arXiv subjects

Fanlong Meng

Publications and source records attributed to Fanlong Meng.

At least 19 recordsLinked to original sources

arXiv2026

Modeling bacterial flow field with regularized singularities

The flow field generated by a swimming bacterium serves as a fundamental building block for understanding hydrodynamic interactions between bacteria. Although the flow field generated by a force dipole (stresslet) well captures the fluid motion in the far field limit, the stresslet description does not work in the near-field limit, which can be important in microswimmer interactions. Here we propose the model combining an anisotropically regularized stresslet with an isotropically regularized source dipole, and it nicely reproduces the flow field around a swimming bacterium, which is validated by the experimental measurements of the flow field around \textit{E. coli} and our boundary-element-method simulations of a helical microswimmer, in both cases of the free space and the confined space with a no-slip wall. This work provides a practical tool for obtaining the flow field of the bacterium, and can be utilised to study the collective responses of bacteria in dense suspensions.

cond-mat.soft↗

arXiv2025

Confinement reduces surface accumulation of swimming bacteria

Many swimming bacteria naturally inhabit confined environments, yet how confinement influences their swimming behaviors remains unclear. Here, we combine experiments, continuum modeling and particle-based simulations to investigate near-surface bacterial swimming in dilute suspensions under varying confinement. Confinement reduces near-surface accumulation and facilitates bacterial escape. These effects are quantitatively captured by models incorporating the force quadrupole, a higher-order hydrodynamic singularity, that generates a rotational flow reorienting bacteria away from surfaces. Under strong confinement, bacterial trajectories straighten due to the balancing torques exerted by opposing surfaces. These findings highlight the role of hydrodynamic quadrupole interactions in near-surface bacterial motility, with implications for microbial ecology, infection control, and industrial applications.

physics.bio-ph↗

arXiv2025

Stochastic Model and Optimal Control of an Active Tracking Particle with Information Processing

Living systems often function with regulatory interactions, but the question of how activity, stochasticity and regulations work together for achieving different goals still remains puzzling. We propose a stochastic model of an active tracking particle with information processing, where the entropy production and information flow are discussed, with the generalised fluctuation theorem serving as a benchmark for verifying the probability setup. Based on the model, the system performance, in terms of the first passage steps and the total energy consumption, are analysed in the variable space of (measurement error, control field), leading to discussions on optimal controls of the system. Not only elucidating the basic concepts involved in a stochastic active system with information processing, this prototypical model could also inspire more elaborated modelings of natural smart organisms and industrial designs of controllable active systems with desired physical performances in the future.

cond-mat.soft↗

arXiv2025

Cross Spatial Temporal Fusion Attention for Remote Sensing Object Detection via Image Feature Matching

Effectively describing features for cross-modal remote sensing image matching remains a challenging task due to the significant geometric and radiometric differences between multimodal images. Existing methods primarily extract features at the fully connected layer but often fail to capture cross-modal similarities effectively. We propose a Cross Spatial Temporal Fusion (CSTF) mechanism that enhances feature representation by integrating scale-invariant keypoints detected independently in both reference and query images. Our approach improves feature matching in two ways: First, by creating correspondence maps that leverage information from multiple image regions simultaneously, and second, by reformulating the similarity matching process as a classification task using SoftMax and Fully Convolutional Network (FCN) layers. This dual approach enables CSTF to maintain sensitivity to distinctive local features while incorporating broader contextual information, resulting in robust matching across diverse remote sensing modalities. To demonstrate the practical utility of improved feature matching, we evaluate CSTF on object detection tasks using the HRSC2016 and DOTA benchmark datasets. Our method achieves state-of-theart performance with an average mAP of 90.99% on HRSC2016 and 90.86% on DOTA, outperforming existing models. The CSTF model maintains computational efficiency with an inference speed of 12.5 FPS. These results validate that our approach to crossmodal feature matching directly enhances downstream remote sensing applications such as object detection.

