arXiv subjects

Kun Zhu

Publications and source records attributed to Kun Zhu.

At least 37 records · Page 2Linked to original sources

arXiv2025

Symplectic tracking through curved three dimensional fields by a method of generating functions

Symplectic integrator plays a pivotal role in the long-term tracking of charged particles within accelerators. To get symplectic maps in accurate simulation of single-particle trajectories, two key components are addressed: precise analytical expressions for arbitrary electromagnetic fields and a robust treatment of the equations of motion. In a source-free region, the electromagnetic fields can be decomposed into harmonic functions, applicable to both scalar and vector potentials, encompassing both straight and curved reference trajectories. These harmonics are constructed according to the boundary surface's field data due to uniqueness theorem. Finding generating functions to meet the Hamilton-Jacobi equation via a perturbative ansatz, we derive symplectic maps that are independent of the expansion order. This method yields a direct mapping from initial to final coordinates in a large step, bypassing the transition of intermediate coordinates. Our developed particle-tracking algorithm translates the field harmonics into beam trajectories, offering a high-resolution integration method in accelerator simulations.

physics.acc-ph↗

arXiv2024

Length Controlled Generation for Black-box LLMs

Large language models (LLMs) have demonstrated impressive instruction following capabilities, while still struggling to accurately manage the length of the generated text, which is a fundamental requirement in many real-world applications. Existing length control methods involve fine-tuning the parameters of LLMs, which is inefficient and suboptimal for practical use. In this paper, we propose a novel iterative sampling framework for text length control, integrating the Metropolis-Hastings algorithm with an importance sampling acceleration strategy. This framework efficiently and reliably regulates LLMs to generate length-constrained text without modifying the underlying parameters, thereby preserving the original capabilities of LLMs. Experimental results demonstrate that our framework achieves almost 100\% success rates of length control on Llama3.1 for tasks such as length-controlled abstractive summarization and length-constrained instruction following, with minimal additional computational overhead. This also highlights the significant potential of our method for precise length control across a broader range of applications, without compromising the versatility of LLMs.

cs.CL↗

arXiv2024

A Semantic Communication System for Real-time 3D Reconstruction Tasks

3D semantic maps have played an increasingly important role in high-precision robot localization and scene understanding. However, real-time construction of semantic maps requires mobile edge devices with extremely high computing power, which are expensive and limit the widespread application of semantic mapping. In order to address this limitation, inspired by cloud-edge collaborative computing and the high transmission efficiency of semantic communication, this paper proposes a method to achieve real-time semantic mapping tasks with limited-resource mobile devices. Specifically, we design an encoding-decoding semantic communication framework for real-time semantic mapping tasks under limited-resource situations. In addition, considering the impact of different channel conditions on communication, this paper designs a module based on the attention mechanism to achieve stable data transmission under various channel conditions. In terms of simulation experiments, based on the TUM dataset, it was verified that the system has an error of less than 0.1% compared to the groundtruth in mapping and localization accuracy and is superior to some novel semantic communication algorithms in real-time performance and channel adaptation. Besides, we implement a prototype system to verify the effectiveness of the proposed framework and designed module in real indoor scenarios. The results show that our system can complete real-time semantic mapping tasks for common indoor objects (chairs, computers, people, etc.) with a limited-resource device, and the mapping update time is less than 1 second.

cs.RO↗

arXiv2024

Generation of circular field harmonics in quasi-polygonal magnet apertures using superconducting canted-cosine-theta coils

Superconducting magnets with non-circular apertures are important for handling unconventional beam profiles and specialized accelerator applications. This paper presents an analytical framework for designing superconducting accelerator magnets with quasi-polygonal apertures, aimed at generating precise circular field harmonics. In Part 1, we explore the relationship between current distributions on quasi-polygonal formers and their corresponding magnetic field harmonics. By employing conformal mapping techniques, we establish a connection between the design of quasi-polygonal bore magnets and traditional circular bore configurations, facilitating the simplification of complex mathematical formulations. Part 2 applies the derived current distributions to the canted cosine theta (CCT) coil magnet concept, focusing on designing analytic winding schemes that generate single or mixed circular harmonics within quasi-polygonal apertures. This work not only advances the design of superconducting magnets but also broadens the scope of CCT technology to accommodate more complex geometries.

