arXiv subjects

Zhiqiang Wang

Publications and source records attributed to Zhiqiang Wang.

At least 91 records · Page 5Linked to original sources

arXiv2024

LLMGeo: Benchmarking Large Language Models on Image Geolocation In-the-wild

Image geolocation is a critical task in various image-understanding applications. However, existing methods often fail when analyzing challenging, in-the-wild images. Inspired by the exceptional background knowledge of multimodal language models, we systematically evaluate their geolocation capabilities using a novel image dataset and a comprehensive evaluation framework. We first collect images from various countries via Google Street View. Then, we conduct training-free and training-based evaluations on closed-source and open-source multi-modal language models. we conduct both training-free and training-based evaluations on closed-source and open-source multimodal language models. Our findings indicate that closed-source models demonstrate superior geolocation abilities, while open-source models can achieve comparable performance through fine-tuning.

cs.CV↗

arXiv2024

Test for BCS-BEC Crossover in the Cuprate Superconductors

In this paper we address the question of whether high-temperature superconductors have anything in common with BCS-BEC crossover theory. Towards this goal, we present a proposal and related predictions which provide a concrete test for the applicability of this theoretical framework. These predictions characterize the behavior of the Ginzburg-Landau coherence length, $ξ_0^{\text{coh}}$, near the transition temperature $T_{\text{c}}$, and across the entire superconducting $T_{\text{c}}$ dome in the phase diagram. That we are lacking a systematic characterization of $ξ_0^{\text{coh}}$ in the entire class of cuprate superconductors is perhaps surprising, as it is one of the most fundamental properties of any superconductor. This paper is written to motivate further experiments and, thus, address this shortcoming. Here we show how measurements of $ξ_0^{\text{coh}}$ contain direct indications for whether or not the cuprates are associated with BCS-BEC crossover and, if so, where within the crossover spectrum a particular superconductor lies.

cond-mat.supr-con↗

arXiv2024

Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems

Cascade ranking is widely used for large-scale top-k selection problems in online advertising and recommendation systems, and learning-to-rank is an important way to optimize the models in cascade ranking. Previous works on learning-to-rank usually focus on letting the model learn the complete order or top-k order, and adopt the corresponding rank metrics (e.g. OPA and NDCG@k) as optimization targets. However, these targets can not adapt to various cascade ranking scenarios with varying data complexities and model capabilities; and the existing metric-driven methods such as the Lambda framework can only optimize a rough upper bound of limited metrics, potentially resulting in sub-optimal and performance misalignment. To address these issues, we propose a novel perspective on optimizing cascade ranking systems by highlighting the adaptability of optimization targets to data complexities and model capabilities. Concretely, we employ multi-task learning to adaptively combine the optimization of relaxed and full targets, which refers to metrics Recall@m@k and OPA respectively. We also introduce permutation matrix to represent the rank metrics and employ differentiable sorting techniques to relax hard permutation matrix with controllable approximate error bound. This enables us to optimize both the relaxed and full targets directly and more appropriately. We named this method as Adaptive Neural Ranking Framework (abbreviated as ARF). Furthermore, we give a specific practice under ARF. We use the NeuralSort to obtain the relaxed permutation matrix and draw on the variant of the uncertainty weight method in multi-task learning to optimize the proposed losses jointly. Experiments on a total of 4 public and industrial benchmarks show the effectiveness and generalization of our method, and online experiment shows that our method has significant application value.

cs.LG↗

arXiv2024

Quantum-geometry-induced anomalous Hall effect in nonunitary superconductors and application to Sr$_2$RuO$_4$

