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Jun Yan

Publications and source records attributed to Jun Yan.

At least 145 records · Page 8Linked to original sources
arXiv2023

Radar detection of wake vortex behind the aircraft: the detection range problem

In this study, we showcased the detection of the wake vortex produced by a medium aircraft at distances exceeding 10 km using an X-band pulse-Doppler radar. We analyzed radar signals within the range profiles behind a Boeing 737 aircraft on February 7, 2021, within the airspace of the Runway Protection Zone (RPZ) at Tianhe Airport, Wuhan, China. The findings revealed that the wake vortex extended up to 6 km from the aircraft, which is 10 km from the radar, displaying distinct stages characterized by scattering patterns and Doppler signatures. Despite the wake vortex exhibiting a scattering power approximately 10 dB lower than that of the aircraft, its Doppler Signal-to-Clutter Ratio (DSCR) values were only 5 dB lower, indicating a notably strong scattering power within a single radar bin. Additionally, certain radar parameters proved inconsistent in the stable detection and tracking of wake vortex, aligning with our earlier concept of cognitive micro-Doppler radar.

eess.SP↗
arXiv2023

An intermediate case of exponential multivalued forbidden matrix configuration

The forbidden number forb$(m,F)$, which denotes the maximum number of distinct columns in an $m$-rowed $(0,1)$-matrix with no submatrix that is a row and column permutation of $F$, has been widely studied in extremal set theory. Recently, this function was extended to $r$-matrices, whose entries lie in $\{0,1,\cdots,r-1\}$. forb$(m,r,F)$ is the maximum number of distinct columns in an $r$-matrix with no submatrix that is a row and column permutation of $F$. While forb$(m,F)$ is polynomial in $m$, forb$(m,r,F)$ is exponential for $r\geq 3$. Recently, forb$(m,r,F)$ was studied for some small $(0,1)$-matrices $F$, and exact values were determined in some cases. In this paper we study forb$(m,r,M)$ for $M=\begin{bmatrix}0&1\\0&1\\1&0\end{bmatrix}$, which is the smallest matrix for which this forbidden number is unknown. Interestingly, it turns out that this problem is closely linked with the following optimisation problem. For each triangle in the complete graph $K_m$, pick one of its edges. Let $m_e$ denote the number of times edge $e$ is picked. For each $α\in\mathbb{R}$, what is $H(m,α)=\max\sum_{e\in E(K_m)}α^{m_e}$? We establish a relationship between forb$(m,r,M)$ and $H(m,(r-1)/(r-2))$, find upper and lower bounds for $H(m,α)$, and use them to significantly improve known bounds for forb$(m,r,M)$.

math.CO↗
arXiv2023

GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks

Automatically evaluating vision-language tasks is challenging, especially when it comes to reflecting human judgments due to limitations in accounting for fine-grained details. Although GPT-4V has shown promising results in various multi-modal tasks, leveraging GPT-4V as a generalist evaluator for these tasks has not yet been systematically explored. We comprehensively validate GPT-4V's capabilities for evaluation purposes, addressing tasks ranging from foundational image-to-text and text-to-image synthesis to high-level image-to-image translations and multi-images to text alignment. We employ two evaluation methods, single-answer grading and pairwise comparison, using GPT-4V. Notably, GPT-4V shows promising agreement with humans across various tasks and evaluation methods, demonstrating immense potential for multi-modal LLMs as evaluators. Despite limitations like restricted visual clarity grading and real-world complex reasoning, its ability to provide human-aligned scores enriched with detailed explanations is promising for universal automatic evaluator.

cs.CV↗
arXiv2023

Ramsey numbers of bounded degree trees versus general graphs

For every $k\ge 2$ and $Δ$, we prove that there exists a constant $C_{Δ,k}$ such that the following holds. For every graph $H$ with $χ(H)=k$ and every tree with at least $C_{Δ,k}|H|$ vertices and maximum degree at most $Δ$, the Ramsey number $R(T,H)$ is $(k-1)(|T|-1)+σ(H)$, where $σ(H)$ is the size of a smallest colour class across all proper $k$-colourings of $H$. This is tight up to the value of $C_{Δ,k}$, and confirms a conjecture of Balla, Pokrovskiy, and Sudakov.

math.CO↗
arXiv2023

Degree sequences of triangular multigraphs

A simple graph is triangular if every edge is contained in a triangle. A sequence of integers is graphical if it is the degree sequence of a simple graph. Egan and Nikolayevsky recently conjectured that every graphical sequence whose terms are all at least 4 is the degree sequence of a triangular simple graph, and proved this in some special cases. In this paper we state and prove the analogous version of this conjecture for multigraphs.

