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Rui Ma

Publications and source records attributed to Rui Ma.

At least 109 records · Page 6Linked to original sources
arXiv2023

Cellular uptake of active nonspherical nanoparticles

Due to the potential applications in biomedical engineering, it becomes more and more important to understand the process of engulfment and internalization of nanoparticles (NPs) by cell membranes. Despite the fact that the interaction between cell membranes and passive particles has been widely studied, the interaction between cell membranes and self-propelled nonspherical NPs remains to be elucidated. Here we present a theoretical model to systematically investigate the influence of the active force, aspect ratio of NPs, particle size and membrane properties (adhesion energy density and membrane tension) on the cellular uptake of a nonspherical nanoparticle. It is found that the active force generated by an NP can trigger a type of first-order wrapping transition from a small partial wrapping state to a large one. In addition, the phase diagram in the force-aspect ratio (particle size, adhesion energy density and membrane tension) space displays more complex behaviors compared with that for the passive wrapping mediated merely by adhesion. These results may provide a useful guidance to the study of activity-driven cellular entry of active particles into cells.

physics.bio-ph↗
arXiv2023

Incoherent illumination for motion-based imaging through thick scattering medium

Object-motion-based speckle correlation can recover hidden objects from any inhomogeneous medium, which takes advantage of the inherent connection that the cross-correlation between speckle patterns can reflect the autocorrelation of object, providing a route for imaging through or inside thick scattering media. However, once the object is phase-modulated, the above-mentioned relation will not be satisfied under coherent illumination, and the objects cannot be recovered using the existing approaches. Here, we propose an incoherent illumination method for object-motion-based imaging. Theoretical analysis and experimental results show that the cross-correlation between the object-motion-based speckle patterns can be directly used to represent the intensity autocorrelation of the object, making it possible to recover hidden objects regardless of whether the object is phase-modulated or not. Moreover, the proposed approach has a lower root-mean-square error for extracting the autocorrelation patterns of the hidden object. The proposed imaging mechanism blazes a way of imaging moving objects with scattering-induced or intrinsic phase profile, which is in favor of complex imaging scenarios such as inhomogeneous object imaging, deep tissue imaging, and passive lighting scattering imaging.

physics.optics↗
arXiv2023

High-efficiency edge couplers enabled by vertically tapering on lithium-niobate photonic chips

In the past decade, photonic integrated circuits (PICs) based on thin-film lithium niobate (TFLN) have advanced in various fields, including optical communication, nonlinear photonics, and quantum optics. A critical component is an efficient edge coupler connecting PICs to light sources or detectors. Here, we propose an innovative edge coupler design with a wedge-shaped TFLN waveguide and a silicon oxynitride (SiON) cladding. Experimental results show that the coupling loss between the TFLN PIC and a 3-μm mode field diameter (MFD) lensed fiber is low at 1.52 dB/facet, with the potential for improvement to 0.43 dB/facet theoretically. The coupling loss between the edge coupler and a UHNA7 fiber with an MFD of 3.2 μm is reduced to 0.92 dB/facet. This design maintains robust fabrication and alignment tolerance. Importantly, the minimum linewidth of the TFLN waveguide of the coupler (600 nm) can be easily achieved using foundry-available i-line stepper lithography. This work benefits the development of TFLN integrated platforms, such as on-chip electro-optic modulators, frequency comb generation, and quantum sensors.

physics.optics↗
arXiv2023

Orbital-angular-momentum dependent speckles for spatial mode sorting and multiplexed data transmission

Characterizing the orbital angular momentum (OAM) of a vortex beam is critically important for OAM-encoded data transfer. However, in typical OAM-based applications where vortex beams transmit through diffusers, the accompanying scattering effect tends to be either deliberately prevented, or characterized and then modulated actively based on complex wavefront shaping and interferometry techniques. Here, we aim to investigate the characteristics of blurred speckles obtained after a vortex beam transmits through a ground glass diffuser. It is theoretically and experimentally demonstrated that a cross-correlation annulus can be identified by implementing the cross-correlation operation between speckle patterns corresponding to vortex beams with different OAM values. Besides, it is worth noting that, the size of the cross-correlation annulus is determined by the absolute value of the topological charge difference between the two corresponding vortex beams. Based on this mechanism, the OAM modes can be easily sorted from the incoherently measured OAM-dependent speckles as well as their cross-correlation. Furthermore, to make full use of the orthogonal feature of the OAM-dependent speckles, demultiplexing of OAM-encoded data transfer is verified using a ground glass diffuser. Both 8-bit grayscale and 24-bit RGB OAM-encoded data transfers are carried out in experiments with superior error rates. We can conclude that the OAM-dependent speckles can be not only utilized as a competitive candidate for the OAM mode sorting function in a simple way but also provide an efficient method for the demultiplexing of OAM-encoded data transfer in a practical application.

