Large Language Models (LLMs) are increasingly integrated into diverse industries, posing substantial security risks due to unauthorized replication and misuse. To mitigate these concerns, robust identification mechani...
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Large Language Models (LLMs) are increasingly integrated into diverse industries, posing substantial security risks due to unauthorized replication and misuse. To mitigate these concerns, robust identification mechanisms are widely acknowledged as an effective strategy. Identification systems for LLMs now rely heavily on watermarking technology to manage and protect intellectual property and ensure data security. However, previous studies have primarily concentrated on the basic principles of algorithms and lacked a comprehensive analysis of watermarking theory and practice from the perspective of intelligent identification. To bridge this gap, firstly, we explore how a robust identity recognition system can be effectively implemented and managed within LLMs by various participants using watermarking technology. Secondly, we propose a mathematical framework based on mutual information theory, which systematizes the identification process to achieve more precise and customized watermarking. Additionally, we present a comprehensive evaluation of performance metrics for LLM watermarking, reflecting participant preferences and advancing discussions on its identification applications. Lastly, we outline the existing challenges in current watermarking technologies and theoretical frameworks, and provide directional guidance to address these challenges. Our systematic classification and detailed exposition aim to enhance the comparison and evaluation of various methods, fostering further research and development toward a transparent, secure, and equitable LLM ecosystem.
Hand exoskeletons have become increasingly crucial for the rehabilitation of hand function, as relevant studies have shown that using the exoskeletons to assist in rehabilitation training can improve hand motor functi...
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作者:
Shi, GenLu, HaoHui, HuiTian, JieBeihang Univ
Sch Engn Med Beijing 100191 Peoples R China Beihang Univ
Sch Biol Sci & Med Engn Beijing 100191 Peoples R China Beihang Univ
Key Lab Big DataBased Precis Med Minist Ind & Informat Technol China Beijing 100191 Peoples R China Chinese Acad Sci
Inst Automat State Key Lab Management & Control Complex Syst Beijing 10086 Peoples R China Chinese Acad Sci
Inst Automat CAS Key Lab Mol Imaging Beijing 100190 Peoples R China Natl Key Lab Kidney Dis
Beijing 100853 Peoples R China
Precise cerebrovascular segmentation in Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) data is crucial for computer-aided clinical diagnosis. The sparse distribution of cerebrovascular structures within TOF-M...
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Precise cerebrovascular segmentation in Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) data is crucial for computer-aided clinical diagnosis. The sparse distribution of cerebrovascular structures within TOF-MRA images often results in high costs for manual data labeling. Leveraging unlabeled TOF-MRA data can significantly enhance model performance. In this study, we have constructed the largest preprocessed unlabeled TOF-MRA dataset to date, comprising 1510 subjects. Additionally, we provide manually annotated segmentation masks for 113 subjects based on existing external image datasets to facilitate evaluation. We propose a simple yet effective pretraining strategy utilizing the Frangi filter, known for its capability to enhance vessel-like structures, to optimize the use of the unlabeled data for 3D cerebrovascular segmentation. This involves a Frangi filter-based preprocessing workflow tailored for large-scale unlabeled datasets and a multi-task pretraining strategy to efficiently utilize the preprocessed data. This approach ensures maximal extraction of useful knowledge from the unlabeled data. The efficacy of the pretrained model is assessed across four cerebrovascular segmentation datasets, where it demonstrates superior performance, improving the clDice metric by approximately 2%-3% compared to the latest semi- and self-supervised methods. Additionally, ablation studies validate the generalizability and effectiveness of our pretraining method across various backbone structures. The code and data have been open source at: https://***/shigen-StoneRoot/FFPN.
Gait recognition is a prominent biometric recognition technique extensively employed in public security. Appearance-based and model-based gait recognition are two categories of methods commonly used. Specifically, app...
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Gait recognition is a prominent biometric recognition technique extensively employed in public security. Appearance-based and model-based gait recognition are two categories of methods commonly used. Specifically, appearance-based methods, which use silhouettes to represent body information, typically outperform model-based methods that rely on skeleton data, making them more popular. Recently, the shift from single-frame templates to multiframe silhouettes has advanced appearance-based gait recognition with better spatiotemporal representation. However, there is a notable lack of comprehensive studies that deepen the understanding of multiframe appearance-based gait recognition methods. This article reviews various methods to trace the evolution of gait recognition. Furthermore, we unify various performant models in one framework, study the overlooked effects on data arrangement, and explore the scaling ability of existing methods. Besides the advancement in gait recognition, we also summarize the current challenges and future prospects to foster future research.
With the rapid development of technologies such as Artificial Intelligence (AI), edge computing, and cloud intelligence, the medical field is undergoing a fundamental transformation [1]. These technologies significant...
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With the rapid development of technologies such as Artificial Intelligence (AI), edge computing, and cloud intelligence, the medical field is undergoing a fundamental transformation [1]. These technologies significantly enhance the medical system's capability to process complex data and also improve the real-time response rate to patient needs. In this wave of technological innovation, parallel intelligence, along with Artificial systems, Computational experiments, and Parallel execution (ACP) approach [2] will play a crucial role. Through parallel interactions between virtual and real systems, this approach optimizes the functionality of medical devices and instruments, enhancing the accuracy of diagnoses and treatments while enabling the autonomous evolution and adaptive adjustment of medical systems.
