arXiv subjects

Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 379 records · Page 21Linked to original sources

arXiv2025

A New Scope and Domain Measure Comparison Method for Global Convergence Analysis in Evolutionary Computation

Convergence analysis is a fundamental research topic in evolutionary computation (EC). The commonly used analysis method models the EC algorithm as a homogeneous Markov chain for analysis, which is not always suitable for different EC variants, and also sometimes causes misuse and confusion due to their complex process. In this article, we categorize the existing researches on convergence analysis in EC algorithms into stable convergence and global convergence, and then prove that the conditions for these two convergence properties are somehow mutually exclusive. Inspired by this proof, we propose a new scope and domain measure comparison (SDMC) method for analyzing the global convergence of EC algorithms and provide a rigorous proof of its necessity and sufficiency as an alternative condition. Unlike traditional methods, the SDMC method is straightforward, bypasses Markov chain modeling, and minimizes errors from misapplication as it only focuses on the measure of the algorithm's search scope. We apply SDMC to two algorithm types that are unsuitable for traditional methods, confirming its effectiveness in global convergence analysis. Furthermore, we apply the SDMC method to explore the gene targeting mechanism's impact on the global convergence in large-scale global optimization, deriving insights into how to design EC algorithms that guarantee global convergence and exploring how theoretical analysis can guide EC algorithm design.

cs.NE↗

arXiv2025

Maximum likelihood estimation for the $λ$-exponential family

The $λ$-exponential family generalizes the standard exponential family via a generalized convex duality motivated by optimal transport. It is the constant-curvature analogue of the exponential family from the information-geometric point of view, but the development of computational methodologies is still in an early stage. In this paper, we propose a fixed point iteration for maximum likelihood estimation under i.i.d.~sampling, and prove using the duality that the likelihood is monotone along the iterations. We illustrate the algorithm with the $q$-Gaussian distribution and the Dirichlet perturbation.

math.ST↗

arXiv2025

Multimodal Deep Learning-Empowered Beam Prediction in Future THz ISAC Systems

Integrated sensing and communication (ISAC) systems operating at terahertz (THz) bands are envisioned to enable both ultra-high data-rate communication and precise environmental awareness for next-generation wireless networks. However, the narrow width of THz beams makes them prone to misalignment and necessitates frequent beam prediction in dynamic environments. Multimodal sensing, which integrates complementary modalities such as camera images, positional data, and radar measurements, has recently emerged as a promising solution for proactive beam prediction. Nevertheless, existing multimodal approaches typically employ static fusion architectures that cannot adjust to varying modality reliability and contributions, thereby degrading predictive performance and robustness. To address this challenge, we propose a novel and efficient multimodal mixture-of-experts (MoE) deep learning framework for proactive beam prediction in THz ISAC systems. The proposed multimodal MoE framework employs multiple modality-specific expert networks to extract representative features from individual sensing modalities, and dynamically fuses them using adaptive weights generated by a gating network according to the instantaneous reliability of each modality. Simulation results in realistic vehicle-to-infrastructure (V2I) scenarios demonstrate that the proposed MoE framework outperforms traditional static fusion methods and unimodal baselines in terms of prediction accuracy and adaptability, highlighting its potential in practical THz ISAC systems with ultra-massive multiple-input multiple-output (MIMO).

eess.SP↗

arXiv2025

WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks

The rapid evolution of wireless networks presents unprecedented challenges in managing complex and dynamic systems. Existing methods are increasingly facing fundamental limitations in addressing these challenges. In this paper, we introduce WirelessAgent, a novel framework that harnesses large language models (LLMs) to create autonomous AI agents for diverse wireless network tasks. This framework integrates four core modules that mirror human cognitive processes: perception, memory, planning, and action. To implement it, we provide a basic usage based on agentic workflows and the LangGraph architecture. We demonstrate the effectiveness of WirelessAgent through a comprehensive case study on network slicing. The numerical results show that WirelessAgent achieves $44.4\%$ higher bandwidth utilization than the \emph{Prompt-based} method, while performing only $4.3\%$ below the \emph{Rule-based optimality}. Notably, WirelessAgent delivers near-optimal network throughput across diverse network scenarios. These underscore the framework's potential for intelligent and autonomous resource management in future wireless networks. The code is available at \url{https://github.com/jwentong/WirelessAgent_R1}.

