arXiv subjects

Chao Sun

Publications and source records attributed to Chao Sun.

At least 37 records · Page 2Linked to original sources

arXiv2025

Marangoni-driven freezing dynamics of supercooled binary droplets

Solidification of droplets is of great importance to various technological applications, drawing considerable attention from scientists aiming to unravel the fundamental physical mechanisms. In the case of multicomponent droplets undergoing solidification, the emergence of concentration gradients may trigger significant interfacial flows that dominate the freezing dynamics. Here, we experimentally investigate the fascinating interfacial freezing dynamics of supercooled ethanol-water droplets, accompanied with the migration and growth of massive ice particles. We reveal that these unique freezing dynamics are driven by solidification-induced solutal Marangoni flow within the droplets. Our model, which incorporates the temperature- and concentration-dependent properties of the ethanol-water mixture, quantitatively predicts both the migration velocity and the growth rate of the ice particles. The former is determined by the solutal Marangoni flow velocity, while the latter is governed by a balance between the latent heat release and the enhanced thermal dissipation by the Marangoni flow. Moreover, we show that the final wrapping state of droplets can be modulated by the concentration of ethanol. Our findings may pave the way for novel insights into the physicochemical hydrodynamics of multicomponent liquids undergoing phase transitions.

physics.flu-dyn↗

arXiv2025

Freezing and ice aging dynamics in saline water under natural convection

Understanding the coupled dynamics of liquid-solid phase change and fluid flows is crucial in a wide range of geophysical and industrial applications. When freezing occurs in saline water, the newly formed ice is mushy, with a porous structure that traps the brine within the ice. In this work, which combines experiments and theoretical analyses, we investigate the long-term evolution of saline ice, comprehensively accounting for the coupled dynamics of multiscale fluid flow, heat and mass transfer, and phase change. We show that in a closed convective system the rapid formation of a mushy ice layer is followed by desalination (i.e, the expulsion of salt from the ice) processes that might lead to a slow asymptotic decrease of the ice thickness. Desalination of mushy ice reduces its porosity, which alters the dynamic thermal equilibrium and ice thickness by weakening buoyancy-driven convection within the mushy layer. In turn, changes in brine convection and ice thickness affect the further desalination of the ice. The long-term dynamics of the system can be accurately predicted by a one-dimensional model based on appropriate parameterizations of global heat and mass transfer properties. Furthermore, within the same theoretical model we explore the ice dynamics across a broader parameter space. Our findings advance the understanding of the coupled phase-change physics of saline solutions in the presence of convective fluid flows and provide a basis for explaining and predicting real-world phenomena such as the aging of sea ice.

physics.flu-dyn↗

arXiv2025

Non-reciprocal coalescence-breakup dynamics in concentrated emulsions

Dense stabilized emulsions are mixtures of immiscible fluids where the high-volume fraction droplet dispersed phase is stabilized against coalescence by steric interactions. The production of emulsions involves high-shear flows and it is well known that at a critical volume fraction the emulsion loses stability, undergoing an extremely rapid process where the fluid components in the emulsion exchange roles. This process, called catastrophic phase inversion, which resembles in several respects a dynamical phase transition, has remained widely elusive from an experimental and theoretical point of view. In this work, we present state-of-the-art experimental and numerical data to support a dynamical-system framework capable of precisely highlighting the dynamics occurring in the system as it approaches the catastrophic phase inversion. Our study clearly highlights that at high volume fractions, dynamical changes in the emulsion morphology, due to coalescence and breakup of droplets, play a critical role in determining emulsion's rheology and stability. Additionally, we show that at approaching the critical volume fractions, the dynamics can be simplified as being controlled by the dynamics of a correlation length represented, in our systems, by the size of the largest droplet. This dynamics shares a close connection with non-reciprocal phase transition where two different physical mechanisms, coalescence and breakups, can get out of balance leading to large non-symmetric periodic excursions in phase space. We clarify the phenomenology observed and quantitatively explain the essential aspect of the highly complex dynamics of stabilized emulsions undergoing catastrophic phase inversion.

cond-mat.soft↗

arXiv2025

Inner-product Functional Encryption with Fine-grained Revocation for Flexible EHR Sharing

