arXiv subjects

Hao Lin

Publications and source records attributed to Hao Lin.

At least 73 records · Page 4Linked to original sources

arXiv2025

Nonlinear dynamic Process of Fluvial Process Based on Theories of Chaos and Dissipative Structure

The analysis of riverbed evolution is an important basis for decision-making on river improvement, flood control project construction, and water conservancy hub dispatching and application. From the perspective of river nonlinear dynamic system, this paper deeply understands the disorder and order in the evolution and development of river bed. Using the chaos theory, the phase diagram is legally used to identify the chaos of the river time series, and the fractal dimension is quantitatively calculated to calculate the characteristic quantities of chaos. Combined with the theory of dissipative structure, the orderliness in evolution and development is understood, and the internal mechanism of river self-organization is analyzed by the principle of minimum entropy generation. The river information entropy is introduced to identify the local scouring and silting pattern of the river section. The results show that the runoff and sediment transport in the three sections are chaotic, and the information entropy fluctuates with a certain range with time, and develops to a decreasing size, that is, the river section as a whole develops in an orderly direction. However, the chaos and disorder of the Sanmenxia section are the most prominent, which may be affected by the imbalance of sediment transport caused by the construction of the reservoir, and the change of beach trough scouring and silting here is significantly affected, and the situation has been improved with the development of bank protection projects.

physics.geo-ph↗

arXiv2025

Energy-as-a-Service for RF-Powered IoE Networks: A Percolation Theory Approach

Due to the involved massive number of devices, radio frequency (RF) energy harvesting is indispensable to realize the foreseen Internet-of-Everything (IoE) within 6G networks. Analogous to the cellular networks concept, shared energy stations (ESs) are foreseen to supply energy-as-a-service (EaaS) in order to recharge devices that belong to different IoE operators who are offering diverse use cases. Considering the capital expenditure (CAPEX) for ES deployment along with their finite wireless energy transfer (WET) zones, spatial energy gaps are plausible. Furthermore, the ESs deployment cannot cover 100% of the energy-harvesting devices of all coexisting IoE use cases. In this context, we utilize percolation theory to characterize the feasibility of large-scale device-to-device (D2D) connectivity of IoE networks operating under EaaS platforms. Assuming that ESs and IoE devices follow independent Poisson point processes (PPPs), we construct a connectivity graph for the IoE devices that are within the WET zones of ESs. Continuum percolation on the construct graph is utilized to derive necessary and sufficient conditions for large-scale RF-powered D2D connectivity in terms of the required IoE device density and communication range along with the required ESs density and WET zone size. Fixing the IoE network parameters along with the size of WET zones, we obtain the approximate critical value of the ES density that ensures large-scale connectivity using the inner-city and Gilbert disk models. By imitating the bounds and combining the approximations, we construct an approximate expression for the critical ES density function, which is necessary to minimize the EaaS CAPEX under the IoE connectivity constraint.

cs.NI↗

arXiv2025

Connectivity of LEO Satellite Mega Constellations: An Application of Percolation Theory on a Sphere

With the advent of the 6G era, global connectivity has become a common goal in the evolution of communications, aiming to bring Internet services to more unconnected regions. Additionally, the rise of applications such as the Internet of Everything and remote education also requires global connectivity. Non-terrestrial networks (NTN), particularly low earth orbit (LEO) satellites, play a crucial role in this future vision. Although some literature already analyze the coverage performance using stochastic geometry, the ability of generating large-scale continuous service area is still expected to analyze. Therefore, in this paper, we mainly investigate the necessary conditions of LEO satellite deployment for large-scale continuous service coverage on the earth. Firstly, we apply percolation theory to a closed spherical surface and define the percolation on a sphere for the first time. We introduce the sub-critical and super-critical cases to prove the existence of the phase transition of percolation probability. Then, through stereographic projection, we introduce the tight bounds and closed-form expression of the critical number of LEO satellites on the same constellation. In addition, we also investigate how the altitude and maximum slant range of LEO satellites affect percolation probability, and derive the critical values of them. Based on our findings, we provide useful recommendations for companies planning to deploy LEO satellite networks to enhance connectivity.