physics.optics↗
arXiv2023

B-Spine: Learning B-Spline Curve Representation for Robust and Interpretable Spinal Curvature Estimation

Spinal curvature estimation is important to the diagnosis and treatment of the scoliosis. Existing methods face several issues such as the need of expensive annotations on the vertebral landmarks and being sensitive to the image quality. It is challenging to achieve robust estimation and obtain interpretable results, especially for low-quality images which are blurry and hazy. In this paper, we propose B-Spine, a novel deep learning pipeline to learn B-spline curve representation of the spine and estimate the Cobb angles for spinal curvature estimation from low-quality X-ray images. Given a low-quality input, a novel SegRefine network which employs the unpaired image-to-image translation is proposed to generate a high quality spine mask from the initial segmentation result. Next, a novel mask-based B-spline prediction model is proposed to predict the B-spline curve for the spine centerline. Finally, the Cobb angles are estimated by a hybrid approach which combines the curve slope analysis and a curve-based regression model. We conduct quantitative and qualitative comparisons with the representative and SOTA learning-based methods on the public AASCE2019 dataset and our new proposed CJUH-JLU dataset which contains more challenging low-quality images. The superior performance on both datasets shows our method can achieve both robustness and interpretability for spinal curvature estimation.

eess.IV↗
arXiv2023

Breaking On-device Training Memory Wall: A Systematic Survey

On-device training has become an increasingly popular approach to machine learning, enabling models to be trained directly on mobile and edge devices. However, a major challenge in this area is the limited memory available on these devices, which can severely restrict the size and complexity of the models that can be trained. In this systematic survey, we aim to explore the current state-of-the-art techniques for breaking on-device training memory walls, focusing on methods that can enable larger and more complex models to be trained on resource-constrained devices. Specifically, we first analyze the key factors that contribute to the phenomenon of memory walls encountered during on-device training. Then, we present a comprehensive literature review of on-device training, which addresses the issue of memory limitations. Finally, we summarize on-device training and highlight the open problems for future research. By providing a comprehensive overview of these techniques and their effectiveness in breaking memory walls, we hope to help researchers and practitioners in this field navigate the rapidly evolving landscape of on-device training.

cs.DC↗
arXiv2023

A new mixed finite element for the linear elasticity problem in 3D

This paper constructs the first mixed finite element for the linear elasticity problem in 3D using $P_3$ polynomials for the stress and discontinuous $P_2$ polynomials for the displacement on tetrahedral meshes under some mild mesh conditions. The degrees of freedom of the stress space as well as the corresponding nodal basis are established by characterizing a space of some piecewise constant symmetric matrices on a patch around each edge. Macro-element techniques are used to define a stable interpolation to prove the discrete inf-sup condition. Optimal convergence is obtained theoretically.

math.NA↗
arXiv2023

Spontaneous stable rotation of flocking flexible active matter

In nature, active matter, such as worms or dogs, tend to spontaneously form a stable rotational cluster when they flock to the same food source on an unregulated and unconfined surface. {In this paper we present an $n$-node flexible active matter model to study the collective motion due to the flocking of individual agents on a two-dimensional surface, and confirm that there exists a spontaneous stable cluster rotation synchronizing with a chirality produced by the alignment of their bodies under the impetus of the active force.} A prefactor of 1.86 is obtained for the linear relationship between normalized angular velocity and chirality. The angular velocity of such a rotation is found to be dependent on the individual flexibility, the number of nodes in each individual, and the magnitude of the active force. The conclusions well explain the spontaneous stable rotation of clusters that exists in many flexible active matter, like worms or {dogs}, when they flock to the same single source.

cond-mat.soft↗
arXiv2023

Random node reinforcement and $K$-core structure of complex networks

To enhance robustness of complex networked systems, a simple method is introducing reinforced nodes which always function during failure propagation. A random scheme of node reinforcement can be considered as a benchmark for finding an optimal reinforcement solution. Yet there still lacks a systematic evaluation on how node reinforcement affects network structure at a mesoscopic level upon failures. Here we study this problem through the lens of $K$-cores of networks. Based on an analytical percolation framework, we first show that, on uncorrelated random graphs, with a critical size of reinforced nodes, an abrupt emergence of $K$-cores is smoothed out to a continuous one, and a detailed phase diagram is derived. We then show that, with a cost-benefit analysis on random reinforcement, for proper weight factors in cost functions with constant and increasing marginal costs, a gain function shows a unimodality, thus we can analytically find an optimal reinforcement fraction by locating the maximal gain. In all, our framework offers a gain-oriented analytical perspective to designing robust interconnected systems.