physics.acc-ph↗

arXiv2024

BFA-YOLO: A balanced multiscale object detection network for building façade attachments detection

The detection of façade elements on buildings, such as doors, windows, balconies, air conditioning units, billboards, and glass curtain walls, is a critical step in automating the creation of Building Information Modeling (BIM). Yet, this field faces significant challenges, including the uneven distribution of façade elements, the presence of small objects, and substantial background noise, which hamper detection accuracy. To address these issues, we develop the BFA-YOLO model and the BFA-3D dataset in this study. The BFA-YOLO model is an advanced architecture designed specifically for analyzing multi-view images of façade attachments. It integrates three novel components: the Feature Balanced Spindle Module (FBSM) that tackles the issue of uneven object distribution; the Target Dynamic Alignment Task Detection Head (TDATH) that enhances the detection of small objects; and the Position Memory Enhanced Self-Attention Mechanism (PMESA), aimed at reducing the impact of background noise. These elements collectively enable BFA-YOLO to effectively address each challenge, thereby improving model robustness and detection precision. The BFA-3D dataset, offers multi-view images with precise annotations across a wide range of façade attachment categories. This dataset is developed to address the limitations present in existing façade detection datasets, which often feature a single perspective and insufficient category coverage. Through comparative analysis, BFA-YOLO demonstrated improvements of 1.8\% and 2.9\% in mAP$_{50}$ on the BFA-3D dataset and the public Façade-WHU dataset, respectively, when compared to the baseline YOLOv8 model. These results highlight the superior performance of BFA-YOLO in façade element detection and the advancement of intelligent BIM technologies.

cs.CV↗

arXiv2024

Discrete Modeling via Boundary Conditional Diffusion Processes

We present an novel framework for efficiently and effectively extending the powerful continuous diffusion processes to discrete modeling. Previous approaches have suffered from the discrepancy between discrete data and continuous modeling. Our study reveals that the absence of guidance from discrete boundaries in learning probability contours is one of the main reasons. To address this issue, we propose a two-step forward process that first estimates the boundary as a prior distribution and then rescales the forward trajectory to construct a boundary conditional diffusion model. The reverse process is proportionally adjusted to guarantee that the learned contours yield more precise discrete data. Experimental results indicate that our approach achieves strong performance in both language modeling and discrete image generation tasks. In language modeling, our approach surpasses previous state-of-the-art continuous diffusion language models in three translation tasks and a summarization task, while also demonstrating competitive performance compared to auto-regressive transformers. Moreover, our method achieves comparable results to continuous diffusion models when using discrete ordinal pixels and establishes a new state-of-the-art for categorical image generation on the Cifar-10 dataset.

cs.LG↗

arXiv2024

Learning to Break: Knowledge-Enhanced Reasoning in Multi-Agent Debate System

Multi-agent debate system (MAD) imitating the process of human discussion in pursuit of truth, aims to align the correct cognition of different agents for the optimal solution. It is challenging to make various agents perform right and highly consistent cognition due to their limited and different knowledge backgrounds (i.e., cognitive islands), which hinders the search for the optimal solution. To address the challenge, we propose a novel \underline{M}ulti-\underline{A}gent \underline{D}ebate with \underline{K}nowledge-\underline{E}nhanced framework (\textbf{MADKE}) to promote the system to find the solution. First, we involve a shared retrieval knowledge pool in the debate process to solve the problem of limited and different knowledge backgrounds. Then, we propose an adaptive knowledge selection method to guarantee the accuracy and personalization of knowledge. This method allows agents to choose whether to use external knowledge in each conversation round according to their own needs. Our experimental results on six datasets show that our method achieves state-of-the-art results compared to existing single-agent and multi-agent methods. Further analysis reveals that the introduction of retrieval knowledge can help the agent to break cognitive islands in the debate process and effectively improve the consistency and correctness of the model. Moreover, MADKE using Qwen1.5-72B-Chat surpasses GPT-4 by +1.26\% on average in six datasets, which validates that our method can help open-source LLMs achieve or even surpass the performance of GPT-4. Our code is available at \url{https://github.com/FutureForMe/MADKE}.

