lucy (RL)-based updating scheme. To estimate high-quality deblurred images without amplified artifacts, we develop a prior estimation network (PEN) to obtain prior information, which is further integrated into the RL scheme. Experimental results demonstrate that the proposed method performs favorably against state-of-the-art algorithms both quantitatively and qualitatively on synthetic and real-world images.

cs.CV↗

arXiv2023

Approaching quantum queer supergroups using finite dimensional superalgebras (Preliminary version)

The idea of using a sequence of finite dimensional algebras to approach a quantum linear group (i.e., a quantum $\mathfrak{gl}_n$) was first introduced by Beilinson-Lusztig-MacPherson [BLM]. In their work, the algebras are convolution algebras of some finite partial flag varieties whose certain structure constants relative to the orbital basis satisfy a stabilization property. This property leads to the definition of an infinite dimensional idempotented algebra. Finally, taking a limit process yields a new realization for the quantum $\mathfrak{gl}_n$. Since then, this work has been modified [DF2] and generalized to quantum affine $\mathfrak{gl}_n$ (see [GV, L] for the geometric approach and [DDF, DF] for the algebraic approach and a new realization) and quantum super $\mathfrak{gl}_{m|n}$ [DG], and, more recently, to convolution algebras arising from type $B/C$ geometry and $i$-quantum groups $\boldsymbol U^\jmath$ and $\boldsymbol U^\imath$; see [BKLW, DWu1, DWu2]. This paper extends the algebraic approach to the quantum queer supergroup $U_{v}(\mathfrak{q}_n)$ via finite dimensional queer $q$-Schur superalgebras.

math.QA↗

arXiv2023

Some multiplication formulas in queer $q$-Schur superalgebras

Building on the work [18], where some standard basis for the queer $q$-Schur superalgebra $\mathcal{Q}_q(n,r;R)$ is defined by a labelling set of matrices and their associated double coset representatives, we investigate the matrix representation of the regular module of $\mathcal{Q}_q(n,r;R)$ with respect to this basis. More precisely, we derive explicitly (resp., partial explicitly) the multiplication formulas of the basis elements by certain even (resp., odd) generators of a queer $q$-Schur superalgebra. These multiplication formulas are highly technical to derive, especially in the odd case. It requires to discover many multiplication (or commutation) formulas in the Hecke--Clifford algebra $\mathcal{H}_{r,R}^c$ associated with the labelling matrices. For example, for a given such a labelling matrix $A^{\!\star}$, there are several matrices $w(A)$, $σ(A), \widetilde A$, and $\widehat A$ associated with the base matrix $A$ of $A^{\!\star}$, where $w(A)$ is used to compute a reduced expression of the distinguished double coset representatives $d_A$, and the other matrices are used to describe the permutation $d_A$ and the SDP (commutation) condition between $T_{d_A}$ and generators of the Clifford subsuperalgebra. With these multiplication formulas, we will construct a new realisation of the quantum queer supergroup in a forthcoming paper [13], and to give new applications to the integral Schur--Olshanski duality and its associated representation theory at roots of unity.

math.RT↗

arXiv2021

The integral Schur-Weyl-Sergeev duality

Degenerating the quantum queer Schur superalgebra ${\mathcal{Q}_q(n,r; R)}$ to the case $q=1$, the queer Schur superalgebra ${\mathcal{Q}(n,r)}$ is obtained. In this article, we reconstruct the universal enveloping algebra ${U({\mathfrak{q}_n})}$ of the queer Lie superalgebra ${\mathfrak{q}_n}$ via ${\mathcal{Q}(n,r)}$, and achieve another explanation of the Schur-Weyl-Sergeev duality. Finally, we depict the Schur-Weyl-Sergeev duality over $\mathbb{Z}$.

math.QA↗

arXiv2021

Four-state reference-frame-independent quantum key distribution with non-qubit sources

The discrepancy between theory and experiment severely limits the development of quantum key distribution (QKD). Reference-frame-independent (RFI) protocol has been proposed to avoid alignment of the reference frame. However, multiple optical modes caused by Trojan horse attacks and equipment loopholes lead to the imperfect emitted signal unavoidably. In this paper, we analyzed the security of the RFI-QKD protocol with non-qubit sources based on generalizing loss-tolerant techniques. The simulation results show that our work can effectively defend against non-qubit sources including a misaligned reference frame, state preparation flaws, multiple optical modes, and Trojan horse attacks. Moreover, it only requires the preparation of four quantum states, which reduces the complexity of the experiment in the future.

quant-ph↗

arXiv2019

Pareto Multi-Task Learning

Multi-task learning is a powerful method for solving multiple correlated tasks simultaneously. However, it is often impossible to find one single solution to optimize all the tasks, since different tasks might conflict with each other. Recently, a novel method is proposed to find one single Pareto optimal solution with good trade-off among different tasks by casting multi-task learning as multiobjective optimization. In this paper, we generalize this idea and propose a novel Pareto multi-task learning algorithm (Pareto MTL) to find a set of well-distributed Pareto solutions which can represent different trade-offs among different tasks. The proposed algorithm first formulates a multi-task learning problem as a multiobjective optimization problem, and then decomposes the multiobjective optimization problem into a set of constrained subproblems with different trade-off preferences. By solving these subproblems in parallel, Pareto MTL can find a set of well-representative Pareto optimal solutions with different trade-off among all tasks. Practitioners can easily select their preferred solution from these Pareto solutions, or use different trade-off solutions for different situations. Experimental results confirm that the proposed algorithm can generate well-representative solutions and outperform some state-of-the-art algorithms on many multi-task learning applications.