math.CO↗
arXiv2023

An introduction to radar Automatic Target Recognition (ATR) technology in ground-based radar systems

This paper presents a brief examination of Automatic Target Recognition (ATR) technology within ground-based radar systems. It offers a lucid comprehension of the ATR concept, delves into its historical milestones, and categorizes ATR methods according to different scattering regions. By incorporating ATR solutions into radar systems, this study demonstrates the expansion of radar detection ranges and the enhancement of tracking capabilities, leading to superior situational awareness. Drawing insights from the Russo-Ukrainian War, the paper highlights three pressing radar applications that urgently necessitate ATR technology: detecting stealth aircraft, countering small drones, and implementing anti-jamming measures. Anticipating the next wave of radar ATR research, the study predicts a surge in cognitive radar and machine learning (ML)-driven algorithms. These emerging methodologies aspire to confront challenges associated with system adaptation, real-time recognition, and environmental adaptability. Ultimately, ATR stands poised to revolutionize conventional radar systems, ushering in an era of 4D sensing capabilities.

eess.SP↗
arXiv2023

Time periodic and almost periodic viscosity solutions of contact Hamilton-Jacobi equations on $\mathbb{T}^n$

This paper concerns with the time periodic viscosity solution problem for a class of evolutionary contact Hamilton-Jacobi equations with time independent Hamiltonians on the torus $\mathbb{T}^n$. Under certain suitable assumptions we show that the equation has a non-trivial $T$-periodic viscosity solution if and only if $T\in D$, where $D$ is a dense subset of $[0,+\infty)$. Moreover, we clarify the structure of $D$. As a consequence, we also study the existence of Bohr almost periodic viscosity solutions.

math.AP↗
arXiv2023

Generating General Preferential Attachment Networks with R Package wdnet

Preferential attachment (PA) network models have a wide range of applications in various scientific disciplines. Efficient generation of large-scale PA networks helps uncover their structural properties and facilitate the development of associated analytical methodologies. Existing software packages only provide limited functions for this purpose with restricted configurations and efficiency. We present a generic, user-friendly implementation of weighted, directed PA network generation with R package wdnet. The core algorithm is based on an efficient binary tree approach. The package further allows adding multiple edges at a time, heterogeneous reciprocal edges, and user-specified preference functions. The engine under the hood is implemented in C++. Usages of the package are illustrated with detailed explanation. A benchmark study shows that wdnet is efficient for generating general PA networks not available in other packages. In restricted settings that can be handled by existing packages, wdnet provides comparable efficiency.

stat.CO↗
arXiv2023

Formation Wing-Beat Modulation (FWM): A Tool for Quantifying Bird Flocks Using Radar Micro-Doppler Signals

Radar echoes from bird flocks contain modulation signals, which we find are produced by the flapping gaits of birds in the flock, resulting in a group of spectral peaks with similar amplitudes spaced at a specific interval. We call this the formation wing-beat modulation (FWM) effect. FWM signals are micro-Doppler modulated by flapping wings and are related to the bird number, wing-beat frequency, and flight phasing strategy. Our X-band radar data show that FWM signals exist in radar signals of a seagull flock, providing tools for quantifying the bird number and estimating the mean wingbeat rate of birds. This new finding could aid in research on the quantification of bird migration numbers and estimation of bird flight behavior in radar ornithology and aero-ecology.

eess.SP↗
arXiv2023

Realising every colour distribution sequence with a Gallai colouring

An edge colouring of $K_n$ with $k$ colours is a Gallai $k$-colouring if it does not contain any rainbow triangle. Gyárfás, Pálvölgyi, Patkós and Wales proved that there exists a number $g(k)$ such that $n\geq g(k)$ if and only if for any colour distribution sequence $(e_1,\cdots,e_k)$ with $\sum_{i=1}^ke_i=\binom{n}{2}$, there exist a Gallai $k$-colouring of $K_n$ with $e_i$ edges having colour $i$. They also showed that $Ω(k)=g(k)=O(k^2)$ and posed the problem of determining the exact order of magnitude of $g(k)$. Feffer, Fu and Yan improved both bounds significantly by proving $Ω(k^{1.5}/\log k)=g(k)=O(k^{1.5})$. We resolve this problem by showing $g(k)=Θ(k^{1.5}/(\log k)^{0.5})$.