The polar Kerr effect and the closely related anomalous charge Hall effect are among the most distinguishing signatures of the superconducting state in Sr$_2$RuO$_4$, as well as in several other compounds. These effects are often thought to be derived from chiral superconducting pairing, and different mechanisms have been invoked for the explanation. However, the intrinsic mechanisms proposed previously often involve unrealistically strong interband Cooper pairing. In this study we show that, even without interband pairing, nonunitary superconducting states can support intrinsic anomalous charge Hall effect, thanks to the quantum geometric properties of the Bloch electrons. The key here is to have a normal-state spin Hall effect, for which a nonzero spin-orbit coupling is essential. A finite charge Hall effect then naturally arises at the onset of a spin-polarized nonunitary superconducting pairing. It depends on both the spin polarization and the normal-state electron Berry curvature, the latter of which is the imaginary part of the quantum geometric tensor of the Bloch states. Applying our results to the weakly-paired Sr$_2$RuO$_4$ we conclude that, if the reported Kerr effect is of intrinsic origin, the superconducting state is most likely nonunitary and has odd-parity. Our theory may be generalized to other superconductors that exhibit polar Kerr effect.

cond-mat.supr-con↗

arXiv2024

Spectrality of random convolutions generated by finitely many Hadamard triples

Let $\{(N_j, B_j, L_j): 1 \le j \le m\}$ be finitely many Hadamard triples in $\mathbb{R}$. Given a sequence of positive integers $\{n_k\}_{k=1}^\infty$ and $ω=(ω_k)_{k=1}^\infty \in \{1,2,\cdots, m\}^\mathbb{N}$, let $μ_{ω,\{n_k\}}$ be the infinite convolution given by $$μ_{ω,\{n_k\}} = δ_{N_{ω_1}^{-n_1} B_{ω_1}} * δ_{N_{ω_1}^{-n_1} N_{ω_2}^{-n_2} B_{ω_2}} * \cdots * δ_{N_{ω_1}^{-n_1} N_{ω_2}^{-n_2} \cdots N_{ω_k}^{-n_k} B_{ω_k} }* \cdots. $$ In order to study the spectrality of $μ_{ω,\{ n_k\}}$, we first show the spectrality of general infinite convolutions generated by Hadamard triples under the equi-positivity condition. Then by using the integral periodic zero set of Fourier transform we show that if $\mathrm{gcd}(B_j - B_j)=1$ for $1 \le j \le m$, then all infinite convolutions $μ_{ω,\{n_k\}}$ are spectral measures. This implies that we may find a subset $Λ_{ω,\{n_k\}}\subseteq \mathbb{R}$ such that $\big\{ e_λ(x) = e^{2πi λx}: λ\in Λ_{ω,\{n_k\}} \big\}$ forms an orthonormal basis for $L^2(μ_{ω,\{ n_k\}})$.

math.CA↗

arXiv2023

Large Language Models Are Zero-Shot Text Classifiers

Retrained large language models (LLMs) have become extensively used across various sub-disciplines of natural language processing (NLP). In NLP, text classification problems have garnered considerable focus, but still faced with some limitations related to expensive computational cost, time consumption, and robust performance to unseen classes. With the proposal of chain of thought prompting (CoT), LLMs can be implemented using zero-shot learning (ZSL) with the step by step reasoning prompts, instead of conventional question and answer formats. The zero-shot LLMs in the text classification problems can alleviate these limitations by directly utilizing pretrained models to predict both seen and unseen classes. Our research primarily validates the capability of GPT models in text classification. We focus on effectively utilizing prompt strategies to various text classification scenarios. Besides, we compare the performance of zero shot LLMs with other state of the art text classification methods, including traditional machine learning methods, deep learning methods, and ZSL methods. Experimental results demonstrate that the performance of LLMs underscores their effectiveness as zero-shot text classifiers in three of the four datasets analyzed. The proficiency is especially advantageous for small businesses or teams that may not have extensive knowledge in text classification.

cs.CL↗

arXiv2023

Reducibility of 1-D quantum harmonic oscillator with new unbounded oscillatory perturbations