E-health record (EHR) contains a vast amount of continuously growing medical data and enables medical institutions to access patient health data conveniently.This provides opportunities for medical data mining which has important applications in identifying high-risk patients and improving disease diagnosis, etc.Since EHR contains sensitive patient information, how to protect patient privacy and enable mining on EHR data is important and challenging.Traditional public key encryption (PKE) can protect patient privacy, but cannot support flexible selective computation on encrypted EHR data.Functional encryption (FE) allows authorised users to compute function values of encrypted data without releasing other information, hence supporting selective computation on encrypted data. Nevertheless, existing FE schemes do not support fine-grained revocation and update, so they are unsuitable for EHR system. In this paper,we first propose an inner-product functional encryption with fine-grained revocation (IPFE-FR) scheme, and then apply it to a flexible EHR sharing system. Our scheme possesses the following features:(1) a group manager can revoke a specific function computation of medical institutions on encrypted EHR data,instead of all function computation rights. (2) a revoked medical institution is not allowed to compute the function value of encrypted EHR data not only generated after the revocation, but also generated before the revocation. (3) secret keys issued to the same medical institution are bound together to prevent collusion attacks. The formal definition and security model of the IPFE-FR scheme are proposed.Furthermore, we present a concrete construction and reduce its security to the Learning with Errors (LWE) assumption which is quantum-resistant. Finally, the theoretical analysis and experimental implementation of our scheme are conducted to show its efficiency.

cs.CR↗

arXiv2025

Gravity drives the flow within the Stewartson layer in centrifugal convection

We conduct three-dimensional numerical simulations on centrifugal convection (CC) in a closed annular container, incorporating gravity and no-slip top and bottom boundaries, to systematically investigate rotation-induced secondary flow. The Stewartson layer, identified by an elongated circulation in mean vertical velocity plots, emerges near the inner and outer cylinders only beyond a critical gravitational forcing. Quantitative analyses confirm that the layer thickness scales as $δ_{st}\sim Ek^{1/3}$ due to rotational effects, consistent with results from rotating Rayleigh-Bénard convection, where $Ek$ represents the Ekman number. The internal circulation strength, however, is determined by both gravitational buoyancy and rotational effects. We propose that gravitational buoyancy drives the internal flow, which balances against viscous forces to establish a terminal velocity. Through theoretical analysis, the vertical velocity amplitude follows $W_{st}\sim Ek^{5/3}Ro^{-1}Ra_gPr^{-1}$, showing good agreement with simulation results across a wide parameter range. Here, $Ro^{-1}$ represents the inverse Rossby number, $Ra_g$ the gravitational Rayleigh number, and $Pr$ the Prandtl number. The theoretical predictions match simulations well, demonstrating that the Stewartson layer is gravity-induced and rotationally constrained through geostrophic balance in the CC system. These findings yield fundamental insights into turbulent flow structures and heat transfer mechanisms in the CC system, offering both theoretical advances and practical engineering applications.

physics.flu-dyn↗

arXiv2025

OmniDPO: A Preference Optimization Framework to Address Omni-Modal Hallucination

Recently, Omni-modal large language models (OLLMs) have sparked a new wave of research, achieving impressive results in tasks such as audio-video understanding and real-time environment perception. However, hallucination issues still persist. Similar to the bimodal setting, the priors from the text modality tend to dominate, leading OLLMs to rely more heavily on textual cues while neglecting visual and audio information. In addition, fully multimodal scenarios introduce new challenges. Most existing models align visual or auditory modalities with text independently during training, while ignoring the intrinsic correlations between video and its corresponding audio. This oversight results in hallucinations when reasoning requires interpreting hidden audio cues embedded in video content. To address these challenges, we propose OmniDPO, a preference-alignment framework designed to mitigate hallucinations in OLLMs. Specifically, OmniDPO incorporates two strategies: (1) constructing text-preference sample pairs to enhance the model's understanding of audio-video interactions; and (2) constructing multimodal-preference sample pairs to strengthen the model's attention to visual and auditory information. By tackling both challenges, OmniDPO effectively improves multimodal grounding and reduces hallucination. Experiments conducted on two OLLMs demonstrate that OmniDPO not only effectively mitigates multimodal hallucinations but also significantly enhances the models' reasoning capabilities across modalities. All code and datasets will be released upon paper acceptance.

