SearcharxivSearch

arXiv subjects

Lijuan Zhang

Publications and source records attributed to Lijuan Zhang.

13 recordsLinked to original sources

Mean-square attractors for non-autonomous Caputo fractional stochastic differential equations

This paper investigates the existence of mean-square attractors for a class of non-autonomous Caputo fractional stochastic differential equations of order $α\in (\frac{1}{2},1)$, with a driving system on a compact base space $P$ and tempered fractional noise. We first construct a mean-square semi-dynamical system on $\mathfrak{C} \times P$ that carries a skew-product semi-flow structure, where $\mathfrak{C}=C(\mathbb{R}^{+}, L^{2}(Ω, \mathcal{F}; \mathbb{R}^d))$ denotes the space of continuous functions from $ \mathbb{R}^{+}$ into $L^2(Ω, \mathcal{F}; \mathbb{R}^d)$. A global forward attracting set is then established in the weak mean-square topology. Moreover, by endowing the function space $\mathfrak{C}_{w}=C(\mathbb{R}^{+}, L^{2}_w(Ω, \mathcal{F}; \mathbb{H}))$ with an appropriate topology that renders it complete, we show that the skew-product semi-flow possesses a bounded and closed mean-square attractor within $\mathfrak{C}_{w} \times P$. It is worth emphasizing that completeness plays a crucial role here: without this property, the attractor need not exist.

math.DS

Rough differential equations driven by TFBM with Hurst index $H\in (\frac{1}{4}, \frac{1}{3})$

We consider the rough differential equations driven by tempered fractional Brownian motion with Hurst index $H\in (\frac{1}{4}, \frac{1}{3})$ and tempered parameter $λ>0$. First, by means of piecewise linear approximation, we canonically lift the tempered fractional Brownian motion to a three-step geometric rough path in an almost sure sense. Subsequently, employing the Doss-Sussmann technique in conjunction with a greedy sequence of stopping times, we construct a suitable transformation that establishes a bijection between the solution of the rough differential equation and that of an associated ordinary differential equation. This yields the existence and uniqueness of a solution to the original equation. Based on this result and appealing to Gronwall's lemma, we further derive an upper bound for the solution norm, thereby providing a quantitative control on its growth.

math.DS

Semantic Search At LinkedIn

Semantic search with large language models (LLMs) enables retrieval by meaning rather than keyword overlap, but scaling it requires major inference efficiency advances. We present LinkedIn's LLM-based semantic search framework for AI Job Search and AI People Search, combining an LLM relevance judge, embedding-based retrieval, and a compact Small Language Model trained via multi-teacher distillation to jointly optimize relevance and engagement. A prefill-oriented inference architecture co-designed with model pruning, context compression, and text-embedding hybrid interactions boosts ranking throughput by over 75x under a fixed latency constraint while preserving near-teacher-level NDCG, enabling one of the first production LLM-based ranking systems with efficiency comparable to traditional approaches and delivering significant gains in quality and user engagement.

cs.IR

Fast Wrong-way Cycling Detection in CCTV Videos: Sparse Sampling is All You Need

Effective monitoring of unusual transportation behaviors, such as wrong-way cycling (i.e., riding a bicycle or e-bike against designated traffic flow), is crucial for optimizing law enforcement deployment and traffic planning. However, accurately recording all wrong-way cycling events is both unnecessary and infeasible in resource-constrained environments, as it requires high-resolution cameras for evidence collection and event detection. To address this challenge, we propose WWC-Predictor, a novel method for efficiently estimating the wrong-way cycling ratio, defined as the proportion of wrong-way cycling events relative to the total number of cycling movements over a given time period. The core innovation of our method lies in accurately detecting wrong-way cycling events in sparsely sampled frames using a light-weight detector, then estimating the overall ratio using an autoregressive moving average model. To evaluate the effectiveness of our method, we construct a benchmark dataset consisting of 35 minutes of video sequences with minute-level annotations.Our method achieves an average error rate of a mere 1.475\% while consuming only 19.12\% GPU time required by conventional tracking methods, validating its effectiveness in estimating the wrong-way cycling ratio. Our source code is publicly available at: https://github.com/VICA-Lab-HKUST-GZ/WWC-Predictor.

cs.CV

Driving with Context: Online Map Matching for Complex Roads Using Lane Markings and Scenario Recognition

Accurate online map matching is fundamental to vehicle navigation and the activation of intelligent driving functions. Current online map matching methods are prone to errors in complex road networks, especially in multilevel road area. To address this challenge, we propose an online Standard Definition (SD) map matching method by constructing a Hidden Markov Model (HMM) with multiple probability factors. Our proposed method can achieve accurate map matching even in complex road networks by carefully leveraging lane markings and scenario recognition in the designing of the probability factors. First, the lane markings are generated by a multi-lane tracking method and associated with the SD map using HMM to build an enriched SD map. In areas covered by the enriched SD map, the vehicle can re-localize itself by performing Iterative Closest Point (ICP) registration for the lane markings. Then, the probability factor accounting for the lane marking detection can be obtained using the association probability between adjacent lanes and roads. Second, the driving scenario recognition model is applied to generate the emission probability factor of scenario recognition, which improves the performance of map matching on elevated roads and ordinary urban roads underneath them. We validate our method through extensive road tests in Europe and China, and the experimental results show that our proposed method effectively improves the online map matching accuracy as compared to other existing methods, especially in multilevel road area. Specifically, the experiments show that our proposed method achieves $F_1$ scores of 98.04% and 94.60% on the Zenseact Open Dataset and test data of multilevel road areas in Shanghai respectively, significantly outperforming benchmark methods. The implementation is available at https://github.com/TRV-Lab/LMSR-OMM.