Enlightened by Lemma 1.7 in \cite{LiangLuo2021}, we prove a similar lemma which is based upon oscillatory integrals and Langer's turning point theory. From it we show that the Schr{ö}dinger equation $${\rm i}\partial_t u = -\partial_x^2 u+x^2 u+ε\langle x\rangle^μ\sum_{k\inΛ}\left(a_k(ωt)\sin(k|x|^β)+b_k(ωt) \cos(k|x|^β)\right) u,\quad u=u(t,x),~x\in\mathbb{R},~ β>1,$$ can be reduced in $\mathcal{H}^1(\mathbb{R})$ to an autonomous system for most values of the frequency vector $ω$, where $Λ\subset\mathbb R\setminus\{0\}$, $|Λ|<\infty$ and $\langle x\rangle:=\sqrt{1+x^2}$. The functions $a_k(θ)$ and $b_k(θ)$ are analytic on $\mathbb T^n_σ$ and $μ\geq 0$ will be chosen according to the value of $β$. Comparing with \cite{LiangLuo2021}, the novelty is that the phase functions of oscillatory integral are more degenerate when $β>1$.

math.AP↗

arXiv2023

Systematic Analysis of Security and Vulnerabilities in Miniapps

The past few years have witnessed a boom of miniapps, as lightweight applications, miniapps are of great importance in the mobile internet sector. Consequently, the security of miniapps can directly impact compromising the integrity of sensitive data, posing a potential threat to user privacy. However, after a thorough review of the various research efforts in miniapp security, we found that their actions in researching the safety of miniapp web interfaces are limited. This paper proposes a triad threat model focusing on users, servers and attackers to mitigate the security risk of miniapps. By following the principle of least privilege and the direction of permission consistency, we design a novel analysis framework for the security risk assessment of miniapps by this model. Then, we analyzed the correlation between the security risk assessment and the threat model associated with the miniapp. This analysis led to identifying potential scopes and categorisations with security risks. In the case study, we identify nine major categories of vulnerability issues, such as SQL injection, logical vulnerabilities and cross-site scripting. We also assessed a total of 50,628 security risk hazards and provided specific examples.

cs.CR↗

arXiv2023

Exponential stability of solutions to the Schr{ö}dinger-Poisson equation

We prove an exponential stability result for the small solutions of the Schr{ö}dinger-Poisson equation on the circle without exterior parameters in Gevrey class. More precisely we prove that for most of the initial data of Gevrey-norm smaller than $\varepsilon$ small enough, the solution of the Schr{ö}dinger-Poisson equation remains smaller than $2\varepsilon$ for times of order $exp(α|\log \varepsilon|^2 / \log |\log \varepsilon|)$. We stress out that this is the optimal time expected for PDEs as conjectured by Jean Bourgain in [Bou04].

math.AP↗

arXiv2023

Counting Manatee Aggregations using Deep Neural Networks and Anisotropic Gaussian Kernel

Manatees are aquatic mammals with voracious appetites. They rely on sea grass as the main food source, and often spend up to eight hours a day grazing. They move slow and frequently stay in group (i.e. aggregations) in shallow water to search for food, making them vulnerable to environment change and other risks. Accurate counting manatee aggregations within a region is not only biologically meaningful in observing their habit, but also crucial for designing safety rules for human boaters, divers, etc., as well as scheduling nursing, intervention, and other plans. In this paper, we propose a deep learning based crowd counting approach to automatically count number of manatees within a region, by using low quality images as input. Because manatees have unique shape and they often stay in shallow water in groups, water surface reflection, occlusion, camouflage etc. making it difficult to accurately count manatee numbers. To address the challenges, we propose to use Anisotropic Gaussian Kernel (AGK), with tunable rotation and variances, to ensure that density functions can maximally capture shapes of individual manatees in different aggregations. After that, we apply AGK kernel to different types of deep neural networks primarily designed for crowd counting, including VGG, SANet, Congested Scene Recognition network (CSRNet), MARUNet etc. to learn manatee densities and calculate number of manatees in the scene. By using generic low quality images extracted from surveillance videos, our experiment results and comparison show that AGK kernel based manatee counting achieves minimum Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The proposed method works particularly well for counting manatee aggregations in environments with complex background.

cs.CV↗

arXiv2023

The Dichotomy Property in Stabilizability of $2\times2$ Linear Hyperbolic Systems