cs.CV↗

arXiv2024

KVDirect: Distributed Disaggregated LLM Inference

Large Language Models (LLMs) have become the new foundation for many applications, reshaping human society like a storm. Disaggregated inference, which separates prefill and decode stages, is a promising approach to improving hardware utilization and service quality. However, due to inefficient inter-node communication, existing systems restrict disaggregated inference to a single node, limiting resource allocation flexibility and reducing service capacity. This paper introduces KVDirect, which optimizes KV cache transfer to enable a distributed disaggregated LLM inference. KVDirect achieves this through the following contributions. First, we propose a novel tensor-centric communication mechanism that reduces the synchronization overhead in traditional distributed GPU systems. Second, we design a custom communication library to support dynamic GPU resource scheduling and efficient KV cache transfer. Third, we introduce a pull-based KV cache transfer strategy that reduces GPU resource idling and improves latency. Finally, we implement KVDirect as an open-source LLM inference framework. Our evaluation demonstrates that KVDirect reduces per-request latency by 55% compared to the baseline across diverse workloads under the same resource constraints.

cs.DC↗

arXiv2024

Curvature induces and enhances transport of spinning colloids through narrow channels

The effect of curvature and how it induces and enhances the transport of colloidal particles driven through narrow channels represent an unexplored research avenue. Here we combine experiments and simulations to investigate the dynamics of magnetically driven colloidal particles confined through a narrow, circular channel. We use an external precessing magnetic field to induce a net torque and spin the particles at a defined angular velocity. Due to the spinning, the particle propulsion emerges from the different hydrodynamic coupling with the inner and outer walls and strongly depends on the curvature. The experimental findings are combined with finite element numerical simulations that predict a positive rotation translation coupling in the mobility matrix. Further, we explore the collective transport of many particles across the curved geometry, making an experimental realization of a driven single file system. With our finding, we elucidate the effect of curvature on the transport of microscopic particles which could be important to understand the complex, yet rich, dynamics of particle systems driven through curved microfluidic channels.

cond-mat.soft↗

arXiv2024

GraphScale: A Framework to Enable Machine Learning over Billion-node Graphs

Graph Neural Networks (GNNs) have emerged as powerful tools for supervised machine learning over graph-structured data, while sampling-based node representation learning is widely utilized in unsupervised learning. However, scalability remains a major challenge in both supervised and unsupervised learning for large graphs (e.g., those with over 1 billion nodes). The scalability bottleneck largely stems from the mini-batch sampling phase in GNNs and the random walk sampling phase in unsupervised methods. These processes often require storing features or embeddings in memory. In the context of distributed training, they require frequent, inefficient random access to data stored across different workers. Such repeated inter-worker communication for each mini-batch leads to high communication overhead and computational inefficiency. We propose GraphScale, a unified framework for both supervised and unsupervised learning to store and process large graph data distributedly. The key insight in our design is the separation of workers who store data and those who perform the training. This separation allows us to decouple computing and storage in graph training, thus effectively building a pipeline where data fetching and data computation can overlap asynchronously. Our experiments show that GraphScale outperforms state-of-the-art methods for distributed training of both GNNs and node embeddings. We evaluate GraphScale both on public and proprietary graph datasets and observe a reduction of at least 40% in end-to-end training times compared to popular distributed frameworks, without any loss in performance. While most existing methods don't support billion-node graphs for training node embeddings, GraphScale is currently deployed in production at TikTok enabling efficient learning over such large graphs.

cs.LG↗

arXiv2024

Molecular Dynamics Simulations of Microscopic Structural Transition and Macroscopic Mechanical Properties of Magnetic Gels

Magnetic gels with embedded micro/nano-sized magnetic particles in crosslinked polymer networks can be actuated by external magnetic fields, with changes in their internal microscopic structures and macroscopic mechanical properties. We investigate the responses of such magnetic gels to an external magnetic field, by means of coarse-grained molecular dynamics simulations. We find that the dynamics of magnetic particles are determined by the interplay of between magnetic dipole-dipole interactions, polymer elasticity and thermal fluctuations. The corresponding microscopic structures formed by the magnetic particles such as elongated chains can be controlled by the external magnetic field. Furthermore, the magnetic gels can exhibit reinforced macroscopic mechanical properties, where the elastic modulus increases algebraically with the magnetic moments of the particles in the form of $\propto(m-m_{\mathrm{c}})^{2}$ when magnetic chains are formed. This simulation work can not only serve as a tool for studying the microscopic and the macroscopic responses of the magnetic gels, but also facilitate future fabrications and practical controls of magnetic composites with desired physical properties.