cs.LG↗

arXiv2019

Discrete Element Method Model of Elastic Fiber Uniaxial Compression

A flexible fiber model based on the discrete element method (DEM) is presented and validated for the simulation of uniaxial compression of flexible fibers in a cylindrical container. It is found that the contact force models in the DEM simulations have a significant impact on compressive forces exerted on the fiber bed. Only when the geometry-dependent normal contact force model and the static friction model are employed, the simulation results are in good agreement with experimental results. Systematic simulation studies show that the compressive force initially increases and eventually saturates with an increase in the fiber-fiber friction coefficient, and the fiber-fiber contact forces follow a similar trend. The compressive force and lateral shear-to-normal stress ratio increase linearly with increasing fiber-wall friction coefficient. In uniaxial compression of frictional fibers, more static friction contacts occur than dynamic friction contacts with static friction becoming more predominant as the fiber-fiber friction coefficient increases.

cond-mat.soft↗

arXiv2018

Nonlinear Collaborative Scheme for Deep Neural Networks

Conventional research attributes the improvements of generalization ability of deep neural networks either to powerful optimizers or the new network design. Different from them, in this paper, we aim to link the generalization ability of a deep network to optimizing a new objective function. To this end, we propose a \textit{nonlinear collaborative scheme} for deep network training, with the key technique as combining different loss functions in a nonlinear manner. We find that after adaptively tuning the weights of different loss functions, the proposed objective function can efficiently guide the optimization process. What is more, we demonstrate that, from the mathematical perspective, the nonlinear collaborative scheme can lead to (i) smaller KL divergence with respect to optimal solutions; (ii) data-driven stochastic gradient descent; (iii) tighter PAC-Bayes bound. We also prove that its advantage can be strengthened by nonlinearity increasing. To some extent, we bridge the gap between learning (i.e., minimizing the new objective function) and generalization (i.e., minimizing a PAC-Bayes bound) in the new scheme. We also interpret our findings through the experiments on Residual Networks and DenseNet, showing that our new scheme performs superior to single-loss and multi-loss schemes no matter with randomization or not.

cs.LG↗

arXiv2018

A Batched Scalable Multi-Objective Bayesian Optimization Algorithm

The surrogate-assisted optimization algorithm is a promising approach for solving expensive multi-objective optimization problems. However, most existing surrogate-assisted multi-objective optimization algorithms have three main drawbacks: 1) cannot scale well for solving problems with high dimensional decision space, 2) cannot incorporate available gradient information, and 3) do not support batch optimization. These drawbacks prevent their use for solving many real-world large scale optimization problems. This paper proposes a batched scalable multi-objective Bayesian optimization algorithm to tackle these issues. The proposed algorithm uses the Bayesian neural network as the scalable surrogate model. Powered with Monte Carlo dropout and Sobolov training, the model can be easily trained and can incorporate available gradient information. We also propose a novel batch hypervolume upper confidence bound acquisition function to support batch optimization. Experimental results on various benchmark problems and a real-world application demonstrate the efficiency of the proposed algorithm.

cs.NE↗

arXiv2017

A Simple Yet Efficient Rank One Update for Covariance Matrix Adaptation

In this paper, we propose an efficient approximated rank one update for covariance matrix adaptation evolution strategy (CMA-ES). It makes use of two evolution paths as simple as that of CMA-ES, while avoiding the computational matrix decomposition. We analyze the algorithms' properties and behaviors. We experimentally study the proposed algorithm's performances. It generally outperforms or performs competitively to the Cholesky CMA-ES.

cs.NE↗

arXiv2016

Generation of arbitrary radially polarized array beams by modulating the correlation structure

We demonstrate a convenient approach for simultaneously manipulating the amplitude and polarization of light beams by means of the modulation of the correlation structure. As an illustration, we constructed a periodic correlation structure that can generate an arbitrary radially polarized array (RPA) beam of a radial or rectangular symmetry array in the focal plane from a radially polarized (RP) beam. The physical realizability conditions for such source and the far-field beam condition are derived. It is illustrated that the beamlet shape and the state of polarization (SOP) can be effectively controlled by the initial correlation structure and the coherence width. Furthermore, by designing the source correlation structure, a tunable OK-shaped RPA beam and an optical cage are demonstrated, which can find widespread applications in non-destructive manipulation of particles and living biological cells. The arbitrariness in the design of correlation structure prompted us to find more convenient approaches for controlling the statistics of light beams in terms of amplitude and polarization.

physics.optics↗

arXiv2016

Benchmarking NFV Software Dataplanes

A key enabling technology of NFV is software dataplane, which has attracted much attention in both academia and industry recently. Yet, till now there is little understanding about its performance in practice. In this paper, we make a benchmark measurement study of NFV software dataplanes in terms of packet processing capability, one of the most fundamental and critical performance metrics. Specifically, we compare two state-of-the-art open-source NFV dataplanes, SoftNIC and ClickOS, using commodity 10GbE NICs under various typical workloads. Our key observations are that (1) both dataplanes have performance issues processing small (<=128B) packets; (2) it is not always best to put all VMs of a service chain on one server due to NUMA effect. We propose resource allocation strategies to remedy the problems, including carefully adding CPU cores and vNICs to VMs, and spreading VMs of a service chain to separate servers. To fundamentally address these problems and scale their performance, SoftNIC and ClickOS could improve the support for NIC queues and multiple cores.

cs.NI↗