cs.NI↗

arXiv2025

Performance Analysis of Infrastructure Sharing Techniques in Cellular Networks: A Percolation Theory Approach

In the context of 5G, infrastructure sharing has been identified as a potential solution to reduce the investment costs of cellular networks. In particular, it can help low-income regions build 5G networks more affordably and further bridge the digital divide. There are two main kinds of infrastructure sharing: passive sharing (i.e. site sharing) and active sharing (i.e. access sharing), which require mobile network operators (MNOs) to share their non-electronic elements or electronic elements, respectively. Because co-construction and sharing can achieve broader coverage with lower investment, through percolation theory, we investigate how different sharing strategies can deliver large-scale continuous services. First, we examine the percolation characteristics in signal-to-interference-plus-noise ratio (SINR) coverage graphs and the necessary conditions for percolation. Second, we propose an 'average coverage radius' to approximate the SINR graph with a low base station (BS) density based on the Gilbert disk model. Finally, we estimate the critical conditions of BS densities of MNOs for different sharing strategies and compare the percolation probabilities under different infrastructure sharing strategies.

eess.SY↗

arXiv2025

Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning

Effective urban traffic management is vital for sustainable city development, relying on intelligent systems with machine learning tasks such as traffic flow prediction and travel time estimation. Traditional approaches usually focus on static road network and trajectory representation learning, and overlook the dynamic nature of traffic states and trajectories, which is crucial for downstream tasks. To address this gap, we propose TRACK, a novel framework to bridge traffic state and trajectory data for dynamic road network and trajectory representation learning. TRACK leverages graph attention networks (GAT) to encode static and spatial road segment features, and introduces a transformer-based model for trajectory representation learning. By incorporating transition probabilities from trajectory data into GAT attention weights, TRACK captures dynamic spatial features of road segments. Meanwhile, TRACK designs a traffic transformer encoder to capture the spatial-temporal dynamics of road segments from traffic state data. To further enhance dynamic representations, TRACK proposes a co-attentional transformer encoder and a trajectory-traffic state matching task. Extensive experiments on real-life urban traffic datasets demonstrate the superiority of TRACK over state-of-the-art baselines. Case studies confirm TRACK's ability to capture spatial-temporal dynamics effectively.

cs.LG↗

arXiv2024

Topological one-way Weyl fiber

Topological photonics enables unprecedented photon manipulation by realizing various topological states, such as corner states, edge states, and surface states. However, achieving a topological fiber state has remained elusive. Here, we demonstrate a topological fiber state in a Weyl gyromagnetic photonic crystal fiber. By applying an in-plane magnetic bias to a gyromagnetic photonic crystal fiber with broken parity-inversion symmetry, we create an asymmetrical Weyl bandgap that supports one-way fiber states associated with type-II Weyl points. Dispersion and topological invariant calculations reveal the transition from Weyl surface states to one-way Weyl fiber states. Electromagnetic field simulations confirm the existence of one-way Weyl fiber states and their robust transport in the presence of metallic obstacle along the transport path. Our findings offer an intriguing pathway for exploring novel topological states and guiding the design of topological fibers.

physics.optics↗

arXiv2024

Discovery of terahertz-frequency orbitally-coupled magnons in a kagome ferromagnet