cs.AI↗

arXiv2025

Active biphasic heat transfer enhancement in vertical natural convection

Vertical natural convection (VC), often cannot meet the high heat transfer demands due to the inherent misalignment of the direction of buoyancy (vertical) with the direction of the heat transfer (horizontal). Here we applied a novel strategy on a water based VC system to enhance the heat transfer. By adding 2% of the total volume with a low-boiling-temperature liquid (HFE-7000) and introducing a gas-liquid layer on top of the VC cell, we create a self-sustained state of pseudo-turbulence with evaporating, circulating and condensing biphasic bubbles. The system achieves 246% heat transfer enhancement at constant superheat $T_{sup}\approx6.4 \text{K}$ when the liquid in the full nucleate boiling state. Using shadowgraphy and Laser Doppler Anemometry (LDA) methods, we validate that the bubbles and biphasic particles induced agitation enhances the heat flux and modifies the temperature field of the heat transfer.

physics.flu-dyn↗

arXiv2025

3DTTNet: Multimodal Fusion-Based 3D Traversable Terrain Modeling for Off-Road Environments

Off-road environments remain significant challenges for autonomous ground vehicles, due to the lack of structured roads and the presence of complex obstacles, such as uneven terrain, vegetation, and occlusions. Traditional perception algorithms, primarily designed for structured environments, often fail in unstructured scenarios. In this paper, traversable area recognition is achieved through semantic scene completion. A novel multimodal method, 3DTTNet, is proposed to generate dense traversable terrain estimations by integrating LiDAR point clouds with monocular images from a forward-facing perspective. By integrating multimodal data, environmental feature extraction is strengthened, which is crucial for accurate terrain modeling in complex terrains. Furthermore, RELLIS-OCC, a dataset with 3D traversable annotations, is introduced, incorporating geometric features such as step height, slope, and unevenness. Through a comprehensive analysis of vehicle obsta cle-crossing conditions and the incorporation of vehicle body structure constraints, four traversability cost labels are generated: lethal, medium-cost, low-cost, and free. Experimental results demonstrate that 3DTTNet outperforms the comparison approaches in 3D traversable area recognition, particularly in off-road environments with irregular geometries and partial occlusions. Specifically, 3DTTNet achieves a 42\% improvement in scene completion IoU compared to other models. The proposed framework is scalable and adaptable to various vehicle platforms, allowing for adjustments to occupancy grid parameters and the integration of advanced dynamic models for traversability cost estimation.

cs.RO↗

arXiv2025

RoadMamba: A Dual Branch Visual State Space Model for Road Surface Classification

Acquiring the road surface conditions in advance based on visual technologies provides effective information for the planning and control system of autonomous vehicles, thus improving the safety and driving comfort of the vehicles. Recently, the Mamba architecture based on state-space models has shown remarkable performance in visual processing tasks, benefiting from the efficient global receptive field. However, existing Mamba architectures struggle to achieve state-of-the-art visual road surface classification due to their lack of effective extraction of the local texture of the road surface. In this paper, we explore for the first time the potential of visual Mamba architectures for road surface classification task and propose a method that effectively combines local and global perception, called RoadMamba. Specifically, we utilize the Dual State Space Model (DualSSM) to effectively extract the global semantics and local texture of the road surface and decode and fuse the dual features through the Dual Attention Fusion (DAF). In addition, we propose a dual auxiliary loss to explicitly constrain dual branches, preventing the network from relying only on global semantic information from the deep large receptive field and ignoring the local texture. The proposed RoadMamba achieves the state-of-the-art performance in experiments on a large-scale road surface classification dataset containing 1 million samples.

cs.CV↗

arXiv2025

A Survey on Efficiency Optimization Techniques for DNN-based Video Analytics: Process Systems, Algorithms, and Applications

The explosive growth of video data in recent years has brought higher demands for video analytics, where accuracy and efficiency remain the two primary concerns. Deep neural networks (DNNs) have been widely adopted to ensure accuracy; however, improving their efficiency in video analytics remains an open challenge. Different from existing surveys that make summaries of DNN-based video mainly from the accuracy optimization aspect, in this survey, we aim to provide a thorough review of optimization techniques focusing on the improvement of the efficiency of DNNs in video analytics. We organize existing methods in a bottom-up manner, covering multiple perspectives such as hardware support, data processing, operational deployment, etc. Finally, based on the optimization framework and existing works, we analyze and discuss the problems and challenges in the performance optimization of DNN-based video analytics.