cs.CL↗

arXiv2024

An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation

Retrieval-augmented generation integrates the capabilities of large language models with relevant information retrieved from an extensive corpus, yet encounters challenges when confronted with real-world noisy data. One recent solution is to train a filter module to find relevant content but only achieve suboptimal noise compression. In this paper, we propose to introduce the information bottleneck theory into retrieval-augmented generation. Our approach involves the filtration of noise by simultaneously maximizing the mutual information between compression and ground output, while minimizing the mutual information between compression and retrieved passage. In addition, we derive the formula of information bottleneck to facilitate its application in novel comprehensive evaluations, the selection of supervised fine-tuning data, and the construction of reinforcement learning rewards. Experimental results demonstrate that our approach achieves significant improvements across various question answering datasets, not only in terms of the correctness of answer generation but also in the conciseness with $2.5\%$ compression rate.

cs.CL↗

arXiv2024

BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering

Large language models (LLMs) have demonstrated strong reasoning capabilities. Nevertheless, they still suffer from factual errors when tackling knowledge-intensive tasks. Retrieval-augmented reasoning represents a promising approach. However, significant challenges still persist, including inaccurate and insufficient retrieval for complex questions, as well as difficulty in integrating multi-source knowledge. To address this, we propose Beam Aggregation Reasoning, BeamAggR, a reasoning framework for knowledge-intensive multi-hop QA. BeamAggR explores and prioritizes promising answers at each hop of question. Concretely, we parse the complex questions into trees, which include atom and composite questions, followed by bottom-up reasoning. For atomic questions, the LLM conducts reasoning on multi-source knowledge to get answer candidates. For composite questions, the LLM combines beam candidates, explores multiple reasoning paths through probabilistic aggregation, and prioritizes the most promising trajectory. Extensive experiments on four open-domain multi-hop reasoning datasets show that our method significantly outperforms SOTA methods by 8.5%. Furthermore, our analysis reveals that BeamAggR elicits better knowledge collaboration and answer aggregation.

cs.CL↗

arXiv2024

Approximated Coded Computing: Towards Fast, Private and Secure Distributed Machine Learning

In a large-scale distributed machine learning system, coded computing has attracted wide-spread attention since it can effectively alleviate the impact of stragglers. However, several emerging problems greatly limit the performance of coded distributed systems. Firstly, an existence of colluding workers who collude results with each other leads to serious privacy leakage issues. Secondly, there are few existing works considering security issues in data transmission of distributed computing systems. Thirdly, the number of required results for which need to wait increases with the degree of decoding functions. In this paper, we design a secure and private approximated coded distributed computing (SPACDC) scheme that deals with the above-mentioned problems simultaneously. Our SPACDC scheme guarantees data security during the transmission process using a new encryption algorithm based on elliptic curve cryptography. Especially, the SPACDC scheme does not impose strict constraints on the minimum number of results required to be waited for. An extensive performance analysis is conducted to demonstrate the effectiveness of our SPACDC scheme. Furthermore, we present a secure and private distributed learning algorithm based on the SPACDC scheme, which can provide information-theoretic privacy protection for training data. Our experiments show that the SPACDC-based deep learning algorithm achieves a significant speedup over the baseline approaches.