This paper is devoted to discuss the stabilizability of a class of $ 2 \times2 $ non-homogeneous hyperbolic systems. Motivated by the example in \cite[Page 197]{CB2016}, we analyze the influence of the interval length $L$ on stabilizability of the system. By spectral analysis, we prove that either the system is stabilizable for all $L>0$ or it possesses the dichotomy property: there exists a critical length $L_c>0$ such that the system is stabilizable for $L\in (0,L_c)$ but unstabilizable for $L\in [L_c,+\infty)$. In addition, for $L\in [L_c,+\infty)$, we obtain that the system can reach equilibrium state in finite time by backstepping control combined with observer. Finally, we also provide some numerical simulations to confirm our developed analytical criteria.

math.AP↗

arXiv2023

Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community

The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Models (LLMs) have demonstrated exceptional capabilities in text understanding, generation, and knowledge inference, opening up exciting avenues for IR research. LLMs not only facilitate generative retrieval but also offer improved solutions for user understanding, model evaluation, and user-system interactions. More importantly, the synergistic relationship among IR models, LLMs, and humans forms a new technical paradigm that is more powerful for information seeking. IR models provide real-time and relevant information, LLMs contribute internal knowledge, and humans play a central role of demanders and evaluators to the reliability of information services. Nevertheless, significant challenges exist, including computational costs, credibility concerns, domain-specific limitations, and ethical considerations. To thoroughly discuss the transformative impact of LLMs on IR research, the Chinese IR community conducted a strategic workshop in April 2023, yielding valuable insights. This paper provides a summary of the workshop's outcomes, including the rethinking of IR's core values, the mutual enhancement of LLMs and IR, the proposal of a novel IR technical paradigm, and open challenges.

cs.IR↗

arXiv2023

A new approach for stability analysis of 1-D wave equation with time delay

In our manuscript, we develop a new approach for stability analysis of one-dimensional wave equation with time delay. The major contribution of our work is to develop a new method for spectral analysis. We derive sufficient and necessary conditions for the feedback gain and time delay which guarantee the exponential stability of the closed-loop system. Comparing with similar conditions developed in the past literatures, we discuss all the situation when the time delay is positive, including when it is irrational. We prove that the exponential stability can be achieved if and only if the time delay is an even number. We also get the general formula term of the stability region of the coupling gain for different even multiples of time delay, and from this we easily obtain the shrink of the stability region as time delay increases. In addition, we explore the impact of slight perturbations in time delay on high frequency robustness.

math.AP↗

arXiv2023

Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models

Vision-language pre-training (VLP) models have shown vulnerability to adversarial examples in multimodal tasks. Furthermore, malicious adversaries can be deliberately transferred to attack other black-box models. However, existing work has mainly focused on investigating white-box attacks. In this paper, we present the first study to investigate the adversarial transferability of recent VLP models. We observe that existing methods exhibit much lower transferability, compared to the strong attack performance in white-box settings. The transferability degradation is partly caused by the under-utilization of cross-modal interactions. Particularly, unlike unimodal learning, VLP models rely heavily on cross-modal interactions and the multimodal alignments are many-to-many, e.g., an image can be described in various natural languages. To this end, we propose a highly transferable Set-level Guidance Attack (SGA) that thoroughly leverages modality interactions and incorporates alignment-preserving augmentation with cross-modal guidance. Experimental results demonstrate that SGA could generate adversarial examples that can strongly transfer across different VLP models on multiple downstream vision-language tasks. On image-text retrieval, SGA significantly enhances the attack success rate for transfer attacks from ALBEF to TCL by a large margin (at least 9.78% and up to 30.21%), compared to the state-of-the-art.

cs.CV↗

arXiv2023

On the union of homogeneous symmetric Cantor set with its translations

Fix a positive integer $N$ and a real number $0< β< 1/(N+1)$. Let $Γ$ be the homogeneous symmetric Cantor set generated by the IFS $$ \Big\{ ϕ_i(x)=βx + i \frac{1-β}{N}: i=0,1,\cdots, N \Big\}. $$ For $m\in\mathbb{Z}_+$ we show that there exist infinitely many translation vectors $\mathbf t=(t_0,t_1,\cdots, t_m)$ with $0=t_0<t_1<\cdots<t_m$ such that the union $\bigcup_{j=0}^m(Γ+t_j)$ is a self-similar set. Furthermore, for $0< β< 1/(2N+1)$, we give a complete characterization on which the union $\bigcup_{j=0}^m(Γ+t_j)$ is a self-similar set. Our characterization relies on determining whether some related directed graph has no cycles, or whether some related adjacency matrix is nilpotent.