In ferromagnetic materials, magnons - quanta of spin waves - typically resonate in the gigahertz range. Beyond conventional magnons, while theoretical studies have predicted magnons associated with orbital magnetic moments, their direct observation has remained challenging. Here, we present the discovery of two distinct terahertz orbitally-coupled magnon resonances in the topological kagome ferromagnet Co3Sn2S2. Using time-resolved Kerr rotation spectroscopy, we pinpoint two magnon resonances at 0.61 and 0.49 THz at 6 K, surpassing all previously reported magnon resonances in ferromagnets due to strong magnetocrystalline anisotropy. These dual modes originate from the strong coupling of localized spin and orbital magnetic moments. These findings unveil a novel category of magnons stemming from orbital magnetic moments, and position Co3Sn2S2 as a promising candidate for high-speed terahertz spintronic applications

cond-mat.str-el↗

arXiv2024

UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style

The rapid advancement of high-quality image generation models based on AI has generated a deluge of anime illustrations. Recommending illustrations to users within massive data has become a challenging and popular task. However, existing anime recommendation systems have focused on text features but still need to integrate image features. In addition, most multi-modal recommendation research is constrained by tightly coupled datasets, limiting its applicability to anime illustrations. We propose the User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style (UMAIR-FPS) to tackle these gaps. In the feature extract phase, for image features, we are the first to combine image painting style features with semantic features to construct a dual-output image encoder for enhancing representation. For text features, we obtain text embeddings based on fine-tuning Sentence-Transformers by incorporating domain knowledge that composes a variety of domain text pairs from multilingual mappings, entity relationships, and term explanation perspectives, respectively. In the multi-modal fusion phase, we novelly propose a user-aware multi-modal contribution measurement mechanism to weight multi-modal features dynamically according to user features at the interaction level and employ the DCN-V2 module to model bounded-degree multi-modal crosses effectively. UMAIR-FPS surpasses the stat-of-the-art baselines on large real-world datasets, demonstrating substantial performance enhancements.

cs.IR↗

arXiv2023

The minimum positive uniform Turán density in uniformly dense $k$-uniform hypergraphs

A $k$-graph (or $k$-uniform hypergraph) $H$ is uniformly dense if the edge distribution of $H$ is uniformly dense with respect to every large collection of $k$-vertex cliques induced by sets of $(k-2)$-tuples. Reiher, Rödl and Schacht [Int. Math. Res. Not., 2018] proposed the study of the uniform Turán density $π_{k-2}(F)$ for given $k$-graphs $F$ in uniformly dense $k$-graphs. Meanwhile, they [J. London Math. Soc., 2018] characterized $k$-graphs $F$ satisfying $π_{k-2}(F)=0$ and showed that $π_{k-2}(\cdot)$ ``jumps" from 0 to at least $k^{-k}$. In particular, they asked whether there exist $3$-graphs $F$ with $π_{1}(F)$ equal or arbitrarily close to $1/27$. Recently, Garbe, Král' and Lamaison [arXiv:2105.09883] constructed some $3$-graphs with $π_{1}(F)=1/27$. In this paper, for any $k$-graph $F$, we give a lower bound of $π_{k-2}(F)$ based on a probabilistic framework, and provide a general theorem that reduces proving an upper bound on $π_{k-2}(F)$ to embedding $F$ in reduced $k$-graphs of the same density using the regularity method for $k$-graphs. By using this result and Ramsey theorem for multicolored hypergraphs, we extend the results of Garbe, Král' and Lamaison to $k\ge 3$. In other words, we give a sufficient condition for $k$-graphs $F$ satisfying $π_{k-2}(F)=k^{-k}$. Additionally, we also construct an infinite family of $k$-graphs with $π_{k-2}(F)=k^{-k}$.

math.CO↗

arXiv2023

Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph

Anomaly detection on attributed graphs is a crucial topic for its practical application. Existing methods suffer from semantic mixture and imbalance issue because they mainly focus on anomaly discrimination, ignoring representation learning. It conflicts with the assortativity assumption that anomalous nodes commonly connect with normal nodes directly. Additionally, there are far fewer anomalous nodes than normal nodes, indicating a long-tailed data distribution. To address these challenges, a unique algorithm,Decoupled Self-supervised Learning forAnomalyDetection (DSLAD), is proposed in this paper. DSLAD is a self-supervised method with anomaly discrimination and representation learning decoupled for anomaly detection. DSLAD employs bilinear pooling and masked autoencoder as the anomaly discriminators. By decoupling anomaly discrimination and representation learning, a balanced feature space is constructed, in which nodes are more semantically discriminative, as well as imbalance issue can be resolved. Experiments conducted on various six benchmark datasets reveal the effectiveness of DSLAD.

