arXiv subjects

Jiafeng Xie

Publications and source records attributed to Jiafeng Xie.

12 recordsLinked to original sources

arXiv2026

Addressing bedload flux variability due to grain shape effects and experimental channel geometry

The study-to-study variability of bedload flux measurements in turbulent sediment transport borders an order of magnitude, even for idealized laboratory conditions. This uncertainty stems from physically poorly supported, empirical methods to account for channel geometry effects in the determination of the transport-driving bed shear stress, and from study-to-study grain-shape variations. Here, we derive a universal method of bed shear stress determination. It consists of a granular-physics-based definition of the bed surface and a channel sidewall correction based on linking Reynolds stress to bulk flow properties via Kolmogorov's theory of turbulence. Application of this method to bedload transport of spherical grains---to rule out grain-shape effects---collapses data from existing laboratory measurements and grain-resolved computation fluid dynamics (CFD) and discrete element method (DEM) simulations for various channel geometries onto a single curve. In contrast, classical sidewall corrections, as well as an alternative bed surface definition, are unable to universally capture these data, especially those from shallow or very narrow channel flows. We then apply our method to an extended grain-shape-controlled data compilation, complemented by literature data for non-spherical grains and from grain-unresolved CFD-DEM simulations. This compilation covers a very diverse range of transport conditions, ranging from very narrow to infinitely wide channels, from shallow to deep channel flows, from mild to steep bed slopes, and from weak to intense transport. We generalize an existing physical bedload flux model to account for grain-shape effects and show that it explains almost all the compiled data within a factor of only $1.3$.

physics.geo-ph↗

arXiv2025

SoK: Can Fully Homomorphic Encryption Support General AI Computation? A Functional and Cost Analysis

Artificial intelligence (AI) increasingly powers sensitive applications in domains such as healthcare and finance, relying on both linear operations (e.g., matrix multiplications in large language models) and non-linear operations (e.g., sorting in retrieval-augmented generation). Fully homomorphic encryption (FHE) has emerged as a promising tool for privacy-preserving computation, but it remains unclear whether existing methods can support the full spectrum of AI workloads that combine these operations. In this SoK, we ask: Can FHE support general AI computation? We provide both a functional analysis and a cost analysis. First, we categorize ten distinct FHE approaches and evaluate their ability to support general computation. We then identify three promising candidates and benchmark workloads that mix linear and non-linear operations across different bit lengths and SIMD parallelization settings. Finally, we evaluate five real-world, privacy-sensitive AI applications that instantiate these workloads. Our results quantify the costs of achieving general computation in FHE and offer practical guidance on selecting FHE methods that best fit specific AI application requirements. Our codes are available at https://github.com/UCF-ML-Research/FHE-AI-Generality.

cs.CR↗

arXiv2023

VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale Driving Scene

High-definition (HD) map serves as the essential infrastructure of autonomous driving. In this work, we build up a systematic vectorized map annotation framework (termed VMA) for efficiently generating HD map of large-scale driving scene. We design a divide-and-conquer annotation scheme to solve the spatial extensibility problem of HD map generation, and abstract map elements with a variety of geometric patterns as unified point sequence representation, which can be extended to most map elements in the driving scene. VMA is highly efficient and extensible, requiring negligible human effort, and flexible in terms of spatial scale and element type. We quantitatively and qualitatively validate the annotation performance on real-world urban and highway scenes, as well as NYC Planimetric Database. VMA can significantly improve map generation efficiency and require little human effort. On average VMA takes 160min for annotating a scene with a range of hundreds of meters, and reduces 52.3% of the human cost, showing great application value. Code: https://github.com/hustvl/VMA.

cs.CV↗

arXiv2023

Monocular Road Planar Parallax Estimation

Estimating the 3D structure of the drivable surface and surrounding environment is a crucial task for assisted and autonomous driving. It is commonly solved either by using 3D sensors such as LiDAR or directly predicting the depth of points via deep learning. However, the former is expensive, and the latter lacks the use of geometry information for the scene. In this paper, instead of following existing methodologies, we propose Road Planar Parallax Attention Network (RPANet), a new deep neural network for 3D sensing from monocular image sequences based on planar parallax, which takes full advantage of the omnipresent road plane geometry in driving scenes. RPANet takes a pair of images aligned by the homography of the road plane as input and outputs a $γ$ map (the ratio of height to depth) for 3D reconstruction. The $γ$ map has the potential to construct a two-dimensional transformation between two consecutive frames. It implies planar parallax and can be combined with the road plane serving as a reference to estimate the 3D structure by warping the consecutive frames. Furthermore, we introduce a novel cross-attention module to make the network better perceive the displacements caused by planar parallax. To verify the effectiveness of our method, we sample data from the Waymo Open Dataset and construct annotations related to planar parallax. Comprehensive experiments are conducted on the sampled dataset to demonstrate the 3D reconstruction accuracy of our approach in challenging scenarios.