cs.DC↗

arXiv2024

Energy-Efficient UAV Swarm Assisted MEC with Dynamic Clustering and Scheduling

In this paper, the energy-efficient unmanned aerial vehicle (UAV) swarm assisted mobile edge computing (MEC) with dynamic clustering and scheduling is studied. In the considered system model, UAVs are divided into multiple swarms, with each swarm consisting of a leader UAV and several follower UAVs to provide computing services to end-users. Unlike existing work, we allow UAVs to dynamically cluster into different swarms, i.e., each follower UAV can change its leader based on the time-varying spatial positions, updated application placement, etc. in a dynamic manner. Meanwhile, UAVs are required to dynamically schedule their energy replenishment, application placement, trajectory planning and task delegation. With the aim of maximizing the long-term energy efficiency of the UAV swarm assisted MEC system, a joint optimization problem of dynamic clustering and scheduling is formulated. Taking into account the underlying cooperation and competition among intelligent UAVs, we further reformulate this optimization problem as a combination of a series of strongly coupled multi-agent stochastic games, and then propose a novel reinforcement learning-based UAV swarm dynamic coordination (RLDC) algorithm for obtaining the equilibrium. Simulations are conducted to evaluate the performance of the RLDC algorithm and demonstrate its superiority over counterparts.

cs.NI↗

arXiv2024

Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities

With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans. However, learning and coordinating movements and actions for a large number of unmanned vehicles in complex and dynamic environments introduce significant challenges to conventional AI methods. Generative AI (GAI), with its capabilities in complex data feature extraction, transformation, and enhancement, offers great potential in solving these challenges of unmanned vehicle swarms. For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms. Specifically, we first present an overview of unmanned vehicles and unmanned vehicle swarms as well as their use cases and existing issues. Then, an in-depth background of various GAI techniques together with their capabilities in enhancing unmanned vehicle swarms are provided. After that, we present a comprehensive review on the applications and challenges of GAI in unmanned vehicle swarms with various insights and discussions. Finally, we highlight open issues of GAI in unmanned vehicle swarms and discuss potential research directions.

cs.RO↗

arXiv2024

Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images

Vision Foundation Models (VFMs) such as the Segment Anything Model (SAM) allow zero-shot or interactive segmentation of visual contents, thus they are quickly applied in a variety of visual scenes. However, their direct use in many Remote Sensing (RS) applications is often unsatisfactory due to the special imaging characteristics of RS images. In this work, we aim to utilize the strong visual recognition capabilities of VFMs to improve the change detection of high-resolution Remote Sensing Images (RSIs). We employ the visual encoder of FastSAM, an efficient variant of the SAM, to extract visual representations in RS scenes. To adapt FastSAM to focus on some specific ground objects in the RS scenes, we propose a convolutional adaptor to aggregate the task-oriented change information. Moreover, to utilize the semantic representations that are inherent to SAM features, we introduce a task-agnostic semantic learning branch to model the semantic latent in bi-temporal RSIs. The resulting method, SAMCD, obtains superior accuracy compared to the SOTA methods and exhibits a sample-efficient learning ability that is comparable to semi-supervised CD methods. To the best of our knowledge, this is the first work that adapts VFMs for the CD of HR RSIs.

cs.CV↗

arXiv2023

Hierarchical Catalogue Generation for Literature Review: A Benchmark

Scientific literature review generation aims to extract and organize important information from an abundant collection of reference papers and produces corresponding reviews while lacking a clear and logical hierarchy. We observe that a high-quality catalogue-guided generation process can effectively alleviate this problem. Therefore, we present an atomic and challenging task named Hierarchical Catalogue Generation for Literature Review as the first step for review generation, which aims to produce a hierarchical catalogue of a review paper given various references. We construct a novel English Hierarchical Catalogues of Literature Reviews Dataset with 7.6k literature review catalogues and 389k reference papers. To accurately assess the model performance, we design two evaluation metrics for informativeness and similarity to ground truth from semantics and structure.Our extensive analyses verify the high quality of our dataset and the effectiveness of our evaluation metrics. We further benchmark diverse experiments on state-of-the-art summarization models like BART and large language models like ChatGPT to evaluate their capabilities. We further discuss potential directions for this task to motivate future research.

cs.CL↗

arXiv2023

Leveraging Optical Communication Fiber and AI for Distributed Water Pipe Leak Detection