cs.LG↗

arXiv2022

Dual Stream Computer-Generated Image Detection Network Based On Channel Joint And Softpool

With the development of computer graphics technology, the images synthesized by computer software become more and more closer to the photographs. While computer graphics technology brings us a grand visual feast in the field of games and movies, it may also be utilized by someone with bad intentions to guide public opinions and cause political crisis or social unrest. Therefore, how to distinguish the computer-generated graphics (CG) from the photographs (PG) has become an important topic in the field of digital image forensics. This paper proposes a dual stream convolutional neural network based on channel joint and softpool. The proposed network architecture includes a residual module for extracting image noise information and a joint channel information extraction module for capturing the shallow semantic information of image. In addition, we also design a residual structure to enhance feature extraction and reduce the loss of information in residual flow. The joint channel information extraction module can obtain the shallow semantic information of the input image which can be used as the information supplement block of the residual module. The whole network uses SoftPool to reduce the information loss of down-sampling for image. Finally, we fuse the two flows to get the classification results. Experiments on SPL2018 and DsTok show that the proposed method outperforms existing methods, especially on the DsTok dataset. For example, the performance of our model surpasses the state-of-the-art by a large margin of 3%.

cs.CV↗

arXiv2022

Temporal Analysis of Transaction Ego Networks with Different Labels on Ethereum

Due to the widespread use of smart contracts, Ethereum has become the second-largest blockchain platform after Bitcoin. Many different types of Ethereum accounts (ICO, Mining, Gambling, etc.) also have quite active trading activities on Ethereum. Studying the transaction records of these specific Ethereum accounts is very important for understanding their particular transaction characteristics, and further labeling the pseudonymous accounts. However, traditional methods are generally based on static and global transaction networks to conduct research, ignoring useful information about dynamic changes. Our work chooses six kinds of important account labels, and builds ego networks for each kind of Ethereum account. We focus on the interaction between the target node and neighbor nodes with temporal analysis. Experiments show that there is a significant difference between various types of accounts in terms of several network features, helping us better understand their transaction patterns. To the best of our knowledge, this is the first work to analyze the dynamic characteristics of Ethereum labeled accounts from the perspective of transaction ego networks.

cs.CR↗

arXiv2022

Integer colorings with no rainbow $k$-term arithmetic progression

In this paper, we study the rainbow Erdős-Rothschild problem with respect to $k$-term arithmetic progressions. For a set of positive integers $S \subseteq [n]$, an $r$-coloring of $S$ is \emph{rainbow $k$-AP-free} if it contains no rainbow $k$-term arithmetic progression. Let $g_{r,k}(S)$ denote the number of rainbow $k$-AP-free $r$-colorings of $S$. For sufficiently large $n$ and fixed integers $r\ge k\ge 3$, we show that $g_{r,k}(S)<g_{r,k}([n])$ for any proper subset $S\subset [n]$. Further, we prove that $\lim_{n\to \infty}g_{r,k}([n])/(k-1)^n= \binom{r}{k-1}$. Our result is asymptotically best possible and implies that, almost all rainbow $k$-AP-free $r$-colorings of $[n]$ use only $k-1$ colors.

math.CO↗

arXiv2021

Improved Xception with Dual Attention Mechanism and Feature Fusion for Face Forgery Detection