eess.SP↗

arXiv2025

Multi-dimensional optical imaging on a chip

Light inherently consists of multiple dimensions beyond intensity, including spectrum, polarization, etc. The coupling among these high-dimensional optical features provides a compressive characterization of intrinsic material properties. Because multiple optical dimensions are intrinsically coupled rather than independent, analyzing their inter-relationships and achieving their simultaneous acquisition is essential. Despite the existing optical techniques to obtain different-dimensional data with cumbersome systems, joint acquisition of multi-dimensional optical information on a chip is still a serious challenge, limited by intensity-only photoelectric detection, single-dimensional optical elements, and finite bandwidth. In this work, we report a multi-dimensional on-chip optical imaging (MOCI) architecture, which is functionally composed of three layers, including a multi-dimensional encoding layer to simultaneously encode different dimensions of incident light, an image acquisition layer to collect coupled intensity data, and a computational reconstruction layer to recover multi-dimensional images from a single frame of coupled measurement. Following the MOCI architecture, we for the first time fabricated a real-time (74 FPS) on-chip polarization-hyperspectral imaging (PHI) sensor, with 2048$\times$2448 pixels at 61 spectral channels covering the VIS-NIR range and 4 polarization states. We applied the PHI sensor for simultaneously resolving hyperspectral and polarization information of complex scenes, and for the first time demonstrated new applications including hyperspectral 3D modeling with normal and height maps, and hyperspectral sensing against strong reflection and glare...

physics.optics↗

arXiv2025

Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization

In this survey, we introduce Meta-Black-Box-Optimization~(MetaBBO) as an emerging avenue within the Evolutionary Computation~(EC) community, which incorporates Meta-learning approaches to assist automated algorithm design. Despite the success of MetaBBO, the current literature provides insufficient summaries of its key aspects and lacks practical guidance for implementation. To bridge this gap, we offer a comprehensive review of recent advances in MetaBBO, providing an in-depth examination of its key developments. We begin with a unified definition of the MetaBBO paradigm, followed by a systematic taxonomy of various algorithm design tasks, including algorithm selection, algorithm configuration, solution manipulation, and algorithm generation. Further, we conceptually summarize different learning methodologies behind current MetaBBO works, including reinforcement learning, supervised learning, neuroevolution, and in-context learning with Large Language Models. A comprehensive evaluation of the latest representative MetaBBO methods is then carried out, alongside an experimental analysis of their optimization performance, computational efficiency, and generalization ability. Based on the evaluation results, we meticulously identify a set of core designs that enhance the generalization and learning effectiveness of MetaBBO. Finally, we outline the vision for the field by providing insight into the latest trends and potential future directions. Relevant literature will be continuously collected and updated at https://github.com/MetaEvo/Awesome-MetaBBO.

cs.NE↗

arXiv2025

XeMap: Contextual Referring in Large-Scale Remote Sensing Environments

Advancements in remote sensing (RS) imagery have provided high-resolution detail and vast coverage, yet existing methods, such as image-level captioning/retrieval and object-level detection/segmentation, often fail to capture mid-scale semantic entities essential for interpreting large-scale scenes. To address this, we propose the conteXtual referring Map (XeMap) task, which focuses on contextual, fine-grained localization of text-referred regions in large-scale RS scenes. Unlike traditional approaches, XeMap enables precise mapping of mid-scale semantic entities that are often overlooked in image-level or object-level methods. To achieve this, we introduce XeMap-Network, a novel architecture designed to handle the complexities of pixel-level cross-modal contextual referring mapping in RS. The network includes a fusion layer that applies self- and cross-attention mechanisms to enhance the interaction between text and image embeddings. Furthermore, we propose a Hierarchical Multi-Scale Semantic Alignment (HMSA) module that aligns multiscale visual features with the text semantic vector, enabling precise multimodal matching across large-scale RS imagery. To support XeMap task, we provide a novel, annotated dataset, XeMap-set, specifically tailored for this task, overcoming the lack of XeMap datasets in RS imagery. XeMap-Network is evaluated in a zero-shot setting against state-of-the-art methods, demonstrating superior performance. This highlights its effectiveness in accurately mapping referring regions and providing valuable insights for interpreting large-scale RS environments.