physics.soc-ph↗
arXiv2023

Photorefraction-assisted self-emergence of dissipative Kerr solitons

Generated in high-Q optical microresonators, dissipative Kerr soliton microcombs constitute broadband optical frequency combs with chip sizes and repetition rates in the microwave to millimeter-wave range. For frequency metrology applications such as spectroscopy, optical atomic clocks and frequency synthesizers, octave-spanning soliton microcombs generated in dispersion optimized microresonator are required, which allow self-referencing for full frequency stabilization. In addition, field-deployable applications require the generation of such soliton microcombs simple, deterministic, and reproducible. Here, we demonstrate a novel scheme to generate self-emerging solitons in integrated lithium niobate microresonators. The single soliton features a broadband spectral bandwidth with dual dispersive waves, allowing 2f-3f self-referencing. Via harnessing the photorefractive effect of lithium niobate to significantly extend the soliton existence range, we observe a spontaneous yet deterministic single-soliton formation. The soliton is immune to external perturbation and can operate continuously over 13 hours without active feedback control. Finally, via integration with a pre-programed DFB laser, we demonstrate turnkey soliton generation. With further improvement of microresonator Q and hybrid integration with chip-scale laser chips, compact soliton microcomb devices with electronic actuation can be created, which can become central elements for future LiDAR, microwave photonics and optical telecommunications.

physics.optics↗
arXiv2023

Self-similarity-based super-resolution of photoacoustic angiography from hand-drawn doodles

Deep-learning-based super-resolution photoacoustic angiography (PAA) is a powerful tool that restores blood vessel images from under-sampled images to facilitate disease diagnosis. Nonetheless, due to the scarcity of training samples, PAA super-resolution models often exhibit inadequate generalization capabilities, particularly in the context of continuous monitoring tasks. To address this challenge, we propose a novel approach that employs a super-resolution PAA method trained with forged PAA images. We start by generating realistic PAA images of human lips from hand-drawn curves using a diffusion-based image generation model. Subsequently, we train a self-similarity-based super-resolution model with these forged PAA images. Experimental results show that our method outperforms the super-resolution model trained with authentic PAA images in both original-domain and cross-domain tests. Specially, our approach boosts the quality of super-resolution reconstruction using the images forged by the deep learning model, indicating that the collaboration between deep learning models can facilitate generalization, despite limited initial dataset. This approach shows promising potential for exploring zero-shot learning neural networks for vision tasks.

eess.IV↗
arXiv2023

Network Algebraization and Port Relationship for Power-Electronic-Dominated Power Systems

Different from the quasi-static network in the traditional power system, the dynamic network in the power-electronic-dominated power system should be considered due to rapid response of converters' controls. In this paper, a nonlinear differential-algebraic model framework is established with algebraic equations for dynamic electrical networks and differential equations for the (source) nodes, by generalizing the Kron reduction. The internal and terminal voltages of source nodes including converters are chosen as ports of nodes and networks. Correspondingly, the impact of dynamic network becomes clear, namely, it serves as a voltage divider and generates the terminal voltage based on the internal voltage of the sources instantaneously, even when the dynamics of inductance are included. With this simplest model, the roles of both nodes and the network become apparent.Simulations verify the proposed model framework in the modified 9-bus system.

eess.SY↗
arXiv2023

MXM-CLR: A Unified Framework for Contrastive Learning of Multifold Cross-Modal Representations

Multifold observations are common for different data modalities, e.g., a 3D shape can be represented by multi-view images and an image can be described with different captions. Existing cross-modal contrastive representation learning (XM-CLR) methods such as CLIP are not fully suitable for multifold data as they only consider one positive pair and treat other pairs as negative when computing the contrastive loss. In this paper, we propose MXM-CLR, a unified framework for contrastive learning of multifold cross-modal representations. MXM-CLR explicitly models and learns the relationships between multifold observations of instances from different modalities for more comprehensive representation learning. The key of MXM-CLR is a novel multifold-aware hybrid loss which considers multiple positive observations when computing the hard and soft relationships for the cross-modal data pairs. We conduct quantitative and qualitative comparisons with SOTA baselines for cross-modal retrieval tasks on the Text2Shape and Flickr30K datasets. We also perform extensive evaluations on the adaptability and generalizability of MXM-CLR, as well as ablation studies on the loss design and effects of batch sizes. The results show the superiority of MXM-CLR in learning better representations for the multifold data. The code is available at https://github.com/JLU-ICL/MXM-CLR.