cs.CV

Edge-aware Hard Clustering Graph Pooling for Brain Imaging

Graph Convolutional Networks (GCNs) can capture non-Euclidean spatial dependence between different brain regions. The graph pooling operator, a crucial element of GCNs, enhances the representation learning capability and facilitates the acquisition of abnormal brain maps. However, most existing research designs graph pooling operators solely from the perspective of nodes while disregarding the original edge features. This confines graph pooling application scenarios and diminishes its ability to capture critical substructures. In this paper, we propose a novel edge-aware hard clustering graph pool (EHCPool), which is tailored to dominant edge features and redefines the clustering process. EHCPool initially introduced the 'Edge-to-Node' score criterion which utilized edge information to evaluate the significance of nodes. An innovative Iteration n-top strategy was then developed, guided by edge scores, to adaptively learn sparse hard clustering assignments for graphs. Additionally, a N-E Aggregation strategy is designed to aggregate node and edge features in each independent subgraph. Extensive experiments on the multi-site public datasets demonstrate the superiority and robustness of the proposed model. More notably, EHCPool has the potential to probe different types of dysfunctional brain networks from a data-driven perspective. Method code: https://github.com/swfen/EHCPool

cs.CV

Giant third-order nonlinear Hall effect in misfit layer compound (SnS)${1.17}$(NbS$_2$)$_3$

Nonlinear Hall effect (NLHE) holds immense significance in recognizing the band geometry and its potential applications in current rectification. Recent discoveries have expanded the study from second-order to third-order nonlinear Hall effect (THE), which is governed by an intrinsic band geometric quantity called the Berry Connection Polarizability (BCP) tensor. Here we demonstrate a giant THE in a misfit layer compound, (SnS)${1.17}$(NbS$_2$)$_3$. While the THE is prohibited in individual NbS$_2$ and SnS due to the constraints imposed by the crystal symmetry and their band structures, a remarkable THE emerges when a superlattice is formed by introducing a monolayer of SnS. The angular-dependent THE and its scaling relationship indicate that the phenomenon could be correlated to the band geometry modulation, concurrently with the symmetry breaking. The resulting strength of THE is orders of magnitude higher compared to recent studies. Our work illuminates the modulation of structural and electronic geometries for novel quantum phenomena through interface engineering.

cond-mat.mes-hall

An Efficient Early-breaking Estimation and Tree-splitting Missing RFID Tag Identification Protocol

Recent statistics have demonstrated that missing items have become the main cause of loss for retailers in inventory management. To quickly identify missing tags, traditional protocols adopt Aloha-based strategies which take a long time, especially when the number of tags is large. Among them, few works considered the effect of unexpected unknown tags on the missing tag identification process. With the presence of unknown tags, some missing tags may be falsely identified as present. Thus, the system's reliability is hardly guaranteed. In this work, we propose an efficient early-breaking estimation and tree-splitting-based missing tag identification (ETMTI) protocol for large-scale RFID systems. In ETMTI, a new early-breaking estimation and deactivation method is developed to effectively estimate the number of unknown tags and deactivate them within a short time. Next, a new tree-splitting-based missing tag identification method is proposed to quickly identify missing tags with a B-ary splitting tree. Besides, a bit-tracking response strategy is designed to further reduce the time cost. The optimal parameters, time cost, and false negative rate of ETMTI are analyzed theoretically. Simulation results are presented to demonstrate that the proposed ETMTI protocol takes a smaller time and has a lower false negative rate than the best-performing benchmarks.

cs.OH

Phase diagram of superconducting vortex ratchet motion in a superlattice with noncentrosymmetry

Ratchet motion of superconducting vortices, which is a directional flow of vortices in superconductors, is highly useful for exploring quantum phenomena and developing superconducting devices, such as superconducting diode and microwave antenna. However, because of the challenges in the quantitative characterization of the dynamic motion of vortices, a phase diagram of the vortex ratchet motion is still missing, especially in the superconductors with low dimensional structures. Here we establish a quantitative phase diagram of the vortex ratchet motion in a highly anisotropic superlattice superconductor, (SnS)1.17NbS2, using nonreciprocal magnetotransport. The (SnS)1.17NbS2, which possesses a layered atomic structure and noncentrosymmetry, exhibits nonreciprocal magnetotransport in a magnetic field perpendicular and parallel to the plane, which is considered a manifest of ratchet motion of superconducting vortices. We demonstrated that the ratchet motion is responsive to current excitation, magnetic field and thermal perturbation. Furthermore, we extrapolated a giant nonreciprocal coefficient (γ), which quantitatively describes the magnitude of the vortex ratchet motion, and eventually established phase diagrams of the ratchet motion of the vortices with a quantitative description. Last, we propose that the ratchet motion originates from the coexistence of pancake vortices (PVs) and Josephson vortices (JVs). The phase diagrams are desirable for controlling the vortex motion in superlattice superconductors and developing next-generation energy-efficient superconducting devices.