eess.IV↗

arXiv2025

Enhancing Non-Core Language Instruction-Following in Speech LLMs via Semi-Implicit Cross-Lingual CoT Reasoning

Large language models have been extended to the speech domain, leading to the development of speech large language models (SLLMs). While existing SLLMs demonstrate strong performance in speech instruction-following for core languages (e.g., English), they often struggle with non-core languages due to the scarcity of paired speech-text data and limited multilingual semantic reasoning capabilities. To address this, we propose the semi-implicit Cross-lingual Speech Chain-of-Thought (XS-CoT) framework, which integrates speech-to-text translation into the reasoning process of SLLMs. The XS-CoT generates four types of tokens: instruction and response tokens in both core and non-core languages, enabling cross-lingual transfer of reasoning capabilities. To mitigate inference latency in generating target non-core response tokens, we incorporate a semi-implicit CoT scheme into XS-CoT, which progressively compresses the first three types of intermediate reasoning tokens while retaining global reasoning logic during training. By leveraging the robust reasoning capabilities of the core language, XS-CoT improves responses for non-core languages by up to 45\% in GPT-4 score when compared to direct supervised fine-tuning on two representative SLLMs, Qwen2-Audio and SALMONN. Moreover, the semi-implicit XS-CoT reduces token delay by more than 50\% with a slight drop in GPT-4 scores. Importantly, XS-CoT requires only a small amount of high-quality training data for non-core languages by leveraging the reasoning capabilities of core languages. To support training, we also develop a data pipeline and open-source speech instruction-following datasets in Japanese, German, and French.

cs.SD↗

arXiv2025

Dynamics of polynomial generalized Liénard system near the origin and infinity

We classify all topological phase portraits of the polynomial generalized Liénard system, determined by three arbitrary polynomials, at the origin and the infinity. This yields a complete characterization of monodromy at the origin and the infinity. Moreover, we obtain a necessary and sufficient condition for the local center via Cherkas' method. When the origin is the only equilibrium and it is a center, there are 5 global phase portraits, including two types of global center. Further, we prove that the global center is not isochronous.

math.DS↗

arXiv2025

CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning

Active learning (AL) reduces human annotation costs for machine learning systems by strategically selecting the most informative unlabeled data for annotation, but performing it individually may still be insufficient due to restricted data diversity and annotation budget. Federated Active Learning (FAL) addresses this by facilitating collaborative data selection and model training, while preserving the confidentiality of raw data samples. Yet, existing FAL methods fail to account for the heterogeneity of data distribution across clients and the associated fluctuations in global and local model parameters, adversely affecting model accuracy. To overcome these challenges, we propose CHASe (Client Heterogeneity-Aware Data Selection), specifically designed for FAL. CHASe focuses on identifying those unlabeled samples with high epistemic variations (EVs), which notably oscillate around the decision boundaries during training. To achieve both effectiveness and efficiency, \model{} encompasses techniques for 1) tracking EVs by analyzing inference inconsistencies across training epochs, 2) calibrating decision boundaries of inaccurate models with a new alignment loss, and 3) enhancing data selection efficiency via a data freeze and awaken mechanism with subset sampling. Experiments show that CHASe surpasses various established baselines in terms of effectiveness and efficiency, validated across diverse datasets, model complexities, and heterogeneous federation settings.

cs.LG↗

arXiv2025

HMI: Hierarchical Knowledge Management for Efficient Multi-Tenant Inference in Pretrained Language Models

The significant computational demands of pretrained language models (PLMs), which often require dedicated hardware, present a substantial challenge in serving them efficiently, especially in multi-tenant environments. To address this, we introduce HMI, a Hierarchical knowledge management-based Multi-tenant Inference system, designed to manage tenants with distinct PLMs resource-efficiently. Our approach is three-fold: Firstly, we categorize PLM knowledge into general, domain-specific, and task-specific. Leveraging insights on knowledge acquisition across different model layers, we construct hierarchical PLMs (hPLMs) by extracting and storing knowledge at different levels, significantly reducing GPU memory usage per tenant. Secondly, we establish hierarchical knowledge management for hPLMs generated by various tenants in HMI. We manage domain-specific knowledge with acceptable storage increases by constructing and updating domain-specific knowledge trees based on frequency. We manage task-specific knowledge within limited GPU memory through parameter swapping. Finally, we propose system optimizations to enhance resource utilization and inference throughput. These include fine-grained pipelining via hierarchical knowledge prefetching to overlap CPU and I/O operations with GPU computations, and optimizing parallel implementations with batched matrix multiplications. Our experimental results demonstrate that the proposed HMI can efficiently serve up to 10,000 hPLMs (hBERTs and hGPTs) on a single GPU, with only a negligible compromise in accuracy.