cs.CV↗

arXiv2023

Turbidity currents propagating down an inclined slope: particle auto-suspension

The Turbidity current (TC), a ubiquitous fluid-particle coupled phenomenon in the natural environment and engineering, can transport over long distances on an inclined terrain due to the suspension mechanism. A large-eddy simulation and discrete element method coupled model is employed to simulate the particle-laden gravity currents over the inclined slope in order to investigate the auto-suspension mechanism from a Lagrangian perspective. The particle Reynolds number in our TC simulation is $0.01\sim0.1$ and the slope angle is $1/20 \sim 1/5$. The influences of initial particle concentration and terrain slope on the particle flow regimes, particle movement patterns, fluid-particle interactions, energy budget and auto-suspension index are explored. The results indicate that the auto-suspension particles predominantly appear near the current head and their number increases and then decreases during the current evolution, which is positively correlated with the coherent structures around the head. When the turbidity current propagates downstream, the average particle Reynolds number of the auto-suspension particles remains basically unchanged, and is higher than that of other transported particles. The average particle Reynolds number of the transported particles exhibits a negative correlation with the Reynolds number of the current. Furthermore, the increase in particle concentration will enhance the particle velocity, which allows the turbidity current to advance faster and improves the perpendicular support, thereby increasing the turbidity current auto-suspension capacity. Increasing slope angle will result in a slightly larger front velocity, while the effect of that on the total force is insignificant.

physics.flu-dyn↗

arXiv2022

AFIA: ATPG-Guided Fault Injection Attack on Secure Logic Locking

The outsourcing of the design and manufacturing of integrated circuits has raised severe concerns about the piracy of Intellectual Properties and illegal overproduction. Logic locking has emerged as an obfuscation technique to protect outsourced chip designs, where the circuit netlist is locked and can only be functional once a secure key is programmed. However, Boolean Satisfiability-based attacks have shown to break logic locking, simultaneously motivating researchers to develop more secure countermeasures. In this paper, we present a novel fault injection attack to break any locking technique that relies on a stored secret key, and denote this attack as AFIA, ATPG-guided Fault Injection Attack. The proposed attack is based on sensitizing a key bit to the primary output while injecting faults at a few other key lines that block the propagation of the targeted key bit. AIFA is very effective in determining a key bit as there exists a stuck-at fault pattern that detects a stuck-at 1 (or stuck-at 0) fault at any key line. The average complexity of number of injected faults for AFIA is linear with the key size and requires only |K| test patterns to determine a secret key, K. AFIA requires a fewer number of injected faults to sensitize a bit to the primary output, compared to 2|K|-1 faults for the differential fault analysis attack [26].

cs.CR↗

arXiv2022

FastRE: Towards Fast Relation Extraction with Convolutional Encoder and Improved Cascade Binary Tagging Framework

Recent work for extracting relations from texts has achieved excellent performance. However, most existing methods pay less attention to the efficiency, making it still challenging to quickly extract relations from massive or streaming text data in realistic scenarios. The main efficiency bottleneck is that these methods use a Transformer-based pre-trained language model for encoding, which heavily affects the training speed and inference speed. To address this issue, we propose a fast relation extraction model (FastRE) based on convolutional encoder and improved cascade binary tagging framework. Compared to previous work, FastRE employs several innovations to improve efficiency while also keeping promising performance. Concretely, FastRE adopts a novel convolutional encoder architecture combined with dilated convolution, gated unit and residual connection, which significantly reduces the computation cost of training and inference, while maintaining the satisfactory performance. Moreover, to improve the cascade binary tagging framework, FastRE first introduces a type-relation mapping mechanism to accelerate tagging efficiency and alleviate relation redundancy, and then utilizes a position-dependent adaptive thresholding strategy to obtain higher tagging accuracy and better model generalization. Experimental results demonstrate that FastRE is well balanced between efficiency and performance, and achieves 3-10x training speed, 7-15x inference speed faster, and 1/100 parameters compared to the state-of-the-art models, while the performance is still competitive.

cs.CL↗

arXiv2022

Fluid-particle interaction regimes during the evolution of turbidity currents from a coupled LES/DEM model