cond-mat.soft↗

arXiv2023

Multiflagellate Swimming Controlled by Interflagella Hydrodynamic Interactions

Many eukaryotic microorganisms propelled by multiple flagella can swim very rapidly with distinct gaits. Here, we model a three-dimensional mutiflagellate swimming strategy, resembling the microalgae, and investigate the effects of interflagella hydrodynamic interactions (iHIs) on the swimming performance. When the flagella are actuated synchronously, the swimming efficiency can be enhanced or reduced by iHIs, determined by the intrinsic tilting angle of the flagella. The asynchronous gait with a phase difference between neighboring flagella is found to be important by both utilizing the iHIs and reducing the oscillatory motion via the basal mechanical coupling. We further demonstrate that an optimal number of flagella could arise when the microswimmer is loaded with a swimmer body. Apart from understanding the role of iHIs in the multiflagellate swimming, this work could also guide laboratory fabrications of novel microswimmers.

cond-mat.soft↗

arXiv2023

Effect of viscoelastic fluid on the lift force in lubricated contacts

We consider a cylinder immersed in viscous fluid moving near a flat substrate covered by an incompressible viscoelastic fluid layer, and study the effect of the fluid viscoelasticity on the lift force exerted on the cylinder. The lift force is zero when the viscoelastic layer is not deformed, but becomes non-zero when it is deformed. We calculate the lift force by considering both the tangential stress and the normal stress applied at the surface of the viscoelastic layer. Our analysis indicates that as the layer changes from the elastic limit to the viscous limit, the lift force decreases with the decrease of the Deborah number (De). For small De, the effect of the layer elasticity is taken over by the surface tension and the lift force can become negative. We also show that the tangential stress and the interface slip velocity (the surface velocity relative to the substrate), which have been ignored in the previous analysis, give important contributions to the lift force. Especially for thin elastic layer, they give dominant contributions to the lift force.

physics.flu-dyn↗

arXiv2023

Scaling transition of active turbulence from two to three dimensions

Turbulent flows are observed in low-Reynolds active fluids. They are intrinsically different from the classical inertial turbulence and behave distinctively in two- and three-dimensions. Understanding the behaviors of this new type of turbulence and their dependence on the system dimensionality is a fundamental challenge in non-equilibrium physics. We experimentally measure flow structures and energy spectra of bacterial turbulence between two large parallel plates spaced by different heights $H$. The turbulence exhibits three regimes as H increases, resulting from the competition of bacterial length, vortex size and H. This is marked by two critical heights ($H_0$ and $H_1$) and a $H^{0.5}$ scaling law of vortex size in the large-$H$ limit. Meanwhile, the spectra display distinct universal scaling laws in quasi-two-dimensional (2D) and three-dimensional (3D) regimes, independent of bacterial activity, length and $H$, whereas scaling exponents exhibit transitions in the crossover. To understand the scaling laws, we develop a hydrodynamic model using image systems to represent the effect of no-slip confining boundaries. This model predicts universal 1 and -4 scaling on large and small length scales, respectively, and -2 and -1 on intermediate length scales in 2D and 3D, respectively, which are consistent with the experimental results. Our study suggests a framework for investigating the effect of dimensionality on non-equilibrium self-organized systems.

cond-mat.soft↗

arXiv2022

Shear hardening in frictionless amorphous solids near the jamming transition

The jamming transition, generally manifested by a rapid increase of rigidity under compression (i.e., compression hardening), is ubiquitous in amorphous materials. Here we study shear hardening in deeply annealed frictionless packings generated by numerical simulations, reporting critical scalings absent in compression hardening. We demonstrate that hardening is a natural consequence of shear-induced memory destruction. Based on an elasticity theory, we reveal two independent microscopic origins of shear hardening: (i) the increase of the interaction bond number and (ii) the emergence of anisotropy and long-range correlations in the orientations of bonds, the latter highlights the essential difference between compression and shear hardening. Through the establishment of physical laws specific to anisotropy, our work completes the criticality and universality of jamming transition, and the elasticity theory of amorphous solids.

cond-mat.soft↗

arXiv2021

Field-controlling patterns of sheared ferrofluid droplets

We investigate how ferrofluid droplets suspended in a wall-bounded shear flow can organise when subjected to an external magnetic field. By tuning the magnitude of the external magnetic field, we find that the ferrofluid droplets form chain-like structures in the flow direction when the magnetic field is weak, while forming a crystal-like pattern in a strong magnetic field. We provide the phase diagram and the critical conditions for this chain-to-crystal transition, by applying both numerical simulations and analytic calculations. We also examine how the organised patterns of the ferrofluid droplets can be controlled by simply changing the direction of the magnetic field. This work demonstrates new aspects of field-controllable ferrofluid droplets as a configurable and reprocessable metamaterial.