Tourism is a critical driver of economic growth and cultural exchange. However, traditional tourism models often struggle to address challenges such as insufficient understanding of diverse travel preferences, limited...
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Tourism is a critical driver of economic growth and cultural exchange. However, traditional tourism models often struggle to address challenges such as insufficient understanding of diverse travel preferences, limited capacity for personalized services, and a lack of intelligent, convenient service solutions. In this paper, we propose a virtual-real interactive Parallel Tourism System (PTS) and a Smart Tourism and Lifestyle Service Large Model (STLS-LM) to handle these challenges. By integrating Decentralized Autonomous Organizations (DAOs) and Foundation Models (FMs) supported by Retrieval-Augmented Generation, PTS elevates tourism services by incorporating agentic intelligence and autonomous intelligence, advancing beyond traditional AI-powered applications. The proposed framework enables a deep understanding of task requirements, precise multi-source information sensing, personalized functionality customization, and timely public feedback adoption, forming a comprehensive intelligent travel service system that caters to the evolving needs of modern tourism.
Traditional cognitive travel modeling typically employs a unified cognitive model to simulate representative travel behaviors, which may usually result in a weak characterization of user heterogeneity in paths, modes,...
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Traditional cognitive travel modeling typically employs a unified cognitive model to simulate representative travel behaviors, which may usually result in a weak characterization of user heterogeneity in paths, modes, and other factors. Large language model (LLM), by contrast, has significantly enhanced the anthropomorphic and personalized features of intelligent systems. To integrate their advantages, this article proposes LLM-driven cognitive modeling to generate more diverse and personalized travel demands. The new method sufficiently exploits LLM such as the llama as a basis and provides personalized travel plans so that more heterogenous travel demands could be generated. Additionally, introducing LLM into cognitive modeling can significantly reduce the time of model development, thus accelerating the research or engineering deployment. By calibrating and testing with one month's data from public transportation (buses and subways) in Beijing, our method, compared to traditional cognitive models, not only achieves better accuracy in reproducing typical travel patterns, but also generates more diverse ones, providing a more comprehensive input for computational experiments on traffic management and control strategies.
The passing of Professor Wolter “Wolt” Fabrycky, an outstanding member and great leader, is a big loss to our international systems engineering professional community. “Wolt was a legend in the systems engineering ...
The passing of Professor Wolter “Wolt” Fabrycky, an outstanding member and great leader, is a big loss to our international systems engineering professional community. “Wolt was a legend in the systems engineering community with his teaching, advising, and dissemination of knowledge through the books he authored.”, as stated by Professor Eileen Aken, a former student of Wolt and the head of the Virginia Tech's Grado Department of Industrial and systems Engineeirng where Wolt had served and led for 30 years and retired as John L. Lawrence Professor emeritus.
To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict lane change intentions of surrounding vehicles in advance and forecast their future trajectories....
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To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict lane change intentions of surrounding vehicles in advance and forecast their future trajectories. Existing motion prediction approaches have ample room for improvement, particularly in terms of long-term prediction accuracy and interpretability. In this study, we address these challenges by proposing a Lane Change-Large Language Model (LC-LLM), an explainable lane change prediction model that leverages the strong reasoning capabilities and self explanation abilities of Large Language Models (LLMs). Essentially, we reformulate the lane change prediction task as a language modeling problem, processing heterogeneous driving scenario information as natural language prompts for LLMs and employing supervised fine-tuning to tailor LLMs specifically for lane change prediction task. Additionally, we finetune the Chain-of-Thought (CoT) reasoning to improve prediction transparency and reliability, and include explanatory requirements in the prompts during the inference stage. Therefore, our LC-LLM not only predicts lane change intentions and trajectories but also provides CoT reasoning and explanations for its predictions, enhancing its interpretability. Extensive experiments based on the large-scale highD dataset demonstrate the superior performance and interpretability of our LC-LLM in lane change prediction task. To the best of our knowledge, this is the first attempt to utilize LLMs for predicting lane change behavior. Our study shows that LLMs can effectively encode comprehensive interaction information for understanding driving behavior.
This article concerns the secure containment control problem for multiple autonomous aerial vehicles. The cyber attacker can manipulate control commands, resulting in containment failure in the position loop. Within a...
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This article concerns the secure containment control problem for multiple autonomous aerial vehicles. The cyber attacker can manipulate control commands, resulting in containment failure in the position loop. Within a zero-sum graphical game framework, secure containment controllers and malicious attackers are regarded as game players, and the attack-defense process is recast as a min-max optimization problem. Acquiring optimal distributed secure control policies requires solving the game-related Hamilton-Jacobi-Isaacs (HJI) equations. Based on the critic-only neural network (NN) structure, the reinforcement learning (RL) method is employed in solving coupled HJI equations. The fixed-time convergence technique is introduced to improve the convergence rate of RL, and the experience replay mechanism is utilized to relax the persistence of excitation condition. The associated NN convergence and closed-loop stability are analyzed. In the attitude loop, the optimal feedback control law is obtained by solving Hamilton-Jacobi-Bellman equations using the fixed-time convergent RL method. The simulation example and the quadrotor experiment are given to show the effectiveness of the proposed scheme.
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