In this paper, fluid-particle interactions in lock-exchange turbidity currents (TCs) over a flat bed are investigated using a model combining LES and DEM (Large-Eddy Simulation and Discrete Element Method). The reliability of this model is demonstrated via comparing the numerical solutions with measurements of the front positions, fluid velocity profile, and particle concentration profile of lock-exchange TCs. The following physical understandings are obtained. The vorticity field plays an important role for the current evolution by affecting the fluid lift force (i.e., in the direction normal to the fluid-particle slip velocity) acting on the particles. At the very beginning, a longitudinal positive lift force due to strong positive vorticity promotes longitudinal particle transport. Afterwards, the longitudinal lift force decreases and eventually becomes negative, with a magnitude that even exceeds that of the positive longitudinal drag force, because more and more of the settling particles are affected by the negative vorticity near the bottom wall caused by surface friction. Interestingly, in spite of the complex behavior of the fluid-particle interaction forces and their role in TC evolution, only a very small fraction of the initial particle gravitational potential energy is actually transformed into TC kinetic energy (both particle and fluid).

physics.flu-dyn↗

arXiv2018

Improving Fast Segmentation With Teacher-student Learning

Recently, segmentation neural networks have been significantly improved by demonstrating very promising accuracies on public benchmarks. However, these models are very heavy and generally suffer from low inference speed, which limits their application scenarios in practice. Meanwhile, existing fast segmentation models usually fail to obtain satisfactory segmentation accuracies on public benchmarks. In this paper, we propose a teacher-student learning framework that transfers the knowledge gained by a heavy and better performed segmentation network (i.e. teacher) to guide the learning of fast segmentation networks (i.e. student). Specifically, both zero-order and first-order knowledge depicted in the fine annotated images and unlabeled auxiliary data are transferred to regularize our student learning. The proposed method can improve existing fast segmentation models without incurring extra computational overhead, so it can still process images with the same fast speed. Extensive experiments on the Pascal Context, Cityscape and VOC 2012 datasets demonstrate that the proposed teacher-student learning framework is able to significantly boost the performance of student network.

cs.CV↗

arXiv2017

A large enhancement of carrier mobility in phosphorene by introducing hexagonal boron nitride substrate

Carrier mobility is a crucial character for electronic devices since it domains power dissipation and switching speed. Materials with certain high carrier mobility, equally, unveil rich unusual physical phenomena elusive in their conventional counterparts. As a consequence, the methods to enhance the carrier mobility of materials receive immense research interests due to their potential applications in more effective electronic devices and enrichment of more unusual phenomena. For instance, introducing a flat hexagonal boron nitride (h-BN) substrate to enhance the carrier mobility has been achieved experimentally. However, the underlying mechanics is not well understood. In this study, we estimate the carrier mobility of phosphorene on h-BN substrate (P/h-BN) within the framework of the phonon-limited scattering model at first-principles level. %Our results are generic. Besides high-$κ$ dielectric property, h-BN also possesses excellent mechanical property of a high two-dimensional elastic modulus. The P/h-BN heterostructure inherits the high elastic modulus of h-BN, leading to an enhanced carrier mobility in phosphorene. Owing to the weak van der Waals interactions between the layers, the unique electronic properties of phosphorene are almost perfectly preserved near the Fermi level, guaranteeing the superior electronic transport in P/h-BN. Our findings offer a new perspective to improve the carrier mobility in phosphorene as well as other 2D materials based field effect transistors.

cond-mat.mtrl-sci↗

arXiv2015

Manifestation of unexpected semiconducting properties in few-layer orthorhombic arsenene

In this express, we demonstrate few-layer orthorhombic arsenene is an ideal semiconductor. Due to the layer stacking, multilayer arsenenes always behave as intrinsic direct bandgap semiconductors with gap values of around 1 eV. In addition, these bandgaps can be further tuned in its nanoribbons. Based on the so-called acoustic phonon limited approach, the carrier mobilities are predicted to approach as high as several thousand square centimeters per volt-second and simultaneously exhibit high directional anisotropy. All these make few-layer arsenene promising for device applications in semiconducting industry.

physics.comp-ph↗

arXiv2015

Negative Poisson's ratios in few-layer orthorhombic arsenic from first-principles calculations

A material exhibiting a negative Poisson's ratio is always one of the leading topics in materials science, which is due to the potential applications in those special areas such as defence and medicine. In this letter, we demonstrate a new material, few-layer orthorhombic arsenic, also possesses the negative Poisson's ratio. For monolayer arsenic, the negative Poisson's ratio is predicted to be around -0.09, originated from the hinge-like structure within the single layer of arsenic. When the layer increases, the negative Poisson's ratio becomes more negative and finally approaches the limit at four-layer, which is very close to the bulk's value of -0.12. The underlying mechanism is proposed for this layer-dependent negative Poisson's ratio, where the internal bond lengths as well as the normal Poisson's ratio within layer play a key role. The study like ours sheds new light on the physics of negative Poisson's ratio in those hinge-like nano-materials.

cond-mat.mtrl-sci↗