cond-mat.supr-con

Incoherent phonon transport dominates heat conduction across van der Waals superlattices

Heat conduction mechanisms in superlattices could be different across different types of interfaces. Van der Waals superlattices are structures physically assembled through weak van der Waals interactions by design, and may host properties beyond the traditional limits of lattice matching and processing compatibility, offering new types of interfaces. In this work, natural van der Waals (SnS)1.17(NbS2)n superlattices are synthesized, and their thermal conductivities are measured by time-domain thermoreflectance as a function of interface density. Our results show that heat conduction of (SnS)1.17(NbS2)n superlattices is dominated by interface scattering when the coherent length of phonons is larger than the superlattice period, indicating incoherent phonon transport dominates cross-plane heat conduction in van der Waals superlattices even when the period is atomically thin and abrupt. Moreover, our result suggests that the widely accepted heat conduction mechanism for conventional superlattices that coherent phonons dominate when the period is short, is not applicable due to symmetry breaking in most van der Waals superlattices. Our findings provide new insight for understanding the thermal behavior of van der Waals superlattices, and devise approaches for effective thermal management of superlattices depending on the distinct types of interfaces.

cond-mat.mtrl-sci

A unified theory to describe the transition of stable nanobubbles to unstable microbubbles on homogeneous surface

Experiments have not only revealed the remarkably long lifetime of nanobubbles, but also demonstrated the diffusive instability of bubbles above micrometers, thus a full-scale physical understanding on the stability of bubbles is in urgent need. Herein, we develop a model that captures the state transition from the stable nanobubbles to the unstable microbubbles on homogeneous surfaces. The transition explains the typical long lifetime, limited height and small contact angle of surface nanobubbles observed in experiments. The consequent phase diagram shows that the bubble size and gas dissolving saturation determine the dynamic behaviors of surface bubbles, namely growth, stability, shrinkage or dissolution.

cond-mat.soft

Two-dimensional Ca-Cl crystals under ambient conditions observed directly by cryo-electron microscopy

Recently, we report the direct observation, under ambient conditions, of Na2Cl and Na3Cl as two-dimensional (2D) Na-Cl crystals, together with regular NaCl, on reduced graphene oxide membranes and on the surfaces of natural graphite powders from salt solutions far below the saturated concentration. However, what are these abnormal stoichiometries for high valence ions, such as calcium ions and copper ions still remain unknown. Here, using cryo-electron microscopy, we report the direct observation of two-dimensional (2D) Ca-Cl crystals on reduced graphene oxide (rGO) membranes, in which the calcium ions are only monovalent (i.e. +1). Remarkably, metallic properties rather than insulating are displayed by those CaCl crystals. We note that such CaCl crystals are obtained by simply incubating rGO membranes in salt solutions below the saturated concentration, under ambient conditions. Theoretical studies show that the formation of those abnormal crystals is attributed to the strong cation-pi interactions of the Ca2+ ions with the aromatic rings in the graphitic surfaces. Since strong cation-pi interactions also exist between other metal ions (such as Mg2+, Fe2+, Co2+, Cu2+, Cd2+, Cr2+ and Pb2+) and graphitic surfaces, similar 2D crystals with abnormal valence state of the metal cations and corresponding abnormal properties are highly expected. The 2D crystals with monovalent calcium ions show unusual electronic properties, and can be applicated in catalyzer, hydrogen storage, high-performance conducting electrodes and sensors. These findings also produce functionalized graphene including compact "graphene-metallic CaCl-insulating CaCl2" junction that can serve as transistors down to the atomic scale, and other devices for magnetic, optical and mechanical applications.

physics.chem-ph

Gas Bubbles and Gas Pancakes at Liquid/Solid Interface: A Continuum Theory Incorporated with Molecular Interactions

The states of gas accumulated at the liquid-solid interface are analyzed based on the continuum theory where the Hamaker constant is used to describe the long-range interaction at the microscopic scale. The Hamaker constant is always negative, whereas the gas spreading coefficient can be either sign. Despite the complexity of gas, including that the density profile may not be uniform due to absorption on both solid and liquid surfaces, we predict three possible gas states at the liquid-solid interface, i.e. complete wetting, partial wetting and pseudopartial wetting. These possible gas states correspond respectively to a gas pancake (or film) surrounded by a wet solid, a gas bubble with a finite contact angle, and a gas bubble(s) coexisting with a gas pancake. Typical thickness of the gas pancakes is at the nanoscale within the force range of the long-range interaction, whereas the radius of the gas bubbles can be large. The state of gas bubble(s) coexisting with a gas film is predicted theoretically for the first time. Our theoretical results can contribute to the development of a unified picture of gas nucleation at the liquid-solid interface.

cond-mat.soft