cs.CV↗
arXiv2023

Membrane buckling and the determination of Gaussian curvature modulus

Biological membranes are able to exhibit various morphology due to the fluidity of the lipid molecules within the monolayers. The shape transformation of membranes has been well described by the classical Helfrich theory, which consists only a few phenomenological parameters, including the mean and the Gaussian curvature modulus. Though various methods have been proposed to measure the mean curvature modulus, determination of the Gaussian curvature modulus remains difficult both in experiments and in simulation. In this paper we study the buckling process of a rectangular membrane and a circular membrane subject to compressive stresses and under different boundary conditions. We find that the buckling of a rectangular membrane takes place continuously, while the buckling of a circular membrane can be discontinous depending on the boundary conditions. Furthermore, our results show that the stress-strain relationship of a buckled circular membrane can be used to effectively determine the Gaussian curvature modulus.

cond-mat.soft↗
arXiv2023

Second harmonic and cascaded third harmonic generation in generalized quasi-periodic poled lithium niobate waveguides

Lithium niobate (LN) thin film has recently emerged as an important platform for nonlinear optical investigations for its large $χ^{(2)}$ nonlinear coefficients and ability of light localization. In this paper, we report the first fabrication of LN on insulator (LNOI) ridge waveguides with generalized quasi-periodic poled superlattices using the electric field polarization technique and microfabrication techniques. Benefiting from the abundant reciprocal vectors, we observed efficient second-harmonic and cascaded third-harmonic signals in the same device, with the normalized conversion efficiency 1735% W$^{-1}$cm$^{-2}$ and 0.41% W$^{-2}$cm$^{-4}$, respectively. This work opens a new direction of nonlinear integrated photonics based on LN thin film.

physics.optics↗
arXiv2023

Forgetting to Remember: A Scalable Incremental Learning Framework for Cross-Task Blind Image Quality Assessment

Recent years have witnessed the great success of blind image quality assessment (BIQA) in various task-specific scenarios, which present invariable distortion types and evaluation criteria. However, due to the rigid structure and learning framework, they cannot apply to the cross-task BIQA scenario, where the distortion types and evaluation criteria keep changing in practical applications. This paper proposes a scalable incremental learning framework (SILF) that could sequentially conduct BIQA across multiple evaluation tasks with limited memory capacity. More specifically, we develop a dynamic parameter isolation strategy to sequentially update the task-specific parameter subsets, which are non-overlapped with each other. Each parameter subset is temporarily settled to Remember one evaluation preference toward its corresponding task, and the previously settled parameter subsets can be adaptively reused in the following BIQA to achieve better performance based on the task relevance. To suppress the unrestrained expansion of memory capacity in sequential tasks learning, we develop a scalable memory unit by gradually and selectively pruning unimportant neurons from previously settled parameter subsets, which enable us to Forget part of previous experiences and free the limited memory capacity for adapting to the emerging new tasks. Extensive experiments on eleven IQA datasets demonstrate that our proposed method significantly outperforms the other state-of-the-art methods in cross-task BIQA. The source code of the proposed method is available at https://github.com/maruiperfect/SILF.

cs.CV↗
arXiv2023

Wrapping dynamics and full uptake conditions for nonspherical active nanoparticles

The cellular uptake of self-propelled nanoparticles (NPs) or viruses, usually nonspherical, by cell membrane is crucial in many biological processes. In this study, using Onsager variational principle, we obtain a general wrapping equation for nonspherical self-propelled nanoparticles. Two analytical critical conditions are theoretically derived, one for the continuous full uptake of prolate particles and the other for snapthrough full wrapping of oblate particles. They capture considerably well the full uptake critical boundaries in the phase diagrams constructed in terms of active force, aspect ratio, adhesion energy density, and membrane tension based on numerical calculations. It is found that enhancing activity (active force), reducing effective dynamic viscosity, increasing adhesion energy density, and decreasing membrane tension, can significantly improve the wrapping efficiency for the self-propelled particles. These results elucidate some of the previous specific investigations conclusively and may offer novel possibilities for designing an effective active NP-based vehicle for controlled drug delivery.

cond-mat.soft↗
arXiv2023

P3DC-Shot: Prior-Driven Discrete Data Calibration for Nearest-Neighbor Few-Shot Classification

Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pretrained deep models. However, NN-based methods are sensitive to the data distribution and may produce false prediction if the samples in the support set happen to lie around the distribution boundary of different classes. To solve this issue, we present P3DC-Shot, an improved nearest-neighbor based few-shot classification method empowered by prior-driven data calibration. Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification. Specifically, we treat the prototypes representing each base class as priors and calibrate each support data based on its similarity to different base prototypes. Then, we perform NN classification using these discretely calibrated support data. Results from extensive experiments on various datasets show our efficient non-learning based method can outperform or at least comparable to SOTA methods which need additional learning steps.

cs.CV↗