math.CO↗
arXiv2023

Distribution of colours in rainbow H-free colourings

An edge colouring of $K_n$ with $k$ colours is a Gallai $k$-colouring if it does not contain any rainbow triangle. Gyárfás, Pálvölgyi, Patkós and Wales proved that there exists a number $g(k)$ such that $n\geq g(k)$ if and only if for any colour distribution sequence $(e_1,\cdots,e_k)$ with $\sum_{i=1}^ke_i=\binom{n}{2}$, there exist a Gallai $k$-colouring of $K_n$ with $e_i$ edges having colour $i$. They also showed that $Ω(k)=g(k)=O(k^2)$ and posed the problem of determining the exact order of magnitude of $g(k)$. Feffer, Fu and Yan improved both bounds significantly by proving $Ω(k^{1.5}/\log k)=g(k)=O(k^{1.5})$. We resolve this problem by showing $g(k)=Θ(k^{1.5}/(\log k)^{0.5})$. Moreover, we generalise these definitions by considering rainbow $H$-free colourings of $K_n$ for any general graph $H$, and the natural corresponding quantity $g(H,k)$. We prove that $g(H,k)$ is finite for every $k$ if and only if $H$ is not a forest, and determine the order of $g(H,k)$ when $H$ contains a subgraph with minimum degree at least 3.

math.CO↗
arXiv2023

A Strength and Sparsity Preserving Algorithm for Generating Weighted, Directed Networks with Predetermined Assortativity

Degree-preserving rewiring is a widely used technique for generating unweighted networks with given assortativity, but for weighted networks, it is unclear how an analog would preserve the strengths and other critical network features such as sparsity level. This study introduces a novel approach for rewiring weighted networks to achieve desired directed assortativity. The method utilizes a mixed integer programming framework to establish a target network with predetermined assortativity coefficients, followed by an efficient rewiring algorithm termed "strength and sparsity preserving rewiring" (SSPR). SSPR retains the node strength distributions and network sparsity after rewiring. It is also possible to accommodate additional properties like edge weight distribution with extra computational cost. The optimization scheme can be used to determine feasible assortativity ranges for an initial network. The effectiveness of the proposed SSPR algorithm is demonstrated through its application to two classes of popular network models.

stat.CO↗
arXiv2023

Introduction to Drone Detection Radar with Emphasis on Automatic Target Recognition (ATR) technology

This paper discusses the challenges of detecting and categorizing small drones with radar automatic target recognition (ATR) technology. The authors suggest integrating ATR capabilities into drone detection radar systems to improve performance and manage emerging threats. The study focuses primarily on drones in Group 1 and 2. The paper highlights the need to consider kinetic features and signal signatures, such as micro-Doppler, in ATR techniques to efficiently recognize small drones. The authors also present a comprehensive drone detection radar system design that balances detection and tracking requirements, incorporating parameter adjustment based on scattering region theory. They offer an example of a performance improvement achieved using feedback and situational awareness mechanisms with the integrated ATR capabilities. Furthermore, the paper examines challenges related to one-way attack drones and explores the potential of cognitive radar as a solution. The integration of ATR capabilities transforms a 3D radar system into a 4D radar system, resulting in improved drone detection performance. These advancements are useful in military, civilian, and commercial applications, and ongoing research and development efforts are essential to keep radar systems effective and ready to detect, track, and respond to emerging threats.

eess.SP↗
arXiv2023

Low-discrepancy Sampling in the Expanded Dimensional Space: An Acceleration Technique for Particle Swarm Optimization

Compared with random sampling, low-discrepancy sampling is more effective in covering the search space. However, the existing research cannot definitely state whether the impact of a low-discrepancy sample on particle swarm optimization (PSO) is positive or negative. Using Niderreiter's theorem, this study completes an error analysis of PSO, which reveals that the error bound of PSO at each iteration depends on the dispersion of the sample set in an expanded dimensional space. Based on this error analysis, an acceleration technique for PSO-type algorithms is proposed with low-discrepancy sampling in the expanded dimensional space. The acceleration technique can generate a low-discrepancy sample set with a smaller dispersion, compared with a random sampling, in the expanded dimensional space; it also reduces the error at each iteration, and hence improves the convergence speed. The acceleration technique is combined with the standard PSO and the comprehensive learning particle swarm optimization, and the performance of the improved algorithm is compared with the original algorithm. The experimental results show that the two improved algorithms have significantly faster convergence speed under the same accuracy requirement.