cs.LG↗

arXiv2025

Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization

Recent research in Cooperative Coevolution~(CC) have achieved promising progress in solving large-scale global optimization problems. However, existing CC paradigms have a primary limitation in that they require deep expertise for selecting or designing effective variable decomposition strategies. Inspired by advancements in Meta-Black-Box Optimization, this paper introduces LCC, a pioneering learning-based cooperative coevolution framework that dynamically schedules decomposition strategies during optimization processes. The decomposition strategy selector is parameterized through a neural network, which processes a meticulously crafted set of optimization status features to determine the optimal strategy for each optimization step. The network is trained via the Proximal Policy Optimization method in a reinforcement learning manner across a collection of representative problems, aiming to maximize the expected optimization performance. Extensive experimental results demonstrate that LCC not only offers certain advantages over state-of-the-art baselines in terms of optimization effectiveness and resource consumption, but it also exhibits promising transferability towards unseen problems.

cs.LG↗

arXiv2025

Study of neutron-deficient nucleus 224Np and its α decay by particle-number-conserving method in the framework of deformed shell model

The particle-number-conserving (PNC) method in the framework of the deformed shell model (DSM) is employed to study the properties of the newly discovered short-lived neutron-deficient nucleus 224Np and its α-decay. The calculated energy of α-particle lies within 300 keV of the experimental data. This is the first application of the PNC method to the region of neutron-deficient nuclei. This work provides the first attempt to combine the microscopic PNC theory with empirical formulas to study the nuclear α decay. The configurations of ground states are assigned as π5/2-[523]{\otimes}ν5/2+[633] for 224Np, π1/2-[530]{\otimes}ν3/2+[642] for 220Pa, π3/2+[651]{\otimes}ν1/2-[501] for 216Ac, and π7/2-[514]{\otimes}ν3/2-[501] for 212Fr. The absence of the Z = 92 subshell closure in 224Np is explained by analyzing the proton single-particle levels. Low-lying excited statesare predicted for nuclei along the α-decay chain by the PNC method. Based on the PNC predicted α-decay energy and the assigned configurations, the α-decay half-lives are calculated by the empirical formulas, in which the angular momentum taken away by the α particle is taken into account. The angular momentum have an important effect on the α-decay half-life. The errors of the empirical formulas calculation are in two orders of magnitude with the experimental data.

nucl-th↗

arXiv2025

Pulmonary electrical impedance tomography based on deep recurrent neural networks

Electrical impedance tomography (EIT) is a non-invasive functional imaging technology. In order to enhance the quality of lung EIT images, novel algorithms, namely LSTM-LSTM, LSTM-BiLSTM, BiLSTM-LSTM, and BiLSTM-BiLSTM, leveraging LSTM or BiLSTM networks, were developed. Simulation results demonstrate that the optimized deep recurrent neural network significantly enhanced the quality of the reconstructed images. Specifically, the correlation coefficients of the LSTM-LSTM and the LSTM-BiLSTM algorithms exhibited maximum increases of 27.5% and 25.4% over the LSTM algorithm, respectively. Moreover, in comparison to the BiLSTM algorithm, the correlation coefficients of the BiLSTM-LSTM and BiLSTM-BiLSTM algorithms increased by 11.7% and 13.4%, respectively. Overall, the quality of EIT images showed notable enhancement. This research offers a valuable approach for enhancing EIT image quality and presents a novel application of LSTM networks in EIT technology.