cond-mat.soft↗

arXiv2021

Scaling regimes of active turbulence with external dissipation

Active fluids exhibit complex turbulent-like flows at low Reynolds number. Recent work predicted that 2d active nematic turbulence follows universal scaling laws. However, experimentally testing these predictions is conditioned by the coupling to the 3d environment. Here, we measure the spectrum of the kinetic energy, $E(q)$, in an active nematic film in contact with a passive oil layer. At small and intermediate scales, we find the scaling regimes $E(q)\sim q^{-4}$ and $E(q)\sim q^{-1}$, respectively, in agreement with the theoretical prediction for 2d active nematics. At large scales, however, we find a new scaling $E(q)\sim q$, which emerges when the dissipation is dominated by the 3d oil layer. In addition, we derive an explicit expression for the spectrum that spans all length scales, thus explaining and connecting the different scaling regimes. This allows us to fit the data and extract the length scale that controls the crossover to the new large-scale regime, which we tune by varying the oil viscosity. Overall, our work experimentally demonstrates the emergence of universal scaling laws in active turbulence, and it establishes how the spectrum is affected by external dissipation.

cond-mat.soft↗

arXiv2020

Magnetic microswimmers exhibit Bose-Einstein-like condensation

We study an active matter system comprised of magnetic microswimmers confined in a microfluidic channel and show that it exhibits a new type of self-organized behavior. Combining analytical techniques and Brownian dynamics simulations, we demonstrate how the interplay of non-equilibrium activity, external driving, and magnetic interactions leads to the condensation of swimmers at the center of the channel via a non-equilibrium phase transition that is formally akin to Bose-Einstein condensation. We find that the effective dynamics of the microswimmers can be mapped onto a diffusivity-edge problem, and use the mapping to build a generalized thermodynamic framework, which is verified by a parameter-free comparison with our simulations. Our work reveals how driven active matter has the potential to generate exotic classical non-equilibrium phases of matter with traits that are analogous to those observed in quantum systems.

cond-mat.stat-mech↗

arXiv2020

Modelling Mullins Effect Induced by Chain Delamination and Reattachment

We propose a continuum theory to model the Mullins effect, which is ubiquitously observed in polymer composites. In the theory, the softening of the materials during the stretching process is accounted for by considering the delamination of polymer chains from nano-/micro-sized fillers, and the recovery effect during the de-stretching process is due to the reattachment of the polymer chains to nano-/micro-sized fillers. By incorporating the chain entanglements, Log-Normal distribution of the mesh size in the network, etc., we can obtain a good agreement between our numerical calculation results and existing experimental data. This physical theory can be easily adapted to meet more practical needs and utilised in analysing mechanic properties of polymer composites.

cond-mat.soft↗

arXiv2020

Modelling Elastically-Mediated Liquid-Liquid Phase Separation

We propose a continuum theory of the liquid-liquid phase separation in an elastic network where phase-separated microscopic droplets rich in one fluid component can form as an interplay of fluids mixing, droplet nucleation, network deformation, thermodynamic fluctuation, \emph{etc}. We find that the size of the phase separated droplets decreases with the shear modulus of the elastic network in the form of $\sim[\mathrm{modulus}]^{-1/3}$ and the number density of the droplet increases almost linearly with the shear modulus $\sim[\mathrm{modulus}]$, which are verified by the experimental observations. Phase diagrams in the space of (fluid constitution, mixture interaction, network modulus) are provided, which can help to understand similar phase separations in biological cells and also to guide fabrications of synthetic cells with desired phase properties.

cond-mat.soft↗

arXiv2020

Designing metachronal waves of cilia

On surfaces with many motile cilia, beats of the individual cilia coordinate to form metachronal waves. We present a theoretical framework that connects the dynamics of an individual cilium to the collective dynamics of a ciliary carpet via systematic coarse-graining. We uncover the criteria that control the selection of frequency and wavevector of stable metchacronal waves of the cilia and examine how they depend on the geometric and dynamical characteristics of single cilia, as well as the geometric properties of the array. We perform agent-based numerical simulations of arrays of cilia with hydrodynamic interactions and find quantitative agreement with the predictions of the analytical framework. Our work sheds light on the question of how the collective properties of beating cilia can be determined using information about the individual units, and as such exemplifies a bottom-up study of a rich active matter system.

cond-mat.soft↗