cs.CV↗

arXiv2025

Sea ice aging by diffusion-driven desalination

Sea ice is a key component of the Earth's climate system, making its aging process an essential focus of current research. The age of sea ice is closely linked to its thermal and mechanical properties, which govern its interactions with the surrounding environment. In this study, we combine experimental techniques and modeling to explore the full dynamical process of mushy ice growth and spontaneous aging in saline water, within a natural convective flow system. We show that the aging of newly formed mushy ice in the present system, characterized by a gradual long-term reduction in porosity, is controlled by diffusion-driven desalination. Moreover, we observe that the system eventually transits into a dense freshwater ice layer adjacent to a well-mixed saline water region. The shape of the ice layer in this asymptotic state is well captured by numerical simulations of non-porous ice. Our findings improve the understanding of the complex physics governing phase changes in aqueous systems and provide a framework for studying sea ice aging in laboratory settings, with implications spanning diverse natural and industrial applications.

physics.flu-dyn↗

arXiv2025

Uncovering the nanoscopic phase behavior of ternary solutions in the presence of electrolytes: from pre-Ouzo to Ouzo region

In this work, we report a comprehensive study of how electrolyte addition governs the structure and stability of surfactant-free microemulsions in a trans-anethol/ethanol/water system. The universal structural response has been validated, spanning the full range of solution dispersed-phase structurings, from sub-10 nm W/O reverse-aggregates to O/W mesoscopic droplets (~100 nm) and classical Ouzo droplets (~1 μm). Experimental results reveal that there is a threshold for electrolyte levels above which oil-in-water nanodroplet coalescence and phase separation are triggered: screening of electrical double layers and "salting out" of hydrophobic components drives hydrotrope into fewer, larger droplets. The total oil volume sequestered in the dispersed phase remains essentially constant, indicating oil redistribution rather than dissolution. In contrast, water-in-oil nanodroplets in a predominantly organic medium display near-complete insensitivity to ionic strength, owing to low dielectric screening and tight interfacial packing that exclude substantial ion uptake. Finally, addition of high salt to Ouzo droplets accelerates their collapse: large droplets fuse and sediment, leaving only residual nanostructures and confirming electrolyte-driven phase demixing. This insight offers clear guidelines for designing additive-free emulsions with tailored lifetimes and nanostructure architectures across pharmaceutical, food, and materials applications.

cond-mat.soft↗

arXiv2025

RoadFormer : Local-Global Feature Fusion for Road Surface Classification in Autonomous Driving

The classification of the type of road surface (RSC) aims to utilize pavement features to identify the roughness, wet and dry conditions, and material information of the road surface. Due to its ability to effectively enhance road safety and traffic management, it has received widespread attention in recent years. In autonomous driving, accurate RSC allows vehicles to better understand the road environment, adjust driving strategies, and ensure a safer and more efficient driving experience. For a long time, vision-based RSC has been favored. However, existing visual classification methods have overlooked the exploration of fine-grained classification of pavement types (such as similar pavement textures). In this work, we propose a pure vision-based fine-grained RSC method for autonomous driving scenarios, which fuses local and global feature information through the stacking of convolutional and transformer modules. We further explore the stacking strategies of local and global feature extraction modules to find the optimal feature extraction strategy. In addition, since fine-grained tasks also face the challenge of relatively large intra-class differences and relatively small inter-class differences, we propose a Foreground-Background Module (FBM) that effectively extracts fine-grained context features of the pavement, enhancing the classification ability for complex pavements. Experiments conducted on a large-scale pavement dataset containing one million samples and a simplified dataset reorganized from this dataset achieved Top-1 classification accuracies of 92.52% and 96.50%, respectively, improving by 5.69% to 12.84% compared to SOTA methods. These results demonstrate that RoadFormer outperforms existing methods in RSC tasks, providing significant progress in improving the reliability of pavement perception in autonomous driving systems.

cs.CV↗

arXiv2025

Decoding Cellular Temperature via Neural Network-Aided Fluorescent Thermometry

The temperature distribution within cells, especially the debates on mitochondrial temperature, has recently attracted widespread attention. Some studies have claimed that the temperature of mitochondria can reach up to 50-53 degrees Celsius. Yet others have questioned that this is due to measurement errors from fluorescent thermometry caused by other factors, like cell viscosity. Here we present a neural network-aided fluorescent thermometry and decouple the effect of cellular viscosity on temperature measurements. We found that cellular viscosity may cause significant deviations in temperature measurements. We investigated the dynamic temperature changes in different organelles within the cell under stimulation and observed a distinct temperature gradient within the cell. Eliminating the influence of viscosity, the upper limit of mitochondrial temperature does not exceed 42-43 degrees Celsius, supporting our knowledge about the inactivation temperature of enzymes. The temperature of mitochondria is closely related to their functions and morphology, such as fission and fusion. Our results help to clarify the question of "how hot are mitochondria?" and promote a better understanding on cellular thermodynamics.