Detecting leaks in water networks is a costly challenge. This article introduces a practical solution: the integration of optical network with water networks for efficient leak detection. Our approach uses a fiber-optic cable to measure vibrations, enabling accurate leak identification and localization by an intelligent algorithm. We also propose a method to access leak severity for prioritized repairs. Our solution detects even small leaks with flow rates as low as 0.027 L/s. It offers a cost-effective way to improve leak detection, enhance water management, and increase operational efficiency.

eess.SY↗

arXiv2023

Co-Imitation Learning without Expert Demonstration

Imitation learning is a primary approach to improve the efficiency of reinforcement learning by exploiting the expert demonstrations. However, in many real scenarios, obtaining expert demonstrations could be extremely expensive or even impossible. To overcome this challenge, in this paper, we propose a novel learning framework called Co-Imitation Learning (CoIL) to exploit the past good experiences of the agents themselves without expert demonstration. Specifically, we train two different agents via letting each of them alternately explore the environment and exploit the peer agent's experience. While the experiences could be valuable or misleading, we propose to estimate the potential utility of each piece of experience with the expected gain of the value function. Thus the agents can selectively imitate from each other by emphasizing the more useful experiences while filtering out noisy ones. Experimental results on various tasks show significant superiority of the proposed Co-Imitation Learning framework, validating that the agents can benefit from each other without external supervision.

cs.LG↗

arXiv2023

Covert D2D Communication Underlaying Cellular Network: A System-Level Security Perspective

In this paper, we aim to secure the D2D communication of the D2D-underlaid cellular network by leveraging covert communication to hide its presence from the vigilant adversary. In particular, there are adversaries aiming to detect D2D communications based on their received signal powers. To avoid being detected, the legitimate entity, i.e., D2D-underlaid cellular network, performs power control so as to hide the presence of the D2D communication. We model the combat between the adversaries and the legitimate entity as a two-stage Stackelberg game. Therein, the adversaries are the followers and aim to minimize their detection errors at the lower stage while the legitimate entity is the leader and aims to maximize its utility constrained by the D2D communication covertness and the cellular quality of service (QoS) at the upper stage. Different from the conventional works, the study of the combat is conducted from the system-level perspective, where the scenario that a large-scale D2D-underlaid cellular network threatened by massive spatially distributed adversaries is considered and the network spatial configuration is modeled by stochastic geometry. We obtain the adversary's optimal strategy as the best response from the lower stage and also both analytically and numerically verify its optimality. Taking into account the best response from the lower stage, we design a bi-level algorithm based on the successive convex approximation (SCA) method to search for the optimal strategy of the legitimate entity, which together with the best response from the lower stage constitute the Stackelberg equilibrium. Numerical results are presented to evaluate the network performance and reveal practical insights that instead of improving the legitimate utility by strengthening the D2D link reliability, increasing D2D transmission power will degrade it due to the security concern.

cs.CR↗

arXiv2022

Compressed domain vibration detection and classification for distributed acoustic sensing

Distributed acoustic sensing (DAS) is a novel enabling technology that can turn existing fibre optic networks to distributed acoustic sensors. However, it faces the challenges of transmitting, storing, and processing massive streams of data which are orders of magnitude larger than that collected from point sensors. The gap between intensive data generated by DAS and modern computing system with limited reading/writing speed and storage capacity imposes restrictions on many applications. Compressive sensing (CS) is a revolutionary signal acquisition method that allows a signal to be acquired and reconstructed with significantly fewer samples than that required by Nyquist-Shannon theorem. Though the data size is greatly reduced in the sampling stage, the reconstruction of the compressed data is however time and computation consuming. To address this challenge, we propose to map the feature extractor from Nyquist-domain to compressed-domain and therefore vibration detection and classification can be directly implemented in compressed-domain. The measured results show that our framework can be used to reduce the transmitted data size by 70% while achieves 99.4% true positive rate (TPR) and 0.04% false positive rate (TPR) along 5 km sensing fibre and 95.05% classification accuracy on a 5-class classification task.

eess.SP↗