math.DS↗

arXiv2023

KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks

Social Internet of Things (SIoT), a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things), paving the way for the next generation of Internet of Things. However, due to the risks and uncertainty, a crucial and urgent problem to be settled is establishing reliable relationships within SIoT, that is, trust evaluation. Graph neural networks for trust evaluation typically adopt a straightforward way such as one-hot or node2vec to comprehend node characteristics, which ignores the valuable semantic knowledge attached to nodes. Moreover, the underlying structure of SIoT is usually complex, including both the heterogeneous graph structure and pairwise trust relationships, which renders hard to preserve the properties of SIoT trust during information propagation. To address these aforementioned problems, we propose a novel knowledge-enhanced graph neural network (KGTrust) for better trust evaluation in SIoT. Specifically, we first extract useful knowledge from users' comment behaviors and external structured triples related to object descriptions, in order to gain a deeper insight into the semantics of users and objects. Furthermore, we introduce a discriminative convolutional layer that utilizes heterogeneous graph structure, node semantics, and augmented trust relationships to learn node embeddings from the perspective of a user as a trustor or a trustee, effectively capturing multi-aspect properties of SIoT trust during information propagation. Finally, a trust prediction layer is developed to estimate the trust relationships between pairwise nodes. Extensive experiments on three public datasets illustrate the superior performance of KGTrust over state-of-the-art methods.

cs.AI↗

arXiv2022

XMAM:X-raying Models with A Matrix to Reveal Backdoor Attacks for Federated Learning

Federated Learning (FL) has received increasing attention due to its privacy protection capability. However, the base algorithm FedAvg is vulnerable when it suffers from so-called backdoor attacks. Former researchers proposed several robust aggregation methods. Unfortunately, many of these aggregation methods are unable to defend against backdoor attacks. What's more, the attackers recently have proposed some hiding methods that further improve backdoor attacks' stealthiness, making all the existing robust aggregation methods fail. To tackle the threat of backdoor attacks, we propose a new aggregation method, X-raying Models with A Matrix (XMAM), to reveal the malicious local model updates submitted by the backdoor attackers. Since we observe that the output of the Softmax layer exhibits distinguishable patterns between malicious and benign updates, we focus on the Softmax layer's output in which the backdoor attackers are difficult to hide their malicious behavior. Specifically, like X-ray examinations, we investigate the local model updates by using a matrix as an input to get their Softmax layer's outputs. Then, we preclude updates whose outputs are abnormal by clustering. Without any training dataset in the server, the extensive evaluations show that our XMAM can effectively distinguish malicious local model updates from benign ones. For instance, when other methods fail to defend against the backdoor attacks at no more than 20% malicious clients, our method can tolerate 45% malicious clients in the black-box mode and about 30% in Projected Gradient Descent (PGD) mode. Besides, under adaptive attacks, the results demonstrate that XMAM can still complete the global model training task even when there are 40% malicious clients. Finally, we analyze our method's screening complexity, and the results show that XMAM is about 10-10000 times faster than the existing methods.

cs.CR↗

arXiv2022

On the centered co-circular central configurations for the n-body problem

For the power-law potential $n$-body problem, we study a special kind of central configurations where all the masses lie on a circle and the center of mass coincides with the center of the circle. It is also called the centered co-circular central configuration. We get some symmetry results for such central configurations. We show that for positive numbers $α>0$ and integers $n\geq3$ satisfying $\frac{1}{n}\sum_{j=1}^{n-1}\csc^α\frac{jπ}{n}\leq1+\fracα{4}$, the regular $n$-gon with equal masses is the unique centered co-circular central configuration for the $n$-body problem with power-law potential $U_α$. It quickly follows that for the Newtonian $n$-body problem (in the case $α=1$) and $n\leq6$, the regular $n$-gon is the unique centered co-circular central configuration.

math.DS↗