With the rapid development of deep learning technology, more and more face forgeries by deepfake are widely spread on social media, causing serious social concern. Face forgery detection has become a research hotspot in recent years, and many related methods have been proposed until now. For those images with low quality and/or diverse sources, however, the detection performances of existing methods are still far from satisfactory. In this paper, we propose an improved Xception with dual attention mechanism and feature fusion for face forgery detection. Different from the middle flow in original Xception model, we try to catch different high-semantic features of the face images using different levels of convolution, and introduce the convolutional block attention module and feature fusion to refine and reorganize those high-semantic features. In the exit flow, we employ the self-attention mechanism and depthwise separable convolution to learn the global information and local information of the fused features separately to improve the classification the ability of the proposed model. Experimental results evaluated on three Deepfake datasets demonstrate that the proposed method outperforms Xception as well as other related methods both in effectiveness and generalization ability.

cs.CV↗

arXiv2020

Spinodal Instability at the Onset of Collective Expansion in Nuclear Collisions

Using transport theory to model central Au + Au collisions in the energy region of 20 - 110 MeV/u, at impact parameters b <= 5 fm, we predict a measurable impact of spinoidal instability as the collective expansion sets in with energy. Two transport models are employed, the pBUU model, solving a Boltzmann-Uehling-Uhlenbeck equation, and the Brownian Motion (BM) model, solving a set of Langevin equations to describe the motion of individual nucleons in a noisy nuclear medium. We find without ambiguity, for the first time, that a combination of delayed equilibration, onset of collective expansion and the spinodal instability produces a pair of transient ring structures, made of the projectile and target remnants, with spectator nucleons predicted to end in the entities reminiscent of stones in jewelry, on the rings. The ring structures, calculated in the configuration space and mapped onto the velocity space, could be detected in experimental collective flow data.

nucl-th↗

arXiv2020

Dirac-vortex topological photonic crystal fibre

The success of photonic crystal fibres relies largely on the endless variety of two-dimensional photonic crystals in the cross-section. Here, we propose a topological bandgap fibre whose bandgaps along in-plane directions are opened by generalized Kekulé modulation of a Dirac lattice with a vortex phase. Then, the existence of mid-gap defect modes is guaranteed to guide light at the core of this Dirac-vortex fibre, where the number of guiding modes equals the winding number of the spatial vortex. The single-vortex design provides a single-polarization single mode for a bandwidth as large as one octave.

physics.optics↗

arXiv2020

E2 distribution and statistical regularity in polygonal planar tessellations

From solar supergranulation to salt flat in Bolivia, from veins on leaves to cells on Drosophila wing discs, polygon-based networks exhibit great complexities, yet similarities persist and statistical distributions can be remarkably consistent. Based on analysis of 99 polygonal tessellations of a wide variety of physical origins, this work demonstrates the ubiquity of an exponential distribution in the squared norm of the deformation tensor, $E^{2}$, which directly leads to the ubiquitous presence of Gamma distributions in polygon aspect ratio. The $E^{2}$ distribution in turn arises as a $χ^{2}$-distribution, and an analytical framework is developed to compute its statistics. $E^{2}$ is closely related to many energy forms, and its Boltzmann-like feature allows the definition of a pseudo-temperature. Together with normality in other key variables such as vertex displacement, this work reveals regularities universally present in all systems alike

cond-mat.stat-mech↗

arXiv2019

Dynamics of one-dimensional correlated nuclear systems within non-equilibrium Green's function theory

Theory of non-equilibrium Green's function (NGF) provides a practical framework for studying quantum many-body systems out of equilibrium. Extending the previous mean field approach developed for nuclear systems in one dimension with NGF, we introduce isospin degrees of freedom to the Green's functions and incorporate short-range two-body interactions in the second-order self-consistent approximation to correlations, which represents the scattering of momentum orbitals in the Born approximation. We discuss the preparation of a finite nuclear system and examine the impact of correlations on the ground state. We also excite a finite symmetric nuclear system to oscillate in an isovector dipole mode and explore the dissipation effects in the oscillation. Finally, we demonstrate how to boost a slab to a constant and stable motion in a box, based on Galilean covariance of the theory. The studies in this paper lay the ground for the future exploration of collisions of correlated nuclear systems in one dimension.

nucl-th↗