physics.bio-ph↗

arXiv2025

AI for CSI Prediction in 5G-Advanced and Beyond

Artificial intelligence (AI) is pivotal in advancing fifth-generation (5G)-Advanced and sixth-generation systems, capturing substantial research interest. Both the 3rd Generation Partnership Project (3GPP) and leading corporations champion AI's standardization in wireless communication. This piece delves into AI's role in channel state information (CSI) prediction, a sub-use case acknowledged in 5G-Advanced by the 3GPP. We offer an exhaustive survey of AI-driven CSI prediction, highlighting crucial elements like accuracy, generalization, and complexity. Further, we touch on the practical side of model management, encompassing training, monitoring, and data gathering. Moreover, we explore prospects for CSI prediction in future wireless communication systems, entailing integrated design with feedback, multitasking synergy, and predictions in rapid scenarios. This article seeks to be a touchstone for subsequent research in this burgeoning domain.

eess.SP↗

arXiv2025

On the Feasibility of Using MultiModal LLMs to Execute AR Social Engineering Attacks

Augmented Reality (AR) and Multimodal Large Language Models (LLMs) are rapidly evolving, providing unprecedented capabilities for human-computer interaction. However, their integration introduces a new attack surface for social engineering. In this paper, we systematically investigate the feasibility of orchestrating AR-driven Social Engineering attacks using Multimodal LLM for the first time, via our proposed SEAR framework, which operates through three key phases: (1) AR-based social context synthesis, which fuses Multimodal inputs (visual, auditory and environmental cues); (2) role-based Multimodal RAG (Retrieval-Augmented Generation), which dynamically retrieves and integrates contextual data while preserving character differentiation; and (3) ReInteract social engineering agents, which execute adaptive multiphase attack strategies through inference interaction loops. To verify SEAR, we conducted an IRB-approved study with 60 participants in three experimental configurations (unassisted, AR+LLM, and full SEAR pipeline) compiling a new dataset of 180 annotated conversations in simulated social scenarios. Our results show that SEAR is highly effective at eliciting high-risk behaviors (e.g., 93.3% of participants susceptible to email phishing). The framework was particularly effective in building trust, with 85% of targets willing to accept an attacker's call after an interaction. Also, we identified notable limitations such as ``occasionally artificial'' due to perceived authenticity gaps. This work provides proof-of-concept for AR-LLM driven social engineering attacks and insights for developing defensive countermeasures against next-generation augmented reality threats.

cs.CR↗

arXiv2025

Imagining the Far East: Exploring Perceived Biases in AI-Generated Images of East Asian Women

Image-generating AI, which allows users to create images from text, is increasingly used to produce visual content. Despite its advancements, cultural biases in AI-generated images have raised significant concerns. While much research has focused on issues within Western contexts, our study examines the perceived biases regarding the portrayal of East Asian women. In this exploratory study, we invited East Asian users to audit three popular models (DALL-E, Midjourney, Stable Diffusion) and identified 18 specific perceived biases, categorized into four patterns: Westernization, overuse or misuse of cultural symbols, sexualization & feminization, and racial stereotypes. This work highlights the potential challenges posed by AI models in portraying Eastern individuals.

cs.HC↗

arXiv2025

Enabling Auditory Large Language Models for Automatic Speech Quality Evaluation

Speech quality assessment typically requires evaluating audio from multiple aspects, such as mean opinion score (MOS) and speaker similarity (SIM) \etc., which can be challenging to cover using one small model designed for a single task. In this paper, we propose leveraging recently introduced auditory large language models (LLMs) for automatic speech quality assessment. By employing task-specific prompts, auditory LLMs are finetuned to predict MOS, SIM and A/B testing results, which are commonly used for evaluating text-to-speech systems. Additionally, the finetuned auditory LLM is able to generate natural language descriptions assessing aspects like noisiness, distortion, discontinuity, and overall quality, providing more interpretable outputs. Extensive experiments have been performed on the NISQA, BVCC, SOMOS and VoxSim speech quality datasets, using open-source auditory LLMs such as SALMONN, Qwen-Audio, and Qwen2-Audio. For the natural language descriptions task, a commercial model Google Gemini 1.5 Pro is also evaluated. The results demonstrate that auditory LLMs achieve competitive performance compared to state-of-the-art task-specific small models in predicting MOS and SIM, while also delivering promising results in A/B testing and natural language descriptions. Our data processing scripts and finetuned model checkpoints can be found at https://github.com/bytedance/SALMONN.

eess.AS↗