cs.NE↗
arXiv2023

DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness

Graph-based semi-supervised node classification has been shown to become a state-of-the-art approach in many applications with high research value and significance. Most existing methods are only based on the original intrinsic or artificially established graph structure which may not accurately reflect the "true" correlation among data and are not optimal for semi-supervised node classification in the downstream graph neural networks. Besides, while existing graph-based methods mostly utilize the explicit graph structure, some implicit information, for example, the density information, can also provide latent information that can be further exploited. To address these limitations, this paper proposes the Dual Hypergraph Neural Network (DualHGNN), a new dual connection model integrating both hypergraph structure learning and hypergraph representation learning simultaneously in a unified architecture. The DualHGNN first leverages a multi-view hypergraph learning network to explore the optimal hypergraph structure from multiple views, constrained by a consistency loss proposed to improve its generalization. Then, DualHGNN employs a density-aware hypergraph attention network to explore the high-order semantic correlation among data points based on the density-aware attention mechanism. Extensive experiments are conducted in various benchmark datasets, and the results demonstrate the effectiveness of the proposed approach.

cs.LG↗
arXiv2023

BITE: Textual Backdoor Attacks with Iterative Trigger Injection

Backdoor attacks have become an emerging threat to NLP systems. By providing poisoned training data, the adversary can embed a "backdoor" into the victim model, which allows input instances satisfying certain textual patterns (e.g., containing a keyword) to be predicted as a target label of the adversary's choice. In this paper, we demonstrate that it is possible to design a backdoor attack that is both stealthy (i.e., hard to notice) and effective (i.e., has a high attack success rate). We propose BITE, a backdoor attack that poisons the training data to establish strong correlations between the target label and a set of "trigger words". These trigger words are iteratively identified and injected into the target-label instances through natural word-level perturbations. The poisoned training data instruct the victim model to predict the target label on inputs containing trigger words, forming the backdoor. Experiments on four text classification datasets show that our proposed attack is significantly more effective than baseline methods while maintaining decent stealthiness, raising alarm on the usage of untrusted training data. We further propose a defense method named DeBITE based on potential trigger word removal, which outperforms existing methods in defending against BITE and generalizes well to handling other backdoor attacks.

cs.CL↗
arXiv2023

A general model-checking procedure for semiparametric accelerated failure time models

We propose a set of goodness-of-fit tests for the semiparametric accelerated failure time (AFT) model, including an omnibus test, a link function test, and a functional form test. This set of tests is derived from a multi-parameter cumulative sum process shown to follow asymptotically a zero-mean Gaussian process. Its evaluation is based on the asymptotically equivalent perturbed version, which enables both graphical and numerical evaluations of the assumed AFT model. Empirical p-values are obtained using the Kolmogorov-type supremum test, which provides a reliable approach for estimating the significance of both proposed un-standardized and standardized test statistics. The proposed procedure is illustrated using the induced smoothed rank-based estimator but is directly applicable to other popular estimators such as non-smooth rank-based estimator or least-squares estimator.Our proposed methods are rigorously evaluated using extensive simulation experiments that demonstrate their effectiveness in maintaining a Type I error rate and detecting departures from the assumed AFT model in practical sample sizes and censoring rates. Furthermore, the proposed approach is applied to the analysis of the Primary Biliary Cirrhosis data, a widely studied dataset in survival analysis, providing further evidence of the practical usefulness of the proposed methods in real-world scenarios. To make the proposed methods more accessible to researchers, we have implemented them in the R package afttest, which is publicly available on the Comprehensive R Archieve Network.

stat.ME↗
arXiv2023

Non-LTE ionization potential depression model for warm and hot dense plasma

For warm and hot dense plasma (WHDP), the ionization potential depression (IPD) is a key physical parameter in determining its ionization balance, therefore a reliable and universal IPD model is highly required to understand its microscopic material properties and resolve those existing discrepancies between the theoretical and experimental results. However, the weak temperature dependence of the nowadays IPD models prohibits their application through much of the WHDP regime, especially for the non-LTE dense plasma produced by short-pulse laser. In this work, we propose a universal non-LTE IPD model with the contribution of the inelastic atomic processes, and found that three-body recombination and collision ionization processes become important in determining the electron distribution and further affect the IPD in warm and dense plasma. The proposed IPD model is applied to treat the IPD experiments available in warm and hot dense plasmas and excellent agreements are obtained in comparison with those latest experiments of the IPD for Al plasmas with wide-range conditions of 70-700 eV temperature and 0.2-3 times of solid density, as well as a typical non-LTE system of hollow Al ions. We demonstrate that the present IPD model has a significant temperature dependence due to the consideration of the inelastic atomic processes. With the low computational cost and wide range applicability of WHDP, the proposed model is expected to provide a promising tool to study the ionization balance and the atomic processes as well as the related radiation and particle transports properties of a wide range of WHDP.

physics.plasm-ph↗