physics.bio-ph↗

arXiv2025

Experimental Study of Fabry-Perot BICs in a Microwave Waveguide

We study Fabry-Perot bound states in the continuum (FP-BIC) in the GHz frequency range, formed by two ceramic discs placed inside a metallic-walled rectangular waveguide, that act as perfect reflectors at the resonant frequency. The energy becomes perfectly trapped between the discs, forming a FP-BIC, when the distance between them matches the Fabry-Perot quantization condition. We present both theoretical and experimental analyses to investigate how the total and radiative quality factors (Q factors) depend on the inter-disk distance. We gain valuable insights into the Fano features observed in the transmission spectra using the quasi-normal mode technique and temporal coupled mode theory. Notably, we find that as the system approaches the BICs, the Fano asymmetry parameters diverge, resulting in a Lorentzian transmission profile. Experimentally, we measure a radiative Q factor on the order of $10^5$, while the total Q factor, limited by material losses, remains around $10^3$. These results offer new opportunities for the application of BICs in microwave technology, significantly advancing the potential for high-performance devices.

physics.optics↗

arXiv2025

Ionic environment-modulated nucleation and stability of multiscale nanodomains in surfactant-free microemulsions

Nano-clustering occurs in the monophasic "pre-Ouzo" region of ternary liquid mixtures without the use of surfactants. This study is proposed to elucidate the nucleation and stability of multiscale nanodomains in a surfactant-free microemulsion (SFME) system composed of trans-anethol, ethanol and water, tuned by aqueous ionic environment. We examined direct- and reverse-SFME structures with different compositions performed by dynamic light scattering and nanoparticle tracking analysis. Our findings demonstrate that extreme pH and ionic strength modulation significantly affect the nucleation and growth of nanodomains, resulting in larger sizes and lower number density of mesoscopic droplets in O/W structuring, whereas the reverse aggregates (W/O structuring) remain stable. Our findings clarify that Ostwald ripening is the primary mechanism to drive the droplet growth. Both theoretical calculation and experiment results match well and reveal that droplet ripening is significantly affected by extreme acidic (pH<3) and alkaline (pH>10) conditions. Electrostatic repulsion in a neutral ionic environment can prevent the coalescence of droplets induced by collision. This research provides an insight into the behavior of multiscale nanodomains in SFME under extensive pH control for applications in material synthesis, drug solubilization and gel preparation.

physics.chem-ph↗

arXiv2025

Distributed Stochastic Optimization for Non-Smooth and Weakly Convex Problems under Heavy-Tailed Noise

In existing distributed stochastic optimization studies, it is usually assumed that the gradient noise has a bounded variance. However, recent research shows that the heavy-tailed noise, which allows an unbounded variance, is closer to practical scenarios in many tasks. Under heavy-tailed noise, traditional optimization methods, such as stochastic gradient descent, may have poor performance and even diverge. Thus, it is of great importance to study distributed stochastic optimization algorithms applicable to the heavy-tailed noise scenario. However, most of the existing distributed algorithms under heavy-tailed noise are developed for convex and smooth problems, which limits their applications. This paper proposes a clipping-based distributed stochastic algorithm under heavy-tailed noise that is suitable for non-smooth and weakly convex problems. The convergence of the proposed algorithm is proven, and the conditions on the parameters are given. A numerical experiment is conducted to demonstrate the effectiveness of the proposed algorithm.

math.OC↗

arXiv2025

Distributed Stochastic Optimization under Heavy-Tailed Noises

This paper studies the distributed optimization problem under the influence of heavy-tailed gradient noises. Here, a heavy-tailed noise means that the noise does not necessarily satisfy the bounded variance assumption. Instead, it satisfies a more general assumption. The commonly-used bounded variance assumption is a special case of the considered noise assumption. A typical example of this kind of noise is a Pareto distribution noise with tail index within (1,2], which has infinite variance. Despite that there has been several distributed optimization algorithms proposed for the heavy-tailed noise scenario, these algorithms need a centralized server in the network which collects the information of all clients. Different from these algorithms, this paper considers that there is no centralized server and the agents can only exchange information with neighbors in a communication graph. A distributed method combining gradient clipping and distributed stochastic subgradient projection is proposed. It is proven that when the gradient descent step-size and the gradient clipping step-size meet certain conditions, the state of each agent converges to the optimal solution of the distributed optimization problem with probability 1. The simulation results validate the algorithm.

math.OC↗